Gene mutant dosage is associated with prognosis and metastatic tropism in 60,000 clinical cancer samples.

The intricate interplay between somatic mutations and copy number alterations critically influences tumour evolution and patient prognosis. However, traditional genomic analyses often treat these alterations independently, overlooking gene mutant dosage - a key emergent property of their interaction. Here, we develop an innovative computational framework that infers mutation copy number and multiplicity directly from clinical targeted sequencing panels without requiring matched normal samples. Using this approach, we derived gene mutant dosage statistics for over 500,000 mutations across 60,000 clinical samples spanning 39 cancer types. By stratifying more than 20,000 patients according to mutant dosage across multiple oncogenes and tumour suppressor genes, we identified 46 tumour type-specific biomarkers predictive of overall survival. Notably, 13 of these biomarkers across 12 tumour types were undetectable using standard binary mutant/wild-type models. Additionally, 26 biomarkers were recurrently associated with metastatic spread in 10 tumour types, and 20 predicted organ-specific metastatic tropism in 5 tumour types. Alongside confirming known roles for established oncogenes and tumour suppressors, our method reveals, for the first time, gene mutant dosage patterns as independent predictors of prognosis, metastatic potential, and site-specific dissemination across diverse solid tumours. This augmented insight into genomic drivers enhances our understanding of cancer progression and metastasis, holding the potential to foster biomarker discovery significantly.