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.

GaiaLab is an evidence-first biological intelligence engine that turns a research question (or gene list) into traceable, cross-validated insights—fast.

In this episode: From Question → Insights

• How GaiaLab frames a biological question

• Evidence pulled from curated sources

• Cross-validation across independent datasets

• Traceable claims + contradictions flagged

• Outputs you can replay and reuse

Watch the video on YouTube: https://www.youtube.com/watch?v=7A0XY-Gf7So
Try GaiaLab: https://gaialab-production.up.railway.app/

Notes:

This content is for research/educational purposes and is not medical advice.