Dissecting Therapeutic Resistance and Progression in Metastatic Melanoma Through Clinical Computational Oncology

项目摘要 靶向治疗(BRAF/Meki)和免疫检查点阻断(ICB)靶向治疗的进展 共抑制受体CTLA-4和PD-1使转移性黑色素瘤的治疗发生了革命性的变化。然而， 只有一小部分患者保持持久的反应，许多人经历了严重的副作用 心理治疗。预测个别患者的治疗反应仍然是一个关键和尚未解决的问题。 此外，推动进展和治疗耐药性的一系列关键基因组和表观遗传学事件是 不完全理解。这一建议的指导性假设是(A)对ICB和靶向的抵抗 治疗是由肿瘤的内在和外在机制介导的，其中一些机制可以通过 肿瘤和肿瘤微环境的系统多模式分子表征；和(B) 应用现代机器学习和统计学方法处理来自患者的分子和临床数据 肿瘤将为开发新的治疗方法和预测模型提供信息，以改善患者状况 关心。 在大型人群中识别和验证对BRAF/Meki和ICB固有耐药性的预测因素 到目前为止，队列一直是有限的。本提案的目标1适用于基因组和转录特征 使用BRAF/Meki、PD-1i和CTLA-4i治疗的大量患者的治疗前肿瘤，以便 发现并验证反应和耐药的分子和临床标记。机器学习 这些方法将把这些标记整合到预测反应的简约模型中。一种差异化分析 将使用相互信息来揭示预测治疗差异反应的标记。 相当大比例的患者对免疫治疗没有反应或维持持续反应， 而且迫切需要确定获得或选择的驱动器的特性，这些驱动器提供了对 免疫疗法。该提案的目标2开发了使用纵向分子表征的算法 收集多个解剖部位的肿瘤样本以发现基因组和表观遗传学驱动因素 以系统发育分析为发现主干的免疫治疗进展与抗药性。 最后，检测以低频率出现的新的肿瘤驱动因素突变的能力与此密切相关 关于队列大小。这项建议的目标3利用所有基因组特征的黑色素瘤来执行Meta- 使用最先进和新的算法进行分析，以发现存在于低频的新的驱动程序突变 重点放在缺乏已知靶向驱动因素的肿瘤亚群上。 这些研究将扩大转移性肿瘤基因组和表观遗传学改变的可操作范围。 黑色素瘤，促进我们对靶向和免疫治疗的内在和获得性耐药的理解 黑色素瘤，并建立一个框架来预测个别患者的反应，这可能会影响患者的护理 在黑色素瘤中，并适用于其他疾病环境。

Project Summary The development of targeted therapy (BRAF/MEKi) and immune checkpoint blockade (ICB) targeting the co-inhibitory receptors CTLA-4 and PD-1 have revolutionized the treatment of metastatic melanoma. However, only a subset of patients maintain durable responses, and many people experience substantial side effects of therapy. Predicting therapeutic response in individual patients remains a critical and unresolved issue. Furthermore, the series of key genomic and epigenetic events driving progression and resistance to therapy is incompletely understood. The guiding hypothesis of this proposal is that (a) resistance to ICB and targeted therapy is mediated by tumor intrinsic and extrinsic mechanisms, some of which may be elucidated by systematic multi-modal molecular characterization of the tumor and tumor microenvironment; and (b) applying modern machine-learning and statistical approaches to molecular and clinical data from patient tumors will inform development of new therapeutic approaches and predictive models to improve patient care. Identifying and validating predictors of intrinsic resistance to BRAF/MEKi and ICB across large human cohorts has been limited to date. Aim 1 of this proposal applies genomic and transcriptomic characterization of pre-treatment tumors to large cohorts of patients treated with BRAF/MEKi, PD-1i, and CTLA-4i in order to discover and to validate molecular and clinical markers of response and resistance. Machine learning approaches will integrate these markers into parsimonious models predicting response. A differential analysis using mutual information will be conducted to reveal markers that predict differential response to therapy. A significant proportion of patients do not respond or maintained sustained responses to immunotherapy, and there is a critical need to characterize the acquisition or selection of drivers that confer resistance to immunotherapy. Aim 2 of this proposal develops algorithms using molecular characterization of longitudinally collected tumor samples across multiple anatomic sites to discover genomic and epigenetic drivers of progression and resistance to immunotherapy using phylogenetic analysis as the backbone of discovery. Finally, the ability to detect novel tumor driver mutations present at low frequencies is strongly dependent on cohort size. Aim 3 of this proposal leverages all genomically characterized melanomas to perform a meta- analysis using state-of-the-art and novel algorithms to discover novel driver mutations present at low frequencies with a focus on tumor subsets that lack known targetable drivers. These studies will expand the actionable landscape of genomic and epigenetic alterations in metastatic melanoma, advance our understanding of intrinsic and acquired resistance to targeted and immunotherapies in melanoma, and establish a framework to predict response in individual patients, which may impact patient care in melanoma and have applicability in other disease settings.