GEO series
Serial Changes of Circulating Tumor Cells in Patients with Hepatocellular Carcinoma Treated with Atezolizumab Plus Bevacizumab
GSE261186
Homo sapiens
Expression profiling by high throughput sequencing
20 samples
2024/07/17
GPL15520
Summary
Background and aims: Immune checkpoint inhibitors have promising outcomes in patients with hepatocellular carcinoma (HCC); however, there is no reliable biomarker for predicting disease progression. Circulating tumor cells (CTCs) derived from peripheral blood have attracted attention in monitoring therapeutic efficacy. In this study, CTCs were serially collected from patients with HCC undergoing atezolizumab plus bevacizumab (Atezo+Bev) treatment, and changes in molecular expression and CTC numbers were analyzed to identify effective biomarkers. Approach and results: The peripheral blood samples were collected serially from 22 patients with HCC treated with Atezo+Bev, and CTCs were enriched using RosetteSep. The CD45(-)PanCK(+) cell counts were measured using flow cytometry. RNA extracted from enriched CTCs underwent targeted RNA sequencing with next-generation sequencing (NGS) and performed unsupervised hierarchical clustering analysis. Changes in CTC numbers during Atezo+Bev treatment reflected the tumor volume. NGS analysis revealed that patients with elevated transforming growth factor (TGF)-β signaling molecules had a poorer response, whereas those with elevated apoptosis signaling molecules had a favorable response (p < 0.05). In addition, compared with changes in CTC counts, changes in TGF-β signaling molecule expression in CTCs accurately and promptly predicted treatment response. Conclusions: NGS analysis of CTC-derived RNA showed that changes in TGF-β signaling molecules predict treatment response earlier than changes in CTC counts. These findings suggest that changes in the expression of TGF-β molecules in CTCs could serve as novel biomarkers for the early prediction of therapeutic response in patients with unresectable HCC undergoing Atezo+Bev therapy.
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Paper (PMID 39001472) ↗
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