Metastatic spread involves tumour cell detachment from a primary tumour, colonization of a secondary tissue and growth in a hostile environment1,2. Advanced metastatic tumours are frequently able to resist aggressive treatment regimes6. Despite the many efforts to understand these phenomena3,7,8,9, we still have limited knowledge of the contribution of genomic changes that equip tumours with these extraordinary capacities. Thus, it is essential to characterize genomic differences between primary and metastatic cancers and quantify their effect on therapy resistance to understand and harness therapeutic interventions that establish more effective and more personalized therapies10.

Although extensive whole-genome analyses of primary or metastatic tumour types have been conducted4,5, large-scale comparative studies between the two tumour stages remain limited due to the logistical challenges associated with obtaining pan-cancer cohorts of primary and metastatic cancers. To circumvent this issue, most comparison studies have relied on unpaired whole-exome data or have adopted more targeted approaches with a specific focus on driver gene landscapes11,12,13. However, these efforts have frequently involved separated processing pipelines for primary and metastatic cohorts, complicating the analysis of genomic features that are highly sensitive to the selected data-processing strategy14,15. A recent study that uniformly analysed more than 25,000 tumours16 has provided a comprehensive overview of the genomic differences, driver alteration patterns and organotropism using clinical gene-panel sequencing as a base. However, this genomic analysis approach prevented the exploration of the full spectrum of genomic alterations that have a role in tumorigenesis, such as structural variation and mutational scarring.

Harmonized whole-genome-sequenced tumours

Here we created a uniformly processed whole-genome-sequenced (WGS) inventory of 7,108 matched tumour and normal genomes from two unpaired primary and metastatic cohorts. We first collated the Hartwig Medical Foundation (Hartwig) dataset5, which included 4,784 samples from 4,375 patients with metastatic cancers. Then, we reprocessed 2,835 primary tumour samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium4 using the open-source Hartwig analytical pipeline5,17 to harmonize somatic calling and annotations of events, and to eliminate processing biases (Extended Data Fig. 1 and Supplementary Table 1). Reassuringly, per-sample comparison of the number of single-base substitutions (SBSs), double-base substitutions (DBSs), indels (IDs) and structural variants (SVs) revealed a strong agreement between our results and the consensus calls originally generated by the PCAWG consortium (Supplementary Note 1). In addition, our processing pipeline strategy was minimally affected by differences in sequencing coverage, enabling a reasonable comparison of WGS samples from heterogeneous sources (Supplementary Note 1). A total of 7,108 tumour samples from 71 cancer types met the processing pipeline quality standards (Methods and Extended Data Fig. 1a) and constitutes one of the largest publicly available datasets of WGS tumours.

We next focused on 23 cancer types from 14 tissues with sufficient sample representation, comprising 5,365 tumour samples (1,914 primary and 3,451 metastatic) to explore genomic differences between primary and metastatic tumours (Fig. 1a and Extended Data Fig. 1a). Within this dataset, patients with metastatic tumours were slightly older at biopsy than patients with primary tumours (mean of 1.67 years older across all cancer types), although patients with metastatic prostate and thyroid carcinomas, and diffuse large B cell lymphoma were markedly older than their primary counterparts. Consistent gender proportions were observed across all cancer types except for thyroid adenocarcinomas (metastatic: 72% male and 28% female; primary: 25% male and 75% female). Treatment information was available for 83.7% of patients with metastatic tumours, which is essential to gauge specific treatment-induced contributions to genomic differences between primary and metastatic tumours (Fig. 1a). Finally, biopsy locations were annotated for 84.2% of patients with metastatic tumours (12.2% from metastatic lesions in the primary tissue (local), 16.2% in lymph nodes and 55.7% in distant locations) and displayed a highly tumour-type-specific distribution pattern, probably reflecting both the dissemination patterns of the tumours and the accessibility for safe clinical sampling.

Fig. 1: Database overview and global genomic features.
figure 1

a, Anatomical location of the 23 cancer types, ordered by tissue or origin, included in this study. From left to right: sample size, age at biopsy, gender, treatment type and biopsy site of the cohort with metastatic tumours. CNS, central nervous system. The image in a was created using BioRender ( b, Mean percentage of clonal mutations in primary (x axis) and metastatic (y axis) tumours. The dots are coloured according to the log2 of the clonality ratio (metastatic divided by primary). The size of the dots is proportional to the total number of samples (primary and metastatic). The red edge lines represent a two-sided Mann–Whitney adjusted P < 0.05. BLCA, bladder urothelial carcinoma; BRCA, breast carcinoma; CESC, cervical carcinoma; CHOL, cholangiocarcinoma; COREAD, colorectal carcinoma; DLBCL, diffuse large B cell lymphoma; ESCA, oesophageal carcinoma; GBM, glioblastoma multiforme; HNSC, upper respiratory tract carcinoma; KIRC, kidney renal clear cell carcinoma; LIHC, hepatocellular carcinoma; LMS, leiomyosarcoma; LPS, liposarcoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; PAAD, pancreatic carcinoma; PANET, pancreatic neuroendocrine tumour; PRAD, prostate carcinoma; OV, ovarian serous adenocarcinoma; SKCM, skin melanoma; STAD, stomach carcinoma; THCA, thyroid carcinoma; UCEC, uterine carcinoma. c, Tumour clonality according to the metastatic biopsy location in breast (left), colorectal (middle) and oesophageal (right) carcinomas. n Refers to the number of samples. P refers to Mann–Whitney two-sided P value. For boxplots, the centre line indicates the median; the box limits denote the first and third quartiles; and the whiskers indicate the lowest or highest data points at the first quartile minus or plus 1.5× the interquartile range. d, Heatmap representing the normalized mean chromosome arm ploidy gains and losses relative to the expected 2n ploidy status in primary (top) and metastatic (bottom) tumours. *Adjusted P < 0.01 (two-sided Mann–Whitney). e, Comparison of four genomic instability indicators between primary (top) and metastatic (bottom) tumours. From left to right: aneuploidy score from ref. 20, the proportion of genome undergoing LOH, and the fraction of samples bearing whole-genome duplication (WGD) and TP53 alterations. The black dots represent the median values. *Adjusted P < 0.01 using two-sided Fisher’s exact test for WGD and TP53, and two-sided Mann–Whitney test for the continuous features.

Comparison of global genomic features

We first explored global genomic differences between primary and metastatic tumours across the aforementioned 23 cancer types. Metastatic tumours showed an overall increase in clonality compared with their primary tumour counterparts (Fig. 1b). Particularly, five cancer types had a significantly higher metastatic average clonality ratio, ranging from 13.6% increased mean clonality in pancreatic carcinoma to 37.2% in thyroid carcinoma. Within the group of patients with metastatic breast carcinoma, distant and lymph node tumour biopsies showed significantly higher clonality ratios than local metastatic lesions (Fig. 1c and Supplementary Note 2). This increase in clonality was also observed in distant tumour biopsies of oesophageal and colorectal carcinomas (Fig. 1c). Nevertheless, the biopsy location did not influence tumour clonality in other cancer types such as lung adenocarcinoma and skin melanoma (Extended Data Fig. 1b), suggesting that patterns of tumour dissemination are highly tumour-type specific9. Our results support the model that metastatic lesions generally have lower intratumour heterogeneity16, which may be explained by a single major subclone seeding event from the primary cancer and/or by severe evolutionary constraints imposed by anticancer therapies.

Comparison of chromosome arm aneuploidy profiles revealed a generally conserved portrait, which was strongly shaped by the cell of origin (Fig. 1d and Supplementary Table 2), supporting the notion that tumour karyotype is generally defined at early stages of tumorigenesis18. Only metastatic kidney renal clear cell, prostate and thyroid carcinomas showed substantial changes compared with primary tumours, encompassing 91% (43 of 47) of all significant discrepancies. Besides the poor prognostic marker 8q gain in metastatic prostate carcinoma19, all discrepancies were associated with an increased prevalence of chromosomal arm losses at the metastatic setting. Remarkably, 30% (14 of 46) of the metastatic-enriched chromosome arm losses were retained when comparing non-WGD tumours (Extended Data Fig. 1c,d), indicating that other factors, aside from whole-genome doubling20,21, have an important role in the accumulation of arm-sized chromosomal aberrations.

The same three cancer types also showed persistent increases in specific genomic instability indicators (that is, chromosomal aneuploidy score20, loss of heterozygosity (LOH) genome fraction, WGD22 and TP53 alterations23) in the metastatic cohort (Fig. 1e and Supplementary Table 2). Although all of these indicators are elevated pan-cancer in WGD tumours (Extended Data Fig. 1c,d), non-WGD metastatic tumours of these three cancer types also had significantly greater aneuploidy and LOH scores. Furthermore, patients with metastatic tumours from other cancer types, including lung and colorectal adenocarcinomas, also displayed a moderate increase in aneuploidy and LOH scores, although they seemed to be primarily associated with higher metastatic WGD rates (Extended Data Fig. 1c). Our results thus revealed that the majority of cancer types have already acquired variable degrees of chromosomal arm aneuploidy in early stages of tumorigenesis. However, in certain cancers, such as kidney renal clear cell, prostate and thyroid carcinomas, significantly increased levels of genomic instability were induced in later evolutionary stages, which were, in turn, associated with substantial additional karyotypic changes.

Tumour mutation burden

We observed that the small variant tumour mutation burden (TMB), collectively encompassing SBSs, DBSs and IDs, was only moderately increased in metastatic tumours compared with primary tumours across the 23 cancer types tested (fold-change increases of 1.25 ± 0.47 for SBSs, 1.55 ± 0.86 for DBSs and 1.45 ± 0.53 for IDs; mean ± standard deviation (s.d.)). In fact, 15 of the 23 cancer types had no significant increase in mutation burden for any mutation type. Only five cancer types (breast, cervical, thyroid and prostate carcinomas and pancreatic neuroendocrine tumour) had a consistent increase for the three mutation types at the metastatic stage, although the mutation profiles lacked systematic differences between primary and metastatic tumours (Fig. 2b and Extended Data Fig. 2a). Finally, further TMB comparisons grouping by tumour subtypes, metastatic biopsy locations and primary clinical progression status generally provided consistent results, although cancer-type-specific particularities are present (Supplementary Note 2). These results show that TMB is not necessarily indicative of tumour progression status and that the overall mutational spectra are tightly shaped by the mutational processes that were already active before and during primary tumour development.

Fig. 2: TMB and mutational processes.
figure 2

a, Cumulative distribution function plot (samples were ranked independently for each variant type) of TMB for each cancer type for SBS (blue), IDs (green) and DBS (red). The horizontal lines represent median values. The fold-change labels are included only when two-sided Mann–Whitney comparison renders a significant adjusted P < 0.05. b, SBS mutational spectra of patients with metastatic (top) and primary (bottom) tumours. Patients are ordered according to their TMB. DDRD, DNA damage repair deficiency; ROS, reactive oxygen species. c, Moon plot representing the SBS mutational burden differences attributed to each mutational signature in metastatic (main plot, left) and primary (main plot, right) tumours. The edge thickness and colours represent significant differences (two-sided Mann–Whitney adjusted P < 0.05, ±1.4× fold change) and the direction of the enrichment, respectively. The size of the circles are proportionate to the mutation burden difference. The bars on the right indicate the number of metastatic cancer types with a mutational signature with significant enrichment. The top stacked bars represent the cumulative signature exposure difference. The thicker bar edge lines represent significance. Bars are coloured according to the annotated aetiology. Only mutational signatures with known aetiology or with at least one cancer type with significant metastatic enrichment are included. 5-FU, 5-fluorouracil; 5mC, 5-methylcytosine; MMRd, mismatch repair deficiency.

Mutational processes activity comparison

To determine whether the TMB differences may be attributed to differential activity of environmental or endogenous mutational processes, we assessed the activities of all operative mutational signatures in a quantitative and relative manner. We found that mutations attributed to cytotoxic treatments were significantly enriched in ten cancer types (Fig. 2c (red top bars) and Extended Data Figs. 2b,c and 3a for relative contributions). Platinum-based chemotherapies (SBS31/SBS35 and DBS5) showed the strongest mutagenic effect with 551 ± 575 (mean ± s.d.) SBS mutations and 32 ± 22 (mean ± s.d.) DBS-attributed mutations on average per sample. In fact, the excess in DBS mutation burden observed in eight cancer types (breast, oesophageal, stomach, cervical, ovarian serous and lung squamous carcinomas, cholangiocarcinoma and lung adenocarcinoma) was uniquely linked to platinum treatment mutations (Extended Data Fig. 2b, top bars). Likewise, median mutation contribution from the radiotherapy ID signature24 (ID8) was enriched in six cancer types commonly exposed to radiation-based treatment (Extended Data Fig. 2c), whereas the 5-fluorouracil25 (SBS17a/b) and polycyclic aromatic hydrocarbon metabolites from chemotreatments26 (DBS2) also displayed greater metastatic mutation burden contribution in a tumour-type-specific manner (Extended Data Fig. 2b,c).

The broad enrichment of SBS2/SBS13 mutations in metastatic cancers suggests enhanced activity of APOBEC mutagenesis during the progression of advanced tumours. Specifically, our results revealed an increase in APOBEC mutation burden of 325 ± 178 (mean ± s.d.) mutations per sample in six metastatic tumours (breast, colorectal, stomach, kidney, prostate and pancreatic neuroendocrine carcinomas) that reached statistical significance, with breast and stomach cancers the types with the strongest increase (more than 500 APOBEC mutations per sample). Other cancer types, such as cervical and bladder urothelial carcinomas, also showed enhanced APOBEC activity (more than 2,500 median mutations per sample), but they did not reach significance due to already high intrinsic APOBEC activity in the primary tumours. The metastatic breast cancer samples also had a higher percentage in clustered APOBEC hypermutation variants than primary tumours (15% versus 5%; Extended Data Fig. 2d and Supplementary Table 3).

Six metastatic cancer types also displayed more mutations from the clock-like mutational processes, including five cancer types (diffuse large B cell lymphoma, breast, prostate, pancreatic neuroendocrine and kidney renal clear cell carcinomas) that exhibited an increased SBS1 contribution and three cancer types (hepatocellular, prostate and thyroid carcinomas) that had an increased SBS5/SBS40 mutation burden. The increase in clock-like mutations in thyroid and prostate cancers, as well as diffuse B cell lymphomas to a lesser extent, may be explained by a larger proportion of older patients with metastatic disease. However, SBS1 metastatic enrichment was also present in cancer types with highly similar age population distributions (Fig. 1a).

Additional focused analyses will be needed to obtain a better understanding of the mutational signature differences that we observed in smaller subsets of cancer types (and subtypes) and metastatic locations (full data in Supplementary Table 3 and Supplementary Data 1).

Differential SBS1 mutation burden

To investigate the SBS1 mutation burden differences in more detail, we evaluated their SBS1 mutation burden by the age of biopsy separately for both cohorts. As expected, the SBS1 mutation burden per year was highly tissue specific27,28 and displayed an increase with age in the majority of cancer types in both primary and metastatic cohorts (Pearson’s R > 0.1, 15 of 23 tumour types; Extended Data Fig. 4a and Supplementary Table 4). However, four cancer types (that is, breast, prostate, kidney renal clear cell and thyroid carcinomas) showed an age-independent and significant enrichment of SBS1 mutations in metastatic lesions (Extended Data Fig. 4a). For instance, metastatic breast cancer had a nearly uniform fold-change increase of 1.46 over primary tumours (188 ± 16 SBS1 mutations, mean ± s.d.) across the ages of biopsies, and which was generally consistent across breast cancer subtypes (Extended Data Fig. 4b and Supplementary Note 3). This pattern was highly cancer-type specific and was not observed for most cancer types, including those with similar intratumour heterogeneity in the primary cohort (for example, colorectal, ovarian serous and pancreatic carcinomas) (Extended Data Fig. 4a). Moreover, this pattern was not explained by differences in tumour genome ploidy (Extended Data Fig. 4c) or by metastatic biopsy location (Supplementary Note 3), was observed in paired primary–metastatic biopsies from individual patients with breast and kidney renal clear cell carcinomas and rendered consistent patterns when relying on independent unpaired cohorts (Supplementary Note 3). Finally, other mutational processes that operate over the evolution of the somatic tissues (for example, clock-like mutations attributed to SBS5/SBS40 that accumulate with age in a cell-cycle-independent manner28,29) did not show such enrichment (Extended Data Fig. 4d).

SBS1 mutation burden has been extensively correlated with estimated stem cell division rates30. Therefore, an increase in age and tumour-type-specific SBS1 mutation burden in treated metastatic tumours may indicate that these tumours have undergone a higher number of cell divisions. However, the estimated number of years to explain the SBS1 mutation burden shift (23 and 71 years for breast and prostate cancers, respectively; see Supplementary Table 4) shows that this is unlikely to be the main cause. Hence, a more plausible explanation, which also supports previous observations31,32, is that these metastatic tumours display accelerated cell division rates compared with their primary tumour counterparts (Extended Data Fig. 4e). Supporting this hypothesis, metastatic tumours also had a lower normalized fraction of clonal SBS1 mutations (Extended Data Fig. 4f). Of note, this pattern was not observed in cancer types with consistently high SBS1 mutagenic dynamics (Extended Data Fig. 4f) and was indistinguishable for SBS5/SBS40 mutations (Supplementary Note 3).

Finally, we observed a negative association between the yearly rate of SBS1 mutation accumulation in primary tumours (a proxy of stem cell division rates30) and the estimated fold change of the SBS1 mutation rate in the metastatic cohort (Extended Data Fig. 4g,h). This suggests that tumours with an intrinsically active turnover rate (for example, colorectal carcinomas) preserve their high proliferation rates, whereas others with lower cell division rates (for example, breast, prostate, kidney and thyroid carcinomas) may acquire higher proliferation rates during the course of cancer progression. Nevertheless, we cannot rule out the contribution of other tissue-type-specific or tumour-type-specific mechanisms, such as higher rates of 5-methylcytosine deamination, decreased fidelity to repair these mismatches or higher contribution from other metastatic-specific mutational processes with overlapping mutational contexts.

SV burden

Comparison of the total number of SVs per tumour revealed an extensive increase in the metastatic tumours (fold change of 2.5 ± 1.3, mean ± s.d.). This increase was observed in 13 of 23 (56%) cancer types (Fig. 3a, Extended Data Fig. 5a and Supplementary Table 5), and was not generally explained by differences in sequencing coverage, tumour clonality (Supplementary Note 1) or cancer-subtype composition (Supplementary Note 2). Moreover, the increased SV burden was also observed for cancer types lacking substantial changes in genomic instability indicators, such as oesophageal and lung squamous cell carcinomas (Fig. 3a). Finally, we observed an increased SV burden in prostate and pancreatic neuroendocrine primary tumours that eventually progressed compared with those with relatively better prognosis, which in both cases were in turn lower than the median values in metastatic tumours (Supplementary Note 2). Overall, compared with TMB, the SV analyses revealed a much more widespread pan-cancer effect, with larger increases per metastatic cancer type that affected almost every cancer type studied.

Fig. 3: SV burden.
figure 3

The top rectangles represent the four genomic instability features defined in Fig. 1e. The red background represents significant enrichment in the metastatic cohort (two-sided Mann–Whitney adjusted P < 0.01). S-plots and cumulative distribution function plots (samples ranked independently for each SV type) of the aggregated SV burden for each cancer type. The horizontal lines represent median values. Backgrounds are coloured according to the relative enrichment, defined as: log10(median SV-type burden in metastatic tumours + 1) − log10(median SV-type burden in primary tumours + 1). Fold-change labels and coloured backgrounds are displayed when Mann–Whitney comparison renders a significant q < 0.05. Fold-change labels are displayed with ‘>’ when the SV burden for primary tumours is 0 (see Methods for more details). For each cancer type, the bottom bar plots represent the relative fraction of each SV type in the metastatic (left) and primary (right) datasets. LINE, long interspersed nuclear element.

Small (less than 10 kb) deletions were the most enriched SV types in metastatic tumours (2.7 ± 1.2 fold change in 15 of 23 cancer types with significant enrichment, mean ± s.d.; Extended Data Fig. 5a,b). Larger (10 kb or larger) deletions and duplications had a similar pan-cancer enrichment, although generally with slightly lower fold changes. Complex SVs with 20 or more breakpoints, encompassing events such as chromothripsis and chromoplexy, were enriched in metastatic prostate carcinomas (more than threefold enrichment). Finally, a strong cancer-type-specific metastatic enrichment was also noted for long interspersed nuclear element insertions (LINE), with an increased fold change of 12.2 and 12.5 in stomach and bladder urothelial carcinomas, respectively.

We next used linear regression models to unravel the underlying features associated with the observed increase in SV burden in metastatic tumours (Extended Data Fig. 6a–k and Supplementary Table 5). Our approach confirmed the role of previously described cancer-type-specific driver-induced SV phenotypes, including homologous recombination deficiency33 in metastatic breast carcinoma tumours, CDK12 (ref. 34) alterations in prostate carcinoma and MDM2 (ref. 35) amplifications in breast ER+/HER2 carcinomas, among others. Genomic instability features, such as genome ploidy and TP53 alterations, showed a strong pan-cancer association with deletions and duplications (Extended Data Fig. 6a,d), and thus very likely contributed to the observed SV increase in metastatic tumours20,22,23. Finally, previous exposure to radiotherapy treatment was strongly associated with small deletions in breast ER+/HER2 and prostate carcinomas36.

Cancer driver gene landscape

Metastatic tumours showed a moderate increase in the total number of driver gene alterations per patient (a mean of 4.5 and 5.3 driver alterations per sample in primary and metastatic tumours, respectively), including 8 (34%) tumour types with a significant increase (Fig. 4a). Prostate carcinoma (average increase of 3.16 driver alterations per sample), pancreatic neuroendocrine tumour (2.16), thyroid carcinoma (1.7) and kidney renal clear cell carcinoma (1.87) showed the strongest increases (more than 1.5 driver alterations per patient), whereas the majority of cancer types showed a mean increase below 1.5 driver alterations per sample. All mutation types (amplifications, deletions and mutations) contributed to the increased driver alterations in metastatic tumours (Extended Data Fig. 7a).

Fig. 4: Driver alterations in primary and metastatic tumours.
figure 4

a, Cancer-type-specific distribution of the number of driver alterations per patient in primary (top) and metastatic (bottom) tumours. The black dots represent the mean values. Labels display mean differences (metastatic to primary) in cancer types with a significant difference (two-sided Mann–Whitney adjusted P < 0.01). b, Heatmap representing the cancer genes displaying significant mutational frequency differences between primary and metastatic tumours (two-sided Fisher’s exact test adjusted P < 0.01). Circles denote mutation frequency enrichment in both cohorts, whereas triangles facing upwards and downwards represent drivers that are exclusively enriched in metastatic and primary cohorts, respectively. Colours represent the direction of the enrichment.

Comparison of gene and cancer-type frequencies revealed that only 12 genes had a significant frequency bias in at least one cancer type (22 gene and cancer-type pairs in total; Fig. 4b, Extended Data Fig. 7b and Supplementary Table 6). The majority (19 out of 22, 86%) of the significant pairs had enrichment towards higher metastatic frequency, including four driver genes that were exclusively mutated in metastatic tumours and were not found in the primary tumour equivalents (PTPRD in kidney renal clear cell carcinoma, CREBBP in pancreatic neuroendocrine tumour, and RET and TP53 alterations in thyroid carcinoma). Most metastatic-enriched cancer drivers had a cancer-type-specific enrichment, including well-established resistance gene drivers associated with anticancer therapies, such as AR and ESR1 alterations in patients with prostate and ER+ breast carcinomas treated with hormone deprivation therapies37,38 (Fig. 4b and Supplementary Note 2). Nevertheless, three driver genes (that is, TP53, CDKN2A and TERT) showed a metastatic enrichment across multiple cancer types (Fig. 4b), indicating that alterations of these genes may enhance aggressiveness by disturbing pan-cancer hallmarks of tumorigenesis.

We next investigated whether the reported driver differences may have an effect on potential clinical actionability. Cancer-type-specific comparison of therapeutically actionable variants revealed an overall larger fraction of patients with therapeutically actionable variants in the metastatic cohort, with high variability across cancer types (Extended Data Fig. 8a and Supplementary Table 7). Subsetting by A-on label variants (that is, approved biomarkers in the specific cancer type) revealed a consistent pattern in which only cholangiocarcinoma (FGFR2 fusions and IDH1 mutations) and lung adenocarcinoma (EGFR alterations) showed a substantial proportional increase in the metastatic cohort (Extended Data Fig. 8a,b). Non-A-on label biomarkers (A-off label, B-on label and B-off label) showed a modest and tumour-type-dependent metastatic increase, which was mainly linked to the increased alteration frequency of KRAS exon 2 mutations and CDKN2A loss in advanced tumour stages (Extended Data Fig. 8b).

Treatment-associated drivers

The presence of treatment resistance driver genes in late-stage tumours prompted us to devise a test that aimed to identify treatment-enriched drivers (TEDs) that were either significantly enriched (that is, treatment enriched) or exclusively found (that is, treatment exclusive) in a cancer-type-specific and treatment-specific manner (Extended Data Fig. 9a and Methods). Our analytical framework provided 61 TEDs associated with 33 treatment groups from 8 cancer types and 4 cancer subtypes (Fig. 5a,b, Supplementary Table 8 and Supplementary Note 4). Of the identified TEDs, 33 of 61 (54%) were coding mutation drivers, 16 (26%) were copy number amplifications, 9 (14%) were non-coding drivers and 3 (6%) were recurrent homozygous deletions (Fig. 5b and Extended Data Fig. 9b,c). Reassuringly, the majority of the top hits were known treatment resistance drivers, including AR-activating mutations and gene amplifications in patients with prostate cancer treated with androgen-deprivation therapy38 (Extended Data Fig. 9d,e), ESR1536–538 mutations in patients with breast cancer treated with aromatase inhibitors37 (Extended Data Fig. 9f), and EGFRT790M mutations (Extended Data Fig. 9g) and EGFR copy number gains in patients with lung adenocarcinoma treated with EGFR inhibitors39,40 (Extended Data Fig. 9h), among others. Moreover, we also found that TP53 alterations were recurrently associated with resistance to multiple treatments, which may indicate that these alterations are prognostic markers for enhanced tumour aggressiveness and plasticity rather than being a cancer-type-specific mechanism of drug resistance (Supplementary Note 4).

Fig. 5: TEDs.
figure 5

a, Workflow representing the number of treatment groups in each step of the analysis. For the external layers of the pie chart, the number of treatments with identified TEDs is coloured by cancer type. For the internal layers of the pie chart, the category of the corresponding treatment is shown. n Refers to the number of treatment groups with at least one TED. MSS, microsatellite stable; TNB, triple negative breast cancer. b, Volcano plots displaying the identified TEDs. Each dot represents one cancer gene alteration type in one treatment group. The x axis displays the effect size (as log2(odds ratio)) and the y axis shows the significance (−log10(q value)). The circle markers denote TEDs exclusively mutated in the treatment group (squared makers are used otherwise). Markers are coloured according to the type of alteration. The thicker edge lines indicate known resistance drivers. CNA, copy number alteration; UTR, untranslated region. c, Global proportion of patients with TEDs treated for metastasis. d, Mean number of driver alterations per patient with a metastatic tumour before (purple circle) and after (purple square) excluding TEDs compared with patients with primary tumours (orange square). The vertical lines indicate s.d. The mean number of driver alterations are labelled. n Metastatic and n primary denote the number of metastatic and primary samples, respectively.

Our results also provided a long tail of candidate drivers of resistance, some of them with orthogonal evidence by independent reports (Fig. 5b and Extended Data Fig. 9b,c). Examples of the latter group include TYMS amplification in patients with breast cancer treated with pyrimidine antagonists41 (Extended Data Fig. 9i), PRNC1 and MYC co-amplifications in patients with prostate cancer treated with androgen deprivation42 (Extended Data Fig. 9j), ACTL6A promoter mutations in patients with triple-negative breast cancer treated with platinum-based therapies43, and FGFR2 promoter mutations in patients with breast cancer treated with CDK4/CDK6 inhibitors44. The full TEDs catalogue is provided in Supplementary Table 8 and constitutes a valuable resource for investigating resistance mechanisms to common cancer therapies.

Overall, 53% of patients with metastatic disease with annotated treatment information had TEDs, including 32% with annotations of known resistance drivers and an additional 21% of patients with candidate resistance drivers derived from our analysis (Fig. 5c). We identified 0.70 ± 0.53 (mean ± s.d.) TEDs per metastatic sample across the 8 cancer types that had reported TEDs (Fig. 5d), with prostate and breast carcinomas displaying the greatest prevalence of TEDs (that is, 1.74 and 1.12 drivers per patient with prostate and breast cancers, respectively). Therefore, after excluding TEDs, primary and metastatic tumours had a 36% reduction of their original differences in the number of drivers per sample (from 5.3 to 5.0 mean drivers per sample in the metastatic cohort after excluding TEDs, compared with 4.5 mean drivers per sample in the primary cohort) (Fig. 5d and Supplementary Table 8), indicating that an important proportion of the metastatic-enriched drivers are probably associated with resistance to anticancer therapies.


In this study, we describe a cohort of more than 7,000 uniformly reprocessed WGS samples from patients with primary untreated and metastatic treated tumours. We compared genomic features across 23 cancer types and confirmed previous cancer-type-specific observations while also providing novel biological findings, such as the clock-like molecular features, the prevalence of SV burden across different tumorigenic stages and the incidence of TEDs in treated patients.

Specifically, metastatic tumours displayed high genomic instability, low intratumour heterogeneity and strong accumulation of SVs. However, the magnitude of genomic differences between primary and metastatic tumours was highly cancer-type specific and was influenced by the exposure to cancer treatments. Overall, five cancer types (prostate, thyroid, kidney renal clear cell, breast and pancreatic neuroendocrine carcinomas) showed an intense transformation of the genomic landscape in advanced tumorigenic stages (Extended Data Fig. 10, labelled as strong). The other cancer types displayed variable genomic differences, although the chromosomal genomic portrait tended to be conserved.

Cancer types with the strongest genomic differences between primary and metastatic settings in our analyses typically have a good prognosis in the primary setting. But then, whether the metastatic tumours representing a unique set of primary patients that eventually progressed (that is primaries from the metastatic cohort were ‘born to be bad’) or whether there are stochastic triggers of metastatic disease in relatively indolent primaries are still to be determined. To fully address these, larger pan-cancer sets of matched biopsies from the same patient, as already implemented in various cancer-type-specific studies45,46, would be needed.

This study faced various limitations, such as the use of different laboratory workups and sequencing parameters used for primary and metastatic tumour samples, although we demonstrated that this does not severely have an effect on the overall detectability of clonal somatic variants (Supplementary Note 1). However, we cannot exclude the possibility of missing highly subclonal driver mutations. Furthermore, our observations are unlikely to be exhaustive, especially for lower frequency events, because of limited cohort (or subcohort) sizes. Expanding cancer cohorts in research or clinical settings will be essential to advance our understanding of tumour progression. Finally, genomic changes alone cannot entirely explain how tumour cells are able to colonize other organs. Therefore, additional information from complementary tumour omics47 and from the tumour microenvironment48 will be needed to further dissect and better understand metastasis and resistance to cancer therapies.

To conclude, our dataset constitutes a valuable resource that can be leveraged to further study other aspects of tumour evolution, such as genomic differences across metastatic biopsy locations (Supplementary Note 2), dedicated analysis for cancer subtypes (Supplementary Note 2), genetic immune escape alterations in primary and metastatic tumours49 as well as for the development of machine learning tools to foster cancer diagnostics50.


Cohort gathering and processing

We have matched tumour–normal WGS data from patients with cancer from two independent cohorts: Hartwig and PCAWG. A detailed description of the Hartwig and PCAWG cohort gathering and processing as well as comprehensive documentation of the PCAWG sample reanalysis with the Hartwig somatic pipeline is described in the Supplementary Note 1.

Tumour clonality analysis

Each mutation in the .vcf files was given a subclonal likelihood by PURPLE. Following PURPLE guidelines, we considered mutations with subclonal scores equal or higher than 0.8 to be subclonal and mutations below the 0.8 threshold to be clonal. For each sample, we then computed the average proportion of clonal mutations by dividing the number of clonal mutations by the total mutation burden (including SBS, multinucleotide variants and IDs). Finally, for each cancer type, we used Mann–Whitney test to assess the significance of the clonality difference between the primary and metastatic tumours. P values were adjusted for false discovery rate (FDR) using the Benjamini–Hochberg procedure. An adjusted P < 0.05 was deemed as significant.

In addition, we leveraged biopsy site data in patient reports to further investigate differences in metastatic tumour clonality according to the metastatic biopsy site (see also Supplementary Note 2). If the metastatic biopsy site was in the same organ or tissue as the primary tumour, we considered them as ‘local’, whereas if the metastatic biopsy site was reported in the lymphoid system or other organs or tissues, they were dubbed as ‘lymph’ and ‘distant’, respectively. Cancer types for which there was a minimum of five samples available for each of the biopsy groups were selected and Mann–Whitney test was used to compare the clonality between the biopsy groups.


Chromosome arm level and genome ploidy was estimated as previously described20.

First, for each chromosome arm, tumour purity and ploidy-adjusted copy number (CN) segments (as determined by PURPLE) were rounded to the nearest integer. Second, arm coverage of each integer CN was calculated as the fraction of chromosome arm bases with the specific CN divided by the chromosome arm length (for example, 60% of all chromosome 5p segments have a CN of 2, 30% have a CN of 1 and 10% have a CN of 3). We defined the arm-level ploidy level as the CN with the highest coverage across the whole arm (in the example above it would be 2). Third, we computed the most recurrent chromosome arm ploidy levels across all chromosome arms per sample (that is, observed genome ploidy).

Next, we estimated the true genome ploidy by taking WGD status (given by PURPLE) into account. If a sample did not undergo WGD, its total expected genome ploidy was deemed to be 2n. If a sample did undergo WGD and its observed genome ploidy was less than six, the estimated genome ploidy was deemed to be 4n, and 8n if the observed genome ploidy was six or more. An observed genome CN of more than eight was not found in our dataset.

Then, for each chromosome arm in each sample, we defined the normalized arm ploidy as the difference between the arm-level ploidy level and the expected genome ploidy. The resulting value was classified as 1 for differences higher than or equal to 1 (representing arm gains), as −1 for differences lower than or equal to −1 (representing arm losses) or as 0 (no difference). Normalized arm ploidy values were averaged across all samples from a cancer type in a cohort-specific manner (that is, separating primary and metastatic samples). A Mann–Whitney test was performed per cancer type and chromosome arm to assess the mean difference in arm gains or losses at the cancer-type level. The resulting P value was FDR adjusted across all arms per cancer type. Finally, q < 0.01 and a normalized arm ploidy difference higher than 0.25 was deemed to be significant.

Genomic instability indicators

To compare the differences in aneuploidy scores and the LOH proportions in each group, a Mann–Whitney test was performed per cancer type. The aneuploidy score represents the number of arms per tumour sample that deviate from the estimated genome ploidy as previously described20. The LOH score of a given sample represents the sum of all LOH regions divided by the GRCh37 total genome length. A genomic region is defined as LOH when the minor allele CN < 0.25 and major allele CN ≥ 0.8.

To compare the fraction of samples with a driver mutation in TP53 as well as the fraction of WGD samples per cohort, a Fisher’s exact test was performed per cancer type. Any TP53 driver alteration (non-synonymous mutation, biallelic deletion and homozygous disruption) was considered in the analysis. Multiple driver mutations per sample in a single gene were considered as one driver event. WGD was defined as present if the sample had more than 10 autosomes with an estimated chromosome CN of more than 1.5. P values were FDR corrected across all cancer types. A q < 0.01 was deemed to be significant for all statistical tests.

Mutational signature analysis

Signature extraction

The number of somatic mutations falling into the 96 SBS, 78 DBS and 83 ID contexts (as described in the COSMIC catalogue51; was determined using the R package mutSigExtractor (, v1.23).

SigProfilerExtractor (v1.1.1) was then used (with default settings) to extract a maximum of 21 SBS, 8 DBS and 10 ID de novo mutational signatures. This was performed separately for each of the 20 tissue types that had at least 30 patients in the entire dataset (aggregating primary and metastatic samples; see Supplementary Table 3). Tissue types with less than 30 patients as well as patients with metastatic tumours with unknown primary location type were combined into an additional ‘Other’ group, resulting in a total of 21 tissue-type groups for signature extraction. To select the optimum rank (that is, the eventual number of signatures) for each tissue type and mutation type, we manually inspected the average stability and mean sample cosine similarity plot output by SigProfilerExtractor. This resulted in 440 de novo signature profiles extracted across the 21 tissue-type groups (Supplementary Table 3). Least squares fitting was then performed (using the fitToSignatures() function from mutSigExtractor) to determine the per-sample contributions to each tissue-type-specific de novo signature.

Aetiology assignment

The extracted de novo mutational signatures with high cosine similarity (≥0.85) to any reference COSMIC mutational signatures with known cancer-type associations51 were labelled accordingly (288 de novo signatures matched to 57 COSMIC reference signatures).

For the remaining 152 unlabelled de novo signatures, we reasoned that there could be one or more signatures from one cancer type that is highly similar to those found in other tissue types, and that these probably represent the same underlying mutational process. We therefore performed clustering to group likely equivalent signatures. Specifically, the following steps were performed:

  1. (1)

    We calculated the pairwise cosine distance between each of the de novo signature profiles.

  2. (2)

    We performed hierarchical clustering and used the base R function cutree() to group signature profiles over the range of all possible cluster sizes (minimum number of clusters = 2; maximum number of clusters = number of signature profiles for the respective mutation type).

  3. (3)

    We calculated the silhouette score at each cluster size to determine the optimum number of clusters.

  4. (4)

    We grouped the signature profiles according to the optimum number of clusters. This yielded 27 SBS, 7 DBS and 8 ID de novo signature clusters (see Supplementary Table 3).

For certain de novo signature clusters, we could manually assign the potential aetiology based on their resemblance to signatures with known aetiology described in COSMIC51, Kucab et al.26 and Signal (access date 1 February 2023)52. Some clusters were an aggregate of two known signatures, such as SBS_denovo_clust_2, which was a combination of SBS2 and SBS13, both linked to APOBEC mutagenesis. Other clusters had characteristic peaks of known signatures, such as DBS_denovo_clust_4, which resembled DBS5 based on having distinctive CT>AA and CT>AC peaks. Finally, DBS_denovo_clust_1 was annotated as a suspected POLE mutation and MMRd, as samples with high contribution (more than 150 mutations) of this cluster are frequently microsatellite instable (MSI) or have POLE mutations. Likewise, DBS_denovo_clust_2 was annotated as a suspected MMRd as the aetiology, as samples with high contribution (more than 250 mutations) of this cluster were all MSI. See Supplementary Table 3 for a list of all the manually assigned aetiologies.

After applying the aetiology assignment, the de novo extraction resulted in 69 SBS, 13 DBS and 18 ID representative mutational signatures (Supplementary Table 3). Most of these (42 of 69 SBSs, 7 of 13 DBSs and 8 of 18 IDs) mapped onto the well-described mutational signatures in human cancer35,53.

Comparing the prevalence of mutational processes between primary and metastatic cancer

We then compared the activity (that is, the number of mutations contributing to) of each mutational process between primary and metastatic tumours. For each sample, we first summed the contributions of signatures of the same mutation type (that is, SBS, DBS or ID) with the same aetiology, hereafter referred to as ‘aetiology contribution’. Per cancer type and per aetiology, we performed two-sided Mann–Whitney tests to determine whether there was a significant difference in aetiology contribution of primary and metastatic tumours. Per cancer type and per mutation type, we used the p.adjust() base R function to perform multiple testing correction using Holm’s method. Next, we added a pseudocount of 1 to the contributions (to avoid dividing by 0) and calculated the median contribution log2 fold change, that is, log2((median contribution in metastatic tumours + 1)/(median contribution in primary tumours + 1)). We considered the aetiology contribution between primary and metastatic tumours to be significantly different when q < 0.05, and log2 fold change ≥ 0.4 or log2 fold change ≤ −0.4 (= ± ×1.4).

Relative contribution

Relative aetiology contribution was calculated by dividing aetiology contribution by the total contribution of the respective mutation type (that is, SBS, DBS or ID). To determine the significant difference in relative aetiology contribution, we performed two-sided Mann–Whitney tests as described above. We also calculated the median difference in contribution (that is, median relative contribution in metastatic tumours − median relative contribution in primary tumours). We considered the relative aetiology contribution between primary and metastatic tumours to be significantly different when q < 0.05 and median difference was 0.01 or more.

We also determined whether there was an increase in the number of samples with high aetiology contribution (that is, hypermutators) in metastatic versus primary cohorts. For each signature, a sample was considered a hypermutator if the aetiology contribution was 10,000 or more for SBS signatures, 500 or more for DBS signatures or 1,000 or more for ID signatures. For each cancer type, for each aetiology, we performed pairwise testing only for cases in which there were five or more hypermutator samples for either metastatic or primary tumours. Each pairwise test involved calculating P values using two-sided Fisher’s exact tests, and effect sizes by multiplying Cramer’s V by the sign of the log2(odds ratio) to calculate a signed Cramer’s V value that ranges from −1 to +1 (indicating enrichment in primary or metastatic, respectively). We then used the p.adjust() base R function to perform multiple testing correction using Bonferroni’s method.

SBS1–age correlations in primary and metastatic tumours

To count the SBS1 mutations, we relied on the definition from ref. 54 that is based on the characteristic peaks of the COSMIC SBS1 signature profile: single-base CpG > TpG mutations in NpCpG context. To ensure that these counts and the downstream analyses are not affected by differential APOBEC exposure in primary and metastatic cohorts, we excluded CpG > TpG in TpCpG, which is also a characteristic peak in the COSMIC SBS2 signature profile. In addition, for skin melanoma, CpG > TpG in [C/T]pCpG, which overlaps with SBS7a, was excluded. To obtain the SBS5 and SBS40 counts, we relied on their exposures derived from the mutational signature analyses performed in this study (described above).

To assess the correlation between SBS1 burden and the age of the patient, at biopsy we performed a cancer-type and cohort-specific linear regression (that is, separate regression for primary and metastatic tumour samples). To avoid spurious effects caused by hypermutated tumours, samples with a TMB greater than 30,000 as well as those with SBS1 burden greater than 5,000 were excluded.

For each cancer type and cohort, we then computed 100 independent linear regressions by randomly selecting 75% of the available samples. We selected the median linear regression (based on the regression slope) as representative regression for further analyses. Similarly, confidence intervals were derived from the 1st and 99th percentile of the computed regressions.

To evaluate the significance of the differences between primary and metastatic representative linear regressions (hereafter referred to as linear regression for simplicity), we first filtered out cancer types that failed to show a positive correlation trend between SBS1 burden and age at biopsy in both primary and metastatic tumours (that is, Pearson’s correlation coefficient of primary and metastatic regression greater than 0.1). Next, for each selected cancer type, we computed the regression residuals of primary and metastatic SBS1 mutation counts using, in both cases, the primary linear regression as baseline. The primary and metastatic residual distributions were then compared using a Mann–Whitney test to evaluate significance. Cancer types with a Mann–Whitney P < 0.01 were deemed as significant. Finally, to ensure that the differences were uniform across different age ranges (that is, not driven by a small subset of patients), we only considered significant cancer types in which the metastatic linear regression intercept was higher than the primary intercept.

SBS5/SBS40 correlations were computed following the same procedure and using the sum of SBS5 and SBS40 exposures for each tumour sample. If none of the mutations were attributed to SBS5/SBS40 mutational signatures, the aggregated value was set to zero. In the ploidy-corrected analyses, we divided the SBS1 mutation counts (and SBS5/SBS40 mutation counts for the SBS5/SBS40 ploidy-corrected regression, respectively) by the PURPLE-estimated tumour genome ploidy.

For each cancer type, the mean fold change (fc) was defined as \(\underline{{\rm{fc}}}=\frac{1}{40}{\sum }_{i=40}^{80}\frac{{{\rm{M}}{\rm{Pred}}}_{i}}{{{\rm{P}}{\rm{Pred}}}_{i}}\) where MPredi and PPredi are the estimated number of SBS1 mutations for a given age ith according to the metastatic and primary linear regressions, respectively. Similarly, the mean estimated SBS1 burden difference (SBS1diff) was defined as: \(\underline{{\rm{SBS1diff}}}=\frac{1}{40}{\sum }_{i=40}^{80}{{\rm{MPred}}}_{i}-{{\rm{PPred}}}_{i}\).

Clonality of clock-like mutations

SBS1 individual mutations were identified as described in the previous section. For SBS5 and SBS40 mutations, we used a maximum likelihood approach to assign individual mutations to the SBS5 and SBS40 mutational signatures in a cancer-type-specific manner.

For every SBS1 (and SBS5/SBS40 mutation), we then assign the clonality according to the PURPLE subclonal likelihood estimation, in which only mutations with subclonal (SUBCL) likelihood ≥ 0.8 were considered as such (see above).

For each tumour sample, the SBS1 clonality ratio (or respectively SBS5/SBS40 clonality ratio) was defined as the ratio between the proportion of clonal SBS1 mutations (\(\frac{{\rm{SBS\; 1\; clonal\; mutations}}}{{\rm{SBS\; 1\; mutations}}}\)) divided by the total proportion of clonal alterations in the sample (\(\frac{{\rm{Total\; clonal\; mutations}}}{{\rm{Total\; mutations}}}\)).

Primary SBS1 mutation rate and metastatic SBS1 age-corrected enrichment

We computed for each primary cancer type the average number of SBS1 per year as the number of SBS1 mutations divided by the age of the patient at biopsy (only considering primary samples and excluding hypermutated samples as described above). We then used a Spearman’s correlation to assess its association with the estimated mean SBS1 mutation rate fold change in metastatic tumours (see above). In addition, to exclude potential biases in our primary cohort, we repeated the same analysis relying on an independent measurement of primary cancer SBS1 yearly accumulation. Specifically, we used the best-estimated accumulation of SBS1 per year from ref. 30 (Supplementary Table 6) and regressed it to the fold-change estimates for the matching cancer types present in both datasets.

SV analysis

Definitions of SV type

LINX55 chains one or more SVs and classifies these SV clusters into various event types (‘ResolvedType’). We defined deletions and duplications as clusters with a ResolvedType of ‘DEL’ or ‘DUP’ whose start and end breakpoints are on the same chromosome (that is, intrachromosomal). Deletions and duplications were split into those less than 10 kb and 10 kb or more in length (small and large, respectively), based on observing bimodal distributions in these lengths across cancer types (Extended Data Fig. 5b). We defined complex SVs as clusters with a ‘COMPLEX’ ResolvedType, an inversion ResolvedType (including: INV, FB_INV_PAIR, RECIP_INV, RECIP_INV_DEL_DUP and RECIP_INV_DUPS) or a translocation ResolvedType (including: RECIP_TRANS, RECIP_TRANS_DEL_DUP, RECIP_TRANS_DUPS, UNBAL_TRANS and UNBAL_TRANS_TI). Complex SVs were split into those with less than 20 and 20 or more SVs (small and large, respectively), based on observing similar unimodal distributions in the number SVs across cancer types whose tail begins at approximately 20 breakpoints (Extended Data Fig. 5b). Finally, we defined long interspersed nuclear element insertions (LINEs) as clusters with a ResolvedType of ‘LINE’. For each sample, we counted the occurrence (that is, SV burden) of each of the seven SV types described above. In addition, we determined the total SV burden by summing counts of the SV types.

Comparing SV burden between primary versus metastatic cancer

We then compared the SV-type burden between primary versus metastatic tumours as shown in Fig. 3a. First, we performed two-sided Mann–Whitney tests per SV type and per cancer type to determine whether there was a statistically significant difference in SV-type burden between primary versus metastatic. The Bonferroni method was used for multiple testing correction on the P values from the Mann–Whitney tests (to obtain q values). Next, we calculated relative enrichment as follows: log10(median SV-type burden in metastatic tumours + 1) − log10(median SV-type burden in primary tumours + 1); and calculated fold change as follows: (median SV-type burden in metastatic tumours + 1) / (median SV-type burden in primary tumours + 1). When calculating relative enrichment and fold change, the pseudocount of 1 was added to avoid the log(0) and divide by zero errors, respectively. Fold changes are displayed with a ‘>’ in Fig. 3a when the SV burden for primary tumours is 0 (that is, when a divide by 0 would occur without the pseudocount). We considered the SV-type burden between primary versus metastatic to be significant when: q < 0.05, and fold change ≥ 1.2 or fold change ≤ 0.8

Identifying features associated with increased SV burden in metastatic cancer

To identify the features that could explain increased SV burden, we correlated SV burden with various tumour genomic features. This included: (1) genome ploidy (determined by PURPLE); (2) homologous recombination deficiency (determined by CHORD33) and MSI (determined by PURPLE) status; (3) the presence of mutations in 345 cancer-associated genes (excluding fragile site genes that are often affected by CN alterations5), hereafter referred to as ‘gene status’; and (4) treatment history, including the presence of radiotherapy, the presence of one of the 79 different cancer therapies as well as the total number of treatments received. All primary samples and all metastatic samples without treatment information were considered to have no treatment. Genome ploidy and total number of treatments received were numeric features, whereas all of the remaining were boolean (that is, true or false) features. In total, there were 429 features.

SV-type burden was transformed to log10(SV-type burden + 1) and was correlated with the 429 features using multivariate linear regression models (LMs). This was performed separately for each of the seven SV types, and for each cancer type (or subtype). In the SV main analysis (Fig. 4b–f), there were 23 cancer types, resulting in a total of 161 (23 cancer types × 7 SV types) LM models.

Each LM model (that is, per SV type and cancer type) involved training of three independent LMs with (1) both metastatic and primary samples (primary + metastatic), (2) only Hartwig samples (metastatic only), and (3) only PCAWG samples (primary only). This was done to filter out correlations between features and increased SV-type burden solely due to differences in feature values between primary and metastatic tumours. We then required features that positively correlated with SV-type burden in the primary + metastatic LM to independently show the same association in the metastatic-only or primary-only LMs. Only genomic features that independently showed positive correlation with the SV burden were further considered as significant (that is, represented in the lollipop plots).

Each of the three LMs was trained as follows:

  1. (1)

    Remove boolean features with too few ‘true’ samples.

  1. (i)

    For the primary + metastatic LM, remove gene status features with less than 15 ‘true’ samples.

  2. (ii)

    For the metastatic-only and primary-only LMs, remove gene status features with less than 10 ‘true’ samples.

  3. (iii)

    For the remaining boolean features, remove features with less than 5% ‘true’ samples.

  1. (2)

    Fit a LM using the lm() base R function to correlate log10(SV-type burden + 1) versus all features.

For each LM analysis, we used the following filtering criteria to identify the features that were correlated with increased SV-type burden:

  1. (1)

    Only keep LM analyses for which there was significant increase in SV-type burden for the respective cancer type (P < 0.01 as described in the previous section ‘Comparing SV burden between primary versus metastatic cancer’).

  2. (2)

    For primary + metastatic LM:

  1. (i)

    Regression P < 0.01

  2. (ii)

    Coefficient P < 0.01

  3. (iii)

    Coefficient more than 0

  1. (3)

    For metastatic-only LM or primary-only LM:

  1. (i)

    Coefficient P < 0.01

  2. (ii)

    Coefficient more than 0

Finally, to determine which features (of those correlated with increased SV-type burden) were enriched in metastatic tumours compared with primary tumours (and vice versa), we calculated Cliff’s delta for numeric features and Cramer’s V for boolean features. Cliff’s delta ranges from −1 to +1, with −1 representing complete enrichment in primary tumours, whereas +1 represents complete enrichment in metastatic tumours. Cramer’s V only ranges from 0 to 1 (with 1 representing enrichment in either primary or metastatic tumours), the sign of the log(odds ratio) was assigned as the sign of the Cramer’s V value so that it ranged from −1 to +1. Features with an effect size of more than 0 were considered as those that could explain the SV burden increase in metastatic cancer when compared with primary cancer.

Driver alterations

We relied on patient-specific cancer driver and fusion catalogues constructed by PURPLE5 and LINX55. Only drivers with a driver likelihood of more than 0.5 were retained. Fusion drivers were filtered for those that were previously reported in the literature. Similarly, we manually curated the list of drivers and removed SMAD3 hotspot mutations because of the high-burden mutations in low-mappability regions. The final driver catalogue contained a total of 453 driver genes and the final fusion catalogue contained 554 reported fusions.

To compare the number of drivers in primary and metastatic tumours, we then combined fusions with the LINX driver variants to calculate a patient-specific number of driver events. Drivers that concern the same driver gene but a different driver type were deemed to be single drivers (for example, TP53 mutation and TP53 deletion in the same sample were considered as one driver event). Cancer-type-specific Mann–Whitney test was performed to assess differences between primary and metastatic tumours. An adjusted q < 0.01 was deemed to be significant.

To assess the driver enrichment, a contingency matrix was constructed from the driver catalogue, containing the frequency of driver mutations per driver type (that is, deletion, amplification or mutations) and cancer type in each cohort (metastatic and primary). A second contingency matrix was constructed for the fusions. Partial amplifications were considered as amplifications, whereas homologous disruptions were considered as deletions. These contingency matrices were filtered for genes that show a minimum frequency of five mutated samples in either the primary or the metastatic cohorts. Then, a two-sided Fisher’s exact test for each gene, cancer type and mutation type was performed and the P value was adjusted for FDR per cancer type. Cramer’s V and the odds ratio were used as effect size measures. An adjusted P < 0.01 was deemed to be significant.

Therapeutic actionability of variants

To determine the amount of actionable variants observed in each sample, we compared our variants annotated by SnpEff (v5.1)56 to those derived from three different databases (OncoKB57, CIViC58 and CGI59) that were classified based on a common clinical evidence level ( as previously described5. In our study we only considered A and B levels of evidence, which represent variants that have been FDA approved for treatment and are currently being evaluated in a late-stage clinical trial, respectively. A variant was determined to be ‘on-label’ when the cancer type matches the cancer type for which the treatment was approved for or is being investigated for, and ‘off-label’ otherwise. Only actionable variants of the sensitive category were considered (that is, tumours containing the variant are sensitive to a certain treatment). Sample-level actionable variants such as TMB high/low or MSI status were not evaluated, because of their tendency to overshadow the other variants, especially in the off-label category. Furthermore, wild-type actionable variants were not considered in this analysis for the same reason. Variants related to gene expression or methylation were not considered due to lack of available data. In addition, we found actionable variants derived from leukaemias to be very different from the solid tumours in our dataset, which is why we excluded them for this analysis. For the analysis of proportion of samples bearing therapeutically actionable variants, we considered that the highest evidence level was retained for each sample following the order A on/off-label to B on/off-label. To assess enrichment of actionable variants globally and at the A on-label level in metastatic tumours, a Fisher’s exact test was performed pan-cancer-wide and per cancer type. An adjusted P < 0.05 was deemed to be significant. Fold changes in frequency are only shown for cancer types with a global significant difference.

To determine which variants contribute the most to the observed significant frequency differences, individual actionable variants were tested for enrichment in metastatic tumours using a Fisher’s exact test per cancer type and tier level. P values were FDR adjusted per cancer type and q < 0.05 was deemed to be significant. In Extended Data Fig. 8, only actionable variants from cancer types with a global significant difference (see above) and that were found at a minimum frequency of 5% in either primary or metastatic cohort and a minimum frequency difference of 5% between them were shown. However, the differences across all screened variants are available as part of Supplementary Table 7.


We aimed to pinpoint drivers that are potentially responsible for lack of response to certain cancer treatments in the metastatic cohort. Hence, we devised a test that identifies driver alterations that are enriched in groups of patients treated with a particular treatment type compared with the untreated group of patients from the same cancer type (see Extended Data Fig. 9a for illustration of the workflow).

Treatments were grouped according to their mechanism of action so that multiple drugs with a shared mechanism of action were grouped into the mechanistic treatment category (for example, cisplatin, oxaliplatin and carboplatin were grouped as platinum). We created 323 treatment and cancer-type groups by grouping patients with treatment annotation according to their treatment record before the biopsy. One patient might be involved in multiple groups if they have received multiple lines of therapy or a simultaneous combination of multiple drugs. Only 92 treatment and cancer-type groups with at least ten patients were further considered in the analysis.

Hence, for each cancer type (or subtype, in the case of breast and colorectal) and treatment group, we performed the following steps:

  1. (1)

    We first performed a driver discovery analysis in treatment and cancer-type (or subtype)-specific manner. We explored three types of somatic alterations: coding mutations, non-coding mutations and CN variants (see below for detailed description of each driver category). Driver elements from each alteration category were selected for further analysis.

  2. (2)

    For each driver alteration from (1), we compared the alteration frequency in the treated group to the untreated group of the same cancer type. Each driver category (coding and non-coding mutations and CN variants) were evaluated independently. We performed a Fisher’s exact test to assess the significance of the frequency differences. Similarly, we computed the odds ratio of the mutation frequencies for each driver alteration. The P values were adjusted with a multiple-testing correction using the Benjamini–Hochberg procedure (α = 0.05). An adjusted P value of 0.05 was used for coding mutations and CN variants. An adjusted P value of 0.1 was used for non-coding variants due to the overall low mutation frequency of the elements included in this category, which hampered the identification of significant differences.

  3. (3)

    We then annotated each driver element with information about the exclusivity in the treatment group. We labelled drivers as treatment exclusive if the mutation frequency in the untreated group was lower than 5% or we annotated as treatment enriched otherwise. In addition, we manually curated the identified drivers with literature references of their association with each treatment category.

  4. (4)

    Finally, the overlap of patients in multiple treatment groups (see above) in the same cancer type prompted us to prioritize the most significant treatment association for each driver gene in a particular cancer type. In other words, for each driver gene that was deemed as significantly associated with multiple treatment groups in the same cancer type, we selected the most significant treatment association, unless a driver-treatment annotation was clearly reported in the literature.

The full catalogue of TEDs and their mutation frequencies can be found in Supplementary Table 8.

Coding mutation drivers

We used dNdScv (v0.0.1)60 with default parameters to identify cancer driver genes from coding mutations. A global q < 0.1 was used as a threshold for significance. Mutation frequencies for each driver gene were extracted from the dNdScv output. We defined the mutation frequency as the number of samples bearing non-synonymous mutations.

Non-coding mutation drivers

We used ActiveDriverWGS61 (v1.1.2, default parameters) to identify non-coding driver elements in five regulatory regions of the genome including 3′ untranslated regions (UTRs), 5′ UTRs, long non-coding RNAs, proximal promoters and splice sites. For each element category, we extracted the genomic coordinates from Ensembl v101. Each regulatory region was independently tested. To select for significant hits, we filtered on adjusted P values (FDR < 0.1) and a minimum of three mutated samples. We defined the mutation frequency as the number of mutated samples for each significantly mutated element in the treatment group.

CN variant drivers

We ran GISTIC2 (ref. 62) (v2.0.23) on each of the 92 treatment and cancer-type groups using the following settings:

gistic2 -b <inputPath> -seg <inputSegmentation> -refgene hg19.UCSC.add_miR.140312.refgene.mat -genegistic 1 -gcm extreme -maxseg 4000 -broad 1 -brlen 0.98 -conf 0.95 -rx 0 -cap 3 -saveseg 0 -armpeel 1 -smallmem 0 -res 0.01 -ta 0.1 -td 0.1 -savedata 0 -savegene 1 -qvt 0.1.

The focal GISTIC peaks (q ≤ 0.1 and <1 Mb) were then annotated with functional elements using the coordinates from Ensembl v101. The frequency differences between treated and untreated cohorts on every gene was assessed with Fisher’s exact test as described above. For this, we first calculated the focal amplification and deep depletion status of every gene within each sample. A gene was amplified when the ploidy level of the gene was 2.5 ploidy levels higher than its genome-wide mean ploidy level (as measured by PURPLE), and deleted when the gene ploidy level was lower than 0.3 (that is, deep deletion). We observed that the majority of the peaks contained multiple significant gene candidates (after multiple correction q < 0.05) and therefore we retained the gene most closely positioned to the peak summit, which is the most significantly enriched region across the treated samples. Next, we also found recurrent peaks across multiple treatment groups per cancer type that are not, or less, present in the untreated control group because most of the Hartwig samples have received multiple treatment types. We therefore merged peaks with overlapping ranges to produce a single peak per genomic region per cancer type. For each collapsed peak, we selected the treatment type showing the lowest q value for the gene near the peak summit. Deletion and amplification peaks were processed separately.

Group-level aggregation of treatment resistance-associated variants

To estimate the contribution of TEDs to the total number of drivers per sample in the metastatic cohort, we excluded any TED from the catalogue of driver mutations (see the above section ‘Driver alterations’) in a cancer-type-specific, gene-specific and driver-type-specific manner.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.