Cancer stem cells: a systems biology view of their role in prognosis and therapy

Abstract

Evidence has accumulated that characterizes highly tumorigenic cancer cells residing in heterogeneous populations. The accepted term for such a subpopulation is cancer stem cells (CSCs). While many questions still remain about their precise role in the origin, progression, and drug resistance of tumors, it is clear they exist. In this review, a current understanding of the nature of CSC, their potential usefulness in prognosis, and the need to target them will be discussed. In particular, separate studies now suggest that the CSC is plastic in its phenotype, toggling between tumorigenic and nontumorigenic states depending on both intrinsic and extrinsic conditions. Because of this, a static view of gene and protein levels defined by correlations may not be sufficient to either predict disease progression or aid in the discovery and development of drugs to molecular targets leading to cures. Quantitative dynamic modeling, a bottom up systems biology approach whereby signal transduction pathways are described by differential equations, may offer a novel means to overcome the challenges of oncology today. In conclusion, the complexity of CSCs can be captured in mathematical models that may be useful for selecting molecular targets, defining drug action, and predicting sensitivity or resistance pathways for improved patient outcomes.

Figure 1

Laboratory Methods Evaluating CSCs. A. Surface Antigen Expression. A population of tumor cells is labeled with fluorescent antibody to antigens associated with stem-like activity. Frequently, two or more antigens may define CSC subpopulations (142). Common markers include CD44, CD24, CD133, and Epcam (CD326). B. Sphere Forming Assay. A population of tumor cells in placed in culture with defined media including certain growth factors (e.g. EGF, FGF) and no serum and sphere formation is evaluated. It is distinct from floating clusters of cells. If the spheres are aggregated and replated, propagation and reformation confirms the presence of CSCs (143). C. Colony Forming Assay. The ability to form colonies a clonal level in soft agar is a measure of tumorigenicity and therefore is considered useful in identifying CSC (144). It is thought that the cell division that occurs is frequently associated with differentiation and therefore, the larger the colony the less differentiated the initiating cell (CSC). D. Side Population Assay. Because CSC are thought to be intrinsically drug resistant, one measure in a population of cells is the ability to extrude drug through ABC transporters such as ABCB5 (145). Tumor cells are incubated with a Hoechst dye and the subpopulation without fluorescence, but above background levels are considered CSC. E. Limiting Dilution Tumorigenicity Assay. This is considered the gold standard assay for identifying CSC. As depicted, a population of tumor cells (presumably containing CSCs) is diluted many 10-fold logs prior to injection reaching levels as low as one to ten cells per mouse. At any given dilution, the fraction of mice possessing tumors can be used to calculate the ratio of CSCs (5). Immunocompromised mice are used and the site of injection may vary. Other factors also seem to vary the readout as the use of highly immunocompromised mice seems to improve tumor formation as does the addition of a commercially available extracellular matrix such as Matrigel (6).

Figure 2

Signaling Pathways. A simplified overview of signal transduction pathways altered in CSCs. In the CSC literature, an emphasis has been placed on those with a role to play in normal development (Wnt, Hedgehog, Notch). Gene expression that follows stimulation or activation of a given receptor is shown (e.g., Wnt). In some instances, subsequent crosstalk with particular pathways is noted (TNF). Additionally, a tumor cell must produce energy and essential metabolites such as nucleotides, fatty acids, and amino acids as building blocks for maintenance. Oxygen gradients are also crucial and likely vary within any given region of a tumor. Growth factors, extracellular matrix and hormones are not depicted, but provide a context in which the signal transduction pathways act.

Figure 3

Tumor Heterogeneity. At present, the development of tumor heterogeneity may be three mechanisms. While depicted singly, some aspects of each may overlap. A. Clonal evolution. Accumulation of sufficient mutations in a differentiated epithelial cell (green) brings uncontrolled growth. Continuation of this mutational process may be aided by genomic instability while selection pressure for survival occurs. Different clones result (variable shades of green and blue). B. CSC hierarchy. CSCs are expected to reside in a niche reflecting their origin, the normal adult tissue stem cell. If this is true, then abnormal self-renewal (hexagonal blue cell) leads to differentiation of the CSC which then comprises the bulk of the tumor and is nontumorigenic. C. Plastic CSC. CSCs may derive from normal adult tissue cells (hexagonal blue cell), but as they divide and differentiate, it is also possible that subsequent progenitor cells may reacquire a more “stem-like” phenotype. This plasticity could explain tumor heterogeneity. The reversion from a differentiated CSC back to a more “stem-like” phenotype may result from environmental cues or therapy.

Figure 4

Systems Biology Approaches. A. Inferential Networks. It is commonplace to evaluate genomic, proteomic and metabolic findings construct networks of connections. The connections are based on statistical correlations and known networks residing in databases. Networks like the one depicted here is typically static in nature. (From: http://www.yaneslab.com/research/ with permission). B. Dynamic Networks. MAPK signaling is illustrated in symbols and from this, mathematical representations of the biology are determined through assignment of reaction rates and other parameters. Input/output relationships over time can be graphed and new connections may emerge.

Figure 5

Dynamic Modeling and Laboratory Result: Effect of a small molecule probe, which promotes CSC characteristics. on its molecular target, ERK. A. A dynamic model of MAPK signaling was constructed and included the inhibition of the presumptive targets (RasGAP and ERK1/2) of SC-1 using in vitro dissociation constants previously determined. B. HT29 colon tumor line was incubated with SC-1 and phospho-ERK1/2 was determined by western blot over time. The steady decrease of phospho-ERK1/2 up to 1 hr and the rebound at 4 hrs is reflected in the simulation results. However, the initial increase phospho-ERK1/2 in simulation is not reflected in the laboratory results. Adjustments to the model will be needed.