Intrahepatic IgA complex induces polarization of cancer-associated fibroblasts to matrix phenotypes in the tumor microenvironment of HCC.

BACKGROUND AND AIMS: Cancer-associated fibroblasts (CAFs) play key roles in the tumor microenvironment. IgA contributes to inflammation and dismantling antitumor immunity in the human liver. In this study, we aimed to elucidate the effects of the IgA complex on CAFs in Pil Soo Sung the tumor microenvironment of HCC. APPROACH AND RESULTS: CAF dynamics in HCC tumor microenvironment were analyzed through single-cell RNA sequencing of HCC samples. CAFs isolated from 50 HCC samples were treated with mock or serum-derived IgA dimers in vitro. Progression-free survival of patients with advanced HCC treated with atezolizumab and bevacizumab was significantly longer in those with low serum IgA levels ( p <0.05). Single-cell analysis showed that subcluster proportions in the CAF-fibroblast activation protein-α matrix were significantly increased in patients with high serum IgA levels. Flow cytometry revealed a significant increase in the mean fluorescence intensity of fibroblast activation protein in the CD68 + cells from patients with high serum IgA levels ( p <0.001). We confirmed CD71 (IgA receptor) expression in CAFs, and IgA-treated CAFs exhibited higher programmed death-ligand 1 expression levels than those in mock-treated CAFs ( p <0.05). Coculture with CAFs attenuated the cytotoxic function of activated CD8 + T cells. Interestingly, activated CD8 + T cells cocultured with IgA-treated CAFs exhibited increased programmed death-1 expression levels than those cocultured with mock-treated CAFs ( p <0.05). CONCLUSIONS: Intrahepatic IgA induced polarization of HCC-CAFs into more malignant matrix phenotypes and attenuates cytotoxic T-cell function. Our study highlighted their potential roles in tumor progression and immune suppression.

Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells.

Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells.