Skip to main content
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2008 Sep 2;46(3):222–229. doi: 10.1016/S1028-4559(08)60024-8

Microarray Analysis of Gene Expression of Cancer to Guide the Use of Chemotherapeutics

Tzu-Hao Wang a,b,*, Angel Chao a
PMCID: PMC7129596  PMID: 17962100

Summary

The beauty of microarray analysis of gene expression (MAGE) is that it can be used to discover some genes that were previously thought to be unrelated to a physiologic or pathologic event. During the period from 1999 to 2007, applications of MAGE in cancer investigation have shifted from molecular profiling, identifying previously undiscovered cancer types, predicting outcomes of cancer patients, revealing metastasis signatures of solid tumors, to guiding the use of therapeutics. The roles of cancer genomic signatures have evolved through three phases. In the first phase, genomic signatures were described in stored cancer specimens and dubbed as molecular portraits of cancer. When gene expression profiles were carefully correlated with sufficient clinical information of cancer patients, new subgroups of cancers with distinct outcomes were revealed. In studies of the second phase, validation of cancer signatures was emphasized and commonly performed with independent groups of cancer specimens or independent data set. In the third phase, cancer genomic signatures have been further expanded beyond depicting the molecular portrait of cancer to predicting patient outcomes and guiding the use of cancer therapeutics. Cancer genomic signatures have become an essential part of a new generation of cancer clinical trials. It is advocated that, in future clinical trials of cancer therapy, the cancer specimens of each participant should be tested for currently available predictor genomic signatures, so that the most effective treatment with the least adverse effects for each patient can be identified. Then, participants can be triaged to an appropriate study group.

Key Words: cancer therapeutics, clinical application, gene expression, genomic signatures, microarrays

References

  • 1.Wang TH, Lee YS, Chen ES. Establishment of cDNA microarray analysis at the Genomic Medicine Research Core Laboratory (GMRCL) of Chang Gung Memorial Hospital. Chang Gung Med J. 2004;27:243–260. [PubMed] [Google Scholar]
  • 2.Chao A, Wang TH, Lai CH. Overview of microarray analysis of gene expression and its applications to cervical cancer investigation. Taiwan J Obstet Gynecol 2007 (In press). [DOI] [PMC free article] [PubMed]
  • 3.Lee YS, Chen CH, Chao A. Molecular signature of clinical severity in recovering patients with severe acute respiratory syndrome coronavirus (SARS-CoV) BMC Genomics. 2005;6:132. doi: 10.1186/1471-2164-6-132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Chao A, Wang TH, Lee YS. Molecular characterization of adenocarcinoma and squamous carcinoma of the uterine cervix using microarray analysis of gene expression. Int J Cancer. 2006;119:91–98. doi: 10.1002/ijc.21813. [DOI] [PubMed] [Google Scholar]
  • 5.Wang TH, Chan YH, Chen CW. Paclitaxel (Taxol) upregulates expression of functional interleukin-6 in human ovarian cancer cells through multiple signaling pathways. Oncogene. 2006;25:4857–4866. doi: 10.1038/sj.onc.1209498. [DOI] [PubMed] [Google Scholar]
  • 6.Tsai MS, Hwang SM, Chen KD, et al. Functional network analysis on the transcriptomes of mesenchymal stem cells derived from amniotic fluid, amniotic membrane, cord blood, and bone marrow. Stem Cells 2007 (In press). [DOI] [PubMed]
  • 7.Tan PK, Downey TJ, Spitznagel EL., Jr Evaluation of gene expression measurements from commercial microar-ray platforms. Nucleic Acids Res. 2003;31:5676–5684. doi: 10.1093/nar/gkg763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Yauk CL, Berndt ML, Williams A, Douglas GR. Comprehensive comparison of six microarray technologies. Nucleic Acids Res. 2004;32:e124. doi: 10.1093/nar/gnh123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Carter SL, Eklund AC, Mecham BH, Kohane IS, Szallasi Z. Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements. BMC Bioinformatics. 2005;6:107. doi: 10.1186/1471-2105-6-107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Wang H, He X, Band M, Wilson C, Liu L. A study of inter-lab and inter-platform agreement of DNA microarray data. BMC Genomics. 2005;6:71. doi: 10.1186/1471-2164-6-71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Fortunel NO, Otu HH, Ng HH. Comment on “;‘Stemness’: transcriptional profiling of embryonic and adult stem cells” and “a stem cell molecular signature”. Science. 2003;302:393. doi: 10.1126/science.1086384. author reply 393. [DOI] [PubMed] [Google Scholar]
  • 12.Marshall E. Getting the noise out of gene arrays. Science. 2004;306:630–631. doi: 10.1126/science.306.5696.630. [DOI] [PubMed] [Google Scholar]
  • 13.Strauss E. Arrays of hope. Cell. 2006;127:657–659. doi: 10.1016/j.cell.2006.11.005. [DOI] [PubMed] [Google Scholar]
  • 14.Tong W, Lucas AB, Shippy R. Evaluation of external RNA controls for the assessment of microarray performance. Nat Biotechnol. 2006;24:1132–1139. doi: 10.1038/nbt1237. [DOI] [PubMed] [Google Scholar]
  • 15.Canales RD, Luo Y, Willey JC. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol. 2006;24:1115–1122. doi: 10.1038/nbt1236. [DOI] [PubMed] [Google Scholar]
  • 16.Patterson TA, Lobenhofer EK, Fulmer-Smentek SB. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat Biotechnol. 2006;24:1140–1150. doi: 10.1038/nbt1242. [DOI] [PubMed] [Google Scholar]
  • 17.Shi L, Reid LH, Jones WD, for MAQC Consortium The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006;24:1151–1161. doi: 10.1038/nbt1239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Guo L, Lobenhofer EK, Wang C. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol. 2006;24:1162–1169. doi: 10.1038/nbt1238. [DOI] [PubMed] [Google Scholar]
  • 19.Liu ET, Karuturi KR. Microarrays and clinical investigations. N Engl J Med. 2004;350:1595–1597. doi: 10.1056/NEJMp048050. [DOI] [PubMed] [Google Scholar]
  • 20.Grimwade D, Haferlach T. Gene-expression profiling in acute myeloid leukemia. N Engl J Med. 2004;350:1676–1678. doi: 10.1056/NEJMe048040. [DOI] [PubMed] [Google Scholar]
  • 21.Febbo PG, Kantoff PW. Noise and bias in microarray analysis of tumor specimens. J Clin Oncol. 2006;24:3719–3721. doi: 10.1200/JCO.2006.06.7942. [DOI] [PubMed] [Google Scholar]
  • 22.Rhodes DR, Chinnaiyan AM. Integrative analysis of the cancer transcriptome. Nat Genet. 2005;37(Suppl):S31–S37. doi: 10.1038/ng1570. [DOI] [PubMed] [Google Scholar]
  • 23.Segal E, Friedman N, Kaminski N, Regev A, Koller D. From signatures to models: understanding cancer using microar-rays. Nat Genet. 2005;37(Suppl):S38–S45. doi: 10.1038/ng1561. [DOI] [PubMed] [Google Scholar]
  • 24.Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genomewide expression patterns. Proc Natl Acad Sci USA. 1998;95:14863–14868. doi: 10.1073/pnas.95.25.14863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Rhodes DR, Yu J, Shanker K. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA. 2004;101:9309–9314. doi: 10.1073/pnas.0401994101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Harris MA, Clark J, Ireland A, for Gene Ontology Consortium The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004;32(Database issue):D258–D261. doi: 10.1093/nar/gkh036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Barrette TR, Ghosh D, Chinnaiyan AM. Mining for regulatory programs in the cancer transcriptome. Nat Genet. 2005;37:579–583. doi: 10.1038/ng1578. [DOI] [PubMed] [Google Scholar]
  • 28.Weinmann AS, Yan PS, Oberley MJ, Huang TH, Farnham PJ. Isolating human transcription factor targets by coupling chromatin immunoprecipitation and CpG island microar-ray analysis. Genes Dev. 2002;16:235–244. doi: 10.1101/gad.943102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Odom DT, Zizlsperger N, Gordon DB. Control of pancreas and liver gene expression by HNF transcription factors. Science. 2004;303:1378–1381. doi: 10.1126/science.1089769. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Mootha VK, Lindgren CM, Eriksson KF. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267–273. doi: 10.1038/ng1180. [DOI] [PubMed] [Google Scholar]
  • 31.Segal E, Friedman N, Koller D, Regev A. A module map showing conditional activity of expression modules in cancer. Nat Genet. 2004;36:1090–1098. doi: 10.1038/ng1434. [DOI] [PubMed] [Google Scholar]
  • 32.Golub TR, Slonim DK, Tamayo P. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537. doi: 10.1126/science.286.5439.531. [DOI] [PubMed] [Google Scholar]
  • 33.Perou CM, Sorlie T, Eisen MB. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. doi: 10.1038/35021093. [DOI] [PubMed] [Google Scholar]
  • 34.Alizadeh AA, Eisen MB, Davis RE. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403:503–511. doi: 10.1038/35000501. [DOI] [PubMed] [Google Scholar]
  • 35.van't Veer LJ, Dai H, van de Vijver MJ. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536. doi: 10.1038/415530a. [DOI] [PubMed] [Google Scholar]
  • 36.Shipp MA, Ross KN, Tamayo P. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 2002;8:68–74. doi: 10.1038/nm0102-68. [DOI] [PubMed] [Google Scholar]
  • 37.Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet. 2003;33:49–54. doi: 10.1038/ng1060. [DOI] [PubMed] [Google Scholar]
  • 38.Potti A, Dressman HK, Bild A. Genomic signatures to guide the use of chemotherapeutics. Nat Med. 2006;12:1294–1300. doi: 10.1038/nm1491. [DOI] [PubMed] [Google Scholar]
  • 39.Lossos IS, Czerwinski DK, Alizadeh AA, Wechser MA, Tibshirani R, Botstein D, Levy R. Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. N Engl J Med. 2004;350:1828–1837. doi: 10.1056/NEJMoa032520. [DOI] [PubMed] [Google Scholar]
  • 40.Perou CM, Jeffrey SS, van de Rijn M. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA. 1999;96:9212–9217. doi: 10.1073/pnas.96.16.9212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.A clinical evaluation of the International Lymphoma Study Group classification of non-Hodgkin's lymphoma. The Non-Hodgkin's Lymphoma Classification Project. Blood. 1997;89:3909–3918. [PubMed] [Google Scholar]
  • 42.Polychemotherapy for early breast cancer: an overview of the randomised trials. Early Breast Cancer Trialists' Collaborative Group. Lancet. 1998;352:930–942. [PubMed] [Google Scholar]
  • 43.Tamoxifen for early breast cancer: an overview of the randomised trials. Early Breast Cancer Trialists' Collaborative Group. Lancet. 1998;351:1451–1467. [PubMed] [Google Scholar]
  • 44.Liotta LA, Kohn EC. Cancer's deadly signature. Nat Genet. 2003;33:10–11. doi: 10.1038/ng0103-10. [DOI] [PubMed] [Google Scholar]
  • 45.Bullinger L, Dohner K, Bair E. Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med. 2004;350:1605–1616. doi: 10.1056/NEJMoa031046. [DOI] [PubMed] [Google Scholar]
  • 46.Valk PJ, Verhaak RG, Beijen MA. Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med. 2004;350:1617–1628. doi: 10.1056/NEJMoa040465. [DOI] [PubMed] [Google Scholar]
  • 47.Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005;365:488–492. doi: 10.1016/S0140-6736(05)17866-0. [DOI] [PubMed] [Google Scholar]
  • 48.Sorlie T, Perou CM, Tibshirani R. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98:10869–10874. doi: 10.1073/pnas.191367098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.van de Vijver MJ, He YD, van't Veer LJ. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999–2009. doi: 10.1056/NEJMoa021967. [DOI] [PubMed] [Google Scholar]
  • 50.Chang HY, Nuyten DS, Sneddon JB. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA. 2005;102:3738–3743. doi: 10.1073/pnas.0409462102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Chang HY, Sneddon JB, Alizadeh AA. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2004;2:E7. doi: 10.1371/journal.pbio.0020007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Rosenwald A, Wright G, Chan WC, for the Lymphoma/Leukemia Molecular Profiling Project The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med. 2002;346:1937–1947. doi: 10.1056/NEJMoa012914. [DOI] [PubMed] [Google Scholar]
  • 53.Fan C, Oh DS, Wessels L. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006;355:560–569. doi: 10.1056/NEJMoa052933. [DOI] [PubMed] [Google Scholar]
  • 54.Bild AH, Yao G, Chang JT. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature. 2006;439:353–357. doi: 10.1038/nature04296. [DOI] [PubMed] [Google Scholar]
  • 55.Breathnach OS, Freidlin B, Conley B. Twenty-two years of phase III trials for patients with advanced non-small-cell lung cancer: sobering results. J Clin Oncol. 2001;19:1734–1742. doi: 10.1200/JCO.2001.19.6.1734. [DOI] [PubMed] [Google Scholar]
  • 56.Staunton JE, Slonim DK, Coller HA. Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci USA. 2001;98:10787–10792. doi: 10.1073/pnas.191368598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Gyorffy B, Surowiak P, Kiesslich O, Denkert C, Schafer R, Dietel M, Lage H. Gene expression profiling of 30 cancer cell lines predicts resistance towards 11 anticancer drugs at clinically achieved concentrations. Int J Cancer. 2006;118:1699–1712. doi: 10.1002/ijc.21570. [DOI] [PubMed] [Google Scholar]
  • 58.Herbst RS, Lippman SM. Molecular signatures of lung cancer—toward personalized therapy. N Engl J Med. 2007;356:76–78. doi: 10.1056/NEJMe068218. [DOI] [PubMed] [Google Scholar]
  • 59.Potti A, Mukherjee S, Petersen R. A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med. 2006;355:570–580. doi: 10.1056/NEJMoa060467. [DOI] [PubMed] [Google Scholar]
  • 60.Dressman HK, Berchuck A, Chan G. An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer. J Clin Oncol. 2007;25:517–525. doi: 10.1200/JCO.2006.06.3743. [DOI] [PubMed] [Google Scholar]

Articles from Taiwanese Journal of Obstetrics & Gynecology are provided here courtesy of Elsevier

RESOURCES