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Cited by 141Ovarian cancer diagnosis and treatmentGene expression and cancer classificationBreast Cancer Treatment Studies

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Gene Expression Profiles Predict Early Relapse in Ovarian Cancer after Platinum-Paclitaxel Chemotherapy

Hartmann, Lynn C., Lu, Karen H., Linette, Gerald P., Cliby, William A., Kalli, Kimberly R., Gershenson, David, Bast, Robert C., Stec, James, Iartchouk, Natalia, Smith, David I., Ross, Jeffrey S., Hoersch, Sebastian, Shridhar, Viji, Lillie, James, Kaufmann, Scott H., Clark, Edwin A., Damokosh, Andrew I.·2005·Clinical Cancer Research
DOI 10.1158/1078-0432.ccr-04-1673
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Abstract

PURPOSE: Women with advanced epithelial ovarian cancer are routinely treated with platinum-paclitaxel chemotherapy following cytoreductive surgery, yet only approximately 20% achieve long-term disease-free survival. We hypothesized that differences in gene expression before treatment could distinguish patients with short versus long time to recurrence after administration of platinum-paclitaxel combination chemotherapy. EXPERIMENTAL DESIGN: To test this hypothesis, gene expression profiling of 79 primary surgically resected tumors from women with advanced-stage, high-grade epithelial ovarian cancer was done using cDNA microarrays containing 30,721 genes. Supervised learning algorithms were applied in an effort to develop a binary classifier that could discriminate women at risk for early (< or =21 months) versus late (>21 months) relapse after initial chemotherapy. RESULTS: A 14-gene predictive model was developed using a set of training samples (n = 51) and subsequently tested using an independent set of test samples (n = 28). This model correctly predicted the outcome of 24 of the 28 test samples (86% accuracy) with 95% positive predictive value for early relapse. CONCLUSIONS: Predictive markers for early recurrence can be identified for platinum-paclitaxel combination chemotherapy in primary ovarian carcinoma. The proposed 14-gene model requires further validation.

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Research works printed but not linked: 45

  1. Jemal A, Tiwari RC, Murray T, et al. Cancer statistics, 2004. CA Cancer J Clin 2004;54:8–29. · DOI · Paper page
  2. Cannistra SA. Cancer of the ovary. N Engl J Med 1993;329:1550–9. · DOI
  3. McGuire WP, Hoskins WJ, Brady MF, et al. Cyclophosphamide and cisplatin compared with paclitaxel and cisplatin in patients with stage III and stage IV ovarian cancer. N Engl J Med 1996;334:1–6. · DOI
  4. Ozols RF, Rubin SC, Thomas SC, Robboy SJ. Epithelial ovarian cancer. In: Hoskins E, editor. Principles and practice of gynecologic oncology. 3rd ed. Philadelphia: Lippincott; 2000.
  5. Ozols RF, Bundy BN, Greer BE, et al. Phase III trial of carboplatin and paclitaxel compared with cisplatin and paclitaxel in patients with optimally resected stage III ovarian cancer: a Gynecologic Oncology Group study. J Clin Oncol 2003;21:3194–200. · DOI
  6. Ozols RF, Bookman MA, Connolly DC, et al. Focus on epithelial ovarian cancer. Cancer Cell 2004;5:19–24. · DOI
  7. Hartmann LC, Podratz KC, Keeney GL, et al. Prognostic significance of p53 immunostaining in epithelial ovarian cancer. J Clin Oncol 1994;12:64–9. · DOI
  8. Makar AP, Baekelandt M, Trope CG, Kristensen GB. The prognostic significance of residual disease, FIGO substage, tumor histology, and grade in patients with FIGO stage III ovarian cancer. Gynecol Oncol 1995;56:175–80. · DOI
  9. Yarbro JW, Page DL, Fielding LP, Partridge EE, Murphy GP. American Joint Committee on Cancer prognostic factors consensus conference. Cancer 1999;86:2436–46. · DOI
  10. Chi DS, Liao JB, Leon LF, et al. Identification of prognostic factors in advanced epithelial ovarian carcinoma. Gynecol Oncol 2001;82:532–7. · DOI
  11. Ramaswamy S, Golub TR. DNA microarrays in clinical oncology. J Clin Oncol 2002;20:1932–41. · DOI · Paper page
  12. Rosenwald A, Wright G, Chan WC, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002;346:1937–47.
  13. Shipp MA, Ross KN, Tamayo P, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002;8:68–74. · DOI
  14. Simon R, Radmacher MD, Dobbin K. Design of studies using DNA microarrays. Genet Epidemiol 2002;23:21–36. · DOI
  15. Ayers M, Symmans WF, Stec J, et al. Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 2004;22:2284–93. · DOI · Paper page
  16. Cancer Committee of the International Federation of Gynecological and Obstetrics. Staging announcement. FIGO Cancer Committee. Gynecol Oncol 1986;25:383–5.
  17. Pusztai L, Ayers M, Stec J, et al. Gene expression profiles obtained from fine-needle aspirations of breast cancer reliably identify routine prognostic markers and reveal large-scale molecular differences between estrogen-negative and estrogen-positive tumors. Clin Cancer Res 2003;9:2406–15.
  18. Chiang LW, Grenier JM, Ettwiller L, et al. An orchestrated gene expression component of neuronal programmed cell death revealed by cDNA array analysis. Proc Natl Acad Sci U S A 2001;98:2814–9. · DOI · Paper page
  19. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A 2001;98:13790–5. · DOI
  20. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998;95:14863–8. · DOI · Paper page
  21. Markman M, Bundy BN, Alberts DS, et al. Phase III trial of standard-dose intravenous cisplatin plus paclitaxel versus moderately high-dose carboplatin followed by intravenous paclitaxel and intraperitoneal cisplatin in small-volume stage III ovarian carcinoma: an intergroup study of the Gynecologic Oncology Group, Southwestern Oncology Group, and Eastern Cooperative Oncology Group. J Clin Oncol 2001;19:1001–7. · DOI
  22. Shridhar V, Lee J, Pandita A, et al. Genetic analysis of early- versus late-stage ovarian tumors. Cancer Res 2001;61:5895–904.
  23. Schaner ME, Ross DT, Ciaravino G, et al. Gene expression patterns in ovarian carcinomas. Mol Biol Cell 2003;14:4376–86. · DOI
  24. Schwartz DR, Kardia SL, Shedden KA, et al. Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas. Cancer Res 2002;62:4722–9.
  25. Sawiris GP, Sherman-Baust CA, Becker KG, Cheadle C, Teichberg D, Morin PJ. Development of a highly specialized cDNA array for the study and diagnosis of epithelial ovarian cancer. Cancer Res 2002;62:2923–8.
  26. Tsuchiya A, Sakamoto M, Yasuda J, et al. Expression profiling in ovarian clear cell carcinoma: identification of hepatocyte nuclear factor-1β as a molecular marker and a possible molecular target for therapy of ovarian clear cell carcinoma. Am J Pathol 2003;163:2503–12. · DOI
  27. Matei D, Graeber TG, Baldwin RL, Karlan BY, Rao J, Chang DD. Gene expression in epithelial ovarian carcinoma. Oncogene 2002;21:6289–98. · DOI
  28. Jazaeri AA, Yee CJ, Sotiriou C, Brantley KR, Boyd J, Liu ET. Gene expression profiles of BRCA1-linked, BRCA2-linked, and sporadic ovarian cancers. J Natl Cancer Inst 2002;94:990–1000. · DOI
  29. Welsh JB, Zarrinkar PP, Sapinoso LM, et al. Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc Natl Acad Sci U S A 2001;98:1176–81. · DOI
  30. Wong KK, Cheng RS, Mok SC. Identification of differentially expressed genes from ovarian cancer cells by MICROMAX cDNA microarray system. Biotechniques 2001;30:670–5. · DOI
  31. Ono K, Tanaka T, Tsunoda T, et al. Identification by cDNA microarray of genes involved in ovarian carcinogenesis. Cancer Res 2000;60:5007–11.
  32. Schummer M, Ng WV, Bumgarner RE, et al. Comparative hybridization of an array of 21,500 ovarian cDNAs for the discovery of genes overexpressed in ovarian carcinomas. Gene 1999;238:375–85. · DOI
  33. Lancaster JM, Dressman HK, Whitaker RS, et al. Gene expression patterns that characterize advanced stage serous ovarian cancers. J Soc Gynecol Investig 2004;11:51–9. · DOI
  34. Wei SH, Chen CM, Strathdee G, et al. Methylation microarray analysis of late-stage ovarian carcinomas distinguishes progression-free survival in patients and identifies candidate epigenetic markers. Clin Cancer Res 2002;8:2246–52.
  35. Holleman A, Cheok MH, den Boer ML, et al. Gene-expression patterns in drug-resistant acute lymphoblastic leukemia cells and response to treatment. N Engl J Med 2004;351:533–42. · DOI
  36. Beck JF, Bohnet B, Brugger D, et al. Expression analysis of protein kinase C isozymes and multidrug resistance associated genes in ovarian cancer cells. Anticancer Res 1998;18:701–5.
  37. Brugger D, Brischwein K, Liu C, et al. Induction of drug resistance and protein kinase C genes in A2780 ovarian cancer cells after incubation with antineoplastic agents at sublethal concentrations. Anticancer Res 2002;22:4229–32.
  38. Swannie HC, Kaye SB. Protein kinase C inhibitors. Curr Oncol Rep 2002;4:37–46. · DOI
  39. Meng XW, Heldebrant MP, Kaufmann SH. Phorbol 12-myristate 13-acetate inhibits death receptor-mediated apoptosis in Jurkat cells by disrupting recruitment of Fas-associated polypeptide with death domain. J Biol Chem 2002;277:3776–83. · DOI
  40. Welcsh PL, Lee MK, Gonzalez-Hernandez RM, et al. BRCA1 transcriptionally regulates genes involved in breast tumorigenesis. Proc Natl Acad Sci U S A 2002;99:7560–5. · DOI
  41. Beger C, Pierce LN, Kruger M, et al. Identification of ID4 as a regulator of BRCA1 expression by using a ribozyme-library-based inverse genomics approach. Proc Natl Acad Sci U S A 2001;98:130–5. · DOI
  42. Boyd J, Sonoda Y, Federici MG, et al. Clinicopathologic features of BRCA-linked and sporadic ovarian cancer. JAMA 2000;283:2260–5. · DOI
  43. Rubin SC, Benjamin I, Behbakht K, et al. Clinical and pathological features of ovarian cancer in women with germ-like mutations of BRCA1. N Engl J Med 1996;335:1413–6. · DOI
  44. Matsuda Y, Suzuki G, Honda S, et al. Large-scale identification and characterization of human genes that activate NF-κB and MAPK signaling pathways. Oncogene 2003;22:3307–18. · DOI
  45. Mabuchi S, Ohmichi M, Nishio Y, et al. Inhibition of NFκB increases the efficacy of cisplatin in in vitro and in vivo ovarian cancer models. J Biol Chem 2004;279:23477–85. · DOI

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Metadata

Authors
Hartmann, L. C., Lu, K. H., Linette, G. P., Cliby, W. A., Kalli, K. R., Gershenson, D., Bast, R. C., Stec, J., Iartchouk, N., Smith, D. I., Ross, J. S., Hoersch, S., Shridhar, V., Lillie, J., Kaufmann, S. H., Clark, E. A., & Damokosh, A. I.
Affiliations
  • Mayo Clinic (US)
  • The University of Texas MD Anderson Cancer Center (US)
  • Takeda Oncology (United States) (US)
  • 3Millennium Pharmaceuticals, Cambridge, Massachusetts
ORCID
Venue
Clinical Cancer Research
Publisher
American Association for Cancer Research (AACR)
Published
2005-03-15
Volume
11
Issue
6
Pages
2149-2155
ISSN
  • 1078-0432 (ISSN-L)
  • 1557-3265
DOI
10.1158/1078-0432.ccr-04-1673
OpenAlex
W2098421558
Cited by
141
References
0
Citations by year
2026: 1 · 2025: 1 · 2023: 5 · 2022: 5 · 2021: 2 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 4 · 2016: 6 · 2015: 9 · 2014: 4 · 2013: 9 · 2012: 12
FWCI
12.66
Type
journal article
Language
en
Open access
closed
Locations
  • Clinical Cancer Research (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Cancer Institute (P50-CA83639, P30 CA 15083-29, P50CA083639, P30CA015083)
Topics
  • Health Sciences › Medicine › Oncology › Ovarian cancer diagnosis and treatment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
epithelial ovarian cancer, early recurrence, gene expression profiling, predictive biomarkers, recurrence risk prediction, primary tumor, cytoreductive surgery
MeSH terms
Adult, Aged, Aged, 80 and over, Antineoplastic Combined Chemotherapy Protocols, Female, Humans, Middle Aged, Neoplasm Recurrence, Local, Neoplasm Staging, Ovarian Neoplasms, Platinum, Biomarkers, Tumor, Gene Expression Regulation, Neoplastic, Survival Rate, Paclitaxel, Carcinoma, Endometrioid, Cystadenocarcinoma, Serous, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling

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