PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
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
Version 1
: Received: 27 February 2024 / Approved: 25 March 2024 / Online: 26 March 2024 (02:45:01 CET)
How to cite:
Choi, Y.; Jung, C. M.; Kang, G.; Jang, J. J.; Chae, L.; Kim, P. H.; Hermida de Viveiros, P. 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. Preprints2024, 2024031496. https://doi.org/10.20944/preprints202403.1496.v1
Choi, Y.; Jung, C. M.; Kang, G.; Jang, J. J.; Chae, L.; Kim, P. H.; Hermida de Viveiros, P. 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. Preprints 2024, 2024031496. https://doi.org/10.20944/preprints202403.1496.v1
Choi, Y.; Jung, C. M.; Kang, G.; Jang, J. J.; Chae, L.; Kim, P. H.; Hermida de Viveiros, P. 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. Preprints2024, 2024031496. https://doi.org/10.20944/preprints202403.1496.v1
APA Style
Choi, Y., Jung, C. M., Kang, G., Jang, J. J., Chae, L., Kim, P. H., & Hermida de Viveiros, P. (2024). 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. Preprints. https://doi.org/10.20944/preprints202403.1496.v1
Chicago/Turabian Style
Choi, Y., Peter Haseok Kim and Pedro Hermida de Viveiros. 2024 "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" Preprints. https://doi.org/10.20944/preprints202403.1496.v1
Abstract
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.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.