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Background <p>The clinical utility of integrated proteogenomic biomarkers for predicting chemotherapy response in triple-negative breast cancer remains underexplored. We prospectively analyzed paired baseline and post-treatment tumor samples from 50 patients with stage II–III TNBC treated with anthracycline- and taxane-based neoadjuvant chemotherapy, integrating whole-exome sequencing, RNA sequencing, global proteomics, and phosphoproteomics.</p> Results <p>Non-negative matrix factorization clustering identifies five proteogenomic subtypes. The immune-enriched subtype demonstrates the highest pathologic complete response rate (55.6%), whereas no pathologic complete response was observed in the xenobiotic metabolism or epithelial–mesenchymal transition subtypes. Immune-related pathways are enriched in tumors with pathologic complete response, while epithelial–mesenchymal transition pathways are enriched in non-pathologic complete response tumors. The estrogen response pathway is selectively enriched in non-pathologic complete response tumors at the proteomic level and inversely correlated with immune activation. Post-translational modification and in vitro analyses suggest estrogen-linked GRK2 activation contributes to chemotherapy resistance. <i>ITGB8</i> copy number loss is associated with higher pathologic complete response rates and immune activation, while non-pathologic complete response tumors of the immunomodulatory subtype show increased expression of <i>AKR1C2</i> and <i>ABCA13</i>. Comparison of baseline and post-treatment tumors reveals AURKB pathway activation in residual disease, with Aurora B kinase inhibition synergizing with paclitaxel. A predictive model incorporating these biomarkers outperforms RNA-based models in predicting response.</p> Conclusion <p>Integrative proteogenomic profiling enables robust prediction of chemotherapy resistance in triple-negative breast cancer and identifies actionable biomarkers providing a framework for advancing personalized therapeutic strategies.</p>

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Proteogenomic decoding of chemotherapy resistance in patients with triple-negative breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Dong Ki Lee,
  • Min Hwan Kim,
  • Yumi Hwang,
  • Seul-Gi Kim,
  • Won-Ji Ryu,
  • Geon-Uk Kim,
  • Hyun Myoung Yun,
  • Shinyoung Park,
  • Jeong Dong Lee,
  • Hyun Ju Han,
  • Gun Min Kim,
  • Kyung-Hee Kim,
  • Jong Bae Park,
  • Min Jung Kim,
  • Ja Seung Koo,
  • Jee Ye Kim,
  • Hyung Seok Park,
  • Seung Il Kim,
  • Heon Yung Gee,
  • Seho Park,
  • Joohyuk Sohn

摘要

Background

The clinical utility of integrated proteogenomic biomarkers for predicting chemotherapy response in triple-negative breast cancer remains underexplored. We prospectively analyzed paired baseline and post-treatment tumor samples from 50 patients with stage II–III TNBC treated with anthracycline- and taxane-based neoadjuvant chemotherapy, integrating whole-exome sequencing, RNA sequencing, global proteomics, and phosphoproteomics.

Results

Non-negative matrix factorization clustering identifies five proteogenomic subtypes. The immune-enriched subtype demonstrates the highest pathologic complete response rate (55.6%), whereas no pathologic complete response was observed in the xenobiotic metabolism or epithelial–mesenchymal transition subtypes. Immune-related pathways are enriched in tumors with pathologic complete response, while epithelial–mesenchymal transition pathways are enriched in non-pathologic complete response tumors. The estrogen response pathway is selectively enriched in non-pathologic complete response tumors at the proteomic level and inversely correlated with immune activation. Post-translational modification and in vitro analyses suggest estrogen-linked GRK2 activation contributes to chemotherapy resistance. ITGB8 copy number loss is associated with higher pathologic complete response rates and immune activation, while non-pathologic complete response tumors of the immunomodulatory subtype show increased expression of AKR1C2 and ABCA13. Comparison of baseline and post-treatment tumors reveals AURKB pathway activation in residual disease, with Aurora B kinase inhibition synergizing with paclitaxel. A predictive model incorporating these biomarkers outperforms RNA-based models in predicting response.

Conclusion

Integrative proteogenomic profiling enables robust prediction of chemotherapy resistance in triple-negative breast cancer and identifies actionable biomarkers providing a framework for advancing personalized therapeutic strategies.