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Ellis and colleagues show that combination of EZH2 inhibition and anti-PD-1 can increase antitumor immune responses in typically ‘immune cold’ prostate cancer, by increasing EZH2-regulated dsRNA–STING–ISG response signaling.
Halmos, Goel and colleagues characterize the rates of SARS-CoV-2 seroconversion in patients with cancer and see higher IgG positivity associated with specific cancer types and therapy modalities.
Ishikawa and colleagues perform integrated genomic and drug-sensitivity screens with extensive PDX modeling and reveal combined XIAP and BCL2 inhibition as a vulnerability hub across AML genetic alterations.
Magrini et. al. study sarcoma development and antitumor immune response in complement-deficient murine hosts, demonstrating a role for the C3a–C3aR axis in promoting immunosuppressive macrophages.
Walsh and colleagues use prospective sequencing in a large cohort of pediatric patients with solid tumors to detect mutations in cancer predisposition genes and guide downstream clinical care.
Zender, Dauch and colleagues demonstrate that pharmacologically induced lipotoxicity by activating LXRα and Raf-1 inhibition provides a metabolic therapeutic strategy for hepatocellular carcinoma.
Eilers and colleagues report that Aurora-A suppresses transcription–replication conflicts in MYCN-driven neuroblastoma, a vulnerability that can be targeted with a combination of Aurora-A and ATR kinase inhibitors.
Ma et al. apply few-shot learning to train a neural network model on cell-line drug-response data, and they subsequently transfer it to distinct biological contexts including different tissues and patient-derived tumor cells and xenografts.
Yang and colleagues perform a network system–pharmacology approach and clinical data integration, and identify LCK and BCL2 signaling as the molecular determinants of dasatinib response in pediatric and adult patients with T-ALL.
Ferrando and colleagues identify FYN–TRAF3IP2 as a recurrent oncogenic gene fusion that promotes angioimmunoblastic T-cell lymphoma and peripheral T-cell lymphoma not otherwise specified through the activation of NF-κB signaling.
Garofano et al. use single-cell RNA-sequencing data to classify glioblastomas along a metabolic axis of mitochondrial and glycolytic/plurimetabolic states and a neurodevelopmental axis of proliferative/progenitor and neuronal states.
Zhang and colleagues report that targeting GLS1 alleviates the glutamine dependence of ARID1A-mutated ovarian clear cell carcinomas, thereby suppressing their growth.
Pugh and colleagues use single-cell RNA sequencing, CRISPR screens and functional assays to define a gradient of developmental and wound-response cell states in glioblastoma stem cells, revealing insights into glioblastoma origins and potential therapeutic targets.