A Quantitative Paradigm for Decision-Making in Precision Oncology

Dalit Engelhardt; Franziska Michor

doi:10.1016/j.trecan.2021.01.006

A Quantitative Paradigm for Decision-Making in Precision Oncology

Trends Cancer. 2021 Apr;7(4):293-300. doi: 10.1016/j.trecan.2021.01.006. Epub 2021 Feb 23.

Authors

Dalit Engelhardt¹, Franziska Michor²

Affiliations

¹ Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: dengelhardt@ds.dfci.harvard.edu.
² Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA; The Broad Institute of Harvard and MIT, Cambridge, MA, USA; The Ludwig Center at Harvard, Boston, MA, USA.

PMID: 33637444
DOI: 10.1016/j.trecan.2021.01.006

Abstract

The complexity and variability of cancer progression necessitate a quantitative paradigm for therapeutic decision-making that is dynamic, personalized, and capable of identifying optimal treatment strategies for individual patients under substantial uncertainty. Here, we discuss the core components and challenges of such an approach and highlight the need for comprehensive longitudinal clinical and molecular data integration in its development. We describe the complementary and varied roles of mathematical modeling and machine learning in constructing dynamic optimal cancer treatment strategies and highlight the potential of reinforcement learning approaches in this endeavor.

Keywords: machine learning; mathematical modeling; personalized medicine; treatment optimization.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review

MeSH terms

Decision Making*
Humans
Machine Learning
Medical Oncology / methods*
Models, Theoretical*
Neoplasms / drug therapy*
Precision Medicine / methods*