Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model

• Type of Material: Article
• Authors: Luke A Torre-Healy, Stony Brook Medicine; Ryan R Kawalerski, Stony Brook Medicine; Ki Oh, Stony Brook Medicine; Lucie Chrastecka, Stony Brook Med; Xianlu L Peng, University of North Carolina; Andrew J Aguirre, Dana-Farber Cancer Institute; Naim U Rashid, University of North Carolina; Jen Jen Yeh, University of North Carolina; Richard Moffitt, Emory University
• Language: English
• Date: 2023-02-10
• Publisher: NATURE PORTFOLIO
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s42003-023-04461-6
• Title of Journal or Parent Work: COMMUNICATIONS BIOLOGY
• Volume: 6
• Issue: 1
• Start Page: 163
• End Page: 163
• Keywords: Life Sciences & Biomedicine - Other Topics; TUMOR; MOLECULAR SUBTYPES; Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; Life Sciences & Biomedicine; Biology
• Research Categories: Health Sciences, Pathology; Biology, Biostatistics; Health Sciences, Medicine and Surgery

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR (http://pdacR.bmi.stonybrook.edu, github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.