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9.9
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Cells
Special Issues
Cell-Cell Interaction Modelling of Cancer Immunotherapy Treatments
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Cell-Cell Interaction Modelling of Cancer Immunotherapy Treatments

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cell Methods".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 10583

Special Issue Editors

Dr. Giovanni Amabile

E-Mail
Guest Editor
Chief Executive Officer, Enthera, Milano, Italy
Interests: induced pluripotent stem cells; epigenetics; hematopoiesis; neurogenesis; early and late stage clinical trials
Special Issues, Collections and Topics in MDPI journals
Dr. Teddy Lazebnik

E-Mail Website
Co-Guest Editor
Department of Cancer Biology, Cancer Institute, University College London, London, UK
Interests: numerical analysis; biomathematical modeling; numerical simulations; ordinary differential equations

Special Issue Information

Dear Colleagues,

With the ever-increasing access to high-performance computing and data alongside advances in theoretical understanding of biological and clinical processes throughout mathematical models, new open questions in theoretical and applied biologically have recently emerged in the scientific community. These include the ability to personalize a clinical treatment as well as predict treatment outcomes without the need to widely test it, etc.

Mathematical modelling is shown to be a useful tool in oncology, allowing to investigate both the disease and possible treatments. A particular group of models that are able to obtain accurate predictions of biological and clinical outcomes are the ones based on cell–cell interactions. In parallel, in recent years, immunotherapy treatments have shown promising results with fewer side effects, long-term effects, and better success rates for multiple diseases and cancer in particular. However, many opportunities in cell–cell-interaction-based models for cancer immunotherapy treatment are still available, enriching both our understanding of biomathematical models, clinical processes, and the interactions between them. Specificities underlying each particular setting usually trigger interesting disclosures concerning models and methods of resolution.

The purpose of this Special Issue is to offer a stage for modern applied mathematical models tackling immunotherapy treatments in multiple forms of cancer in order to straighten the connection between theoretical and applied biology and present novel ideas for clinical investigation. We invite authors to submit research articles that fit this purpose.

Dr. Giovanni Amabile
Dr. Teddy Lazebnik
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cells is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • immunotherapy
  • treatment model
  • cancer pharmacokinetics model
  • cancer pharmacodynamics model
  • ODE cancer spread
  • cancer treatment dynamics

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Published Papers (4 papers)

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Research

10 pages, 3361 KiB  
Article
A Mathematical Model for Predicting Patient Responses to Combined Radiotherapy with CTLA-4 Immune Checkpoint Inhibitors
by Yongjin Kim, Bo-Young Choe, Tae Suk Suh and Wonmo Sung
Cells 2023, 12(9), 1305; https://doi.org/10.3390/cells12091305 - 3 May 2023
Cited by 8 | Viewed by 2232
Abstract
The purpose of this study was to develop a cell–cell interaction model that could predict a tumor’s response to radiotherapy (RT) combined with CTLA-4 immune checkpoint inhibition (ICI) in patients with hepatocellular carcinoma (HCC). The previously developed model was extended by adding a [...] Read more.
The purpose of this study was to develop a cell–cell interaction model that could predict a tumor’s response to radiotherapy (RT) combined with CTLA-4 immune checkpoint inhibition (ICI) in patients with hepatocellular carcinoma (HCC). The previously developed model was extended by adding a new term representing tremelimumab, an inhibitor of CTLA-4. The distribution of the new immune activation term was derived from the results of a clinical trial for tremelimumab monotherapy (NCT01008358). The proposed model successfully reproduced longitudinal tumor diameter changes in HCC patients treated with tremelimumab (complete response = 0%, partial response = 17.6%, stable disease = 58.8%, and progressive disease = 23.6%). For the non-irradiated tumor control group, adding ICI to RT increased the clinical benefit rate from 8% to 32%. The simulation predicts that it is beneficial to start CTLA-4 blockade before RT in terms of treatment sequences. We developed a mathematical model that can predict the response of patients to the combined CTLA-4 blockade with radiation therapy. We anticipate that the developed model will be helpful for designing clinical trials with the ultimate aim of maximizing the efficacy of ICI-RT combination therapy. Full article
(This article belongs to the Special Issue Cell-Cell Interaction Modelling of Cancer Immunotherapy Treatments)
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20 pages, 3114 KiB  
Article
Distinct Dynamics of Migratory Response to PD-1 and CTLA-4 Blockade Reveals New Mechanistic Insights for Potential T-Cell Reinvigoration following Immune Checkpoint Blockade
by Fateme Safaeifard, Bahram Goliaei, Amir R. Aref, Mohammad-Hadi Foroughmand-Araabi, Sama Goliaei, Jochen Lorch, Russell W. Jenkins, David A. Barbie, Seyed Peyman Shariatpanahi and Curzio Rüegg
Cells 2022, 11(22), 3534; https://doi.org/10.3390/cells11223534 - 8 Nov 2022
Cited by 3 | Viewed by 2794
Abstract
Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1), two clinically relevant targets for the immunotherapy of cancer, are negative regulators of T-cell activation and migration. Optimizing the therapeutic response to CTLA-4 and PD-1 blockade calls for a more comprehensive [...] Read more.
Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1), two clinically relevant targets for the immunotherapy of cancer, are negative regulators of T-cell activation and migration. Optimizing the therapeutic response to CTLA-4 and PD-1 blockade calls for a more comprehensive insight into the coordinated function of these immune regulators. Mathematical modeling can be used to elucidate nonlinear tumor–immune interactions and highlight the underlying mechanisms to tackle the problem. Here, we investigated and statistically characterized the dynamics of T-cell migration as a measure of the functional response to these pathways. We used a previously developed three-dimensional organotypic culture of patient-derived tumor spheroids treated with anti-CTLA-4 and anti-PD-1 antibodies for this purpose. Experiment-based dynamical modeling revealed the delayed kinetics of PD-1 activation, which originates from the distinct characteristics of PD-1 and CTLA-4 regulation, and followed through with the modification of their contributions to immune modulation. The simulation results show good agreement with the tumor cell reduction and active immune cell count in each experiment. Our findings demonstrate that while PD-1 activation provokes a more exhaustive intracellular cascade within a mature tumor environment, the time-delayed kinetics of PD-1 activation outweighs its preeminence at the individual cell level and consequently confers a functional dominance to the CTLA-4 checkpoint. The proposed model explains the distinct immunostimulatory pattern of PD-1 and CTLA-4 blockade based on mechanisms involved in the regulation of their expression and may be useful for planning effective treatment schemes targeting PD-1 and CTLA-4 functions. Full article
(This article belongs to the Special Issue Cell-Cell Interaction Modelling of Cancer Immunotherapy Treatments)
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15 pages, 1107 KiB  
Article
Cell-Level Spatio-Temporal Model for a Bacillus Calmette–Guérin-Based Immunotherapy Treatment Protocol of Superficial Bladder Cancer
by Teddy Lazebnik
Cells 2022, 11(15), 2372; https://doi.org/10.3390/cells11152372 - 2 Aug 2022
Cited by 11 | Viewed by 1938
Abstract
Bladder cancer is one of the most widespread types of cancer. Multiple treatments for non-invasive, superficial bladder cancer have been proposed over the last several decades with a weekly Bacillus Calmette–Guérin immunotherapy-based therapy protocol, which is considered the gold standard today. Nonetheless, due [...] Read more.
Bladder cancer is one of the most widespread types of cancer. Multiple treatments for non-invasive, superficial bladder cancer have been proposed over the last several decades with a weekly Bacillus Calmette–Guérin immunotherapy-based therapy protocol, which is considered the gold standard today. Nonetheless, due to the complexity of the interactions between the immune system, healthy cells, and cancer cells in the bladder’s microenvironment, clinical outcomes vary significantly among patients. Mathematical models are shown to be effective in predicting the treatment outcome based on the patient’s clinical condition at the beginning of the treatment. Even so, these models still have large errors for long-term treatments and patients that they do not fit. In this work, we utilize modern mathematical tools and propose a novel cell-level spatio-temporal mathematical model that takes into consideration the cell–cell and cell–environment interactions occurring in a realistic bladder’s geometric configuration in order to reduce these errors. We implement the model using the agent-based simulation approach, showing the impacts of different cancer tumor sizes and locations at the beginning of the treatment on the clinical outcomes for today’s gold-standard treatment protocol. In addition, we propose a genetic-algorithm-based approach to finding a successful and time-optimal treatment protocol for a given patient’s initial condition. Our results show that the current standard treatment protocol can be modified to produce cancer-free equilibrium for deeper cancer cells in the urothelium if the cancer cells’ spatial distribution is known, resulting in a greater success rate. Full article
(This article belongs to the Special Issue Cell-Cell Interaction Modelling of Cancer Immunotherapy Treatments)
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18 pages, 1242 KiB  
Article
Validation of a Mathematical Model Describing the Dynamics of Chemotherapy for Chronic Lymphocytic Leukemia In Vivo
by Ekaterina Guzev, Suchita Suryakant Jadhav, Eleonora Ela Hezkiy, Michael Y. Sherman, Michael A. Firer and Svetlana Bunimovich-Mendrazitsky
Cells 2022, 11(15), 2325; https://doi.org/10.3390/cells11152325 - 28 Jul 2022
Cited by 6 | Viewed by 2590
Abstract
In recent years, mathematical models have developed into an important tool for cancer research, combining quantitative analysis and natural processes. We have focused on Chronic Lymphocytic Leukemia (CLL), since it is one of the most common adult leukemias, which remains incurable. As the [...] Read more.
In recent years, mathematical models have developed into an important tool for cancer research, combining quantitative analysis and natural processes. We have focused on Chronic Lymphocytic Leukemia (CLL), since it is one of the most common adult leukemias, which remains incurable. As the first step toward the mathematical prediction of in vivo drug efficacy, we first found that logistic growth best described the proliferation of fluorescently labeled murine A20 leukemic cells injected in immunocompetent Balb/c mice. Then, we tested the cytotoxic efficacy of Ibrutinib (Ibr) and Cytarabine (Cyt) in A20-bearing mice. The results afforded calculation of the killing rate of the A20 cells as a function of therapy. The experimental data were compared with the simulation model to validate the latter’s applicability. On the basis of these results, we developed a new ordinary differential equations (ODEs) model and provided its sensitivity and stability analysis. There was excellent accordance between numerical simulations of the model and results from in vivo experiments. We found that simulations of our model could predict that the combination of Cyt and Ibr would lead to approximately 95% killing of A20 cells. In its current format, the model can be used as a tool for mathematical prediction of in vivo drug efficacy, and could form the basis of software for prediction of personalized chemotherapy. Full article
(This article belongs to the Special Issue Cell-Cell Interaction Modelling of Cancer Immunotherapy Treatments)
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