Abstract
To understand genetic evolution in cancer during metastasis, we analyzed genomic profiles of 3,732 cancer patients in whom several tumor sites were longitudinally biopsied. During distant metastasis, tumors were observed to accumulate copy number alterations (CNAs) to a much greater degree than mutations. In particular, the development of whole genome duplication was a common event during metastasis, emerging de novo in 28% of patients. Loss of 9p (including CDKN2A) developed during metastasis in 11% of patients. To a lesser degree, mutations and allelic loss in human leukocyte antigen class I and other genes associated with antigen presentation also emerged. Increasing CNA, but not increasing mutational load, was associated with immune evasion in patients treated with immunotherapy. Taken together, these data suggest that CNA, rather than mutational accumulation, is enriched during cancer metastasis, perhaps due to a more favorable balance of enhanced cellular fitness versus immunogenicity.
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Data availability
A deidentified dataset containing the clinical features and processed data that underlie the results reported in this article derived from MSK-IMPACT tumor sequencing is available via Zenodo at https://zenodo.org/records/14538739 (ref. 55). The original sequencing reads cannot be publicly deposited due to privacy restrictions since sequencing was performed as part of clinical care.
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Acknowledgements
We are grateful to our patients and their families for their bravery and support of cancer research. We thank members of the Morris laboratory and the Center for Molecular Oncology at MSK for illuminating discussions. This study was supported by the Department of Defense Peer Reviewed Cancer Research Program and Rare Cancer Research Program, The Geoffrey Beene Cancer Research Center, Cycle for Survival: Team Fearless4Jen, The Jayme Flowers Fund, The Larry De Shon Fund, The Raquel and Riccardo Di Capua Fund, The Geri Herbert and David Yasenak Fund (to L.G.T.M.), the Weill Cornell CTSC Grant 2UL1-TR-002384 (to K.Z.), the Area of Concentration Program at Weill Cornell Medical College (to K.Z.), and the NIH/NCI Cancer Center Support Grant P30 CA008748 (institutional, to MSKCC). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.
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K.Z., J.V., C.Y.H., X.K.Z. and L.G.T.M. conceived and designed the study. K.Z., J.V., S.L., L.A.B., D.M., C.Y.H., C.V., M.L., C.F., A.S.L., M.P., S.J., X.D., T.A.C., M.F.B., X.K.Z. and L.G.T.M. acquired, analyzed and interpreted the data. K.Z., J.V., X.K.Z. and L.G.T.M. drafted the paper. All authors carried out a critical revision of the paper for important intellectual content. K.Z., J.V., X.K.Z. and L.G.T.M. carried out the statistical analysis. L.G.T.M. obtained the funding. L.A.B. provided administrative, technical or material support. C.B., X.K.Z. and L.G.T.M. supervised the study.
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M.F.B. reports personal fees from AstraZeneca and Paige.AI; research support from Boundless Bio; and intellectual property rights with SOPHiA Genetics. T.A.C. acknowledges grant funding from Bristol Myers Squibb, AstraZeneca, Illumina, Pfizer, AN2H and Eisai, has served as an advisor for Bristol Myers Squibb, Illumina, Eisai and AN2H, holds equity in AN2H and is a cofounder of Gritstone Oncology and holds equity in the company. T.A.C. and L.G.T.M. are listed inventors on intellectual property held by Memorial Sloan Kettering Cancer Center, unrelated to this work. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Correlation between tumor mutational burden and fraction of genome altered.
Spearman’s rho of log2(TMB + 1) and FGA. TMB = tumor mutational burden. FGA = fraction of genome altered.
Extended Data Fig. 2 Mixed effects model results in original dataset and dataset with more stringent mutation matching criteria.
Comparison of forest plots in original dataset (a, c) to dataset with more stringent mutation matching criteria (b, d; see Methods).
Extended Data Fig. 3 De novo analyses results in original dataset and dataset with more stringent mutation matching criteria.
Comparison of de novo mutation analyses in original dataset (a, c) to dataset with more stringent mutation matching criteria (b, d; see Methods).
Extended Data Fig. 4 Mixed effects model results with different minimum purity requirements.
Estimate of TMB (a) and FGA (b) metastasis estimate from mixed effects models run with increasing minimum purity requirements. TMB = tumor mutational burden. FGA = fraction of genome altered.
Extended Data Fig. 5 Estimated effect of metastatic sample on tumor mutational burden and fraction of genome altered by cancer subtype.
(a, b) Subtype analysis of infiltrating ductal carcinoma, and the estimated effect of metastatic sample on TMB and FGA. HR = hormone receptor. (c, d) Subtype analysis of pancreatic cancer, and the estimated effect of metastatic sample on TMB and FGA. NET = neuroendocrine tumor. (e, f) Subtype analysis of non-small cell lung cancer, and the estimated effect of metastatic sample on TMB and FGA. The plots show the point estimate (dot), with the whiskers representing the 95% confidence interval. FGA = fraction of genome altered.
Extended Data Fig. 6 Tumor mutational burden and fraction of genome altered changes by quantile.
The ratio of Metastasis > Primary to Metastasis < Primary cases, alongside the mean difference in TMB (a) and FGA (b) for each quartile of primary tumor scores. Mixed effects model results for the patients in the lowest (first quartile) of TMB and FGA values for their first available sample (c, e) and in the highest (fourth quartile) of TMB and FGA values for their first available sample (d, f). TMB = tumor mutational burden. FGA = fraction of genome altered.
Extended Data Fig. 7 Association of tumor mutational burden and fraction of genome altered with overall survival, progression free survival, and immunotherapy response.
Forest plots depicting multivariable regression models examining associations with overall survival in Cox multivariable regression (a), progression-free survival in Cox multivariable regression (b), and tumor response to immunotherapy in logistic regression (c). Covariates in the models included tumor mutational burden (TMB), fraction of genome altered (FGA), cancer stage, and cancer type. Hazard or odds ratios with 95% confidence intervals and corresponding p-values are shown on the right.
Extended Data Fig. 8 De novo development of different genetic alterations between paired primary and metastatic sites by cancer type.
Percentage of patients with paired primary and metastatic samples demonstrating de novo mutations in antigen presenting mechanism (APM) genes, including B2M, TAP1, TAP2, HLA-A, HLA-B, and HLA-C. All samples were filtered to ensure a sample purity of greater than 20%.
Extended Data Fig. 9 Clonality status between paired primary and metastatic samples.
(a) Scatterplot showing paired mutations that appeared in both a primary sample and a metastatic sample from the same patient. Cancer cell fraction (CCF) of the mutation in the primary sample was plotted against CCF of the mutation in the metastatic sample. (b) Alluvial plot showing fate of cancer cell fraction (CCF) of mutations among patients with both a primary and a metastatic site sampled. This plot tracks how the clonality status, determined by CCF, changes in the metastatic site. Ind = indeterminate.
Extended Data Fig. 10 Relationship between fraction of genome with loss of heterozygosity and HLA-I loss of heterozygosity status.
The difference in fraction of genome with loss of heterozygosity (FGLOH) between paired primary and metastatic samples (a) and paired initial and subsequent primary samples (b) in different subgroups based on de novo HLA-I LOH status.
Supplementary information
Supplementary Tables 1–6
Supplementary Table 1, Patient and sample characteristics in the whole cohort and by sample type. Supplementary Table 2, Mixed effects model results at different purity cutoffs. Red shading indicates that P value is <0.05. Supplementary Table 3, Number of samples by cancer type and metastatic sample site (Fig. 4). Supplementary Table 4, Results of analysis of de novo WGD at different purity stringency cutoffs. Supplementary Table 5, Results of analysis of de novo mutations and alterations at different purity stringency cutoffs. Supplementary Table 6, Results of analysis of de novo HLA-I LOH at different LOHHLA significance thresholds.
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Zhao, K., Vos, J., Lam, S. et al. Longitudinal and multisite sampling reveals mutational and copy number evolution in tumors during metastatic dissemination. Nat Genet 57, 1504–1511 (2025). https://doi.org/10.1038/s41588-025-02204-3
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DOI: https://doi.org/10.1038/s41588-025-02204-3
