Overcoming Drug Resistance in Multiple Myeloma

总体摘要 MM是骨髓内的浆细胞肿瘤，具有显著的复杂性， 分子水平上的异质性。尽管临床结果有所改善， 随着新疗法的实现，对治疗反应的个体间差异很大， 这是实现一致的治疗效果或治愈的主要限制。并非所有患者 对初始治疗有反应（先天性耐药），而那些经常复发的患者， 难治性疾病（获得性耐药性）。我们要解决的核心假设是 MM的合理治疗开发应基于对以下因素的理解： 肿瘤的潜在遗传学和生物学， 目前和未来的药物。在这个骨髓瘤-DRSC中，我们提出了3个项目，将： 1)为骨髓瘤细胞系开发高通量药物筛选平台， MM生物学的广泛多样性;并使用该平台筛选原发性患者肿瘤 最有效的反应，包括联合疗法; 2）开发一种 与药物相关的综合突变和生殖系变异组 体外和体内反应以及毒性; 3）确定 MM中使用的两种最常见治疗药物的反应和耐药性 治疗：IMiDS和蛋白酶体抑制剂; 4）发展遗传和免疫表型 有效预测肿瘤反应、耐药性和患者毒性的特征; 5） 鉴定可能导致出现耐药性的肿瘤亚群;以及6） 确定预测耐药性的策略和克服耐药性的方法。

OVERALL ABSTRACT MM is a plasma cell neoplasm within the bone marrow with significant complexity and heterogeneity at the molecular level. Despite improvements in the clinical outcomes achieved with newer therapies, wide inter-individual variation in response to treatment is a major limitation in achieving consistent therapeutic effects, or cures. Not all patients respond to initial therapies (innate resistance), and those that do frequently relapse with refractory disease (acquired resistance). The central hypothesis we are addressing is that rational therapeutic development in MM should be based on an understanding of the underlying genetics and biology of the tumor that identify sensitivity and resistance to current and future drugs. In this Myeloma-DRSC we are proposing 3 Projects that will: 1) develop a high throughput drug screening platform for myeloma cell lines representing the wide diversity of MM biology; and use this platform to screen primary patient tumor cells for most effective responses, including combination therapies; 2) develop a comprehensive mutational and germline variation panel that will be correlated to drug responses in vitro and in vivo, as well as toxicities; 3) identify the mechanism of response and resistance for two of the most common therapeutics used in MM treatment: IMiDS and proteasome inhibitors; 4) develop genetic and immunophenotypic signatures that effectively predict tumor response, resistance, and patient toxicities; 5) identify tumor sub-populations that may contribute to emerging resistance; and 6) identify strategies to predict drug resistance and approaches to overcome resistance.