Abstract A16: Evolutionary dynamics and optimal management of high-risk multiple myeloma
Authors: Kenneth Shain, Zayar Khin, Ariosto Silva
Journal: Clinical Cancer Research (2015)

Abstract Multiple myeloma (MM) remains an incurable malignancy of bone marrow resident plasma cells. Both disease- and host-specific variables contribute to significant heterogeneity in overall survival (OS). While low-risk (LR) MM has benefited from the advent of novel agents and contemporary treatment strategies, reaching an OS of over 10 years for low risk patients, median OS in high-risk (HR) MM remains less than 2 years. This disparity demands a focus on the unmet medical need of HRMM patients, and indicates that novel therapeutic strategies are needed for this population. Initial response in HRMM is followed by successive relapses with rapidly evolving cross-resistant disease, due to innate and tumor microenvironmental (TME) mechanisms. Viewed as an evolutionary process, the development of therapeutic resistance relies on Darwinian dynamics within the bone marrow. The primary objective of our research is to develop novel therapeutic strategies to increase overall survival in HRMM patients. We propose to achieve this by characterizing and then exploiting the evolutionary principles that lead to emergence of chemoresistant clones. Different strategies will be explored to promote intra-tumoral competition, seeking to induce an alternating clonal dominance. In this context, clones with different chemoresistance profiles will alternate dominance; thereby, suppressing the expansion of those chemoresistant clones associated with treatment failure. We anticipate that we will introduce a new paradigm for treatment of HRMM patients, and for other cancers characterized by similar clonal dynamics. We hypothesize that emergence of cross-resistance in MM -specifically in HRMM- and consequent therapy failure is due to a combination of evolving cell-innate and TME-modulated mechanisms of chemoresistance that facilitate the expansion of drug resistant clones at the expense of drug sensitive clones. To investigate this hypothesis, we have developed a patient-parameterized computational system that integrates evolutionary dynamics of changes in tumor burden during treatment, assessment of innate and environment-mediated drug resistance in a novel microfluidic ex vivo platform, and quantitative immunohistochemistry (qIHC) of TME elements from bone marrow (BM) biopsies. Like hurricane prediction models, simulations from this system will be used to monitor and estimate patient's future course in response to current or novel therapeutic strategies, aiming to delay emergence of cross-resistance. Our preliminary studies have shown that relapse in multiple myeloma has multi-factorial dynamics: it occurs at cellular level (increase in ex vivo chemoresistance to previously used drugs), population level (turnover of the tumor population characterized by extinction of sub-populations and emergence of new ones), as well as at microenvironmental level (increase in expression of myeloma adhesion molecules and preferential localization of multiple myeloma cells in regions of the marrow richer in fibronectin). Using predictive computational models parameterized by pre-treatment information (pre-treatment bone marrow biopsy) as well as early clinical response information (using serum paraprotein as tumor burden surrogate), we have proposed that patient-tailored regimens could significantly increase clinical benefit (progression-free survival) of high-risk multiple myeloma patients. Citation Format: Kenneth Shain, Zayar Khin, Ariosto Silva. Evolutionary dynamics and optimal management of high-risk multiple myeloma. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr A16.