DNA-damaging chemotherapy and radiation therapy are integrated into the treatment paradigm of the majority of cancer patients. Recently, immunotherapy that targets the immunosuppressive interaction between programmed death 1 (PD-1) and its ligand PD-L1 has been approved for malignancies including non–small cell lung cancer, melanoma, and head and neck squamous cell carcinoma. ATR is a DNA damage–signaling kinase activated at damaged replication forks, and ATR kinase inhibitors potentiate the cytotoxicity of DNA-damaging chemotherapies. We show here that the ATR kinase inhibitor AZD6738 combines with conformal radiation therapy to attenuate radiation-induced CD8⁺ T cell exhaustion and potentiate CD8⁺ T cell activity in mouse models of Kras-mutant cancer. Mechanistically, AZD6738 blocks radiation-induced PD-L1 upregulation on tumor cells and dramatically decreases the number of tumor-infiltrating Tregs. Remarkably, AZD6738 combines with conformal radiation therapy to generate immunologic memory in complete responder mice. Our work raises the possibility that a single pharmacologic agent may enhance the cytotoxic effects of radiation while concurrently potentiating radiation-induced antitumor immune responses.

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relationship between the transcriptome and the proteome. Integrating these data dictates a reliable mapping between the identifier nomenclature resultant from the two high-throughput platforms. However, this kind of analysis is well known to be hampered by lack of standardization of identifier nomenclature among proteins, genes, and microarray probe sets. Therefore data integration may also play a role in critiquing the fallible gene identifications that both platforms emit.