Single-cell buoyant mass reveals CD8+ T-cell fitness and predicts melanoma checkpoint response 2251212

Abstract

Abstract Introduction Reliable, label-free evaluations of systemic T-cell fitness are lacking. Most biomarkers are tumor-intrinsic (e.g., TMB, PD-L1) or rely on molecular readouts (gene expression, cytokines) that provide only partial insight into a T cell’s integrated functional potential. We hypothesized that an intrinsic physical property of resting CD8+ T cells could integrate composition and state to reveal functional heterogeneity. We therefore asked whether single-cell buoyant mass can resolve hidden CD8+ states and predict response to immune checkpoint blockade (ICB) in a clinical study. Methods We engineered a live-cell, label-free SMR microfluidic sorter and paired it with transcriptomic/proteomic profiling. Ex vivo functional assays quantified proliferation, memory re-expression, and exhaustion propensity. In a neoadjuvant melanoma cohort, we profiled pre-treatment circulating CD8+ T cells and computed two features: mean heavy-cell mass and fraction of heavy cells. Results Resting CD8+ T cells showed a bimodal buoyant-mass distribution of light and heavy populations. Heavy cells were biosynthetically primed, expanded faster, re-expressed CD62L more readily, and resisted exhaustion; light cells accumulated mitochondrial mass, proliferated more slowly, and exhausted earlier after delayed activation. In a neoadjuvant melanoma cohort, pre-treatment heavy-cell metrics predicted pathologic response to checkpoint blockade and outperformed tumor mutational burden. Predictive information resided in subtle heavy-population shifts captured by SMR. Conclusion Buoyant mass resolves hidden T-cell heterogeneity and provides an integrated, stimulation-independent biomarker of systemic immune fitness. This fast, label-free readout supports patient stratification and broader immunotherapy applications. As the first/lead author, I will present these findings and the translational path to a clinical diagnostic. Funding Source D.K. Ludwig Fund for Cancer Research, MIT Center for Precision Cancer Medicine, and Stand Up to Cancer Convergence Program 3.1416. Topic Categories Translational and Interventional Immunology (TI)