Charting co-mutation patterns associated with actionable drivers in intrahepatic cholangiocarcinoma.

Autor: Kendre G; Dept. of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany., Murugesan K; Foundation Medicine, Inc., Cambridge, MA, USA., Brummer T; Institute of Molecular Medicine and Cell Research, ZBMZ, Faculty of Medicine, University of Freiburg, 79104, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany; Comprehensive Cancer Center Freiburg (CCCF), Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany; Center for Biological Signalling Studies BIOSS, University of Freiburg, 79104, Freiburg, Germany., Segatto O; Translational Oncology Research Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy., Saborowski A; Dept. of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany. Electronic address: saborowski.anna@mh-hannover.de., Vogel A; Dept. of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany. Electronic address: Vogel.Arndt@mh-hannover.de.
Jazyk: angličtina
Zdroj: Journal of hepatology [J Hepatol] 2023 Mar; Vol. 78 (3), pp. 614-626. Date of Electronic Publication: 2022 Dec 15.
DOI: 10.1016/j.jhep.2022.11.030
Abstrakt: Background & Aims: In recent years, intrahepatic cholangiocarcinoma (iCCA) has evolved as a "role model" for precision oncology in gastrointestinal cancers. However, its rarity, paired with its genomic heterogeneity, challenges the development and evolution of targeted therapies. Interrogating large datasets drives better understanding of the characteristics of molecular subgroups of rare cancers and enables the identification of genomic patterns that remain unrecognized in smaller cohorts.
Methods: We performed a retrospective analysis of 6,130 patients diagnosed with iCCA from the FoundationCORE database who received diagnostic panel sequencing on the FoundationOne platform. Short variants/fusion-rearrangements and copy number alterations in >300 tumor-associated genes were evaluated, and the tumor mutational burden (TMB) as well as the microsatellite instability (MSI) status were available for the majority of the cohort.
Results: We provide a highly representative cartography of the genomic landscape of iCCA and outline the co-mutational spectra of seven therapeutically relevant oncogenic driver genes: IDH1/2, FGFR2, ERBB2, BRAF, MDM2, BRCA1/2, MET and KRAS G12C . We observed a negative selection of RTK/RAS/ERK pathway co-alterations, and an enrichment of epigenetic modifiers such as ARID1A and BAP1 in patients with IDH1/2 and FGFR2 alterations. RNF43 as well as KMT2D occurred with high frequency in MSI high and TMB high tumors.
Conclusion: Detailed knowledge of the most prevalent genomic constellations is key to the development of effective treatment strategies for iCCA. Our study provides a valuable resource that could be used to assess the feasibility of clinical trials and subgroup analyses, spurs the development of translationally relevant preclinical models, and serves as a knowledge base to predict potential mechanisms of resistance to targeted therapies in genomically defined subgroups.
Impact and Implications: Due to the high frequency of targetable alterations, molecular diagnostics is recommended in patients with biliary tract cancers, and especially in those with iCCA. The identification of an actionable lesion, however, does not guarantee therapeutic success, and the co-mutational spectrum may act as a critical modifier of drug response. Using a large dataset of comprehensive panel sequencing results from 6,130 patients with iCCA, we provide a detailed analysis of the co-mutational spectrum of the most frequent druggable genetic alterations, which is meant to serve as a reference to establish genetically relevant preclinical models, develop hypothesis-driven combination therapies and identify recurrent genetic profiles.
(Copyright © 2022. Published by Elsevier B.V.)
Databáze: MEDLINE
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