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Butner JD, Dogra P, Chung C, Pasqualini R, Arap W, Lowengrub J, Cristini V, Wang Z. Mathematical modeling of cancer immunotherapy for personalized clinical translation. NATURE COMPUTATIONAL SCIENCE 2022; 2:785-796. [PMID: 38126024 PMCID: PMC10732566 DOI: 10.1038/s43588-022-00377-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2023]
Abstract
Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - John Lowengrub
- Department of Mathematics, University of California at Irvine, Irvine, CA, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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Bitsouni V, Tsilidis V. Mathematical modeling of tumor-immune system interactions: the effect of rituximab on breast cancer immune response. J Theor Biol 2022; 539:111001. [PMID: 34998860 DOI: 10.1016/j.jtbi.2021.111001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/25/2021] [Indexed: 12/11/2022]
Abstract
tBregs are a newly discovered subcategory of B regulatory cells, which are generated by breast cancer, resulting in the increase of Tregs and therefore in the death of NK cells. In this study, we use a mathematical and computational approach to investigate the complex interactions between the aforementioned cells as well as CD8+ T cells, CD4+ T cells and B cells. Furthermore, we use data fitting to prove that the functional response regarding the lysis of breast cancer cells by NK cells has a ratio-dependent form. Additionally, we include in our model the concentration of rituximab - a monoclonal antibody that has been suggested as a potential breast cancer therapy - and test its effect, when the standard, as well as experimental dosages, are administered.
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Affiliation(s)
- Vasiliki Bitsouni
- Department of Mathematics, National and Kapodistrian University of Athens, Panepistimioupolis, GR-15784 Athens, Greece; School of Science and Technology, Hellenic Open University, 18 Parodos Aristotelous Str., GR-26335 Patras, Greece.
| | - Vasilis Tsilidis
- School of Science and Technology, Hellenic Open University, 18 Parodos Aristotelous Str., GR-26335 Patras, Greece.
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Kumbhari A, Rose D, Lee PP, Kim PS. A minimal model of T cell avidity may identify subtherapeutic vaccine schedules. Math Biosci 2021; 334:108556. [PMID: 33539903 DOI: 10.1016/j.mbs.2021.108556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/24/2021] [Accepted: 01/25/2021] [Indexed: 11/17/2022]
Abstract
T cells protect the body from cancer by recognising tumour-associated antigens. Recognising these antigens depends on multiple factors, one of which is T cell avidity, i.e., the total interaction strength between a T cell and a cancer cell. While both high- and low-avidity T cells can kill cancer cells, durable anti-cancer immune responses require the selection of high-avidity T cells. Previous experimentation with anti-cancer vaccines, however, has shown that most vaccines elicit low-avidity T cells. Optimising vaccine schedules may remedy this by preferentially selecting high-avidity T cells. Here, we use mathematical modelling to develop a simple, phenomenological model of avidity selection that may identify vaccine schedules that disproportionately favour low-avidity T cells. We calibrate our model to our prior, more complex model, and then validate it against several experimental data sets. We find that the sensitivity of the model's parameters change with vaccine dosage, which allows us to use a patient's data and clinical history to screen for suitable vaccine strategies.
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Affiliation(s)
- Adarsh Kumbhari
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia
| | - Danya Rose
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia
| | - Peter P Lee
- Department of Immuno-Oncology, City of Hope and Beckman Research Institute, Duarte, CA, USA
| | - Peter S Kim
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia.
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Improving Convergence in Therapy Scheduling Optimization: A Simulation Study. MATHEMATICS 2020. [DOI: 10.3390/math8122114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The infusion times and drug quantities are two primary variables to optimize when designing a therapeutic schedule. In this work, we test and analyze several extensions to the gradient descent equations in an optimal control algorithm conceived for therapy scheduling optimization. The goal is to provide insights into the best strategies to follow in terms of convergence speed when implementing our method in models for dendritic cell immunotherapy. The method gives a pulsed-like control that models a series of bolus injections and aims to minimize a cost a function, which minimizes tumor size and to keep the tumor under a threshold. Additionally, we introduce a stochastic iteration step in the algorithm, which serves to reduce the number of gradient computations, similar to a stochastic gradient descent scheme in machine learning. Finally, we employ the algorithm to two therapy schedule optimization problems in dendritic cell immunotherapy and contrast our method’s stochastic and non-stochastic optimizations.
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Griffiths JI, Wallet P, Pflieger LT, Stenehjem D, Liu X, Cosgrove PA, Leggett NA, McQuerry JA, Shrestha G, Rossetti M, Sunga G, Moos PJ, Adler FR, Chang JT, Sharma S, Bild AH. Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy. Proc Natl Acad Sci U S A 2020; 117:16072-16082. [PMID: 32571915 PMCID: PMC7355015 DOI: 10.1073/pnas.1918937117] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The extent to which immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single-cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled in a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients but not in those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached tumor burden decreases, similar to models of predator-prey interactions; these dynamic patterns were absent in nonresponder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single-cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients' tumor-immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.
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Affiliation(s)
- Jason I Griffiths
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010
- Department of Mathematics, University of Utah, Salt Lake City, UT 84112
| | - Pierre Wallet
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010
| | - Lance T Pflieger
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010
| | - David Stenehjem
- College of Pharmacy, University of Minnesota, Duluth, MN 55812
| | - Xuan Liu
- Department of Integrative Biology and Pharmacology, School of Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Patrick A Cosgrove
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010
| | - Neena A Leggett
- Department of Integrative Biology and Pharmacology, School of Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Jasmine A McQuerry
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010
- Department of Oncological Sciences, School of Medicine, University of Utah, Salt Lake City, UT 84112
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112
| | - Gajendra Shrestha
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112
| | - Maura Rossetti
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, Los Angeles, CA 90095
| | - Gemalene Sunga
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, Los Angeles, CA 90095
| | - Philip J Moos
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112
| | - Frederick R Adler
- Department of Mathematics, University of Utah, Salt Lake City, UT 84112
| | - Jeffrey T Chang
- Department of Integrative Biology and Pharmacology, School of Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Sunil Sharma
- Translational Oncology Research & Drug Discovery, Translational Genomics Research Institute, Phoenix, AZ 85004
| | - Andrea H Bild
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010;
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Long S, Chen J, Hu A, Liu H, Chen Z, Zheng D. Microaneurysms detection in color fundus images using machine learning based on directional local contrast. Biomed Eng Online 2020; 19:21. [PMID: 32295576 PMCID: PMC7161183 DOI: 10.1186/s12938-020-00766-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 04/06/2020] [Indexed: 02/07/2023] Open
Abstract
Background As one of the major
complications of diabetes, diabetic retinopathy (DR) is a leading
cause of visual impairment and blindness due to delayed diagnosis
and intervention. Microaneurysms appear as the earliest symptom of
DR. Accurate and reliable detection of microaneurysms in color
fundus images has great importance for DR screening. Methods A microaneurysms' detection method
using machine learning based on directional local contrast (DLC) is
proposed for the early diagnosis of DR. First, blood vessels were
enhanced and segmented using improved enhancement function based on
analyzing eigenvalues of Hessian matrix. Next, with blood vessels
excluded, microaneurysm candidate regions were obtained using shape
characteristics and connected components analysis. After image
segmented to patches, the features of each microaneurysm candidate
patch were extracted, and each candidate patch was classified into
microaneurysm or non-microaneurysm. The main contributions of our
study are (1) making use of directional local contrast in
microaneurysms' detection for the first time, which does make sense
for better microaneurysms' classification. (2) Applying three
different machine learning techniques for classification and
comparing their performance for microaneurysms' detection. The
proposed algorithm was trained and tested on e-ophtha MA database,
and further tested on another independent DIARETDB1 database.
Results of microaneurysms' detection on the two databases were
evaluated on lesion level and compared with existing algorithms. Results The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively. Conclusions The proposed method
using machine learning based on directional local contrast of image
patches can effectively detect microaneurysms in color fundus images
and provide an effective scientific basis for early clinical DR
diagnosis.
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Affiliation(s)
- Shengchun Long
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Jiali Chen
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China.
| | - Ante Hu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Haipeng Liu
- Research Center of Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, CV1 5RW, UK
| | - Zhiqing Chen
- Eye Center, The second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Dingchang Zheng
- Research Center of Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, CV1 5RW, UK
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