Genome biology · 2026
Proteogenomic decoding of chemotherapy resistance in patients with triple-negative breast cancer.
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Lee Dong Ki, Kim Min Hwan, Hwang Yumi, Kim Seul-Gi, Ryu Won-Ji, Kim Geon-Uk, Yun Hyun Myoung, Park Shinyoung, Lee Jeong Dong, Han Hyun Ju, Kim Gun Min, Kim Kyung-Hee, Park Jong Bae, Kim Min Jung, Koo Ja Seung, Kim Jee Ye, Park Hyung Seok, Kim Seung Il, Gee Heon Yung, Park Seho, Sohn Joohyuk
Аннотация
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.
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.