Mathematical Modeling of Therapy-induced Cancer Drug Resistance: Connecting Cancer Mechanisms to Population Survival Rates

Cyclical Treatment of Colorectal Tumor Spheroids Induces Resistance to MEK Inhibitors

Adaptive drug resistance is a major obstacle to successful treatment of colorectal cancers. Physiologic tumor models of drug resistance are crucial to understand mechanisms of treatment failure and improve therapy by developing new therapeutics and treatment strategies. Using our aqueous two-phase system microtechnology, we developed colorectal tumor spheroids and periodically treated them with sub-lethal concentrations of three Mitogen Activated Kinase inhibitors (MEKi) used in clinical trials. We used long-term, periodic treatment and recovery of spheroids to mimic cycles of clinical chemotherapy and implemented a growth rate metric to quantitatively assess efficacy of the MEKi during treatment. Our results showed that efficacy of the MEKi significantly reduced with increased treatment cycles. Using a comprehensive molecular analysis, we established that resistance of colorectal tumor spheroids to the MEKi developed through activation of the PI3K/AKT/mTOR pathway. We also showed that other potential feedback mechanisms, such as STAT3 activation or amplified B-RAF, did not account for resistance to the MEKi. We combined each of the three MEKi with a PI3K/mTOR inhibitor and showed that the combination treatments synergistically blocked resistance to the MEKi. Importantly, and unlike the individual inhibitors, we demonstrated that synergistic concentrations of combinations of MEK and PI3K/mTOR inhibitors effectively inhibited growth of colorectal tumor spheroids in long-term treatments. This proof-of-concept study to model treatment-induced drug resistance of cancer cells using 3D cultures offers a unique approach to identify underlying molecular mechanisms and develop effective treatments.

A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008-2017

BACKGROUND

The application of artificial intelligence techniques for processing electronic health records data plays increasingly significant role in advancing clinical decision support. This study conducts a quantitative comparison on the research of utilizing artificial intelligence on electronic health records between the USA and China to discovery their research similarities and differences.

METHODS

Publications from both Web of Science and PubMed are retrieved to explore the research status and academic performances of the two countries quantitatively. Bibliometrics, geographic visualization, collaboration degree calculation, social network analysis, latent dirichlet allocation, and affinity propagation clustering are applied to analyze research quantity, collaboration relations, and hot research topics.

RESULTS

There are 1031 publications from the USA and 173 publications from China during 2008-2017 period. The annual numbers of publications from the USA and China increase polynomially. JAMIA with 135 publications and JBI with 13 publications are the top prolific journals for the USA and China, respectively. Harvard University with 101 publications and Zhejiang University with 12 publications are the top prolific affiliations for the USA and China, respectively. Massachusetts is the most prolific region with 211 publications for the USA, while for China, Taiwan is the top 1 with 47 publications. China has relatively higher institutional and international collaborations. Nine main research areas for the USA are identified, differentiating 7 for China.

CONCLUSIONS

There is a steadily growing presence and increasing visibility of utilizing artificial intelligence on electronic health records for the USA and China over the years. The results of the study demonstrate the research similarities and differences, as well as strengths and weaknesses of the two countries.

Therapeutic Schedule Evaluation for Brain-Metastasized Non-Small Cell Lung Cancer with A Probabilistic Linguistic ELECTRE II Method

With the rapid development of modern medicine, therapeutic schedules of brain-metastasized non-small cell lung cancer (NSCLC) are expanding. To assist a patient who suffers from brain-metastasized NSCLC to select the most suitable therapeutic schedule, firstly, we establish an indicator system for evaluating the therapeutic schedules; then, we propose a probabilistic linguistic ELECTRE II method to handle the corresponding evaluation problem for the following reasons: (1) probabilistic linguistic information is effective to depict the uncertainty of the therapeutic process and the fuzziness of an expert’s cognition; (2) the ELECTRE II method can deal with evaluation indicators that do not meet a fully compensatory relationship. Simulation tests on the parameters in the proposed method are provided to discuss their impacts on the final rankings. Furthermore, we apply the proposed method to help a patient with brain-metastasized NSCLC at the Sichuan Cancer Hospital and Institute to choose the optimal therapeutic schedule, and we present some sensitive analyses and comparative analyses to demonstrate the stability and applicability of the proposed method.

Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System

Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

Pharmacodynamic modelling of resistance to epidermal growth factor receptor inhibition in brain metastasis mouse models

Purpose

Epidermal growth factor receptor (EGFR) is thought to play a role in the regulation of cell proliferation; with its activation stimulating tumour growth. EGFR inhibitors have shown promise in the treatment of cancer, particularly in non-small cell lung cancer, however, resistance is observed in the majority of patients. A tumour growth model was developed aiming to explain this resistance.

Methods

The model incorporating populations of both sensitive and resistant cells were fitted to data from a study of EGFR inhibitor AZD3759 in brain metastasis mouse models. The observed regrowth of tumours in higher dose groups suggested the development of resistance to treatment. The bioluminescence observations were highly variable, covering many orders of magnitude, so to assess how reliable the model was, the parameter estimates were compared to those found in less noisy subcutaneous mouse models.

Results

The fitted model suggested that resistance was mainly due to a proportion of cells being resistant at baseline, and the contribution of mutations occurring during the study leading to resistance was negligible. Estimated growth rate and dose–response was found to be comparable between brain metastasis and subcutaneous mouse models.

Conclusions

The developed model to describe resistance suggests that the resistance to EGFR-inhibition seen in these xenografts is best described by assuming a small percentage of cells are resistant to treatment at baseline. This model suggests changes to dosing and dosing schedule may not prevent resistance to treatment developing, and that additional treatments would need to be used in combination to overcome resistance.

Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination

The efficacy of pharmacological perturbation to the signaling transduction network depends on the network topology. However, whether and how signaling dynamics mediated by crosstalk contributes to the drug resistance are not fully understood and remain to be systematically explored. In this study, motivated by a realistic signaling network linked by crosstalk between EGF/EGFR/Ras/MEK/ERK pathway and HGF/HGFR/PI3K/AKT pathway, we develop kinetic models for several small networks with typical crosstalk modules to investigate the role of the architecture of crosstalk in inducing drug resistance. Our results demonstrate that crosstalk inhibition diminishes the response of signaling output to the external stimuli. Moreover, we show that signaling crosstalk affects the relative sensitivity of drugs, and some types of crosstalk modules that could yield resistance to the targeted drugs were identified. Furthermore, we quantitatively evaluate the relative efficacy and synergism of drug combinations. For the modules that are resistant to the targeted drug, we identify drug targets that can not only increase the relative drug efficacy but also act synergistically. In addition, we analyze the role of the strength of crosstalk in switching a module between drug-sensitive and drug-resistant. Our study provides mechanistic insights into the signaling crosstalk-mediated mechanisms of drug resistance and provides implications for the design of synergistic drug combinations to reduce drug resistance.

Spatial Heterogeneity and Evolutionary Dynamics Modulate Time to Recurrence in Continuous and Adaptive Cancer Therapies

Treatment of advanced cancers has benefited from new agents that supplement or bypass conventional therapies. However, even effective therapies fail as cancer cells deploy a wide range of resistance strategies. We propose that evolutionary dynamics ultimately determine survival and proliferation of resistant cells. Therefore, evolutionary strategies should be used with conventional therapies to delay or prevent resistance. Using an agent-based framework to model spatial competition among sensitive and resistant populations, we applied antiproliferative drug treatments to varying ratios of sensitive and resistant cells. We compared a continuous maximum-tolerated dose schedule with an adaptive schedule aimed at tumor control via competition between sensitive and resistant cells. Continuous treatment cured mostly sensitive tumors, but with any resistant cells, recurrence was inevitable. We identified two adaptive strategies that control heterogeneous tumors: dose modulation controls most tumors with less drug, while a more vacation-oriented schedule can control more invasive tumors. These findings offer potential modifications to treatment regimens that may improve outcomes and reduce resistance and recurrence.Significance: By using drug dose modulation or treatment vacations, adaptive therapy strategies control the emergence of tumor drug resistance by spatially suppressing less fit resistant populations in favor of treatment sensitive ones. Cancer Res; 78(8); 2127-39. ©2018 AACR.

A Multifunctional Micellar Nanoplatform with pH-Triggered Cell Penetration and Nuclear Targeting for Effective Cancer Therapy and Inhibition to Lung Metastasis

The enhancement of cellular internalization and subsequent achievement of a nuclear targeting of nanocarriers play an important role in maximizing the therapeutic potency and minimizing the side effects of encapsulated drugs. Herein, a multifunctional micellar nanoplatform simultaneously with high cell penetration and nuclear targeting through pH-triggered surface charge reversal is presented. The miscellar system is constructed from poly(ethylene glycol)-poly(ε-caprolactone) with 2,3-dimethylmaleic anhydride-Tat decoration (PECL/DA-Tat). DA groups are used to mask the positive charge of Tat to prolong blood circulation of the nanocarriers. In the mildly acidic environment of tumor tissue, the system exhibits ultrasensitive negative to positive charge reversal, facilitating the cell internalization and subsequent nuclear targeting. The chemotherapeutic 10-hydroxycamptothecin conjugated to methoxy polyethylene glycol, which is loaded in this micelle, obviously enhances cytotoxicity against tumor cells. The in vivo therapy in mice bearing 4T1 breast tumor reveals that the system has a significant enhancement of both the endocytosis and nuclear enrichment, showing a highly effective antitumor efficacy and inhibition to lung metastasis.

Interference of P-REX2a may inhibit proliferation and reverse the resistance of SGC7901 cells to doxorubicin

Drug resistance inhibits the efficacy of doxorubicin in gastric cancer. Phosphatidylinositol 3,4,5-trisphosphate RAC exchanger 2a (P-REX2a) activates the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt) signaling pathway by binding to and inactivating phosphatase and tensin homolog (PTEN), which functions as a tumor promoter in a number of types of cancer. However, there is no research concerning the association between P-REX2a expression and drug resistance in gastric cancer. In the present study, the expression of P-REX2a in clinical gastric cancer tissues was detected, and the mechanism of doxorubicin resistance in the gastric cancer cell line SGC7901 was investigated. Using reverse transcription-quantitative polymerase chain reaction and western blotting, it was demonstrated that the mRNA and protein expression of P-REX2a was increased in gastric cancer tissues. MTT assays were also used to determine proliferation, and proliferation was revealed to be reduced following transfection of P-REX2a small interfering (si)RNA. When the cells were treated with 0.3 µM doxorubicin for 24 h, the rate of apoptosis in the siRNA-transfected groups significantly increased and no marked changes in of PTEN and Akt expression were observed. By contrast, the activity of PTEN increased, and the expression of p-Akt (S473) decreased in the P-REX2a siRNA-transfected group compared with the control. The detection of PTEN enzymatic activity in the present study was based on phosphatidylinositol-3,4,5-trisphosphate. Therefore, it was concluded that P-REX2a may participate in the generation of resistance to doxorubicin in gastric cancer, and this may be associated with the upregulation of the PI3K/Akt signaling pathway via inactivation of PTEN.

A Spatio-Temporal Model of Macrophage-Mediated Drug Resistance in Glioma Immunotherapy

The emergence of drug resistance is often an inevitable obstacle that limits the long-term effectiveness of clinical cancer chemotherapeutics. Although various forms of cancer cell-intrinsic mechanisms of drug resistance have been experimentally revealed, the role and the underlying mechanism of tumor microenvironment in driving the development of acquired drug resistance remain elusive, which significantly impedes effective clinical cancer treatment. Recent experimental studies have revealed a macrophage-mediated drug resistance mechanism in which the tumor microenvironment undergoes adaptation in response to macrophage-targeted colony-stimulating factor-1 receptor (CSF1R) inhibition therapy in gliomas. In this study, we developed a spatio-temporal model to quantitatively describe the interplay between glioma cells and CSF1R inhibitor-targeted macrophages through CSF1 and IGF1 pathways. Our model was used to investigate the evolutionary kinetics of the tumor regrowth and the associated dynamic adaptation of the tumor microenvironment in response to the CSF1R inhibitor treatment. The simulation result obtained using this model was in agreement with the experimental data. The sensitivity analysis revealed the key parameters involved in the model, and their potential impacts on the model behavior were examined. Moreover, we demonstrated that the drug resistance is dose-dependent. In addition, we quantitatively evaluated the effects of combined CSFR inhibition and IGF1 receptor (IGF1R) inhibition with the goal of designing more effective therapies for gliomas. Our study provides quantitative and mechanistic insights into the microenvironmental adaptation mechanisms that operate during macrophage-targeted immunotherapy and has implications for drug dose optimization and the design of more effective combination therapies. Mol Cancer Ther; 17(4); 814-24. ©2018 AACR.

Evaluation of drug combination effect using a Bliss independence dose-response surface model

To test the anticancer effect of combining two drugs targeting different biological pathways, the popular way to show synergistic effect of drug combination is a heat map or surface plot based on the percent excess the Bliss prediction using the average response measures at each combination dose. Such graphs, however, are inefficient in the drug screening process and it doesn't give a statistical inference on synergistic effect. To make a statistically rigorous and robust conclusion for drug combination effect, we present a two-stage Bliss independence response surface model to estimate an overall interaction index (τ) with 95% confidence interval (CI). By taking into all data points account, the overall τ with 95% CI can be applied to determine if the drug combination effect is synergistic overall. Using some example data, the two-stage model was compared to a couple of classic models following Bliss rule. The data analysis results obtained from our model reflect the pattern shown from other models. The application of overall τ helps investigators to make decision easier and accelerate the preclinical drug screening.

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Positive feedback loops drive immune cell polarization toward a pro-tumor phenotype that accentuates immunosuppression and tumor angiogenesis. This phenotypic switch leads to the escape of cancer cells from immune destruction. These positive feedback loops are generated by cytokines such as TGF-β, Interleukin-10 and Interleukin-4, which are responsible for the polarization of monocytes and M1 macrophages into pro-tumor M2 macrophages, and the polarization of naive helper T cells intopro-tumor Th2 cells. In this article, we present a deterministic ordinary differential equation (ODE) model that includes key cellular interactions and cytokine signaling pathways that lead to immune cell polarization in the tumor microenvironment. The model was used to simulate various cancer treatments in silico. We identified combination therapies that consist of M1 macrophages or Th1 helper cells, coupled with an anti-angiogenic treatment, that are robust with respect to immune response strength, initial tumor size and treatment resistance. We also identified IL-4 and IL-10 as the targets that should be neutralized in order to make these combination treatments robust with respect to immune cell polarization. The model simulations confirmed a hypothesis based on published experimental evidence that a polarization into the M1 and Th1 phenotypes to increase the M1-to-M2 and Th1-to-Th2 ratios plays a significant role in treatment success. Our results highlight the importance of immune cell reprogramming as a viable strategy to eradicate a highly vascularized tumor when the strength of the immune response is characteristically weak and cell polarization to the pro-tumor phenotype has occurred.

Integrating Models to Quantify Environment-Mediated Drug Resistance

Drug resistance is the single most important driver of cancer treatment failure for modern targeted therapies, and the dialog between tumor and stroma has been shown to modulate the response to molecularly targeted therapies through proliferative and survival signaling. In this work, we investigate interactions between a growing tumor and its surrounding stroma and their role in facilitating the emergence of drug resistance. We used mathematical modeling as a theoretical framework to bridge between experimental models and scales, with the aim of separating intrinsic and extrinsic components of resistance in BRAF-mutated melanoma; the model describes tumor-stroma dynamics both with and without treatment. Integration of experimental data into our model revealed significant variation in either the intensity of stromal promotion or intrinsic tissue carrying capacity across animal replicates. Cancer Res; 77(19); 5409-18. ©2017 AACR.

Low-density lipoprotein-coupled micelles with reduction and pH dual sensitivity for intelligent co-delivery of paclitaxel and siRNA to breast tumor

Multidrug resistance (MDR) is a major obstacle for the clinical therapy of malignant human cancers. The discovery of RNA interference provides efficient gene silencing within tumor cells for reversing MDR. In this study, a new “binary polymer” low-density lipoprotein–N-succinyl chitosan–cystamine–urocanic acid (LDL–NSC–SS–UA) with dual pH/redox sensitivity and targeting effect was synthesized for the co-delivery of breast cancer resistance protein small interfering RNA (siRNA) and paclitaxel (PTX). In vivo, the co-delivering micelles can accumulate in tumor tissue via the enhanced permeability and retention effect and the specific recognition and combination of LDL and LDL receptor, which is overexpressed on the surface of tumor cell membranes. The siRNA–PTX-loaded micelles inhibited gene and drug release under physiological conditions while promoting fast release in an acid microenvironment or in the presence of glutathione. The micelles escaped from the lysosome through the proton sponge effect. Additionally, the micelles exhibited superior antitumor activity and downregulated the protein and mRNA expression levels of breast cancer resistance protein in MCF-7/Taxol cells. The biodistribution and antitumor studies proved that the siRNA–PTX-loaded micelles possessed prolonged circulation time with a remarkable tumor-targeting effect and effectively inhibited tumor growth. Therefore, the novel dual pH/redox-sensitive polymers co-delivering siRNA and PTX with excellent biocompatibility and effective reversal of MDR demonstrate a considerable potential in cancer therapy.

Molecular Mimics of Classic P-Glycoprotein Inhibitors as Multidrug Resistance Suppressors and Their Synergistic Effect on Paclitaxel

P-glycoprotein (Pgp) is a membrane bound efflux pump spread in a variety of tumor cells and considered as a main component of multidrug resistance (MDR) to chemotherapies. In this work, three groups of compounds (imidazolone, oxazolone and vinyl dipeptide derivatives) were synthesized aiming to develop a molecular framework that effectively suppresses MDR. When tested for their influence on Pgp activity, four compounds coded Cur1-01, Cur1-12V, Curox-1 and Curox-3 significantly decreased remaining ATP concentration indicating Pgp substrate site blocking. On the other hand, Cur-3 and Cur-10 significantly increased remaining ATP concentration, which is indicative of Pgp ATPase inhibition. The cytotoxicity of synthesized compounds was examined against Pgp expressing/highly resistant colorectal cancer cell lines (LS-174T). Compounds Cur-1 and Cur-3 showed considerable cytotoxicity with IC50 values of 7.6 and 8.9 μM, respectively. Equitoxic combination (at IC50 concentrations) of PTX and Cur-3 greatly diminished resistant cell clone from 45.7% to 2.5%, albeit with some drop in potency from IC50 of 7.9 nM to IC50 of 23.8 nM. On the other hand, combination of PTX and the non-cytotoxic Cur1-12V (10 μM) significantly decreased the IC50 of PTX to 3.8 nM as well as the resistant fraction to 16.2%. The combination test was confirmed using the same protocol but on another resistant CRC cell line (HCT-116) as we obtained similar results. Both Cur-3 and Cur1-12V (10 μM) significantly increased the cellular entrapment of Pgp probe (doxorubicin) elevating its intracellular concentration from 1.9 pmole/cell to 3.0 and 2.9 pmole/cell, respectively.

Looking beyond the cancer cell for effective drug combinations

Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient’s response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs—in terms of both computational and data requirements—that will truly empower such combinations.

Effect of Dynamic Interaction between microRNA and Transcription Factor on Gene Expression

MicroRNAs (miRNAs) are endogenous noncoding RNAs which participate in diverse biological processes in animals and plants. They are known to join together with transcription factors and downstream gene, forming a complex and highly interconnected regulatory network. To recognize a few overrepresented motifs which are expected to perform important elementary regulatory functions, we constructed a computational model of miRNA-mediated feedforward loops (FFLs) in which a transcription factor (TF) regulates miRNA and targets gene. Based on the different dynamic interactions between miRNA and TF on gene expression, four possible structural topologies of FFLs with two gate functions (AND gate and OR gate) are introduced. We studied the dynamic behaviors of these different motifs. Furthermore, the relationship between the response time and maximal activation velocity of miRNA was investigated. We found that the curve of response time shows nonmonotonic behavior in Co1 loop with OR gate. This may help us to infer the mechanism of miRNA binding to the promoter region. At last we investigated the influence of important parameters on the dynamic response of system. We identified that the stationary levels of target gene in all loops were insensitive to the initial value of miRNA.

Stochastic modeling suggests that noise reduces differentiation efficiency by inducing a heterogeneous drug response in glioma differentiation therapy

Background

Glioma differentiation therapy is a novel strategy that has been used to induce glioma cells to differentiate into glia-like cells. Although some advances in experimental methods for exploring the molecular mechanisms involved in differentiation therapy have been made, a model-based comprehensive analysis is still needed to understand these differentiation mechanisms and improve the effects of anti-cancer therapeutics. This type of analysis becomes necessary in stochastic cases for two main reasons: stochastic noise inherently exists in signal transduction and phenotypic regulation during targeted therapy and chemotherapy, and the relationship between this noise and drug efficacy in differentiation therapy is largely unknown.

Results

In this study, we developed both an additive noise model and a Chemical-Langenvin-Equation model for the signaling pathways involved in glioma differentiation therapy to investigate the functional role of noise in the drug response. Our model analysis revealed an ultrasensitive mechanism of cyclin D1 degradation that controls the glioma differentiation induced by the cAMP inducer cholera toxin (CT). The role of cyclin D1 degradation in human glioblastoma cell differentiation was then experimentally verified. Our stochastic simulation demonstrated that noise not only renders some glioma cells insensitive to cyclin D1 degradation during drug treatment but also induce heterogeneous differentiation responses among individual glioma cells by modulating the ultrasensitive response of cyclin D1. As such, the noise can reduce the differentiation efficiency in drug-treated glioma cells, which was verified by the decreased evolution of differentiation potential, which quantified the impact of noise on the dynamics of the drug-treated glioma cell population.

Conclusion

Our results demonstrated that targeting the noise-induced dynamics of cyclin D1 during glioma differentiation therapy can increase anti-glioma effects, implying that noise is a considerable factor in assessing and optimizing anti-cancer drug interventions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0316-x) contains supplementary material, which is available to authorized users.