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Rapid learning for precision oncology

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Key Points

  • In Precision Oncology 3.0 sophisticated algorithms analyse panomic data to hypothesize the molecular pathways that drive an individual patient's tumour, and hypothesize personalized treatments, using combinations of narrowly targeted therapies

  • At the molecular level, where Precision Oncology 3.0 operates, there are far too many combinations of driver mutations and possible treatments to be efficiently searched by current clinical trial methodologies

  • The 'Rapid Learning Precision Oncology' paradigm considers each patient encounter as an experiment, continuously gathering and analysing all the data to inform each subsequent encounter with the same or similar patients

  • All patient encounters can be coordinated through a 'Global Cumulative Treatment Analysis' (GCTA) methodology, which chooses treatments according to their continuously updated performance statistics

  • The Rapid Learning approach can help to overcome some of the technical and structural barriers facing Precision Oncology 3.0, including the facilitation of the off-label uses of targeted drugs

Abstract

The emerging paradigm of Precision Oncology 3.0 uses panomics and sophisticated methods of statistical reverse engineering to hypothesize the putative networks that drive a given patient's tumour, and to attack these drivers with combinations of targeted therapies. Here, we review a paradigm termed Rapid Learning Precision Oncology wherein every treatment event is considered as a probe that simultaneously treats the patient and provides an opportunity to validate and refine the models on which the treatment decisions are based. Implementation of Rapid Learning Precision Oncology requires overcoming a host of challenges that include developing analytical tools, capturing the information from each patient encounter and rapidly extrapolating it to other patients, coordinating many patient encounters to efficiently search for effective treatments, and overcoming economic, social and structural impediments, such as obtaining access to, and reimbursement for, investigational drugs.

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Figure 1: Precision Oncology 3.0 in outline.
Figure 2: Precision Oncology 3.0 core algorithms and components.
Figure 3: Global Cumulative Treatment Analysis (GCTA).

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Acknowledgements

We especially thank C. A. Blau for his significant contributions to both the concepts and content of this paper. Others who provided significant feedback include A. Califano, O. Capote, J. Doroshow, G. S. Ginsberg, S. Greene, P. Howard, P. Huber, R. Lehrer, L. Marton, G. B. Mills, K. Polacek, A. Schiffman, E. Shtivelman, and several anonymous reviewers.

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Both authors researched data for article, made a substantial contribution to discussion of the content, and wrote and edited the manuscript before submission.

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Correspondence to Jeff Shrager.

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Shrager, J., Tenenbaum, J. Rapid learning for precision oncology. Nat Rev Clin Oncol 11, 109–118 (2014). https://doi.org/10.1038/nrclinonc.2013.244

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