Abstract 4887: Unveiling the functional impact of glioma non-coding germline variants using large language models

Abstract Efforts to identify the risk for developing gliomas through Genome-Wide Association Studies (GWAS) face relevant challenges, such as accurately pinpointing the genomic positions of germline variants, primarily found in non-coding regions, with unclear functional implications in glioma development. In this context, the GET method, a large language model designed to unveil regulatory grammars across 213 human cell types, emerges as a valuable tool for elucidating the mechanisms of non-coding mutations. Our aims include: I) Validate the efficacy of the GET method using a well-documented case and prioritize relevant variants for analysis; II) Refine the genomic positions of these variants and exploring their linkage disequilibrium (LD); III) Identify potential motifs and genes that might be influenced by these high-impact variants. After examining published GWAS, 39 non-coding SNPs in gliomas were found. Adding 316 variants in high LD (R2 > 0.8) expanded the dataset to 355 variants for analysis. Using the GET method, we calculated impact scores (ISs) for each variant, considering changes in motif binding score (ΔBM) and motif gradient (GM) across human astrocytes. We focused on rare variants (risk allele frequency <0.25) with the higher odds ratios (OR). The table presents GET results for a well-known case-control, showing its role in increasing glioma risk through OCT4 motif disruption and enhanced MYC expression, as previously was confirmed experimentally. Moreover, it details results concerning the 5 rare germline variants exhibiting the highest ISs, including precise genomic positions and the most affected motifs and genes that could potentially be impacted by each variant. Using a large language model, this study overcomes the challenge of mere variant identification, aiming to establish a framework for uncovering the underlying mechanism of risk variants. While further validation is essential, this is a promising approach to explore the impact of germline mutations. Variant ID Genomic Position Altered Motif Affected Gene Ref allele Alt allele Case-control rs55705857 Chr8:129633445 OCT4+SOX2:POU MYC A G H-I variant #1 rs78378222 Chr17:7668433 HD/18 PER1 T G H-I variant #2 rs518394 Chr9:22019673 KLF/SP/2 CDKN2A G C H-I variant #3 rs79495512 Chr15:76199995 KLF/SP/2 LINGO1 T C H-I variant #4 rs78185702 Chr15:76317893 SMAD LINGO1 C T H-I variant #5 rs2538067 Chr7:54907724 CTCF EGFR G A Citation Format: Maria del Mar Alvarez-Torres, Xi Fu, Romella K Sagatelian, Alejandro L Buendia, Raul Rabadan. Unveiling the functional impact of glioma non-coding germline variants using large language models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4887.