Diagnosis and treatment of cancers with microrna located in or near cancer-associated chromosomal features

INCORPORATION BY REFERENCE OF MATERIAL IN ASCII TEXT FILE

This application incorporates by reference the Sequence Listing contained in the following ASCII text file being submitted concurrently herewith:

- a) File name: 35891018020SEQLIST.txt; created Sep. 4, 2015; 128 KB in size.

FIELD OF THE INVENTION

The invention relates to the diagnosis of cancers, or the screening of individuals for the predisposition to cancer, by evaluating the status of at least one miR gene located in close proximity to chromosomal features, such as cancer-associated genomic regions, fragile sites, human papilloma virus integration sites, and homeobox genes and gene clusters. The invention also relates to the treatment of cancers by altering the amount of gene product produced from miR genes located in close proximity to these chromosomal features. The invention further provides methods of diagnosing CLL and other cancers by screening for mutations in miR genes.

BACKGROUND OF THE INVENTION

Taken as a whole, cancers are a significant source of mortality and morbidity in the U.S. and throughout the world. However, cancers are a large and varied class of diseases with diverse etiologies. Researchers therefore have been unable to develop treatments or diagnostic tests which cover more than a few types of cancer.

For example, cancers are associated with many different classes of chromosomal features. One such class of chromosomal features are perturbations in the genomic structure of certain genes, such as the deletion or mutation of tumor suppressor genes. The activation of proto-oncogenes by gene amplification or promoter activation (e.g., by viral integration), epigenetic modifications (e.g., a change in DNA methylation) and chromosomal translocations can also cause cancerigenesis. Such perturbations in the genomic structure which are involved in the etiology of cancers are called “cancer-associated genomic regions” or “CAGRs.”

Chromosomal fragile sites are another class of chromosomal feature implicated in the etiology of cancers. Chromosomal fragile sites are regions of genomic DNA which show an abnormally high occurrence of gaps or breaks when DNA synthesis is perturbed during metaphase. These fragile sites are categorized as “rare” or “common.” As their name suggests, rare fragile sites are uncommon. Such sites are associated with di- or tri-nucleotide repeats, can be induced in metaphase chromosomes by folic acid deficiency, and segregate in a Mendelian manner. An exemplary rare fragile site is the Fragile X site.

Common fragile sites are revealed when cells are grown in the presence of aphidocolin or 5-azacytidine, which inhibit DNA polymerase. At least eighty-nine common fragile sites have been identified, and at least one such site is found on every human chromosome. Thus, while their function is poorly understood, common fragile sites represent a basic component of the human chromosome structure.

Induction of fragile sites in vitro leads to increased sister-chromatid exchange and a high rate of chromosomal deletions, amplifications and translocations, while fragile sites have been colocalized with chromosome breakpoints in vivo. Also, most common fragile sites studied in tumor cells contain large, intra-locus deletions or translocations, and a number of tumors have been identified with deletions in multiple fragile sites. Chromosomal fragile sites are therefore mechanistically involved in producing many of the chromosomal lesions commonly seen in cancer cells.

Cervical cancer, which is the second leading cause of female cancer mortality worldwide, is highly associated with human papillomavirus (HPV) infection. Indeed, sequences from the HPV 16 or HPV 18 viruses are found in cells from nearly every cervical tumor cell examined. In malignant forms of cervical cancer, the HPV genome is found integrated into the genome of the cancer cells. HPV preferentially integrates in or near common chromosomal fragile sites. HPV integration into a host cell genome can cause large amplification, deletions or rearrangements near the integration site. Expression of cellular genes near the HPV integration site can therefore be affected, which may contribute to the oncogenesis of the infected cell. These sites of HPV integration into a host cell genome are therefore considered another class of chromosomal feature that is associated with a cancer.

Homeobox genes are a conserved family of regulatory genes that contain the same 183-nucleotide sequence, called the “homeobox.” The homeobox genes encode nuclear transcription factors called “homeoproteins,” which regulate the expression of numerous downstream genes important in development. The homeobox sequence itself encodes a 61 amino acid “homeodomain” that recognizes and binds to a specific DNA binding motif in the target developmental genes. Homeobox genes are categorized as “class I” or “clustered” homeobox genes, which regulate antero-posterior patterning during embryogenesis, or “class II” homeobox genes, which are dispersed throughout the genome. Altogether, the homeobox genes account for more than 0.1% of the vertebrate genome.

The homeobox genes are believed to “decode” external inductive stimuli that signal a given cell to proceed down a particular developmental lineage. For example, specific homeobox genes might be activated in response to various growth factors or other external stimuli that activate signal transduction pathways in a cell. The homeobox genes then activate and/or repress specific programs of effector or developmental genes (e.g., morphogenetic molecules, cell-cycle regulators, pro- or anti-apoptotic proteins, etc.) to induce the phenotype “ordered” by the external stimuli. The homeobox system is clearly highly coordinated during embryogenesis and morphogenesis, but appears to be dysregulated during oncogenesis. Such dysregulation likely occurs because of disruptions in the genomic structure or chromosomal architecture surrounding the homeobox genes or gene clusters. The homeobox genes or gene clusters are therefore considered yet another chromosomal feature which are associated with cancers.

Micro RNAs (miRs) are naturally-occurring 19 to 25 nucleotide transcripts found in over one hundred distinct organisms, including fruit flies, nematodes and humans. The miRs are typically processed from 60- to 70-nucleotide foldback RNA precursor structures, which are transcribed from the miR gene. The miR precursor processing reaction requires Dicer RNase III and Argonaute family members (Sasaki et al. (2003), Genomics 82, 323-330). The miR precursor or processed miR products are easily detected, and an alteration in the levels of these molecules within a cell can indicate a perturbation in the chromosomal region containing the miR gene.

To date, at least 222 separate miR genes have been identified in the human genome. Two miR genes (miR15a and miR16a) have been localized to a homozygously deleted region on chromosome 13 that is correlated with chronic lymphocytic leukemia (Calin et al. (2002), Proc. Natl. Acad. Sci. USA 99:15524-29), and the miR-143/miR145 gene cluster is downregulated in colon cancer (Michael et al. (2003), Mol. Cancer Res. 1:882-91). However, the distribution of miR genes throughout the genome, and the relationship of the miR genes to the diverse chromosomal features discussed herein, has not been systematically studied.

A method for reliably and accurately diagnosing, or for screening individuals for a predisposition to, cancers associated with such diverse chromosomal features as CAGRs, fragile sites, HPV integration sites and homeobox genes is needed. A method of treating cancers associated with these diverse chromosomal features is also highly desired.

SUMMARY OF THE INVENTION

It has now been discovered that miR genes are commonly associated with chromosomal features involved in the etiology of different cancers. The perturbations in the genomic structure or chromosomal architecture of a cell caused by a cancer-associated chromosomal feature can affect the expression of the miR gene(s) located in close proximity to that chromosomal feature. Evaluation of miR gene expression can therefore be used to indicate the presence of a cancer-causing chromosomal lesion in a subject. As the change in miR gene expression level caused by a cancer-associated chromosomal feature may also contribute to cancerigenesis, a given cancer can be treated by restoring the level of miR gene expression to normal.

The invention therefore provides a method of diagnosing cancer in a subject. The cancer can be any cancer associated with a cancer-associated chromosomal feature. As used herein, a cancer-associated chromosomal feature includes, but is not limited to, a cancer-associated genomic region, a chromosomal fragile site, a human papillomavirus integration site on a chromosome of the subject, and a homeobox gene or gene cluster on a chromosome of the subject. The cancer can also be any cancer associated with one or more adverse prognostic markers, including cancers associated with positive ZAP-70 expression, an unmutated IgV_Hgene, positive CD38 expression, deletion at chromosome 11q23, and loss or mutation of TP53. In one embodiment, the diagnostic method comprises the following steps. In a sample obtained from a subject suspected of having a cancer associated with a cancer-associated chromosomal feature, the status of at least one miR gene located in close proximity to the cancer-associated chromosomal feature is evaluated by measuring the level of at least one miR gene product from the miR gene in the sample, provided the miR genes are not miR-15, miR-16, miR-143 or miR-145. An alteration in the level of miR gene product in the sample relative to the level of miR gene product in a control sample is indicative of the presence of the cancer in the subject. In a related embodiment, the diagnostic method comprises evaluating in a sample obtained from a subject suspected of having a cancer associated with a cancer-associated chromosomal feature, the status of at least one miR gene located in close proximity to the cancer-associated chromosomal feature, provided the miR gene is not miR-15 or miR-16, by measuring the level of at least one miR gene product from the miR gene in the sample. An alteration in the level of miR gene product in the sample relative to the level of miR gene product in a control sample is indicative of the presence of the cancer in the subject.

The status of the at least one miR gene in the subject's sample can also be evaluated by analyzing the at least one miR gene for a deletion, mutation and/or amplification. The detection of a deletion, mutation and/or amplification in the miR gene relative to the miR gene in a control sample is indicative of the presence of the cancer in the subject. The status of the at least one miR gene in the subject's sample can also be evaluated by measuring the copy number of the at least one miR gene in the sample, wherein a copy number other than two for miR genes located on any chromosome other than a Y chromosome, and other than one for miR genes located on a Y chromosome, is indicative of the subject either having or being at risk for having a cancer. In one embodiment, the diagnostic method comprises analyzing at least one miR gene in the sample for a deletion, mutation and/or amplification, wherein detection of a deletion, mutation and/or amplification in the miR gene relative to the miR gene in a control sample is indicative of the presence of the cancer in the subject. In a related embodiment, the diagnostic method comprises analyzing at least one miR gene in the sample for a deletion, mutation or amplification, provided the miR gene is not miR-15 or miR-16, wherein detection of a deletion, mutation and/or amplification in the miR gene relative to the miR gene in a control sample is indicative of the presence of the cancer in the subject. In a further embodiment, the diagnostic method comprises analyzing the miR-16 gene in the sample for a specific mutation, depicted in SEQ ID NO. 642, wherein detection of the mutation in the miR-16 gene relative to a miR-16 gene in a control sample is indicative of the presence of the cancer in the subject.

The invention also provides a method of screening subjects for a predisposition to develop a cancer associated with a cancer-associated chromosomal feature, by evaluating the status of at least one miR gene located in close proximity to the cancer-associated chromosomal feature in the same manner described herein for the diagnostic method. The cancer can be any cancer associated with a cancer-associated chromosomal feature.

In one embodiment, the level of the at least one miR gene product from the sample is measured by quantitatively reverse transcribing the miR gene product to form a complementary target oligodeoxynucleotide, and hybridizing the target oligodeoxynucleotide to a microarray comprising a probe oligonucleotide specific for the miR gene product. In another embodiment, the levels of multiple miR gene products in a sample are measured in this fashion, by quantitatively reverse transcribing the miR gene products to form complementary target oligodeoxynucleotides, and hybridizing the target oligodeoxynucleotides to a microarray comprising probe oligonucleotides specific for the miR gene products. In another embodiment, the multiple miR gene products are simultaneously reverse transcribed, and the resulting set of target oligodeoxynucleotides are simultaneously exposed to the microarray.

In a related embodiment, the invention provides a method of diagnosing cancer in a subject, comprising reverse transcribing total RNA from a sample from the subject to provide a set of labeled target oligodeoxynucleotides; hybridizing the target oligodeoxynucleotides to a microarray comprising miRNA-specific probe oligonucleotides to provide a hybridization profile for the sample; and comparing the sample hybridization profile to the hybridization profile generated from a control sample, an alteration in the profile being indicative of the subject either having, or being at risk for developing, a cancer. The microarray of miRNA-specific probe oligonucleotides preferably comprises miRNA-specific probe oligonucleotides for a substantial portion of the human miRNome, the full complement of microRNA genes in a cell. The microarray more preferably comprises at least about 60%, 70%, 80%, 90%, or 95% of the human miRNome. In one embodiment, the cancer is associated with a cancer-associated chromosomal feature, such as a cancer-associated genomic region or a chromosomal fragile site. In another embodiment, the cancer is associated with one or more adverse prognostic markers. In a particular embodiment, the cancer is B-cell chronic lymphocytic leukemia. In a further embodiment, the cancer is a subset of B-cell chronic lymphocytic leukemia that is associated with one or more adverse prognostic markers. As used herein, an adverse prognostic marker is any indicator, such as a specific genetic alteration or a level of expression of a gene, whose presence suggests an unfavorable prognosis concerning disease progression, the severity of the cancer, and/or the likelihood of developing the cancer.

The invention further provides a method of treating a cancer associated with a cancer-associated chromosomal feature in a subject. The cancer can be any cancer associated with a cancer-associated chromosomal feature, for example, cancers associated with a cancer-associated genomic region, a chromosomal fragile site, a human papillomavirus integration site on a chromosome of the subject, or a homeobox gene or gene cluster on a chromosome of the subject. Furthermore, the cancer is a cancer associated with a cancer-associated chromosomal feature in which at least one isolated miR gene product from a miR gene located in close proximity to the cancer-associated chromosomal feature is down-regulated or up-regulated in cancer cells of the subject, as compared to control cells. When the at least one isolated miR gene product is down regulated in the subject's cancer cells, the method comprises administering to the subject, an effective amount of at least one isolated miR gene product from the at least one miR gene, such that proliferation of cancer cells in the subject is inhibited. When the at least one isolated miR gene product is up-regulated in the cancer cells, an effective amount of at least one compound for inhibiting expression of the at least one miR gene is administered to the subject, such that proliferation of cancer cells in the subject is inhibited.

The invention further provides a method of treating cancer associated with a cancer-associated chromosomal feature in a subject, comprising the following steps. The amount of miR gene product expressed from at least one miR gene located in close proximity to the cancer-associated chromosomal region in cancer cells from the subject is determined relative to control cells. If the amount of the miR gene product expressed in the cancer cells is less than the amount of the miR gene product expressed in control cells, the amount of miR gene product expressed in the cancer cells is altered by administering to the subject an effective amount of at least one isolated miR gene product from the miR gene, such that proliferation of cancer cells in the subject is inhibited. If the amount of the miR gene product expressed in the cancer cells is greater than the amount of the miR gene product expressed in control cells, the amount of miR gene product expressed in the cancer cells is altered by administering to the subject an effective amount of at least one compound for inhibiting expression of the at least one miR gene, such that proliferation of cancer cells in the subject is inhibited.

The invention further provides pharmaceutical compositions comprising a pharmaceutically acceptable carrier and at least one miR gene product, or a nucleic acid expressing at least one miR gene product, from an miR gene located in close proximity to a cancer-associated chromosomal feature, provided the miR gene product is not miR-15 or miR-16.

The invention still further provides for the use of at least one miR gene product, or a nucleic acid expressing at least one miR gene product, from an miR gene located in close proximity to a cancer-associated chromosomal feature for the manufacture of a medicament for the treatment of a cancer associated with a cancer-associated chromosomal region.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

FIG. 1 is an image of a Northern blot analysis of the expression of miR-16a (upper panel), miR-26a (middle panel), and miR-99a (lower panel) in normal human lung (lane 1) and human lung cancer cells (lanes 2-8). Below the three blots is an image of an ethidium bromide-stained gel indicating the 5S RNA lane loading control. The genomic location and the type of alteration are indicated.

FIG. 2 is a schematic representation demonstrating the position of various miR genes on human chromosomes in relation to HOX gene clusters.

FIG. 3 shows an miRNome expression analysis of 38 individual CLL samples. The main miR-associated CLL clusters are presented. The control samples are: MNC, mononuclear cells; Ly, Diffuse large B cell lymphoma; CD5, selected CD5+ B lymphocytes.

FIG. 4 is an image of a Northern blot analysis of the expression of miR-16a (upper panel), miR-26a (middle panel), and miR-99a (lower panel) in 12 B-CLL samples. Below the three blots is an image of an ethidium bromide-stained gel indicating the 5S RNA lane loading control. miR-16a expression levels varied in these B-CLL cases, and were either low or absent in several of the samples tested. However, the expression levels of miR-26a and miR-99a, both regions not involved in B-CLL, were relatively constant in the tested samples.

FIG. 5 shows Kaplan-Meier curves depicting the relationship between miRNA expression levels and the time from diagnosis to either the time of initial therapy or the present, if therapy had not commenced. The proportion of untreated patients with CLL is plotted against time since diagnosis. The patients are grouped according to the expression profile generated by 11 microRNA genes.

FIG. 6 shows the expression levels of miR-16-1 and miR-15a miRNAs in samples from two patients with a miR-16-1 mutation (see SEQ ID NO. 642) and in CD5+ cell samples from normal patients, both by Northern blot analysis (upper panels) and by miRNACHIP (expression level indicated by numbers below panels).

FIG. 7A is a schematic depicting the locations of mutations affecting various miRNAs. The mutated (below chromosome) and normal (above chromosome) nucleotide base is presented for each mutation/polymorphism. The figure is not drawn to scale.

FIG. 7B depicts the RT-PCR amplification products of primary transcripts corresponding to various mutant miR gene products for which mutations have been identified in B-CLL cells, as well as the length of the amplified genomic DNA (G). GAPDH levels were used for normalization; RT+=reverse transcription, RT−=control without reverse transcription, G=genomic control.

FIG. 7C presents the chromatograms for the genomic regions of samples having either normal miR-16-1/15a (top) or mutated miR-16-1/15a (CtoT)+7 (bottom). The precise position of the precursor (line with period at end) and the location of the mutation (arrowheads) are indicated.

FIG. 7D shows the expression levels by miRNACHIP (MAr) and Northern blot (NB) analysis for miR-16-1 and miR-15a in samples from two normal CD5 pools (CD5+) and from both of the patients carrying the germline (CtoT)+7 mutation (CLL). The Northern blot band intensities were quantified using ImageQuantTL (Nonlinear Dynamics Ltd.). Data are presented as arbitrary units.

FIG. 7E is a Northern blot showing that the germline mutation in the pri-miR-16-1 is associated with abnormal expression of the active, mature miR-16-1 molecule. Levels of expression were assessed in 293 cells transfected with miR-16-1-WT, miR-16-1-MUT or Empty vector (Empty V), as indicated. Untransfected 293 cells were tested as a control. Normalization for loading was performed with a U6 probe (miR-15a; left panel) and the transfection levels were normalized with anti-GFP signal on cell lysates from the same pellet as that used for Northern blotting (miR-16-1; right panel).

DETAILED DESCRIPTION OF THE INVENTION

All nucleic acid sequences herein are given in the 5′ to 3′ direction. In addition, genes are represented by italics, and gene products are represented by normal type; e.g., mir-17 is the gene and miR-17 is the gene product.

It has now been discovered that the genes that comprise the miR gene complement of the human genome (or “miRNome”) are non-randomly distributed throughout the genome in relation to each other. For example, of 222 human miR genes, at least ninety are located in thirty-six gene clusters, typically with two or three miR genes per cluster (median=2.5). The largest cluster is composed of six genes located on chromosome 13 at 13q31; the miR genes in this cluster are miR-17/miR-18/miR-19a/miR-20/miR-19b1/miR-92-1.

The human miR genes are also non-randomly distributed across the human chromosomal complement. For example, chromosome 4 has a less-than-expected rate of miRs, and chromosomes 17 and 19 contain significantly more miR genes than expected based on chromosome size. Indeed, six of the thirty-six miR gene clusters (17%), containing 16 of 90 clustered genes (18%), are located on chromosomes 17 and 19, which account for only 5% of the entire human genome.

The sequences of the gene products of 187 miR genes are provided in Table 1. The location and distribution of these 187 miR genes in the human genome is given in Tables 2 and 3; see also Example 1. All Tables are located in the Examples section below. As used herein, an “miR gene product” or “miRNA” means the unprocessed or processed RNA transcript from an miR gene. As the miR gene products are not translated into a protein, the term “miR gene products” does not include proteins.

A used herein, “probe oligonucleotide” refers to an oligonucleotide that is capable of hybridizing to a target oligonucleotide. “Target oligonucleotide” or “target oligodeoxynucleotide” refers to a molecule to be detected (e.g., in a hybridization). By “miR-specific probe oligonucleotide” or “probe oligonucleotide specific for an miR” is meant a probe oligonucleotide that has a sequence selected to hybridize to a specific miR gene product, or to a reverse transcript of the specific miR gene product.

The unprocessed miR gene transcript is also called an “miR precursor,” and typically comprises an RNA transcript of about 70 nucleotides in length. The miR precursor can be processed by digestion with an RNAse (such as, Dicer, Argonaut, or RNAse III, e.g., E. coli RNAse III)) into an active 19-25 nucleotide RNA molecule. This active 19-25 nucleotide RNA molecule is also called the “processed miR gene transcript.”

The active 19-25 nucleotide RNA molecule can be obtained from the miR precursor through natural processing routes (e.g., using intact cells or cell lysates) or by synthetic processing routes (e.g., using isolated processing enzymes, such as isolated Dicer, Argonaut, or RNAase III). It is understood that the active 19-25 nucleotide RNA molecule can also be produced directly by biological or chemical syntheses, without having been processed from the miR precursor. For ease of discussion, such a directly produced active 19-25 nucleotide RNA molecule is also referred to as a “processed miR gene product.”

As used herein, “miR gene expression” refers to the production of miR gene products from an miR gene, including processing of the miR precursor into a processed miR gene product.

The human miR genes are closely associated with different classes of chromosomal features that are themselves associated with cancer. As used herein, a “cancer-associated chromosomal feature” refers to a region of a given chromosome, which, when perturbed, is correlated with the occurrence of at least one human cancer. As used herein, a chromosomal feature is “correlated” with a cancer when the feature and the cancer occur together in individuals of a study population in a manner not expected on the basis of chance alone.

A region of a chromosome is “perturbed” when the chromosomal architecture or genomic DNA sequence in that region is disturbed or differs from the normal architecture or sequence in that region. Exemplary perturbations of chromosomal regions include, e.g., chromosomal breakage and translocation, mutations, deletions or amplifications of genomic DNA, a change in the methylation pattern of genomic DNA, the presence of fragile sites, and the presence of viral integration sites. One skilled in the art would recognize that other chromosomal perturbations associated with a cancer are possible.

It is understood that a cancer-associated chromosomal feature can be a chromosomal region where perturbations are known to occur at a higher rate than at other regions in the genome, but where the perturbation has not yet occurred. For example, a common chromosomal breakpoint or fragile site is considered a cancer-associated chromosomal feature, even if a break has not yet occurred. Likewise, a region in the genomic DNA known as a mutational “hotspot” can be a cancer-associated chromosomal feature, even if no mutations have yet occurred in the region.

One class of cancer-associated chromosomal feature which is closely associated with miR genes in the human genome is a “cancer-associated genomic region” or “CAGR” (see Table 4). As used herein, a “CAGR” includes any region of the genomic DNA that comprises a genetic or epigenetic change (or the potential for a genetic or epigenetic change) that differs from normal DNA, and which is correlated with a cancer. Exemplary genetic changes include single- and double-stranded breaks (including common breakpoint regions in or near possible oncogenes or tumor-suppressor genes); chromosomal translocations; mutations, deletions, insertions (including viral, plasmid or transposon integrations) and amplifications (including gene duplications) in the DNA; minimal regions of loss-of-heterozygosity (LOH) suggestive of the presence of tumor-suppressor genes; and minimal regions of amplification suggestive of the presence of oncogenes. Exemplary epigenetic changes include any changes in DNA methylation patterns (e.g., DNA hyper- or hypo-methylation, especially in promoter regions). As used herein, “cancer-associated genomic region” or “CAGR” specifically excludes chromosomal fragile sites or human papillomavirus insertion sites.

Many of the known miR genes in the human genome are in or near CAGRs, including 80 miR genes that are located exactly in minimal regions of LOH or minimal regions of amplification correlated to a variety of cancers. Other miR genes are located in or near breakpoint regions, deleted areas, or regions of amplification. The distribution of miR genes in the human genome relative to CAGRs is given in Tables 6 and 7 and in Example 4A below.

As used herein, an miR gene is “associated” with a given CAGR when the miR gene is located in close proximity to the CAGR; i.e., when the miR is located within the same chromosomal band or within 3 megabases (3 Mb) of the CAGR. See Tables 6 and 7 and Example 4A below for a description of cancers which are correlated with CAGRs, and a description of miRs associated with those CAGRs.

For example, cancers associated with CAGRs include leukemia (e.g., AML, CLL, pro-lymphocytic leukemia), lung cancer (e.g., small cell and non-small cell lung carcinoma), esophageal cancer, gastric cancer, colorectal cancer, brain cancer (e.g., astrocytoma, glioma, glioblastoma, medulloblastoma, meningioma, neuroblastoma), bladder cancer, breast cancer, cervical cancer, epithelial cancer, nasopharyngeal cancer (e.g., oral or laryngeal squamous cell carcinoma), lymphoma (e.g., follicular lymphoma), uterine cancer (e.g., malignant fibrous histiocytoma), hepatic cancer (e.g., hepatocellular carcinoma), head-and-neck cancer (e.g., head-and-neck squamous cell carcinoma), renal cancer, male germ cell tumors, malignant mesothelioma, myelodysplastic syndrome, ovarian cancer, pancreatic or biliary cancer, prostate cancer, thyroid cancer (e.g., sporadic follicular thyroid tumors), and urothelial cancer.

Examples of miR genes associated with CAGRs include miR-153-2, let-7i, miR-33a, miR-34a-2, miR 34a-1, let-7a-1, let-7d; let-7f-1, miR-24-1, miR-27b, miR-23b, miR-181a; miR-199b, miR-218-1, miR-31, let-7a-2, let-7g, miR-21, miR-32a-1, miR-33b, miR-100, miR-101-1, miR-125b-1, miR-135-1, miR-142as, miR-142s; miR-144, miR-301, miR-297-3, miR-155(BIC), miR-26a, miR-17, miR-18, miR-19a, miR-19b1, miR-20, miR-92-1, miR-128a, miR-7-3, miR-22, miR-123, miR-132, miR-149, miR-161; miR-177, miR-195, miR-212, let-7c, miR-99a, miR-125b-2, miR-210, miR-135-2, miR-124a-1, miR-208, miR-211, miR-180, miR-145, miR-143, miR-127, miR-136, miR-138-1, miR-154, miR-134, miR-299, miR-203, miR-34, miR-92-2, miR-19b-2, miR-108-1, miR-193, miR-106a, miR-29a, miR-29b, miR-129-1, miR-182s, miR-182as, miR-96, miR-183, miR-32, miR-159-1, miR-192 and combinations thereof.

Specific groupings of miR gene(s) that are associated with a particular cancer are evident from Tables 6 and 7, and are preferred. For example, acute myeloid leukemia (AML) is associated with miR-153-2, and adenocarcinoma of the lung or esophagus is associated with let-7i. Where more than one miR gene is listed in Tables 6 and 7, it is understood that the cancer associated with those genes can be diagnosed by evaluating any one of the listed miR genes, or by evaluating any combination of the listed miR genes. Subgenera of CAGRs or associated with miR gene(s) would also be evident to one of ordinary skill in the art from Tables 6 and 7.

Another class of cancer-associated chromosomal feature which is closely associated with miR genes in the human genome is a “chromosomal fragile site” or “FRAs” (see Table 4 and Example 2). As used herein, a “FRA” includes any rare or common fragile site in a chromosome; e.g., one that can be induced by subjecting a cell to stress during DNA replication. For example, a rare FRA can be induced by subjecting the cell to folic acid deficiency during DNA replication. A common FRA can be induced by treating the cell with aphidocolin or 5-azacytidine during DNA replication. The identification or induction of chromosomal fragile sites is within the skill in the art; see, e.g., Arlt et al. (2003), Cytogenet. Genome Res. 100:92-100 and Arlt et al. (2002), Genes, Chromosomes and Cancer 33:82-92, the entire disclosures of which are herein incorporated by reference.

Approximately 20% of the known human miR genes are located in (13 miRs) or within 3 Mb (22 miRs) of cloned FRAs. Indeed, the relative incidence of miR genes inside fragile sites occurs at a rate 9.12 times higher than in non-fragile sites. Moreover, after studying 113 fragile sites in a human karyotype, it was found that 61 miR genes are located in the same chromosomal band as a FRA. The distribution of miR genes in the human genome relative to FRAs is given in Table 5 and in Example 2.

As used herein, an miR gene is “associated” with a given FRA when the miR gene is located in close proximity to the FRA; i.e., when the miR is located within the same chromosomal band or within 3 megabases (3 Mb) of the FRA. See Table 5 and Example 2 for a description of cancers which are correlated with FRAs, and a description of miRs associated with those FRAs.

For example, cancers associated with FRAs include bladder cancer, esophageal cancer, lung cancer, stomach cancer, kidney cancer, cervical cancer, ovarian cancer, breast cancer, lymphoma, Ewing sarcoma, hematopoietic tumors, solid tumors and leukemia.

Examples of miR genes associated with FRAs include miR-186, miR-101-1, miR-194, miR-215, miR-106b, miR-25, miR-93, miR-29b, miR-29a, miR-96, miR-182s, miR-182as, miR-183, miR-129-1, let7a-1, let-7d, let-7f-1, miR-23b, miR-24-1, miR-27b, miR-32, miR-159-1, miR-192, miR-125b-1, let-7a-2, miR-100, miR-196-2, miR-148b, miR-190, miR-21, miR-301, miR-142s, miR-142as, miR-105-1, miR-175 and combinations thereof.

Specific groupings of miR gene(s) that are associated with a particular cancer and FRA are evident from Table 5, and are preferred. For example, FRA7H is correlated with esophageal cancer, and is associated with miR-29b, miR-29a, miR-96, miR-182s, miR-182as, miR-183, and miR-129-1. FRA9D is correlated with bladder cancer, and is associated with let7a-1, let-7d, let-7f-1, miR-23b, miR-24-1, and miR-27b. Where more than one miR gene is listed in Table 5 in association with a FRA, it is understood that the cancer associated with those miR genes can be diagnosed by evaluating any one of the listed miR genes, or by evaluating any combination of the listed miR genes. Subgenera of CAGRs and/or associated with miR gene(s) would also be evident to one of ordinary skill in the art from Table 5.

Another class of cancer-associated chromosomal feature which is closely associated with miR genes in the human genome is a “human papillomavirus (HPV) integration site” (see Table 4 and Example 3). As used herein, an “HPV integration site” includes any site in a chromosome of a subject where some or all of an HPV genome can insert into the genomic DNA, or any site where some or all of an HPV genome has inserted into the genomic DNA. HPV integration sites are often associated with common FRAs, but are distinct from FRAs for purposes of the present invention. Any species or strain of HPV can insert some or all of its genome into an HPV integration site. However, the most common strains of HPV which insert some or all of their genomes into an HPV integration site are HPV 16 and HPV 18. The identification of HPV integration sites in the human genome is within the skill in the art; see, e.g., Thorland et al. (2000), Cancer Res. 60:5916-21, the entire disclosure of which is herein incorporated by reference.

Thirteen miR genes (7%) are located within 2.5 Mb of seven of the seventeen (45%) cloned integration sites in the human genome. The relative incidence of miRs at HPV16 integration sites occurred at a rate 3.22 times higher than in the rest of the genome. Indeed, four miR genes (miR-21, miR-301, miR-142s and miR-142as) were located within one cluster of integration sites at chromosome 17q23, in which there are three HPV 16 integration events spread over roughly 4 Mb of genomic sequence.

As used herein, an miR gene is “associated” with a given HPV integration site when the miR gene is located in close proximity to the HPV integration site; i.e., when the miR is located within the same chromosomal band or within 3 megabases (3 Mb), preferably within 2.5 Mb, of the HPV integration site. See Table 5 and Example 3 for a description of miRs associated with HPV integration sites.

Insertion of HPV sequences into the genome of subject is correlated with the occurrence of cervical cancer. Examples of miR genes associated with HPV integration sites on human chromosomes include miR-21, miR-301, miR-142as, miR-142s, miR-194, miR-215, miR-32 and combinations thereof.

Specific groupings of miR gene(s) that are associated with a particular HPV integration site are evident from Table 5, and are preferred. For example, the HPV integration site located in or near FRA9E is associated with miR-32. The HPV integration site located in or near FRA1H is associated with miR-194 and miR-215. The HPV integration site located in or near FRA17B is associated with miR-21, miR-301, miR-142s, and miR-142as. Where more than one miR gene is listed in Table 5 in relation to an HPV integration site, it is understood that the cancer associated with those miR genes can be diagnosed by evaluating any one of the listed miR genes, or by evaluating any combination of the listed miR genes.

Another class of cancer-associated chromosomal feature which is closely associated with miR genes in the human genome is a “homeobox gene or gene cluster” (see Table 4 and Example 5). As used herein, a “homeobox gene or gene cluster” is a single gene or a grouping of genes, characterized in that the gene or genes have been classified by sequence or function as a class I or class II homeobox gene or contain the 183-nucleotide “homeobox” sequence. Identification and characterization of homeobox genes or gene clusters are within the skill in the art; see, e.g., Cillo et al. (1999), Exp. Cell Res. 248:1-9 and Pollard et al. (2000), Current Biology 10:1059-62, the entire disclosures of which are herein incorporated by reference.

Of the four known class I homeobox gene clusters in the human genome, three contain miR genes: miR-10a and miR-196-1 are in the HOX B cluster on 17q21; miR-196-2 is in the HOX C cluster at 12q13; and miR-10b is in the HOX D cluster at 2q31. Three other miRs (miR-148, miR-152 and miR-148b) are located within 1 Mb of a HOX gene cluster. miR genes are also found within class II homeobox gene clusters; for example, seven microRNAs (miR-129-1, miR-153-2, let-7a-1, let-7f-1, let-7d, miR-202 and miR-139) are located within 0.5 Mb of class II homeotic genes. See Example 5 and FIG. 2 for a description of miRs associated with homeobox genes or gene clusters in the human genome.

Examples of homeobox genes associated with miR genes in the human genome include genes in the HOXA cluster, genes in the HOXB cluster, genes in the HOXC cluster, genes in the HOXD cluster, NK1, NK3, NK4, Lbx, Tlx, Emx, Vax, Hmx, NK6, Msx, Cdx, Xlox, Gsx, En, HB9, Gbx, Msx-1, Msx-2, GBX2, HLX, HEX PMX1, DLX, LHX2 and CDX2. Examples of homeobox gene clusters associated with miR genes in the human genome include HOXA, HOXB, HOXC, HOXD, extended Hox, NKL, ParaHox, and EHGbox, PAX, PBX, MEIS, REIG and PREP/KNOX1.

Examples of cancers associated with homeobox genes or gene clusters include renal cancer, Wilm's tumor, colorectal cancer, small cell lung cancer, melanoma, breast cancer, prostate cancer, skin cancer, osteosarcoma, neuroblastoma, leukemia (acute lymphocytic leukemia, acute myeloid leukemia, chronic lymphocytic leukemia), glioblastoma multiform, medulloblastoma, lymphoplasmacytoid lymphoma, thyroid cancer, rhabdomyosarcoma and solid tumors.

Examples of miR genes associated with homeobox genes or gene clusters include miR-148, miR-10a, miR-196-1, miR-152, miR-196-2, miR-148b, miR-10b, miR-129-1, miR-153-2, miR-202, miR-139, let-7a, let-7f, let-7d and combinations thereof.

Specific groupings of miR gene(s) that are associated with particular homeobox genes or gene cluster are evident from Example 5 and FIG. 2, and are preferred. For example, homeobox gene cluster HOXA is associated with miR-148. Homeobox gene cluster HOXB is associated with miR-148, miR-10a, miR-196-1, miR-152 and combinations thereof. Homeobox gene cluster HOXC is associated with miR-196-2, miR-148b or a combination thereof. Homeobox gene cluster HOXD, is associated with miR-10b. Where more than one miR gene is associated with a homeobox gene or gene cluster, it is understood that the cancer associated with those genes can be diagnosed by evaluating any one of the miR genes, or by evaluating any combination of the miR genes. In one embodiment, the miR gene or gene product that is measured or analyzed is not miR-15, miR-16, miR-143 and/or miR-145.

Without wishing to be bound by any theory, it is believed that perturbations in the genomic structure or chromosomal architecture of a cell which comprise the cancer-associated chromosomal feature can affect the expression of the miR gene(s) associated with the feature in that cell. For example, a CAGR can comprise an amplification of the region containing an miR gene(s), causing an up-regulation of miR gene expression. Likewise, the CAGR can comprise a chromosomal breakpoint or a deletion that disrupts gene expression, and results in a down-regulation of miR gene expression. HPV integrations and FRAs can cause deletions, amplifications or rearrangement of the surrounding DNA, which can also affect the structure or expression of any associated miR genes. The factors which cause the collected dysregulation of homeobox genes or gene clusters would cause similar disruptions to any associated miR genes. A change in the status of at least one of the miR genes associated with a cancer-associated chromosomal feature in a tissue or cell sample from a subject, relative to the status of that miR gene in a control sample, therefore is indicative of the presence of a cancer, or a susceptability to cancer, in a subject.

Without wishing to be bound by any theory, it is also believed that a change in status of miR genes associated with a cancer-associated chromosomal feature can be detected prior to, or in the early stages of, the development of transformed or neoplastic phenotypes in cells of a subject. The invention therefore also provides a method of screening subjects for a predisposition to developing a cancer associated with a cancer-associated chromosomal feature, by evaluating the status of at least one miR gene associated with a cancer-associated chromosomal feature in a tissue or cell sample from a subject, relative to the status of that miR gene in a control sample. Subjects with a change in the status of one or more miR genes associated with a cancer-associated chromosomal feature are candidates for further testing to determine or confirm that the subjects have cancer. Such further testing can comprise histological examination of blood or tissue samples, or other techniques within the skill in the art.

As used herein, the “status of an miR gene” refers to the condition of the miR gene in terms of its physical sequence or structure, or its ability to express a gene product. Thus, the status of an miR gene in cells of a subject can be evaluated by any technique suitable for detecting genetic or epigenetic changes in the miR gene, or by any technique suitable for detecting the level of miR gene product produced from the miR gene.

For example, the level of at least one miR gene product produced from an miR gene can be measured in cells of a biological sample obtained from the subject. An alteration in the level (i.e., an up- or down-regulation) of miR gene product in the sample obtained from the subject relative to the level of miR gene product in a control sample is indicative of the presence of the cancer in the subject. As used herein, a “subject” is any mammal suspected of having a cancer associated with a cancer-associated chromosomal feature. In one embodiment, the subject is a human suspected of having a cancer associated with a cancer-associated chromosomal feature. As used herein, expression of an miR gene is “up-regulated” when the amount of miR gene product produced from that gene in a cell or tissue sample from a subject is greater than the amount produced from the same gene in a control cell or tissue sample. Likewise, expression of an miR gene is “down-regulated” when the amount of miR gene product produced from that gene in a cell or tissue sample from a subject is less than the amount produced from the same gene in a control cell or tissue sample.

Methods for determining RNA expression levels in cells from a biological sample are within the level of skill in the art. For example, tissue sample can be removed from a subject suspected of having cancer associated with a cancer-associated chromosomal feature by conventional biopsy techniques. In another example, a blood sample can be removed from the subject, and white blood cells isolated for DNA extraction by standard techniques. The blood or tissue sample is preferably obtained from the subject prior to initiation of radiotherapy, chemotherapy or other therapeutic treatment. A corresponding control tissue or blood sample can be obtained from unaffected tissues of the subject, from a normal human individual or population of normal individuals, or from cultured cells corresponding to the majority of cells in the subject's sample. The control tissue or blood sample is then processed along with the sample from the subject, so that the levels of miR gene product produced from a given miR gene in cells from the subject's sample can be compared to the corresponding miR gene product levels from cells of the control sample.

For example, the relative miR gene expression in the control and normal samples can be conveniently determined with respect to one or more RNA expression standards. The standards can comprise, for example, a zero miR gene expression level, the miR gene expression level in a standard cell line, or the average level of miR gene expression previously obtained for a population of normal human controls.

Suitable techniques for determining the level of RNA transcripts of a particular gene in cells are within the skill in the art. According to one such method, total cellular RNA can be purified from cells by homogenization in the presence of nucleic acid extraction buffer, followed by centrifugation. Nucleic acids are precipitated, and DNA is removed by treatment with DNase and precipitation. The RNA molecules are then separated by gel electrophoresis on agarose gels according to standard techniques, and transferred to nitrocellulose filters by, e.g., the so-called “Northern” blotting technique. The RNA is then immobilized on the filters by heating. Detection and quantification of specific RNA is accomplished using appropriately labeled DNA or RNA probes complementary to the RNA in question. See, for example, Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition, Cold Spring Harbor Laboratory Press, 1989, Chapter 7, the entire disclosure of which is incorporated by reference.

Suitable probes for Northern blot hybridization of a given miR gene product can be produced from the nucleic acid sequences provided in Table 1. Methods for preparation of labeled DNA and RNA probes, and the conditions for hybridization thereof to target nucleotide sequences, are described in Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition, Cold Spring Harbor Laboratory Press, 1989, Chapters 10 and 11, the disclosures of which are herein incorporated by reference.

For example, the nucleic acid probe can be labeled with, e.g., a radionuclide such as ³H, ³²P, ³³P, ¹⁴C, or ³⁵S; a heavy metal; or a ligand capable of functioning as a specific binding pair member for a labeled ligand (e.g., biotin, avidin or an antibody), a fluorescent molecule, a chemiluminescent molecule, an enzyme or the like.

Probes can be labeled to high specific activity by either the nick translation method of Rigby et al. (1977), J. Mol. Biol. 113:237-251 or by the random priming method of Fienberg et al. (1983), Anal. Biochem. 132:6-13, the entire disclosures of which are herein incorporated by reference. The latter is the method of choice for synthesizing ³²P-labeled probes of high specific activity from single-stranded DNA or from RNA templates. For example, by replacing preexisting nucleotides with highly radioactive nucleotides according to the nick translation method, it is possible to prepare ³²P-labeled nucleic acid probes with a specific activity well in excess of 10⁸cpm/microgram. Autoradiographic detection of hybridization can then be performed by exposing hybridized filters to photographic film. Densitometric scanning of the photographic films exposed by the hybridized filters provides an accurate measurement of miR gene transcript levels. Using another approach, miR gene transcript levels can be quantified by computerized imaging systems, such the Molecular Dynamics 400-B 2D Phosphorimager available from Amersham Biosciences, Piscataway, N.J.

Where radionuclide labeling of DNA or RNA probes is not practical, the random-primer method can be used to incorporate an analogue, for example, the dTTP analogue 5-(N—(N-biotinyl-epsilon-aminocaproyl)-3-aminoallyl)deoxyuridine triphosphate, into the probe molecule. The biotinylated probe oligonucleotide can be detected by reaction with biotin-binding proteins, such as avidin, streptavidin, and antibodies (e.g., anti-biotin antibodies) coupled to fluorescent dyes or enzymes that produce color reactions.

In addition to Northern and other RNA blotting hybridization techniques, determining the levels of RNA transcripts can be accomplished using the technique of in situ hybridization. This technique requires fewer cells than the Northern blotting technique, and involves depositing whole cells onto a microscope cover slip and probing the nucleic acid content of the cell with a solution containing radioactive or otherwise labeled nucleic acid (e.g., cDNA or RNA) probes. This technique is particularly well-suited for analyzing tissue biopsy samples from subjects. The practice of the in situ hybridization technique is described in more detail in U.S. Pat. No. 5,427,916, the entire disclosure of which is incorporated herein by reference. Suitable probes for in situ hybridization of a given miR gene product can be produced from the nucleic acid sequences provided in Table 1, as described above.

The relative number of miR gene transcripts in cells can also be determined by reverse transcription of miR gene transcripts, followed by amplification of the reverse-transcribed transcripts by polymerase chain reaction (RT-PCR). The levels of miR gene transcripts can be quantified in comparison with an internal standard, for example, the level of mRNA from a “housekeeping” gene present in the same sample. A suitable “housekeeping” gene for use as an internal standard includes, e.g., myosin or glyceraldehyde-3-phosphate dehydrogenase (G3PDH). The methods for quantitative RT-PCR and variations thereof are within the skill in the art.

In some instances, it may be desirable to simultaneously determine the expression level of a plurality of different of miR genes in a sample. In certain instances, it may be desirable to determine the expression level of the transcripts of all known miR genes correlated with cancer. Assessing cancer-specific expression levels for hundreds of miR genes is time consuming and requires a large amount of total RNA (at least 20 μg for each Northern blot) and autoradiographic techniques that require radioactive isotopes. To overcome these limitations, an oligolibrary in microchip format may be constructed containing a set of probe oligonucleotides specific for a set of miR genes. In one embodiment, the oligolibrary contains probes corresponding to all known miRs from the human genome. The microchip oligolibrary may be expanded to include additional miRNAs as they are discovered.

The microchip is prepared from gene-specific oligonucleotide probes generated from known miRNAs. According to one embodiment, the array contains two different oligonucleotide probes for each miRNA, one containing the active sequence and the other being specific for the precursor of the miRNA. The array may also contain controls such as one or more mouse sequences differing from human orthologs by only a few bases, which can serve as controls for hybridization stringency conditions. tRNAs from both species may also be printed on the microchip, providing an internal, relatively stable positive control for specific hybridization. One or more appropriate controls for non-specific hybridization may also be included on the microchip. For this purpose, sequences are selected based upon the absence of any homology with any known miRNAs.

The microchip may be fabricated by techniques known in the art. For example, probe oligonucleotides of an appropriate length, e.g., 40 nucleotides, are 5′-amine modified at position C6 and printed using commercially available microarray systems, e.g., the GeneMachine OmniGrid™ 100 Microarrayer and Amersham CodeLink™ activated slides. Labeled cDNA oligomer corresponding to the target RNAs is prepared by reverse transcribing the target RNA with labeled primer. Following first strand synthesis, the RNA/DNA hybrids are denatured to degrade the RNA templates. The labeled target cDNAs thus prepared are then hybridized to the microarray chip under hybridizing conditions, e.g. 6×SSPE/30% formamide at 25° C. for 18 hours, followed by washing in 0.75×TNT at 37° C. for 40 minutes. At positions on the array where the immobilized probe DNA recognizes a complementary target cDNA in the sample, hybridization occurs. The labeled target cDNA marks the exact position on the array where binding occurs, allowing automatic detection and quantification. The output consists of a list of hybridization events, indicating the relative abundance of specific cDNA sequences, and therefore the relative abundance of the corresponding complementary miRs, in the patient sample. According to one embodiment, the labeled cDNA oligomer is a biotin-labeled cDNA, prepared from a biotin-labeled primer. The microarray is then processed by direct detection of the biotin-containing transcripts using, e.g., Streptavidin-Alexa647 conjugate, and scanned utilizing conventional scanning methods. Images intensities of each spot on the array are proportional to the abundance of the corresponding miR in the patient sample.

The use of the array has several advantages for miRNA expression detection. First, the global expression of several hundred genes can be identified in a same sample at one time point. Second, through careful design of the oligonucleotide probes, expression of both mature and precursor molecules can be identified. Third, in comparison with Northern blot analysis, the chip requires a small amount of RNA, and provides reproducible results using 2.5 μg of total RNA. The relatively limited number of miRNAs (a few hundred per species) allows the construction of a common microarray for several species, with distinct oligonucleotide probes for each. Such a tool would allow for analysis of trans-species expression for each known miR under various conditions.

In addition to use for quantitative expression level assays of specific miRs, a microchip containing miRNA-specific probe oligonucleotides corresponding to a substantial portion of the miRNome, preferably the entire miRNome, may be employed to carry out miR gene expression profiling, for analysis of miR expression patterns. Distinct miR signatures may be associated with established disease markers, or directly with a disease state. As described hereinafter in Example 11, two distinct clusters of human B-cell chronic lymphocytic leukemia (CLL) samples are associated with the presence or the absence of Zap-70 expression, a predictor of early disease progression. As described in Examples 11 and 12, two miRNA signatures were associated with the presence of absence of prognostic markers of disease progression, including Zap-70 expression, mutations in the expressed immunoglobulin variable-region gene IgV_Hand deletions at 13q14. Therefore, miR gene expression profiles can be used for diagnosing the disease state of a cancer, such as whether a cancer is malignant or benign, based on whether or not a given profile is representative of a cancer that is associated with one or more established adverse prognostic markers. Prognostic markers that are suitable for this method include ZAP-70 expression, unmutated IgV_Hgene, CD38 expression, deletion at chromosome 11q23, loss or mutation of TP53, and any combination thereof.

According to the expression profiling method in one embodiment, total RNA from a sample from a subject suspected of having a cancer is quantitatively reverse transcribed to provide a set of labeled target oligodeoxynucleotides complementary to the RNA in the sample. The target oligodeoxynucleotides are then hybridized to a microarray comprising miRNA-specific probe oligonucleotides to provide a hybridization profile for the sample. The result is a hybridization profile for the sample representing the expression pattern of miRNA in the sample. The hybridization profile comprises the signal from the binding of the target oligodeoxynucleotides from the sample to the miRNA-specific probe oligonucleotides in the microarray. The profile may be recorded as the presence or absence of binding (signal vs. zero signal). More preferably, the profile recorded includes the intensity of the signal from each hybridization. The profile is compared to the hybridization profile generated from a normal, i.e., noncancerous, control sample. An alteration in the signal is indicative of the presence of the cancer in the subject.

Other techniques for measuring miR gene expression are also within the skill in the art, and include various techniques for measuring rates of RNA transcription and degradation.

The status of an miR gene in a cell of a subject can also be evaluated by analyzing at least one miR gene or gene product in the sample for a deletion, mutation or amplification, wherein detection of a deletion, mutation or amplification in the miR gene or gene product relative to the miR gene or gene product in a control sample is indicative of the presence of the cancer in the subject. As used herein, a mutation is any alteration in the sequence of a gene of interest that results from one or more nucleotide changes. Such changes include, but are not limited to, allelic polymorphisms, and may affect gene expression and/or function of the gene product.

A deletion, mutation or amplification in an miR gene or gene product can be detected by determining the structure or sequence of an miR gene or gene product in cells from a biological sample from a subject suspected of having cancer associated with a cancer-associated chromosomal feature, and comparing this with the structure or sequence of a corresponding gene or gene product in cells from a control sample. Subject and control samples can be obtained as described herein. Especially suitable candidate miR genes for this type of analysis include, but are not limited to, miR-16-1, miR-27b, miR-206, miR-29b-2 and miR-187. As described in Examples 13 and 14 herein, specific mutations in these five miR genes have been identified in samples from CLL patients.

In certain embodiments, the present invention provides methods for diagnosing whether a subject has, or is at risk for developing, a cancer, comprising analyzing a miR gene or gene product in a test sample from the subject, wherein the detection of a mutation in the miR gene or gene product in the test sample, relative to a control sample, is indicative of the subject having, or being at risk for developing, cancer. In one embodiment, the method comprises analyzing the status of a miR-16-1 gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-16-1 gene for the presence of a mutation, wherein the mutation is a C to T nucleotide substitution at +7 base pairs 3′ of the miR-16-1 precursor coding region (see, e.g., SEQ ID NOS: 641 and 642). Suitable cancers to be diagnosed by this method include CLL, among others. In another embodiment, the method comprises analyzing the status of a miR-27b gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-27b gene for the presence of a mutation, wherein the mutation is a G to A nucleotide substitution at +50 base pairs 3′ of the miR-27b precursor coding region (see, e.g., SEQ ID NOS: 645 and 646). Suitable cancers to be diagnosed by this method include, but are not limited to, CLL, throat cancer, and lung cancer. In an additional embodiment, the method comprises analyzing the status of a miR-206 gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-206 gene for the presence of a mutation, wherein the mutation is a G to T nucleotide substitution at position 49 of the miR-206 precursor coding region (see, e.g., SEQ ID NOS:657 and 658). In a related embodiment, the method comprises analyzing the status of a miR-206 gene for the presence of a mutation, wherein the mutation is an A to T substitution at −116 base pairs 5′ of the miR-206 precursor coding region (see, e.g., SEQ ID NOS:657 and 659). Suitable cancers to be diagnosed by this method include, but are not limited to, CLL and other leukemias, esophogeal cancer, prostate cancer and breast cancer. In yet another embodiment the method comprises analyzing the status of a miR-29b-2 gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-29b-2 gene for the presence of a mutation, wherein the mutation is a G to A nucleotide substitution at +212 base pairs 3′ of the miR-29b-2 precursor coding region (see, e.g., SEQ ID NOS:651 and 652). In a related embodiment, the method comprises analyzing the status of a miR-206 gene for the presence of a mutation, wherein the mutation is an A nucleotide insertion at +107 base pairs 3′ of the miR-29b-2 precursor coding region (see, e.g., SEQ ID NOS:651 and 653). Suitable cancers to be diagnosed by this method include, but are not limited to, CLL and other leukemias, as well as breast cancer. In a further embodiment, the method comprises analyzing the status of a miR-187 gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-187 gene for the presence of a mutation, wherein the mutation is a T to C nucleotide substitution at +73 base pairs 3′ of the miR-187 precursor coding region (see, e.g., SEQ ID NOS:654 and 655). Suitable cancers to be diagnosed by this method include, CLL, among others.

Any technique suitable for detecting alterations in the structure or sequence of genes can be used in the practice of the present method. For example, the presence of miR gene deletions, mutations or amplifications can be detected by Southern blot hybridization of the genomic DNA from a subject, using nucleic acid probes specific for miR gene sequences.

Southern blot hybridization techniques are within the skill in the art. For example, genomic DNA isolated from a subject's sample can be digested with restriction endonucleases. This digestion generates restriction fragments of the genomic DNA that can be separated by electrophoresis, for example, on an agarose gel. The restriction fragments are then blotted onto a hybridization membrane (e.g., nitrocellulose or nylon), and hybridized with labeled probes specific for a given miR gene or genes. A deletion or mutation of these genes is indicated by an alteration of the restriction fragment patterns on the hybridization membrane, as compared to DNA from a control sample that has been treated identically to the DNA from the subject's sample. Probe labeling and hybridization conditions suitable for detecting alterations in gene structure or sequence can be readily determined by one of ordinary skill in the art. The miR gene nucleic acid probes for Southern blot hybridization can be designed based upon the nucleic acid sequences provided in Table 1, as described herein. Nucleic acid probe hybridization can then be detected by exposing hybridized filters to photographic film, or by employing computerized imaging systems, such the Molecular Dynamics 400-B 2D Phosphorimager available from Amersham Biosciences, Piscataway, N.J.

Deletions, mutations and/or amplifications of an miR gene can also be detected by amplifying a fragment of these genes by polymerase chain reaction (PCR), and analyzing the amplified fragment by sequencing or by electrophoresis to determine if the sequence and/or length of the amplified fragment from the subject's DNA sample is different from that of a control DNA sample. Suitable reaction and cycling conditions for PCR amplification of DNA fragments can be readily determined by one of ordinary skill in the art.

Deletions of an miR gene can also be identified by detecting deletions of chromosomal markers that are closely linked to the miR gene. Mutations in an miR gene can also be detected by the technique of single strand conformational polymorphism (SSCP), for example, as described in Orita et al. (1989), Genomics 5:874-879 and Hayashi (1991), PCR Methods and Applic. 1:34-38, the entire disclosures of which are herein incorporated by reference. The SSCP technique consists of amplifying a fragment of the gene of interest by PCR; denaturing the fragment and electrophoresing the two denatured single strands under non-denaturing conditions. The single strands assume a complex sequence-dependent intrastrand secondary structure that affects the strands electrophoretic mobility.

The status of an miR gene in cells of a subject can also be evaluated by measuring the copy number of the at least one miR gene in the sample, wherein a gene copy number other than two for miR genes on somatic chromosomes and sex chromosomes in a female, or other than one for miR genes on sex chromosomes in a male, is indicative of the presence of the cancer in the subject.

Any technique suitable for detecting gene copy number can be used in the practice of the present method, including the Southern blot and PCR amplification techniques described above. An alternative method of determining the miR gene copy number in a sample of tissue relies on the fact that many miR genes or gene clusters are closely linked to chromosomal markers or other genes. The loss of a copy of an miR gene in an individual who is heterozygous at a marker or gene closely linked to the miR gene can be inferred from the loss of heterozygosity in the closely linked marker or gene. Methods for determining loss of heterozygosity of chromosomal markers are within the skill in the art.

As discussed above, the human miR genes are closely associated with different classes of chromosomal features that are themselves associated with cancer. These cancers are likely caused, in part, by the perturbation in the chromosome or genomic DNA caused by the cancer-associated chromosomal feature, which can affect expression of oncogenes or tumor-suppressor genes located near the site of perturbation. Without wishing to be bound by any theory, it is believed that the perturbations caused by the cancer-associated chromosomal features also affect the expression level of miR genes associated with the feature, and that this also may also contribute to cancerigenesis. Therefore, a given cancer can be treated by restoring the level of miR gene expression associated with that cancer to normal. For example, if the level of miR gene expression is down-regulated in cancer cells of a subject, then the cancer can be treated by raising the miR expression level. Likewise, if the level of miR gene expression is up-regulated in cancer cells of a subject, then the cancer can be treated by reducing the miR expression level.

The cancers associated with different cancer-associated chromosomal features, and the miR genes associated with these features, are described above and in Tables 5, 6 and 7 and FIG. 2. In the practice of the present method, expression the appropriate miR gene or genes associated with a particular cancer and/or cancer-associated chromosomal features is altered by the compositions and methods described herein. As before, specific groupings of miR gene(s) that are associated with a particular cancer-associated chromosomal feature and/or cancer are evident from Tables 5, 6 and 7 and in FIG. 2, and are preferred. In one embodiment, the method of treatment comprising administering an miR gene product. In another embodiment, the method of treatment comprises administering an miR gene product, provided the miR gene product is not miR-15, miR-16, miR-143 and/or miR-145.

In one embodiment of the present method, the level of at least one miR gene product in cancer cells of a subject is first determined relative to control cells. Techniques suitable for determining the relative level of miR gene product in cells are described above. If miR gene expression is down-regulated in the cancer cell relative to control cells, then the cancer cells are treated with an effective amount of a compound comprising the isolated miR gene product from the miR gene which is down-regulated. If miR gene expression is up-regulated in cancer cells relative to control cells, then the cancer cells are treated with an effective amount of a compound that inhibits miR gene expression. In one embodiment, the level of miR gene product in a cancer cell is not determined beforehand, for example, in those cancers where miR gene expression is known to be up- or down-regulated.

Thus, in the practice of the present treatment methods, an effective amount of at least one isolated miR gene product can be administered to a subject. As used herein, an “effective amount” of an isolated miR gene product is an amount sufficient to inhibit proliferation of a cancer cell in a subject suffering from a cancer associated with a cancer-associated chromosomal feature. One skilled in the art can readily determine an effective amount of an miR gene product to be administered to a given subject, by taking into account factors such as the size and weight of the subject; the extent of disease penetration; the age, health and sex of the subject; the route of administration; and whether the administration is regional or systemic.

For example, an effective amount of isolated miR gene product can be based on the approximate weight of a tumor mass to be treated. The approximate weight of a tumor mass can be determined by calculating the approximate volume of the mass, wherein one cubic centimeter of volume is roughly equivalent to one gram. An effective amount of the isolated miR gene product based on the weight of a tumor mass can be at least about 10 micrograms/gram of tumor mass, and is preferably between about 10-500 micrograms/gram of tumor mass. More preferably, the effective amount is at least about 60 micrograms/gram of tumor mass. Particularly preferably, the effective amount is at least about 100 micrograms/gram of tumor mass. It is preferred that an effective amount based on the weight of the tumor mass be injected directly into the tumor.

An effective amount of an isolated miR gene product can also be based on the approximate or estimated body weight of a subject to be treated. Preferably, such effective amounts are administered parenterally or enterally, as described herein. For example, an effective amount of the isolated miR gene product is administered to a subject can range from about 5-3000 micrograms/kg of body weight, and is preferably between about 700-1000 micrograms/kg of body weight, and is more preferably greater than about 1000 micrograms/kg of body weight.

One skilled in the art can also readily determine an appropriate dosage regimen for the administration of an isolated miR gene product to a given subject. For example, an miR gene product can be administered to the subject once (e.g., as a single injection or deposition). Alternatively, an miR gene product can be administered once or twice daily to a subject for a period of from about three to about twenty-eight days, more preferably from about seven to about ten days. In a preferred dosage regimen, an miR gene product is administered once a day for seven days. Where a dosage regimen comprises multiple administrations, it is understood that the effective amount of the miR gene product administered to the subject can comprise the total amount of gene product administered over the entire dosage regimen.

As used herein, an “isolated” miR gene product is one which is synthesized, or altered or removed from the natural state through human intervention. For example, an miR gene product naturally present in a living animal is not “isolated.” A synthetic miR gene product, or an miR gene product partially or completely separated from the coexisting materials of its natural state, is “isolated.” An isolated miR gene product can exist in substantially purified form, or can exist in a cell into which the miR gene product has been delivered. Thus, an miR gene product which is deliberately delivered to, or expressed in, a cell is considered an “isolated” miR gene product. An miR gene product produced inside a cell by from an miR precursor molecule is also considered to be “isolated” molecule.

Isolated miR gene products can be obtained using a number of standard techniques. For example, the miR gene products can be chemically synthesized or recombinantly produced using methods known in the art. Preferably, miR gene products are chemically synthesized using appropriately protected ribonucleoside phosphoramidites and a conventional DNA/RNA synthesizer. Commercial suppliers of synthetic RNA molecules or synthesis reagents include, e.g., Proligo (Hamburg, Germany), Dharmacon Research (Lafayette, Colo., USA), Pierce Chemical (part of Perbio Science, Rockford, Ill., USA), Glen Research (Sterling, Va., USA), ChemGenes (Ashland, Mass., USA) and Cruachem (Glasgow, UK).

Alternatively, the miR gene products can be expressed from recombinant circular or linear DNA plasmids using any suitable promoter. Suitable promoters for expressing RNA from a plasmid include, e.g., the U6 or H1 RNA pol III promoter sequences, or the cytomegalovirus promoters. Selection of other suitable promoters is within the skill in the art. The recombinant plasmids of the invention can also comprise inducible or regulatable promoters for expression of the miR gene products in cancer cells.

The miR gene products that are expressed from recombinant plasmids can be isolated from cultured cell expression systems by standard techniques. The miR gene products which are expressed from recombinant plasmids can also be delivered to, and expressed directly in, the cancer cells. The use of recombinant plasmids to deliver the miR gene products to cancer cells is discussed in more detail below.

The miR gene products can be expressed from a separate recombinant plasmid, or can be expressed from the same recombinant plasmid. Preferably, the miR gene products are expressed as the RNA precursor molecules from a single plasmid, and the precursor molecules are processed into the functional miR gene product by a suitable processing system, including processing systems extant within a cancer cell. Other suitable processing systems include, e.g., the in vitro Drosophila cell lysate system as described in U.S. published application 2002/0086356 to Tuschl et al. and the E. coli RNAse III system described in U.S. published patent application 2004/0014113 to Yang et al., the entire disclosures of which are herein incorporated by reference.

Selection of plasmids suitable for expressing the miR gene products, methods for inserting nucleic acid sequences into the plasmid to express the gene products, and methods of delivering the recombinant plasmid to the cells of interest are within the skill in the art. See, for example, Zeng et al. (2002), Molecular Cell 9:1327-1333; Tuschl (2002), Nat. Biotechnol, 20:446-448; Brummelkamp et al. (2002), Science 296:550-553; Miyagishi et al. (2002), Nat. Biotechnol. 20:497-500; Paddison et al. (2002), Genes Dev. 16:948-958; Lee et al. (2002), Nat. Biotechnol. 20:500-505; and Paul et al. (2002), Nat. Biotechnol. 20:505-508, the entire disclosures of which are herein incorporated by reference.

In one embodiment, a plasmid expressing the miR gene products comprises a sequence encoding a miR precursor RNA under the control of the CMV intermediate-early promoter. As used herein, “under the control” of a promoter means that the nucleic acid sequences encoding the miR gene product are located 3′ of the promoter, so that the promoter can initiate transcription of the miR gene product coding sequences.

The miR gene products can also be expressed from recombinant viral vectors. It is contemplated that the miR gene products can be expressed from two separate recombinant viral vectors, or from the same viral vector. The RNA expressed from the recombinant viral vectors can either be isolated from cultured cell expression systems by standard techniques, or can be expressed directly in cancer cells. The use of recombinant viral vectors to deliver the miR gene products to cancer cells is discussed in more detail below.

The recombinant viral vectors of the invention comprise sequences encoding the miR gene products and any suitable promoter for expressing the RNA sequences. Suitable promoters include, for example, the U6 or H1 RNA pol III promoter sequences, or the cytomegalovirus promoters. Selection of other suitable promoters is within the skill in the art. The recombinant viral vectors of the invention can also comprise inducible or regulatable promoters for expression of the miR gene products in a cancer cell.

Any viral vector capable of accepting the coding sequences for the miR gene products can be used; for example, vectors derived from adenovirus (AV); adeno-associated virus (AAV); retroviruses (e.g., lentiviruses (LV), Rhabdoviruses, murine leukemia virus); herpes virus, and the like. The tropism of the viral vectors can be modified by pseudotyping the vectors with envelope proteins or other surface antigens from other viruses, or by substituting different viral capsid proteins, as appropriate.

For example, lentiviral vectors of the invention can be pseudotyped with surface proteins from vesicular stomatitis virus (VSV), rabies, Ebola, Mokola, and the like. AAV vectors of the invention can be made to target different cells by engineering the vectors to express different capsid protein serotypes. For example, an AAV vector expressing a serotype 2 capsid on a serotype 2 genome is called AAV 2/2. This serotype 2 capsid gene in the AAV 2/2 vector can be replaced by a serotype 5 capsid gene to produce an AAV 2/5 vector. Techniques for constructing AAV vectors which express different capsid protein serotypes are within the skill in the art; see, e.g., Rabinowitz J. E. et al. (2002), J Virol 76:791-801, the entire disclosure of which is herein incorporated by reference.

Selection of recombinant viral vectors suitable for use in the invention, methods for inserting nucleic acid sequences for expressing RNA into the vector, methods of delivering the viral vector to the cells of interest, and recovery of the expressed RNA products are within the skill in the art. See, for example, Dornburg (1995), Gene Therap. 2:301-310; Eglitis (1988), Biotechniques 6:608-614; Miller (1990), Hum. Gene Therap. 1:5-14; and Anderson (1998), Nature 392:25-30, the entire disclosures of which are herein incorporated by reference.

Preferred viral vectors are those derived from AV and AAV. A suitable AV vector for expressing the miR gene products, a method for constructing the recombinant AV vector, and a method for delivering the vector into target cells, are described in Xia et al. (2002), Nat. Biotech. 20:1006-1010, the entire disclosure of which is herein incorporated by reference. Suitable AAV vectors for expressing the miR gene products, methods for constructing the recombinant AAV vector, and methods for delivering the vectors into target cells are described in Samulski et al. (1987), J. Virol. 61:3096-3101; Fisher et al. (1996), J. Virol., 70:520-532; Samulski et al. (1989), J. Virol. 63:3822-3826; U.S. Pat. No. 5,252,479; U.S. Pat. No. 5,139,941; International Patent Application No. WO 94/13788; and International Patent Application No. WO 93/24641, the entire disclosures of which are herein incorporated by reference. Preferably, the miR gene products are expressed from a single recombinant AAV vector comprising the CMV intermediate early promoter.

In one embodiment, a recombinant AAV viral vector of the invention comprises a nucleic acid sequence encoding an miR precursor RNA in operable connection with a polyT termination sequence under the control of a human U6 RNA promoter. As used herein, “in operable connection with a polyT termination sequence” means that the nucleic acid sequences encoding the sense or antisense strands are immediately adjacent to the polyT termination signal in the 5′ direction. During transcription of the miR sequences from the vector, the polyT termination signals act to terminate transcription.

In the practice of the present treatment methods, an effective amount of at least one compound which inhibits miR gene expression can also be administered to the subject. As used herein, “inhibiting miR gene expression” means that the production of miR gene product from the miR gene in the cancer cell after treatment is less than the amount produced prior to treatment. One skilled in the art can readily determine whether miR gene expression has been inhibited in a cancer cell, using for example the techniques for determining miR transcript level discussed above for the diagnostic method.

As used herein, an “effective amount” of a compound that inhibits miR gene expression is an amount sufficient to inhibit proliferation of a cancer cell in a subject suffering from a cancer associated with a cancer-associated chromosomal feature. One skilled in the art can readily determine an effective amount of an miR gene expression-inhibiting compound to be administered to a given subject, by taking into account factors such as the size and weight of the subject; the extent of disease penetration; the age, health and sex of the subject; the route of administration; and whether the administration is regional or systemic.

For example, an effective amount of the expression-inhibiting compound can be based on the approximate weight of a tumor mass to be treated. The approximate weight of a tumor mass can be determined by calculating the approximate volume of the mass, wherein one cubic centimeter of volume is roughly equivalent to one gram. An effective amount based on the weight of a tumor mass can be at least about 10 micrograms/gram of tumor mass, and is preferably between about 10-500 micrograms/gram of tumor mass. More preferably, the effective amount is at least about 60 micrograms/gram of tumor mass. Particularly preferably, the effective amount is at least about 100 micrograms/gram of tumor mass. It is preferred that an effective amount based on the weight of the tumor mass be injected directly into the tumor.

An effective amount of a compound that inhibits miR gene expression can also be based on the approximate or estimated body weight of a subject to be treated. Preferably, such effective amounts are administered parenterally or enterally, as described herein. For example, an effective amount of the expression-inhibiting compound administered to a subject can range from about 5-3000 micrograms/kg of body weight, and is preferably between about 700-1000 micrograms/kg of body weight, and is more preferably greater than about 1000 micrograms/kg of body weight.

One skilled in the art can also readily determine an appropriate dosage regimen for administering a compound that inhibits miR gene expression to a given subject. For example, an expression-inhibiting compound can be administered to the subject once (e.g., as a single injection or deposition). Alternatively, an expression-inhibiting compound can be administered once or twice daily to a subject for a period of from about three to about twenty-eight days, more preferably from about seven to about ten days. In a preferred dosage regimen, an expression-inhibiting compound is administered once a day for seven days. Where a dosage regimen comprises multiple administrations, it is understood that the effective amount of the expression-inhibiting compound administered to the subject can comprise the total amount of compound administered over the entire dosage regimen.

Suitable compounds for inhibiting miR gene expression include double-stranded RNA (such as short- or small-interfering RNA or “siRNA”), antisense nucleic acids, and enzymatic RNA molecules such as ribozymes. Each of these compounds can be targeted to a given miR gene product and destroy or induce the destruction of the target miR gene product.

For example, expression of a given miR gene can be inhibited by inducing RNA interference of the miR gene with an isolated double-stranded RNA (“dsRNA”) molecule which has at least 90%, for example 95%, 98%, 99% or 100%, sequence homology with at least a portion of the miR gene product. In a preferred embodiment, the dsRNA molecule is a “short or small interfering RNA” or “siRNA.”

siRNA useful in the present methods comprise short double-stranded RNA from about 17 nucleotides to about 29 nucleotides in length, preferably from about 19 to about 25 nucleotides in length. The siRNA comprise a sense RNA strand and a complementary antisense RNA strand annealed together by standard Watson-Crick base-pairing interactions (hereinafter “base-paired”). The sense strand comprises a nucleic acid sequence which is substantially identical to a nucleic acid sequence contained within the target miR gene product.

As used herein, a nucleic acid sequence in an siRNA which is “substantially identical” to a target sequence contained within the target mRNA is a nucleic acid sequence that is identical to the target sequence, or that differs from the target sequence by one or two nucleotides. The sense and antisense strands of the siRNA can comprise two complementary, single-stranded RNA molecules, or can comprise a single molecule in which two complementary portions are base-paired and are covalently linked by a single-stranded “hairpin” area.

The siRNA can also be altered RNA that differs from naturally-occurring RNA by the addition, deletion, substitution and/or alteration of one or more nucleotides. Such alterations can include addition of non-nucleotide material, such as to the end(s) of the siRNA or to one or more internal nucleotides of the siRNA, or modifications that make the siRNA resistant to nuclease digestion, or the substitution of one or more nucleotides in the siRNA with deoxyribonucleotides.

One or both strands of the siRNA can also comprise a 3′ overhang. As used herein, a “3′ overhang” refers to at least one unpaired nucleotide extending from the 3′-end of a duplexed RNA strand. Thus, in one embodiment, the siRNA comprises at least one 3′ overhang of from 1 to about 6 nucleotides (which includes ribonucleotides or deoxyribonucleotides) in length, preferably from 1 to about 5 nucleotides in length, more preferably from 1 to about 4 nucleotides in length, and particularly preferably from about 2 to about 4 nucleotides in length. In a preferred embodiment, the 3′ overhang is present on both strands of the siRNA, and is 2 nucleotides in length. For example, each strand of the siRNA can comprise 3′ overhangs of dithymidylic acid (“TT”) or diuridylic acid (“uu”).

The siRNA can be produced chemically or biologically, or can be expressed from a recombinant plasmid or viral vector, as described above for the isolated miR gene products. Exemplary methods for producing and testing dsRNA or siRNA molecules are described in U.S. published patent application 2002/0173478 to Gewirtz and in U.S. published patent application 2004/0018176 to Reich et al., the entire disclosures of which are herein incorporated by reference.

Expression of a given miR gene can also be inhibited by an antisense nucleic acid. As used herein, an “antisense nucleic acid” refers to a nucleic acid molecule that binds to target RNA by means of RNA-RNA or RNA-DNA or RNA-peptide nucleic acid interactions, which alters the activity of the target RNA. Antisense nucleic acids suitable for use in the present methods are single-stranded nucleic acids (e.g., RNA, DNA, RNA-DNA chimeras, PNA) that generally comprise a nucleic acid sequence complementary to a contiguous nucleic acid sequence in an miR gene product. Preferably, the antisense nucleic acid comprises a nucleic acid sequence that is 50-100% complementary, more preferably 75-100% complementary, and most preferably 95-100% complementary to a contiguous nucleic acid sequence in an miR gene product. Nucleic acid sequences for the miR gene products are provided in Table 1. Without wishing to be bound by any theory, it is believed that the antisense nucleic acids activate RNase H or some other cellular nuclease that digests the miR gene product/antisense nucleic acid duplex.

Antisense nucleic acids can also contain modifications to the nucleic acid backbone or to the sugar and base moieties (or their equivalent) to enhance target specificity, nuclease resistance, delivery or other properties related to efficacy of the molecule. Such modifications include cholesterol moieties, duplex intercalators such as acridine or the inclusion of one or more nuclease-resistant groups.

Antisense nucleic acids can be produced chemically or biologically, or can be expressed from a recombinant plasmid or viral vector, as described above for the isolated miR gene products. Exemplary methods for producing and testing are within the skill in the art; see, e.g., Stein and Cheng (1993), Science 261:1004 and U.S. Pat. No. 5,849,902 to Woolf et al., the entire disclosures of which are herein incorporated by reference.

Expression of a given miR gene can also be inhibited by an enzymatic nucleic acid. As used herein, an “enzymatic nucleic acid” refers to a nucleic acid comprising a substrate binding region that has complementarity to a contiguous nucleic acid sequence of an miR gene product, and which is able to specifically cleave the miR gene product. Preferably, the enzymatic nucleic acid substrate binding region is 50-100% complementary, more preferably 75-100% complementary, and most preferably 95-100% complementary to a contiguous nucleic acid sequence in an miR gene product. The enzymatic nucleic acids can also comprise modifications at the base, sugar, and/or phosphate groups. An exemplary enzymatic nucleic acid for use in the present methods is a ribozyme.

The enzymatic nucleic acids can be produced chemically or biologically, or can be expressed from a recombinant plasmid or viral vector, as described above for the isolated miR gene products. Exemplary methods for producing and testing dsRNA or siRNA molecules are described in Werner and Uhlenbeck (1995), Nucl. Acids Res. 23:2092-96; Hammann et al. (1999), Antisense and Nucleic Acid Drug Dev. 9:25-31; and U.S. Pat. No. 4,987,071 to Cech et al, the entire disclosures of which are herein incorporated by reference.

Administration of at least one miR gene product, or at least one compound for inhibiting miR gene expression, will inhibit the proliferation of cancer cells in a subject who has a cancer associated with a cancer-associated chromosomal feature. As used herein, to “inhibit the proliferation of a cancer cell” means to kill the cell, or permanently or temporarily arrest or slow the growth of the cell. Inhibition of cancer cell proliferation can be inferred if the number of such cells in the subject remains constant or decreases after administration of the miR gene products or miR gene expression-inhibiting compounds. An inhibition of cancer cell proliferation can also be inferred if the absolute number of such cells increases, but the rate of tumor growth decreases.

The number of cancer cells in a subject's body can be determined by direct measurement, or by estimation from the size of primary or metastatic tumor masses. For example, the number of cancer cells in a subject can be measured by immunohistological methods, flow cytometry, or other techniques designed to detect characteristic surface markers of cancer cells.

The size of a tumor mass can be ascertained by direct visual observation, or by diagnostic imaging methods, such as X-ray, magnetic resonance imaging, ultrasound, and scintigraphy. Diagnostic imaging methods used to ascertain size of the tumor mass can be employed with or without contrast agents, as is known in the art. The size of a tumor mass can also be ascertained by physical means, such as palpation of the tissue mass or measurement of the tissue mass with a measuring instrument, such as a caliper.

The miR gene products or miR gene expression-inhibiting compounds can be administered to a subject by any means suitable for delivering these compounds to cancer cells of the subject. For example, the miR gene products or miR expression inhibiting compounds can be administered by methods suitable to transfect cells of the subject with these compounds, or with nucleic acids comprising sequences encoding these compounds. Preferably, the cells are transfected with a plasmid or viral vector comprising sequences encoding at least one miR gene product or miR gene expression inhibiting compound.

Transfection methods for eukaryotic cells are well known in the art, and include, e.g., direct injection of the nucleic acid into the nucleus or pronucleus of a cell; electroporation; liposome transfer or transfer mediated by lipophilic materials; receptor mediated nucleic acid delivery, bioballistic or particle acceleration; calcium phosphate precipitation, and transfection mediated by viral vectors.

For example, cells can be transfected with a liposomal transfer compound, e.g., DOTAP (N-[1-(2,3-dioleoyloxy)propyl]-N,N,N-trimethyl-ammonium methylsulfate, Boehringer—Mannheim) or an equivalent, such as LIPOFECTIN. The amount of nucleic acid used is not critical to the practice of the invention; acceptable results may be achieved with 0.1-100 micrograms of nucleic acid/10⁵cells. For example, a ratio of about 0.5 micrograms of plasmid vector in 3 micrograms of DOTAP per 10⁵cells can be used.

An miR gene product or miR gene expression inhibiting compound can also be administered to a subject by any suitable enteral or parenteral administration route. Suitable enteral administration routes for the present methods include, e.g., oral, rectal, or intranasal delivery. Suitable parenteral administration routes include, e.g., intravascular administration (e.g., intravenous bolus injection, intravenous infusion, intra-arterial bolus injection, intra-arterial infusion and catheter instillation into the vasculature); peri- and intra-tissue injection (e.g., peri-tumoral and intra-tumoral injection, intra-retinal injection, or subretinal injection); subcutaneous injection or deposition, including subcutaneous infusion (such as by osmotic pumps); direct application to the tissue of interest, for example by a catheter or other placement device (e.g., a retinal pellet or a suppository or an implant comprising a porous, non-porous, or gelatinous material); and inhalation. Preferred administration routes are injection, infusion and direct injection into the tumor.

In the present methods, an miR gene product or miR gene expression inhibiting compound can be administered to the subject either as naked RNA, in combination with a delivery reagent, or as a nucleic acid (e.g., a recombinant plasmid or viral vector) comprising sequences that express the miR gene product or expression inhibiting compound. Suitable delivery reagents include, e.g, the Minis Transit TKO lipophilic reagent; lipofectin; lipofectamine; cellfectin; polycations (e.g., polylysine), and liposomes.

Recombinant plasmids and viral vectors comprising sequences that express the miR gene products or miR gene expression inhibiting compounds, and techniques for delivering such plasmids and vectors to cancer cells, are discussed above.

In a preferred embodiment, liposomes are used to deliver an miR gene product or miR gene expression-inhibiting compound (or nucleic acids comprising sequences encoding them) to a subject. Liposomes can also increase the blood half-life of the gene products or nucleic acids.

Liposomes suitable for use in the invention can be formed from standard vesicle-forming lipids, which generally include neutral or negatively charged phospholipids and a sterol, such as cholesterol. The selection of lipids is generally guided by consideration of factors such as the desired liposome size and half-life of the liposomes in the blood stream. A variety of methods are known for preparing liposomes, for example, as described in Szoka et al. (1980), Ann. Rev. Biophys. Bioeng. 9:467; and U.S. Pat. Nos. 4,235,871, 4,501,728, 4,837,028, and 5,019,369, the entire disclosures of which are herein incorporated by reference.

The liposomes for use in the present methods can comprise a ligand molecule that targets the liposome to cancer cells. Ligands which bind to receptors prevalent in cancer cells, such as monoclonal antibodies that bind to tumor cell antigens, are preferred.

The liposomes for use in the present methods can also be modified so as to avoid clearance by the mononuclear macrophage system (“MMS”) and reticuloendothelial system (“RES”). Such modified liposomes have opsonization-inhibition moieties on the surface or incorporated into the liposome structure. In a particularly preferred embodiment, a liposome of the invention can comprise both opsonization-inhibition moieties and a ligand.

Opsonization-inhibiting moieties for use in preparing the liposomes of the invention are typically large hydrophilic polymers that are bound to the liposome membrane. As used herein, an opsonization inhibiting moiety is “bound” to a liposome membrane when it is chemically or physically attached to the membrane, e.g., by the intercalation of a lipid-soluble anchor into the membrane itself, or by binding directly to active groups of membrane lipids. These opsonization-inhibiting hydrophilic polymers form a protective surface layer that significantly decreases the uptake of the liposomes by the MMS and RES; e.g., as described in U.S. Pat. No. 4,920,016, the entire disclosure of which is herein incorporated by reference.

Opsonization inhibiting moieties suitable for modifying liposomes are preferably water-soluble polymers with a number-average molecular weight from about 500 to about 40,000 daltons, and more preferably from about 2,000 to about 20,000 daltons. Such polymers include polyethylene glycol (PEG) or polypropylene glycol (PPG) derivatives; e.g., methoxy PEG or PPG, and PEG or PPG stearate; synthetic polymers such as polyacrylamide or poly N-vinyl pyrrolidone; linear, branched, or dendrimeric polyamidoamines; polyacrylic acids; polyalcohols, e.g., polyvinylalcohol and polyxylitol to which carboxylic or amino groups are chemically linked, as well as gangliosides, such as ganglioside GM1. Copolymers of PEG, methoxy PEG, or methoxy PPG, or derivatives thereof, are also suitable. In addition, the opsonization inhibiting polymer can be a block copolymer of PEG and either a polyamino acid, polysaccharide, polyamidoamine, polyethyleneamine, or polynucleotide. The opsonization inhibiting polymers can also be natural polysaccharides containing amino acids or carboxylic acids, e.g., galacturonic acid, glucuronic acid, mannuronic acid, hyaluronic acid, pectic acid, neuraminic acid, alginic acid, carrageenan; aminated polysaccharides or oligosaccharides (linear or branched); or carboxylated polysaccharides or oligosaccharides, e.g., reacted with derivatives of carbonic acids with resultant linking of carboxylic groups. Preferably, the opsonization-inhibiting moiety is a PEG, PPG, or derivatives thereof. Liposomes modified with PEG or PEG-derivatives are sometimes called “PEGylated liposomes.”

The opsonization inhibiting moiety can be bound to the liposome membrane by any one of numerous well-known techniques. For example, an N-hydroxysuccinimide ester of PEG can be bound to a phosphatidyl-ethanolamine lipid-soluble anchor, and then bound to a membrane. Similarly, a dextran polymer can be derivatized with a stearylamine lipid-soluble anchor via reductive amination using Na(CN)BH₃and a solvent mixture, such as tetrahydrofuran and water in a 30:12 ratio at 60° C.

Liposomes modified with opsonization-inhibition moieties remain in the circulation much longer than unmodified liposomes. For this reason, such liposomes are sometimes called “stealth” liposomes. Stealth liposomes are known to accumulate in tissues fed by porous or “leaky” microvasculature. Thus, tissue characterized by such microvasculature defects, for example solid tumors, will efficiently accumulate these liposomes; see Gabizon, et al. (1988), Proc. Natl. Acad. Sci., USA, 18:6949-53. In addition, the reduced uptake by the RES lowers the toxicity of stealth liposomes by preventing significant accumulation of the liposomes in the liver and spleen. Thus, liposomes that are modified with opsonization-inhibition moieties are particularly suited to deliver the miR gene products or miR gene expression inhibition compounds (or nucleic acids comprising sequences encoding them) to tumor cells.

The miR gene products or miR gene expression inhibition compounds are preferably formulated as pharmaceutical compositions, sometimes called “medicaments,” prior to administering to a subject, according to techniques known in the art. Pharmaceutical compositions of the present invention are characterized as being at least sterile and pyrogen-free. As used herein, “pharmaceutical formulations” include formulations for human and veterinary use. Methods for preparing pharmaceutical compositions of the invention are within the skill in the art, for example as described in Remington's Pharmaceutical Science, 17th ed., Mack Publishing Company, Easton, Pa. (1985), the entire disclosure of which is herein incorporated by reference.

The present pharmaceutical formulations comprise at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them) (e.g., 0.1 to 90% by weight), or a physiologically acceptable salt thereof, mixed with a pharmaceutically-acceptable carrier. The pharmaceutical formulations of the invention can also comprise at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them) which are encapsulated by liposomes and a pharmaceutically-acceptable carrier. In one embodiment, the pharmaceutical compositions comprise an miR gene or gene product that is not miR-15, miR-16, miR-143 and/or miR-145.

Preferred pharmaceutically-acceptable carriers are water, buffered water, normal saline, 0.4% saline, 0.3% glycine, hyaluronic acid and the like.

In a preferred embodiment, the pharmaceutical compositions of the invention comprise at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them) which is resistant to degradation by nucleases. One skilled in the art can readily synthesize nucleic acids which are nuclease resistant, for example by incorporating one or more ribonucleotides that are modified at the 2′-position into the miR gene products. Suitable 2′-modified ribonucleotides include those modified at the 2′-position with fluoro, amino, alkyl, alkoxy, and O-allyl.

Pharmaceutical compositions of the invention can also comprise conventional pharmaceutical excipients and/or additives. Suitable pharmaceutical excipients include stabilizers, antioxidants, osmolality adjusting agents, buffers, and pH adjusting agents. Suitable additives include, e.g., physiologically biocompatible buffers (e.g., tromethamine hydrochloride), additions of chelants (such as, for example, DTPA or DTPA-bisamide) or calcium chelate complexes (such as, for example, calcium DTPA, CaNaDTPA-bisamide), or, optionally, additions of calcium or sodium salts (for example, calcium chloride, calcium ascorbate, calcium gluconate or calcium lactate). Pharmaceutical compositions of the invention can be packaged for use in liquid form, or can be lyophilized.

For solid pharmaceutical compositions of the invention, conventional nontoxic solid pharmaceutically-acceptable carriers can be used; for example, pharmaceutical grades of mannitol, lactose, starch, magnesium stearate, sodium saccharin, talcum, cellulose, glucose, sucrose, magnesium carbonate, and the like.

For example, a solid pharmaceutical composition for oral administration can comprise any of the carriers and excipients listed above and 10-95%, preferably 25%-75%, of the at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them). A pharmaceutical composition for aerosol (inhalational) administration can comprise 0.01-20% by weight, preferably 1%-10% by weight, of the at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them) encapsulated in a liposome as described above, and a propellant. A carrier can also be included as desired; e.g., lecithin for intranasal delivery.

The invention will now be illustrated by the following non-limiting examples.

EXAMPLES

The following techniques were used in the Examples.

General Methods:

The miR Gene Database

A set of 187 human miR genes was compiled (see Table 1). The set comprises 153 miRs identified in the miR Registry (maintained by the Wellcome Trust Sanger Institute, Cambridge, UK), and 36 other miRs manually curated from published papers (Lim et al., 2003, Science 299:1540; Lagos-Quintana et al., 2001, Science 294:853-858; Lau et al., 2001, Science 294:858-862; Lee et al., 2001, Science 294:862-864; Mourelatos et al., 2002, Genes Dev. 16:720-728; Lagos-Quintana et al., 2002, Curr. Biol. 12:735-739; Dostie et al., 2003, RNA 9:180-186; Houbaviy et al., 2003, Dev. Cell. 5:351-8) or found in the GenBank database accessed through the National Center for Biotechnology Information (NCBI) website, maintained by the National Institutes of Health and the National Library of Medicine Nineteen new human miRs (approximately 10% of the miR set) were found based on their homology with cloned miRs from other species (mainly mouse). For all miRs, the sequence of the precursor was identified using the M Zucker RNA folding program and selecting the precursor sequence that gave the best score for the hairpin structure. The program is available and is maintained by Michael Zucker of Rensselaer Polytechnic Institute.

TABLE 1

Human miR Gene Product Sequences

SEQ ID

Name
Precursor Sequence (5′ to 3′)*
NO.

has-let-7a-1-prec
CACTGTGGGATGAGGTAGTAGGTTGTATAGTTTTAGG
1

GTCACACCCACCACTGGGAGATAACTATACAATCTAC

TGTCTTTCCTAACGTG

hsa-let-7a-2-prec
AGGTTGAGGTAGTAGGTTGTATAGTTTAGAATTACAT
2

CAAGGGAGATAACTGTACAGCCTCCTAGCTTTCCT

hsa-let-7a-3-prec
GGGTGAGGTAGTAGGTTGTATAGTTTGGGGCTCTGCC
3

CTGCTATGGGATAACTATACAATCTACTGTCTTTCCT

hsa-let-7a-4-prec
GTGACTGCATGCTCCCAGGTTGAGGTAGTAGGTTGTA
4

TAGTTTAGAATTACACAAGGGAGATAACTGTACAGCC

TCCTAGCTTTCCTTGGGTCTTGCACTAAACAAC

hsa-let-7b-prec
GGCGGGGTGAGGTAGTAGGTTGTGTGGTTTCAGGGCA
5

GTGATGTTGCCCCTCGGAAGATAACTATACAACCTAC

TGCCTTCCCTG

hsa-let-7c-prec
GCATCCGGGTTGAGGTAGTAGGTTGTATGGTTTAGAG
6

TTACACCCTGGGAGTTAACTGTACAACCTTCTAGCTT

TCCTTGGAGC

hsa-let-7d-prec
CCTAGGAAGAGGTAGTAGGTTGCATAGTTTTAGGGCA
7

GGGATTTTGCCCACAAGGAGGTAACTATACGACCTGC

TGCCTTTCTTAGG

hsa-let-7d-v1-prec
CTAGGAAGAGGTAGTAGTTTGCATAGTTTTAGGGCAA
8

AGATTTTGCCCACAAGTAGTTAGCTATACGACCTGCA

GCCTTTTGTAG

hsa-let-7d-v2-prec
CTGGCTGAGGTAGTAGTTTGTGCTGTTGGTCGGGTTG
9

TGACATTGCCCGCTGTGGAGATAACTGCGCAAGCTAC

TGCCTTGCTAG

hsa-let-7e-prec
CCCGGGCTGAGGTAGGAGGTTGTATAGTTGAGGAGGA
10

CACCCAAGGAGATCACTATACGGCCTCCTAGCTTTCC

CCAGG

hsa-let-7f-1-prec
TCAGAGTGAGGTAGTAGATTGTATAGTTGTGGGGTAG
11

TGATTTTACCCTGTTCAGGAGATAACTATACAATCTA

TTGCCTTCCCTGA

hsa-let-7f-2-prec
CTGTGGGATGAGGTAGTAGATTGTATAGTTGTGGGGT
12

AGTGATTTTACCCTGTTCAGGAGATAACTATACAATC

TATTGCCTTCCCTGA

hsa-let-7f-2-prec
CTGTGGGATGAGGTAGTAGATTGTATAGTTTTAGGGT
13

CATACCCCATCTTGGAGATAACTATACAGTCTACTGT

CTTTCCCACGG

hsa-let-7g-prec
TTGCCTGATTCCAGGCTGAGGTAGTAGTTTGTACAGT
14

TTGAGGGTCTATGATACCACCCGGTACAGGAGATAAC

TGTACAGGCCACTGCCTTGCCAGGAACAGCGCGC

hsa-let-7i-prec
CTGGCTGAGGTAGTAGTTTGTGCTGTTGGTCGGGTTG
15

TGACATTGCCCGCTGTGGAGATAACTGCGCAAGCTAC

TGCCTTGCTAG

hsa-mir-001b-1-prec
ACCTACTCAGAGTACATACTTCTTTATGTACCCATAT
16

GAACATACAATGCTATGGAATGTAAAGAAGTATGTAT

TTTTGGTAGGC

hsa-mir-001b-1-prec
CAGCTAACAACTTAGTAATACCTACTCAGAGTACATA
17

CTTCTTTATGTACCCATATGAACATACAATGCTATGG

AATGTAAAGAAGTATGTATTTTTGGTAGGCAATA

hsa-mir-001b-2-prec
GCCTGCTTGGGAAACATACTTCTTTATATGCCCATAT
18

GGACCTGCTAAGCTATGGAATGTAAAGAAGTATGTAT

CTCAGGCCGGG

hsa-mir-001b-prec
TGGGAAACATACTTCTTTATATGCCCATATGGACCTG
19

CTAAGCTATGGAATGTAAAGAAGTATGTATCTCA

hsa-mir-001d-prec
ACCTACTCAGAGTACATACTTCTTTATGTACCCATAT
20

GAACATACAATGCTATGGAATGTAAAGAAGTATGTAT

TTTTGGTAGGC

hsa-mir-007-1
TGGATGTTGGCCTAGTTCTGTGTGGAAGACTAGTGAT
21

TTTGTTGTTTTTAGATAACTAAATCGACAACAAATCA

CAGTCTGCCATATGGCACAGGCCATGCCTCTACA

hsa-mir-007-1-prec
TTGGATGTTGGCCTAGTTCTGTGTGGAAGACTAGTGA
22

TTTTGTTGTTTTTAGATAACTAAATCGACAACAAATC

ACAGTCTGCCATATGGCACAGGCCATGCCTCTACAG

hsa-mir-007-2
CTGGATACAGAGTGGACCGGCTGGCCCCATCTGGAAG
23

ACTAGTGATTTTGTTGTTGTCTTACTGCGCTCAACAA

CAAATCCCAGTCTACCTAATGGTGCCAGCCATCGCA

hsa-mir-007-2-prec
CTGGATACAGAGTGGACCGGCTGGCCCCATCTGGAAG
24

ACTAGTGATTTTGTTGTTGTCTTACTGCGCTCAACAA

CAAATCCCAGTCTACCTAATGGTGCCAGCCATCGCA

hsa-mir-007-3
AGATTAGAGTGGCTGTGGTCTAGTGCTGTGTGGAAGA
25

CTAGTGATTTTGTTGTTCTGATGTACTACGACAACAA

GTCACAGCCGGCCTCATAGCGCAGACTCCCTTCGAC

hsa-mir-007-3-prec
AGATTAGAGTGGCTGTGGTCTAGTGCTGTGTGGAAGA
26

CTAGTGATTTTGTTGTTCTGATGTACTACGACAACAA

GTCACAGCCGGCCTCATAGCGCAGACTCCCTTCGAC

hsa-mir-009-1
CGGGGTTGGTTGTTATCTTTGGTTATCTAGCTGTATG
27

AGTGGTGTGGAGTCTTCATAAAGCTAGATAACCGAAA

GTAAAAATAACCCCA

hsa-mir-009-2
GGAAGCGAGTTGTTATCTTTGGTTATCTAGCTGTATG
28

AGTGTATTGGTCTTCATAAAGCTAGATAACCGAAAGT

AAAAACTCCTTCA

hsa-mir-009-3
GGAGGCCCGTTTCTCTCTTTGGTTATCTAGCTGTATG
29

AGTGCCACAGAGCCGTCATAAAGCTAGATAACCGAAA

GTAGAAATGATTCTCA

hsa-mir-010a-prec
GATCTGTCTGTCTTCTGTATATACCCTGTAGATCCGA
30

ATTTGTGTAAGGAATTTTGTGGTCACAAATTCGTATC

TAGGGGAATATGTAGTTGACATAAACACTCCGCTCT

hsa-mir-010b-prec
CCAGAGGTTGTAACGTTGTCTATATATACCCTGTAGA
31

ACCGAATTTGTGTGGTATCCGTATAGTCACAGATTCG

ATTCTAGGGGAATATATGGTCGATGCAAAAACTTCA

hsa-mir-015a-2-prec
GCGCGAATGTGTGTTTAAAAAAAATAAAACCTTGGAG
32

TAAAGTAGCAGCACATAATGGTTTGTGGATTTTGAAA

AGGTGCAGGCCATATTGTGCTGCCTCAAAAATAC

hsa-mir-015a-prec
CCTTGGAGTAAAGTAGCAGCACATAATGGTTTGTGGA
33

TTTTGAAAAGGTGCAGGCCATATTGTGCTGCCTCAAA

AATACAAGG

hsa-mir-015b-prec
CTGTAGCAGCACATCATGGTTTACATGCTACAGTCAA
34

GATGCGAATCATTATTTGCTGCTCTAG

hsa-mir-015b-prec
TTGAGGCCTTAAAGTACTGTAGCAGCACATCATGGTT
35

TACATGCTACAGTCAAGATGCGAATCATTATTTGCTG

CTCTAGAAATTTAAGGAAATTCAT

hsa-mir-016a-chr13
GTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGTT
36

AAGATTCTAAAATTATCTCCAGTATTAACTGTGCTGC

TGAAGTAAGGTTGAC

hsa-mir-016b-chr3
GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGA
37

AATATATATTAAACACCAATATTACTGTGCTGCTTTA

GTGTGAC

hsa-mir-016-prec-13
GCAGTGCCTTAGCAGCACGTAAATATTGGCGTTAAGA
38

TTCTAAAATTATCTCCAGTATTAACTGTGCTGCTGAA

GTAAGGT

hsa-mir-017-prec
GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGT
39

GATATGTGCATCTACTGCAGTGAAGGCACTTGTAGCA

TTATGGTGAC

hsa-mir-018-prec
TGTTCTAAGGTGCATCTAGTGCAGATAGTGAAGTAGA
40

TTAGCATCTACTGCCCTAAGTGCTCCTTCTGGCA

hsa-mir-018-prec-13
TTTTTGTTCTAAGGTGCATCTAGTGCAGATAGTGAAG
41

TAGATTAGCATCTACTGCCCTAAGTGCTCCTTCTGGC

ATAAGAA

hsa-mir-019a-prec
GCAGTCCTCTGTTAGTTTTGCATAGTTGCACTACAAG
42

AAGAATGTAGTTGTGCAAATCTATGCAAAACTGATGG

TGGCCTGC

hsa-mir-019a-prec-13
CAGTCCTCTGTTAGTTTTGCATAGTTGCACTACAAGA
43

AGAATGTAGTTGTGCAAATCTATGCAAAACTGATGGT

GGCCTG

hsa-mir-019b-1-
CACTGTTCTATGGTTAGTTTTGCAGGTTTGCATCCAG
44

prec
CTGTGTGATATTCTGCTGTGCAAATCCATGCAAAACT

GACTGTGGTAGTG

hsa-mir-019b-2-
ACATTGCTACTTACAATTAGTTTTGCAGGTTTGCATT
45

prec
TCAGCGTATATATGTATATGTGGCTGTGCAAATCCAT

GCAAAACTGATTGTGATAATGT

hsa-mir-019b-
TTCTATGGTTAGTTTTGCAGGTTTGCATCCAGCTGTG
46

prec-13
TGATATTCTGCTGTGCAAATCCATGCAAAACTGACTG

TGGTAG

hsa-mir-019b-
TTACAATTAGTTTTGCAGGTTTGCATTTCAGCGTATA
47

prec-X
TATGTATATGTGGCTGTGCAAATCCATGCAAAACTGA

TTGTGAT

hsa-mir-020-prec
GTAGCACTAAAGTGCTTATAGTGCAGGTAGTGTTTAG
48

TTATCTACTGCATTATGAGCACTTAAAGTACTGC

hsa-mir-021-prec
TGTCGGGTAGCTTATCAGACTGATGTTGACTGTTGAA
49

TCTCATGGCAACACCAGTCGATGGGCTGTCTGACA

hsa-mir-021-prec-
ACCTTGTCGGGTAGCTTATCAGACTGATGTTGACTGT
50

17
TGAATCTCATGGCAACACCAGTCGATGGGCTGTCTGA

CATTTTG

hsa-mir-022-prec
GGCTGAGCCGCAGTAGTTCTTCAGTGGCAAGCTTTAT
51

GTCCTGACCCAGCTAAAGCTGCCAGTTGAAGAACTGT

TGCCCTCTGCC

hsa-mir-023a-prec
GGCCGGCTGGGGTTCCTGGGGATGGGATTTGCTTCCT
52

GTCACAAATCACATTGCCAGGGATTTCCAACCGACC

hsa-mir-023b-prec
CTCAGGTGCTCTGGCTGCTTGGGTTCCTGGCATGCTG
53

ATTTGTGACTTAAGATTAAAATCACATTGCCAGGGAT

TACCACGCAACCACGACCTTGGC

hsa-mir-023-prec-
CCACGGCCGGCTGGGGTTCCTGGGGATGGGATTTGCT
54

19
TCCTGTCACAAATCACATTGCCAGGGATTTCCAACCG

ACCCTGA

hsa-mir-024-1-prec
CTCCGGTGCCTACTGAGCTGATATCAGTTCTCATTTT
55

ACACACTGGCTCAGTTCAGCAGGAACAGGAG

hsa-mir-024-2-prec
CTCTGCCTCCCGTGCCTACTGAGCTGAAACACAGTTG
56

GTTTGTGTACACTGGCTCAGTTCAGCAGGAACAGGG

hsa-mir-024-prec-
CCCTGGGCTCTGCCTCCCGTGCCTACTGAGCTGAAAC
57

19
ACAGTTGGTTTGTGTACACTGGCTCAGTTCAGCAGGA

ACAGGGG

hsa-mir-024-prec-
CCCTCCGGTGCCTACTGAGCTGATATCAGTTCTCATT
58

9
TTACACACTGGCTCAGTTCAGCAGGAACAGCATC

hsa-mir-025-prec
GGCCAGTGTTGAGAGGCGGAGACTTGGGCAATTGCTG
59

GACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGAC

AGTGCCGGCC

hsa-mir-026a-prec
AGGCCGTGGCCTCGTTCAAGTAATCCAGGATAGGCTG
60

TGCAGGTCCCAATGGCCTATCTTGGTTACTTGCACGG

GGACGCGGGCCT

hsa-mir-026b-prec
CCGGGACCCAGTTCAAGTAATTCAGGATAGGTTGTGT
61

GCTGTCCAGCCTGTTCTCCATTACTTGGCTCGGGGAC

CGG

hsa-mir-027a-prec
CTGAGGAGCAGGGCTTAGCTGCTTGTGAGCAGGGTCC
62

ACACCAAGTCGTGTTCACAGTGGCTAAGTTCCGCCCC

CCAG

hsa-mir-027b-prec
AGGTGCAGAGCTTAGCTGATTGGTGAACAGTGATTGG
63

TTTCCGCTTTGTTCACAGTGGCTAAGTTCTGCACCT

hsa-mir-027b-prec
ACCTCTCTAACAAGGTGCAGAGCTTAGCTGATTGGTG
64

AACAGTGATTGGTTTCCGCTTTGTTCACAGTGGCTAA

GTTCTGCACCTGAAGAGAAGGTG

hsa-mir-027-prec-
CCTGAGGAGCAGGGCTTAGCTGCTTGTGAGCAGGGTC
65

19
CACACCAAGTCGTGTTCACAGTGGCTAAGTTCCGCCC

CCCAGG

hsa-mir-028-prec
GGTCCTTGCCCTCAAGGAGCTCACAGTCTATTGAGTT
66

ACCTTTCTGACTTTCCCACTAGATTGTGAGCTCCTGG

AGGGCAGGCACT

hsa-mir-029a-2
CCTTCTGTGACCCCTTAGAGGATGACTGATTTCTTTT
67

GGTGTTCAGAGTCAATATAATTTTCTAGCACCATCTG

AAATCGGTTATAATGATTGGGGAAGAGCACCATG

hsa-mir-029a-prec
ATGACTGATTTCTTTTGGTGTTCAGAGTCAATATAAT
68

TTTCTAGCACCATCTGAAATCGGTTAT

hsa-mir-029c-prec
ACCACTGGCCCATCTCTTACACAGGCTGACCGATTTC
69

TCCTGGTGTTCAGAGTCTGTTTTTGTCTAGCACCATT

TGAAATCGGTTATGATGTAGGGGGAAAAGCAGCAGC

hsa-mir-030a-prec
GCGACTGTAAACATCCTCGACTGGAAGCTGTGAAGCC
70

ACAGATGGGCTTTCAGTCGGATGTTTGCAGCTGC

hsa-mir-030b-prec
ATGTAAACATCCTACACTCAGCTGTAATACATGGATT
71

GGCTGGGAGGTGGATGTTTACGT

hsa-mir-030b-prec
ACCAAGTTTCAGTTCATGTAAACATCCTACACTCAGC
72

TGTAATACATGGATTGGCTGGGAGGTGGATGTTTACT

TCAGCTGACTTGGA

hsa-mir-030c-prec
AGATACTGTAAACATCCTACACTCTCAGCTGTGGAAA
73

GTAAGAAAGCTGGGAGAAGGCTGTTTACTCTTTCT

hsa-mir-030d-prec
GTTGTTGTAAACATCCCCGACTGGAAGCTGTAAGACA
74

CAGCTAAGCTTTCAGTCAGATGTTTGCTGCTAC

hsa-mir-031-prec
GGAGAGGAGGCAAGATGCTGGCATAGCTGTTGAACTG
75

GGAACCTGCTATGCCAACATATTGCCATCTTTCC

hsa-mir-032-prec
GGAGATATTGCACATTACTAAGTTGCATGTTGTCACG
76

GCCTCAATGCAATTTAGTGTGTGTGATATTTTC

hsa-mir-033b-prec
GGGGGCCGAGAGAGGCGGGCGGCCCCGCGGTGCATTG
77

CTGTTGCATTGCACGTGTGTGAGGCGGGTGCAGTGCC

TCGGCAGTGCAGCCCGGAGCCGGCCCCTGGCACCAC

hsa-mir-033-prec
CTGTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGG
78

TACCCATGCAATGTTTCCACAGTGCATCACAG

hsa-mir-034-prec
GGCCAGCTGTGAGTGTTTCTTTGGCAGTGTCTTAGCT
79

GGTTGTTGTGAGCAATAGTAAGGAAGCAATCAGCAAG

TATACTGCCCTAGAAGTGCTGCACGTTGTGGGGCCC

hsa-mir-091-prec-
TCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTG
80

13
ATATGTGCATCTACTGCAGTGAAGGCACTTGTAGCAT

TATGGTGA

hsa-mir-092-prec-
CTTTCTACACAGGTTGGGATCGGTTGCAATGCTGTGT
81

13 = 092-1
TTCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGT

TTGG

hsa-mir-092-prec-
TCATCCCTGGGTGGGGATTTGTTGCATTACTTGTGTT
82

X = 092-2
CTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

A

hsa-mir-093-prec-
CTGGGGGCTCCAAAGTGCTGTTCGTGCAGGTAGTGTG
83

7.1=093-1
ATTACCCAACCTACTGCTGAGCTAGCACTTCCCGAGC

CCCCGG

hsa-mir-093-prec-
CTGGGGGCTCCAAAGTGCTGTTCGTGCAGGTAGTGTG
84

7.2=093-2
ATTACCCAACCTACTGCTGAGCTAGCACTTCCCGAGC

CCCCGG

hsa-mir-095-prec-
AACACAGTGGGCACTCAATAAATGTCTGTTGAATTGA
85

4
AATGCGTTACATTCAACGGGTATTTATTGAGCACCCA

CTCTGTG

hsa-mir-096-prec-
TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTC
86

7
TCTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGG

GAAA

hsa-mir-098-prec-
GTGAGGTAGTAAGTTGTATTGTTGTGGGGTAGGGATA
87

X
TTAGGCCCCAATTAGAAGATAACTATACAACTTACTA

CTTTCC

hsa-mir-099b-prec-
GGCACCCACCCGTAGAACCGACCTTGCGGGGCCTTCG
88

19
CCGCACACAAGCTCGTGTCTGTGGGTCCGTGTC

hsa-mir-099-prec-
CCCATTGGCATAAACCCGTAGATCCGATCTTGTGGTG
89

21
AAGTGGACCGCACAAGCTCGCTTCTATGGGTCTGTGT

CAGTGTG

hsa-mir-100-1/2-
AAGAGAGAAGATATTGAGGCCTGTTGCCACAAACCCG
90

prec

TAGATCCGAACTTGTGGTATTAGTCCGCACAAGCTTG

TATCTATAGGTATGTGTCTGTTAGGCAATCTCAC

hsa-mir-100-prec-
CCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTA
91

11
TTAGTCCGCACAAGCTTGTATCTATAGGTATGTGTCT

GTTAGG

hsa-mir-101-1/2-
AGGCTGCCCTGGCTCAGTTATCACAGTGCTGATGCTG
92

prec
TCTATTCTAAAGGTACAGTACTGTGATAACTGAAGGA

TGGCAGCCATCTTACCTTCCATCAGAGGAGCCTCAC

hsa-mir-101-prec
TCAGTTATCACAGTGCTGATGCTGTCCATTCTAAAGG
93

TACAGTACTGTGATAACTGA

hsa-mir-101-prec-
TGCCCTGGCTCAGTTATCACAGTGCTGATGCTGTCTA
94

1
TTCTAAAGGTACAGTACTGTGATAACTGAAGGATGGC

A

hsa-mir-101-prec-
TGTCCTTTTTCGGTTATCATGGTACCGATGCTGTATA
95

9
TCTGAAAGGTACAGTACTGTGATAACTGAAGAATGGT

G

hsa-mir-102-prec-
CTTCTGGAAGCTGGTTTCACATGGTGGCTTAGATTTT
96

1
TCCATCTTTGTATCTAGCACCATTTGAAATCAGTGTT

TTAGGAG

hsa-mir-102-prec-
CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTA
97

7.1
AATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTC

TTGGGGG

hsa-mir-102-prec-
CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTA
98

7.2
AATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTC

TTGGGGG

hsa-mir-103-2-prec
TTGTGCTTTCAGCTTCTTTACAGTGCTGCCTTGTAGC
99

ATTCAGGTCAAGCAACATTGTACAGGGCTATGAAAGA

ACCA

hsa-mir-103-prec-
TTGTGCTTTCAGCTTCTTTACAGTGCTGCCTTGTAGC
100

20
ATTCAGGTCAAGCAACATTGTACAGGGCTATGAAAGA

ACCA

hsa-mir-103-prec-
TACTGCCCTCGGCTTCTTTACAGTGCTGCCTTGTTGC
101

5 = 103-1
ATATGGATCAAGCAGCATTGTACAGGGCTATGAAGGC

ATTG

hsa-mir-104-prec-
AAATGTCAGACAGCCCATCGACTGGTGTTGCCATGAG
102

17
ATTCAACAGTCAACATCAGTCTGATAAGCTACCCGAC

AAGG

hsa-mir-105-prec-
TGTGCATCGTGGTCAAATGCTCAGACTCCTGTGGTGG
103

X.1 = 105-1
CTGCTCATGCACCACGGATGTTTGAGCATGTGCTACG

GTGTCTA

hsa-mir-105-prec-
TGTGCATCGTGGTCAAATGCTCAGACTCCTGTGGTGG
104

X.2 = 105-2
CTGCTCATGCACCACGGATGTTTGAGCATGTGCTACG

GTGTCTA

hsa-mir-106-prec-
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCT
105

X
TTTTGAGATCTACTGCAATGTAAGCACTTCTTACATT

ACCATGG

hsa-mir-107-prec-
CTCTCTGCTTTCAGCTTCTTTACAGTGTTGCCTTGTG
106

10
GCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAA

GCACAGA

hsa-mir-122a-prec
CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGT
107

GTCTAAACTATCAAACGCCATTATCACACTAAATAGC

TACTGCTAGGC

hsa-mir-122a-prec
AGCTGTGGAGTGTGACAATGGTGTTTGTGTCCAAACT
108

ATCAAACGCCATTATCACACTAAATAGCT

hsa-mir-123-prec
ACATTATTACTTTTGGTACGCGCTGTGACACTTCAAA
109

CTCGTACCGTGAGTAATAATGCGC

hsa-mir-124a-1-prec
tccttcctCAGGAGAAAGGCCTCTCTCTCCGTGTTCA
110

CAGCGGACCTTGATTTAAATGTCCATACAATTAAGGC

ACGCGGTGAATGCCAAGAATGGGGCT

hsa-mir-124a-1-prec
AGGCCTCTCTCTCCGTGTTCACAGCGGACCTTGATTT
111

AAATGTCCATACAATTAAGGCACGCGGTGAATGCCAA

GAATGGGGCTG

hsa-mir-124a-2-prec
ATCAAGATTAGAGGCTCTGCTCTCCGTGTTCACAGCG
112

GACCTTGATTTAATGTCATACAATTAAGGCACGCGGT

GAATGCCAAGAGCGGAGCCTACGGCTGCACTTGAAG

hsa-mir-124a-3-prec
CCCGCCCCAGCCCTGAGGGCCCCTCTGCGTGTTCACA
113

GCGGACCTTGATTTAATGTCTATACAATTAAGGCACG

CGGTGAATGCCAAGAGAGGCGCCTCCGCCGCTCCTT

hsa-mir-124a-3-prec
TGAGGGCCCCTCTGCGTGTTCACAGCGGACCTTGATT
114

TAATGTCTATACAATTAAGGCACGCGGTGAATGCCAA

GAGAGGCGCCTCC

hsa-mir-124a-prec
CTCTGCGTGTTCACAGCGGACCTTGATTTAATGTCTA
115

TACAATTAAGGCACGCGGTGAATGCCAAGAG

hsa-mir-124b-prec
CTCTCCGTGTTCACAGCGGACCTTGATTTAATGTCAT
116

ACAATTAAGGCACGCGGTGAATGCCAAGAG

hsa-mir-125a-prec
TGCCAGTCTCTAGGTCCCTGAGACCCTTTAACCTGTG
117

AGGACATCCAGGGTCACAGGTGAGGTTCTTGGGAGCC

TGGCGTCTGGCC

hsa-mir-125a-prec
GGTCCCTGAGACCCTTTAACCTGTGAGGACATCCAGG
118

GTCACAGGTGAGGTTCTTGGGAGCCTGG

hsa-mir-125b-1
ACATTGTTGCGCTCCTCTCAGTCCCTGAGACCCTAAC
119

TTGTGATGTTTACCGTTTAAATCCACGGGTTAGGCTC

TTGGGAGCTGCGAGTCGTGCTTTTGCATCCTGGA

hsa-mir-125b-1
TGCGCTCCTCTCAGTCCCTGAGACCCTAACTTGTGAT
120

GTTTACCGTTTAAATCCACGGGTTAGGCTCTTGGGAG

CTGCGAGTCGTGCT

hsa-mir-125b-2-prec
ACCAGACTTTTCCTAGTCCCTGAGACCCTAACTTGTG
121

AGGTATTTTAGTAACATCACAAGTCAGGCTCTTGGGA

CCTAGGCGGAGGGGA

hsa-mir-125b-2-prec
CCTAGTCCCTGAGACCCTAACTTGTGAGGTATTTTAG
122

TAACATCACAAGTCAGGCTCTTGGGACCTAGGC

hsa-mir-126-prec
CGCTGGCGACGGGACATTATTACTTTTGGTACGCGCT
123

GTGACACTTCAAACTCGTACCGTGAGTAATAATGCGC

CGTCCACGGCA

hsa-mir-126-prec
ACATTATTACTTTTGGTACGCGCTGTGACACTTCAAA
124

CTCGTACCGTGAGTAATAATGCGC

hsa-mir-127-prec
TGTGATCACTGTCTCCAGCCTGCTGAAGCTCAGAGGG
125

CTCTGATTCAGAAAGATCATCGGATCCGTCTGAGCTT

GGCTGGTCGGAAGTCTCATCATC

hsa-mir-127-prec
CCAGCCTGCTGAAGCTCAGAGGGCTCTGATTCAGAAA
126

GATCATCGGATCCGTCTGAGCTTGGCTGGTCGG

hsa-mir-128a-prec
TGAGCTGTTGGATTCGGGGCCGTAGCACTGTCTGAGA
127

GGTTTACATTTCTCACAGTGAACCGGTCTCTTTTTCA

GCTGCTTC

hsa-mir-128b-prec
GCCCGGCAGCCACTGTGCAGTGGGAAGGGGGGCCGAT
128

ACACTGTACGAGAGTGAGTAGCAGGTCTCACAGTGAA

CCGGTCTCTTTCCCTACTGTGTCACACTCCTAATGG

hsa-mir-128-prec
GTTGGATTCGGGGCCGTAGCACTGTCTGAGAGGTTTA
129

CATTTCTCACAGTGAACCGGTCTCTTTTTCAGC

hsa-mir-129-prec
TGGATCTTTTTGCGGTCTGGGCTTGCTGTTCCTCTCA
130

ACAGTAGTCAGGAAGCCCTTACCCCAAAAAGTATCTA

hsa-mir-130a-prec
TGCTGCTGGCCAGAGCTCTTTTCACATTGTGCTACTG
131

TCTGCACCTGTCACTAGCAGTGCAATGTTAAAAGGGC

ATTGGCCGTGTAGTG

hsa-mir-131-1-prec
gccaggaggcggGGTTGGTTGTTATCTTTGGTTATCT
132

AGCTGTATGAGTGGTGTGGAGTCTTCATAAAGCTAGA

TAACCGAAAGTAAAAATAACCCCATACACTGCGCAG

hsa-mir-131-3-prec
CACGGCGCGGCAGCGGCACTGGCTAAGGGAGGCCCGT
133

TTCTCTCTTTGGTTATCTAGCTGTATGAGTGCCACAG

AGCCGTCATAAAGCTAgataaccgaaagtagaaatg

hsa-mir-131-prec
GTTGTTATCTTTGGTTATCTAGCTGTATGAGTGTATT
134

GGTCTTCATAAAGCTAGATAACCGAAAGTAAAAAC

hsa-mir-132-prec
CCGCCCCCGCGTCTCCAGGGCAACCGTGGCTTTCGAT
135

TGTTACTGTGGGAACTGGAGGTAACAGTCTACAGCCA

TGGTCGCCCCGCAGCACGCCCACGCGC

hsa-mir-132-prec
GGGCAACCGTGGCTTTCGATTGTTACTGTGGGAACTG
136

GAGGTAACAGTCTACAGCCATGGTCGCCC

hsa-mir-133a-1
ACAATGCTTTGCTAGAGCTGGTAAAATGGAACCAAAT
137

CGCCTCTTCAATGGATTTGGTCCCCTTCAACCAGCTG

TAGCTATGCATTGA

hsa-mir-133a-2
GGGAGCCAAATGCTTTGCTAGAGCTGGTAAAATGGAA
138

CCAAATCGACTGTCCAATGGATTTGGTCCCCTTCAAC

CAGCTGTAGCTGTGCATTGATGGCGCCG

hsa-mir-133-prec
GCTAGAGCTGGTAAAATGGAACCAAATCGCCTCTTCA
139

ATGGATTTGGTCCCCTTCAACCAGCTGTAGC

hsa-mir-134-prec
CAGGGTGTGTGACTGGTTGACCAGAGGGGCATGCACT
140

GTGTTCACCCTGTGGGCCACCTAGTCACCAACCCTC

hsa-mir-134-prec
AGGGTGTGTGACTGGTTGACCAGAGGGGCATGCACTG
141

TGTTCACCCTGTGGGCCACCTAGTCACCAACCCT

hsa-mir-135-1-prec
AGGCCTCGCTGTTCTCTATGGCTTTTTATTCCTATGT
142

GATTCTACTGCTCACTCATATAGGGATTGGAGCCGTG

GCGCACGGCGGGGACA

hsa-mir-135-2-prec
AGATAAATTCACTCTAGTGCTTTATGGCTTTTTATTC
143

CTATGTGATAGTAATAAAGTCTCATGTAGGGATGGAA

GCCATGAAATACATTGTGAAAAATCA

hsa-mir-135-prec
CTATGGCTTTTTATTCCTATGTGATTCTACTGCTCAC
144

TCATATAGGGATTGGAGCCGTGG

hsa-mir-136-prec
TGAGCCCTCGGAGGACTCCATTTGTTTTGATGATGGA
145

TTCTTATGCTCCATCATCGTCTCAAATGAGTCTTCAG

AGGGTTCT

hsa-mir-136-prec
GAGGACTCCATTTGTTTTGATGATGGATTCTTATGCT
146

CCATCATCGTCTCAAATGAGTCTTC

hsa-mir-137-prec
CTTCGGTGACGGGTATTCTTGGGTGGATAATACGGAT
147

TACGTTGTTATTGCTTAAGAATACGCGTAGTCGAGG

hsa-mir-138-1-prec
CCCTGGCATGGTGTGGTGGGGCAGCTGGTGTTGTGAA
148

TCAGGCCGTTGCCAATCAGAGAACGGCTACTTCACAA

CACCAGGGCCACACCACACTACAGG

hsa-mir-138-2-prec
CGTTGCTGCAGCTGGTGTTGTGAATCAGGCCGACGAG
149

CAGCGCATCCTCTTACCCGGCTATTTCACGACACCAG

GGTTGCATCA

hsa-mir-138-prec
CAGCTGGTGTTGTGAATCAGGCCGACGAGCAGCGCAT
150

CCTCTTACCCGGCTATTTCACGACACCAGGGTTG

hsa-mir-139-prec
GTGTATTCTACAGTGCACGTGTCTCCAGTGTGGCTCG
151

GAGGCTGGAGACGCGGCCCTGTTGGAGTAAC

hsa-mir-140
TGTGTCTCTCTCTGTGTCCTGCCAGTGGTTTTACCCT
152

ATGGTAGGTTACGTCATGCTGTTCTACCACAGGGTAG

AACCACGGACAGGATACCGGGGCACC

hsa-mir-140as-prec
TCCTGCCAGTGGTTTTACCCTATGGTAGGTTACGTCA
153

TGCTGTTCTACCACAGGGTAGAACCACGGACAGGA

hsa-mir-140s-prec
CCTGCCAGTGGTTTTACCCTATGGTAGGTTACGTCAT
154

GCTGTTCTACCACAGGGTAGAACCACGGACAGG

hsa-mir-141-prec
CGGCCGGCCCTGGGTCCATCTTCCAGTACAGTGTTGG
155

ATGGTCTAATTGTGAAGCTCCTAACACTGTCTGGTAA

AGATGGCTCCCGGGTGGGTTC

hsa-mir-141-prec
GGGTCCATCTTCCAGTACAGTGTTGGATGGTCTAATT
156

GTGAAGCTCCTAACACTGTCTGGTAAAGATGGCCC

hsa-mir-142as-prec
ACCCATAAAGTAGAAAGCACTACTAACAGCACTGGAG
157

GGTGTAGTGTTTCCTACTTTATGGATG

hsa-mir-142-prec
GACAGTGCAGTCACCCATAAAGTAGAAAGCACTACTA
158

ACAGCACTGGAGGGTGTAGTGTTTCCTACTTTATGGA

TGAGTGTACTGTG

hsa-mir-142s-pres
ACCCATAAAGTAGAAAGCACTACTAACAGCACTGGAG
159

GGTGTAGTGTTTCCTACTTTATGGATG

hsa-mir-143-prec
GCGCAGCGCCCTGTCTCCCAGCCTGAGGTGCAGTGCT
160

GCATCTCTGGTCAGTTGGGAGTCTGAGATGAAGCACT

GTAGCTCAGGAAGAGAGAAGTTGTTCTGCAGC

hsa-mir-143-prec
CCTGAGGTGCAGTGCTGCATCTCTGGTCAGTTGGGAG
161

TCTGAGATGAAGCACTGTAGCTCAGG

hsa-mir-144-prec
TGGGGCCCTGGCTGGGATATCATCATATACTGTAAGT
162

TTGCGATGAGACACTACAGTATAGATGATGTACTAGT

CCGGGCACCCCC

hsa-mir-144-prec
GGCTGGGATATCATCATATACTGTAAGTTTGCGATGA
163

GACACTACAGTATAGATGATGTACTAGTC

hsa-mir-145-prec
CACCTTGTCCTCACGGTCCAGTTTTCCCAGGAATCCC
164

TTAGATGCTAAGATGGGGATTCCTGGAAATACTGTTC

TTGAGGTCATGGTT

hsa-mir-145-prec
CTCACGGTCCAGTTTTCCCAGGAATCCCTTAGATGCT
165

AAGATGGGGATTCCTGGAAATACTGTTCTTGAG

hsa-mir-146-prec
CCGATGTGTATCCTCAGCTTTGAGAACTGAATTCCAT
166

GGGTTGTGTCAGTGTCAGACCTCTGAAATTCAGTTCT

TCAGCTGGGATATCTCTGTCATCGT

hsa-mir-146-prec
AGCTTTGAGAACTGAATTCCATGGGTTGTGTCAGTGT
167

CAGACCTGTGAAATTCAGTTCTTCAGCT

hsa-mir-147-prec
AATCTAAAGACAACATTTCTGCACACACACCAGACTA
168

TGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT

hsa-mir-148-prec
GAGGCAAAGTTCTGAGACACTCCGACTCTGAGTATGA
169

TAGAAGTCAGTGCACTACAGAACTTTGTCTC

hsa-mir-149-prec
GCCGGCGCCCGAGCTCTGGCTCCGTGTCTTCACTCCC
170

GTGCTTGTCCGAGGAGGGAGGGAGGGACGGGGGCTGT

GCTGGGGCAGCTGGA

hsa-mir-149-prec
GCTCTGGCTCCGTGTCTTCACTCCCGTGCTTGTCCGA
171

GGAGGGAGGGAGGGAC

hsa-mir-150-prec
CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTG
172

CTGGGCTCAGACCCTGGTACAGGCCTGGGGGACAGGG

ACCTGGGGAC

hsa-mir-150-prec
CCCTGTCTCCCAACCCTTGTACCAGTGCTGGGCTCAG
173

ACCCTGGTACAGGCCTGGGGGACAGGG

hsa-mir-151-prec
CCTGCCCTCGAGGAGCTCACAGTCTAGTATGTCTCAT
174

CCCCTACTAGACTGAAGCTCCTTGAGGACAGG

hsa-mir-152-prec
TGTCCCCCCCGGCCCAGGTTCTGTGATACACTCCGAC
175

TCGGGCTCTGGAGCAGTCAGTGCATGACAGAACTTGG

GCCCGGAAGGACC

hsa-mir-152-prec
GGCCCAGGTTCTGTGATACACTCCGACTCGGGCTCTG
176

GAGCAGTCAGTGCATGACAGAACTTGGGCCCCGG

hsa-mir-153-1-prec
CTCACAGCTGCCAGTGTCATTTTTGTGATCTGCAGCT
177

AGTATTCTCACTCCAGTTGCATAGTCACAAAAGTGAT

CATTGGCAGGTGTGGC

hsa-mir-153-1-prec
tctctctctccctcACAGCTGCCAGTGTCATTGTCAC
178

AAAAGTGATCATTGGCAGGTGTGGCTGCTGCATG

hsa-mir-153-2-prec
AGCGGTGGCCAGTGTCATTTTTGTGATGTTGCAGCTA
179

GTAATATGAGCCCAGTTGCATAGTCACAAAAGTGATC

ATTGGAAACTGTG

hsa-mir-153-2-prec
CAGTGTCATTTTTGTGATGTTGCAGCTAGTAATATGA
180

GCCCAGTTGCATAGTCACAAAAGTGATCATTG

hsa-mir-154-prec
GTGGTACTTGAAGATAGGTTATCCGTGTTGCCTTCGC
181

TTTATTTGTGACGAATCATACACGGTTGACCTATTTT

TCAGTACCAA

hsa-mir-154-prec
GAAGATAGGTTATCCGTGTTGCCTTCGCTTTATTTGT
182

GACGAATCATACACGGTTGACCTATTTTT

hsa-mir-155-prec
CTGTTAATGCTAATCGTGATAGGGGTTTTTGCCTCCA
183

ACTGACTCCTACATATTAGCATTAACAG

hsa-mir-16-2-prec
CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGG
184

CGTTAAGATTCTAAAATTATCTCCAGTATTAACTGTG

CTGCTGAAGTAAGGTTGACCATACTCTACAGTTG

hsa-mir-181a-prec
AGAAGGGCTATCAGGCCAGCCTTCAGAGGACTCCAAG
185

GAACATTCAACGCTGTCGGTGAGTTTGGGATTTGAAA

AAACCACTGACCGTTGACTGTACCTTGGGGTCCTTA

hsa-mir-181b-prec
TGAGTTTTGAGGTTGCTTCAGTGAACATTCAACGCTG
186

TCGGTGAGTTTGGAATTAAAATCAAAACCATCGACCG

TTGATTGTACCCTATGGCTAACCATCATCTACTCCA

hsa-mir-181c-prec
CGGAAAATTTGCCAAGGGTTTGGGGGAACATTCAACC
187

TGTCGGTGAGTTTGGGCAGCTCAGGCAAACCATCGAC

CGTTGAGTGGACCCTGAGGCCTGGAATTGCCATCCT

hsa-mir-182-as-prec
GAGCTGCTTGCCTCCCCCCGTTTTTGGCAATGGTAGA
188

ACTCACACTGGTGAGGTAACAGGATCCGGTGGTTCTA

GACTTGCCAACTATGGGGCGAGGACTCAGCCGGCAC

hsa-mir-182-prec
TTTTTGGCAATGGTAGAACTCACACTGGTGAGGTAAC
189

AGGATCCGGTGGTTCTAGACTTGCCAACTATGG

hsa-mir-183-prec
CCGCAGAGTGTGACTCCTGTTCTGTGTATGGCACTGG
190

TAGAATTCACTGTGAACAGTCTCAGTCAGTGAATTAC

CGAAGGGCCATAAACAGAGCAGAGACAGATCCACGA

hsa-mir-184-prec
CCAGTCACGTCCCCTTATCACTTTTCCAGCCCAGCTT
191

TGTGACTGTAAGTGTTGGACGGAGAACTGATAAGGGT

AGGTGATTGA

hsa-mir-184-prec
CCTTATCACTTTTCCAGCCCAGCTTTGTGACTGTAAG
192

TGTTGGACGGAGAACTGATAAGGGTAGG

hsa-mir-185-prec
AGGGGGCGAGGGATTGGAGAGAAAGGCAGTTCCTGAT
193

GGTCCCCTCCCCAGGGGCTGGCTTTCCTCTGGTCCTT

CCCTCCCA

hsa-mir-185-prec
AGGGATTGGAGAGAAAGGCAGTTCCTGATGGTCCCCT
194

CCCCAGGGGCTGGCTTTCCTCTGGTCCTT

hsa-mir-186-prec
TGCTTGTAACTTTCCAAAGAATTCTCCTTTTGGGCTT
195

TCTGGTTTTATTTTAAGCCCAAAGGTGAATTTTTTGG

GAAGTTTGAGCT

hsa-mir-186-prec
ACTTTCCAAAGAATTCTCCTTTTGGGCTTTCTGGTTT
196

TATTTTAAGCCCAAAGGTGAATTTTTTGGGAAGT

hsa-mir-187-prec
GGTCGGGCTCACCATGACACAGTGTGAGACTCGGGCT
197

ACAACACAGGACCCGGGGCGCTGCTCTGACCCCTCGT

GTCTTGTGTTGCAGCCGGAGGGACGCAGGTCCGCA

hsa-mir-188-prec
TGCTCCCTCTCTCACATCCCTTGCATGGTGGAGGGTG
198

AGCTTTCTGAAAACCCCTCCCACATGCAGGGTTTGCA

GGATGGCGAGCC

hsa-mir-188-prec
TCTCACATCCCTTGCATGGTGGAGGGTGAGCTTTCTG
199

AAAACCCCTCCCACATGCAGGGTTTGCAGGA

hsa-mir-189-prec
CTGTCGATTGGACCCGCCCTCCGGTGCCTACTGAGCT
200

GATATCAGTTCTCATTTTACACACTGGCTCAGTTCAG

CAGGAACAGGAGTCGAGCCCTTGAGCAA

hsa-mir-189-prec
CTCCGGTGCCTACTGAGCTGATATCAGTTCTCATTTT
201

ACACACTGGCTCAGTTCAGCAGGAACAGGAG

hsa-mir-190-prec
TGCAGGCCTCTGTGTGATATGTTTGATATATTAGGTT
202

GTTATTTAATCCAACTATATATCAAACATATTCCTAC

AGTGTCTTGCC

hsa-mir-190-prec
CTGTGTGATATGTTTGATATATTAGGTTGTTATTTAA
203

TCCAACTATATATCAAACATATTCCTACAG

hsa-mir-191-prec
CGGCTGGACAGCGGGCAACGGAATCCCAAAAGCAGCT
204

GTTGTCTCCAGAGCATTCCAGCTGCGCTTGGATTTCG

TCCCCTGCTCTCCTGCCT

hsa-mir-191-prec
AGCGGGCAACGGAATCCCAAAAGCAGCTGTTGTCTCC
205

AGAGCATTCCAGCTGCGCTTGGATTTCGTCCCCTGCT

hsa-mir-192-2/3
CCGAGACCGAGTGCACAGGGCTCTGACCTATGAATTG
206

ACAGCCAGTGCTCTCGTCTCCCCTCTGGCTGCCAATT

CCATAGGTCACAGGTATGTTCGCCTCAATGCCAG

hsa-mir-192-prec
GCCGAGACCGAGTGCACAGGGCTCTGACCTATGAATT
207

GACAGCCAGTGCTCTCGTCTCCCCTCTGGCTGCCAAT

TCCATAGGTCACAGGTATGTTCGCCTCAATGCCAGC

hsa-mir-193-prec
CGAGGATGGGAGCTGAGGGCTGGGTCTTTGCGGGCGA
208

GATGAGGGTGTCGGATCAACTGGCCTACAAAGTCCCA

GTTCTCGGCCCCCG

hsa-mir-193-prec
GCTGGGTCTTTGCGGGCGAGATGAGGGTGTCGGATCA
209

ACTGGCCTACAAAGTCCCAGT

hsa-mir-194-prec
ATGGTGTTATCAAGTGTAACAGCAACTCCATGTGGAC
210

TGTGTACCAATTTCCAGTGGAGATGCTGTTACTTTTG

ATGGTTACCAA

hsa-mir-194-prec
GTGTAACAGCAACTCCATGTGGACTGTGTACCAATTT
211

CCAGTGGAGATGCTGTTACTTTTGAT

hsa-mir-195-prec
AGCTTCCCTGGCTCTAGCAGCACAGAAATATTGGCAC
212

AGGGAAGCGAGTCTGCCAATATTGGCTGTGCTGCTCC

AGGCAGGGTGGTG

hsa-mir-195-prec

TAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCT

213

GCCAATATTGGCTGTGCTGCT

hsa-mir-196-1-prec
CTAGAGCTTGAATTGGAACTGCTGAGTGAATTAGGTA
214

GTTTCATGTTGTTGGGCCTGGGTTTCTGAACACAACA

ACATTAAACCACCCGATTCACGGCAGTTACTGCTCC

hsa-mir-196-1-prec
GTGAATTAGGTAGTTTCATGTTGTTGGGCCTGGGTTT
215

CTGAACACAACAACATTAAACCACCCGATTCAC

hsa-mir-196-2-prec
TGCTCGCTCAGCTGATCTGTGGCTTAGGTAGTTTCAT
216

GTTGTTGGGATTGAGTTTTGAACTCGGCAACAAGAAA

CTGCCTGAGTTACATCAGTCGGTTTTCGTCGAGGGC

hsa-mir-196-prec
GTGAATTAGGTAGTTTCATGTTGTTGGGCCTGGGTTT
217

CTGAACACAACAACATTAAACCACCCGATTCAC

hsa-mir-197-prec
GGCTGTGCCGGGTAGAGAGGGCAGTGGGAGGTAAGAG
218

CTCTTCACCCTTCACCACCTTCTCCACCCAGCATGGC

C

hsa-mir-198-prec
TCATTGGTCCAGAGGGGAGATAGGTTCCTGTGATTTT
219

TCCTTCTTCTCTATAGAATAAATGA

hsa-mir-199a-1-prec
GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTC
220

TCAATGTGTACAGTAGTCTGCACATTGGTTAGGC

hsa-mir-199a-2-prec
AGGAAGCTTCTGGAGATCCTGCTCCGTCGCCCCAGTG
221

TTCAGACTACCTGTTCAGGACAATGCCGTTGTACAGT

AGTCTGCACATTGGTTAGACTGGGCAAGGGAGAGCA

hsa-mir-199b-prec
CCAGAGGACACCTCCACTCCGTCTACCCAGTGTTTAG
222

ACTATCTGTTCAGGACTCCCAAATTGTACAGTAGTCT

GCACATTGGTTAGGCTGGGCTGGGTTAGACCCTCGG

hsa-mir-199s-prec
GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTC
223

TCAATGTGTACAGTAGTCTGCACATTGGTTAGGC

hsa-mir-200a-prec
GCCGTGGCCATCTTACTGGGCAGCATTGGATGGAGTC
224

AGGTCTCTAATACTGCCTGGTAATGATGACGGC

hsa-mir-200b-prec
CCAGCTCGGGCAGCCGTGGCCATCTTACTGGGCAGCA
225

TTGGATGGAGTCAGGTCTCTAATACTGCCTGGTAATG

ATGACGGCGGAGCCCTGCACG

hsa-mir-202-prec
GTTCCTTTTTCCTATGCATATACTTCTTTGAGGATCT
226

GGCCTAAAGAGGTATAGGGCATGGGAAGATGGAGC

hsa-mir-203-prec
GTGTTGGGGACTCGCGCGCTGGGTCCAGTGGTTCTTA
227

ACAGTTCAACAGTTCTGTAGCGCAATTGTGAAATGTT

TAGGACCACTAGACCCGGCGGGCGCGGCGACAGCGA

hsa-mir-204-prec
GGCTACAGTCTTTCTTCATGTGACTCGTGGACTTCCC
228

TTTGTCATCCTATGCCTGAGAATATATGAAGGAGGCT

GGGAAGGCAAAGGGACGTTCAATTGTCATCACTGGC

hsa-mir-205-prec
AAAGATCCTCAGACAATCCATGTGCTTCTCTTGTCCT
229

TCATTCCACCGGAGTCTGTCTCATACCCAACCAGATT

TCAGTGGAGTGAAGTTCAGGAGGCATGGAGCTGACA

hsa-mir-206-prec
TGCTTCCCGAGGCCACATGCTTCTTTATATCCCCATA
230

TGGATTACTTTGCTATGGAATGTAAGGAAGTGTGTGG

TTTCGGCAAGTG

hsa-mir-206-prec
AGGCCACATGCTTCTTTATATCCCCATATGGATTACT
231

TTGCTATGGAATGTAAGGAAGTGTGTGGTTTT

hsa-mir-208-prec
TGACGGGCGAGCTTTTGGCCCGGGTTATACCTGATGC
232

TCACGTATAAGACGAGCAAAAAGCTTGTTGGTCA

hsa-mir-210-prec
ACCCGGCAGTGCCTCCAGGCGCAGGGCAGCCCCTGCC
233

CACCGCACACTGCGCTGCCCCAGACCCACTGTGCGTG

TGACAGCGGCTGATCTGTGCCTGGGCAGCGCGACCC

hsa-mir-211-prec
TCACCTGGCCATGTGACTTGTGGGCTTCCCTTTGTCA
234

TCCTTCGCCTAGGGCTCTGAGCAGGGCAGGGACAGCA

AAGGGGTGCTCAGTTGTCACTTCCCACAGCACGGAG

hsa-mir-212-prec
CGGGGCACCCCGCCCGGACAGCGCGCCGGCACCTTGG
235

CTCTAGACTGCTTACTGCCCGGGCCGCCCTCAGTAAC

AGTCTCCAGTCACGGCCACCGACGCCTGGCCCCGCC

hsa-mir-213-prec
CCTGTGCAGAGATTATTTTTTAAAAGGTCACAATCAA
236

CATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAA

GCTCACTGAACAATGAATGCAACTGTGGCCCCGCTT

hsa-mir-213-prec-
GAGTTTTGAGGTTGCTTCAGTGAACATTCAACGCTGT
237

LIM
CGGTGAGTTTGGAATTAAAATCAAAACCATCGACCGT

TGATTGTACCCTATGGCTAACCATCATCTACTCC

hsa-mir-214-prec
GGCCTGGCTGGACAGAGTTGTCATGTGTCTGCCTGTC
238

TACACTTGCTGTGCAGAACATCCGCTCACCTGTACAG

CAGGCACAGACAGGCAGTCACATGACAACCCAGCCT

hsa-mir-215-prec
ATCATTCAGAAATGGTATACAGGAAAATGACCTATGA
239

ATTGACAGACAATATAGCTGAGTTTGTCTGTCATTTC

TTTAGGCCAATATTCTGTATGACTGTGCTACTTCAA

hsa-mir-216-prec
GATGGCTGTGAGTTGGCTTAATCTCAGCTGGCAACTG
240

TGAGATGTTCATACAATCCCTCACAGTGGTCTCTGGG

ATTATGCTAAACAGAGCAATTTCCTAGCCCTCACGA

hsa-mir-217-prec
AGTATAATTATTACATAGTTTTTGATGTCGCAGATAC
241

TGCATCAGGAACTGATTGGATAAGAATCAGTCACCAT

CAGTTCCTAATGCATTGCCTTCAGCATCTAAACAAG

hsa-mir-218-1-prec
GTGATAATGTAGCGAGATTTTCTGTTGTGCTTGATCT
242

AACCATGTGGTTGCGAGGTATGAGTAAAACATGGTTC

CGTCAAGCACCATGGAACGTCACGCAGCTTTCTACA

hsa-mir-218-2-prec
GACCAGTCGCTGCGGGGCTTTCCTTTGTGCTTGATCT
243

AACCATGTGGTGGAACGATGGAAACGGAACATGGTTC

TGTCAAGCACCGCGGAAAGCACCGTGCTCTCCTGCA

hsa-mir-219-prec
CCGCCCCGGGCCGCGGCTCCTGATTGTCCAAACGCAA
244

TTCTCGAGTCTATGGCTCCGGCCGAGAGTTGAGTCTG

GACGTCCCGAGCCGCCGCCCCCAAACCTCGAGCGGG

hsa-mir-220-prec
GACAGTGTGGCATTGTAGGGCTCCACACCGTATCTGA
245

CACTTTGGGCGAGGGCACCATGCTGAAGGTGTTCATG

ATGCGGTCTGGGAACTCCTCACGGATCTTACTGATG

hsa-mir-221-prec
TGAACATCCAGGTCTGGGGCATGAACCTGGCATACAA
246

TGTAGATTTCTGTGTTCGTTAGGCAACAGCTACATTG

TCTGCTGGGTTTCAGGCTACCTGGAAACATGTTCTC

hsa-mir-222-prec
GCTGCTGGAAGGTGTAGGTACCCTCAATGGCTCAGTA
247

GCCAGTGTAGATCCTGTCTTTCGTAATCAGCAGCTAC

ATCTGGCTACTGGGTCTCTGATGGCATCTTCTAGCT

hsa-mir-223-prec
CCTGGCCTCCTGCAGTGCCACGCTCCGTGTATTTGAC
248

AAGCTGAGTTGGACACTCCATGTGGTAGAGTGTCAGT

TTGTCAAATACCCCAAGTGCGGCACATGCTTACCAG

hsa-mir-224-prec
GGGCTTTCAAGTCACTAGTGGTTCCGTTTAGTAGATG
249

ATTGTGCATTGTTTCAAAATGGTGCCCTAGTGACTAC

AAAGCCC

hsA-mir-29b-
CTTCTGGAAGCTGGTTTCACATGGTGGCTTAGATTTT
250

1 = 102-prec1
TCCATCTTTGTATCTAGCACCATTTGAAATCAGTGTT

TTAGGAG

hsA-mir-29b-
CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTA
251

2 = 102prec7.1 = 7.2
AATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTC

TTGGGGG

hsA-mir-29b-
CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTA
252

3 = 102prec7.1 = 7.2
AATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTC

TTGGGGG

hsa-mir-30* = mir-
GTGAGCGACTGTAAACATCCTCGACTGGAAGCTGTGA
253

097-prec-6
AGCCACAGATGGGCTTTCAGTCGGATGTTTGCAGCTG

CCTACT

mir-033b
ACCAAGTTTCAGTTCATGTAAACATCCTACACTCAGC
254

TGTAATACATGGATTGGCTGGGAGGTGGATGTTTACT

TCAGCTGACTTGGA

mir-101-
TGCCCTGGCTCAGTTATCACAGTGCTGATGCTGTCTA
255

precursor-9 = mir-
TTCTAAAGGTACAGTACTGTGATAACTGAAGGATGGC

101-3
A

mir-108-1-small
ACACTGCAAGAACAATAAGGATTTTTAGGGGCATTAT
256

GACTGAGTCAGAAAACACAGCTGCCCCTGAAAGTCCC

TCATTTTTCTTGCTGT

mir-108-2-small
ACTGCAAGAGCAATAAGGATTTTTAGGGGCATTATGA
257

TAGTGGAATGGAAACACATCTGCCCCCAAAAGTCCCT

CATTTT

mir-123-prec =
CGCTGGCGACGGGACATTATTACTTTTGGTACGCGCT
258

mir-126-prec
GTGACACTTCAAACTCGTACCGTGAGTAATAATGCGC

CGTCCACGGCA

mir-123-prec =
ACATTATTACTTTTGGTACGCGCTGTGACACTTCAAA
259

mir-126-prec
CTCGTACCGTGAGTAATAATGCGC

mir-129-1-prec
TGGATCTTTTTGCGGTCTGGGCTTGCTGTTCCTCTCA
260

ACAGTAGTCAGGAAGCCCTTACCCCAAAAAGTATCTA

mir-129-small-
TGCCCTTCGCGAATCTTTTTGCGGTCTGGGCTTGCTG
261

2 = 129b?
TACATAACTCAATAGCCGGAAGCCCTTACCCCAAAAA

GCATTTGCGGAGGGCG

mir-133b-small
GCCCCCTGCTCTGGCTGGTCAAACGGAACCAAGTCCG
262

TCTTCCTGAGAGGTTTGGTCCCCTTCAACCAGCTACA

GCAGGG

mir-135-small-
AGATAAATTCACTCTAGTGCTTTATGGCTTTTTATTC
263

CTATGTGATAGTAATAAAGTCTCATGTAGGGATGGAA

GCCATGAAATACATTGTGAAAAATCA

mir-148b-small
AAGCACGATTAGCATTTGAGGTGAAGTTCTGTTATAC
264

ACTCAGGCTGTGGCTCTCTGAAAGTCAGTGCAT

mir-151-prec
CCTGTCCTCAAGGAGCTTCAGTCTAGTAGGGGATGAG
265

ACATACTAGACTGTGAGCTCCTCGAGGGCAGG

mir-155-
CTGTTAATGCTAATCGTGATAGGGGTTTTTGCCTCCA
266

prec(BIC)
ACTGACTCCTACATATTAGCATTAACAG

mir-156 = mir-
CCTAACACTGTCTGGTAAAGATGGCTCCCGGGTGGGT
267

157 = overlap mir-
TCTCTCGGCAGTAACCTTCAGGGAGCCCTGAAGACCA

141
TGGAGGAC

mir-158-small =
GCCGAGACCGAGTGCACAGGGCTCTGACCTATGAATT
268

mir-192

GACAGCCAGTGCTCTCGTCTCCCCTCTGGCTGCCAAT

TCCATAGGTCACAGGTATGTTCGCCTCAATGCCAGC

mir-159-1-small
TCCCGCCCCCTGTAACAGCAACTCCATGTGGAAGTGC
269

CCACTGGTTCCAGTGGGGCTGCTGTTATCTGGGGCGA

GGGCCA

mir-161-small
AAAGCTGGGTTGAGAGGGCGAAAAAGGATGAGGTGAC
270

TGGTCTGGGCTACGCTATGCTGCGGCGCTCGGG

mir-163-1b-small
CATTGGCCTCCTAAGCCAGGGATTGTGGGTTCGAGTC
271

CCACCCGGGGTAAAGAAAGGCCGAATT

mir-163-3-small
CCTAAGCCAGGGATTGTGGGTTCGAGTCCCACCTGGG
272

GTAGAGGTGAAAGTTCCTTTTACGGAATTTTTT

mir-175-
GGGCTTTCAAGTCACTAGTGGTTCCGTTTAGTAGATG
273

small = mir-224
ATTGTGCATTGTTTCAAAATGGTGCCCTAGTGACTAC

AAAGCCC

mir-177-small
ACGCAAGTGTCCTAAGGTGAGCTCAGGGAGCACAGAA
274

ACCTCCAGTGGAACAGAAGGGCAAAAGCTCATT

mir-180-small
CATGTGTCACTTTCAGGTGGAGTTTCAAGAGTCCCTT
275

CCTGGTTCACCGTCTCCTTTGCTCTTCCACAAC

mir-187-prec
GGTCGGGCTCACCATGACACAGTGTGAGACTCGGGCT
276

ACAACACAGGACCCGGGGCGCTGCTCTGACCCCTCGT

GTCTTGTGTTGCAGCCGGAGGGACGCAGGTCCGCA

mir-188-prec
TGCTCCCTCTCTCACATCCCTTGCATGGTGGAGGGTG
277

AGCTTTCTGAAAACCCCTCCCACATGCAGGGTTTGCA

GGATGGCGAGCC

mir-190-prec
TGCAGGCCTCTGTGTGATATGTTTGATATATTAGGTT
278

GTTATTTAATCCAACTATATATCAAACATATTCCTAC

AGTGTCTTGCC

mir-197-2
GTGCATGTGTATGTATGTGTGCATGTGCATGTGTATG
279

TGTATGAGTGCATGCGTGTGTGC

mir-197-prec
GGCTGTGCCGGGTAGAGAGGGCAGTGGGAGGTAAGAG
280

CTCTTCACCCTTCACCACCTTCTCCACCCAGCATGGC

C

mir-202-prec
GTTCCTTTTTCCTATGCATATACTTCTTTGAGGATCT
281

GGCCTAAAGAGGTATAGGGCATGGGAAGATGGAGC

mir-294-1 (chr16)
CAATCTTCCTTTATCATGGTATTGATTTTTCAGTGCT
282

TCCCTTTTGTGTGAGAGAAGATA

mir-hes1
ATGGAGCTGCTCACCCTGTGGGCCTCAAATGTGGAGG
283

AACTATTCTGATGTCCAAGTGGAAAGTGCTGCGACAT

TTGAGCGTCACCGGTGACGCCCATATCA

mir-hes2
GCATCCCCTCAGCCTGTGGCACTCAAACTGTGGGGGC
284

ACTTTCTGCTCTCTGGTGAAAGTGCCGCCATCTTTTG

AGTGTTACCGCTTGAGAAGACTCAACC

mir-hes3
CGAGGAGCTCATACTGGGATACTCAAAATGGGGGCGC
285

TTTCCTTTTTGTCTGTTACTGGGAAGTGCTTCGATTT

TGGGGTGTCCCTGTTTGAGTAGGGCATC

hsa-mir-29b-1
CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTA
664

AATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTC

TTGGGGG

*An underlined sequence within a precursor sequence represents a processed miR transcript. All sequences are human.

Genome Analysis

The BUILD 33 and BUILD 34 Version 1 of the Homo sapiens genome, available at the NCBI website (see above), was used for genome analysis. For each human miR present in the miR database, a BLAST search was performed using the default parameters against the human genome to find the precise location, followed by mapping using the maps available at the Human Genome Resources at the NCBI website. See also Altschul et al. (1990), J. Mol. Biol. 215:403-10 and Altschul et al. (1997), Nucleic Acids Res. 25:3389-3402, the entire disclosures of which are herein incorporated by reference, for a discussion of the BLAST search algorithm. Also, as a confirmation of the data, the human clone corresponding to each miR was identified and mapped to the human genome (see Table 2). Perl scripts for the automatic submission of BLAST jobs and for the retrieval of the search results were based on the LPW, HTML, and HTPP Perl modules and BioPerl modules.

Fragile Site Database

This database was constructed using the Virtual Gene Nomenclature Workshop, maintained by the HUGO Gene Nomenclature Committee at University College, London. For each FRA locus, the literature was screened for publications reporting the cloning of the locus. In ten cases, genomic positions for both centromeric and telomeric ends were found. The total genomic length of these FRA loci is 26.9 Mb. In twenty-nine cases, only one anchoring marker was identified. It was determined, based on the published data, that 3 Mb can be used as the median length for each FRA locus. Therefore, 3 Mb was used as a guideline or window length for considering whether miR were in close proximity to the FRA sites.

The human clones for seventeen HPV16 integration sites (IS) were also precisely mapped on the human genome. By analogy with the length of a FRA, in the case of HPV16 integration sites, “close” vicinity was defined to be a distance of less than 2 Mb.

PubMed Database

The PubMed database was screened on-line for publications describing cancer-related abnormalities such as minimal regions of loss-of-heterozygosity (minimal LOH) and minimal regions of amplification (minimal amplicons) using the words “LOH and genome-wide,” “amplification and genome-wide” and “amplicon and cancer.” The PubMed database is maintained by the NCBI and was accessed via its website. The data obtained from thirty-two papers were used to screen for putative CAGRs, based on markers with high frequency of LOH/amplification. As a second step, a literature search was performed to determine the presence or absence of the above three types of alterations and to determine the precise location of miRs with respect to CAGRs (see above). Search phrases included the combinations “minimal regions of LOH AND cancer”, and “minimal region amplification AND cancer.” A total of 296 publications were found and manually curated to find regions defined by both telomeric and centromeric markers. One hundred fifty-four minimally deleted regions (median length—4.14 Mb) and 37 minimally amplified regions (median length-2.45 Mb) were identified with precise genomic mapping for both telomeric and centromeric ends involving all human chromosomes except Y. To identify common breakpoint regions, PubMed was searched with the combination “translocation AND cloning AND breakpoint AND cancer.” The search yielded 308 papers, which were then manually curated. Among these papers, 45 translocations with at least one breakpoint precisely mapped were reported.

Statistical Analyses

The incidence of miR genes and their association with specific chromosomes and chromosome regions, such as FRAs and amplified or deleted regions in cancer, was analyzed with random effect Poisson regression models. Under these models, “events” are defined as the number of miR genes, and non-overlapping lengths of the region of interest defined exposure “time” (i.e., fragile site versus non-fragile site, etc.). The “length” of a region was exactly ±1 Mb, if known, or estimated as ±1 Mb if unknown. The random effect used was chromosomal location, in that data within a chromosome were assumed to be correlated. The fixed effect in each model consisted of an indicator variable(s) for the type of region. This model provided the incidence rate ratio (IRR), 2-sided 95% confidence interval of the IRR, and 2-sided p-values for testing the hypothesis that the IRR is 1.0. An IRR significantly greater than 1 indicates an increase in the number of miR genes within a region.

Each model was repeated considering the distribution of miR genes only in the transcriptionally active portion of the genome (about 43% of the genome using the published data), rather than the entire chromosome length, and similar results were obtained. Considering the distribution of miRs only in the transcriptionally active portion of the genome is more conservative, and takes into account the phenomenon of clustering that was observed for the miR genes' genomic location. All computations were completed using STATA v7.0.

Patient Samples and Cell Lines

Patient samples were obtained from twelve chronic lymphocytic leukemia (CLL) patients, and mononuclear cells were isolated through Ficoll-Hypaque gradient centrifugation (Amersham Pharmacia Biotech, Piscataway, N.J.), as previously described (Calin et al., Proc. Natl. Acad. Sci. USA 2002, 99:15524-15529). Samples were then processed for RNA and DNA extraction according to standard protocols as described in Sambrook J et al. (1989), Molecular cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.), the entire disclosure of which is herein incorporated by reference.

Seven human lung cancer cell lines were obtained from the American Type Culture Collection (ATCC; Manassas, Va.) and maintained according to ATCC instructions. These cell lines were: Calu-3, H1299, H522, H460, H23, H1650 and H1573.

Northern Blotting

Total RNA isolation from patient samples and cell lines described above was performed using the Tri-Reagent protocol (Molecular Research Center, Inc). RNA samples (30 μg each) were run on 15% acryl amide denaturing (urea) Criterion recast gels (Bio-Red Laboratories, Hercules, Calif.) and then transferred onto Hyoid-N+ membrane (Amersham Pharmacia Biotech), as previously described (Calin et al., Proc. Natl. Acad. Sci. USA 2002, 99:15524-15529). Hybridization with gamma-³²P ATP labeled probes was performed at 42° C. in 7% SDS, 0.2 M Na₂PO₄, pH 7.0 overnight. Membranes were washed at 42° C., twice in 2×SSPE, 0.1% SDS and twice with 0.5×SSPE, 0.1% SDS. Blots were stripped by boiling in 0.1% aqueous SDS/0.1×SSC for 10 minutes, and were reprobed several times. As a gel loading control, 5S rRNA was also loaded and was stained with ethidium bromide. Lung tissue RNA was utilized as the normal control; normal lung total RNA was purchased from Clontech (Palo Alto, Calif.).

Example 1
miR Genes are Non-Randomly Distributed in the Human Genome

One hundred eighty-six human genes representing known or predicted miR genes were mapped, based on mouse homology or computational methods, as described above in the General Methods. The results are presented in Table 2. The names were as in the miRNA Registry; for new miR genes, sequential names were assigned. miR 213 from Sanger database is different from miR 213 described in Lim et al. (2003, Science 299:1540). MiR genes in clusters are separated by a forward slash “/”. The approximate location in Mb of each clone is presented in the last column.

TABLE 2

miR Database: Chromosome Location and Clustering

Chromosome

Loc (Mb)

Name
location
Genes in Cluster
(built 33)

let-7a-1
09q22.2
let-7a-1/let-7f-1/let-7d
90.2-.3

let-7a-2
11q24.1
miR-125b-1/let-7a-2/miR-100
121.9-122.15

let-7a-3
22q13.3
let-7a-3/let-7b
44.7-.8

let-7b
22q13.3
let-7a-3/let-7b
44.7-.8

let-7c
21q11.2
miR-99a/let-7c/miR-125b-2
16.7-.9

let-7d
09q22.2
let-7a-1/let-7f-1/let7d
90.2-.3

let-7e
19q13.4
miR-99b/let-7e/miR-125a
56.75-57

let-7f
09q22.2
let-7a-1/let-7f-1/let7d
90.2-.3

let-7f-2
Xp11.2
miR-98/let-7f-2
52.2-.3

let-7g
03p21.3
let-7g/miR-135-1
52.1-.3

let-7i
12q14.1

62.7-.9

miR-001b-2
20q13.3
miR-133a-2/miR-1b-2
61.75-.8

miR-001d
18q11.1
miR-133a-1/miR-1d
19.25-.4

miR-007-1
09q21.33

80-80.1

miR-007-2
15q25

86.7-.8

miR-007-3
19p13.3

4.7-.75

miR-009-1
01q22

153.1-.2

(=miR-131-1)

miR-009-2
05q14

87.85-88

(=miR-131-2)

miR-009-3
15q25.3

87.5

(=miR-131-3)

miR-010a
17q21.3
miR-196-1/miR-10a
46.95-47.05

miR-010b
02q31

176.85-177

miR-015a
13q14
miR-16a/miR-15a
49.5-.8

miR-015b
03q26.1
miR-15b/miR-16b
161.35-.5

miR-016a
13q14
miR-16a/miR-15a
49.5-.8

miR-016b
03q26.1
miR-15b/miR-16b
161.35-.5

miR-017
13q31
miR-17/miR-18/miR-19a/miR-20/miR-
90.82

(=miR-91)

19b-1/miR-92-1

miR-018
13q31
miR-17/miR-18/miR-19a/miR-20/miR-
90.82

19b-1/miR-92-1

miR-019a
13q31
miR-17/miR-18/miR-19a/miR-20/miR-
90.82

19b-1/miR-92-1

miR-019b-1
13q31
miR-17/miR-18/miR-19a/miR-20/miR-
90.82

19b-1/miR-92-1

miR-019b-2
Xq26.2
miR-92-2/miR-19b-2/miR-106a
131.2-.3

miR-020
13q31
miR-17/miR-18/miR-19a/miR-20/miR-
90.82

19b-1/miR-92-1

miR-021
17q23.2

58.25-.35

(=miR104-as)

miR-022
17p13.3

1.4-.6

miR-023a
19p13.2
miR-24-2/miR-27a/miR-23a/miR-181c
13.75-.95

miR-023b
09q22.1
miR-24-1/miR27b/miR-23b
90.8-91

miR-024-1
09q22.1
miR-24-1/miR27b/miR-23b
90.8-91

(=miR-189)

miR-024-2
19p13.2
miR-24-2/miR-27a/miR-23a/miR-181c
13.75-.95

miR-025
07q22
miR-106b/miR-25/miR-93-1
99.25-.4

miR-026a
03p21

37.8-.9

miR-026b
02q35

219.1-.3

miR-027a
19p13.2
miR-24-2/miR-27a/miR-23a/miR-181c
13.75-.95

miR-027b
09q22.1
miR-24-1/miR27b/miR-23b
90.8-91

miR-028
03q28

189.65-.85

miR-029a
07q32
miR-29a/miR29b
129.9-130.1

miR-029b
07q32
miR-29a/miR29b
129.9-130.1

(=miR-102-7.1)

miR-029c
01q32.2-32.3
miR-29c/miR-102
204.6-.7

miR-030a-as
06q12-13

72.05-.2

miR-030a-s
06q12-13

72.05-.2

(=miR-097)

miR-030b
08q24.2
miR-30d/miR-30b
135.5

miR-030c
06q13

71.95-72.1

miR-030d
08q24.2
miR-30d/miR-30b
135.5

miR-031
09p21

21.3-.5

miR-032
09q31.2

105.1-.3

miR-033a
22q13.2

40.5-.8

miR-033b
17p11.2

17.6-.7

miR-034
01p36.22

8.8

(=miR-170)

miR-034a-1
11q23
miR-34a-2/miR 34a-1
111.3-.5

miR-034a-2
11q23
miR-34a-2/miR 34a-1
111.3-.5

miR-092-1
13q31
miR-17/miR-18/miR-19a/miR-20/miR-
90.82

19b-1/miR-92-1

miR-092-2
Xq26.2
miR-92-2/miR-19b-2/miR-106a
131.2-.3

miR-093-1
07q22
miR-106b/miR-25/miR-93-1
99.25-.4

miR-095
04p16

8-.2

miR-096
07q32
miR-182s/miR-182as/miR-96/miR-183
128.9-129

miR-098
Xp11.2
miR-98/let-7f-2
52.2-.3

miR-099a
21q11.2
miR-99a/let-7c/miR-125b-2
16.7-.9

miR-099b
19q13.4
miR-99b/let-7e/miR-125a
56.75-57

miR-100
11q24.1
miR-125b-1/let-7a-2/miR-100
121.9-122.15

miR-101-1
01p31.3

64.85-95

miR-101-2
09p24

4.8-5

miR-102
01q32.2-32.3
miR-29c/miR-102
204.5-.7

miR-103-1
05q35.1

167.8-.95

miR-103-2
20p13

3.82-.90

miR-105-1
Xq28

149.3-.4

miR-106b
07q22
miR-106b/miR-25/miR-93-1
99.25-.4

(=miR-94)

miR-106a
Xq26.2
miR-92-2/miR-19b-2/miR-106a
131.2-.3

miR-107
10q23.31

91.45-.6

miR-108-1
17q11.1
miR-108-1/miR-193
29.6-.8

miR-108-2
16p13.1

14.3-.5

miR-122a
18q21

55.85-56

miR-123
09q34

132.9-133.05

(=miR-126)

miR-124a-1
08p23

9.5-.65

miR-124a-2
08q12.2

64.9-65.1

miR-124a-3
20q13.33

62.4-.55

miR-125a
19q13.4
miR-99b/let-7e/miR-125a
56.75-57

miR-125b-1
11q24.1
miR-125b1/let-7a-2/miR-100
121.9-122.1

miR-125b-2
21q11.2
miR-99a/let-7c/miR-125b-2
16.7-.9

miR-127
14q32
miR-127/miR-136
99.2-.4

miR-128a
02q21

136.3-.5

miR-128b
03p22

35.45-.6

miR-129-1
07q32

127.25-.4

miR-129-2
11p11.2

43.65-.75

miR-130a
11q12

57.6-.7

miR-130b
22q11.1

20.2-.4

miR-132
17p13.3
miR-212/miR-132
1.85-2

miR-133a-1
18q11.1
miR-133a-1/miR-1d
19.25-.4

miR-133a-2
20q13.3
miR-133a-2/miR-1b-2
61.75-.8

miR-133b
06p12
miR-206/miR-133b
51.9-52

miR-134
14q32
miR-154/miR-134/miR-299
99.4-.6

miR-135-1
03p21.3
let-7g/miR-135-1
52.1-.3

miR-135-2
12q23

97.85-98

miR-136
14q32
miR-127/miR-136
99.2-.4

miR-137
01p21-22

97.75

miR-138-1
03p21

43.85-.95

miR-138-2
16q12-13

56.55-.7

miR-139
11q13

72.55-.7

miR-140as
16q22.1

69.6-.8

miR-140s
16q22.1

69.6-.8

miR-141
12p13
overlap miR-156 - cluster
6.9-7.05

(=overlap

miR-156)

miR-142-as
17q23

56.75-.9

miR-142-s
17q23

56.75-.9

miR-143
05q32-33
miR-145/miR-143
148.65-.8

miR-144
17q11.2

27.05

miR-145
05q32-33
miR-145/miR-143
148.65-.8

miR-146
05q34

159.8-.9

miR-147
09q33

116.35-.55

miR-148
07p15

25.6-.8

miR-148b
12q13

54.35-.45

miR-149
02q37.3

241.3-.4

miR-150
19q13

54.6-.8

miR-151
08q24.3

141.4-.5

miR-152
17q21

46.4-.5

miR-153-1
02q36

220.1-.2

miR-153-2
07q36

156.5-.7

miR-154
14q32
miR-154/miR-134/miR-299
99.4-.6

miR-155
21q21

25.85

(BIC)

miR-156
12p13
overlap miR-141 - cluster
6.9-7.05

(=miR-157)

miR-159-1
11q13
miR-159-1/miR-192
64.9-65

miR-161
08p21

21.8-.9

miR-175
Xq28

148.8-.9

(=miR-224)

miR-177
08p21

21.25-.35

miR-180
22q11.21-12.2

26.45

miR-181a
09q33.1-34.13

120.85-.95

(=miR-178-2)

miR-181b
01q31.2-q32.1
miR-213 S/miR-181b
195.2-.35

(=miR-178 =

miR-213 -LIM)

miR-181c
19p13.3
miR-24-2/miR-27a/miR-23a/miR-181c
13.75-.95

miR-182-as
07q32
miR-182s/miR-182as/miR-96/miR-183
128.9-129

miR-182-s
07q32
miR-182s/miR-182as/miR-96/miR-183
128.9-129

miR-183
07q32
miR-182s/miR-182as/miR-96/miR-183
128.9-129

(=miR-174)

miR-184
15q24

76.9-77.1

miR-185
22q11.2

18.35-.45

miR-186
01p31

70.9-71

miR-187
18q12.1

33.25-.4

miR-188
Xp11.23-p11.2

48.35-.5

miR-190
15q21

60.6-.8

miR-191
03p21

48.85-.95

miR-192
11q13
miR-159-1/miR-192
64.9-65

(=miR-158)

miR-193
17q11.2
miR-108-1/miR-193
29.6-.8

miR-194
01q41
miR-215/miR-194
216.7-.8

(=miR-159-2)

miR-195
17p13

6.75-.85

miR-196-1
17q21
miR-196-1/miR-10a
46.9-47.1

miR-196-2
12q13

54-54.15

miR-197
01p13

109.2-.3

miR-198
03q13.3

121.3-.4

miR-199a-1
19p13.2

10.75-.8

(=miR-199s)

miR-199a-2
01q23.3
miR-214/miR-199a-2
168.7-.8

miR-199as
19p13.2

10.75-.8

(=antisense

miR-199a-1)

miR-199b
09q34

124.3-.5

(=miR-164)

miR-200
01p36.3

0.9-1

miR-202
10q26.3

135

miR-203
14q32.33

102.4-.6

miR-204
09q21.1

66.9-67

miR-205
01q32.2

206.2-.3

miR-206
06p12
miR-206/miR-133b
51.9-52

miR-208
14q11.2

21.8-22

miR-210
11p15

0.55-0.75

miR-211
15q11.2-q12

28.9-29.1

miR-212
17p13.3
miR-212/miR-132
1.9-2.1

miR-213 -
01q31.3-q32.1
miR-213 S/miR-181b
195.2-.35

SANGER

miR-214
01q23.3
miR-214/miR-199a-2
168.7-.8

miR-215
01q41
miR-215/miR-194
216.7-.8

miR-216
02p16
miR-217/miR-216
56.2-.4

miR-217
02p16
miR-217/miR-216
56.2-.4

miR-218-1
04p15.32

20.15-.35

miR-218-2
05q35.1

168-.15

miR-219
06p21.2-21.31

33.1-.25

miR-220
Xq25

120.6-.8

miR-221
Xp11.3
miR-222/miR-221
44.35-.45

miR-222
Xp11.3
miR-222/miR-221
44.35-.45

miR-223
Xq12-13.3

63.4-.5

miR-294-1
16q22

65.1-.3

miR-297-3
20q13.2

52.25-.35

miR-299
14q32
miR-154/miR-134/miR-299
99.4-.6

miR-301
17q23

57.5-.7

miR-302
04q25

113.9-114

mir-hes1
19q13.4
miR-hes1/miR-hes2/miR-hes3
58.9-59.05

miR-hes2
19q13.4
miR-hes1/miR-hes2/miR-hes3
58.9-59.05

miR-hes3
19q13.4
miR-hes1/miR-hes2/miR-hes3
58.9-59.05

The distribution of the 186 human miR genes was found to be non-random. Ninety miR genes were located in 36 clusters, usually with two or three genes per cluster (median=2.5). The largest cluster found comprises six genes (miR-17/miR-18/miR-19a/miR-20/miR-19b1/miR-92-1) and is located at 13q31 (Table 2). A significant association of the incidence of miR genes with specific chromosomes was found. Chromosome 4 was found to have a lower than expected rate of miR genes (IRR=0.27; p=0.035). Chromosomes 17 and 19 were found to have significantly more miR genes than expected, based on chromosome size (IRR=2.97, p=0.002 and IRR=3.39, p=0.001, respectively). Six of the 36 miR gene clusters (17%), which contain 16 of 90 clustered genes (18%), are located on these two small chromosomes, which account for only 5% of the entire genome.

Similar results were obtained using a model considering the distribution of miR genes only in the transcriptionally active portion of the genome (see Table 3).

Chromosome 1 is used as the baseline in the model with a rate of miR gene incidence of ˜0.057, which is approximately equal to the overall rate of miR gene incidence across the genome.

TABLE 3

Location of miRs by chromosome and results

of mixed effects Poisson regression model.

Chromosome
Length
# of miRs
IRR
p

1
279
16
—
—

2
251
7
0.49
0.112

3
221
10
0.79
0.557

4
197
3
0.27
0.035

5
198
6
0.53
0.183

6
176
6
0.59
0.277

7
163
13
1.39
0.377

8
148
6
0.71
0.469

9
140
15
1.87
0.082

10
143
2
0.24
0.060

11
148
11
1.29
0.508

12
142
6
0.74
0.523

13
118
8
0
1.000

14
107
7
1.14
0.771

15
100
5
0.87
0.789

16
104
5
0.84
0.731

17
88
15
2.97
0.002

18
86
4
0.81
0.708

19
72
14
3.39
0.001

20
66
5
1.32
0.587

21
45
4
1.55
0.433

22
48
6
2.09
0.123

X
163
12
1.28
0.513

Y
51
0
0
1.000

Example 2
miR Genes are Located in or Near Fragile Sites

Thirty-five of 186 miRs (19%) were found in (13 miR genes), or within 3 Mb (22 miR genes) of cloned fragile sites (FRA). A set of 39 fragile sites with available cloning information was used in the analysis. Data were available for the exact dimension (mean 2.69 Mb) and position of ten of these cloned fragile sites (see General Methods above). The relative incidence of miR genes inside fragile sites occurred at a rate 9.12 times higher than in non-fragile sites (p<0.001, using mixed effect Poisson regression models; see Tables 3 and 4). The same very high statistical significance was also found when only the 13 miRs located exactly inside a FRA or exactly in the vicinity of the “anchoring” marker mapped for a FRA were considered (IRR=3.21, p<0.001). Among the four most active common fragile sites (FRA3B, FRA16D, FRA6E, and FRA7H), the data demonstrate seven miRs in (miR-29a and miR-29b) or close (miR-96, miR-182s, miR-182as, miR-183, and miR-129-1) to FRA7H, the only fragile site where no candidate tumor suppressor (TS) gene has been found. The other three of the four most active sites contain known or candidate TS genes; i.e., FHIT, WWOX and PARK2, respectively (Ohta et al., 1996, Cell 84:587-597; Paige et al., 2001, Proc. Natl. Acad. Sci. USA 98:11417-11422; Cesari et al., 2003, Proc. Natl. Acad. Sci. USA 100:5956-5961).

Analysis of 113 fragile sites scattered in the human karyotype showed that 61 miR genes are located in the same cytogenetic positions with FRAs. Thirty-five miR genes were located inside twelve cloned FRAs. These data indicate that more miRs are located in or near FRAs, and that the results described herein represent an underestimation of miR gene/FRA association, likely because the mapping of these unstable regions is not complete.

TABLE 4

Mixed Effect Poisson Regression Results for the Association

Between microRNAs and Several Types of Regions of Interest

Incidence Rate
95% CI

Region of interest
Ratio (IRR)
IRR
p

Cloned Fragile sites vs. non-
9.12
6.22, 13.38
<0.001

fragile sites

HPV16 insertion vs. all other
3.22
1.55, 6.68
<0.002

Deleted region vs. all other
4.08
2.99, 5.56
<0.001

Amplified region vs. all other
3.97
2.31, 6.83
<0.001

HOX Clusters vs. all other
15.77
7.39, 33.62
<0.001

Homeobox genes vs. all other
2.95
1.63, 5.34
<0.001

Note:

“all other” means all the genome except the regions of interest.

Example 3
miR Genes are Located in or Near Human Papilloma Virus (HPV) Integration Sites

Because common fragile sites are preferential targets for HPV 16 integration in cervical tumors, and infection with HPV 16 or HPV 18 is the major risk factor for developing cervical cancer, the association between miR gene locations and HPV16 integration sites in cervical tumors was analyzed. The data indicate that thirteen miR genes (7%) are located within 2.5 Mb of seven of seventeen (45%) cloned integration sites. The relative incidence of miRs at HPV 16 integration sites occurred at a rate 3.22 times higher than in the rest of the genome (p<0.002) (Tables 4 and 5). In one cluster of integration sites at chromosome 17q23, where three HPV 16 integration sites are spread over roughly 4 Mb of genomic sequence, four miR genes (miR-21, miR-301, miR-142s and miR-142as) were found.

TABLE 5

Analyzed FRA Sites, Cancer Correlation and HPV Integration Sites

Distance

Location
Closest
miR-FRA
HPV16

Symbol
Chromosome
Cancer correlation
Type
(Mb)
miR(s)
(Mb)
integration*

FRA1A
1p36

FRA1C
1p31

aphidicolin type,
67.87
miR-186;
3; 3

common

miR-101-1

FRA1F
1q21
bladder

FRA1H
1q42.1
cervical
5-azacytidine,
216.5
miR-194;
exact
YES

common

miR-215

FRA2G
2q31
RCC

FRA2I
2q33
chronic myelogenous

leukemia

FRA3B
3p14.2
esophageal carcinoma,

lung, stomach, kidney,

cervical cancer

FRA4B
4q12

FRA4C
4q31.1

FRA5C
5q31.1

FRA5E
5p14

FRA6E
6q26
ovarian

FRA6F
6q21
leukemias and solid

tumors

FRA7E
7q21.2 or

21.11

FRA7F
7q22

aphidicolin type,
100.2-107
miR-106b;
less than 1

common

miR-25;

miR-93

FRA7G
7q31.2
ovarian

FRA7H
7q32.3
esophageal
aphidicolin type,
129.8-130.4
miR-29b;
exact; 1

common

miR-29a;
and 2.5

miR-96;

miR-182s;

miR-182as;

miR-183;

miR-129-1

FRA7I
7q35
breast

FRA8B
8q22.1

FRA8E
8q24.1

FRA9D
9q22.1
bladder
aphidicolin type,
89.5-92
let7a-1; let-7d;
exact

common

let-7f-1

miR-23b;

miR-24-1;

miR-27b

FRA9E
9q32-33.1
ovarian, bladder,
aphidicolin type,
101.3-111.9
miR-32
exact
YES

cervical
common

FRA10B
10q25.2

FRA10C
10q21

FRA10D
10q22.1

FRA11A
11q13.3
hematopoietic and solid
folic acid type,
66.18-66.9
miR-159-1;
1.2

tumors
rare

miR-192

FRA11B
11q23.3

folic acid type,
119.1-.2
miR-125b-1;
2

rare

let-7a-2;

miR-100

FRA12A
12q13.1

folic acid type,
53.55
miR-196-2;
1

rare

miR-148b

FRA13C
13q21.2

FRA15A
15q22

aphidicolin type,
60.93
miR-190
exact

common

FRA16D
16q23.2
gastric

adenocarcinoma,

adenocarcinomas of

stomach, colon, lung

and ovary

FRA16E
16p12.1

FRA17B
17q23.1

aphidicolin type,
58.25-58.35
miR-21
exact
YES

common

miR-301
0.5

miR-142s ;
1.5

miR-142as

FRA18A
18q12.2
esophageal carcinoma

FRA22A
22q13

FRAXA
Xq27.31

FRAXB
Xp22.3

FRAXE
Xq28

FRAXF
Xq28

146.58
miR-105-1;
2.2

miR-175

Note:

*other microRNAs located close to HPV16 integration sites were found in relation to FRA5C, FRA11C, FRA12B and FRA12E. Positions are indicated according to Build 33 of the Human Genome.

Example 4A
miR Genes are Located in or Near Cancer Associated Genomic Regions

Because the miR-FRA-HPV16 association has significance for cancer pathogenesis, miR genes might be involved in malignancies through other mechanisms, such as deletion, amplification, or epigenetic modifications. Thus, a search was performed for reported genomic alterations in human cancers, located in regions containing miR genes. PubMed was searched for reports of CAGR such as minimal regions of loss-of-heterozygosity (LOH) suggestive of the presence of tumor-suppressor genes (TSs), minimal regions of amplification suggestive of the presence of oncogenes (OGs), and common breakpoint regions in or near possible OGs or TSs (see General Methods above). Overall, 98 of 187 (52.5%) miR genes were found to be located in CAGRs (see Tables 6 and 7). Eighty of the miR genes (43%) were found to be located exactly within minimal regions of LOH or minimal regions of amplification described in a variety of tumors, such as lung, breast, ovarian, colon, gastric and hepatocellular carcinoma, as well as leukemias and lymphomas (see Tables 6 and 7).

The analysis showed that on chromosome 9, eight of fifteen mapped miR genes (including six located in clusters), were located inside two regions of deletion on 9q (Simoneau et al., 1999, Oncogene 7:157-163): the clusters let-7a-1/let-7f-1/let-7d and miR-23b/miR-27b/miR-24-1 inside region B at 9q22.3 and miR-181a and miR-199b inside region D at 9q33-34.1 (Table 6). Furthermore, five other miR genes were located less than 2 Mb from the markers with the highest rate of LOH: miR-31 near IFNA, miR-204 near D9S15, miR-181 and miR-147 near GSN, and miR-123 near D9S67.

In breast carcinomas, two different regions of loss at 11q23, independent from the ATM locus, have been studied extensively: the first spans about 2 Mb between loci D11S1347 and D11S927; the second is located between loci D11S1345 and D11S1316 and is estimated at about 1 Mb (di Iasio et al., 1999, Oncogene 25:1635-1638). Despite extensive effort, the only candidate TS gene found was the PPP2R1B gene, involved in less than 10% of reported cases (Calin et al., 2002, Proc. Natl. Acad. Sci. USA 99:15524-15529; Wang et al., 1998, Science 282:284-7). Both of these minimal LOH regions contained numerous microRNAs: the cluster miR-34-a1/miR-34-a2 in the first and the cluster miR-125b1/let-7a-2/miR-100 in the second.

High frequency LOH at 17p13.3 and relatively low TP53 mutation frequency in cases of hepatocellular carcinomas (HCC), lung cancers and astrocytomas indicate the presence of other TSs involved in the development of these tumors. One minimal LOH region correlated with HCC, and located telomeric to TP53 between markers D17S1866 and D17S1574 on chromosome 17, contained three miR genes: miR-22, miR-132, and miR-212. miR-195 is located between ENO3 and TP53 on chromosome 17.

Homozygous deletions (HD) in cancer can indicate the presence of TSs (Huebner et al., 1998, Annu Rev. Genet. 32:7-31), and several miR genes are located in homozygously deleted regions without known TSs. In addition to miR-15a and miR-16a located at 13q14 HD region in B-CLL, the cluster miR-99a/let-7c/miR-125b-2 mapped in a 21p11.1 region of HD in lung cancers and miR-32 at 9q31.2 in a region of HD in various types of cancer. Among the seven regions of LOH and HD on the short arm of chromosome 3, three of the regions harbor miRs: miR-26a in region AP20, miR-138-1 in region 5 at 3p21.3 and the cluster let-7g/miR-135-1 in region 3 at 3p21.1-p21.2. The locations of the miR genes/gene clusters are not likely to be random, because it was found that overall, the relative incidence of miRs in both deleted and amplified regions is highly significant (IRR=4.08, p<0.001 and IRR=3.97, p=0.001, respectively) (Table 4). Thus, these miRs expand the spectrum of candidate TSs.

TABLE 6

Examples of microRNAs Located in Minimal Deleted Regions, Minimal Amplified

Regions, and Breakpoint Regions Involved in Human Cancers *

Location
Size

Known

Chromosome
(defining markers)
Mb
MiR Gene
Histotype
OG/TS

3p21.1-21.2-D
ARP-
7
let-7g/miR-135-1
lung, breast cancer
—

DRR1

3p21.3(AP20)-D
GOLGA4 -
0.75
miR-26a
epithelial cancer
—

VILL

3p23-21.31
D3S1768 -
12.32
miR-26a; miR-138-1
nasopharyngeal
—

(MDR2)-D
D3S1767

cancer

5q32-D
ADRB2 -
2.92
miR-145/miR-143
myelodysplastic
—

ATX1

syndrome

9q22.3-D
D9S280 -
1.46
miR-24-1/mir-27b/miR-
urothelial cancer
PTC, FANCC

D9S1809

23b; let-7a-1/let-7f-1/let-7d

9q33-D
D9S1826 -
0.4
miR-123
NSCLC
—

D9S158

11q23-q24-D
D11S927 -
1.994
miR-34a-1/miR-34a-2
breast, lung cancer
PPP2R 1B

D11S1347

11q23-q24-D
D11S1345 -
1.725
miR-125b-1/let-7a-2/miR-100
breast, lung, ovary,
—

D11S1328

cervix cancer

13q14.3-D
D13S272 -
0.54
miR-15a/miR-16a
B-CLL
—

D13S25

13q32-33-A
stSG15303 -
7.15
miR-17/miR-18/miR-
follicular lymphoma
—

stSG31624

19a/miR-20/miR-19b-1/miR-

92-1

17p13.3-D
D17S1866 -
1.899
miR-22; miR-132; miR-
HCC
—

D17S1574

212

17p13.3-D
ENO3 -
2.275
miR-195
lung cancer
TP53

TP53

17q22-t(8; 17)
miR-142s/

miR-142s; miR-142as
prolymphocytic leukemia
c-MYC

c-MYC

17q23-A
CLTC -
0.97
miR-21
neuroblastoma
—

PPM1D

20q13- A
FLJ33887-
0.55
miR-297-3
colon cancer
—

ZNF217

21q11.1-D
D21S1911 -
2.84
miR-99a/let-7c/miR-125b
lung cancer
—

ANA

Note:

* OG—oncogene; TS—tumor suppressor gene; D—deleted region; A—amplified region; NSCLC—Non-Small Cell Lung Cancer; HCC—Hepatocellular carcinoma; B-CLL—B-Chronic Lymphocytic Leukemia; PTC—patched homolog (Drosophila); FANCC—Fanconi anemia, complementation group C; PPP2R1B—protein phosphatase 2, regulatory subunit A (PR 65), β isoform. miR genes in a cluster are separated by a slash.

TABLE 7

MicroRNAs Located in Minimal Deleted Regions, Minimal Amplified Regions and Breakpoint Regions Involved in Human Cancers

Type

Size/

miR

of region

Position

Position
Distance

Location

Chromosome
(name)
Marker 1
(Mb)
Marker 2
(Mb)
(Mb)
Histotype
Closest miR
(Mb)

01p31
D
D1S2638
62.92
ARHI
67.885
4.96
ovarian and
miR-101-1
64.9

breast cancer

01p36.3
D
D1S468
3.36
D1s2697
15.23
0
Non Small Cell
miR-34
8.8

Lung Ca.

02q21
D
D2S1334
136.66

0.1
gastric ca.
miR-128a
136.55

02q37
D
D2S125
241.5

0.2
hepatocellular
miR-149
241.65

carcinoma

(HCC)

03p21.1-21.2
D
ARP
51.5
DRR1
58.5
7
lung, breast ca.
let-7g/miR-135-1
52.3

03p21.3
D (AP20)
GOLGA4
37.25
VILL
38
0.75
epithelial
miR-26a
38

malignancies

03p23-21.31
D (MDR2)
D3S1768
34.59
D3S1767
46.91
12.32
nasopharyngeal
miR-26a; miR-
38; 44

carcinoma
138-1

03q27
t(3;11)(q27;
LAZ3/BCL6
188.75
BOB1/
110.78

B cell leukemia
miR-34a-2/miR
110.9

q23.1)

OBF1

line (Karpas 231)
34a-1

04p15.3
D
D4S1608
18.83
D4S404
23.98
5.15
primary bladder
miR-218-1
20.25

ca.

05q31-33
D
D5S1480
144.17
D5S820
156.1
11.93
prostate ca.
miR-145/miR-
148.7

aggressiveness
143

05q32
D
ADRB2
148.23
ATX1
151.15
2.92
myelodysplastic
miR-145/miR-
148.7

syndrome
143

07q32
D
D7S3061
122.84
D7S1804
131.25
8.41
prostate ca.
miR-129-1; miR-
127.3;

(aggressiveness)
182s/miR-
129; 130

182as/miR-

96/miR-183;

miR29a/miR-29b

07q32-q33
D
D7S2531
130.35
D7S1804
131.69
1.34
prostate ca.
miR-29a/miR-
130

(aggressiveness)
29b

08p21
D (MRL1)
D8S560
21.61
D8S1820
28.02
6.41
HCC
miR-161; miR-
22; 21.5

177

08p21
D
D8S282
21.42

0.1
HCC
miR-177
21.5

08p22
D
D8S254
16.62
SFTP2
22.05
5.43
oral and
miR-161; miR-
22; 21.5

laryngeal
177

squamous

carcinoma.

08p23.1
A
D8S1819
6.737
D8S550
10.919
4.18
malignant
miR-124a-1
9.75

fibrous

histiocytomas

(MFHs)

09p21
D (LOH)
IFNA
21.2
D9S171/
22.07
0.87
primary bladder
miR-31
21.4

S1814

tumor

09p21
D
IFNA
21.5

0
lung
miR-31
21.4

adenocarcinoma

09p21
D
IFNA
21.5

0
gastric ca.
miR-31
21.4

09p21
D
CDKN2A,
21.9

0.5
breast ca.
miR-31
21.4

CDKN2B

09q22
D
D9S280
92.47
D9S1809
93.93
1.46
urothelial ca.
miR-24-1/miR-
92.9; 92.3

27b/miR-23b;

let-7a-1/let-

7f1/let-7d

09q22.3
D (reg B)
D9S12
91.21
D9S180 R
96.03
4.82
bladder ca.
let-7a-1/let-
92.3; 92.9

7f1/let-7d; miR-

24-1/miR-

27b/miR-23b

09q32
D
D9S1677
107.35

0.2
Small Cell Lung
miR-32
107.15

Ca., Non-Small

Cell Lung Ca.

09q33
D
D9S1826
133.88
D9S158
134.53
0.4
NSCLC
miR-123
134.95

09q33-34.1
D (reg D)
GSN
119.45
D9S260
127.09
7.64
bladder ca.
miR-181a; miR-
122.85;

199b
126.3

09q34
D
D9S158
134.54

0.4
HCC
miR-123
134.95

11p15
D
D11S2071
0.23

0.4
ovarian ca.
miR-210
0.6

11p15.5
D (LOH11B)
HRAS
0.52
D11S1363
1.05
0.53
lung ca.
miR-210
0.6

11q13
D
D11S4946
64.35
D11S4939
64.54
0.19
sporadic
miR-159-1/miR-
64.45

follicular thyroid
192

tumor

11q22
D
D11S940/
100.65
CD3D/
118.7
18.05
lung
miR-34a-1/miR-
111

S1782

D11S4104

adenocarcinoma
34a-2

11q22.1-23.2
D (MDR3)
D11S2017
107.05
D11S965
111.3
4.25
nasopharyngeal
miR-34a-1/miR-
111

carcinoma
34a-2

11q22.3-q25
D
D11S1340
116.12
D11S912
128.16
12.04
ovarian ca.
miR125b-1/let-
121.5

7a-2/miR-100

11q22-q23
D
D11S2106/
108.76
D11S1356
117.454
8.7
chronic
miR-34a-1/miR-
111

S2220

lymphocytic
34a-2

leukemia

11q23
D
D11S1647
110.34
NCAM2/
112.5
2.16
lung ca.
miR-34a-1/miR-
111

NCAM1

34a-2

11q23
D
D11S1345
121.83
D11S1328
123.56
1.73
lung
miR125b-1/let-
121.5

adenocarcinoma
7a-2/miR-100

11q23
D
D11S1345
121.83
D11S1328
123.56
1.73
lung
miR125b-1/let-
121.5

adenocarcinoma
7a-2/miR-100

11q23.1-23.2
D (LOH)
D11S4167
121.68
D11S4144
122.96
1.28
cervical ca.
miR125b-1/let-
121.5

7a-2/miR-100

11q23-q24
D
D11S927
109.676
D11S1347
111.67
1.994
breast, lung ca.
miR-34a-1/miR-
111

(LOH11CR1)

34a-2

11q23-q24
D
D11S1345
121.835
D11S1328
123.56
1.725
breast, lung,
miR125b-1/let-
121.5

(LOH11CR2)

ovary, cervix ca.
7a-2/miR-100

12p13
t(7;12)(q36;
TEL(ETV6)
11.83
near HLXB9
156.21

acute myeloid
miR-153-2
156.6

p13)

leukemia (AML)

12q13-q14
A
DGKA
54.6
BLOV1
67.4
12.8
adenocarcinomas
let-7i
61.35

of lung and

esophagus

12q13-q15
A
GLI
56.15
MDM2
67.5
11.35
bladder ca.
let-7i
61.3-.45

12q22
D
D12S1716
95.45
P382A8AG/
97.47
2.02
male germ cell
miR-135-2
96.5

D12S296

tumors

12q22
D
D12S377/
94.1
D12S296
97.47
3.37
male germ cell
miR-135-2
96.5

D12S101

tumors.

13q14.3
D
D13S319/
48.5
D13S25
49.04
0.54
B-Chronic
miR-15a/miR-
48.5

D13S272

Lymphocytic
16a

Leuk (B-CLL)

13q14
D
D13S260
30.23
AFMa301wb5
48.62
18.39
adult
miR-15a/miR-
48.5

lymphoblastic
16a

leukemia

13q14
D
Rb1
46.77
BCMS
48.46
1.69
lipoma
miR-15a/miR16a
48.5

(DLEU-1)

13q14.3
D (RMD)
D13S272
48.5
AF077401
48.765
0.265
CLL
miR-15a/miR-
48.5

16a

13q14.3
D (Reg II)
D13S153
46.68
D13S1289
62.43
15.75
head-and-neck
miR-15a/miR-
48.5

squamous-cell
16a

carcinoma

13q14.3
D
D13S273
48.11
D13S176
58.31
10.2
oral ca.
miR-15a/miR-
48.5

16a

13q14.3
D
D13S1168
48.28
D13S25
49.04
0.76
B-CLL
miR-15a/miR-
48.5

16a

13q32-33
A
stSG15303
89.7
stSG31624
96.85
7.15
follicular
miR-17/miR-
89.7

lymphoma
18/miR-19a/miR-

20/miR-19b-

1/miR-92-1

14q11.1-q12
D
D14S283
20.67
D14S64
22.55
1.88
malignant
miR-208
21.8

mesothelioma

14q32
D
D14S51
95.56
telomere
105.2
9.64
nasopharyngeal
miR-127/miR-
99.3; 99.5;

carcinoma
136; miR-
102.5

154/miR-

134/miR-299;

miR-203

15q11.1-15
D
D15S128
22.67
D15S1012
36.72
14.05
malignant
miR-211
29

mesothelioma.

17p11.2
A
PNMT
17.351

0.5
breast ca
miR-33b
17.8

17p11.2
D
D17S1857
16.61
D17S805/
20.79
4.18
kidney ca (Birt-
miR-33b
17.9

S959

Hogg-Dube sy)

17p11.2
D
D117S1857
16.61
D17S805/
20.79
4.18
medulloblastoma
miR-33b
17.9

S959

(Smith-Magenis

syndrome)

17p13
D
D17S578
7.025

0
HCC
miR-195
7

17p13.3
D
D17S1866
0.121
D17S1574
2.02
1.9
HCC
miR-22; miR-
1.75; 2.2

132; miR-212

17p13.3
D
ENO3
5.5
TP53
7.775
2.275
lung ca.
miR-195
7

17p13.3
D
D17S1574
2.02
D17S379
2.46
0.44
lung ca.
miR-132; miR-
2.2

212

17q11.1
D
NF1
29.7

0.3
ovarian ca.
miR-108-1
30

17q11.1
D
NF1
29.7

0.3
ovarian ca.
miR-193
30

17q11.2
A
MLN 62
27.22

0.1
primary breast
miR-144
27.35

(TRAF4)

ca.

17q11.2
D (NF1 locus)
CYTOR4
29.25
WI-12393
30.52
1.27
NF1
miR-108-1/miR-
30; 30

microdeletion
193

17q22
t(8;17)
“BCL3”
56.95
c-MYC
128.7

prolymphocytic
miR-142s/miR-
56.95

leukemia
142as

17q23
A
RAD51C
57.116

0.25
breast ca.
miR-142s/miR-
56.95

142as

17q23
A
RAD51C
57.116

0.5
breast ca.
miR-301
57.7

17q23
A
CLTC
58.21
PPM1D
59.18
0.97
neuroblastoma
miR-21
58.45

17q25
A (SRO2)
D17S1306
53.76
D17S1604
58.45
4.69
breast ca.
miR-142s; miR-
56.95;

142as; miR-301;
57.7;

miR-21
58.45

19p13.3
D
D19S886
0.95
D19S216
4.9
3.95
lung
miR-7-3
4.75

adenocarcinoma

19p13.3
D (LOH)
D19S216
4.9
D19S549
5.44
0.54
gynecological
miR-7-3
4.75

tumor in Peutz-

Jegher's sy

19p13.3
D (HZYG)
D19S894
4.34
D19S395
7.32
2.98
gynecological
miR-7-3
4.75

tumor in Peutz-

Jegher's sy

19p13.3
D (LOH)
D19S886
0.95
D19S216
4.9
3.95
pancreatic and
miR-7-3
4.75

biliary ca

20q13
A
FLJ33887
52.2
ZNF217
52.75
0.55
colon ca
miR-297-3
52.35

20q13.1
A
ZNF217
52.285

0
ovarian
miR-297-3
52.35

20q13.2
A
D20S854
52.68
D20S120
53.69
1.01
gastric
miR-297-3
52.35

adenocarcinoma

20q13.2
A
ZNF217
52.85

0.5
head/neck
miR-297-3
52.35

squamous

carcinoma

21q11.1
D
D21S120/
15.06
ANA
17.9
2.84
lung ca. (cell
miR-99a/let-
16.8

S1911

line MA17)
7c/miR-125b-2

21q21
A
BIC
25.8
BIC
25.9
0.1
colon ca.
miR-155(BIC)
25.85

22q12.2-
D
D22S280
31.53
D22S274
43.54
12.01
colorectal ca.
miR-33a
40.6

q13.33

22q12.3-
D
D22S280
31.53
D22S282
42.1
10.57
astrocytomas
miR-33a
40.6

q13.33

22q12.1
t(4;22)

MN1
26.5

meningioma
miR-180
26.45

Xq25-26.1
D
DXS1206
125.08
HPRT
132.31
7.23
advanced
miR-92-2/miR-
132

ovarian ca.
19b-2/miR-106a

Note:

D—deletion; A—amplification; ca.—cancer; sy—syndrome. The distance (in Mb) from the markers used in genome-wide analysis is shown. miRs in clusters are separated by a slash. Positions are according to BUILD 34, version 1, of the Human Genome

Example 4B
Effect of Genomic Location on miR Gene Expression

In order to investigate whether the genomic location in deleted regions influences miR gene expression, a set of lung cancer cell lines was analyzed. miR-26a and miR-99a, located at 3p21 and 21q1.2, respectively, are not expressed or are expressed at low levels in lung cancer cell lines. The locations of miR-26a and miR-99a correlate with regions of LOH/HD in lung tumors. However, the expression of miR-16a (located at 13q14) was unchanged in the majority of lung tumor cell lines as compared to normal lung (see FIG. 1).

Several miR genes are located near breakpoint regions, including miR-142s at 50 nt from the t(8; 17) translocation involving chromosome 17 and MYC, and miR-180 at 1 kb from the MN1 gene involved in a t(4; 22) translocation in meningioma (Table 6). The t(8; 17) translocation brings the MYC gene near the miR gene promoter, with consequent MYC over-expression, while the t(4; 22) translocation inactivates the MN1 gene, and possibly inactivates the miR gene located in the same position. Other miR genes are located relatively close to chromosomal breakpoints, such as the cluster miR 34a-1/34a-2 and miR-153-2 (see Table 7). Further supporting a role for miR-122a in cancer, it was found herein that human miR-122a is located in the minimal amplicon around MALT1 in aggressive marginal zone lymphoma (MZL), and was found to be about 160 kb from the breakpoint region of translocation t(11; 18) in mucosa-associated lymphoid tissue (MALT) lymphoma (Sanchez-Izquierdo et al., 2003, Blood 101:4539-4546). Apart from miR-122a, several other miR genes were located in regions particularly prone to cancer-specific abnormalities, such as miR-142s and miR-142as, located at 17q23 close to a t(8; 17) breakpoint in B cell acute leukemia, and also located within the minimal amplicon in breast cancer and near the FRA17B site, which is also a target for HPV16 integration in cervical tumors (see Tables 5 and 7).

Example 5
MicroRNAs are Located in or Near HOX Gene Clusters

Homeobox-containing genes are a family of transcription factor genes that play crucial roles during normal development and in oncogenesis. HOXB4, HOXB5, HOXC9, HOXC10, HOXD4 and HOXD8, all with miR gene neighbors, are deregulated in a variety of solid and hematopoietic cancers (Cillo et al., 1999, Exp. Cell Res. 248:1-9; Owebs et al., 2002, Stem Cells 20:364-379). A strong correlation was found between the location of specific miR genes and homeobox (HOX) genes. The miR-10a and miR-196-1 genes are located within the HOX B cluster on 17q21, while miR-196-2 is within the HOX C cluster at 12q13, and miR-10b maps to the HOX D cluster at 2q31 (see FIG. 2). Moreover, three other miRs (miR-148, miR-152 and miR-148b) are close to HOX clusters (less than 1 Mb; see FIG. 2). The 1 Mb distance was selected because some form of long-range coordinated regulation of gene expression was shown to expand up to one megabase to HOX clusters (Kamath et al., 2003, Nature 421:231-7). Such proximity of miR genes to HOX gene clusters is unlikely to have occurred by chance (IRR=15.77; p<0.001) (Table 4). Because collinear expression of, and cooperation between, HOX genes is well demonstrated, these data indicated that miRs are altered along with the HOX genes in human cancers.

Next, it was determined whether miR genes were located within class II HOX gene clusters as well. Fourteen additional human HOX gene clusters (Pollard et al., 2000, Current Biology 10:1059-1062) were analyzed, and seven miR genes (miR-129-1, miR-153-2, let-7a-1, let-7f-1, let-7d, miR-202 and miR-139) were located within 0.5 Mb of class II homeotic genes, a result which was highly unlikely to occur by chance (IRR=2.95, p<0.001) (Table 4).

Example 6
Expression of miR Gene Products in Human Cells

The cDNA sequence encoding the entire miR precursor transcript of an miR gene is separately cloned into the context of an irrelevant mRNA expressed under the control of the cytomegalovirus immediate early (CMV-IE) promoter, according to the procedure of Zeng et al., 2002, Mol. Cell 9:1327-1333, the entire disclosure of which is herein incorporated by reference.

Briefly, Xho I linkers are placed on the end of double-stranded cDNA sequences encoding an miR precursor, and this construct is separately cloned into the Xho I site present in the pBC12/CMV plasmid. The pBC12/CMV plasmid is described in Cullen, 1986, Cell 46:973-982, the entire disclosure of which is herein incorporated by reference.

pCMV plasmid containing the miR precursor coding sequence is transfected into cultured human 293T cells by standard techniques using the FuGene 6 reagent (Roche). Total RNA is extracted as described above, and the presence of the processed miR transcript is detected by Northern blot analysis with an miR probe specific for the miR transcript.

pCMV-miR is also transfected into cultured human normal cells or cells with proliferative disorders, such as cancer cells. For example, the proliferative disease or cancer cell types include ovarian cancer, breast cancer, small cell lung cancer, sporadic follicular thyroid tumor, chronic lymphocytic leukemia, cervical cancer, acute myeloid leukemia, adenocarcinomas, male germ cell tumor, non-small cell lung cancer, gastric cancer, hepatocellular carcinoma, lung cancer, nasopharyngeal cancer, B-chronic lymphocytic leukemia, lipoma, mesothelioma, kidney cancer, NF1 microdeletion, neuroblastoma, medulloblastoma, pancreatic cancer, biliary cancer, colon cancer, gastric adenocarcinoma, head/neck squamous carcinoma, astrocytoma, meningioma, B cell leukemia, primary bladder cancer, prostate cancer, myelodysplastic syndrome, oral cavity carcinoma, laryngeal squamous carcinoma, and urothelial cancer. Total RNA is extracted as described above, and the presence of processed miR transcripts in the cancer cells is detected by Northern blot analysis with miR specific probes. The transfected cells are also evaluated for changes in morphology, the ability to overcome contact inhibition, and other markers indicative of a transformed phenotype.

Example 7
Preparation of Liposomes Encapsulating miR Gene Products

Liposome Preparation 1—Liposomes composed of lactosyl cerebroside, phosphatidylglycerol, phosphatidylcholine, and cholesterol in molar ratios of 1:1:4:5 are prepared by the reverse phase evaporation method described in U.S. Pat. No. 4,235,871, the entire disclosure of which is herein incorporated by reference. The liposomes are prepared in an aqueous solution of 100 μg/ml processed miR transcripts or 500 μg/ml pCMV-microRNA. The liposomes thus prepared encapsulate either the processed microRNA, or the pCMV-microRNA plasmids.

The liposomes are then passed through a 0.4 polycarbonate membrane and suspended in saline, and are separated from non-encapsulated material by column chromatography in 135 mM sodium chloride, 10 mM sodium phosphate (pH 7.4). The liposomes are used without further modification, or are modified as described herein.

A quantity of the liposomes prepared above are charged to an appropriate reaction vessel to which is added, with stirring, a solution of 20 mM sodium metaperiodate, 135 mM sodium chloride and 10 mM sodium phosphate (pH 7.4). The resulting mixture is allowed to stand in darkness for 90 minutes at a temperature of about 20° C. Excess periodate is removed by dialysis of the reaction mixture against 250 ml of buffered saline (135 mM sodium chloride, 10 mM sodium phosphate, pH 7.4) for 2 hours. The product is a liposome having a surface modified by oxidation of carbohydrate hydroxyl groups to aldehyde groups. Targeting groups or opsonization inhibiting moieties are conjugated to the liposome surface via these aldehyde groups.

Liposome Preparation 2—A second liposome preparation composed of maleimidobenzoyl-phosphatidylethanolamine (MBPE), phosphatidylcholine and cholesterol is obtained as follows. MBPE is an activated phospholipid for coupling sulfhydryl-containing compounds, including proteins, to the liposomes.

Dimyristoylphosphatidylethanolamine (DMPE) (100 mmoles) is dissolved in 5 ml of anhydrous methanol containing 2 equivalents of triethylamine and 50 mg of m-maleimidobenzoyl N-hydroxysuccinimide ester, as described in Kitagawa et al. (1976), J. Biochem. 79:233-236, the entire disclosure of which is herein incorporated by reference. The resulting reaction is allowed to proceed under a nitrogen gas atmosphere overnight at room temperature, and is subjected to thin layer chromatography on Silica gel H in chloroform/methanol/water (65/25/4), which reveals quantitative conversion of the DMPE to a faster migrating product. Methanol is removed under reduced pressure and the products re-dissolved in chloroform. The chloroform phase is extracted twice with 1% sodium chloride and the maleimidobenzoyl-phosphatidylethanolamine (MBPE) purified by silicic acid chromatography with chloroform/methanol (4/1) as the solvent. Following purification, thin-layer chromatography indicates a single phosphate containing spot that is ninhydrin negative.

Liposomes are prepared with MBPE, phosphatidylcholine and cholesterol in molar ratios of 1:9:8 by the reverse phase evaporation method of U.S. Pat. No. 4,235,871, supra, in an aqueous solution of 100 μg/ml processed microRNA or a solution of 500 μg/ml pCMV-miR (see above). Liposomes are separated from non-encapsulated material by column chromatography in 100 mM sodium chloride-2 mM sodium phosphate (pH 6.0).

Example 8
Attachment of Anti-Tumor Antibodies to Liposomes

An appropriate vessel is charged with 1.1 ml (containing about 10 mmoles) of Liposome Preparation 1 (see above) carrying reactive aldehyde groups, or Liposome Preparation 2 (see above). 0.2 ml of a 200 mM sodium cyanoborohydride solution and 1.0 ml of a 3 mg/ml solution of a monoclonal antibody directed against a tumor cell antigen is added to the preparation, with stirring. The resulting reaction mixture is allowed to stand overnight while maintained at a temperature of 4° C. The reaction mixture is separated on a Biogel A5M agarose column (Biorad, Richmond, Ca.; 1.5×37 cm).

Example 9
Inhibition of Human Tumor Growth In Vivo with miR Gene Products

A cancer cell line, such as one of the lung cancer cell lines described above or a tumor-derived cell, is inoculated into nude mice, and the mice are divided into treatment and control groups. When tumors in the mice reach 100 to 250 cubic millimeters, processed miR transcripts encapsulated in liposomes are injected directly into the tumors of the test group. The tumors of the control group are injected with liposomes encapsulating carrier solution only. Tumor volume is measured throughout the study.

Example 10
Oligonucleotide Microchip for Genome-Wide miRNA Profiling
Introduction

A micro-chip microarray was prepared as follows, containing 368 gene-specific oligonucleotide probes generated from 248 miRNAs (161 human, 84 mouse, and 3 arabidopsis) and 15 tRNAs (8 human and 7 mouse). These sequences correspond to human and mouse miRNAs found in the miRNA Registry (June 2003) (Griffiths-Jones, S. (2004) Nucleic Acids Res. 32, D109-D111) or collected from published literature (Lagos-Quintana, M., Rauhut, R., Lendeckel, W. & Tuschl, T. (2001) Science 294, 853-858; Lim, L. P., Glasner, M. E., Yekta, S., Burge, C. B. & Bartel, D. P. (2003) Science 299, 1540; Mourelatos, Z., Dostie, J., Paushkin, S., Sharma, A., Charroux, B., Abel, L., Rappsilber, J., Mann, M. & Dreyfuss, G. (2002) Genes Dev 16, 720-728). For 76 miRNAs, two different oligonucleotide probes were designed, one containing the active sequence and the other specific for the precursor. Using these distinct sequences, we were able to separately analyze the expression of miRNA and pre-miRNA transcripts for the same gene.

Various specificity controls were used to validate data. For intra-assay validation, individual oligonucleotide-probes were printed in triplicate. Fourteen oligonucleotides had a total of six replicates because of identical mouse and human sequences and therefore were spotted on both human and mouse sections of the array. Several mouse and human orthologs differ only in few bases, serving as controls for the hybridization stringency conditions. tRNAs from both species were also printed on the microchip, providing an internal, relatively stable positive, control for specific hybridization, while Arabidopsis sequences were selected, based on the absence of any homology with known miRNAs from other species, and used as controls for non-specific hybridization.

Materials and Methods

The following materials and methods were employed in designing and testing the microchip.

miRNA Oligonucleotide Probe Design. A total of 281 miRNA precursor sequences (190 Homo sapiens, 88 Mus musculus, and 3 Arabidopsis thaliana) with annotated active sites were selected for oligonucleotide design. These correspond to human and mouse miRNAs found in the miRNA Registry or collected from published literature. All of the sequences were confirmed by BLAST alignment with the corresponding genome and the hairpin structures were analyzed. When two precursors with different length or slightly different base composition for the same miRNAs were found, both sequences were included in the database and the one that satisfied the highest number of design criteria was used. The sequences were clustered by organism using the LEADS platform (Sorek, R., Ast, G. & Graur, D. (2002) Genome Research 12, 1060-1067), resulting in 248 clusters (84 mouse, 161 human, and 3 arabidopsis). For each cluster, all 40-mer oligonucleotides were evaluated for their cross-homology to all genes of the relevant organism, number of bases in alignment to a repetitive element, amount of low-complexity sequence, maximum homopolymeric stretch, global and local G+C content, and potential hairpins (self 5-mers). The best oligonucleotide was selected that contained each active site of each miRNA. This produced a total of 259 oligonucleotides; there were 11 clusters with multiple annotated active sites. Next, we attempted to design an oligonucleotide that did not contain the active site for each cluster, when it was possible to choose such an oligonucleotide that did not overlap the selected oligonucleotide(s) by more than 10 nt. To design each of these additional oligonucleotides, we required <75% global cross-homology and <20 bases in any 100% alignment to the relevant organism, <16 bases in alignments to repetitive elements, <16 bases of low-complexity, homopolymeric stretches of no more than 6 bases, G+C content between 30-70% and no more than 11 windows of size 10 with G+C content outside 30-70%, and no self 5-mers. A total of 76 additional oligonucleotides were designed. In addition, we designed oligonucleotides for 7 mouse tRNAs and 8 human tRNAs, using similar design criteria. We selected a single oligonucleotide for each, with the exception of the human and mouse initiators, Met-tRNA-i, for which we selected two oligonucleotides each (Table 8).

TABLE 8

Oligonucleotides used for the miRNA microarray chip and

correspondence with specific human and mouse microRNAs.

Covers

SEQ

Corresponding
Oligonucleotide
active

ID

Oligonucleotide_name
miRNA
sequence
site?
Notes
NO.

ath-miR156a-#1
ath-miR156a
TGACAGAAGAGAGTGAGCAC
yes

286

ACAAAGGCAATTTGCATATC

ath-miR156a-#2
ath-miR156a
CATTGCACTTGCTTCTCTTG
no

287

CGTGCTCACTGCTCTTTCTG

ath-miR157a-#1
ath-miR157a
GTGTTGACAGAAGATAGAGA
yes

288

GCACAGATGATGAGATACAA

ath-miR157a-#2
ath-miR157a
CATCTTACTCCTTTGTGCTC
no

289

TCTAGCCTTCTGTCATCACC

ath-miR180a-#1
ath-miR180
GATGGACGGTGGTGATTCAC
no

290

TCTCCACAAAGTTCTCTATG

ath-miR180a-#2
ath-miR180
TGAGAATCTTGATGATGCTG
yes

291

CATCGGCAATCAACGACTAT

hsa-let-7a-1-prec
let-7a-1
TGAGGTAGTAGGTTGTATAG
yes

292

TTTTAGGGTCACACCCACCA

hsa-let-7a-2-prec-#1
let-7a-2
TACAGCCTCCTAGCTTTCCT
no

293

TGGGTCTTGCACTAAACAAC

hsa-let-7a-2-prec-#2
let-7a-2
ACTGCATGCTCCCAGGTTGA
yes

294

GGTAGTAGGTTGTATAGTTT

hsa-let-7a-3-prec
let-7a-3
GGGTGAGGTAGTAGGTTGTA
yes

295

TAGTTTGGGGCTCTGCCCTG

hsa-let-7b-prec
let-7b
TGAGGTAGTAGGTTGTGTGG
yes

296

TTTCAGGGCAGTGATGTTGC

hsa-let-7c-prec
let-7c
GCATCCGGGTTGAGGTAGTA
yes

297

GGTTGTATGGTTTAGAGTTA

hsa-let-7d-prec
let-7d
CCTAGGAAGAGGTAGTAGGT
yes

298

TGCATAGTTTTAGGGCAGGG

hsa-let-7d-v1-prec
let-7d
CTAGGAAGAGGTAGTAGTTT
yes

299

(= 7d-v1)
GCATAGTTTTAGGGCAAAGA

hsa-let-7d-v2-prec-#1
let-7i
TTGGTCGGGTTGTGACATTG
no

300

(= let-7d-v2)
CCCGCTGTGGAGATAACTGC

hsa-let-7d-v2-prec-#2
let-7i
GCTGAGGTAGTAGTTTGTGC
yes
idem mmu-let-
301

(= let-7d-v2)
TGTTGGTCGGGTTGTGACAT

7i-prec

hsa-let-7e-prec
let-7e
GGCTGAGGTAGGAGGTTGTA
yes

302

TAGTTGAGGAGGACACCCAA

hsa-let-7f-1-prec-#1
let-7f-1
GGTAGTGATTTTACCCTGTT
no

303

CAGGAGATAACTATACAATC

hsa-let-7f-1-prec-#2
let-7f-1
GGGATGAGGTAGTAGATTGT
yes

304

ATAGTTGTGGGGTAGTGATT

hsa-let-7f-2-prec2
let-7f-2
TGAGGTAGTAGATTGTATAG
yes

305

TTTTAGGGTCATACCCCATC

hsa-let-7g-prec-#1
let-7g
CTGATTCCAGGCTGAGGTAG
yes

306

TAGTTTGTACAGTTTGAGGG

hsa-let-7g-prec-#2
let-7g
TTGAGGGTCTATGATACCAC
no

307

CCGGTACAGGAGATAACTGT

hsa-miR-001b-1-prec1
miR-001
AATGCTATGGAATGTAAAGA
yes

308

AGTATGTATTTTTGGTAGGC

hsa-miR-001b-2-prec
miR-001
TAAGCTATGGAATGTAAAGA
yes

309

AGTATGTATCTCAGGCCGGG

hsa-miR-007-1-prec
miR-007-1
TGTTGGCCTAGTTCTGTGTG
yes

310

GAAGACTAGTGATTTTGTTG

hsa-miR-007-2-prec-#1
miR-007-2
TACTGCGCTCAACAACAAAT
no

311

CCCAGTCTACCTAATGGTGC

hsa-miR-007-2-prec-#2
miR-007-2
GGACCGGCTGGCCCCATCTG
yes

312

GAAGACTAGTGATTTTGTTG

hsa-miR-007-3-prec-#1
miR-007-3
AGATTAGAGTGGCTGTGGTC
no

313

TAGTGCTGTGTGGAAGACTA

hsa-miR-007-3-prec-#2
miR-007-3
TGGAAGACTAGTGATTTTGT
yes

314

TGTTCTGATGTACTACGACA

hsa-miR-009-1-#1
miR-009-1
TCTTTGGTTATCTAGCTGTA
yes

315

(miR-131-1)
TGAGTGGTGTGGAGTCTTCA

hsa-miR-009-1-#2
miR-009-1
TAAAGCTAGATAACCGAAAG
yes

316

(miR-131-1)
TAAAAATAACCCCATACACT

hsa-miR-009-2-#1
miR-009-2
GAAGCGAGTTGTTATCTTTG
yes

317

(miR-131-2)
GTTATCTAGCTGTATGAGTG

hsa-miR-009-2-#2
miR-009-2
GAGTGTATTGGTCTTCATAA
yes
idem mmu-miR-
318

(miR-131-2)
AGCTAGATAACCGAAAGTAA

009-prec-#2

hsa-miR-009-3-#1
miR-009-3
GGGAGGCCCGTTTCTCTCTT
yes

319

(miR-131-3)
TGGTTATCTAGCTGTATGAG

hsa-miR-009-3-#2
miR-009-3
GTGCCACAGAGCCGTCATAA
yes

320

(miR-131-3)
AGCTAGATAACCGAAAGTAG

hsa-miR-010a-prec-#1
miR-010a
GTCTGTCTTCTGTATATACC
yes

321

CTGTAGATCCGAATTTGTGT

hsa-miR-010a-prec-#2
miR-010a
GTGGTCACAAATTCGTATCT
no

322

AGGGGAATATGTAGTTGACA

hsa-miR-010b-prec-#1
miR-010b
TACCCTGTAGAACCGAATTT
yes

323

GTGTGGTATCCGTATAGTCA

hsa-miR-010b-prec-#2
miR-010b
GTCACAGATTCGATTCTAGG
no

324

GGAATATATGGTCGATGCAA

hsa-miR-015a-2-prec-#1
miR-15-a
CCTTGGAGTAAAGTAGCAGC
yes

325

ACATAATGGTTTGTGGATTT

hsa-miR-015a-2-prec-#2
miR-15-a
TTTGTGGATTTTGAAAAGGT
no

326

GCAGGCCATATTGTGCTGCC

hsa-miR-015b-prec-#1
miR-015-b
GGCCTTAAAGTACTGTAGCA
yes

327

GCACATCATGGTTTACATGC

hsa-miR-015b-prec-#2
miR-015-b
TGCTACAGTCAAGATGCGAA
no

328

TCATTATTTGCTGCTCTAGA

hsa-miR-016a-chr13
miR-016-1
CAATGTCAGCAGTGCCTTAG
yes

329

CAGCACGTAAATATTGGCGT

hsa-miR-016b-chr3
miR-016-2
GTTCCACTCTAGCAGCACGT
yes

330

AAATATTGGCGTAGTGAAAT

hsa-miR-017-prec-#1
miR-017
GCATCTACTGCAGTGAAGGC
yes

331

(miR-091)
ACTTGTAGCATTATGGTGAC

hsa-miR-017-prec-#2
miR-017
GTCAGAATAATGTCAAAGTG
yes

332

(miR-091)
CTTACAGTGCAGGTAGTGAT

hsa-miR-018-prec
miR-018
TAAGGTGCATCTAGTGCAGA
yes

333

TAGTGAAGTAGATTAGCATC

hsa-miR-019a-prec
miR-019a
TGTAGTTGTGCAAATCTATG
yes

334

CAAAACTGATGGTGGCCTGC

hsa-miR-019b-1-prec
miR-019b-1
TTCTGCTGTGCAAATCCATG
yes

335

CAAAACTGACTGTGGTAGTG

hsa-miR-019b-2-prec
miR-019b-2
GTGGCTGTGCAAATCCATGC
yes

336

AAAACTGATTGTGATAATGT

hsa-miR-020-prec
miR-020
TAAAGTGCTTATAGTGCAGG
yes

337

TAGTGTTTAGTTATCTACTG

hsa-miR-021-prec-17-#1
miR-021
GTCGGGTAGCTTATCAGACT
yes

338

GATGTTGACTGTTGAATCTC

hsa-miR-021-prec-17-#2
miR-021
TTCAACAGTCAACATCAGTC
yes

339

TGATAAGCTACCCGACAAGG

hsa-miR-022-prec
miR-022
TGTCCTGACCCAGCTAAAGC
yes

340

TGCCAGTTGAAGAACTGTTG

hsa-miR-023a-prec
miR-023a
TCCTGTCACAAATCACATTG
yes

341

CCAGGGATTTCCAACCGACC

hsa-miR-023b-prec
miR-023b
AATCACATTGCCAGGGATTA
yes

342

CCACGCAACCACGACCTTGG

hsa-miR-024-1-prec-#1
miR-024-1
TTTTACACACTGGCTCAGTT
yes

343

CAGCAGGAACAGGAGTCGAG

hsa-miR-024-1-prec-#2
miR-024-1
TCCGGTGCCTACTGAGCTGA
yes

344

TATCAGTTCTCATTTTACAC

hsa-miR-024-2-prec
miR-024-2
AGTTGGTTTGTGTACACTGG
yes

345

CTCAGTTCAGCAGGAACAGG

hsa-miR-025-prec
miR-025
ACGCTGCCCTGGGCATTGCA
yes

346

CTTGTCTCGGTCTGACAGTG

hsa-miR-026a-prec-#1
miR-026a
TTCAAGTAATCCAGGATAGG
yes

347

CTGTGCAGGTCCCAATGGCC

hsa-miR-026a-prec-#2
miR-026a
TCCCAATGGCCTATCTTGGT
no

348

TACTTGCACGGGGACGCGGG

hsa-miR-026b-prec
miR-026b
TTCAAGTAATTCAGGATAGG
yes

349

TTGTGTGCTGTCCAGCCTGT

hsa-miR-027a-prec
miR-027a
GTCCACACCAAGTCGTGTTC
yes

350

ACAGTGGCTAAGTTCCGCCC

hsa-miR-027b-prec
miR-027b
CCGCTTTGTTCACAGTGGCT
yes

351

AAGTTCTGCACCTGAAGAGA

hsa-miR-028-prec
miR-028
AAGGAGCTCACAGTCTATTG
yes

352

AGTTACCTTTCTGACTTTCC

hsa-miR-029a-2-#1
miR-029a
CTAGCACCATCTGAAATCGG
yes

353

TTATAATGATTGGGGAAGAG

hsa-miR-029a-2-#2
miR-029a
CCCCTTAGAGGATGACTGAT
no

354

TTCTTTTGGTGTTCAGAGTC

hsa-miR-029b-2 =
miR-029b
AGTGATTGTCTAGCACCATT
yes

355

102prec7.1 = 7.2
(= miR-102-
TGAAATCAGTGTTCTTGGGG

7.1 = 7.2)

hsa-miR-029c-prec
miR-029c
TTTTGTCTAGCACCATTTGA
yes

356

AATCGGTTATGATGTAGGGG

hsa-miR-030a-prec-#1
miR-030a-as
GCGACTGTAAACATCCTCGA
yes

357

CTGGAAGCTGTGAAGCCACA

hsa-miR-030a-prec-#2
miR-030a-s
CACAGATGGGCTTTCAGTCG
yes

358

GATGTTTGCAGCTGCCTACT

hsa-miR-030b-prec-#1
miR-030b
TGTAAACATCCTACACTCAG
yes

359

CTGTAATACATGGATTGGCT

hsa-miR-030b-prec-#2
miR-030b
ATGGATTGGCTGGGAGGTGG
no

360

ATGTTTACTTCAGCTGACTT

hsa-miR-030c-prec
miR-030c
TACTGTAAACATCCTACACT
yes

361

CTCAGCTGTGGAAAGTAAGA

hsa-miR-030d-prec-#1
miR-030d
TAAGACACAGCTAAGCTTTC
no

362

AGTCAGATGTTTGCTGCTAC

hsa-miR-030d-prec-#2
miR-030d
TTGTAAACATCCCCGACTGG
yes

363

AAGCTGTAAGACACAGCTAA

hsa-miR-031-prec
miR-031
GGCAAGATGCTGGCATAGCT
yes

364

GTTGAACTGGGAACCTGCTA

hsa-miR-032-prec-#1
miR-032
TGTCACGGCCTCAATGCAAT
no

365

TTAGTGTGTGTGATATTTTC

hsa-miR-032-prec-#2
miR-032
GGAGATATTGCACATTACTA
yes

366

AGTTGCATGTTGTCACGGCC

hsa-miR-033b-prec
miR-033b
GTGCATTGCTGTTGCATTGC
yes

367

ACGTGTGTGAGGCGGGTGCA

hsa-miR-033-prec
miR-33
GTGGTGCATTGTAGTTGCAT
yes

368

TGCATGTTCTGGTGGTACCC

hsa-miR-034-prec-#1
miR-034
GAGTGTTTCTTTGGCAGTGT
yes

369

(= miR-170)
CTTAGCTGGTTGTTGTGAGC

hsa-miR-034-prec-#2
miR-034
AGTAAGGAAGCAATCAGCAA
no

370

(= miR-170)
GTATACTGCCCTAGAAGTGC

hsa-miR-092-prec-
miR-092-1
ACAGGTTGGGATCGGTTGCA
no

371

13 = 092-1-#1

ATGCTGTGTTTCTGTATGGT

hsa-miR-092-prec-
miR-092-1
TCTGTATGGTATTGCACTTG
yes

372

13 = 092-1-#2

TCCCGGCCTGTTGAGTTTGG

hsa-miR-092-prec-
miR-092-2
GTTCTATATAAAGTATTGCA
yes

373

X = 092-2

CTTGTCCCGGCCTGTGGAAG

hsa-miR-093-prec-
miR-093-1
CCAAAGTGCTGTTCGTGCAG
yes

374

7.1 = 093-1

GTAGTGTGATTACCCAACCT

hsa-miR-095-prec-4
miR-095
CGTTACATTCAACGGGTATT
yes

375

TATTGAGCACCCACTCTGTG

hsa-miR-096-prec-7-#1
miR-096
CTCCGCTCTGAGCAATCATG
no

376

TGCAGTGCCAATATGGGAAA

hsa-miR-096-prec-7-#2
miR-096
TGGCCGATTTTGGCACTAGC
yes

377

ACATTTTTGCTTGTGTCTCT

hsa-miR-098-prec-X
miR-098
TGAGGTAGTAAGTTGTATTG
yes

378

TTGTGGGGTAGGGATATTAG

hsa-miR-099b-prec-19-#1
miR-099b
GCCTTCGCCGCACACAAGCT
no
idem mmu-miR-
379

CGTGTCTGTGGGTCCGTGTC

099b-prec-#1

hsa-miR-099b-prec-19-#2
miR-099b
CACCCGTAGAACCGACCTTG
yes
idem mmu-miR-
380

CGGGGCCTTCGCCGCACACA

099b-prec-#2

hsa-miR-099-prec-21
miR-099a
ATAAACCCGTAGATCCGATC
yes

381

(= miR-099-
TTGTGGTGAAGTGGACCGCA

prec2l)

hsa-miR-100-1/2-prec
miR-100
TGAGGCCTGTTGCCACAAAC
yes

382

CCGTAGATCCGAACTTGTGG

hsa-miR-101-1/2-prec-#1
miR-101-1
CCCTGGCTCAGTTATCACAG
no

383

TGCTGATGCTGTCTATTCTA

hsa-miR-101-1/2-prec-#2
miR-101-1
TACAGTACTGTGATAACTGA
yes

384

AGGATGGCAGCCATCTTACC

hsa-miR-101-prec-9
miR-101-2
GCTGTATATCTGAAAGGTAC
yes

385

AGTACTGTGATAACTGAAGA

hsa-miR-102-prec-1
miR-102
TCTTTGTATCTAGCACCATT
yes

386

TGAAATCAGTGTTTTAGGAG

hsa-miR-103-2-prec
miR-103-2
GTAGCATTCAGGTCAAGCAA
yes

387

CATTGTACAGGGCTATGAAA

hsa-miR-103-prec-
miR-103-1
TATGGATCAAGCAGCATTGT
yes

388

5 = 103-1
(= miR-103-5)
ACAGGGCTATGAAGGCATTG

hsa-miR-105-prec-
miR-105-1
ATCGTGGTCAAATGCTCAGA
yes

389

X.1 = 105-1
(= miR-105-
CTCCTGTGGTGGCTGCTCAT

prec-X)

hsa-miR-106-prec-X
miR-106a
CCTTGGCCATGTAAAAGTGC
yes

390

TTACAGTGCAGGTAGCTTTT

hsa-miR-107-prec-10
miR-107
GGCATGGAGTTCAAGCAGCA
yes

391

TTGTACAGGGCTATCAAAGC

hsa-miR-122a-prec
miR-122a
CCTTAGCAGAGCTGTGGAGT
yes

392

GTGACAATGGTGTTTGTGTC

hsa-miR-123-prec-#1
miR-123 =
GACGGGACATTATTACTTTT
yes

393

miR-126
GGTACGCGCTGTGACACTTC

hsa-miR-123-prec-#2
miR-123 =
TGTGACACTTCAAACTCGTA
yes

394

miR-126
CCGTGAGTAATAATGCGCCG

hsa-miR-124a-1-prec1
miR-124a-1
ATACAATTAAGGCACGCGGT
yes

395

GAATGCCAAGAATGGGGCTG

hsa-miR-124a-2-prec
miR-124a-2
TTAAGGCACGCGGTGAATGC
yes

396

CAAGAGCGGAGCCTACGGCT

hsa-miR-124a-3-prec
miR-124a-3
TTAAGGCACGCGGTGAATGC
yes

397

CAAGAGAGGCGCCTCCGCCG

hsa-miR-125a-prec-#1
miR-125a
TCTAGGTCCCTGAGACCCTT
yes

398

TAACCTGTGAGGACATCCAG

hsa-miR-125a-prec-#2
miR-125a
CAGGGTCACAGGTGAGGTTC
no

399

TTGGGAGCCTGGCGTCTGGC

hsa-miR-125b-1
miR-125b-1
TCCCTGAGACCCTAACTTGT
yes

400

GATGTTTACCGTTTAAATCC

hsa-miR-125b-2-prec-#1
miR-125b-2
TAGTAACATCACAAGTCAGG
no

401

CTCTTGGGACCTAGGCGGAG

hsa-miR-125b-2-prec-#2
miR-125b-2
ACCAGACTTTTCCTAGTCCC
yes

402

TGAGACCCTAACTTGTGAGG

hsa-miR-127-prec
miR-127
TCGGATCCGTCTGAGCTTGG
yes

403

CTGGTCGGAAGTCTCATCAT

hsa-miR-128a-prec-#1
miR-128a
TTGGATTCGGGGCCGTAGCA
no
idem mmu-miR-
404

CTGTCTGAGAGGTTTACATT

128-prec-#2

hsa-miR-128a-prec-#2
miR-128a
ACATTTCTCACAGTGAACCG
yes

405

GTCTCTTTTTCAGCTGCTTC

hsa-miR-128b-prec-#1
miR-128b
TCACAGTGAACCGGTCTCTT
yes

406

TCCCTACTGTGTCACACTCC

hsa-miR-128b-prec-#2
miR-128b
GGGGGCCGATACACTGTACG
no

407

AGAGTGAGTAGCAGGTCTCA

hsa-miR-129-prec-#1
miR-129-1/2
TGGATCTTTTTGCGGTCTGG
yes

408

GCTTGCTGTTCCTCTCAACA

hsa-miR-129-prec-#2
miR-129-1/2
CCTCTCAACAGTAGTCAGGA
no

409

AGCCCTTACCCCAAAAAGTA

hsa-miR-130a-prec-#1
miR-130a
CCAGAGCTCTTTTCACATTG
no

410

TGCTACTGTCTGCACCTGTC

hsa-miR-130a-prec-#2
miR-130a
TGTCTGCACCTGTCACTAGC
yes

411

AGTGCAATGTTAAAAGGGCA

hsa-miR-132-prec-#1
miR-132
TGTGGGAACTGGAGGTAACA
yes

412

GTCTACAGCCATGGTCGCCC

hsa-miR-132-prec-#2
miR-132
TCCAGGGCAACCGTGGCTTT
no

413

CGATTGTTACTGTGGGAACT

hsa-miR-133a-1
miR-133a-1
CCTCTTCAATGGATTTGGTC
yes

414

(= miR-133c)
CCCTTCAACCAGCTGTAGCT

hsa-miR-133a-2
miR-133a-2
TTGGTCCCCTTCAACCAGCT
yes

415

(= miR-133d)
GTAGCTGTGCATTGATGGCG

hsa-miR-134-prec-#1
miR-134
ATGCACTGTGTTCACCCTGT
no

416

GGGCCACCTAGTCACCAACC

hsa-miR-134-prec-#2
miR-134
GTGTGTGACTGGTTGACCAG
yes

417

AGGGGCATGCACTGTGTTCA

hsa-miR-135-1-prec
miR-135-1
GCCTCGCTGTTCTCTATGGC
yes

418

(= miR-135)
TTTTTATTCCTATGTGATTC

hsa-miR-135-2-prec
miR-135-2
CACTCTAGTGCTTTATGGCT
yes

419

TTTTATTCCTATGTGATAGT

hsa-miR-136-prec-#1
miR-136
ATGCTCCATCATCGTCTCAA
no

420

ATGAGTCTTCAGAGGGTTCT

hsa-miR-136-prec-#2
miR-136
TGAGCCCTCGGAGGACTCCA
yes

421

TTTGTTTTGATGATGGATTC

hsa-miR-137-prec
miR-137
GGATTACGTTGTTATTGCTT
yes
idem mmu-miR-
422

AAGAATACGCGTAGTCGAGG

137-prec

hsa-miR-138-1-prec
miR-138-1
AGCTGGTGTTGTGAATCAGG
yes

423

CCGTTGCCAATCAGAGAACG

hsa-miR-138-2-prec
miR-138-2
AGCTGGTGTTGTGAATCAGG
yes
idem mmu-miR-
424

CCGACGAGCAGCGCATCCTC

138-prec

hsa-miR-139-prec
miR-139
GTGTATTCTACAGTGCACGT
yes

425

GTCTCCAGTGTGGCTCGGAG

hsa-miR-140-#1
miR-140-as
GCCAGTGGTTTTACCCTATG
no

426

GTAGGTTACGTCATGCTGTT

hsa-miR-140-#2
miR-140-as
TTCTACCACAGGGTAGAACC
yes

427

ACGGACAGGATACCGGGGCA

hsa-miR-141-prec-#1
miR-141
TTGTGAAGCTCCTAACACTG
yes

428

TCTGGTAAAGATGGCTCCCG

hsa-miR-141-prec-#2
miR-141
ATCTTCCAGTACAGTGTTGG
no

429

ATGGTCTAATTGTGAAGCTC

hsa-miR-142-prec
miR-142-as
CCCATAAAGTAGAAAGCACT
yes
idem mmu-miR-
430

ACTAACAGCACTGGAGGGTG

142-prec

hsa-miR-143-prec
miR-143
CTGGTCAGTTGGGAGTCTGA
yes

431

GATGAAGCACTGTAGCTCAG

hsa-miR-144-prec-#1
miR-144
CGATGAGACACTACAGTATA
yes

432

GATGATGTACTAGTCCGGGC

hsa-miR-144-prec-#2
miR-144
CCCTGGCTGGGATATCATCA
no

433

TATACTGTAAGTTTGCGATG

hsa-miR-145-prec
miR-145
CCTCACGGTCCAGTTTTCCC
yes

434

AGGAATCCCTTAGATGCTAA

hsa-miR-146-prec
miR-146
TGAGAACTGAATTCCATGGG
yes

435

TTGTGTCAGTGTCAGACCTC

hsa-miR-147-prec
miR-147
GACTATGGAAGCCAGTGTGT
yes

436

GGAAATGCTTCTGCTAGATT

hsa-miR-148-prec
miR-148
TGAGTATGATAGAAGTCAGT
yes

437

GCACTACAGAACTTTGTCTC

hsa-miR-149-prec
miR-149
CGAGCTCTGGCTCCGTGTCT
yes

438

TCACTCCCGTGCTTGTCCGA

hsa-miR-150-prec
miR-150
CTCCCCATGGCCCTGTCTCC
yes

439

CAACCCTTGTACCAGTGCTG

hsa-miR-151-prec
miR-151
GTATGTCTCATCCCCTACTA
yes

440

GACTGAAGCTCCTTGAGGAC

hsa-miR-152-prec-#1
miR-152
ACTCGGGCTCTGGAGCAGTC
yes
idem mmu-miR-
441

AGTGCATGACAGAACTTGGG

152-prec

hsa-miR-152-prec-#2
miR-152
CCCCGGCCCAGGTTCTGTGA
no

442

TACACTCCGACTCGGGCTCT

hsa-miR-153-1-prec1
miR-153-1
CAGTTGCATAGTCACAAAAG
yes

443

TGATCATTGGCAGGTGTGGC

hsa-miR-153-1-prec2
miR-153-1
CACAGCTGCCAGTGTCATTG
yes

444

TCACAAAAGTGATCATTGGC

hsa-miR-153-2-prec
miR-153-2
GCCCAGTTGCATAGTCACAA
yes

445

AAGTGATCATTGGAAACTGT

hsa-miR-154-prec1-#1
miR-154
GTGGTACTTGAAGATAGGTT
yes

446

ATCCGTGTTGCCTTCGCTTT

hsa-miR-154-prec1-#2
miR-154
GCCTTCGCTTTATTTGTGAC
no

447

GAATCATACACGGTTGACCT

hsa-miR-155-prec
miR-155(BIC)
TTAATGCTAATCGTGATAGG
yes

448

GGTTTTTGCCTCCAACTGAC

hsa-miR-181a-prec-#1
miR-181a
TCAGAGGACTCCAAGGAACA
yes

449

(= miR-178-2)
TTCAACGCTGTCGGTGAGTT

hsa-miR-181a-prec-#2
miR-181a
GAAAAAACCACTGACCGTTG
no

450

(= miR-178-2)
ACTGTACCTTGGGGTCCTTA

hsa-miR-181b-prec-#1
miR-181b
TGAGGTTGCTTCAGTGAACA
yes

451

(= miR-178)
TTCAACGCTGTCGGTGAGTT

hsa-miR-181b-prec-#2
miR-181b
ACCATCGACCGTTGATTGTA
yes

452

(= miR-178)
CCCTATGGCTAACCATCATC

hsa-miR-181c-prec-#1
miR-181c
TGCCAAGGGTTTGGGGGAAC
yes

453

ATTCAACCTGTCGGTGAGTT

hsa-miR-181c-prec-#2
miR-181c
ATCGACCGTTGAGTGGACCC
no

454

TGAGGCCTGGAATTGCCATC

hsa-miR-182-prec-#1
miR-182-s
AGGTAACAGGATCCGGTGGT
no

455

TCTAGACTTGCCAACTATGG

hsa-miR-182-prec-#2
miR-182-s
TTGGCAATGGTAGAACTCAC
yes

456

ACTGGTGAGGTAACAGGATC

hsa-miR-183-prec-#1
miR-183
GACTCCTGTTCTGTGTATGG
yes

457

(= miR-174)
CACTGGTAGAATTCACTGTG

hsa-miR-183-prec-#2
miR-183
GTCTCAGTCAGTGAATTACC
no

458

(= miR-174)
GAAGGGCCATAAACAGAGCA

hsa-miR-184-prec-#1
miR-184
GACTGTAAGTGTTGGACGGA
yes

459

GAACTGATAAGGGTAGGTGA

hsa-miR-184-prec-#2
miR-184
CGTCCCCTTATCACTTTTCC
no

460

AGCCCAGCTTTGTGACTGTA

hsa-miR-185-prec-#1
miR-185
GCGAGGGATTGGAGAGAAAG
yes

461

GCAGTTCCTGATGGTCCCCT

hsa-miR-185-prec-#2
miR-185
CCTCCCCAGGGGCTGGCTTT
no

462

CCTCTGGTCCTTCCCTCCCA

hsa-miR-186-prec
miR-186
CTTGTAACTTTCCAAAGAAT
yes

463

TCTCCTTTTGGGCTTTCTGG

hsa-miR-187-prec-#1
miR-187
CTCGTGTCTTGTGTTGCAGC
yes

464

CGGAGGGACGCAGGTCCGCA

hsa-miR-187-prec-#2
miR-187
TCACCATGACACAGTGTGAG
no

465

ACTCGGGCTACAACACAGGA

hsa-miR-188-prec
miR-188
TCACATCCCTTGCATGGTGG
yes

466

AGGGTGAGCTTTCTGAAAAC

hsa-miR-190-prec
miR-190
GCAGGCCTCTGTGTGATATG
yes

467

TTTGATATATTAGGTTGTTA

hsa-miR-191-prec
miR-191
CAACGGAATCCCAAAAGCAG
yes
idem mmu-miR-
468

CTGTTGTCTCCAGAGCATTC

191-prec

hsa-miR-192-2/3-#1
miR-192
TCTGACCTATGAATTGACAG
yes

469

CCAGTGCTCTCGTCTCCCCT

hsa-miR-192-2/3-#2
miR-192
CCAATTCCATAGGTCACAGG
no

470

TATGTTCGCCTCAATGCCAG

hsa-miR-193-prec-#1
miR-193
AGATGAGGGTGTCGGATCAA
yes

471

CTGGCCTACAAAGTCCCAGT

hsa-miR-193-prec-#2
miR-193
AGGATGGGAGCTGAGGGCTG
no

472

GGTCTTTGCGGGCGAGATGA

hsa-miR-194-prec-#1
miR-194
TGTAACAGCAACTCCATGTG
yes

473

GACTGTGTACCAATTTCCAG

hsa-miR-194-prec-#2
miR-194
CCAATTTCCAGTGGAGATGC
no

474

TGTTACTTTTGATGGTTACC

hsa-miR-195-prec
miR-195
TCTAGCAGCACAGAAATATT
yes

475

GGCACAGGGAAGCGAGTCTG

hsa-miR-196-1-prec-#1
miR-196-1
CTGCTGAGTGAATTAGGTAG
yes

476

TTTCATGTTGTTGGGCCTGG

hsa-miR-196-1-prec-#2
miR-196-1
ACACAACAACATTAAACCAC
no

477

CCGATTCACGGCAGTTACTG

hsa-miR-196-2-prec-#1
miR-196-2
AGAAACTGCCTGAGTTACAT
no

478

CAGTCGGTTTTCGTCGAGGG

hsa-miR-196-2-prec-#2
miR-196-2
GCTGATCTGTGGCTTAGGTA
yes

479

GTTTCATGTTGTTGGGATTG

hsa-miR-197-prec
miR-197
TAAGAGCTCTTCACCCTTCA
yes

480

CCACCTTCTCCACCCAGCAT

hsa-miR-198-prec
miR-198
TCATTGGTCCAGAGGGGAGA
yes

481

TAGGTTCCTGTGATTTTTCC

hsa-miR-199a-1-prec
miR-199a-1
GCCAACCCAGTGTTCAGACT
yes

482

(= 199s)
ACCTGTTCAGGAGGCTCTCA

hsa-miR-199a-2-prec
miR-199a-2
TCGCCCCAGTGTTCAGACTA
yes

483

CCTGTTCAGGACAATGCCGT

hsa-miR-199b-prec-#1
miR-199b
GTCTGCACATTGGTTAGGCT
no

484

GGGCTGGGTTAGACCCTCGG

hsa-miR-199b-prec-#2
miR-199b
ACCTCCACTCCGTCTACCCA
yes

485

GTGTTTAGACTATCTGTTCA

hsa-miR-200a-prec
miR-200a
GTCTCTAATACTGCCTGGTA
yes

486

ATGATGACGGCGGAGCCCTG

hsa-miR-202-prec
miR-202
GATCTGGCCTAAAGAGGTAT
yes

487

AGGGCATGGGAAGATGGAGC

hsa-miR-203-prec-#1
miR-203
GTTCTGTAGCGCAATTGTGA
yes

488

AATGTTTAGGACCACTAGAC

hsa-miR-203-prec-#2
miR-203
TGGGTCCAGTGGTTCTTAAC
no

489

AGTTCAACAGTTCTGTAGCG

hsa-miR-204-prec-#1
miR-204
CGTGGACTTCCCTTTGTCAT
yes

490

CCTATGCCTGAGAATATATG

hsa-miR-204-prec-#2
miR-204
AGGCTGGGAAGGCAAAGGGA
no

491

CGTTCAATTGTCATCACTGG

hsa-miR-205-prec
miR-205
TCCTTCATTCCACCGGAGTC
yes

492

TGTCTCATACCCAACCAGAT

hsa-miR-206-prec-#1
miR-206
TTGCTATGGAATGTAAGGAA
yes

493

GTGTGTGGTTTCGGCAAGTG

hsa-miR-206-prec-#2
miR-206
TGCTTCCCGAGGCCACATGC
no

494

TTCTTTATATCCCCATATGG

hsa-miR-208-prec
miR-208
ACCTGATGCTCACGTATAAG
yes

495

ACGAGCAAAAAGCTTGTTGG

hsa-miR-210-prec
miR-210
AGACCCACTGTGCGTGTGAC
yes

496

AGCGGCTGATCTGTGCCTGG

hsa-miR-211-prec-#1
miR-211
TTCCCTTTGTCATCCTTCGC
yes

497

CTAGGGCTCTGAGCAGGGCA

hsa-miR-211-prec-#2
miR-211
GCAGGGACAGCAAAGGGGTG
no

498

CTCAGTTGTCACTTCCCACA

hsa-miR-212-prec-#1
miR-212
CCTCAGTAACAGTCTCCAGT
yes

499

CACGGCCACCGACGCCTGGC

hsa-miR-212-prec-#2
miR-212
CGGACAGCGCGCCGGCACCT
no

500

TGGCTCTAGACTGCTTACTG

hsa-miR-213-prec-#1
miR-213
AACATTCATTGCTGTCGGTG
yes
idem mmu-miR-
501

GGTTGAACTGTGTGGACAAG

213-prec

hsa-miR-213-prec-#2
miR-213
TGTGGACAAGCTCACTGAAC
no

502

AATGAATGCAACTGTGGCCC

hsa-miR-214-prec
miR-214
TGTACAGCAGGCACAGACAG
yes
idem mmu-miR-
503

GCAGTCACATGACAACCCAG

214-prec

hsa-miR-215-prec-#1
miR-215
CAGGAAAATGACCTATGAAT
yes

504

TGACAGACAATATAGCTGAG

hsa-miR-215-prec-#2
miR-215
CATTTCTTTAGGCCAATATT
no

505

CTGTATGACTGTGCTACTTC

hsa-miR-216-prec-#1
miR-216
CTGGGATTATGCTAAACAGA
no

506

GCAATTTCCTAGCCCTCACG

hsa-miR-216-prec-#2
miR-216
GATGGCTGTGAGTTGGCTTA
yes

507

ATCTCAGCTGGCAACTGTGA

hsa-miR-217-prec-#1
miR-217
GAATCAGTCACCATCAGTTC
no

508

CTAATGCATTGCCTTCAGCA

hsa-miR-217-prec-#2
miR-217
TGTCGCAGATACTGCATCAG
yes

509

GAACTGATTGGATAAGAATC

hsa-miR-218-1-prec
miR-218-1
GTTGTGCTTGATCTAACCAT
yes

510

GTGGTTGCGAGGTATGAGTA

hsa-miR-218-2-prec-#1
miR-218-2
TGGTGGAACGATGGAAACGG
no

511

AACATGGTTCTGTCAAGCAC

hsa-miR-218-2-prec-#2
miR-218-2
TCGCTGCGGGGCTTTCCTTT
yes

512

GTGCTTGATCTAACCATGTG

hsa-miR-219-prec
miR-219
ATTGTCCAAACGCAATTCTC
yes

513

GAGTCTATGGCTCCGGCCGA

hsa-miR-220-prec
miR-220
TGTGGCATTGTAGGGCTCCA
yes

514

CACCGTATCTGACACTTTGG

hsa-miR-221-prec
miR-221
CAACAGCTACATTGTCTGCT
yes
idem mmu-miR-
515

GGGTTTCAGGCTACCTGGAA

221-prec-#1

hsa-miR-222-prec-#1
miR-222
CTTTCGTAATCAGCAGCTAC
yes

516

ATCTGGCTACTGGGTCTCTG

hsa-miR-222-prec-#2
miR-222
GCTGCTGGAAGGTGTAGGTA
no

517

CCCTCAATGGCTCAGTAGCC

hsa-miR-223-prec
miR-223
GAGTGTCAGTTTGTCAAATA
yes

518

CCCCAAGTGCGGCACATGCT

hsa-miR-224-prec
miR-224
GGCTTTCAAGTCACTAGTGG
yes

519

TTCCGTTTAGTAGATGATTG

HSHELA01
-
GGCCGCAGCAACCTCGGTTC
-

520

GTATCCGAGTCACGGCACCA

HSTRNL
-
TCCGGATGGAGCGTGGGTTC
-

521

GAATCCCACTTCTGACACCA

HUMTRAB
-
ATGGTAGAGCGCTCGCTTTG
-

522

CTTGCGAGAGGTAGCGGGAT

HUMTRF
-
GATCTAAAGGTCCCTGGTTC
-

523

GATCCCGGGTTTCGGCACCA

HUMTRMI-#1
-
AGCAGAGTGGCGCAGCGGAA
-
idem MUSTRMI-#1
524

GCGTGCTGGGCCCATAACCC

HUMTRMI-#2
-
AACCCAGAGGTCGATGGATC
-

525

GAAACCATCCTCTGCTACCA

HUMTRN
-
CAATCGGTTAGCGCGTTCGG
-

526

CTGTTAACCGAAAGGTTGGT

HUMTRS
-
TCTAGCGACAGAGTGGTTCA
-

527

ATTCCACCTTTCGGGCGCCA

HUMTRV1A
-
ACGCGAAAGGTCCCCGGTTC
-

528

GAAACCGGGCGGAAACACCA

mmu-let-7g-prec
mmu-let-7g
CTGAGGTAGTAGTTTGTACA
yes

529

GTTTGAGGGTCTATGATACC

mmu-let-7i-prec
mmu-let-7i
GCTGAGGTAGTAGTTTGTGC
yes
idem hsa-let-
530

TGTTGGTCGGGTTGTGACAT

7d-v2-prec-#2

mmu-miR-001b-prec
mmu-miR-001b
ATTCAGTGCTATGGAATGTA
yes

531

AAGAAGTATGTATTTTGGGT

mmu-miR-001d-prec
mmu-miR-001d
CTGCTAAGCTATGGAATGTA
yes

532

AAGAAGTATGTATTTCAGGC

mmu-miR-009-prec-#1
mmu-miR-009
ATCTTTGGTTATCTAGCTGT
yes

533

ATGAGTGTATTGGTCTTCAT

mmu-miR-009-prec-#2
mmu-miR-009-
GAGTGTATTGGTCTTCATAA
yes
idem hsa-miR-
534

AGCTAGATAACCGAAAGTAA

009-2-#2

mmu-miR-010b-prec
mmu-miR-010b
TACCCTGTAGAACCGAATTT
yes

535

GTGTGGTACCCACATAGTCA

mmu-miR-023b-prec
mmu-miR-023b
TTGAGATTAAAATCACATTG
yes

536

CCAGGGATTACCACGCAACC

mmu-miR-027b-prec
mmu-miR-027b
TTGGTTTCCGCTTTGTTCAC
yes

537

AGTGGCTAAGTTCTGCACCT

mmu-miR-029b-prec
mmu-miR-029b
TAAATAGTGATTGTCTAGCA
yes

538

CCATTTGAAATCAGTGTTCT

mmu-miR-030b-prec
mmu-miR-030b
TGTAAACATCCTACACTCAG
yes

539

CTGTCATACATGCGTTGGCT

mmu-miR-030e-prec
mmu-miR-030e
TGTAAACATCCTTGACTGGA
yes

540

AGCTGTAAGGTGTTGAGAGG

mmu-miR-099a-prec
mmu-miR-099a
CATAAACCCGTAGATCCGAT
yes

541

CTTGTGGTGAAGTGGACCGC

mmu-miR-099b-prec-#1
mmu-miR-099b
GCCTTCGCCGCACACAAGCT
no
idem hsa-miR-
542

CGTGTCTGTGGGTCCGTGTC

099b-prec-19-#1

mmu-miR-099b-prec-#2
mmu-miR-099b
CACCCGTAGAACCGACCTTG
yes
idem hsa-miR-
543

CGGGGCCTTCGCCGCACACA

099b-prec-19-#2

mmu-miR-100-prec
mmu-miR-100
TGCCACAAACCCGTAGATCC
yes

544

GAACTTGTGCTGATTCTGCA

mmu-miR-101-prec
mmu-miR-101
GCTGTCCATTCTAAAGGTAC
yes

545

AGTACTGTGATAACTGAAGG

mmu-miR-122a-prec-#1
mmu-miR-122a
GTGTCCAAACCATCAAACGC
no

546

CATTATCACACTAAATAGCT

mmu-miR-122a-prec-#2
mmu-miR-122a
GCTGTGGAGTGTGACAATGG
yes

547

TGTTTGTGTCCAAACCATCA

mmu-miR-123-prec-#1
mmu-miR-123
CATTATTACTTTTGGTACGC
yes

548

GCTGTGACACTTCAAACTCG

mmu-miR-123-prec-#2
mmu-miR-123
GACACTTCAAACTCGTACCG
yes

549

TGAGTAATAATGCGCGGTCA

mmu-miR-124a-prec
mmu-miR-124a
TAATGTCTATACAATTAAGG
yes

550

CACGCGGTGAATGCCAAGAG

mmu-miR-125a-prec
mmu-miR-125a
TCCCTGAGACCCTTTAACCT
yes

551

GTGAGGACGTCCAGGGTCAC

mmu-miR-125b-prec-#1
mmu-miR-125b
GCCTAGTCCCTGAGACCCTA
yes

552

ACTTGTGAGGTATTTTAGTA

mmu-miR-125b-prec-#2
mmu-miR-125b
ATTTTAGTAACATCACAAGT
no

553

CAGGTTCTTGGGACCTAGGC

mmu-miR-127-prec
mmu-miR-127
TTCAGAAAGATCATCGGATC
yes

554

CGTCTGAGCTTGGCTGGTCG

mmu-miR-128-prec-#1
mmu-miR-128
AGGTTTACATTTCTCACAGT
yes

555

GAACCGGTCTCTTTTTCAGC

mmu-miR-128-prec-#2
mmu-miR-128
TTGGATTCGGGGCCGTAGCA
no
idem hsa-miR-
556

CTGTCTGAGAGGTTTACATT

128a-prec-#1

mmu-miR-129b-prec
mmu-miR-129b
CTTTTTGCGGTCTGGGCTTG
yes

557

CTGTACATAACTCAATAGCC

mmu-miR-129-prec
mmu-miR-129
CTTTTTGCGGTCTGGGCTTG
yes

558

CTGTTTTCTCGACAGTAGTC

mmu-miR-130-prec
mmu-miR-130
GTCTAACGTGTACCGAGCAG
yes

559

TGCAATGTTAAAAGGGCATC

mmu-miR-131-3-prec
mmu-miR-131-3
AGTGGTGTGGAGTCTTCATA
yes

560

AAGCTAGATAACCGAAAGTA

mmu-miR-132-prec
mmu-miR-132
TGTGGGAACCGGAGGTAACA
yes

561

GTCTACAGCCATGGTCGCCC

mmu-miR-133-prec
mmu-miR-133
ATCGCCTCTTCAATGGATTT
yes

562

GGTCCCCTTCAACCAGCTGT

mmu-miR-134-prec-#1
mmu-miR-134
GCACTCTGTTCACCCTGTGG
no

563

GCCACCTAGTCACCAACCCT

mmu-miR-134-prec-#2
mmu-miR-134
TGTGTGACTGGTTGACCAGA
yes

564

GGGGCGTGCACTCTGTTCAC

mmu-miR-135-prec
mmu-miR-135
CTATGGCTTTTTATTCCTAT
yes

565

GTGATTCTATTGCTCGCTCA

mmu-miR-136-prec
mmu-miR-136
GAGGACTCCATTTGTTTTGA
yes

566

TGATGGATTCTTAAGCTCCA

mmu-miR-137-prec
mmu-miR-137
GGATTACGTTGTTATTGCTT
yes
idem hsa-miR-
567

AAGAATACGCGTAGTCGAGG

137-prec

mmu-miR-138-prec
mmu-miR-138
AGCTGGTGTTGTGAATCAGG
yes
idem hsa-miR-
568

CCGACGAGCAGCGCATCCTC

138-2-prec

mmu-miR-140s-prec
mmu-miR-140s
TTACGTCATGCTGTTCTACC
yes

569

ACAGGGTAGAACCACGGACA

mmu-miR-141-prec
mmu-miR-141
GAAGTATGAAGCTCCTAACA
yes

570

CTGTCTGGTAAAGATGGCCC

mmu-miR-142-prec
mmu-miR-142
CCCATAAAGTAGAAAGCACT
yes
idem hsa-miR-
571

ACTAACAGCACTGGAGGGTG

142-prec

mmu-miR-143-prec
mmu-miR-143
TGGTCAGTTGGGAGTCTGAG
yes

572

ATGAAGCACTGTAGCTCAGG

mmu-miR-144-prec
mmu-miR-144
GTTTGTGATGAGACACTACA
yes

573

GTATAGATGATGTACTAGTC

mmu-miR-145-prec
mmu-miR-145
ACGGTCCAGTTTTCCCAGGA
yes

574

ATCCCTTGGATGCTAAGATG

mmu-miR-146-prec
mmu-miR-146
TGAGAACTGAATTCCATGGG
yes

575

TTATATCAATGTCAGACCTG

mmu-miR-149-prec
mmu-miR-149
GCTCTGGCTCCGTGTCTTCA
yes

576

CTCCCGTGTTTGTCCGAGGA

mmu-miR-150-prec
mmu-miR-150
TGTCTCCCAACCCTTGTACC
yes

577

AGTGCTGTGCCTCAGACCCT

mmu-miR-151-prec
mmu-miR-151
TATGTCTCCTCCCTACTAGA
yes

578

CTGAGGCTCCTTGAGGGACA

mmu-miR-152-prec
mmu-miR-152
ACTCGGGCTCTGGAGCAGTC
yes
idem hsa-miR-
579

AGTGCATGACAGAACTTGGG

152-prec-#1

mmu-miR-153-prec
mmu-miR-153
TAATATGAGCCCAGTTGCAT
yes

580

AGTGACAAAAGTGATCATTG

mmu-miR-154-prec
mmu-miR-154
AGATAGGTTATCCGTGTTGC
yes

581

CTTCGCTTTATTCGTGACGA

mmu-miR-155-prec
mmu-miR-155
TTAATGCTAATTGTGATAGG
yes

582

GGTTTTGGCCTCTGACTGAC

mmu-miR-181-prec
mmu-miR-181
CCATGGAACATTCAACGCTG
yes

583

TCGGTGAGTTTGGGATTCAA

mmu-miR-182-prec
mmu-miR-182
TTTGGCAATGGTAGAACTCA
yes

584

CACCGGTAAGGTAATGGGAC

mmu-miR-183-prec-#1
mmu-miR-183
AACAGTCTCAGTCAGTGAAT
no

585

TACCGAAGGGCCATAAACAG

mmu-miR-183-prec-#2
mmu-miR-183
TATGGCACTGGTAGAATTCA
yes

586

CTGTGAACAGTCTCAGTCAG

mmu-miR-184-prec
mmu-miR-184
TGTGACTCTAAGTGTTGGAC
yes

587

GGAGAACTGATAAGGGTAGG

mmu-miR-185-prec
mmu-miR-185
GGGATTGGAGAGAAAGGCAG
yes

588

TTCCTGATGGTCCCCTCCCA

mmu-miR-186-prec
mmu-miR-186
CAAAGAATTCTCCTTTTGGG
yes

589

CTTTCTCATTTTATTTTAAG

mmu-miR-187-prec
mmu-miR-187
GGGCGCTGCTCTGACCCCTC
yes

590

GTGTCTTGTGTTGCAGCCGG

mmu-miR-188-prec
mmu-miR-188
TCACATCCCTTGCATGGTGG
yes

591

AGGGTGAGCTCTCTGAAAAC

mmu-miR-189-prec
mmu-miR-189
CGGTGCCTACTGAGCTGATA
yes

592

TCAGTTCTCATTTCACACAC

mmu-miR-190-prec
mmu-miR-190
CTGTGTGATATGTTTGATAT
yes

593

ATTAGGTTGTTATTTAATCC

mmu-miR-191-prec
mmu-miR-191
CAACGGAATCCCAAAAGCAG
yes
idem hsa-miR-
594

CTGTTGTCTCCAGAGCATTC

191-prec

mmu-miR-192-2/3-prec
mmu-miR-192-2/3
CTGACCTATGAATTGACAGC
yes

595

CAGTGCTCTCGTCTCCCCTC

mmu-miR-193-prec
mmu-miR-193
TGAGAGTGTCAGTTCAACTG
yes

596

GCCTACAAAGTCCCAGTCCT

mmu-miR-194-prec
mmu-miR-194
ATCGGGTGTAACAGCAACTC
yes

597

CATGTGGACTGTGCTCGGAT

mmu-miR-195-prec
mmu-miR-195
TAGCAGCACAGAAATATTGG
yes

598

CATGGGGAAGTGAGTCTGCC

mmu-miR-196-prec
mmu-miR-196
GTAGGTAGTTTCATGTTGTT
yes

599

GGGCCTGGCTTTCTGAACAC

mmu-miR-199as-prec
mmu-miR-199as
GAGGCTGGGACATGTACAGT
yes

600

AGTCTGCACATTGGTTAGGC

mmu-miR-200a-prec-#1
mmu-miR-200a
TAGTGTCTGATCTCTAATAC
yes

601

TGCCTGGTAATGATGACGGC

mmu-miR-200a-prec-#2
mmu-miR-200a
CCGTGGCCATCTTACTGGGC
no

602

AGCATTGGATAGTGTCTGAT

mmu-miR-201-prec
mmu-miR-201
TACCTTACTCAGTAAGGCAT
yes

603

TGTTCTTCTATATTAATAAA

mmu-miR-202-prec
mmu-miR-202
GATCTGGTCTAAAGAGGTAT
yes

604

AGCGCATGGGAAGATGGAGC

mmu-miR-203-prec-#1
mmu-miR-203
GGTCCAGTGGTTCTTGACAG
no

605

TTCAACAGTTCTGTAGCACA

mmu-miR-203-prec-#2
mmu-miR-203
GTAGCACAATTGTGAAATGT
yes

606

TTAGGACCACTAGACCCGGC

mmu-miR-204-prec
mmu-miR-204
TTCCCTTTGTCATCCTATGC
yes

607

CTGAGAATATATGAAGGAGG

mmu-miR-205-prec
mmu-miR-205
GTCCTTCATTCCACCGGAGT
yes

608

CTGTCTTATGCCAACCAGAT

mmu-miR-206-prec
mmu-miR-206
TAGATATCTCAGCACTATGG
yes

609

AATGTAAGGAAGTGTGTGGT

mmu-miR-207-prec
mmu-miR-207
GCTGCGGCTTGCGCTTCTCC
yes

610

TGGCTCTCCTCCCTCTCCTT

mmu-miR-212-prec-#1
mmu-miR-212
CTTCAGTAACAGTCTCCAGT
yes

611

CACGGCCACCGACGCCTGGC

mmu-miR-212-prec-#2
mmu-miR-212
AGCGCGCCGGCACCTTGGCT
no

612

CTAGACTGCTTACTGCCCGG

mmu-miR-213-prec
mmu-miR-213
AACATTCATTGCTGTCGGTG
yes
idem hsa-miR-
613

GGTTGAACTGTGTGGACAAG

213-prec-#1

mmu-miR-214-prec
mmu-miR-214
TGTACAGCAGGCACAGACAG
yes
idem hsa-miR-
614

GCAGTCACATGACAACCCAG

214-prec

mmu-miR-215-prec
mmu-miR-215
CAGGAGAATGACCTATGATT
yes

615

TGACAGACCGTGCAGCTGTG

mmu-miR-216-prec-#1
mmu-miR-216
GAGATGTCCCTATCATTCCT
no

616

CACAGTGGTCTCTGGGATTA

mmu-miR-216-prec-#2
mmu-miR-216
ATGGCTATGAGTTGGTTTAA
yes

617

TCTCAGCTGGCAACTGTGAG

mmu-miR-217-prec-#1
mmu-miR-217
GCAGATACTGCATCAGGAAC
yes

618

TGACTGGATAAGACTTAATC

mmu-miR-217-prec-#2
mmu-miR-217
CCCCATCAGTTCCTAATGCA
no

619

TTGCCTTCAGCATCTAAACA

mmu-miR-218-2-prec-#1
mmu-miR-218-2
GGGCTTTCCTTTGTGCTTGA
yes

620

TCTAACCATGTGGTGGAACG

mmu-miR-218-2-prec-#2
mmu-miR-218-2
GTGGTGGAACGATGGAAACG
no

621

GAACATGGTTCTGTCAAGCA

mmu-miR-219-prec-#1
mmu-miR-219
TCCTGATTGTCCAAACGCAA
yes

622

TTCTCGAGTCTCTGGCTCCG

mmu-miR-219-prec-#2
mmu-miR-219
CTCTGGCTCCGGCCGAGAGT
no

623

TGCGTCTGGACGTCCCGAGC

mmu-miR-221-prec-#1
mmu-miR-221
CAACAGCTACATTGTCTGCT
yes
idem hsa-miR-
624

GGGTTTCAGGCTACCTGGAA

221-prec

mmu-miR-221-prec-#2
mmu-miR-221
GGCATACAATGTAGATTTCT
no

625

GTGTTTGTTAGGCAACAGCT

mmu-miR-222-prec
mmu-miR-222
TTGGTAATCAGCAGCTACAT
yes

626

CTGGCTACTGGGTCTCTGGT

mmu-miR-223-prec
mmu-miR-223
AGAGTGTCAGTTTGTCAAAT
yes

627

ACCCCAAGTGTGGCTCATGC

mmu-miR-224-
mmu-miR-224-
TAAGTCACTAGTGGTTCCGT
yes

628

precformer175-#1
(miR-175)
TTAGTAGATGGTCTGTGCAT

mmu-miR-224-
mmu-miR-224-
TGCATTGTTTCAAAATGGTG
no

629

precformer175-#2
(miR-175)
CCCTAGTGACTACAAAGCCC

MUSTRF
—
TAGACTGAAGATCTAAAGGT

630

CCCTGGTTCGATCCCGGGTT

MUSTRM4

AATCTGAAGGTCGTGAGTTC

631

GATCCTCACACGGGGCACCA

MUSTRMI-#1

AGCAGAGTGGCGCAGCGGAA

idem
632

GCGTGCTGGGCCCATAACCC

HUMTRMI-#1

MUSTRMI-#2

CCCATAACCCAGAGGTCGAT

633

GGATCGAAACCATCCTCTGC

MUSTRNAH

TGCGTTGTGGCCGCAGCAAC

634

CTCGGTTCGAATCCGAGTCA

MUSTRP2

GCTCGTTGGTCTAGGGGTAT

635

GATTCTCGCTTTGGGTGCGA

MUSTRS

AGCTGTTTAGCGACAGAGTG

636

GTTCAATTCCACCTTTCGGG

MUSTRV1MN

TTCCGTAGTGTAGTGGTTAT

637

CACGCTCGCCTGACACGCGA

Oligonucleotide

5′ biotin-AAA-AAA-

638

primer #1

AAA-AAA-(biotin)AAA-

AAA-AAA-AAA-NNN-NNN-

NN 3′

Oligonucleotide

5′ biotin-(biotin)-

639

primer #2

AAA-NNN-NNN-NN 3′

Oligonucleotide

5′ GCC-AGT-GAA-TTG-

640

primer #3

TAA-TAC-GAC-TCA-CTA-

TAG-GGA-GGC-GGN-NNN-

NNN-N 3′

miRNA Microarray Fabrication. 40-mer 5′ amine modified C6 oligonucleotides were resuspended in 50 mM phosphate buffer pH 8.0 at 20 mM concentration. The individual oligonucleotide-probe was printed in triplicate on Amersham CodeLink™ activated slides under 45% humidity by GeneMachine OmniGrid™ 100 Microarrayer in 2×2 pin configuration and 20×20 spot configuration of each subarray. The spot diameter was 100 μm and distance from center to center was 200 μm. The printed miRNA microarrays were further chemically covalently-coupled under 70% humidity overnight. The miRNA microarrays were ready for sample hybridization after additional blocking and washing steps.

Target Preparation. Five μg of total RNA were separately added to a reaction mix in a final volume of 12 μl, containing 1 μg of [3′(N)8-(A)12-biotin-(A)12-biotin 5′] oligonucleotide primer. The mixture was incubated for 10 min at 70° C. and chilled on ice. With the mixture remaining on ice, 4 μl of 5× first-strand buffer, 2 μl 0.1 M DTT, 1 μl of 10 mM dNTP mix and 1 μl Superscript™ II RNaseff reverse transcriptase (200 U/μl) was added to a final volume of 20 μl, and the mixture incubated for 90 min in a 37° C. water bath. After incubation for first strand cDNA synthesis, 3.5 μl of 0.5 M NaOH/50 mM EDTA was added into 20 μl of first strand reaction mix and incubated at 65° C. for 15 min to denature the RNA/DNA hybrids and degrade RNA templates. Then 5 μl of 1 M Tris-HCl, pH 7.6 (Sigma) was added to neutralize the reaction mix and labeled targets were stored in 28.5 μl at −80° C. until chip hybridization.

Array Hybridization. Labeled targets from 5 μg of total RNA were used for hybridization on each KCC/TJU miRNA microarray containing 368 probes in triplicate, corresponding to 245 human and mouse miRNA genes. All probes on these microarrays were 40-mer oligonucleotides spotted by contacting technologies and covalently attached to a polymeric matrix. The microarrays were hybridized in 6×SSPE/30% formamide at 25° C. for 18 hours, washed in 0.75×TNT at 37° C. for 40 min, and processed using direct detection of the biotin-containing transcripts by Streptavidin-Alexa647 conjugate. Processed slides were scanned using a Perkin Elmer ScanArray® XL5K Scanner with the laser set to 635 nm, at Power 80 and PMT 70 setting, and a scan resolution of 10 microns.

Data Analysis. Images were quantified by QuantArray® Software (PerkinElmer). Signal intensities for each spot were calculated by subtracting local background (based on the median intensity of the area surrounding each spot) from total intensities. Raw data were normalized and analyzed using the GeneSpring® software version 6.1.1 (Silicon Genetics, Redwood City, Calif.). GeneSpring generates an average value of the three spot replicates of each miRNA. Following data transformation (to convert any negative value to 0.01), normalization was performed by using a per-chip 50th percentile method that normalizes each chip on its median allowing comparison among chips. Hierarchical clustering for both genes and conditions were then generated by using standard correlation as a measure of similarity. To highlight genes that characterize each tissue, a per-gene on median normalization was performed, which normalizes the expression of every miRNA on its median among samples.

Samples. HeLa cells were purchased from ATCC and grown as recommended. Mouse macrophage cell line RAW264.7 (established from BALB/c mice) was also used (Dumitru, C. D., Ceci, J. D., Tsatsanis, C., Kontoyiannis, D., Stamatakis, K., Lin, J. H., Patriotis, C., Jenkins, N. A., Copeland, N. G., Kollias, G. & Tsichlis, P. N. (2000) Cell 103, 1071-83). RNA from 20 normal human tissues, including 18 of adult origin (7 hematopoietic: bone marrow, lymphocytes B, T, and CD5+ cells from 2 individuals, peripheral blood leukocytes derived from three healthy donors, spleen, and thymus; and 11 solid tissues, including brain, breast, ovary, testis, prostate, lung, heart, kidney, liver, skeletal muscle, and placenta) and 2 of fetal origin (fetal liver and fetal brain) were assessed for miRNA expression. Each RNA was labeled and hybridized in duplicate and the average expression was calculated. For all the normal tissues, except lymphocytes B, T and CD5+ cells, total RNA was purchased from Ambion (Austin, Tex.).

Cell Preparation. Mononuclear cells (MNC) from peripheral blood of normal donors were separated by Ficoll-Hypaque density gradients. T cells were purified from these MNC by rosetting with neuraminidase treated SRBC and depletion of contaminant monocytes (Cd11b+), natural killer cells (CD16+) and B lymphocytes (CD19+) were purified using magnetic beads (Dynabeads, Unipath, Milano, Italy) and specific monoclonal antibodies (Becton Dickinson, San Jose, Calif.). Total B cells and CD5+ B cells were prepared from tonsils as described (Dono, M., Zupo, S., Leanza, N., Melioli, G., Fogli, M., Melagrana, A., Chiorazzi, N. & Ferrarini, M. (2000) J. Immunol 164, 5596-604). Briefly, tonsils were obtained from patients in the pediatric age group undergoing routine tonsillectomies, after informed consent. Purified B cells were prepared by rosetting T cells from MNC cells with neuraminidase treated SRBC. In order to obtain CD5+ B cells, purified B cells were incubated with anti CD5 monoclonal antibody followed by goat anti mouse Ig conjugated with magnetic microbeads. CD5+ B cells were positively selected by collecting the cells retained on the magnetic column MS by Mini MACS system (Miltenyi Biotec, Auburn, Calif.). The degree of purification of the cell preparations was higher than 95%, as assessed by flow cytometry.

RNA Extraction and Northern Blots. Total RNA isolation and blots were performed as described (Calin, et al., (2002) Proc Natl Acad Sc USA. 99, 15524-15529). After RNA isolation, the washing step with ethanol was not performed, or if performed, the tube walls were rinsed with 75% ethanol without perturbing the RNA pellet (Lagos-Quintana, et al., (2001) Science 294, 853-858). For reuse, blots were stripped by boiling in 0.1% aqueous SDS/0.1×SSC for 10 min, and were reprobed. 5S rRNA stained with ethidium bromide served as a loading control.

Quantitative RT-PCR for miRNA Precursors. Quantitative RT-PCR was performed as described (Schmittgen, T. D., Jiang, J., Liu, Q. & Yang, L. (2004) Nucleic Acid Research 32, 43-53). Briefly, RNA was reverse transcribed to cDNA with gene-specific primers and Thermoscript, and the relative amount of each miRNA to both U6 RNA and tRNA for initiator methionine was described using the equation 2^−dCT, where dC_T=(C_TmiRNA−C_{TU6 or HUMTMI RNA}). The miRNAs analyzed included miR-15a, miR-164, miR-18, miR-20, miR-21, miR-28-2, miR-30d, miR-93-1, miR-105, miR-124a-2, miR-147, miR-216, miR-219, and miR-224. The primers used were as published (Schmittgen, T. D., Jiang, J., Liu, Q. & Yang, L. (2004) Nucleic Acid Research 32, 43-53).

Microarray Data Submission. All data were submitted using MIAMExpress to Array Express database and each of the 44 samples described here received an ID number ranging from SAMPLE169150SUB621 to SAMPLE 169193SIUB621.

Results

Hybridization Sensitivity. The hybridization sensitivity of the miRNA microarray was tested using various quantities of total RNA from HeLa cells, starting from 2.5 μg up to 20 μg. The coefficients of correlation between the 5 μg experiment versus the 2.5, 10 and 20 μg experiments, were 0.98, 0.99 and 0.97 respectively. These results clearly show high inter-assay reproducibility, even in the presence of large differences in RNA quantities. In addition, standard deviation calculated for miRNA triplicates was below 10% for the vast majority (>95%) of oligonucleotides. All other experiments described here were performed with 5 μg of total RNA.

Microarray specificity. To test the specificity of the microchip, miRNA expression in human blood leukocytes from three healthy donors and 2 samples of mouse macrophages was analyzed. Samples derived from the same type of tissue presented homogenous patterns of miRNA expression. Furthermore, the pattern of hybridization is different for the two species. To confirm microarray results, the same RNA samples from mouse macrophages and HeLa cells were also analyzed by quantitative RT-PCR for a randomly selected set of 14 miRNAs (Schmittgen, T. D., Jiang, J., Liu, Q. & Yang, L. (2004) Nucleic Acid Research 32, 43-53). When we were able to amplify a miRNA precursor for which a correspondent oligonucleotide was present on the chip (hsa-miR-15a, hsa-mir-30d, mmu-miR-219 and mmu-miR-224) the concordance between the two techniques was 100%. Furthermore, it has been reported that expression levels of the active miRNA and the precursor pre-miRNA are different in the same sample (Calin, et al. (2002) Proc Natl Acad Sc USA. 99, 15524-15529; Mourelatos, et al. (2002) Genes Dev 16, 720-728; Lagos-Quintana, et al. (2002) Curr Biol 12, 735-739); in fact, for another 10 miRNAs for which only the oligonucleotide corresponding to the active version was present on the chip, no concordance with quantitative real-time PCR results was observed for the precursor.

The stringency of hybridization was, in several instances, sufficient to distinguish nucleotide mismatches for members of closely related miRNA families and very similar sequences gave distinct expression profiles (for example let-7a-1 and let-7f-2 which are 89% similar in an 88 nucleotide sequence). Therefore, each quantified result represents the specific expression of a single miRNA member and not the combined expression of the entire family. In other cases, when a portion of oligonucleotide was 100% identical for two probes (for example, the 23mer of active molecule present in the 40-mer oligonucleotides for both mir-16 sequences from chromosome 13 and chromosome 3), very similar profiles were observed. Therefore, both sequence similarity and secondary structure influence the cross-hybridization between different molecules on this type of microarray.

miRNA Expression in Normal Human Tissues. To further validate reliability of the microarray, we analyzed a panel of 20 RNAs from human normal tissues, including 18 of adult origin (7 hematopoietic and 11 solid tissues) and 2 of fetal origin (fetal liver and brain). For 15 of them, at least two different RNA samples or two replicates from the same preparation were used (for a detailed list of samples see the above Methods). The results demonstrated that different tissues have distinctive patterns of miRNome expression (defined as the full complement of miRNAs in a cell) with each tissue presenting a specific signature. Using unsupervised hierarchical clustering, the same types of tissue from different individuals clustered together. The hematopoietic tissues presented two distinct clusters, the first one containing CD5+ cells, T lymphocytes, and leukocytes and the second cluster containing bone marrow, fetal liver and B lymphocytes. Of note, RNA of fetal or adult type from the same tissue origin (brain) present different miRNA expression pattern. The results demonstrated that some miRNAs are highly expressed in only one or few tissues, such as miR-1b-2 or miR-99b in brain, and the closely related members miR-133a and miR-133b in skeletal muscle, heart and prostate. The types of normalization of the GeneSpring software (on 50% with or without a per-gene on median normalization) did not influence these results.

To verify these data, Northern blot analysis was performed on total RNA used in the microarray experiments, using four miRNA probes: miR-16-1, miR-26a, miR-99a and miR-223. In each case, the concordance between the two techniques was high: in all instances the highest and the lowest expression levels were concordant. For example high levels of miR-223 expression were found by both techniques in spleen, for miR-16-1 in CD5+ cells, while very low levels were found in brain for both miRNAs. Moreover, in several instances (for example miR-15a), we were able to identify the same pattern of expression for the precursor and the active miR with both microchip and Northern blots.

We also compared the published expression data for cloned human and mouse miRNAs by Northern blot analyses against the microarray results. We found that the concordance with the chip data is high for both pattern and intensity of expression. For example, miR-133 was reported to be strongly expressed only in the skeletal muscle and heart (Sempere, et al. (2003) Genome Biol. 5, R13), precisely as was found with the microarray, while miR-125 and mir-128 were reported to be highly expressed in brain (Sempere, et al. (2003) Genome Biol. 5, R13), a finding confirmed on the microchip.

Example 11
miRNA Profiling of B-Cell Chronic Lymphocytic Leukemia Samples
Introduction

The miRNome expression in 38 individual human B-cell chronic lymphocytic leukemia (CLL) cell samples was determined utilizing the microchip of Example 10. One normal lymph node sample and 5 samples from healthy donors, including two tonsillar CD5+ B lymphocyte samples and three blood mononuclear cell (MNC) samples, were included for comparison. As hereinafter demonstrated, two distinct clusters of CLL samples associated with the presence or the absence of Zap-70 expression, a predictor of early disease progression. Two miRNA signatures were associated with presence or absence of mutations in the expressed immunoglobulin variable-region genes or with deletions at 13q14 respectively.

Materials and Methods

The following methods were employed in the miRNome expression study.

Tissue Samples and CLL Samples. 47 samples were used for this study, including 41 samples from 38 patients with CLL, and 6 normal samples, including one lymph node, tonsillar CD5+ B cells from two normal donors and blood mononuclear cells from three normal donors. For three cases, two independent samples were collected and processed. CLL samples were obtained after informed consent from patients diagnosed with CLL at the CLL Research Consortium institutions. Briefly, blood was obtained from CLL patients, mononuclear cells were isolated through Ficoll/Hypaque gradient centrifugation (Amersham Pharmacia Biotech) and processed for RNA extraction according to described protocols (M. Lagos-Quintana, R. Rauhut, W. Lendeckel, T. Tuschl, Science 294, 853-858 (2001)). For the majority of samples clinical and biological information, such as age at diagnosis, sex, Rai stage, presence/absence of treatment, ZAP-70 expression, IgV_Hgene mutation status were available, as provided in Table 9:

TABLE 9

Clinical and biological data for the

patients in the two CLL clusters*

Semnification
Dx Age
Sex
% Zap
VH gene
Mut

CLL cluster 1
50.68
F
30.4
VH4-04
Neg

CLL cluster 1
57.4
F
50.6
VH3-33
Pos

CLL cluster 1
67.49
M
0.5
VH3-23
Pos

CLL cluster 1
59.74
M
31.5
VH3-09
Pos

CLL cluster 1
77.49
F
0.3
VH5-51
Pos

CLL cluster 1
58.19
F
3.6
VH3-30/3-30.5
Pos

CLL cluster 1
43
M
41.9
VH4-30.1/4-31
Neg

CLL cluster 1
61.82
M
83.2
VH1-03
Neg

CLL cluster 1
48.44
F
69.3
VH1-69
Neg

CLL cluster 2
72.59
M
2.2
VH3-72
Pos

CLL cluster 2
45.19
M
7.3
VH1-69
Pos

CLL cluster 2
56.39
F
0.6
VH3-15
Pos

CLL cluster 2
61.85
F
0.1
VH3-30
Neg

CLL cluster 2
60.89
F
0.1
VH2-05
Pos

CLL cluster 2
62.66
M
1
VH3-07
Pos

CLL cluster 2
49.85
M
3.6
VH3-74
Pos

CLL cluster 2
70.62
M
0.2
VH3-13
Pos

CLL cluster 2
68.02
F
0.9
VH3-30.3
Pos

CLL cluster 2
46.84
M
62.2
VH3-30/3-30.5
Neg

CLL cluster 2
51.31
F
91.9
VH4-59
Neg

CLL cluster 2
52.6
F
10.6
VH3-07
Pos

CLL cluster 2
56.04
F
0.4
VH3-72
Pos

CLL cluster 2
61.67
M
77.9
VH3-74
Neg

CLL cluster 2
62.14
F
46
VH1-02
Pos

CLL cluster 2
39.29
F
10.1
VH3-07
Neg

*data for ZAP-70 expression were available for 25 patients (25/38, 66%).

Cell Preparation. Mononuclear cells (MNC) from peripheral blood of normal donors were separated by Ficoll-Hypaque density gradients. T cells were purified from these MNC by rosetting with neuraminidase-treated sheep red blood cells (SRBC) and depletion of contaminant monocytes (Cd11b+), natural killer cells (CD16+) and B lymphocytes (CD19+) were purified using magnetic beads (Dynabeads, Unipath, Milano, Italy) and specific monoclonal antibodies (Becton Dickinson, San Jose, Calif.). Total B cells and CD5+ B cells were prepared from tonsillar lymphocytes as described (M. Dono et al., J. Immunol 164, 5596-604. (2000)). Briefly, tonsils were obtained from patients in the pediatric age group undergoing routine tonsillectomies, after informed consent. Purified B cells were prepared by rosetting T cells from MNC cells with neuraminidase treated SRBC. In order to obtain CD5+ B cells, purified B cells were incubated with anti CD5 monoclonal antibody followed by goat anti mouse Ig conjugated with magnetic microbeads. CD5+ B cells were positively selected by collecting the cells retained on the magnetic column MS by Mini MACS system (Miltenyi Biotec, Auburn, Calif.). The degree of purification of the cell preparations was higher than 95%, as assessed by flow cytometry.

RNA Extraction and Northern Blots. Total RNA isolation and blots were performed as described (G. A. Calin et al., Proc Natl Acad Sc USA. 99, 15524-15529 (2002)). After RNA isolation, the washing step with ethanol was not performed, or if performed, the tube walls were rinsed with 75% ethanol without perturbing the RNA pellet (M. Lagos-Quintana, R. Rauhut, W. Lendeckel, T. Tuschl, Science 294, 853-858 (2001)). For reuse, blots were stripped by boiling in 0.1% aqueous SDS/0.1×SSC for 10 minutes, and were reprobed. 5S rRNA stained with ethidium bromide served as a sample loading control.

Microarray Experiments. RNA blot analysis was performed as described in Example 10, utilizing the microchip of Example 10. Briefly, labeled targets from 5 μg of total RNA was used for hybridization on each miRNA microarray chip containing 368 probes in triplicate, corresponding to 245 human and mouse miRNA genes. The microarrays were hybridized in 6×SSPE/30% formamide at 25° C. for 18 hrs, washed in 0.75×TNT at 37° C. for 40 min, and processed using a method of direct detection of the biotin-containing transcripts by Streptavidin-Alexa647 conjugate. Processed slides were scanned using a Perkin Elmer ScanArray® XL5K Scanner, with the laser set to 635 nm, at Power 80 and PMT 70 setting, and a scan resolution of 10 microns.

Data Analysis. Expression profiles were analyzed in duplicate independent experiments starting from the same cell sample. Raw data were normalized and analyzed in GeneSpring® software version 6.1.1 (Silicon Genetics, Redwood City, Calif.). GeneSpring generated an average value of the three spot replicates of each miRNA. Following data transformation (to convert any negative value to 0.01), normalization was performed by using a per-chip on median normalization method and a normalization to specific samples, expressly to the two CD5+ B cell samples, used as common reference for miRNA expression. Hierarchical clustering for both genes and conditions were generated by using standard correlation as a measure of similarity. To identify genes with statistically significant differences between sample groups (i.e. CLL cells and CD5+ B cells, CLL and MNC, CLL samples with or without IgV_Hmutations or CLL cases with or without 13q14.3 deletion), a Welch's approximate t-test for two groups (variances not assumed equal) with a p-value cutoff of 0.05 and Benjamini and Hochberg False Discovery Rate as multiple testing correction were performed.

Real Time PCR. Quantitative real-time PCR was performed as described by T. D. Schmittgen, J. Jiang, Q. Liu, L. Yang, Nucleic Acid Research 32, 43-53 (2004). Briefly, RNA was reverse transcribed to cDNA with gene-specific primers and Thermoscript and the relative amount of each miRNA to tRNA for initiator methionine was described, using the equation 2^−dCT, where dC_T=(C_TmiRNA−C_{TU6 or HUMTMI RNA}). The set of analyzed miRNAs included miR-15a, miR-16-1, miR-18, miR-20, and miR-21. The primers used were as published (Id.).

Western Blotting. Protein lysates were prepared from the leukemia cells of 7 CLL patients and from isolated tonsillar CD5+ B cells. Western blot analysis was performed with a polyclonal Pten antibody (Cell Signaling Technology, Beverly, Mass.) and was normalized using an anti-actin antibody (Sigma, St. Louis, Mo.).

Microarray Data Submission. All data were submitted using MIAMExpress to the Array Express database and each of the 39 CLL samples described here received an ID number ranging from SAMPLE 169194SUB621 to SAMPLE 169234SIUB621.

Results

Comparison of miRNA expression in CLL cells vs. normal CD5+ B cells and normal blood mononuclear cells. Normal CD5+ B cells utilized in this study are considered as normal cell counterparts to CLL B cells. As described in Table 10, two groups of differentially expressed miRNAs, the first composed of 5S genes and the second of 29 genes, had statistically significant differences in expression levels between the various groups (p<0.05 using Welch t-test as described in Materials and Methods, above). Only 6 miRNA are shared between the two lists, confirming the results of Example 10 showing distinct miRNome signatures in CD5⁺ B cells and leukocytes. When both pre-miRNA and mature miRNA were observed to be dysregulated (such as for miR-123, miR-132 or miR-136), the same type of variation in CLL samples with respect to CD5 or MNC was noted in every case. Also, for some miRNA genomic clusters all members were aberrantly regulated (such as the up-regulated 7q32 group encompassing miR-96-miR182-miR183), while for others only some members were abnormally expressed (such as the 13q31 genomic cluster where two out of six members, miR-19 and miR-92-1, were strongly up-regulated and two, miR-17 and miR-20, were moderately down-regulated). Without wishing to be bound by any theory, the results illustrate the complexity of the patterns of miRNA expression in CLL and indicate the existence of mechanisms regulating individual miRNA genes that map in the same chromosome region. In confirmation of the accuracy of the data, miR-223, reported to be expressed at high levels in granulocytes (M. Lagos-Quintana et al., Curr Biol 12, 735-739 (2002)), was expressed at significantly lower levels in the CLL samples than in the MNC, but at about the same level as that noted for CD5⁺ B cells (which generally constitute less than a few percent of blood MNC).

TABLE 10

Differentially expressed miRNAs in CLLs versus CD5+ cells or CLLs versus MNC (bold) *

Chr
FRA

Oligonucleotide probe
microRNA
location
associated
P-value
Type

hsa-let-7a-2-precNo1

let-7a-2
11q24.1

0.014

Down

hsa-let-7d-v2-precNo2
let-7d-v2-prec
12q14.1

4.29E−04
Down

hsa-let-7f-1-precNo1

let-7f-1
09q22.2
FRA9D

3.09E−29

Down

hsa-mir-009-2No1
miR-9-2
5q14

0.013
up

hsa-mir-010a-precNo2
miR-10a-prec
17q21.3

0.007
up

hsa-mir-010b-precNo1
mir-10b
02q31

1.10E−15
up

hsa-mir-015b-precNo2
mir-15b-prec
03q26.1

5.79E−14
up

hsa-mir-017-precNo2
mir-17-prec
13q31

0.042

Down

hsa-mir-017-precNo2
mir-17-prec
13q31

0.049
Down

hsa-mir-019a-prec
mir-19a
13q31

5.16E−17
up

hsa-mir-020-prec
mir-20a
13q31

0.038
Down

hsa-mir-021-prec-17No2
mir-21-prec
17q23.2
FRA17B
0.044
up

hsa-mir-022-prec
mir-22
17p13.3

7.16E−04
up

hsa-mir-023a-prec
mir-23a
19p13.2

0.011
Down

hsa-mir-024-1-precNo1

mir-24-1
09q22.1
FRA9D

0.002

Down

hsa-mir-024-1-precNo2
mir-24-1-prec
09q22.1
FRA9D
7.35E−20
up

hsa-mir-024-2-prec

mir-24-2
19p13.2

5.69E−17

Down

hsa-mir-025-prec
mir-25
07q22
FRA7F
9.52E−04
Down

hsa-mir-027b-prec

mir-27b
09q22.1

FRA9D

0.046

Down

hsa-mir-029a-2No1

mir-29a-2
07q32
FRA7H

0.013

up

hsa-mir-029a-2No2
mir-29a-2-prec
07q32
FRA7H
0.001
up

hsa-mir-029c-prec

mir-29c
01q32.2-32.3

0.002

up

hsa-mir-030a-precNo1

mir-30a
06q12-13

0.004

Down

hsa-mir-030a-precNo2
mir-30a-prec
06q12-13

0.034
Down

hsa-mir-030d-precNo2
mir-30d-prec
08q24.2

0.008
Down

hsa-mir-033-prec
mir-33
22q13.2

1.56E−18
up

hsa-mir-034precNo1
mir-34
01p36.22

6.00E−06
up

hsa-mir-092-prec-13 = 092-1No1
mir-92-1
13q31

1.70E−12
up

hsa-mir-092-prec-13 = 092-1No2

mir-92-prec
13q31

0.021

Down

hsa-mir-092-prec-X = 092-2

mir-92-2
Xq26.2

3.38E−04

Down

hsa-mir-092-prec-X = 092-2
mir-92-2
Xq26.2

0.042
Down

hsa-mir-096-prec-7No1
mir-96
07q32
FRA7H
1.79E−04
up

hsa-mir-099-prec-21

mir-99
21q11.2

0.001

Down

hsa-mir-101-1/2-precNo1
mir-101
01p31.3
FRA1C
1.26E−08
up

hsa-mir-101-1/2-precNo2
mir-101-prec
01p31.3

0.017
up

hsa-mir-103-prec-5 = 103-1
mir-103-1
05q35.1

0.002
Down

hsa-mir-103-prec-5 = 103-1

mir-103-1
05q35.1

0.007

Down

hsa-mir-105-prec-X.1 = 105-1
mir-105-1
Xq28
FRAXF
1.55E−05
up

hsa-mir-107-prec-10

mir-107
10q23.31

0.002

Down

hsa-mir-123-precNo1
mir-123
09q34

2.80E−16
up

hsa-mir-123-precNo1

mir-123
09q34

0.021

Down

hsa-mir-123-precNo2

mir-123-prec
09q34

0.021

Down

hsa-mir-124a-2-prec
mir-124a-2
08q12.2

4.33E−06
up

hsa-mir-128b-precNo1

mir-128b
03p22

5.05E−07

Down

hsa-mir-128b-precNo2
mir-128-prec
03p22

0.007
up

hsa-mir-130a-precNo2

mir-130a-prec
11q12

0.010

Down

hsa-mir-130a-precNo2
mir-130a-prec
1q12

0.050
up

hsa-mir-132-precNo1
mir-132
11q12

1.68E−07
up

hsa-mir-132-precNo2
mir-132-prec
17p13.3

8.62E−04
up

hsa-mir-134-precNo1
mir-134
14q32

6.01E−08
up

hsa-mir-136-precNo1
mir-136
14q32

0.003
up

hsa-mir-136-precNo2
mir-136-prec
14q32

7.44E−04
up

hsa-mir-137-prec
mir-137
01p21-22

0.013
up

hsa-mir-138-1-prec

mir-138-1
03p21

2.53E−04

up

hsa-mir-140No1
mir-140
16q22.1

2.41E−16
up

hsa-mir-141-precNo1
mir-141
12p13

7.91E−08
up

hsa-mir-141-precNo2
mir-141-prec
12p13

1.39E−08
up

hsa-mir-142-prec
mir-142
17q23
FRA17B
0.004
Down

hsa-mir-145-prec
mir-145
05q32-33

0.021
Down

hsa-mir-146-prec

mir-146
05q34

1.03E−08

Down

hsa-mir-148-prec
mir-148
07p15

3.48E−05
up

hsa-mir-152-precNo1
mir-152
17q21

0.003
up

hsa-mir-152-precNo2
mir-152-prec
17q21

3.35E−05
up

hsa-mir-153-1-prec1
mir-153
02q36

0.005
up

hsa-mir-153-1-prec2
mir-153-prec
02q36

1.48E−08
up

hsa-mir-154-prec1No1
mir-154
14q32

1.14E−10
up

hsa-mir-155-prec

mir-155
21q21

0.029

up

hsa-mir-181b-precNo2
mir-181b-prec
01q31.2-q32.1

3.26E−06
up

hsa-mir-181c-precNo2

mir-181c-prec
19p13.3

0.003

up

hsa-mir-182-precNo2
mir-182-prec
07q32
FRA7H
0.001
up

hsa-mir-183-precNo2
mir-183-prec
07q32
FRA7H
1.26E−23
up

hsa-mir-184-precNo1
mir-184
15q24

0.007
up

hsa-mir-188-prec
mir-188
Xp11.23-p1.2

6.08E−11
up

hsa-mir-190-prec
mir-190
15q21
FRA15A
1.48E−20
up

hsa-mir-191-prec

mir-191
03p21

9.14E−05

Down

hsa-mir-192-2/3No1
mir-192
11q13

2.00E−07
Down

hsa-mir-193-precNo2

mir-193-prec
17q1.2

9.14E−05

up

hsa-mir-194-precNo1

mir-194
01q41
FRA1H

0.002

up

hsa-mir-196-2-precNo1
mir-196-2
12q13
FRA12A
4.94E−08
up

hsa-mir-196-2-precNo2
mir-196-2-prec
12q13
FRA12A
0.040
up

hsa-mir-197-prec
mir-197
01p13

0.003
Down

hsa-mir-200a-prec

mir-200a
01p36.3

9.14E−05

up

hsa-mir-204-precNo2

mir-204-prec
09q21.1

8.55E−04

up

hsa-mir-206-precNo1
mir-206
06p12

0.003
Down

hsa-mir-210-prec
mir-210
11p15

0.009
Down

hsa-mir-212-precNo1
mir-212
17p13.3

0.045
Down

hsa-mir-213-precNo1
mir-213
01q31.3-q32.1

1.47E−33
Down

hsa-mir-217-precNo2
mir-217
02p16

3.85E−09
up

hsa-mir-220-prec
mir-220
Xq25

2.14E−09
Down

hsa-mir-220-prec

mir-220
Xq25

3.16E−05

Down

hsa-mir-221-prec

mir-221
Xp11.3

1.39E−05

Down

hsa-mir-223-prec

mir-223
Xq12-13.3

9.04E−04

Down

* The correlation with fragile sites (FRA) location is as published in Calin et al., Proc Natl Acad Sci USA. 101, 2999-3004 (2004).

As indicated in the CLL vs. CD5+ B cell list of Table 10, several miRNAs located exactly inside fragile sites (miR-183 at FRA7H, miR-190 at FRA15A and miR-24-1 at FRA9D) and miR-213. The mature miR-213 molecule is expressed at lower levels in all the CLL samples, and the precursor miR-213 is reduced in expression in 62.5% of the samples. miR-16-1, at 13q14.3, which we previously reported to be down-regulated in the majority of CLL cases by microarray analysis (G. A. Calin et al., Proc Natl Acad Sc USA. 99, 15524-15529 (2002), was expressed at low levels in 45% of CLL samples. An identical mature miR-16 exists on chromosome 3; because the 40-mer oligonucleotide for both miR-16 sequences from chromosome 13 (miR-16-1) and chromosome 3 (miR-16-2) exhibit the same 23-mer mature sequence, very similar profiles were observed. However, since we observed very low levels of miR-16-2 expression in CLL samples by Northern blot, the expression observed is mainly contributed by miR-16-1. The other miRNA of 13q14.3, miR-15a, was expressed at low levels in ˜25% of CLL cases. Overall, these data demonstrate that CLL is a malignancy with extensive alterations of miRNA expression.

Validation of the microarray data was supplied for four miRNAs by Northern blot analyses: miR-16-1, located within the region of deletion at 13q14.3, miR-26a, on chromosome 3 in a region not involved in the pathogeneses of CLL, and miR-206 and miR-223 that are down-regulated (see above) in the majority of samples. For all four miRNAs, the Northern blot analyses confirmed the data obtained using the microarray. We also performed real-time PCR to measure expression levels of precursor molecules for five genes (miR-15a, miR-16-1, miR-18, miR-21, and miR-30d) and we found results concordant with the chip data.

Unsupervised hierarchical clustering generated two clearly distinguishable miRNA signatures within the set of CLL samples, one closer to the miRNA expression profile observed in human leukocytes and the other clearly different (FIG. 3). A list of the microRNAs differentially expressed between the two main CLL clusters is given in Table 11. The name of each miRNA is as in the miRNA Registry. The disregulation of either active molecule or precursor is specified in the name. The location in minimally deleted or minimally amplified or breakpoint regions or in fragile sites is presented. The top 25 differentially expressed miRNA in these two signatures (at p<0.001) include genes known or suggested to be involved in cancer. The precursor of miR-155 is over-expressed in the majority of childhood Burkitt's lymphoma (M. Metzler, M. Wilda, K. Busch, S. Viehmann, A. Borkhardt, Genes Chromosomes Cancer. 39, 167-9. (2004)), miR-21 is located at the fragile site FRA17B (G. A. Calin et al., Proc Natl Acad Sci US A. 101, 2999-3004. (2004)), miR-26a is at 3p21.3, a region frequently deleted region in epithelial cancers, while miR-92-1 and miR-17 are at 13q32, a region amplified in follicular lymphoma (Id.).

TABLE 11

microRNAs differentially expressed between the two main CLL clusters*.

Oligonucleotide
miRNA
Chr location
P-value
Cancer-associated genomic regions

hsa-miR-017-precNo2
miR-17-prec
13q31
0.00000000
Amp - Folicular Ly/Del - HCC

hsa-miR-020-prec
miR-20
13q31
0.00000000
Amp - Folicular Ly/Del - HCC

hsa-miR-103-2-prec
miR-103-2
20p13
0.00000001

hsa-miR-030d-precNo2
miR-30d-prec
08q24.2
0.00000002

hsa-miR-106-prec-X
miR-106
Xq26.2
0.00000006
Del - advanced ovarian ca.

hsa-miR-026b-prec
miR-26b
02q35
0.00000006

hsa-miR-103-prec-5 = 103-1
miR-103-1
05q35.1
0.00000006

hsa-miR-025-prec
miR-25
07q22
0.00000007
FRA7F

hsa-miR-030a-precNo1
miR-30a
06q12-13
0.00000008

hsa-miR-021-prec-17No1
miR-21
17q23.2
0.00000008
Amp - Neuroblastoma; FRA17B

hsa-miR-107-prec-10
miR-107
10q23.31
0.00000008

hsa-miR-092-prec-13 = 092-1No2
miR-92-1-prec
13q31
0.00000024
Amp - Follicular Ly.

hsa-miR-027a-prec
miR-27a
19p13.2
0.00000024

hsa-miR-023a-prec
miR-23a
19p13.2
0.00000032

hsa-miR-092-prec-X = 092-2
miR-92-2
Xq26.2
0.00000040
Del - Advanced Ovarian ca.

hsa-miR-030b-precNo1
miR-30b
08q24.2
0.000004

hsa-miR-026a-precNo1
miR-26a
03p21
0.000009
Del - Epithelial malignancies

hsa-miR-093-prec-7.1 = 093-1
miR-93-1
07q22
0.000009
Amp - Folicular Ly/Del - HCC; FRA7F

hsa-miR-194-precNo1
miR-194
01q41
0.000015
FRA1H

hsa-miR-155-prec
miR-155
21q21
0.000028
Amp - Colon ca; Childhood Burkit Ly

hsa-miR-153-2-prec
miR-153-2
07q36
0.000028
t(7; 12)(q36; p13) - Acute Myeloid Leukemia

hsa-miR-193-precNo2
miR-193-prec
17q11.2
0.000044
Del - Ovarian ca.

hsa-miR-130a-precNo1
miR-130a
11q12
0.0001

hsa-miR-023b-prec
miR-23b
09q22.1
0.0001
Del - Urothelial Ca.; FRA9D

hsa-miR-030c-prec
miR-30c
06q13
0.0001

hsa-miR-139-prec
miR-139
11q13
0.0001

hsa-miR-144-precNo2
miR-144-prec
17q1.2
0.0001
Amp - Primary Breast ca.

hsa-miR-29b-2 = 102prec7.1 = 7.2
miR-29b-2
07q32
0.0002
Del - Prostate ca agressiveness; FRA7H

hsa-miR-125a-precNo2
miR-125a-prec
19q13.4
0.0002

hsa-miR-224-prec
miR-224
Xq28
0.0002

hsa-miR-211-precNo1
miR-211
Xp11.3
0.0002
Del - Malignant Mesothelioma.

hsa-miR-221-prec
miR-221
Xp11.3
0.0002

hsa-miR-191-prec
miR-191
03p21
0.0002

hsa-miR-018-prec
miR-18
13q31
0.0003
Amp - Follicular Lymphoma

hsa-miR-203-precNo2
miR-203-prec
14q32.33
0.0004
Del - Nasopharyngeal ca.

hsa-miR-217-precNo2
miR-217-prec
02p16
0.0004

hsa-miR-204-precNo2
miR-204-prec
09q21.1
0.0004

hsa-miR-199a-1-prec
miR-199a-1
19p13.2
0.0005

hsa-miR-128b-precNo1
miR-128b
03p22
0.0005

hsa-miR-102-prec-1
miR-102
01q32.2-32.3
0.0005
Del - Prostate ca agressiveness

hsa-miR-140No2
miR-140-prec
16q22.1
0.0006

hsa-miR-199a-2-prec
miR-199a-2
01q23.3
0.0007

hsa-miR-010b-precNo2
miR-10b-prec
02q31
0.0008

hsa-miR-029a-2No1
miR-29a-2
07q32
0.0008
Del - Prostate ca agressiveness; FRA7H

hsa-miR-125a-precNo1
miR-125a
19q13.4
0.0010

hsa-miR-204-precNo1
miR-204
09q21.1
0.0011

hsa-miR-181a-precNo1
miR-181a
09q33.1-34.13
0.0014
Del - Bladder ca

hsa-miR-188-prec
miR-188
Xp11.23-p11.2
0.0014

hsa-miR-200a-prec
miR-200a
01p36.3
0.0014

hsa-miR-024-2-prec
miR-24-2
19p13.2
0.0014

hsa-miR-134-precNo2
miR-134-prec
14q32
0.0016
Del - Nasopharyngeal ca.

hsa-miR-010a-precNo2
miR-10a-prec
17q21.3
0.0018

hsa-miR-029c-prec
miR-29c
01q32.2-32.3
0.0021

hsa-miR-010a-precNo1
miR-10a
17q21.3
0.0022

hsa-let-7d-v2-precNo1
let-7d-v2
12q14.1
0.0022
Del - Urothelial carc; FRA9D

hsa-miR-205-prec
miR-205
01q32.2
0.0023

hsa-miR-129-precNo1
miR-129
07q32
0.0023
Del - Prostate ca agressiveness

hsa-miR-032-precNo2
miR-32-prec
09q31.2
0.0026
Del - Lung ca.; FRA9E

hsa-miR-187-precNo2
miR-187-prec
18q12.1
0.0035

hsa-miR-125b-2-precNo1
miR-125b-2
21q11.2
0.0036
Del - Lung ca. (MA17)

hsa-miR-181c-precNo1
miR-181c
19p13.3
0.0036

hsa-miR-132-precNo2
miR-132-prec
17p13.3
0.0036
Del - HCC

hsa-miR-215-precNo1
miR-215
01q41
0.0036
FRA1H

hsa-miR-136-precNo1
miR-136
14q32
0.0036
Del - Nasopharyngeal ca.

hsa-miR-030a-precNo2
miR-30a-prec
06q12-13
0.0040

hsa-miR-100-1/2-prec
miR-100
11q24.1
0.0040
Del - 0varian Ca.; FRA11B

hsa-miR-218-2-precNo1
miR-218-2
05q35.1
0.0040

hsa-miR-193-precNo1
miR-193
17q1.2
0.0052
Del - Ovarian ca.

hsa-miR-027b-prec
miR-27b
09q22.1
0.0058
Del - Bladder ca; FRA9D

hsa-miR-220-prec
miR-220
Xq25
0.0065

hsa-miR-024-1-precNo1
miR-24-1
09q22.1
0.0065
Del - Urothelial ca.

hsa-miR-019a-prec
miR-19a
13q31
0.0071
Amp - Follicular Ly

hsa-miR-196-2-precNo1
miR-196-2
12q13
0.0082
FRA12A

hsa-miR-022-prec
miR-22
17p13.3
0.0086
Del - HCC

hsa-miR-183-precNo2
miR-183-prec
07q32
0.0086
Del - Prostate ca agressiveness; FRA7H

hsa-miR-128a-precNo2
miR-128a-prec
02q21
0.0105
Del - Gastric Ca

hsa-miR-203-precNo1
miR-203
14q32.33
0.0109
Del - Nasopharyngeal ca.

hsa-miR-033b-prec
miR-33b
17p1.2
0.0109
Amp - Breast ca.

hsa-miR-030d-precNo1
miR-30d
08q24.2
0.0111

hsa-miR-133a-1
miR-133a-1
18q11.1
0.0119

hsa-miR-007-3-precNo2
miR-7-3-prec
22q13.3
0.0128

hsa-miR-021-prec-17No2
miR-21-prec
17q23.2
0.0131
Amp - Neuroblastoma

hsa-miR-208-prec
miR-208
14q11.2
0.0134
Del - Malignant Mesothelioma

hsa-miR-154-prec1No2
miR-154-prec
14q32
0.0146
Del - Nasopharyngeal ca.

hsa-miR-141-precNo2
miR-141-prec
12p13
0.0154

hsa-miR-024-1-precNo2
miR-024-1-prec
09q22.1
0.0169
Del - Urothelial carc; FRA9D

hsa-miR-128a-precNo1
miR-128a
02q21
0.0170
Del - Gastric Ca

hsa-miR-184-precNo2
miR-184-prec
15q24
0.0219

hsa-miR-019b-2-prec
miR-19b-2
13q31
0.0302

hsa-miR-132-precNo1
miR-132
17p13.3
0.0303
Del - Hepatocellular ca. (HCC)

hsa-miR-127-prec
miR-127
14q32
0.0326
Del - Nasopharyngeal ca.

hsa-miR-202-prec
miR-202
10q26.3
0.0333

hsa-let-7g-precNo2
let-7g-prec
03p21.3
0.0350
Del - Lung Ca., Breast Ca.

hsa-miR-222-precNo1
miR-222
Xp11.3
0.0351

hsa-miR-009-1No2
miR-009-1-prec
05q14
0.0382

hsa-miR-136-precNo2
miR-136-prec
14q32
0.0391
Del - Nasopharyngeal ca.

hsa-miR-010b-precNo1
miR-10b
02q31
0.0403

hsa-miR-223-prec
miR-223
Xq12-13.3
0.0407

*The location in minimally deleted or minimally amplified or breakpoint regions or in fragile sites is presented.

HCC—Hepatocellular ca.; AML—acute myeloid leukemia.

The two clusters may be distinguished by at least one clinico-biological factor. A high difference in the levels of ZAP-70 characterized the two groups: 66% (6/9) patients from the first cluster vs. 25% (4/16) patients from the second one have low levels of ZAP-70 (<20%) (P=0.04 at chi test) (Table 9). The mean value of ZAP-70 was 19% (±31% S.D.) vs. 35% (±30% S.D.), respectively or otherwise the two clusters can discriminate between patients who express and who do not express this protein (at levels <20% ZAP-70 is considered as non-expressed) (Table 9). ZAP-70 is a tyrosine kinase, which is a strong predictor of early disease progression, and low levels of expression are proved to be a finding associated with good prognosis (J. A. Orchard et al., Lancet 363, 105-11 (2004)).

The microarray data revealed specific molecular signatures predictive for subsets of CLL that differ in clinical behavior. CLL cases harbor deletions at chromosome 13q14.3 in approximately 50% of cases (F. Bullrich, C. M. Croce, Chronic Lymphoid leukemia. B. D. Chenson, Ed. (Dekker, New York, 2001)). As a single cytogenetic defect, these CLL patients have a relatively good prognosis, compared with patients with leukemia cells harboring complex cytogenetic changes (H. Dohner et al., N Engl J Med. 343, 1910-6. (2000)). It was also shown that deletion at 13q14.3 was associated with the presence of mutated immunoglobulin V_H(IgV_H) genes (D. G. Oscier et al., Blood. 100, 1177-84 (2002)), another good prognostic factor. By comparing expression data of CLL samples with or without deletions at 13q14, we found that miR-16-1 was expressed at low levels in leukemias harboring deletions at 13q14 (p=0.03, ANOVA test). We also found that miR-24-2, miR-195, miR-203, miR-220 and miR-221 are expressed at significantly reduced levels, while miR-7-1, miR-19a, miR-136, miR-154, miR-217 and the precursor of miR-218-2 are expressed at significantly higher levels in the samples with 13q14.3 deletions, respectively (Table 12). All these genes are located in different regions of the genome and differ in their nucleotide sequences, excluding the possibility of cross-hybridization. Without wishing to be bound by any theory, these results suggest the existence of functional miRNA networks in which hierarchical regulation may be present, with some miRNA (such as miR-16-1) controlling or influencing the expression of other miRNA

TABLE 12

microRNAs signatures associated with prognosis in B-CLL ¹.

Chr.

miRNA
location
P-value
Association
Observation

miR-7-1
9q21.33
0.030
13q14 normal

miR-16-1
13q14.3
0.030
IGVH mutations negative

0.023
13q14 deleted

miR-19a
13q31
0.024
13q14 normal

miR-24-2
19p13.2
0.033
13q14 deleted

miR-29c
1q32.2-32.3
0.018
IGVH mutations positive
cluster miR-29c-miR 102

miR-102
1q32.2-32.3
0.023
IGVH mutations positive
cluster miR-29c-miR 102

miR-132
17p13.3
0.033
IGVH mutations negative

miR-136
14q32
0.045
13q14 normal

miR-154
14q32
0.020
13q14 normal

miR-186
1p31
0.038
IGVH mutations negative

mir-195
17p13
0.036
13q14 deleted

miR-203
14q32.33
0.026
13q14 deleted

miR-217-prec
2p16
0.005
13q14 normal

miR-218-2
5q35.1
0.019
13q14 normal

miR-220
Xq25
0.026
13q14 deleted

miR-221
Xp11.3
0.021
13q14 deleted

¹The name of each miRNA is as in miRNA Registry and the disregulation of either active molecule or precursor is specified in the name.

The expression of mutated IgV_His a favorable prognostic marker (D. G. Oscier et al., Blood. 100, 1177-84 (2002)). We found a distinct miRNA signature composed of 5 differentially expressed genes (miR-186, miR-132, miR-16-1, miR-102 and miR-29c) that distinguished CLL samples that expressed mutated IgV_Hgene from those that expressed unmutated IgV_Hgenes, indicating that miRNA expression profiles have prognostic significance in CLL. As a confirmation of our results is the observation that the common element between the del 13q14.3-related and the IgV_H-related signatures is miR-16-1. This gene is located in the common deleted region 13q14.3 and the presence of this particular deletion is associated with good prognosis. Therefore, miRNAs expand the spectrum of adverse prognostic markers in CLL, such as expression of ZAP-70, unmutated IgV_H, CD38, deletion at chromosome 11q23, or loss or mutation of TP53.

Example 12
Identification of miRNA Signature Profiles Associated with Prognostic Factors and Disease Survival in B-Cell Chronic Leukemia Samples
Introduction

Knowing that the expression profile of miRNome, the full complement of microRNAs in a cell, is different between malignant CLL cells and normal corresponding cells, we asked whether microarray analysis using the miRNACHIP could reveal specific molecular signatures predictive for subsets of CLL that differ in clinical behavior. The miRNome expression in 94 CLL samples was determined utilizing the microchip of Example 10. miRNA expression profiles were analyzed to determine if distinct molecular signatures are associated with the presence or absence of two prognostic markers, ZAP-70 expression and mutation of the IgV_Hgene. The microarray data revealed that two specific molecular signatures were associated with the presence or absence of each of these markers. An analysis of expression profiles from Zap-70 positive/IgV_Hunmutated (Umut) vs. Zap-70 negative/IgV_Hmutated (Mut) CLL samples revealed a unique signature of 17 genes that can distinguish these two subsets. Our results indicate that miRNA expression profiles have prognostic significance in CLL.

Materials and Methods

Patient Samples and Clinical Database. 94 CLL samples were used for this study, which were obtained after informed consent from patients diagnosed with CLL at the CLL Research Consortium institutions (L. Z. Rassenti et al. N. Engl. J. Med. 351(9):893-901 (2004)). Briefly, blood was obtained from CLL patients and mononuclear cells were isolated through Ficoll/Hypaque gradient centrifugation (Amersham Pharmacia Biotech) and processed for RNA extraction according to described protocols (G. A. Calin et al., Proc. Natl. Acad. Sc. U.S.A 99, 15524-15529 (2002)). For each sample, clinical and biological information, such as sex, age at diagnosis, Rai stage, presence/absence of treatment, time between diagnosis and therapy, ZAP-70 expression, and IgV_Hgene mutation status, were available and are described in Table 13.

TABLE 13

Characteristics of patients analyzed with the miRNACHIP.

Characteristic
Value

Male sex - no. of patients (%)
58 (61.7)

Age at diagnosis - years

median
57.3

range
38.2

Therapy begun

No

No. of patients
53

Time since diagnosis - months
87.07

Yes

No. of patients
41

Time between diagnosis & therapy - months
40.27

ZAP-70 level

≦20%
48

>20%
46

IgV_H

Unmutated (≧98% homology)
57

Mutated (<98% homology)
37

RNA Extraction and Northern Blots. Total RNA isolation and RNA blotting were performed as described (G. A. Calin et al., Proc Proc. Natl. Acad. Sc. U.S.A 99, 15524-15529 (2002)).

Microarray Experiments. Microarray experiments were performed as described in Example 11. Of note, for 76 microRNAs on the miRNACHIP, two specific oligonucleotides were synthesized—one identifying the active 22 nucleotide part of the molecule and the other identifying the 60-110 nucleotide precursor. All probes on these microarrays are 40-mer oligonucleotides spotted by contacting technologies and covalently attached to a polymeric matrix.

Data Analysis. After construction of the expression table with Genespring, data normalization was performed by using Bioconductor package. Analyses were carried out using the PAM package (Prediction Analysis of Microarrays) and SAM (Significance Analysis of Microarrays) software. The data were confirmed by Northern blotting for 4 microRNAs in 20 CLL samples, each. All data were submitted using MIAMExpress to the Array Express database.

Analysis of ZAP-70 and Sequence analysis of expressed IgV_H. Analyses were performed as described previously (L. Z. Rassenti et al. N. Engl. J. Med. 351(9):893-901 (2004)). Briefly, ZAP-70 expression was assessed by immunoblot analysis and flow cytometry, while the analysis of expressed IgV_Hwas performed by direct sequencing.

Results

Comparison of miRNA expression in ZAP-70 positive vs. ZAP-70 negative CLL cells. Using 20% as a cutoff for defining ZAP-70 positivity, we constructed two classes that were constituted of 48 ZAP-70-negative and 46 ZAP-70-positive CLL samples, respectively. The analyses carried out using the PAM package identified an expression signature composed of 14 microRNAs (14/190 miRNAs on chip, 7.35%) with a PAM score >±0.02 (Table 14). Using the expression of these microRNAs, it is possible to predict with a low misclassification error (about 0.2 at cross-validation) the type of ZAP-70 expression in a patient's malignant B cells.

Comparison of miRNA expression in IgV_Hpositive vs. IgV_Hnegative CLL cells. The expression of a mutated IgV_Hgene is a favorable prognostic marker (D. G. Oscier et al., Blood. 100, 1177-84 (2002)). ZAP-70 expression is well correlated with the status of the IgV_Hgene. Therefore, we asked whether a specific microRNA signature can predict the mutated (Mut) vs. unmutated (Umut) status of this gene. Using the 98% cutoff for homology with germ-line IgV_H, we identified two groups of patients composed of 37 Umut 98% homology) and 57 Mut (<98% homology). Based on this analysis, 12 microRNAs can be used to correctly predict the Umut vs. Mut status of the gene with a low error (0.02) (Table 14). All of these genes are included in the previous signature.

Comparison of miRNA expression in Zap-70 positive/IgV_HUmut vs. Zap-70 negative/IgV_HMut CLL cells. We divided the 94 CLL cases into 4 groups (Zap-70 positive/IgV_HUmut, Zap-70 positive/IgV_HMut, Zap-70 negative/IgV_HUmut and Zap-70 negative/IgV_HMut), and have found, using the PAM package, that the same unique signature composed of 17 genes can discriminate between the two main groups of patients, Zap70 positive/IgV_HUmut and Zap-70 negative/IgV_HMut. In this case, we observed the lowest classification error (0.015 at cross validation). Only one patient was Zap-70 negative and IgV_HUmut, and therefore was not used in the classification. When the remaining three classes were analyzed, the 10 patients belonging to the Zap-70 positive/IgV_HMut class were always misclassified, which indicates that there are no microRNAs on the miRNACHIP that can compose a different signature. The same unique signature was identified using another algorithm of microarray analysis, SAM, thereby confirming the reproducibility of our results. These results indicate that miRNA expression profiles have prognostic significance in CLL and can be used for diagnosing the disease state of a particular cancer by determining whether or not a given profile is characteristic of a cancer associated with one or more adverse prognostic markers.

TABLE 14

A miRNA signature associated with prediction factors and disease survival in CLL patients.

Short vs. Long

Signature

Zap70+/IgV_HUmut vs.
time to

component
ZAP-70+ vs. Zap-70−
IgV_HMut vs. IgV_HUmut
Zap70−/IgV_HMut
initial therapy
Observation

mir-015a
−0.0728 vs. 0.076
NA
−0.0372 vs. 0.0485
NA
cluster 15a/16-1

del CLL, prostate

ca. 13q13.4 (G. A.

Calin et al., Proc.

Natl. Acad. Sic. USA.

99, 15524-15529

(2002))

mir-016-1
−0.1396 vs. 0.1457
−0.0852 vs. 0.1312
−0.1444 vs. 0.1886
NA
del CLL, prostate

ca. 13q13.4 (G. A.

Calin et al., Proc.

Natl. Acad. Sci. USA.

99, 15524-15529

(2002))

mir-016-2
−0.1615 vs. 0.1685
−0.0969 vs. 0.1493
−0.1619 vs. 0.2113
NA
identical 16-1/16-2

mir-023a
−0.0235 vs. 0.0245
0.0647 vs. 0.0997
−0.0748 vs. 0.0977
0.0587 vs. −0.019
cluster 23a/24-2

mir-023b
−0.0658 vs. 0.0686
−0.0663 vs. 0.1021
−0.0909 vs. 0.1187
0.0643 vs. −0.0208
cluster 24-1/23b

FRA 9D; del

Urothelial ca. 9q22.

(G. A Calin et al.

Proc. Natl. Acad. Sci.

U.S.A.

101(32): 11755-60

(2004))

mir-024-1
NA
−0.042 vs. 0.0648
−0.0427 vs. 0.0558
NA
FRA 9D; del

Urothelial ca. 9q22

(ref (G. A. Calin et al.

Proc. Natl. Acad. Sci.

U.S.A.

101(32): 11755-60

(2004))

mir-024-2
NA
NA
−0.0272 vs. 0.0355
0.0696 vs. −0.0225

mir-029a

0.0806 vs. −00842
0.0887 vs. −0.1367
0.1139 vs. −0.1487
NA
cluster 29a/29b-1

FRA7H; del

Prostate ca. 7q32

(G. A. Calin et al.

Proc. Natl. Acad. Sci.

U.S.A.

101(32): 11755-60

(2004))

mir-29b-2
0.1284 vs. −0.134
0.1869 vs. −0.2879
0.2065 vs. −0.2696
NA
1q32.2-32.3

mir-029c
0.1579 vs. −0.1648
0.1846 vs. −0.2844
0.2174 vs. −0.2839
−0.0221 vs. 0.0072

mir-146
−0.1518 vs. 0.1584
−0.1167 vs. 0.1798
−0.1803 vs. 0.2354
0.07 vs. −0.0227

mir-155
−.0.1015 vs. 0.1059
−0.0743 vs. 0.1145
−0.1155 vs. 0.1508
0.1409 vs. −0.0456
amp child Burkitt's

lymphoma, colon

ca. (M. Metzler et al.

Genes Chromosomes

Cancer.

Feb; 39(2): 167-9

(2004)) and (M. Z.

Michael et al.

Mol Cancer Res.

1(12): 882-91 (2003)).

mir-181a
−0.0473 vs. 0.0494
NA
−0.0279 vs. 0.0364
0.1862 vs. −0.0603
Up-regulated in

differentiated B ly

(C. Z. Chen et al.

Science.

303(5654): 83-6

(2004)).

mir-195
−0.0679 vs. 0.0708
NA
−0.053 vs. 0.0692
NA

mir-221
−0.0812 vs. 0.0848
−0.0839 vs. 0.1292
−0.1157 vs. 0.1511
0.0343 vs. −0.0111
cluster 221/222

mir-222
NA
NA
−0.022 vs. 0.0288
0.0458 vs. −0.0148

mir-223
0.0522 vs. −0.0544
0.1036 vs. −0.1596
0.1056 vs. −0.1379
NA
Normally expression

restricted to

myeloid lineage

(C. Z. Chen et al.

Science.

303(5654): 83-6

(2004)).

Note:

ZAP-70 negative = ZAP-70 expression ≦20%; ZAP-70 positive = ZAP-70 expression >20%; IgV_Hunmutated = homology ≧98%; IgV_Hmutated = homology <98%. The numbers indicate the PAM scores in the two classes (n score and y score). mir-29b-2 was previously named mir-102.

Association between miRNA expression and time to initial therapy. Treatment of patients according to the National Cancer Institute Working Group criteria (B. D. Cheson et al. Blood. 87(12):4990-7 (1996)) was performed when symptomatic or progressive disease developed. Of the 94 patients studied, 41 had initiated therapy (Table 13). We examined the relationship between the expression of 190 microRNA genes and either the time from diagnosis to initial therapy (for patients that have begun treatment) or from the time of diagnosis to the present (for those patients who haven't begun treatment), collectively representing the total group of 94 patients in the study. We found that the expression profile generated by a spectrum of 9 microRNAs, all components of the unique signature, can differentiate between two subsets of patients in the group of 94 tested—one subset with a short interval from diagnosis to initial therapy and the second subset with a significantly longer interval (see Table 14 and FIG. 5). The significance of Kaplan-Meier curves improves if we restrict the analyses to the two main groups of 83 patients (the Zap-70 positive/IgV_HUmut and Zap-70 negative/IgV_HMut groups) or if we use only the 17 microRNAs from the signature (P decreases from <0.01 to P<0.005 and P<0.001, respectively). All of the microRNAs which can predict the time to initial therapy, with the exception of mir-29c, are overexpressed in the group characterized by a short interval from diagnosis to initial therapy.

Example 13
Identification of Sequence Alterations in miR Genes Associated with CLL
Introduction

Using tumor DNA from CLL samples, we screened more than 700 kb of tumor DNAs (mean 39 patients/miRNA for mean 500 bp/miRNA) for sequence alterations in each of 35 different miR genes. Very rare polymorphisms or tumor specific mutations were identified in 4 of the 39 CLL cases, affecting one of three different miR genes: miR-16-1, miR-27b and miR-206. In two other miR genes, miR-34b and miR-100, polymorphisms were identified in both CLL and normal samples with similar frequencies.

Materials and Methods

Detection of microRNA mutations. Thirty-five miR genes were analyzed for the presence of a mutation, including 16 members of the miR expression signature identified in Example 12 (mir-15a, mir-16-1, mir-23a, mir-23b, mir-24-1, mir-24-2, mir-27a, mir-27b, mir-29b-2, mir-29c, mir-146, mir-155, mir-181a, mir-221, mir-222, mir-223) and 19 other miR genes selected randomly (let-7a2, let-7b, mir-21, mir-30a, mir-30b, mir-30c, mir-30d, mir-30e, mir-32, mir-100, mir-108, mir-125b1, mir-142-5p, mir-142-3p, mir-193, mir-181a, mir-206, mir-213 and mir-224).

The algorithm for screening for miR gene mutations in CLL samples was performed as follows: the genomic region corresponding to each precursor miRNA from either 39 CLL samples or 3 normal mononuclear cell samples from healthy individuals was amplified, including at least 50 base pairs in the 5′ and 3′ extremities. For the miRNAs located in clusters covering less than one kilobase, the entire corresponding genomic region was amplified and sequenced using the Applied Biosystems Model 377 DNA sequencing system (PE, Applied Biosystems, Foster City, Calif.). When a deviation from the normal sequence was found, a panel of blood DNAs from 95 normal individuals was screened to confirm that the deviation represented a polymorphism. If the sequencing data were normal, an additional panel of 37 CLL cases was screened to determine the frequency of mutations in a total of 76 cancer patients. If additional mutations were found, another set of 65 normal DNAs was screened, to assess the frequency of the specific alteration in a total of 160 normal samples.

In vivo studies of mir-16-1 mutant effects. We constructed two mir-16-1/mir-15a expression vectors—one containing an 832 base pair genomic sequence that included both mir-16-1 and mir-15a, and another nearly identical construct containing the C to T mir-16-1 substitution, as shown in SEQ ID NO. 642—by ligating the relevant open reading frame in a sense orientation into the mammalian expression vector, pSR-GFP-Neo (OligoEngine, Seattle, Wash.). These vectors are referred to as mir-16-1-WT and mir-16-1-MUT, respectively. All sequenced constructs were transfected into 293 cells using Lipofectamine 2000 according to the manufacturer's protocol (Invitrogen, Carlsbad, Calif.). The expression of both mir-16-1-WT and mir-16-1-MUT constructs was assessed by Northern blotting as previously described (G. A. Calin et al., Proc. Natl. Acad. Sc. U.S.A 99, 15524-15529 (2002)).

Results

Very rare polymorphisms or tumor specific mutations were identified in 4 of the 39 CLL cases, affecting one of three different miR genes: miR-16-1, miR-27b and miR-206 (Tables 15 and 16). In two other miR genes, miR-34b and miR-100, polymorphisms were identified in both CLL and normal samples with similar frequencies (see Tables 15, 16 and Results section below).

TABLE 15

Genetic variations in the genomic sequences of miR genes

in CLL patients.

Other

miRNA

miRNA
Mutation
CLL (%)
allele
Normals
CHIP
Observation

mir-16-1
C to T
2/76 (2.6)
Deleted
0/160 (0)
Reduced
Heterozygous in

(see SEQ ID

(FISH,

expression
normal cells

NO. 642)

LOH)

from both

patients;

Previous breast

cancer; Mother

died with CLL;

sister died with

breast cancer.

mir-27b
G to A
1/39 (2.6)
Normal
0/98 (0)
Normal

(see SEQ ID

expression

NO. 646)

mir-206
G to A
1/39 (2.6)
Normal
NA
NA

(see SEQ ID

NO. 647)

mir-100
G to A
17/39 (43.5)
Normal
2/3
NA

(see SEQ ID

NO. 644)

TABLE 16

Sequences showing genetic variations

in the miR genes of CLL patients.

Precursor Sequence
SEQ ID

Name
(5′ to 3′)
NO.

hsa-mir-16-
GTCAGCAGTGCCTTAGCAGCACGT
641

1-normal
AAATATTGGCGTTAAGATTCTAAA

ATTATCTCCAGTATTAACTGTGCT

GCTGAAGTAAGGTTGACCATACTC

TAC

hsa-mir-16-
GTCAGCAGTGCCTTAGCAGCACGT
642

1-MUT
AAATATTGGCGTTAAGATTCTAAA

ATTATCTCCAGTATTAACTGTGCT

GCTGAAGTAAGGTTGACCATACTT

TAC

hsa-mir-100
CCTGTTGCCACAAACCCGTAGATC
643

CGAACTTGTGGTATTAGTCCGCAC

AAGCTTGTATCTATAGGTATGTGT

CTGTTAGGCAATCTCACGGACC

hsa-mir-100-
CCTGTTGCCACAAACCCGTAGATC
644

MUT
CGAACTTGTGGTATTAGTCCGCAC

AAGCTTGTATCTATAGGTATGTGT

CTGTTAGGCAATCTCACAGACC

hsa-mir-27b-
ACCTCTCTAACAAGGTGCAGAGCT
645

normal
TAGCTGATTGGTGAACAGTGATTG

GTTTCCGCTTTGTTCACAGTGGCT

AAGTTCTGCACCTGAAGAGAAGGT

GAGATGGGGACAGTTAAGTTGGAG

CCGCTGGGGCAGAGGCCGTTGCTG

ACGGGC

hsa-mir-27b-
ACCTCTCTAACAAGGTGCAGAGCT
646

MUT
TAGCTGATTGGTGAACAGTGATTG

GTTTCCGCTTTGTTCACAGTGGCT

AAGTTCTGCACCTGAAGAGAAGGT

GAGATGGGGACAGTTAAGTTGGAG

CCGCTGGGGCAGAGGCCGTTGCTG

ACAGGC

has-mir-206
TGCTTCCCGAGGCCACATGCTTCT
230

TTATATCCCCATATGGATTACTTT

GCTATGGAATGTAAGGAAGTGTGT

GGTTTCGGCAAGTG

has-mir-206-
TGCTTCCCGAGGCCACATGCTTCT
647

MUT
TTATATCCCCATATGGATTACTTT

A
CTATGGAATGTAAGGAAGTGTGT

GGTTTCGGCAAGTG

hsa-mir-34b-
GTGCTCGGTTTGTAGGCAGTGTCA
648

normal
TTAGCTGATTGTACTGTGGTGGTT

ACAATCACTAACTCCACTGCCATC

AAAACAAGGCACAGCATCACCGCC

G

hsa-mir-34b-
GTGCTCGGTTTGTAGGCAGTGTCA
650

MUT
TTAGCTGATTGTACTGTGGTGGTT

ACAATCACTAACTCCACTGCCATC

AAAACAAGGCACAGCATCACCACC

G

Note:

Each mutation/polymorphism is underlined and indicated in bold in the sequences marked “MUT”.

The miR-16-1 gene is located at 13q13.4. In 2 CLL patients out of 76 screened (2.6%), we found a homozygous C to T polymorphism (compare SEQ ID NO: 641 to SEQ ID NO: 642; Table 16), which is located in a 3′ region of the miR-16-1 precursor (FIG. 7C) with strong conservation in all of the primates analyzed (E. Berezikov et al., Cell 120(1):21-4 (2005)), suggesting that this polymorphism has functional implications. By RT-PCR and Northern blotting we have shown that the precursor miRNA includes the 3′ region harboring the base substitution. Both patients have a significant reduction in mir-16-1 expression in comparison with normal CD5+ cells by miRNACHIP and Northern blotting (FIG. 6, FIG. 7D). Further suggesting a pathogenic role, by FISH and LOH, we found a monoallelic deletion at 13q14.3 in the majority of examined cells. This substitution was not found in any of 160 normal control samples (p<0.05 using chi square analysis). In both patients, the normal cells from mucal mucosa were heterozygous for this abnormality. Therefore, this change is a very rare polymorphism or a germ-line mutation. In support of the latter is the fact that one of the patients has two relatives (mother and sister) who have been diagnosed with CLL and breast cancer, respectively. Therefore, this family fulfills the minimal criteria for “familial” CLL, i.e., two or more cases of B-CLL in first-degree living relatives (N. Ishibe et al., Leuk Lymphoma 42 (1-2):99-108 (2001)).

To identify a possible pathogenic effect for this substitution, we inserted both the wild-type sequence of the mir-15a/mir-16-1 cluster, as well as the mutated sequence, into separate expression vectors. We transfected 293 cells, which have a low endogenous expression of this cluster. As a control, 293 cells transfected with an empty vector were tested. The expression levels of both mir-15a and mir-16-1 were significantly reduced in transfectants expressing the mutant construct in comparison to transfectants expressing the wild-type construct (FIG. 7E). The level of expression in transfectants expressing the mutant construct was comparable with the level of endogenous expression in 293 cells (FIG. 7E). Therefore, we conclude that the C to T change in miR-16-1 affects the processing of the pre-miRNA in mature miRNA.

The miR-27b gene is located on chromosome 9. A heterozygous mutation caused by a G to A change in the 3′ region of the miR-27b precursor (compare SEQ ID NO: 645 to SEQ ID NO: 646; Table 16), but within the transcript of the 23b-27b-24-1 cluster, was identified in one out of 39 CLL samples. miRCHIP analysis indicated that miR-27b expression was reduced in this sample. This change has not been found in any of the 98 normal individuals screened to date.

The miR-34b gene is located at 11q23. Four CLL patients out of 39 carried two associated polymorphisms, a G to A polymorphism, as shown in SEQ. ID NO. 650, and a T to G polymorphism located in the 3′ region of the miR-34b precursor. Both polymorphisms were within the transcript of the mir-34b-mir-34c cluster. One patient was found to be homozygous (presenting by FISH heterozygous abnormal chromosome 11q23), while the other three were heterozygous for the polymorphisms. The same frequency of mutation was found in 35 normal individuals tested.

Example 14
Identification of Abnormalities in the Genomic Sequences of miR Genes Associated with CLL
Introduction

Abnormally expressed cancer genes are frequently targets for genetic abnormalities, e.g., mutations that can either activate or inactivate their function. Therefore, we screened 42 microRNAs for germline or somatic mutations.

Materials and Methods

Detection of microRNA Gene Mutations.

The genomic region corresponding to each precursor miRNA, including at least 50 additional base pairs (bp) in the 5′ and 3′ extremities (i.e., flanking sequences), was amplified from 40 CLL samples and normal mononuclear cell samples from 3 healthy individuals. For the miRNAs located in clusters that were less than one kilobase (kb) in length, the entire corresponding genomic region was amplified and sequenced using the Applied Biosystems Model 377 DNA sequencing system (PE, Applied Biosystems, Foster City, Calif.). When a deviation from the normal sequence was found, a panel of blood DNAs from 160 normal individuals, as well as an additional panel of 35 CLL cases (total of 75 leukemia patients), were screened to confirm polymorphisms. All subjects were Caucasian, as indicated by medical records of CLL patients and information obtained during an interview for control patients. For 46 CLL patients, personal and/or familial cancer history was known. Forty-two miR genes were screened for germline or somatic mutations, including 15 members of the specific signature identified in Example 12, or members of the same cluster: miR-15a, miR-16-1, miR-23a, miR-23b, miR-24-1, miR-24-2, miR-27a, miR-27b, miR-29b-2, miR-29c, miR-146, miR-155, miR-221, miR-222, miR-223, as well as 27 other microRNAs that were selected randomly: let-7a2, let-7b, miR-17-3p, miR-17-5p, miR-18, miR-19a, miR-19b-1, miR-20, miR-21, miR-30b, miR-30c-1, miR-30d, miR-30e, miR-32, miR-100, miR-105-1, miR-108, miR-122, miR-125b-1, miR-142-5p, miR-142-3p, miR-193, miR-181a, miR-187, miR-206, miR-224, miR-346.

Results

Germline or somatic mutations were identified in miRNA genomic regions in 11 out of 75 (15%) CLL samples. Five different miRNAs were affected by mutations (5/42 miR genes analyzed, 12%): miR-16-1, miR-27b, miR-206, miR-29b-2 and miR-187. None of these mutations were found in a set of 160 individuals without cancer (p<0.0001) (see Table 17). The positions of the various mutations are shown relative to the position of the miR gene in FIG. 7A. All the abnormalities are localized in regions that are transcribed, as shown by RT-PCR (FIG. 7B). Eight of the 11 (73%) patients with abnormal miRNA sequences have a known personal or familial history of CLL or other hematopoietic or solid tumors (Table 17). Sequences containing the identified miR gene mutations, as well as their corresponding wild-type sequences, are shown in Table 16 for miR-16-1 and miR-27b and in Table 18 for miR-29b-2, miR-187 and miR-206. Two mutations were identified in miR-29b-2 and miR-206 (labeled MUTT and MUT2, respectively, in Table 18). In addition, a polymorphism was detected in both CLL and normal samples with similar frequencies for three other miR genes: miR-29c, miR-122a and miR-187 (labeled MUT2) (see Tables 17 and 18).

TABLE 17

Genetic variations in the genomic sequences of miR genes in CLL patients.

miRNACHIP

miRNA
Location **
CLL
Normals
expression
a) Observations

miR-16-1
Germline;
2/75
0/160
Reduced to
Normal allele deleted in CLL

pri-

15% and 40%
cells in both patients (FISH,

miRNA: C to T

of normal,
LOH). For one patient:

substitution

respectively
History of previous breast

at +7 bp in

cancer; mother with CLL

the 3′

(deceased); sister with breast

flanking

cancer (deceased).

miR-27b
Germline;
1/75
0/160
Normal
Mother with throat and lung

pri-

cancer at age 58. Father with

miRNA: G to A

lung cancer at age 57.

substitution

at +50 bp in

3′ flanking

sequence

miR-29b-2
pri-
1/75
0/160
Reduced to 75%
Sister with breast cancer at

miRNA: G to A

age 88 (still living). Brother

substitution

with “some type of blood

at +212 in

cancer” at age 70.

3′ flanking

sequence

miR-29b-2
pri-
3/75
0/160
Reduced to 80%
Both patients have a family

miRNA: A

history of unspecified

insertion

cancer.

at +107 in

3′ flanking

sequence

miR-187
pri-
1/75
0/160
NA
Unknown

miRNA: T to C

substitution

at +73 in

3′ flanking

sequence

miR-206
pre-
2/75
0/160
Reduced to 25%
Prostate cancer; mother with

miRNA: G to T

esophogeal cancer. Brother

substitution

with prostate cancer; sister

at position

with breast cancer

49 of

precursor

miR-206
Somatic;
1/75
0/160
Reduced to 25%
Aunt with leukemia

pri-

(data only for
(deceased)

miRNA: A to T

one pt)

substitution

at −116 in

5′ flanking

sequence

miR-29c
pri-
2/75
1/160
NA
Paternal grandmother with

miRNA: G to A

CLL; sister with breast

substitution

cancer.

at −31 in

5′ flanking

sequence

miR-122a
pre-
1/75
2/160
Reduced to 33%
Paternal uncle with colon

miRNA: C to T

cancer.

substitution

at position

53 of

precursor

miR-187
pre-
1/75
1/160
NA
Grandfather with

miRNA: G to A

polycythemia vera. Father

substitution

has a history of cancer but

at position

not lymphoma.

34 of

precursor

For each CLL patient/normal control, more than 12 kb of genomic DNA was sequenced. In total, ~627 kb of tumor DNA and about 700 kb of normal DNA was screened by direct sequencing. The positions of the mutations are reported with respect to the precursor miRNA molecule.

** When normal corresponding DNA from bucal mucosa was available, the alteration was identified as germline when present or somatic when absent, respectively. FISH = fluorescence in situ hybridization; LOH = loss of heterozygosity; NA = not available.

TABLE 18

Sequences showing genetic variations

in the miR genes of CLL patients.

Precursor Sequence

(5′ to 3′) +/−

5′ or 3′ flanking
SEQ ID

Name
genomic sequence
NO.

hsa-mir-29b-
CTTCTGGAAGCTGGTTTCACATGG
651

2-normal
TGGCTTAGATTTTTCCATCTTTGT

ATCTAGCACCATTTGAAATCAGTG

TTTTAGGAGTAAGAATTGCAGCAC

AGCCAAGGGTGGACTGCAGAGGAA

CTGCTGCTCATGGAACTGGCTCCT

CTCCTCTTGCCACTTGAGTCTGTT

CGAGAAGTCCAGGGAAGAACTTGA

AGAGCAAAATACACTCTTGAGTTT

GTTGGGTTTTGGGAGAGGTGACAG

TAGAGAAGGGGGTTGTGTTTAAAA

TAAACACAGTGGCTTGAGCAGGGG

CAGAGG

hsa-mir-29b-2-
CTTCTGGAAGCTGGTTTCACATGG
652

MUT1 (G to A
TGGCTTAGATTTTTCCATCTTTGT

substitution
ATCTAGCACCATTTGAAATCAGTG

at +212 in 3′
TTTTAGGAGTAAGAATTGCAGCAC

flanking
AGCCAAGGGTGGACTGCAGAGGAA

sequence)
CTGCTGCTCATGGAACTGGCTCCT

CTCCTCTTGCCACTTGAGTCTGTT

CGAGAAGTCCAGGGAAGAACTTGA

AGAGCAAAATACACTCTTGAGTTT

GTTGGGTTTTGGGAGAGGTGACAG

TAGAGAAGGGGGTTGTGTTTAAAA

TAAACACAGTGGCTTGAGCAGGGG

CAGAAG

hsa-mir-29b-
CTTCTGGAAGCTGGTTTCACATGG
653

2-MUT2 (A
TGGCTTAGATTTTTCCATCTTTGT

insertion
ATCTAGCACCATTTGAAATCAGTG

at +107 in 3′
TTTTAGGAGTAAGAATTGCAGCAC

flanking
AGCCAAGGGTGGACTGCAGAGGAA

sequence)
CTGCTGCTCATGGAACTGGCTCCT

CTCCTCTTGCCACTTGAGTCTGTT

CGAGAAGTCCAGGGAAGAAACTTG

AAGAGCAAAATACACTCTTGAGTT

TGTTGGGTTTTGGGAGAGGTGACA

GTAGAGAAGGGGGTTGTGTTTAAA

ATAAACACAGTGGCTTGAGCAGGG

GCAGAGG

hsa-mir-
GGTCGGGCTCACCATGACACAGTG
654

187-normal
TGAGACCTCGGGCTACAACACAGG

ACCCGGGCGCTGCTCTGACCCCTC

GTGTCTTGTGTTGCAGCCGGAGGG

ACGCAGGTCCGCAGCAGAGCCTGC

TCCGCTTGTCCTGAGGGACTCGAC

ACAGGGGACTGCACAGAGACCATG

GGAAAGTCCAGGCTC

hsa-mir-187-
GGTCGGGCTCACCATGACACAGTG
655

MUT1 (T to C
TGAGACCTCGGGCTACAACACAGG

substitution
ACCCGGGCGCTGCTCTGACCCCTC

at +73 in 3′
GTGTCTTGTGTTGCAGCCGGAGGG

flanking
ACGCAGGTCCGCAGCAGAGCCTGC

sequence)
TCCGCTTGTCCTGAGGGACTCGAC

ACAGGGGACTGCACAGAGACCATG

GGAAAGTCCAGGCCC

hsa-mir-187-
GGTCGGGCTCACCATGACACAGTG
656

MUT2 (G to A
TGAGACTCGAGCTACAACACAGGA

substitution
CCCGGGGCGCTGCTCTGACCCCTC

at position
GTGTCTTGTGTTGCAGCCGGAGGG

34 of
ACGCAGGTCCGCAGCAGAGCCTGC

precursor)
TCCGCTTGTCCTGAGGGACTCGAC

ACAGGGGACTGCACAGAGACCATG

GGAAAGTCCAGGCTC

has-mir-206
GATTTAGGATGAGTTGAGATCCCA
657

GTGATCTTCTCGCTAAGAGTTTCC

TGCCTGGGCAAGGAGGAAAGATGC

TACAAGTGGCCCACTTCTGAGATG

CGGGCTGCTTCTGGATGACACTGC

TTCCCGAGGCCACATGCTTCTTTA

TATCCCCATATGGATTACTTTGCT

ATGGAATGTAAGGAAGTGTGTGGT

TTCGGCAAGTG

has-mir-206-
GATTTAGGATGAGTTGAGATCCCA
658

MUT1 (G to T
GTGATCTTCTCGCTAAGAGTTTCC

substitution
TGCCTGGGCAAGGAGGAAAGATGC

at position
TACAAGTGGCCCACTTCTGAGATG

49 of
CGGGCTGCTTCTGGATGACACTGC

precursor)
TTCCCGAGGCCACATGCTTCTTTA

TATCCCCATATGGATTACTTTTCT

ATGGAATGTAAGGAAGTGTGTGGT

TTCGGCAAGTG

has-mir-206-
GTTTTAGGATGAGTTGAGATCCCA
659

MUT2 (A to T
GTGATCTTCTCGCTAAGAGTTTCC

substitution
TGCCTGGGCAAGGAGGAAAGATGC

at -116 in 5′
TACAAGTGGCCCACTTCTGAGATG

flanking
CGGGCTGCTTCTGGATGACACTGC

sequence)
TTCCCGAGGCCACATGCTTCTTTA

TATCCCCATATGGATTACTTTTCT

ATGGAATGTAAGGAAGTGTGTGGT

TTCGGCAAGTG

hsa-mir-
CGAGGTGCAGACCCTGGGAGCACC
660

29c-normal
ACTGGCCCATCTCTTACACAGGCT

GACCGATTTCTCCTGGTGTTCAGA

GTCTGTTTTTGTCTAGCACCATTT

GAAATCGGTTATGATGTAGGGGGA

hsa-mir-29c-
CAAGGTGCAGACCCTGGGAGCACC
661

MUT (G to A
ACTGGCCCATCTCTTACACAGGCT

substitution
GACCGATTTCTCCTGGTGTTCAGA

at -31 in 5′
GTCTGTTTTTGTCTAGCACCATTT

flanking
GAAATCGGTTATGATGTAGGGGGA

sequence)

hsa-mir-
CCTTAGCAGAGCTGTGGAGTGTGA
662

122a-normal
CAATGGTGTTTGTGTCTAAACTAT

CAAACGCCATTATCACACTAAATA

GCTACTGCTAGGC

hsa-mir-122a-
CCTTAGCAGAGCTGTGGAGTGTGA
663

MUT (C to T
CAATGGTGTTTGTGTCTAAACTAT

substitution
CAAATGCCATTATCACACTAAATA

at position 53
GCTACTGCTAGGC

of precursor)

Note:

The position of each mutation/polymorphism is underlined and indicated in bold in the sequences marked “MUT”.

Example 15
A Unique MicroRNA Signature Associated with Prognostic Factors and Disease Progression in Chronic Lymphocytic Leukemia

Introduction: In spite of extensive effort, little is known regarding the pathogenic events leading to the initiation and progression of B cell CLL, the most frequent adult leukemia in the Western world. On the contrary, several factors predicting the clinical course have been defined. CLL cells with few or no mutations in the immunoglobulin heavy-chain variable-region gene (IgV_H) or with high expression of the 70-kD zeta-associated protein positive (ZAP-70+) have an aggressive course, whereas patients with mutated clones or few ZAP-70+ B cells have an indolent course (Chiorazzi, N., et al., N. Engl. J. Med. 352:804-815 (2005)). It was also found that genomic aberrations in CLL are important independent predictors of disease progression and survival (Dohner, H., et al., N. Engl. J. Med. 343(26):1910-1916 (2000)). However, the molecular basis of these associations is largely unknown. Here, we performed genome wide expression profiling with the miRNACHIP in a large series of CLL samples with extensive clinical data to examine whether expression of these noncoding genes is associated with factors predicting the clinical course.

Materials and Methods

Patient samples and clinical database. Samples used for this study are described in detail in Example 12.

RNA extraction, Northern blots and miRNACHIP experiments. Procedures were performed as described (Calin, G. A., et al., Proc. Natl. Acad. Sci. USA 101(32):1175-1160 (2004); Liu, C.-G., et al., Proc. Natl. Acad. Sci. USA 101(26): 9740-9744 (2004)). Briefly, labeled targets from 5 μg of total RNA was used for hybridization on each miRNACHIP microarray chip containing 368 probes in triplicate, corresponding to 245 human and mouse miRNA genes. Of note, for 76 microRNAs on the miRNACHIP two specific oligos were synthesized one identifying the active 22nt part of the molecule and the other for the 60-110nt precursor (Liu, C.-G., et al., Proc. Natl. Acad. Sci. USA 101(26): 9740-9744 (2004)).

Data analysis. Raw data were normalized and analyzed in GeneSpring® software version 7.2 (Silicon Genetics, Redwood City, Calif.). Expression data were median centered using both GeneSpring normalization option or Global Median normalization of the Bioconductor package, without any substantial difference. Statistical comparisons were done both using the GeneSpring ANOVA tool and the SAM software (Significance Analysis of Microarray). MiRNA predictors were calculated by using PAM software (Prediction Analysis of Microarrays); the Support Vector Machine tool of GeneSpring was used for the Cross-validation and Test-set prediction. The Kaplan-Meier plot (“survival analysis” of the PAM software) was used to identify an association between miRNA expression and the time elapsing from CLL diagnosis and the beginning of therapy. miRNAs able to best separate the two groups were identified at the same time. All data were submitted using MIAMExpress to the Array Express database (accession numbers to be received upon revision). We validated the microarray data for 4 miRNAs (miR-16-1, miR-26a, miR-206 and miR-223) in 11 CLL samples and normal CD5 cells by solution hybridization detection as presented elsewhere (Calin, G. A., et al., Proc. Natl. Acad. Sci. USA 101 (32):11755-11760 (2004)). Furthermore, miR-15a and miR-16-1 expression in the patients with germline mutation was confirmed by Northern blot.

Analysis of ZAP-70 and Sequence analysis of expressed IgV_H. These experiments were performed as described in Example 12.

Results

Comparison of miRNA expression in Zap-70 positive/IgV_HUmut vs. Zap-70 negative/IgV_HMut CLL cells. In Example 12, a unique signature that can discriminate between the two main groups of CLL patients (i.e., Zap70 positive/IgV_HUmut and Zap-70 negative/IgV_HMut), composed of 17 genes, was identified using the PAM package. Using additional algorithms for statistical and prediction analysis (i.e., SAM and GeneSpring) to validate the PAM signature, we found that a signature composed of 13 mature microRNAs could discriminate (at P<0.01) between Zap70 positive/IgV_HUmut and Zap-70 negative/IgV_HMut patients (Tables 19 and 20). Furthermore, the prediction made using Support Vector Machine correctly classified all patients (Table 20). The majority of miRNAs (9 out of 13) were significantly overexpressed in the group with poor prognosis. The 10 patients belonging to the Zap-70 positive and VhMut group were equally assigned to groups good or poor prognosis, suggesting either that there are no microRNAs on the miRNACHIP whose expression can distinguish these two groups, that these two groups are not different with regard to microRNA expression profiles or that the groups are too small to be correctly classified.

We used the Support Vector Machine algorithm also to predict an additional independent set of 50 CLL samples with known ZAP-70 status (Table 21). When the 13 miRNAs of the identified signature were used, the prediction was made correctly in all cases, confirming, thereby confirming our results. Also confirming the microarray specificity, as reported in Liu, C.-G., et al., Proc. Natl. Acad. Sci. USA 101(26): 9740-9744 (2004), the signature did not include very similar members of the same families, such as miR-23a (1 base difference from miR-23b) and miR-15b (four bases difference from miR-15a), while the identical mature miRNAs miR-16-1 and miR-16-2 were both identified, indicating that the chip is able to discriminate between highly similar isoforms.

TABLE 19

miRNA signature associated with prognostic factors (ZAP70 and IgVH mutations) and

disease progression in CLL patients*.

Group 4

Nr. Crt.
Component
Map
value
expression**
Putative targets ***
Observation****

1
miR-15a
13q14.3
0.018
high
NA
cluster 15a/16-1

del CLL & Prostate ca.

2
miR-195
17p13
0.017
high
NA
del HCC

3
miR-221
Xp11.3
0.010
high
HECTD2, CDKN1B, NOVA1,
cluster 221/222

ZFPM2, PHF2

4
miR-23b
9q22.1
0.009
high
FNBP1L, WTAP,
cluster 24-1/23b

PDE4B, SATB1, SEMA6D
FRA 9D; del Urothelial ca.

5
miR-155
21q21
0.009
high
ZNF537, PICALM, RREB1,
amp child Burkitt's lymphoma

BDNF, QKI

6
miR-223
Xq12-13.3
0.007
low
PTBP2, SYNCRIP, WTAP,
normally expression restricted

FBXW7, QKI
to myeloid lineage

7
miR-29a-2
7q32
0.004
low
NA
cluster 29a-2/29b-1

FRA7H; del Prostate ca.

8
miR-24-1
9q22.1
0.003
high
TOP1, FLJ45187, RSBN1L,
cluster 24-1/23b

RAP2C, PRPF4B
FRA 9D; del Urothelial ca.

9
miR-29b-2 (miR-102)
1q32.2-32.3
0.0007
low
NA

10
miR-146
5q34
0.0007
high
NOVA1, NFE2L1, C1orf16,

ABL2, ZFYVE1

11
miR-16-1
13q14.3
0.0004
high
BCL2, CNOT6L, USP15,
cluster 15a/16-1

PAFAH1B1, ESRRG
del CLL, prostate ca.

12
miR-16-2
3q26.1
0.0003
high
see miR-16-1
identical miR-16-1

13
miR-29c
1q32.2-32.3
0.0002
low
NA

Note:

*All the members of the signature are mature miRNAs;

**Group 4 includes patients with IgVh mutated and Zap-70 negative, both predictors of poor prognosis.

***—top five predictions using TargetScan (Lewis, B. P., et al., Cell 120:15-20 (2005)) were included. NA— not available; for specific gene names—see the NCBI site.

****FRA = fragile site; del = deletion; HCC = hepatocellular carcinoma; ca. = carcinoma.

TABLE 20

List of miRNAs associated with prognostic factors and disease

progression in CLL patients selected by Prediction Analysis of Microarrays (PAM) and

ANOVA analysis (GeneSpring)*.

Nr.
PAM
n−
y+

GeneSpring
Anova

crt.
signature
score
score
map
signature
p-value
map

1
mir-222
−0.022
0.0288
Xp11.2
mir-34-prec
0.048
1p36.22

2
mir-24-2
−0.0272
0.0355
19p13.12
mir-192-2/3-prec
0.0457
11q13

3
mir-181a
−0.0279
0.0364
1q32.1
mir-15a-prec
0.0353
13q14.3

4
mir-15a
−0.0372
0.0485
13q14.3
mir-17
0.0257
13q31

5
mir-24-1
−0.0427
0.0558
9q22.1
mir-15a
0.018
13q14.3

6
mir-195
−0.053
0.0692
17p13
mir-195
0.0175
17p13

7
mir-23a
−0.0748
0.0977
19p13.12
mir-213-prec
0.0153
1q31.3-

q32.1

8
mir-23b
−0.0909
0.1187
9q22.1
mir-221
0.0105
Xp11.3

9
mir-223
0.1056
−0.1379
Xq12-13.3
mir-023b
0.00964
9q22.1

10
mir-29a-2
−0.1139
−0.1487
7q32
mir-155
0.00959
21q21

11
mir-155
−0.1155
0.1508
21q21
mir-223
0.00774
Xq12-

13.3

12
mir-221
−0.1157
0.1511
Xp11.3
mir-132
0.00461
17p13.3

13
mir-16-1
−0.1444
0.1886
13q14.3
mir-029a-2
0.00446
7q32

14
mir-16-2
−0.1619
0.2113
3q26.1
mir-024-1
0.00311
9q22.1

15
mir-146
−0.1803
0.2354
5q34
mir-29b-2 (102)
0.000778
1q32.2-

32.3

16
mir-29b-2
0.2065
−0.2696
1q32.2-32.3
mir-146
0.000753
5q34

(102)

17
mir-029c
0.2174
−0.2839
1q32.2-32.3
mir-016-1
0.00042
13q14.3

18

mir-016-2
0.000327
3q26.1

19

mir-029c
0.000216
1q32.2-

32.3

*the list of genes is in ascending order of significance, as represented by score or p value, respectively.

TABLE 21

Predictions of ZAP-70 status and Immunoglobulin heavy chain variable

gene status according to miRNA expression in CLL patients*.

CLL
True Value
Prediction
n margin
y margin

PANEL 1 -
CLL01
Zap70 < 20 VhM
Zap70 < 20 VhM
1.278
−1.327

83 correct
CLL02
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

predictions,
CLL03
Zap70 < 20 VhM
Zap70 < 20 VhM
1.247
−1.348

0 incorrect
CLL04
Zap70 < 20 VhM
Zap70 < 20 VhM
1.16
−1.388

predictions
CLL05
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL06
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL07
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1.122

CLL08
Zap70 < 20 VhM
Zap70 < 20 VhM
1.391
−1.595

CLL09
Zap70 < 20 VhM
Zap70 < 20 VhM
0.953
−1.048

CLL10
Zap70 < 20 VhM
Zap70 < 20 VhM
1.059
−1.333

CLL11
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL12
Zap70 < 20 VhM
Zap70 < 20 VhM
0.997
−1.261

CLL13
Zap70 < 20 VhM
Zap70 < 20 VhM
1.488
−1.841

CLL14
Zap70 < 20 VhM
Zap70 < 20 VhM
2.171
−2.582

CLL15
Zap70 < 20 VhM
Zap70 < 20 VhM
1.252
−1.352

CLL16
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1.188

CLL17
Zap70 < 20 VhM
Zap70 < 20 VhM
1.19
−1.284

CLL18
Zap70 < 20 VhM
Zap70 < 20 VhM
1.747
−2.062

CLL19
Zap70 < 20 VhM
Zap70 < 20 VhM
1.503
−1.833

CLL20
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL21
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL22
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL23
Zap70 < 20 VhM
Zap70 < 20 VhM
2.047
−2.27

CLL24
Zap70 < 20 VhM
Zap70 < 20 VhM
1.464
−1.527

CLL25
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL26
Zap70 < 20 VhM
Zap70 < 20 VhM
1.034
−1.034

CLL27
Zap70 < 20 VhM
Zap70 < 20 VhM
1.479
−1.617

CLL28
Zap70 < 20 VhM
Zap70 < 20 VhM
2.355
−2.57

CLL29
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL30
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL31
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL32
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL33
Zap70 < 20 VhM
Zap70 < 20 VhM
2.229
−2.496

CLL34
Zap70 < 20 VhM
Zap70 < 20 VhM
2.683
−2.931

CLL35
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL36
Zap70 < 20 VhM
Zap70 < 20 VhM
2.578
−2.768

CLL37
Zap70 < 20 VhM
Zap70 < 20 VhM
2.079
−2.34

CLL38
Zap70 < 20 VhM
Zap70 < 20 VhM
1.745
−1.814

CLL39
Zap70 < 20 VhM
Zap70 < 20 VhM
1.559
−1.699

CLL40
Zap70 < 20 VhM
Zap70 < 20 VhM
2.608
−3.005

CLL41
Zap70 < 20 VhM
Zap70 < 20 VhM
2.357
−2.676

CLL42
Zap70 < 20 VhM
Zap70 < 20 VhM
1.102
−1.303

CLL43
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL44
Zap70 < 20 VhM
Zap70 < 20 VhM
2.464
−2.629

CLL45
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL46
Zap70 < 20 VhM
Zap70 < 20 VhM
1
−1

CLL47
Zap70 < 20 VhM
Zap70 < 20 VhM
2.074
−2.271

CLL48
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL49
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.179
1.487

CLL50
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
0.88

CLL51
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL52
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.836
2.405

CLL53
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL54
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.334
1.649

CLL55
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1.229

CLL56
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL57
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL58
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.171
1.566

CLL59
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL60
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL61
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.505
1.976

CLL62
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.095
1.46

CLL63
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−2.297
2.717

CLL64
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.187
1.381

CLL65
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL66
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.344
1.479

CLL67
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.876
2.049

CLL68
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL69
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.89
1.987

CLL70
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−2.658
2.938

CLL71
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.556
1.967

CLL72
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−2.574
2.81

CLL73
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL74
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL75
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL76
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−2.671
3.041

CLL77
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1.376

CLL78
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL79
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.678
1.914

CLL80
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−2.416
2.953

CLL81
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1
1

CLL82
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−1.782
1.846

CLL83
Zap70 > 20 VhUM
Zap70 > 20 VhUM
−2.307
2.716

PANEL 2 -
CLL95
Zap70 < 20
Zap70 < 20
8.494
−8.494

50 correct
CLL96
Zap70 < 20
Zap70 < 20
1
−1

predictions,
CLL97
Zap70 < 20
Zap70 < 20
0.763
−0.763

0 incorrect
CLL98
Zap70 < 20
Zap70 < 20
11.19
−11.19

predictions
CLL99
Zap70 < 20
Zap70 < 20
7.561
−7.561

CLL100
Zap70 < 20
Zap70 < 20
14.51
−14.51

CLL101
Zap70 < 20
Zap70 < 20
5.585
−5.585

CLL102
Zap70 < 20
Zap70 < 20
1
−1

CLL103
Zap70 < 20
Zap70 < 20
10.09
−10.09

CLL104
Zap70 < 20
Zap70 < 20
5.521
−5.521

CLL105
Zap70 < 20
Zap70 < 20
7.33
−7.33

CLL106
Zap70 < 20
Zap70 < 20
3.264
−3.264

CLL107
Zap70 < 20
Zap70 < 20
7.774
−7.774

CLL108
Zap70 < 20
Zap70 < 20
5.3
−5.3

CLL109
Zap70 < 20
Zap70 < 20
4.34
−4.34

CLL110
Zap70 < 20
Zap70 < 20
1.822
−1.822

CLL111
Zap70 < 20
Zap70 < 20
3.879
−3.879

CLL112
Zap70 < 20
Zap70 < 20
8.514
−8.514

CLL113
Zap70 < 20
Zap70 < 20
5.866
−5.866

CLL114
Zap70 < 20
Zap70 < 20
10.69
−10.69

CLL115
Zap70 < 20
Zap70 < 20
4.141
−4.141

CLL116
Zap70 < 20
Zap70 < 20
1
−1

CLL117
Zap70 < 20
Zap70 < 20
1
−1

CLL118
Zap70 < 20
Zap70 < 20
1
−1

CLL119
Zap70 < 20
Zap70 < 20
10.11
−10.11

CLL120
Zap70 > 20
Zap70 > 20
−3.109
3.109

CLL121
Zap70 > 20
Zap70 > 20
−4.722
4.722

CLL122
Zap70 > 20
Zap70 > 20
−5.166
5.166

CLL123
Zap70 > 20
Zap70 > 20
−7.828
7.828

CLL124
Zap70 > 20
Zap70 > 20
−7.468
7.468

CLL125
Zap70 > 20
Zap70 > 20
−11.44
11.44

CLL126
Zap70 > 20
Zap70 > 20
−1
1

CLL127
Zap70 > 20
Zap70 > 20
−6.617
6.617

CLL128
Zap70 > 20
Zap70 > 20
−7.011
7.011

CLL129
Zap70 > 20
Zap70 > 20
−7.479
7.479

CLL130
Zap70 > 20
Zap70 > 20
−9.568
9.568

CLL131
Zap70 > 20
Zap70 > 20
−5.286
5.286

CLL132
Zap70 > 20
Zap70 > 20
−5.045
5.045

CLL133
Zap70 > 20
Zap70 > 20
−1
1

CLL134
Zap70 > 20
Zap70 > 20
−1
1

CLL135
Zap70 > 20
Zap70 > 20
−1.324
1.324

CLL136
Zap70 > 20
Zap70 > 20
−1
1

CLL137
Zap70 > 20
Zap70 > 20
−1
1

CLL138
Zap70 > 20
Zap70 > 20
−9.649
9.649

CLL139
Zap70 > 20
Zap70 > 20
−9.264
9.264

CLL140
Zap70 > 20
Zap70 > 20
−7.13
7.13

CLL141
Zap70 > 20
Zap70 > 20
−11.77
11.77

CLL142
Zap70 > 20
Zap70 > 20
−2.986
2.986

CLL143
Zap70 > 20
Zap70 > 20
−1
1

CLL144
Zap70 > 20
Zap70 > 20
−1
1

*Prediction for 83 CLLs, from groups 1 and 4 (see text). Classification was generated by the ‘Support Vector Machines’ algorithm (Kernel Function used: Polynomial Dot Product (Order 2). Diagonal Scaling Factor: 0).

The miRNA signature associated with prognostic factors was generated using panel 1 samples and then tested to cross validate the panel 1 and to predict the status of samples from panel 2.

Association between miRNA expression and time to initial therapy.

This analysis was performed as described in Example 12. All of the microRNAs which can predict the time to initial therapy, with the exception of mir-29c, are overexpressed in the group characterized by a short interval from diagnosis to initial therapy (Table 22). The PAM score for each of the components of microRNA signature associated with the time from diagnosis to initial therapy is presented in Table 23.

TABLE 22

Relative expression levels of microRNAs predictive of

the time interval from diagnosis to initial therapy.

Short
Long

interval
interval

microarray expression

hsa-mir-181a
High
Low

hsa-mir-155
High
Low

hsa-mir-146
High
Low

hsa-mir-024-2
High
Low

hsa-mir-023b
High
Low

hsa-mir-023a
High
Low

hsa-mir-222
High
Low

hsa-mir-221
High
Low

hsa-mir-029c
Low
High

TABLE 23

PAM score for each of the components of microRNA signature

associated with the time from diagnosis to initial therapy.

1 score
2 score

hsa-mir-181a
0.1862
−0.0603

hsa-mir-155
0.1409
−0.0456

hsa-mir-146
0.07
−0.0227

hsa-mir-024-2
0.0696
−0.0225

hsa-mir-023b
0.0643
−0.0208

hsa-mir-023a
0.0587
−0.019

hsa-mir-222
0.0458
−0.0148

hsa-mir-221
0.0343
−0.0111

hsa-mir-029c
−0.0221
0.0072

Note:

Score 1 characterize the short time; score 2 the long time from diagnosis to initial therapy in a panel of 94 CLL patients.

The relevant teachings of all publications cited herein that have not explicitly been incorporated by reference, are incorporated herein by reference in their entirety. One skilled in the art will readily appreciate that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof and, accordingly, reference should be made to the appended claims, rather than to the foregoing specification, as indicating the scope of the invention.

Number	Name	Date	Kind
7723030	Croce et al.	May 2010	B2
8778676	Croce et al.	Jul 2014	B2
20080261908	Croce et al.	Oct 2008	A1
20080306006	Croce et al.	Dec 2008	A1
20080306017	Croce et al.	Dec 2008	A1
20080306018	Croce et al.	Dec 2008	A1
20100203544	Croce et al.	Aug 2010	A1
20110052502	Croce et al.	Mar 2011	A1
20140256040	Croce et al.	Sep 2014	A1

Number	Date	Country
WO 03029459	Apr 2003	WO
WO 03029459	Apr 2003	WO
WO 2004043387	May 2004	WO
WO 2005013901	Feb 2005	WO

Number	Date	Country
60543119	Feb 2004	US
60542929	Feb 2004	US
60542963	Feb 2004	US
60542940	Feb 2004	US
60580959	Jun 2004	US
60580797	Jun 2004	US

	Number	Date	Country
Parent	14281756	May 2014	US
Child	14846193		US
Parent	12767279	Apr 2010	US
Child	13972759		US

	Number	Date	Country
Parent	13972759	Aug 2013	US
Child	14281756		US
Parent	11194055	Jul 2005	US
Child	12767279		US

Diagnosis and treatment of cancers with microrna located in or near cancer-associated chromosomal features

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

Agents

CPC

Field of Search

CPC

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Abstract

Description

Claims

RELATED APPLICATIONS

GOVERNMENT SUPPORT

US Referenced Citations (9)

Foreign Referenced Citations (4)

Non-Patent Literature Citations (67)

Related Publications (1)

Provisional Applications (6)

Divisions (2)

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Continuation in Parts (1)

Entry
Amendment After Allowance dated Feb. 2, 2010, U.S. Appl. No. 11/194,055, “Diagnosis and Treatment of Cancers With MicroRNA Located in or Near Cancer-Associated Chromosomal Features.”
Notice of Non-Compliant Amendment (37 CFR 1.121) dated Oct. 7, 2009, U.S. Appl. No. 11/194,055, “Diagnosis and Treatment of Cancers With MicroRNA Located in or Near Cancer-Associated Chromosomal Features.”
Office Action dated Jun. 13, 2012, U.S. Appl. No. 12/751,463, “Diagnosis and Treatment Of Cancers With MicroRNA Located in or Near Cancer Associated Chromosomal Features.”
Office Action dated Mar. 18, 2009, U.S. Appl. No. 11/194,055, “Diagnosis and Treatment of Cancers With MicroRNA Located in or Near Cancer-Associated Chromosomal Features.”
Reply dated Jul. 16, 2009, U.S. Appl. No. 11/194,055, “Diagnosis and Treatment Of Cancers With MicroRNA Located in or Near Cancer-Associated Chromosomal Features.”
Reply to Restriction Requirement dated Dec. 16, 2008, U.S. Appl. No. 11/194,055, “Diagnosis and Treatment of Cancers With MicroRNA Located in or Near Cancer-Associated Chromosomal Features.”
Reply to Restriction Requirement dated Jul. 7, 2008, U.S. Appl. No. 11/194,055, “Diagnosis and Treatment of Cancers With MicroRNA Located in or Near Cancer-Associated Chromosomal Features.”
Reply to Restriction Requirement dated Mar. 5, 2008, U.S. Appl. No. 11/194,055, “Diagnosis and Treatment of Cancers With MicroRNA Located in or Near Cancer-Associated Chromosomal Features.”
Restriction Requirement dated Feb. 13, 2012, U.S. Appl. No. 12/751,463, “Diagnosis and Treatment of Cancers With MicroRNA Located in or Near Cancer Associated Chromosomal Features.”
Restriction Requirement dated May 23, 2008, U.S. Appl. No. 11/194,055, “Diagnosis and Treatment of Cancers With MicroRNA Located in or Near Cancer-Associated Chromosomal Features.”
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