Cosine Similarity Conflates Clinically Distinct Cancer Variants: A Case for Typed-Graph Retrieval in Precision Oncology Decision Support

Cancer variant interpretation increasingly relies on retrieval from biomedical knowledge bases, with cosine similarity over neural text embeddings now the dominant retrieval substrate. Whether these embeddings preserve the entity-level distinctions that variant interpretation requires that BRAF V600E and V600K are distinct alleles, that EGFR L858R is a sensitizing mutation while T790M is a resistance mutation has not been systematically measured. We hypothesize that cosine-similarity retrieval over biomedical embeddings conflates clinically distinct cancer variants at high rates, while a typed-graph approach in which each variant is a discrete node preserves variant identity by construction. We constructed a benchmark of 9 cancer variant pairs known to have differential FDA-approved therapy indications or distinct molecular biology, curated from theCIViC clinical evidence database and primary clinical literature. Pairs included BRAF V600E vs V600K (melanoma), EGFR L858R vs T790M (NSCLC