Genomic foundation model-derived disruption profiling links somatic mutations to cancer biology and clinical outcomes
Synopsis
Using AlphaGenome and AlphaMissense to quantify the disruption imposed by somatic mutations across 8,800 patients and 33 cancer types from The Cancer Genome Atlas, this study found that recurrent hotspot mutations showed substantially larger predicted protein-level effects while non-hotspot mutations exhibited larger regulatory effects across most cancer types; aggregating variant-level predictions into patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing yielded gene- and modality-specific profiles that reflected tissue of origin, cancer type, and microsatellite-instability status while retaining information beyond tumor mutational burden; among patients lacking recurrent hotspot mutations, higher predicte
Interpretation
At the individual-variant level, recurrent hotspot mutations showed substantially larger predicted protein-level effects, whereas non-hotspot mutations exhibited larger regulatory effects across most cancer types. Prior cancer genomics concentrated on individual mutations; this work separates predicted effects by variant class (hotspot versus non-hotspot), proposing that hotspots are enriched for strong protein-level effects while regulatory consequences are distributed more broadly across other variants. Based on somatic mutations from 8,800 patients and 33 cancer types in TCGA, quantified with AlphaGenome and AlphaMissense, representing large-scale computational prediction evidence.
Aggregating variant-level predictions constructed patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing; these profiles were gene- and modality-specific, reflected tissue of origin, cancer type, and microsatellite-instability status, and retained information beyond tumor mutational burden. Shifts from a discrete driver-mutation view to a continuous, multidimensional view of gene perturbation, integrating multiple regulatory modalities into patient-gene profiles. Aggregation analysis on the same TCGA cohort, showing associations with tissue of origin, cancer type, and microsatellite-instability status, and reporting retention of information beyond tumor mutational burden.
Among patients lacking recurrent hotspot mutations in a given cancer gene, higher predicted disruption was associated with overall survival, with the strongest and most consistent signal observed for chromatin accessibility; in an independent treatment-annotated cohort, gene-level disruption was also associated with survival within treatment-defined subgroups. Links foundation model-derived disruption profiles to clinical outcomes (overall survival) and observes associations within treatment-defined subgroups in an independent treatment-annotated cohort. Survival association analysis in the TCGA cohort, with associations also observed in an independent treatment-annotated cohort, representing observational association evidence.
Perspective
This work applies to research settings that quantify somatic mutation disruption from DNA sequence, especially in patient groups lacking recurrent hotspot mutations; its disruption profiles can characterize tissue of origin, cancer type, and microsatellite-instability status and explore associations with survival within treatment-defined subgroups.
This summary is based on abstract-level text and does not include figures or supplementary materials; disruption quantification is model prediction rather than direct experimental measurement, survival associations are observational, and the specific effect sizes and statistical details behind the finding that chromatin accessibility showed the strongest and most consistent signal await confirmation in the original figures.