Why Static Biomarkers Often Fall Short: Circadian Immune Coherence as a Missing Dimension in Immunotherapy

<ns5:p>Despite major advances in cancer immunotherapy, clinical decision-making still relies heavily on static biomarkers derived from single time-point measurements, such as PD-L1 expression or baseline inflammatory indices. While informative, these markers fail to capture the dynamic and time-dependent nature of host–tumor immune interactions. Accumulating evidence from circadian biology shows that immune cell trafficking, cytokine release, antigen presentation, and checkpoint pathway activity are under robust endogenous rhythmic control. Ignoring this temporal dimension may therefore lead to biological misclassification, inconsistent trial results, and suboptimal therapeutic strategies. Here, we argue that the limitations of static biomarkers reflect a conceptual gap: the absence of a system-level, time-aware framework capable of integrating longitudinal immune dynamics. We introduce Circadian Immune Coherence (CIC) as a systems-level property describing the temporal organization, rhythmic stability, and phase alignment of immune trajectories over time. Rather than focusing on isolated values, CIC captures the structured temporal behavior of the immune system. Preliminary application of this framework to longitudinal real-world clinical data (manuscript under review) supports its feasibility and suggests the presence of distinct immune coherence phenotypes associated with treatment outcomes. Time-series and spectral approaches can operationalize CIC by revealing latent rhythmic patterns in immune data that remain invisible to static analyses. Incorporating time-aware stratification and adaptive scheduling into biomarker development and clinical trial design may help reconcile conflicting results and improve therapeutic precision. Recognizing time as an intrinsic biological variable is therefore essential for advancing biomarker science in immunotherapy.</ns5:p>