Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis

Src-mediated regulation of the PI3K pathway in advanced papillary and anaplastic thyroid cancer

Advanced stages of papillary and anaplastic thyroid cancer continue to be plagued by a dismal prognosis, which is a result of limited effective therapies for these cancers. Due to the high proportion of thyroid cancers harboring mutations in the MAPK pathway, the MAPK pathway has become a focal point for therapeutic intervention in thyroid cancer. Unfortunately, unlike melanoma, a similar responsiveness to MAPK pathway inhibition has yet to be observed in thyroid cancer patients. To address this issue, we have focused on targeting the non-receptor tyrosine kinase, Src, and we and others have demonstrated that targeting Src results in inhibition of growth, invasion, and migration both in vitro and in vivo, which can be enhanced through the combined inhibition of Src and the MAPK pathway. Therefore, we examined the efficacy of the combination therapy across a panel of thyroid cancer cell lines representing common oncogenic drivers (BRAF, RAS, and PIK3CA). Interestingly, combined inhibition of Src and the MAPK pathway overcomes intrinsic dasatinib resistance in cell lines where both the MAPK and PI3K pathways are inhibited, which we show is likely due to the regulation of the PI3K pathway by Src in these responsive cells. Interestingly, we have mapped downstream phosphorylation of rpS6 as a key biomarker of response, and cells that maintain rpS6 phosphorylation likely represent drug tolerant persisters. Altogether, the combined inhibition of Src and the MAPK pathway holds great promise for improving the overall survival of advanced thyroid cancer patients with BRAF and RAS mutations, and activation of the PI3K pathway and rpS6 phosphorylation represent important biomarkers of response for patients treated with this therapy.

5-Aminolevulinic Acid Guided Sampling of Glioblastoma Microenvironments Identifies Pro-Survival Signaling at Infiltrative Margins

Glioblastoma (GBM) contains diverse microenvironments with uneven distributions of oncogenic alterations and signaling networks. The diffusely infiltrative properties of GBM result in residual tumor at neurosurgical resection margins, representing the source of relapse in nearly all cases and suggesting that therapeutic efforts should be focused there. To identify signaling networks and potential druggable targets across tumor microenvironments (TMEs), we utilized 5-ALA fluorescence-guided neurosurgical resection and sampling, followed by proteomic analysis of specific TMEs. Reverse phase protein array (RPPA) was performed on 205 proteins isolated from the tumor margin, tumor bulk, and perinecrotic regions of 13 previously untreated, clinically-annotated and genetically-defined high grade gliomas. Differential protein and pathway signatures were established and then validated using western blotting, immunohistochemistry, and comparable TCGA RPPA datasets. We identified 37 proteins differentially expressed across high-grade glioma TMEs. We demonstrate that tumor margins were characterized by pro-survival and anti-apoptotic proteins, whereas perinecrotic regions were enriched for pro-coagulant and DNA damage response proteins. In both our patient cohort and TCGA cases, the data suggest that TMEs possess distinct protein expression profiles that are biologically and therapeutically relevant.

Drosophila Cancer Models Identify Functional Differences between Ret Fusions

We generated and compared Drosophila models of RET fusions CCDC6-RET and NCOA4-RET. Both RET fusions directed cells to migrate, delaminate, and undergo EMT, and both resulted in lethality when broadly expressed. In all phenotypes examined, NCOA4-RET was more severe than CCDC6-RET, mirroring their effects on patients. A functional screen against the Drosophila kinome and a library of cancer drugs found that CCDC6-RET and NCOA4-RET acted through different signaling networks and displayed distinct drug sensitivities. Combining data from the kinome and drug screens identified the WEE1 inhibitor AZD1775 plus the multi-kinase inhibitor sorafenib as a synergistic drug combination that is specific for NCOA4-RET. Our work emphasizes the importance of identifying and tailoring a patient's treatment to their specific RET fusion isoform and identifies a multi-targeted therapy that may prove effective against tumors containing the NCOA4-RET fusion.

Ginsenoside Rg1 Ameliorates Behavioral Abnormalities and Modulates the Hippocampal Proteomic Change in Triple Transgenic Mice of Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, so far, there are no effective measures to prevent and cure this deadly condition. Ginsenoside Rg1 (Rg1) was shown to improve behavioral abnormalities in AD; however, the potential mechanisms remain unclear. In this study, we pretreated 7-month-old 3xTg-AD mice for 6 weeks with Rg1 and evaluated the effects of Rg1 on the behaviors and the protein expression of hippocampal tissues. The behavioral tests showed that Rg1 could improve the memory impairment and ameliorate the depression-like behaviors of 3xTg-AD mice. Proteomic results revealed a total of 28 differentially expressed hippocampal proteins between Rg1-treated and nontreated 3xTg-AD mice. Among these proteins, complexin-2 (CPLX2), synapsin-2 (SYN2), and synaptosomal-associated protein 25 (SNP25) were significantly downregulated in the hippocampus of 3xTg-AD mice compared with the WT mice, and the treatment of Rg1 modulated the expression of CPLX2 and SNP25 in the hippocampus of 3xTg-AD mice. The expression of CPLX2, SYN2, and SNP25 was further validated by Western blot analysis. Taken together, we concluded that Rg1 could be a potential candidate drug to improve the behavioral deficits in AD via modulating the expression of the proteins (i.e., CPLX2, SYN2, and SNP25).

Next-Generation CDK2/9 Inhibitors and Anaphase Catastrophe in Lung Cancer

Background

The first generation CDK2/7/9 inhibitor seliciclib (CYC202) causes multipolar anaphase and apoptosis in lung cancer cells with supernumerary centrosomes (known as anaphase catastrophe). We investigated a new and potent CDK2/9 inhibitor, CCT68127 (Cyclacel).

Methods

CCT68127 was studied in lung cancer cells (three murine and five human) and control murine pulmonary epithelial and human immortalized bronchial epithelial cells. Robotic CCT68127 cell-based proliferation screens were used. Cells undergoing multipolar anaphase and inhibited centrosome clustering were scored. Reverse phase protein arrays (RPPAs) assessed CCT68127 effects on signaling pathways. The function of PEA15, a growth regulator highlighted by RPPAs, was analyzed. Syngeneic murine lung cancer xenografts (n = 4/group) determined CCT68127 effects on tumorigenicity and circulating tumor cell levels. All statistical tests were two-sided.

Results

CCT68127 inhibited growth up to 88.5% (SD = 6.4%, P < .003) at 1 μM, induced apoptosis up to 42.6% (SD = 5.5%, P < .001) at 2 μM, and caused G1 or G2/M arrest in lung cancer cells with minimal effects on control cells (growth inhibition at 1 μM: 10.6%, SD = 3.6%, P = .32; apoptosis at 2 μM: 8.2%, SD = 1.0%, P = .22). A robotic screen found that lung cancer cells with KRAS mutation were particularly sensitive to CCT68127 ( P = .02 for IC 50 ). CCT68127 inhibited supernumerary centrosome clustering and caused anaphase catastrophe by 14.1% (SD = 3.6%, P < .009 at 1 μM). CCT68127 reduced PEA15 phosphorylation by 70% (SD = 3.0%, P = .003). The gain of PEA15 expression antagonized and its loss enhanced CCT68127-mediated growth inhibition. CCT68127 reduced lung cancer growth in vivo ( P < .001) and circulating tumor cells ( P = .004). Findings were confirmed with another CDK2/9 inhibitor, CYC065.

Conclusions

Next-generation CDK2/9 inhibition elicits marked antineoplastic effects in lung cancer via anaphase catastrophe and reduced PEA15 phosphorylation.

Protein network construction using reverse phase protein array data

In this paper, we introduce a novel computational method for constructing protein networks based on reverse phase protein array (RPPA) data to identify complex patterns in protein signaling. The method is applied to phosphoproteomic profiles of basal expression and activation/phosphorylation of 76 key signaling proteins in three breast cancer cell lines (MCF7, LCC1, and LCC9). Temporal RPPA data are acquired at 48h, 96h, and 144h after knocking down four genes in separate experiments. These genes are selected from a previous study as important determinants for breast cancer survival. Interaction networks are constructed by analyzing the expression levels of protein pairs using a multivariate analysis of variance model. A new scoring criterion is introduced to determine relevant protein pairs. Through a network topology based analysis, we search for wiring patterns to identify key proteins that are associated with significant changes in expression levels across various experimental conditions.

Endometrial Cancers With Activating KRas Mutations Have Activated Estrogen Signaling and Paradoxical Response to MEK Inhibition

OBJECTIVES

The aims of this study were to determine if activating KRas mutation alters estrogen signaling in endometrial cancer (EC) and to explore the potential therapeutic impact of these alterations.

METHODS

The Cancer Genome Atlas was queried for changes in estrogen-regulated genes in EC based on KRas mutation status. In vitro studies were conducted to evaluate estrogen receptor α (ERα) phosphorylation changes and related kinase changes in KRas mutant EC cells. The resulting effect on response to MEK inhibition, using trametinib, was evaluated. Immunohistochemistry was performed on KRas mutant and wild-type EC tumors to test estrogen signaling differences.

RESULTS

KRas mutant tumors in The Cancer Genome Atlas showed decreased progesterone receptor expression (P = 0.047). Protein analysis in KRas mutant EC cells also showed decreased expression of ERα (P < 0.001) and progesterone receptor (P = 0.001). Although total ERα is decreased in KRas mutant cells, phospho-ERα S118 was increased compared with wild type. Treatment with trametinib in KRas mutant cells increased phospho-ERα S167 and increased expression of estrogen-regulated genes. While MEK inhibition blocked estradiol-stimulated phosphorylation of ERK1/2 and p90RSK in wild-type cells, phospho-ERK1/2 and phospho-p90RSK were substantially increased in KRas mutants. KRas mutant EC tumor specimens showed similar changes, with increased phospho-ERα S118 and phospho-ERα S167 compared with wild-type EC tumors.

CONCLUSIONS

MEK inhibition in KRas mutant cells results in activation of ER signaling and prevents the abrogation of signaling through ERK1/2 and p90RSK that is achieved in KRas wild-type EC cells. Combination therapy with MEK inhibition plus antiestrogen therapy may be necessary to improve response rates in patients with KRas mutant EC.

Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer

Reprogramming of energy metabolism is a hallmark of cancer that enables the cancer cells to meet the increased energetic requirements due to uncontrolled proliferation. One prominent example is pancreatic ductal adenocarcinoma, an aggressive form of cancer with an overall 5-year survival rate of 5%. The reprogramming mechanism in pancreatic cancer involves deregulated uptake of glucose and glutamine and other opportunistic modes of satisfying energetic demands in a hypoxic and nutrient-poor environment. In the current study, we apply systems biology approaches to enable a better understanding of the dynamics of the distinct metabolic alterations in KRAS-mediated pancreatic cancer, with the goal of impeding early cell proliferation by identifying the optimal metabolic enzymes to target. We have constructed a kinetic model of metabolism represented as a set of ordinary differential equations that describe time evolution of the metabolite concentrations in glycolysis, glutaminolysis, tricarboxylic acid cycle and the pentose phosphate pathway. The model is comprised of 46 metabolites and 53 reactions. The mathematical model is fit to published enzyme knockdown experimental data. We then applied the model to perform in silico enzyme modulations and evaluate the effects on cell proliferation. Our work identifies potential combinations of enzyme knockdown, metabolite inhibition, and extracellular conditions that impede cell proliferation. Excitingly, the model predicts novel targets that can be tested experimentally. Therefore, the model is a tool to predict the effects of inhibiting specific metabolic reactions within pancreatic cancer cells, which is difficult to measure experimentally, as well as test further hypotheses toward targeted therapies.

Exploring the key genes and signaling transduction pathways related to the survival time of glioblastoma multiforme patients by a novel survival analysis model

BACKGROUND

This study is to explore the key genes and signaling transduction pathways related to the survival time of glioblastoma multiforme (GBM) patients.

RESULTS

Our results not only showed that mutually explored GBM survival time related genes and signaling transduction pathways are closely related to the GBM, but also demonstrated that our innovated constrained optimization algorithm (CoxSisLasso strategy) are better than the classical methods (CoxLasso and CoxSis strategy).

CONCLUSION

We analyzed why the CoxSisLasso strategy can outperform the existing classical methods and discuss how to extend this research in the distant future.

Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology

Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology.

Network-Oriented Approaches to Anticancer Drug Response

A long-standing paradigm in drug discovery has been the concept of designing maximally selective drugs to act on individual targets considered to underlie a disease of interest. Nonetheless, although some drugs have proven to be successful, many more potential drugs identified by the "one gene, one drug, one disease" approach have been found to be less effective than expected or to cause notable side effects. Advances in systems biology and high-throughput in-depth genomic profiling technologies along with an analysis of the successful and failed drugs uncovered that the prominent factor to determine drug sensitivity is the intrinsic robustness of the response of biological systems in the face of perturbations. The complexity of the molecular and cellular bases of systems responses to drug interventions has fostered an increased interest in systems-oriented approaches to drug discovery. Consonant with this knowledge of the multifactorial mechanistic basis of drug sensitivity and resistance is the application of network-based approaches for the identification of molecular (multi-)feature signatures associated with desired (multi-)drug phenotypic profiles. This chapter illustrates the principal network analysis and inference techniques which have found application in systems-oriented drug design and considers their benefits and drawbacks in relation to the nature of the data produced by network pharmacology.

Mig-6 Mouse Model of Endometrial Cancer

Endometrial cancer is a frequently occurring gynecological disorder. Estrogen-dependent endometrioid carcinoma is the most common type of gynecological cancer. One of the major pathologic phenomena of endometrial cancer is the loss of estrogen (E2) and progesterone (P4) control over uterine epithelial cell proliferation. P4 antagonizes the growth-promoting properties of E2 in the uterus. P4 prevents the development of endometrial cancer associated with unopposed E2 by blocking E2 actions. Mitogen inducible gene 6 (Mig-6, Errfi1, RALT, or gene 33) is an immediate early response gene that can be induced by various mitogens and common chronic stress stimuli. Mig-6 has been identified as an important component of P4-mediated inhibition of E2 signaling in the uterus. Decreased expression of MIG-6 is observed in human endometrial carcinomas. Transgenic mice with Mig-6 ablation in the uterus develop endometrial hyperplasia and E2-dependent endometrial cancer. Thus, MIG-6 has a tumor suppressor function in endometrial tumorigenesis. The following discussion summarizes our current knowledge of Mig-6 mouse models and their role in understanding the molecular mechanisms of endometrial tumorigenesis and in the development of therapeutic approaches for endometrial cancer.

Combine and conquer: challenges for targeted therapy combinations in early phase trials

Our increasing understanding of cancer biology has led to the development of molecularly targeted anticancer drugs. The full potential of these agents has not, however, been realised, owing to the presence of de novo (intrinsic) resistance, often resulting from compensatory signalling pathways, or the development of acquired resistance in cancer cells via clonal evolution under the selective pressures of treatment. Combinations of targeted treatments can circumvent some mechanisms of resistance to yield a clinical benefit. We explore the challenges in identifying the best drug combinations and the best combination strategies, as well as the complexities of delivering these treatments to patients. Recognizing treatment-induced toxicity and the inability to use continuous pharmacodynamically effective doses of many targeted treatments necessitates creative intermittent scheduling. Serial tumour profiling and the use of parallel co-clinical trials can contribute to understanding mechanisms of resistance, and will guide the development of adaptive clinical trial designs that can accommodate hypothesis testing, in order to realize the full potential of combination therapies.

Novel biomarkers of nasopharyngeal carcinoma metastasis risk identified by reverse phase protein array based tumor profiling with consideration of plasma Epstein-Barr virus DNA load

PURPOSE

In patients with Epstein-Barr virus (EBV) associated nasopharyngeal carcinoma (NPC), intertumor heterogeneity causes interpatient heterogeneity in the risk of distant metastasis. We aimed to identify novel biomarkers of metastasis risk using reverse phase protein array (RPPA) profiling of NPC patients at risk for metastasis and considering plasma EBV DNA load.

EXPERIMENTAL DESIGN

A total of 98 patients with NPC with and without metastasis after treatment, matched with respect to clinical parameters, are enrolled. Total protein expression is measured by RPPA, and protein functions are analyzed by pathway bioinformatics.

RESULTS

The RPPA analysis revealed a profile of 70 proteins that are differentially expressed in metastatic and nonmetastatic tumors. Plasma EBV DNA load after treatment correlated with protein expression level better than plasma EBV DNA load before treatment did. The biomarkers of NPC metastasis identified by proteomics regulate signaling pathways involved in cell cycle progression, apoptosis, and epithelial-mesenchymal transition. The authors identified 26 biomarkers associated with 5-year distant failure-free survival in univariate analysis; five biomarkers remained significant in multivariate analysis.

CONCLUSIONS AND CLINICAL RELEVANCE

A comprehensive RPPA profiling study is warranted to identify novel metastasis-related biomarkers and further examine the activation state of signaling proteins to improve estimation of metastasis risk for patients with EBV-associated NPC.

Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Current Trends in Multidrug Optimization

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways

Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro.

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)-driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.

Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature

Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer.

Musashi-2 (MSI2) supports TGF-β signaling and inhibits claudins to promote non-small cell lung cancer (NSCLC) metastasis

Non-small cell lung cancer (NSCLC) has a 5-y survival rate of ∼16%, with most deaths associated with uncontrolled metastasis. We screened for stem cell identity-related genes preferentially expressed in a panel of cell lines with high versus low metastatic potential, derived from NSCLC tumors of Kras(LA1/+);P53(R172HΔG/+) (KP) mice. The Musashi-2 (MSI2) protein, a regulator of mRNA translation, was consistently elevated in metastasis-competent cell lines. MSI2 was overexpressed in 123 human NSCLC tumor specimens versus normal lung, whereas higher expression was associated with disease progression in an independent set of matched normal/primary tumor/lymph node specimens. Depletion of MSI2 in multiple independent metastatic murine and human NSCLC cell lines reduced invasion and metastatic potential, independent of an effect on proliferation. MSI2 depletion significantly induced expression of proteins associated with epithelial identity, including tight junction proteins [claudin 3 (CLDN3), claudin 5 (CLDN5), and claudin 7 (CLDN7)] and down-regulated direct translational targets associated with epithelial-mesenchymal transition, including the TGF-β receptor 1 (TGFβR1), the small mothers against decapentaplegic homolog 3 (SMAD3), and the zinc finger proteins SNAI1 (SNAIL) and SNAI2 (SLUG). Overexpression of TGFβRI reversed the loss of invasion associated with MSI2 depletion, whereas overexpression of CLDN7 inhibited MSI2-dependent invasion. Unexpectedly, MSI2 depletion reduced E-cadherin expression, reflecting a mixed epithelial-mesenchymal phenotype. Based on this work, we propose that MSI2 provides essential support for TGFβR1/SMAD3 signaling and contributes to invasive adenocarcinoma of the lung and may serve as a predictive biomarker of NSCLC aggressiveness.

Prospective Design of Anti-Transferrin Receptor Bispecific Antibodies for Optimal Delivery into the Human Brain

Anti‐transferrin receptor (TfR)‐based bispecific antibodies have shown promise for boosting antibody uptake in the brain. Nevertheless, there are limited data on the molecular properties, including affinity required for successful development of TfR‐based therapeutics. A complex nonmonotonic relationship exists between affinity of the anti‐TfR arm and brain uptake at therapeutically relevant doses. However, the quantitative nature of this relationship and its translatability to humans is heretofore unexplored. Therefore, we developed a mechanistic pharmacokinetic‐pharmacodynamic (PK‐PD) model for bispecific anti‐TfR/BACE1 antibodies that accounts for antibody‐TfR interactions at the blood‐brain barrier (BBB) as well as the pharmacodynamic (PD) effect of anti‐BACE1 arm. The calibrated model correctly predicted the optimal anti‐TfR affinity required to maximize brain exposure of therapeutic antibodies in the cynomolgus monkey and was scaled to predict the optimal affinity of anti‐TfR bispecifics in humans. Thus, this model provides a framework for testing critical translational predictions for anti‐TfR bispecific antibodies, including choice of candidate molecule for clinical development.

A Set of miRNAs, Their Gene and Protein Targets and Stromal Genes Distinguish Early from Late Onset ER Positive Breast Cancer

Breast cancer (BC) in young adult patients (YA) has a more aggressive biological behavior and is associated with a worse prognosis than BC arising in middle aged patients (MA). We proposed that differentially expressed miRNAs could regulate genes and proteins underlying aggressive phenotypes of breast tumors in YA patients when compared to those arising in MA patients.

OBJECTIVE

Using integrated expression analyses of miRs, their mRNA and protein targets and stromal gene expression, we aimed to identify differentially expressed profiles between tumors from YA-BC and MA-BC.

METHODOLOGY AND RESULTS

Samples of ER+ invasive ductal breast carcinomas, divided into two groups: YA-BC (35 years or less) or MA-BC (50-65 years) were evaluated. Screening for BRCA1/2 status according to the BOADICEA program indicated low risk of patients being carriers of these mutations. Aggressive characteristics were more evident in YA-BC versus MA-BC. Performing qPCR, we identified eight miRs differentially expressed (miR-9, 18b, 33b, 106a, 106b, 210, 518a-3p and miR-372) between YA-BC and MA-BC tumors with high confidence statement, which were associated with aggressive clinicopathological characteristics. The expression profiles by microarray identified 602 predicted target genes associated to proliferation, cell cycle and development biological functions. Performing RPPA, 24 target proteins differed between both groups and 21 were interconnected within a network protein-protein interactions associated with proliferation, development and metabolism pathways over represented in YA-BC. Combination of eight mRNA targets or the combination of eight target proteins defined indicators able to classify individual samples into YA-BC or MA-BC groups. Fibroblast-enriched stroma expression profile analysis resulted in 308 stromal genes differentially expressed between YA-BC and MA-BC.

CONCLUSION

We defined a set of differentially expressed miRNAs, their mRNAs and protein targets and stromal genes that distinguish early onset from late onset ER positive breast cancers which may be involved with tumor aggressiveness of YA-BC.

Predictive Model of Lymphocyte-Specific Protein Tyrosine Kinase (LCK) Autoregulation

Lymphocyte-specific protein tyrosine kinase (LCK) is a key activator of T cells; however, little is known about the specific autoregulatory mechanisms that control its activity. We have constructed a model of LCK autophosphorylation and phosphorylation by the regulating kinase CSK. The model was fit to existing experimental data in the literature that presents an in vitro reconstituted membrane system, which provides more physiologically relevant kinetic measurements than traditional solution-based systems. The model is able to predict a robust mechanism of LCK autoregulation. It provides insights into the molecular causes of key site-specific phosphorylation differences between distinct experimental conditions. Probing the model also provides new hypotheses regarding the influence of individual binding and catalytic rates, which can be tested experimentally. This minimal model is required to elucidate the mechanistic interactions of LCK and CSK and can be further expanded to better understand T cell activation from a systems perspective. Our computational model enables the evaluation of LCK protein interactions that mediate T cell activation on a more quantitative level, providing new insights and testable hypotheses.

HER2+ Cancer Cell Dependence on PI3K vs. MAPK Signaling Axes Is Determined by Expression of EGFR, ERBB3 and CDKN1B

Understanding the molecular pathways by which oncogenes drive cancerous cell growth, and how dependence on such pathways varies between tumors could be highly valuable for the design of anti-cancer treatment strategies. In this work we study how dependence upon the canonical PI3K and MAPK cascades varies across HER2+ cancers, and define biomarkers predictive of pathway dependencies. A panel of 18 HER2+ (ERBB2-amplified) cell lines representing a variety of indications was used to characterize the functional and molecular diversity within this oncogene-defined cancer. PI3K and MAPK-pathway dependencies were quantified by measuring in vitro cell growth responses to combinations of AKT (MK2206) and MEK (GSK1120212; trametinib) inhibitors, in the presence and absence of the ERBB3 ligand heregulin (NRG1). A combination of three protein measurements comprising the receptors EGFR, ERBB3 (HER3), and the cyclin-dependent kinase inhibitor p27 (CDKN1B) was found to accurately predict dependence on PI3K/AKT vs. MAPK/ERK signaling axes. Notably, this multivariate classifier outperformed the more intuitive and clinically employed metrics, such as expression of phospho-AKT and phospho-ERK, and PI3K pathway mutations (PIK3CA, PTEN, and PIK3R1). In both cell lines and primary patient samples, we observed consistent expression patterns of these biomarkers varies by cancer indication, such that ERBB3 and CDKN1B expression are relatively high in breast tumors while EGFR expression is relatively high in other indications. The predictability of the three protein biomarkers for differentiating PI3K/AKT vs. MAPK dependence in HER2+ cancers was confirmed using external datasets (Project Achilles and GDSC), again out-performing clinically used genetic markers. Measurement of this minimal set of three protein biomarkers could thus inform treatment, and predict mechanisms of drug resistance in HER2+ cancers. More generally, our results show a single oncogenic transformation can have differing effects on cell signaling and growth, contingent upon the molecular and cellular context.

Biomarkers capable of accurately predicting patient responses to alternate therapies are critical to realizing the vision of precision medicine. Identifying such biomarkers is, however, challenging due to the inherent complexity of biological networks. Here we sought to identify molecular features that predict how a genetically defined subset of cancers (HER2+) differentially depend on two oncogenic signaling pathways, the PI3K/AKT and MAPK/ERK cascades. We find that combined measurement of three non-intuitive proteins (EGFR, ERBB3, and CDKN1B) accurately predicts cellular dependence on these signaling pathways, and responsiveness to drugs targeting their constituents. Notably, this three-biomarker model outperformed both biological intuition (phosho-AKT and phospho-ERK) and current clinical practice (PIK3CA mutations). More broadly, this exemplifies how the functional consequences of a single oncogenic driver (HER2) can depend upon molecular and cellular context. Expression of these markers also varies by indication, such that breast cancers are biased toward PI3K-dependnece, while non-breast indications (lung, ovarian, and gastric) are particularly MAPK-dependent, and thus may respond differently to therapeutic strategies developed for breast cancer. Together, we believe that our results will aid the design of novel, stratified treatment strategies for HER2+ disease.

HSP70 Inhibition Limits FAK-Dependent Invasion and Enhances the Response to Melanoma Treatment with BRAF Inhibitors

The stress-inducible chaperone protein HSP70 (HSPA1) is implicated in melanoma development, and HSP70 inhibitors exert tumor-specific cytotoxic activity in cancer. In this study, we documented that a significant proportion of melanoma tumors express high levels of HSP70, particularly at advanced stages, and that phospho-FAK (PTK2) and BRAF are HSP70 client proteins. Treatment of melanoma cells with HSP70 inhibitors decreased levels of phospho-FAK along with impaired migration, invasion, and metastasis in vitro and in vivo Moreover, the HSP70 inhibitor PET-16 reduced levels of mutant BRAF, synergized with the BRAF inhibitor PLX4032 in vitro, and enhanced the durability of response to BRAF inhibition in vivo Collectively, these findings provide strong support for HSP70 inhibition as a therapeutic strategy in melanoma, especially as an adjuvant approach for overcoming the resistance to BRAF inhibitors frequently observed in melanoma patients. Cancer Res; 76(9); 2720-30. ©2016 AACR.

Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells

Cancer therapy has traditionally focused on eliminating fast-growing populations of cells. Yet, an increasing body of evidence suggests that small subpopulations of cancer cells can evade strong selective drug pressure by entering a 'persister' state of negligible growth. This drug-tolerant state has been hypothesized to be part of an initial strategy towards eventual acquisition of bona fide drug-resistance mechanisms. However, the diversity of drug-resistance mechanisms that can expand from a persister bottleneck is unknown. Here we compare persister-derived, erlotinib-resistant colonies that arose from a single, EGFR-addicted lung cancer cell. We find, using a combination of large-scale drug screening and whole-exome sequencing, that our erlotinib-resistant colonies acquired diverse resistance mechanisms, including the most commonly observed clinical resistance mechanisms. Thus, the drug-tolerant persister state does not limit--and may even provide a latent reservoir of cells for--the emergence of heterogeneous drug-resistance mechanisms.

Quantitative analysis of insulin-like growth factor 2 receptor and insulin-like growth factor binding proteins to identify control mechanisms for insulin-like growth factor 1 receptor phosphorylation

Background

The insulin-like growth factor (IGF) system impacts cellular development by regulating proliferation, differentiation, and apoptosis, and is an attractive therapeutic target in cancer. The IGF system is complex, with two ligands (IGF1, IGF2), two receptors (IGF1R, IGF2R), and at least six high affinity IGF-binding proteins (IGFBPs) that regulate IGF ligand bioavailability. While the individual components of the IGF system are well studied, the question of how these different components integrate as a system to regulate cell behavior is less clear.

Results

To analyze the relative importance of different mechanisms that control IGF network activity, we developed a mass-action kinetic model incorporating cell surface binding, phosphorylation, and intracellular trafficking events. The model was calibrated and validated using experimental data collected from OVCAR5, an immortalized ovarian cancer cell line. We then performed model analysis to examine the ability of IGF2R or IGFBPs to counteract phosphorylation of IGF1R, a critical step for IGF network activation. This analysis suggested that IGF2R levels would need to be 320-fold greater than IGF1R in order to decrease pIGF1R by 25 %, while IGFBP levels would need to be 390-fold greater. Analysis of The Cancer Genome Atlas (TCGA) data set suggested that this level of overexpression is unlikely for IGF2R in ovarian, breast, and colon cancer. In contrast, IGFBPs can likely reach these levels, suggesting that IGFBPs are the more critical regulator of IGF1R network activity. Levels of phosphorylated IGF1R were insensitive to changes in parameters regulating the IGF2R arm of the network.

Conclusions

Using a mass-action kinetic model, we determined that IGF2R plays a minor role in regulating the activity of IGF1R under a variety of conditions and that due to their high expression levels, IGFBPs are the dominant mechanism to regulating IGF network activation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0263-6) contains supplementary material, which is available to authorized users.

Suppression of RAF/MEK or PI3K synergizes cytotoxicity of receptor tyrosine kinase inhibitors in glioma tumor-initiating cells

Background

The majority of glioblastomas have aberrant receptor tyrosine kinase (RTK)/RAS/phosphoinositide 3 kinase (PI3K) signaling pathways and malignant glioma cells are thought to be addicted to these signaling pathways for their survival and proliferation. However, recent studies suggest that monotherapies or inappropriate combination therapies using the molecular targeted drugs have limited efficacy possibly because of tumor heterogeneities, signaling redundancy and crosstalk in intracellular signaling network, indicating necessity of rationale and methods for efficient personalized combination treatments. Here, we evaluated the growth of colonies obtained from glioma tumor-initiating cells (GICs) derived from glioma sphere culture (GSC) in agarose and examined the effects of combination treatments on GICs using targeted drugs that affect the signaling pathways to which most glioma cells are addicted.

Methods

Human GICs were cultured in agarose and treated with inhibitors of RTKs, non-receptor kinases or transcription factors. The colony number and volume were analyzed using a colony counter, and Chou-Talalay combination indices were evaluated. Autophagy and apoptosis were also analyzed. Phosphorylation of proteins was evaluated by reverse phase protein array and immunoblotting.

Results

Increases of colony number and volume in agarose correlated with the Gompertz function. GICs showed diverse drug sensitivity, but inhibitions of RTK and RAF/MEK or PI3K by combinations such as EGFR inhibitor and MEK inhibitor, sorafenib and U0126, erlotinib and BKM120, and EGFR inhibitor and sorafenib showed synergy in different subtypes of GICs. Combination of erlotinib and sorafenib, synergistic in GSC11, induced apoptosis and autophagic cell death associated with suppressed Akt and ERK signaling pathways and decreased nuclear PKM2 and β-catenin in vitro, and tended to improve survival of nude mice bearing GSC11 brain tumor. Reverse phase protein array analysis of the synergistic treatment indicated involvement of not only MEK and PI3K signaling pathways but also others associated with glucose metabolism, fatty acid metabolism, gene transcription, histone methylation, iron transport, stress response, cell cycle, and apoptosis.

Conclusion

Inhibiting RTK and RAF/MEK or PI3K could induce synergistic cytotoxicity but personalization is necessary. Examining colonies in agarose initiated by GICs from each patient may be useful for drug sensitivity testing in personalized cancer therapy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0803-2) contains supplementary material, which is available to authorized users.

Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

A major obstacle in precision cancer medicine is the inevitable resistance to targeted therapies. Tremendous effort and progress has been made over the past few years to understand the biochemical and genetic mechanisms underlying drug resistance, with the goal to eventually overcome such daunting challenges. Diverse mechanisms, such as secondary mutations, oncogene bypass, and epigenetic alterations, can all lead to drug resistance, and the number of known involved genes is growing rapidly, thus providing many possibilities to overcome resistance. The finding of these mechanisms and genes invariably requires the application of genomic and functional genomic approaches to tumors or cancer models. In this review, we briefly highlight the major drug-resistance mechanisms known today, and then focus primarily on the technological approaches leading to the advancement of this field.

Clinical utility of reverse phase protein array for molecular classification of breast cancer

Reverse Phase Protein Array (RPPA) represents a sensitive and high-throughput technique allowing simultaneous quantitation of protein expression levels in biological samples. This study aimed to confirm the ability of RPPA to classify archival formalin-fixed paraffin-embedded (FFPE) breast cancer tissues into molecular classes used in the Nottingham prognostic index plus (NPI+) determined by immunohistochemistry (IHC). Proteins were extracted from FFPE breast cancer tissues using three extraction protocols: the Q-proteome FFPE Tissue Kit (Qiagen, Hilden, Germany) and two in-house methods using Laemmli buffer with either incubation for 20 min or 2 h at 105 °C. Two preparation methods, full-face sections and macrodissection, were used to assess the yield and quality of protein extracts. Ten biomarkers used for the NPI+ (ER, PgR, HER2, Cytokeratins 5/6 and 7/8, EGFR, HER3, HER4, p53 and Mucin 1) were quantified using RPPA and compared to results determined by IHC. The Q-proteome FFPE Tissue Kit produced significantly higher protein concentration and signal intensities. The intra- and inter-array reproducibility assessment indicated that RPPA using FFPE lysates was a highly reproducible and robust technique. Expression of the biomarkers individually and in combination using RPPA was highly consistent with IHC results. Macrodissection of the invasive tumour component gave more reliable results with the majority of biomarkers determined by IHC, (80 % concordance) compared with full-face sections (60 % concordance). Our results provide evidence for the technical feasibility of RPPA for high-throughput protein expression profiling of FFPE breast cancer tissues. The sensitivity of the technique is related to the quality of extracted protein and purity of tumour tissue. RPPA could provide a quantitative technique alternative to IHC for the biomarkers used in the NPI+.

A streamlined search technology for identification of synergistic drug combinations

A major key to improvement of cancer therapy is the combination of drugs. Mixing drugs that already exist on the market may offer an attractive alternative. Here we report on a new model-based streamlined feedback system control (s-FSC) method, based on a design of experiment approach, for rapidly finding optimal drug mixtures with minimal experimental effort. We tested combinations in an in vitro assay for the viability of a renal cell adenocarcinoma (RCC) cell line, 786-O. An iterative cycle of in vitro testing and s-FSC analysis was repeated a few times until an optimal low dose combination was reached. Starting with ten drugs that target parallel pathways known to play a role in the development and progression of RCC, we identified the best overall drug combination, being a mixture of four drugs (axitinib, erlotinib, dasatinib and AZD4547) at low doses, inhibiting 90% of cell viability. The removal of AZD4547 from the optimized drug combination resulted in 80% of cell viability inhibition, while still maintaining the synergistic interaction. These optimized drug combinations were significantly more potent than monotherapies of all individual drugs (p < 0.001, CI < 0.3).

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.

Tumor-Targeted Synergistic Blockade of MAPK and PI3K from a Layer-by-Layer Nanoparticle

PURPOSE

Cross-talk and feedback between the RAS/RAF/MEK/ERK and PI3K/AKT/mTOR cell signaling pathways is critical for tumor initiation, maintenance, and adaptive resistance to targeted therapy in a variety of solid tumors. Combined blockade of these pathways-horizontal blockade-is a promising therapeutic strategy; however, compounded dose-limiting toxicity of free small molecule inhibitor combinations is a significant barrier to its clinical application.

EXPERIMENTAL DESIGN

AZD6244 (selumetinib), an allosteric inhibitor of Mek1/2, and PX-866, a covalent inhibitor of PI3K, were co-encapsulated in a tumor-targeting nanoscale drug formulation-layer-by-layer (LbL) nanoparticles. Structure, size, and surface charge of the nanoscale formulations were characterized, in addition to in vitro cell entry, synergistic cell killing, and combined signal blockade. In vivo tumor targeting and therapy was investigated in breast tumor xenograft-bearing NCR nude mice by live animal fluorescence/bioluminescence imaging, Western blotting, serum cytokine analysis, and immunohistochemistry.

RESULTS

Combined MAPK and PI3K axis blockade from the nanoscale formulations (160 ± 20 nm, -40 ± 1 mV) was synergistically toxic toward triple-negative breast (MDA-MB-231) and RAS-mutant lung tumor cells (KP7B) in vitro, effects that were further enhanced upon encapsulation. In vivo, systemically administered LbL nanoparticles preferentially targeted subcutaneous MDA-MB-231 tumor xenografts, simultaneously blocked tumor-specific phosphorylation of the terminal kinases Erk and Akt, and elicited significant disease stabilization in the absence of dose-limiting hepatotoxic effects observed from the free drug combination. Mice receiving untargeted, but dual drug-loaded nanoparticles exhibited progressive disease.

CONCLUSIONS

Tumor-targeting nanoscale drug formulations could provide a more safe and effective means to synergistically block MAPK and PI3K in the clinic.

Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.

Systems biology approaches for advancing the discovery of effective drug combinations

Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations.

Graphical abstract

Mig-6 suppresses endometrial cancer associated with Pten deficiency and ERK activation

PTEN mutations are the most common genetic alterations in endometrial cancer. Loss of PTEN and subsequent AKT activation stimulate estrogen receptor α-dependent pathways that play an important role in endometrial tumorigenesis. The major pathologic phenomenon of endometrial cancer is the loss of ovarian steroid hormone control over uterine epithelial cell proliferation and apoptosis. However, the precise mechanism of PTEN/AKT signaling in endometrial cancer remains poorly understood. The progesterone signaling mediator MIG-6 suppresses estrogen signaling and it has been implicated previously as a tumor suppressor in endometrial cancer. In this study, we show that MIG-6 also acts as a tumor suppressor in endometrial cancers associated with PTEN deficiency. Transgenic mice, where Mig-6 was overexpressed in progesterone receptor-expressing cells, exhibited a relative reduction in uterine tumorigenesis caused by Pten deficiency. ERK1/2 was phosphorylated in uterine tumors and administration of an ERK1/2 inhibitor suppressed cancer progression in PR(cre/+)Pten(f/f) mice. In clinical specimens of endometrial cancer, MIG-6 expression correlated inversely with ERK1/2 phosphorylation during progression. Taken together, our findings suggest that Mig-6 regulates ERK1/2 phosphorylation and that it is crucial for progression of PTEN-mutant endometrial cancers, providing a mechanistic rationale for the evaluation of ERK1/2 inhibitors as a therapeutic treatment in human endometrial cancer.

α-Tubulin acetylation elevated in metastatic and basal-like breast cancer cells promotes microtentacle formation, adhesion, and invasive migration

Metastatic cases of breast cancer pose the primary challenge in clinical management of this disease, demanding the identification of effective therapeutic strategies that remain wanting. In this study, we report that elevated levels of α-tubulin acetylation are a sufficient cause of metastatic potential in breast cancer. In suspended cell culture conditions, metastatic breast cancer cells exhibited high α-tubulin acetylation levels that extended along microtentacle (McTN) protrusions. Mutation of the acetylation site on α-tubulin and enzymatic modulation of this posttranslational modification exerted a significant impact on McTN frequency and the reattachment of suspended tumor cells. Reducing α-tubulin acetylation significantly inhibited migration but did not affect proliferation. In an analysis of more than 140 matched primary and metastatic tumors from patients, we found that acetylation was maintained and in many cases increased in lymph node metastases compared with primary tumors. Proteomic analysis of an independent cohort of more than 390 patient specimens further documented the relationship between increased α-tubulin acetylation and the aggressive behaviors of basal-like breast cancers, with a trend toward increased risk of disease progression and death in patients with high-intensity α-tubulin acetylation in primary tumors. Taken together, our results identify a tight correlation between acetylated α-tubulin levels and aggressive metastatic behavior in breast cancer, with potential implications for the definition of a simple prognostic biomarker in patients with breast cancer.

Computational systems biology approaches to anti-angiogenic cancer therapeutics

Angiogenesis is an exquisitely regulated process that is required for physiological processes and is also important in numerous diseases. Tumors utilize angiogenesis to generate the vascular network needed to supply the cancer cells with nutrients and oxygen, and many cancer drugs aim to inhibit tumor angiogenesis. Anti-angiogenic therapy involves inhibiting multiple cell types, molecular targets, and intracellular signaling pathways. Computational tools are useful in guiding treatment strategies, predicting the response to treatment, and identifying new targets of interest. Here, we describe progress that has been made in applying mathematical modeling and bioinformatics approaches to study anti-angiogenic therapeutics in cancer.

Analysis of the quantitative balance between insulin-like growth factor (IGF)-1 ligand, receptor, and binding protein levels to predict cell sensitivity and therapeutic efficacy

Background

The insulin-like growth factor (IGF) system impacts cell proliferation and is highly activated in ovarian cancer. While an attractive therapeutic target, the IGF system is complex with two receptors (IGF1R, IGF2R), two ligands (IGF1, IGF2), and at least six high affinity IGF-binding proteins (IGFBPs) that regulate the bioavailability of IGF ligands. We hypothesized that a quantitative balance between these different network components regulated cell response.

Results

OVCAR5, an immortalized ovarian cancer cell line, were found to be sensitive to IGF1, with the dose of IGF1 (i.e., the total mass of IGF1 available) a more reliable predictor of cell response than ligand concentration. The applied dose of IGF1 was depleted by both cell-secreted IGFBPs and endocytic trafficking, with IGFBPs sequestering up to 90% of the available ligand. To explore how different variables (i.e., IGF1, IGFBPs, and IGF1R levels) impacted cell response, a mass-action steady-state model was developed. Examination of the model revealed that the level of IGF1-IGF1R complexes per cell was directly proportional to the extent of proliferation induced by IGF1. Model analysis suggested, and experimental results confirmed, that IGFBPs present during IGF1 treatment significantly decreased IGF1-mediated proliferation. We utilized this model to assess the efficacy of IGF1 and IGF1R antibodies against different network compositions and determined that IGF1R antibodies were more globally effective due to the receptor-limited state of the network.

Conclusions

Changes that affect IGF1R occupancy have predictable effects on IGF1-induced proliferation and our model captured these effects. Analysis of this model suggests that IGF1R antibodies will be more effective than IGF1 antibodies, although the difference was minimal in conditions with low levels of IGF1 and IGFBPs. Examining how different components of the IGF system influence cell response will be critical to improve our understanding of the IGF signaling network in ovarian cancer.

Quantitative phenotypic and pathway profiling guides rational drug combination strategies

Advances in target-based drug discovery strategies have enabled drug discovery groups in academia and industry to become very effective at generating molecules that are potent and selective against single targets. However, it has become apparent from disappointing results in recent clinical trials that a major challenge to the development of successful targeted therapies for treating complex multifactorial diseases is overcoming heterogeneity in target mechanism among patients and inherent or acquired drug resistance. Consequently, reductionist target directed drug-discovery approaches are not appropriately tailored toward identifying and optimizing multi-targeted therapeutics or rational drug combinations for complex disease. In this article, we describe the application of emerging high-content phenotypic profiling and analysis tools to support robust evaluation of drug combination performance following dose-ratio matrix screening. We further describe how the incorporation of high-throughput reverse phase protein microarrays with phenotypic screening can provide rational drug combination hypotheses but also confirm the mechanism-of-action of novel drug combinations, to facilitate future preclinical and clinical development strategies.

Towards structural systems pharmacology to study complex diseases and personalized medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles

BACKGROUND

The Fibroblast Growth Factor (FGF) pathway is driving various aspects of cellular responses in both normal and malignant cells. One interesting characteristic of this pathway is the biphasic nature of the cellular response to some FGF ligands like FGF2. Specifically, it has been shown that phenotypic behaviors controlled by FGF signaling, like migration and growth, reach maximal levels in response to intermediate concentrations, while high levels of FGF2 elicit weak responses. The mechanisms leading to the observed biphasic response remains unexplained.

RESULTS

A combination of experiments and computational modeling was used to understand the mechanism behind the observed biphasic signaling responses. FGF signaling involves a tertiary surface interaction that we captured with a computational model based on Ordinary Differential Equations (ODEs). It accounts for FGF2 binding to FGF receptors (FGFRs) and heparan sulfate glycosaminoglycans (HSGAGs), followed by receptor-phosphorylation, activation of the FRS2 adapter protein and the Ras-Raf signaling cascade. Quantitative protein assays were used to measure the dynamics of phosphorylated ERK (pERK) in response to a wide range of FGF2 ligand concentrations on a fine-grained time scale for the squamous cell lung cancer cell line H1703. We developed a novel approach combining Particle Swarm Optimization (PSO) and feature-based constraints in the objective function to calibrate the computational model to the experimental data. The model is validated using a series of extracellular and intracellular perturbation experiments. We demonstrate that in silico model predictions are in accordance with the observed in vitro results.

CONCLUSIONS

Using a combined approach of computational modeling and experiments we found that competition between binding of the ligand FGF2 to HSGAG and FGF receptor leads to the biphasic response. At low to intermediate concentrations of FGF2 there are sufficient free FGF receptors available for the FGF2-HSGAG complex to enable the formation of the trimeric signaling unit. At high ligand concentrations the ligand binding sites of the receptor become saturated and the trimeric signaling unit cannot be formed. This insight into the pathway is an important consideration for the pharmacological inhibition of this pathway.

Reverse-phase protein array analysis to identify biomarker proteins in human pancreatic cancer

BACKGROUND

Pancreatic cancer is the fourth leading cause of cancer death in the United States. The high mortality rate of patients with pancreatic cancer is primarily due to the difficulty of early diagnosis and a lack of effective therapies. There is an urgent need to discover novel molecular targets for early diagnosis and new therapeutic approaches to improve the clinical outcome of this deadly disease.

AIM

We utilized the reverse-phase protein assay (RPPA) to identify differentially expressed biomarker proteins in tumors and matched adjacent, normal-appearing tissue samples from 15 pancreatic cancer patients.

METHODS

The antibody panel used for the RPPA included 130 key proteins involved in various cancer-related pathways. The paired t test was used to determine the significant differences between matched pairs, and the false discovery rate-adjusted p values were calculated to take into account the effect of multiple comparisons.

RESULTS

After correcting for multiple comparisons, we found 19 proteins that had statistically significant differences in expression between matched pairs. However, only four (AKT, β-catenin, GAB2, and PAI-1) of them met the conservative criteria (both a q value <0.05 and a fold-change of ≥3/2 or ≤2/3) to be considered differentially expressed. Overexpression of AKT, β-catenin, and GAB2 in pancreatic cancer tissues identified by RPPA has also been further confirmed by western blot analysis. Further analysis identified several significantly associated canonical pathways and overrepresented network functions.

CONCLUSION

GAB2, a newly identified protein in pancreatic cancer, may provide additional insight into this cancer's pathogenesis. Future studies in a larger population are warranted to further confirm our results.

Principles and methods of integrative genomic analyses in cancer

Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and protein expression, point to biological functions and molecular pathways being deregulated in multiple cancers. Genomic, metabolomic and clinical data from various solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring. The integrative genomics methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they can seem daunting. The objectives, methods and computational tools of integrative genomics that are available to date are reviewed here, as is their implementation in cancer research.

Mapping network motif tunability and robustness in the design of synthetic signaling circuits

Cellular networks are highly dynamic in their function, yet evolutionarily conserved in their core network motifs or topologies. Understanding functional tunability and robustness of network motifs to small perturbations in function and structure is vital to our ability to synthesize controllable circuits. In establishing core sets of network motifs, we selected topologies that are overrepresented in mammalian networks, including the linear, feedback, feed-forward, and bifan circuits. Static and dynamic tunability of network motifs were defined as the motif ability to respectively attain steady-state or transient outputs in response to pre-defined input stimuli. Detailed computational analysis suggested that static tunability is insensitive to the circuit topology, since all of the motifs displayed similar ability to attain predefined steady-state outputs in response to constant inputs. Dynamic tunability, in contrast, was tightly dependent on circuit topology, with some motifs performing superiorly in achieving observed time-course outputs. Finally, we mapped dynamic tunability onto motif topologies to determine robustness of motif structures to changes in topology and identify design principles for the rational assembly of robust synthetic networks.