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Limited evolution of the actionable metastatic cancer genome under therapeutic pressure

Abstract

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.

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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 (www.hartwigmedicalfoundation.nl). 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 (https://github.com/hartwigmedical/pipeline5).

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Acknowledgements

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

Authors

Contributions

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.

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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.

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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.

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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). https://doi.org/10.1038/s41591-021-01448-w

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