Evaluation of in silico tools and Chat-GPT in identifying the impact of missense variants of immune-related genes associated with immunotherapy outcomes for solid tumors

Yoonhee Choi; Chan Mi Jung; Grace Kang; Jessica Jooeun Jang; Lena Chae; Peter Haseok Kim; Pedro Hermida de Viveiros

doi:10.20944/preprints202403.1496.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 February 2024 / Approved: 25 March 2024 / Online: 26 March 2024 (02:45:01 CET)

Understanding clinical significance of variants of unknown significance (VUS) reported in next-generation sequencing (NGS) has become essential in cancer treatment. Our study examined six widely used in silico tools: PolyPhen-2, Align-GVGD, MutationTaster2, CADD, REVEL, and Chat-GPT. We utilized a dataset of gene variants known to potentially affect immune therapy. No single tool could comprehensively determine mutation variant pathogenicity. MutationTaster2021 showed the highest overall accuracy and MCC among the tools. Notably, REVEL and Chat-GPT exhibited 100% specificity, suggesting their proficiency in accurately identifying pathogenic variants and minimizing false positives. In contrast, CADD displayed optimal sensitivity, making it suitable for effectively ruling out benign variants.

solid tumor; Artificial intelligence; Variant classification; Pathogenicity; Variants of uncertain significance

Medicine and Pharmacology, Oncology and Oncogenics

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Evaluation of in silico tools and Chat-GPT in identifying the impact of missense variants of immune-related genes associated with immunotherapy outcomes for solid tumors

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