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Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among common cancers. The genomic landscape of PDAC is defined by four mutational pathways: kirsten rat sarcoma virus (KRAS), cellular tumor antigen p53 (TP53), cyclin dependent kinase inhibitor 2A (CDKN2A), and SMAD family...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404641/ https://www.ncbi.nlm.nih.gov/pubmed/34471425 http://dx.doi.org/10.1177/17588359211038478 |
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author | Hong, Jung Yong Cho, Hee Jin Kim, Seung Tae Park, Young Suk Shin, Sang Hyun Han, In Woong Lee, Jeeyun Heo, Jin Seok Park, Joon Oh |
author_facet | Hong, Jung Yong Cho, Hee Jin Kim, Seung Tae Park, Young Suk Shin, Sang Hyun Han, In Woong Lee, Jeeyun Heo, Jin Seok Park, Joon Oh |
author_sort | Hong, Jung Yong |
collection | PubMed |
description | BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among common cancers. The genomic landscape of PDAC is defined by four mutational pathways: kirsten rat sarcoma virus (KRAS), cellular tumor antigen p53 (TP53), cyclin dependent kinase inhibitor 2A (CDKN2A), and SMAD family member 4 (SMAD4). However, there is a paucity of data on the molecular features associated with clinical outcomes after surgery or chemotherapy. METHODS: We performed comprehensive molecular characterization of tumor specimens from 83 patients with PDAC who received surgery, using whole-exome sequencing and ribonucleic acid sequencing on tumor and matched normal tissues derived from patients. We also systematically performed integrative analysis, combining genomic, transcriptomic, and clinical features to identify biomarkers and possible therapeutic targets. RESULTS: KRAS (75%), TP53 (67%), CDKN2A (12%), SMAD4 (20%), and ring finger protein 43 (RNF43) (13%) were identified as significantly mutated genes. The tumor-specific transcriptome was classified into two clusters (tumor S1 and tumor S2), which resembled the Moffitt tumor classification. Tumor S1 displayed two distinct subclusters (S1-1 and S1-2). The transcriptome of tumor S1-1 overlapped with the exocrine-like (Collisson)/ADEX (Bailey) subtype, while tumor S1-2 mostly consisted of the classical (Collisson)/progenitor (Bailey) subtype. In the analysis of combinatorial gene alterations, concomitant mutations of KRAS with low-density lipoprotein receptor related protein 1B (LRP1B) were associated with significantly worse disease-free survival after surgery (p = 0.034). One patient (1.2%) was an ultrahypermutant with microsatellite instability. We also identified high protein kinase C lota (PRKCI) expression as an overlapping, poor prognostic marker between our dataset and the TCGA dataset. CONCLUSION: We identified potential prognostic biomarkers and therapeutic targets of patients with PDAC. Understanding these molecular aberrations that determine patient outcomes after surgery and chemotherapy has the potential to improve the treatment outcomes of PDAC patients. |
format | Online Article Text |
id | pubmed-8404641 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-84046412021-08-31 Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer Hong, Jung Yong Cho, Hee Jin Kim, Seung Tae Park, Young Suk Shin, Sang Hyun Han, In Woong Lee, Jeeyun Heo, Jin Seok Park, Joon Oh Ther Adv Med Oncol Original Research BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among common cancers. The genomic landscape of PDAC is defined by four mutational pathways: kirsten rat sarcoma virus (KRAS), cellular tumor antigen p53 (TP53), cyclin dependent kinase inhibitor 2A (CDKN2A), and SMAD family member 4 (SMAD4). However, there is a paucity of data on the molecular features associated with clinical outcomes after surgery or chemotherapy. METHODS: We performed comprehensive molecular characterization of tumor specimens from 83 patients with PDAC who received surgery, using whole-exome sequencing and ribonucleic acid sequencing on tumor and matched normal tissues derived from patients. We also systematically performed integrative analysis, combining genomic, transcriptomic, and clinical features to identify biomarkers and possible therapeutic targets. RESULTS: KRAS (75%), TP53 (67%), CDKN2A (12%), SMAD4 (20%), and ring finger protein 43 (RNF43) (13%) were identified as significantly mutated genes. The tumor-specific transcriptome was classified into two clusters (tumor S1 and tumor S2), which resembled the Moffitt tumor classification. Tumor S1 displayed two distinct subclusters (S1-1 and S1-2). The transcriptome of tumor S1-1 overlapped with the exocrine-like (Collisson)/ADEX (Bailey) subtype, while tumor S1-2 mostly consisted of the classical (Collisson)/progenitor (Bailey) subtype. In the analysis of combinatorial gene alterations, concomitant mutations of KRAS with low-density lipoprotein receptor related protein 1B (LRP1B) were associated with significantly worse disease-free survival after surgery (p = 0.034). One patient (1.2%) was an ultrahypermutant with microsatellite instability. We also identified high protein kinase C lota (PRKCI) expression as an overlapping, poor prognostic marker between our dataset and the TCGA dataset. CONCLUSION: We identified potential prognostic biomarkers and therapeutic targets of patients with PDAC. Understanding these molecular aberrations that determine patient outcomes after surgery and chemotherapy has the potential to improve the treatment outcomes of PDAC patients. SAGE Publications 2021-08-28 /pmc/articles/PMC8404641/ /pubmed/34471425 http://dx.doi.org/10.1177/17588359211038478 Text en © The Author(s), 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Hong, Jung Yong Cho, Hee Jin Kim, Seung Tae Park, Young Suk Shin, Sang Hyun Han, In Woong Lee, Jeeyun Heo, Jin Seok Park, Joon Oh Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer |
title | Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer |
title_full | Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer |
title_fullStr | Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer |
title_full_unstemmed | Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer |
title_short | Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer |
title_sort | comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404641/ https://www.ncbi.nlm.nih.gov/pubmed/34471425 http://dx.doi.org/10.1177/17588359211038478 |
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