Peritumoral hepatocytes are critical components of the liver cancer microenvironment, However, the role of peritumoral hepatocytes in the local tumor immune interface and the underlying molecular mechanisms have not been elucidated. YTHDF2, an RNA N6-methyladenosine (m6A) reader, is critical for liver tumor progression. The function and regulatory roles of YTHDF2 in peritumoral hepatocytes are unknown. This study demonstrated that oxaliplatin (OXA) upregulated m6A modification and YTHDF2 expression in hepatocytes. Studies using tumor-bearing liver-specific Ythdf2 knockout mice revealed that hepatocyte YTHDF2 suppresses liver tumor growth through CD8+ T cell recruitment and activation. Additionally, YTHDF2 mediated the response to immunotherapy. Mechanistically, OXA upregulated YTHDF2 expression by activating the cGAS-STING signaling pathway and consequently enhanced the therapeutic outcomes of immunotherapeutic interventions. Ythdf2 stabilized Cx3cl1 transcripts in an m6A-dependent manner, regulating the interplay between CD8+ T cells and the progression of liver malignancies. Thus, this study elucidated the novel role of hepatocyte YTHDF2, which promotes therapy-induced antitumor immune responses in the liver. The findings of this study provide valuable insights into the mechanism underlying the therapeutic benefits of targeting YTHDF2.
Background: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days.
Methods: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets.
Results: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options.
Conclusions: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.
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