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Map every state a cancer cell can be in, and how drugs and the surrounding tissue move it between states, into an open computational model anyone can query and improve.
Single-cell and spatial atlases (Human Tumor Atlas Network, Human Cell Atlas) describe cell states; perturbation screens and foundation models trained on them begin to predict responses. The proposal is a coordinated, openly licensed effort to generate perturbation-response single-cell data across hundreds of models and patient samples, train and release a foundation model of cancer cell state transitions, and benchmark it prospectively against drug response in organoids and trials, with the data, weights and benchmarks all public.
Shares CRISPR functional genomics, Patient-derived organoids, Lab models that fail to predict what happens in patients, Tumour heterogeneity and clonal evolution.
Shares Broad Institute of MIT and Harvard, Preclinical results do not reproduce, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares 10x Genomics, Single-cell & spatial profiling, Tumour heterogeneity and clonal evolution.
Shares CRISPR functional genomics, Preclinical results do not reproduce, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Single-cell & spatial profiling, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Broad Institute of MIT and Harvard, CRISPR functional genomics, Tumour heterogeneity and clonal evolution.
Shares Broad Institute of MIT and Harvard, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Single-cell & spatial profiling, Patient-derived organoids, Lab models that fail to predict what happens in patients.