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Review
. 2022 Jun:20:101375.
doi: 10.1016/j.tranon.2022.101375. Epub 2022 Mar 23.

The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis

Jinlong Cao  1 , Xin Yang  2 , Siyu Chen  3 , Jirong Wang  3 , Xinpeng Fan  3 , Shengjun Fu  3 , Li Yang  4
Affiliations
Review

The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis

Jinlong Cao et al. Transl Oncol. 2022 Jun.

Abstract

Purpose: To explore the predictive efficacy of tumor mutation burden (TMB) as a potential biomarker for cancer patients treated with Immune checkpoint inhibitors (ICIs).

Methods: We systematically searched PubMed, Cochrane Library, Embase and Web of Science for clinical studies (published between Jan 1, 2014 and Aug 30, 2021) comparing immunotherapy patients with high TMB to patients with low TMB. Our main endpoints were objective response rate (ORR), durable clinical benefit (DCB), overall survival (OS) and progress-free Survival (PFS). Moreover, we downloaded simple nucleotide variation (SNV) data of 33 major cancer types from the TCGA database as non-ICIs group, and compared the high TMB patients' OS between the non-ICIs group and meta-analysis results.

Results: Of 10,450 identified studies, 41 were eligible and were included in our analysis (7713 participants). Compared with low TMB patients receiving ICIs, high TMB yielded a better ORR (RR = 2.73; 95% CI: 2.31-3.22; P = 0.043) and DCB (RR = 1.93; 95% CI: 1.64-2.28; P = 0.356), and a significantly increased OS (HR =0.24; 95% CI: 0.21-0.28; P < 0.001) and PFS (HR = 0.38; 95% CI: 0.34-0.42; P < 0.001). Furthermore, compared with non-ICIs group from the TCGA database, immunotherapy can improve OS in some cancer types with high TMB and better prognosis, including colorectal cancer, gastric cancer, lung cancer, melanoma and pan-cancer.

Conclusion: TMB is a promising therapeutic and prognostic biomarker for immunotherapy, which indicates a better ORR, DCB, OS and PFS. If there is a standard for TMB assessment and cut-off, it could improve the management of different cancers.

Keywords: Bioinformatics; Immune checkpoint inhibitors; Immunotherapy; Meta-analysis; Tumor mutation burden.

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Conflict of interest statement

All the authors declare no conflicts of interest.

Figures

Image, graphical abstract
Graphical abstract
Fig 1
Fig. 1
Study selection process.
Fig 2
Fig. 2
The forest plot of ORR in patients with high TMB compared to those with low TMB.
Fig 3
Fig. 3
The forest plot of DCB in patients with high TMB compared to those with low TMB.
Fig 4
Fig. 4
The forest plot of OS in patients with high TMB compared to those with low TMB.
Fig 5
Fig. 5
The forest plot of PFS in patients with high TMB compared to those with low TMB.
Fig 6
Fig. 6
Subgroup analysis in OS of patients with high/low TMB based on different cancer types.
Fig 7
Fig. 7
Subgroup analysis in OS of patients with high/low TMB based on different cancer types.
Fig 8
Fig. 8
TMB distribution and its relationship with prognosis in 33 major cancer types from TCGA. (A) The TMB distribution of 33 tumors from TCGA database was presented in the form of boxplot; (B) The results of K-M survival analysis for 33 cancer types were presented in the form of forest map.
Fig 9
Fig. 9
Meta-analysis results compared with non-ICIs group from the TCGA database. Among them, names begin with TCGA were no-ICIs group, and the others were ICIs-treated results obtained by meta-analysis.
See this image and copyright information in PMC

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