- •The Cancer Genome Atlas (TCGA) analysis proposed multi-omics classifications for hepato-pancreato-biliary (HPB) cancers.
- •Status of 649 genes from whole-exome sequencing of about 2700 patients was analysed.
- •Artificial neural networks (ANNs) were developed to identify TCGA classifications.
- •ANNs proved to precisely predict TCGA cell-of-origin patterns and molecular subtypes.
- •TCGA classifications improved the ability to predict the prognosis of patients with HPB.
Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts.
Patients and methods
Data from whole-exome sequencing (WES) of The Cancer Genome Atlas (TCGA) patients were used as an input for the artificial neural network (ANN) to predict the anatomical site, iClusters (cell-of-origin patterns) and molecular subtype classifications. The Ohio State University (OSU) and the International Cancer Genome Consortium (ICGC) patients with HPB cancer were included in external validation cohorts. TCGA, OSU and ICGC data were merged, and survival analyses were performed using both the ‘classic’ survival analysis and a machine learning algorithm (random survival forest).
Although the ANN predicting the anatomical site of the tumour (i.e. cholangiocarcinoma, hepatocellular carcinoma of the liver, pancreatic ductal adenocarcinoma) demonstrated a low accuracy in TCGA test cohort, the ANNs predicting the iClusters (cell-of-origin patterns) and molecular subtype classifications demonstrated a good accuracy of 75% and 82% in TCGA test cohort, respectively. The random survival forest analysis and Cox’ multivariable survival models demonstrated that models for HPB cancers that integrated clinical data with molecular classifications (iClusters, molecular subtypes) had an increased prognostic accuracy compared with standard staging systems.
The analyses of genetic status (i.e. WES, gene panels) of patients with HPB cancers might predict the classifications proposed by TCGA project and help to select patients suitable to targeted therapies. The molecular classifications of HPB cancers when integrated with clinical information could improve the ability to predict the prognosis of patients with HPB cancer.
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- Cell-of-Origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer.Cell. 2018; 173: 291-304 e6
- Cells of origin in cancer.Nature. 2011; 469: 314-322
- The cancer genome Atlas pan-cancer analysis project.Nat Genet. 2013; 45: 1113-1120
- Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.Cell. 2014; 158: 929-944
- International network of cancer genome projects.Nature. 2010; 464: 993-998
- Pan-cancer analysis of whole genomes.Nature. 2020; 578: 82-93
- Comprehensive characterization of cancer driver genes and mutations.Cell. 2018; 173 (371-85 e18)
- Oncogenic signaling pathways in the cancer genome Atlas.Cell. 2018; 173 (321-37 e10)
- Integrative genomic analysis of cholangiocarcinoma identifies distinct IDH-mutant molecular profiles.Cell Rep. 2017; 18: 2780-2794
- Electronic address wbe, cancer genome Atlas Research N. Comprehensive and integrative genomic characterization of hepatocellular carcinoma.Cell. 2017; 169 (e23): 1327-1341
- Electronic address aadhe, cancer genome Atlas Research N. Integrated genomic characterization of pancreatic ductal adenocarcinoma.Canc Cell. 2017; 32 (e13): 185-203
- Exploring the host desmoplastic response to pancreatic carcinoma: gene expression of stromal and neoplastic cells at the site of primary invasion.Am J Pathol. 2002; 160: 91-99
- Pathology and molecular genetics of pancreatic neoplasms.Canc J. 2012; 18: 492-501
- Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines.Cell Syst. 2018; 6 (e7): 271-281
- Ohio supercomputer center.http://osc.edu/ark:/19495/f5s1ph73Date accessed: January 18, 2021
- Explaining cancer type specific mutations with transcriptomic and epigenomic features in normal tissues.Sci Rep. 2018; 8: 11456
- Mutational heterogeneity in cancer and the search for new cancer-associated genes.Nature. 2013; 499: 214-218
- Cell-of-origin chromatin organization shapes the mutational landscape of cancer.Nature. 2015; 518: 360-364
- Anatomical, histomorphological and molecular classification of cholangiocarcinoma.Liver Int. 2019; 39: 7-18
- Pathologic classification of cholangiocarcinoma: new concepts.Best Pract Res Clin Gastroenterol. 2015; 29: 277-293
- New horizons for precision medicine in biliary tract cancers.Canc Discov. 2017; 7: 943-962
- The limitations of standard clinicopathologic features to accurately risk-stratify prognosis after resection of intrahepatic cholangiocarcinoma.J Gastrointest Surg. 2018; 22: 477-485
- Identification of prognostic markers in cholangiocarcinoma using altered DNA methylation and gene expression profiles.Front Genet. 2020; 11: 522125
- Comprehensive molecular and immunological characterization of hepatocellular carcinoma.EBioMedicine. 2019; 40: 457-470
- Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling.Hepatology. 2004; 40: 667-676
- Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma.Canc Res. 2009; 69: 7385-7392
- Transcriptome classification of HCC is related to gene alterations and to new therapeutic targets.Hepatology. 2007; 45: 42-52
- Focal gains of VEGFA and molecular classification of hepatocellular carcinoma.Canc Res. 2008; 68: 6779-6788
- TRIM25 promotes the cell survival and growth of hepatocellular carcinoma through targeting Keap1-Nrf2 pathway.Nat Commun. 2020; 11: 348
- Dysregulation of Nrf2 in hepatocellular carcinoma: role in cancer progression and chemoresistance.Cancers. 2018; 10
- Molecular subtyping of hepatocellular carcinoma: a step toward precision medicine.Canc Commun. 2020; 40: 681-693
- Liver cancer: translating '-omics' results into precision medicine for hepatocellular carcinoma.Nat Rev Gastroenterol Hepatol. 2017; 14: 571-572
- Role of epigenetic aberrations in the development and progression of human hepatocellular carcinoma.Canc Lett. 2014; 342: 223-230
- Histo-molecular oncogenesis of pancreatic cancer: from precancerous lesions to invasive ductal adenocarcinoma.World J Gastrointest Oncol. 2018; 10: 317-327
- Pancreatic intraepithelial neoplasia: a new nomenclature and classification system for pancreatic duct lesions.Am J Surg Pathol. 2001; 25: 579-586
- Molecular genetics of pancreatic intraepithelial neoplasia.J Hepatobiliary Pancreat Surg. 2007; 14: 224-232
- Pancreatic ductal adenocarcinoma and its variants.Surg Pathol Clin. 2016; 9: 547-560
- From genetic alterations to tumor microenvironment: the ariadne's string in pancreatic cancer.Cells. 2020; 9
- Cell of origin affects tumour development and phenotype in pancreatic ductal adenocarcinoma.Gut. 2019; 68: 487-498
- Genomic analyses identify molecular subtypes of pancreatic cancer.Nature. 2016; 531: 47-52
- Real-time targeted genome profile Analysis of pancreatic ductal adenocarcinomas identifies genetic alterations that might Be targeted with existing drugs or used as biomarkers.Gastroenterology. 2019; 156: 2242-22453 e4
- Comprehensive characterisation of pancreatic ductal adenocarcinoma with microsatellite instability: histology, molecular pathology and clinical implications.Gut. 2021; 70: 148-156
Published online: March 26, 2021
Accepted: January 29, 2021
Received in revised form: January 20, 2021
Received: October 23, 2020
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