Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

Sha, Dan; Jin, Zhaohui; Budczies, Jan; Kluck, Klaus; Stenzinger, Albrecht; Sinicrope, Frank A.

doi:10.1158/2159-8290.cd-20-0522

Cited by 859Cancer Immunotherapy and BiomarkersCancer Genomics and DiagnosticsImmunotherapy and Immune Responses

Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

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Sha, Dan, Jin, Zhaohui, Budczies, Jan, Kluck, Klaus, Stenzinger, Albrecht, Sinicrope, Frank A.·2020·Cancer Discovery

DOI 10.1158/2159-8290.cd-20-0522PMC PMC7710563

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Abstract

Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, varies across malignancies. Panel sequencing–based estimates of TMB have largely replaced whole-exome sequencing–derived TMB in the clinic. Retrospective evidence suggests that TMB can predict the efficacy of immune checkpoint inhibitors, and data from KEYNOTE-158 led to the recent FDA approval of pembrolizumab for the TMB-high tumor subgroup. Unmet needs include prospective validation of TMB cutoffs in relationship to tumor type and patient outcomes. Furthermore, standardization and harmonization of TMB measurement across test platforms are important to the successful implementation of TMB in clinical practice. Significance: Evaluation of TMB as a predictive biomarker creates the need to harmonize panel-based TMB estimation and standardize its reporting. TMB can improve the predictive accuracy for immunotherapy outcomes, and has the potential to expand the candidate pool of patients for treatment with immune checkpoint inhibitors.

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Nonresponse due to low tumor mutational burden, low pre-existing T cell-mediated immune responses, or a cold TME may overlap with nonresponse due to dysbiosis.
“One might anticipate that nonresponse due to low tumor mutational burden, low pre-existing T cell-mediated immune responses, or a cold TME may overlap with nonresponse due to dysbiosis.171,172”
From Melanoma and microbiota: Current understanding and future directions (2024) · cites this paper as [172]

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Authors: Sha, D., Jin, Z., Budczies, J., Kluck, K., Stenzinger, A., & Sinicrope, F. A.
Affiliations: Mayo Clinic (US)
University Hospital Heidelberg (DE)
ORCID: Sha, Dan
Jin, Zhaohui
Budczies, Jan
Kluck, Klaus
Stenzinger, Albrecht
Sinicrope, Frank A.
Venue: Cancer Discovery
Publisher: American Association for Cancer Research (AACR)
Published: 2020-12-01
Volume: 10
Issue: 12
Pages: 1808-1825
ISSN: 2159-8274 (ISSN-L)
2159-8290
DOI: 10.1158/2159-8290.cd-20-0522
PMC: PMC7710563
OpenAlex: W3096112873
Cited by: 859
References: 144
Citations by year: 2026: 105 · 2025: 165 · 2024: 197 · 2023: 190 · 2022: 152 · 2021: 48 · 2013: 1
FWCI: 35.85
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Cancer Discovery (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: NCI (R01 CA210509-01A1)
Topics: Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
Keywords: tumor mutational burden, predictive biomarkers, solid tumors, immune checkpoint inhibitors, pembrolizumab, KEYNOTE-158, panel sequencing, test standardization, immunotherapy response prediction, somatic mutations
MeSH terms: Humans, Neoplasms, Biomarkers, Tumor, Tumor Burden

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Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

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