Association of survival and genomic mutation signature with immunotherapy in patients with hepatocellular carcinoma

Abstract

Background: Current guidelines lack recommendations for the use of immunotherapy and immune-related biomarkers for hepatocellular carcinoma (HCC). We aim to provide reliable evidence of the association of survival with HCC immunotherapy and to demonstrate that genomic mutation signature could be an effective biomarker to predict immunotherapy efficacy of HCC patients.

Methods: We conducted a meta-analysis of 17 randomized trials with 2055 patients and an individual patient-level analysis of 31 patients. Trial data were identified in PubMed, EMBASE and Cochrane Central library, and individual patient data were obtained from the cBioPortal database. Overall survival (OS) and progression-free survival (PFS) were assessed with the hazard ratio (HR) and 95% CI. This study is registered with PROSPERO, number CRD42018083991.

Results: The meta-analysis showed that compared to conventional therapy, immunotherapy resulted in prolonged OS (HR =0.65, P<0.0001, high quality) and PFS (HR =0.81, P<0.0001, high quality); the benefits were observed for cellular immunotherapy, tumor vaccine, and cytokine immunotherapy. Findings were robust to subgroup and trial sequential analyses. In the individual patient-level analysis of patients treated with immune checkpoint inhibitor, mutations in TERT, CTNNB1, BRD4, or MLL, and co-mutations in TP53 and TERT or BRD4 were associated with significantly worse survival. These oncogenes were used to develop a novel integrated mutation risk score, which exhibited better utility in predicting survival than the tumor mutation burden (TMB). Patients with low- versus high- mutation risk score had longer OS (HR =0.18, P=0.02) and PFS (HR =0.33, P=0.018). A nomogram comprising the mutation risk score and essential clinical factors further improved the predictive accuracy (AUC =0.840 for both 1- and 2-year OS).

Conclusions: Immunotherapy showed longer OS and PFS than conventional therapy among HCC patients, especially patients with a low mutation risk score. The nomogram based on genomic and clinical characteristics is effective in predicting survival of HCC patients undergoing immune checkpoint inhibitor.

Keywords: Hepatocellular carcinoma (HCC); efficacy; genomic mutation; immunotherapy.

Figure 1

Pooled hazard ratios for overall survival (A) and progression-free survival (B) of patients undergoing immunotherapy versus conventional therapy. CI, confidence interval.

Figure 2

Landscape of oncogenic driver mutations in patients treated by immune checkpoint inhibitor.

Figure 3

Progression-free survival analysis (A) and overall survival analysis (B) stratified by TP53 and TERT co-mutation status. HR, hazard ratio; CI, confidence interval.

Figure 4

Progression-free survival analysis (A) and overall survival analysis (B) stratified by mutation risk score. HR, hazard ratio; CI, confidence interval.

Figure 5

Nomogram to predict the survival of patients with hepatocellular carcinoma undergoing immunotherapy. HCV, hepatitis C virus; PFS, progression-free survival.

Figure S1

PRISMA flow diagram of the meta-analysis.

Figure S2

Risk of bias graph.

Figure S3

Risk of bias summary.

Figure S4

Pooled analysis of overall survival rate of immunotherapy versus conventional therapy. Pooled analysis of 1-year overall survival rate (A), 3-year overall survival rate (B), and 5-year overall survival rate (C). CI, confidence interval.

Figure S5

Trial sequential analyses of trials comparing immunotherapy with conventional therapy. Trial sequential analyses for 1-year overall survival rate (A), 3-year overall survival rate (B), and 5-year overall survival rate (C). The solid yellow cumulative Z curves indicate the cumulative Z score obtained from the inverse variance model Z statistic when a new trial is added. The solid yellow cumulative Z curves cross the dashed blue trial sequential alpha for monitoring boundaries. The horizontal dotted blue lines illustrate the traditional level of statistical significance (P=0.05). Pc = event proportion in the conventional therapy group. RRR, relative risk reduction.

Figure S6

Survival analysis stratified by tumor mutation burden. (A) Progression-free survival analysis and (B) overall survival analysis. HHR, hazard ratio; CI, confidence interval.

Figure S7

Progression-free survival analysis in the MSK cohort stratified by gene mutation status. (A) Progression-free survival analysis on immune checkpoint inhibitor for patients with CTNNB1 mutation versus CTNNB1 wild-type tumors. (B) The same analysis as (A), but patients with MLL mutations were compared MLL wild-type tumors. HR, hazard ratio; CI, confidence interval.

Figure S8

Overall survival analysis in the MSK cohort stratified by gene mutation status. (A) Overall survival analysis on immune checkpoint inhibitor for patients with BRD4 mutation versus BRD4 wild-type tumors. (B) The same analysis as in (A), but patients with a TERT mutation were compared with patients with TERT wild-type tumors. HR, hazard ratio; CI, confidence interval.

Figure S9

Survival analysis based on risk stratification using the nomogram. (A) Progression-free survival; (B) overall survival. HR, hazard ratio; CI, confidence interval.

Figure S10

Calibration curve for the nomogram to predict overall survival of immunotherapy-treated patients. OS, overall survival.

Figure S11

Pooled analysis of the ratio of hazard ratios between overall survival and progression-free survival. CI, confidence interval.

Figure S12

Weighted linear correlation between overall survival and progression-free survival in meta-analysis. The correlation was described in all trials (A), cellular and cytokine immunotherapy trials (B), cytokine immunotherapy trials (C), and cellular immunotherapy trials (D). HR, hazard ratio; OS, overall survival; PFS, progression-free survival.

Figure S13

Weighted linear correlation between overall survival and progression-free survival in the MSK cohort. HR, hazard ratio; OS, overall survival; PFS, progression-free survival.