Faculty, Staff and Student Publications
Publication Date
8-15-2023
Journal
Clinical Cancer Research
Abstract
PURPOSE: Therapeutic resistance to frontline therapy develops rapidly in small cell lung cancer (SCLC). Treatment options are also limited by the lack of targetable driver mutations. Therefore, there is an unmet need for developing better therapeutic strategies and biomarkers of response. Aurora kinase B (AURKB) inhibition exploits an inherent genomic vulnerability in SCLC and is a promising therapeutic approach. Here, we identify biomarkers of response and develop rational combinations with AURKB inhibition to improve treatment efficacy.
EXPERIMENTAL DESIGN: Selective AURKB inhibitor AZD2811 was profiled in a large panel of SCLC cell lines (n = 57) and patient-derived xenograft (PDX) models. Proteomic and transcriptomic profiles were analyzed to identify candidate biomarkers of response and resistance. Effects on polyploidy, DNA damage, and apoptosis were measured by flow cytometry and Western blotting. Rational drug combinations were validated in SCLC cell lines and PDX models.
RESULTS: AZD2811 showed potent growth inhibitory activity in a subset of SCLC, often characterized by, but not limited to, high cMYC expression. Importantly, high BCL2 expression predicted resistance to AURKB inhibitor response in SCLC, independent of cMYC status. AZD2811-induced DNA damage and apoptosis were suppressed by high BCL2 levels, while combining AZD2811 with a BCL2 inhibitor significantly sensitized resistant models. In vivo, sustained tumor growth reduction and regression was achieved even with intermittent dosing of AZD2811 and venetoclax, an FDA-approved BCL2 inhibitor.
CONCLUSIONS: BCL2 inhibition overcomes intrinsic resistance and enhances sensitivity to AURKB inhibition in SCLC preclinical models.
Keywords
Humans, Antineoplastic Agents, Apoptosis, Aurora Kinase B, Cell Line, Tumor, Lung Neoplasms, Proteomics, Proto-Oncogene Proteins c-bcl-2, Small Cell Lung Carcinoma, Xenograft Model Antitumor Assays
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons
Comments
Supplementary Materials
PMID: 37289191