Optimizing Detection and Interventions Against Rare Pre-existing Drug Resistance Mutations

摘要 导致药物失败的基因突变是许多疾病的主要障碍，包括癌症、艾滋病毒、 巨细胞病毒和结核病。治疗诱导的对赋予抗性的畸变的选择可能会出现 要么来自新的突变，要么来自细胞或病毒的小亚群中预先存在的突变。这个 后一种关于预先存在的亚克隆耐药性的观点在各种疾病中得到了充分研究，并且没有 关于实施抗耐药疗法的最佳方法的一般临床共识。这其中很大一部分是由于 以足够的分辨率和足够高的吞吐量检测亚群的技术困难。 在这里，我们利用了一种新颖的高灵敏度 DNA 突变检测技术，多重阻断剂置换 扩增和黑色素瘤作为研究多基因亚克隆耐药性的模型系统 数百个治疗前患者样本。我们将把这项临床研究与新的亚克隆小鼠模型配对 优化抗耐药治疗的风险回报策略。我们的初步数据来自 黑色素瘤患者与其他癌症的数据一致，表明等位基因频率非常低 亚克隆耐药突变可能预先存在于超过三分之一的患者肿瘤中。因此，我们的总体方法 旨在确定如何最好地治疗具有潜在亚克隆耐药性的患者，首先是通过改善突变 其次是确定哪些突变阳性患者最能从中受益 最佳时机的反抵抗干预。尽管我们以黑色素瘤作为模型系统开始，但我们的 该方法将作为识别和克服先前存在的亚克隆的广泛适用的蓝图 阻力。

Abstract Genetic mutations that cause drug failure are a major obstacle in many diseases including cancer, HIV, cytomegalovirus, and tuberculosis. Therapy-induced selection for resistance-conferring aberrations can arise either from new mutations or from those that pre-existed in a small subpopulation of the cells or viruses. This latter idea of pre-existing subclonal drug resistance is highly understudied across diseases, and there is no general clinical consensus on the best way to implement counter-resistance therapies. A large part of this is due to technical difficulties in detecting the subpopulations at both sufficient resolution and high enough throughput. Here, we leverage a novel high-sensitivity DNA mutation detection technology, multiplex blocker displacement amplification, and melanoma as a model system to study subclonal drug resistance for multiple genes inhundreds of pre-therapy patient samples. We will pair this clinical study with novel mouse models of subclonal resistance to optimize risk-reward strategies for counter-resistance therapies. Our preliminary data from melanoma patients are consistent with data from other cancers suggesting that very low allelic-frequency subclonal resistance mutations could pre-exist in over a third of patients' tumors. Therefore, our overall approach is aimed at determining how to best treat patients with potential subclonal resistance, first by improving mutation detection in patients and second by determining which mutation-positive patients would most benefit from optimally-timed counter-resistance interventions. Although we start with melanoma as a model system, our approach will serve as a broadly-applicable blueprint for recognizing and overcoming pre-existing subclonal resistance.