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Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. To comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, we employed single-cell RNA-seq (scRNA-seq) to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases, and identified diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, in PDAC. We found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, we found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, our findings provide a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.

There is growing evidence that tumour neoantigens have important roles in generating spontaneous antitumour immune responses and predicting clinical responses to immunotherapies1,2. Despite the presence of numerous neoantigens in patients, complete tumour elimination is rare, owing to failures in mounting a sufficient and lasting antitumour immune response3,4. Here we show that durable neoantigen-specific immunity is regulated by mRNA N6-methyadenosine (m6A) methylation through the m6A-binding protein YTHDF15. In contrast to wild-type mice, Ythdf1-deficient mice show an elevated antigen-specific CD8+ T cell antitumour response. Loss of YTHDF1 in classical dendritic cells enhanced the cross-presentation of tumour antigens and the cross-priming of CD8+ T cells in vivo. Mechanistically, transcripts encoding lysosomal proteases are marked by m6A and recognized by YTHDF1. Binding of YTHDF1 to these transcripts increases the translation of lysosomal cathepsins in dendritic cells, and inhibition of cathepsins markedly enhances cross-presentation of wild-type dendritic cells. Furthermore, the therapeutic efficacy of PD-L1 checkpoint blockade is enhanced in Ythdf1-/- mice, implicating YTHDF1 as a potential therapeutic target in anticancer immunotherapy.

Introduction Acute myeloid leukemia (AML) is an aggressive disease with genetic and phenotypic heterogeneity that results in a highly variable response to standard chemotherapy. Azacitidine (AZA) is a hypomethylating agent (HMA) and has been investigated in combination with intensive chemotherapy as an epigenetic primer to sensitize leukemic cells to treatment. In a phase 1 trial, this regimen was safe and well-tolerated with overall response rate (CR+CRi) of 61% and complete remission rate of 41% (Cahill et al, Blood Adv 2020). Predictive biomarkers for response to this treatment strategy have not yet been identified. Since 5-hydroxymethylcytosine (5hmC) is an epigenetic biomarker in cancer, we hypothesized that Nano-5hmC-Seal sequencing technology may serve as a novel approach to identifying 5hmC profiles predictive of treatment response to epigenetic priming. Methods We performed RNA-seq gene expression and Nano-5hmC-Seal DNA profiling from peripheral blood/bone marrow samples of patients with high-risk AML to identify potential 5hmC profile biomarkers and gene expression changes (Figure 1A). Patients (n=46) were treated in a 3+3 dose-escalation scheme of AZA (37.5 mg/m 2, 50 mg/m 2, or 75 mg/m 2) on days 1-5 followed by high-dose cytarabine (3000 mg/m 2) and mitoxantrone (30 mg/m 2) (AZA-HiDAC-Mito) on day 6 and day 10 in a phase 1 trial previously reported (Cahill et al, Blood Adv 2020). We compared pre-treatment RNA-seq gene expression and 5hmC DNA profiles between responders (CR+CRi) and non-responders, as well as between pre-treatment and after 5 days of AZA for individual patients. We used an XGBoost machine learning model in Python based on a training set of patients to develop a 5hmC gene signature to predict response to AZA-HiDAC-Mito in an independent test set of patients. We compared continuous variables with two-tailed Student's t-test and used the Kaplan-Meier method with log-rank test for survival analysis. Results Thirty-three patients (72%) had adequate RNA samples for RNA-seq gene expression analysis. Eighteen responded to treatment (CR +CRi) and were enriched with gene expression patterns involved in cell-cell interaction and activation of cell cycle, while non-responders (n=15) had a higher expression of leukemic stem cell (LSC) signatures. There was no difference in gene expression profile when comparing pre-treatment samples to day 5 samples after AZA exposure. From the 5hmC profiling [n=40 (87%) patients with adequate samples], increased 5hmC in LSC genes was associated with treatment resistance to AZA-HiDAC-Mito (p=0.044). The number of differentially hydroxy-methylated genes (DhMGs) increased with higher doses of AZA exposure suggesting a dose-dependent epigenetic effect from AZA. Patients with a greater number of DhMGs following 5 days of AZA treatment had improved survival (p=0.015) (Figure 1B). Using the 5hmC-based XGBoost machine learning model comparing 5hmC profiles between responders to non-responders from a training set of patients (n=22), we developed an 11-gene 5hmC pre-treatment signature (including SKP1, WNT8A, CYP2E1, and NBPF9) to predict treatment response. The model was highly effective in predicting response to therapy, with an area under the curve (AUC) of 0.86 in an independent test set of patients (n=18) treated with AZA-HiDAC-Mito (Figure 1C). Conclusion In patients with AML treated with AZA-HiDAC-Mito, a pre-treatment LSC gene expression signature enriched with 5hmC was associated with treatment resistance. More DhMGs at day 5 appear to be a dose-dependent epigenetic effect that is induced by AZA and is associated with longer survival despite the absence of an immediate change in gene expression levels. An 11-gene 5hmC pre-treatment signature may be a predictive biomarker for AZA-HiDAC-Mito therapy and other HMA-based approaches. These findings warrant validation in a larger prospective trial. Figure 1 Figure 1. Disclosures Zhang: Bristol-Myers Squibb: Current Employment. Stock: Pfizer: Consultancy, Honoraria, Research Funding; amgen: Honoraria; agios: Honoraria; jazz: Honoraria; kura: Honoraria; kite: Honoraria; morphosys: Honoraria; servier: Honoraria; syndax: Consultancy, Honoraria; Pluristeem: Consultancy, Honoraria. Odenike: Celgene, Incyte, AstraZeneca, Astex, NS Pharma, AbbVie, Gilead, Janssen, Oncotherapy, Agios, CTI/Baxalta, Aprea: Research Funding; AbbVie, Celgene, Impact Biomedicines, Novartis, Taiho Oncology, Takeda: Consultancy. He: Epican Genetech: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company.

Summary Tumor-associated macrophages (TAMs) can dampen the antitumor activity of T cells, yet the underlying mechanism remains incompletely understood. Here, we show that C1q+ TAMs are regulated by an RNA N6-methyladenosine (m6A) program and modulate tumor-infiltrating CD8+ T cells by expressing multiple immunomodulatory ligands. Macrophage-specific knockout of an m6A methyltransferase Mettl14 drives CD8+ T cell differentiation along a dysfunctional trajectory, impairing CD8+ T cells to eliminate tumors. Mettl14-deficient C1q+ TAMs show a decreased m6A abundance on and a higher level of transcripts of Ebi3, a cytokine subunit. In addition, neutralization of EBI3 leads to reinvigoration of dysfunctional CD8+ T cells and overcomes immunosuppressive impact in mice. We show that the METTL14-m6A levels are negatively correlated with dysfunctional T cell levels in patients with colorectal cancer, supporting the clinical relevance of this regulatory pathway. Thus, our study demonstrates how an m6A methyltransferase in TAMs promotes CD8+ T cell dysfunction and tumor progression.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. To comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, we employed single-cell RNA-seq (scRNA-seq) to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases, and identified diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, in PDAC. We found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, we found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, our findings provide a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.