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BMC Cancer
Published in: BMC Cancer 1/2015

Open Access 01-12-2015 | Research Article

Prediction of resistance to chemotherapy in ovarian cancer: a systematic review

Authors: Katherine L Lloyd, Ian A Cree, Richard S Savage

Published in: BMC Cancer | Issue 1/2015

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Abstract

Background

Patient response to chemotherapy for ovarian cancer is extremely heterogeneous and there are currently no tools to aid the prediction of sensitivity or resistance to chemotherapy and allow treatment stratification. Such a tool could greatly improve patient survival by identifying the most appropriate treatment on a patient-specific basis.

Methods

PubMed was searched for studies predicting response or resistance to chemotherapy using gene expression measurements of human tissue in ovarian cancer.

Results

42 studies were identified and both the data collection and modelling methods were compared. The majority of studies utilised fresh-frozen or formalin-fixed paraffin-embedded tissue. Modelling techniques varied, the most popular being Cox proportional hazards regression and hierarchical clustering which were used by 17 and 11 studies respectively. The gene signatures identified by the various studies were not consistent, with very few genes being identified by more than two studies. Patient cohorts were often noted to be heterogeneous with respect to chemotherapy treatment undergone by patients.

Conclusions

A clinically applicable gene signature capable of predicting patient response to chemotherapy has not yet been identified. Research into a predictive, as opposed to prognostic, model could be highly beneficial and aid the identification of the most suitable treatment for patients.
Appendix
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Literature
1.
Office for National Statistics: Cancer Incidence and Mortality in the United Kingdom. 2008–2010. http://​www.​ons.​gov.​uk/​ons/​rel/​cancer-unit/​cancer-incidence-and-mortality/​2008-2010/​rft-cancer-incidence-and-mortality-in-the-uk--2008-2010.​xls.
2.
Office for National Statistics: Cancer Survival Rates, Cancer Survival in England, Patients Diagnosed 2005-2009 and Followed up to 2010. http://​www.​ons.​gov.​uk/​ons/​rel/​cancer-unit/​cancer-survival-rates/​2005-2009--followed-up-to-2010/​tbl-cancer-survival.​xls.
3.
Li M, Yin J, Mao N, Pan L. Upregulation of phosphorylated cofilin 1 correlates with taxol resistance in human ovarian cancer in vitro and in vivo. Oncology Rep. 2013; 29(1):58–66.
4.
Rubin SC, Randall TC, Armstrong KA, Chi DS, Hoskins WJ. Ten-year follow-up of ovarian cancer patients after second-look laparotomy with negative findings. Obstet Gynecol. 1999; 93(1):21–4.PubMed
5.
Armstrong DK. Relapsed ovarian cancer: challenges and management strategies for a chronic disease. The Oncologist. 2002; 7(Supplement 5):20–8.CrossRefPubMed
6.
7.
National Institute for Health and Care Excellence: Guidance on the use of paclitaxel in the treatment of ovarian cancer. http://​www.​nice.​org.​uk/​guidance/​ta55.
8.
9.
Winter WE, Maxwell GL, Tian C, Carlson JW, Ozols RF, Rose PG, et al.Prognostic factors for stage iii epithelial ovarian cancer: a gynecologic oncology group study. J Clin Oncol. 2007; 25(24):3621–7.CrossRefPubMed
10.
Cancer Research UK: Ten-year survival over time. http://​www.​cancerresearchuk​.​org/​prod_​consump/​groups/​cr_​common/​@nre/​@sta/​documents/​generalcontent/​surv_​10yrtrends_​selcancers_​xls.​xls.
11.
Slodkowska EA, Ross JS. Mammaprint™ 70-gene signature: another milestone in personalized medical care for breast cancer patients. Expert Rev Mol Diagn. 2009; 9(5):417–22.CrossRefPubMed
12.
Oncotype DX®;: Underlying Technology. http://​breast-cancer.​oncotypedx.​com/​en-US/​Professional-Invasive/​WhatIsTheOncotyp​eDXBreastCancerT​est/​UnderlyingTechno​logy.​aspx.
13.
14.
15.
Efron B. The efficiency of cox’s likelihood function for censored data. J Am Stat Assoc. 1977; 72(359):557–65.CrossRef
16.
Gillet J-P, Calcagno AM, Varma S, Marino M, Green LJ, Vora MI, et al.Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc Nat Acad Sci. 2011; 108(46):18708–13.CrossRefPubMedPubMedCentral
17.
Cree IA, Glaysher S, Harvey AL. Efficacy of anti-cancer agents in cell lines versus human primary tumour tissue. Curr Opin Pharmacol. 2010; 10(4):375–9.CrossRefPubMed
18.
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al.Quadas-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011; 155(8):529–36.CrossRefPubMed
19.
The Oxford 2011 Levels of Evidence (OCEBM Levels of Evidence Working Group). http://​www.​cebm.​net/​index.​aspx?​o=​5653.
20.
Wang X, Terfve C, Rose JC, Markowetz F. Htsanalyzer: an r/bioconductor package for integrated network analysis of high-throughput screens. Bioinformatics. 2011; 27(6):879.CrossRefPubMedPubMedCentral
21.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995; 57(1):289–300.
22.
Jeong W, Kim S-B, Sohn BH, Park Y-Y, PARK ES, Kim SC, et al.Activation of yap1 is associated with poor prognosis and response to taxanes in ovarian cancer. Anticancer Res. 2014; 34(2):811–7.PubMedPubMedCentral
23.
Lisowska KM, Olbryt M, Dudaladava V, Pamula-Pilat J, Kujawa K, Grzybowska E, et al.Gene expression analysis in ovarian cancer - faults and hints from dna microarray study. Front Oncol. 2014; 4:6.CrossRefPubMedPubMedCentral
24.
Roque DM, Buza N, Glasgow M, Bellone S, Bortolomai I, Gasparrini S, et al.Class iii beta-tubulin overexpression within the tumor microenvironment is a prognostic biomarker for poor overall survival in ovarian cancer patients treated with neoadjuvant carboplatin/paclitaxel. Clin Exp Metastasis. 2014; 31(1):101–10.CrossRefPubMed
25.
Schwede M, Spentzos D, Bentink S, Hofmann O, Haibe-Kains B, Harrington D, et al.Stem cell-like gene expression in ovarian cancer predicts type ii subtype and prognosis. PLoS One. 2013; 8:57799.CrossRef
26.
Verhaak RG, Tamayo P, Yang JY, Hubbard D, Zhang H, Creighton CJ, et al.Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Invest. 2013; 123(1):517–25.PubMed
27.
Obermayr E, Castillo-Tong DC, Pils D, Speiser P, Braicu I, Van Gorp T, et al.Molecular characterization of circulating tumor cells in patients with ovarian cancer improves their prognostic significance – a study of the ovcad consortium. Gynecol Oncol. 2013; 128(1):15–21.CrossRefPubMed
28.
Han Y, Huang H, Xiao Z, Zhang W, Cao Y, Qu L, et al.Integrated analysis of gene expression profiles associated with response of platinum/paclitaxel-based treatment in epithelial ovarian cancer. PLoS One. 2012; 7:52745.CrossRef
29.
Hsu FH, Serpedin E, Hsiao TH, Bishop AJ, Dougherty ER, Chen Y. Reducing confounding and suppression effects in tcga data: an integrated analysis of chemotherapy response in ovarian cancer. BMC Genomics. 2012; 13 Suppl 6:13.CrossRef
30.
Liu Y, Sun Y, Broaddus R, Liu J, Sood AK, Shmulevich I, et al.Integrated analysis of gene expression and tumor nuclear image profiles associated with chemotherapy response in serous ovarian carcinoma. PLoS One. 2012; 7:36383.CrossRef
31.
Kang J, D’Andrea AD, Kozono D. A dna repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy. J Natl Cancer Inst. 2012; 104:670–81.CrossRefPubMedPubMedCentral
32.
Gillet JP, Calcagno AM, Varma S, Davidson B, Bunkholt Elstrand M, Ganapathi R, et al.Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma. Clin Cancer Res. 2012; 18:3197–206.CrossRefPubMedPubMedCentral
33.
Ferriss JS, Kim Y, Duska L, Birrer M, Levine DA, Moskaluk C, et al.Multi-gene expression predictors of single drug responses to adjuvant chemotherapy in ovarian carcinoma: predicting platinum resistance. PLoS One. 2012; 7:30550.CrossRef
34.
Brun JL, Cortez A, Lesieur B, Uzan S, Rouzier R, Darai E. Expression of mmp-2, -7, -9, mt1-mmp and timp-1 and -2 has no prognostic relevance in patients with advanced epithelial ovarian cancer. Oncol Rep. 2012; 27(4):1049–57.PubMedPubMedCentral
35.
Skirnisdottir I, Seidal T. The apoptosis regulators p53, bax and puma: Relationship and impact on outcome in early stage (figo i-ii) ovarian carcinoma after post-surgical taxane-based treatment. Oncol Rep. 2012; 27(3):741–7.PubMed
36.
Brenne K, Nymoen DA, Hetland TE, Trope’ CG, Davidson B. Expression of the ets transcription factor ehf in serous ovarian carcinoma effusions is a marker of poor survival. Hum Pathol. 2012; 43(4):496–505.CrossRefPubMed
37.
Sabatier R, Finetti P, Bonensea J, Jacquemier J, Adelaide J, Lambaudie E, et al.A seven-gene prognostic model for platinum-treated ovarian carcinomas. Br J Cancer. 2011; 105:304–11.CrossRefPubMedPubMedCentral
38.
Gillet JP, Wang J, Calcagno AM, Green LJ, Varma S, Bunkholt Elstrand M, et al.Clinical relevance of multidrug resistance gene expression in ovarian serous carcinoma effusions. Mol Pharm. 2011; 8(6):2080–8.CrossRefPubMedPubMedCentral
39.
Chao SY, Chiang JH, Huang AM, Chang WS. An integrative approach to identifying cancer chemoresistance-associated pathways. BMC Med Genomics. 2011; 4:23.CrossRefPubMedPubMedCentral
40.
Schlumbrecht MP, Xie SS, Shipley GL, Urbauer DL, Broaddus RR. Molecular clustering based on eralpha and eig121 predicts survival in high-grade serous carcinoma of the ovary/peritoneum. Mod Pathol. 2011; 24:453–62.CrossRefPubMed
41.
Glaysher S, Gabriel FG, Johnson P, Polak M, Knight LA, Parker K, et al.Molecular basis of chemosensitivity of platinum pre-treated ovarian cancer to chemotherapy. Br J Cancer. 2010; 103:656–62.CrossRefPubMedPubMedCentral
42.
Yan X, Yin J, Yao H, Mao N, Yang Y, Pan L. Increased expression of annexin a3 is a mechanism of platinum resistance in ovarian cancer. Cancer Res. 2010; 70:1616–24.CrossRefPubMed
43.
Yoshihara K, Tajima A, Yahata T, Kodama S, Fujiwara H, Suzuki M, et al.Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets. PLoS One. 2010; 5(3):9615.CrossRef
44.
Williams PD, Cheon S, Havaleshko DM, Jeong H, Cheng F, Theodorescu D, et al.Concordant gene expression signatures predict clinical outcomes of cancer patients undergoing systemic therapy. Cancer Res. 2009; 69:8302–9.CrossRefPubMedPubMedCentral
45.
Denkert C, Budczies J, Darb-Esfahani S, Gyorffy B, Sehouli J, Konsgen D, et al.A prognostic gene expression index in ovarian cancer - validation across different independent data sets. J Pathol. 2009; 218(2):273–80.CrossRefPubMed
46.
Matsumura N, Huang Z, Baba T, Lee PS, Barnett JC, Mori S, et al.Yin yang 1 modulates taxane response in epithelial ovarian cancer. Mol Cancer Res. 2009; 7:210–20.CrossRefPubMedPubMedCentral
47.
Crijns AP, Fehrmann RS, de Jong S, Gerbens F, Meersma GJ, Klip HG, et al.Survival-related profile, pathways, and transcription factors in ovarian cancer. PLoS Med. 2009; 6:24.CrossRef
48.
Mendiola M, Barriuso J, Redondo A, Marino-Enriquez A, Madero R, Espinosa E, et al.Angiogenesis-related gene expression profile with independent prognostic value in advanced ovarian carcinoma. PLoS One. 2008; 3(12):4051.CrossRef
49.
Gevaert O, De Smet F, Van Gorp T, Pochet N, Engelen K, Amant F, et al.Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation. BMC Cancer. 2008; 8:18.CrossRefPubMedPubMedCentral
50.
Bachvarov D, L’Esperance S, Popa I, Bachvarova M, Plante M, Tetu B. Gene expression patterns of chemoresistant and chemosensitive serous epithelial ovarian tumors with possible predictive value in response to initial chemotherapy. Int J Oncol. 2006; 29(4):919–33.PubMed
51.
Netinatsunthorn W, Hanprasertpong J, Dechsukhum C, Leetanaporn R, Geater A. Wt1 gene expression as a prognostic marker in advanced serous epithelial ovarian carcinoma: an immunohistochemical study. BMC Cancer. 2006; 6:90.CrossRefPubMedPubMedCentral
52.
De Smet F, Pochet NL, Engelen K, Van Gorp T, Van Hummelen P, Marchal K, et al.Predicting the clinical behavior of ovarian cancer from gene expression profiles. Int J Gynecol Cancer. 2006; 16 Suppl 1:147–51.CrossRefPubMed
53.
Helleman J, Jansen MP, Span PN, van Staveren IL, Massuger LF, Meijer-van Gelder ME, et al.Molecular profiling of platinum resistant ovarian cancer. Int J Cancer. 2006; 118(8):1963–71.CrossRefPubMed
54.
Spentzos D, Levine DA, Kolia S, Otu H, Boyd J, Libermann TA, et al.Unique gene expression profile based on pathologic response in epithelial ovarian cancer. J Clin Oncol. 2005; 23:7911–8.CrossRefPubMed
55.
Jazaeri AA, Awtrey CS, Chandramouli GV, Chuang YE, Khan J, Sotiriou C, et al.Gene expression profiles associated with response to chemotherapy in epithelial ovarian cancers. Clin Cancer Res. 2005; 11:6300–10.CrossRefPubMed
56.
Raspollini MR, Amunni G, Villanucci A, Boddi V, Taddei GL. Increased cyclooxygenase-2 (cox-2) and p-glycoprotein-170 (mdr1) expression is associated with chemotherapy resistance and poor prognosis. analysis in ovarian carcinoma patients with low and high survival. Int J Gynecol Cancer. 2005; 15:255–60.CrossRefPubMed
57.
Hartmann LC, Lu KH, Linette GP, Cliby WA, Kalli KR, Gershenson D, et al.Gene expression profiles predict early relapse in ovarian cancer after platinum-paclitaxel chemotherapy. Clin Cancer Res. 2005; 11:2149–55.CrossRefPubMed
58.
Spentzos D, Levine DA, Ramoni MF, Joseph M, Gu X, Boyd J, et al.Gene expression signature with independent prognostic significance in epithelial ovarian cancer. J Clin Oncol. 2004; 22:4700–10.CrossRefPubMed
59.
Selvanayagam ZE, Cheung TH, Wei N, Vittal R, Lo KW, Yeo W, et al.Prediction of chemotherapeutic response in ovarian cancer with dna microarray expression profiling. Cancer Genet Cytogenet. 2004; 154:63–6.CrossRefPubMed
60.
Iba T, Kigawa J, Kanamori Y, Itamochi H, Oishi T, Simada M, et al.Expression of the c-myc gene as a predictor of chemotherapy response and a prognostic factor in patients with ovarian cancer. Cancer Sci. 2004; 95(5):418–23.CrossRefPubMed
61.
Kamazawa S, Kigawa J, Kanamori Y, Itamochi H, Sato S, Iba T, et al.Multidrug resistance gene-1 is a useful predictor of paclitaxel-based chemotherapy for patients with ovarian cancer. Gynecol Oncol. 2002; 86:171–6.CrossRefPubMed
62.
Vogt U, Falkiewicz B, Bielawski K, Bosse U, Schlotter CM. Relationship of c-myc and erbb oncogene family gene aberrations and other selected factors to ex vivo chemosensitivity of ovarian cancer in the modified atp-chemosensitivity assay. Acta Biochim Pol. 2000; 47(1):157–64.PubMed
63.
Medeiros F, Rigl CT, Anderson GG, Becker SH, Halling KC. Tissue handling for genome-wide expression analysis: a review of the issues, evidence, and opportunities. Arch Pathol Lab Med. 2007; 131(12):1805–16.PubMed
64.
Rentoft M, Coates PJ, Laurell G, Nylander K. Transcriptional profiling of formalin fixed paraffin embedded tissue: pitfalls and recommendations for identifying biologically relevant changes. PloS One. 2012; 7(4):35276.CrossRef
65.
Takano M, Tsuda H, Sugiyama T. Clear cell carcinoma of the ovary: is there a role of histology-specific treatment. J Exp Clin Cancer Res. 2012; 31(53):35.
66.
Hess V, A’Hern R, Nasiri N, King DM, Blake PR, Barton DP, et al.Mucinous epithelial ovarian cancer: a separate entity requiring specific treatment. J Clin Oncol. 2004; 22(6):1040–4.CrossRefPubMed
67.
Oldenhuis C, Oosting S, Gietema J, De Vries E. Prognostic versus predictive value of biomarkers in oncology. Eur J Cancer. 2008; 44(7):946–53.CrossRefPubMed
68.
Tezak Z, Kondratovich MV, Mansfield E. Us fda and personalized medicine: in vitro diagnostic regulatory perspective. Personalized Med. 2010; 7(5):517–30.CrossRef
69.
Tapia G, Diaz-Padilla I. Molecular mechanisms of platinum resistance in ovarian cancer In: Diaz-Padilla I, editor. Ovarian Cancer - A Clinical and Translational Update. InTech: 2013.
70.
Citri A, Yarden Y. Egf–erbb signalling: towards the systems level. Nat Rev Mol Cell Biol. 2006; 7(7):505–16.CrossRefPubMed
71.
Tan F, Thiele CJ, Zhijie L. Neurotrophin signaling in cancer In: Kostrzewa RM, editor. Handbook of Neurotoxicity. New York: Springer: 2014.
72.
McLean GW, Carragher NO, Avizienyte E, Evans J, Brunton VG, Frame MC. The role of focal-adhesion kinase in cancer–a new therapeutic opportunity. Nat Rev Cancer. 2005; 5(7):505–15.CrossRefPubMed
73.
Elliott RL, Blobe GC. Role of transforming growth factor beta in human cancer. J Clin Oncol. 2005; 23(9):2078–93.CrossRefPubMed
74.
Levine AJ, Bargonetti J, Bond GL, Hoh J, Onel K, Overholtzer M, et al.The p53 network In: Zambetti GP, editor. The P53 Tumor Suppressor Pathway and Cancer. US: Springer: 2005.
75.
Neumann AS, Sturgis EM, Wei Q. Nucleotide excision repair as a marker for susceptibility to tobacco-related cancers: A review of molecular epidemiological studies. Mol Carcinog. 2005; 42(2):65–92.CrossRefPubMed
76.
Martin LP, Hamilton TC, Schilder RJ. Platinum resistance: the role of dna repair pathways. Clin Cancer Res. 2008; 14(5):1291–5.CrossRefPubMed
77.
Galletti E, Magnani M, Renzulli ML, Botta M. Paclitaxel and docetaxel resistance: molecular mechanisms and development of new generation taxanes. ChemMedChem. 2007; 2(7):920–42.CrossRefPubMed
78.
Jekunen A, Christen R, Shalinsky D, Howell S. Synergistic interaction between cisplatin and taxol in human ovarian carcinoma cells in vitro. British J Cancer. 1994; 69(2):299.CrossRef
79.
Hall A, Tilby M. Mechanisms of action of, and modes of resistance to, alkylating agents used in the treatment of haematological malignancies. Blood Rev. 1992; 6(3):163–73.CrossRefPubMed
80.
81.
Dhillon A, Hagan S, Rath O, Kolch W. Map kinase signalling pathways in cancer. Oncogene. 2007; 26(22):3279–90.CrossRefPubMed
82.
Hembruff SL, Cheng N. Chemokine signaling in cancer: Implications on the tumor microenvironment and therapeutic targeting. Cancer Ther. 2009; 7(A):254.PubMedPubMedCentral
83.
Kowanetz M, Ferrara N. Vascular endothelial growth factor signaling pathways: therapeutic perspective. Clinical Cancer Res. 2006; 12(17):5018–22.CrossRef
84.
85.
Bernstein C, Prasad AR, Nfonsam V, Bernstein H. Dna damage, dna repair and cancer In: Chen C, editor. New Research Directions in DNA Repair. InTech: 2013.
86.
87.
Heagerty PJ, Lumley T, Pepe MS. Time-dependent roc curves for censored survival data and a diagnostic marker. Biometrics. 2000; 56(2):337–44.CrossRefPubMed
88.
Altman DG, Royston P. Statistics notes: the cost of dichotomising continuous variables. BMJ: British Med J. 2006; 332(7549):1080.CrossRef
89.
Metadata
Title
Prediction of resistance to chemotherapy in ovarian cancer: a systematic review
Authors
Katherine L Lloyd
Ian A Cree
Richard S Savage
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2015
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-015-1101-8

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