(PQD1) Clonal heterogeneity and targeted therapy resistance in melanoma

描述（由申请人提供）：PQD1：黑色素瘤的克隆异质性和靶向治疗耐药性。癌细胞的分子不稳定性产生了多样性，而多样性反过来又促进了肿瘤的进化。来自靶向治疗的强大选择压力可以提供戏剧性的临床反应，但也会推动这种进化产生快速的耐药肿瘤生长。为了有效地应对现代肿瘤学的这一关键挑战，必须更好地了解患者体内耐药进化的基本机制。我们假设晚期癌症对靶向治疗的临床耐药性的发展是肿瘤异质性和克隆选择的功能，而不是细胞对治疗压力的适应。我们的建议将使用靶向抑制黑色素瘤中的致癌BRAF作为模型系统来验证这一假设。为了实现这一目标，我们将采用一种高度创新的方法，将基于流式细胞术的肿瘤细胞核分选直接从实体瘤存档的石蜡包埋临床样本中与下一代测序相结合，为利用梅奥诊所存档的临床注释黑色素瘤组织的广泛资源提供独特的机会。从一系列患者匹配的敏感和耐药组织中获得的不同克隆肿瘤群体的基因组序列数据将用于定义克隆肿瘤谱系和与耐药相关的各自分子事件。在本提案中，我们还将利用我们Stand Up To Cancer (SU2C) Melanoma Dream Team的新颖而强大的信息学资源，对基因组数据进行临床相关的解释。最后，我们将探索在一个独特的、高度临床相关的动物模型系统中调节患者体内产生的耐药性发展的方法。使用来自患者匹配的治疗前（敏感）和复发（耐药）组织的肿瘤移植物，我们将测试改变药物剂量和时间的影响并探索

DESCRIPTION (provided by applicant): PQD1: Clonal heterogeneity and targeted therapy resistance in melanoma. Molecular instability of cancer cells generates diversity that, in turn, enables tumor evolution. A strong selective pressure from targeted therapies can provide dramatic clinical responses but also drive such evolution to yield rapid drug resistant tumor growth. To effectively address this key challenge of modern day oncology, it is imperative to better understand the basic mechanisms underlying evolution of resistance in patients in vivo. We hypothesize that development of clinical resistance to targeted therapy in advanced cancers is a function of marked tumor heterogeneity and clonal selection rather than cellular adaptation to therapeutic pressure. Our proposal will test this hypothesis using targeted inhibition of oncogenic BRAF in melanoma as a model system. To achieve this we will employ a highly innovative approach that combines flow cytometry-based sorting of tumor nuclei directly from solid tumor archived paraffin-embedded clinical samples with next generation sequencing, providing a unique opportunity to exploit the extensive Mayo Clinic resources of archived clinically annotated melanoma tissues. Genomic sequence data obtained in these experiments from distinct clonal tumor populations sorted from a series of patient matched sensitive and resistant tissues will be used to define clonal tumor lineages and respective molecular events associated with resistance. We will also leverage the novel and robust informatics resources of our Stand Up To Cancer (SU2C) Melanoma Dream Team effort for clinically relevant interpretation of genomic data in this proposal. Finally, we will explore approaches to modulate development of resistance arising in patients in vivo in a unique, highly clinically relevant anima model system. Using tumorgrafts derived from patient's matched pre-treatment (sensitive) and relapsed (resistant) tissues, we will test the effects of altering drug dose and timing and explore rational combination treatments based on genomic data from paired tissues. The key issue addressed in this proposal holds relevance not only to melanoma but solid tumors in general. The outcomes of our proposed research have potential to significantly impact our understanding of drug resistance and the development of clinically relevant approaches to overcome it, which is of particular relevance as numerous targeted agents are reaching the clinic.