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

通过临床计算肿瘤学剖析转移性黑色素瘤的治疗耐药性和进展

• 批准号: 10475605
• 负责人: David Liu
• 金额: $ 25.08万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-18 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10475605
• 关键词: Algorithms; Automobile Driving; BRAF gene; Binding; Biological; Biological Markers; CTLA4 blockade; CTLA4 gene; Classification; Clinical; Clinical Data; Clinical Markers; Combined Modality Therapy; Computational Biology; Data; Development; Disease; Epigenetic Process; Event; Frequencies; Genomics; Goals; Human; Immunotherapy; Light; Machine Learning; Mediating; Meta-Analysis; Metastatic Melanoma; Modernization; Molecular; Multiple Anatomic Sites; Mutation; NF1 mutation; Oncology; Pathway interactions; Patient Care; Patient-Focused Outcomes; Patients; Persons; Phylogenetic Analysis; Prediction of Response to Therapy; Prognosis; Resistance; Sampling; Series; Site; Standardization; Tail; Therapeutic; Time; Vertebral column; clinical biomarkers; clinically relevant; cohort; driver mutation; experience; immune checkpoint blockade; improved; individual patient; insight; longitudinal analysis; melanoma; molecular marker; multimodality; new therapeutic target; novel; novel therapeutic intervention; patient subsets; predicting response; predictive marker; predictive modeling; programmed cell death protein 1; receptor; response; response biomarker; side effect; statistical and machine learning; targeted treatment; therapy resistant; transcriptomics; treatment response; tumor; tumor microenvironment; tumor progression

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.

项目摘要 靶向治疗（BRAF/MEKi）和免疫检查点阻断（ICB）的发展， 共抑制受体CTLA-4和PD-1已经彻底改变了转移性黑素瘤的治疗。然而，在这方面， 只有一部分患者保持持久的反应，许多人经历了严重的副作用， 疗法预测个体患者的治疗反应仍然是一个关键且尚未解决的问题。 此外，一系列关键的基因组和表观遗传事件驱动的进展和耐药性的治疗， 不完全理解。这项建议的指导假设是：（a）对国际竞争性细菌的耐药性和针对性 治疗是由肿瘤内在和外在机制介导的，其中一些可以通过以下方式阐明： 肿瘤和肿瘤微环境的系统多模式分子表征;和（B） 将现代机器学习和统计方法应用于患者的分子和临床数据 肿瘤将为新的治疗方法和预测模型的开发提供信息， 在乎 在大型人类中识别和验证对BRAF/MEKi和ICB的内在抗性的预测因子 迄今为止，队列数量有限。该提案的目的1应用了以下基因组和转录组学表征： 将治疗前肿瘤转移至接受BRAF/MEKi、PD-1 i和CTLA-4 i治疗的大型患者队列， 发现和验证反应和耐药性的分子和临床标志物。机器学习 这些方法将把这些标记物整合到预测响应的简约模型中。的差分分析 将使用互信息来揭示预测对治疗的不同反应的标记。 很大一部分患者对免疫疗法没有反应或维持持续反应， 并且迫切需要表征赋予抗真菌性的驱动因子的获得或选择， 免疫疗法。该提案的目的2开发了使用纵向分子表征的算法， 收集了多个解剖部位的肿瘤样本，以发现基因组和表观遗传驱动因素， 使用系统发育分析作为发现的支柱来研究免疫疗法的进展和抗性。 最后，检测以低频率存在的新型肿瘤驱动突变的能力强烈依赖于 在队列规模上。该提案的目的3利用所有基因组特征的黑色素瘤来执行Meta， 使用最先进的新算法进行分析，以发现低频存在的新驱动突变 重点在于缺乏已知靶向驱动因子的肿瘤亚群。 这些研究将扩大转移性乳腺癌中基因组和表观遗传学改变的可操作范围， 黑色素瘤，推进我们对靶向和免疫治疗的内在和获得性抗性的理解， 黑色素瘤，并建立一个框架来预测个体患者的反应，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。