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Vascular endothelial growth factor A as predictive marker for mTOR inhibition in relapsing high-grade serous ovarian cancer.

Andorfer P, Heuwieser A, Heinzel A, Lukas A, Mayer B, Perco P - BMC Syst Biol (2016)

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria.

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.

No MeSH data available.


Related in: MedlinePlus

Data analysis workflow. Schematic representation of the data analysis workflow with used datasets, methods, and results indicated by grey, white, and green boxes respectively
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Fig1: Data analysis workflow. Schematic representation of the data analysis workflow with used datasets, methods, and results indicated by grey, white, and green boxes respectively

Mentions: A data analysis overview is depicted in Fig. 1 with individual analysis steps and results described in the following sections.Fig. 1


Vascular endothelial growth factor A as predictive marker for mTOR inhibition in relapsing high-grade serous ovarian cancer.

Andorfer P, Heuwieser A, Heinzel A, Lukas A, Mayer B, Perco P - BMC Syst Biol (2016)

Data analysis workflow. Schematic representation of the data analysis workflow with used datasets, methods, and results indicated by grey, white, and green boxes respectively
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4836190&req=5

Fig1: Data analysis workflow. Schematic representation of the data analysis workflow with used datasets, methods, and results indicated by grey, white, and green boxes respectively
Mentions: A data analysis overview is depicted in Fig. 1 with individual analysis steps and results described in the following sections.Fig. 1

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, 1180, Vienna, Austria.

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.

No MeSH data available.


Related in: MedlinePlus