Unsupervised class discovery in pancreatic ductal adenocarcinoma reveals cell-intrinsic mesenchymal features and high concordance between existing classification systems

Abstract

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis of all common cancers. However, divergent outcomes exist between patients, suggesting distinct underlying tumor biology. Here, we delineated this heterogeneity, compared interconnectivity between classification systems, and experimentally addressed the tumor biology that drives poor outcome. RNA-sequencing of 90 resected specimens and unsupervised classification revealed four subgroups associated with distinct outcomes. The worst-prognosis subtype was characterized by mesenchymal gene signatures. Comparative (network) analysis showed high interconnectivity with previously identified classification schemes and high robustness of the mesenchymal subtype. From species-specific transcript analysis of matching patient-derived xenografts we constructed dedicated classifiers for experimental models. Detailed assessments of tumor growth in subtyped experimental models revealed that a highly invasive growth pattern of mesenchymal subtype tumor cells is responsible for its poor outcome. Concluding, by developing a classification system tailored to experimental models, we have uncovered subtypespecific biology that should be further explored to improve treatment of a group of PDAC patients that currently has little therapeutic benefit from surgical treatment.

Microdissected pancreatic cancer proteomes reveal tumor heterogeneity and therapeutic targets

“…RNA-Seq of PDX models and correlation with protein expression. RNA-Seq of PDX was previously performed and described (EMBL-EBI ArrayExpress code E-MTAB-6830) (46). In short, total RNA was extracted from PDX model tissue and amplified with the Total Prep RNA amplification kit (Illumina).…”

Macrophage-secreted MMP9 induces mesenchymal transition in pancreatic cancer cells via PAR1 activation

“…Gene expression datasets were derived from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/gds) using the R2 microarray analysis and visualization platform (http://r2.amc.nl). Pancreatic tumor expression datasets (GSE62452 [28], GSE28735 [29], GSE15471 [30], TCGA-PDAC [31], E-MTAB-6830 [32], GSE93326 [33] and GSE49149 [34]) were used for expression analysis of PAR1 (F2R), CD68 and CD163. The datasets were dichotomized for F2R, CD68, or CD163 based on the median expression and further analyzed on the same platform.…”

Basal‐like and classical cells coexist in pancreatic cancer revealed by single‐cell analysis on biopsy‐derived pancreatic cancer organoids from the classical subtype

“…2,3 Recently, two main PDAC subtypes have been identified by molecular characterization: a classical subtype, which is more frequently resectable, presents with a higher level of differentiation, often associated with fibrosis and inflammation and a basal-like subtype presents with a poorer clinical outcome and loss of differentiation. 4,5 While this binary classification is well accepted, some groups proposed novel subclasses, [6][7][8] often subdividing the classical subtype that represent the largest subgroup (approximately 80%) of PDAC. These well-established classifications may be inadequate and fail to well describe PDAC heterogeneity if tumors contain many cellular phenotypes at the same time.…”