Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue

Effector T cells under hypoxia have an altered transcriptome similar to tumor-stressed T cells found in non-responsive melanoma patients

BACKGROUND

In the tumor microenvironment (TME), hypoxia stands as a significant factor that modulates immune responses, especially those driven by T cells. As T cell-based therapies often fail to work in solid tumors, this study aims to investigate the effects of hypoxia on T cell topo-distribution in the TME, gene expression association with T cell states, and clinical responses in melanoma.

METHODS

To generate detailed information on tumor oxygenation and T cell accessibility, we used mathematical modeling of human melanoma tissue microarrays that incorporate oxygen supply from vessels, intratumoral diffusion, and cellular uptake. We created tumor maps and derived plots showing the fraction of CD4 and CD8 T cells against the distance to the nearest vessel and oxygen pressure. To assess their function and transcriptional changes caused by hypoxia, effector T cells were generated and cultured under hypoxia (0.5% oxygen) or normoxia (21% oxygen). The T cell hypoxia-transcriptional signature was compared against datasets from msigDB, iATLAS (clinical trials of melanoma patients treated with immune checkpoint inhibitors (ICIs)), ORIEN AVATAR (real-world melanoma patients treated with ICIs), and a single-cell atlas of tumor-infiltrating lymphocytes.

RESULTS

We made three specific observations: (1) in melanoma T cells preferentially accumulated in oxygenated areas close to blood vessels (50-100 µm from the vasculature in the regions of high oxygen availability) but not in hypoxic areas far from blood vessels. (2) Our analysis confirmed that under hypoxia, T cell functions were significantly reduced compared with normoxic conditions and accompanied by a unique gene signature. Furthermore, this hypoxic gene signature was prevalent in resting and non-activated T cells. Notably and clinically relevant, the hypoxic T cell gene set was found to correlate with reduced overall survival and reduced progression-free survival in melanoma patients, which was more pronounced in non-responder patients undergoing ICI therapy. (3) Finally, compared with a single-cell atlas of tumor-infiltrating T cells, our hypoxia signature aligned with a population of cells at a state termed stress response state (TSTR).

CONCLUSIONS

Our study highlights the critical role of hypoxia in shaping T cell distribution and its correlation with clinical outcomes in melanoma. We revealed a preferential accumulation of T cells in oxygenated areas. Moreover, hypoxic T cells develop a distinct hypoxic gene signature prevalent in resting, non-activated T cells and TSTR that was also associated with poorer outcomes, particularly pronounced among non-responders to ICIs.

Exploring chronic and transient tumor hypoxia for predicting the efficacy of hypoxia-activated pro-drugs

Hypoxia, a low level of oxygen in the tissue, arises due to an imbalance between the vascular oxygen supply and oxygen demand by the surrounding cells. Typically, hypoxia is viewed as a negative marker of patients' survival, because of its implication in the development of aggressive tumors and tumor resistance. Several drugs that specifically target the hypoxic cells have been developed, providing an opportunity for exploiting hypoxia to improve cancer treatment. Here, we consider combinations of hypoxia-activated pro-drugs (HAPs) and two compounds that transiently increase intratumoral hypoxia: a vasodilator and a metabolic sensitizer. To effectively design treatment protocols with multiple compounds we used mathematical micro-pharmacology modeling and determined treatment schedules that take advantage of heterogeneous and dynamically changing oxygenation in tumor tissue. Our model was based on data from murine pancreatic cancers treated with evofosfamide (as a HAP) and either hydralazine (as a vasodilator), or pyruvate (as a metabolic sensitizer). Subsequently, this model was used to identify optimal schedules for different treatment combinations. Our simulations showed that schedules of HAPs with the vasodilator had a bimodal distribution, while HAPs with the sensitizer showed an elongated plateau. All schedules were more successful than HAP monotherapy. The three-compound combination had three local optima, depending on the HAPs clearance from the tissue interstitium, each two-fold more effective than baseline HAP treatment. Our study indicates that the three-compound therapy administered in the defined order will improve cancer response and that designing complex schedules could benefit from the use of mathematical modeling.

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming

Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.

A hybrid spatiotemporal model of PCa dynamics and insights into optimal therapeutic strategies

Using a hybrid cellular automaton with stochastic elements, we investigate the effectiveness of multiple drug therapies on prostate cancer (PCa) growth. The ability of Androgen Deprivation Therapy to reduce PCa growth represents a milestone in prostate cancer treatment, nonetheless most patients eventually become refractory and develop castration-resistant prostate cancer. In recent years, a "second generation" drug called enzalutamide has been used to treat advanced PCa, or patients already exposed to chemotherapy that stopped responding to it. However, tumour resistance to enzalutamide is not well understood, and in this context, preclinical models and in silico experiments (numerical simulations) are key to understanding the mechanisms of resistance and to assessing therapeutic settings that may delay or prevent the onset of resistance. In our mathematical system, we incorporate cell phenotype switching to model the development of increased drug resistance, and consider the effect of the micro-environment dynamics on necrosis and apoptosis of the tumour cells. The therapeutic strategies that we explore include using a single drug (enzalutamide), and drug combinations (enzalutamide and everolimus or cabazitaxel) with different treatment schedules. Our results highlight the effectiveness of alternating therapies, especially alternating enzalutamide and cabazitaxel over a year, and a comparison is made with data taken from TRAMP mice to verify our findings.

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.

The role of memory in non-genetic inheritance and its impact on cancer treatment resistance

Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.

A computational diffusion model to study antibody transport within reconstructed tumor microenvironments

Background

Antibodies revolutionized cancer treatment over the past decades. Despite their successfully application, there are still challenges to overcome to improve efficacy, such as the heterogeneous distribution of antibodies within tumors. Tumor microenvironment features, such as the distribution of tumor and other cell types and the composition of the extracellular matrix may work together to hinder antibodies from reaching the target tumor cells. To understand these interactions, we propose a framework combining in vitro and in silico models. We took advantage of in vitro cancer models previously developed by our group, consisting of tumor cells and fibroblasts co-cultured in 3D within alginate capsules, for reconstruction of tumor microenvironment features.

Results

In this work, an experimental-computational framework of antibody transport within alginate capsules was established, assuming a purely diffusive transport, combined with an exponential saturation effect that mimics the saturation of binding sites on the cell surface. Our tumor microenvironment in vitro models were challenged with a fluorescent antibody and its transport recorded using light sheet fluorescence microscopy. Diffusion and saturation parameters of the computational model were adjusted to reproduce the experimental antibody distribution, with root mean square error under 5%. This computational framework is flexible and can simulate different random distributions of tumor microenvironment elements (fibroblasts, cancer cells and collagen fibers) within the capsule. The random distribution algorithm can be tuned to follow the general patterns observed in the experimental models.

Conclusions

We present a computational and microscopy framework to track and simulate antibody transport within the tumor microenvironment that complements the previously established in vitro models platform. This framework paves the way to the development of a valuable tool to study the influence of different components of the tumor microenvironment on antibody transport.

Drug-Induced Resistance in Micrometastases: Analysis of Spatio-Temporal Cell Lineages

Resistance to anti-cancer drugs is a major cause of treatment failure. While several intracellular mechanisms of resistance have been postulated, the role of extrinsic factors in the development of resistance in individual tumor cells is still not fully understood. Here we used a hybrid agent-based model to investigate how sensitive tumor cells develop drug resistance in the heterogeneous tumor microenvironment. We characterized the spatio-temporal evolution of lineages of the resistant cells and examined how resistance at the single-cell level contributes to the overall tumor resistance. We also developed new methods to track tumor cell adaptation, to trace cell viability trajectories and to examine the three-dimensional spatio-temporal lineage trees. Our findings indicate that drug-induced resistance can result from cells adaptation to the changes in drug distribution. Two modes of cell adaptation were identified that coincide with microenvironmental niches—areas sheltered by cell micro-communities (protectorates) or regions with limited drug penetration (refuga or sanctuaries). We also recognized that certain cells gave rise to lineages of resistant cells (precursors of resistance) and pinpointed three temporal periods and spatial locations at which such cells emerged. This supports the hypothesis that tumor micrometastases do not need to harbor cell populations with pre-existing resistance, but that individual tumor cells can adapt and develop resistance induced by the drug during the treatment.

A Review on Applications of Computational Methods in Drug Screening and Design

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

Hybrid Automata Library: A flexible platform for hybrid modeling with real-time visualization

The Hybrid Automata Library (HAL) is a Java Library developed for use in mathematical oncology modeling. It is made of simple, efficient, generic components that can be used to model complex spatial systems. HAL's components can broadly be classified into: on- and off-lattice agent containers, finite difference diffusion fields, a GUI building system, and additional tools and utilities for computation and data collection. These components are designed to operate independently and are standardized to make them easy to interface with one another. As a demonstration of how modeling can be simplified using our approach, we have included a complete example of a hybrid model (a spatial model with interacting agent-based and PDE components). HAL is a useful asset for researchers who wish to build performant 1D, 2D and 3D hybrid models in Java, while not starting entirely from scratch. It is available on GitHub at https://github.com/MathOnco/HAL under the MIT License. HAL requires the Java JDK version 1.8 or later to compile and run the source code.

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

Immunological Approaches Towards Cancer and Inflammation: A Cross Talk

The inflammation is the protective response of the body against various harmful stimuli; however, the aberrant and inappropriate activation tends to become harmful. The acute inflammatory response tends to resolved once the offending agent is subside but this acute response becomes chronic in nature when the body is unable to successfully neutralized the noxious stimuli. This chronic inflammatory microenvironment is associated with the release of various pro-inflammatory and oncogenic mediators such as nitric oxide (NO), cytokines [IL-1β, IL-2, interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)], growth factor, and chemokines. These mediators make the inflammatory microenvironment more vulnerable toward tumorigenesis. The pro-inflammatory mediators released during the chronic inflammation tends to induce several molecular signaling cascades such as nuclear factor kappa B, MAPKinase, nuclear factor erythroid 2-related factor 2, phosphoinositide-3-kinase, Janus kinases/STAT, Wnt/B-catenin, and cyclic AMP response element binding protein. The immune system and its components have a pleiotropic effect on inflammation and cancer progression. Immune components such as T cells, natural killer cells, macrophages, and neutrophils either inhibit or enhance tumor initiation depending on the type of tumor and immune cells involved. Tumor-associated macrophages and tumor-associated neutrophils are pro-tumorigenic cells highly prevalent in inflammation-mediated tumors. Similarly, presence of T regulatory (Treg) cells in an inflammatory and tumor setting suppresses the immune system, thus paving the way for oncogenesis. However, Treg cells also inhibit autoimmune inflammation. By contrast, cytotoxic T cells and T helper cells confer antitumor immunity and are associated with better prognosis in patients with cancer. Cytotoxic T cells inflict a direct cytotoxic effect on cells expressing oncogenic markers. Currently, several anti-inflammatory and antitumor therapies are under trials in which these immune cells are exploited. Adoptive cell transfer composed of tumor-infiltrating lymphocytes has been tried for the treatment of tumors after their ex vivo expansion. Mediators released by cells in a tumorigenic and inflammatory microenvironment cross talk with nearby cells, either promoting or inhibiting inflammation and cancer. Recently, several cytokine-based therapies are either being developed or are under trial to treat such types of manifestations. Monoclonal antibodies directed against TNF-α, VEGF, and IL-6 has shown promising results to ameliorate inflammation and cancer, while direct administration of IL-2 has been shown to cause tumor regression.