Abstract 2171: A multivariate biomarker predicts sacituzumab govitecan response in solid tumors

Abstract

Abstract Despite the recent clinical success of antibody drug conjugates (ADC) in oncology, predictive biomarkers are lacking, potentially limiting their impact. Herein, we evaluated the ability of candidate biomarkers alone and in combination to predict objective response rates observed in solid tumor patients treated with the TROP2-targeted ADC, sacituzumab govitecan (SG), as determined in the IMMU-12-01 basket trial. We leveraged available next generation sequencing (NGS)-based molecular profiling data from an independent advanced solid tumor cohort (n = 23,968) and developed a multivariate biomarker algorithm that produced biomarker positive rates correlating with the objective response rates (ORR) observed in IMMU-12-01. Candidate biomarkers evaluated included TROP2 gene expression, proliferation (by gene expression) and tumor cellularity. Notably, while TROP2 gene expression was highly correlated with protein expression across 45 tumor types (r = 0.93), TROP2 gene expression alone did not significantly predict ORR across 9 tumor types (r = 0.40, p = 0.29). In contrast, a biomarker algorithm combining TROP2 and proliferation by gene expression with tumor cellularity strongly predicted response both when using tumor type-specific biomarker rates in a discovery cohort (r = 0.83, p = 0.006) and in an independent validation cohort (r = 0.82, p = 0.007). These results indicate that the multivariate biomarker accounts for 67% of the variability observed in response rates and may thus identify patients likely to benefit from SG. Among tumor types with objective responses in IMMU-12-01, biomarker positive rates ranged from 9.9% in colorectal cancer to 57.4% in bladder cancer. Additional tumor types with biomarker positive rates >30% included cancers of the head and neck, cervix, salivary gland, skin (non-melanoma) and ovary, all with positive biomarker rates >30%. Interestingly, most tumor types had biomarker positive rates >5%, suggesting the potential for a tumor type-agnostic approach to patient selection. Considering SG and other ADC’s mechanism of action, a plausible model for response is that (1) higher target expression increases ADC drug delivery, (2) higher tumor cellularity increases ADC bystander effect and (3) higher tumor cell proliferation increases tumor cell death. In summary, we uncovered a novel biomarker algorithm capable of predicting SG response across solid tumors that may be generalizable to ADCs as a class, with the potential to further optimize use and maximize benefit. Citation Format: Nickolay A. Khazanov, Laura E. Lamb, Daniel H. Hovelson, Kat Kwiatkowski, D. Bryan Johnson, Daniel R. Rhodes, Scott A. Tomlins. A multivariate biomarker predicts sacituzumab govitecan response in solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2171.