Literature DB >> 33436630

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

Ji-Hye Oh1,2, Se Jin Jang2,3, Jihun Kim2,3, Insuk Sohn4, Ji-Young Lee1,2, Eun Jeong Cho1,2, Sung-Min Chun5,6, Chang Ohk Sung7,8,9.   

Abstract

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.

Year:  2020        PMID: 33436630     DOI: 10.1038/s41525-019-0107-6

Source DB:  PubMed          Journal:  NPJ Genom Med        ISSN: 2056-7944            Impact factor:   8.617


  1 in total

1.  Establishment of Immunoglobulin Heavy (IGH) Chain Clonality Testing by Next-Generation Sequencing for Routine Characterization of B-Cell and Plasma Cell Neoplasms.

Authors:  Maria E Arcila; Wayne Yu; Mustafa Syed; Hannah Kim; Lidia Maciag; JinJuan Yao; Caleb Ho; Kseniya Petrova; Christine Moung; Paulo Salazar; Ivelise Rijo; Tessara Baldi; Ahmet Zehir; Ola Landgren; Jae Park; Mikhail Roshal; Ahmet Dogan; Khedoudja Nafa
Journal:  J Mol Diagn       Date:  2018-12-25       Impact factor: 5.568

  1 in total

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