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Cancers
Special Issues
Quantitative Approaches to Cancer Immunotherapy
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Quantitative Approaches to Cancer Immunotherapy

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Immunology and Immunotherapy".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 13175

Special Issue Editors

Dr. Mohammad Kohandel

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Guest Editor
Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
Interests: integrative cancer biology; computational approaches; combinations of cancer therapies; immunotherapy; nano-scale drug delivery systems; tumour microenvironment; cancer stem cells; biomechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Michelle Przedborski

E-Mail
Guest Editor
Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
Interests: cancer immunotherapy; systems biology; mathematical modeling; data science; genetic pathways; apoptosis; metabolic networks

Special Issue Information

Dear Colleagues,

Immunotherapy has the potential to revolutionize cancer treatment by improving patient prognosis for several aggressive cancers. Patients who respond to immunotherapy tend to have durable, long-lasting benefits of treatment. However, there are many unanswered questions regarding how to identify ideal candidates for immunotherapy and how patient-specific biology impacts the treatment response dynamics. This Special Issue welcomes submissions that shed light on these unanswered questions. The issue will focus on quantitative approaches, including mathematical, computational, and machine learning approaches, that can be used to help understand more about the role of patient-specific biology and the tumor microenvironment in the response to cancer immunotherapy. We welcome work that addresses emerging cancer immunotherapies and novel therapeutic combinations that may help to elicit a stronger immune response.

Dr. Mohammad Kohandel
Dr. Michelle Przedborski
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. Cancers 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 2900 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

  • cancer immunotherapy
  • tumor microenvironment
  • mathematical and computational modeling
  • patient response dynamics

Published Papers (3 papers)

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Research

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33 pages, 63412 KiB  
Article
A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor–Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy
by Chang Gong, Alvaro Ruiz-Martinez, Holly Kimko and Aleksander S. Popel
Cancers 2021, 13(15), 3751; https://doi.org/10.3390/cancers13153751 - 26 Jul 2021
Cited by 16 | Viewed by 5233
Abstract
Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that [...] Read more.
Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer–immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials. Full article
(This article belongs to the Special Issue Quantitative Approaches to Cancer Immunotherapy)
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Review

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25 pages, 2411 KiB  
Review
The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy
by Olga Kuczkiewicz-Siemion, Kamil Sokół, Beata Puton, Aneta Borkowska and Anna Szumera-Ciećkiewicz
Cancers 2022, 14(15), 3833; https://doi.org/10.3390/cancers14153833 - 08 Aug 2022
Cited by 4 | Viewed by 2424
Abstract
Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. However, not all patients benefit from immunotherapy equally. The prediction of patient response to this type of therapy [...] Read more.
Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. However, not all patients benefit from immunotherapy equally. The prediction of patient response to this type of therapy is mainly based on conventional immunohistochemistry, which is limited by intraobserver variability, semiquantitative assessment, or single-marker-per-slide evaluation. Multiplex imaging techniques and digital image analysis are powerful tools that could overcome some issues concerning tumour-microenvironment studies. This novel approach to biomarker assessment offers a better understanding of the complicated interactions between tumour cells and their environment. Multiplex labelling enables the detection of multiple markers simultaneously and the exploration of their spatial organisation. Evaluating a variety of immune cell phenotypes and differentiating their subpopulations is possible while preserving tissue histology in most cases. Multiplexing supported by digital pathology could allow pathologists to visualise and understand every cell in a single tissue slide and provide meaning in a complex tumour-microenvironment contexture. This review aims to provide an overview of the different multiplex imaging methods and their application in PD-L1 biomarker assessment. Moreover, we discuss digital imaging techniques, with a focus on slide scanners and software. Full article
(This article belongs to the Special Issue Quantitative Approaches to Cancer Immunotherapy)
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22 pages, 2366 KiB  
Review
Circadian and Immunity Cycle Talk in Cancer Destination: From Biological Aspects to In Silico Analysis
by Mina Mirian, Amirali Hariri, Mahtasadat Yadollahi and Mohammad Kohandel
Cancers 2022, 14(6), 1578; https://doi.org/10.3390/cancers14061578 - 20 Mar 2022
Cited by 7 | Viewed by 4392
Abstract
Cancer is the leading cause of death and a major problem to increasing life expectancy worldwide. In recent years, various approaches such as surgery, chemotherapy, radiation, targeted therapies, and the newest pillar, immunotherapy, have been developed to treat cancer. Among key factors impacting [...] Read more.
Cancer is the leading cause of death and a major problem to increasing life expectancy worldwide. In recent years, various approaches such as surgery, chemotherapy, radiation, targeted therapies, and the newest pillar, immunotherapy, have been developed to treat cancer. Among key factors impacting the effectiveness of treatment, the administration of drugs based on the circadian rhythm in a person and within individuals can significantly elevate drug efficacy, reduce adverse effects, and prevent drug resistance. Circadian clocks also affect various physiological processes such as the sleep cycle, body temperature cycle, digestive and cardiovascular processes, and endocrine and immune systems. In recent years, to achieve precision patterns for drug administration using computational methods, the interaction of the effects of drugs and their cellular pathways has been considered more seriously. Integrated data-derived pathological images and genomics, transcriptomics, and proteomics analyses have provided an understanding of the molecular basis of cancer and dramatically revealed interactions between circadian and immunity cycles. Here, we describe crosstalk between the circadian cycle signaling pathway and immunity cycle in cancer and discuss how tumor microenvironment affects the influence on treatment process based on individuals’ genetic differences. Moreover, we highlight recent advances in computational modeling that pave the way for personalized immune chronotherapy. Full article
(This article belongs to the Special Issue Quantitative Approaches to Cancer Immunotherapy)
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