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<p>Homologous recombination repair deficiency (HRD) is a key biomarker for targeting breast cancer (BC) with PARP inhibitors (e.g., olaparib), but its detection usually requires a comprehensive genomic analysis. We explored whether metabolic signatures could indicate HRD status. We refined published lactate metabolism gene sets to develop an HRD-related lactate signature using least absolute shrinkage and selection operator (LASSO) regression. Using this signature, we stratifiedmore than1000 TCGA BC samples into high- and low-risk groups. We compared tumor immune-cell composition between groups using CIBERSORT and assessed associations with sensitivity to over 500 anticancer drugs using the GDSC2 and CTRP2 databases. A two-gene lactate metabolism signature (HPDL and SLC16A8) was constructed to stratify patients into high- and low-risk prognostic groups. The high-risk group had significantly worse overall survival. High-risk tumors presented immunosuppressive features (reduced CD8<sup>+</sup> T cells and follicular helper T cells, and increased M2 macrophages) and higher lactate metabolism scores than low-risk tumors did. Drug response analysis revealed that the low-risk group was more sensitive to several targeted agents, notably Nutlin-3 (an MDM2–p53 pathway inhibitor). We established a novel lactate metabolism signature that is predictive of prognosis and HRD in patients withBC. This signature provides mechanistic insight into the link between tumor metabolism and DNA repair and may help guide targeted therapy selection (e.g., MDM2 inhibitors) forbreast cancer patients.</p>

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Development of a lactate metabolism signature for predicting homologous recombination repair status in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Yejue Lin,
  • Lingting Jiang,
  • Shiyao Hu,
  • Yun Zhang,
  • Ming Luo

摘要

Homologous recombination repair deficiency (HRD) is a key biomarker for targeting breast cancer (BC) with PARP inhibitors (e.g., olaparib), but its detection usually requires a comprehensive genomic analysis. We explored whether metabolic signatures could indicate HRD status. We refined published lactate metabolism gene sets to develop an HRD-related lactate signature using least absolute shrinkage and selection operator (LASSO) regression. Using this signature, we stratifiedmore than1000 TCGA BC samples into high- and low-risk groups. We compared tumor immune-cell composition between groups using CIBERSORT and assessed associations with sensitivity to over 500 anticancer drugs using the GDSC2 and CTRP2 databases. A two-gene lactate metabolism signature (HPDL and SLC16A8) was constructed to stratify patients into high- and low-risk prognostic groups. The high-risk group had significantly worse overall survival. High-risk tumors presented immunosuppressive features (reduced CD8+ T cells and follicular helper T cells, and increased M2 macrophages) and higher lactate metabolism scores than low-risk tumors did. Drug response analysis revealed that the low-risk group was more sensitive to several targeted agents, notably Nutlin-3 (an MDM2–p53 pathway inhibitor). We established a novel lactate metabolism signature that is predictive of prognosis and HRD in patients withBC. This signature provides mechanistic insight into the link between tumor metabolism and DNA repair and may help guide targeted therapy selection (e.g., MDM2 inhibitors) forbreast cancer patients.