Variance as a Universal Defection Signal: A Framework for Early Detection of Cooperative Failure in Biological Systems

We propose that temporal variance in gene expression is a sufficient signal to distinguish cooperative (healthy) cells from defecting (cancerous) cells. Drawing on cooperative game theory, we reframe malignant cells as defectors that have ceased cooperating with the cellular collective while evading immune detection. Simulations demonstrate: (1) defectors in cooperative networks exhibit measurably higher output variance with perfect classification (F1=1.000, AUC=1.000); (2) simulated cancer cells with broken gene regulatory feedback produce variance separation exceeding 1,800x; (3) a variance-based threshold can identify defectors before macroscopic tumor formation. Open-source Python code provided for validation against TCGA, Human Cell Atlas, and GEO single-cell RNA sequencing datasets. Code: https://github.com/dot-protocol/variance-detection