Genetic Determinants of Evolutionary Trajectories and Clinical Course in Pancreatic Cancer

项目摘要 肿瘤内遗传异质性（gITH）与癌症进展相关，被认为是肿瘤发生的重要因素。 治疗抵抗的主要因素。然而，不同的基因驱动因素在多大程度上出现在 致癌作用特别影响随后的进化轨迹，临床过程仍然未知。 一些具有长期临床病程的癌症类型，例如透明细胞肾细胞癌，通常遵循 达尔文的生长模式，表现出广泛的gITH，其特征是异质性体细胞突变， 多个亚克隆驱动程序。相比之下，侵袭性肿瘤类型，如胰腺导管腺癌， （PDAC）特征性地具有多个克隆驱动事件，其由体细胞编码突变和 拷贝数的改变和随后的进化轨迹似乎受到遗传的限制。但无论 肿瘤类型包含已观察到不同遗传特征和临床过程的异常值。 最终，深入了解多种肿瘤类型内部和之间的进化轨迹，以及 治疗前后可以区分患者的生物学或寡转移性疾病 从那些传播更快和临床过程的进展。这种见解有可能 便于临床试验分层和疾病管理。 该项目的目的是确定驱动基因的多样性和进化时间的程度， 突变影响单一癌症类型PDAC的临床病程。虽然有已知的驱动基因， PDAC，这些驱动因素的数量，质量或时间顺序影响肿瘤演变的程度仍然存在 不清楚因此，我们将对多区域采样的人PDAC进行批量和单细胞DNA测序， 评估GITH。与传统的活检不同，多区域采样更全面，有助于消除 关于特定突变是否存在于给定位点的假阴性和假阳性结论。 将使用多种计算方法和系统发育树对亚克隆进行深入分析 将针对个别癌症患者进行重建。然后，这些数据将与 临床病程，包括诊断时的分期、总生存期、治疗状态、治疗方式，以及 转移负担，当这些信息可用。通过提高我们对肿瘤亚克隆 架构，我们预计这些知识将有助于改善有关疾病复发的风险预测， 转移级联反应此外，确定允许次要亚克隆支持或限制 肿瘤生长可能为靶向干预和肿瘤控制提供新的机会。

PROJECT SUMMARY Genetic intratumoral heterogeneity (gITH) has been associated with cancer progression and is thought to be a major contributor to treatment resistance. However, the extent to which different genetic drivers that arise during carcinogenesis specifically influence subsequent evolutionary trajectories and clinical course remains unknown. Some cancer types with a protracted clinical course, e.g. clear cell renal cell carcinoma, generally follow a Darwinian growth pattern, displaying extensive gITH characterized by heterogeneous somatic mutations and multiple subclonal drivers. By contrast, aggressive tumor types such as pancreatic ductal adenocarcinoma (PDAC) characteristically have multiple clonal driver events consisting of both somatic coding mutations and copy number alterations, and subsequent evolutionary trajectories appear genetically restrained. However, both tumor types contain outliers for which different genetic features and clinical courses have been observed. Ultimately, a deep understanding of evolutionary trajectories within and across multiple tumor types, as well as before and after treatment may distinguish patients with more indolent disease biology or oligometastatic progression from those with more rapid dissemination and clinical courses. Such insights have the potential to facilitate clinical trial stratification and disease management. The aim of this project is to determine the extent to which diversity and evolutionary timing of driver gene mutations impacts clinical disease course in a single cancer type, PDAC. While there are known driver genes in PDAC, the extent to which the quantity, quality, or chronology of these drivers impacts tumor evolution remains unclear. Therefore, we will perform bulk and single cell DNA sequencing on multiregion sampled human PDACs to assess gITH. Unlike traditional biopsies, multiregion sampling is more comprehensive and helps to eliminate false negative and false positive conclusions regarding whether a particular mutation is present at a given site. In-depth analyses of subclones will be performed using multiple computational methods and phylogenetic trees will be reconstructed for individual patients’ cancers. These data will then be correlated with various metrics of clinical disease course, including stage at diagnosis, overall survival, treatment status, mode of treatment, and metastatic burden when such information is available. By improving our understanding of a tumor’s subclonal architecture, we anticipate that this knowledge will help improve risk predictions regarding disease relapse and the metastatic cascade. Additionally, identifying genetic factors that allow minor subclones to support or restrict tumor growth may provide new opportunities for targeted intervention and tumor control.