Back

Multi-region spatial transcriptome analysis reveals cellular networks and pathways associated with hepatocellular carcinoma recurrence

Aiderus, A.; Seshachalam, P. V.; Idzham, K.; Caldez, M.; Shanmugam, R.; Sari, I. N.; Ying, D. H. H.; Chong, S. L.; Sekar, K.; Chew, S. C.; Chen, G. B.; Chung, A. Y.-F.; Cheow, P. C.; Kam, J. H.; Koh, A. W.-C.; Ganpathi, I. S.; Loong, S.; Leow, W.-Q.; Chen, K.; Chanwat, R.; de Villa, V. H.; Koh, P. S.; Bonney, G. K.; Goh, B. K. P.; Tam, W. L.; Tergaonkar, V.; Chow, P. K. H.

2023-07-26 cancer biology
10.1101/2023.07.26.549242 bioRxiv
Show abstract

Hepatocellular carcinomas (HCC) are driven by various etiologies and molecular diversity at presentation. Patient prognosis post-surgery is generally dismal, and the majority respond poorly to adjuvant targeted and/or immuno-therapies. Tumours are an ecosystem comprised of organization and interaction between different cell types that may contribute to clinically significant outcomes, such as disease recurrence. To better understand this phenomenon, we leveraged on a local cohort of patients with or without recurrence to generate spatial transcriptome profiles from multiple sectors from each tumour. We identified widespread gene expression intra- and inter tumour heterogeneity observed across the tumour sectors. Our analysis also revealed the cell type enrichment and localization, and ligand-receptor interactions identify a specific subset of endothelial cell enriched in primary tumours of patients with recurrence. Altogether, this study describes the spatial gene expression landscape in HCC patients associated with disease recurrence.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.