Cargando…

Pancreatic cancer survival analysis defines a signature that predicts outcome

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the US. Despite multiple large-scale genetic sequencing studies, identification of predictors of patient survival remains challenging. We performed a comprehensive assessment and integrative analysis of large-scale...

Descripción completa

Detalles Bibliográficos
Autores principales: Raman, Pichai, Maddipati, Ravikanth, Lim, Kian Huat, Tozeren, Aydin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084949/
https://www.ncbi.nlm.nih.gov/pubmed/30092011
http://dx.doi.org/10.1371/journal.pone.0201751
_version_ 1783346256165208064
author Raman, Pichai
Maddipati, Ravikanth
Lim, Kian Huat
Tozeren, Aydin
author_facet Raman, Pichai
Maddipati, Ravikanth
Lim, Kian Huat
Tozeren, Aydin
author_sort Raman, Pichai
collection PubMed
description Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the US. Despite multiple large-scale genetic sequencing studies, identification of predictors of patient survival remains challenging. We performed a comprehensive assessment and integrative analysis of large-scale gene expression datasets, across multiple platforms, to enable discovery of a prognostic gene signature for patient survival in pancreatic cancer. PDAC RNA-Sequencing data from The Cancer Genome Atlas was stratified into Survival+ (>2-year survival) and Survival–(<1-year survival) cohorts (n = 47). Comparisons of RNA expression profiles between survival groups and normal pancreatic tissue expression data from the Gene Expression Omnibus generated an initial PDAC specific prognostic differential expression gene list. The candidate prognostic gene list was then trained on the Australian pancreatic cancer dataset from the ICGC database (n = 103), using iterative sampling based algorithms, to derive a gene signature predictive of patient survival. The gene signature was validated in 2 independent patient cohorts and against existing PDAC subtype classifications. We identified 707 candidate prognostic genes exhibiting differential expression in tumor versus normal tissue. A substantial fraction of these genes was also found to be differentially methylated between survival groups. From the candidate gene list, a 5-gene signature (ADM, ASPM, DCBLD2, E2F7, and KRT6A) was identified. Our signature demonstrated significant power to predict patient survival in two distinct patient cohorts and was independent of AJCC TNM staging. Cross-validation of our gene signature reported a better ROC AUC (≥ 0.8) when compared to existing PDAC survival signatures. Furthermore, validation of our signature through immunohistochemical analysis of patient tumor tissue and existing gene expression subtyping data in PDAC, demonstrated a correlation to the presence of vascular invasion and the aggressive squamous tumor subtype. Assessment of these genes in patient biopsies could help further inform risk-stratification and treatment decisions in pancreatic cancer.
format Online
Article
Text
id pubmed-6084949
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-60849492018-08-18 Pancreatic cancer survival analysis defines a signature that predicts outcome Raman, Pichai Maddipati, Ravikanth Lim, Kian Huat Tozeren, Aydin PLoS One Research Article Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the US. Despite multiple large-scale genetic sequencing studies, identification of predictors of patient survival remains challenging. We performed a comprehensive assessment and integrative analysis of large-scale gene expression datasets, across multiple platforms, to enable discovery of a prognostic gene signature for patient survival in pancreatic cancer. PDAC RNA-Sequencing data from The Cancer Genome Atlas was stratified into Survival+ (>2-year survival) and Survival–(<1-year survival) cohorts (n = 47). Comparisons of RNA expression profiles between survival groups and normal pancreatic tissue expression data from the Gene Expression Omnibus generated an initial PDAC specific prognostic differential expression gene list. The candidate prognostic gene list was then trained on the Australian pancreatic cancer dataset from the ICGC database (n = 103), using iterative sampling based algorithms, to derive a gene signature predictive of patient survival. The gene signature was validated in 2 independent patient cohorts and against existing PDAC subtype classifications. We identified 707 candidate prognostic genes exhibiting differential expression in tumor versus normal tissue. A substantial fraction of these genes was also found to be differentially methylated between survival groups. From the candidate gene list, a 5-gene signature (ADM, ASPM, DCBLD2, E2F7, and KRT6A) was identified. Our signature demonstrated significant power to predict patient survival in two distinct patient cohorts and was independent of AJCC TNM staging. Cross-validation of our gene signature reported a better ROC AUC (≥ 0.8) when compared to existing PDAC survival signatures. Furthermore, validation of our signature through immunohistochemical analysis of patient tumor tissue and existing gene expression subtyping data in PDAC, demonstrated a correlation to the presence of vascular invasion and the aggressive squamous tumor subtype. Assessment of these genes in patient biopsies could help further inform risk-stratification and treatment decisions in pancreatic cancer. Public Library of Science 2018-08-09 /pmc/articles/PMC6084949/ /pubmed/30092011 http://dx.doi.org/10.1371/journal.pone.0201751 Text en © 2018 Raman et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Raman, Pichai
Maddipati, Ravikanth
Lim, Kian Huat
Tozeren, Aydin
Pancreatic cancer survival analysis defines a signature that predicts outcome
title Pancreatic cancer survival analysis defines a signature that predicts outcome
title_full Pancreatic cancer survival analysis defines a signature that predicts outcome
title_fullStr Pancreatic cancer survival analysis defines a signature that predicts outcome
title_full_unstemmed Pancreatic cancer survival analysis defines a signature that predicts outcome
title_short Pancreatic cancer survival analysis defines a signature that predicts outcome
title_sort pancreatic cancer survival analysis defines a signature that predicts outcome
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084949/
https://www.ncbi.nlm.nih.gov/pubmed/30092011
http://dx.doi.org/10.1371/journal.pone.0201751
work_keys_str_mv AT ramanpichai pancreaticcancersurvivalanalysisdefinesasignaturethatpredictsoutcome
AT maddipatiravikanth pancreaticcancersurvivalanalysisdefinesasignaturethatpredictsoutcome
AT limkianhuat pancreaticcancersurvivalanalysisdefinesasignaturethatpredictsoutcome
AT tozerenaydin pancreaticcancersurvivalanalysisdefinesasignaturethatpredictsoutcome