Project 2: Targeting Cooperative Phenotypes Common in Spatial Heterogeneity

摘要 最近对原发性肿瘤的研究发现，肿瘤内存在显著程度的异质性， 肿瘤由一系列表现出多种表型的亚克隆组成，包括分子谱， 增殖能力和对治疗的反应。虽然异质性现在被广泛报道，但很少有研究 研究了晚期转移性肿瘤的异质性，尽管转移性癌症 据估计，超过90%的癌症死亡是由它造成的。对于乳腺癌和卵巢癌， 转移的进展是治疗难治的。因此，非常需要确定机制， 亚克隆多样性可通过其影响转移表型并成为治疗困难的基础。 由于收集患者组织的挑战，研究转移性肿瘤是困难的。虽然原代组织 通常通过活检获得，很少对转移部位进行活检。为了解决这一难题，我们 我已经开发了一种快速尸检策略，我们在几小时内收集转移性肿瘤的新鲜样本， 患者死亡，以及转移性肿瘤活检的收集在临床试验设置之前和之后， 药物治疗这些收集使我们能够描绘多个转移部位，并调查相关性。 在同基因背景下的转移位点和亚克隆进化之间。我们建议利用这种独特的 研究转移进展期间肿瘤亚克隆演变与 这些肿瘤的表型特征 我们假设，尽管转移性肿瘤的基因突变谱具有多样性， 克隆动态，最终导致更常见的合作表型的收敛 网络，而针对这个网络中的关键依赖将导致更多的崩溃， 转移性肿瘤人群。为了研究这一点，我们将通过全基因组测序来分析肿瘤， 全外显子组测序和单细胞RNA测序。这些数据，加上我们新开发的 使用树重建算法解剖亚克隆群体的算法， 使用贝叶斯统计的基因表达谱，以及使用数学 模型，将使我们能够理解（目标1）的亚克隆异质性，基础转移 启动和进展;（目标2）合作功能如何演变为化学难治性信号网络， 以及靶向它的治疗策略;以及（目的3）这些动力学如何在临床中表现为人类肿瘤 审判 我们的研究代表了第一个大的多位点转移性肿瘤的克隆动力学特征， 队列，并将提供一个新的框架，了解和治疗终末期肿瘤的基础上的演变 合作的表型。我们将在患者样本上开发这些模型，并在一个独特的临床试验中进行测试。 试验，确保生理，如果不是临床，我们的研究结果的相关性。

ABSTRACT Recent studies in primary tumors have found a remarkable degree of intratumor heterogeneity, where a single tumor is comprised of a range of subclones exhibiting a diversity of phenotypes, including molecular profiles, proliferation capacity, and response to therapies. Although heterogeneity is now widely reported, few studies have investigated the heterogeneity of metastatic tumors at the end stage, despite the fact that metastatic cancer is estimated to be responsible for over 90% of cancer deaths. For breast and ovarian cancer, tumors that progress to metastasis are refractory to treatment. Therefore, there is a great need to determine the mechanisms by which subclonal diversity can affect the metastatic phenotype and underlie the difficulties in treatment. Studying metastatic tumors is difficult due to the challenges in collecting patient tissues. While primary tissues are typically obtained through biopsy, this is rarely performed for metastatic sites. To address this difficulty, we have developed both a rapid autopsy strategy where we collect fresh samples of metastatic tumors within hours of patient death, as well as collections of metastatic tumor biopsies in the clinical trial setting prior to and after drug treatment. These collections enable us to profile multiple metastatic sites and investigate the association between metastatic sites and subclonal evolution in an isogenic background. We propose to leverage this unique data set to investigate the relationship between evolution of tumor subclones during metastatic progression and the phenotypic profiles of these tumors. We hypothesize that, despite the diversity in their genetic mutation profiles, metastatic tumors exhibit clonal dynamics that ultimately leads to convergence on more common cooperative phenotypic networks, and that targeting the key dependencies within this network will lead to increased collapse of the metastatic tumor population. To investigate this, we will profile the tumors by whole genome sequencing, whole exome sequencing, and single cell RNA sequencing. This data, coupled with our newly developed algorithms for dissecting subclonal populations using tree reconstruction algorithms, for eliciting phenotypes from gene expression profiles using Bayesian statistics, and for simulating phenotypic evolution using mathematical models from ecology; will enable us to understand (Aim 1) the subclonal heterogeneity that underlies metastatic initiation and progression; (Aim 2) how cooperative functions evolve to a chemo-refractory signaling network, and therapeutic strategies to target it; and (Aim 3) how these dynamics are manifested human tumors in a clinical trial. Our investigations represent the first characterization of the clonal dynamics of a large multisite metastatic cohort, and will provide a new framework for understanding and treating end-stage tumors based on the evolution of cooperative phenotypes. We will develop these models on patient samples and test them in a unique clinical trial, ensuring the physiological, if not clinical, relevance of our findings.