Comprehensive transcriptomic analysis to identify biological and clinical differences in cholangiocarcinoma

Author:

Silvestri Marco12ORCID,Nghia Vu Trung3,Nichetti Federico45,Niger Monica4,Di Cosimo Serena1ORCID,De Braud Filippo4,Pruneri Giancarlo6,Pawitan Yudi3,Calza Stefano2,Cappelletti Vera1ORCID

Affiliation:

1. Department of Applied Research and Technological Development Fondazione IRCCS Istituto Nazionale dei Tumori di Milano Milan Italy

2. Unit of Biostatistics, Department of Molecular and Translational Medicine University of Brescia Brescia Italy

3. Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden

4. Department of Medical Oncology Fondazione IRCCS Istituto Nazionale dei Tumori di Milano Milan Italy

5. Computational Oncology Group, Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ) Heidelberg Germany

6. Department Pathology and Laboratory Medicine Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy

Abstract

AbstractBackgroundCholangiocarcinoma (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.MethodsA 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.ResultsBy 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.ConclusionsThe 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.

Funder

Associazione Italiana per la Ricerca sul Cancro

Publisher

Wiley

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology

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