학술논문
Comprehensive transcriptomic analysis to identify biological and clinical differences in cholangiocarcinoma.
Document Type
Academic Journal
Author
Silvestri M; Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.; Unit of Biostatistics, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.; Nghia Vu T; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.; Nichetti F; Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.; Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany.; Niger M; Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.; Di Cosimo S; Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.; De Braud F; Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.; Pruneri G; Department Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.; Pawitan Y; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.; Calza S; Unit of Biostatistics, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.; Cappelletti V; Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.
Source
Publisher: John Wiley & Sons Ltd Country of Publication: United States NLM ID: 101595310 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-7634 (Electronic) Linking ISSN: 20457634 NLM ISO Abbreviation: Cancer Med Subsets: MEDLINE
Subject
Language
English
Abstract
Background: Cholangiocarcinoma (CC) is a rare and aggressive disease with limited therapeutic options and a poor prognosis. All available public records of cohorts reporting transcriptomic data on intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) were collected with the aim to provide a comprehensive gene expression-based classification with clinical relevance.
Methods: A total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques.
Results: By unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis.
Conclusions: The developed class-predictor allows identification of CC subgroups with specific biological features and clinical behavior at single-sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice.
(© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)
Methods: A total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques.
Results: By unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis.
Conclusions: The developed class-predictor allows identification of CC subgroups with specific biological features and clinical behavior at single-sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice.
(© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)