Inhibition of METTL3 Results in a Cell-Intrinsic Interferon Response That Enhances Antitumor Immunity [ONT direct RNA-seq]

Therapies that enhance antitumor immunity have altered the natural history of many cancers. Consequently, leveraging nonoverlapping mechanisms to increase immunogenicity of cancer cells remains a priority. Using a novel enzymatic inhibitor of the RNA methyl­transferase METTL3, we demonstrate a global decrease in N6-methyladenosine (m6A) results in double-stranded RNA (dsRNA) formation and a profound cell-intrinsic interferon response. Through unbiased CRISPR screens, we establish dsRNA-sensing and interferon signaling are primary mediators that potentiate T-cell killing of cancer cells following METTL3 inhibition. We show in a range of immunocompetent mouse models that although METTL3 inhibition is equally efficacious to anti–PD-1 therapy, the combination has far greater preclinical activity. Using SPLINTR barcoding, we demonstrate that anti–PD-1 therapy and METTL3 inhibition target distinct malignant clones, and the combination of these therapies overcomes clones insensitive to the single agents. These data provide the mole­cular and preclinical rationale for employing METTL3 inhibitors to promote antitumor immunity in the clinic. Overall design: Nanopore sequencing was undertaken on AT3 cells treated with DMSO, STM2457 20µM or STM3006 2µM for 48 hours. All samples were processed in duplicate. Total RNA was isolated using TRIzol LS reagent (Invitrogen) according to manufacturer's instructions and ethanol precipitation. RNA concentration was quantified with a Qubit Fluorometer (Thermo Fisher Scientific). Extracted total RNA was processed to obtain polyA-tailed transcripts with Dynabeads (Invitrogen) according to the manufacturer's instructions. A target amount of 200ng was used to create a readable transcript library for a single flowcell (type FLO-MIN106) using a protocol based on Oxford Nanopore Technology's DirectRNA SQK-RNA002 kit protocol. Each sample was sequenced on a single flowcell with version R9.4.1 chemistry. A GridION instrument controlled by MINKNOW software (v20.10.6) was loaded with four flowcells and four channel libraries were run separately and simultaneously. Readfish (v3.0.0) selective sequencing32 was used during the GridION run to eject abundant and irrelevant transcripts. Briefly, the accessions of 81 mouse transcripts comprising mitochondrial transcripts and cytoplasmic ribsomal protein-encoding transcripts were used to generate an exclude list. Reads were basecalled in real time using Guppy (v4.2.3). FAST5 files were recorded for later processing by Nanopolish (v0.11.2)33. Basecalled reads and Fast5 signal files were processed to prepare for Nanocompore (v1.0.3) comparison analysis according to the author's instructions17. Briefly, fastq files were aligned to a mouse transcriptome version M20 with minimap2 (v2.2.0)34 to obtain bam alignment files, then Nanopolish's eventalign module was used to estimate kmer pore transition ('dwell') times from the alignment and the Fast5 signal files. The Nanopolish-compress tool was used to average nanopolish output by kmer. Nanocompore was used to discern significant differences in signal current intensity and dwell time between the control and treatment replicate pairs, using the GMM-logit option to use Gaussian Mixture Modelling to discern the presence of two different nucleotide modification states, and logit regression analysis to calculate the difference in site characteristics between the samples.