Simplifying the complexity of resistance heterogeneity in metastasis.
The main goal of treatment regimens for metastasis is to control growth rates, not eradicate all cancer cells. Mathematical models offer methodologies that incorporate high-throughput data with dynamic effects on net growth. The ideal approach would simplify, but not over-simplify, a complex problem into meaningful and manageable estimators that predict the response of a patient to specific treatments. We explore here three fundamental approaches with different assumptions concerning resistance mechanisms in which the cells are categorized into either discrete compartments or described by a continuous range of resistance levels. We argue in favor of modeling resistance as a continuum, and demonstrate how integrating cellular growth rates, density-dependent versus exponential growth, and intratumoral heterogeneity improves predictions concerning the resistance heterogeneity of metastases.
Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Orit
O
Lavi
Department of Mathematics and Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, MD, USA.
James M
JM
Greene
Department of Mathematics and Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, MD, USA.
Doron
D
Levy
Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. Electronic address: mgottesman@nih.gov.
Michael M
MM
Gottesman
D045744:Cell Line, Tumor; D019008:Drug Resistance, Neoplasm; D006801:Humans; D008962:Models, Theoretical; D009362:Neoplasm Metastasis / Q000473:pathology*
cell density; clinical application; mathematical model; metastasis; resistance level; tumor heterogeneity