CoChat
Sign Up
Cited by 87Ovarian cancer diagnosis and treatmentGene expression and cancer classificationBreast Cancer Treatment Studies

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

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

Open access
Lloyd, Katherine L, Cree, Ian A, Savage, Richard S·2015·BMC Cancer
DOI 10.1186/s12885-015-1101-8PMC PMC4371880
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12885-015-1101-8

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 79

  1. 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. · DOI
  2. 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.
  3. Armstrong DK. Relapsed ovarian cancer: challenges and management strategies for a chronic disease. The Oncologist. 2002; 7(Supplement 5):20–8. · DOI
  4. Di Nicolantonio F, Mercer SJ, Knight LA, Gabriel FG, Whitehouse PA, Sharma S, et al.Cancer cell adaptation to chemotherapy. BMC Cancer. 2005; 5(1):78. · DOI
  5. Goff BA. Advanced ovarian cancer: what should be the standard of care?J Gynecol Oncol. 2013; 24(1):83–91. · DOI
  6. 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. · DOI
  7. 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. · DOI
  8. Efron B. The efficiency of cox’s likelihood function for censored data. J Am Stat Assoc. 1977; 72(359):557–65. · DOI
  9. 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. · DOI
  10. The Oxford 2011 Levels of Evidence (OCEBM Levels of Evidence Working Group). http://www.cebm.net/index.aspx?o=5653 .
  11. 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. · DOI
  12. 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. · DOI · Paper page
  13. 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.
  14. 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. · DOI · Paper page
  15. 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. · DOI
  16. 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. · DOI
  17. 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.
  18. 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. · DOI
  19. 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. · DOI
  20. 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. · DOI
  21. 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. · DOI
  22. 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. · DOI · Paper page
  23. 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. · DOI
  24. 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. · DOI
  25. 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. · DOI
  26. 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.
  27. 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. · DOI
  28. 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. · DOI · Paper page
  29. 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. · DOI
  30. Chao SY, Chiang JH, Huang AM, Chang WS. An integrative approach to identifying cancer chemoresistance-associated pathways. BMC Med Genomics. 2011; 4:23. · DOI
  31. 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. · DOI
  32. 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. · DOI
  33. 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. · DOI
  34. 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. · DOI
  35. 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. · DOI
  36. 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. · DOI
  37. 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. · DOI
  38. 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. · DOI
  39. 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. · DOI
  40. 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. · DOI
  41. 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.
  42. 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. · DOI
  43. 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. · DOI
  44. 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. · DOI
  45. 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. · DOI
  46. 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. · DOI · Paper page
  47. 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. · DOI
  48. 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. · DOI · Paper page
  49. 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. · DOI
  50. 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. · DOI
  51. 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. · DOI
  52. 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. · DOI
  53. 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. · DOI
  54. 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. · DOI
  55. 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. · DOI
  56. 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.
  57. 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. · DOI
  58. 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. · DOI
  59. Tezak Z, Kondratovich MV, Mansfield E. Us fda and personalized medicine: in vitro diagnostic regulatory perspective. Personalized Med. 2010; 7(5):517–30. · DOI
  60. 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. · DOI
  61. Citri A, Yarden Y. Egf–erbb signalling: towards the systems level. Nat Rev Mol Cell Biol. 2006; 7(7):505–16. · DOI
  62. Tan F, Thiele CJ, Zhijie L. Neurotrophin signaling in cancer In: Kostrzewa RM, editor. Handbook of Neurotoxicity. New York: Springer: 2014.
  63. 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. · DOI
  64. Elliott RL, Blobe GC. Role of transforming growth factor beta in human cancer. J Clin Oncol. 2005; 23(9):2078–93. · DOI
  65. 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.
  66. 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. · DOI
  67. Martin LP, Hamilton TC, Schilder RJ. Platinum resistance: the role of dna repair pathways. Clin Cancer Res. 2008; 14(5):1291–5. · DOI
  68. 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. · DOI
  69. 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. · DOI
  70. 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. · DOI
  71. Pópulo H, Lopes JM, Soares P. The mtor signalling pathway in human cancer. Int J Mol Sci. 2012; 13(2):1886–918. · DOI · Paper page
  72. Hembruff SL, Cheng N. Chemokine signaling in cancer: Implications on the tumor microenvironment and therapeutic targeting. Cancer Ther. 2009; 7(A):254.
  73. Kowanetz M, Ferrara N. Vascular endothelial growth factor signaling pathways: therapeutic perspective. Clinical Cancer Res. 2006; 12(17):5018–22. · DOI
  74. Cree IA. Designing personalised cancer treatments. J Control Release. 2013; 172(2):405–9. · DOI
  75. 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. · DOI
  76. Royston P, Altman DG. External validation of a cox prognostic model: principles and methods. BMC Med Res Methodol. 2013; 13(1):33. · DOI
  77. Heagerty PJ, Lumley T, Pepe MS. Time-dependent roc curves for censored survival data and a diagnostic marker. Biometrics. 2000; 56(2):337–44. · DOI
  78. Altman DG, Royston P. Statistics notes: the cost of dichotomising continuous variables. BMJ: British Med J. 2006; 332(7549):1080. · DOI · Paper page
  79. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000; 100(1):57–70. · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Lloyd, K. L., Cree, I. A., & Savage, R. S.
Affiliations
University of Warwick (GB)
Corresponding author
Lloyd, Katherine L
ORCID
Venue
BMC Cancer
Publisher
Springer Science and Business Media LLC
Published
2015-03-11
Volume
15
Issue
1
Pages
117-117
ISSN
1471-2407 (ISSN-L)
DOI
10.1186/s12885-015-1101-8
PMC
PMC4371880
OpenAlex
W2016226316
Cited by
87
References
3
Citations by year
2026: 2 · 2025: 5 · 2024: 4 · 2023: 9 · 2022: 8 · 2021: 14 · 2020: 8 · 2019: 12 · 2018: 9 · 2017: 8 · 2016: 7 · 2015: 1
FWCI
1.26
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Cancer (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
Funders
  • Medical Research Council
  • Engineering and Physical Sciences Research Council (EP/F500378, EP/F500378/1, 1229056)
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
ovarian cancer, chemoresistance, chemotherapy response prediction, gene expression signatures, predictive biomarkers, treatment stratification, Cox proportional hazards model, hierarchical clustering, patient heterogeneity
MeSH terms
Animals, Antineoplastic Agents, Female, Humans, Ovarian Neoplasms, Predictive Value of Tests, Drug Resistance, Neoplasm

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review