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Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study


Whole-genome sequencing (WGS) brings comprehensive insights to cancer genome interpretation. To explore the clinical value of WGS, we sequenced 254 triple-negative breast cancers (TNBCs) for which associated treatment and outcome data were collected between 2010 and 2015 via the population-based Sweden Cancerome Analysis Network–Breast (SCAN-B) project ( ID:NCT02306096). Applying the HRDetect mutational-signature-based algorithm to classify tumors, 59% were predicted to have homologous-recombination-repair deficiency (HRDetect-high): 67% explained by germline/somatic mutations of BRCA1/BRCA2, BRCA1 promoter hypermethylation, RAD51C hypermethylation or biallelic loss of PALB2. A novel mechanism of BRCA1 abrogation was discovered via germline SINE-VNTR-Alu retrotransposition. HRDetect provided independent prognostic information, with HRDetect-high patients having better outcome on adjuvant chemotherapy for invasive disease-free survival (hazard ratio (HR) = 0.42; 95% confidence interval (CI) = 0.2–0.87) and distant relapse-free interval (HR = 0.31, CI = 0.13–0.76) compared to HRDetect-low, regardless of whether a genetic/epigenetic cause was identified. HRDetect-intermediate, some possessing potentially targetable biological abnormalities, had the poorest outcomes. HRDetect-low cancers also had inadequate outcomes: ~4.7% were mismatch-repair-deficient (another targetable defect, not typically sought) and they were enriched for (but not restricted to) PIK3CA/AKT1 pathway abnormalities. New treatment options need to be considered for now-discernible HRDetect-intermediate and HRDetect-low categories. This population-based study advocates for WGS of TNBC to better inform trial stratification and improve clinical decision-making.

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Fig. 1: CONSORT diagram of the study.
Fig. 2: HRDetect classification and genomic characteristics in population-based TNBC.
Fig. 3: Genetic characteristics of RAD51C- and PALB2-altered TNBCs.
Fig. 4: Association of HRDetect classification with clinical outcomes in an unselected population-based TNBC cohort.

Data availability

Somatic mutational data are available at

Raw sequence data may be obtained by contacting the Swedish corresponding authors with a request that is compliant with Swedish regulations on data protection, ethical permissions and patient consent.


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We thank patients and clinicians participating in the SCAN-B study, the staff at the central SCAN-B laboratory at the Division of Oncology and Pathology, Lund University, the Swedish national breast cancer quality registry (NKBC), RBC Syd, the South Sweden Breast Cancer Group (SSBCG), the CASM IT and Wellcome Sanger Institute sequencing team for support, R. Harris for administrative, technical and coordination support, K. Lövgren and E. Rambech for technical assistance with MMRd cases and P.-O. Bendahl for statistical comments. Financial support for this study was provided by the Swedish Cancer Society (CAN 2016/659, CAN 2018/685 and Senior Investigator Award SIA190013), the Mrs Berta Kamprad Foundation (FBKS-2018-3-166 and FBKS-2018-4-146), the Crafoord Foundation (20180543), the Swedish Research Council, the Lund-Lausanne L2-Bridge/Biltema Foundation (F 2016/1330), the Mats Paulsson Foundation (IACD 2017), the Gustav V’s Jubilee Foundation (174271) and Governmental Funding of Clinical Research within the National Health Service (ALF) (2018/40612). Whole-genome sequencing and analysis was funded by a Wellcome Trust Intermediate Clinical Fellowship (WT100183MA), a CRUK Advanced Clinician Scientist Award (C60100/A23916) and a CRUK Grand Challenge Award (C60100/A25274).

Author information

Authors and Affiliations



Conception and design were provided by J.S., S.N.-Z. and Å.B. Collection and assembly of data were carried out by J.S., S.N.-Z., D.G., Å.B., C.R., J.V.-C., J.H. and C.H. Study material or patients were provided by C.L., N.L., L.R., Å.B., M.M., A.E., L.H.S., C.H. and H.E. Data analysis and interpretation were performed by J.S., D.G., S.N.-Z., H.R.D., T.D.A., A.D. and A.K. Financial support was provided by S.N.-Z., Å.B. and J.S. Administrative support was provided by J.S., S.N.-Z., D.G., J.V.-C., C.H. and J.H. The manuscript was written by all authors. Final approval of manuscript was provided by all authors. All authors agree to be accountable for all aspects of the work.

Corresponding authors

Correspondence to Johan Staaf or Serena Nik-Zainal.

Ethics declarations

Competing interests

D.G., H.R.D. and S.N.-Z. are inventors on a patent encompassing the code and intellectual principle of the HRDetect algorithm. The remaining authors declare no competing interests.

Additional information

Peer review information 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 Sweden Cancerome Analysis Network – Breast (SCAN-B).

In the Skåne healthcare region (Region Skåne) four main hospitals are participating in the SCAN-B study: Lund, Malmö, Helsingborg and Kristianstad. a, SCAN-B overall enrolment rate at all participating hospitals, including Skåne healthcare region, during the period 1 September 2010 to 31 March 2015, corresponding to the same time period from which the TNBC cases in the current study were selected. The statistics are restricted to the seven hospitals where enrolment was operational from the start in 2010. b, Overall accrual rate per quarter of a year (Q1–Q4) for the SCAN-B study since the start in 2010 Q4 up until 2018 Q1. The red line corresponds to the cumulative number of enrolled patients, reaching nearly 12,000 in 2018 Q1. c, Illustration of the population-based nature of the SCAN-B study for primary resectable breast cancer. Based on data from the national breast cancer quality registry in Sweden (NKBC), a background population of primary resectable breast cancers from the entire SCAN-B catchment region during the period 1 September 2010 to 31 March 2015 was identified (same time period from which the TNBC cases in the current study were selected), comprising 8,587 patients. Of these 8,587 patients, 5,417 were enrolled in SCAN-B, with 3,520 patients having RNA sequencing data passing basic quality criteria. The lower panels demonstrate the clinicopathological characteristics of the different subgroups in the consort diagram, demonstrating the representativity of the end RNA sequencing cohort compared to all enrolled SCAN-B patients and the total patient population in the catchment region. Note that the RNA sequencing cohort has a slightly lower inclusion of smaller tumors, due to the fact that the SCAN-B tissue sampling is performed by a pathologist after enough tissue has been secured for routine diagnostics. d, Demonstration of the year to year representativity of molecular subtypes in breast cancer (PAM50, top panel) and administered treatments based on data from the NKBC (lower panel) for patients identified in c. The bars show patients in the RNA sequencing cohort from c, stratified by year of diagnosis (all patients diagnosed in a particular year are included). PAM50 subtyping was performed using the AIMS method (Paquet et al. J. Natl Cancer Inst. 107, 357, 2014) (as for the TNBC cases in the current study) as this classifier is a single sample classifier that does not rely on a mean centering of gene expression data across a cohort (thus is not sensitive to, for example, potential bias in year to year inclusion). ACT, adjuvant chemotherapy.

Extended Data Fig. 2 Similar genomic characteristics of SCAN-B TNBC cases compared to previously reported WGS-analysed TNBCs.

a, Comparison of copy number alterations (CNA) as defined by Nik-Zainal et al. (Nature, 534, 47–54, 2016) in the 237 SCAN-B TNBC cases versus 162 TNBC cases from Nik-Zainal et al. A frequency below 0 means frequency of copy number loss. b, Comparison of frequency of LOH defined as in Nik-Zainal et al. (Nature, 2016) between the same SCAN-B cases and Nik-Zainal et al. (Nature, 2016) TNBC cases. c, Comparison of copy number neutral (cnn) LOH defined as in Nik-Zainal et al. (Nature, 2016) between the same set of samples. d, Comparison of the frequency of driver gene amplifications between the same set of samples. Only amplifications matched in both cohorts are displayed. Driver gene list was obtained from Nik-Zainal et al. (Nature, 2016). e, Comparison of the frequency of homozygous deletions based on ASCAT data, as described in Nik-Zainal et al. (Nature, 2016), for the same set of samples. Only deletions matched in both cohorts are shown. f, Frequency of somatic substitutions and indels for driver genes from Nik-Zainal et al. (Nature, 2016) in the two cohorts. Only genes with >1% mutation frequency in Nik-Zainal et al. are displayed. g, Exposure to mutation substitution signatures as defined in Nik-Zainal et al. (Nature, 2016) for the same set of samples. The line corresponds to a 1:1 relationship. h, Exposure to rearrangement signatures (RS1–RS6) as defined in Nik-Zainal et al. (Nature, 2016) for the same set of samples. The line corresponds to a 1:1 relationship.

Extended Data Fig. 3 Clinicopathological and genomic characteristics of HRDetect groups.

a, Expression of checkpoint proliferation (left), steroid (center) and basal (right) metagenes from Fredlund et al. (Breast Cancer Research, 2012) across HRDetect groups stratified by BRCA status. HRDetect-inter, intermediate subgroup; BRCA1pm, BRCA1 promoter hypermethylated; BRCAgerm, BRCA1/2 germline carriers; BRCAsom, BRCA1/2 somatic cases. b, Distribution of patient age (left), Ki67 staining (%, center) and clinical grade (right) across the same groups (same set of patient numbers). c, Distribution of number of detected substitutions (left), indels (center) and rearrangements (right) for the same groups limited to cases with 30-fold sequence coverage. Two-sided P values were calculated using the Kruskal–Wallis test. d, Frequency of the genome altered by copy number gain and loss (CN-FGA, left), LOH (LOH-FGA, center) and copy number neutral LOH (cnnLOH-FGA, right) defined as in Nik-Zainal et al. (Nature, 534, 47–54, 2016). e, Frequency of copy number gain (above zero centerline) and copy number loss across the genome for HRDetect-high tumors versus HRDetect-low tumors defined as in Nik-Zainal et al. (Nature, 2016). HRDetect-intermediate tumors were omitted due to small numbers. f, Frequency of amplification of driver genes from Nik-Zainal et al. (Nature, 2016) across HRDetect groups (left) and putative homozygous deletions (HD) called using ASCAT (right) as defined in Nik-Zainal et al. g, Comparison of somatic mutation frequency (substitutions, indels and curated rearrangements) for driver genes from Nik-Zainal et al. (Nature, 2016) versus HRDetect groups. Two-sided P values calculated using the chi-squared test. h, Violin plot of the distribution of rearrangement signature (RS) proportions per sample defined in Nik-Zainal et al. (Nature, 2016) versus HRDetect groups for patients with at least 20 called rearrangements. Violin plot line elements: center line, median; thick limits, upper and lower quartiles; whiskers, 1.5× interquartile range. In all boxplots, the top axis shows the number of patients in each group. Boxplot elements: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range. Kruskal, Kruskal–Wallis test; ChiSq, chi-squares test. All calculated P values are two-sided.

Extended Data Fig. 4 Unsupervised and supervised gene expression analyses versus HRDetect groups.

In all analyses, raw expression data (FPKM) have been offset by the addition of +0.1, followed by log2 transformation before further analyses. Only RefSeq annotated genes were used. A total of 232 cases with gene expression were included in all analyses. In all consensus cluster analyses, clustering was performed using Pearson correlation and ward.d2 linkage, with 2,000 repetitions using the R ConsensusClusterPlus package. For PCA analyses pItem = 0.8 and pFeature = 0.98 were used in the consensus cluster function. For non-PCA analyses, the corresponding values were 0.8 and 0.8. a, Consensus clustering of PCA components from PCA analysis of 19,102 genes using a two-group solution. The heatmap to the left shows consensus, with blue indicating that samples often cluster together across repetitions (rows = samples = columns). Bars to the right show the proportion of HRDetect groups in different consensus clusters according to the legend. PCA captures all variation in the data in the different principal components on which clustering was performed. b, Same as in a, but for a three-group consensus solution. c, Same as in a but for a four-group solution. d, Consensus clustering performed on 16,364 genes with mean-centered log2 data as input (that is, no PCA). HRDetect-high implies probabilities >0.7, HRDetect-low probabilities <0.2, that is, according to the main text definitions. Heatmaps show the percentage of samples for a group in respective consensus clusters (x axis), across different cluster solutions (y axis). For example, for HRDetect-high cases (left heatmap) using a k = 2 solution, >70% of these tumors are located in cluster 1, together with 40–70% of HRDetect-low samples (as seen in the right heatmap). e, Same visualization as in d, but now for 6,776 genes with a standard deviation of >0.6 in expression across samples. f, Supervised prediction of HRDetect-high (P > 0.7) and HRDetect-low (P < 0.2) according to main text definitions based on the top 10,000 varying RefSeq genes across all 232 cases using seven different types of machine learning method. FPKM values were offset by +0.1 and then log2 transformed. The 10,000 most varying genes across all relevant cases were selected. For each method, cases were divided into training (70% of cohort) and test (30%), balanced for age, lymph node status and grade. HRDetect-intermediate cases were omitted. Training and test cohorts were individually mean-centered. ROC was used as the optimization metric and fourfold cross-validation was repeated 10 times for training using the training cohort. The optimized model was applied to the test set. The entire procedure was repeated 10 times through an outer loop, with a different division of samples in the training and test set in each loop to assure that sample selection was not skewing results. For each model this generated, for example, 10 ROC metrics as each outer loop iteration created a (potentially) new model. The summarized results are shown to the left. For all methods, bar height corresponds to the average metric across the 10 iterations with one standard deviation range shown in red and individual values in orange. All analyses were performed using the Caret R-package using the classifier names indicated in the plot and with the tuneLength variable set to 10. g, The same analysis as in f, but instead using PCA components as input data for machine learning. PCA components were derived originally in a to capture all variation in the data and are now used as input for supervised prediction using the same set-up and parameters as in f. h, Gene expression (log2(FPKM + offset)) of prototypical immunomarkers versus HRDetect groups. Two-sided P values were calculated using the Kruskal–Wallis test. s.d., standard deviation. In all boxplots the top axis shows the number of patients in each group. Boxplot elements: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range.

Extended Data Fig. 5 MMRd SCAN-B tumors.

Note, unlike in colorectal cancer, mismatch repair deficient (MMRd) tumors are also able to carry signs of chromosomal or genomic instability as seen in PD31144a (BRCA1 promoter hypermethylated case) and PD31040a. Thus the mutational processes driving these two features are not mutually exclusive in breast cancer.

Extended Data Fig. 6 Characteristics of expanded HRDetect-intermediate cases.

a, Comparison of driver amplifications from Nik-Zainal et al. (Nature, 534, 47–54, 2016) between HRDetect groups defined from a broadened intermediate group (0.1–0.9 in HRDetect score). HRDetect (0.9–1) = 127 cases; HRDetect (0.1–0.9) = 32 cases; HRDetect (0–0.1) = 78 cases. b, Comparison of somatic driver mutations (substitutions, indels) for driver genes defined in Nik-Zainal et al. (Nature, 2016). For the specific set of genes curated for rearrangements in Nik-Zainal et al. (for example, RB1 and PTEN) these are included as events in the analysis (that is, RB1 includes both mutations and rearrangements). c, Distribution of mutational signature exposure for signatures S3 (e.3) and 5 (e.5) defined in Nik-Zainal et al. (Nature, 2016), and a copy number–based HRD score defined by Telli et al. (Clin. Cancer Res., 22, 3764–3773, 2016) (originally based on SNP arrays, ‘genomic scars’) across HRDetect subgroups defined by a broadened intermediate group. In all boxplots the top axis shows the number of patients in each group. Boxplot elements: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range. d, Distribution of total number of detected substitutions, indels and rearrangements for 30-fold sequenced cases across HRDetect subgroups defined by a broadened intermediate group. In all boxplots, the top axis shows the number of patients in each group. Boxplot elements: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range. Two-sided P values were calculated using the Kruskal–Wallis test. e, Distribution of exposure (displayed as a violin plot) to the six rearrangement signatures defined in Nik-Zainal et al. (Nature, 2016) versus HRDetect subgroups defined by a broadened intermediate group. Only cases with at least 20 rearrangements are included in the plots. Violin plot line elements: center line, median; thick limits, upper and lower quartiles; whiskers, 1.5× interquartile range. f, Outcome analysis for original HRDetect-groups (left panels) and new division with a broadened HRDetect-intermediate group (right panels) stratified by treatment status using invasive disease-free survival (IDFS) as clinical endpoint. The top two panels show IDFS for patients receiving adjuvant chemotherapy (ACT) and the bottom two panels show IDFS for untreated patients according to division by HRDetect score. Log-rank P values are two-sided. g, Distribution of different molecular subtypes in the broadened HRDetect-intermediate group based on 232 cases with gene expression data. mApo, molecular apocrine; BL1, basal-like 1; BL 2, basal-like 2; IM, immunomodulatory; M, mesenchymal; MSL, mesenchymal stem-like; LAR, luminal androgen receptor; UNS, uncertain.

Extended Data Fig. 7 Tumor cellularity versus HRDetect probability scores and characteristic rearrangement signature proportions for BRCA1-null (biallelic alteration or promoter hypermethylation) and BRCA2-null (biallelic alterations) tumors.

a, HRDetect probabilities versus WGS estimated tumor cell content based on the ASCAT algorithm (n = 84 cases). b, HRDetect probabilities versus a pathological assessment of the invasive cancer proportion from a section adjacent to the extracted tumor piece (n = 67 cases). Tumors are further stratified by their intended sequencing depth (30-fold or 15-fold) in a and b. c, Proportions of rearrangement signature 3 (Nik-Zainal et al., Nature, 534, 47–54, 2016) for BRCA1-null cases. Two of the 237 cases do not have a value for the signature. d, Proportions of rearrangement signature 5 for BRCA2-null cases. One outlier exists, corresponding to a tumor with concurrent BRCA1 hypermethylation that has a genetic phenotype very similar to a BRCA1-null tumor rather than a BRCA2-null tumor. Two of the 237 cases do not have a value for the signature. In all boxplots the top axis shows the number of patients in each group. Box-plot elements: center line, median; box limits, upper and lower quartiles; whiskers, = 1.5× interquartile range.

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Staaf, J., Glodzik, D., Bosch, A. et al. Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study. Nat Med 25, 1526–1533 (2019).

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