Molecular Classification and Tumor Microenvironment Characterization of Gallbladder Cancer by Comprehensive Genomic and Transcriptomic Analysis

Abstract

Gallbladder cancer (GBC), a rare but lethal disease, is often diagnosed at advanced stages. So far, molecular characterization of GBC is insufficient, and a comprehensive molecular portrait is warranted to uncover new targets and classify GBC. We performed a transcriptome analysis of both coding and non-coding RNAs from 36 GBC fresh-frozen samples. The results were integrated with those of comprehensive mutation profiling based on whole-genome or exome sequencing. The clustering analysis of RNA-seq data facilitated the classification of GBCs into two subclasses, characterized by high or low expression levels of TME (tumor microenvironment) genes. A correlation was observed between gene expression and pathological immunostaining. TME-rich tumors showed significantly poor prognosis and higher recurrence rate than TME-poor tumors. TME-rich tumors showed overexpression of genes involved in epithelial-to-mesenchymal transition (EMT) and inflammation or immune suppression, which was validated by immunostaining. One non-coding RNA, miR125B1, exhibited elevated expression in stroma-rich tumors, and miR125B1 knockout in GBC cell lines decreased its invasion ability and altered the EMT pathway. Mutation profiles revealed TP53 (47%) as the most commonly mutated gene, followed by ELF3 (13%) and ARID1A (11%). Mutations of ARID1A, ERBB3, and the genes related to the TGF-β signaling pathway were enriched in TME-rich tumors. This comprehensive analysis demonstrated that TME, EMT, and TGF-β pathway alterations are the main drivers of GBC and provides a new classification of GBCs that may be useful for therapeutic decision-making.

Gallbladder Cancer: Current Insights in Genetic Alterations and Their Possible Therapeutic Implications

“…Out of 92 articles, 30 were excluded due to: no data for adenocarcinoma of the gallbladder available (N = 12), overlapping cohorts (N = 8), the wrong type of article (N = 5), no genetic alteration frequency data available (N = 3), wrong study population (N = 1), and not written in English (N = 1). A total of 62 articles were included for final data extraction, describing a total of 3893 GBC samples from individual patients [ 18 , 19 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ,…”

“… Korea Chae 2019 [ 79 ] 3/54 6% NGS AC Greece Papadopoulou 2018 [ 40 ] 3/39 8% WES/WGS N.A. Japan Ebata 2021 [ 57 ] 19/144 13% WES AC India, Korea, Chile Pandey 2020 [ 46 ] 2/12 17% NGS N.A. China Li 2020 [ 47 ] 7/58 12% …”

“… America Abdel-Wahab 2020 [ 19 ] ELF3 18.6% 29/144 20% WES AC India, Korea, Chile Pandey 2020 [ 46 ] 5/39 13% WES/WGS N.A. Japan Ebata 2021 [ 57 ] ERBB2 6.7% 0/21 0% SNaPshot AC America Moy 2015 [ 53 ] 0/46 0% mass array AC India Kumari 2014 [ 68 ] 0/4 …”

“… America Okamura 2021 [ 27 ] 3/39 8% WES/WGS N.A. Japan Ebata 2021 [ 57 ] 4/47 9% WES AC China Yang 2021 [ 56 ] 22/144 15% WES AC India, Korea, Chile Pandey 2020 [ 46 ] 6/111 5% NGS N.A. America Mondaca 2019 [ 38 ] 5/12 42% …”

“… America Okamura 2021 [ 27 ] 0/32 0% targeted NGS AC Chile, Japan Narayan 2019 [ 73 ] 3/39 8% WES/WGS N.A. Japan Ebata 2021 [ 57 ] 4/47 9% WES AC China Yang 2021 [ 56 ] 3/50 6% NGS N.A. India Patel 2020 [ 39 ] 3/54 6% …”