📊CrisPRO Precision Oncology Platform

In-Silico Generative and Discrimative Oncology

Predict Drug Efficacy Before Treatment. Generate Novel Therapeutics before wet labs

By translating standard pre-treatment genomic data into an 8-dimensional biological fingerprint, CrisPRO successfully stratifies clinical trial responders from non-responders.

✅Validated: 5 Major Trials Validated (Proof of Concept)

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Cas9 • 1,368 aa

On-target: 0.92

Off-target: 2

The 8-Dimensional Biological Fingerprint

Every tumor has a unique biological signature — an 8-dimensional mechanism vector covering DDR, IO, PI3K, MAPK, Efflux, and Replication Stress. CrisPRO reads this fingerprint to predict which drugs will work and which will fail.

Three Validated, Connected Engines

Click each engine to explore its live visualization — real interactive components, not static text.

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Live Interactive Visualization • Research Use Only

Research Scenario — LATIFY Trial

LATIFY Scenario, 62, NSCLC cancer. IO-refractory advanced NSCLC with STK11/KEAP1 co-loss — cold tumor, pembrolizumab failed

Match Score

0.9852

Cosine similarity rank

Outcome Prediction

MATCH: Ceralasertib + Durvalumab

Rank #1 of 806 trials. Delta: +0.3658. All 3 gates passed.

Phase III validated (NCT05450692). Cancer Cell 2025 (PMID 40645185). HUDSON subgroup confirmed (PMCID PMC10957481). $4-7B annual wasted IO spend addressable.

Three Validated Engines

From Target Discovery to Resistance Detection

Each engine is independently validated and connected — covering the full precision oncology pipeline from target identification through treatment monitoring.

🎯

Target-Lock: 0.988 AUROC

INTERCEPTION (CRISPR)

Therapeutic target identification via 4-signal composite (Evo2 + Enformer) across 304 gene-step combinations. 11/11 FDA-approved targets prospectively predicted.

Validated Capabilities

Target-Lock composite score (4-signal: Functionality, Essentiality, Regulatory, Chromatin)
Stage-specific targeting across 8 metastatic steps
AlphaFold3 structural pass rate: 100% (mean pLDDT 65.6)

0.988 AUROCIn silico

🛡️

8-Pathway Model: AUC 0.806

IO ENGINE

Will IO work for this patient? 8-pathway transcriptomic model predicts IO response with held-out AUC 0.806 and KEYNOTE-158 proxy delta +0.358.

Validated Capabilities

8-pathway transcriptomic scoring (EXHAUSTION, TIL, T_EFFECTOR, ANGIOGENESIS, etc.)
3x responder enrichment (10-15% → 30-50%)
KEYNOTE-158 proxy validated: delta +0.358 (3.5x threshold)

AUC 0.806Held-out + external validated

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680 Patients / 6 Datasets

KILL CHAIN + SPE

What resistance class is active right now? Monitors 10 resistance classes validated across 680 patients from 6 independent datasets with temporal ctDNA modeling.

Validated Capabilities

10 resistance class detection (BRCA reversion, ABCB1 efflux, SLFN11, lineage plasticity)
6 independent datasets (ARIEL, Patch, Christie, TCGA-OV, Abbott, MSK-SPECTRUM)
Temporal ctDNA resistance modeling (27 paired ARIEL profiles)

680 Patients6 Datasets

Three Questions. Three Engines. Clear Answers.

What targets should we pursue?

Target-Lock evaluates targets with a 4-signal composite (Evo2 + Enformer) across 304 gene-step combinations — achieving 0.988 AUROC and prospectively predicting 11/11 newly FDA-approved targets.

Will IO work for this patient?

The 8-pathway transcriptomic model achieves held-out AUC 0.806 and a KEYNOTE-158 proxy delta of +0.358 — enriching responder identification by 3x (from 10-15% to 30-50%).

What resistance is active right now?

Kill Chain monitors 10 resistance classes validated across 680 patients from 6 independent datasets (ARIEL, TCGA-OV, MSK-SPECTRUM) — with temporal ctDNA modeling and SLFN11 33.6% dual-resistance detection.