Simple SummaryImmune checkpoint inhibitors (ICIs) offer high efficacy of cancer treatment, but their use is limited to a subset of patients who respond well to the resetting of the immune system. To attempt to identify patients and predict response to ICI, tumour-based biomarkers such as PD-L1 expression or mutational tumour burden have been widely used but proved to be of insufficient accuracy. Here, we have deployed epigenetic profiling that detects specific chromosome conformations in the blood of the patients. It has been successfully used for predictive and prognostic applications. In this study, we developed and validated blood biomarkers for a checkpoint inhibitor response test that offers a significant increase in the accuracy of predicting positive response to ICI across multiple oncological indications. This new test is accurate, rapid, and minimally invasive. It could assist in treatment decisions, help to improve patient selection, and more efficiently manage costs.AbstractBackground: Unprecedented advantages in cancer treatment with immune checkpoint inhibitors (ICIs) remain limited to only a subset of patients. Systemic analyses of the regulatory 3D genome architecture linked to individual epigenetic and immunogenetic controls associated with tumour immune evasion mechanisms and immune checkpoint pathways reveal a highly prevalent molecular profile predictive of response to PD-1/PD-L1 ICIs. A clinical blood test based on a set of eight (8) 3D genomic biomarkers has been developed and validated on the basis of an observational trial to predict response to ICI therapy. Methods: The predictive eight biomarker set is derived from prospective observational clinical trials, representing 280 treatments with Pembrolizumab, Atezolizumab, Durvalumab, Nivolumab, and Avelumab in a broad range of indications: melanoma, lung, hepatocellular, renal, breast, bladder, colon, head and neck, bone, brain, lymphoma, prostate, vulvar, and cervical cancers. Results: The 3D genomic eight biomarker panel for response to immune checkpoint therapy achieved a high accuracy of 85%, sensitivity of 93%, and specificity of 82%. Conclusions: This study demonstrates that a 3D genomic approach can be used to develop a predictive clinical assay for response to PD-1/PD-L1 checkpoint inhibition in cancer patients.