Deciphering the Leading-Edge Spatiotemporal Microenvironment of Hepatocellular Carcinoma for Targeted Drug Discovery...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• The leading edge (LE) of hepatocellular carcinoma (HCC) is vital for tumor progression and linked to poor patient outcomes and significant intratumoral heterogeneity.
• Understanding LE mechanisms is crucial for developing effective HCC therapies.

Purpose of the Study:

• To investigate the molecular drivers and spatial characteristics of the HCC leading edge.
• To develop a novel computational model for analyzing spatial heterogeneity in HCC.
• To identify potential therapeutic targets and drug candidates for HCC.

Main Methods:

• Multi-omics data integration to identify key genes (SPARC, IGFBP7) in the HCC LE.
• Cell-cell communication and pathway analyses to understand LE-associated signaling.
• Development and validation of the SpaPred model for spatial analysis of HCC.
• In silico trajectory-perturbation and pharmacogenomic analyses for drug discovery.

Main Results:

• Elevated SPARC and IGFBP7 expression in the HCC LE correlates with stromal remodeling and an immune-depleted microenvironment.
• SpaPred accurately infers spatiotemporal heterogeneity in HCC, surpassing existing algorithms.
• Potential links identified between LE stromal states and pro-invasive signaling.
• Oxaliplatin, Belinostat, and Temsirolimus are prioritized as candidate drugs targeting LE-related programs.

Conclusions:

• SpaPred offers a novel framework for studying HCC spatial heterogeneity and identifying therapeutic vulnerabilities.
• Targeting LE-associated pathways presents a promising strategy for HCC treatment.
• Computational drug predictions require experimental validation to confirm efficacy.