4.5 Article

Spontaneous mutations in the single TTN gene represent high tumor mutation burden

Journal

NPJ GENOMIC MEDICINE
Volume 5, Issue 1, Pages -

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/s41525-019-0107-6

Keywords

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Funding

  1. Basic Science Research Program of the National Research Foundation of Korea (NRFK) [NRF-2017R1C1B2009899, NRF-2019R1A2C1084460]
  2. Bio and Medical Technology Development Program of the NRFK [NRF-2017M3A9G5061671, NRF-2019M3E5D4066900]
  3. Technology Innovation Program for Fostering New Post-Genome Industry - Ministry of Trade, Industry, and Energy (MOTIE) of the Korean government [10067796, 10067407]
  4. Korea Evaluation Institute of Industrial Technology (KEIT) [10067796] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
  5. National Research Foundation of Korea [2017M3A9G5061671, 2019M3E5D4066900] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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Tumor mutation burden (TMB) is an emerging biomarker, whose calculation requires targeted sequencing of many genes. We investigated if the measurement of mutation counts within a single gene is representative of TMB. Whole-exome sequencing (WES) data from the pan-cancer cohort (n = 10,224) of TCGA, and targeted sequencing (tNGS) and TTN gene sequencing from 24 colorectal cancer samples (AMC cohort) were analyzed. TTN was identified as the most frequently mutated gene within the pan-cancer cohort, and its mutation number best correlated with TMB assessed by WES (rho = 0.917, p < 2.2e-16). Colorectal cancer was one of good candidates for the application of this diagnostic model of TTN-TMB, and the correlation coefficients were 0.936 and 0.92 for TMB by WES and TMB by tNGS, respectively. Higher than expected TTN mutation frequencies observed in other FLAGS (FrequentLy mutAted GeneS) are associated with late replication time. Diagnostic accuracy for high TMB group did not differ between TTN-TMB and TMB assessed by tNGS. Classification modeling by machine learning using TTN-TMB for MSI-H diagnosis was constructed, and the diagnostic accuracy was 0.873 by area under the curve in external validation. TTN mutation was enriched in samples possessing high immunostimulatory signatures. We suggest that the mutation load within TTN represents high TMB status.

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