# Brakepoint

- **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:** [Chengchen Duan](https://cerebralvalley.ai/u/Sam_Duan)
- **GitHub:** https://duanchengchen-oss.github.io/brakepoint/deliverables/
- **Demo video:** https://youtu.be/Jksufatf51A
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/48

Brakepoint is a genome-scale discovery engine for the next generation of cancer-immunotherapy drug targets. The best checkpoint drugs work by cutting the "brakes" off a patient's own T cells — but only a handful of those brakes have ever been drugged, and most patients still don't respond. Brakepoint goes looking for the rest, across the whole genome.

WHAT I BUILT. A fully reproducible pipeline that reads a public 2,638,736-cell, genome-wide human CD4+ T-cell CRISPRi Perturb-seq screen (Marson lab, Gladstone Institutes; 12,449 gene knockdowns) and, for every knockdown, asks two questions at once: how hard did switching the gene off hit the cell (a coverage-equalized energy-distance effect size), and which way did it push the cell — toward a stronger "fighter" (effector) state or a weaker (exhausted) one (a signed direction axis: a 16-gene effector program minus a 13-gene exhaustion program).

WHAT I FOUND. At 2.6M cells, ~97.5% of knockdowns clear significance, so p-values can no longer rank targets — and ranking by raw effect size points straight at essential machinery (18 of the top 20 hits are genes the T cell needs to survive, including its own TCR-signaling core). Adding the signed axis fixes this: it separates a candidate brake from essential machinery. With zero prior hints, Brakepoint rediscovered CBLB — a brake already advancing through clinical trials (NX-1607, HST-1011) — then surfaced four more candidates: CD5, DGKA, SMAD3, and UBASH3A, each scored across seven independent lines of evidence (causal effect, direction, donor consistency, screen fitness, target tractability, immune genetics, and clinical precedent).

WHY IT MATTERS. Recovering a target the industry already drugs — blind, from raw data — validates the method; the other four are concrete, testable hypotheses for the next experiment. Brakepoint is a blueprint for AI-native target discovery: one person, one week, on public data, with every result a versioned artifact carrying its exact code and reasoning trail.

LINKS. Live project + interactive explorer: https://duanchengchen-oss.github.io/brakepoint/deliverables/ | Open-source code (MIT): https://github.com/duanchengchen-oss/brakepoint | Methods deep-dive: https://github.com/duanchengchen-oss/brakepoint/blob/main/pipeline/METHODS.md

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