Publications by Year: 2024

The tumor microenvironment (TME) is a heterogeneous ecosystem containing cancer cells, immune cells, stromal cells, cytokines, and chemokines which together govern tumor progression and response to immunotherapies. Methyltransferase-like 3 (METTL3), a core catalytic subunit for RNA N6-methyladenosine (m6A) modification, plays a crucial role in regulating various physiological and pathological processes. Whether and how METTL3 regulates the TME and anti-tumor immunity in non-small-cell lung cancer (NSCLC) remain poorly understood. Here, we report that METTL3 elevates expression of pro-tumorigenic chemokines including CXCL1, CXCL5, and CCL20, and destabilizes PD-L1 mRNA in an m6A-dependent manner, thereby shaping a non-inflamed TME. Thus, inhibiting METTL3 reprograms a more inflamed TME that renders anti-PD-1 therapy more effective in several murine lung tumor models. Clinically, NSCLC patients who exhibit low-METTL3 expression have a better prognosis when receiving anti-PD-1 therapy. Collectively, our study highlights targeting METTL3 as a promising strategy to improve immunotherapy in NSCLC patients.

Severe COVID-19 leads to widespread transcriptomic changes in the human brain, mimicking diminished cognitive performance. As long noncoding RNAs (lncRNAs) play crucial roles in the regulation of gene expression, identification of the lncRNAs differentially expressed upon COVID-19 may nominate key regulatory nodes underpinning cognitive changes. Here we identify hundreds of lncRNAs differentially expressed in the brains of COVID-19 patients relative to uninfected age/sex-matched controls, many of which are associated with decreased cognitive performance and inflammatory cytokine response. Our analyses reveal pervasive transcriptomic changes in lncRNA expression upon severe COVID-19, which may serve as key regulators of neurocognitive changes in the brain.

The landscape of non-coding mutations in cancer progression and immune evasion is largely unexplored. Here, we identify transcrptome-wide somatic and germline 3' untranslated region (3'-UTR) variants from 375 gastric cancer patients from The Cancer Genome Atlas. By performing gene expression quantitative trait loci (eQTL) and immune landscape QTL (ilQTL) analysis, we discover 3'-UTR variants with cis effects on expression and immune landscape phenotypes, such as immune cell infiltration and T cell receptor diversity. Using a massively parallel reporter assay, we distinguish between causal and correlative effects of 3'-UTR eQTLs in immune-related genes. Our approach identifies numerous 3'-UTR eQTLs and ilQTLs, providing a unique resource for the identification of immunotherapeutic targets and biomarkers. A prioritized ilQTL variant signature predicts response to immunotherapy better than standard-of-care PD-L1 expression in independent patient cohorts, showcasing the untapped potential of non-coding mutations in cancer.