Dynamic metrics-based biomarkers to predict responders to anti-PD-1 immunotherapy

BACKGROUND: Anti-PD-1 immunotherapies have shown clinical benefit in multiple cancers, but response was only observed in a subset of patients. Predicting which patients will respond is an urgent clinical need, but current companion diagnosis based on PD-L1 IHC staining shows limited predictability. METHODS: A dynamic, metrics-based biomarker was developed to discriminate responders from non-responders for anti-PD-1 immunotherapy in B16F10 melanoma-bearing mice. RESULTS: Similar to patients, there was considerable heterogeneity in response to anti-PD-1 immunotherapy in mice. Compared with the control group, 45% of anti-PD-1 antibody-treated mice displayed improved survival (defined as responders) and the remainder only gained little, if any, survival benefit from PD-1 blockade (non-responders). Interestingly, the dynamics of IFN-γ secretion by peripheral lymphocytes was associated with faster secretion onset (shorter lag time), stronger exponential phase, shorter time to half magnitude, and higher magnitude of secretion in responders at day 10 after tumour inoculation. To sufficiently predict responders from non-responders, IFN-γ secretion descriptors as well as phenotypic markers were subjected to multivariate analysis using orthogonal partial least-squares discriminant analysis (OPLS-DA). CONCLUSIONS: By integrating phenotypic markers, IFN-γ secretion descriptors sufficiently predict response to anti-PD-1 immunotherapy. Such a dynamic, metrics-based biomarker holds high diagnostic potential for anti-PD-1 checkpoint immunotherapy.