# Concinnity

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Taras Nazarov](https://cerebralvalley.ai/u/concinnity)
- **GitHub:** https://github.com/different-change/organelle-direction
- **Demo video:** https://youtu.be/T2vFfWQJ5Wc
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/32

We built a directional readout of organelle dynamics. Standard analysis measures how much of an organelle is present - it can't separate a cell building an organelle up from one blocking its clearance, and the clearance genes (selective-autophagy receptors like BNIP3) aren't in conventional organelle signatures. We score the building and clearing programs separately and subtract: one signed number whose sign is the biology.

What we found: reading direction, not amount, predicts mitochondrial vulnerability in cancer. Across 1,066 DepMap cell lines, higher mito-biogenesis direction means stronger genetic dependency on that machinery (ρ=−0.35, p=1.7×10⁻³²) - and it's specific (the same test on ribosomes is null). From expression alone it also ranks drug response: the clinical Complex-I inhibitor IACS-010759 lands in the top 3% of 1,514 drugs, and MitoQ ranks #1. Direction beats amount on the vulnerability endpoint (AUROC 0.67 vs 0.61).

Why it matters: OXPHOS-targeting cancer drugs keep stalling on patient selection - no one can tell in advance which tumors depend on their mitochondria. This reads exactly that, from data already collected.

Honest bounds: effect sizes are modest, the cancer results are in cell lines, and our one patient-survival test (TCGA-KIRC, 510-tumor cohort) is a clean null at power - reported. All public data.

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