Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression profiling of pancreatic ductal adenocarcinoma

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis of all common cancers, but divergent outcomes are apparent between patients. To delineate the intertumor heterogeneity that contributes to this, we aimed to identify clinically distinct gene expression-based subgroups. From a cohort of 345 resected pancreatic cancer cases, 90 samples with confirmed diagnosis of PDAC and sufficient tumor content were available for gene expression analysis by RNA sequencing. Unsupervised classification was applied, and a classifier was constructed. Species-specific transcript analysis on matching patient-derived xenografts (PDX, N=14) allowed construction of tumor- and stroma-specific classifiers for use on PDX models and cell lines.

INSTRUMENT(S): Illumina HiSeq 2500

ORGANISM(S): Homo sapiens

SUBMITTER: Maarten Bijlsma

PROVIDER: E-MTAB-6830 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

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

Dijk Frederike F Veenstra Veronique L VL Soer Eline C EC Dings Mark P G MPG Zhao Lan L Halfwerk Johannes B JB Hooijer Gerrit K GK Damhofer Helene H Marzano Marco M Steins Anne A Waasdorp Cynthia C Busch Olivier R OR Besselink Marc G MG Tol Johanna A JA Welling Lieke L van Rijssen Lennart B LB Klompmaker Sjors S Wilmink Hanneke W HW van Laarhoven Hanneke W HW Medema Jan Paul JP Vermeulen Louis L van Hooff Sander R SR Koster Jan J Verheij Joanne J van de Vijver Marc J MJ Wang Xin X Bijlsma Maarten F MF

Scientific reports 20200115 1

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-pr ...[more]

PMID: 31941932

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Project description:Engraftment of primary pancreas ductal adenocarcinomas (PDAC) in mice to generate patient derived xenograft (PDX) models is a promising platform to for biological and therapeutic studies in this disease. However, these models are still incompletely characterized. Here, we measured the impact of the murine environment on the gene expression of the engrafted human tumoral cells. We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published microarray data from PDAC and hepatocellular carcinoma (HCC). Our results showed that PDX models derived from PDAC, or HCC, were clearly different to the cell lines derived from the same cancer tissues. Indeed, PDAC- and HCC-derived cell lines are indistinguishable one from the other based in their gene expression profiles. In contrast, the transcriptomes of PDAC and HCC PDX models are clearly different and more similar to their original tumor than to PDX models from the other tumor type. Interestingly, the main differences between pancreatic PDX models and human PDAC is the expression of genes involved in pathways related with extracellular matrix interactions and cell cycle regulation likely reflecting the adaptations of the tumors to the new environment. Furthermore, most of these differences are detected in the first passages after the tumor engraftment, indicating early phases of the adaptation process. In conclusion, different from conventional cancer cell lines, PDX models of PDAC retain similar gene expression profiles of PDAC. Expression changes are mainly related to genes involved in stromal pathways likely reflecting the adaptation to new environments. We also provide evidence of the stability of gene expression patterns over subsequent passages. We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published in GEO microarray data. We used PDX models, primary tumors and cell lines from PDAC and hepatocellular carcinoma. All these public data were re-process in order to compare with our 35 samples

Dataset Information

Expression profiling of pancreatic ductal adenocarcinoma

Publications

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

Similar Datasets

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