Taylor Gonzalez David J, Djulbegovic Mak B, Sharma Meghan, Antonietti Michael, Kim Colin K, Uversky Vladimir N, Karp Carol L, Shields Carol L, Wilson Matthew W
Bascom Palmer Eye Institute, University of Miami, Miami, Florida.
Wills Eye Hospital, Thomas Jefferson University, Philadelphia, Pennsylvania.
Ophthalmol Sci. 2024 Dec 6;5(3):100673. doi: 10.1016/j.xops.2024.100673. eCollection 2025 May-Jun.
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.
A cross-sectional bioinformatics exploratory data analysis of genetic mutations from UM cases.
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.
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.
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 , and .
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.
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.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
葡萄膜黑色素瘤（UM）因其多变的基因格局带来了重大的诊断和预后挑战。我们探索使用一种新型深度学习工具来评估UM中基因突变的功能影响。
对UM病例的基因突变进行横断面生物信息学探索性数据分析。
分析了被诊断为UM的患者的基因数据，特别关注来自癌症体细胞突变目录（COSMIC）数据库的错义突变。
我们使用COSMIC数据库识别UM中经常观察到的错义突变，使用AlphaMissense评估其潜在致病性，并使用AlphaFold可视化突变。临床意义通过与ClinVar数据库中的条目进行交叉验证。
测量的主要结果是AlphaMissense预测与ClinVar注释之间关于与UM相关的关键基因（如 、 和 ）中突变致病性的一致率。
在COSMIC上发现的UM突变中，错义替换占91.35%（n = 1310）。在最常突变的基因中分析的151个独特错义突变中，只有40.4%（n = 61）在ClinVar中有相应数据。值得注意的是，AlphaMissense为27.2%（n = 41）的突变提供了明确分类，这些突变在ClinVar中被标记为“意义不明”，凸显了其在模糊病例中提供更多清晰度的潜力。当排除这些意义不确定的突变时，AlphaMissense在所有分析基因中与ClinVar显示出完美一致（100%），表明在预测为“致病”的突变被分类为“良性”或反之的情况中没有差异。
通过AlphaMissense整合深度学习为理解UM的突变格局提供了一种有前景的方法。我们的方法有潜力改善基因组诊断并为UM个性化治疗策略的制定提供信息。
在本文末尾的脚注和披露中可能会发现专有或商业披露信息。
