Machine learning-driven estimation of mutational burden highlights DNAH5 as a prognostic marker in colorectal cancer.

ABSTRACT:

Background

Tumor Mutational Burden (TMB) have emerged as pivotal predictive biomarkers in determining prognosis and response to immunotherapy in colorectal cancer (CRC) patients. While Whole Exome Sequencing (WES) stands as the gold standard for TMB assessment, carry substantial costs and demand considerable time commitments. Additionally, the heterogeneity among high-TMB patients remains poorly characterized.

Methods

We employed eight advanced machine learning algorithms to develop gene-panel-based models for TMB estimation. To rigorously compare and validate these TMB estimation models, four external cohorts, involving 1,956 patients, were used. Furthermore, we computed the Pearson correlation coefficient between the estimated TMB and tumor neoantigen levels to elucidate t

SUBMITTER: Fang Y 

PROVIDER: S-EPMC11566893 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature