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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.
Shares Shared reference organoid and PDX panels that every lab can test against, DepMap (Cancer Dependency Map), Functional (ex vivo) drug testing, Lab models that fail to predict what happens in patients.
Shares Cancer Models (PDCM Finder) & HCMI, DepMap (Cancer Dependency Map), Functional (ex vivo) drug testing, Patient-derived organoids.
Shares Cancer Models (PDCM Finder) & HCMI, DepMap (Cancer Dependency Map), Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares DepMap (Cancer Dependency Map), Functional (ex vivo) drug testing, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares An open engine that ranks every drug pair by predicted synergy before anyone runs a trial, Functional (ex vivo) drug testing, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Cancer Models (PDCM Finder) & HCMI, Functional (ex vivo) drug testing, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Functional (ex vivo) drug testing, Patient-derived organoids, Lab models that fail to predict what happens in patients, Too many combinations to test.
Shares Functional (ex vivo) drug testing, Patient-derived organoids, Lab models that fail to predict what happens in patients.