Genetic Determinants of Evolutionary Trajectories and Clinical Course in Pancreatic Cancer

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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.