Genomic analysis and prediction of therapeutic vulnerabilities of small cell lung cancer from rovalpituzumab tesirine phase III trial (MERU)

8577 Background: Predicting variable therapeutic responses that are driven by the genetic and transcriptomic heterogeneity of Small Cell Lung Cancer (SCLC) offers an opportunity for implementing precision therapies within the cancer cells and tumor microenvironment (TME). MERU was a Phase III study of Rovalpituzumab Tesirine (Rova-T) as maintenance therapy following first-line platinum-based chemotherapy in participants with extensive stage SCLC. In this study, we comprehensively analyzed the baseline genomic data in the MERU cohort to interrogate SCLC’s heterogeneous TME and tumor-intrinsic molecular and genetic drivers for therapeutic vulnerabilities. Methods: RNA-seq and Whole-Exome-Sequencing (WES) data were collected from archival tumor samples of 306 of 740 subjects enrolled in MERU. RNA-seq reads were aligned with STAR and quantified for gene expression by HTSeq. WES reads were analyzed for somatic mutation and copy number variation using AbbVie’s tumor-only WES pipeline. TME heterogeneity was evaluated using gene-set variation analysis of pan-cancer TME gene signatures. SCLC subtyping was performed using expression of 4 transcriptional factors (TF): ASCL1, NEUROD1, POU2F3, and YAP1. A computational framework of mapping MERU transcriptome expression profile to Cancer Dependency Map (DepMap) dataset was developed to synthetically screen MERU samples’ drug sensitivity. Results: TF subtyping reveals high prevalence of SCLC-A and -N subtypes in the MERU cohort, consistent with high expression of DLL3 in these two neuroendocrine subtypes. Correlation of TME gene signature scores identified two distinct clusters in the MERU cohort with correspondingly polarized immune-suppressive and -inflamed phenotypes. The pro-inflammatory score combining IFN-gamma and TGF-beta signatures, predicted prognosis in the MERU cohort better than the previously reported SCLC-I signature (Hazard Ratio: 0.71 [95% CI 0.38-1.3] vs. 1.29 [95% CI 0.65-2.6]). WES analysis identified high prevalence of TP53 and RB1 mutations, in line with the reported prevalence of these genetic drivers in SCLC. The clinical characteristics including gender, smoking status, and prior treatment, are not significantly associated with either TF or TME subgroup. The computational drug screen framework maps 83% of MERU samples to DepMap SCLC cell lines. The correlation of subtype TF expression with drug sensitivity was highly concordant between MERU and DepMap. Collectively, this approach demonstrated that molecular subtyping can be leveraged to broadly predict drug response in SCLC patients. Conclusions: Our comprehensive genomic analysis of the MERU cohort provides new insights into SCLC heterogeneity from both tumor-intrinsic and tumor-immune interaction perspectives and shall contribute to the development of predictive biomarkers and therapeutic opportunities for SCLC.