Genome-wide CRISPR screens identify PKMYT1 as a therapeutic target in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with an overall 5-year survival rate of <12% due to the lack of effective treatments. Novel treatment strategies are urgently needed. Here, PKMYT1 is identified through genome-wide CRISPR screens as a non-mutant, genetic vulnerability of PDAC. Higher PKMYT1 expression levels indicate poor prognosis in PDAC patients. PKMYT1 ablation inhibits tumor growth and proliferation in vitro and in vivo by regulating cell cycle progression and inducing apoptosis. Moreover, pharmacological inhibition of PKMYT1 shows efficacy in multiple PDAC cell models and effectively induces tumor regression without overt toxicity in PDAC cell line-derived xenograft and in more clinically relevant patient-derived xenograft models. Mechanistically, in addition to its canonical function of phosphorylating CDK1, PKMYT1 functions as an oncogene to promote PDAC tumorigenesis by regulating PLK1 expression and phosphorylation. Finally, TP53 function and PRKDC activation are shown to modulate the sensitivity to PKMYT1 inhibition. These results define PKMYT1 dependency in PDAC and identify potential therapeutic strategies for clinical translation.

Tumor cell-intrinsic SETD2 inactivation sensitizes cancer cells to immune checkpoint blockade through the NR2F1-STAT1 pathway.

BACKGROUND: Cancer immunotherapies can induce durable tumor regression, but most patients do not respond. SETD2 mutation has been linked to the efficacy of immune checkpoint inhibitors (ICIs) immunotherapy. The functional importance of the SETD2 inactivation and how to modulate immunotherapy response remains unclear. METHODS: To explore the function of SETD2 in immunotherapy, knockout and subsequent functional experiments were conducted. Bulk RNA-seq, ATAC-seq, Chip-seq and single-cell RNA-seq were performed to dissect the mechanism and explore the immune microenvironment of mouse tumor. Flow cytometry was used to assess cell surface antigen and intratumoral T cell levels. RESULTS: We comprehensively determine the effect of SETD2 inactivation in ICIs therapy and elucidate the mechanistic impact on tumor immunity. Murine syngeneic tumors harboring Setd2 inactivation are sensitive to ICIs. By bulk and single-cell RNA-seq, we further reveal that SETD2 inactivation reprograms intratumoral immune cells and inflames the tumor microenvironment, which is characterized by high infiltration of T cells and enhanced antigen presentation to activate CD8+ T cell-mediated killing. Mechanistically, via an integrated multiomics analysis using ATAC-seq, ChIP-seq and RNA-seq, we demonstrate that SETD2 inactivation reduces NR2F1 transcription by impairing H3K36me3 deposition and chromatin accessibility, which activates the STAT1 signaling pathway to promote chemokines and programmed cell death protein-1 (PD-1) expression and enhance antigen presentation. All these regulatory mechanisms synergistically promote the effects of anti-programmed cell death ligand 1 immunotherapy in Setd2-knockout syngeneic mouse models. The SETD2-NR2F1-STAT1 regulatory axis is conserved in human and murine cancers. Finally, cancer patients harboring SETD2 mutations who received ICIs show increased durable clinical benefits and survival. CONCLUSIONS: These findings provide novel insights into the biology of SETD2 inactivation regulation and reveal a new potential therapeutic biomarker for ICIs immunotherapy in various refractory cancers.

A simple pan-specific RNN model for predicting HLA-II binding peptides.

The prediction of human leukocyte antigen (HLA) class II binding peptides plays important roles in understanding the mechanism of immune recognition and developing effective epitope-based vaccines. In this work, gated recurrent unit (GRU)-based recurrent neural network (RNN) was successfully employed to establish a pan-specific prediction model of HLA-II-binding peptides by using only the HLA and peptide sequence information. In comparison with the existing pan-specific models of HLA-II-binding peptides, the GRU-based RNN model covered a broad spectrum of HLA-II molecules including 50 HLA-DR, 47 HLA-DQ, and 19 HLA-DP molecules with peptide lengths varying from 8 to 43 mers. The results demonstrated strong discriminant capabilities of the GRU-based RNN model, of which the AUC values were 0.92, 0.88, and 0.88 for the training, validation, and test sets, respectively. Also, the GRU-based model showed state-of-the-art performances in predicting the binding peptides with the length ranging from 8-32 mers, which provides an efficient method for predicting HLA-II-binding peptides of longer lengths in comparison with the available methods. Overall, taking the advantages of the RNN architecture, the established pan-specific GRU model can be used for predicting accurately the HLA-II-binding peptides in a simple and direct manner.