Journal
CANCER RESEARCH
Volume 75, Issue 15, Pages 2987-2998Publisher
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/0008-5472.CAN-14-3242
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Funding
- Academy of Finland (Center of Excellence in Cancer Genetics Research)
- Sigrid Juselius Foundation
- Finnish Cancer Associations
- Finnish Doctoral Programme in Computational Sciences
- Ida Montini's Foundation
- Turku University Hospital Research Funds
- European Regional Development Fund [A31859]
- Cancer Foundation Finland sr [140103, 130083] Funding Source: researchfish
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Disseminated high-grade serous ovarian cancer (HGS-OvCa) is an aggressive disease treated with platinum and taxane combination therapy. While initial response can be favorable, the disease typically relapses and becomes resistant to treatment. As genomic alterations in HGS-OvCa are heterogeneous, identification of clinically meaningful molecular markers for outcome prediction is challenging. We developed a novel computational approach (PSFinder) that fuses transcriptomics and clinical data to identify HGS-OvCa prognostic subgroups for targeted treatment. Application of PSFinder to transcriptomics data from 180 HGS-OvCa patients treated with platinum-taxane therapy revealed 61 transcript isoforms that characterize two poor and one good survival-associated groups (P = 0.007). These groups were validated in eight independent data sets, including a prospectively collected ovarian cancer cohort. Two poor prognostic groups have distinct expression profiles and are characteristic by increased hypermethylation and stroma-related genes. Integration of the PSFinder signature and BRCA1/2 mutation status allowed even better stratification of HGS-OvCa patients' prognosis. The herein introduced novel and generally applicable computational approach can identify outcome-related subgroups and facilitate the development of precision medicine to overcome drug resistance. A limited set of biomarkers divides HGS-OvCa into three prognostic groups and predicts patients in need of targeted therapies. (C) 2015 AACR.
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