The intricate interplay between somatic mutations and copy number alterations critically influences tumour evolution and patient prognosis. Traditional genomic studies often overlook this interplay by analysing these two biomarker types in isolation. Leveraging an innovative computational model capable of detecting allele-specific copy number alterations from clinical targeted panels without matched normal, we conducted a comprehensive analysis of over 500,000 mutations across 60,000 clinical samples spanning 39 cancer types. Our findings uncovered 11 genes and 6 hotspots exhibiting recurrent tumour-specific patterns of co-existing mutations and copy-number alterations across 17 tumours. By stratifying more than 24,000 patients based on these composite genotypes across multiple oncogenes and tumour suppressor genes, we identified 66 groups with distinct prognostic significance, 25% more than using a standard mutation-centric stratification. Notably, 7 groups displayed a heightened propensity for metastasis, while 16 were associated with site-specific patterns of metastatic dissemination. This augmented insight into genomic drivers enhances our understanding of cancer progression and metastasis, holding the potential to significantly foster biomarker discovery.

The authors have declared no competing interest.

The research leading to these results has received funding from AIRC under MFAG 2020, ID. 24913 project, P.I. Caravagna Giulio. This research was also financially supported through funding Ricerca Corrente from the IRCCS Regina Elena National Cancer Institute granted by the Italian Ministry of Health. We wish to thank Area Science Park for computational support through the ORFEO (Open Research Facility for Epigenomics and Other) data centre.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

No new human data was generated in the course of this study. Previously published data include: - PCAWG and TCGA data ( mutations with matched validated CNAs) available at https://doi.org/10.5281/zenodo.6410935 - MSK MetTropism data from cohort msk_met_2021 downloaded from the CBioPortal at https://www.cbioportal.org/study/summary?id=msk_met_2021. - AACR GENIE-DFCI data downloaded at https://www.synapse.org/# through access codes syn50678641, syn50678411, syn50678410, syn50678644, syn50678531, syn50678642, syn50678530, syn50678532, syn50678295, syn50678640, syn50678653, syn50678296.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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PCAWG and TCGA data used in this paper ( mutations with matched validated CNAs) are available at https://doi.org/10.5281/zenodo.6410935, following 42. MSK MetTropism data has been downloaded from cohort msk_met_2021 at the CBioPortal, following link https://www.cbioportal.org/study/summary?id=msk_met_2021. AACR GENIE-DFCI data has been downloaded at https://www.synapse.org/# through access codes syn50678641, syn50678411, syn50678410, syn50678644, syn50678531, syn50678642, syn50678530, syn50678532, syn50678295, syn50678640, syn50678653, syn50678296.