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Paper cited by one bottleneck page and 18 idea pages, indexed on Europe PMC as PubMed record 29245013 and published in Cell; the citing pages link this DOI, which is how the record was matched.
Combination cancer therapies aim to improve the probability and magnitude of therapeutic responses and reduce the likelihood of acquired resistance in an individual patient. However, drugs are tested in clinical trials on genetically diverse patient populations. We show here that patient-to-patient variability and independent drug action are sufficient to explain the superiority of many FDA-approved drug combinations in the absence of drug synergy or additivity. This is also true for combinations tested in patient-derived tumor xenografts. In a combination exhibiting independent drug action, each patient benefits solely from the drug to which his or her tumor is most sensitive, with no added benefit from other drugs. Even when drug combinations exhibit additivity or synergy in pre-clinical models, patient-to-patient variability and low cross-resistance make independent action the dominant mechanism in clinical populations. This insight represents a different way to interpret trial data and a different way to design combination therapies.
Indexed on Europe PMC as PubMed record 29245013 (DOI 10.1016/j.cell.2017.11.009). Matched by DOI alone: one bottleneck page and 18 idea pages cite this DOI among their external links (the pages are listed under Related), and this page was written so that the citation resolves inside OnCo. No figure has been checked by an editor.
One bottleneck page and 18 idea pages on OnCo cite this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing pages listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
Shares A patent pool for combination method-of-use claims, Every approved cancer drug ships with a public combination-readiness data pack, Too many combinations to test.
Shares Two-week pre-operative windows to compare combination biology head to head, Let the trial learn: response-adaptive allocation across many combination arms, Too many combinations to test.
Shares A registry of every treatment sequence patients actually receive, with outcomes, Registry-embedded randomisation of treatment order in routine care, Sequential multiple-assignment randomised trials to find the best order of ADCs, Too many combinations to test.
Shares Automated combination discovery: patient-sample screens feeding Bayesian platform trials, Grow each trial patient's tumour as organoids to decide which platform arm opens next, Too many combinations to test.
Shares Combination baskets defined by resistance mechanism rather than by cancer type, Sequential multiple-assignment randomised trials to find the best order of ADCs, Too many combinations to test.
Shares A non-profit phase 1b combination unit that any drug owner can use, Too many combinations to test.
Shares An open forecasting tournament on which combination trials will succeed, Too many combinations to test.
Shares Every approved cancer drug ships with a public combination-readiness data pack, Too many combinations to test.