title: Vascular endothelial growth factor A as predictive marker for mTOR inhibition in relapsing high-grade serous ovarian cancer.
abstract: BACKGROUND
  Development 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.
  
  
  RESULTS
  Consolidation 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.
  
  
  CONCLUSIONS
  Stratifying 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.
authors.affiliation: emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria.
authors.forename: Peter
authors.initials: P
authors.lastname: Andorfer
authors.affiliation: emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria.
authors.forename: Alexander
authors.initials: A
authors.lastname: Heuwieser
authors.affiliation: emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Heinzel
authors.affiliation: emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria.
authors.forename: Arno
authors.initials: A
authors.lastname: Lukas
authors.affiliation: emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria.
authors.forename: Bernd
authors.initials: B
authors.lastname: Mayer
authors.affiliation: emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria. paul.perco@emergentec.com.
authors.forename: Paul
authors.initials: P
authors.lastname: Perco
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D002945:Cisplatin / Q000494:pharmacology / Q000627:therapeutic use; D019008:Drug Resistance, Neoplasm; D005260:Female; D006801:Humans; D008954:Models, Biological; D060787:Neoplasm Grading; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000378:metabolism* / Q000473:pathology*; D017239:Paclitaxel / Q000494:pharmacology / Q000627:therapeutic use; D011379:Prognosis; D012008:Recurrence; D058570:TOR Serine-Threonine Kinases / Q000037:antagonists & inhibitors*; D042461:Vascular Endothelial Growth Factor A / Q000378:metabolism*
chemical_list: D014408:Biomarkers, Tumor; D042461:Vascular Endothelial Growth Factor A; D058570:TOR Serine-Threonine Kinases; D017239:Paclitaxel; D002945:Cisplatin
keywords: Data integration; Disease modeling; Drug resistance; Network biology; Ovarian cancer; Predictive biomarker