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Objective <p>Accurate cancer risk prediction is hindered by complex, multi-layered immune interactions, and traditional tissue biopsies are invasive and lack scalability for large-scale or repeated assessments. Peripheral blood offers a minimally invasive and accessible alternative for immune profiling. This study aims to develop CAMFormer, a deep learning framework that integrates multimodal peripheral blood-derived immune features for precise, non-invasive early cancer risk prediction.</p> Methods <p>CAMFormer combines mRNA expression, immune cell frequencies, and TCR diversity index, leveraging a cross-attention-based multimodal Transformer to capture cross-scale immune interactions.</p> Results <p>In five-fold cross-validation, CAMFormer achieved an AUC of 0.92 and an F1-score of 0.85 on the validation set, outperforming unimodal and baseline methods.</p> Conclusion <p>These results highlight the potential benefits of integrating multimodal immune features with cross-attention mechanisms for early cancer detection and for guiding future personalized immunotherapy studies.</p>

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Peripheral blood multimodal integration via cross-attention for cancer immune profiling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月6日

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  • Xiong Li,
  • Yi Hua,
  • Hongwei Liu,
  • Juan Zhou,
  • Yuejin Zhang,
  • Haowen Chen

摘要

Objective

Accurate cancer risk prediction is hindered by complex, multi-layered immune interactions, and traditional tissue biopsies are invasive and lack scalability for large-scale or repeated assessments. Peripheral blood offers a minimally invasive and accessible alternative for immune profiling. This study aims to develop CAMFormer, a deep learning framework that integrates multimodal peripheral blood-derived immune features for precise, non-invasive early cancer risk prediction.

Methods

CAMFormer combines mRNA expression, immune cell frequencies, and TCR diversity index, leveraging a cross-attention-based multimodal Transformer to capture cross-scale immune interactions.

Results

In five-fold cross-validation, CAMFormer achieved an AUC of 0.92 and an F1-score of 0.85 on the validation set, outperforming unimodal and baseline methods.

Conclusion

These results highlight the potential benefits of integrating multimodal immune features with cross-attention mechanisms for early cancer detection and for guiding future personalized immunotherapy studies.