-Methyladenosine (mA) modification is the most pervasive modification of human mRNA molecules. It is reversible regulation of mA modification methyltransferase, demethylase and proteins that preferentially recognize mA modification as "writers", "erasers" and "readers", respectively. Altered expression levels of the mA modification key regulators substantially affect their function, leading to significant phenotype changes in the cell and organism. Recent studies have proved that the mA modification plays significant roles in regulation of metabolism, stem cell self-renewal, and metastasis in a variety of human cancers. In this review, we describe the potential roles of mA modification in human cancers and summarize their underlying molecular mechanisms. Moreover, we will highlight potential therapeutic approaches by targeting the key mA modification regulators for cancer drug development.

Lenvatinib, a second-generation multi-receptor tyrosine kinase inhibitor approved by the FDA for first-line treatment of advanced liver cancer, facing limitations due to drug resistance. Here, we applied a multidimensional, high-throughput screening platform comprising patient-derived resistant liver tumor cells (PDCs), organoids (PDOs), and xenografts (PDXs) to identify drug susceptibilities for conquering lenvatinib resistance in clinically relevant settings. Expansion and passaging of PDCs and PDOs from resistant patient liver tumors retained functional fidelity to lenvatinib treatment, expediting drug repurposing screens. Pharmacological screening identified romidepsin, YM155, apitolisib, NVP-TAE684 and dasatinib as potential antitumor agents in lenvatinib-resistant PDC and PDO models. Notably, romidepsin treatment enhanced antitumor response in syngeneic mouse models by triggering immunogenic tumor cell death and blocking the EGFR signaling pathway. A combination of romidepsin and immunotherapy achieved robust and synergistic antitumor effects against lenvatinib resistance in humanized immunocompetent PDX models. Collectively, our findings suggest that patient-derived liver cancer models effectively recapitulate lenvatinib resistance observed in clinical settings and expedite drug discovery for advanced liver cancer, providing a feasible multidimensional platform for personalized medicine.