Abstract 159: Pancreatic ductal adenocarcinoma developmental cell state signatures identified by single cell RNA sequencing are prognostic when applied to bulk RNA-seq data
Authors: Chati Prathamesh Mandar , Storrs Erik , Usmani Abul , Krasnick Bradley , Wetzel Chris , Hollander Thomas , Quium Faridi , Sloan Ian , Anthony Hephzibah , Shahed Badiyan , Lang Gabriel D. , Cosgrove Natalie D. , Kushnir Vladimir M. , Early Dayna S. , Hawkins William G. , Ding Li , Fields Ryan C. , Das Koushik K. , Chaudhuri Aadel A. 
Published: July 2021

Abstract
INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer type with a poor prognosis. Patients with the classical histologic subtype typically have a better prognosis than those with a squamous-like histology. Still, survival outcomes vary significantly, even in early-stage patients, making it challenging to personalize treatment via subtyping. Here, we utilize CytoTRACE to better classify PDAC based on tumor cell-intrinsic developmental states, to more accurately prognosticate patients from the time of initial surgical resection.
METHODS: We performed core needle pancreatic biopsies in 13 patients and surgical PDAC resections in five patients, and analyzed the resulting single-cell RNA sequencing (scRNA-seq) data to identify tumor cell clusters. We then applied CytoTRACE for developmental state analysis. Following developmental state quantification, we classified PDAC tumor cells into 3 distinct subtypes: squamous-like, classical early developmental (ED), and classical late developmental (LD). We developed a gene signature for each subtype, which we then applied to two external bulk RNA-seq datasets - 1) The Cancer Genome Atlas (TCGA): 125 early-stage PDAC tumors, and 2) Bailey et al (Nature 2016): 86 predominantly early-stage PDAC tumors.
RESULTS: scRNA-seq data was partitioned into two subtypes, classical and squamous-like, based on marker gene expression. The classical subtype was further partitioned into ED versus LD cell states using the developmental index from CytoTRACE. For the squamous-like group, we identified the top 20 differentially expressed genes (squamous-like gene signature). For the ED and LD subtypes, we identified the top 20 genes correlating with the CytoTRACE developmental index (ED gene signature). Using a multivariate cox proportional hazards regression, we showed that the squamous-like signature was associated with significantly worse overall survival in TCGA (HR = 6.8, P = .01). Strikingly, our newly derived ED cell state signature was also associated with inferior overall survival in TCGA (HR = 5.9, P = .02). Kaplan-Meier analysis using optimized cutpoints between squamous-like and classical subtype scores, and between ED and LD cell state scores, again showed that patients with predominantly squamous-like tumors had significantly worse survival (HR = 4.4, P = .04); and that predominantly classical tumors enriched for the ED cell state had significantly inferior overall survival compared to LD (median 15.0 vs. 22.0 months, HR = 4.6, P = .03). The same trends were observed in the less-powered Bailey et al cohort.
CONCLUSION: We showed that three developmental cell states, learned through the analysis of PDAC scRNA-seq data, can prognosticate patients with bulk RNA-seq expression data. This could help facilitate more personalized risk-adapted approaches for PDAC in the future.
Citation Format: Prathamesh Mandar Chati, Erik Storrs, Abul Usmani, Bradley Krasnick, Chris Wetzel, Thomas Hollander, Faridi Quium, Ian Sloan, Hephzibah Anthony, Badiyan Shahed, Gabriel D. Lang, Natalie D. Cosgrove, Vladimir M. Kushnir, Dayna S. Early, William G. Hawkins, Li Ding, Ryan C. Fields, Koushik K. Das, Aadel A. Chaudhuri. Pancreatic ductal adenocarcinoma developmental cell state signatures identified by single cell RNA sequencing are prognostic when applied to bulk RNA-seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 159.