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

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.

Identifying Bayesian optimal experiments for uncertain biochemical pathway models.

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Complex display of putative tumor stem cell markers in the NCI60 tumor cell line panel.

Tumor stem cells or cancer initiating cells (CICs) are single tumor cells that can regenerate a tumor or a metastasis. The identification and isolation of CICs remain challenging, and a variety of putative CIC markers have been described. We hypothesized that cell lines of the NCI60 panel contain CICs and express putative CIC markers. We investigated expression of putative CIC surface markers (CD15, CD24, CD44, CD133, CD166, CD326, PgP) and the activity of aldehyde dehydrogenase in the NCI60 panel singly and in combination by six-color fluorescence-activated cell sorting analysis. All investigated markers were expressed in cell lines of the NCI60 panel. Expression levels of individual markers varied widely across the 60 cell lines, and neither single marker expression nor simple combinations nor co-expression patterns correlated with the colony-formation capacity of cell lines. Rather, marker expression patterns correlated with tumor types in multidimensional analysis. Whereas some expression patterns correlated with tumor entities such as basal breast cancer, other expression patterns occurred across different tumor types and largely related to expression of a more mesenchymal phenotype in individual breast, lung, renal, and melanoma cell lines. Our data for the first time demonstrate that tumor cell lines display CIC markers in a complex pattern that relates to the tumor type. The complexity and tumor type specificity of marker display creates challenges for the application of cell sorting and other approaches to isolation of putative tumor stem cell populations and suggests that therapeutic targeting strategies will need to take this into account.

Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning.

Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer a priori and molecular targets in the Machine Learning setting.

In vitro evaluation of dimethane sulfonate analogues with potential alkylating activity and selective renal cell carcinoma cytotoxicity.

We identified five structurally related dimethane sulfonates with putative selective cytotoxicity in renal cancer cell lines. These compounds have a hydrophobic moiety linked to a predicted alkylating group. A COMPARE analysis with the National Cancer Institute Anticancer Drug Screen standard agent database found significant correlations between the IC50 of the test compounds and the IC50 of alkylating agents (e.g., r = 0.68, P < 0.00001 for chlorambucil). In this report, we examined whether these compounds had activities similar to those of conventional alkylating agents. In cytotoxicity studies, chlorambucil-resistant Walker rat carcinoma cells were 4- to 11-fold cross-resistant to the test compounds compared with 14-fold resistant to chlorambucil. To determine effects on cell cycle progression, renal cell carcinoma (RCC) line 109 was labeled with bromodeoxyuridine prior to drug treatment. Complete cell cycle arrest occurred in cells treated with an IC90 dose of NSC 268965. p53 protein levels increased as much as 5.7-fold in RCC line 109 and as much as 20.4-fold in breast cancer line MCF-7 following an 18-hour drug exposure. Finally, DNA-protein cross-links were found following a 6-hour pretreatment with all compounds. Thus, the dimethane sulfonate analogues have properties expected of some alkylating agents but, unlike conventional alkylating agents, appear to possess activity against RCC.

A small molecule (pluripotin) as a tool for studying cancer stem cell biology: proof of concept.

BACKGROUND: Cancer stem cells (CSC) are thought to be responsible for tumor maintenance and heterogeneity. Bona fide CSC purified from tumor biopsies are limited in supply and this hampers study of CSC biology. Furthermore, purified stem-like CSC subpopulations from existing tumor lines are unstable in culture. Finding a means to overcome these technical challenges would be a useful goal. In a first effort towards this, we examined whether a chemical probe that promotes survival of murine embryonic stem cells without added exogenous factors can alter functional characteristics in extant tumor lines in a fashion consistent with a CSC phenotype. METHODOLOGY/PRINCIPAL FINDINGS: The seven tumor lines of the NCI60 colon subpanel were exposed to SC-1 (pluripotin), a dual kinase and GTPase inhibitor that promotes self-renewal, and then examined for tumorigenicity under limiting dilution conditions and clonogenic activity in soft agar. A statistically significant increase in tumor formation following SC-1 treatment was observed (p<0.04). Cloning efficiencies and expression of putative CSC surface antigens (CD133 and CD44) were also increased. SC-1 treatment led to sphere formation in some colon tumor lines. Finally, SC-1 inhibited in vitro kinase activity of RSK2, and another RSK2 inhibitor increased colony formation implicating a role for this kinase in eliciting a CSC phenotype. CONCLUSIONS/SIGNIFICANCE: These findings validate a proof of concept study exposure of extant tumor lines to a small molecule may provide a tractable in vitro model for understanding CSC biology.

The metastasis-associated gene S100A4 is a novel target of beta-catenin/T-cell factor signaling in colon cancer.

BACKGROUND & AIMS: Activation of the Wnt/beta-catenin pathway is frequently observed in colorectal cancers. Our aim was to elucidate the impact of gain-of-function beta-catenin on the metastasis-associated gene S100A4 in human colon cancer cell lines and tumors. METHODS: We analyzed cell lines heterozygous for gain-of-function and wild-type beta-catenin, and variants homozygous for gain- or loss-of-function mutation in beta-catenin, for S100A4 expression, cell motility, and in vivo metastasis. beta-catenin-mediated S100A4 promoter activation was tested by reporter assays. For human colon carcinomas, S100A4 expression, beta-catenin genotype, and metachronous metastasis were correlated. RESULTS: We identified S100A4 as the most regulated gene by gain-of-function beta-catenin using a 10K microarray. Cell lines with gain-of-function beta-catenin expressed up to 60-fold elevated S100A4 levels, displayed strongly increased migration and invasion in vitro, and induced metastasis in mice. S100A4 small interfering RNA, beta-catenin small interfering RNA, or dominant negative T-cell factor (TCF) knocked down S100A4 and blocked biological effects. S100A4 complementary DNA transfection increased migration and invasion. We identified a TCF binding site within the S100A4 promoter and demonstrated the direct binding of heterodimeric beta-catenin/TCF complexes. Reporter assays confirmed the beta-catenin-induced S100A4 promoter activity. Furthermore, S100A4 mRNA expression was increased in primary colon cancers, which later developed distant metastases, compared to non-metastasizing tumors. Colon tumors heterozygous for gain-of-function beta-catenin showed concomitant nuclear beta-catenin localization, high S100A4 expression, and metastases. CONCLUSIONS: S100A4 is a direct beta-catenin/TCF target, induces migration and invasion in vitro and metastasis in vivo, and has value for prognosis of metastasis formation in colon cancer patients.