Abstract 1706: Individualized therapy identified using simulation for bortezomib resistant patient with <i>ex-vivo</i> validation
Authors: Nicole A. Doudican, Shireen Vali, Annette Leon, Ansu Kumar, Neeraj Kumar Singh, Anuj Tyagi, Shweta Kapoor, Zeba Sultana, Taher Abbasi, Amitabha Mazumder
Journal: Cancer Research (2014)

Abstract Background: The unique signature of a patient's tumor mandates the need to rationally design personalized therapies employing N=1 segmentation conceptually. By focusing on rationally designed personalized treatments, our strategy targets key pathways to address the clinical problem of therapy resistance. To overcome bortezomib resistance, we have (1) employed predictive simulation modeling using patient genomic profiling to design patient specific combinatorial therapeutic regimens and (2) validated designed therapy ex-vivo in patient-derived cell lines. Methods: Clinical patient samples were analyzed for chromosomal alterations using array Comparative Genomic Hybridization (aCGH) by GenPath Diagnostics and cytogenetic chromosome analysis by NYU. Using this information, we created a patient simulation avatar. To identify effective personalized therapeutics, we focused on molecularly targeted agents with clinical data. The predictive simulation based approach from Cellworks provides a comprehensive representation of plasma cell myeloma (PCM) disease physiology incorporating signaling and metabolic networks with an integrated phenotype view. Therapeutic hits were shortlisted from over 1000 pharmacodynamic dose-response simulation studies using efficacy and synergy criteria. Simulation modeling identified therapeutic mechanisms that impact proliferation and agent combinations that overcome bortezomib resistance. These predictive findings are in the process of being assessed ex vivo using patient primary cells and prospectively validated. Results: A loss of the CUL1 due to deletion of chromosome 7q36.1 region and CCND1 as a result of gain of chromosome 11q13 region was detected. Simulation of this patient's avatar showed high proliferation phenotype as a consequence of stated aberrations. CUL1 deletion caused increase in NFKB and CTNNB1 and a synergistic increase in CCND1. CUL1 deletion excludes target proteins from proteasomal degradation, thereby making cells resistant bortezomib effect. Simulation screening identified adjuvant NVP-BEZ235 (pan PI3K/mTORC1 and mTORC2 inhibitor) with current background of bortezomib to show enhanced efficacy in this patient. NFKB is reduced by this mechanism and along with reduction in translation of pro-proliferative genes mediated by mTOR inhibition. IC20 concentrations with respect to proliferation of the single agents in combination showed 38% inhibition of proliferation in simulation. These findings are currently being prospectively validated ex-vivo in patient primary cells. Conclusions: This study demonstrates and validates simulation for developing novel therapeutics and technologies to truly leverage “big data”. This level of individualization, beyond linking point mutations to associated drugs targeting the same mutations, truly incorporates the complete patient tumor signature with a clinical translation pathway. Citation Format: Nicole A. Doudican, Shireen Vali, Annette Leon, Ansu Kumar, Neeraj Kumar Singh, Anuj Tyagi, Shweta Kapoor, Zeba Sultana, Taher Abbasi, Amitabha Mazumder. Individualized therapy identified using simulation for bortezomib resistant patient with ex-vivo validation. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1706. doi:10.1158/1538-7445.AM2014-1706