Abstract LB384: H3K27ac-HiChIP variant-to-gene mapping confirms the importance of cancer drivers and oncogenic signaling pathways in melanoma risk

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Abstract We performed a new melanoma GWAS meta-analysis, roughly doubling the sample size to ∼70,000 cases compared to the most recent study by Landi and colleagues. In the new meta-analysis, we identify 116 independent risk loci, replicating 52/54 loci previously reported. Post-GWAS variant-to-gene mapping remains critical to functionally interpret new loci discovered by this analysis. Most loci contain many candidate causal variants in linkage disequilibrium, with very few altering protein-coding sequence, suggesting cis-regulatory function underlying the majority of loci. Quantitative trait locus (QTL) colocalization methods can effectively prioritize target genes at susceptibility loci but many loci remain without assigned targets, perhaps reflecting context-specific variant effects not well-reflected in QTL datasets. To complement QTLs and more comprehensively identify potential causal genes, including those beyond 1 Mb, we applied H3K27ac-HiChIP in human primary melanocytes, the cell type of origin for melanoma. H3K27ac-HiChIP combines a Hi-C approach with a H3K27ac ChIP step to detect enhancer-promoter interactions at the risk loci. Statistically significant chromatin interactions were identified using the FitHiChIP pipeline. We subsequently performed variant-to-gene (V2G) mapping at all genome-wide significant melanoma loci, nominating target genes where fine-mapped variants overlapped or physically interacted with their respective promoters. HiChIP-based V2G mapping approach identified target genes at 94% loci (779 genes nominated at 109/116 loci), outperforming other gene nomination approaches (QTL colocalization, protein-coding variants), which nominated 102 candidate genes at 57% of risk loci (67/116 loci). Among genes identified by melanocyte or melanoma eQTL colocalization, a majority (19 of 32) were also nominated by V2G mapping. Likewise, 3 of 5 splice QTL genes and 21 of 34 meQTL genes overlapped with the V2G gene set. Next, we compared gene sets including the V2G mapping-identified candidates to those identified solely by QTLs and protein-coding variants. The gene set incorporating V2G candidates showed significant enrichment for oncogenic signaling pathways, including WNT/β-catenin signaling (FDR: Pwith V2G gene set= 6.3 × 10-05 vs Pwithout V2G gene set= 0.2), aryl hydrocarbon receptor signaling (P= 1.6 × 10-4 vs 0.2), and signaling by NOTCH1 (P=0.002 vs0.5). Notably, V2G mapping linked risk-associated variants to known cancer drivers (e.g. PIK3CA, NOTCH2, MDM4etc.) at 46% of loci (53/116; nominated 76 cancer drivers), with several located >1Mb away. Strikingly, we detected a highly significant long-range interaction (∼2 Mb) connecting fine-mapped variants near the locus 8q24.21 to cancer driver MYC. Overall, H3K27ac-HiChIP-based V2G mapping greatly improves the interpretation of melanoma susceptibility loci by identifying distant susceptibility genes, highlighting known cancer drivers as potential targets, and revealing that these loci converge on key oncogenic signaling pathways. Citation Format: Rohit Thakur, G J M Shanika R Jayasinghe, Mai Xu, Linh Bui-Raborn, Jianxin Shi, Diptavo Dutta, Phuc H. Hoang, Mathias Seviiri, Christopher I. Amos, Andrew Bakshi, Anne E. Cust, Florence Demenais, David L. Duffy, Lars G. Fritsche, Jiali Han, Nicholas K. Hayward, Kiarash Khosrotehrani, Rajiv Kumar, John F. Thompson, Stuart MacGregor, Miguel Renteria, Diane T. Smelser, Sarah V. Ward, Maria Concetta Fargnoli, Paola Ghiorzo, Alisa M. Goldstein, Chiara Menin, David Millan-Esteban, Eduardo Nagore, Cristina Pellegrini, Susana Puig, Alex Stratigos, David C. Whiteman, Melanoma Meta-Analysis Consortium, Lee E. Whelees, Rebecca I. Hartman, Maria Teresa Landi, Matthew H. Law, Kevin M. Brown. H3K27ac-HiChIP variant-to-gene mapping confirms the importance of cancer drivers and oncogenic signaling pathways in melanoma risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB384.

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