Multiomics Identifies Metabolic Subtypes Based on Fatty Acid Degradation Allocating Personalized Treatment in Hepatocellular Carcinoma

Background And Aims: Molecular classification is a promising tool for prognosis prediction and optimizing precision therapy for HCC. Here, we aimed to develop a molecular classification of HCC based on the fatty acid degradation (FAD) pathway, fully characterize it, and evaluate its ability in guiding personalized therapy.

Approach And Results: We performed RNA sequencing (RNA-seq), PCR-array, lipidomics, metabolomics, and proteomics analysis of 41 patients with HCC, in which 17 patients received anti-programmed cell death-1 (PD-1) therapy. Single-cell RNA sequencing (scRNA-seq) was performed to explore the tumor microenvironment. Nearly, 60 publicly available multiomics data sets were analyzed. The associations between FAD subtypes and response to sorafenib, transarterial chemoembolization (TACE), immune checkpoint inhibitor (ICI) were assessed in patient cohorts, patient-derived xenograft (PDX), and spontaneous mouse model ls. A novel molecular classification named F subtype (F1, F2, and F3) was identified based on the FAD pathway, distinguished by clinical, mutational, epigenetic, metabolic, and immunological characteristics. F1 subtypes exhibited high infiltration with immunosuppressive microenvironment. Subtype-specific therapeutic strategies were identified, in which F1 subtypes with the lowest FAD activities represent responders to compounds YM-155 and Alisertib, sorafenib, anti-PD1, anti-PD-L1, and atezolizumab plus bevacizumab (T + A) treatment, while F3 subtypes with the highest FAD activities are responders to TACE. F2 subtypes, the intermediate status between F1 and F3, are potential responders to T + A combinations. We provide preliminary evidence that the FAD subtypes can be diagnosed based on liquid biopsies.

Conclusions: We identified 3 FAD subtypes with unique clinical and biological characteristics, which could optimize individual cancer patient therapy and help clinical decision-making.