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

• Published in: BMC cancer Vol. 15; no. 1; p. 117
• Main Authors: Lloyd, Katherine L, Cree, Ian A, Savage, Richard S
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.03.2015; BioMed Central
• Subjects: Analysis; Animals; Antineoplastic Agents - pharmacology; Antineoplastic Agents - therapeutic use; Cancer; Care and treatment; Chemotherapy; Drug Resistance, Neoplasm - drug effects; Drug Resistance, Neoplasm - genetics; Female; Formaldehyde; Gene expression; Genes; Health aspects; Humans; Medical research; Medicine, Experimental; Ovarian cancer; Ovarian Neoplasms - diagnosis; Ovarian Neoplasms - drug therapy; Ovarian Neoplasms - genetics; Predictive Value of Tests; Prognosis
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-015-1101-8

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