Hacking the Triple-Negative Resistance Network (TP53, BRCA1, EGFR) — GaiaLab
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Hacking the Triple-Negative Resistance Network (TP53, BRCA1, EGFR) — GaiaLab
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In this episode, I use GaiaLab (evidence-first biological intelligence) to analyze TP53, BRCA1, and EGFR in triple-negative breast cancer (TNBC) — surfacing prioritized pathways, therapeutic strategies, and testable hypotheses.
Highlights
• Top pathway signal: EGFR tyrosine kinase inhibitor (TKI) resistance (p = 1.68e-4)
• Therapy lanes: EGFR-targeted approaches + PARP inhibitors in BRCA-mutated TNBC
• Hypotheses to validate: BRCA1–EGFR resistance axis, TP53–metabolism targeting, and a BRCA1+EGFR biomarker panel
Trust & limits (important)
• Consensus 52% vs Contention 48%
• Evidence Density 34% (sparse)
• Contradiction Index 100% (high) — treat this as hypothesis-generating
• Research synthesis only — not medical advice
Slides (PDF)
https://drive.google.com/file/d/1095BEy5QBoy7B0qLEYZaSe5Qwb_mO7tY/view?usp=sharing
Next episode:
I’ll publish the YouTube video version and open submissions where you can send 2–5 genes + a disease context for analysis.
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