A tumor-agnostic network medicine framework reveals topological rewiring of systemic T-cell immunity in long-surviving cancer patients

Objective: Although a particular subset of patients treated with immune-checkpoint inhibitors (ICIs) consistently shows long-term survival, the immune mechanisms behind such phenomena remain largely unknown. Standard analyses of flow-cytometric data often rely on univariate statistics, thus minimizing the underlying complexity of the immune system. We propose a network medicine framework that reliably uncovers non-obvious patterns among target T-cell subgroups in flow-cytometric data. Our objective is to investigate the topological rewiring of T-cell response in long survivors (LS) across different solid tumor cohorts, with the aim of uncovering a tumor-agnostic immune signature. Methods: Peripheral blood mononuclear cells were collected from 93 patients with distinct solid tumors undergoing ICI therapy and 52 healthy donors and then analyzed using multiparametric flow-cytometry for a total of 31 T-cell populations. Patients were divided according to clinical parameters: LS (Overall-Survival > 18/24 months), Early Progressors (EP) (Progression-Free-Survival (PFS) ≤ 3 months), the remaining patients were defined as Intermediate (INT). To address the bounded, non-normal nature of cytometric percentages and ensure statistical rigor, we applied a logit transformation with limit of detection handling coupled with James-Stein shrinkage partial correlation, isolating direct cellular interdependencies. To robustly investigate immune signatures among subgroups, we implemented a differential overlay pipeline: network edges were first validated for stability via bootstrap resampling, followed by permutation testing to assess significant rewiring across clinical outcomes. Stable edges were then topologically classified (conserved, specific, or inverted). Finally, topological metrics (degree, betweenness, and Jaccard index) were integrated with sPLS-DA feature loadings to identify systemic master regulators. Results: Comparison between clinical outcomes exposed a profound topological shift. The EP network is highly centralized around activation compartments (CD3+PD1+Effector, degree=11). Interestingly, Both LS and INT patients lose this activation centrality (degree drops to 5). However, while INT patients lose this signature, only the LS network undergoes extensive structural rewiring to establish a costimulatory, memory-driven topology. Here, the central hub is represented by CD3+CD137+Central memory (degree=12). Interestingly, Non-Suppressive Tregs act as a critical structural bottleneck specifically in the LS network, exhibiting the highest systemic betweenness (0.101). This transition is orchestrated by specific master regulators: notably, CD3+CD137+Naïve exhibits major discriminating power (sPLS-DA loading=0.919) and undergoes massive structural rewiring (Jaccard=0.083). This node-centric divergence is driven by statistically robust edge inversions, prominently the interactions between CD3+CD137+Effector memory RA+ and CD3+PD1+Naive (p=0.0015 in EP vs LS), and between CD3+Central memory and CD3+Effector memory (p=0.0065 in EP vs LS), proving that long-term survival relies on reversing specific immunological axes rather than global immune restoration. Conclusion: From our results, long-term survival seems supported by rewired CD137+ memory T-cell networks. Our framework demonstrates that integrating edge inversions and nodal metrics from flow-cytometry data provides deep biological insights invisible to standard univariate analyses.