The Role of Exosomes in Pancreatic Cancer Microenvironment

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer

Mathematical modelling has proven to be a valuable tool in predicting the delivery and efficacy of molecular, antibody-based, nano and cellular therapy in solid tumours. Mathematical models based on our understanding of the biological processes at subcellular, cellular and tissue level are known as mechanistic models that, in turn, are divided into continuous and discrete models. Continuous models are further divided into lumped parameter models - for describing the temporal distribution of medicine in tumours and normal organs - and distributed parameter models - for studying the spatiotemporal distribution of therapy in tumours. Discrete models capture interactions at the cellular and subcellular levels. Collectively, these models are useful for optimizing the delivery and efficacy of molecular, nanoscale and cellular therapy in tumours by incorporating the biological characteristics of tumours, the physicochemical properties of drugs, the interactions among drugs, cancer cells and various components of the tumour microenvironment, and for enabling patient-specific predictions when combined with medical imaging. Artificial intelligence-based methods, such as machine learning, have ushered in a new era in oncology. These data-driven approaches complement mechanistic models and have immense potential for improving cancer detection, treatment and drug discovery. Here we review these diverse approaches and suggest ways to combine mechanistic and artificial intelligence-based models to further improve patient treatment outcomes.

Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer

Patients with advanced metastatic colorectal cancer (mCRC) typically exhibit significant interindividual differences in treatment responses and face poor survival outcomes. To systematically analyze the heterogeneous tumor progression and recurrence observed in advanced mCRC patients, we developed a quantitative cancer-immunity cycle (QCIC) model. The QCIC model employs differential equations to capture the biological mechanisms underlying the cancer-immunity cycle and predicts tumor evolution dynamics under various treatment strategies through stochastic computational methods. We introduce the treatment response index (TRI) to quantify disease progression in virtual clinical trials and the death probability function (DPF) to estimate overall survival. Additionally, we investigate the impact of predictive biomarkers on survival prognosis in advanced mCRC patients, identifying tumor-infiltrating CD8+ cytotoxic T lymphocytes (CTLs) as key predictors of disease progression and the tumor-infiltrating CD4+ Th1/Treg ratio as a significant determinant of survival outcomes. This study presents an approach that bridges the gap between diverse clinical data sources and the generation of virtual patient cohorts, providing valuable insights into interindividual treatment variability and survival forecasting in mCRC patients.

Small extracellular vesicles (sEVs) in pancreatic cancer progression and diagnosis

Pancreatic cancer is one of the most aggressive malignancies with poor prognostic outcomes, necessitating the exploration of novel biomarkers and therapeutic targets for early detection and effective treatment. Small extracellular vesicles (sEVs) secreted by cells, have gained considerable attention in cancer research due to their role in intercellular communication and their potential as non-invasive biomarkers. This review focuses on the role of sEVs in the progression of pancreatic cancer and their application as biomarkers. We delve into the biogenesis, composition, and functional implications of sEVs in pancreatic tumor biology, emphasizing their involvement in processes such as tumor growth, metastasis, immune modulation, and chemotherapy resistance. In addition, we discuss the challenges in isolating and characterizing sEVs. The review also highlights recent advances in the utilization of sEV-derived biomarkers for the early diagnosis, prognosis, and monitoring of pancreatic cancer. By synthesizing the latest findings, we aim to underscore the significance of sEVs in pancreatic cancer and their potential to revolutionize patient management through improved diagnostics and targeted therapies.

Spatio-temporal model of combining ADT and chemotherapy with senolytic treatment in metastatic prostate cancer

Prostate cancer cells depend on androgen for their survival. A standard treatment of metastatic prostate cancer (mPC) is androgen deprivation treatment (ADT). However, after a period of remission, some cancer cells changed into androgen-independent cells, and then treatment proceeds with a combination of ADT and chemotherapy. Senescent cells are cells that stop dividing but sustain viability. Senescence cancer cells are common in cancer, and they affect cancer treatment negatively by secreting inflammatory cytokines and pro-cancer VEGF. In this paper, we include the effect of senescence in a model of mPC. We consider combinations of ADT, chemotherapy, and senolytic drug, which eliminate senescent cells, in a spatio-temporal partial differential equations model, and demonstrate that simulations of the model are in agreement with experimental results. We evaluate the synergy between different doses of chemotherapy and senolytic drugs, at different fixed doses of ADT. We also consider optimal scheduling of the drugs, and the hypothesis that, in optimal schedule, a senolytic drug is to be administered immediately following the chemotherapy drug.

Adaptive Immunity Determines the Cancer Treatment Outcome of Oncolytic Virus and Anti-PD-1

The immune checkpoint inhibitor, anti-programmed death protein-1 (anti-PD-1), enhances adaptive immunity to kill tumor cells, and the oncolytic virus (OV) triggers innate immunity to clear the infected tumor cells. We create a mathematical model to investigate how the interaction between adaptive and innate immunities under OV and anti-PD-1 affects tumor reduction. For different immunity strength, we create the corresponding virtual baseline patients and cohort patients to decipher the major factors determining the treatment outcome. Global sensitivity analysis indicates that adaptive immunity has more control on the treatment outcome than innate immunity, and whether anti-PD-1 cancels out the OV treatment efficacy depends on the OV dosage and the balance between clearance of infected tumor cells and OV by T cells. The optimal OV infection rate and dosage suggest that OV treatment is more sensitive to adaptive immunity than innate immunity. Our model prediction also indicates that tumor reduction is more sensitive to anti-PD-1 efficacy as adaptive immunity becomes stronger, and anti-PD-1 trends to cancel out the OV treatment efficacy as innate immunity becomes stronger. Based on these results, the recommended treatment protocol for patients with different immunity strength can be determined.

Spatio-temporal model of combining chemotherapy with senolytic treatment in lung cancer

Senescent cells are cells that stop dividing but sustain viability. Cellular senescence is the hallmark of aging, but senescence also appears in cancer, triggered by cells stress, tumor suppression of gene activation, and oncogene activity. In lung cancer, senescent cancer cells secrete VEGF, which initiates a process of angiogenesis, enabling the cancer to grow and proliferate. Chemotherapy kills cancer cells, but some cancer cells become senescent. Hence, a senolytic drug, a drug that eliminates senescent cells, should significantly improve the efficacy of chemotherapy. In this paper, we developed a mathematical spatio-temporal model of combination chemotherapy with senolytic drug in treatment of lung cancer. Model's simulations of tumor volume growth are shown to agree with mouse experiments in the case where cyclophosphamide is combined with the senolytic drug fisetin. It is then shown how the model can be used to assess the benefits of treatments with different combinations and different schedules of the two drugs in order to achieve optimal tumor volume reduction.

Investigating nano-sized tumor-derived extracellular vesicles in enhancing anti-PD-1 immunotherapy

Anti-PD1 immune checkpoint blockade (ICB) has shown promising results for treating several aggressive cancers, enhancing patient survival rates. The variability in clinical response to anti-PD1 ICB is thought to be driven by patient-specific biology and heterogeneity within the tumor microenvironment. Tumor-derived extracellular vesicles (TDEVs), nano-sized particles released from tumor cells, can modulate the tumor microenvironment, leading to immunosuppression and tumor progression. Hence, TDEVs may contribute to the variability in treatment response and play a crucial role in the failure of anti-PD1 immunotherapy. In this study, we develop a systems biology approach to interrogate the role of TDEVs on the response dynamics for anti-PD1 blockade. Our results suggest that the detection and profiling of TDEVs can help screen patients for anti-PD-1 immunotherapy. Moreover, the results in this study suggest that TDEVs and IL-12 can potentially be liquid biopsy biomarkers to profile patient response to anti-PD1 ICB and tailor patient-specific treatment protocols. Importantly, the methodology is generalizable to other types of cancer immunotherapies. Therefore, the collection of patient-specific liquid biopsy data, and the implementation of those data into the systems biology framework, may offer the opportunity to discover new biomarkers for patient drug screening and enable the continuous monitoring of patient response to treatment and adaptation of patient-specific immunotherapy treatment protocols to overcome therapeutic resistance.

Mathematical Modeling of Tumor Immune Interactions: The Role of Anti-FGFR and Anti-PD-1 in the Combination Therapy

Bladder cancer poses a significant global health burden with high incidence and recurrence rates. This study addresses the therapeutic challenges in advanced bladder cancer, focusing on the competitive mechanisms of ligand or drug binding to receptors. We developed a refined mathematical model that integrates the dynamics of tumor cells and immune responses, particularly targeting fibroblast growth factor receptor 3 (FGFR3) and immune checkpoint inhibitors (ICIs). This study contributes to understanding combination therapies by elucidating the competitive binding dynamics and quantifying the synergistic effects. The findings highlight the importance of personalized immunotherapeutic strategies, considering factors such as drug dosage, dosing schedules, and patient-specific parameters. Our model further reveals that ligand-independent activated-state receptors are the most essential drivers of tumor proliferation. Moreover, we found that PD-L1 expression rate was more important than PD-1 in driving the dynamic evolution of tumor and immune cells. The proposed mathematical model provides a comprehensive framework for unraveling the complexities of combination therapies in advanced bladder cancer. As research progresses, this multidisciplinary approach contributes valuable insights toward optimizing therapeutic strategies and advancing cancer treatment paradigms.

Mathematical modeling and analysis of cancer treatment with radiation and anti-PD-L1

In cancer treatment, radiation therapy (RT) induces direct tumor cell death due to DNA damage, but it also enhances the deaths of radiosensitive immune cells and is followed by local relapse and up-regulation of immune checkpoint ligand PD-L1. Since the binding between PD-1 and PD-L1 curtails anti-tumor immunities, combining RT and PD-L1 inhibitor, anti-PD-L1, is a potential method to improve the treatment efficacy by RT. Some experiments support this hypothesis by showing that the combination of ionizing irradiation (IR) and anti-PD-L1 improves tumor reduction comparing to the monotherapy of IR or anti-PD-L1. In this work, we create a simplified ODE model to study the order of tumor growths under treatments of IR and anti-PD-L1. Our synergy analysis indicates that both IR and anti-PD-L1 improve the tumor reduction of each other, when IR and anti-PD-L1 are given simultaneously. When giving IR and anti-PD-L1 separately, a high dosage of IR should be given first to efficiently reduce tumor load and then followed by anti-PD-L1 with strong efficacy to maintain the tumor reduction and slow down the relapse. Increasing the duration of anti-PD-L1 improves the tumor reduction, but it cannot prolong the duration that tumor relapses to the level of the control case. Under some simplification, we also prove that the model has an unstable tumor free equilibrium and a locally asymptotically stable tumor persistent equilibrium. Our bifurcation diagram reveals a transition from tumor elimination to tumor persistence, as the tumor growth rate increases. In the tumor persistent case, both anti-PD-L1 and IR can reduce tumor amount in the long term.

Investigating the synergistic effects of immunotherapy and normalization treatment in modulating tumor microenvironment and enhancing treatment efficacy

We developed a comprehensive mathematical model of cancer immunotherapy that takes into account: i) Immune checkpoint blockers (ICBs) and the interactions between cancer cells and the immune system, ii) characteristics of the tumor microenvironment, such as the tumor hydraulic conductivity, interstitial fluid pressure, and vascular permeability, iii) spatial and temporal variations of the modeled components within the tumor and the surrounding host tissue, iv) the transport of modeled components through the vasculature and between the tumor-host tissue with convection and diffusion, and v) modeling of the tumor draining lymph nodes were the antigen presentation and the development of cytotoxic immune cells take place. Our model successfully reproduced experimental data from various murine tumor types and predicted immune system profiling, which is challenging to achieve experimentally. It showed that combination of ICB therapy and normalization treatments, that aim to improve tumor perfusion, decreases interstitial fluid pressure and increases the concentration of both innate and adaptive immune cells at the tumor center rather than the periphery. Furthermore, using the model, we investigated the impact of modeled components on treatment outcomes. The analysis found that the number of functional vessels inside the tumor region and the ICB dose administered have the largest impact on treatment outcomes.

IL-27 in combination with anti-PD-1 can be anti-cancer or pro-cancer

Interleukin-27 (IL-27) is known to play opposing roles in immunology. The present paper considers, specifically, the role IL-27 plays in cancer immunotherapy when combined with immune checkpoint inhibitor anti-PD-1. We first develop a mathematical model for this combination therapy, by a system of Partial Differential Equations, and show agreement with experimental results in mice injected with melanoma cells. We then proceed to simulate tumor volume with IL-27 injection at a variable dose F and anti-PD-1 at a variable dose g. We show that in some range of "small" values of g, as f increases tumor volume decreases as long as fFc(g), where Fc(g) is a monotone increasing function of g. This demonstrates that IL-27 can be both anti-cancer and pro-cancer, depending on the ranges of both anti-PD-1 and IL-27.

Mathematical modeling of intratumoral immunotherapy yields strategies to improve the treatment outcomes

Intratumoral injection of immunotherapy aims to maximize its activity within the tumor. However, cytokines are cleared via tumor vessels and escape from the tumor periphery into the host-tissue, reducing efficacy and causing toxicity. Thus, understanding the determinants of the tumor and immune response to intratumoral immunotherapy should lead to better treatment outcomes. In this study, we developed a mechanistic mathematical model to determine the efficacy of intratumorally-injected conjugated-cytokines, accounting for properties of the tumor microenvironment and the conjugated-cytokines. The model explicitly incorporates i) the tumor vascular density and permeability and the tumor hydraulic conductivity, ii) conjugated-cytokines size and binding affinity as well as their clearance via the blood vessels and the surrounding tissue, and iii) immune cells-cancer cells interactions. Model simulations show how the properties of the tumor and of the conjugated-cytokines determine treatment outcomes and how selection of proper parameters can optimize therapy. A high tumor tissue hydraulic permeability allows for the uniform distribution of the cytokines into the tumor, whereas uniform tumor perfusion is required for sufficient access and activation of immune cells. The permeability of the tumor vessels affects the blood clearance of the cytokines and optimal values depend on the size of the conjugates. A size >5 nm in radius was found to be optimal, whereas the binding of conjugates should be high enough to prevent clearance from the tumor into the surrounding tissue. In conclusion, development of strategies to improve vessel perfusion and tissue hydraulic conductivity by reprogramming the microenvironment along with optimal design of conjugated-cytokines can enhance intratumoral immunotherapy.

Different mechanisms of CD200-CD200R induce diverse outcomes in cancer treatment

The CD200 is a cell membrane protein expressed by tumor cells, and its receptor CD200 receptor (CD200R) is expressed by immune cells including macrophages and dendritic cells. The formation of CD200-CD200R inhibits the cellular functions of the targeted immune cells, so CD200 is one type of the immune checkpoint and blockade CD200-CD200R formation is a potential cancer treatment. However, the CD200 blockade has opposite treatment outcomes in different types of cancers. For instance, the CD200R deficient mice have a higher tumor load than the wild type (WT) mice in melanoma suggesting that CD200-CD200R inhibits melanoma. On the other hand, the antibody anti-CD200 treatment in pancreatic ductal adenocarcinoma (PDAC) and head and neck squamous cell carcinoma (HNSCC) significantly reduces the tumor load indicating that CD200-CD200R promotes PDAC and HNSCC. In this work, we hypothesize that different mechanisms of CD200-CD200R in tumor microenvironment could be one of the reasons for the diverse treatment outcomes of CD200 blockade in different types of cancers. We create one Ordinary Differential Equations (ODEs) model for melanoma including the inhibition of CCL8 and regulatory T cells and the switching from M2 to M1 macrophages by CD200-CD200R to capture the tumor inhibition by CD200-CD200R. We also create another ODEs model for PDAC and HNSCC including the promotion of the polarization and suppressive activities of M2 macrophages by CD200-CD200R to generate the tumor promotion by CD200-CD200R. Furthermore, we use these two models to investigate the treatment efficacy of the combination treatment between the CD200-CD200R blockade and the other immune checkpoint inhibitor, anti-PD-1. Our result shows that different mechanisms of CD200-CD200R can induce different treatment outcomes in combination treatments, namely, only the CD200-CD200R blockade reduces tumor load in melanoma and only the anti-PD-1 and CD200 knockout decrease tumor load in PDAC and HNSCC. Moreover, in melanoma, the CD200-CD200R mainly utilizes the inhibitions on M1 macrophages and dendritic cells to inhibit tumor growth, instead of M2 macrophages.

Pancreatic cancer and exosomes: role in progression, diagnosis, monitoring, and treatment

Pancreatic cancer (PC) is one of the most dangerous diseases that threaten human life, and investigating the details affecting its progression or regression is particularly important. Exosomes are one of the derivatives produced from different cells, including tumor cells and other cells such as Tregs, M2 macrophages, and MDSCs, and can help tumor growth. These exosomes perform their actions by affecting the cells in the tumor microenvironment, such as pancreatic stellate cells (PSCs) that produce extracellular matrix (ECM) components and immune cells that are responsible for killing tumor cells. It has also been shown that pancreatic cancer cell (PCC)-derived exosomes at different stages carry molecules. Checking the presence of these molecules in the blood and other body fluids can help us in the early stage diagnosis and monitoring of PC. However, immune system cell-derived exosomes (IEXs) and mesenchymal stem cell (MSC)-derived exosomes can contribute to PC treatment. Immune cells produce exosomes as part of the mechanisms involved in the immune surveillance and tumor cell-killing phenomenon. Exosomes can be modified in such a way that their antitumor properties are enhanced. One of these methods is drug loading in exosomes, which can significantly increase the effectiveness of chemotherapy drugs. In general, exosomes form a complex intercellular communication network that plays a role in developing, progressing, diagnosing, monitoring, and treating pancreatic cancer.

Patient-Specific Mathematical Model of the Clear Cell Renal Cell Carcinoma Microenvironment

The interactions between cells and molecules in the tumor microenvironment can give insight into the initiation and progression of tumors and their optimal treatment options. In this paper, we developed an ordinary differential equation (ODE) mathematical model of the interaction network of key players in the clear cell renal cell carcinoma (ccRCC) microenvironment. We then performed a global gradient-based sensitivity analysis to investigate the effects of the most sensitive parameters of the model on the number of cancer cells. The results indicate that parameters related to IL-6 have high a impact on cancer cell growth, such that decreasing the level of IL-6 can remarkably slow the tumor's growth.

Mathematical modeling for the combination treatment of IFN- γ and anti-PD-1 in cancer immunotherapy

When the immune-checkpoint programmed death-1 (PD-1) binds to its ligand programmed death ligand 1 (PD-L1) to form the complex PD-1-PD-L1, this complex inactivates immune cells resulting in cell apoptosis, downregulation of immune reaction, and tumor evasion. The antibody, anti-PD-1 or anti-PD-L1, blocks the PD-1-PD-L1 complex formation to restore the functions of T cells. Combination of anti-PD-1 with other treatment shows promising in different types of cancer treatments. Interferon-gamma (IFN-γ) plays an important role in immune responses. It is mainly regarded as a pro-inflammatory cytokine that promotes the proliferation of CD8⁺ T cell and cytotoxic T cell, enhances the activation of Th1 cells and CD8⁺ T cells, and enhances tumor elimination. However, recent studies have been discovering many anti-inflammatory functions of IFN-γ, such as promotion of the PD-L1 expression, T cell apoptosis, and tumor metastasis, as well as inhibition of the immune recognition and the killing rates by T cells. In this work, we construct a mathematical model incorporating pro-inflammatory and anti-inflammatory functions of IFN-γ to capture tumor growth under anti-PD-1 treatment in the wild type and IFN-γ null mutant melanoma. Our simulation results qualitatively fit experimental data that IFN-γ null mutant with anti-PD-1 obtains the highest tumor reduction comparing to IFN-γ null mutant without anti-PD-1 and wild type tumor with anti-PD-1 therapy. Moreover, our synergy analysis indicates that, in the combination treatment, the tumor volume decreases as either the dosage of anti-PD-1 increases or the IFN-γ production efficiency decreases. Thus, the combination of anti-PD-1 and IFN-γ blockade improves the tumor reduction comparing to the monotherapy of anti-PD-1 or the monotherapy of IFN-γ blockade. We also find a threshold curve of the minimal dosage of anti-PD-1 corresponding to the IFN-γ production efficiency to ensure the tumor reduction under the presence of IFN-γ.

Data-driven causal model discovery and personalized prediction in Alzheimer's disease

With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Proteomes of Extracellular Vesicles From Pancreatic Cancer Cells and Cancer-Associated Fibroblasts

OBJECTIVES

Extracellular vesicles (EVs) are lipid bound vesicles secreted by cells into the extracellular environment. Studies have implicated EVs in cell proliferation, epithelial-mesenchymal transition, metastasis, angiogenesis, and mediating the interaction of tumor cells and microenvironment. A systematic characterization of EVs from pancreatic cancer cells and cancer-associated fibroblasts (CAFs) would be valuable for studying the roles of EV proteins in pancreatic tumorigenesis.

METHODS

Proteomic and functional analyses were applied to characterize the proteomes of EVs released from 5 pancreatic cancer lines, 2 CAF cell lines, and a normal pancreatic epithelial cell line (HPDE).

RESULTS

More than 1400 nonredundant proteins were identified in each EV derived from the cell lines. The majority of the proteins identified in the EVs from the cancer cells, CAFs, and HPDE were detected in all 3 groups, highly enriched in the biological processes of vesicle-mediated transport and exocytosis. Protein networks relevant to pancreatic tumorigenesis, including epithelial-mesenchymal transition, complement, and coagulation components, were significantly enriched in the EVs from cancer cells or CAFs.

CONCLUSIONS

These findings support the roles of EVs as a potential mediator in transmitting epithelial-mesenchymal transition signals and complement response in the tumor microenvironment and possibly contributing to coagulation defects related to cancer development.

Analysis of the Equilibrium Phase in Immune-Controlled Tumors Provides Hints for Designing Better Strategies for Cancer Treatment

When it comes to improving cancer therapies, one challenge is to identify key biological parameters that prevent immune escape and maintain an equilibrium state characterized by a stable subclinical tumor mass, controlled by the immune cells. Based on a space and size structured partial differential equation model, we developed numerical methods that allow us to predict the shape of the equilibrium at low cost, without running simulations of the initial-boundary value problem. In turn, the computation of the equilibrium state allowed us to apply global sensitivity analysis methods that assess which and how parameters influence the residual tumor mass. This analysis reveals that the elimination rate of tumor cells by immune cells far exceeds the influence of the other parameters on the equilibrium size of the tumor. Moreover, combining parameters that sustain and strengthen the antitumor immune response also proves more efficient at maintaining the tumor in a long-lasting equilibrium state. Applied to the biological parameters that define each type of cancer, such numerical investigations can provide hints for the design and optimization of cancer treatments.

A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice

The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells' location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells' diffusion and immune cells' influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes' diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer.

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

During cancer progression, tumors shed different biomarkers into the bloodstream, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). The analysis of these biomarkers in the blood, known as 'liquid biopsy' (LB), is a promising approach for early cancer detection and treatment monitoring, and more recently, as a means for cancer therapy. Previous reviews have discussed the role of CTCs and ctDNA in cancer progression; however, ctDNA and EVs are rapidly evolving with technological advancements and computational analysis and are the subject of enormous recent studies in cancer biomarkers. In this review, first, we introduce these cell-released cancer biomarkers and briefly discuss their clinical significance in cancer diagnosis and treatment monitoring. Second, we present conventional and novel approaches for the isolation, profiling, and characterization of these markers. We then investigate the mathematical and in silico models that are developed to investigate the function of ctDNA and EVs in cancer progression. We convey our views on what is needed to pave the way to translate the emerging technologies and models into the clinic and make the case that optimized next-generation techniques and models are needed to precisely evaluate the clinical relevance of these LB markers.

A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

The Cellular and Biological Impact of Extracellular Vesicles in Pancreatic Cancer

Simple Summary

The increased incidence and global failure of ongoing therapies project pancreatic cancer as the second deadliest cancer worldwide. While our knowledge of pancreatic cancer cells’ abilities and specificities has drastically improved based on multi-scaled omics, one must consider that much more remains to be uncovered on the role and impact of stromal cells and the established network of communication with tumor cells. This review article discusses how tumor cells communicate with the various cells composing the stroma and its implication in tumor cells’ abilities, PDA (pancreatic ductal adenocarcinoma) carcinogenesis and therapeutic response. We will focus on extracellular vesicles-mediated crosstalk and how this multifaceted dialogue impacts both cellular compartments and its subsequent impact on PDA biology.

Abstract

Deciphering the interactions between tumor and stromal cells is a growing field of research to improve pancreatic cancer-associated therapies and patients’ care. Indeed, while accounting for 50 to 90% of the tumor mass, many pieces of evidence reported that beyond their structural role, the non-tumoral cells composing the intra-tumoral microenvironment influence tumor cells’ proliferation, metabolism, cell death and resistance to therapies, among others. Simultaneously, tumor cells can influence non-tumoral neighboring or distant cells in order to shape a tumor-supportive and immunosuppressive environment as well as influencing the formation of metastatic niches. Among intercellular modes of communication, extracellular vesicles can simultaneously transfer the largest variety of signals and were recently reported as key effectors of cell–cell communication in pancreatic cancer, from its development to its evolution as well as its ability to resist available treatments. This review focuses on extracellular vesicles-mediated communication between different cellular components of pancreatic tumors, from the modulation of cellular activities and abilities to their biological and physiological relevance. Taking into consideration the intra-tumoral microenvironment and its extracellular-mediated crosstalk as main drivers of pancreatic cancer development should open up new therapeutic windows.

Conceptual Analogies Between Multi-Scale Feeding and Feedback Cycles in Supermassive Black Hole and Cancer Environments

Adopting three physically-motivated scales (“micro” – “meso” – “macro”, which refer to mpc – kpc – Mpc, respectively) is paramount for achieving a unified theory of multiphase active galactic nuclei feeding and feedback, and it represents a keystone for astrophysical simulations and observations in the upcoming years. In order to promote this multi-scale idea, we have decided to adopt an interdisciplinary approach, exploring the possible conceptual similarities between supermassive black hole feeding and feedback cycles and the dynamics occurring in human cancer microenvironment.

Modeling of Modulated Exosome Release From Differentiated Induced Neural Stem Cells for Targeted Drug Delivery

A novel implantable and externally controllable stem-cell-based platform for the treatment of Glioblastoma brain cancer has been proposed to bring hope to patients who suffer from this devastating cancer type. Induced Neural Stem Cells (iNSCs), known to have potent therapeutic effects through exosomes-based molecular communication, play a pivotal role in this platform. Transplanted iNSCs demonstrate long-term survival and differentiation into neurons and glia which then fully functionally integrate with the existing neural network. Recent studies have shown that specific types of calcium channels in differentiated neurons and astrocytes are inhibited or activated upon cell depolarization leading to the increased intracellular calcium concentration levels which, in turn, interact with mobilization of multivesicular bodies and exosomal release. In order to provide a platform towards treating brain cancer with the optimum therapy dosage, we propose mathematical models to compute the therapeutic exosomal release rate that is modulated by cell stimulation patterns applied from the external wearable device. This study serves as an initial and required step in the evaluation of controlled exosomal secretion and release via induced stimulation with electromagnetic, optical and/or ultrasonic waves.

TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model

Drug resistance is a primary obstacle in cancer treatment. In many patients who at first respond well to treatment, relapse occurs later on. Various mechanisms have been explored to explain drug resistance in specific cancers and for specific drugs. In this paper, we consider resistance to anti-PD-1, a drug that enhances the activity of anti-cancer T cells. Based on results in experimental melanoma, it is shown, by a mathematical model, that resistances to anti-PD-1 can be significantly reduced by combining it with anti-TNF-α. The model is used to simulate the efficacy of the combined therapy with different range of doses, different initial tumor volume, and different schedules. In particular, it is shown that under a course of treatment with 3-week cycles where each drug is injected in the first day of either week 1 or week 2, injecting anti-TNF-α one week after anti-PD-1 is the most effective schedule in reducing tumor volume.

A size and space structured model describing interactions of tumor cells with immune cells reveals cancer persistent equilibrium states in tumorigenesis

The recent success of immunotherapies for the treatment of cancer has highlighted the importance of the interactions between tumor and immune cells. Mathematical models of tumor growth are needed to faithfully reproduce and predict the spatiotemporal dynamics of tumor growth. We introduce a mathematical model intended to describe by means of a system of partial differential equations the early stages of the interactions between effector immune cells and tumor cells. The model is structured in size and space, and it takes into account the migration of the tumor antigen-specific cytotoxic effector cells towards the tumor micro-environment by a chemotactic mechanism. We investigate on numerical grounds the role of the key parameters of the model such as the division and growth rates of the tumor cells, and the conversion and death rates of the immune cells. Our main findings are two-fold. Firstly, the model exhibits a possible control of the tumor growth by the immune response; nevertheless, the control is not complete in the sense that the asymptotic equilibrium states keep residual tumors and activated immune cells. Secondly, space heterogeneities of the source of immune cells can significantly reduce the efficiency of the control dynamics, making patterns of remission-recurrence appear.

Overcoming Drug Resistance to BRAF Inhibitor

One of the most frequently found mutations in human melanomas is in the B-raf gene, making its protein BRAF a key target for therapy. However, in patients treated with BRAF inhibitor (BRAFi), although the response is very good at first, relapse occurs within 6 months, on the average. In order to overcome this drug resistance to BRAFi, various combinations of BRAFi with other drugs have been explored, and some are being applied clinically, such as a combination of BRAF and MEK inhibitors. Experimental data for melanoma in mice show that under continuous treatment with BRAFi, the pro-cancer MDSCs and chemokine CCL2 initially decrease but eventually increase to above their original level, while the anticancer T cells continuously decrease. In this paper, we develop a mathematical model that explains these experimental results. The model is used to explore the efficacy of combinations of BRAFi with anti-CCL2, anti-PD-1 and anti-CTLA-4, with the aim of eliminating or reducing drug resistance to BRAFi.

Recent advances and challenges in the recovery and purification of cellular exosomes

Exosomes are nanovesicles secreted by most cellular types that carry important biochemical compounds throughout the body with different purposes, playing a preponderant role in cellular communication. Because of their structure, physicochemical properties and stability, recent studies are focusing in their use as nanocarriers for different therapeutic compounds for the treatment of different diseases ranging from cancer to Parkinson's disease. However, current bioseparation protocols and methodologies are selected based on the final exosome application or intended use and present both advantages and disadvantages when compared among them. In this context, this review aims to present the most important technologies available for exosome isolation while discussing their advantages and disadvantages and the possibilities of being combined with other strategies. This is critical since the development of novel exosome-based therapeutic strategies will be constrained to the effectiveness and yield of the selected downstream purification methodologies for which a thorough understanding of the available technological resources is needed.

Exosomal lncRNAs and cancer: connecting the missing links

Motivation

Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long non-coding RNAs (lncRNAs).

Results

This review outlines the links between EVs, lncRNAs and cancer. We highlight communication networks involving the tumor microenvironment, the immune system and metastasis. We show examples supporting the value of exosomal lncRNAs as cancer biomarkers and therapeutic targets. We demonstrate how a system biology approach can be used to model cell-cell communication via exosomal lncRNAs and to simulate effects of therapeutic interventions. In addition, we introduce algorithms and bioinformatics resources for the discovery of tumor-specific lncRNAs and tools that are applied to determine exosome content and lncRNA function. Finally, this review provides a comprehensive collection and guide to databases for exosomal lncRNAs.

Supplementary information

Supplementary data are available at Bioinformatics online.

miRNA and Gene Expression in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a challenging disease that is mostly diagnosed late in the course of the illness. Unlike other cancers in which measurable successes have been achieved with traditional chemotherapy, targeted therapy, and, recently, immunotherapy, PDAC has proved to be poorly responsive to these treatments, with only marginal to modest incremental benefits using conventional cytotoxic therapy. There is, therefore, a great unmet need to develop better therapies based on improved understanding of biology and identification of predictive and prognostic biomarkers that would guide therapy. miRNAs are small noncoding RNAs that regulate the expression of some key genes by targeting their 3'-untranslated mRNA region. Aberrant expression of miRNAs has been linked to the development of various malignancies, including PDAC. A series of miRNAs have been identified as potential tools for early diagnosis, prediction of treatment response, and prognosis of patients with PDAC. In this review, we present a summary of the miRNAs that have been studied in PDAC in the context of disease biology.

How to schedule VEGF and PD-1 inhibitors in combination cancer therapy?

BACKGROUND

One of the questions in the design of cancer clinical trials with combination of two drugs is in which order to administer the drugs. This is an important question, especially in the case where one agent may interfere with the effectiveness of the other agent.

RESULTS

In the present paper we develop a mathematical model to address this scheduling question in a specific case where one of the drugs is anti-VEGF, which is known to affect the perfusion of other drugs. As a second drug we take anti-PD-1. Both drugs are known to increase the activation of anticancer T cells. Our simulations show that in the case where anti-VEGF reduces the perfusion, a non-overlapping schedule is significantly more effective than a simultaneous injection of the two drugs, and it is somewhat more beneficial to inject anti-PD-1 first.

CONCLUSION

The method and results of the paper can be extended to other combinations, and they could play an important role in the design of clinical trials with combination therapy, where scheduling strategies may significantly affect the outcome.

Targeting dendritic cells in pancreatic ductal adenocarcinoma

Dendritic cells (DC) are an integral part of the tumor microenvironment. Pancreatic cancer is characterized by reduced number and function of DCs, which impacts antigen presentation and contributes to immune tolerance. Recent data suggest that exosomes can mediate communication between pancreatic cancer cells and DCs. Furthermore, levels of DCs may serve as prognostic factors. There is also growing evidence for the effectiveness of vaccination with DCs pulsed with tumor antigens to initiate adaptive cytolytic immune responses via T cells. Most experience with DC-based vaccination has been gathered for MUC1 and WT1 antigens, where clinical studies in advanced pancreatic cancer have provided encouraging results. In this review, we highlight the role of DC in the course, prognosis and treatment of pancreatic cancer.

Combination therapy for cancer with oncolytic virus and checkpoint inhibitor: A mathematical model

Oncolytic virus (OV) is a replication competent virus that selectively invades cancer cells; as these cells die under the viral burden, the released virus particles proceed to infect other cancer cells. Oncolytic viruses are designed to also be able to stimulate the anticancer immune response. Thus, one may represent an OV by two parameters: its replication potential and its immunogenicity. In this paper we consider a combination therapy with OV and a checkpoint inhibitor, anti-PD-1. We evaluate the efficacy of the combination therapy in terms of the tumor volume at some later time, for example, 6 months from initial treatment. Since T cells kill not only virus-free cancer cells but also virus-infected cancer cells, the following question arises: Does increasing the amount of the checkpoint inhibitor always improve the efficacy? We address this question, by a mathematical model consisting of a system of partial differential equations. We use the model to construct, by simulations, an efficacy map in terms of the doses of the checkpoint inhibitor and the OV injection. We show that there are regions in the map where an increase in the checkpoint inhibitor actually decreases the efficacy of the treatment. We also construct efficacy maps with checkpoint inhibitor vs. the replication potential of the virus that show the same antagonism, namely, an increase in the checkpoint inhibitor may actually decrease the efficacy. These results have implications for clinical trials.

Pancreatic Cancer Diagnosis and Management: Has the Time Come to Prick the Bubble?

Pancreatic cancer (PC) is associated with poor prognosis and very dismal survival rates. The most effective possibility of cure is tumor resection, which is only possible in about 15% of patients diagnosed at early stages of disease progression. Recent whole-genome sequencing studies pointed genetic alterations in 12 core signaling pathways in PC. These observations hint at the possibility that the initial mutation in PC might appear nearly 20 years before any symptoms occur, suggesting that a large window of opportunity may exist for early detection. Biomarkers with the potential to identify pre-neoplastic disease or very early stages of cancer are of great promise to improve patient survival. The concept of liquid biopsy refers to a minimally invasive sampling and analysis of liquid biomarkers that can be isolated from body fluids, primarily blood, urine and saliva. A myriad of circulating molecules may be useful as tumor markers, including cell-free DNA (cfDNA), cell-free RNA (cfRNA), circulating tumor cells (CTC), circulating tumor proteins, and extracellular vesicles, more specifically exosomes. In this review, we discuss with more detail the potential role of exosomes in several aspects related to PC, from initiation to tumor progression and its applicability in early detection and treatment. Exosomes are small circulating extracellular vesicles of 50-150 nm in diameter released from the plasma membrane by almost all cells and exhibit some advantages over other biomarkers. Exosomes are central players of intercellular communication and they have been implicated in a series of biological process, including tumorigenesis, migration and metastasis. Several exosomal microRNAs and proteins have been observed to distinguish PC from benign pancreatic diseases and healthy controls. Besides their possible role in diagnosis, understanding exosomes functions in cancer has clarified the importance of microenvironment in PC progression as well as its influence in proliferation, metastasis and resistance to chemotherapy. Increasing knowledge on cancer exosomes provides valuable insights on new therapeutic targets and can potentially open new strategies to treat this disease. Continuous research is needed to ascertain the reliability of using exosomes and their content as potential biomarkers, so that, hopefully, in the near future, they will provide the opportunity for early diagnosis, treatment intervention and increase survival of PC patients.

Combination therapy for melanoma with BRAF/MEK inhibitor and immune checkpoint inhibitor: a mathematical model

BACKGROUND

The B-raf gene is mutated in up to 66% of human malignant melanomas, and its protein product, BRAF kinase, is a key part of RAS-RAF-MEK-ERK (MAPK) pathway of cancer cell proliferation. BRAF-targeted therapy induces significant responses in the majority of patients, and the combination BRAF/MEK inhibitor enhances clinical efficacy, but the response to BRAF inhibitor and to BRAF/MEK inhibitor is short lived. On the other hand, treatment of melanoma with an immune checkpoint inhibitor, such as anti-PD-1, has lower response rate but the response is much more durable, lasting for years. For this reason, it was suggested that combination of BRAF/MEK and PD-1 inhibitors will significantly improve overall survival time.

RESULTS

This paper develops a mathematical model to address the question of the correlation between BRAF/MEK inhibitor and PD-1 inhibitor in melanoma therapy. The model includes dendritic and cancer cells, CD 4+ and CD 8+ T cells, MDSC cells, interleukins IL-12, IL-2, IL-6, IL-10 and TGF- β, PD-1 and PD-L1, and the two drugs: BRAF/MEK inhibitor (with concentration γ B ) and PD-1 inhibitor (with concentration γ A ). The model is represented by a system of partial differential equations, and is used to develop an efficacy map for the combined concentrations (γ B ,γ A ). It is shown that the two drugs are positively correlated if γ B and γ A are at low doses, that is, the growth of the tumor volume decreases if either γ B or γ A is increased. On the other hand, the two drugs are antagonistic at some high doses, that is, there are zones of (γ B ,γ A ) where an increase in one of the two drugs will increase the tumor volume growth, rather than decrease it.

CONCLUSIONS

It will be important to identify, by animal experiments or by early clinical trials, the zones of (γ B ,γ A ) where antagonism occurs, in order to avoid these zones in more advanced clinical trials.

Combination therapy of cancer with cancer vaccine and immune checkpoint inhibitors: A mathematical model

In this paper we consider a combination therapy of cancer. One drug is a vaccine which activates dendritic cells so that they induce more T cells to infiltrate the tumor. The other drug is a checkpoint inhibitor, which enables the T cells to remain active against the cancer cells. The two drugs are positively correlated in the sense that an increase in the amount of each drug results in a reduction in the tumor volume. We consider the question whether a treatment with combination of the two drugs at certain levels is preferable to a treatment by one of the drugs alone at 'roughly' twice the dosage level; if that is the case, then we say that there is a positive 'synergy' for this combination of dosages. To address this question, we develop a mathematical model using a system of partial differential equations. The variables include dendritic and cancer cells, CD4+ and CD8+ T cells, IL-12 and IL-2, GM-CSF produced by the vaccine, and a T cell checkpoint inhibitor associated with PD-1. We use the model to explore the efficacy of the two drugs, separately and in combination, and compare the simulations with data from mouse experiments. We next introduce the concept of synergy between the drugs and develop a synergy map which suggests in what proportion to administer the drugs in order to achieve the maximum reduction of tumor volume under the constraint of maximum tolerated dose.