Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy

A robust optimal control framework for controlling aberrant RTK signaling pathways in esophageal cancer

This study presents a new framework for obtaining personalized optimal treatment strategies targeting aberrant signaling pathways in esophageal cancer, such as the epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) signaling pathways. A new pharmacokinetic model is developed taking into account specific heterogeneities of these signaling mechanisms. The optimal therapies are designed to be obtained using a three step process. First, a finite-dimensional constrained optimization problem is solved to obtain the parameters of the pharmacokinetic model, using discrete patient data measurements. Next, a sensitivity analysis is carried out to determine which of the parameters are sensitive to the evolution of the variants of EGF receptors and VEGF receptors. Finally, a second optimal control problem is solved based on the sensitivity analysis results, using a modified pharmacokinetic model that incorporates two representative drugs Trastuzumab and Bevacizumab, targeting EGF and VEGF, respectively. Numerical results with the combination of the two drugs demonstrate the efficiency of the proposed framework.

Cellular barcoding tracks heterogeneous clones through selective pressures and phenotypic transitions

Genomic DNA barcoding has emerged as a sensitive and flexible tool to measure the fates of clonal subpopulations within a heterogeneous cancer cell population. Coupling cellular barcoding with single-cell transcriptomics permits the longitudinal analysis of molecular mechanisms with detailed clone-level resolution. Numerous recent studies have employed these tools to track clonal cell states in cancer progression and treatment response. With these new technologies comes the opportunity to examine longstanding questions about the origins and contributions of tumor cell heterogeneity and the roles of selection and phenotypic plasticity in disease progression and treatment.

Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways

Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.

Protein degradation: expanding the toolbox to restrain cancer drug resistance

Despite significant progress in clinical management, drug resistance remains a major obstacle. Recent research based on protein degradation to restrain drug resistance has attracted wide attention, and several therapeutic strategies such as inhibition of proteasome with bortezomib and proteolysis-targeting chimeric have been developed. Compared with intervention at the transcriptional level, targeting the degradation process seems to be a more rapid and direct strategy. Proteasomal proteolysis and lysosomal proteolysis are the most critical quality control systems responsible for the degradation of proteins or organelles. Although proteasomal and lysosomal inhibitors (e.g., bortezomib and chloroquine) have achieved certain improvements in some clinical application scenarios, their routine application in practice is still a long way off, which is due to the lack of precise targeting capabilities and inevitable side effects. In-depth studies on the regulatory mechanism of critical protein degradation regulators, including E3 ubiquitin ligases, deubiquitylating enzymes (DUBs), and chaperones, are expected to provide precise clues for developing targeting strategies and reducing side effects. Here, we discuss the underlying mechanisms of protein degradation in regulating drug efflux, drug metabolism, DNA repair, drug target alteration, downstream bypass signaling, sustaining of stemness, and tumor microenvironment remodeling to delineate the functional roles of protein degradation in drug resistance. We also highlight specific E3 ligases, DUBs, and chaperones, discussing possible strategies modulating protein degradation to target cancer drug resistance. A systematic summary of the molecular basis by which protein degradation regulates tumor drug resistance will help facilitate the development of appropriate clinical strategies.

Computational modeling implicates protein scaffolding in p38 regulation of Akt

Cells process environmental cues by activating intracellular signaling pathways with numerous interconnections and opportunities for cross-regulation. We employed a systems biology approach to investigate intersections of kinase p38, a context-dependent tumor suppressor or promoter, with Akt and ERK, two kinases known to promote cell survival, proliferation, and drug resistance in cancer. Using live, single cell microscopy, multiplexed fluorescent reporters of p38, Akt, and ERK activities, and a custom automated image-processing pipeline, we detected marked heterogeneity of signaling outputs in breast cancer cells stimulated with chemokine CXCL12 or epidermal growth factor (EGF). Basal activity of p38 correlated inversely with amplitude of Akt and ERK activation in response to either ligand. Remarkably, small molecule inhibitors of p38 immediately decreased basal activities of Akt and ERK but increased the proportion of cells with high amplitude ligand-induced activation of Akt signaling. To identify mechanisms underlying cross-talk of p38 with Akt signaling, we developed a computational model incorporating subcellular compartmentalization of signaling molecules by scaffold proteins. Dynamics of this model revealed that subcellular scaffolding of Akt accounted for observed regulation by p38. The model also predicted that differences in the amount of scaffold protein in a subcellular compartment captured the observed single cell heterogeneity in signaling. Finally, our model predicted that reduction in kinase signaling can be accomplished by both scaffolding and direct kinase inhibition. However, scaffolding inhibition can potentiate future kinase activity by redistribution of pathway components, potentially amplifying oncogenic signaling. These studies reveal how computational modeling can decipher mechanisms of cross-talk between the p38 and Akt signaling pathways and point to scaffold proteins as central regulators of signaling dynamics and amplitude.

Predominant contribution of the dose received from constituent heavy-ions in the induction of gastrointestinal tumorigenesis after simulated space radiation exposure

Gastrointestinal (GI) cancer risk among astronauts after encountering galactic cosmic radiation (GCR) is predicted to exceed safe permissible limits in long duration deep-space missions. Current predictions are based on relative biological effectiveness (RBE) values derived from in-vivo studies using single-ion beams, while GCR is essentially a mixed radiation field composed of protons (H), helium (He), and heavy ions. Therefore, a sequentially delivered proton (H) → Helium (He) → Oxygen (O) → Silicon (Si) beam was designed to simulate simplified-mixed-field GCR (Smf-GCR), and Apc^1638N/+ mice were total-body irradiated to sham or γ (¹⁵⁷Cs) or Smf-GCR followed by assessment of GI-tumorigenesis at 150 days post-exposure. Further, GI-tumor data from equivalent doses of heavy-ions (i.e., 0.05 Gy of O and Si) in 0.5 Gy of Smf-GCR were compared to understand the contributions of heavy-ions in GI-tumorigenesis. The Smf-GCR-induced tumor and carcinoma count were significantly greater than γ-rays, and male preponderance for GI-tumorigenesis was consistent with our earlier findings. Comparison of tumor data from Smf-GCR and equivalent doses of heavy ions revealed an association between higher GI-tumorigenesis where dose received from heavy-ions contributed to > 95% of the total GI-tumorigenic effect observed after Smf-GCR. This study provides the first experimental evidence that cancer risk after GCR exposure could largely depend on doses received from constituent heavy-ions.

Mutant KRAS-Associated Proteome Is Mainly Controlled by Exogenous Factors

Understanding how mutant KRAS signaling is modulated by exogenous stimuli is of utmost importance to elucidate resistance mechanisms underlying pathway inhibition failure, and to uncover novel therapeutic targets for mutant KRAS patients. Hence, aiming at perceiving KRAS-autonomous versus -non autonomous mechanisms, we studied the response of two mutant KRAS colorectal cancer cell lines (HCT116 and LS174T) upon KRAS silencing and treatment with rhTGFβ1-activated fibroblasts secretome. A proteomic analysis revealed that rhTGFβ1-activated fibroblast-secreted factors triggered cell line-specific proteome alterations and that mutant KRAS governs 43% and 38% of these alterations in HCT116 and LS174T cells, respectively. These KRAS-dependent proteins were localized and displayed molecular functions that were common to both cell lines (e.g., extracellular exosome, RNA binding functions). Moreover, 67% and 78% of the KRAS-associated proteome of HCT116 and LS174T cells, respectively, was controlled in a KRAS-non-autonomous manner, being dependent on fibroblast-secreted factors. In HCT116 cells, KRAS-non-autonomously controlled proteins were mainly involved in proteoglycans in cancer, p53, and Rap1 signaling pathways; whereas in LS174T cells, they were associated with substrate adhesion-dependent cell-spreading and involved in metabolic processes. This work highlights the context-dependency of KRAS-associated signaling and reinforces the importance of integrating the tumor microenvironment in the study of KRAS-associated effects.

Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth

The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.

A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach

OBJECTIVES

Computational modeling of biological systems is a powerful tool to clarify diverse processes contributing to cancer. The aim is to clarify the complex biochemical and mechanical interactions between cells, the relevance of intracellular signaling pathways in tumor progression and related events to the cancer treatments, which are largely ignored in previous studies.

MATERIALS AND METHODS

A three-dimensional multiscale cell-based model is developed, covering multiple time and spatial scales, including intracellular, cellular, and extracellular processes. The model generates a realistic representation of the processes involved from an implementation of the signaling transduction network.

RESULTS

Considering a benign tumor development, results are in good agreement with the experimental ones, which identify three different phases in tumor growth. Simulating tumor vascular growth, results predict a highly vascularized tumor morphology in a lobulated form, a consequence of cells' motile behavior. A novel systematic study of chemotherapy intervention, in combination with targeted therapy, is presented to address the capability of the model to evaluate typical clinical protocols. The model also performs a dose comparison study in order to optimize treatment efficacy and surveys the effect of chemotherapy initiation delays and different regimens.

CONCLUSIONS

Results not only provide detailed insights into tumor progression, but also support suggestions for clinical implementation. This is a major step toward the goal of predicting the effects of not only traditional chemotherapy but also tumor-targeted therapies.

Modeling the effects of EMT-immune dynamics on carcinoma disease progression

During progression from carcinoma in situ to an invasive tumor, the immune system is engaged in complex sets of interactions with various tumor cells. Tumor cell plasticity alters disease trajectories via epithelial-to-mesenchymal transition (EMT). Several of the same pathways that regulate EMT are involved in tumor-immune interactions, yet little is known about the mechanisms and consequences of crosstalk between these regulatory processes. Here we introduce a multiscale evolutionary model to describe tumor-immune-EMT interactions and their impact on epithelial cancer progression from in situ to invasive disease. Through simulation of patient cohorts in silico, the model predicts that a controllable region maximizes invasion-free survival. This controllable region depends on properties of the mesenchymal tumor cell phenotype: its growth rate and its immune-evasiveness. In light of the model predictions, we analyze EMT-inflammation-associated data from The Cancer Genome Atlas, and find that association with EMT worsens invasion-free survival probabilities. This result supports the predictions of the model, and leads to the identification of genes that influence outcomes in bladder and uterine cancer, including FGF pathway members. These results suggest new means to delay disease progression, and demonstrate the importance of studying cancer-immune interactions in light of EMT.

Agent-based modeling reveals benefits of heterogeneous and stochastic cell populations during cGAS-mediated IFNβ production

MOTIVATION

The cGAS pathway is a component of the innate immune system responsible for the detection of pathogenic DNA and upregulation of interferon beta (IFNβ). Experimental evidence shows that IFNβ signaling occurs in highly heterogeneous cells and is stochastic in nature; however, the benefits of these attributes remain unclear. To investigate how stochasticity and heterogeneity affect IFNβ production, an agent-based model is developed to simulate both DNA transfection and viral infection.

RESULTS

We show that heterogeneity can enhance IFNβ responses during infection. Furthermore, by varying the degree of IFNβ stochasticity, we find that only a percentage of cells (20-30%) need to respond during infection. Going beyond this range provides no additional protection against cell death or reduction of viral load. Overall, these simulations suggest that heterogeneity and stochasticity are important for moderating immune potency while minimizing cell death during infection.

AVAILABILITY AND IMPLEMENTATION

Model repository is available at: https://github.com/ImmuSystems-Lab/AgentBasedModel-cGASPathway.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Unsupervised logic-based mechanism inference for network-driven biological processes

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.

Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling

INTRODUCTION

CXCR4 and epidermal growth factor receptor (EGFR) represent two major families of receptors, G-protein coupled receptors and receptor tyrosine kinases, with central functions in cancer. While utilizing different upstream signaling molecules, both CXCR4 and EGFR activate kinases ERK and Akt, although single-cell activation of these kinases is markedly heterogeneous. One hypothesis regarding the origin of signaling heterogeneity proposes that intercellular variations arise from differences in pre-existing intracellular states set by extrinsic noise. While pre-existing cell states vary among cells, each pre-existing state defines deterministic signaling outputs to downstream effectors. Understanding causes of signaling heterogeneity will inform treatment of cancers with drugs targeting drivers of oncogenic signaling.

METHODS

We built a single-cell computational model to predict Akt and ERK responses to CXCR4- and EGFR-mediated stimulation. We investigated signaling heterogeneity through these receptors and tested model predictions using quantitative, live-cell time-lapse imaging.

RESULTS

We show that the pre-existing cell state predicts single-cell signaling through both CXCR4 and EGFR. Computational modeling reveals that the same set of pre-existing cell states explains signaling heterogeneity through both EGFR and CXCR4 at multiple doses of ligands and in two different breast cancer cell lines. The model also predicts how phosphatidylinositol-3-kinase (PI3K) targeted therapies potentiate ERK signaling in certain breast cancer cells and that low level, combined inhibition of MEK and PI3K ablates potentiated ERK signaling.

CONCLUSIONS

Our data demonstrate that a conserved motif exists for EGFR and CXCR4 signaling and suggest potential clinical utility of the computational model to optimize therapy.

Synthetic gut microbiome: Advances and challenges

An exponential rise in studies regarding the association among human gut microbial communities, human health, and diseases is currently attracting the attention of researchers to focus on human gut microbiome research. However, even with the ever-growing number of studies on the human gut microbiome, translation into improved health is progressing slowly. This hampering is due to the complexities of the human gut microbiome, which is composed of >1,000 species of microorganisms, such as bacteria, archaea, viruses, and fungi. To overcome this complexity, it is necessary to reduce the gut microbiome, which can help simplify experimental variables to an extent, such that they can be deliberately manipulated and controlled. Reconstruction of synthetic or established gut microbial communities would make it easier to understand the structure, stability, and functional activities of the complex microbial community of the human gut. Here, we provide an overview of the developments and challenges of the synthetic human gut microbiome, and propose the incorporation of multi-omics and mathematical methods in a better synthetic gut ecosystem design, for easy translation of microbiome information to therapies.

Modeling How Heterogeneity in Cell Cycle Length Affects Cancer Cell Growth Dynamics in Response to Treatment

Tumors are complex, dynamic, and adaptive biological systems characterized by high heterogeneity at genetic, epigenetic, phenotypic, as well as tissue microenvironmental level. In this work, utilizing cellular automata methods, we focus on intrinsic heterogeneity with respect to cell cycle duration and explore whether and to what extent this heterogeneity affects cancer cell growth dynamics when cytotoxic treatment is applied. We assume that treatment acts on cancer cells specifically during mitosis and compare it with a (cell cycle-non-specific) cytotoxic treatment that acts randomly regardless of the cell cycle phase. We simulate the spatiotemporal evolution of tumor cells with different initial spatial configurations and different cell length probability distributions. We observed that in heterogeneous populations, strong selection forces act on cancer cells favoring the faster cells, when the death rates are lower than the proliferation rates. However, at higher mitotic death rates, selection of the slower proliferative cells is favored, leading to slower post-treatment regrowth rates, as compared to untreated growth. Of note, random cell death progressively eliminates the slower proliferative cells, consistently, favoring highly proliferative phenotypes. Interestingly, compared to the monoclonal populations that exhibit complete response at high random death rates, emergent resistance arises naturally in heterogeneous populations during treatment. As divergent selection forces may act on a heterogeneous cancer cell population, we argue that treatment plan selection can considerably alter the post-treatment tumor dynamics, cell survival, and emergence of resistance, proving its significant biological and therapeutic impact.

An in-silico study of cancer cell survival and spatial distribution within a 3D microenvironment

3D cell cultures are in-vitro models representing a significant improvement with respect to traditional monolayers. Their diffusion and applicability, however, are hampered by the complexity of 3D systems, that add new physical variables for experimental analyses. In order to account for these additional features and improve the study of 3D cultures, we here present SALSA (ScAffoLd SimulAtor), a general purpose computational tool that can simulate the behavior of a population of cells cultured in a 3D scaffold. This software allows for the complete customization of both the polymeric template structure and the cell population behavior and characteristics. In the following the technical description of SALSA will be presented, together with its validation and an example of how it could be used to optimize the experimental analysis of two breast cancer cell lines cultured in collagen scaffolds. This work contributes to the growing field of integrated in-silico/in-vitro analysis of biological systems, which have great potential for the study of complex cell population behaviours and could lead to improve and facilitate the effectiveness and diffusion of 3D cell culture models.

The Interactions of Quantum Dot-Labeled Silk Fibroin Micro/Nanoparticles with Cells

When silk fibroin particles are used for controlled drug delivery, particle size plays a key role in the location of the carrier on the cells as well as the transport pathway, utilization efficiency, and therapeutic effect of the drugs. In this study, the interactions of different-sized silk fibroin particles and cell lines were investigated. Silk fibroin microparticles with dry size of 1.9 ± 0.4 μm (2.7 ± 0.3 μm in wet state) and silk fibroin nanoparticles with dry size of 51.5 ± 11.0 nm (174.8 ± 12.5 nm in wet state) were prepared by salting-out method and high-voltage electrospray method, respectively. CdSe/ZnS quantum dots were coupled to the surface of the micro/nanoparticles. Photostability observations indicated that the fluorescence stability of the quantum dots was much higher than that of fluorescein isothiocyanate. In vitro, microparticles and nanoparticles were co-cultured with human umbilical vein endothelial cells EA.hy 926 and cervical cancer cells HeLa, respectively. The fluorescence test and cell viability showed that the EA.hy926 cells tended to be adhered to the microparticle surfaces and the cell proliferation was significantly promoted, while the nanoparticles were more likely to be internalized in HeLa cells and the cell proliferation was notably inhibited. Our findings might provide useful information concerning effective drug delivery that microparticles may be preferred if the drugs need to be delivered to normal cell surface, while nanoparticles may be preferred if the drugs need to be transmitted in tumor cells.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution

Cancer is a complex, dynamic disease that despite recent advances remains mostly incurable. Inter- and intratumoral heterogeneity are generally considered major drivers of therapy resistance, metastasis, and treatment failure. Recent advances in high-throughput experimentation have produced a wealth of data on tumor heterogeneity and researchers are increasingly turning to mathematical modeling to aid in the interpretation of these complex datasets. In this mini-review, we discuss three important classes of approaches for modeling cellular dynamics within heterogeneous tumors: agent-based models, population dynamics, and multiscale models. An important new focus, for which we provide an example, is the role of intratumoral cell-cell interactions.

Hybrid modeling frameworks of tumor development and treatment

Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments.

This article is characterized under:

Analytical and Computational Methods > Computational Methods

Translational, Genomic, and Systems Medicine > Therapeutic Methods

Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models

Assessment of intratumoral heterogeneity with mutations and gene expression profiles

Intratumoral heterogeneity (ITH) refers to the presence of distinct tumor cell populations. It provides vital information for the clinical prognosis, drug responsiveness, and personalized treatment of cancer patients. As genomic ITH in various cancers affects the expression patterns of genes, the expression profile could be utilized for determining ITH level. Herein, we present a novel approach to directly detect high ITH defined as a larger number of subclones from the gene expression pattern through machine learning approaches. We examined associations between gene expression profile and ITH of 12 cancer types from The Cancer Genome Atlas (TCGA) database. Using stomach adenocarcinoma (STAD) showing high association, we evaluated the performance of our method in predicting ITH by employing three machine learning algorithms using gene expression profile data. We classified tumors into high and low heterogeneity groups using the learning model through the selection of LASSO feature. The result showed that support vector machines (SVMs) outperformed other algorithms (AUC = 0.84 in SVMs and 0.82 in Naïve Bayes) and we were able to improve predictive power by using both combined data from mutation and expression. Furthermore, we evaluated the prediction ability of each model using simulation data generated by mixing cell lines of the Cancer Cell Line Encyclopedia (CCLE), and obtained consistent results with using real dataset. Our approach could be utilized for discriminating tumors with heterogeneous cell populations to characterize ITH.

Short-term cellular memory tunes the signaling responses of the chemokine receptor CXCR4

The chemokine receptor CXCR4 regulates fundamental processes in development, normal physiology, and diseases, including cancer. Small subpopulations of CXCR4-positive cells drive the local invasion and dissemination of malignant cells during metastasis, emphasizing the need to understand the mechanisms controlling responses at the single-cell level to receptor activation by the chemokine ligand CXCL12. Using single-cell imaging, we discovered that short-term cellular memory of changes in environmental conditions tuned CXCR4 signaling to Akt and ERK, two kinases activated by this receptor. Conditioning cells with growth stimuli before CXCL12 exposure increased the number of cells that initiated CXCR4 signaling and the amplitude of Akt and ERK activation. Data-driven, single-cell computational modeling revealed that growth factor conditioning modulated CXCR4-dependent activation of Akt and ERK by decreasing extrinsic noise (preexisting cell-to-cell differences in kinase activity) in PI3K and mTORC1. Modeling established mTORC1 as critical for tuning single-cell responses to CXCL12-CXCR4 signaling. Our single-cell model predicted how combinations of extrinsic noise in PI3K, Ras, and mTORC1 superimposed on different driver mutations in the ERK and/or Akt pathways to bias CXCR4 signaling. Computational experiments correctly predicted that selected kinase inhibitors used for cancer therapy shifted subsets of cells to states that were more permissive to CXCR4 activation, suggesting that such drugs may inadvertently potentiate pro-metastatic CXCR4 signaling. Our work establishes how changing environmental inputs modulate CXCR4 signaling in single cells and provides a framework to optimize the development and use of drugs targeting this signaling pathway.

Computational algorithms for in silico profiling of activating mutations in cancer

Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.

Modelling of Protein Kinase Signaling Pathways in Melanoma and Other Cancers

Melanoma is a highly aggressive tumor with a strong dependence on intracellular signaling pathways. Almost half of all melanomas are driven by mutations in the v-Raf murine sarcoma viral oncogene homolog B (BRAF) with BRAFV600E being the most prevalent mutation. Recently developed targeted treatment directed against mutant BRAF and downstream mitogen-activated protein kinase (MAPK) MAP2K1 (also termed MEK1) have improved overall survival of melanoma patients. However, the MAPK signaling pathway is far more complex than a single chain of consecutively activated MAPK enzymes and it contains nested-, inherent feedback mechanisms, crosstalk with other signaling pathways, epigenetic regulatory mechanisms, and interacting small non-coding RNAs. A more complete understanding of this pathway is needed to better understand melanoma development and mechanisms of treatment resistance. Network reconstruction, analysis, and modelling under the systems biology paradigm have been used recently in different malignant tumors including melanoma to analyze and integrate 'omics' data, formulate mechanistic hypotheses on tumorigenesis, assess and personalize anticancer therapy, and propose new drug targets. Here we review the current knowledge of network modelling approaches in cancer with a special emphasis on melanoma.

Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology

The tumor microenvironment (TME) is a multifaceted ecosystem characterized by profound cellular heterogeneity, dynamicity, and complex intercellular cross-talk. The striking responses obtained with immune checkpoint blockers, i.e., antibodies targeting immune-cell regulators to boost antitumor immunity, have demonstrated the enormous potential of anticancer treatments that target TME components other than tumor cells. However, as checkpoint blockade is currently beneficial only to a limited fraction of patients, there is an urgent need to understand the mechanisms orchestrating the immune response in the TME to guide the rational design of more effective anticancer therapies. In this Mini Review, we give an overview of the methodologies that allow studying the heterogeneity of the TME from multi-omics data generated from bulk samples, single cells, or images of tumor-tissue slides. These include approaches for the characterization of the different cell phenotypes and for the reconstruction of their spatial organization and inter-cellular cross-talk. We discuss how this broader vision of the cellular heterogeneity and plasticity of tumors, which is emerging thanks to these methodologies, offers the opportunity to rationally design precision immuno-oncology treatments. These developments are fundamental to overcome the current limitations of targeted agents and checkpoint blockers and to bring long-term clinical benefits to a larger fraction of cancer patients.