Patent Grant
10169530
The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Oct. 24, 2013, is named GHI-0056-PCT_SL.txt and is 1,261,876 bytes in size.
The present invention relates to gene fusions and genes comprising alternative spliced junctions associated with breast cancer. The present invention also relates to methods of identifying gene fusions and genes comprising alternative spliced junctions in samples obtained from a patient with cancer. Furthermore, the present invention relates to method of predicting the prognosis of a patient with breast cancer based on the number of gene fusion events.
Genomic aberrations resulting in gene fusions and alternatively spliced genes play an important role in cancer. Gene fusions, for example, have been estimated to account for about 20% of human cancer morbidity. Mitelman et al., Nature Reviews Cancer 7:233-245 (2007). Gene fusions are hybrids created by joining two previously separate genes via genomic aberrations such as translocations, deletions, and inversions, or trans-splicing between precursor mRNAs. Gene fusions may up-regulate expression of oncogenic genes by fusing a strong promoter to an oncogene. The first gene fusion identified in human neoplasia was BCR-ABL1 in chronic myelogenous leukemia (CML). The protein resulting from this fusion exhibits constitutive tyrosine kinase activity. Discovery of BCR-ABL1 led to development of a targeted treatment for CML using the tyrosine kinase inhibitor imatinib, which was approved in 2001. Druker et al., New England Journal of Medicine 344:1038-1042 (2001). Most of the known gene fusions have been found in hematological disorders; however, with the advent of next-generation sequencing technology, rare recurrent gene fusion events have been identified in common solid tumors. See Kohno et al., (2012) Nature Medicine 18: 375-377 (2012); Takeuchi et al., Nature Medicine 18: 378-381 (2012); Lipson et al., Nature Medicine, 18: 382-384 (2012); and Ju, et al., Genome Res., 22: 436-445 (2012).
In cancer, aberrantly spliced pre-mRNAs escape the quality control mechanisms within cells (e.g., the nonsense mediated mRNA decay pathway) and are, therefore, translated into aberrant proteins. He et al., PLoS ONE 4(3):e4732 (2009). For example, alternative splicing is known to be related to the pathogenesis of colon cancer and has been described to occur in lung adenocarcinoma. Seo et al., Genome Research 1-11 (October 2012).
Transcriptome sequencing enables detection of transcriptional variants such as gene fusions and alternative splicing events. Current methods, such as ChimeranScan (Robinson, et al., Nature Medicine 17: 1646-1651 (2011)), SnowShoes-FTD (Asmann et al., Cancer Res, 72: 1921-1928 (2012)), GSTRUCT-fusions (Seshagiri, S. et al., Nature 488: 660-664 (2012)), and GFP (Ju et al., Genome Res., 22: 436-445 (2012)), use paired-end data obtained from fresh frozen tissue samples to detect gene fusions. Other methods, such as TopHat-Fusion (Kim and Salzberg, Genome Biol 12: R72 (2011)), FusionMap (Ge et al. Bioinformatics, 27: 1922-1928 (2011)), and FusionFinder (Francis et al. PLoS One, 7(6):e39987 (2012)) can use single-end data from cell lines or fresh frozen tissue samples to detect gene fusions.
Because standard clinical practices include generating formalin-fixed, paraffin-embedded (FFPE) tissue samples from biopsies and surgical resections, FFPE samples provide an enormous repository of information for cancer research. Nonetheless, current methods are not well suited for investigating RNA from FFPE samples as the RNA from such samples is often degraded and libraries generated from those samples have low complexity and small insert sizes.
The present bioinformatics approaches identify gene fusions and alternative spliced junctions from FFPE RNA-sequencing datasets at base-pair resolution.
A bioinformatics approach was developed to identify gene fusion junctions using FFPE RNA-sequencing datasets. The present invention provides gene fusion junctions that are present in breast cancer tissue samples. These gene fusions are provided in Tables A and B. The present invention also provides a bioinformatics approach to identify alternative spliced junctions. The present invention provides alternative spliced junctions that are present in breast cancer tissue samples. These alternative spliced junctions are present in Table 5.
The present invention accommodates the use of archived paraffin-embedded biopsy material for assay of gene fusion transcripts, and therefore is compatible with the most widely available type of biopsy material. It is also compatible with other different methods of tumor tissue harvest, for example, via core biopsy or fine needle aspiration.
A multiplexed, whole genome sequencing methodology was used to enable whole transcriptome-wide gene fusion and alternative spliced junction discovery using low amounts of FFPE tissue. The methods described herein support the use of single end or paired end sequence reads.
In one aspect, the invention provides a method for identifying a gene fusion in a biological sample obtained from a patient with cancer. The method comprises obtaining a plurality of reads from RNA sequencing of the biological sample. The read is then mapped to the human genome. Next, the method comprises determining whether the read comprises a distant spliced junction and selecting the read comprising a distant spliced junction. A candidate gene fusion comprising the distant spliced junction is then identified. The method also comprises creating a first set of templates for the candidate gene fusion. The first set of templates comprises: (1) a fusion template comprising 50 base pairs (bp) of exonic sequence of a preserved region of a donor gene and 50 bp of exonic sequence of a preserved region of an acceptor gene, (2) a donor template comprising 50 bp of exonic sequence of a preserved region of a donor gene and 50 bp of exonic sequence of a discarded region of an donor gene, (3) an acceptor template comprising 50 bp of exonic sequence of a discarded region of a acceptor gene and 50 bp of exonic sequence of a preserved region of an acceptor gene, (4) a donor genomic template comprising 50 bp upstream genomic sequence of a donor splicing site and 50 bp downstream genomic sequence of a donor splicing site, and (5) an acceptor genomic template comprising 50 bp upstream genomic sequence of an acceptor splicing site and 50 bp downstream genomic sequence of an acceptor splicing site. The first set of templates is used to filter false positives and provide accurate read alignment information. A candidate gene fusion is removed if any of the first template set sequences are identical, but map to different genes in the human genome. Next, a second set of templates is created. The second set of templates comprises (a) a fusion template comprising 150 bp of exonic sequence of a preserved region of a donor gene and 150 bp of exonic sequence of a preserved region of an acceptor gene, (b) a donor template comprising 150 bp of exonic sequence of a preserved region of a donor gene and 150 bp of exonic sequence of a discarded region of an donor gene, (c) an acceptor template comprising 150 bp of exonic sequence of a discarded region of a acceptor gene and 150 bp of exonic sequence of a preserved region of an acceptor gene, (d) a donor genomic template comprising 150 bp upstream genomic sequence of a donor splicing site and 150 bp downstream genomic sequence of a donor splicing site, and (e) an acceptor genomic template comprising 150 bp upstream genomic sequence of an acceptor splicing site and 150 bp downstream genomic sequence of an acceptor splicing site. The second set of templates is also used to filter false positives by determining the homology between templates (b) and (c) and between templates (d) and (e) and removing the candidate gene fusion if templates (b) and (c) are homologous or if templates (d) and (e) are homologous. Next, a read obtained from RNA sequencing of the biological sample is aligned to the first set of templates and the read that maps to the fusion template of the first set of templates is selected.
In some embodiments, a gene fusion is identified by a candidate gene fusion having at least two non-duplicate reads that map to the fusion template of the first set of templates.
In other embodiments, a gene fusion is identified by a candidate gene fusion having one non-duplicate read that maps to the fusion template of the first set of templates. The method then comprises determining and comparing the expression levels of the exons and introns of the preserved regions of the donor gene and the acceptor gene to the expression levels of the exons and introns of the discarded regions of the donor gene and the acceptor gene. A gene fusion is then identified as having increased expression levels of exons and introns of the preserved regions of the donor gene and the acceptor gene compared to the expression levels of the exons and introns of the discarded regions of the donor gene and the acceptor gene.
In another aspect, the present invention provides a method for predicting the presence of a gene fusion in a biological sample obtained from a patient with cancer. The method comprises identifying a gene fusion according to any of claims 1-3 in a first biological sample. Next, a second biological sample that does not have reads that map to the gene fusion is obtained. Then, the method comprises determining in the second biological sample the expression levels of exons and introns of preserved and discarded regions of a donor gene and an acceptor gene of the gene fusion identified in any of claims 1-3. The expression level of the second biological sample is compared to the expression levels of the first biological sample. The presence of the gene fusion in the second biological sample is predicted based on having a similar expression profile compared to the first biological sample.
In yet another aspect, the invention provides a method of predicting a likelihood of poor prognosis in a breast cancer patient. Gene fusion events in a breast tumor sample from the patient are identified and the number of gene fusion events in the breast tumor sample is determined. The presence of three or more gene fusion events is positively correlated with an increased likelihood of poor prognosis.
In a further aspect, the present invention provides a method of identifying an alternatively spliced junction in a biological sample obtained from a patient with cancer. The method comprises obtaining a read from RNA sequencing of the biological sample. Next, the read is mapped to the human genome. It is then determined whether the read comprises a distant spliced junction and the read that comprises the distant spliced junction is selected. It is next determined whether the distant spliced junction is present in a single gene. The distant spliced junction that is present in a single gene is selected. In some embodiments, the method further comprises preparing a report based on the identification of an alternative spliced junction.
In still a further aspect, the present invention provides a method for predicting a risk of recurrence of breast cancer. The method comprises determining the presence of an alternative spliced junction in a breast cancer tumor sample obtained from said patient. The alternative spliced junction is selected from Table 5. The presence of junction −chr3:196118684_−chr3:196129890 in UBXN, junction −chr12:24366277_−chr12:24048958 in SOX5, junction −chr9:114148657_−chr9:114154104 in KIAA0368, junction +chr18:39629569_+chr18:39623697 in PIK3C3, or junction+chr1:155695810_chr1:155695173 is correlated with an decreased risk of recurrence, and wherein the presence of junction −chr2:99786013_−chr2:99787892 in MITD1 is correlated with an increased risk of recurrence. The presence of the alternative spliced junction can be determined by whole transcriptome sequencing or reverse transcriptase polymerase chain reaction (RT-PCR).
In some embodiments, an isolated polynucleotide comprises a gene fusion, wherein the isolated polynucleotide comprises a sequence selected from SEQ ID NO:1 to SEQ ID NO:100 is provided
In other embodiments, an isolated polynucleotide comprises an alternative spliced junction selected from −chr12:24366277_−chr12:24048958; −chr9:114148657_−chr9:114154104; +chr18:39629569_+chr18:39623697; +chr1:155695810_chr1:155695173; and −chr2:99786013_−chr2:99787892 is provided.
The top panel shows a scatter plot of the expression data. The x-axis shows the number of normalized counts for the indicated fusion donor and the y-axis shows the number of normalized counts for the indicated fusion acceptor. The closed circles represent samples that are positive for the indicated fusion and the open circles shows the remaining samples in the cohort that are negative for the fusion.
The middle panel shows a bar plot of the expression of the exons and introns of the indicated fusion donor. The x-axis shows the relative genomic location of the donor's exons and introns and the y-axis shows the number of normalized counts by length. The vertical line separates the donor exons and introns into those that preserved in the fusion (black bars) and those that are discarded from the fusion (gray bars). As shown on the x-axis, the symbol “d” indicates that the exon or intron is a donor exon or intron; the symbol “l” indicates that the exon or intron is located to the left of the vertical line separating discarded and preserved exons and introns; the symbol “r” indicates that the exon or intron is located to the right of the vertical line separating discarded and preserved exons and introns.
The bottom panel shows a bar plot of the expression of the exons and introns of the indicated fusion acceptor. The x-axis shows the relative genomic location of the acceptor's exons and introns and the y-axis shows the number of normalized counts by length. The vertical line separates the acceptor exons and introns into those that preserved in the fusion (black bars) and those that are discarded from the fusion (gray bars). As shown on the x-axis, the symbol “a” indicates that the exon or intron is an acceptor exon or intron; the symbol “l” indicates that the exon or intron is located to the left of the vertical line separating discarded and preserved exons and introns; the symbol “r” indicates that the exon or intron is located to the right of the vertical line separating discarded and preserved exons and introns.
Before the present invention and specific exemplary embodiments of the invention are described, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.
Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either both of those included limits are also included in the invention.
As used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “an RNA transcript” includes a plurality of such RNA transcripts.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. For example, Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), provide one skilled in the art with a general guide to many of the terms used in the present application.
Additionally, the practice of the present invention will employ, unless otherwise indicated, conventional techniques of molecular biology (including recombinant techniques), microbiology, cell biology, and biochemistry, which are within the skill of the art. Such techniques are explained fully in the literature, such as, “Molecular Cloning: A Laboratory Manual”, 2nd edition (Sambrook et al., 1989); “Oligonucleotide Synthesis” (M. J. Gait, ed., 1984); “Animal Cell Culture” (R. I. Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.); “Handbook of Experimental Immunology”, 4th edition (D. M. Weir & C. C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene Transfer Vectors for Mammalian Cells” (J. M. Miller & M. P. Calos, eds., 1987); “Current Protocols in Molecular Biology” (F. M. Ausubel et al., eds., 1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds., 1994).
One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described. For purposes of the present invention, the following terms are defined below.
The term “annotate” refers to adding biological information to a genome sequence.
The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer include but are not limited to, breast cancer, colon cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer, pancreatic cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, and brain cancer.
The term “correlates” or “correlating” as used herein refers to a statistical association between instances of two events, where events may include numbers, data sets, and the like. For example, when the events involve numbers, a positive correlation (also referred to herein as a “direct correlation”) means that as one increases, the other increases as well. A negative correlation (also referred to herein as an “inverse correlation”) means that as one increases, the other decreases. The present invention provides gene fusions and alternative spliced junctions which may be correlated with a particular outcome measure. For example, the presence of a gene fusion or an alternative spliced junction may be positively correlated with a likelihood of a good clinical outcome for the patient, such as an increased likelihood of long-term survival without recurrence and/or a positive response to a chemotherapy, and the like. Such a positive correlation may be demonstrated statistically in various ways, e.g. by a low hazard ratio. In another example, the presence of a gene fusion or an alternative spliced junction may be negatively correlated with a likelihood of good clinical outcome for the patient. In this case, for example, the patient may have a decreased likelihood of long-term survival without recurrence of the cancer and/or a positive response to a chemotherapy, and the like. Such a negative correlation indicates that the patient likely has a poor prognosis or will respond poorly to a chemotherapy, and this may be demonstrated statistically in various ways, e.g., by a high hazard ratio.
As used herein, the term “exon” refers to any segment of an interrupted gene that is represented in the mature RNA product (B. Lewin. Genes IV Cell Press, Cambridge Mass. 1990). As used herein, the terms “intron” and “intronic sequence” refer to any non-coding region found within genes.
The term “expression product” as used herein refers to an expression product of a coding RNA transcript. Thus, the term refers to a polypeptide or protein.
As used herein, the term “intergenic region” refers to a stretch of DNA or RNA sequences located between clusters of genes that contain few or no genes. Intergenic regions are different from intragenic regions (or “introns”), which are non-coding regions that are found between exons within genes. An intergenic region may be comprised of one or more “intergenic sequences.”
As used herein, the term “gene fusion” refers to a chimeric molecule derived from two separate genes—a donor gene and an acceptor gene. The donor gene is generally located upstream of the acceptor gene. The regions of the donor gene and the acceptor gene that are present in the gene fusion are referred to herein as a “preserved region” of the donor gene and a “preserved region” of the acceptor gene, respectively. The regions of the donor gene and the acceptor gene that are not present in the gene fusion are referred to herein as a “discarded region” of the donor gene and a “discarded region” of the acceptor gene, respectively. A gene fusion may arise from a chromosomal aberration, such as a translocation, deletion, or inversion, within a chromosome or between chromosomes. A gene fusion may result in an expression product with a new or different function compared to the fusion partners. Alternatively, a proto-oncogene may be fused to a strong promoter, resulting in expression of an oncogene. A gene fusion is recurrent when it is present in samples from two or more patients with the same type of cancer, for example, breast cancer.
As used herein, the term “homology” with regard to template sequences, refers to the degree of similarity between two sequences. In some embodiments, a 300 bp donor template and a 300 bp acceptor template are homologous if they share sequence identity of more than 14 bp. In other embodiments, a 300 bp donor genomic template and a 300 bp acceptor genomic template are homologous if they share sequence identity of more than 14 bp.
As used herein, the term “isolated” refers to a molecule that is separated from other constituents. For example, an isolated DNA molecule may be cleaved from genomic DNA or synthesized to include a portion of a naturally occurring DNA molecule. Isolated DNA is a free-standing portion of the larger, natural DNA molecule. Isolated DNA molecules, therefore, are not naturally occurring DNA molecules or native DNA molecules.
As used herein, the term “level” refers to qualitative or quantitative determination of the number of reads of exons and introns in the genes that comprise a gene fusion. An exon or an intron exhibits an “increased level” when the level of the exon or intron is higher in a first sample, such as in a preserved acceptor or donor region of a gene fusion, than in a second sample, such as in a discarded acceptor or donor region of a gene fusion.
The term “long-term” survival as used herein refers to survival for at least 3 years. In other embodiments, it may refer to survival for at least 5 years, or for at least 10 years following surgery or other treatment.
As used herein, the term “pathology” of cancer includes all phenomena that comprise the well-being of the patient. This includes, without limitation, abnormal or uncontrollable cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological response, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or organs, such as lymph nodes.
A “patient response” may be assessed using any endpoint indicating a benefit to the patient, including, without limitation, (1) inhibition, to some extent, of tumor growth, including slowing down and complete growth arrest; (2) reduction in the number of tumor cells; (3) reduction in tumor size; (4) inhibition (i.e., reduction, slowing down or complete stopping) of tumor cell infiltration into adjacent peripheral organs and/or tissues; (5) inhibition (i.e. reduction, slowing down or complete stopping) of metastasis; (6) enhancement of anti-tumor immune response, which may, but does not have to, result in the regression or rejection of the tumor; (7) relief, to some extent, of one or more symptoms associated with the cancer; (8) increase in the length of survival following treatment; and/or (9) decreased mortality at a given point of time following treatment.
The term “polynucleotide” when used in singular or plural, generally refers to any polyribonucleotide or polydeoxribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides as defined herein include, without limitation, single- and double-stranded DNA, DNA including single- and double-stranded regions, single- and double-stranded RNA, and RNA including single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or include single- and double-stranded regions. In addition, the term “polynucleotide” as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an oligonucleotide. The term “polynucleotide” specifically includes cDNAs. The term includes DNAs (including cDNAs) and RNAs that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons are “polynucleotides” as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term “polynucleotides” as defined herein. In general, the term “polynucleotide” embraces all chemically, enzymatically and/or metabolically modified forms of unmodified polynucleotides, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including simple and complex cells.
The term “prognosis” as used herein, refers to the prediction of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of neoplastic disease, such as breast cancer. The term “prediction” is used herein to refer to the likelihood that a patient will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses, or that a patient will survive, following surgical removal of the primary tumor and/or chemotherapy for a certain period of time without cancer recurrence. The methods of the present invention can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient. The methods of the present invention are tools in predicting if a patient is likely to respond favorably to a treatment regimen, such as surgical intervention, chemotherapy with a given drug or drug combination, and/or radiation therapy, or whether long-term survival of the patient without cancer recurrence is likely, following surgery and/or termination of chemotherapy or other treatment modalities.
The term “recurrence,” as used herein, refers to local or distant (metastasis) recurrence of cancer. For example, breast cancer can come back as a local recurrence (in the treated breast or near the tumor surgical site) or as a distant recurrence in the body. The most common sites of breast cancer recurrence include the lymph nodes, bones, liver, or lungs.
As used herein, the term “RefSeq RNA” refers to an RNA that can be found in the Reference Sequence (RefSeq) database, a collection of publicly available nucleotide sequences and their protein products built by the National Center for Biotechnology Information (NCBI). The RefSeq database provides an annotated, non-redundant record for each natural biological molecule (i.e. DNA, RNA or protein) included in the database. Thus, a sequence of a RefSeq RNA is well-known and can be found in the RefSeq database at http://www.ncbi.nlm.nih.gov/RefSeq/. See also Pruitt et al., Nucl. Acids Res. 33(Supp 1):D501-D504 (2005). Accession numbers for donor and acceptor RefSeq are provided in Table A.
As used herein, the term “RNA transcript” refers to the RNA transcription product of DNA and includes coding and non-coding RNA transcripts. RNA transcripts include, for example, mRNA, an unspliced RNA, a splice variant mRNA, a microRNA, fragmented RNA, long intergenic non-coding RNAs (lincRNAs), intergenic RNA sequences or regions, and intronic RNAs.
The terms “read” and “sequence read” are used interchangeably herein to refer to sequence information obtained from an RNA sequencing experiment. A read may comprise, for example, 50 bases to 150 bases, 50 bases to 100 bases, 50 bases to 55 bases, 55 bases to 60 bases, 60 bases to 65 bases, 65 bases to 70 bases, 70 bases to 75 bases, 75 bases to 80 bases, 80 bases to 85 bases, 85 bases to 90 bases, 90 bases to 95 bases, 95 bases to 100 bases, 100 bases to 105, 105 bases to 110, 110 bases to 115, 115 bases to 120, 120 bases to 125, 125 bases to 130, 130 bases to 135, 135 bases to 140, 140 bases to 145, or 145 bases to 150 bases. A read may be a single read or a paired-end read. A single read refers to a read that is sequenced from one end. A paired-end read refers to a read that is sequenced from both ends.
As used herein, the term “splicing” refers to the process of removing introns and joining exons from pre-mRNA to generate mRNA. The terms “splice site” and “splice junction” are used interchangeably to refer to a region where a splicing event takes place, for example, at an exon-intron junction in a pre-mRNA molecule. For example, a slice donor site may be present at the 5′ end of an intron and a splice acceptor site may be present at the 3′ end of an intron. As used herein, a “distant spliced site” includes sites used in splicing events that occur between different genes or chromosomes. Distant splicing events may also include splicing events occurring within the same gene, but in the opposite transcription direction. Distant splicing events may include translocations, inversions, and the like. As used herein, “alternative splicing” refers to a process whereby identical pre-mRNA molecules are spliced in various ways to yield different mRNA molecules. The different mRNA molecules may be translated into different protein isoforms.
In some embodiments, the alternative spliced junction is present UBXN7, SOX5, KIAA0368, PIKC3C, DAP3, or MITD1. In other embodiments, the alternative spliced junction within UBXN7 comprises the junction −chr3:196118684_−chr3:196129890; the alternative spliced junction within SOX5 comprises the junction −chr12:24366277_−chr12:24048958; the alternative spliced junction within KIAA0368 comprises the junction −chr9:114148657_−chr9:114154104; the alternative spliced junction within PIK3C3 comprises the junction+chr18:39629569_+chr18:39623697; the alternative spliced junction within DAP3 comprises the junction+chr1:155695810_chr1:155695173; and the alternative spliced junction within MITD1 comprises the junction −chr2:99786013_−chr2:99787892.
The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a mammal being assessed for treatment and/or being treated. In an embodiment, the mammal is a human. The terms “subject,” “individual,” and “patient” thus encompass individuals having cancer (e.g., breast cancer), including those who have undergone or are candidates for resection (surgery) to remove cancerous tissue.
As used herein, the term “surgery” applies to surgical methods undertaken for removal of cancerous tissue, including mastectomy, lumpectomy, lymph node removal, sentinel lymph node dissection, prophylactic mastectomy, prophylactic ovary removal, cryotherapy, and tumor biopsy. The tumor samples used for the methods of the present invention may have been obtained from any of these methods.
As used herein, the term “template” refers to a nucleotide sequence against which another nucleotide sequence may be compared. The templates used in the methods of the present invention include (1) a fusion template comprising 50 or 150 bp of exonic sequence of a preserved region of a donor gene and 50 or 150 bp of exonic sequence of a preserved region of an acceptor gene, (2) a donor template comprising 50 or 150 bp of exonic sequence of a preserved region of a donor gene and 50 or 150 bp of exonic sequence of a discarded region of an donor gene, (3) an acceptor template comprising 50 or 150 bp of exonic sequence of a discarded region of a acceptor gene and 50 or 150 bp of exonic sequence of a preserved region of an acceptor gene, (4) a donor genomic template comprising 50 or 150 bp upstream genomic sequence of a donor splicing site and 50 or 150 bp downstream genomic sequence of a donor splicing site, and (5) an acceptor genomic template comprising 50 or 150 bp upstream genomic sequence of an acceptor splicing site and 50 or 150 bp downstream genomic sequence of an acceptor splicing site. In some embodiments, the method comprises determining the homology between various templates. In other embodiments, the method comprises aligning a read obtained from RNA sequencing of a biological sample to the templates and selecting the read that maps to the fusion template.
The term “tumor” as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.
The term “tumor sample” as used herein refers to a sample comprising tumor material obtained from a cancer patient. The term encompasses tumor tissue samples, for example, tissue obtained by surgical resection and tissue obtained by biopsy, such as for example, a core biopsy or a fine needle biopsy. In a particular embodiment, the tumor sample is a fixed, wax-embedded tissue sample, such as a formalin-fixed, paraffin-embedded tissue sample. Additionally, the term “tumor sample” encompasses a sample comprising tumor cells obtained from sites other than the primary tumor, e.g., circulating tumor cells. The term also encompasses cells that are the progeny of the patient's tumor cells, e.g. cell culture samples derived from primary tumor cells or circulating tumor cells. The term further encompasses samples that may comprise protein or nucleic acid material shed from tumor cells in vivo, e.g., bone marrow, blood, plasma, serum, and the like. The term also encompasses samples that have been enriched for tumor cells or otherwise manipulated after their procurement and samples comprising polynucleotides and/or polypeptides that are obtained from a patient's tumor material.
As used herein, “whole transcriptome sequencing” refers to the use of high throughput sequencing technologies to sequence the entire transcriptome in order to get information about a sample's RNA content. Whole transcriptome sequencing can be done with a variety of platforms for example, the Genome Analyzer or HiSeq 2000/2500 (Illumina, Inc., San Diego, Calif.) and the SOLiD™ Sequencing System (Life Technologies, Carlsbad, Calif.). However, any platform useful for whole transcriptome sequencing may be used.
The term “RNA-Seq” or “transcriptome sequencing” or “RNA sequencing” refers to sequencing performed on RNA (or cDNA) instead of DNA, where typically, the primary goal is to measure expression levels, detect fusion transcripts, alternative splicing, and other genomic alterations that can be better assessed from RNA. RNA-Seq includes whole transcriptome sequencing as well as target specific sequencing.
The term “computer-based system,” as used herein, refers to the hardware means, software means, and data storage means used to analyze information. The minimum hardware of a patient computer-based system comprises a central processing unit (CPU), input means, output means, and data storage means. A skilled artisan can readily appreciate that many of the currently available computer-based system are suitable for use in the present invention and may be programmed to perform the specific measurement and/or calculation functions of the present invention.
To “record” data, programming or other information on a computer readable medium refers to a process for storing information, using any such methods as known in the art. Any convenient data storage structure may be chosen, based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g. word processing text file, database format, etc.
A “processor” or “computing means” references any hardware and/or software combination that will perform the functions required of it. For example, any processor herein may be a programmable digital microprocessor such as available in the form of an electronic controller, mainframe, server or personal computer (desktop or portable). Where the processor is programmable, suitable programming can be communicated from a remote location to the processor, or previously saved in a computer program product (such as a portable or fixed computer readable storage medium, whether magnetic, optical or solid state device based). For example, a magnetic medium or optical disk may carry the programming, and can be read by a suitable reader communicating with each processor at its corresponding station.
The present invention provides gene fusions and alternative spliced junctions that are associated with breast cancer. These gene fusions are listed in Tables A and B and the alternative spliced junctions are provided in Table 5. The present invention also provides a method for identifying gene fusions and a method for identifying alternative spliced junctions in a biological sample obtained from a patient with cancer. The present invention further provides a method for predicting a gene fusion in a biological sample obtained from a patient with cancer.
The gene fusions and alternative spliced junctions and associated information provided by the present invention also have utility in the development of therapies to treat cancers and screening patients for inclusion in clinical trials. The gene fusions and alternative spliced junctions and associated information may further be used to design or produce a reagent that modulates the level or activity of the gene fusion and alternative spliced junction. Such reagents may include, but are not limited to, a drug, an antisense RNA, a small inhibitory RNA (siRNA), a ribozyme, a small molecule, a monoclonal antibody, and a polyclonal antibody.
In various embodiments of the methods of the present invention, various technological approaches are available for determining the presence of gene fusions or alternative spliced junctions, including, without limitation, whole transcriptome sequencing, RT-PCR, microarrays, and serial analysis of gene expression (SAGE), which are described in more detail below.
Correlating the Presence of a Gene Fusion or an Alternative Spliced Junction to a Clinical Outcome
One skilled in the art will recognize that there are many statistical methods that may be used to determine whether there is a correlation between an outcome of interest (e.g., likelihood of survival) and the presence of a gene fusion or an alternative spliced junction. This relationship can be presented as a continuous recurrence score (RS), or patients may be stratified into risk groups (e.g., low, intermediate, high). For example, a Cox proportional hazards regression model may fit to a particular clinical endpoint (e.g., RFI, DFS, OS). One assumption of the Cox proportional hazards regression model is the proportional hazards assumption, i.e. the assumption that effect parameters multiply the underlying hazard. Assessments of model adequacy may be performed including, but not limited to, examination of the cumulative sum of martingale residuals. One skilled in the art would recognize that there are numerous statistical methods that may be used (e.g., Royston and Parmer (2002), smoothing spline, etc.) to fit a flexible parametric model using the hazard scale and the Weibull distribution with natural spline smoothing of the log cumulative hazards function, with effects for treatment (chemotherapy or observation) and RS allowed to be time-dependent. (See, e.g., P. Royston, M. Parmer, Statistics in Medicine 21(15:2175-2197 (2002).)
In an exemplary embodiment, power calculations are carried out for the Cox proportional hazards model with a single non-binary covariate using the method proposed by F. Hsieh and P. Lavori, Control Clin Trials 21:552-560 (2000) as implemented in PASS 2008.
Methods of Assaying Gene Fusions and Alternative Spliced Junctions
Methods of assaying gene fusions and alternative spliced junctions include methods based on sequencing of polynucleotides, methods based on hybridization analysis of polynucleotides, and proteomics-based methods. Representative methods for sequencing-based analysis include Massively Parallel Sequencing (see e.g., Tucker et al., The American J. Human Genetics 85:142-154, 2009) and Serial Analysis of Gene Expression (SAGE). Exemplary methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106:247-283 (1999)); RNAse protection assays (Hod, Biotechniques 13:852-854 (1992)); and PCR-based methods, such as reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)). Antibodies may be employed that can recognize sequence-specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes.
Nucleic Acid Sequencing-Based Methods
Nucleic acid sequencing technologies are suitable methods for expression analysis. The principle underlying these methods is that the number of times a cDNA sequence is detected in a sample is directly related to the relative RNA levels corresponding to that sequence. These methods are sometimes referred to by the term Digital Gene Expression (DGE) to reflect the discrete numeric property of the resulting data. Early methods applying this principle were Serial Analysis of Gene Expression (SAGE) and Massively Parallel Signature Sequencing (MPSS). See, e.g., S. Brenner, et al., Nature Biotechnology 18(6):630-634 (2000).
More recently, the advent of “next-generation” sequencing technologies has made DGE simpler, higher throughput, and more affordable. As a result, more laboratories are able to utilize DGE to screen the expression of more nucleic acids in more individual patient samples than previously possible. See, e.g., J. Marioni, Genome Research 18(9):1509-1517 (2008); R. Morin, Genome Research 18(4):610-621 (2008); A. Mortazavi, Nature Methods 5(7):621-628 (2008); N. Cloonan, Nature Methods 5(7):613-619 (2008). Massively parallel sequencing methods have also enabled whole genome or transcriptome sequencing, allowing the analysis of not only coding but also non-coding sequencees. As reviewed in Tucker et al., The American J. Human Genetics 85:142-154 (2009), there are several commercially available massively parallel sequencing platforms, such as the Illumina Genome Analyzer or HiSeq 2000/2500 (Illumina, Inc., San Diego, Calif.), Applied Biosystems SOLiD™ Sequencer (Life Technologies, Carlsbad, Calif.), Roche GS-FLX 454 Genome Sequencer (Roche Applied Science, Germany), and the Helicos® Genetic Analysis Platform (Helicos Biosciences Corp., Cambridge, Mass.). Other developing technologies may be used.
Reverse Transcriptase PCR (RT-PCR)
The starting material is typically total RNA isolated from a human tumor, usually from a primary tumor. Optionally, normal tissues from the same patient can be used as an internal control. RNA can be extracted from a tissue sample, e.g., from a sample that is fresh, frozen (e.g. fresh frozen), or fixed and paraffin-embedded (e.g. formalin-fixed).
General methods for RNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andrés et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MasterPure™ Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from fresh frozen tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from a tumor sample can be isolated, for example, by cesium chloride density gradient centrifugation. The isolated RNA may then be depleted of ribosomal RNA as described in U.S. Pub. No. 2011/0111409.
The sample containing the RNA is then subjected to reverse transcription to produce cDNA from the RNA template, followed by exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of the assay. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, CA, USA), following the manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.
PCR-based methods use a thermostable DNA-dependent DNA polymerase, such as a Taq DNA polymerase. For example, TaqMan® PCR typically utilizes the 5′-nuclease activity of Taq or Tth polymerase to hydrolyze a hybridization probe bound to its target amplicon, but any enzyme with equivalent 5′ nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction product. A third oligonucleotide, or probe, can be designed to facilitate detection of a nucleotide sequence of the amplicon located between the hybridization sites of the two PCR primers. The probe can be detectably labeled, e.g., with a reporter dye, and can further be provided with both a fluorescent dye, and a quencher fluorescent dye, as in a TaqMan® probe configuration. Where a TaqMan® probe is used, during the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the released reporter dye is free from the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.
TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, ABI PRISM 7900™ Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In a preferred embodiment, the 5′ nuclease procedure is run on a real-time quantitative PCR device such as the ABI PRISM 7900™ Sequence Detection System™. The system consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system amplifies samples in a 384-well format on a thermocycler. The RT-PCR may be performed in triplicate wells with an equivalent of 2 ng RNA input per 10 μL-reaction volume. During amplification, laser-induced fluorescent signal is collected in real-time through fiber optics cables for all wells, and detected at the CCD. The system includes software for running the instrument and for analyzing the data.
5′-Nuclease assay data are generally initially expressed as a threshold cycle (“Ct”). Fluorescence values are recorded during every cycle and represent the amount of product amplified to that point in the amplification reaction. The threshold cycle (Ct) is generally described as the point when the fluorescent signal is first recorded as statistically significant.
To minimize errors and the effect of sample-to-sample variation, RT-PCR is usually performed using an internal standard. The ideal internal standard gene (also referred to as a reference gene) is expressed at a constant level among cancerous and non-cancerous tissue of the same origin (i.e., a level that is not significantly different among normal and cancerous tissues), and is not significantly affected by the experimental treatment (i.e., does not exhibit a significant difference in expression level in the relevant tissue as a result of exposure to chemotherapy). RNAs most frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin. Gene expression measurements can be normalized relative to the mean of one or more (e.g., 2, 3, 4, 5, or more) reference genes. Reference-normalized expression measurements can range from 0 to 15, where a one unit increase generally reflects a 2-fold increase in RNA quantity.
Real time PCR is compatible both with quantitative competitive PCR, where an internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a normalization gene contained within the sample, or a housekeeping gene for RT-PCR. For further details see, e.g. Held et al., Genome Research 6:986-994 (1996).
Design of PCR Primers and Probes
PCR primers and probes can be designed based upon exon, intron, or intergenic sequences present in the RNA transcript of interest. Primer/probe design can be performed using publicly available software, such as the DNA BLAT software developed by Kent, W. J., Genome Res. 12(4):656-64 (2002), or by the BLAST software including its variations.
Where necessary or desired, repetitive sequences of the target sequence can be masked to mitigate non-specific signals. Exemplary tools to accomplish this include the Repeat Masker program available on-line through the Baylor College of Medicine, which screens DNA sequences against a library of repetitive elements and returns a query sequence in which the repetitive elements are masked. The masked sequences can then be used to design primer and probe sequences using any commercially or otherwise publicly available primer/probe design packages, such as Primer Express (Applied Biosystems); MGB assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers. In: Rrawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, N.J., pp 365-386).
Other factors that can influence PCR primer design include primer length, melting temperature (Tm), and G/C content, specificity, complementary primer sequences, and 3′-end sequence. In general, optimal PCR primers are generally 17-30 bases in length, and contain about 20-80%, such as, for example, about 50-60% G+C bases, and exhibit Tm's between 50 and 80° C., e.g. about 50 to 70° C.
For further guidelines for PCR primer and probe design see, e.g. Dieffenbach, C W. et al, “General Concepts for PCR Primer Design” in: PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press, New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs” in: PCR Protocols, A Guide to Methods and Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer and probe design. Methods MoI. Biol. 70:520-527 (1997), the entire disclosures of which are hereby expressly incorporated by reference.
MassARRAY® System
In MassARRAY-based methods, such as the exemplary method developed by Sequenom, Inc. (San Diego, Calif.) following the isolation of RNA and reverse transcription, the obtained cDNA is spiked with a synthetic DNA molecule (competitor), which matches the targeted cDNA region in all positions, except a single base, and serves as an internal standard. The cDNA/competitor mixture is PCR amplified and is subjected to a post-PCR shrimp alkaline phosphatase (SAP) enzyme treatment, which results in the dephosphorylation of the remaining nucleotides. After inactivation of the alkaline phosphatase, the PCR products from the competitor and cDNA are subjected to primer extension, which generates distinct mass signals for the competitor- and cDNA-derived PCR products. After purification, these products are dispensed on a chip array, which is pre-loaded with components needed for analysis with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. The cDNA present in the reaction is then quantified by analyzing the ratios of the peak areas in the mass spectrum generated. For further details see, e.g. Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059-3064 (2003).
Other PCR-Based Methods
Further PCR-based techniques that can find use in the methods disclosed herein include, for example, BeadArray® technology (Illumina, San Diego, Calif.; Oliphant et al., Discovery of Markers for Disease (Supplement to Biotechniques), June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000)); BeadsArray for Detection of Gene Expression® (BADGE), using the commercially available LuminexlOO LabMAP® system and multiple color-coded microspheres (Luminex Corp., Austin, Tex.) in a rapid assay for gene expression (Yang et al., Genome Res. 11:1888-1898 (2001)); and high coverage expression profiling (HiCEP) analysis (Fukumura et al., Nucl. Acids. Res. 31(16) e94 (2003).
Microarrays
In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are arrayed on a substrate. The arrayed sequences are then contacted under conditions suitable for specific hybridization with detectably labeled cDNA generated from RNA of a sample. The source of RNA typically is total RNA isolated from a tumor sample, and optionally from normal tissue of the same patient as an internal control or cell lines. RNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples.
For example, PCR amplified inserts of cDNA clones of a gene to be assayed are applied to a substrate in a dense array. Usually at least 10,000 nucleotide sequences are applied to the substrate. For example, the microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After washing under stringent conditions to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance.
With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pair wise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et at, Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)). Microarray analysis can be performed on commercially available equipment, following the manufacturer's protocols, such as by using the Affymetrix GenChip® technology, or Incyte's microarray technology.
Isolating RNA from Body Fluids
Methods of isolating RNA for expression analysis from blood, plasma and serum (see for example, Tsui N B et al. (2002) Clin. Chem. 48, 1647-53 and references cited therein) and from urine (see for example, Boom R et al. (1990) J Clin Microbiol. 28, 495-503 and reference cited therein) have been described.
Immunohistochemistry
Immunohistochemistry methods are also suitable for detecting the presence of gene fusions and alternative spliced junctions and applied to the method disclosed herein. Antibodies (e.g., monoclonal antibodies) that specifically bind a gene product of a gene of interest can be used in such methods. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as biotin, or an enzyme such as horse radish peroxidase or alkaline phosphatase. Alternatively, unlabeled primary antibody can be used in conjunction with a labeled secondary antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.
Proteomics
The term “proteome” is defined as the totality of the proteins present in a sample (e.g. tissue, organism, or cell culture) at a certain point of time. Proteomics includes, among other things, study of the global changes of protein expression in a sample (also referred to as “expression proteomics”). Proteomics typically includes the following steps: (1) separation of individual proteins in a sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of the individual proteins recovered from the gel, e.g. my mass spectrometry or N-terminal sequencing, and (3) analysis of the data using bioinformatics.
General Description of the RNA Isolation and Preparation from Fixed, Paraffin-Embedded Samples for Whole Transcriptome Sequencing
The steps of a representative protocol for profiling gene expression levels using fixed, paraffin-embedded tissues as the RNA source are provided in various published journal articles. (See, e.g., T. E. Godfrey et al., J. Molec. Diagnostics 2: 84-91 (2000); K. Specht et al., Am. J. Pathol. 158: 419-29 (2001), M. Cronin, et al., Am J Pathol 164:35-42 (2004)). Modified methods can used for whole transcriptome sequencing as described in the Examples section. Briefly, a representative process starts with cutting a tissue sample section (e.g. about 10 μm thick sections of a paraffin-embedded tumor tissue sample). The RNA is then extracted, and ribosomal RNA may be deleted as described in U.S. Pub. No. 2011/0111409. cDNA sequencing libraries may be prepared that are directional and allowed for single or paired-end sequencing using commercially available kits such as the ScriptSeg™ mRNA-Seq Library Preparation Kit (Illumina). The libraries may also be barcoded for multiplex sequencing using commercially available barcode primers such as the RNA-Seq Barcode Primers from Illumina. PCR is then carried out to generate the second strand of cDNA to incorporate the barcodes and to amplify the libraries. After the libraries are quantified, the sequencing libraries may be sequenced as described herein.
Kits of the Invention
The materials for use in the methods of the present invention are suited for preparation of kits produced in accordance with well known procedures. The present invention thus provides kits comprising agents, which may include primers and/or probes, for quantitating the level of the disclosed gene fusions or alternative spliced junctions or their expression products via methods such as whole transcriptome sequencing or RT-PCR for predicting prognostic outcome. Such kits may optionally contain reagents for the extraction of RNA from tumor samples, in particular, fixed paraffin-embedded tissue samples and/or reagents for whole transcriptome sequencing. In addition, the kits may optionally comprise the reagent(s) with an identifying description or label or instructions relating to their use in the methods of the present invention. The kits may comprise containers (including microliter plates suitable for use in an automated implementation of the method), each with one or more of the various reagents (typically in concentrated form) utilized in the methods, including, for example, pre-fabricated microarrays, buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP), reverse transcriptase, DNA polymerase, RNA polymerase, and one or more probes and primers of the present invention (e.g., appropriate length poly(T) or random primers linked to a promoter reactive with the RNA polymerase). Mathematical algorithms used to estimate or quantify prognostic information are also potential components of kits.
Reports
The methods of this invention are suited for the preparation of reports summarizing the findings of the methods of the present invention. A “report” as described herein, is an electronic or tangible document that includes elements that provide information of interest relating the presence of gene fusions in a sample, the presence of alternative spliced junctions in a sample, or a likelihood assessment and its results. A subject report can be completely or partially electronically generated, e.g., presented on an electronic display (e.g., computer monitor). A report can further include one or more of: 1) information regarding the testing facility; 2) service provider information; 3) patient data; 4) sample data; 5) an interpretive report, which can include various information including: a) indication; b) test data, where test data can include information regarding the presence of a gene fusion or alternative spliced junction of interest, and 6) other features.
The present invention therefore provides methods of creating reports and the reports resulting therefrom. The report may include a summary of the gene fusions or alternative spliced junctions, in the cells obtained from the patient's tumor sample. The report may include a prediction that the patient has an increased likelihood of breast cancer recurrence or the report may include a prediction that the subject has a decreased likelihood of breast cancer recurrence. The report may include a recommendation for a treatment modality such as surgery alone or surgery in combination with chemotherapy. The report may be presented in electronic format or on paper.
Thus, in some embodiments, the methods of the present invention further include generating a report that includes information regarding the patient's likelihood of long-term survival without breast cancer recurrence. For example, the methods of the present invention can further include a step of generating or outputting a report providing the results of a patient response likelihood assessment, which can be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium).
A report that includes information regarding the likelihood that a patient will exhibit breast cancer recurrence is provided to a user. An assessment as to the likelihood that a cancer patient will exhibit breast cancer recurrence is referred to as a “likelihood assessment.” A person or entity who prepares a report (“report generator”) may also perform the likelihood assessment. The report generator may also perform one or more of sample gathering, sample processing, and data generation, e.g., the report generator may also perform one or more of: a) sample gathering; b) sample processing; and c) determining the presence of a gene fusion or an alternative spliced junction. Alternatively, an entity other than the report generator can perform one or more sample gathering, sample processing, and data generation.
The term “user” or “client” refers to a person or entity to whom a report is transmitted, and may be the same person or entity who does one or more of the following: a) collects a sample; b) processes a sample; c) provides a sample or a processed sample; and d) generates data for use in the likelihood assessment. In some cases, the person or entity who provides sample collection and/or sample processing and/or data generation, and the person who receives the results and/or report may be different persons, but are both referred to as “users” or “clients.” In certain embodiments, e.g., where the methods are completely executed on a single computer, the user or client provides for data input and review of data output. A “user” can be a health professional (e.g., a clinician, a laboratory technician, a physician (e.g., an oncologist, surgeon, pathologist), etc.).
In embodiments where the user only executes a portion of the method, the individual who, after computerized data processing according to the methods of the invention, reviews data output (e.g., results prior to release to provide a complete report, a complete, or reviews an “incomplete” report and provides for manual intervention and completion of an interpretive report) is referred to herein as a “reviewer.” The reviewer may be located at a location remote to the user (e.g., at a service provided separate from a healthcare facility where a user may be located).
Where government regulations or other restrictions apply (e.g., requirements by health, malpractice, or liability insurance), all results, whether generated wholly or partially electronically, are subjected to a quality control routine prior to release to the user.
Computer-Based Systems and Methods
The methods and systems described herein can be implemented in numerous ways. In one embodiment of the invention, the methods involve use of a communications infrastructure, for example, the internet. Several embodiments of the invention are discussed below. The present invention may also be implemented in various forms of hardware, software, firmware, processors, or a combination thereof. The methods and systems described herein can be implemented as a combination of hardware and software. The software can be implemented as an application program tangibly embodied on a program storage device, or different portions of the software implemented in the user's computing environment (e.g., as an applet) and on the reviewer's computing environment, where the reviewer may be located at a remote site (e.g., at a service provider's facility).
In an embodiment of the invention, during or after data input by the user, portions of the data processing can be performed in the user-side computing environment. For example, the user-side computing environment can be programmed to provide for defined test codes to denote a likelihood “score,” where the score is transmitted as processed or partially processed responses to the reviewer's computing environment in the form of test code for subsequent execution of one or more algorithms to provide a result and/or generate a report in the reviewer's computing environment. The score can be a numerical score (representative of a numerical value) or a non-numerical score representative of a numerical value or range of numerical values (e.g., “A”: representative of a 90-95% likelihood of a positive response; “High”: representative of a greater than 50% chance of a positive response (or some other selected threshold of likelihood); “Low”: representative of a less than 50% chance of a positive response (or some other selected threshold of likelihood), and the like.
As a computer system, the system generally includes a processor unit. The processor unit operates to receive information, which can include test data (e.g., the presence of a gene fusion or an alternative spliced junction) and may also include other data such as patient data. This information received can be stored at least temporarily in a database, and data analyzed to generate a report as described above.
Part or all of the input and output data can also be sent electronically. Certain output data (e.g., reports) can be sent electronically or telephonically (e.g., by facsimile, using devices such as fax back). Exemplary output receiving devices can include a display element, a printer, a facsimile device and the like. Electronic forms of transmission and/or display can include email, interactive television, and the like. In an embodiment of the invention, all or a portion of the input data and/or output data (e.g., usually at least the final report) are maintained on a web server for access, preferably confidential access, with typical browsers. The data may be accessed or sent to health professionals as desired. The input and output data, including all or a portion of the final report, can be used to populate a patient's medical record that may exist in a confidential database as the healthcare facility.
The present invention also contemplates a computer-readable storage medium (e.g., CD-ROM, memory key, flash memory card, diskette, etc.) having stored thereon a program which, when executed in a computing environment, provides for implementation of algorithms to carry out all or a portion of the results of a likelihood assessment as described herein. Where the computer-readable medium contains a complete program for carrying out the methods described herein, the program includes program instructions for collecting, analyzing and generating output, and generally includes computer readable code devices for interacting with a user as described herein, processing that data in conjunction with analytical information, and generating unique printed or electronic media for that user.
Where the storage medium includes a program that provides for implementation of a portion of the methods described herein (e.g., the user-side aspect of the methods (e.g., data input, report receipt capabilities, etc.)), the program provides for transmission of data input by the user (e.g., via the internet, via an intranet, etc.) to a computing environment at a remote site. Processing or completion of processing of the data is carried out at the remote site to generate a report. After review of the report, and completion of any needed manual intervention, to provide a complete report, the complete report is then transmitted back to the user as an electronic document or printed document (e.g., fax or mailed paper report). The storage medium containing a program according to the invention can be packaged with instructions (e.g., for program installation, use, etc.) recorded on a suitable substrate or a web address where such instructions may be obtained. The computer-readable storage medium can also be provided in combination with one or more reagents for carrying out a likelihood assessment (e.g., primers, probes, arrays, or such other kit components).
Having described the invention, the same will be more readily understood through reference to the following Examples, which are provided by way of illustration, and are not intended to limit the invention in any way. All citations through the disclosure are hereby expressly incorporated by reference.
Patients
One hundred and thirty-six primary breast cancer FFPE tumor specimens with clinical outcomes were provided by Providence St. Joseph Medical Center (Burbank, Calif.), with institutional review board approval. The time to first recurrence of breast cancer or death due to breast cancer (including death due to unknown cause) was determined from these records. Patients who were still alive without breast cancer recurrence or who died due to known other causes were considered censored at the time of last follow-up or death. These tumor specimens were used for biomarker discovery in the development of the Oncotype DX® assay. See e.g., U.S. Pat. No. 7,081,340; S. Paik et al., The New England Journal of Medicine 351, 2817 (2004). For the present study, 136 specimens had adequate RNA remaining. Among the 136 patients, 26 experienced breast cancer recurrence or death due to breast cancer. Clinical characteristics of the patients in the Providence cohort are described in Sinicropi et al., PLoS ONE 7(7):e40092 (2012) which is incorporated by reference in its entirety.
RNA-Seq Sample Preparation and Sequencing
Transcriptome RNA-Seq analysis of the Providence cohort is described in Sinicropi et al., PLoS ONE 7(7):e40092 (2012). Total RNA was prepared from three 10-μm-thick sections of FFPE tumor tissue as previously described using the MasterPure™ Purification Kit (Epicentre® Biotechnologies, Madison, Wis.). M. Cronin et al., The American Journal of Pathology 164, 35 (January 2004). One hundred nanograms of the isolated RNA were depleted of ribosomal RNA as described. See U.S. Pub. No. 2011/0111409. Sequencing libraries for whole transcriptome analysis were prepared using ScriptSeg™ mRNA-Seq Library Preparation Kits (Epicentre® Biotechnologies, Madison, Wis.). During the cDNA synthesis step, additional incubation for 90 minutes at 37° C. was implemented in the reverse transcription step to increase library yield. After 3′-terminal tagging, the di-tagged cDNA was purified using MinElute® PCR Purification Kits (Qiagen, Valencia, Calif.). Two 6 base index sequences were used to prepare barcoded libraries for duplex sequencing (RNA-Seq Barcode Primers; Epicentre® Biotechnologies, Madison, Wis.). PCR was carried out through 16 cycles to generate the second strand of cDNA, incorporate barcodes, and amplify libraries. The amplified libraries were size-selected by a solid phase reversible immobilization, paramagnetic bead-based process (Agencourt® AMPure® XP System; Beckman Coulter Genomics, Danvers, Mass.). Libraries were quantified by PicoGreen® assay (Life Technologies, Carlsbad, Calif.) and visualized with an Agilent Bioanalyzer using a DNA 1000 kit (Agilent Technologies, Waldbronn, Germany).
Two RNA-Seq libraries with different index barcodes were loaded into each lane of flow cells. The cluster generation in flow cells was carried out in an Illumina cBOT™ instrument using TruSeq™ SR Cluster Kits v2 following the manufacturer's protocol (Illumina Inc.; San Diego, Calif.). The flow cells were subsequently transferred to an Illumina HiSeq®2000 instrument (Illumina, Inc.) for sequence analysis using TruSeq SBS Kit v3-HS (50 cycles) following the manufacturer's protocol. The single-read runs were carried out for a total of 57 cycles including 7 cycles for the index sequences and 50 cycles (i.e. 50 bases) for the insert sequences.
Data Quality Assessment
Each sequencing lane was duplexed with two patient sample libraries using a 6 base barcode to differentiate between them. The mean read ratio+/−SD between the two samples in each lane was 1.05±0.38 and the mean+/−SD percentage of un-discerned barcodes was 2.08%±1.63%. Using principal components analysis and other exploratory data analysis methods, no systematic differences were found among samples associated with flow cell or barcode.
In a run-in phase of the study, duplicate libraries were prepared for 8 samples selected at random from the study set of 136. RefSeq RNA coverage for these libraries ranged between 3.1M and 6.7M uniquely mapped reads. Log count Pearson correlations among duplicate libraries ranged between 0.947 and 0.985. Single libraries were prepared for the remaining 128 samples and distributed in duplex mode among the lanes of 8 flow-cells. Sequencing in 3 lanes failed. Two libraries had low yield, resulting in low coverage. Three lanes were flagged by various Illumina process monitoring indices: low Q30 (coverage=2.8M and 4.2M), high cluster density (coverage=1.6M and 1.8M), or inadequate imaging (coverage=3.3M and 3.1M). For the remaining lanes, sample coverage ranged between 2.5M and 7.3M reads. New libraries for the samples that had low yield were prepared and sequenced. Libraries in the failed and flagged lanes, as well as some of the low coverage samples, were re-sequenced. Replicate correlations among all sequenced samples were very high, 0.985 for the samples with the high cluster density in the original run, and over 0.990 for all others. For the analysis data set, data for one of each of the duplicate libraries from the run-in experiment were kept. For the samples for which new libraries were prepared and for the samples in the failed and flagged lanes, the reads from the subsequent run were used. For the samples with low coverage for which the library was reprocessed, reads from the two runs were pooled. For the rest of the samples, the reads from the single lane were used. Results differed little when other data analysis procedures were used, for example, using only the second run when libraries were reprocessed.
78 patient samples as described in Cobleigh et al., Clin. Cancer Res. 11:8623-8631 (2005) and in U.S. Pat. No. 7,569,345 were obtained from women with invasive breast cancer and ≥10 positive nodes with no evidence of metastatic disease who had surgery at Rush University Medical Center from 1979 to 1999. Clinical outcome data were available for all patients. Patients who were still alive without breast cancer recurrence or who died due to known other causes were considered censored at the time of last follow-up or death. For the present study, 76 specimens had adequate RNA remaining for RNA-Seq.
An overview of the bioinformatics approach used to identify gene fusions in samples from the Providence and Rush cohorts is depicted in
The underlying gene fusion method is based on the detection of distant splicing within a single read feature of a RNA-seq aligner GSNAP (Wu, T. D. and Nacu, S. (2010) Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics, 26, 873-881). The utility of GSNAP for gene fusion detection has been demonstrated in gene fusion detection methods such as GSTRUCT-fusions and GFP (Seshagiri, S. et al. (2012) Recurrent R-spondin fusions in colon cancer. Nature, 488, 660-664; Ju, Y. S. et al. (2012) A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing. Genome Res., 22, 436-445). Both methods depend on GSNAP to provide fusion read candidates, and apply a set of filtering modules to remove false positives in paired-end RNA-seq datasets. In RNA-seq paired end libraries prepared from fresh frozen tissue, bridging reads mapped to each side of fusion junction sites provide a very powerful filter in both GSTRUCT and GFP approaches, therefore single end read datasets are disadvantaged. To compensate for the short FFPE RNA length with median library size around 100 bp in Providence, we leverage data from the two patient cohorts as shown in
Step 1: Mapping FASTQ Files to the Human Genome Using GSNAP
Raw sequencing data from the Providence and Rush cohorts were converted to FASTQ files using CASAVA software. The FASTQ files were mapped to the human genome (version GHCh37/hg19) along with RefSeq splicing sites using the RNA-Seq aligner GSNAP. An important feature of GSNAP is its ability to detect a distant spliced junction within a single read. Local spliced junctions derive from splicing events within a single gene in a consistent transcription direction, whereas distant sliced junctions derive from splicing events between different genes or chromosomes. Distant splicing events can also include splicing events occurring within the same gene, but in the opposite transcription direction. Distant splicing events, therefore, include translocations, inversions, and the like.
Two filters were installed to remove low quality and unwanted reads. Good quality reads were identified as reads in which at least 30% of the bases have Sanger quality score 20 or above. Reads failing this threshold were flagged as low-quality and removed from alignment (BAM) files. Simultaneously, a number of abundant sequences including biological sequences (e.g., ribosomal RNA and mitochondrial sequences), and sequences introduced during library prep (e.g., phiX), were considered undesirable for gene fusion detection and were removed from alignment (BAM) files. Only reads passing both filtering thresholds and uniquely mapped to human genome were retained for the further analyses. Such reads were considered distant spliced reads.
The uniquely mapped reads in the cleaned BAM files were converted to (non-normalized) gene feature counts that provide expression values for exonic, intronic, and intergenic regions. The gene feature count is the number of aligned bases from reads mapped within the feature region. The gene level expression values were calculated by aggregating counts for exonic or intronic features. These gene feature counts are referred to as “gene tables” in
Step 2: Retesting Reads Using GSNAP
In order to remove false positives, reads that mapped to the human genome in Step 1 were retested using GSNAP parameters that favor local alignment. Each alignment from the GSNAP rerun was examined, and any reads meeting all following criteria were considered as having false positive distant splicing reads in the original GSNAP output, and thus removed for the further analyses: (1) the total matched length was 44 bp or more; (2) the insertion length was 1 bp or 0; and (3) the deletion length was 1 bp or 0. For the Providence cohort, Step 2 filtered out 18% of the distant spliced junctions. Reads that successfully passed through this step were considered to include a distant spliced junction.
Step 3: Extracting Gene Fusions
In Step 3, the resulting distant splicing junctions were then annotated and candidate gene fusions were selected. Specifically, the alignments of reads that passed GSNAP re-testing step were examined, and reads with any mismatches within 5 bp of the distant splicing junction site or mapped to the anti-sense strand of annotated genes were removed from further analyses. The remaining reads were grouped according to the distant splicing junction sites, and each junction site was annotated based on UCSC refseq sequence annotation. Junctions mapped to a pseudogene or multiple mapped refseq genes were removed. At this stage, candidate gene fusions met one of the following criteria: (1) they mapped to different chromosomes; (2) they mapped to different refseq genes; (3) they were in opposite directions on same chromosome; or (4) they were at least 1 MB apart if on the same chromosome. For the Providence cohort, Step 3 filtered out 20% of the distant spliced junctions. Steps 1-3 make up the sample based approach identified in
Step 4: Building Templates
A five template set was created to remove false positives introduced by homologous template sequences and to enable accurate mapping of supporting reads. At this stage, information from both the Providence and Rush cohorts was combined. The features of the five template set are depicted in
Donor and acceptor exon only or genomic containing template sequences were used as controls. The sequence of each template in the 5 template set was retrieved and annotated for each candidate gene fusion. Candidate gene fusions were considered to be false positives and were removed if any of its templates had the identical sequence, but were mapped to different locations on the human genome.
BLAST was used to investigate the homology of the remaining candidate gene fusions. A second five template set for each of the remaining candidate gene fusions was created. This template included the following individual templates, each of which included 300 bp:
Homology between (b) the donor template and (c) the acceptor template and between (d) the donor genomic template and (e) the acceptor genomic template was evaluated. The fusion template (a) was used to provide sequence information for RT-PCR experiments. Any candidate gene fusion meeting the following criteria was removed from further analysis: (1) sequence identity of more than 14 bp of 300 bp of the donor template and acceptor template; (2) sequence identity of more than 14 bp of 300 bp of the donor genomic template and acceptor genomic template; and (3) less than 50 bp exonic sequence on either side of fusion, donor, or acceptor template sequences. This step removed 27% of the candidate gene fusions from the Providence cohort.
Step 5: Retrieving Reads
The templates from the remaining candidate gene fusions from both the Providence and the Rush cohorts were used to create a genomic index using a tool from the GSNAP package. Based on the genomic location of all candidate fusion template sets, all short reads mapped near any junction sites and reads not mapped in the original GSNAP BAM file per RNA-seq library were selected. The selected reads were re-mapped into the built template genomic index by GSNAP with the splicing detection parameter turn off. The alignments between supporting reads and fusion templates were screened to allow minimum of 5 bp exact match sequence as overhang across the junction site.
Only reads uniquely mapped to the fusion template were kept. Reads were examined according to the below parameters to determine if they mapped to the fusion template with good quality: (1) no mismatch around 5 bp of junctions of any template; (2) number of reads with indels is no more than 75% of all reads mapped to a given template; (3) reads with splicing evidence in the original BAM files were not allowed to be mapped to fusion template; (4) no splicing or distant splicing allowed on these 100 bp template sequences; (5) no soft clipping of more than 3 bp on each read side; (6) no deletion more than 1 bp at a given indel location; and (7) no insertion more than 1 bp at a given indel location. Step 5 filtered out 5% of the candidate gene fusions from the Providence cohort.
Step 6: Expression Profiling
Expression profiles provide additional evidence for gene fusions. The utilization of expression data for gene fusion detection is a feature of the COPA (Cancer Outlier Profiling Analysis) method that was devised for analysis of microarray databases (Tomlins et al., 2005). Cancer related genes identified as expression outliers in microarray experiments led to the discovery of TMPRSS2 to ETS transcription factors, the first known recurrent gene fusions in common solid carcinomas. Gene fusion RNAs are expected to exhibit a marked expression discontinuity between the preserved side and discarded side of a given fusion junction, compared to samples without that gene fusion. Recently published gene fusions detected using RNA-seq have displayed this discrete expression pattern at acceptor fusion junction sites under RNA-seq platform (Lipson, D. et al. (2012) Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies. Nature Medicine, 18, 382-384; Ju, Y. S. et al. (2012) A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing. Genome Res., 22, 436-445). Multiple bioinformatics approaches including FusionSeq (Sboner, A. et al. (2010) FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data. Genome Biol, 11, R104), deFuse (McPherson, A. et al. (2011) deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data. PLoS Comput Biol, 7) and TopHat-Fusion (Kim, D. and Salzberg, S. L. (2011) TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol, 12, R72) have used expression data in their pipelines. However all these methods are based on an analysis of subjects, one by one. The cohort-based approach described here compares expression levels across the cohort of subjects to find expression outliers as well as exon/intron level expression interruption corresponding to matching fusion junctions. Due to the large proportion of sequences that map to introns in FFPE RNA-seq data (Sinicropi, D. et al. (2012) Whole Transcriptome RNA-Seq Analysis of Breast Cancer Recurrence Risk Using Formalin-Fixed Paraffin-Embedded Tumor Tissue. PLoS ONE, 7, e40092), introns were used along with exons to enhance expression measurements. The expression profiling step can nominate candidate fusions despite the existence of very limited reads. In fact, here we used the expression profile data to predict known fusions in samples having no detected fusion sequences
Methods for expression profiling included the following: The gene table described in Step 1 was normalized by scaling factors as described by R package DEseq, which is available at http://bioconductor.org/packages/release/bioc/html/DESeq.html). Anders and Huber, Genome Biology 11:R106 (2010). The intron immediately before the splicing site on the acceptor gene and the intron immediately after the splicing site on the donor gene were identified and excluded from expression analyses. The remaining exons and introns were analyzed and classified as either discarded or preserved based on their expression level. Discarded exons and introns having counts below 250 were padded to 250. Such counts were equivalent to 5 reads prior to normalization. The gene feature (exon or exon/intron) counts were normalized by the sequence length. The expression interrupt ratios of normalized counts between preserved and discarded sides were calculated for donor and acceptor genes for each sample according to the following formula:
As an exemplary case, the fusion RABEP1→DNAH9 (a tier 1 fusion) was initially found in a single Providence sample and was supported by two non-redundant reads across the fusion template (
Expression profiling results for the candidate gene fusions are shown in
Step 7: Review Evidence
Data were manually reviewed to identify candidate gene fusions. The following rules were used to select the final candidate gene fusions: (1) multiple samples sharing the same hits, but without good expression evidence were removed; (2) fusions with a minimum of two non-duplicate reads and a minimum of 15 bp overhang were kept; and (3) expression profiling evidence was reviewed to select candidates with favorable expression evidence. Steps 4-7 make up the cohort based approached identified in
Gene Fusion List
Overall, 108 fusion events consisting of 100 unique fusion junctions were identified in the two cohorts (Tables A and B). Table A provides information regarding the cohort, the fusion junction, the fusion genes, COSMIC gene, donor Entrez gene ID, donor gene type, donor HUGO gene symbol, donor gene description, acceptor Entrez gene ID, acceptor gene type, acceptor HUGO gene symbol, and acceptor gene description. The symbol “→” indicates the splicing direction of the gene fusion. The symbol “_” is used interchangeably with the symbol “→” to denote junctions in the figures and tables. Splice donors are located to the left of the arrow and splice acceptors are located to the right of the arrow. The “+” symbol denoted in the junctions, refers to the plus-strand of the chromosome, whereas the “−” symbol denotes the minus-strand of the chromosome. Table B provides the gene fusion and the nucleotide sequence of the 100 unique gene fusions.
The majority of gene fusions are intra-chromosomal genomic rearrangements (69 out of total 100 fusion junctions), and this is consistent with findings of others (Robinson, D. R. et al. (2011) Functionally recurrent rearrangements of the MAST kinase and Notch gene families in breast cancer. Nature Medicine, 17, 1646-1651; Edgren, H. et al. (2011) Identification of fusion genes in breast cancer by paired-end RNA-sequencing. Genome Biol, 12, R6. Of the 100 unique fusions, only TFG→GPR128 has been discovered previously (Mitelman, F. et al. (2012) Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer; Asmann, Y. W. et al. (2012) Detection of Redundant Fusion Transcripts as Biomarkers or Disease-Specific Therapeutic Targets in Breast Cancer. Cancer Res, 72, 1921-1928). It is noteworthy that some of these rare fusions are detected in both of the examined patient cohorts. Here, we validated 3 recurrent gene fusions including TFG→GPR128, ESR1→AKAP12 and RABEP1→DNAH9 by TaqMan assay using amplified RNA from 6, 3 and 2 patients respectively in the two cohorts of 212 total patients. Interestingly, among three ESR1→AKAP12 fusion events in three different patients, there are two unique fusion junctions sharing the same acceptor junction site but differing at the donor junction sites by one exon. Since both ESR1→AKAP12 fusion junctions are in frame and the differed ESR1 exon doesn't harbor any known functional domains, these two fusion transcripts can be assumed possessing the same biological function. Further protein domain analysis showed both fusion proteins replace ESR1 ligand binding site with functional domains of AKAP12 (
Also, multiple recurrent partners fused to different partners were found within the two cohorts. In the Providence sample harboring ESR1→AKAP12, another fusion ESR1→C6orf211 was found and validated, it suggests multiple copies of ESR1 existed and they were fused to different acceptors. LRP5 was also found and validated to be fused to different acceptors KAT6A and SLC22A24 in the same patient. However ADK was found and validated to be an acceptor in the fusion DLG5→ADK in one patient, and a donor in the fusion ADK→C10orf11 in another patient. Similarly, the gene ACACA was also found and validated as the donor of ACACA→M512 in one patient, and the acceptor of UTP18→ACACA in another patient. We further searched the Mitelman fusion database with all 184 unique fusion partners including donors and acceptors from the final 108 fusion list, and 29 partners were found fused to various different partners in the database (Mitelman, F. et al. (2012) Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer). Among them, ACACA, BCAS3, DDX5, FBXL20, IKZF3, RAF1, TFG and TRPS1 were fused to more than one partner in the database. These observations suggest fusion events are unlikely random although they appear to be rare in solid tumors.
The identified fusion partners also tend to be cancer related, and 82% of the total 83 gene fusions identified from the Providence cohort have at least one partner belonging to COSMIC database which contains many frequently altered cancer specific genes. This is consistent with other evidence for frequently mutated genes prone to genomic rearrangements in the cancer genomes (Ju, Y. S. et al. (2012) A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing. Genome Res., 22, 436-445). The discovery of gene fusions containing partners that regulate repair of DNA double-strand breaks and homologous recombination such as RAD21, RDM1, BRCA2 and SHFM1 is consistent with abundant evidence for DNA replication infidelity in cancer.
60 of the 100 fusion junctions were selected based on cancer relevance of fused partner genes, and tested by quantitative RT-PCR assay (TaqMan®) using amplified RNA samples from selected patients harboring corresponding candidate fusions. Reverse transcription was carried out using the Omniscript™ RT Kit (Qiagen) by incubating amplified RNA with random hexamers and gene-specific primers at 37° C. for 1 hour. Primer, probe, and amplicon sequences are shown in Supplementary Table 2. Fluorogenic probes were dual-labeled with 5′-FAM as a reporter and 3′-BHQ-2 as a quencher. Primers and probes were designed using the Primer3 program restricting amplicon sizes to 65-85 bps (http://frodo.wi.mit.edu/). When Primer3 failed, primer and probe sequences were optimized manually to ensure optimal performance of the TaqMan® assay design for the chimeric transcripts. Reverse transcription reaction in the absence of RNA template (i.e., water) was always used as a negative control in all assays. The samples that were previously validated as positive or negative for a particular gene fusion junction were served as controls when needed. Since the RT reaction was multiplexed by using a pooled gene specific primer set, the cDNA derived from a RNA sample was tested with all fusion gene qPCR assays within a validation gene set. All RNA samples were assayed in triplicate qPCR reactions with 10 ul per well. Thermalcycling conditions were standard for all assays (A heat activation step of 95° C. for 10 minutes followed by 40 cycles of 95° C. for 20 seconds and 60° C. for 45 seconds). Table C indicates the fusion genes, the fusion junction, primer design method, amplicon length, and primer, probe, and amplicon sequences.
At the end of this process, 83 fusion junctions representing only 0.56% of candidate fusion junctions from Step 1 were selected in the Providence dataset. Overall, 108 fusion events consisting of 100 unique fusion junctions were identified in the two cohorts (Tables A and B). Candidate fusions were classified into 3 tiers based on the levels of supporting evidence (
An important feature of the gene fusion detection pipeline described here is using expression profiling to select tier 2 and tier 3 candidate gene fusions with minimal sequencing data at fusion junctions. Generally, functionally important gene fusions in cancer are characterized by donor genes that are expressed at relatively high levels in non-fused state, by acceptor genes that are expressed at relatively low levels in non-fused state. The strong promoter of a donor gene may up-regulate expression of an oncogenic acceptor gene to contribute to the disease pathology (Mitelman, F. et al. (2007) The impact of translocations and gene fusions on cancer causation. Nature Reviews Cancer, 7, 233-245). Among 31 validated tier 1 fusions, only 7 (23%) fail to show an interrupted expression patterns at either donor or acceptor fusion junctions. Therefore, the filtered false negative gene fusions by expression profiling are probably low and also less likely to be less pathologically relevant. As an internal control, we performed TaqMan assays on 4 fusion candidates that had single non-duplicate reads but without interrupted expression patterns, and only one, and only ESR1→C6orf211, was validated. These two assessments suggest the false negative rate of our pipeline at Step 6 at around 25%. It has been observed fused genes tend to have high copy number variation (Supper, J. et al. (2012) Detecting and visualizing gene fusions. Methods; Kangaspeska, S. et al. (2012) Reanalysis of RNA-Sequencing Data Reveals Several Additional Fusion Genes with Multiple Isoforms. PLoS ONE, 7, e48745). Multiple normal copies of candidates with a single copy of a fused gene can mask the expression profiling of the fused genes, which can lead to the false negative fusions undetected by expression profiling approach.
Fusion transcripts may result from genomic rearrangements or transcript level rearrangements such as trans-splicing which is also biologically relevant. Another type of trans-splicing is known reverse transcriptase artifacts derived from sequence homology (Houseley, J. and Tollervey, D. (2010) Apparent Non-Canonical Trans-Splicing Is Generated by Reverse Transcriptase In Vitro. PLoS ONE, 5, e12271). Although our method cannot distinguish genomic rearrangement derived gene fusions from trans-splicing derived, we used homology sequence search between templates to remove false positive fusions potentially caused by homologous sequences introduced by mapping algorithm or RT errors. This strategy should sufficiently reduce these errors. The existence of vast amount of true negative gene fusions validated by TaqMan (Table 1) also supports the very limited RT based trans-splicing artifact in this study.
Although the gene fusion event is relatively low per patient, which could be attributed to the low quality of FFPE RNA-seq libraries, patient stratification across a breast cancer cohort based on fusion frequencies demonstrates the clinical prognostic power of fusion detection. This was further validated in the biological pathway and network analysis as fusion signature genes were highlighted in the known cancer related network (
Table 1 shows a summary of the results from the RT-PCR experiments of the 108 fusion events. Shown is the average CT from triplicate 10 ul TaqMan assays.
Heatmaps and bar plots generated in Example 3 (Step 6: Expression Profiling) were analyzed to identify gene fusions present in multiple patients from the Providence and Rush cohorts. Results are shown in Table 2, which lists the gene fusion, number of Providence patient samples positive for the gene fusion via heatmap inspection and the number of reads spanning the gene fusion junction (split reads) per Providence patient sample, number of Rush patient samples positive for the gene fusion via heatmap inspection and the number of reads spanning the gene fusion junction (split reads) per Rush sample. The symbol “†” indicates that multiple junctions were observed for the ESR1→AKAP12 gene fusion. The asterisks “*” in Table 2 indicate that the identified fusion was predicted to be recurrent because split reads were not identified in all patient samples that were positive for the gene fusion via expression profiling evaluation. For example, the data for the TFG→GPR12 gene fusion indicates that 2 patients positive for the gene fusion via heatmap inspection in Providence cohort, 1 patient in the Providence cohort that was positive for that gene fusion via expression profiling evaluation had 3 supporting split reads, and 1 patient has 0 split read. However, in the Rush cohort, of the 4 patients positive for the gene fusion via heatmap inspection, 2 patient samples had 3 split reads, 1 patient sample had 1 split read, and 1 patient sample had 0 split reads. The symbol “‡” indicates that TFG_GPR128 TFG→GPR128 has been discovered previously Mitelman, F. et al. (2012) Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer; Asmann, Y. W. et al. (2012) Detection of Redundant Fusion Transcripts as Biomarkers or Disease-Specific Therapeutic Targets in Breast Cancer. Cancer Res, 72, 1921-1928. The symbol “--” in various samples from the Rush cohort indicates that there were no samples positive for the indicated gene fusion and there were no split reads supporting the gene fusion. Accordingly, for those gene fusions, recurrence was observed among the Providence patient samples only.
The list of candidate gene fusions was also analyzed to identify recurrent gene fusions within a given sample in order to identify genes that may be hotspots for chromosomal aberrations that cause gene fusions. Table 3 shows gene fusions that were recurrent in a single sample.
The list of candidate gene fusions was further analyzed to identify recurrent gene fusions present in different samples (either within a single or cohort or between cohorts) in order to identify genes that may be hotspots for chromosomal aberrations that cause gene fusions. Results are shown in Table 4.
Also, multiple recurrent partners fused to different partners were found within the two cohorts. In the Providence sample harboring ESR1→AKAP12, another fusion ESR1→C6orf211 was found and validated, it suggests multiple copies of ESR1 existed and they were fused to different acceptors. LRP5 was also found and validated to be fused to different acceptors KAT6A and SLC22A24 in the same patient. However ADK was found and validated to be an acceptor in the fusion DLG5→ADK in one patient, and a donor in the fusion ADK→C10orf11 in another patient. Similarly, the gene ACACA was also found and validated as the donor of ACACA→MSI2 in one patient, and the acceptor of UTP18→ACACA in another patient. We further searched the Mitelman fusion database with all 184 unique fusion partners including donors and acceptors from the final 108 fusion list, and 29 partners were found fused to various different partners in the database (Mitelman, F. et al. (2012) Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer). Among them, ACACA, BCAS3, DDX5, FBXL20, IKZF3, RAF1, TFG and TRPS1 were fused to more than one partner in the database. These observations suggest fusion events are unlikely random although they appear to be rare in solid tumors.
An overview of the method for identifying alternative spliced junctions is shown in
Six candidate alternative splicing junctions were identified using the above method. The candidate alternative splicing junctions are shown in Table 5. Table 5 shows the gene symbol, the alternative spliced junction within the gene, Fisher's p value, the non-recurrent ratio, the recurrent ratio, the non-recurrence sample count, the recurrence sample count, and the splice type identified by the method described herein. Alternative spliced junctions in UBXN7, SOX5, KIAA0368, PIKC3C, and DAP3 correlated with non-recurrence of breast cancer whereas an alternative spliced junction MITD1 correlated with recurrence of breast cancer. Furthermore, investigation of the alternative spliced junction in PIKC3 predicts a fusion protein with a junction at amino acids 701 and 887 (data not shown).
Validation of the alternative splice junctions is determined using quantitative RT-PCR. Quantitative RT-PCR analysis using TaqMan® RT PCR is used to investigate the six alternative spliced junctions identified in Example 7. Reverse transcription is carried out using the OmniPure RT kit (Qiagen). Reverse transcription is performed by combining random hexamers and gene-specific primers at 37° C. for 1 hour.
Fluorogenic probes are dual-labeled with 5′-FAM as a reporter and 3′-BHQ-2 as a quencher. Primers and probes are designed using the Primer3 program (http://frodo.wi.mit.edu/). In some cases, primer and probe sequences are optimized manually to ensure optimal performance of the TaqMan® assay design for FFPE samples. The TaqMan® assay designs are manually optimized to select an amplicon size less than 100 bases in length and to enable the probe to approximately span the alternative spliced junction. No template (i.e., water) is used as a negative control.
Results from quantitative RT-PCR are obtained and analyzed to investigate the validity of the alternative spliced junctions identified using the bioinformatics approach.
The average fusion events per patient across Providence and Rush cohorts are 0.63 and 0.29 respectively, far less than the average of 4.2 fusions in fresh frozen breast cancer biopsies (Robinson, D. R. et al. (2011) Functionally recurrent rearrangements of the MAST kinase and Notch gene families in breast cancer. Nature Medicine, 17, 1646-1651; Asmann, Y. W. et al. (2012) Detection of Redundant Fusion Transcripts as Biomarkers or Disease-Specific Therapeutic Targets in Breast Cancer. Cancer Res, 72, 1921-1928). This difference can reasonably be attributed to the poor quality of FFPE RNAs. This is born out in the Rush and Providence data sets, the former having older archival ages therefore poorer quality RNA and yielding a far fewer identified gene fusions (
Within each patient cohort we investigated whether the number of fusion events detected within individual tumors related to the likelihood of disease recurrence. We thus stratified patients according to the numbers of fusion events within each cohort (
In order to assess the biological significance of the fusion frequency, we identified genes differentially expressed between the multiple fusion group versus no fusion group in the Providence cohort. Since ER status can effect gene expression greatly, an additive model of edgeR using ER status as background was applied to remove ER effect in identifying genes related to fusion frequency. We took extra cautions to examine the pairwise comparisons between any Providence fusion frequency groups, and confirmed the differentially expressed genes between multiple fusion samples versus no fusion samples are specific to this comparison, which achieved maximum differentially expression among all comparisons tested (Table 6). Accordingly, Table 6 shows pairwise differentially expressed (DE) gene analysis between sample categories by fusion number in Providence show differentially expressed fusion gene signatures are specific to the comparison of multiple fusion samples to no fusion samples.
Both multiple fusion samples and no fusion samples were also segregated according to ER status, and the overlapped genes between each category were compared (
These fusion signature genes were clustered into five related functional steps (
Methods Regarding RNA-Seq Expression Analysis
The differentially expressed genes were analyzed by edgeR (Robinson, M. D. et al. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140) based on base count tables tallied from GSNAP mapping results. For the comparison of Providence multiple fusions versus no fusion samples, the low expression filter requires minimum 8 samples with at least 250 base counts which equivalent to 5 reads, and the Cox-Reid profile-adjusted likelihood method was used to estimate mean-variance relationships. Due to the strong influence of ER status on gene expression profiling, the additive model of edgeR was used to get differentially expressed genes between multiple fusion samples versus no fusion samples adjusting for differences between ER positive and negative status. The false discovery rate of differentially expressed genes was set at 0.05 of Bonferroni-Holm method adjusted p values.
The differentially expressed genes were mapped to a cancer focused protein-protein interaction database, Reactome FI database as instructed by the Reactome FIs Cytoscape plugin. The network was clustered by a built-in spectral partition based clustering algorithm, and nodes in different network modules (
In other experiments, based on the expression profiling, the acceptor exon boundary is identified precisely. A chimera database is built with that fixed on the right hand of the sequence; the left part of the sequence is one of the 300,000 exons from RefSeq (all 20,000+ genes). Mapping each read against the chimera database potentially identifies the donor exon/gene.
In a separate set of experiments, outlier expression analysis was used to identify an ESR1-AKAP12 fusion in a patient sample from the Rush cohort. In summary, the fusion between ESR1 and AKAP12 in the Rush cohort was identified by a combination of identifying the expression outlier of AKAP12 from the entire cohort followed by the sequence comparison for AKAP12 and ESR1.
First, expression analysis was used to identify the original read counts for the entire Rush cohort. The original read counts were normalized by Q3 then log 2 transformed to assess the expression level for each gene. AKAP12 gene expression was plotted in a histogram to identify the expression outlier(s), defined by the 3 standard deviation of the mean level.
Second, reads were investigated to identify those spanning the fusion between ESR1 and AKAP12. All of the reads from the above patient were mapped against the human genome by Bowtie. The fusion reads were in the unmapped category. Using ESR1 and AKAP12 as the two separate targets, the unmapped reads were aligned against them. The reads which could be aligned both to ESR1 and AKAP12 were then identified. This process identified the following read maps to the junction between ESR1 and AKAP12 at +chr6:152201906(ESR1)→+chr6:151669846(AKAP12).
All references cited throughout the disclosure, including the examples, are hereby expressly incorporated by reference for their entire disclosure.
While the present invention has been described with reference to what is considered to be specific embodiments, it is to be understood that the invention is not so limited. To the contrary, the invention is intended to cover various modifications and equivalents included within the spirit and scope of the appended claims.
This application is a national phase application under 35 U.S.C. § 371 of International Application No. PCT/US2013/068236, filed Nov. 4, 2013, and claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 61/722,634, filed Nov. 5, 2012, and U.S. Provisional Application No. 61/766,561, filed Feb. 19, 2013, the disclosures of which are hereby incorporated herein by reference in their entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2013/068236 | 11/4/2013 | WO | 00 |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2014/071279 | 5/8/2014 | WO | A |
| Number | Name | Date | Kind |
|---|---|---|---|
| 6849400 | Harvey et al. | Feb 2005 | B1 |
| 20040115686 | Dolginow et al. | Jun 2004 | A1 |
| 20050214823 | Blume et al. | Sep 2005 | A1 |
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