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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

HGSOCr molecular model. a Each node represents a molecular process, the node diameter scales with the number of protein coding genes included. Edges between molecular processes indicate a significant number of protein-protein interactions between protein coding genes across molecular processes. Color-coding scales with the sum of individual biomarker frequencies in LASSO selection based on bootstrap runs of the transcript feature set classifier for explaining variance in PFS. b Subgraph representation of molecular process 4. Each node codes for a protein coding gene, edges represent interactions according to the underlying interaction network. Genes of specific relevance are highlighted in red (VEGFA, mTOR, CCND1)
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Fig2: HGSOCr molecular model. a Each node represents a molecular process, the node diameter scales with the number of protein coding genes included. Edges between molecular processes indicate a significant number of protein-protein interactions between protein coding genes across molecular processes. Color-coding scales with the sum of individual biomarker frequencies in LASSO selection based on bootstrap runs of the transcript feature set classifier for explaining variance in PFS. b Subgraph representation of molecular process 4. Each node codes for a protein coding gene, edges represent interactions according to the underlying interaction network. Genes of specific relevance are highlighted in red (VEGFA, mTOR, CCND1)

Mentions: Mapping the HGSOCr feature set on the selected hybrid interaction network results in an induced subgraph holding 1062 protein nodes. This subgraph resembles one giant component with a path from each protein coding gene (network node) to all other gene nodes. Applying a segmentation algorithm for identifying densely connected gene sets provides 24 molecular process segments holding in total 409 molecular features, with molecular process size ranging from 3 to 95 nodes (Fig. 2a).Fig. 2


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)

HGSOCr molecular model. a Each node represents a molecular process, the node diameter scales with the number of protein coding genes included. Edges between molecular processes indicate a significant number of protein-protein interactions between protein coding genes across molecular processes. Color-coding scales with the sum of individual biomarker frequencies in LASSO selection based on bootstrap runs of the transcript feature set classifier for explaining variance in PFS. b Subgraph representation of molecular process 4. Each node codes for a protein coding gene, edges represent interactions according to the underlying interaction network. Genes of specific relevance are highlighted in red (VEGFA, mTOR, CCND1)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: HGSOCr molecular model. a Each node represents a molecular process, the node diameter scales with the number of protein coding genes included. Edges between molecular processes indicate a significant number of protein-protein interactions between protein coding genes across molecular processes. Color-coding scales with the sum of individual biomarker frequencies in LASSO selection based on bootstrap runs of the transcript feature set classifier for explaining variance in PFS. b Subgraph representation of molecular process 4. Each node codes for a protein coding gene, edges represent interactions according to the underlying interaction network. Genes of specific relevance are highlighted in red (VEGFA, mTOR, CCND1)
Mentions: Mapping the HGSOCr feature set on the selected hybrid interaction network results in an induced subgraph holding 1062 protein nodes. This subgraph resembles one giant component with a path from each protein coding gene (network node) to all other gene nodes. Applying a segmentation algorithm for identifying densely connected gene sets provides 24 molecular process segments holding in total 409 molecular features, with molecular process size ranging from 3 to 95 nodes (Fig. 2a).Fig. 2

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