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Background <p>The marked heterogeneity of primary liver cancer (PLC), together with the dynamic influence of the tumor microenvironment (TME), remains a major challenge to precision oncology. As currently available therapies provide limited benefit for many patients and robust preclinical platforms for rapid, patient-specific treatment stratification are lacking, there is an urgent need for models that faithfully preserve individual tumor architecture and cellular diversity.</p> Methods <p>We established three-dimensional bioprinted primary liver cancer (3DP-PLC) models comprising 61 patient-derived monocultures and 34 co-culture models with patient-matched cancer-associated fibroblasts (CAFs). To explore the correlation between ex vivo models and clinical drug response, we developed a clinically anchored hybrid stratification framework for response classification. In parallel, the 3DP-PLC/CAF co-culture biobank was used to investigate stromal regulation of therapeutic response.</p> Results <p>These 3DP-PLC constructs preserved the histological features, biomarker expression patterns, mutational landscapes, and transcriptomic profiles of their corresponding parental tumors. Drug-sensitivity profiling across the biobank revealed substantial intertumoral heterogeneity in therapeutic responses. The clinically anchored hybrid stratification framework integrating ex vivo pharmaceutical profiles with external clinical benchmark data enabled interpretable response classification and demonstrated clinical relevance in patients receiving neoadjuvant or adjuvant targeted therapy. In parallel, the 3DP-PLC/CAF co-culture biobank, combined with single-cell transcriptomic analysis, recapitulated fibrous ring-like architecture and revealed CAF-associated drug-resistant states.</p> Conclusions <p>These findings support 3DP-PLC as a high-fidelity and scalable platform for personalized therapeutic stratification and mechanistic investigation of tumor–stroma interactions in PLC.</p>

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Exploring clinically anchored therapeutic stratification and tumor-stroma heterogeneity using patient-derived 3D bioprinted primary liver cancer models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Kai Zhang,
  • Liwei Du,
  • Minghao Sun,
  • Yuce Lu,
  • Mingchang Pang,
  • Shangze Jiang,
  • Xiyue Liu,
  • Jiaxun Dong,
  • Bao Jin,
  • Fu Xu,
  • Hang Sun,
  • Jiangang Zhang,
  • Huiyu Yang,
  • Xinting Sang,
  • Shunda Du,
  • Haitao Zhao,
  • Haifeng Xu,
  • Yilei Mao,
  • Huayu Yang

摘要

Background

The marked heterogeneity of primary liver cancer (PLC), together with the dynamic influence of the tumor microenvironment (TME), remains a major challenge to precision oncology. As currently available therapies provide limited benefit for many patients and robust preclinical platforms for rapid, patient-specific treatment stratification are lacking, there is an urgent need for models that faithfully preserve individual tumor architecture and cellular diversity.

Methods

We established three-dimensional bioprinted primary liver cancer (3DP-PLC) models comprising 61 patient-derived monocultures and 34 co-culture models with patient-matched cancer-associated fibroblasts (CAFs). To explore the correlation between ex vivo models and clinical drug response, we developed a clinically anchored hybrid stratification framework for response classification. In parallel, the 3DP-PLC/CAF co-culture biobank was used to investigate stromal regulation of therapeutic response.

Results

These 3DP-PLC constructs preserved the histological features, biomarker expression patterns, mutational landscapes, and transcriptomic profiles of their corresponding parental tumors. Drug-sensitivity profiling across the biobank revealed substantial intertumoral heterogeneity in therapeutic responses. The clinically anchored hybrid stratification framework integrating ex vivo pharmaceutical profiles with external clinical benchmark data enabled interpretable response classification and demonstrated clinical relevance in patients receiving neoadjuvant or adjuvant targeted therapy. In parallel, the 3DP-PLC/CAF co-culture biobank, combined with single-cell transcriptomic analysis, recapitulated fibrous ring-like architecture and revealed CAF-associated drug-resistant states.

Conclusions

These findings support 3DP-PLC as a high-fidelity and scalable platform for personalized therapeutic stratification and mechanistic investigation of tumor–stroma interactions in PLC.