Solid tumors and hematological cancers contain small population of tumor cells that are
believed to play a critical role in the development and progression of the disease. These
cells, named Cancer Stem Cells (CSCs), have been found in leukemia, myeloma, breast,
prostate, pancreas, colon, brain and lung cancers. It is also thought that CSCs drive the
metastatic spread of cancer. The CSC compartment features a specific and phenotypically
defined cell population characterized with self-renewal (through mutations), quiescence or
slow cycling, overexpression of anti-apoptotic proteins, multidrug resistance and impaired
differentiation. CSCs show resistance to a number of conventional therapies, and it is
believed that this explains why it is difficult to completely eradicate the disease and
why recurrence is an ever-present threat. A hierarchical phenomenological model is
proposed based on eight compartments following the stem cell lineage at the normal and
cancer cell levels. As an empirical test, the tumor grading and progression, typically
collected in the pathologic lab, is used to correlate the outcome of this model with the
tumor development stages. In addition, the model is able to quantitatively account for the
temporal development of the population of observed cell types. Two types of therapeutic
treatment models are considered, with dose-density chemotherapy (a pulsatile scenario) as
well as continuous, metronomic delivery. The drug hit is considered at the stem cell
progenitor and early differentiated specialized cell levels for both normal and cancer
cells, while the quiescent stem cell and fully differentiated compartments are considered
favorable outcome for cancer treatment. Circulating progenitors are neglected in this
analysis. The model provides a number of experimentally testable predictions. The relative
importance of the cell kill and survival is demonstrated through a deterministic
parametric study. The significance of the stem cell compartment is underlined based on
this simulation study. This predictive mathematical model for cancer stem cell hypothesis
is used to understand tumor responses to chemotherapeutic agents and judge the
efficacy.