OnCo
ideasIdea

A public atlas of drug-pair responses across a thousand patient-derived organoids

Build a large, openly shared dataset of how tumour organoids respond to drug pairs, so that anyone can look up which combinations might work for which tumour type.

Existing combination screens use cell lines (NCI ALMANAC, AZ-DREAM). Organoids preserve more of the patient's tumour biology but no large public pairwise dataset exists. A consortium screening 1,000 characterised organoids (with genomics, transcriptomics and, where available, donor outcome) against a matrix of 100 approved and late-stage drugs would be the training set for every in silico combination model.

Hypothesis
Models trained on the atlas will predict clinical combination outcomes (randomised phase 2 success) with better calibration than models trained on cell-line matrices alone, measured against the next fifty combination readouts.
Rationale
Data scale drove progress in every predictive field. The Human Cancer Models Initiative built the organoids; nobody has systematically screened them in combination.
What would test it
Screen the first 200 organoids across 50 drugs in full pairwise matrices, release the data, and run an open prediction challenge scored on held-out organoids and on existing clinical combination results.
Maturity
preclinical evidence
Who has to act
philanthropy
Cost to try
Large (over $50M)
Years to first evidence
4
Bottlenecks it attacks

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