Genomic alterations in biliary tract cancer predict prognosis and immunotherapy outcomes

Abstract

Background: Recently, immunotherapy with immune checkpoint inhibitors (ICIs) has shown promising efficacy in biliary tract cancer (BTC), which includes gallbladder cancer (GBC) and cholangiocarcinoma (CHOL). Understanding the association between immunotherapy outcomes and the genomic profile of advanced BTC may further improve the clinical benefits from immunotherapy.

Methods: Genomic tumor DNA was isolated from 98 Chinese patients with advanced BTC and used for targeted next-generation sequencing of 416 cancer-related genes to identify the genomic alterations common to advanced BTC. Thirty-four patients had received ICI camrelizumab plus gemcitabine and oxaliplatin (from the NCT03486678 trial) as a first-line treatment. Tumor-infiltrating immune cells were evaluated using immunofluorescence staining.

Results: KRAS and TP53 mutations were much more frequent in the advanced-stage BTC cohort than in other cohorts with mostly early stage disease. Specifically, KRAS-TP53 co-mutations were favored in advanced CHOL, with a favorable response to immunotherapy, while single KRAS mutations predicted poor prognosis and immunotherapy outcomes for CHOL. Compared with GBC, CHOL had more mutations in genes involved in KRAS signaling; a high mutation load in these genes correlated with poor immunotherapy outcomes and may subsequently cause inferior immunotherapy outcomes for CHOL relative to GBC. Furthermore, a genomic signature including 11 genes was developed; their mutated subtype was associated with poor prognosis and immunotherapy outcomes in both CHOL and GBC. Transcriptome analyses suggested immune dysfunction in the signature mutated subtype, which was validated by tumor microenvironment (TME) evaluation based on detection of immune cell infiltration. Importantly, the signature wild-type subtype with favorable TME may be an advantageous population of immunotherapy.

Conclusions: Genomic alterations in advanced BTC were associated with specific prognosis and immunotherapy outcomes. Combining genomic classification with TME evaluation further improved the stratification of immunotherapy outcomes.

Keywords: biomarkers; genetic markers; immunotherapy; tumor; tumor microenvironment.

Figure 1

A flow diagram for the analysis process. JPH, Jiangsu Province Hospital; NGS, next-generation sequencing; CHOL, cholangiocarcinoma; GBC, gallbladder cancer; TCGA, The Cancer Genome Atlas; OS, overall survival; ICI, immune checkpoint inhibitor; LASSO, least absolute shrinkage and selection operator; GSEA, gene set enrichment analysis; IHC, immunohistochemistry; TME, tumor microenvironment.

Figure 2

Genomic characteristics of advanced biliary tract cancer. (A and B) Waterfall plots showing the frequency and types of mutations found in the TOP20 mutated genes in advanced CHOL (A) and GBC (B). (C) Waterfall plots showing the frequency and types of mutations in KRAS and TP53 between the JPH cohort and other cohorts. (D) Overall response rate of immunotherapy stratified by both KRAS and TP53 mutational status in advanced CHOL. (E) Overall survival (OS) of all patients stratified by both KRAS and TP53 mutational status in advanced CHOL. (F) OS of patients treated by immunotherapy and stratified by both KRAS and TP53 mutational status in advanced CHOL. CHOL, cholangiocarcinoma; GBC, gallbladder cancer; TCGA, The Cancer Genome Atlas; JPH, Jiangsu Province Hospital.

Figure 3

Genomic characteristics of advanced cholangiocarcinoma (CHOL) versus advanced gallbladder cancer (GBC). (A) Waterfall plots showing the frequency and types of mutations in genes that were more frequently mutated in CHOL than in GBC. (B) Proteins encoded by genes in a interact in the KRAS signaling network. (C) Overall response rate (ORR) after immunotherapy according to tumor mutation burden (TMB) of genes in a associated with KRAS signaling (K-TMB). (D) Overall survival (OS) of patients treated with immunotherapy and stratified by K-TMB. (E) Immunotherapy ORR of advanced CHOL and GBC. (F) OS of patients treated with immunotherapy in advanced CHOL and GBC, respectively.

Figure 4

Genomic signature and prognosis. (A) HRs of genes with mutations that were significantly associated with overall survival (OS) in univariate COX models. (B) Least absolute shrinkage and selection operator (LASSO) coefficient profiles of the fractions of genes in a. (C) OS of patients in the JPH cohort according to mutation status of genomic signature constructed through the LASSO model in all patients, patients with CHOL, and patients with GBC, respectively. (D) OS of patients in the TCGA and Shanghai cohorts according to mutation status of the genomic signature in all patients, patients with CHOL, and patients with GBC, respectively. CHOL, cholangiocarcinoma; GBC, gallbladder cancer; TCGA, The Cancer Genome Atlas; WT, wild-type; mut, mutated type; JPH, Jiangsu Province Hospital.

Figure 5

Genomic signature and immunotherapy outcomes. (A) Overall response rate of immunotherapy according to mutation status of the genomic signature. (B-D) Overall survival of immunotherapy according to mutation status of the genomic signature in all patients (B), patients with CHOL (C), and patients witj GBC (D), respectively. CHOL, cholangiocarcinoma; GBC, gallbladder cancer; mut, mutated type; WT, wild-type.

Figure 6

Transcriptome features associated with the genomic signature. (A) Heat map of TOP50 genes differentially expressed between the signature mutated and wild-type subgroups in the TCGA CHOL cohort. (B) Gene set enrichment analysis (GSEA) of the signature mutated subtype versus wild-type subtype. Selected entries of biological process (BP) in gene ontology (GO) and pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG) are shown. (C) Enrichment plots of GSEA for several crucial GO terms or KEGG pathways. (D) T cell dysfunction, T cell exclusion and TIDE Scores according to the signature subtypes. (E) The abundance of infiltrating CD8 +T cells according to the signature subtypes. (F) The predicted responses to immune checkpoint inhibitors (ICIs) by the TIDE algorithm according to the signature subtypes. (G) T cell dysfunction and exclusion scores according to the signature subtypes in tumors predicted to fail in ICI therapy. mut, mutated type; WT, wild-type; ORR, objective response rate.

Figure 7

Tumor microenvironment (TME) associated with the genomic signature. (A) Typical micrographs for multiplexed immunohistochemistry (mIHC) staining of surface biomarkers of immune cells in tissues, at 200× magnification. 1, CD8; 2, CD56; 3, CD68 (green) and HLA-DR (red); 4, the reconstructed image for all surface biomarkers. (B) Typical micrographs of programmed death ligand-1 (PD-L1)-positive and negative tumors, at 200× magnification. (C) Clustering based on the density of infiltrating immune cells in tumor parenchyma identified cluster 1 (C1) and cluster 2 (C2). (D) The positive rate of PD-L1 expression and the signature mutations according to clusters. (E and F) ORR (E) and overall survival (F) of immunotherapy according to the clusters and the genomic signature typing. ORR, objective response rate; mut, mutated type; WT, wild-type.