Genetic Determinants of Evolutionary Trajectories and Clinical Course in Pancreatic Cancer

项目总结 遗传性肿瘤内异质性(Gith)与癌症的进展有关，被认为是一种 治疗耐药性的主要贡献者。然而，在不同的遗传驱动因素出现的程度上 癌症的发生特别影响随后的进化轨迹，临床过程尚不清楚。 一些临床病程较长的癌症类型，例如肾透明细胞癌，通常遵循 达尔文式的生长模式，表现出广泛的胃，其特征是异质性体细胞突变和 多个亚克隆驱动程序。相比之下，胰腺导管腺癌等侵袭性肿瘤类型 (PDAC)具有多个克隆性驱动事件，包括体细胞编码突变和 拷贝数的改变，以及随后的进化轨迹似乎受到了基因的限制。然而，两者 肿瘤类型包含异常值，其不同的遗传特征和临床过程已被观察到。 最终，深入了解多种肿瘤类型内部和之间的进化轨迹，以及 治疗前后可区分疾病生物学较惰性或转移较少的患者 从那些有更快传播和临床病程的患者进展。这样的洞察力有可能 促进临床试验分层和疾病管理。 这个项目的目的是确定司机基因的多样性和进化时机的程度 突变会影响单一癌症类型PDAC的临床病程。虽然已知的驾驶基因在 PDAC，这些驱动因素的数量、质量或年代对肿瘤进化的影响程度仍然存在 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 对吉斯进行评估。与传统的活检不同，多区域抽样更全面，有助于消除 关于特定突变是否存在于给定位置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 将针对个别患者的癌症进行重建。然后，这些数据将与以下各项指标相关联 临床病程，包括诊断阶段、总存活率、治疗状况、治疗方式和 当有这样的信息时，转移的负担。通过提高我们对肿瘤亚克隆的理解 架构，我们预计这一知识将有助于改善对疾病复发和 转移级联反应。此外，识别允许次要亚克隆支持或限制的遗传因素 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.