Patient-derived organotypic tumor spheroids as a functional platform for predicting immunotherapeutic responses and guiding precision treatment for hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality worldwide. While immune checkpoint blockade has revolutionized the treatment of many cancers, responses in HCC remain limited. Robust functional platforms capable of predicting individual responses to immunotherapy are urgently needed. In this study, we developed a patient-derived organotypic tumor spheroid (PDOTS) model that preserves the heterogeneity and immune microenvironment of HCC, enabling rapid and reliable assessment of targeted and immunotherapeutic responses.
Tumor tissues from 30 HCC patients were processed using a "Five-Point Clock" sampling method and cultured within a three-dimensional microfluidic chip supplemented with IL-2 and CD3/28 activator to maintain tumor-infiltrating lymphocyte activity. The genomic, immune, and histopathological fidelity of PDOTS relative to parental tumors was evaluated. Drug responses were assessed ex vivo and validated in matched patient-derived xenograft (PDX) models. Transcriptomic profiling was subsequently performed to identify gene signatures associated with treatment sensitivity and to construct a transcriptomic predictive model.
The PDOTS retained ≥60% viability over 7 days and faithfully maintained the genomic, immune, and histopathological profiles of parental tumors. Functionally, PDOTS exhibited immune-dependent responses to PD-1 blockade and predicted treatment responses with 80% concordance in matched PDX models. Transcriptomic profiling revealed distinct metabolic and immune signatures in sensitive and resistant tumors, which were used to derive the "Organoid Killing Index (OKI)". The OKI gene-derived index, a composite index integrating the enrichment scores of gene signatures associated with the OKI, strongly correlated (R=0.829, p<0.001) and predicted clinical outcomes in an external patient cohort treated with atezolizumab plus bevacizumab.
These findings establish PDOTS as an immune-competent ex vivo platform for functional precision oncology and support the integration of functional testing with transcriptomic prediction to guide individualized immunotherapy in HCC.
Tumor tissues from 30 HCC patients were processed using a "Five-Point Clock" sampling method and cultured within a three-dimensional microfluidic chip supplemented with IL-2 and CD3/28 activator to maintain tumor-infiltrating lymphocyte activity. The genomic, immune, and histopathological fidelity of PDOTS relative to parental tumors was evaluated. Drug responses were assessed ex vivo and validated in matched patient-derived xenograft (PDX) models. Transcriptomic profiling was subsequently performed to identify gene signatures associated with treatment sensitivity and to construct a transcriptomic predictive model.
The PDOTS retained ≥60% viability over 7 days and faithfully maintained the genomic, immune, and histopathological profiles of parental tumors. Functionally, PDOTS exhibited immune-dependent responses to PD-1 blockade and predicted treatment responses with 80% concordance in matched PDX models. Transcriptomic profiling revealed distinct metabolic and immune signatures in sensitive and resistant tumors, which were used to derive the "Organoid Killing Index (OKI)". The OKI gene-derived index, a composite index integrating the enrichment scores of gene signatures associated with the OKI, strongly correlated (R=0.829, p<0.001) and predicted clinical outcomes in an external patient cohort treated with atezolizumab plus bevacizumab.
These findings establish PDOTS as an immune-competent ex vivo platform for functional precision oncology and support the integration of functional testing with transcriptomic prediction to guide individualized immunotherapy in HCC.
Authors
Song Song, Lu Lu, Sun Sun, Cheng Cheng, Li Li, Liu Liu, Li Li, Wang Wang, Zheng Zheng, Zhong Zhong, Xu Xu, Xu Xu, Wang Wang, Bai Bai, Xie Xie, Man Man, Fan Fan, Zhou Zhou, Chen Chen, Yang Yang
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