A logic-gated target-discovery pipeline for triple-negative breast cancer (TNBC). I built a pipeline that finds combinations of targets rather than single antigens, because cancer therapy must balance two opposing goals — tumor specificity and tumor-coverage — that no single target satisfies. It pairs a cell-surface antigen ("address") with an extracellular tumor protease ("permit"), scores each on separate axes, and auto-recommends logic gates of the form (antigen A OR antigen B) AND (protease). Five data sources were integrated: SURFY surfaceome, TCGA-BRCA Basal-like (TNBC proxy, n=171), GTEx 54 normal tissues, CELLxGENE single-cell TNBC (161k cells), and UCSC Xena TOIL. I found that single antigens fall short — the best, EPCAM, covers only ~47% of malignant cells and leaks into the kidney, lung, and pancreas. OR-ing two antigens widens coverage, and AND-gating with the stromal protease MMP11 (near-absent in normal tissue, active in TNBC) restores tumor-only specificity. The lead recommendation, (EPCAM OR VTCN1) AND MMP11 (coverage 51%, safety 0.96), maps directly onto real modalities such as logic-gated CAR-T or protease-activated ADCs. TNBC is just the first query — the real goal is a generalizable pipeline that programs medicines by logic rather than by a single target. Here I used only two signal categories (surface antigen + extracellular protease), which fill just 2 cells of a general 3×5 (WHERE × WHAT) space; the same framework extends across the full spectrum of readable cell signals — proteome, transcriptome, metabolome, and beyond — and to other tumor types. Long-term vision: given a cell's genotype and environmental conditions, infer the optimal target combination and design the corresponding programmable medicine.
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