Patent Application
20050074793
Cancer of the colon and/or rectum (referred to as “colorectal cancer”) is significant in Western populations, particularly in the United States. Cancers of the colon and rectum occur in both men and women, most commonly after the age of 50. Colorectal cancer is the second leading cancer killer in the United States, and the third most common cancer overall. This year, more than 50,000 Americans will die from colorectal cancer and approximately 131,600 new cases will be diagnosed.
Mutations in tumor-suppressor genes, proto-oncogenes, and DNA repair genes are factors known to influence the development of tumorigenesis. For example, inactivating both alleles of the adenomatous polyposis coli (APC) gene, a tumor suppressor gene, appears to be one of the earliest events in colorectal cancer, and may even be the initiating event. Other genes implicated in colorectal cancer include the MCC gene, the p53 gene, the DCC (deleted in colorectal carcinoma) gene and other chromosome 18q genes, and genes in the TGF-β signaling pathway (for a review, see Molecular Biology of Colorectal Cancer, pp. 238-299, in Curr. Probl. Cancer, September/October 1997; see also Willams, Colorectal Cancer (1996); Kinsella & Schofield, Colorectal Cancer: A Scientific Perspective (1993); Colorectal Cancer: Molecular Mechanisms, Premalignant State and its Prevention Schmiegel & Scholmerich eds., 2000; Colorectal Cancer: New Aspects of Molecular Biology and Their Clinical Applications (Hanski et al., eds 2000); McArdle et al., Colorectal Cancer (2000); Wanebo, Colorectal Cancer (1993); Levin, The American Cancer Society: Colorectal Cancer (1999); Treatment of Hepatic Metastases of Colorectal Cancer (Nordlinger & Jaeck eds., 1993); Management of Colorectal Cancer (Dunitz et al., eds. 1998); Cancer: Principles and Practice of Oncology (Devita et al., eds. 2001); Surgical Oncology: Contemporary Principles and Practice (Kirby et al., eds. 2001); Offit, Clinical Cancer Genetics: Risk Counseling and Management (1997); Radioimmunotherapy of Cancer (Abrams & Fritzberg eds. 2000); Fleming, AJCC Cancer Staging Handbook (1998); Textbook of Radiation Oncology (Leibel & Phillips eds. 2000); and Clinical Oncology (Abeloff et al., eds. 2000).
As with all cancers, there are stages of disease progression, as well as expected survival rates for these different stages. The American Cancer Society reports that the 5-year relative survival rate is 90% for people whose colorectal cancer is treated in an early stage, before it has spread. But, only 37% of colorectal cancers are found at that early stage. Once the cancer has spread to nearby organs or lymph nodes, the 5-year relative survival rate goes down to 65%. For people whose colorectal cancer has spread to distant parts of the body such as the liver or lungs, the 5-year relative survival rate is 9%. Thus, metastasis of the tumor to the liver lungs and regional lymph nodes are important prognostic factors (see, e.g., PET in Oncology: Basics and Clinical Application (Ruhlmann et al. eds. 1999).
Since tumor metastases is the principal cause of death for cancer patients, a better understanding of the various factors involved in this process, especially about the gene expression exhibited by these cancers, will have prognostic and diagnostic value. Indeed, patterns of gene expression associated with the various stages of these cancers would provide an important tool in the selection of treatment alternatives.
Comparing the gene expression profiles of different cells and tissues can provide information about the identity of the tissue, the health status of the tissue and other properties. For example, genes that are differentially expressed in healthy and pathologic cells can function as diagnostic markers. Additionally, such genes are candidate targets for regulation by therapeutic intervention.
There are numerous methods presently in use for generating gene expression profiles of a cell or tissue. However, there remains a need in the art for methods that utilize the information embodied in a gene expression profile for the benefit of diagnosing, treating or determining the probable prognosis of disease.
Accordingly, provided herein are methods that can be used in diagnosis and prognosis evaluation of metastatic colorectal cancer. Further provided are methods that can be used to screen candidate therapeutic agents for the ability to modulate, e.g., treat, colorectal cancer. Additionally, provided herein are molecular targets and compositions for therapeutic intervention in metastatic colorectal disease and other metastatic cancers.
The present invention provides materials and methods for characterizing biological samples, thereby providing diagnostic methods for identifying cells and tissues and evaluating their physiological status. The methods involve obtaining a biological sample, generating a gene expression profile of the biological sample, and comparing the gene expression profile of a select group of genes from the biological sample with gene expression profile represented by the reference sets of the Tables 1-6.
The select groups of genes used for comparison, identification, and diagnosis of the health status of a biological sample comprise the reference sets of the Tables 1-6. The reference sets of the Tables 1-6 comprise genes selected for their high signal-to-noise ratio in reference samples. These genes, herein referred to as “classifier genes” provide maximum information regarding the nature and identity of a given biological sample.
In one aspect the invention provides a method of diagnosing the health status of a biological sample comprising the steps of; generating a gene expression pattern of the biological sample, and comparing the gene expression pattern of the biological sample with the reference sets of the Tables 1-6, wherein a match between the gene expression pattern of one or more genes in the biological sample and one or more genes of the Tables 1-6 provides a diagnosis of the biological sample. In one embodiment, the biological sample comprises cells obtained from a biopsy sample. In another embodiment, the biological sample is diagnosed as healthy tissue. In yet another embodiment, the biological sample is diagnosed as having metastatic colorectal cancer.
In one embodiment analysis of the gene expression pattern of the biological sample indicates that the colon cancer is likely to develop future metastasis.
In one embodiment, the diagnosis of the biological sample is made with reference to at least five different classifier genes from Tables 1-6.
In another embodiment, comparison of the gene expression pattern of the biological sample and the reference sets identifies the tissue origin of the metastatic cancer.
In one embodiment, the comparison of the gene expression pattern of the biological sample and the reference sets is made by comparing RNA expression profiles.
In another embodiment, the comparison of the gene expression pattern of the biological sample and the reference sets is made by comparing protein expression profiles.
In one embodiment, the protein expression profile is evaluated using antibodies.
In one aspect, the invention provides a method for prognosis evaluation of metastatic colorectal cancer comprising the steps of; generating a gene expression pattern of the biological sample, and comparing the gene expression pattern of the biological sample with the reference sets of the Tables 1-6, wherein a match between the gene expression pattern of the biological sample and one or more reference sets provides a prognosis evaluation of the metastatic potential of the colorectal cancer. In one embodiment, a match between the gene expression pattern of the biological sample and the reference set representing colon cancer hepatic metastases is indicative of poor prognosis.
In another aspect the invention provides a method for evaluating the progress of treatment of metastatic colorectal cancer comprising the steps of; generating a first gene expression pattern of a first biological sample from a patient, comparing the first gene expression pattern of the first biological sample with the reference sets of the Tables 1-6, obtaining a match between the first gene expression pattern of the first biological sample and one or more reference sets of the Tables 1-6, thereby providing an initial diagnosis of metastatic colorectal cancer, then administering to the patient a therapeutically effective amount of a compound that modulates the metastatic colorectal cancer, generating a second gene expression profile of a second biological sample from the patient, and comparing the second gene expression pattern of the second biological sample with the reference sets of the Tables 1-6, then comparing the match between the second gene expression pattern of the second biological sample and the match between the first gene expression pattern of the first biological sample wherein the comparison indicates the progress of the treatment for metastatic colorectal cancer.
In another aspect, the invention provides a method for evaluating the efficacy of drug candidates for the treatment of metastatic colorectal cancer, comprising the steps of; contacting a cell or tissue culture that has a gene expression profile indicative of metastatic colorectal cancer with an effective amount of a test compound, generating a gene expression profile of the contacted cell or tissue culture, and comparing the gene expression pattern of the contacted cell culture with the defined sets of genes of the Tables 1-6, obtaining a match between the gene expression pattern of the contacted cell culture and thereby determining the efficacy of the drug compound for the treatment of metastatic colorectal cancer.
In another aspect, the invention provides a kit for identifying the gene expression pattern of a biological sample comprising; nucleic acid probes that specifically bind to nucleotide sequences from reference sets of the Tables 1-6, and means of labeling nucleic acids. In one embodiment the kit comprises nucleic acid probes that identify metastatic cancer derived from a primary tumor in an organ selected from the group consisting of heart, lung, pancreas, breast, prostate, and colon.
In another aspect, the invention provides a kit for identifying the gene expression pattern of a biological sample comprising; antibodies or ligands that specifically bind to polypeptides encoded by a genes of the reference sets of the Tables 1-6, and means of labeling the antibodies or ligands that specifically bind to polypeptides encoded by genes of the reference sets of the Tables 1-6. In one aspect, the kit provides antibodies or ligands that identify metastatic cancer derived from a primary tumor in an organ selected from the group consisting of lung, pancreas, breast, prostate, and colon.
Definitions
By “metastatic colorectal cancer” herein is meant a colon and/or rectal tumor or cancer that is classified as Dukes stage C or D (see, e.g., Cohen et al., Cancer of the Colon, in Cancer: Principles and Practice of Oncology, pp. 1144-1197 (Devita et al., eds., 5th ed. 1997); see also Harrison's Principles of Internal Medicine, pp. 1289-129 (Wilson et al., eds., 12th ed., 1991). “Treatment, monitoring, detection or modulation of metastatic colorectal cancer” includes treatment, monitoring, detection, or modulation of metastatic colorectal disease in those patients who have metastatic colorectal disease (Dukes stage C or D). In Dukes stage A, the tumor has penetrated into, but not through, the bowel wall. In Dukes stage B, the tumor has penetrated through the bowel wall but there is not yet any lymph involvement. In Dukes stage C, the cancer involves regional lymph nodes. In Dukes stage D, there is distant metastasis, e.g., liver, lung, etc.
The term “metastasis” refers to the process by which a disease shifts from one part of the body to another. This process may include the spreading of neoplasms from the site of a primary tumor to distant parts of the body.
The term “metastatic cancer” refers to any cancer in any part of the body which has its origins in primary cancer at a site distant from the location of the secondary tumor. Metastatic cancer includes, but is not limited to true “metastatic tumors” as well as pre-metastatic primary tumor cells in the process of developing a metastatic phenotype.
The term “metastatic potential” refers to the like hood that a particular tumor will metastasize. A tumor with metastatic potential has a high likelihood of progressing to metastatic cancer.
The term “secondary tumor” refers to a metastatic tumor that has developed at a site distant from the location of the original, primary cancer.
“Classifier genes” are genes selected for the purpose of comparison and identification of biological samples. Classifier genes are selected by virtue of the high signal-to-noise ratio and reproducibility they display when measured in reference samples. Classifier genes are considered “maximally informative genes” because the ability to clearly and reliably detect them provides maximum information regarding the nature and identity of a given biological sample.
A specific classifier gene may or may not be uniquely expressed in a particular cell, tissue, or organ. In some applications, the classifier gene may be tissue-specific; that is, expressed exclusively in a particular tissue or cell type. In other applications the classifier gene may be expressed predominantly in one tissue type, but could also be expressed in other cells, tissues or organs, but in a different relationship with the other classifier genes of the set. Thus, the level of expression of a classifier gene, and its relationship within a pattern of co-expressed genes creates a unique profile that can be used to infer the identity and physiology of an unknown biological sample.
Classifier genes may encode intracellular molecules, e.g., cellular nucleic acids, intracellular proteins, and the intracellular domains of transmembrane proteins, or extracellular molecules such as the extracellular domains of transmembrane proteins or secreted proteins. Intracellular and extracellular classifier molecules are equally suitable.
The protein product of a classifier gene may be referred to herein as a “classifier protein”. Similarly, “classifier molecule” may be used herein to refer collectively to both classifier genes and classifier proteins.
Subsets of classifier genes representative of the gene expression patterns of different cells, tissues, organs and physiological states of disease and health are organized into the reference sets of the Tables 1-6.
The term “metastatic colorectal cancer classifier protein” or “metastatic colorectal cancer classifier polynucleotide” or “metastatic colorectal cancer classifier gene sequences” refers to nucleic acid and polypeptide polymorphic variants, alleles, mutants, and interspecies homologs that: (1) have a nucleotide sequence that has greater than about 60% nucleotide sequence identity, 65%, 70%, 75%, 80%, 85%, 90%, preferably 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% or greater nucleotide sequence identity, preferably over a region of over a region of at least about 25, 50, 100, 200, 500, 1000, or more nucleotides, to a nucleotide sequence of or associated with a UniGene cluster of Tables 1-6; (2) bind to antibodies, e.g., polyclonal antibodies, raised against an immunogen comprising an amino acid sequence encoded by a nucleotide sequence of or associated with a UniGene cluster of Tables 1-6, and conservatively modified variants thereof; (3) specifically hybridize under stringent hybridization conditions to a nucleic acid sequence, or the complement thereof of Tables 1-6 and conservatively modified variants thereof or (4) have an amino acid sequence that has greater than about 60% amino acid sequence identity, 65%, 70%, 75%, 80%, 85%, 90%, preferably 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% or greater amino sequence identity, preferably over a region of over a region of at least about 25, 50, 100, 200, 500, 1000, or more amino acid, to an amino acid sequence encoded by a nucleotide sequence of or associated with a UniGene cluster of Tables 1-6. A polynucleotide or polypeptide sequence is typically from a mammal including, but not limited to, primate, e.g., human; rodent, e.g., rat, mouse, hamster; cow, pig, horse, sheep, or other mammal. A “metastatic colorectal cancer classifier gene sequence” a includes both naturally occurring or recombinant nucleotide and protein sequences.
“Reference set” refers to defined sets of classifier genes that characterize a particular tissue, organ, cell, cell culture or physiological state of a biological sample. The reference set may form part of an organized hierarchical structure for the classification of individual tissues or organs. If the reference set is part of an organized hierarchical structure, it may be used to identify or distinguish a sample at either the highest or lowest level of classification, or it may contain defined sets of genes representing one or more levels of classification for a given tissue or organ and therefore use several levels simultaneously to identify a sample.
Table 1 illustrates the hierarchical structure of classification that orders the defined sets of classifier genes comprising the reference sets of the invention. These defined sets of classifier genes can be used to characterize individual tissues and organs from humans. The defined sets of genes are organized hierarchically to permit identification of a sample on several levels of detail. For example, using the reference sets of classifier genes of Tables 1-6, it is possible to determine that a sample comprises adipose tissue. Within the context of this reference set that identifies adipose tissue, further analysis could reveal other defined sets of classifier genes which, when compared to the reference sets of classifier genes in Tables 1-6 identify the sample as being mammary tissue as opposed to omental tissue or simple adipose tissue. The sample could be still further analyzed within the context of the reference set that characterizes adipose tissue, to determine that the sample is a sample of breast tissue.
A “signature” refers to a specific pattern of gene expression as reflected in a particular defined set of classifier genes of the Tables 1-6. The “signature” of a biological sample is a unique identifier of the sample.
A “tissue” refers to a complex, integrated group of cohesive, typically spatially aggregated cells; certain “tissues” are disperse, e.g., blood cells or skin that share a common structure and/or function. Alternatively, complex assemblies of tissues form functional systems of organs. See, e.g., Rohen, et al. (2002) Color Atlas of Anatomy: A Photographic Study of the Human Body Lippincott; Hiatt, et al. (2000) Color Atlas of Histology Lippincott.
“Biological sample” refers to a sample derived from a virus, cell, tissue, organ, or organism including, without limitation, cell, tissue or organ lysates or homogenates, or body fluid samples, such as blood, urine, sputum, or cerebrospinal fluid. Such samples include, but are not limited to, tissue isolated from humans, or explants, primary, and transformed cell cultures derived therefrom. Biological samples may also include sections of tissues such as frozen sections taken for histologic purposes. A biological sample can be obtained from a eukaryotic organism such as fungi, plants, insects, protozoa, birds, fish, reptiles, and preferably a mammal such as rat, mouse, cow, dog, guinea pig, or rabbit, and most preferably a primate such as cynomologous monkeys, rhesus monkeys, chimpanzees, or humans.
“Encoding” refers to the property of specific sequences of nucleotides in a polynucleotide, such as a gene, a cDNA, or an mRNA, to serve as templates for synthesis of other polymers and macromolecules in biological processes having either a defined sequence of nucleotides (e.g., rRNA, tRNA, and mRNA) or a defined sequence of amino acids and the biological properties resulting therefrom. A gene encodes a protein if transcription and translation of mRNA produced by that gene produces the protein in a cell or other biological system. Both the coding strand, the nucleotide sequence of which is identical to the mRNA sequence and is usually provided in sequence listings, and non-coding strand, used as the template for transcription, of a gene or cDNA, can be referred to as encoding the protein or other product of that gene or cDNA. Unless otherwise specified, a “nucleotide sequence encoding an amino acid sequence” includes all nucleotide sequences that are degenerate versions of each other and that encode the same amino acid sequence. Nucleotide sequences that encode proteins and RNA may include introns. See, e.g., Lodish, et al. (2000) Mol. Cell Biol. (4th ed.) Freeman; Alberts, et al. (1994) Mol. Biol. Cell Garland.
“Differential expression” or grammatical equivalents as used herein, refers to qualitative or quantitative differences in the temporal and/or cellular gene expression patterns within and among cells and tissue. Thus, a differentially expressed gene can qualitatively have its expression altered, including an activation or inactivation, in, e.g., normal versus metastatic colorectal cancer tissue. Genes may be turned on or turned off in a particular state, relative to another state thus permitting comparison of two or more states. A qualitatively regulated gene will exhibit an expression pattern within a state or cell type which is detectable by standard techniques. Some genes will be expressed in one state or cell type, but not in both. Alternatively, the difference in expression may be quantitative, e.g., in that expression is increased or decreased; i.e., gene expression is either upregulated, resulting in an increased amount of transcript, or downregulated, resulting in a decreased amount of transcript. The degree to which expression differs need only be large enough to quantify via standard characterization techniques as outlined below, such as by use of Affymetrix GeneChip™ expression arrays, Lockhart, Nature Biotechnology 14:1675-1680 (1996), hereby expressly incorporated by reference. Other techniques include, but are not limited to, quantitative reverse transcriptase PCR, northern analysis and RNase protection.
A component of a biological sample is differentially expressed between two samples if the difference in amount of the component in one sample vs. the amount in the other sample is statistically significant. For example, preferably the change in expression (i.e., upregulation or downregulation) is typically at least about 50%, more preferably at least about 100%, more preferably at least about 150%, more preferably at least 180%, 200%, 300%, 500%, 700%, 900%, or 1000% the amount in the other sample, or if it is detectable in one sample and not detectable in the other.
“Gene expression profile” refers to the identification of at least one mRNA or protein expressed in a biological sample.
“Nucleic acid array” refers to an array of addressable locations (e.g., a location characterized by a distinctive, interrogatable address), each addressable location comprising a characteristic nucleic acid attached thereto. A nucleic acid as defined herein, may be a naturally occurring or synthetic nucleic acid, e.g., an oligonucleotide or polynucleotide. In an oligonucleotide array, the nucleic acid is an oligonucleotide (e.g., corresponding to an exon, EST, or a portion of a gene, transcript, or cDNA); in an EST array the nucleic acid is an EST or portion thereof; in an mRNA array the nucleic acid is an mRNA or portion thereof, or a corresponding cDNA. An oligonucleotide can be from 4, 6, 8, 10, or 12 nucleotides or longer in length, often 10, 30, 40, or 50 nucleotides in length, up to about 100 nucleotides in length. See Kohane, et al. (2002) Microarrays for Integrative Genomics MIT Press; Baldi and Hatfield (2002) DNA Microarrays and Gene Expression Cambridge Univ. Press.
“Detect” refers to identifying the presence, absence or amount of the object to be detected. “Detectable moiety” or a “label” refers to a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, or chemical means. For example, useful labels include 32P, 35S, fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonly used in an ELISA), biotin-streptavidin, digoxigenin, haptens and proteins for which antisera or monoclonal antibodies are available, or nucleic acid molecules with a sequence complementary to a target. The detectable moiety often generates a measurable signal, such as a radioactive, chromogenic, or fluorescent signal, that can be used to quantify the amount of bound detectable moiety in a sample. Quantitation of the signal is achieved by, e.g., scintillation counting, densitometry, or flow cytometry.
As used herein a “nucleic acid probe or oligonucleotide” is defined as a nucleic acid capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation. As used herein, a probe may include natural (e.g., A, G, C, or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in a probe may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. Thus, for example, probes may be peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages. It will be understood by one of skill in the art that probes may bind target sequences lacking complete complementarity with the probe sequence depending upon the stringency of the hybridization conditions. The probes are preferably directly labeled as with isotopes, chromophores, lumiphores, chromogens, or indirectly labeled such as with biotin to which a streptavidin complex may later bind. By assaying for the presence or absence of the probe, one can detect the presence or absence of the select sequence or subsequence.
A “labeled nucleic acid probe or oligonucleotide” is one that is bound, either covalently, through a linker or a chemical bond, or noncovalently, through ionic, van der Waals, electrostatic, or hydrogen bonds to a label such that the presence of the probe may be detected by detecting the presence of the label bound to the probe. “Antibody” refers to a polypeptide comprising a framework region from an immunoglobulin gene or fragments thereof that specifically binds and recognizes an antigen. The recognized immunoglobulin genes include the kappa, lambda, alpha, gamma, delta, epsilon, and mu constant region genes, as well as the myriad immunoglobulin variable region genes. Light chains are classified as either kappa or lambda. Heavy chains are classified as gamma, mu, alpha, delta, or epsilon, which in turn define the immunoglobulin classes, IgG, IgM, IgA, IgD and IgE, respectively. See Paul (1999) Fundamental Immunology (4th ed.) Raven.
An exemplary immunoglobulin (antibody) structural unit comprises a tetramer. Each tetramer is composed of two identical pairs of polypeptide chains, each pair having one “light” (about 25 kD) and one “heavy” chain (about 50-70 kD). The N-terminus of each chain defines a variable region of about 100 to 110 or more amino acids primarily responsible for antigen recognition. The terms variable light chain (VL) and variable heavy chain (VH) refer to these light and heavy chains respectively.
Antibodies exist, e.g., as intact immunoglobulins or as a number of well-characterized fragments produced by digestion with various peptidases. Thus, for example, pepsin digests an antibody below the disulfide linkages in the hinge region to produce F(ab)′2, a dimer of Fab which itself is a light chain joined to VH-CH1 by a disulfide bond. The F(ab)′2 may be reduced under mild conditions to break the disulfide linkage in the hinge region, thereby converting the F(ab)′2 dimer into an Fab′ monomer. The Fab′ monomer is essentially Fab with part of the hinge region (see Fundamental Immunology (Paul ed., 4th ed. 1999)). While various antibody fragments are defined in terms of the digestion of an intact antibody, one of skill will appreciate that such fragments may be synthesized de novo either chemically or by using recombinant DNA methodology. Thus, the term antibody, as used herein, also includes antibody fragments either produced by the modification of whole antibodies, or those synthesized de novo using recombinant DNA methodologies (e.g., single chain Fv, diabodies [dimers of scFv], minibodies [scFv-CH3 fusion proteins]) or those identified using phage display libraries (see, e.g., McCafferty et al., Nature 348:552-554 (1990)).
Monoclonal or polyclonal antibodies my be prepared by many techniques. See, e.g., Kohler & Milstein, Nature 256:495-497 (1975); Kozbor et al., Immunology Today 4: 72 (1983); Cole et al., pp. 77-96 in Monoclonal Antibodies and Cancer Therapy, Alan R. Liss, Inc. (1985). Techniques for the production of single chain antibodies (U.S. Pat. No. 4,946,778) can be adapted to produce antibodies to polypeptides of this invention. Also, transgenic mice, or other organisms such as other mammals, may be used to express humanized antibodies. Alternatively, phage display technology can be used to identify antibodies and heteromeric Fab fragments that specifically bind to selected antigens. See, e.g., McCafferty et al., Nature 348:552-554 (1990); Marks et al., Biotechnology 10:779-783 (1992).
A “chimeric antibody” is an antibody molecule in which (a) the constant region, or a portion thereof, is altered, replaced or exchanged so that the antigen binding site (variable region) is linked to a constant region of a different or altered class, effector function and/or species, or an entirely different molecule which confers new properties to the chimeric antibody, e.g., an enzyme, toxin, hormone, growth factor, drug, etc.; or (b) the variable region, or a portion thereof, is altered, replaced or exchanged with a variable region having a different or altered antigen specificity.
The term “immunoassay” is an assay that uses an antibody to specifically bind an antigen. The immunoassay is characterized by the use of specific binding properties of a particular antibody to isolate, target, and/or quantify the antigen. See Coligan, et al. (1993 and supplements) Current Protocols in Immunology Wiley.
When used in the context of an antibody-antigen reaction, “specific” or “selective binding” of an antibody refers to a binding reaction that is determinative of the presence of the antigen in a heterogeneous population of proteins and other biologics. Thus, under designated immunoassay conditions, the specified antibodies bind to a particular protein at least two times the background and do not substantially bind in a significant amount to other proteins present in the sample. Specific binding to an antibody under such conditions may require an antibody that is selected for its specificity for a particular protein. For example, polyclonal antibodies raised to a polypeptide encoded by a polynucleotide of Tables 2-5, or splice variants, or portions thereof, can be selected to obtain only those polyclonal antibodies that are specifically immunoreactive with the selected polypeptide and not with other proteins. Where the target protein is a member of a family such as GPCRs, this selection may be achieved by subtracting out antibodies that cross-react with molecules such as other GPCR family members. In addition, polyclonal antibodies raised to target polymorphic variants, alleles, orthologs, and conservatively modified variants can be selected to obtain only those antibodies that recognize the target protein, but not other GPCR family members. In addition, antibodies reactive to human target proteins but not homologs from other species can be selected in the same manner. A variety of immunoassay formats may be used to select antibodies specifically immunoreactive with a particular protein. For example, solid-phase ELISA immunoassays are routinely used to select antibodies specifically immunoreactive with a protein (see, e.g., Harlow and Lane, Using Antibodies: A Laboratory Manual, New York: Cold Spring Harbor Laboratory Press (1998). for a description of immunoassay formats and conditions that can be used to determine specific immunoreactivity).
The terms “isolated,” “purified,” or “biologically pure” refer to material that is substantially or essentially free from components that normally accompany it as found in its native state. Purity and homogeneity are typically determined using analytical chemistry techniques such as polyacrylamide gel electrophoresis or high performance liquid chromatography. A protein that is the predominant species present in a preparation is substantially purified. In particular, an isolated nucleic acid of Tables 2-6 encoding a polypeptide is separated from open reading frames that flank the polypeptide coding sequence gene and encode proteins other than the polypeptide of interest. The term “purified” denotes that a nucleic acid or protein gives rise to essentially one band in an electrophoretic gel. Particularly, it means that the nucleic acid or protein is at least 85% pure, more preferably at least 95% pure, and most preferably at least 99% pure. See, e.g., Walsh (2002) Proteins: Biochemistry and Biotechnology Wiley; Hardin, et al. (eds. 2001) Cloning, Gene Expression and Protein Purification Oxford Univ. Press; Wilson, et al. (eds. 2000) Encyclopedia of Separation Science Academic Press.
“Nucleic acid” refers to deoxyribonucleotides or ribonucleotides and polymers thereof in either single- or double-stranded form. The term encompasses nucleic acids containing known nucleotide analogs or modified backbone residues or linkages, which are synthetic, naturally occurring, and non-naturally occurring, which have similar binding properties as the reference nucleic acid, and which are metabolized in a manner similar to the reference nucleotides. Examples of such analogs include, without limitation, phosphorothioates, phosphoramidates, methyl phosphonates, chiral-methyl phosphonates, 2-O-methyl ribonucleotides, peptide-nucleic acids (PNAs).
Unless otherwise indicated, a particular nucleic acid sequence also implicitly encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions) and complementary sequences, as well as the sequence explicitly indicated. Specifically, degenerate codon substitutions may be achieved by generating sequencesin which the third position of one or more selected (or all) codons is substituted with mixed-base and/or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)). The term nucleic acid is used interchangeably with gene, cDNA, mRNA, oligonucleotide, and polynucleotide.
A particular nucleic acid sequence also implicitly encompasses “splice variants.” Similarly, a particular protein encoded by a nucleic acid implicitly encompasses any protein encoded by a splice variant of that nucleic acid. “Splice variants,” as the name suggests, are products of alternative splicing of a gene. After transcription, an initial nucleic acid transcript may be spliced such that different (alternate) nucleic acid splice products encode different polypeptides. Mechanisms for the production of splice variants vary, but include alternate splicing of exons. Alternate polypeptides derived from the same nucleic acid by read-through transcription are also encompassed by this definition. Products of a splicing reaction, including recombinant forms of the splice products, are included in this definition.
The terms “polypeptide,” “peptide” and “protein” are used interchangeably herein to refer to a polymer of amino acid residues. The terms apply to amino acid polymers in which one or more amino acid residue is an artificial chemical mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymers.
The term “amino acid” refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to the naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those amino acids that are later modified, e.g., hydroxyproline, γ-carboxyglutamate, and O-phosphoserine. Amino acid analog refers to compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., a carbon that is bound to a hydrogen, a carboxyl group, an amino group, and an R group, e.g., homoserine, norleucine, methionine sulfoxide, methionine methyl sulfonium. Such analogs have modified R groups (e.g., norleucine) or modified peptide backbones, but retain the same basic chemical structure as a naturally occurring amino acid. Amino acid mimetics refers to chemical compounds that have a structure that is different from the general chemical structure of an amino acid, but that functions in a manner similar to a naturally occurring amino acid.
Amino acids may be referred to herein by either their commonly known three letter symbols or by the one-letter symbols recommended by the IUPAC-IUB Biochemical Nomenclature Commission. Nucleotides, likewise, may be referred to by their commonly accepted single-letter codes.
“Conservatively modified variants” applies to both amino acid and nucleic acid sequences. With respect to particular nucleic acid sequences, conservatively modified variants refers to those nucleic acids which encode identical or essentially identical amino acid sequences, or where the nucleic acid does not encode an amino acid sequence, to essentially identical sequences. Because of the degeneracy of the genetic code, a large number of functionally identical nucleic acids encode any given protein. For instance, the codons GCA, GCC, GCG and GCU all encode the amino acid alanine. Thus, at every position where an alanine is specified by a codon, the codon can be altered to any of the corresponding codons described without altering the encoded polypeptide. Such nucleic acid variations are “silent variations,” which are one species of conservatively modified variations. Every nucleic acid sequence herein which encodes a polypeptide also describes every possible silent variation of the nucleic acid. One of skill will recognize that each codon in a nucleic acid (except AUG, which is ordinarily the only codon for methionine, and TGG, which is ordinarily the only codon for tryptophan) can be modified to yield a functionally identical molecule. Accordingly, each silent variation of a nucleic acid which encodes a polypeptide is implicit in each described sequence.
As to amino acid sequences, one of skill will recognize that individual substitutions, deletions or additions to a nucleic acid, peptide, polypeptide, or protein sequence which alters, adds or deletes a single amino acid or a small percentage of amino acids in the encoded sequence is a “conservatively modified variant” where the alteration results in the substitution of an amino acid with a chemically similar amino acid. Conservative substitution tables providing functionally similar amino acids are well known in the art. Such conservatively modified variants are in addition to and do not exclude polymorphic variants, interspecies homologs, and alleles of the invention.
The following eight groups each contain amino acids that are conservative substitutions for one another: Alanine (A), Glycine (G); Aspartic acid (D), Glutamic acid (E); Asparagine (N), Glutamine (Q); Arginine (R), Lysine (K); Isoleucine (I), Leucine (L), Methionine (M), Valine (V); Phenylalanine (F), Tyrosine (Y), Tryptophan (W); Serine (S), Threonine (T); and Cysteine (C), Methionine (M). See, e.g., Creighton, Proteins (1984) Freeman).
The term “recombinant” when used with reference, e.g., to a cell, or nucleic acid, protein, or vector, indicates that the cell, nucleic acid, protein or vector, has been modified by the introduction of a heterologous nucleic acid or protein or the alteration of a native nucleic acid or protein, or that the cell is derived from a cell so modified. Thus, for example, recombinant cells express genes that are not found within the native (non-recombinant) form of the cell or express native genes that are otherwise abnormally expressed, under expressed or not expressed at all. See Ausubel (ed. 1993) Current Protocols in Molecular Biology Wiley.
A “promoter” is defined as an array of nucleic acid control sequences that direct transcription of a nucleic acid. As used herein, a promoter includes necessary nucleic acid sequences near the start site of transcription, such as, in the case of a polymerase II type promoter, a TATA element. A promoter also optionally includes distal enhancer or repressor elements, which can be located as much as several thousand base pairs from the start site of transcription. A “constitutive” promoter is a promoter that is active under most environmental and developmental conditions. An “inducible” promoter is a promoter that is active under environmental or developmental regulation. The term “operably linked” refers to a functional linkage between a nucleic acid expression control sequence (such as a promoter, or array of transcription factor binding sites) and a second nucleic acid sequence, wherein the expression control sequence directs transcription of the nucleic acid corresponding to the second sequence. See, e.g., Lodish, et al. (2000) Mol. Cell Biol. (4th ed.) Freeman; Alberts, et al. (1994) Mol. Biol. Cell Garland.
The term “heterologous” when used with reference to portions of a nucleic acid indicates that the nucleic acid comprises two or more subsequences that are not found in the same relationship to each other in nature. For instance, the nucleic acid is typically recombinantly produced, having two or more sequences from unrelated genes arranged to make a new functional nucleic acid, e.g., a promoter from one source and a coding region from another source. Similarly, a heterologous protein indicates that the protein comprises two or more subsequences that are not found in the same relationship to each other in nature (e.g., a fusion protein).
An “expression vector” is a nucleic acid construct, generated recombinantly or synthetically, with a series of specified nucleic acid elements that permit transcription of a particular nucleic acid in a host cell. The expression vector can be part of a plasmid, virus, or nucleic acid fragment. Typically, the expression vector includes a nucleic acid to be transcribed operably linked to a promoter.
The term “identify” in the context of the invention means to be able to recognize a particular gene expression pattern as being characteristic of a particular cell, tissue, organ, physiological state, or in the case of testing for compatibility of transplant donors and recipients the gene expression pattern may be characteristic of a particular individual.
The terms “identical” or percent “identity,” in the context of two or more nucleic acids or polypeptide sequences, refer to two or more sequences or subsequences that are the same or have a specified percentage of amino acid residues or nucleotides that are the same (i.e., 60% identity, 65%, 70%, 75%, 80%, preferably 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or higher identity to a nucleotide sequence such as those of Tables 2-5, or to an amino acid sequence encoded by a polynucleotide of Tables 2-5, when compared and aligned for maximum correspondence over a comparison window, or designated region as measured using one of the following sequence comparison algorithms or by manual alignment and visual inspection. Such sequences are then said to be “substantially identical.” This definition also refers to the compliment of a test sequence. Preferably, the identity exists over a region that is at least about 25 amino acids or nucleotides in length, or more preferably over a region that is 50-100 amino acids or nucleotides in length or larger, e.g., 200-500 or more. See, e.g., Baxevanis, et al. (2001) Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins Wiley; Mount (2000) Bioinformatics: Sequence and Genome Analysis CSH Press; Ewens and Grant (2001) Statistical Methods in Bioinformatics: An Introduction Springer-Verlag; Sensen (ed. 2002) Essentials of Genomics and Bioinformatics Wiley.
For sequence comparison, typically one sequence acts as a reference sequence, to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are entered into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. Default program parameters can be used, or alternative parameters can be designated. The sequence comparison algorithm then calculates the percent sequence identities for the test sequences relative to the reference sequence, based on the program parameters. For sequence comparison of nucleic acids and proteins, the BLAST and BLAST 2.0 algorithms and the default parameters discussed below are used.
A “comparison window”, as used herein, includes reference to a segment of any one of the number of contiguous positions selected from the group consisting of from 20 to 600, usually about 50 to about 200, more usually about 100 to about 150 in which a sequence may be compared to a reference sequence of the same number of contiguous positions after the two sequences are optimally aligned. Methods of alignment of sequences for comparison are well-known in the art. Optimal alignment of sequences for comparison can be conducted, e.g., by the local homology algorithm of Smith & Waterman, Adv. Appl. Math. 2:482 (1981), by the homology alignment algorithm of Needleman & Wunsch, J. Mol. Biol. 48:443 (1970), by the search for similarity method of Pearson & Lipman, Proc. Nat'l. Acad. Sci. USA 85:2444 (1988), by computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Dr., Madison, Wis.), or by manual alignment and visual inspection (see, e.g., Current Protocols in Molecular Biology (Ausubel et al., eds. 2001 supplement)).
A preferred example of an algorithm that is suitable for determining percent sequence identity and sequence similarity are the BLAST and BLAST 2.0 algorithms, which are described in Altschul et al., Nuc. Acids Res. 25:3389-3402 (1977) and Altschul et al., J. Mol. Biol. 215:403-410 (1990), respectively. BLAST and BLAST 2.0 are used, with the parameters described herein, to determine percent sequence identity for the nucleic acids and proteins of the invention. Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/). This algorithm involves first identifying high scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence, which either match or satisfy some positive-valued threshold score T when aligned with a word of the same length in a database sequence. T is referred to as the neighborhood word score threshold (Altschul et al., supra). These initial neighborhood word hits act as seeds for initiating searches to find longer HSPs containing them. The word hits are extended in both directions along each sequence for as far as the cumulative alignment score can be increased. Cumulative scores are calculated using, for nucleotide sequences, the parameters M (reward score for a pair of matching residues; always >0) and N (penalty score for mismatching residues; always <0). For amino acid sequences, a scoring matrix is used to calculate the cumulative score. Extension of the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment. The BLASTN program (for nucleotide sequences) uses as defaults a wordlength (W) of 11, an expectation (E) of 10, M=5, N=−4 and a comparison of both strands. For amino acid sequences, the BLASTP program uses as defaults a wordlength of 3, and expectation (E) of 10, and the BLOSUM62 scoring matrix (see Henikoff & Henikoff, Proc. Natl. Acad. Sci. USA 89:10915 (1989)) alignments (B) of 50, expectation (E) of 10, M=5, N=−4, and a comparison of both strands.
The BLAST algorithm also performs a statistical analysis of the similarity between two sequences (see, e.g., Karlin & Altschul, Proc. Nat'l. Acad. Sci. USA 90:5873-5787 (1993)). One measure of similarity provided by the BLAST algorithm is the smallest sum probability (P(N)), which provides an indication of the probability by which a match between two nucleotide or amino acid sequences would occur by chance. For example, a nucleic acid is considered similar to a reference sequence if the smallest sum probability in a comparison of the test nucleic acid to the reference nucleic acid is less than about 0.2, more preferably less than about 0.01, and most preferably less than about 0.001.
An indication that two nucleic acid sequences or polypeptides are substantially identical is that the polypeptide encoded by the first nucleic acid is immunologically cross reactive with the antibodies raised against the polypeptide encoded by the second nucleic acid, as described below. Thus, a polypeptide is typically substantially identical to a second polypeptide, for example, where the two peptides differ only by conservative substitutions. Another indication that two nucleic acid sequences are substantially identical is that the two molecules or their complements hybridize to each other under stringent conditions, as described below. Yet another indication that two nucleic acid sequences are substantially identical is that the same primers can be used to amplify the sequence.
The phrase “selectively (or specifically) hybridizes to” refers to the binding, duplexing, or hybridizing of a molecule only to a particular nucleotide sequence under stringent hybridization conditions when that sequence is present in a complex mixture (e.g., total cellular or library DNA or RNA). See, e.g., Andersen (1998) Nucleic Acid Hybridization Springer-Verlag; Ross (ed. 1997) Nucleic Acid Hybridization Wiley.
The phrase “stringent hybridization conditions” refers to conditions under which a probe will hybridize to its target subsequence, typically in a complex mixture of nucleic acid, but to no other sequences. Stringent conditions are sequence-dependent and will be different in different circumstances. Longer sequences hybridize specifically at higher temperatures. An extensive guide to the hybridization of nucleic acids is found in Tijssen, Techniques in Biochemistry and Molecular Biology—Hybridization with Nucleic Probes, “Overview of principles of hybridization and the strategy of nucleic acid assays” (1993). Generally, stringent conditions are selected to be about 5-10° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength pH. The Tm is the temperature (under defined ionic strength, pH, and nucleic concentration) at which 50% of the probes complementary to the target hybridize to the target sequence at equilibrium (as the target sequences are present in excess, at Tm, 50% of the probes are occupied at equilibrium). Stringent conditions will be those in which the salt concentration is less than about 1.0 M sodium ion, typically about 0.01 to 1.0 M sodium ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 30° C. for short probes (e.g., 10 to 50 nucleotides) and at least about 60° C. for long probes (e.g., greater than 50 nucleotides). Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide. For high stringency hybridization, a positive signal is at least two times background, preferably 10 times background hybridization. Exemplary high stringency or stringent hybridization conditions include: 50% formamide, 5× SSC and 1% SDS incubated at 42° C. or 5× SSC and 1% SDS incubated at 65° C., with a wash in 0.2×SSC and 0.1% SDS at 65° C. For PCR, a temperature of about 36° C. is typical for low stringency amplification, although annealing temperatures may vary between about 32° C. and 48° C. depending on primer length. For high stringency PCR amplification, a temperature of about 62° C. is typical, although high stringency annealing temperatures can range from about 50-65° C., depending on the primer length and specificity. Typical cycle conditions for both high and low stringency amplifications include a denaturation phase of 90-95° C. for 30-120 sec, an annealing phase lasting 30-120 sec., and an extension phase of about 72° C. for 1-2 min.
Nucleic acids that do not hybridize to each other under stringent conditions are still substantially identical if the polypeptides that they encode are substantially identical. This occurs, for example, when a copy of a nucleic acid is created using the maximum codon degeneracy permitted by the genetic code. In such cases, the nucleic acids typically hybridize under moderately stringent hybridization conditions. Exemplary “moderately stringent hybridization conditions” include a hybridization in a buffer of 40% formamide, 1 M NaCl, 1% SDS at 37° C., and a wash in 1× SSC at 45° C. A positive hybridization is at least twice background. Those of ordinary skill will readily recognize that alternative hybridization and wash conditions can be utilized to provide conditions of similar stringency.
Introduction
In accordance with the objects outlined above, the present invention provides materials and methods for characterizing the nature of biological samples, thereby permitting one to identify a biological sample and/or evaluate its physiological state. In particular, the invention provides novel methods for diagnosis and treatment of colon and/or rectal cancer (e.g., colorectal cancer), including metastatic colorectal cancers, as well as methods for screening for compositions which modulate colorectal cancer. The method is also useful for differentiating between particular stages of cancer, for example Duke's stage A, B, C, or D colorectal cancers. The method is also effective for determining the origin of metastatic cancer.
The methods of the present invention allow one to compare a set of genes expressed in a biological sample with reference set, and to thereby identify a cell culture, tissue or organ from which a biological sample is derived. Alternatively, the comparison may yield information useful for diagnosing the health status of tissue or organ sample. In some embodiments the invention is permits the prognosis evaluation of a patient with cancer, particularly colorectal cancer. In other embodiments the invention provides a method for monitoring the progress of therapeutic intervention to cure metastatic colorectal cancer.
The invention comprises reference sets of classifier genes whose characteristic patterns of expression can be used to determine the physiological state of a biological sample. The genes comprising the reference sets are selected for their high signal to noise ratio in a reference sample. These genes are considered “maximally informative genes” or “classifier genes”. Any particular classifier gene of a reference set may or may not be uniquely expressed in a particular biological sample. However, the level of expression of such a gene, and its relationship within a pattern of co-expressed genes creates a unique profile that can be used to infer the identity and/or physiology of a biological sample. Reference sets, representing the gene expression pattern characteristic of metastatic tumors or tumors with metastatic potential are shown in the Tables 1-6. The genes indicative of a tumor with metastatic potential, may be either up-regulated or down-regulated with respect to samples from tumor or tissue that does not show metastatic potential.
Classifier genes may be a portion of a larger polynucleotide comprising a polynucleotide as shown in the Tables 1-6 (e.g., a full length mRNA or cDNA). Alternatively classifier genes may be a portion of a polypeptide encoded by a larger polynucleotide comprising a polynucleotide as shown in the Tables 1-6. “Genes” in this context includes coding regions, non-coding regions, and mixtures of coding and non-coding regions. Accordingly, as will be appreciated by those in the art, using the sequences provided herein, extended sequences, in either direction, of the metastatic colorectal cancer genes can be obtained, using techniques well known in the art for cloning either longer sequences or the full length sequences; see Current Protocols in Molecular Biology (Ausubel et al., eds., 1994). Selection of an appropriate portion of a polynucleotide for sequence hybridization, or of an appropriate portion of a polypeptide for immunological or other recognition, is dictated by optimal hybridization or immunogenicity and may be accomplished by the methods described herein e.g. microarray techniques.
Selection of the classifier polynucleotide or polypeptide is in accordance with the particular analysis to which the biological sample will be subjected. A general property of classifier genes and their corresponding polypeptides is that expression of defined sets of classifier genes can be compared with the reference sets of the Tables 1-6 to determine the metastatic potential of a biological sample. In some applications, it is desirable for the classifier gene to be tissue-specific or disease-specific that is, expressed exclusively in the tissue, cells or disease of interest. In other applications, the classifier gene may be expressed predominantly in one tissue type, or disease state, but could also be expressed in other tissues, or in a healthy state, but in a different relationship with the other classifier genes of the set. For example, a particular classifier gene may be expressed at different levels in biological sample comprising a colon liver metastasis, compared to a non-metastatic colon cancer (e.g. Duke's stage B colorectal cancer that was cured by surgery).
Classifier genes may encode either intracellular molecules e.g., cellular nucleic acids, intracellular proteins, and the intracellular domains of transmembrane proteins, or may encode extracellular molecules, such as the extracellular domains of transmembrane proteins. Intracellular and extracellular classifier genes are equally suitable.
Protein expression patterns may be evaluated by methods other than hybridization or antibody based detection. For example: chromatographic separation of proteins; ELISA or Ab based separations; affinity chromatography, 2d gels; general protein separation methods with analysis of individual “classifier” proteins all may be used (Padzikill (2002) Proteomics Kluwer; Liebler (2001) Introduction to Proteomics: Tools for the New Biology Humana; Suhai (ed. 2000) Genomics and Proteomics: Functional and Computational Aspects Kluwer; Rabilloud (ed. 2001) Proteome Research: Two Dimensional Gel Electrophoresis and Detection Methods Springer-Verlag; Hames and Rickwood (eds. 2001) Gel Electrophoresis of Proteins: A Practical Approach Oxford Univ. Press; James (ed. 2000) Proteome Research: Mass Spectrometry Springer-Verlag; Kyriakidis, et al. (eds. 2001) Proteome and Protein Analysis Springer-Verlag.)
Gene Expression Profiling
A first step in the methods of the invention is performing gene expression profiling of a sample of interest. Gene expression profiling refers to examining expression of one or more RNAs or proteins in a cell or tissue. Often at least or up to 10, 100, 1000, 10,000 or more different RNAs or proteins are examined in a single experiment. The profile of the sample is the compared with the reference sets of the Tables 1-6. In some embodiments, a given classifier gene may have a similar expression pattern in different cells. In other embodiments, the gene of interest may have lower or higher expression in one cell, tissue, organ or physiological state as compared to another.
The evaluating assays of the invention may be of any type. High-density expression arrays can be used, but other techniques are also contemplated. Methods for examining gene expression, often but not always hybridization based, include, e.g., Northern blots; dot blots; primer extension; nuclease protection; subtractive hybridization and isolation of non-duplexed molecules using, e.g., hydroxyapatite; solution hybridization; filter hybridization; amplification techniques such as RT-PCR and other PCR-related techniques such as differential display, LCR, AFLP, RAP, etc. (see, e.g., U.S. Pat. Nos. 4,683,195 and 4,683,202; PCR Protocols: A Guide to Methods and Applications (Innis et al., eds, 1990); Liang & Pardee, Science 257:967-971 (1992); Hubank & Schatz, Nuc. Acids Res. 22:5640-5648 (1994); Perucho et al., Methods Enzymol. 254:275-290 (1995)), fingerprinting, e.g., with restriction endonucleases (Ivanova et al., Nuc. Acids. Res. 23:2954-2958 (1995); Kato, Nuc. Acids Res. 23:3685-3690 (1995); and Shimkets et al., Nature Biotechnology 17:798-803, see also U.S. Pat. No. 5,871,697)); and the use of structure specific endonucleases (see, e.g., De Francesco, The Scientist 12:16 (1998)). mRNA expression can also be analyzed using mass spectrometry techniques (e.g., MALDI or SELDI), liquid chromatography, and capillary gel electrophoresis, as described below.
For a general description of these techniques, see also Sambrook et al., Molecular Cloning, A Laboratory Manual (2nd ed. 1989), see, e.g., pages 7.37-7.39, 7.53-7.54, 7.58-7.66, and 7.71-7.79; Kriegler, Gene Transfer and Expression: A Laboratory Manual (1990); and Current Protocols in Molecular Biology (Ausubel et al., eds., 1994).
Techniques have been developed that expedite expression analysis and sequencing of large numbers of nucleic acids samples. For example, nucleic acid arrays have been developed for high density and high throughput expression analysis (see, e.g., Granjeuad et al., BioEssays 21:781-790 (1999); Lockhart & Winzeler, Nature 405:827-836 (2000)). Nucleic acid arrays refer to large numbers (e.g., tens, hundreds, thousands, tens of thousands, or more) of different nucleic acid probes bound to solid substrates, such as nylon, glass, or silicon wafers (see, e.g., Fodor et al., Science 251:767-773 (1991); Brown & Botstein, Nature Genet. 21:33-37 (1999); Eberwine, Biotechniques 20:584-591 (1996)). A single array can contain probes corresponding to an entire genome, to all genes expressed by the genome, or to a selected subset of genes. The probes on the array can be DNA oligonucleotide arrays (e.g., GeneChip®, see, e.g., Lipshutz et al., Nat. Genet. 21:20-24 (1999)), mRNA arrays, cDNA arrays, EST arrays, or optically encoded arrays on fiber optic bundles (e.g., BeadArray™). The samples applied to the arrays for expression analysis can be, e.g., PCR products, cDNA, mRNA, etc.
Additional techniques for rapid gene sequencing and analysis of gene expression include, for example, SAGE (serial analysis of gene expression). For SAGE, a short segment of the original transcript (typically about 14 bp) is cleaved from the transcript for analysis. This sequence contains sufficient information to uniquely identify a transcript, and is referred to as a sequence tag. Sequence tags are collected from all the mRNA transcripts of a sample by binding of the poly-A tail of the mRNAs to a poly-T column. The sequence tags are linked together to form long concatameric molecules that are cloned, amplified, and sequenced. Analysis of the resulting sequence data will identify each transcript and reveal the number of times a particular tag is observed. Thus the method permits the expression level of the corresponding transcript to be determined (see, e.g., Velculescu et al., Science 270:484-487 (1995); Velculescu et al., Cell 88 (1997); and de Waard et al., Gene 226:1-8 (1999)).
Embodiments of the Invention
As described herein, each of these techniques can be used, alone or in combination, to identify a classifier gene or set of classifier genes expressed in a cell, tissue organ or disease state. Classifier genes may encode, for example, ion channels, receptors, G protein coupled receptors, cytokines, chemokines, signal transduction proteins, housekeeping proteins, cell cycle regulation proteins, transcription factors, zinc finger proteins, chromatin remodeling proteins, etc. Once a classifier gene or set of classifier genes is analyzed in a particular biological sample, the results are compared to the reference sets of the Tables 1-6. The physiological state of the sample can then be determined. Information gained from the analysis of classifier genes in a sample can be used in to diagnose the potential for the disease to progress, the actual stage to which a disease has progressed (e.g. metastatic colorectal cancer), or to monitor the efficacy of therapeutic regimens given to a patient.
RNA or protein can be isolated and assayed from a biological sample using any techniques, for example, they can be isolated from fresh or frozen biopsy, from formalin-fixed tissue, from body fluids, such as blood, plasma, serum, urine, or sputum. Of course the present invention is not limited to the nature of the samples or the nature of the comparison, and will find use in a variety of applications.
The treatment of cancer has been hampered by the fact that there is considerable heterogeneity even within one type of cancer. Some cancers, for example, have the ability to invade tissues and display an aggressive course of growth characterized by metastases. These tumors generally are associated with a poor outcome for the patient. And yet, without a means of identifying such tumors and distinguishing such tumors from non-invasive cancer, the physician is at a loss to change and/or optimize therapy.
The present invention may be used to compare normal tissue with cancer tissue, as well as to differentiate between cancer tissue that is non-metastatic, cancer that is metastatic, and cancer tissue that has a potential to metastasize.
In yet another embodiment, the present invention may be used to determine the health status of a cell culture, tissue, or organ.
The present invention also finds use in drug screening. For example, samples treated with different candidate drugs can be subjected to the methods of the present invention to determine the ability of the compounds to alter the expression of classifier genes known to be implicated in the disease state. For example, if a particular classifier gene is known to be over-expressed in cancer cells, one can look for drugs that reduce the expression of the suspect gene or set of genes to normal levels.
Analysis of gene expression may be at the gene transcript or the protein level. The amount of gene expression may be evaluated using nucleic acid probes to the DNA or RNA equivalent of the gene transcript. Alternatively, the final gene product itself (protein) can be monitored, for example, with antibodies to the classifier protein and standard immunoassays (ELISAs, etc.) or other techniques, including mass spectroscopy assays, 2D gel electrophoresis assays, etc. Proteomics and separation techniques may also allow quantification of expression.
In a preferred embodiment, gene expression monitoring is performed simultaneously on a number of genes. Multiple protein expression monitoring can be performed as well.
In one embodiment, the classifier gene nucleic acid probes are attached to biochips as outlined herein for the detection and quantification of nucleotide sequences in a particular cell or tissue.
General Recombinant DNA Methods
This invention relies on routine techniques in the field of recombinant genetics. Basic texts disclosing the general methods of use in this invention include Sambrook et al., Molecular Cloning, A Laboratory Manual (2nd ed. 1989); Kriegler, Gene Transfer and Expression: A Laboratory Manual (1990); and Current Protocols in Molecular Biology (Ausubel et al., eds., 1994)).
For nucleic acids, sizes are given in either kilobases (kb) or base pairs (bp). These are estimates derived from agarose or acrylamide gel electrophoresis, from sequenced nucleic acids, or from published DNA sequences. For proteins, sizes are given in kilodaltons (kD) or amino acid residue numbers. Proteins sizes are estimated from gel electrophoresis, from sequenced proteins, from derived amino acid sequences, or from published protein sequences.
Oligonucleotides that are not commercially available can be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage & Caruthers, Tetrahedron Letts. 22:1859-1862 (1981), using an automated synthesizer, as described in Van Devanter et. al., Nucleic Acids Res. 12:6159-6168 (1984). Purification of oligonucleotides is by either native acrylamide gel electrophoresis or by anion-exchange HPLC as described in Pearson & Reanier, J. Chrom. 255:137-149 (1983).
The sequence of the cloned genes and synthetic oligonucleotides can be verified after cloning using, e.g., the chain termination method for sequencing double-stranded templates of Wallace et al., Gene 16:21-26 (1981).
Cloning Methods for the Isolation of Nucleotide Sequences
In general, nucleic acid sequences are cloned from cDNA and genomic DNA libraries or isolated using amplification techniques such as polymerase chain reaction (PCR). The primers used for PCR may amplify either the full length sequence or a probe of one to several hundred nucleotides, which is subsequently used to screen a library for full-length clones. Various combinations of oligonucleotides can be used to amplify coding and non-coding regions of the nucleotide sequence.
Nucleic acids can also be isolated from expression libraries using antibodies as probes. Polyclonal or monoclonal antibodies can be raised using the translation of a coding sequence, or any immunogenic portion thereof.
To make a cDNA library, one should choose a source that is rich in mRNA of the molecule one desires to clone. The mRNA is then made into cDNA using reverse transcriptase, ligated into a recombinant vector, and transfected into a recombinant host for propagation, screening and cloning. Methods for making and screening cDNA libraries are well known (see, e.g., Gubler & Hoffman, Gene 25:263-269 (1983); Sambrook et al., supra; Ausubel et al., supra).
For a genomic library, the DNA is extracted from the tissue and either mechanically sheared or enzymatically digested to yield fragments of about 12-20 kb. The fragments are then separated by gradient centrifugation from undesired sizes and are constructed in bacteriophage lambda vectors. These vectors and phage are packaged in vitro. Recombinant phage are analyzed by plaque hybridization as described in Benton & Davis, Science 196:180-182 (1977). Colony hybridization is carried out as generally described in Grunstein et al., Proc. Natl. Acad. Sci. USA., 72:3961-3965 (1975).
An alternative method of isolating specific nucleic acids and their orthologs, alleles, mutants, polymorphic variants, and conservatively modified variants combines the use of synthetic oligonucleotide primers and amplification of an RNA or DNA template (see U.S. Pat. Nos. 4,683,195 and 4,683,202; PCR Protocols: A Guide to Methods and Applications (Innis et al., eds, 1990)). Methods such as polymerase chain reaction (PCR) and ligase chain reaction (LCR) can be used to amplify nucleic acid sequences of target molecules directly from mRNA, from cDNA, from genomic libraries or cDNA libraries. Degenerate oligonucleotides can be designed to amplify target molecules homologs using the sequences provided herein. Restriction endonuclease sites can be incorporated into the primers. Polymerase chain reaction or other in vitro amplification methods may also be useful, for example, to clone nucleic acid sequences that code for proteins to be expressed, to make nucleic acids to use as probes for detecting the presence of target molecule-encoding mRNA in physiological samples, for nucleic acid sequencing, or for other purposes. Genes amplified by the PCR reaction can be purified from agarose gels and cloned into an appropriate vector.
Once isolated the nucleic acid is typically cloned into intermediate vectors before transformation into prokaryotic or eukaryotic cells for replication and/or expression. These intermediate vectors are typically prokaryote vectors, e.g., plasmids, or shuttle vectors.
Expression of Cloned Nucleotide Sequences in Prokaryotes and Eukaryotes
To obtain high level expression of a cloned gene, one typically subclones the gene into an expression vector that contains a strong promoter to direct transcription, a transcription/translation terminator, and if for a nucleic acid encoding a protein, a ribosome binding site for translational initiation. Suitable bacterial promoters are well known in the art and described, e.g., in Sambrook et al., and Ausubel et al., supra. Bacterial expression systems for expressing the target proteins are available in, e.g., E. coli, Bacillus sp., and Salmonella (Palva et al., Gene 22:229-235 (1983); Mosbach et al., Nature 302:543-545 (1983). Kits for such expression systems are commercially available. Eukaryotic expression systems for mammalian cells, yeast, and insect cells are well known in the art and are also commercially available.
Selection of the promoter used to direct expression of a heterologous nucleic acid depends on the particular application. The promoter is preferably positioned about the same distance from the heterologous transcription start site as it is from the transcription start site in its natural setting. As is known in the art, however, some variation in this distance can be accommodated without loss of promoter function.
In addition to the promoter, the expression vector typically contains a transcription unit or expression cassette that contains all the additional elements required for the expression of the target molecule-encoding nucleic acid in host cells. A typical expression cassette thus contains a promoter operably linked to the nucleic acid sequence encoding target molecules and signals required for efficient polyadenylation of the transcript, ribosome binding sites, and translation termination. Additional elements of the cassette may include enhancers and, if genomic DNA is used as the structural gene, introns with functional splice donor and acceptor sites.
In addition to a promoter sequence, the expression cassette should also contain a transcription termination region downstream of the structural gene to provide for efficient termination. The termination region may be obtained from the same gene as the promoter sequence or may be obtained from different genes.
The particular expression vector used to transport the genetic information into the cell is not particularly critical. Any of the conventional vectors used for expression in eukaryotic or prokaryotic cells may be used. Standard bacterial expression vectors include plasmids such as pBR322 based plasmids, pSKF, pET23D, and fusion expression systems such as MBP, GST, and LacZ. Epitope tags can also be added to recombinant proteins to provide convenient methods of isolation, e.g., c-myc.
Expression vectors containing regulatory elements from eukaryotic viruses are typically used in eukaryotic expression vectors, e.g., SV40 vectors, papilloma virus vectors, and vectors derived from Epstein-Barr virus. Other exemplary eukaryotic vectors include pMSG, pAV009/A+, pMTO10/A+, pMAMneo-5, baculovirus pDSVE, and any other vector allowing expression of proteins under the direction of the CMV promoter, SV40 early promoter, SV40 later promoter, metallothionein promoter, murine mammary tumor virus promoter, Rous sarcoma virus promoter, polyhedrin promoter, or other promoters shown effective for expression in eukaryotic cells.
Expression of proteins from eukaryotic vectors can be also be regulated using inducible promoters. With inducible promoters, expression levels are tied to the concentration of inducing agents, such as tetracycline or ecdysone, by the incorporation of response elements for these agents into the promoter. Generally, high level expression is obtained from inducible promoters only in the presence of the inducing agent; basal expression levels are minimal. Inducible expression vectors are often chosen if expression of the protein of interest is detrimental to eukaryotic cells.
Some expression systems have markers that provide gene amplification such as thymidine kinase and dihydrofolate reductase. Alternatively, high yield expression systems not involving gene amplification are also suitable, such as using a baculovirus vector in insect cells, with a target molecule-encoding sequence under the direction of the polyhedrin promoter or other strong baculovirus promoters.
The elements that are typically included in expression vectors also include a replicon that functions in E. coli, a gene encoding antibiotic resistance to permit selection of bacteria that harbor recombinant plasmids, and unique restriction sites in nonessential regions of the plasmid to allow insertion of eukaryotic sequences. The particular antibiotic resistance gene chosen is not critical—any of the many resistance genes known in the art are suitable. The prokaryotic sequences are preferably chosen such that they do not interfere with the replication of the DNA in eukaryotic cells, if necessary.
Standard transfection methods are used to produce bacterial, mammalian, yeast or insect cell lines that express large quantities of target protein, which are then purified using standard techniques (see, e.g., Colley et al., J. Biol. Chem. 264:17619-17622 (1989); Guide to Protein Purification, in Methods in Enzymology, vol. 182 (Deutscher, ed., 1990)). Transformation of eukaryotic and prokaryotic cells are performed according to standard techniques (see, e.g., Morrison, J. Bact. 132:349-351 (1977); Clark-Curtiss & Curtiss, Methods in Enzymology 101:347-362 (Wu et al., eds, 1983).
Any of the well-known procedures for introducing foreign nucleotide sequences into host cells may be used. These include the use of calcium phosphate transfection, polybrene, protoplast fusion, electroporation, biolistics, liposomes, microinjection, plasma vectors, viral vectors and any of the other well known methods for introducing cloned genomic DNA, cDNA, synthetic DNA or other foreign genetic material into a host cell (see, e.g., Sambrook et al., supra). It is only necessary that the particular genetic engineering procedure used be capable of successfully introducing at least one gene into the host cell capable of expressing the gene.
After the expression vector is introduced into the cells, the transfected cells are cultured under conditions favoring expression of the gene or gene fragment. The product of the expressed gene or gene fragment is then recovered from the culture using standard techniques identified below.
Purification of Classifier Gene Polypeptides
Either naturally occurring or recombinant proteins can be purified and used to generate antibodies. Naturally occurring proteins can be purified from a variety of sources. However, in a preferred embodiment the proteins are isolated from mammalian tissue. In a particularly preferred embodiment, the proteins are isolated from human tissue. Recombinant classifier proteins can be purified from any suitable expression system.
The proteins may be purified to substantial purity by standard techniques, including selective precipitation with such substances as ammonium sulfate; column chromatography, immunopurification methods, and others (see, e.g., Scopes, Protein Purification: Principles and Practice (1982); U.S. Pat. No. 4,673,641; Ausubel et al., supra; and Sambrook et al., supra).
A number of procedures can be employed when recombinant proteins are being purified all are familiar to those of skill in the art. For example, proteins having established molecular adhesion properties can be reversibly fused to another protein. With the appropriate ligand, the protein of interest may be selectively adsorbed to a purification column and then freed from the column in a relatively pure form. The fused protein is then removed by enzymatic activity. Finally, if antibodies to a portion of the protein are available, the protein may be purified using immunoaffinity columns.
Antibodies to Classifier Gene Polypeptides
Where the classifier gene product is a polypeptide encoded by a polynucleotide of the Tables 1-6, gene expression profiling can be examined using antibodies to the expressed classifier proteins.
To make effective antibodies, the classifier protein should share at least one epitope or determinant with the full length protein. By “epitope” or “determinant” herein is typically meant a portion of a protein which will generate and/or bind an antibody or T-cell receptor in the context of MHC. Thus, in most instances, antibodies made to a smaller classifier protein will be able to bind to the full-length protein, particularly linear epitopes. In a preferred embodiment, the epitope is unique; that is, antibodies generated to a unique epitope show little or no cross-reactivity.
Both polyclonal and monoclonal antibodies may be raised against the classifier proteins encoded by the classifier genes shown in the reference sets of the Tables 1-6. Methods of producing polyclonal and monoclonal antibodies that react specifically with specific proteins are known to those of skill in the art (see, e.g., Coligan, Current Protocols in Immunology (1991); Harlow & Lane, supra; Goding, Monoclonal Antibodies: Principles and Practice (2d ed. 1986); and Kohler & Milstein, Nature 256:495-497 (1975)). Such techniques include antibody preparation by selection of antibodies from libraries of recombinant antibodies in phage or similar vectors (see Winthrop et al., Q J Nucl Med 44:284-95 (2000)), as well as preparation of polyclonal and monoclonal antibodies by immunizing rabbits or mice (see, e.g., Huse et al., Science 246:1275-1281 (1989); Ward et al., Nature 341:544-546 (1989)). For some applications, recombinant antibody fragments derived from monoclonal antibodies—such as single-chain antibodies, diabodies, and minibodies—are preferred (see Wu and Yazaki, Q J Nucl Med 44:268-83 (2000)).
A number of immunogens comprising portions of classifier proteins encoded by the classifier genes of the Tables 1-6 may be used to produce antibodies specifically reactive with classifier proteins. For example, recombinant classifier proteins, or an antigenic fragment thereof can be isolated as is known in the art. Recombinant protein can be expressed in eukaryotic or prokaryotic cells, and then purified by well established methods known in the art. Recombinant protein is the preferred immunogen for the production of monoclonal or polyclonal antibodies. Alternatively, a synthetic peptide derived from the sequences disclosed herein and conjugated to a carrier protein can be used an immunogen. Naturally occurring protein may also be used either in pure or impure form. The product is then injected into an animal capable of producing antibodies. Either monoclonal or polyclonal antibodies may be generated, for subsequent use in immunoassays to measure the protein.
Methods of production of polyclonal antibodies are known to those of skill in the art. An inbred strain of mice (e.g., BALB/C mice) or rabbits is immunized with the protein using a standard adjuvant, such as Freund's adjuvant, and a standard immunization protocol. The animal's immune response to the immunogen preparation is monitored by taking test bleeds and determining the titer of reactivity to the immunogen. When appropriately high titers of antibody to the immunogen are obtained, blood is collected from the animal, and antisera are prepared. Further fractionation of the antisera to enrich for antibodies reactive to the protein can be done if desired (see, Harlow & Lane, supra).
Monoclonal antibodies and polyclonal sera are collected and titered against the immunogen protein in an immunoassay, for example, a solid phase immunoassay with the immunogen immobilized on a solid support. Typically, polyclonal antisera with a titer of 104 or greater are selected and tested for their cross reactivity against non-homologous proteins and other family proteins, using a competitive binding immunoassay. Specific polyclonal antisera and monoclonal antibodies will usually bind with a Kd of at least about 0.1 mM, more usually at least about 1 μM, preferably at least about 0.1 μM or better, and most preferably, 0.01 μM or better. Antibodies specific only for a particular protein ortholog can also be made, by subtracting out other cross-reacting orthologs from a species such as a non-human mammal.
Methods for Comparing Gene Expression Profiles with Reference Sets of the Tables 1-6
Patterns of gene expression can be compared to the reference set of the Tables 1-6 manually (by a person) or by a computer or other machine. An algorithm can be used to detect similarities and differences. The algorithm may score and compare, for example, the genes which are expressed and the genes which are not expressed. If the genes are expressed, the algorithm may further be used to quantify the expression by looking for relative changes in intensity of expression of a particular gene. A variety of algorithms for such comparisons are known in the art (see e.g. Breiman L, Friedman JH., Olshen RA, and Stone CJ. (1984) Classification and Regression Trees. Wadsworth and Brooks/Cole, Monterey Calif.)
Similarities in the gene expression profile of the classifier genes in a biological sample and a reference set may be determined with reference to which genes are expressed in both samples and/or which genes are not expressed in both samples. Alternatively, the relative differences in intensity of expression of two or more classifier genes in a sample, may be a basis for deciding similarity or difference. Differences in gene expression are considered significant when they are greater than 2-fold, 3-fold or 5-fold from the value defined by expression in a reference set of classifier genes.
Mathematical approaches can also be used to conclude whether similarities or differences in the gene expression exhibited by different samples are significant. See, e.g., Golub et al., Science 286, 531 (1999); Duda, et al. (2001) Pattern Classification Wiley; and Hastie, et al. (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer-Verlag. One approach to determine whether a sample is more similar to or has maximum similarity with a given condition between the sample and one or more pools representing different conditions for comparison; the pool with the smallest vector angle is then chosen as the most similar to the biological sample among the pools compared.
The gene expression patterns of the tissue sample will be compared against the expression patterns designated in the Tables 1-6. This comparison will lead to the determination of whether or not a sample has metastatic potential.
Differences in gene expression are considered significant when the differences in mean expressions across samples is detected with statistical significance and such that the level of falsely detected signficant genes is near zero (Efron B, Tibshirani R, Storey JD, and Tusher V. (2001) Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association, 96: 1151-1160.)
Since the comparison of gene expression profiles can be made with computers or other machines as well as manually, the invention also provides for the storage and retrieval of a collection of data in a computer data storage apparatus, which can include magnetic disks, optical disks, magneto-optical disks, DRAM, SRAM, SGRAM, SDRAM, RDRAM, DDR RAM, magnetic bubble memory devices, and other data storage devices, including CPU registers and on-CPU data storage arrays. Typically, the data records are stored as a bit pattern in an array of magnetic domains on a magnetizable medium or as an array of charge states or transistor gate states, such as an array of cells in a DRAM device (e.g., each cell comprised of a transistor and a charge storage area, which may be on the transistor). In one embodiment, the invention provides such storage devices, and computer systems built therewith, comprising a bit pattern encoding a protein expression fingerprint record comprising unique identifiers for at least 10 data records cross-tabulated with source.
The invention preferably provides a method for identifying peptide or nucleic acid sequences and determining the level of similarity or difference to a reference set, comprising performing a computerized comparison between a peptide or nucleic acid expression profiling record stored in or retrieved from a computer storage device or database and a reference set. The comparison can include a comparison algorithm or computer program embodiment thereof (e.g., FASTA, TFASTA, GAP, BESTFIT) and/or the comparison may be of the absolute or relative amount of a peptide or nucleic acid sequence in a pool of determined from a polypeptide or nucleic acid sample of a specimen.
The invention also provides a magnetic disk, such as an IBM-compatible (DOS, Windows, Windows95/98/2000, Windows NT, OS/2) or other format (e.g., Linux, SunOS, Solaris, AIX, SCO Unix, VMS, MV, Macintosh, etc.) floppy diskette or hard (fixed, Winchester) disk drive, comprising a bit pattern encoding data from an assay of the invention in a file format suitable for retrieval and processing in a computerized sequence analysis, comparison, or relative quantitation method.
The invention also provides a network, comprising a plurality of computing devices linked via a data link, such as an Ethernet cable (coax or 10BaseT), telephone line, ISDN line, wireless network, optical fiber, or other suitable signal transmission medium, whereby at least one network device (e.g., computer, disk array, etc.) comprises a pattern of magnetic domains (e.g., magnetic disk) and/or charge domains (e.g., an array of DRAM cells) composing a bit pattern encoding data acquired from an assay of the invention.
The invention also provides a method for transmitting expression profiling data that includes generating an electronic signal on an electronic communications device, such as a modem, ISDN terminal adapter, DSL, cable modem, ATM switch, or the like, wherein the signal includes (in native or encrypted format) a bit pattern encoding data from an assay or a database comprising a plurality of assay results obtained by the method of the invention.
In a preferred embodiment, the invention provides a computer system for comparing a query target to a database containing an array of data structures, such as an expression profiling result obtained by the method of the invention, and ranking database based on the degree of identity with one or more reference sets of the Tables 1-6. A central processor is preferably initialized to load and execute the computer program for comparison of the expression profiling results. Data for a query target is entered into the central processor via an I/O device. Execution of the computer program results in the central processor retrieving the expression profiling data from the data file, which comprises a binary description of an expression profiling result.
The expression profiling data and the computer program can be transferred to secondary memory, which is typically random access memory (e.g., DRAM, SRAM, SGRAM, or SDRAM). Expression profiles are ranked according to the degree of correspondence between an expression profile and one or more reference sets of the Tables 1-6. Results are output via an I/O device. For example, a central processor can be a conventional computer (e.g., Intel Pentium, PowerPC, Alpha, PA-8000, SPARC, MIPS 4400, MIPS 10000, VAX, etc.); a program can be a commercial or public domain molecular biology software package (e.g., UWGCG Sequence Analysis Software, Darwin); a data file can be an optical or magnetic disk, a data server, a memory device (e.g., DRAM, SRAM, SGRAM, SDRAM, EPROM, bubble memory, flash memory, etc.); an I/O device can be a terminal comprising a video display and a keyboard, a modem, an ISDN terminal adapter, an Ethernet port, a punched card reader, a magnetic strip reader, or other suitable I/O device.
The invention also provides the use of a computer system, such as that described above, which comprises: (1) a computer; (2) a stored bit pattern encoding a collection of expression profiles obtained by the methods of the invention, which may be stored in the computer; (3) reference sets of the Tables 1-6, and (4) a program for comparison, typically with rank-ordering of comparison results on the basis of computed similarity values.
RNA can be extracted from tissue samples, and the presence or absence on metastatic colorectal cancer can be determined by comparing the expression profile of classifier genes in the sample to the defined sets of genes of the Tables 1-6. Analysis of the expression profile can be carried out by measuring expression levels of classifier gene mRNA or protein.
For example, tissue from a non-metastatic Duke's stage B primary tumor, and from colorectal cancer that has progressed to end stage liver metastasis. Expression profiles of classifier genes from each sample are generated by creating an expression profile of either nucleic acid based data, or protein based data. The information obtained in the expression profiling is then analyzed and compared so that the relative expression levels of classifier genes in the two samples is used to create reference sets of genes such as those provided in the Tables 1-6. Expression patterns from samples whose disease state is unknown can then be compared to the defined sets of classifier genes in the Tables 1-6 and the presence or absence of metastatic colorectal cancer is diagnosed. If metastatic colorectal cancer is diagnosed, then further analysis of the data can reveal the stage of the disease and the probable prognosis.
The analysis of mRNA is preferred. For mRNA analysis, labeled, e.g., fluorescent or biotinylated, RNA from the unknown sample may be analyzed with an oligonucleotide microarray comprising sequences corresponding to the classifier genes of the Tables 1-6. Techniques for analysis and set up of the microarrays are known in the art.
Results of the analysis are used to identify which classifier genes are expressed and the level of their expression (as judged by the intensity of the signal). The pattern generated by the microarray analysis is then compared to the defined sets of genes of the Tables 1-6, and a determination of whether metastatic colorectal cancer is present is made. If metastatic disease is present the stage of the disease can also be determined.
In another embodiment, an expression profile of a sample is generated by examining the protein expression pattern of the sample. In this embodiment, total protein is extracted from a sample of the tissue (e.g., liver). Total protein is run on an acrylamide gel, then analyzed by western blot using antibodies to classifier genes of the Tables 1-6. As in the case of mRNA analysis, the expression pattern revealed in the western blot is compared to the defined sets of genes of the Tables 1-6. A match between the expression pattern of the sample with a particular defined set or sets of genes of the Tables 1-6 will permit the determination of whether or not cancer is present.
The defined sets of classifier genes of the Tables 1-6 are superior in their predictive power, because their expression strongly correlates with colorectal cancer metastasis. These defined sets of genes therefore provide ready tools for the diagnosis and prognosis evaluation of cancer, particularly metastatic colorectal cancer.
The expression pattern of classifier genes can be determined from the expression pattern of the corresponding proteins. Classifier proteins can be identified, e.g., by their positions on a gel following 2-dimensional gel electrophoresis of a sample of tissue subject to analysis.
Methods of 2-dimensional gel electrophoresis are well known in the art. Well characterized proteins, such as the classifier genes of the Tables 1-6, can be isolated from their unique placement within a gel after separation according to, for example, isoelectric point in the first dimension and molecular size in the second dimension. Thus, it is possible to determine expression levels of classifier proteins in a sample, as well as absolute expression levels of classifier proteins without the need for preparation of classifier protein specific antibodies.
Expression profiles of classifier genes generated in this manner can by compared with the defined sets of genes of the Tables 1-6 and the metastatic potential of the sample can thereby be determined.
Homo sapiens cDNA FLJ12366 fis, clone MAMMA1002411
Homo sapiens cDNA: FLJ21532 fis, clone COL06049
Homo sapiens cDNA FLJ14279 fis, clone PLACE1005574
Homo sapiens eukaryotic translation initiation factor 3, subunit 9 eta, 116 kDa (EIF3S9)
Homo sapiens cDNA: FLJ21429 fis, clone COL04205
Homo sapiens mRNA; cDNA DKFZp434D0818 (from clone DKFZp434D0818)
Homo sapiens cDNA FLJ13825 fis, clone THYRO1000558
Homo sapiens cDNA FLJ11533 fis, clone HEMBA1002678
Homo sapiens cDNA FLJ13625 fis, clone PLACE1011032
Homo sapiens clone IMAGE: 713177, mRNA sequence
Homo sapiens mRNA full length insert cDNA clone EUROIMAGE 113222
Homo sapiens cDNA: FLJ21532 fis, clone COL06049
Homo sapiens cDNA FLJ13825 fis, clone THYRO1000558
Homo sapiens cDNA FLJ12366 fis, clone MAMMA1002411
Homo sapiens, clone IMAGE: 3948909, mRNA, partial
Homo sapiens clone IMAGE: 713177, mRNA sequence
Homo sapiens cDNA FLJ11533 fis, clone
All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference.
Although the foregoing invention has been described in some detail by way of illustration and example for clarity and understanding, it will be readily apparent to one of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit and scope of the appended claims.
As can be appreciated from the disclosure provided above, the present invention has a wide variety of applications. Accordingly, the following examples are offered for illustration purposes and are not intended to be construed as a limitation on the invention in any way. Those of skill in the art will readily recognize a variety of non-critical parameters that could be changed or modified to yield essentially similar results.
This application claims priority from U.S. Provisional Application No. 60/460,892 filed Apr. 4, 2003, which is hereby incorporated by reference herein in its entirety.
This invention was made at least in part with assistance from the United States Federal Government, under Grant No. U01 CA88130 from the National Institutes of Health. As a result, the government may have certain rights to this invention.
| Number | Date | Country | |
|---|---|---|---|
| 60460892 | Apr 2003 | US |
