AlphaMissense Predictions and ClinVar Annotations: A Deep Learning Approach to Uveal Melanoma

• Published in: Ophthalmology Science
• Main Authors: David J. Taylor Gonzalez, MD, Mak B. Djulbegovic, MD, MSc, Meghan Sharma, MD, MPH, Michael Antonietti, BS, Colin K. Kim, BS, Vladimir N. Uversky, PhD, DSc, Carol L. Karp, MD, Carol L. Shields, MD, Matthew W. Wilson, MD
• Format: Article
• Language: English
• Published: Elsevier 2025-05-01
• Subjects: AlphaFold; AlphaMissense; ClinVar; COSMIC; Missense mutations; Uveal melanoma
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666914524002094

Objective: Uveal melanoma (UM) poses significant diagnostic and prognostic challenges due to its variable genetic landscape. We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM. Design: A cross-sectional bioinformatics exploratory data analysis of genetic mutations from UM cases. Subjects: Genetic data from patients diagnosed with UM were analyzed, explicitly focusing on missense mutations sourced from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Methods: We identified missense mutations frequently observed in UM using the COSMIC database, assessed their potential pathogenicity using AlphaMissense, and visualized mutations using AlphaFold. Clinical significance was cross-validated with entries in the ClinVar database. Main Outcome Measures: The primary outcomes measured were the agreement rates between AlphaMissense predictions and ClinVar annotations regarding the pathogenicity of mutations in critical genes associated with UM, such as GNAQ, GNA11, SF3B1, EIF1AX, and BAP1. Results: Missense substitutions comprised 91.35% (n = 1310) of mutations in UM found on COSMIC. Of the 151 unique missense mutations analyzed in the most frequently mutated genes, only 40.4% (n = 61) had corresponding data in ClinVar. Notably, AlphaMissense provided definitive classifications for 27.2% (n = 41) of the mutations, which were labeled as “unknown significance” in ClinVar, underscoring its potential to offer more clarity in ambiguous cases. When excluding these mutations of uncertain significance, AlphaMissense showed perfect agreement (100%) with ClinVar across all analyzed genes, demonstrating no discrepancies where a mutation predicted as “pathogenic” was classified as “benign” or vice versa. Conclusions: Integrating deep learning through AlphaMissense offers a promising approach to understanding the mutational landscape of UM. Our methodology holds the potential to improve genomic diagnostics and inform the development of personalized treatment strategies for UM. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.