Abstract

Malignant pleural mesothelioma (MPM) has relatively ineffective first/second-line therapy for advanced disease and only 18% five-year survival for early disease. Drug-induced mitochondrial priming measured by dynamic BH3 profiling identifies efficacious drugs in multiple disease settings. We use high throughput dynamic BH3 profiling (HTDBP) to identify drug combinations that prime primary MPM cells derived from patient tumors, which also prime patient derived xenograft (PDX) models. A navitoclax (BCL-xL/BCL-2/BCL-w antagonist) and AZD8055 (mTORC1/2 inhibitor) combination demonstrates efficacy in vivo in an MPM PDX model, validating HTDBP as an approach to identify efficacious drug combinations. Mechanistic investigation reveals AZD8055 treatment decreases MCL-1 protein levels, increases BIM protein levels, and increases MPM mitochondrial dependence on BCL-xL, which is exploited by navitoclax. Navitoclax treatment increases dependency on MCL-1 and increases BIM protein levels. These findings demonstrate that HTDBP can be used as a functional precision medicine tool to rationally construct combination drug regimens in MPM and other cancers.

Malignant pleural mesothelioma (MPM) is an aggressive malignancy with few effective treatment options available. Here, the authors use dynamic BH3 profiling to measure drug-induced mitochondrial priming and identify AZD8055 and navitoclax as a pro-apoptotic drug combination in ex vivo and preclinical MPM models.

Details

Title
Dynamic BH3 profiling identifies pro-apoptotic drug combinations for the treatment of malignant pleural mesothelioma
Author
Potter, Danielle S. 1 ; Du, Ruochen 1 ; Bohl, Stephan R. 1 ; Chow, Kin-Hoe 2 ; Ligon, Keith L. 3   VIAFID ORCID Logo  ; Bueno, Raphael 4 ; Letai, Anthony 1   VIAFID ORCID Logo 

 Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, USA (GRID:grid.65499.37) (ISNI:0000 0001 2106 9910); Harvard Medical School, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X) 
 Dana-Farber Cancer Institute, Department of Oncologic Pathology, Boston, USA (GRID:grid.65499.37) (ISNI:0000 0001 2106 9910); Dana-Farber Cancer Institute, Center for Patient Derived Models, Boston, USA (GRID:grid.65499.37) (ISNI:0000 0001 2106 9910) 
 Harvard Medical School, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Dana-Farber Cancer Institute, Department of Oncologic Pathology, Boston, USA (GRID:grid.65499.37) (ISNI:0000 0001 2106 9910); Dana-Farber Cancer Institute, Center for Patient Derived Models, Boston, USA (GRID:grid.65499.37) (ISNI:0000 0001 2106 9910); Brigham and Women’s Hospital, Department of Pathology, Boston, USA (GRID:grid.62560.37) (ISNI:0000 0004 0378 8294); Broad Institute of MIT and Harvard, Cancer Biology Program, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623) 
 Harvard Medical School, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Brigham and Women’s Hospital, Department of Surgery, Boston, USA (GRID:grid.62560.37) (ISNI:0000 0004 0378 8294) 
Pages
2897
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2816242027
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.