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Background <p>Although breast cancer is a significant heterogeneous disease with an increasing global prevalence, precise prognostic evaluation is a vital aspect of designing personalized therapy strategies and upholding patients’ survival rates. With the incorporation of artificial intelligence technology, in particular, machine learning, cancer prognosis and prediction have significantly been redefined.</p> Methods <p>In this study, we adopted a ten-fold cross-validation method to construct a Machine Learning-Derived Transcription Factor Signature (MDTS) across 108 algorithmic combinations. The optimal model was selected based on the highest average C-index across ten cohorts. We integrated single-cell data with multi-omics analysis to comprehensively assess the robustness of the MDTS model at both molecular and genomic levels. The MDTS demonstrated superior predictive power, outperforming 103 existing signatures and accurately predicting breast cancer outcomes across 10 independent cohorts.</p> Results <p>Our findings revealed that patients with low MDTS scores are more likely to benefit from immunotherapy, while the PAC-1 drug was identified as the most targeted agents to the chemotherapy with high MDTS score.</p> Conclusions <p>These insights will open the door to delivering cutting-edge MDTS strategies to customizing breast cancer therapies.</p>

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Machine learning on transcription factor expression profiles for precision breast cancer therapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月9日

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  • Xiaonan Zhang,
  • Simin Min,
  • Ning Zhang,
  • Xiaoyu Shi,
  • Zhaogen Cai,
  • Di Yang,
  • Zixin Meng,
  • Yunxia Zhao,
  • Ni Ni,
  • Tao Wang

摘要

Background

Although breast cancer is a significant heterogeneous disease with an increasing global prevalence, precise prognostic evaluation is a vital aspect of designing personalized therapy strategies and upholding patients’ survival rates. With the incorporation of artificial intelligence technology, in particular, machine learning, cancer prognosis and prediction have significantly been redefined.

Methods

In this study, we adopted a ten-fold cross-validation method to construct a Machine Learning-Derived Transcription Factor Signature (MDTS) across 108 algorithmic combinations. The optimal model was selected based on the highest average C-index across ten cohorts. We integrated single-cell data with multi-omics analysis to comprehensively assess the robustness of the MDTS model at both molecular and genomic levels. The MDTS demonstrated superior predictive power, outperforming 103 existing signatures and accurately predicting breast cancer outcomes across 10 independent cohorts.

Results

Our findings revealed that patients with low MDTS scores are more likely to benefit from immunotherapy, while the PAC-1 drug was identified as the most targeted agents to the chemotherapy with high MDTS score.

Conclusions

These insights will open the door to delivering cutting-edge MDTS strategies to customizing breast cancer therapies.