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

Agent-based approaches for biological modeling in oncology: A literature review

CONTEXT

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions.

METHODOLOGY

This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023.

RESULTS

Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

Musashi-2 (MSI2) regulation of DNA damage response in lung cancer

Lung cancer is one of the most common types of cancer worldwide. Non-small cell lung cancer (NSCLC), typically caused by KRAS and TP53 driver mutations, represents the majority of all new lung cancer diagnoses. Overexpression of the RNA-binding protein (RBP) Musashi-2 (MSI2) has been associated with NSCLC progression. To investigate the role of MSI2 in NSCLC development, we compared the tumorigenesis in mice with lung-specific Kras-activating mutation and Trp53 deletion, with and without Msi2 deletion (KPM2 versus KP mice). KPM2 mice showed decreased lung tumorigenesis in comparison with KP mice. In addition, KPM2 lung tumors showed evidence of decreased proliferation, but increased DNA damage, marked by increased levels of phH2AX (S139) and phCHK1 (S345), but decreased total and activated ATM. Using cell lines from KP and KPM2 tumors, and human NSCLC cell lines, we found that MSI2 directly binds ATM mRNA and regulates its translation. MSI2 depletion impaired DNA damage response (DDR) signaling and sensitized human and murine NSCLC cells to treatment with PARP inhibitors in vitro and in vivo. Taken together, we conclude that MSI2 supports NSCLC tumorigenesis, in part, by supporting repair of DNA damage by controlling expression of DDR proteins. These results suggest that targeting MSI2 may be a promising strategy for lung cancers treated with DNA-damaging agents.

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

MOTIVATION

Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties.

RESULTS

Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced.

AVAILABILITY AND IMPLEMENTATION

All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.

NSD1 supports cell growth and regulates autophagy in HPV-negative head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Despite advances in therapeutic management and immunotherapy, the 5-year survival rate for head and neck cancer remains at ~66% of all diagnosed cases. A better definition of drivers of HPV-negative HNSCC that are targetable points of tumor vulnerability could lead to significant clinical advances. NSD1 is a histone methyltransferase that catalyzes histone H3 lysine 36 di-methylation (H3K36me2); mutations inactivating NSD1 have been linked to improved outcomes in HNSCC. In this study, we show that NSD1 induces H3K36me2 levels in HNSCC and that the depletion of NSD1 reduces HNSCC of cell growth in vitro and in vivo. We also find that NSD1 strongly promotes activation of the Akt/mTORC1 signaling pathway. NSD1 depletion in HNSCC induces an autophagic gene program activation, causes accumulation of the p62 and LC3B-II proteins, and decreases the autophagic signaling protein ULK1 at both protein and mRNA levels. Reflecting these signaling defects, the knockdown of NSD1 disrupts autophagic flux in HNSCC cells. Taken together, these data identify positive regulation of Akt/mTORC1 signaling and autophagy as novel NSD1 functions in HNSCC, suggesting that NSD1 may be of value as a therapeutic target in this cancer.

The Marine Natural Compound Dragmacidin D Selectively Induces Apoptosis in Triple-Negative Breast Cancer Spheroids

Cancer cells grown in 3D spheroid cultures are considered more predictive for clinical efficacy. The marine natural product dragmacidin D induces apoptosis in MDA-MB-231 and MDA-MB-468 triple-negative breast cancer (TNBC) spheroids within 24 h of treatment while showing no cytotoxicity against the same cells grown in monolayers and treated for 72 h. The IC50 for cytotoxicity based on caspase 3/7 cleavage in the spheroid assay was 8 ± 1 µM in MDA-MB-231 cells and 16 ± 0.6 µM in MDA-MB-468 cells at 24 h. No cytotoxicity was seen at all in 2D, even at the highest concentration tested. Thus, the IC50 for cytotoxicity in the MTT assay (2D) in these cells was found to be >75 µM at 72 h. Dragmacidin D exhibited synergy when used in conjunction with paclitaxel, a current treatment for TNBC. Studies into the signaling changes using a reverse-phase protein array showed that treatment with dragmacidin D caused significant decreases in histones. Differential protein expression was used to hypothesize that its potential mechanism of action involves acting as a protein synthesis inhibitor or a ribonucleotide reductase inhibitor. Further testing is necessary to validate this hypothesis. Dragmacidin D also caused a slight decrease in an invasion assay in the MDA-MB-231 cells, although this failed to be statistically significant. Dragmacidin D shows intriguing selectivity for spheroids and has the potential to be a treatment option for triple-negative breast cancer, which merits further research into understanding this activity.

NSD1 supports cell growth and regulates autophagy in HPV-negative head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Despite advances in therapeutic management and immunotherapy, the five-year survival rate for head and neck cancer remains at ~66% of all diagnosed cases. A better definition of drivers of HPV-negative HNSCC that are targetable points of tumor vulnerability could lead to significant clinical advances. NSD1 is a histone methyltransferase which catalyzes histone H3 lysine 36 di-methylation (H3K36me2); mutations inactivating NSD1 have been linked to improved outcomes in HNSCC. In this study, we show that NSD1 induces H3K36me2 levels in HNSCC, and that the depletion of NSD1 reduces HNSCC of cell growth in vitro and in vivo. We also find that NSD1 strongly promotes activation of the Akt/mTORC1 signaling pathway. NSD1 depletion in HNSCC induces an autophagic gene program activation, causes accumulation of the p62 and LC3B-II proteins, and decreases the autophagic signaling protein ULK1 at both protein and mRNA levels. Reflecting these signaling defects, knockdown of NSD1 disrupts autophagic flux in HNSCC cells. Taken together, these data identify positive regulation of Akt/mTORC1 signaling and autophagy as novel NSD1 functions in HNSCC, suggesting that NSD1 may be of value as a therapeutic target in this cancer.

Experiment-based computational model predicts that IL-6 classic and trans-signaling exhibit similar potency in inducing downstream signaling in endothelial cells

Inflammatory cytokine mediated responses are important in the development of many diseases that are associated with angiogenesis. Targeting angiogenesis as a prominent strategy has shown limited effects in many contexts such as cardiovascular diseases and cancer. One potential reason for the unsuccessful outcome is the mutual dependent role between inflammation and angiogenesis. Inflammation-based therapies primarily target inflammatory cytokines such as interleukin-6 (IL-6) in T cells, macrophages, cancer cells, and muscle cells, and there is a limited understanding of how these cytokines act on endothelial cells. Thus, we focus on one of the major inflammatory cytokines, IL-6, mediated intracellular signaling in endothelial cells by developing a detailed computational model. Our model quantitatively characterized the effects of IL-6 classic and trans-signaling in activating the signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling to phosphorylate STAT3, extracellular regulated kinase (ERK) and Akt, respectively. We applied the trained and validated experiment-based computational model to characterize the dynamics of phosphorylated STAT3 (pSTAT3), Akt (pAkt), and ERK (pERK) in response to IL-6 classic and/or trans-signaling. The model predicts that IL-6 classic and trans-signaling induced responses are IL-6 and soluble IL-6 receptor (sIL-6R) dose-dependent. Also, IL-6 classic and trans-signaling showed similar potency in inducing downstream signaling; however, trans-signaling induces stronger downstream responses and plays a dominant role in the overall effects from IL-6 due to the in vitro experimental setting of abundant sIL-6R. In addition, both IL-6 and sIL-6R levels regulate signaling strength. Moreover, our model identifies the influential species and kinetic parameters that specifically modulate the downstream inflammatory and/or angiogenic signals, pSTAT3, pAkt, and pERK responses. Overall, the model predicts the effects of IL-6 classic and/or trans-signaling stimulation quantitatively and provides a framework for analyzing and integrating experimental data. More broadly, this model can be utilized to identify potential targets that influence IL-6 mediated signaling in endothelial cells and to study their effects quantitatively in modulating STAT3, Akt, and ERK activation.

Musashi-2 (MSI2) regulation of DNA damage response in lung cancer

Lung cancer is one of the most common types of cancers worldwide. Non-small cell lung cancer (NSCLC), typically caused by KRAS and TP53 driver mutations, represents the majority of all new lung cancer diagnoses. Overexpression of the RNA-binding protein (RBP) Musashi-2 (MSI2) has been associated with NSCLC progression. To investigate the role of MSI2 in NSCLC development, we compared the tumorigenesis in mice with lung-specific Kras -activating mutation and Trp53 deletion, with and without Msi2 deletion (KP versus KPM2 mice). KPM2 mice showed decreased lung tumorigenesis in comparison with KP mice what supports published data. In addition, using cell lines from KP and KPM2 tumors, and human NSCLC cell lines, we found that MSI2 directly binds ATM/Atm mRNA and regulates its translation. MSI2 depletion impaired DNA damage response (DDR) signaling and sensitized human and murine NSCLC cells to treatment with PARP inhibitors in vitro and in vivo . Taken together, we conclude that MSI2 supports lung tumorigenesis, in part, by direct positive regulation of ATM protein expression and DDR. This adds the knowledge of MSI2 function in lung cancer development. Targeting MSI2 may be a promising strategy to treat lung cancer.

Significance

This study shows the novel role of Musashi-2 as regulator of ATM expression and DDR in lung cancer.

Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway

Several signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between normoxia and hypoxia can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. We calibrate and fit the model parameters based on well-established modeling techniques. Our results indicate that the main pathways involved in the patterning of tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia affects how a reaction affects patterning. Interestingly, the interaction of receptors with Neuropilin1 is also relevant for cell patterning. Our simulations under different oxygen concentrations indicate time- and oxygen-availability-dependent responses for the two cells. Following simulations with various stimuli, our model suggests that factors such as period under hypoxia and oxygen availability must be considered for pattern control. This project provides insights into the signaling and patterning of endothelial cells under hypoxia, contributing to studies in the field.

Regulation of VEGFR2 and AKT signaling by Musashi-2 in lung cancer

Lung cancer is the most frequently diagnosed cancer type and the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) represents most of the lung cancer. Vascular endothelial growth factor receptor-2 (VEGFR2) is a member of the VEGF family of receptor tyrosine kinase proteins, expressed on both endothelial and tumor cells which is one of the key proteins contributing to cancer development and involved in drug resistance. We previously showed that Musashi-2 (MSI2) RNA-binding protein is associated with NSCLC progression by regulating several signaling pathways relevant to NSCLC. In this study, we performed Reverse Protein Phase Array (RPPA) analysis of murine lung cancer which nominated VEGFR2 protein as strongly positively regulated by MSI2. Next, we validated VEGFR2 protein regulation by MSI2 in several human NSCLC cell line models. Additionally, we found that MSI2 affected AKT signaling via negative PTEN mRNA translation regulation. In silico prediction analysis suggested that both VEGFR2 and PTEN mRNAs have predicted binding sites for MSI2. We next performed RNA immunoprecipitation coupled with quantitative PCR which confirmed that MSI2 directly binds to VEGFR2 and PTEN mRNAs, suggesting direct regulation mechanism. Finally, MSI2 expression positively correlated with VEGFR2 and VEGF-A protein levels in human NSCLC samples. We conclude that MSI2/VEGFR2 axis contributes to NSCLC progression and is worth further investigations and therapeutic targeting.

Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer

Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERα + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα + /HER2-/PI3Kα-mutant subtype of breast cancer. The 3-drug combination targets ERα in combination with PI3Kα and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.

Experiment-based Computational Model Predicts that IL-6 Trans-Signaling Plays a Dominant Role in IL-6 mediated signaling in Endothelial Cells

Inflammatory cytokine mediated responses are important in the development of many diseases that are associated with angiogenesis. Targeting angiogenesis as a prominent strategy has shown limited effects in many contexts such as peripheral arterial disease (PAD) and cancer. One potential reason for the unsuccessful outcome is the mutual dependent role between inflammation and angiogenesis. Inflammation-based therapies primarily target inflammatory cytokines such as interleukin-6 (IL-6) in T cells, macrophages, cancer cells, muscle cells, and there is a limited understanding of how these cytokines act on endothelial cells. Thus, we focus on one of the major inflammatory cytokines, IL-6, mediated intracellular signaling in endothelial cells by developing a detailed computational model. Our model quantitatively characterized the effects of IL-6 classic and trans-signaling in activating the signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling to phosphorylate STAT3, extracellular regulated kinase (ERK) and Akt, respectively. We applied the trained and validated experiment-based computational model to characterize the dynamics of phosphorylated STAT3 (pSTAT3), Akt (pAkt), and extracellular regulated kinase (pERK) in response to IL-6 classic and/or trans-signaling. The model predicts that IL-6 classic and trans-signaling induced responses are IL-6 and soluble IL-6 receptor (sIL-6R) dose-dependent. Also, IL-6 trans-signaling induces stronger downstream signaling and plays a dominant role in the overall effects from IL-6. In addition, both IL-6 and sIL-6R levels regulate signaling strength. Moreover, our model identifies the influential species and kinetic parameters that specifically modulate the pSTAT3, pAkt, and pERK responses, which represent potential targets for inflammatory cytokine mediated signaling and angiogenesis-based therapies. Overall, the model predicts the effects of IL-6 classic and/or trans-signaling stimulation quantitatively and provides a framework for analyzing and integrating experimental data. More broadly, this model can be utilized to identify targets that influence inflammatory cytokine mediated signaling in endothelial cells and to study the effects of angiogenesis- and inflammation-based therapies.

BCL2L11 Induction Mediates Sensitivity to Src and MEK1/2 Inhibition in Thyroid Cancer

Patients with advanced thyroid cancer, including advanced papillary thyroid cancer and anaplastic thyroid cancer (ATC), have low survival rates because of the lack of efficient therapies available that can combat their aggressiveness. A total of 90% of thyroid cancers have identifiable driver mutations, which often are components of the MAPK pathway, including BRAF, RAS, and RET-fusions. In addition, Src is a non-receptor tyrosine kinase that is overexpressed and activated in thyroid cancer, which we and others have shown is a clinically relevant target. We have previously demonstrated that combined inhibition of Src with dasatinib and the MAPK pathway with trametinib synergistically inhibits growth and induces apoptosis in BRAF- and RAS-mutant thyroid cancer cells. Herein, we identified the pro-apoptotic protein BCL2L11 (BIM) as being a key mediator of sensitivity in response to combined dasatinib and trametinib treatment. Specifically, cells that are sensitive to combined dasatinib and trametinib treatment have inhibition of FAK/Src, MEK/ERK, and AKT, resulting in the dramatic upregulation of BIM, while cells that are resistant lack inhibition of AKT and have a dampened induction of BIM. Inhibition of AKT directly sensitizes resistant cells to combined dasatinib and trametinib but will not be clinically feasible. Importantly, targeting BCL-XL with the BH3-mimeitc ABT-263 is sufficient to overcome lack of BIM induction and sensitize resistant cells to combined dasatinib and trametinib treatment. This study provides evidence that combined Src and MEK1/2 inhibition is a promising therapeutic option for patients with advanced thyroid cancer and identifies BIM induction as a potential biomarker of response.

Exercise Promotes Pro-Apoptotic Ceramide Signaling in a Mouse Melanoma Model

Simple Summary

Exercise has been shown to improve the efficacy of chemotherapy against several tumor models using mice through modulating tumor vascular perfusion, immune function, circulating growth factors, hypoxia, and metabolism in tumor cells and their surrounding microenvironment. However, little is known about the effect of exercise on tumor-cell-intrinsic death mechanisms, such as apoptosis. Ceramide is a bioactive lipid that can promote cell death. The strategy of increasing intracellular ceramide has potential as an anticancer treatment for melanoma with dysregulated ceramide metabolism, but there is not yet a clinically relevant method to do so. We found that moderate aerobic exercise increases pro-apoptotic ceramide in melanoma in mice, and activates p53 signaling, promoting tumor cell apoptosis. This finding suggests that exercise may be most effective as an adjuvant therapy to sensitize cancer cells to anticancer treatments in tumors that exhibit downregulated ceramide generation to evade cell death.

Abstract

Ceramides are essential sphingolipids that mediate cell death and survival. Low ceramide content in melanoma is one mechanism of drug resistance. Thus, increasing the ceramide content in tumor cells is likely to increase their sensitivity to cytotoxic therapy. Aerobic exercise has been shown to modulate ceramide metabolism in healthy tissue, but the relationship between exercise and ceramide in tumors has not been evaluated. Here, we demonstrate that aerobic exercise causes tumor cell apoptosis and accumulation of pro-apoptotic ceramides in B16F10 but not BP melanoma models using mice. B16F10 tumor-bearing mice were treated with two weeks of moderate treadmill exercise, or were control, unexercised mice. A reverse-phase protein array was used to identify canonical p53 apoptotic signaling as a key pathway upregulated by exercise, and we demonstrate increased apoptosis in tumors from exercised mice. Consistent with this finding, pro-apoptotic C16-ceramide, and the ceramide generating enzyme ceramide synthase 6 (CerS6), were higher in B16F10 tumors from exercised mice, while pro-survival sphingosine kinase 1 (Sphk1) was lower. These data suggest that exercise contributes to B16F10 tumor cell death, possibly by modulating ceramide metabolism toward a pro-apoptotic ceramide/sphingosine-1-phosphate balance. However, these results are not consistent in BP tumors, demonstrating that exercise can have different effects on tumors of different patient or mouse origin with the same diagnosis. This work indicates that exercise might be most effective as a therapeutic adjuvant with therapies that kill tumor cells in a ceramide-dependent manner.

KRASG12C-independent feedback activation of wild-type RAS constrains KRASG12C inhibitor efficacy

Although KRAS has long been considered undruggable, direct KRASG12C inhibitors have shown promising initial clinical efficacy. However, the majority of patients still fail to respond. Adaptive feedback reactivation of RAS-mitogen-activated protein kinase (MAPK) signaling has been proposed by our group and others as a key mediator of resistance, but the exact mechanism driving reactivation and the therapeutic implications are unclear. We find that upstream feedback activation of wild-type RAS, as opposed to a shift in KRASG12C to its active guanosine triphosphate (GTP)-bound state, is sufficient to drive RAS-MAPK reactivation in a KRASG12C-independent manner. Moreover, multiple receptor tyrosine kinases (RTKs) can drive feedback reactivation, potentially necessitating targeting of convergent signaling nodes for more universal efficacy. Even in colorectal cancer, where feedback is thought to be primarily epidermal growth factor receptor (EGFR)-mediated, alternative RTKs drive pathway reactivation and limit efficacy, but convergent upstream or downstream signal blockade can enhance activity. Overall, these data provide important mechanistic insight to guide therapeutic strategies targeting KRAS.

Multiple Source Detection based on Spatial Clustering and Its Applications on Wearable OPM-MEG

OBJECTIVE

Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields of brain activity. In particular, a new type of optically pumped magnetometer (OPM)-based wearable MEG system has been developed in recent years. Source localization in MEG can provide signals and locations of brain activity. However, conventional source localization methods face the difficulty of accurately estimating multiple sources. The present study presented a new parametric method to estimate the number of sources and localize multiple sources. In addition, we applied the proposed method to a constructed wearable OPM-MEG system.

METHODS

We used spatial clustering of the dipole spatial distribution to detect sources. The spatial distribution of dipoles was obtained by segmenting the MEG data temporally into slices and then estimating the parameters of the dipoles on each data slice using the particle swarm optimization algorithm. Spatial clustering was performed using the spatial-temporal density-based spatial clustering of applications with a noise algorithm. The performance of our approach for detecting multiple sources was compared with that of four typical benchmark algorithms using the OPM-MEG sensor configuration.

RESULTS

The simulation results showed that the proposed method had the best performance for detecting multiple sources. Moreover, the effectiveness of the method was verified by a multimodel sensory stimuli experiment on a real constructed 31-channel OPM-MEG.

CONCLUSION

Our study provides an effective method for the detection of multiple sources.

SIGNIFICANCE

With the improvement of the source localization methods, MEG may have a wider range of applications in neuroscience and clinical research.

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

Chimeric antigen receptor (CAR) T-cell therapy is potentially an effective targeted immunotherapy for glioblastoma, yet there is presently little known about the efficacy of CAR T-cell treatment when combined with the widely used anti-inflammatory and immunosuppressant glucocorticoid, dexamethasone. Here we present a mathematical model-based analysis of three patient-derived glioblastoma cell lines treated in vitro with CAR T-cells and dexamethasone. Advanced in vitro experimental cell killing assay technologies allow for highly resolved temporal dynamics of tumor cells treated with CAR T-cells and dexamethasone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a nonautonomous, two-species predator-prey interaction of tumor cells and CAR T-cells, with explicit time-dependence in the clearance rate of dexamethasone. Using time as a bifurcation parameter, we show that (1) dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells in a dose-dependent manner and (2) as dexamethasone is cleared from the system, a stable coexistence equilibrium returns in the form of a Hopf bifurcation. With the model fit to experimental data, we demonstrate that high concentrations of dexamethasone antagonizes CAR T-cell efficacy by exhausting, or reducing the activity of CAR T-cells, and by promoting tumor cell growth. Finally, we identify a critical threshold in the ratio of CAR T-cell death to CAR T-cell proliferation rates that predicts eventual treatment success or failure that may be used to guide the dose and timing of CAR T-cell therapy in the presence of dexamethasone in patients.

Bioengineering and gene-editing technologies have paved the way for advance immunotherapies that can target patient-specific tumor cells. One of these therapies, chimeric antigen receptor (CAR) T-cell therapy has recently shown promise in treating glioblastoma, an aggressive brain cancer often with poor patient prognosis. Dexamethasone is a commonly prescribed anti-inflammatory medication due to the health complications of tumor associated swelling in the brain. However, the immunosuppressant effects of dexamethasone on the immunotherapeutic CAR T-cells are not well understood. To address this issue, we use mathematical modeling to study in vitro dynamics of dexamethasone and CAR T-cells in three patient-derived glioblastoma cell lines. We find that in each cell line studied there is a threshold of tolerable dexamethasone concentration. Below this threshold, CAR T-cells are successful at eliminating the cancer cells, while above this threshold, dexamethasone critically inhibits CAR T-cell efficacy. Our modeling suggests that in the presence of high dexamethasone reduced CAR T-cell efficacy, or increased exhaustion, can occur and result in CAR T-cell treatment failure.

Microcrater-Arrayed Chemiluminescence Cell Chip to Boost Anti-Cancer Drug Administration in Zebrafish Tumor Xenograft Model

PURPOSE

The aim of this study was to develop a rapid and automatic drug screening platform using microcrater-arrayed (µCA) cell chips.

METHODS

The µCA chip was fabricated using a laser direct writing technique. The fabrication time required for one 9 × 9 microarray wax chip was as quick as 1 min. On a nanodroplet handling platform, the chip was pre-coated with anti-cancer drugs, including cyclophosphamide, cisplatin, doxorubicin, oncovin, etoposide, and 5-fluorouracil, and their associated mixtures. Cell droplets containing 100 SK-N-DZ or MCF-7 cells were then loaded onto the chip. Cell viability was examined directly through a chemiluminescence assay on the chip using the CellTiter-Glo assay.

RESULTS

The time needed for the drug screening assay was demonstrated to be less than 30 s for a total of 81 tests. The prediction of optimal drug synergy from the µCA chip was found by matching it to that of the zebrafish MCF-7 tumor xenograft model, instead of the conventional 96-well plate assay. In addition, the critical reagent volume and cell number for each µCA chip test were 200 nL and 100 cells, respectively, which were significantly lower than 100 µL and 4000 cells, which were achieved using the 96-well assay.

CONCLUSION

Our study for the µCA chip platform could improve the high-throughput drug synergy screening targeting the applications of tumor cell biology.

Mechanistic characterization of endothelial sprouting mediated by pro-angiogenic signaling

Objective

We aim to quantitatively characterize the crosstalk between VEGF‐ and FGF‐mediated angiogenic signaling and endothelial sprouting, to gain mechanistic insights and identify novel therapeutic strategies.

Methods

We constructed an experimentally validated hybrid agent‐based mathematical model that characterizes endothelial sprouting driven by FGF‐ and VEGF‐mediated signaling. We predicted the total sprout length, number of sprouts, and average length by the mono‐ and co‐stimulation of FGF and VEGF.

Results

The experimentally fitted and validated model predicts that FGF induces stronger angiogenic responses in the long‐term compared with VEGF stimulation. Also, FGF plays a dominant role in the combination effects in endothelial sprouting. Moreover, the model suggests that ERK and Akt pathways and cellular responses contribute differently to the sprouting process. Last, the model predicts that the strategies to modulate endothelial sprouting are context‐dependent, and our model can identify potential effective pro‐ and anti‐angiogenic targets under different conditions and study their efficacy.

Conclusions

The model provides detailed mechanistic insight into VEGF and FGF interactions in sprouting angiogenesis. More broadly, this model can be utilized to identify targets that influence angiogenic signaling leading to endothelial sprouting and to study the effects of pro‐ and anti‐angiogenic therapies.

Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model

MOTIVATION

The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway.

RESULTS

We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway.

AVAILABILITY AND IMPLEMENTATION

We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Marine Natural Product Furospinulosin 1 Induces Apoptosis in MDA-MB-231 Triple Negative Breast Cancer Cell Spheroids, But Not in Cells Grown Traditionally with Longer Treatment

Cancer cells grown in spheroid conditions interact with each other and the extracellular matrix, providing a better representation of the in vivo environment than two-dimensional cultures and are a more clinically relevant model. A discrete screening of genetically diverse marine samples in the spheroid assay led to the identification of a novel activity for the known compound furospinulosin 1. This compound shows activity against MDA-MB-231 triple negative breast cancer cells grown as spheroids and treated for 24 or 48 h. No cytotoxicity was seen in traditional two-dimensional adherent cultures treated for a longer time (72 h). A reverse phase protein array (RPPA) confirmed the limited activity of the compound in cells grown traditionally and revealed changes in protein expression when cells are grown as spheroids that are associated with better clinical prognosis. Analysis of the RPPA data through the Broad institute’s connectivity map suggested the hypothesis that furospinulosin 1 functions as an MEK inhibitor. Analysis of the RPPA data through STRING supports the apoptosis observed. The selectivity exhibited by furospinulosin 1 for triple negative breast cancer cells only when grown as spheroids makes it an interesting compound with strong therapeutic potential that merits further study.

Musashi-2 (MSI2) regulates epidermal growth factor receptor (EGFR) expression and response to EGFR inhibitors in EGFR-mutated non-small cell lung cancer (NSCLC)

Non-small cell lung cancer (NSCLC) has limited treatment options. Expression of the RNA-binding protein (RBP) Musashi-2 (MSI2) is elevated in a subset of non-small cell lung cancer (NSCLC) tumors upon progression, and drives NSCLC metastasis. We evaluated the mechanism of MSI2 action in NSCLC to gain therapeutically useful insights. Reverse phase protein array (RPPA) analysis of MSI2-depleted versus control Kras^LA1/+; Trp53^R172HΔG/+ NSCLC cell lines identified EGFR as a MSI2-regulated protein. MSI2 control of EGFR expression and activity in an NSCLC cell line panel was studied using RT-PCR, Western blots, and RNA immunoprecipitation. Functional consequences of MSI2 depletion were explored for cell growth and response to EGFR-targeting drugs, in vitro and in vivo. Expression relationships were validated using human tissue microarrays. MSI2 depletion significantly reduced EGFR protein expression, phosphorylation, or both. Comparison of protein and mRNA expression indicated a post-transcriptional activity of MSI2 in control of steady state levels of EGFR. RNA immunoprecipitation analysis demonstrated that MSI2 directly binds to EGFR mRNA, and sequence analysis predicted MSI2 binding sites in the murine and human EGFR mRNAs. MSI2 depletion selectively impaired cell proliferation in NSCLC cell lines with activating mutations of EGFR (EGFR^mut). Further, depletion of MSI2 in combination with EGFR inhibitors such as erlotinib, afatinib, and osimertinib selectively reduced the growth of EGFR^mut NSCLC cells and xenografts. EGFR and MSI2 were significantly co-expressed in EGFR^mut human NSCLCs. These results define MSI2 as a direct regulator of EGFR protein expression, and suggest inhibition of MSI2 could be of clinical value in EGFR^mut NSCLC.

Broadband vectorial ultrathin optics with experimental efficiency up to 99% in the visible region via universal approximators

Integrating conventional optics into compact nanostructured surfaces is the goal of flat optics. Despite the enormous progress in this technology, there are still critical challenges for real-world applications due to the limited operational efficiency in the visible region, on average lower than 60%, which originates from absorption losses in wavelength-thick (≈ 500 nm) structures. Another issue is the realization of on-demand optical components for controlling vectorial light at visible frequencies simultaneously in both reflection and transmission and with a predetermined wavefront shape. In this work, we developed an inverse design approach that allows the realization of highly efficient (up to 99%) ultrathin (down to 50 nm thick) optics for vectorial light control with broadband input-output responses in the visible and near-IR regions with a desired wavefront shape. The approach leverages suitably engineered semiconductor nanostructures, which behave as a neural network that can approximate a user-defined input-output function. Near-unity performance results from the ultrathin nature of these surfaces, which reduces absorption losses to near-negligible values. Experimentally, we discuss polarizing beam splitters, comparing their performance with the best results obtained from both direct and inverse design techniques, and new flat-optics components represented by dichroic mirrors and the basic unit of a flat-optics display that creates full colours by using only two subpixels, overcoming the limitations of conventional LCD/OLED technologies that require three subpixels for each composite colour. Our devices can be manufactured with a complementary metal-oxide-semiconductor (CMOS)-compatible process, making them scalable for mass production at low cost.

Topsentinol L Trisulfate, a Marine Natural Product That Targets Basal-like and Claudin-Low Breast Cancers

Patients diagnosed with basal-like breast cancer suffer from poor prognosis and limited treatment options. There is an urgent need to identify new targets that can benefit patients with basal-like and claudin-low (BL-CL) breast cancers. We screened fractions from our Marine Invertebrate Compound Library (MICL) to identify compounds that specifically target BL-CL breast cancers. We identified a previously unreported trisulfated sterol, i.e., topsentinol L trisulfate (TLT), which exhibited increased efficacy against BL-CL breast cancers relative to luminal/HER2+ breast cancer. Biochemical investigation of the effects of TLT on BL-CL cell lines revealed its ability to inhibit activation of AMP-activated protein kinase (AMPK) and checkpoint kinase 1 (CHK1) and to promote activation of p38. The importance of targeting AMPK and CHK1 in BL-CL cell lines was validated by treating a panel of breast cancer cell lines with known small molecule inhibitors of AMPK (dorsomorphin) and CHK1 (Ly2603618) and recording the increased effectiveness against BL-CL breast cancers as compared with luminal/HER2+ breast cancer. Finally, we generated a drug response gene-expression signature and projected it against a human tumor panel of 12 different cancer types to identify other cancer types sensitive to the compound. The TLT sensitivity gene-expression signature identified breast and bladder cancer as the most sensitive to TLT, while glioblastoma multiforme was the least sensitive.

A Novel CDK2/9 Inhibitor CYC065 Causes Anaphase Catastrophe and Represses Proliferation, Tumorigenesis, and Metastasis in Aneuploid Cancers

Cyclin-dependent kinase 2 (CDK2) antagonism inhibits clustering of excessive centrosomes at mitosis, causing multipolar cell division and apoptotic death. This is called anaphase catastrophe. To establish induced anaphase catastrophe as a clinically tractable antineoplastic mechanism, induced anaphase catastrophe was explored in different aneuploid cancers after treatment with CYC065 (Cyclacel), a CDK2/9 inhibitor. Antineoplastic activity was studied in preclinical models. CYC065 treatment augmented anaphase catastrophe in diverse cancers including lymphoma, lung, colon, and pancreatic cancers, despite KRAS oncoprotein expression. Anaphase catastrophe was a broadly active antineoplastic mechanism. Reverse phase protein arrays (RPPAs) revealed that along with known CDK2/9 targets, focal adhesion kinase and Src phosphorylation that regulate metastasis were each repressed by CYC065 treatment. Intriguingly, CYC065 treatment decreased lung cancer metastases in in vivo murine models. CYC065 treatment also significantly reduced the rate of lung cancer growth in syngeneic murine and patient-derived xenograft (PDX) models independent of KRAS oncoprotein expression. Immunohistochemistry analysis of CYC065-treated lung cancer PDX models confirmed repression of proteins highlighted by RPPAs, implicating them as indicators of CYC065 antitumor response. Phospho-histone H3 staining detected anaphase catastrophe in CYC065-treated PDXs. Thus, induced anaphase catastrophe after CYC065 treatment can combat aneuploid cancers despite KRAS oncoprotein expression. These findings should guide future trials of this novel CDK2/9 inhibitor in the cancer clinic.

ERK and Akt exhibit distinct signaling responses following stimulation by pro-angiogenic factors

Background

Angiogenesis plays an important role in the survival of tissues, as blood vessels provide oxygen and nutrients required by the resident cells. Thus, targeting angiogenesis is a prominent strategy in many different settings, including both tissue engineering and cancer treatment. However, not all of the approaches that modulate angiogenesis lead to successful outcomes. Angiogenesis-based therapies primarily target pro-angiogenic factors such as vascular endothelial growth factor-A (VEGF) or fibroblast growth factor (FGF) in isolation, and there is a limited understanding of how these promoters combine together to stimulate angiogenesis. Targeting one pathway could be insufficient, as alternative pathways may compensate, diminishing the overall effect of the treatment strategy.

Methods

To gain mechanistic insight and identify novel therapeutic strategies, we have developed a detailed mathematical model to quantitatively characterize the crosstalk of FGF and VEGF intracellular signaling. The model focuses on FGF- and VEGF-induced mitogen-activated protein kinase (MAPK) signaling to promote cell proliferation and the phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) pathway, which promotes cell survival and migration. We fit the model to published experimental datasets that measure phosphorylated extracellular regulated kinase (pERK) and Akt (pAkt) upon FGF or VEGF stimulation. We validate the model with separate sets of data.

Results

We apply the trained and validated mathematical model to characterize the dynamics of pERK and pAkt in response to the mono- and co-stimulation by FGF and VEGF. The model predicts that for certain ranges of ligand concentrations, the maximum pERK level is more responsive to changes in ligand concentration compared to the maximum pAkt level. Also, the combination of FGF and VEGF indicates a greater effect in increasing the maximum pERK compared to the summation of individual effects, which is not seen for maximum pAkt levels. In addition, our model identifies the influential species and kinetic parameters that specifically modulate the pERK and pAkt responses, which represent potential targets for angiogenesis-based therapies.

Conclusions

Overall, the model predicts the combination effects of FGF and VEGF stimulation on ERK and Akt quantitatively and provides a framework to mechanistically explain experimental results and guide experimental design. Thus, this model can be utilized to study the effects of pro- and anti-angiogenic therapies that particularly target ERK and/or Akt activation upon stimulation with FGF and VEGF.

Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction

Motivation

Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data.

Results

This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans.

Availability and implementation

DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction.

Supplementary information

Supplementary data are available at Bioinformatics online.

DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy

Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.

Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network

Background

Although targeted drugs have contributed to impressive advances in the treatment of cancer patients, their clinical benefits on tumor therapies are greatly limited due to intrinsic and acquired resistance of cancer cells against such drugs. Drug combinations synergistically interfere with protein networks to inhibit the activity level of carcinogenic genes more effectively, and therefore play an increasingly important role in the treatment of complex disease.

Results

In this paper, we combined the drug similarity network, protein similarity network and known drug-protein associations into a drug-protein heterogenous network. Next, we ran random walk with restart (RWR) on the heterogenous network using the combinatorial drug targets as the initial probability, and obtained the converged probability distribution as the feature vector of each drug combination. Taking these feature vectors as input, we trained a gradient tree boosting (GTB) classifier to predict new drug combinations. We conducted performance evaluation on the widely used drug combination data set derived from the DCDB database. The experimental results show that our method outperforms seven typical classifiers and traditional boosting algorithms.

Conclusions

The heterogeneous network-derived features introduced in our method are more informative and enriching compared to the primary ontology features, which results in better performance. In addition, from the perspective of network pharmacology, our method effectively exploits the topological attributes and interactions of drug targets in the overall biological network, which proves to be a systematic and reliable approach for drug discovery.

Identification of prognostic biomarker in predicting hepatocarcinogenesis from cirrhotic liver using protein and gene signatures

INTRODUCTION

Cirrhosis primes the liver for hepatocellular carcinoma (HCC) development. However, biomarkers that predict HCC in cirrhosis patients are lacking. Thus, we aimed to identify a biomarker directly from protein analysis and relate it with transcriptomic data to validate in larger cohorts.

MATERIAL AND METHOD

Forty-six patients who underwent hepatectomy for HCC that arose from cirrhotic liver were enrolled. Reverse-phase protein array and microarray data of these patients were analyzed. Clinical validation was performed in two independent cohorts and functional validation using cell and tissue microarray (TMA).

RESULTS

Systematic analysis performed after selecting 20 proteins from 201 proteins with AUROC >70 effectively categorized patients into high (n = 20) or low (n = 26) risk HCC groups. Proteome-derived late recurrence (PDLR)-gene signature comprising 298 genes that significantly differed between high and low risk groups predicted HCC well in a cohort of 216 cirrhosis patients and also de novo HCC recurrence in a cohort of 259 patients who underwent hepatectomy. Among 20 proteins that were selected for analysis, caveolin-1 (CAV1) was the most dominant protein that categorized the patients into high and low risk groups (P < .001). In a multivariate analysis, compared with other clinical variables, the PDLR-gene signature remained as a significant predictor of HCC (HR 1.904, P = .01). In vitro experiments revealed that compared with mock-transduced immortalized liver cells, CAV1-transduced cells showed significantly increased proliferation (P < .001) and colony formation in soft agar (P < .033). TMA with immunohistochemistry showed that tissues with CAV1 expression were more likely to develop HCC than tissues without CAV1 expression (P = .047).

CONCLUSION

CAV1 expression predicts HCC development, making it a potential biomarker and target for preventive therapy.

A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression

Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation. Construction of an accurate and reliable prediction model for the volatility changes in energy consumption can provide valuable reference information for policy makers of the government and for the energy industry. In view of this, a novel improved model is developed in this article by integrating the modified state transition algorithm (MSTA) with the Gaussian processes regression (GPR) approach for non-fossil energy consumption predictions for China at the end of the 13th Five-Year Project, in which the MSTA is utilized for effective optimization of hyper-parameters in GPR. Aiming for validating the superiority of MSTA, several comparisons are conducted on two well-known functions and the optimization results show the effectiveness of modification in the state transition algorithm (STA). Then, based on the latest statistical renewable energy consumption data, the MSTA-GPR model is utilized to generate consumption predictions for overall renewable energy and each single renewable energy source, including hydropower, wind, solar, geothermal, biomass and other energies, respectively. The forecasting results reveal that the proposed improved GPR can promote the forecasting ability of basic GPR and obtain the best prediction effect among all the other comparison models. Finally, combined with the forecasting results, the trend of each renewable energy source is analyzed.

Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization

Parameter estimation is an important part in the modeling of a hydro-turbine regulation system (HTRS), and the results determine the final accuracy of a model. A hydro-turbine is normally a non-minimum phase system with strong nonlinearity and time-varying parameters. For the parameter estimation of such a nonlinear system, heuristic algorithms are more advantageous than traditional mathematical methods. However, most heuristics based algorithms and their improved versions are not adaptive, which means that the appropriate parameters of an algorithm need to be manually found to keep the algorithm performing optimally in solving similar problems. To solve this problem, an adaptive fuzzy particle swarm optimization (AFPSO) algorithm that dynamically tunes the parameters according to model error is proposed and applied to the parameter estimation of the HTRS. The simulation studies show that the proposed AFPSO contributes to lower model error and higher identification accuracy compared with some traditional heuristic algorithms. Importantly, it avoids a possible deterioration in the performance of an algorithm caused by inappropriate parameter selection.

Designing combination therapies with modeling chaperoned machine learning

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.

Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets

Breast cancer is one of the most common malignancies among women throughout the world and is the major cause of most cancer-related deaths. Several explanations account for the high rate of mortality of breast cancer, and metastasis to vital organs is identified as the principal cause. Over the past few years, intensive efforts have demonstrated that breast cancer exhibits metastatic heterogeneity with distinct metastatic precedence to various organs, giving rise to differences in prognoses and responses to therapy in breast cancer patients. Bone, lung, liver, and brain are generally accepted as the primary target sites of breast cancer metastasis. However, the underlying molecular mechanism of metastatic heterogeneity of breast cancer remains to be further elucidated. Recently, the advent of novel genomic and pathologic approaches as well as technological breakthroughs in imaging analysis and animal modelling have yielded an unprecedented change in our understanding of the heterogeneity of breast cancer metastasis and provided novel insight for establishing more effective therapeutics. This review summarizes recent molecular mechanisms and emerging concepts on the metastatic heterogeneity of breast cancer and discusses the potential of identifying specific molecules against tumor cells or tumor microenvironments to thwart the development of metastatic disease and improve the prognosis of breast cancer patients.

A nanodroplet cell processing platform facilitating drug synergy evaluations for anti-cancer treatments

Therapeutic drug synergism intervened in cancer treatments has been demonstrated to be more effective than using a single effector. However, it remains inherently challenging, with a limited cell count from tumor samples, to achieve potent personalized drug cocktails. To address the issue above, we herein present a nanodroplet cell processing platform. The platform incorporates an automatic nanodroplet dispenser with cell array ParaStamp chips, which were fabricated by a new wax stamping approach derived from laser direct writing. Such approach enables not only the on-demand de-wetting with hydrophobic wax films on substrates but also the mask-less fabrication of non-planar microstructures (i.e. no photolithography process). The ParaStamp chip was pre-occupied with anti-cancer drugs and their associate mixtures, enabling for the spatially addressable screening of optimal drug combinations simultaneously. Each droplet with a critical volume of 200 nl containing with 100 cells was utilized. Results revealed that the optimal combination reduces approximate 28-folds of conducted doses compared with single drugs. Tumor inhibition with the optimally selected drug combination was further confirmed by using PC-3 tumor-bearing mouse models. Together, the nanodroplet cell processing platform could therefore offer new opportunities to power the personalized cancer medicine at early-stage drug screening and discovery.

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.

Drug Combinations: Mathematical Modeling and Networking Methods

Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. A description of existing data and software resources is provided that can support investigations in drug combination therapies. Finally, the article concludes with a summary of future directions for investigation by the research community.

GPNMB augments Wnt-1 mediated breast tumor initiation and growth by enhancing PI3K/AKT/mTOR pathway signaling and β-catenin activity

Glycoprotein Nmb (GPNMB) is overexpressed in triple-negative and basal-like breast cancers and its expression is predictive of poor prognosis within this aggressive breast cancer subtype. GPNMB promotes breast cancer growth, invasion, and metastasis; however, its role in mammary tumor initiation remains unknown. To address this question, we overexpressed GPNMB in the mammary epithelium to generate MMTV/GPNMB transgenic mice and crossed these animals to the MMTV/Wnt-1 mouse model, which is known to recapitulate features of human basal breast cancers. We show that GPNMB alone does not display oncogenic properties; however, its expression dramatically accelerates tumor onset in MMTV/Wnt-1 mice. MMTV/Wnt-1 × MMTV/GPNMB bigenic mice also exhibit a significant increase in the growth rate of established primary tumors, which is attributable to increased proliferation and decreased apoptosis. To elucidate molecular mechanisms underpinning the tumor-promoting effects of GPNMB in this context, we interrogated activated pathways in tumors derived from the MMTV/Wnt-1 and MMTV/Wnt-1 × MMTV/GPNMB mice using RPPA analysis. These data revealed that MMTV/Wnt-1 × MMTV/GPNMB bigenic tumors exhibit a pro-growth signature characterized by elevated PI3K/AKT/mTOR signaling and increased β-catenin activity. Furthermore, we extended these observations to an independent Wnt-1 expressing model of aggressive breast cancer, and confirmed that GPNMB enhances canonical Wnt pathway activation, as evidenced by increased β-catenin transcriptional activity, in breast cancer cells and tumors co-expressing Wnt-1 and GPNMB. GPNMB-dependent engagement of β-catenin occurred, in part, through AKT activation. Taken together, these data ascribe a novel, pro-growth role for GPNMB in Wnt-1 expressing basal breast cancers.

Discovery of Small-Molecule Inhibitors of the HSP90-Calcineurin-NFAT Pathway against Glioblastoma

Glioblastoma (GBM) is among the most common and malignant types of primary brain tumors in adults, with a dismal prognosis. Although alkylating agents such as temozolomide are widely applied as the first-line treatment for GBM, they often cause chemoresistance and remain ineffective with recurrent GBM. Alternative therapeutics against GBM are urgently needed in the clinic. We report herein the discovery of a class of inhibitors (YZ129 and its derivatives) of the calcineurin-NFAT pathway that exhibited potent anti-tumor activity against GBM. YZ129-induced GBM cell-cycle arrest at the G2/M phase promoted apoptosis and inhibited tumor cell proliferation and migration. At the molecular level, YZ129 directly engaged HSP90 to antagonize its chaperoning effect on calcineurin to abrogate NFAT nuclear translocation, and also suppressed other proto-oncogenic pathways including hypoxia, glycolysis, and the PI3K/AKT/mTOR signaling axis. Our data highlight the potential for targeting the cancer-promoting HSP90 chaperone network to treat GBM.

New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling

RAS is the most frequently mutated gene across human cancers, but developing inhibitors of mutant RAS has proven to be challenging. Given the difficulties of targeting RAS directly, drugs that impact the other components of pathways where mutant RAS operates may potentially be effective. However, the system-level features, including different localizations of RAS isoforms, competition between downstream effectors, and interlocking feedback and feed-forward loops, must be understood to fully grasp the opportunities and limitations of inhibiting specific targets. Mathematical modeling can help us discern the system-level impacts of these features in normal and cancer cells. New technologies enable the acquisition of experimental data that will facilitate development of realistic models of oncogenic RAS behavior. In light of the wealth of empirical data accumulated over decades of study and the advancement of experimental methods for gathering new data, modelers now have the opportunity to advance progress toward realization of targeted treatment for mutant RAS-driven cancers.

Drug Screening Platforms and RPPA

Since its inception as a scalable and cost-effective method for precise quantification of the abundance of multiple protein analytes and post-translational epitopes across large sample sets, reverse phase protein array (RPPA) has been utilized as a drug discovery tool. Key RPPA drug discovery applications include primary screening of abundance or activation state of nominated protein targets, secondary screening for toxicity and selectivity, mechanism-of-action profiling, biomarker discovery, and drug combination discovery. In recent decades, drug discovery strategies have evolved dramatically in response to continual advances in technology platforms supporting high-throughput screening, structure-based drug design, new therapeutic modalities, and increasingly more complex and disease-relevant cell-based and in vivo preclinical models of disease. Advances in biological laboratory capabilities in drug discovery are complemented by significant developments in bioinformatics and computational approaches for integrating large complex datasets. Bioinformatic and computational analysis of integrated molecular, pathway network and phenotypic datasets enhance multiple stages of the drug discovery process and support more informative drug target hypothesis generation and testing. In this chapter we discuss and present examples demonstrating how the latest advances in RPPA complement and integrate with other emerging drug screening platforms to support a new era of more informative and evidence-led drug discovery strategies.

Mechanistic insight into activation of MAPK signaling by pro-angiogenic factors

Background

Angiogenesis is important in physiological and pathological conditions, as blood vessels provide nutrients and oxygen needed for tissue growth and survival. Therefore, targeting angiogenesis is a prominent strategy in both tissue engineering and cancer treatment. However, not all of the approaches to promote or inhibit angiogenesis lead to successful outcomes. Angiogenesis-based therapies primarily target pro-angiogenic factors such as vascular endothelial growth factor-A (VEGF) or fibroblast growth factor (FGF) in isolation. However, pre-clinical and clinical evidence shows these therapies often have limited effects. To improve therapeutic strategies, including targeting FGF and VEGF in combination, we need a quantitative understanding of the how the promoters combine to stimulate angiogenesis.

Results

In this study, we trained and validated a detailed mathematical model to quantitatively characterize the crosstalk of FGF and VEGF intracellular signaling. This signaling is initiated by FGF binding to the FGF receptor 1 (FGFR1) and heparan sulfate glycosaminoglycans (HSGAGs) or VEGF binding to VEGF receptor 2 (VEGFR2) to promote downstream signaling. The model focuses on FGF- and VEGF-induced mitogen-activated protein kinase (MAPK) signaling and phosphorylation of extracellular regulated kinase (ERK), which promotes cell proliferation. We apply the model to predict the dynamics of phosphorylated ERK (pERK) in response to the stimulation by FGF and VEGF individually and in combination. The model predicts that FGF and VEGF have differential effects on pERK. Additionally, since VEGFR2 upregulation has been observed in pathological conditions, we apply the model to investigate the effects of VEGFR2 density and trafficking parameters. The model predictions show that these parameters significantly influence the response to VEGF stimulation.

Conclusions

The model agrees with experimental data and is a framework to synthesize and quantitatively explain experimental studies. Ultimately, the model provides mechanistic insight into FGF and VEGF interactions needed to identify potential targets for pro- or anti-angiogenic therapies.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0668-5) contains supplementary material, which is available to authorized users.

Computational Model of Chimeric Antigen Receptors Explains Site-Specific Phosphorylation Kinetics

Chimeric antigen receptors (CARs) have recently been approved for the treatment of hematological malignancies, but our lack of understanding of the basic mechanisms that activate these proteins has made it difficult to optimize and control CAR-based therapies. In this study, we use phosphoproteomic mass spectrometry and mechanistic computational modeling to quantify the in vitro kinetics of individual tyrosine phosphorylation on a variety of CARs. We show that each of the 10 tyrosine sites on the CD28-CD3ζ CAR is phosphorylated by lymphocyte-specific protein-tyrosine kinase (LCK) with distinct kinetics. The addition of CD28 at the N-terminal of CD3ζ increases the overall rate of CD3ζ phosphorylation. Our computational model identifies that LCK phosphorylates CD3ζ through a mechanism of competitive inhibition. This model agrees with previously published data in the literature and predicts that phosphatases in this system interact with CD3ζ through a similar mechanism of competitive inhibition. This quantitative modeling framework can be used to better understand CAR signaling and T cell activation.

Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology

Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://github.com/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.

Integrative meta-modeling identifies endocytic vesicles, late endosome and the nucleus as the cellular compartments primarily directing RTK signaling

Recently, intracellular receptor signaling has been identified as a key component mediating cell responses for various receptor tyrosine kinases (RTKs). However, the extent each endocytic compartment (endocytic vesicle, early endosome, recycling endosome, late endosome, lysosome and nucleus) contributes to receptor signaling has not been quantified. Furthermore, our understanding of endocytosis and receptor signaling is complicated by cell- or receptor-specific endocytosis mechanisms. Therefore, towards understanding the differential endocytic compartment signaling roles, and identifying how to achieve signal transduction control for RTKs, we delineate how endocytosis regulates RTK signaling. We achieve this via a meta-analysis across eight RTKs, integrating computational modeling with experimentally derived cell (compartment volume, trafficking kinetics and pH) and ligand-receptor (ligand/receptor concentration and interaction kinetics) physiology. Our simulations predict the abundance of signaling from eight RTKs, identifying the following hierarchy in RTK signaling: PDGFRβ > IGFR1 > EGFR > PDGFRα > VEGFR1 > VEGFR2 > Tie2 > FGFR1. We find that endocytic vesicles are the primary cell signaling compartment; over 43% of total receptor signaling occurs within the endocytic vesicle compartment for these eight RTKs. Mechanistically, we found that high RTK signaling within endocytic vesicles may be attributed to their low volume (5.3 × 10^-19 L) which facilitates an enriched ligand concentration (3.2 μM per ligand molecule within the endocytic vesicle). Under the analyzed physiological conditions, we identified extracellular ligand concentration as the most sensitive parameter to change; hence the most significant one to modify when regulating absolute compartment signaling. We also found that the late endosome and nucleus compartments are important contributors to receptor signaling, where 26% and 18%, respectively, of average receptor signaling occurs across the eight RTKs. Conversely, we found very low membrane-based receptor signaling, exhibiting <1% of the total receptor signaling for these eight RTKs. Moreover, we found that nuclear translocation, mechanistically, requires late endosomal transport; when we blocked receptor trafficking from late endosomes to the nucleus we found a 57% reduction in nuclear translocation. In summary, our research has elucidated the significance of endocytic vesicles, late endosomes and the nucleus in RTK signal propagation.

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.