Features of the reversible sensitivity-resistance transition in PI3K/PTEN/AKT signalling network after HER2 inhibition

Role of Digital Microfluidics in Enabling Access to Laboratory Automation and Making Biology Programmable

Digital microfluidics (DMF) is a liquid handling technique that has been demonstrated to automate biological experimentation in a low-cost, rapid, and programmable manner. This review discusses the role of DMF as a "digital bioconverter"-a tool to connect the digital aspects of the design-build-learn cycle with the physical execution of experiments. Several applications are reviewed to demonstrate the utility of DMF as a digital bioconverter, namely, genetic engineering, sample preparation for sequencing and mass spectrometry, and enzyme-, immuno-, and cell-based screening assays. These applications show that DMF has great potential in the role of a centralized execution platform in a fully integrated pipeline for the production of novel organisms and biomolecules. In this paper, we discuss how the function of a DMF device within such a pipeline is highly dependent on integration with different sensing techniques and methodologies from machine learning and big data. In addition to that, we examine how the capacity of DMF can in some cases be limited by known technical and operational challenges and how consolidated efforts in overcoming these challenges will be key to the development of DMF as a major enabling technology in the computer-aided biology framework.

Role of protein kinase CK2 in antitumor drug resistance

Drug resistance represents the major reason of pharmacological treatment failure. It is supported by a broad spectrum of mechanisms, whose molecular bases have been frequently correlated to aberrant protein phosphorylation. CK2 is a constitutively active protein kinase which phosphorylates hundreds of substrates; it is expressed in all cells, but its level is commonly found higher in cancer cells, where it plays anti-apoptotic, pro-migration and pro-proliferation functions. Several evidences support a role for CK2 in processes directly responsible of drug resistance, such as drug efflux and DNA repair; moreover, CK2 intervenes in signaling pathways which are crucial to evade drug response (as PI3K/AKT/PTEN, NF-κB, β-catenin, hedgehog signaling, p53), and controls the activity of chaperone machineries fundamental in resistant cells. Interestingly, a panel of specific and effective inhibitors of CK2 is available, and several examples are known of their efficacy in resistant cells, with synergistic effect when used in combination with conventional drugs, also in vivo. Here we analyze and discuss evidences supporting the hypothesis that CK2 targeting represents a valuable strategy to overcome drug resistance.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.

Kinetic modelling of in vitro data of PI3K, mTOR1, PTEN enzymes and on-target inhibitors Rapamycin, BEZ235, and LY294002

The phosphatidylinositide 3-kinases (PI3K) and mammalian target of rapamycin-1 (mTOR1) are two key targets for anti-cancer therapy. Predicting the response of the PI3K/AKT/mTOR1 signalling pathway to targeted therapy is made difficult because of network complexities. Systems biology models can help explore those complexities but the value of such models is dependent on accurate parameterisation. Motivated by a need to increase accuracy in kinetic parameter estimation, and therefore the predictive power of the model, we present a framework to integrate kinetic data from enzyme assays into a unified enzyme kinetic model. We present exemplar kinetic models of PI3K and mTOR1, calibrated on in vitro enzyme data and founded on Michaelis-Menten (MM) approximation. We describe the effects of an allosteric mTOR1 inhibitor (Rapamycin) and ATP-competitive inhibitors (BEZ235 and LY294002) that show dual inhibition of mTOR1 and PI3K. We also model the kinetics of phosphatase and tensin homolog (PTEN), which modulates sensitivity of the PI3K/AKT/mTOR1 pathway to these drugs. Model validation with independent data sets allows investigation of enzyme function and drug dose dependencies in a wide range of experimental conditions. Modelling of the mTOR1 kinetics showed that Rapamycin has an IC₅₀ independent of ATP concentration and that it is a selective inhibitor of mTOR1 substrates S6K1 and 4EBP1: it retains 40% of mTOR1 activity relative to 4EBP1 phosphorylation and inhibits completely S6K1 activity. For the dual ATP-competitive inhibitors of mTOR1 and PI3K, LY294002 and BEZ235, we derived the dependence of the IC₅₀ on ATP concentration that allows prediction of the IC₅₀ at different ATP concentrations in enzyme and cellular assays. Comparison of drug effectiveness in enzyme and cellular assays showed that some features of these drugs arise from signalling modulation beyond the on-target action and MM approximation and require a systems-level consideration of the whole PI3K/PTEN/AKT/mTOR1 network in order to understand mechanisms of drug sensitivity and resistance in different cancer cell lines. We suggest that using these models in a systems biology investigation of the PI3K/AKT/mTOR1 signalling in cancer cells can bridge the gap between direct drug target action and the therapeutic response to these drugs and their combinations.

Empirical inference of circuitry and plasticity in a kinase signaling network

Our understanding of physiology and disease is hampered by the difficulty of measuring the circuitry and plasticity of signaling networks that regulate cell biology, and how these relate to phenotypes. Here, using mass spectrometry-based phosphoproteomics, we systematically characterized the topology of a network comprising the PI3K/Akt/mTOR and MEK/ERK signaling axes and confirmed its biological relevance by assessing its dynamics upon EGF and IGF1 stimulation. Measuring the activity of this network in models of acquired drug resistance revealed that cells chronically treated with PI3K or mTORC1/2 inhibitors differed in the way their networks were remodeled. Unexpectedly, we also observed a degree of heterogeneity in the network state between cells resistant to the same inhibitor, indicating that even identical and carefully controlled experimental conditions can give rise to the evolution of distinct kinase network statuses. These data suggest that the initial conditions of the system do not necessarily determine the mechanism by which cancer cells become resistant to PI3K/mTOR targeted therapies. The patterns of signaling network activity observed in the resistant cells mirrored the patterns of response to several drug combination treatments, suggesting that the activity of the defined signaling network truly reflected the evolved phenotypic diversity.

Systems analysis of drug-induced receptor tyrosine kinase reprogramming following targeted mono- and combination anti-cancer therapy

The receptor tyrosine kinases (RTKs) are key drivers of cancer progression and targets for drug therapy. A major challenge in anti-RTK treatment is the dependence of drug effectiveness on co-expression of multiple RTKs which defines resistance to single drug therapy. Reprogramming of the RTK network leading to alteration in RTK co-expression in response to drug intervention is a dynamic mechanism of acquired resistance to single drug therapy in many cancers. One route to overcome this resistance is combination therapy. We describe the results of a joint in silico, in vitro, and in vivo investigations on the efficacy of trastuzumab, pertuzumab and their combination to target the HER2 receptors. Computational modelling revealed that these two drugs alone and in combination differentially suppressed RTK network activation depending on RTK co-expression. Analyses of mRNA expression in SKOV3 ovarian tumour xenograft showed up-regulation of HER3 following treatment. Considering this in a computational model revealed that HER3 up-regulation reprograms RTK kinetics from HER2 homodimerisation to HER3/HER2 heterodimerisation. The results showed synergy of the trastuzumab and pertuzumab combination treatment of the HER2 overexpressing tumour can be due to an independence of the combination effect on HER3/HER2 composition when it changes due to drug-induced RTK reprogramming.

Microenvironment, oncoantigens, and antitumor vaccination: lessons learned from BALB-neuT mice

The tyrosine kinase human epidermal growth factor receptor 2 (HER2) gene is amplified in approximately 20% of human breast cancers and is associated with an aggressive clinical course and the early development of metastasis. Its crucial role in tumor growth and progression makes HER2 a prototypic oncoantigen, the targeting of which may be critical for the development of effective anticancer therapies. The setup of anti-HER2 targeting strategies has revolutionized the clinical outcome of HER2⁺ breast cancer. However, their initial success has been overshadowed by the onset of pharmacological resistance that renders them ineffective. Since the tumor microenvironment (TME) plays a crucial role in drug resistance, the design of more effective anticancer therapies should depend on the targeting of both cancer cells and their TME as a whole. In this review, starting from the successful know-how obtained with a HER2⁺ mouse model of mammary carcinogenesis, the BALB-neuT mice, we discuss the role of TME in mammary tumor development. Indeed, a deeper knowledge of antigens critical for cancer outbreak and progression and of the mechanisms that regulate the interplay between cancer and stromal cell populations could advise promising ways for the development of the best anticancer strategy.

Customizing the therapeutic response of signaling networks to promote antitumor responses by drug combinations

Drug resistance, de novo and acquired, pervades cellular signaling networks (SNs) from one signaling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anti-cancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where SN sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potential. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of combination therapies, and design methods to determine drug targets for combination regimens. Based on a joint systems analysis of cellular SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyze the targets of drug combinations. Our method explores mechanisms of sensitizing the SN through a combination of two drugs targeting vertical signaling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to customize the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the down-stream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects together with the capability of drug combinations to suppress resistance mechanisms before they become clinically manifest.

PTEN modulators: a patent review

INTRODUCTION

PTEN (phosphatase and tensin homolog deleted on chromosome 10) plays a pivotal role in controlling intracellular signaling for cell survival and proliferation by inhibiting the PI3K/Akt pathway, and its dysfunction is associated with several neoplastic diseases. PTEN is frequently found mutated in many pathological conditions highlighting its importance in normal physiological function. Unlike several cellular proteins which are activated by phosphorylation, PTEN is inactivated upon phosphorylation by specific kinases which phosphorylate serine and threonine residues in its C-terminal region. Therefore, development of therapeutic agents that specifically target kinases and kinase-domain-containing proteins affecting PTEN would lead to the treatment of PTEN-related diseases.

AREAS COVERED

With increasing evidence on the role of PTEN in many human diseases, the present review focuses on the clinical relevance of PTEN with a comprehensive list of currently identified modulators of PTEN, and proposes potentially novel molecular targets which could aid in the development of drug candidates for the treatment of PTEN-related diseases such as cardiovascular diseases, diabetes, obesity, cancer, autism, Parkinson's and Alzheimer's diseases.

EXPERT OPINION

This review describes several target sites that could help in the development of novel drug candidates to regulate or restore the normal physiological functions of PTEN and are essential in the treatment of human diseases where PTEN plays a pivotal role.

SPRY2 loss enhances ErbB trafficking and PI3K/AKT signalling to drive human and mouse prostate carcinogenesis

Loss of SPRY2 and activation of receptor tyrosine kinases are common events in prostate cancer (PC). However, the molecular basis of their interaction and clinical impact remains to be fully examined. SPRY2 loss may functionally synergize with aberrant cellular signalling to drive PC and to promote treatment-resistant disease. Here, we report evidence for a positive feedback regulation of the ErbB-PI3K/AKT cascade by SPRY2 loss in in vitro as well as pre-clinical in vivo models and clinical PC. Reduction in SPRY2 expression resulted in hyper-activation of PI3K/AKT signalling to drive proliferation and invasion by enhanced internalization of EGFR/HER2 and their sustained signalling at the early endosome in a PTEN-dependent manner. This involved p38 MAPK activation by PI3K to facilitate clathrin-mediated ErbB receptor endocytosis. Finally, in vitro and in vivo inhibition of PI3K suppressed proliferation and invasion, supporting PI3K/AKT as a target for therapy particularly in patients with PTEN-haploinsufficient-, low SPRY2- and ErbB-expressing tumours. In conclusion, SPRY2 is an important tumour suppressor in PC since its loss drives the PI3K/AKT pathway via functional interaction with the ErbB system.

The oncoprotein HBXIP uses two pathways to up-regulate S100A4 in promotion of growth and migration of breast cancer cells

We have reported that hepatitis B X-interacting protein (HBXIP) promotes the proliferation and migration of breast cancer cells. However, the underlying mechanism is poorly understood. In this study, we report that HBXIP works in the event through up-regulating S100A4. We observed that HBXIP expression was positively correlated to that of S100A4 in 87 clinical breast cancer tissue samples. Then, we identified that HBXIP was able to up-regulate S100A4 expression in breast cancer cells. Notably, we observed the HBXIP nuclear localization, implying that HBXIP may be associated with the promoter of S100A4. Chromatin immunoprecipitation assay (ChIP) and electrophoretic mobility shift assay (EMSA) showed that HBXIP was able to bind to the nucleotides +200~+239 region of S100A4 promoter, containing two putative recognition motif of transcription factor STAT4 and GRβ. It suggests that HBXIP is able to activate S100A4 promoter via interacting with STAT4 in breast cancer cells, leading to the up-regulation of S100A4. In addition, we identified another pathway of S100A4 up-regulation mediated by HBXIP. We found that HBXIP activated the PTEN/PI3K/AKT signaling by inducing DNA methylation of PTEN, which subsequently boosted S100A4 expression. In function, we demonstrated that HBXIP enhanced the growth or migration of breast cancer cells through S100A4 in vivo and in vitro. Collectively, we conclude that HBXIP up-regulates S100A4 through activating S100A4 promoter involving STAT4 and inducing PTEN/PI3K/AKT signaling to promote growth and migration of breast cancer cells. Our finding provides new insight into the mechanism of HBXIP in promotion of the development of breast cancer.