Abstract
BackgroundDevelopment of resistance against first line drug therapy including cisplatin and paclitaxel in high-grade serous ovarian cancer (HGSOC) presents a major challenge. Identifying drug candidates breaking resistance, ideally combined with predictive biomarkers allowing precision use are needed for prolonging progression free survival of ovarian cancer patients.Modeling of molecular processes driving drug resistance in tumor tissue further combined with mechanism of action of drugs provides a strategy for identification of candidate drugs and associated predictive biomarkers.ResultsConsolidation of transcriptomics profiles and biomedical literature mining results provides 1242 proteins linked with ovarian cancer drug resistance. Integrating this set on a protein interaction network followed by graph segmentation results in a molecular process model representation of drug resistant HGSOC embedding 409 proteins in 24 molecular processes. Utilizing independent transcriptomics profiles with follow-up data on progression free survival allows deriving molecular biomarker-based classifiers for predicting recurrence under first line therapy. Biomarkers of specific relevance are identified in a molecular process encapsulating TGF-beta, mTOR, Jak-STAT and Neurotrophin signaling. Mechanism of action molecular model representations of cisplatin and paclitaxel embed the very same signaling components, and specifically proteins afflicted with the activation status of the mTOR pathway become evident, including VEGFA. Analyzing mechanism of action interference of the mTOR inhibitor sirolimus shows specific impact on the drug resistance signature imposed by cisplatin and paclitaxel, further holding evidence for a synthetic lethal interaction to paclitaxel mechanism of action involving cyclin D1.ConclusionsStratifying drug resistant high grade serous ovarian cancer via VEGFA, and specifically treating with mTOR inhibitors in case of activation of the pathway may allow adding precision for overcoming resistance to first line therapy.
Highlights
Development of resistance against first line drug therapy including cisplatin and paclitaxel in highgrade serous ovarian cancer (HGSOC) presents a major challenge
In this work we present a HGSOC molecular process model resting on an interaction network of molecular features associated with platinum-based drug resistance as identified in transcriptomics studies, further complemented with protein coding genes mined from scientific publications
Data integration on interaction networks allows identifying a connected core feature set, which based on adding interactions includes a biological constraint on top of varying evidence of the features identified in individual experimental settings [30]
Summary
Development of resistance against first line drug therapy including cisplatin and paclitaxel in highgrade serous ovarian cancer (HGSOC) presents a major challenge. Identifying drug candidates breaking resistance, ideally combined with predictive biomarkers allowing precision use are needed for prolonging progression free survival of ovarian cancer patients. Modeling of molecular processes driving drug resistance in tumor tissue further combined with mechanism of action of drugs provides a strategy for identification of candidate drugs and associated predictive biomarkers. Development of drug resistance represents a major challenge in cancer therapy. In case of high-grade serous ovarian cancer (HGSOC), accounting for 60–80 % of epithelial ovarian carcinoma, initial sensitivity to standard platinum-based therapy is around 80 %. Several molecular mechanisms leading to drug resistance, being generic across drug classes or specific for a selected drug are described [5].
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