Abstract 4025: Data-driven computational modeling to identify biomarkers of response to lenvatinib (E7080) in melanoma.

Background: Lenvatinib is an oral tyrosine kinase inhibitor targeting VEGFR1-3, FGFR1-4, RET, KIT and PDGFRβ. Anti-tumor activity has been observed in melanoma patients in Phase I studies. We applied an integrative supercomputer-driven analysis approach to identify biomarkers of lenvatinib treatment response in melanoma patients. Methods: Clinical data sets including tumor response data (RECIST), progression free survival (PFS), pharmacokinetic parameters (PK) and molecular data sets including baseline tumor gene expression (Affymetrix U133Plus2) and BRAF and NRAS mutational status, were collected from 18 patients with metastatic melanoma who received lenvatinib 10 mg orally twice daily in 28-day cycles. These clinical and molecular data sets were used to generate computational models developed using REFS (Reverse Engineering and Forward Simulation) modeling platform, which utilizes Bayesian network inference and simulations. Simulations were performed to identify biomarkers of lenvatinib treatment response. These potential predictive biomarkers were then tested for their ability to predict response to lenvatinib in preclinical models. Results: Using a model comprising gene expression, mutational status and PK data, REFS identified a panel of 18 potential predictive biomarkers of lenvatinib treatment response. Identified biomarkers were able to predict up to 89% of the observed variance in the tumor response data. A total of 32 identified genes including 6 candidate predictive biomarkers (TARBP2, CACNA1, C7ORF, RAP2A, SHMT1, IL22RA2) were further validated in a preclinical melanoma model system (n=12) and tissue bank samples with matched normal adjacent tissue (n=21). Expression of 14 genes correlated with relative tumor volume (r>0.35 or r 2). Conclusions: Potential predictive biomarkers of lenvatinib treatment response in melanoma patients were identified by computational modeling and validated in a preclinical model system and tumor tissue bank samples. The identified biomarkers will be tested for their predictive value in an ongoing Phase 2 trial. Citation Format: Tadashi Kadowaki, Yasuhiro Funahashi, Junji Matsui, Kumar Pavan, Pallavi Sachdev, Jim O9Brien, Heming Xing, Paul D. McDonagh, Iya Khalil, Razelle Kurzrock, David S. Hong, John Nemunaitis. Data-driven computational modeling to identify biomarkers of response to lenvatinib (E7080) in melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4025. doi:10.1158/1538-7445.AM2013-4025