Combined inhibition of MEK and Aurora A kinase in KRAS/PIK3CA double-mutant colorectal cancer models

Aurora A kinase and MEK inhibitors induce different, and potentially complementary, effects on the cell cycle of malignant cells, suggesting a rational basis for utilizing these agents in combination. In this work, the combination of an Aurora A kinase and MEK inhibitor was evaluated in pre-clinical colorectal cancer models, with a focus on identifying a subpopulation in which it might be most effective. Increased synergistic activity of the drug combination was identified in colorectal cancer cell lines with concomitant KRAS and PIK3CA mutations. Anti-proliferative effects were observed upon treatment of these double-mutant cell lines with the drug combination, and tumor growth inhibition was observed in double-mutant human tumor xenografts, though effects were variable within this subset. Additional evaluation suggests that degree of G2/M delay and p53 mutation status affect apoptotic activity induced by combination therapy with an Aurora A kinase and MEK inhibitor in KRAS and PIK3CA mutant colorectal cancer. Overall, in vitro and in vivo testing was unable to identify a subset of colorectal cancer that was consistently responsive to the combination of a MEK and Aurora A kinase inhibitor

Association of the Epithelial-to-Mesenchymal Transition (EMT) Phenotype with Responsiveness to the p21-Activated Kinase Inhibitor, PF-3758309, in Colon Cancer Models

The p21-activated kinase (PAK) family of serine/threonine kinases, which are overexpressed in several cancer types, are critical mediators of cell survival, motility, mitosis, transcription, and translation. In the study presented here we utilized a panel of CRC cell lines to identify potential biomarkers of sensitivity or resistance that may be used to individualize therapy to the PAK inhibitor PF-03758309. We observed a wide range of proliferative responses in the CRC cell lines exposed to PF-03758309, this response was recapitulated in other phenotypic assays such as anchorage-independent growth, three dimensional tumor spheroid formation, and migration. Interestingly, we observed that cells most sensitive to PF-03758309 exhibited up regulation of genes associated with a mesenchymal phenotype (CALD1, VIM, ZEB1) and cells more resistant had an up regulation of genes associated with an epithelial phenotype (CLDN2, CDH1, CLDN3, CDH17) allowing us to derive an epithelial-to-mesenchymal transition (EMT) gene signature for this agent. We assessed the functional role of EMT-associated genes in mediating responsiveness to PF-3758309, by targeting known genes and transcriptional regulators of EMT. We observed that suppression of genes associated with the mesenchymal phenotype conferred resistance to PF-3758309, in vitro and in vivo. These results indicate that PAK inhibition is associated with a unique response phenotype in CRC and that further studies should be conducted to facilitate both patient selection and rational combination strategies with these agents

The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models

BACKGROUND: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. RESULTS: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. CONCLUSIONS: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances

Utilization of Quantitative In Vivo Pharmacology Approaches to Assess Combination Effects of Everolimus and Irinotecan in Mouse Xenograft Models of Colorectal Cancer

<div><p>Purpose</p><p>The PI3K/AKT/mTOR pathway is frequently dysregulated in cancers and inhibition of mTOR has demonstrated the ability to modulate pro-survival pathways. As such, we sought to determine the ability of the mTOR inhibitor everolimus to potentiate the antitumor effects of irinotecan in colorectal cancer (CRC).</p> <p>Experimental Design</p><p>The combinatorial effects of everolimus and irinotecan were evaluated <i>in vitro</i> and <i>in vivo</i> in CRC cell lines harboring commonly found mutations in <i>PIK3CA</i>, <i>KRAS</i> and/or <i>BRAF</i>. Pharmacokinetically-directed dosing protocols of everolimus and irinotecan were established and used to assess the in vivo antitumor effects of the agents. At the end of treatment, 3–6 tumors per treatment arm were harvested for biomarker analysis by NMR metabolomics.</p> <p>Results</p><p>Everolimus and irinotecan/SN38 demonstrated synergistic anti-proliferative effects in multiple CRC cell lines <i>in vitro</i>. Combination effects of everolimus and irinotecan were determined in CRC xenograft models using clinically-relevant dosing protocols. Everolimus demonstrated significant tumor growth inhibition alone and when combined with irinotecan in HT29 and HCT116 tumor xenografts. Metabolomic analysis showed that HT29 tumors were more metabolically responsive than HCT116 tumors. Everolimus caused a decrease in glycolysis in both tumor types whilst irinotecan treatment resulted in a profound accumulation of lipids in HT29 tumors indicating a cytotoxic effect.</p> <p>Conclusions</p><p>Quantitative analysis of tumor growth and metabolomic data showed that the combination of everolimus and irinotecan was more beneficial in the <i>BRAF/PIK3CA</i> mutant HT29 tumor xenografts, which had an additive effect, than the <i>KRAS/PIK3CA</i> mutant HCT116 tumor xenografts, which had a less than additive effect.</p> </div

<i>In vivo</i> effects of everolimus (RAD), irinotecan (IRI) and combination of the two agents on CRC tumor xenografts.

<p>(A) HT29 and HCT116 tumor xenograft growth curves. Animals were treated for 28 days with vehicles, RAD, IRI, or the combination of RAD+IRI. Data represents the average ± SEM of 9–12 tumors per group. ^*P<0.05 versus vehicle. (B) Pharmacokinetic-pharmacodynamic modeling was performed on to quantitatively assess the intensity of the RAD+IRI combination in HT29 and HCT116 tumor xenografts. This is a graphical representation of the interaction term (ψ) for the RAD+IRI combination. Each bar represents the ψ value for an individual tumor. ψ values>1.3 are synergistic, 1.3> ψ>0.7 are additive, 0.7> ψ>0 are less than additive, and ψ<0 are antagonistic.</p

Pharmacokinetic parameters for everolimus^a in humans and mice.

a<p>All PK data obtained for everolimus is for daily oral administration.</p>b<p>Steady-state parameter.</p>c<p>Value of C_max for simulated data is taken at 0.5 h for comparison to data measured at this time.</p>d<p>Data estimated from graph in references.</p>e<p>Data for 5 mg/kg RAD001 is based on simulations.</p

Metabolic heat-maps based on quantitative NMR spectroscopic data sets in HT29 and HCT116 xenografts at the end of the study.

<p>¹H-, ¹³C-, and³¹P-NMR data are represented as mean ± SEM of 3–6 measurements. The metabolites, their ratios and metabolic fluxes were grouped based on their biochemical relevance. For the control group, all intracellular metabolite levels are given as µmol per gram cell wet weight and metabolite ratios are unitless. Metabolic pathways which were undisturbed by treatment are presented as yellow maps. A decrease in metabolic end-point is indicated by red, while an increase by green spots. Statistical significance for metabolite changes are based on multivariate analysis of metabolic fluxes with p<0.02. The interactive metabolic profile array database was custom-based <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058089#pone.0058089-Boros1" target="_blank">[23]</a>.</p

<i>In vitro</i> anti-proliferative effects of everolimus (RAD) and SN38 combinations in colorectal cancer cell lines.

<p>The combinatorial anti-proliferative effects were evaluated in HT29, HCT116, HCT8 and LS180 cells to assess potential additive or synergistic interactions. Growth inhibition was measured by the sulforhodmine B assay (SRB) following a 72 hour incubation with SN38 (0, 2, 4 or 8 nM) and RAD (0, 2, 20 or 200 nM - indicated by the triangels under the graphs). Data on the graphs represents the mean ± standard deviation of at least 3 separate experiments. The combination index (CI) values were calculated for for all combinations and the values ± the standard deviation are presented in the colored tables below each graph.. The %CV for replicates ranged from 5–40% and averaged ∼20% therefore, we determined that CI values>1.2 are considered antagonistic, 1.2> CI>0.8 are additive, and CI<0.8 are synergistic. Note that SN38 is used in <i>in vitro</i> assays instead of irinotecan since it is the active metabolite.</p