Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Limited evolution of the actionable metastatic cancer genome under therapeutic pressure


Genomic profiling is critical for the identification of treatment options for patients with metastatic cancer, but it remains unclear how frequently this procedure should be repeated during the course of the disease. To address this, we analyzed whole-genome sequencing (WGS) data of 250 biopsy pairs, longitudinally collected over the treatment course of 231 adult patients with a representative variety of metastatic solid malignancies. Within the biopsy interval (median, 6.4 months), patients received one or multiple lines of (mostly) standard-of-care (SOC) treatments, with all major treatment modalities being broadly represented. SOC biomarkers and biomarkers for clinical trial enrollment could be identified in 23% and 72% of biopsies, respectively. For SOC genomic biomarkers, we observed full concordance between the first and the second biopsy in 99% of pairs. Of the 219 biomarkers for clinical trial enrollment that were identified in the first biopsies, we recovered 94% in the follow-up biopsies. Furthermore, a second WGS analysis did not identify additional biomarkers for clinical trial enrollment in 91% of patients. More-frequent genomic evolution was observed when considering specific genes targeted by small-molecule inhibitors or hormonal therapies (21% and 22% of cases, respectively). Together, our data demonstrate that there is limited evolution of the actionable genome of treated metastases. A single WGS analysis of a metastatic biopsy is generally sufficient to identify SOC genomic biomarkers and to identify investigational treatment opportunities.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Cohort overview.
Fig. 2: Genome-wide concordance of somatic profiles between tumor biopsy pairs.
Fig. 3: The genomic somatic driver landscape of metastatic cancers under therapeutic pressure.
Fig. 4: The evolution of SOC genomic biomarkers for on-label treatments of metastatic cancers under therapeutic pressure.
Fig. 5: The evolution of genomic biomarkers for investigational treatment indications for metastatic cancers under therapeutic pressure.
Fig. 6: Somatic genomic evolution of genes encoding the target proteins of small-molecule inhibitors, hormonal therapies and monoclonal antibodies administered within the biopsy interval.

Data availability

Raw and processed genomics data (for sample identifiers, see Supplementary Table 1) and clinical data are freely available from the Hartwig Medical Foundation through standardized procedures and request forms ( Additional clinical data collected for this study, including detailed information about treatments, treatment outcomes and biopsy sites, are available as Supplementary Tables 1–5 of this manuscript.

Code availability

Bioinformatics analyses of WGS data were performed with an optimized pipeline based on open-source tools, which are freely available on GitHub (


  1. Hyman, D. M., Taylor, B. S. & Baselga, J. Implementing genome-driven oncology. Cell 168, 584–599 (2017).

    CAS  Article  Google Scholar 

  2. Stockley, T. L. et al. Molecular profiling of advanced solid tumors and patient outcomes with genotype-matched clinical trials: the Princess Margaret IMPACT/COMPACT trial. Genome Med. 8, 109 (2016).

    Article  Google Scholar 

  3. Larkin, J. et al. Combined vemurafenib and cobimetinib in BRAF-mutated melanoma. N. Engl. J. Med. 371, 1867–1876 (2014).

    Article  Google Scholar 

  4. Marabelle, A. et al. Efficacy of pembrolizumab in patients with noncolorectal high microsatellite instability/mismatch repair-deficient cancer: results from the phase II KEYNOTE-158 study. J. Clin. Oncol. 38, 1–10 (2020).

    CAS  Article  Google Scholar 

  5. Riaz, N. et al. Pan-cancer analysis of bi-allelic alterations in homologous recombination DNA repair genes. Nat. Commun. 8, 857 (2017).

    Article  Google Scholar 

  6. Okamura, R. et al. Analysis of NTRK alterations in pan-cancer adult and pediatric malignancies: implications for NTRK-targeted therapeutics. JCO Precis. Oncol. 2, 1–20 (2018).

  7. Bonneville, R. et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis. Oncol. 1, 1–15 (2017).

  8. van der Velden, D. L. et al. The Drug Rediscovery protocol facilitates the expanded use of existing anticancer drugs. Nature 574, 127–131 (2019).

    Article  Google Scholar 

  9. Hyman, D. M. et al. HER kinase inhibition in patients with HER2- and HER3-mutant cancers. Nature 554, 189–194 (2018).

    CAS  Article  Google Scholar 

  10. Hyman, D. M. et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N. Engl. J. Med. 373, 726–736 (2015).

    CAS  Article  Google Scholar 

  11. Subbiah, V. et al. Pan-cancer efficacy of vemurafenib in BRAFV600-mutant non-melanoma cancers. Cancer Discov. 10, 657–663 (2020).

    CAS  Article  Google Scholar 

  12. Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    CAS  Article  Google Scholar 

  13. Drilon, A. et al. Efficacy of larotrectinib in TRK fusion-positive cancers in adults and children. N. Engl. J. Med. 378, 731–739 (2018).

    CAS  Article  Google Scholar 

  14. Aitken, M., Villa, P., Bennett, K., Tewary, V. & Lech, C. Optimizing Oncology Care Through Biomarker Adoption: Barriers and Solutions (IQVIA Institute for Human Data Science, 2020).

  15. Roepman, P. et al. Clinical validation of whole genome sequencing for cancer diagnostics. J. Mol. Diagnostics 23, 816–833 (2021).

    CAS  Article  Google Scholar 

  16. Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).

    CAS  Article  Google Scholar 

  17. Reiter, J. G. et al. Minimal functional driver gene heterogeneity among untreated metastases. Science 361, 1033–1037 (2018).

    CAS  Article  Google Scholar 

  18. Pich, O. et al. The mutational footprints of cancer therapies. Nat. Genet. 51, 1732–1740 (2019).

    CAS  Article  Google Scholar 

  19. Kucab, J. E. et al. A compendium of mutational signatures of environmental agents. Cell 177, 821–836 (2019).

    CAS  Article  Google Scholar 

  20. Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

    CAS  Article  Google Scholar 

  21. McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).

    CAS  Article  Google Scholar 

  22. Johnson, B. E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014).

    CAS  Article  Google Scholar 

  23. Visakorpi, T. et al. In vivo amplification of the androgen receptor gene and progression of human prostate cancer. Nat. Genet. 9, 401–406 (1995).

    CAS  Article  Google Scholar 

  24. Waltering, K. K., Urbanucci, A. & Visakorpi, T. Androgen receptor (AR) aberrations in castration-resistant prostate cancer. Mol. Cell. Endocrinol. 360, 38–43 (2012).

    CAS  Article  Google Scholar 

  25. Robinson, D. R. et al. Activating ESR1 mutations in hormone-resistant metastatic breast cancer. Nat. Genet. 45, 1446–1451 (2013).

    CAS  Article  Google Scholar 

  26. Toy, W. et al. ESR1 ligand-binding domain mutations in hormone-resistant breast cancer. Nat. Genet. 45, 1439–1445 (2013).

    CAS  Article  Google Scholar 

  27. Angus, L. et al. The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat. Genet. 51, 1450–1458 (2019).

    CAS  Article  Google Scholar 

  28. Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).

    CAS  Article  Google Scholar 

  29. Levine, A. J. p53: 800 million years of evolution and 40 years of discovery. Nat. Rev. Cancer 20, 471–480 (2020).

    CAS  Article  Google Scholar 

  30. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 1, 1–16 (2017).

  31. Tamborero, D. et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 10, 25 (2018).

    Article  Google Scholar 

  32. Griffith, M. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49, 170–174 (2017).

    CAS  Article  Google Scholar 

  33. Mateo, J. et al. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO scale for clinical actionability of molecular targets (ESCAT). Ann. Oncol. 29, 1895–1902 (2018).

    CAS  Article  Google Scholar 

  34. Fribbens, C. et al. Plasma ESR1 mutations and the treatment of estrogen receptor-positive advanced breast cancer. J. Clin. Oncol. 34, 2961–2968 (2016).

    CAS  Article  Google Scholar 

  35. Samsom, K. G. et al. Study protocol: whole genome sequencing implementation in standard diagnostics for every cancer patient (WIDE). BMC Med. Genomics 13, 169 (2020).

    Article  Google Scholar 

  36. Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

    CAS  Article  Google Scholar 

  37. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  Google Scholar 

  38. Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv (2017).

  39. Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    CAS  Article  Google Scholar 

  40. Cameron, D. L. et al. GRIDSS, PURPLE, LINX: unscrambling the tumor genome via integrated analysis of structural variation and copy number. Preprint at bioRxiv (2019).

  41. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041 (2017).

    CAS  Article  Google Scholar 

  42. Forbes, S. A. et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 45, D777–D783 (2017).

    CAS  Article  Google Scholar 

  43. Ni Huang, M. et al. MSIseq: software for assessing microsatellite instability from catalogs of somatic mutations. Sci. Rep. 5, 13321 (2015).

    Article  Google Scholar 

  44. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).

    CAS  Article  Google Scholar 

  45. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    CAS  Article  Google Scholar 

Download references


We acknowledge the Hartwig Medical Foundation and the Center of Personalised Cancer Treatment (CPCT) for providing the data used in this the study. This work was supported by funding from the Josephine Nefkens Foundation and the Oncode Institute.

Author information

Authors and Affiliations



Original concept was developed by J.v.d.H., L.R.H. and E.E.V. Coordination of clinical data collection was carried out by L.R.H. Representatives of main patient accrual sites: M.P.L., H.M.W.V., H.G., A.J.d.L., E.F.S. and E.E.V. Primary data analyses were performed by J.v.d.H., L.R.H. and P.R. Coordination of genomics data collection was done by E.C. The initial draft was written by J.v.d.H., L.R.H., L.F.A.W. and E.E.V. Figures were created by J.v.d.H. All authors critically reviewed all analyses and results of this work and edited and approved the final manuscript. This work was jointly supervised by L.F.A.W. and E.E.V.

Corresponding author

Correspondence to Emile E. Voest.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Medicine thanks Mark Cowley, Funda Meric-Bernstam and the other, anonymous, reviewer(s) for their contribution to the peer review this work. Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Tumor type distribution of the cohort.

Percentage of biopsies per anatomical site of primary tumor, for the paired biopsy cohort (red; n = 481) and full HMF cohort as published (gray; n = 2921).

Extended Data Fig. 2 Tumor purity differences among paired biopsies.

Barplot denoting the absolute difference (Δ) of tumor purity for each paired biopsy (n = 250), where positive values indicate an increase in the second biopsy as compared to the first. The samples are ranked from high to low Δ purity. The P-value for the two-sided paired Wilcoxon test is shown.

Extended Data Fig. 3 The influence of intertumoral heterogeneity on the discordance of genome-wide somatic profiles between paired biopsies.

a, Boxplot of the fraction of genomic alterations private to the first biopsy (lost alterations, as a fraction of the total number of alterations in the first biopsy), stratified for pairs obtained from the same lesion (n = 83 pairs) or pairs obtained from different lesions (n = 92 pairs). Different classes of genomic alterations are plotted separately: nucleotide substitutions (all, clonal, subclonal), indels (all, clonal, subclonal), structural variants (SVs). Clonal alterations have a probability of clonality above >95%, whereas subclonal alterations have a probability of subclonality >95%. Box center lines, box ranges, whiskers, and dots indicate medians, quartiles, 1.5 interquartile ranges, and outliers, respectively. Two-sided Wilcoxon rank sum test-based P-values are shown (red for significant associations). Only paired biopsies for which it was known whether or not the two biopsies were obtained from the same lesion were used for this analysis (n = 175). b, As A, but the y-axis represents the fraction of alterations private to the second biopsy (gained alterations, as a fraction of the total number of alterations in the second biopsy). c, As A, but the y-axis indicates the Pearson correlation coefficient for the copy number profiles of biopsy pairs.

Extended Data Fig. 4 The influence of time between the biopsies on the discordance of genome-wide somatic profiles between paired biopsies.

a, Dot plots of the ascsin-transformed fractions of genomic alterations private to the first biopsy (lost alterations, as a fraction of the total number of alterations in the first biopsy; top row of plots; y-axis), or the ascsin-transformed fraction of genomic alterations private to the second biopsy (gained alterations, as a fraction of the total number of alterations in the second biopsy; bottom row of plots; y-axis) for all biopsy pairs (n = 250), against the time between biopsies (x-axis). Different classes of somatic alterations are plotted separately: nucleotide substitutions (clonal, subclonal), indels (clonal, subclonal), and structural variants (SVs), as indicated at each subplot’s title. Clonal alterations have a probability of clonality above >95%, whereas subclonal alterations have a probability of subclonality >95%. Arcsin transformation was performed to achieve normality of the inherently non-normally distributed fractions. Linear fits and bootstrapping-obtained 95% confidence intervals are plotted. Pearson correlation coefficients (r) and accompanying two-sided P-values are shown (red for significant associations). b, As A, but the y-axis indicates the Pearson correlation coefficient (r) for the copy number profiles of biopsy pairs. Analysis is based on all paired biopsies (n = 250).

Extended Data Fig. 5 The number of standard of care (left) or investigational (right) biomarkers per biopsy, for patients included in the DRUP study (blue) vs all others (orange).

The y-axis denotes the percentage of biopsies relative to the total number of biopsies from patients in the DRUP study (blue; n = 140 biopsies) or all other patients (orange; n = 341 biopsies). Some patients included in the DRUP study lack actionable biomarkers, for two reasons. First, some patients were included in the DRUP study based on a high tumor mutational burden, which is not considered as an actionable biomarker in the current study. Second, some patients were included in the DRUP study based on the absence of a particular resistance marker (for example, the absence of a KRAS mutation), without the need for the presence of a particular biomarker. Wilcoxon rank sum test-based two-sided P-values are shown.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1 | Treatment information and clinical outcome of all regimens administered within the biopsy interval. Supplementary Table 2 | Somatic genomic drivers detected in the cohort. Supplementary Table 3 | Comparison of actionable genomic alterations between paired biopsies. Supplementary Table 4 | Comparison of genomic alterations in drug targets between paired biopsies, for treatments with small-molecule inhibitors, hormonal therapies and monoclonal antibodies. Supplementary Table 5 | Overview of all drugs and their targets used in this study.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

van de Haar, J., Hoes, L.R., Roepman, P. et al. Limited evolution of the actionable metastatic cancer genome under therapeutic pressure. Nat Med 27, 1553–1563 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer