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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
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.
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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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