Abstract B41: Compartment deconvolution in pancreatic cancer with biologic and clinical implications

Pancreatic ductal adenocarcinoma (PDAC) is characterized by relatively low tumor purity and an abundant tumor microenvironment. To dissect the contribution of the biologic components, we developed DECODER, which performs de novo compartment deconvolution and weight estimation of tumor samples. DECODER is a sophisticated framework that integrates runs of non-negative matrix factorization (NMF) and non-negative least square (NNLS) algorithms and can be applied to any non-negative matrices without the need to know the number of resultant factors or compartments. DECODER was used to deconvolve the TCGA pancreatic adenocarcinoma (PAAD) RNA-seq dataset, which resulted in the identification of 7 major compartments (basal tumor, classical tumor, activated stroma, normal stroma, immune, endocrine, and exocrine), confirming prior manual NMF-based solutions. These results were then used for single-sample based weight estimation in the COMPASS trial and ICGC PACA-AU RNA-seq dataset. We saw a significant positive correlation between DECODER immune weight and leukocyte fraction (r = 0.757, p Citation Format: Xianlu L. Peng, Richard A. Moffitt, Robert J. Torphy, Keith E. Volmar, Jen Jen Yeh. Compartment deconvolution in pancreatic cancer with biologic and clinical implications [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr B41.