Abstract 477: High-dimensional analysis to deconstruct pancreatic ductal adenocarcinoma and identify tumor cellular communities with prognostic and potentially predictive value
Authors: Storrs Erik P. , Usmani Abul , Chati Prathamesh , Krasnick Bradley , Ding Li , Fields Ryan C. , Das Koushik K. , Chaudhuri Aadel A. 
Published: June 2022

Abstract
Introduction: 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.
Methods: 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 assessed by Seurat v3 and differentiation status assessed by CytoTRACE, 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 these 20 cell state fractions representing tumor, leukocyte and stromal cells, we identified 7 unique clustering patterns representing combinations of tumor cellular and microenvironmental cell states present in PDAC tumors, which we termed communities, and correlated these with survival, tumor ecotypes, and tumor cellular differentiation status.
Results: We identified 7 distinct cellular communities in bulk RNA sequencing data after CIBERSORTx and unsupervised clustering. The community associated with worst overall survival contained basal tumor cells, exhausted CD4 and CD8 T cells, and was enriched for fibroblasts. In contrast, highest overall survival was associated with tumors enriched for differentiated classical tumor cells, NK cells and endothelial cells. The differentiation state of tumor cells (assessed by CytoTRACE) also correlated with survival in a dose-dependent fashion. We corroborated the community structures we identified with our unsupervised clustering approach with ecotypes obtained using Ecotyper and observed a significant correlation. We further identified a subset of PDAC samples that were significantly enriched for activated CD8 T cells that achieved a 3-year overall survival rate of 40%, suggesting we can identify PDAC patients with improved prognoses and with potentially higher sensitivity to immunotherapy.
Conclusion: Discovered cellular communities from tumor bulk RNA-seq shed new insight into the composition of PDAC and could pave the way towards better upfront risk stratification and more personalized tumor biology-driven clinical decision-making.
Citation Format: Erik P. Storrs, Abul Usmani, Prathamesh Chati, Bradley Krasnick, Li Ding, Ryan C. Fields, Koushik K. Das, Aadel A. Chaudhuri. High-dimensional analysis to deconstruct pancreatic ductal adenocarcinoma and identify tumor cellular communities with prognostic and potentially predictive value [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 477.