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

摘要 导致药物失效的基因突变是许多疾病的主要障碍，包括癌症、艾滋病毒、 巨细胞病毒和肺结核。可能会出现治疗诱导的耐药异常选择 要么来自新的突变，要么来自先前存在于一小部分细胞或病毒中的突变。这 后一种关于先前存在的亚克隆性耐药性的想法在各种疾病中研究得很少，而且没有 就实施抗耐药疗法的最佳方式达成普遍临床共识。这其中很大一部分是应该得到的 由于在足够的分辨率和足够高的吞吐量下检测亚群的技术困难。 在这里，我们利用了一种新的高灵敏度的DNA突变检测技术，多重阻滞剂置换 以黑色素瘤为模型系统研究多基因亚克隆耐药 在数百个治疗前的患者样本中。我们将把这项临床研究与新的亚克隆小鼠模型配对。 抵抗，以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，表明非常低的等位基因频率 亚克隆性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，我们的总体做法是 旨在确定如何最好地治疗潜在的亚克隆耐药患者，首先是通过改进突变 在患者中进行检测，第二，通过确定哪些突变阳性患者将从 最佳时机的抗药性干预措施。虽然我们从黑色素瘤开始作为一个模型系统，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 抵抗。

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.