Predictive biomarker of BET bromodomain inhibitor in Small cell lung cancer

Division Of Basic Sciences - Nci

Investigators

Linked publications, trials & patents

Abstract

Through analysis of published RNA-Seq data, we have identified one gene signature that can predict sensitivity of SCLC to BETi. By examining 50 SCLC cell lines, we confirmed that the candidate biomarker has 100% specificity in identifying SCLCs sensitive to BETi. In the past year, we have been studying the mechanism underlying sensitivity to BETi in SCLC cell lines with the identified gene signature. We have found that the candidate biomarker is connected to a specific subtype of SCLC. Knockdown the identified biomarker drastically changed the transcriptome of SCLC and the genome-wide profile of superenhancer on ChIP-Seq analysis. By using SCLC PDX models, we have confirmed that the SCLCs with the candidate biomarker is sensitive to BETi in vivo. BETi treatment resulted in significantly better control of tumor growth and significantly extended median overall survival in SCLC PDX models. Based on the findings of this study, we have been interested in studying the susceptibility of each subtype of SCLC to existing oncological therapeutics. By analyzing a public high-throughput drug screen database, we have found heat shock protein 90 inhibitors (HSP90is) are effective in a particular subtype of SCLC. By investigating the mechanisms underlying the sensitivity, we found that HSP90i depletes cell cycle checkpoint proteins and synergizes with cytotoxic chemotherapy drugs. We validated these findings in SCLC xenograft animal models. We plan to reach out to pharmaceutical companies to initiate a clinical trial to test this drug combination in patients with relapsed SCLC. In another project extended from this research, we developed new immunohistochemical assays to detect molecular subtype markers of primary SCLC tumors. In addition, we explored the associations between molecular subtypes and the biomarkers of NE differentiation and therapeutic response, including synaptophysin, CD8+ T-cells, MYC paralogs, and SLFN11.