Erik Storrs is currently a PhD candidate in the Computation and Systems Biology program at Washington University in Saint Louis. He is particularly interested in applications of machine learning to further understanding of the tumor microenvironment.
Erik Storrs, Abul Usmani, Prathamesh Chati, Bradley Krasnick, Ryan Fields, Li Ding, Koushik Das, Aadel Chaudhuri
Washington University School of Medicine, St. Louis, MO, USA
Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers worldwide. Bulk and single-cell technologies have recently been leveraged to better understand its genomic underpinnings. The PDAC tumor microenvironment (TME) has also been explored, revealing an immunosuppressive milieu. However, efforts to utilize TME features to facilitate more effective treatments have largely failed. Here, we performed single-cell RNA sequencing (scRNA-seq) on a cohort of treatment-naive PDAC biopsy samples (n=22) and surgical samples (n=6), integrated with 3 public datasets (n=49), resulting in ~150,000 individual cells from 77 patients. Based on expression markers and differentiation status, we divided the resulting tumor cellular clusters into 5 molecular subtypes: Basal, Mixed Basal/Classical, Less differentiated Classical, More differentiated Classical, and ADEX. We then queried these 5 tumor cell profiles along with 15 scRNA-seq-derived tumor microenvironmental cellular profiles in 391 bulk RNA-seq samples from 4 published datasets of localized PDAC with associated clinical metadata using CIBERSORTx. Through unsupervised clustering analysis of the resulting cell state fractions, we identified 7 unique clustering patterns, which we term communities, representing combinations of tumor cellular and microenvironmental cell states that are correlated with overall survival, tumor ecotypes, and tumor cellular differentiation status. Overall, discovered tumor microenvironmental communities from high-dimensional analysis of PDAC RNA sequencing data reveal new connections between tumor microenvironmental composition and patient survival that could lead to better upfront risk stratification and more personalized clinical decision-making.