Key Points
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The TNM staging system (based on a combination of tumour size or depth (T), lymph node spread (N), and presence or absence of metastases (M)) provides a basis for prediction of survival, choice of initial treatment, stratification of patients in clinical trials, accurate communication among healthcare providers, and uniform reporting of the end result of cancer management.
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There is a dilemma in TNM staging: frequent revisions to include new biomarkers would undermine the value conferred by the stability and universality of TNM, but a static formulation of TNM risks falling behind the state of the art in diagnostic techniques, biological concepts and biomarkers.
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Biomarkers initially considered for cancer screening or risk assessment might also prove useful for cancer staging or grading.
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A biomarker for use in staging or grading need not be as specific as it must be for screening, early detection or risk assessment.
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As molecularly targeted cancer therapeutics become more common, assessing the intended target will more often be deemed necessary for prediction of clinical response, independent of TNM stage. Targeted therapies and their associated biomarkers will often 'co-evolve'.
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The ideal biomarker assay for staging should be sensitive, specific, cost-effective, fast, and robust against inter-operator and inter-institutional variability. It must also demonstrate clinical value beyond that of the other types of information that are already available at the time of diagnosis.
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Biomarker candidates must undergo clinical validation before receiving US Food and Drug Administration approval. For most candidate markers, that process is just beginning.
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Despite all of the potentially useful biomarkers — for example, those identified from microarray or mass spectrometry studies — almost none have been incorporated into formal TNM staging.
Abstract
Advances in genomics, proteomics and molecular pathology have generated many candidate biomarkers with potential clinical value. Their use for cancer staging and personalization of therapy at the time of diagnosis could improve patient care. However, translation from bench to bedside outside of the research setting has proved more difficult than might have been expected. Understanding how and when biomarkers can be integrated into clinical care is crucial if we want to translate the promise into reality.
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References
Sobin, L. H. TNM: evolution and relation to other prognostic factors. Semin. Surg. Oncol. 21, 3–7 (2003). Historical perspective of the TNM staging guidelines.
Hammond, M. E. & Taube, S. E. Issues and barriers to development of clinically useful tumor markers: a development pathway proposal. Semin. Oncol. 29, 213–221 (2002). Provides useful guidelines for the discovery, evaluation and validation of clinical biomarkers.
Negm, R. S., Verma, M. & Srivastava, S. The promise of biomarkers in cancer screening and detection. Trends Mol. Med. 8, 288–293 (2002).
US Department of Health & Human Services; US Food and Drug Administration. Challenge and Opportunity on the Critical Path of New Medical Products. http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html (2004).
Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002).
Nishizuka, S. et al. Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling. Cancer Res. 63, 5243–5250 (2003). Clear demonstration that numerous genomic, proteomic and tissue-based arrays can be used in concert to distinguish between colon and ovarian carcinomas.
Leong, P. P. et al. Distinguishing second primary tumors from lung metastases in patients with head and neck squamous cell carcinoma. J. Natl Cancer Inst. 90, 972–977 (1998).
Califano, J. et al. Unknown primary head and neck squamous cell carcinoma: molecular identification of the site of origin. J. Natl Cancer Inst. 91, 599–604 (1999).
Califano, J. et al. Genetic progression and clonal relationship of recurrent premalignant head and neck lesions. Clin. Cancer Res. 6, 347–352 (2000).
Ma, X. et al. Gene expression profiles of human breast cancer progression. Proc. Natl Acad. Sci. USA 100, 5974–5979 (2003).
Abulafia, O. & Sherer, D. M. Automated cervical cytology: meta-analyses of the performance of the PAPNET system. Obst. Gynecol. Surv. 54, 253–264 (1999).
Destounis, S. V. et al. Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience. Radiology 232, 578–584 (2004).
Awai, K. et al. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. Radiology 230, 347–352 (2004).
Erickson, B. J. & Bartholmai, B. Computer-aided detection and diagnosis at the start of the third millennium. J. Digit. Imaging 15, 59–68 (2002).
American Joint Committee on Cancer & American Cancer Society. AJCC Cancer Staging Handbook: from the AJCC Cancer Staging Manual (eds Greene, F. L. et al.) (Springer, New York, 2002). AJCC guidelines for cancer staging.
McQuade, P. & Knight, L. C. Radiopharmaceuticals for targeting the angiogenesis marker αvβ3 . Q. J. Nucl. Med. 47, 209–220 (2003).
Blankenberg, F. G. et al. In vivo detection and imaging of phosphatidylserine expression during programmed cell death. Proc. Natl Acad. Sci. USA 95, 6349–6354 (1998).
Kuge, Y. et al. Feasibility of 99mTc-annexin V for repetitive detection of apoptotic tumor response to chemotherapy: an experimental study using a rat tumor model. J. Nucl. Med. 45, 309–312 (2004).
Krohn, K. A. Evaluation of alternative approaches for imaging cellular growth. Q. J. Nucl. Med. 45, 174–178 (2001).
Kostakoglu, L., Agress, H. Jr & Goldsmith, S. J. Clinical role of FDG PET in evaluation of cancer patients. Radiographics 23, 315–340 (2003).
Kostakoglu, L. & Goldsmith, S. J. 18F-FDG PET evaluation of the response to therapy for lymphoma and for breast, lung, and colorectal carcinoma. J. Nucl. Med. 44, 224–239 (2003).
Britz-Cunningham, S. H. & Adelstein, S. J. Molecular targeting with radionuclides: state of the science. J. Nucl. Med. 44, 1945–1961 (2003).
Agrawal, M. et al. Increased 99mTc-sestamibi accumulation in normal liver and drug-resistant tumors after the administration of the glycoprotein inhibitor, XR9576. Clin. Cancer Res. 9, 650–656 (2003).
Revillion, F., Bonneterre, J. & Peyrat, J. P. ERBB2 oncogene in human breast cancer and its clinical significance. Eur. J. Cancer 34, 791–808 (1998).
Press, M. F. et al. Amplification and overexpression of HER-2/neu in carcinomas of the salivary gland: correlation with poor prognosis. Cancer Res. 54, 5675–5682 (1994).
Singletary, S. E. et al. Revision of the American Joint Committee on Cancer staging system for breast cancer. J. Clin. Oncol. 20, 3628–3636 (2002).
Druker, B. J. et al. Activity of a specific inhibitor of the BCR–ABL tyrosine kinase in the blast crisis of chronic myeloid leukemia and acute lymphoblastic leukemia with the Philadelphia chromosome. N. Engl. J. Med. 344, 1038–1042 (2001).
Balmain, A., Gray, J. & Ponder, B. The genetics and genomics of cancer. Nature Genet. 33 (Suppl.), 238–244 (2003).
Lynch, T. J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004).
Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004). Evidence that subtle mutations in EGFR can affect the clinical response to gefitinib.
Kaneda, H. et al. Retrospective analysis of the predictive factors associated with the response and survival benefit of gefitinib in patients with advanced non-small-cell lung cancer. Lung Cancer 46, 247–254 (2004).
Ravdin, P. M. et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J. Clin. Oncol. 19, 980–991 (2001).
Relling, M. & Dervieux, T. Pharmacogenetics and cancer therapy. Nature Rev. Cancer 1, 99–108 (2001).
Lenz, H. J. The use and development of germline polymorphisms in clinical oncology. J. Clin. Oncol. 22, 2519–2521 (2004).
Leon, S. A., Shapiro, B., Sklaroff, D. M. & Yaros, M. J. Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res. 37, 646–650 (1977).
Sidransky, D. Emerging molecular markers of cancer. Nature Rev. Cancer 2, 210–219 (2002). In-depth review of diverse biomarkers undergoing clinical evaluation.
Kahn, H. J. et al. Enumeration of circulating tumor cells in the blood of breast cancer patients after filtration enrichment: correlation with disease stage. Breast Cancer Res. Treat. 86, 237–247 (2004).
Stathopoulou, A. et al. Real-time quantification of CK-19 mrna-positive cells in peripheral blood of breast cancer patients using the lightcycler system. Clin. Cancer Res. 9, 5145–5151 (2003).
Hoon, D. S. et al. Molecular markers in blood as surrogate prognostic indicators of melanoma recurrence. Cancer Res. 60, 2253–2257 (2000).
Muller, V. & Pantel, K. Bone marrow micrometastases and circulating tumor cells: current aspects and future perspectives. Breast Cancer Res. 6, 258–261 (2004).
Pantel, K. & Brakenhoff, R. H. Dissecting the metastatic cascade. Nature Rev. Cancer 4, 448–456 (2004).
Meng, S. et al. Circulating tumor cells in patients with breast cancer dormancy. Clin. Cancer Res. 10, 8152–8162 (2004).
Brennon, J. A. et al. Molecular assesment of histopathological staging in squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 332, 429–435 (1995).
Levine, A. J. p53, the cellular gatekeeper for growth and division. Cell 88, 323–331 (1997).
Nigro, J. M., et al. Mutations in the p53 gene occur in diverse tumor types. Nature 342, 705–708 (1989).
Kobayashi, S. et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 352, 786–792 (2005).
Merlo, A. et al. 5′ CpG island methylation is associated with transcriptional silencing of the tumour suppressor p16/CDKN2/MTS1 in human cancers. Nature Med. 1, 686–692 (1995).
Herman, J. G. et al. Silencing of the VHL tumor-suppressor gene by dna methylation in renal carcinoma. Proc. Natl Acad. Sci. USA 91, 9700–9704 (1994).
Jenuwein, T. & Allis, C. Translating the histone code. Science 293, 1074–1080 (2001).
Esteller, M., Corn, P. G., Baylin, S. B. & Herman, J. G. A gene hypermethylation profile of human cancer. Cancer Res. 61, 3225–3229 (2001).
Herman, J. G., Graff, J. R., Myohanen, S., Nelkin, B. D. & Baylin, S. B. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc. Natl Acad. Sci. USA 93, 9821–9826 (1996).
Frommer, M. et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual dna strands. Proc. Natl Acad. Sci. USA 89, 1827–1831 (1992).
Jeronimo, C. et al. Quantitation of GTSP1 methylation in non-neoplastic prostatic tissue and organ-confined prostate adenocarcinoma. J. Natl Cancer Inst. 93, 1747–1752 (2001).
Rosas, S. L. et al. Promoter hypermethylation patterns of p16, o6-methylguanine-DNA-methyltransferase, and death-associated protein kinase in tumors and saliva of head and neck cancer patients. Cancer Res. 61, 939–942 (2001).
Belinsky, S. A. et al. Aberrant methylation of p16INK4a is an early event in lung cancer and a potential biomarker for early diagnosis. Proc. Natl Acad. Sci. USA 95, 11891–11896 (1998).
Palmisano, W. A. et al. Predicting lung cancer by detecting aberrant promoter methylation in sputum. Cancer Res. 60, 5954–5958 (2000).
Ahrendt, S. A. et al. Molecular detection of tumor cells in bronchoalveolar lavage fluid from patients with early stage lung cancer. J. Natl Cancer Inst. 91, 332–339 (1999).
Ramirez, J. L. et al. Methylation patterns and K-ras mutations in tumor and paired serum of resected non-small-cell lung cancer patients. Cancer Lett. 193, 207–216 (2003).
Usadel, H. et al. Quantitative adenomatous polyposis coli promoter methylation analysis in tumor tissue, serum, and plasma DNA of patients with lung cancer. Cancer Res. 62, 371–375 (2002).
Valenzuela, M. T. et al. Assessing the use of p16INK4a promoter gene methylation in serum for detection of bladder cancer. Eur. Urol. 42, 622–630 (2002).
Nakayama, H. et al. Molecular detection of p16 promoter methylation in the serum of recurrent colorectal cancer patients. Int. J. Cancer 105, 491–493 (2003).
Yamaguchi, S., Asao, T., Nakamura, J. I., Ide, M. & Kuwano, H. High frequency of DAP-kinase gene promoter methylation in colorectal cancer specimens and its identification in serum. Cancer Lett. 194, 99–105 (2003).
Esteller, M. et al. Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents. N. Engl J. Med. 343, 1350–1354 (2000).
Weinstein, J. N. Pharmacogenomics — teaching old drugs new tricks. N. Engl. J. Med. 343, 1408–1409 (2000).
Esteller, M. DNA methylation and cancer therapy: new developments and expectations. Curr. Opin. Oncol. 17, 55–60 (2005).
Cheng, J. C. et al. Preferential response of cancer cells to zebularine. Cancer Cell 6, 151–158 (2004).
Piekarz, R. & Bates, S. A review of depsipeptide and other histone deacetylase inhibitors in clinical trials. Curr. Pharm. Des. 10, 2289–2298 (2004).
Piekarz, R. L. et al. T-cell lymphoma as a model for the use of histone deacetylase inhibitors in cancer therapy: impact of depsipeptide on molecular markers, therapeutic targets, and mechanisms of resistance. Blood 103, 4636–4643 (2004).
Mischel, P. S., Cloughesy, T. F. & Nelson, S. F. DNA-microarray analysis of brain cancer: molecular classification for therapy. Nature Rev. Neurosci. 5, 782–792 (2004).
Gray, J. W. & Collins, C. Genome changes and gene expression in human solid tumors. Carcinogenesis 21, 443–452 (2000).
Velculescu, V. E., Zhang, L., Vogelstein, B. & Kinzler, K. W. Serial analysis of gene expression. Science 270, 484–487 (1995).
Weigl, B. H., Bardell, R. L. & Cabrera, C. R. Lab-on-a-chip for drug development. Adv. Drug. Deliv. Rev. 55, 349–377 (2003).
Weinstein, J. N. et al. An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343–349 (1997).
Papin, J. A, Hunter, T., Palsson, B. O. & Subramaniam, S. Reconstruction of cellular signalling networks and analysis of their properties. Nature Rev. Mol. Cell Biol. 6, 99–111 (2005).
Petricoin, E. F., Zoon, K. C., Kohn, E. C., Barrett, J. C. & Liotta, L. A. Clinical proteomics: translating benchside promise into bedside reality. Nature Rev. Drug Discov. 1, 683–695 (2002). Comprehensive review of clinical proteomic technologies and associated pattern-based protein markers.
Perou, C. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).
Sorlie, T., Perou, C., Brown, P., Botstein, D. & Borresen-Dale, A. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869–10874 (2001).
Sotiriou, C. et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Natl Acad. Sci. USA 100, 10393–10398 (2003).
van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002).
van't Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).
Chang, J. C. et al. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362, 362–369 (2003).
Sotiriou, C. et al. Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer. Breast Cancer Res. 4, R3 (2002).
Ramaswamy, S., Ross, K., Lander, E. & Golub, T. A molecular signature of metastasis in primary solid tumors. Nature Genet. 33, 49–54 (2003).
Rosell, R. et al. Ribonucleotide reductase messenger RNA expression and survival in gemcitabine/cisplatin-treated advanced non-small cell lung cancer patients. Clin. Cancer Res. 10, 1318–1325 (2004).
Salonga, D. et al. Colorectal tumors responding to 5-fluorouracil have low gene expression levels of dihydropyrimidine dehydrogenase, thymidylate synthase, and thymidine phosphorylase. Clin. Cancer Res. 6, 1322–1327 (2000).
Bittner, M. et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536–540 (2000).
Carr, K. M., Bittner, M. & Trent, J. M. Gene-expression profiling in human cutaneous melanoma. Oncogene 22, 3076–3080 (2003).
Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).
Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).
Lossos, I. S. et al. Transformation of follicular lymphoma to diffuse large-cell lymphoma: alternative patterns with increased or decreased expression of c-myc and its regulated genes. Proc. Natl Acad. Sci. USA 99, 8886–8891 (2002).
Garber, M. E. et al. Diversity of gene expression in adenocarcinoma of the lung. Proc. Natl Acad. Sci. USA 98, 13784–13789 (2001).
Best, C. J. et al. Molecular differentiation of high- and moderate-grade human prostate cancer by cDNA microarray analysis. Diagn. Mol. Pathol. 12, 63–70 (2003).
Zou, T. T. et al. Application of cDNA microarrays to generate a molecular taxonomy capable of distinguishing between colon cancer and normal colon. Oncogene 21, 4855–4862 (2002).
Zhou, G. et al. 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers. Mol. Cell. Proteomics 1, 117–124 (2002).
Nishizuka, S. et al. Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays. Proc. Natl Acad. Sci. USA 100, 14229–14234 (2003).
Paweletz, C. P. et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20, 1981–1989 (2001).
Verma, M., Wright, G. L. Jr, Hanash, S. M., Gopal-Srivastava, R. & Srivastava, S. Proteomic approaches within the NCI early detection research network for the discovery and identification of cancer biomarkers. Ann. NY Acad. Sci. 945, 103–115 (2001).
Wulfkuhle, J. D., Liotta, L. A. & Petricoin, E. F. Proteomic applications for the early detection of cancer. Nature Rev. Cancer 3, 267–275 (2003).
Sano, T., Smith, C. L. & Cantor, C. R. Immuno-PCR: very sensitive antigen detection by means of specific antibody–DNA conjugates. Science 258, 120–122 (1992).
Nam, J. M., Thaxton, C. S. & Mirkin, C. A. Nanoparticle-based bio-bar codes for the ultrasensitive detection of proteins. Science 301, 1884–1886 (2003).
Service, R. F. American Chemical Society Meeting. Tiny transistors scout for cancer. Science 300, 242–243 (2003).
Seydel, C. Quantum dots get wet. Science 300, 80–81 (2003).
Wu, X. et al. Immunofluorescent labeling of cancer marker Her2 and other cellular targets with semiconductor quantum dots. Nature Biotechnol. 21, 41–46 (2003).
Ben-Ari, E. T. Nanoscale quantum dots hold promise for cancer applications. J. Natl Cancer Inst. 95, 502–504 (2003).
Igor, M. et al. Self-assembled nanoscale biosensors based on quantum dot FRET donors. Nature Mater. 2, 630–638 (2003).
Wulfkuhle, J. D. et al. Proteomics of human breast ductal carcinoma in situ. Cancer Res. 62, 6740–6749 (2002).
Wulfkuhle, J. D. et al. New approaches to proteomic analysis of breast cancer. Proteomics 1, 1205–1215 (2001).
Jones, M. B. et al. Proteomic analysis and identification of new biomarkers and therapeutic targets for invasive ovarian cancer. Proteomics 2, 76–84 (2002).
Patel, V., Leethanakul, C. & Gutkind, J. S. New approaches to the understanding of the molecular basis of oral cancer. Crit. Rev. Oral Biol. Med. 12, 55–63 (2001).
Ornstein, D. K. et al. Proteomic analysis of laser capture microdissected human prostate cancer and in vitro prostate cell lines. Electrophoresis 21, 2235–2242 (2000).
Pepe, M. S. et al. Phases of biomarker development for early detection of cancer. J. Natl Cancer Inst. 93, 1054–1061 (2001).
Barker, P. E. Cancer biomarker validation: standards and process: roles for the National Institute of Standards and Technology (NIST). Ann. NY Acad. Sci. 983, 142–150 (2003).
Ransohoff, D. F. Rules of evidence for cancer molecular marker discovery and validation. Nature Rev. Cancer 4, 309–314 (2004). Effectively summarizes the problem of data overfitting in discovery-based research and the need for 'rules of evidence' to ensure clinical validity.
Gutman, S. Regulatory issues in tumor marker development. Semin. Oncol. 29, 294–300 (2002). Examines the regulatory environment surrounding biomarker approval by the FDA and CMS.
Taube, S. E. & Freiberg, G. P. Regulatory issues related to marker development. Urol. Oncol. 5, 214–216 (2000).
Hackett, J. L. & Lesko, L. J. Microarray data — the US FDA, industry and academia. Nature Biotechnol. 21, 742–743 (2003).
Mendelsohn, A. R. & Brent, R. Postgenomic protein analysis: the next bend in the river. Nature Biotechnol. 16, 520–521 (1998).
Robson, M. E. et al. A combined analysis of outcome following breast cancer: differences in survival based on BRCA1/BRCA2 mutation status and administration of adjuvant treatment. Breast Cancer Res. 6, R8–R17 (2004).
Einspahr, J. G. et al. Surrogate end-point biomarkers as measures of colon cancer risk and their use in cancer chemoprevention trials. Cancer Epidemiol. Biomarkers Prev. 6, 37–48 (1997).
Togashi, K. Ovarian cancer: the clinical role of US, CT, and MRI. Eur. Radiol. 13 (Suppl. 4), L87–L104 (2003).
Schlieman, M. G., Ho, H. S. & Bold, R. J. Utility of tumor markers in determining resectability of pancreatic cancer. Arch. Surg. 138, 951–956 (2003).
Depres-Brummer, P. et al. The usefulness of CA15.3, mucin-like carcinoma-associated antigen and carcinoembryonic antigen in determining the clinical course in patients with metastatic breast cancer. J. Cancer Res. Clin. Oncol. 121, 419–422 (1995).
Duncan, J. L., Price, A. & Rogers, K. The use of CA15.3 as a serum tumour marker in breast carcinoma. Eur. J. Surg. Oncol 17, 16–19 (1991).
Gion, M. et al. Tumor markers in breast cancer monitoring should be scheduled according to initial stage and follow-up time: a prospective study on 859 patients. Cancer J. 7, 181–190 (2001).
Chang, B. L. et al. Polymorphisms in the CYP1A1 gene are associated with prostate cancer risk. Int. J. Cancer 106, 375–378 (2003).
Lee, S. G. et al. Genetic polymorphisms of XRCC1 and risk of gastric cancer. Cancer Lett. 187, 53–60 (2002).
Petrij-Bosch, A. et al. BRCA1 genomic deletions are major founder mutations in Dutch breast cancer patients. Nature Genet. 17, 341–345 (1997).
Newman, B. et al. Frequency of breast cancer attributable to BRCA1 in a population-based series of American women. JAMA 279, 915–921 (1998).
Wu, X. et al. p53 genotypes and haplotypes associated with lung cancer susceptibility and ethnicity. J. Natl Cancer Inst. 94, 681–690 (2002).
Bonnen, P. E., Wang, P. J., Kimmel, M., Chakraborty, R. & Nelson, D. L. Haplotype and linkage disequilibrium architecture for human cancer-associated genes. Genome Res. 12, 1846–1853 (2002).
Johnson, G. & Todd, J. Haplotype tagging for the identification of common disease genes. Nature Genet. 29, 233–237 (2001).
Hoque, M. O., Lee, C. C., Cairns, P., Schoenberg, M. & Sidransky, D. Genome-wide genetic characterization of bladder cancer: a comparison of high-density single-nucleotide polymorphism arrays and PCR-based microsatellite analysis. Cancer Res. 63, 2216–2222 (2003).
Janne, P. A. et al. High-resolution single-nucleotide polymorphism array and clustering analysis of loss of heterozygosity in human lung cancer cell lines. Oncogene 23, 2716–2726 (2004).
Boshoff, C. et al. Kaposi's sarcoma-associated herpesvirus infects endothelial and spindle cells. Nature Med. 1, 1274–1278 (1995).
To, E. W. H. et al. Rapid clearance of plasma Epstein-Barr virus DNA after surgical treatment of nasopharyngeal carcinoma. Clin. Cancer Res. 9, 3254–3259 (2003).
Leung, S. F. et al. Pretherapy quantitative measurement of circulating Epstein-Barr virus DNA is predictive of posttherapy distant failure in patients with early-stage nasopharyngeal carcinoma of undifferentiated type. Cancer 98, 288–291 (2003).
Mueller, N. Overview: viral agents and cancer. Environ. Health Perspect. 103 (Suppl. 8), 259–261 (1995).
Koutsky, L. A. et al. A controlled trial of a human papillomavirus type 16 vaccine. N. Engl. J. Med. 347, 1645–1651 (2002).
Pellet, C. et al. Virologic and immunologic parameters that predict clinical response of AIDS-associated Kaposi's sarcoma to highly active antiretroviral therapy. J. Invest. Dermatol. 117, 858–863 (2001).
Polyak, K. et al. Somatic mutations of the mitochondrial genome in human colorectal tumours. Nature Genet. 20, 291–293 (1998).
Bianchi, N. O., Bianchi, M. S. & Richard, S. M. Mitochondrial genome instability in human cancers. Mutat. Res. 488, 9–23 (2001).
Welter, C., Kovacs, G., Seitz, G. & Blin, N. Alteration of mitochondrial DNA in human oncocytomas. Genes Chromosomes Cancer 1, 79–82 (1989).
Fliss, M. S. et al. Facile detection of mitochondrial DNA mutations in tumors and bodily fluids. Science 287, 2017–2019 (2000).
Weinstein, J. N. Fishing expeditions. Science 282, 628–629 (1998).
Weinstein, J. N. 'Omic' and hypothesis-driven research in the molecular pharmacology of cancer. Curr. Opin. Pharmacol. 2, 361–365 (2002).
Acknowledgements
We thank Maria Chan (FDA) for guidance regarding Table 1 and Anna Maria Calcagno (NCI) for her constructive scientific input. This work was supported by the Intramural Research Program of the NIH, the National Cancer Institute and the Center for Cancer Research. We are grateful to Anna Barker and the reviewers whose constructive comments and suggestions significantly improved the manuscript.
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Glossary
- SINGLE-NUCLEOTIDE POLYMORPHISMS
-
Single-nucleotide changes in DNA that differ among individuals.
- BCR–ABL TRANSLOCATION
-
Translocation between human chromosomes 9 and 22 t(9q34;22q11), resulting in an abnormal Philadephia chromosome that codes for a fusion protein causally linked to chronic myelogenous leukaemia.
- MICROSATELLITE INSTABILITY
-
Genetic instability in diploid tumours owing to a high mutation rate, primarily in short nucleotide repeats. This phenotype is associated with defects in DNA mismatch-repair genes.
- POSITRON-EMISSION TOMOGRAPHY
-
Imaging technique that detects nuclides as they decay by positron emission. The emitted positron collides with a free electron, resulting in the conversion of matter to two γ-rays, which emerge in opposite directions.
- COMPUTER-AIDED DIAGNOSTIC SYSTEM
-
A computer algorithm for interpreting digital images or laboratory tests to provide a diagnosis.
- PATTERN-BASED BIOMARKER
-
A biomarker constructed from a pattern of individual markers that, when evaluated together, can be used for risk assessment, screening, diagnosis, staging, selection of therapy and/or monitoring of therapy. The specific markers that make up the pattern may or may not have been identified.
- SINGLE-PHOTON EMISSION COMPUTED TOMOGRAPHY
-
Imaging technology in which a photon detector array is rotated around the body to acquire data from many angles following the injection of a γ-emitting radionuclide.
- DECISION SUPPORT SYSTEM
-
A computerized information system that supports decision-making activities.
- DIFFERENTIAL DISPLAY
-
A gel-based technique used to identify transcripts that are differentially expressed between cell or tissue samples.
- SERIAL ANALYSIS OF GENE EXPRESSION
-
(SAGE). A technique for identification and quantitation of transcript expression levels. SAGE is based on a process in which short oligonucleotide 'tags' from defined locations within a transcript are spliced together and sequenced for identification of the transcript.
- BEAD-BASED METHODS
-
Methods of measurement based on small or microscopic beads (as opposed, for example, to the flat surfaces characteristic of microarrays).
- MICROFLUIDICS
-
Technology that allows the use of very small volumes of reagents, shortening reaction times and facilitating scale-up of molecular methods.
- HAPLOTYPE
-
A way of denoting the collective genotype of a number of closely linked loci on a chromosome that tend to be inherited together in a population.
- SUPERVISED ALGORITHM
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A method of statistical or machine learning in which a model is fitted to observations. The algorithm, in effect, learns by example.
- LASER-CAPTURE MICRODISSECTION
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A laser-based technology used to obtain materials from selected regions of cut tissue or tumour sections on glass slides. The method is used, for example, to obtain relatively pure populations of tumour cells from the heterogeneous mixture of cells in a tumour.
- CYTOKERATIN
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A protein component of intermediate filaments found in epithelial cells.
- REVERSE-PHASE MICROARRAY
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A microarray spotted with numerous tissue or cell lysates and subsequently incubated with a detection ligand (usually an antibody) to quantitate protein in the lysates.
- IMMUNO-PCR
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A sensitive method for detection of proteins using a combination of PCR and conventional immuno-detection. A bi-specific linker molecule with affinity for DNA and an antibody is used to attach a DNA marker to a specific antigen, resulting in an antigen–antibody–DNA complex that can be quantified using PCR.
- FIELD EFFECT TRANSISTOR-BASED PROTEIN DETECTION
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Technology for detecting proteins based on their completion of a circuit between two electrodes in a transistor, thereby resulting in a measurable increase in current.
- QUANTUM DOTS
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Semiconductor particles with size-dependent fluorescence-emission wavelengths visualized by laser-excitation spectrometry.
- OVERFITTING
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In multivariate predictive analysis, a statistical model can be overfitted if it has too many free parameters for the number and type of cases in the training set. The result can be a model that fits the training data set very well but does poorly when applied to other data.
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Ludwig, J., Weinstein, J. Biomarkers in Cancer Staging, Prognosis and Treatment Selection. Nat Rev Cancer 5, 845–856 (2005). https://doi.org/10.1038/nrc1739
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DOI: https://doi.org/10.1038/nrc1739
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