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Clinical potential of whole-genome data linked to mortality statistics in patients with breast cancer in the UK : a retrospective analysis

Black, Daniella ; Davies, Helen Ruth ; Koh, Gene Ching Chiek ; Chmelova, Lucia ; Cubric, Marko ; Chalivelaki Chan, Georgia ; Degasperi, Andrea ; Czarnecki, Jan ; Toong, Ping Jing and Memari, Yasin , et al. (2025) In The Lancet Oncology 26(11). p.1417-1431
Abstract

Background Breast cancer is the most frequently diagnosed cancer in women. Survival is generally considered favourable, yet some patients remain at risk of early death. We aimed to assess whether comprehensive whole-genome sequencing (WGS) linked to mortality data could add prognostic value to existing clinical measures and identify patients who might respond to targeted therapeutics. Methods In this integrative, retrospective analysis, we analysed 2445 breast cancer tumours (any stage and molecular subtype) collected from 2403 patients recruited through 13 National Health Service Genomic Medicine Centres or hospitals in England affiliated to the 100 000 Genomes Project (100kGP) between 2012 and 2018. We linked 2208 (90%) cases with... (More)

Background Breast cancer is the most frequently diagnosed cancer in women. Survival is generally considered favourable, yet some patients remain at risk of early death. We aimed to assess whether comprehensive whole-genome sequencing (WGS) linked to mortality data could add prognostic value to existing clinical measures and identify patients who might respond to targeted therapeutics. Methods In this integrative, retrospective analysis, we analysed 2445 breast cancer tumours (any stage and molecular subtype) collected from 2403 patients recruited through 13 National Health Service Genomic Medicine Centres or hospitals in England affiliated to the 100 000 Genomes Project (100kGP) between 2012 and 2018. We linked 2208 (90%) cases with clinical data; mortality data were obtained for 1188 patients. Following high-depth WGS of tumour and matched normal DNA, we performed comprehensive WGS profiling seeking driver mutations, mutational signatures, and compound algorithmic scores for homologous recombination repair deficiency (HRD), mismatch repair deficiency, and tumour mutational burden. Data from 1803 additional patients with breast cancer from three independent cohorts were used to validate various findings. To evaluate the prognostic value of WGS features, we performed univariable and multivariable Cox regression on data from patients with stage I–III, ER-positive, HER2-negative breast cancer with a cancer-specific mortality endpoint (around 5-year follow-up). Findings Among 2445 tumours in the 100kGP breast cancer cohort, we observed genomic characteristics with immediate personalised medicine potential in 656 (26·8%), including features reporting HRD (298 [12·2%] total cases and 76 [6·3%] ER-positive, HER2-negative cases), highly individualised driver events, mutations underpinning resistance to endocrine therapy, and mutational signatures indicating therapeutic vulnerabilities. 373 (15·2%) cases had WGS features with potential for translational research, including compromised base excision repair and non-homologous end-joining dependency. Structural variation burden (hazard ratio 3·9 [95 CI% 2·4–6·2]; p<0·0001), high levels of APOBEC signatures (2·5 [1·6–4·1]; p<0·0001), and TP53 drivers (3·9 [2·4–6·2]; p<0·0001) were independently prognostic of customary clinical measures (age at diagnosis, stage, and grade) in patients with ER-positive, HER2-negative breast cancer. We developed a prognosticator for ER-positive, HER2-negative breast cancer capable of identifying patients who require either increased intervention or therapy de-escalation, validating the framework in the independent Swedish Cancerome Analysis Network-Breast (SCAN-B) dataset. Interpretation We show that breast cancer genomes are rich in predictive and prognostic value. We propose a two-step model for effective clinical application. First, the identification of candidates for targeted therapies or clinical trials using highly individualised genomic markers. Second, for patients without such features, the implementation of enhanced prognostication using genomic features alongside existing clinical decision-making factors. Funding National Institute of Health Research, Breast Cancer Research Foundation, Dr Josef Steiner Cancer Research Award 2019, Basser Gray Prime Award 2020, Cancer Research UK, Sir Jeffrey Cheah Early Career Fellowship, the Mats Paulsson Foundation, the Fru Berta Kamprads Foundation, and the Swedish Research Council.

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Contribution to journal
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published
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The Lancet Oncology
volume
26
issue
11
pages
15 pages
publisher
Elsevier
external identifiers
  • pmid:41072453
  • scopus:105020626900
ISSN
1470-2045
DOI
10.1016/S1470-2045(25)00400-0
language
English
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yes
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Publisher Copyright: © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
id
6215e94a-6927-40e3-bf3f-8cd9758e6920
date added to LUP
2025-12-15 15:32:51
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2025-12-16 02:59:35
@article{6215e94a-6927-40e3-bf3f-8cd9758e6920,
  abstract     = {{<p>Background Breast cancer is the most frequently diagnosed cancer in women. Survival is generally considered favourable, yet some patients remain at risk of early death. We aimed to assess whether comprehensive whole-genome sequencing (WGS) linked to mortality data could add prognostic value to existing clinical measures and identify patients who might respond to targeted therapeutics. Methods In this integrative, retrospective analysis, we analysed 2445 breast cancer tumours (any stage and molecular subtype) collected from 2403 patients recruited through 13 National Health Service Genomic Medicine Centres or hospitals in England affiliated to the 100 000 Genomes Project (100kGP) between 2012 and 2018. We linked 2208 (90%) cases with clinical data; mortality data were obtained for 1188 patients. Following high-depth WGS of tumour and matched normal DNA, we performed comprehensive WGS profiling seeking driver mutations, mutational signatures, and compound algorithmic scores for homologous recombination repair deficiency (HRD), mismatch repair deficiency, and tumour mutational burden. Data from 1803 additional patients with breast cancer from three independent cohorts were used to validate various findings. To evaluate the prognostic value of WGS features, we performed univariable and multivariable Cox regression on data from patients with stage I–III, ER-positive, HER2-negative breast cancer with a cancer-specific mortality endpoint (around 5-year follow-up). Findings Among 2445 tumours in the 100kGP breast cancer cohort, we observed genomic characteristics with immediate personalised medicine potential in 656 (26·8%), including features reporting HRD (298 [12·2%] total cases and 76 [6·3%] ER-positive, HER2-negative cases), highly individualised driver events, mutations underpinning resistance to endocrine therapy, and mutational signatures indicating therapeutic vulnerabilities. 373 (15·2%) cases had WGS features with potential for translational research, including compromised base excision repair and non-homologous end-joining dependency. Structural variation burden (hazard ratio 3·9 [95 CI% 2·4–6·2]; p&lt;0·0001), high levels of APOBEC signatures (2·5 [1·6–4·1]; p&lt;0·0001), and TP53 drivers (3·9 [2·4–6·2]; p&lt;0·0001) were independently prognostic of customary clinical measures (age at diagnosis, stage, and grade) in patients with ER-positive, HER2-negative breast cancer. We developed a prognosticator for ER-positive, HER2-negative breast cancer capable of identifying patients who require either increased intervention or therapy de-escalation, validating the framework in the independent Swedish Cancerome Analysis Network-Breast (SCAN-B) dataset. Interpretation We show that breast cancer genomes are rich in predictive and prognostic value. We propose a two-step model for effective clinical application. First, the identification of candidates for targeted therapies or clinical trials using highly individualised genomic markers. Second, for patients without such features, the implementation of enhanced prognostication using genomic features alongside existing clinical decision-making factors. Funding National Institute of Health Research, Breast Cancer Research Foundation, Dr Josef Steiner Cancer Research Award 2019, Basser Gray Prime Award 2020, Cancer Research UK, Sir Jeffrey Cheah Early Career Fellowship, the Mats Paulsson Foundation, the Fru Berta Kamprads Foundation, and the Swedish Research Council.</p>}},
  author       = {{Black, Daniella and Davies, Helen Ruth and Koh, Gene Ching Chiek and Chmelova, Lucia and Cubric, Marko and Chalivelaki Chan, Georgia and Degasperi, Andrea and Czarnecki, Jan and Toong, Ping Jing and Memari, Yasin and Whitworth, James and Zhao, Salome Jingchen and Kumar, Yogesh and Basyuni, Shadi and Rinaldi, Giuseppe and Shooter, Scott and Dembrovskyi, Vladyslav and Davies, Rosie and Chatzou Dunford, Maria and Copson, Ellen and Palmieri, Carlo and Borg, Åke and Ambrose, John and Bunce, Catey and Sosinsky, Alona and Arumugam, Prabhu and Brown, Matthew Arthur and Staaf, Johan and Turner, Nicholas and Nik-Zainal, Serena}},
  issn         = {{1470-2045}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{1417--1431}},
  publisher    = {{Elsevier}},
  series       = {{The Lancet Oncology}},
  title        = {{Clinical potential of whole-genome data linked to mortality statistics in patients with breast cancer in the UK : a retrospective analysis}},
  url          = {{http://dx.doi.org/10.1016/S1470-2045(25)00400-0}},
  doi          = {{10.1016/S1470-2045(25)00400-0}},
  volume       = {{26}},
  year         = {{2025}},
}