The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis
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 Ja...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-06-01
|
| Series: | Translational Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523322000377 |
| _version_ | 1828491761988665344 |
|---|---|
| author | Jinlong Cao Xin Yang Siyu Chen Jirong Wang Xinpeng Fan Shengjun Fu Li Yang |
| author_facet | Jinlong Cao Xin Yang Siyu Chen Jirong Wang Xinpeng Fan Shengjun Fu Li Yang |
| author_sort | Jinlong Cao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T11:08:28Z |
| format | Article |
| id | doaj.art-ebec2c22f21c4b569985b36165c5ae0d |
| institution | Directory Open Access Journal |
| issn | 1936-5233 |
| language | English |
| last_indexed | 2024-12-11T11:08:28Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj.art-ebec2c22f21c4b569985b36165c5ae0d2022-12-22T01:09:38ZengElsevierTranslational Oncology1936-52332022-06-0120101375The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysisJinlong Cao0Xin Yang1Siyu Chen2Jirong Wang3Xinpeng Fan4Shengjun Fu5Li Yang6Department of Urology, The Second Hospital of Lanzhou University, No.82 Cuiyingmen, Lanzhou, Gansu 730000, China; Key Laboratory of Urological Diseases of Gansu Provincial, Lanzhou 730000, ChinaReproductive Medicine Center, The Second Hospital of Lanzhou University, Lanzhou 730000, ChinaDepartment of Urology, The Second Hospital of Lanzhou University, No.82 Cuiyingmen, Lanzhou, Gansu 730000, China; Key Laboratory of Urological Diseases of Gansu Provincial, Lanzhou 730000, ChinaDepartment of Urology, The Second Hospital of Lanzhou University, No.82 Cuiyingmen, Lanzhou, Gansu 730000, China; Key Laboratory of Urological Diseases of Gansu Provincial, Lanzhou 730000, ChinaDepartment of Urology, The Second Hospital of Lanzhou University, No.82 Cuiyingmen, Lanzhou, Gansu 730000, China; Key Laboratory of Urological Diseases of Gansu Provincial, Lanzhou 730000, ChinaDepartment of Urology, The Second Hospital of Lanzhou University, No.82 Cuiyingmen, Lanzhou, Gansu 730000, China; Key Laboratory of Urological Diseases of Gansu Provincial, Lanzhou 730000, ChinaDepartment of Urology, The Second Hospital of Lanzhou University, No.82 Cuiyingmen, Lanzhou, Gansu 730000, China; Key Laboratory of Urological Diseases of Gansu Provincial, Lanzhou 730000, China; Corresponding author at: Department of Urology, The Second Hospital of Lanzhou University, No.82 Cuiyingmen, Lanzhou, Gansu 730000, China.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.http://www.sciencedirect.com/science/article/pii/S1936523322000377Tumor mutation burdenImmunotherapyImmune checkpoint inhibitorsMeta-analysisBioinformatics |
| spellingShingle | Jinlong Cao Xin Yang Siyu Chen Jirong Wang Xinpeng Fan Shengjun Fu Li Yang The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis Translational Oncology Tumor mutation burden Immunotherapy Immune checkpoint inhibitors Meta-analysis Bioinformatics |
| title | The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis |
| title_full | The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis |
| title_fullStr | The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis |
| title_full_unstemmed | The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis |
| title_short | The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis |
| title_sort | predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types a meta analysis and bioinformatics analysis |
| topic | Tumor mutation burden Immunotherapy Immune checkpoint inhibitors Meta-analysis Bioinformatics |
| url | http://www.sciencedirect.com/science/article/pii/S1936523322000377 |
| work_keys_str_mv | AT jinlongcao thepredictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT xinyang thepredictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT siyuchen thepredictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT jirongwang thepredictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT xinpengfan thepredictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT shengjunfu thepredictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT liyang thepredictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT jinlongcao predictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT xinyang predictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT siyuchen predictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT jirongwang predictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT xinpengfan predictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT shengjunfu predictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis AT liyang predictiveefficacyoftumormutationburdeninimmunotherapyacrossmultiplecancertypesametaanalysisandbioinformaticsanalysis |