Synthetic lethality guiding selection of drug combinations in ovarian cancer.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2019
Historique:
received: 27 02 2018
accepted: 03 01 2019
entrez: 26 1 2019
pubmed: 27 1 2019
medline: 23 10 2019
Statut: epublish

Résumé

Synthetic lethality describes a relationship between two genes where single loss of either gene does not trigger significant impact on cell viability, but simultaneous loss of both gene functions results in lethality. Targeting synthetic lethal interactions with drug combinations promises increased efficacy in tumor therapy. We established a set of synthetic lethal interactions using publicly available data from yeast screens which were mapped to their respective human orthologs using information from orthology databases. This set of experimental synthetic lethal interactions was complemented by a set of predicted synthetic lethal interactions based on a set of protein meta-data like e.g. molecular pathway assignment. Based on the combined set, we evaluated drug combinations used in late stage clinical development (clinical phase III and IV trials) or already in clinical use for ovarian cancer with respect to their effect on synthetic lethal interactions. We furthermore identified a set of drug combinations currently not being tested in late stage ovarian cancer clinical trials that however have impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer. Twelve of the tested drug combinations addressed a synthetic lethal interaction with the anti-VEGF inhibitor bevacizumab in combination with paclitaxel being the most studied drug combination addressing the synthetic lethal pair between VEGFA and BCL2. The set of 84 predicted drug combinations for example holds the combination of the PARP inhibitor olaparib and paclitaxel, which showed efficacy in phase II clinical studies. A set of drug combinations currently not tested in late stage ovarian cancer clinical trials was identified having impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.

Sections du résumé

BACKGROUND
Synthetic lethality describes a relationship between two genes where single loss of either gene does not trigger significant impact on cell viability, but simultaneous loss of both gene functions results in lethality. Targeting synthetic lethal interactions with drug combinations promises increased efficacy in tumor therapy.
MATERIALS AND METHODS
We established a set of synthetic lethal interactions using publicly available data from yeast screens which were mapped to their respective human orthologs using information from orthology databases. This set of experimental synthetic lethal interactions was complemented by a set of predicted synthetic lethal interactions based on a set of protein meta-data like e.g. molecular pathway assignment. Based on the combined set, we evaluated drug combinations used in late stage clinical development (clinical phase III and IV trials) or already in clinical use for ovarian cancer with respect to their effect on synthetic lethal interactions. We furthermore identified a set of drug combinations currently not being tested in late stage ovarian cancer clinical trials that however have impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.
RESULTS
Twelve of the tested drug combinations addressed a synthetic lethal interaction with the anti-VEGF inhibitor bevacizumab in combination with paclitaxel being the most studied drug combination addressing the synthetic lethal pair between VEGFA and BCL2. The set of 84 predicted drug combinations for example holds the combination of the PARP inhibitor olaparib and paclitaxel, which showed efficacy in phase II clinical studies.
CONCLUSION
A set of drug combinations currently not tested in late stage ovarian cancer clinical trials was identified having impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.

Identifiants

pubmed: 30682083
doi: 10.1371/journal.pone.0210859
pii: PONE-D-18-06274
pmc: PMC6347359
doi:

Substances chimiques

BCL2 protein, human 0
Phthalazines 0
Piperazines 0
Poly(ADP-ribose) Polymerase Inhibitors 0
Proto-Oncogene Proteins c-bcl-2 0
VEGFA protein, human 0
Vascular Endothelial Growth Factor A 0
Bevacizumab 2S9ZZM9Q9V
Paclitaxel P88XT4IS4D
olaparib WOH1JD9AR8

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0210859

Déclaration de conflit d'intérêts

AL is managing partner of emergentec biodevelopment GmbH, Austria. AH, PP, and PM at the time of performing the analysis were employees of emergentec biodevelopment GmbH. Commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. All authors declare that they have no competing interests.

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Auteurs

Andreas Heinzel (A)

Emergentec biodevelopment GmbH, Vienna, Austria.
Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria.

Maximilian Marhold (M)

Department of Oncology, Division of Internal Medicine I, Medical University of Vienna, Vienna, Austria.

Paul Mayer (P)

Emergentec biodevelopment GmbH, Vienna, Austria.

Michael Schwarz (M)

Department of Oncology, Division of Internal Medicine I, Medical University of Vienna, Vienna, Austria.

Erwin Tomasich (E)

Department of Oncology, Division of Internal Medicine I, Medical University of Vienna, Vienna, Austria.

Arno Lukas (A)

Emergentec biodevelopment GmbH, Vienna, Austria.

Michael Krainer (M)

Department of Oncology, Division of Internal Medicine I, Medical University of Vienna, Vienna, Austria.

Paul Perco (P)

Emergentec biodevelopment GmbH, Vienna, Austria.
Department of Internal Medicine IV, Medical University of Innsbruck, Innsbruck, Austria.

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Classifications MeSH