Has anyone tried finding cross-cancer similarity using SNP data and deep learning?

Hi everyone,
I’m exploring an idea that looks at whether cancers might share genetic fingerprints at the SNP or variant level. The idea is to use a deep neural network to learn embeddings or representations of cancer genomes (from datasets like TCGA or PCAWG) and then see if cancers with similar mutation mechanisms end up close together in that space.

Most of the pan-cancer research I’ve seen focuses on gene expression or somatic mutation data, not germline SNPs. I’m wondering if there’s a reason for that. Is it mostly due to data access issues, the size of SNP data, weak biological signal, or something else?

If anyone has tried a similar approach, or knows of papers, datasets, or tools that explored this kind of cross-cancer genomic similarity, I’d really appreciate your insights.

Thanks in advance!