Can whole genome data analysis be used to personalize breast cancer care?

The investigators (1) performed an integrative retrospective analysis of 2,445 breast cancers from 2,403 patients recruited between 2012 and 2018 from 13 hospitals in England affiliated with the 100,000 Genomes Project (100kGP). Clinical data were available for 90.3% of cases, and mortality data for 49.4% of patients. Whole genome sequencing (WGS) was performed on all tumor and matched normal samples, with profiling conducted for evidence of homologous recombination deficiency (HRD), mismatch repair (MMR) deficiency, and tumor mutational burden. Results were validated by evaluating breast cancer samples from three independent cohorts.

They found genomic characteristics with what they described as “immediate personalized medicine potential” in 26.8% of samples, including evidence of HRD in 12.2% overall and 6.3% in estrogen receptor-positive (ER⁺)/human epidermal growth factor receptor 2-negative (HER2⁻) cases. Moreover, they found that three genomic changes (structural variation burden, high levels of APOBEC signatures, and TP53 drivers) were prognostic in ER⁺/HER2⁻ cancers, independent of commonly used clinical features, suggesting that they might be used to tailor increased or decreased treatment.

This is a well-conceived and conducted investigation. This database has been interrogated multiple times, thus far leading to multiple interesting findings, including that WGS led to a new diagnosis in 25% of the 4,660 people studied, and that in 14% of the new diagnoses, genomic variations would have been missed by non-WGS methods (2). Notably, the WGS findings discussed, including HRD, tumor mutational burden (a proxy for MMR defects), and estrogen receptor 1 (ESR1) mutations, all have actionable/potentially actionable interventions. For tumors that demonstrate HRD, poly(ADP-ribose) polymerase (PARP) inhibitors such as olaparib, rucaparib, niraparib, and talaoparib have demonstrated efficacy (1,3). For those with tumor mutational burden/MMR deficiency, immunotherapy such as pembrolizumab, nivolumab, and atezolizumab (4); also possibly effective are CDK4/6 inhibitors such as palbociclib (5), and PARP inhibitors such as olaparib (6). WGS can also identify resistance markers such as ESR1 driver mutations (Figure 1), which demonstrate resistance to endocrine therapy. Selective estrogen receptor degraders (SERDs) such as fulvestrant and elacestrant have demonstrated efficacy against ESR1 mutations (7). Moreover, the identification of rare drivers (such as MYB-NF1B, VIT1A-TCF10, and ETV6-NTRK3 fusions), as discussed by the authors (1), seems to be an advantage of WGS.

Figure 1 Potential clinical usefulness of WGS stratification in breast cancer. ER⁺, estrogen receptor-positive; ESR1, estrogen receptor 1; HER2⁻, human epidermal growth factor receptor 2-negative; HRD, homologous recombination deficiency; PARP, poly(ADP-ribose) polymerase; SERDs, selective estrogen receptor degraders; SV, stroke volume; WGS, whole genome sequencing.

The obvious question is how this information could be incorporated into clinical practice to optimize care. One way that comes to mind is to discuss the findings at the molecular tumor board, should the treating physicians have access to this.

There are limitations to the study and its findings. First, the sample size is rather modest. Second, less than half of the patients identified in the 100kGP were linked to both clinical and mortality data. Finally, the findings mainly apply to ER⁺/HER2⁻ tumors. Future efforts which incorporate WGS and similar analyses should focus on other breast subtypes, which are, on average, more deadly.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research. The article did not undergo external peer review.

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0147/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Disclaimer: Opinions expressed by the authors are their own and this material should not be interpreted as representing the official viewpoint of the U.S. Department of Health and Human Services, the National Institutes of Health, or the National Cancer Institute.

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