Abstract
Background: Tumor mutation burden (TMB) is the total exonic mutation count per megabase of tumor DNA. Recent advances in precision medicine occasionally detect Lynch syndrome (LS) by germline sequencing for mismatch-repair (g.MMR) genes but not using TMB. The current study analyzes the utility of TMB in detecting LS. Methods: Whole-exome sequencing (ion-semiconductor sequencing) was performed for somatic and germline DNA from 2,501 various cancer patients to detect TMB and g.MMR sequencing. MMR IHC was conducted when high TMB (≥10) was detected in LS-related cancers with an additional condition of wild-type BRAF in colorectal cancers. Target sequencing and multiplex ligation-dependent probe amplification (MLPA) were further performed for g.MMR genes in MMR-deficient cancers (TMB-based g.MMR target sequencing). We compared universal sequencing and TMB-based target sequencing in their sensitivity for detecting LS. Results: LS was detected in 16 (0.6%) of the 2,501 patients: 1.1% (9/826) of colorectal cancer patients, 16.2% (6/37) of endometrial cancer patients, and 14.3% (1/7) of small intestine cancer patients. TMB-based g.MMR target sequencing (81.3%) showed superior sensitivity for detecting LS than universal g.MMR sequencing (56.3%; P = 0.127) but missed 3 LS patients (1 with a low-TMB cancer, 1 with a BRAF-mutant colorectal cancer, and 1 with an MMR-proficient cancer). Ion-semiconductor sequencing could detect single-nucleotide substitutions but not large deletions. POL-mutated cancers showed extremely high TMBs (48.4-749.2). Conclusions: g.MMR target sequencing, combined with TMB, somatic BRAF mutation, and MMR IHC is an effective strategy for detecting LS. Impact: TMB can be a biomarker for detecting LS in precision medicine.
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CITATION STYLE
Kiyozumi, Y., Matsubayashi, H., Higashigawa, S., Horiuchi, Y., Kado, N., Hirashima, Y., … Yamaguchi, K. (2021). Role of tumor mutation burden analysis in detecting lynch syndrome in Precision Medicine: Analysis of 2,501 Japanese cancer patients. Cancer Epidemiology Biomarkers and Prevention, 30(1), 166–174. https://doi.org/10.1158/1055-9965.EPI-20-0694
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