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Schirru M, Charef H, Ismaili KE, Fenneteau F, Zugaj D, Tremblay PO, Nekka F. Predicting efficacy assessment of combined treatment of radiotherapy and nivolumab for NSCLC patients through virtual clinical trials using QSP modeling. J Pharmacokinet Pharmacodyn 2024:10.1007/s10928-024-09903-0. [PMID: 38493439 DOI: 10.1007/s10928-024-09903-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/05/2024] [Indexed: 03/19/2024]
Abstract
Non-Small Cell Lung Cancer (NSCLC) remains one of the main causes of cancer death worldwide. In the urge of finding an effective approach to treat cancer, enormous therapeutic targets and treatment combinations are explored in clinical studies, which are not only costly, suffer from a shortage of participants, but also unable to explore all prospective therapeutic solutions. Within the evolving therapeutic landscape, the combined use of radiotherapy (RT) and checkpoint inhibitors (ICIs) emerged as a promising avenue. Exploiting the power of quantitative system pharmacology (QSP), we undertook a study to anticipate the therapeutic outcomes of these interventions, aiming to address the limitations of clinical trials. After enhancing a pre-existing QSP platform and accurately replicating clinical data outcomes, we conducted an in-depth study, examining different treatment protocols with nivolumab and RT, both as monotherapy and in combination, by assessing their efficacy through clinical endpoints, namely time to progression (TTP) and duration of response (DOR). As result, the synergy of combined protocols showcased enhanced TTP and extended DOR, suggesting dual advantages of extended response and slowed disease progression with certain combined regimens. Through the lens of QSP modeling, our findings highlight the potential to fine-tune combination therapies for NSCLC, thereby providing pivotal insights for tailoring patient-centric therapeutic interventions.
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Affiliation(s)
- Miriam Schirru
- Laboratoire de recherche en pharmacométrie, Faculté de pharmacie, Université de Montréal, Montreal, Canada.
| | - Hamza Charef
- Laboratoire de recherche en pharmacométrie, Faculté de pharmacie, Université de Montréal, Montreal, Canada
| | - Khalil-Elmehdi Ismaili
- Laboratoire de recherche en pharmacométrie, Faculté de pharmacie, Université de Montréal, Montreal, Canada
| | - Frédérique Fenneteau
- Laboratoire de recherche en pharmacométrie, Faculté de pharmacie, Université de Montréal, Montreal, Canada
| | - Didier Zugaj
- Clinical Pharmacology, Syneos Health, Quebec, Quebec G1P 0A2, Canada
| | | | - Fahima Nekka
- Laboratoire de recherche en pharmacométrie, Faculté de pharmacie, Université de Montréal, Montreal, Canada
- Centre de recherches mathématiques (CRM), Université de Montréal, Montreal, Canada
- Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM), McGill University, Montreal, Canada
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2
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Jo S, Jeon J, Park G, Do HK, Kang J, Ahn KJ, Ma SY, Choi YM, Kim D, Youn B, Ki Y. Aerobic Exercise Improves Radiation Therapy Efficacy in Non-Small Cell Lung Cancer: Preclinical Study Using a Xenograft Mouse Model. Int J Mol Sci 2024; 25:2757. [PMID: 38474004 DOI: 10.3390/ijms25052757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/15/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
The "oxygen effect" improves radiation efficacy; thus, tumor cell oxygen concentration is a crucial factor for improving lung cancer treatment. In the current study, we aimed to identify aerobic exercise-induced changes in oxygen concentrations in non-small cell lung cancer (NSCLC) cells. To this end, an NSCLC xenograft mouse model was established using human A549 cells. Animals were subsequently subjected to aerobic exercise and radiation three times per week for 2 weeks. Aerobic exercise was performed at a speed of 8.0 m/m for 30 min, and the tumor was irradiated with 2 Gy of 6 MV X-rays (total radiation dose 12 Gy). Combined aerobic exercise and radiation reduced NSCLC cell growth. In addition, the positive effect of aerobic exercise on radiation efficacy through oxygenation of tumor cells was confirmed based on hypoxia-inducible factor-1 and carbonic anhydrase IX expression. Finally, whole-transcriptome analysis revealed the key factors that induce oxygenation in NSCLC cells when aerobic exercise was combined with radiation. Taken together, these results indicate that aerobic exercise improves the effectiveness of radiation in the treatment of NSCLC. This preclinical study provides a basis for the clinical application of aerobic exercise to patients with NSCLC undergoing radiation therapy.
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Affiliation(s)
- Sunmi Jo
- Department of Radiation Oncology, Haeundae Paik Hospital, Inje University School of Medicine, Busan 48108, Republic of Korea
| | - Jaewan Jeon
- Department of Radiation Oncology, Haeundae Paik Hospital, Inje University School of Medicine, Busan 48108, Republic of Korea
| | - Geumju Park
- Department of Radiation Oncology, Haeundae Paik Hospital, Inje University School of Medicine, Busan 48108, Republic of Korea
| | - Hwan-Kwon Do
- Department of Physical Medicine and Rehabilitation, Haeundae Paik Hospital, Inje University School of Medicine, Busan 48108, Republic of Korea
| | - JiHoon Kang
- Department of Hematology and Medical Oncology, Winship Cancer Institute of Emory, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Ki Jung Ahn
- Department of Radiation Oncology, Busan Paik Hospital, Inje University School of Medicine, Busan 48108, Republic of Korea
| | - Sun Young Ma
- Department of Radiation Oncology, Kosin University Gospel Hospital, Kosin University College of Medicine, Busan 49267, Republic of Korea
| | - Young Min Choi
- Department of Radiation Oncology, Dong-A University College of Medicine, Busan 49315, Republic of Korea
| | - Donghyun Kim
- Department of Radiation Oncology and Biomedical Research Institute, Pusan National University School of Medicine, Busan 49241, Republic of Korea
| | - BuHyun Youn
- Department of Biological Sciences, Pusan National University, Busan 46241, Republic of Korea
| | - Yongkan Ki
- Department of Radiation Oncology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan 50612, Republic of Korea
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Ganesh SR, Roth CM, Parekkadan B. Simulating Interclonal Interactions in Diffuse Large B-Cell Lymphoma. Bioengineering (Basel) 2023; 10:1360. [PMID: 38135951 PMCID: PMC10740451 DOI: 10.3390/bioengineering10121360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of cancers, accounting for 37% of B-cell tumor cases globally. DLBCL is known to be a heterogeneous disease, resulting in variable clinical presentations and the development of drug resistance. One underexplored aspect of drug resistance is the evolving dynamics between parental and drug-resistant clones within the same microenvironment. In this work, the effects of interclonal interactions between two cell populations-one sensitive to treatment and the other resistant to treatment-on tumor growth behaviors were explored through a mathematical model. In vitro cultures of mixed DLBCL populations demonstrated cooperative interactions and revealed the need for modifying the model to account for complex interactions. Multiple best-fit models derived from in vitro data indicated a difference in steady-state behaviors based on therapy administrations in simulations. The model and methods may serve as a tool for understanding the behaviors of heterogeneous tumors and identifying the optimal therapeutic regimen to eliminate cancer cell populations using computer-guided simulations.
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Affiliation(s)
- Siddarth R. Ganesh
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
| | - Charles M. Roth
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
| | - Biju Parekkadan
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
- Department of Medicine, Rutgers Biomedical Health Sciences, New Brunswick, NJ 08852, USA
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Sulimanov R, Koshelev K, Makarov V, Mezentsev A, Durymanov M, Ismail L, Zahid K, Rumyantsev Y, Laskov I. Mathematical Modeling of Non-Small-Cell Lung Cancer Biology through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids. Life (Basel) 2023; 13:2228. [PMID: 38004368 PMCID: PMC10672646 DOI: 10.3390/life13112228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/06/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023] Open
Abstract
Mathematical models of non-small-cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with "omics" approaches will improve our understanding of the nature of non-small-cell lung cancer. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.
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Affiliation(s)
- Rushan Sulimanov
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 173003 Veliky Novgorod, Russia; (R.S.); (K.K.); (V.M.); (A.M.); (M.D.); (I.L.)
| | - Konstantin Koshelev
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 173003 Veliky Novgorod, Russia; (R.S.); (K.K.); (V.M.); (A.M.); (M.D.); (I.L.)
- Ivannikov Institute for System Programming of the Russian Academy of Science, 109004 Moscow, Russia
| | - Vladimir Makarov
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 173003 Veliky Novgorod, Russia; (R.S.); (K.K.); (V.M.); (A.M.); (M.D.); (I.L.)
| | - Alexandre Mezentsev
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 173003 Veliky Novgorod, Russia; (R.S.); (K.K.); (V.M.); (A.M.); (M.D.); (I.L.)
- School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (L.I.); (K.Z.)
| | - Mikhail Durymanov
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 173003 Veliky Novgorod, Russia; (R.S.); (K.K.); (V.M.); (A.M.); (M.D.); (I.L.)
- School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (L.I.); (K.Z.)
| | - Lilian Ismail
- School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (L.I.); (K.Z.)
| | - Komal Zahid
- School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (L.I.); (K.Z.)
| | - Yegor Rumyantsev
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 173003 Veliky Novgorod, Russia; (R.S.); (K.K.); (V.M.); (A.M.); (M.D.); (I.L.)
| | - Ilya Laskov
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 173003 Veliky Novgorod, Russia; (R.S.); (K.K.); (V.M.); (A.M.); (M.D.); (I.L.)
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5
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Smieja J. Mathematical Modeling Support for Lung Cancer Therapy-A Short Review. Int J Mol Sci 2023; 24:14516. [PMID: 37833963 PMCID: PMC10572824 DOI: 10.3390/ijms241914516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 10/15/2023] Open
Abstract
The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.
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Affiliation(s)
- Jaroslaw Smieja
- Department of Systems Biology and Engineering, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
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Cho YB, Yoon N, Suh JH, Scott JG. Radio-immune response modelling for spatially fractionated radiotherapy. Phys Med Biol 2023; 68:165010. [PMID: 37459862 PMCID: PMC10409909 DOI: 10.1088/1361-6560/ace819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/06/2023] [Accepted: 07/17/2023] [Indexed: 07/28/2023]
Abstract
Objective.Radiation-induced cell death is a complex process influenced by physical, chemical and biological phenomena. Although consensus on the nature and the mechanism of the bystander effect were not yet made, the immune process presumably plays an important role in many aspects of the radiotherapy including the bystander effect. A mathematical model of immune response during and after radiation therapy is presented.Approach.Immune response of host body and immune suppression of tumor cells are modelled with four compartments in this study; viable tumor cells, T cell lymphocytes, immune triggering cells, and doomed cells. The growth of tumor was analyzed in two distinctive modes of tumor status (immune limited and immune escape) and its bifurcation condition.Main results.Tumors in the immune limited mode can grow only up to a finite size, named as terminal tumor volume analytically calculated from the model. The dynamics of the tumor growth in the immune escape mode is much more complex than the tumors in the immune limited mode especially when the status of tumor is close to the bifurcation condition. Radiation can kill tumor cells not only by radiation damage but also by boosting immune reaction.Significance.The model demonstrated that the highly heterogeneous dose distribution in spatially fractionated radiotherapy (SFRT) can make a drastic difference in tumor cell killing compared to the homogeneous dose distribution. SFRT cannot only enhance but also moderate the cell killing depending on the immune response triggered by many factors such as dose prescription parameters, tumor volume at the time of treatment and tumor characteristics. The model was applied to the lifted data of 67NR tumors on mice and a sarcoma patient treated multiple times over 1200 days for the treatment of tumor recurrence as a demonstration.
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Affiliation(s)
- Young-Bin Cho
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, United States of America
- Department of Radiation Oncology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, United States of America
- Department of Biomedical Engineering, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, United States of America
| | - Nara Yoon
- Departmentof Mathematics and Computer Science, Adelphi University, New York, United States of America
| | - John H Suh
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, United States of America
- Department of Radiation Oncology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, United States of America
| | - Jacob G Scott
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, United States of America
- Department of Radiation Oncology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, United States of America
- Department of Translational Hematology and Oncology Research, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, United States of America
- Department of Physics, Case Western Reserve University, Cleveland, United States of America
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Kim Y, Choe BY, Suh TS, Sung W. A Mathematical Model for Predicting Patient Responses to Combined Radiotherapy with CTLA-4 Immune Checkpoint Inhibitors. Cells 2023; 12:cells12091305. [PMID: 37174706 PMCID: PMC10177154 DOI: 10.3390/cells12091305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/27/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
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 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.
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Affiliation(s)
- Yongjin Kim
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Bo-Young Choe
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Tae Suk Suh
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Wonmo Sung
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Bai J, Yu Q, Wang Y, Xu L, Wang J, Zhai J, Bao Q, Guo W, Wu C, Zhang K, Shou W, Zhu G. Iodine-125 brachytherapy suppresses tumor growth and alters bone metabolism in a H1299 xenograft mouse model. MEDICAL ONCOLOGY (NORTHWOOD, LONDON, ENGLAND) 2023; 40:72. [PMID: 36607460 DOI: 10.1007/s12032-022-01937-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 12/16/2022] [Indexed: 01/07/2023]
Abstract
The present study aimed to investigate the efficacy of Iodine-125 (I-125) brachytherapy in a mouse model of non-small cell lung cancer, to further explore the efficacy and appropriate method of implantation of the I-125 radioactive seed. This study also aimed to determine the impact of brachytherapy on bone metabolism. A total of 18 mice were used to establish H1299 xenograft models, and were randomly assigned to three groups. These included non-radioactive seed implantation (Sham IM), fractionated I-125 seed implantation (Fractionated IM) and single I-125 seed implantation (Single IM) groups. Mice were euthanized after 28 days of implantation. H&E staining, Ki67 immunohistochemistry, CD31 morphometric analysis and TUNEL immunofluorescence assays were respectively used to determine the histopathological changes, proliferation, micro-angiogenesis and apoptosis of tumors. In addition, bone volume and microstructure were evaluated using trabecular bone area (Tb.Ar), trabecular thickness (Tb.Th), trabecular number (Tb.N) and cortical thickness. Bone metabolic status was analyzed using histomorphometric staining of tartrate-resistant acid phosphate (TRAP) and alkaline phosphatase (ALP) expression in the femur, and using an ELISA assay to determine the expression of C-telopeptide of type 1 collagen (CTX-1) and procollagen type 1 n-terminal propeptide (P1NP) in the serum. Moreover, reverse transcription-quantitative PCR and western blotting were carried out for the analysis of bone remodeling-related gene expression in the bone tissue. Results of the present study demonstrated that compared with the Sham IM group, both the I-125 seed implantation groups, including Fractionated IM and Single IM, demonstrated significant therapeutic effects in both tumor volume and weight. More specifically, the most significant therapeutic effects on tumor inhibition were observed in the Fractionated IM group. Results of Ki67 and CD31 immunohistochemical staining suggested a notable reduction in tumor cell proliferation and micro-angiogenesis, and results of the TUNEL assay demonstrated an increase in tumor cell apoptosis. Although the cortical bone appeared thinner and more fragile in both I-125 seed implantation groups, no notable adverse changes in the morphology of the cancellous bone were observed, and the index of Tb.Ar, Tb.Th and Tb.n was not significantly different among Sham IM and I-125 implantation groups. However, alterations in bone metabolism were characterized by a decrease in CTX-1 and P1NP expression, accompanied by an increase in TRAP activity and a decrease in ALP activity. Results of the present study also demonstrated the notable suppression of osteocalcin and runt-related transcription factor 2. I-125 seed implantation may be an effective and safe antitumor strategy. Moreover, the use of fractionated implantation patterns based on tumor shape exhibited improved therapeutic effect on tumor suppression when the total number of I-125 seeds was equivalent along with reduced complications associated with bone loss.
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Affiliation(s)
- Jiangtao Bai
- Institute of Radiation Medicine, Fudan University, Shanghai, China
- Shanghai Municipal Center for Disease Control & Prevention, Shanghai, China
| | - Qiquan Yu
- Longhua Hospital Affiliated to Shanghai TCM University, 725, South Wanping Road, Shanghai, China
| | - Yuyang Wang
- Institute of Radiation Medicine, Fudan University, Shanghai, China
- Shanghai Municipal Center for Disease Control & Prevention, Shanghai, China
| | - Linshan Xu
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Jianping Wang
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Jianglong Zhai
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Qi Bao
- Longhua Hospital Affiliated to Shanghai TCM University, 725, South Wanping Road, Shanghai, China
| | - Wentao Guo
- Longhua Hospital Affiliated to Shanghai TCM University, 725, South Wanping Road, Shanghai, China
| | - Chunxiao Wu
- Longhua Hospital Affiliated to Shanghai TCM University, 725, South Wanping Road, Shanghai, China
| | - Kun Zhang
- Longhua Hospital Affiliated to Shanghai TCM University, 725, South Wanping Road, Shanghai, China
| | - Weizhen Shou
- Longhua Hospital Affiliated to Shanghai TCM University, 725, South Wanping Road, Shanghai, China.
| | - Guoying Zhu
- Institute of Radiation Medicine, Fudan University, Shanghai, China.
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Chu J, Zhang Y, Huang F, Si L, Huang S, Huang Z. Disentangled representation for sequential treatment effect estimation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107175. [PMID: 36242866 DOI: 10.1016/j.cmpb.2022.107175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Treatment effect estimation, as a fundamental problem in causal inference, focuses on estimating the outcome difference between different treatments. However, in clinical observational data, some patient covariates (such as gender, age) not only affect the outcomes but also affect the treatment assignment. Such covariates, named as confounders, produce distribution discrepancies between different treatment groups, thereby introducing the selection bias for the estimation of treatment effects. The situation is even more complicated in longitudinal data, because the confounders are time-varying that are subject to patient history and meanwhile affect the future outcomes and treatment assignments. Existing methods mainly work on cross-sectional data obtained at a specific time point, but cannot process the time-varying confounders hidden in the longitudinal data. METHODS In this study, we address this problem for the first time by disentangled representation learning, which considers the observational data as consisting of three components, including outcome-specific factors, treatment-specific factors, and time-varying confounders. Based on this, the proposed approach adopts a recurrent neural network-based framework to process sequential information and learn the disentangled representations of the components from longitudinal observational sequences, captures the posterior distributions of latent factors by multi-task learning strategy. Moreover, mutual information-based regularization is adopted to eliminate the time-varying confounders. In this way, the association between patient history and treatment assignment is removed and the estimation can be effectively conducted. RESULTS We evaluate our model in a realistic set-up using a model of tumor growth. The proposed model achieves the best performance over benchmark models for both one-step ahead prediction (0.70% vs 0.74% for the-state-of-the-art model, when γ = 3. Measured by normalized root mean square error, the lower the better) and five-step ahead prediction (1.47% vs 1.83%) in most cases. By increasing the effect of confounders, our proposed model always shows superiority against the state-of-the-art model. In addition, we adopted T-SNE to visualize the disentangled representations and present the effectiveness of disentanglement explicitly and intuitively. CONCLUSIONS The experimental results indicate the powerful capacity of our model in learning disentangled representations from longitudinal observational data and dealing with the time-varying confounders, and demonstrate the surpassing performance achieved by our proposed model on dynamic treatment effect estimation.
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Affiliation(s)
- Jiebin Chu
- Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Yaoyun Zhang
- Alibaba Group, Hangzhou, Zhejiang Province, China
| | - Fei Huang
- Alibaba Group, Hangzhou, Zhejiang Province, China
| | - Luo Si
- Alibaba Group, Hangzhou, Zhejiang Province, China
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10
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Artificial Intelligence for Outcome Modeling in Radiotherapy. Semin Radiat Oncol 2022; 32:351-364. [DOI: 10.1016/j.semradonc.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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11
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Yang EY, Howard GR, Brock A, Yankeelov TE, Lorenzo G. Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin. Front Mol Biosci 2022; 9:972146. [PMID: 36172049 PMCID: PMC9510895 DOI: 10.3389/fmolb.2022.972146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/17/2022] [Indexed: 11/20/2022] Open
Abstract
The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the cytotoxic effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that treatment with doxorubicin induces a compartmentalization of the breast cancer cell population into surviving cells, which continue proliferating after treatment, and irreversibly damaged cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor cell dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the cytotoxic effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.
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Affiliation(s)
- Emily Y. Yang
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Grant R. Howard
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
- Interdisciplinary Life Sciences Program, The University of Texas at Austin, Austin, TX, United States
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, The University of Texas at Austin, Austin, TX, United States
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- *Correspondence: Guillermo Lorenzo, ,
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12
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Cardinal O, Burlot C, Fu Y, Crosley P, Hitt M, Craig M, Jenner AL. Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using
in silico
clinical trials. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2022. [DOI: 10.1002/cso2.1035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Olivia Cardinal
- Department of Mathematics and Statistics Université de Montréal Montréal Quebec Canada
| | - Chloé Burlot
- Department of Mathematics and Statistics Université de Montréal Montréal Quebec Canada
| | - Yangxin Fu
- Department of Oncology University of Alberta Edmonton Alberta Canada
| | - Powel Crosley
- Department of Oncology University of Alberta Edmonton Alberta Canada
| | - Mary Hitt
- Department of Oncology University of Alberta Edmonton Alberta Canada
| | - Morgan Craig
- Department of Mathematics and Statistics Université de Montréal Montréal Quebec Canada
- Research Centre Sainte‐Justine University Hospital Montréal Quebec Canada
| | - Adrianne L. Jenner
- Department of Mathematics and Statistics Université de Montréal Montréal Quebec Canada
- Research Centre Sainte‐Justine University Hospital Montréal Quebec Canada
- School of Mathematical Sciences Queensland University of Technology Brisbane Queensland
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13
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Sharma M, Bakshi AK, Mittapelly N, Gautam S, Marwaha D, Rai N, Singh N, Tiwari P, Aggarwal N, Kumar A, Mishra PR. Recent updates on innovative approaches to overcome drug resistance for better outcomes in cancer. J Control Release 2022; 346:43-70. [DOI: 10.1016/j.jconrel.2022.04.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/04/2022] [Accepted: 04/05/2022] [Indexed: 02/07/2023]
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14
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Mathur D, Barnett E, Scher HI, Xavier JB. Optimizing the future: how mathematical models inform treatment schedules for cancer. Trends Cancer 2022; 8:506-516. [PMID: 35277375 DOI: 10.1016/j.trecan.2022.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 01/25/2022] [Accepted: 02/14/2022] [Indexed: 11/18/2022]
Abstract
For decades, mathematical models have influenced how we schedule chemotherapeutics. More recently, mathematical models have leveraged lessons from ecology, evolution, and game theory to advance predictions of optimal treatment schedules, often in a personalized medicine manner. We discuss both established and emerging therapeutic strategies that deviate from canonical standard-of-care regimens, and how mathematical models have contributed to the design of such schedules. We first examine scheduling options for single therapies and review the advantages and disadvantages of various treatment plans. We then consider the challenge of scheduling multiple therapies, and review the mathematical and clinical support for various conflicting treatment schedules. Finally, we propose how a consilience of mathematical and clinical knowledge can best determine the optimal treatment schedules for patients.
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Affiliation(s)
- Deepti Mathur
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ethan Barnett
- Genitourinary Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Howard I Scher
- Genitourinary Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joao B Xavier
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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15
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Liu J, Hormuth DA, Yang J, Yankeelov TE. A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy Parameterized via Time-Resolved Microscopy Data. Front Oncol 2022; 12:811415. [PMID: 35186747 PMCID: PMC8855115 DOI: 10.3389/fonc.2022.811415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/17/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose Conventional radiobiology models, including the linear-quadratic model, do not explicitly account for the temporal effects of radiation, thereby making it difficult to make time-resolved predictions of tumor response to fractionated radiation. To overcome this limitation, we propose and validate an experimental-computational approach that predicts the changes in cell number over time in response to fractionated radiation. Methods We irradiated 9L and C6 glioma cells with six different fractionation schemes yielding a total dose of either 16 Gy or 20 Gy, and then observed their response via time-resolved microscopy. Phase-contrast images and Cytotox Red images (to label dead cells) were collected every 4 to 6 hours up to 330 hours post-radiation. Using 75% of the total data (i.e., 262 9L curves and 211 C6 curves), we calibrated a two-species model describing proliferative and senescent cells. We then applied the calibrated parameters to a validation dataset (the remaining 25% of the data, i.e., 91 9L curves and 74 C6 curves) to predict radiation response. Model predictions were compared to the microscopy measurements using the Pearson correlation coefficient (PCC) and the concordance correlation coefficient (CCC). Results For the 9L cells, we observed PCCs and CCCs between the model predictions and validation data of (mean ± standard error) 0.96 ± 0.007 and 0.88 ± 0.013, respectively, across all fractionation schemes. For the C6 cells, we observed PCCs and CCCs between model predictions and the validation data were 0.89 ± 0.008 and 0.75 ± 0.017, respectively, across all fractionation schemes. Conclusion By proposing a time-resolved mathematical model of fractionated radiation response that can be experimentally verified in vitro, this study is the first to establish a framework for quantitative characterization and prediction of the dynamic radiobiological response of 9L and C6 gliomas to fractionated radiotherapy.
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Affiliation(s)
- Junyan Liu
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States
| | - Jianchen Yang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, The University of Texas at Austin, Austin, TX, United States
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Thomas E. Yankeelov,
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16
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Cui T, Liu P, Din A. Fractal-fractional and stochastic analysis of norovirus transmission epidemic model with vaccination effects. Sci Rep 2021; 11:24360. [PMID: 34934111 PMCID: PMC8692620 DOI: 10.1038/s41598-021-03732-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/07/2021] [Indexed: 11/09/2022] Open
Abstract
In this paper, we investigate an norovirus (NoV) epidemic model with stochastic perturbation and the new definition of a nonlocal fractal-fractional derivative in the Atangana-Baleanu-Caputo (ABC) sense. First we present some basic properties including equilibria and the basic reproduction number of the model. Further, we analyze that the proposed stochastic system has a unique global positive solution. Next, the sufficient conditions of the extinction and the existence of a stationary probability measure for the disease are established. Furthermore, the fractal-fractional dynamics of the proposed model under Atangana-Baleanu-Caputo (ABC) derivative of fractional order "[Formula: see text]" and fractal dimension "[Formula: see text]" have also been addressed. Besides, coupling the non-linear functional analysis with fixed point theory, the qualitative analysis of the proposed model has been performed. The numerical simulations are carried out to demonstrate the analytical results. It is believed that this study will comprehensively strengthen the theoretical basis for comprehending the dynamics of the worldwide contagious diseases.
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Affiliation(s)
- Ting Cui
- School of Economics, Guangdong University of Finance and Economics, Guangzhou, 510320, People's Republic of China
| | - Peijiang Liu
- School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, 510320, People's Republic of China. .,School of Statistics and Mathematics, Guangdong University of Finance and Economics, Big Data and Educational Statistics Application Laboratory, Guangzhou, 510320, People's Republic of China.
| | - Anwarud Din
- Department of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
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17
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Alfonso JCL, Grass GD, Welsh E, Ahmed KA, Teer JK, Pilon-Thomas S, Harrison LB, Cleveland JL, Mulé JJ, Eschrich SA, Torres-Roca JF, Enderling H. Tumor-immune ecosystem dynamics define an individual Radiation Immune Score to predict pan-cancer radiocurability. Neoplasia 2021; 23:1110-1122. [PMID: 34619428 PMCID: PMC8502777 DOI: 10.1016/j.neo.2021.09.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 01/10/2023] Open
Abstract
Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of antitumor immune responses. We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability. We show that iRIS correlates with local control and survival in a separate cohort of 59 lung cancer patients treated with radiation. In combination, iRIS and RSI predict radiation-induced TIES shifts in individual patients and identify candidates for radiation de-escalation and treatment escalation. This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy.
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Affiliation(s)
- Juan C L Alfonso
- Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - G Daniel Grass
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Eric Welsh
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Kamran A Ahmed
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jamie K Teer
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Shari Pilon-Thomas
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Louis B Harrison
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - John L Cleveland
- Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - James J Mulé
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Steven A Eschrich
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Javier F Torres-Roca
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Heiko Enderling
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
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18
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Moradi Kashkooli F, Soltani M, Momeni MM. Computational modeling of drug delivery to solid tumors: A pilot study based on a real image. J Drug Deliv Sci Technol 2021. [DOI: 10.1016/j.jddst.2021.102347] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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19
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Kozłowska E, Suwiński R, Giglok M, Świerniak A, Kimmel M. Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet. PLoS Comput Biol 2020; 16:e1008234. [PMID: 33017420 PMCID: PMC7561182 DOI: 10.1371/journal.pcbi.1008234] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 10/15/2020] [Accepted: 08/10/2020] [Indexed: 11/23/2022] Open
Abstract
We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004–2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers. Lung cancer is usually diagnosed at an advanced stage because of non-specific symptoms. The most common subtype of lung cancer is non-small cell lung cancer, which constitutes 80% of lung cancer cases. Here, we developed the methodology for finding the optimal treatment schedule for patients treated with palliative intent. The goal is not to cure the patients who are at an advanced stage but to prolong their survival by the administration of platinum-based chemotherapy. The method is based on the mathematical model describing the growth of tumors and its response to chemotherapy which is calibrated using real clinical data.
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Affiliation(s)
- Emilia Kozłowska
- Department of Systems Biology and Engineering, Silesian University of Technology, Akademicka Gliwice, Poland
- * E-mail:
| | - Rafał Suwiński
- The 2 Radiotherapy and Chemotherapy Clinic, M. Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Monika Giglok
- The 2 Radiotherapy and Chemotherapy Clinic, M. Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Andrzej Świerniak
- Department of Systems Biology and Engineering, Silesian University of Technology, Akademicka Gliwice, Poland
| | - Marek Kimmel
- Department of Systems Biology and Engineering, Silesian University of Technology, Akademicka Gliwice, Poland
- Departments of Statistics and Bioengineering, Rice University, Houston Texas, United States of America
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20
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Dehghan M, Narimani N. Radial basis function-generated finite difference scheme for simulating the brain cancer growth model under radiotherapy in various types of computational domains. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105641. [PMID: 32726719 DOI: 10.1016/j.cmpb.2020.105641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 06/28/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES We extend the original mathematical model, i.e., Swanson's reaction-diffusion equation to the surfaces with no boundary, and we find a new numerical method based on a meshless approach for solving numerically Swanson's reaction-diffusion model in the square and on the sphere. METHODS To solve numerically the Swanson's reaction-diffusion model and its extension version, a collocation meshless technique, namely radial basis function-generated finite difference (RBF-FD) scheme is employed for approximating the spatial variables in the square domain and on the sphere, respectively. Also, to approximate the time variable of the studied models, a first-order semi-implicit backward Euler scheme is used. The resulting fully discrete scheme is a linear system of algebraic equations per time step that is solved via the biconjugate gradient stabilized (BiCGSTAB) iterative algorithm with a zero-fill incomplete lower-upper (ILU) preconditioner. RESULTS The numerical simulations show the growth of untreated and treated brain tumors with radiotherapy using estimated and clinical data (given from magnetic resonance imaging (MRI) scans of patients). Moreover, the results reported here can be used for improving the treatment strategies of the invasive brain tumor. CONCLUSIONS Using the developed numerical scheme in this paper, we can simulate the behavior of the invasive form of brain tumor response to radiotherapy. Also, we can see the effects of radiation response on the brain tumor cell concentration of individual patients. The proposed meshless technique, which is applied for solving numerically the studied model, does not depend on any background mesh or triangulation for approximation in comparison with mesh-dependent methods. Moreover, we apply this technique to the sphere via any set of distributed points easily.
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Affiliation(s)
- Mehdi Dehghan
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914, Iran.
| | - Niusha Narimani
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914, Iran.
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21
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McClatchy DM, Willers H, Hata AN, Piotrowska Z, Sequist LV, Paganetti H, Grassberger C. Modeling Resistance and Recurrence Patterns of Combined Targeted-Chemoradiotherapy Predicts Benefit of Shorter Induction Period. Cancer Res 2020; 80:5121-5133. [PMID: 32907839 DOI: 10.1158/0008-5472.can-19-3883] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/17/2020] [Accepted: 09/03/2020] [Indexed: 12/23/2022]
Abstract
Optimal integration of molecularly targeted therapies, such as tyrosine kinase inhibitors (TKI), with concurrent chemotherapy and radiation (CRT) to improve outcomes in genotype-defined cancers remains a current challenge in clinical settings. Important questions regarding optimal scheduling and length of induction period for neoadjuvant use of targeted agents remain unsolved and vary among clinical trial protocols. Here, we develop and validate a biomathematical framework encompassing drug resistance and radiobiology to simulate patterns of local versus distant recurrences in a non-small cell lung cancer (NSCLC) population with mutated EGFR receiving TKIs and CRT. Our model predicted that targeted induction before CRT, an approach currently being tested in clinical trials, may render adjuvant targeted therapy less effective due to proliferation of drug-resistant cancer cells when using very long induction periods. Furthermore, simulations not only demonstrated the competing effects of drug-resistant cell expansion versus overall tumor regression as a function of induction length, but also directly estimated the probability of observing an improvement in progression-free survival at a given cohort size. We thus demonstrate that such stochastic biological simulations have the potential to quantitatively inform the design of multimodality clinical trials in genotype-defined cancers. SIGNIFICANCE: A biomathematical framework based on fundamental principles of evolution and radiobiology for in silico clinical trial design allows clinicians to optimize administration of TKIs before chemoradiotherapy in oncogene-driven NSCLC.
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Affiliation(s)
- David M McClatchy
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts.
| | - Henning Willers
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Aaron N Hata
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital Cancer Center, Charlestown, Massachusetts
| | - Zofia Piotrowska
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital Cancer Center, Charlestown, Massachusetts
| | - Lecia V Sequist
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital Cancer Center, Charlestown, Massachusetts
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts.
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22
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Sung W, Grassberger C, McNamara AL, Basler L, Ehrbar S, Tanadini-Lang S, Hong TS, Paganetti H. A tumor-immune interaction model for hepatocellular carcinoma based on measured lymphocyte counts in patients undergoing radiotherapy. Radiother Oncol 2020; 151:73-81. [PMID: 32679308 DOI: 10.1016/j.radonc.2020.07.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE The impact of radiation therapy on the immune system has recently gained attention particularly when delivered in combination with immunotherapy. However, it is unclear how different treatment fractionation regimens influence the interaction between the immune system and radiation. The goal of this work was to develop a mathematical model that quantifies both the immune stimulating as well as the immunosuppressive effects of radiotherapy and simulates the effects of different fractionation regimens based on patient data. METHODS AND MATERIALS The framework describes the temporal evolution of tumor cells, lymphocytes, and inactivated dying tumor cells releasing antigens during radiation therapy, specifically modeling how recruited lymphocytes inhibit tumor progression. The parameters of the model were partly taken from the literature and in part extracted from blood samples (circulating lymphocytes: CLs) collected from hepatocellular carcinoma patients undergoing radiotherapy and their outcomes. The dose volume histograms to circulating lymphocytes were calculated with a probability-based model. RESULTS Based on the fitted parameters, the model enabled a study into the depletion and recovery of CLs in patients as a function of fractionation regimen. Our results quantify the ability of short fractionation regimens to lead to shorter periods of lymphocyte depletion and predict faster recovery after the end of treatment. The model shows that treatment breaks between fractions can prolong the period of lymphocyte depletion and should be avoided. CONCLUSIONS This study introduces a mathematical model for tumor-immune interactions using clinically extracted radiotherapy patient data, which can be applied to design trials aimed at minimizing lymphocyte depleting effects in radiation therapy.
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Affiliation(s)
- Wonmo Sung
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States
| | - Aimee Louise McNamara
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States
| | - Lucas Basler
- Department of Radiation Oncology, Paul Scherrer Institut, Villigen, Switzerland
| | - Stefanie Ehrbar
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | | | - Theodore S Hong
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States.
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23
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Bica I, Alaa AM, Lambert C, van der Schaar M. From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges. Clin Pharmacol Ther 2020; 109:87-100. [PMID: 32449163 DOI: 10.1002/cpt.1907] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 05/14/2020] [Indexed: 12/21/2022]
Abstract
Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real-world observational data, such as electronic health records (EHRs), contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modeling choices of the state-of-the-art machine learning methods for causal inference, developed for estimating treatment effects both in the cross-section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging EHRs and machine learning for making individualized treatment recommendations. We also discuss how experimental data from RCTs and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on RCTs and known disease processes, physiology, and pharmacology into these machine learning models based on EHRs to fully optimize the opportunity these data present.
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Affiliation(s)
- Ioana Bica
- University of Oxford, Oxford, UK.,The Alan Turing Institute, London, UK
| | - Ahmed M Alaa
- University of California - Los Angeles, Los Angeles, California, USA
| | - Craig Lambert
- Clinical Pharmacology and Safety Sciences, Research and Development, AstraZeneca, Cambridge, UK
| | - Mihaela van der Schaar
- The Alan Turing Institute, London, UK.,University of California - Los Angeles, Los Angeles, California, USA.,University of Cambridge, Cambridge, UK
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24
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Butner JD, Elganainy D, Wang CX, Wang Z, Chen SH, Esnaola NF, Pasqualini R, Arap W, Hong DS, Welsh J, Koay EJ, Cristini V. Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy. SCIENCE ADVANCES 2020; 6:eaay6298. [PMID: 32426472 PMCID: PMC7190324 DOI: 10.1126/sciadv.aay6298] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 01/21/2020] [Indexed: 05/20/2023]
Abstract
We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti-CTLA-4 or anti-PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common (n = 18) and rare (n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Dalia Elganainy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles X. Wang
- MD/PhD Program, Drexel University College of Medicine, Philadelphia, PA 19102, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shu-Hsia Chen
- Immunotherapy Research Center, Houston Methodist Research Institute, Houston, TX, USA
- Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
| | - Nestor F. Esnaola
- Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Surgical Oncology, Fox Chase Cancer Center—Temple Health, Philadelphia, PA, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey at University Hospital, Newark, NJ, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey at University Hospital, Newark, NJ, USA
- Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - David S. Hong
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James Welsh
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Corresponding author. (V.C.); (E.J.K.)
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- MD/PhD Program, Drexel University College of Medicine, Philadelphia, PA 19102, USA
- Corresponding author. (V.C.); (E.J.K.)
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25
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Grassberger C, McClatchy D, Geng C, Kamran SC, Fintelmann F, Maruvka YE, Piotrowska Z, Willers H, Sequist LV, Hata AN, Paganetti H. Patient-Specific Tumor Growth Trajectories Determine Persistent and Resistant Cancer Cell Populations during Treatment with Targeted Therapies. Cancer Res 2019; 79:3776-3788. [PMID: 31113818 DOI: 10.1158/0008-5472.can-18-3652] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 04/10/2019] [Accepted: 05/17/2019] [Indexed: 12/30/2022]
Abstract
The importance of preexisting versus acquired drug resistance in patients with cancer treated with small-molecule tyrosine kinase inhibitors (TKI) remains controversial. The goal of this study is to provide a general estimate of the size and dynamics of a preexisting, drug-resistant tumor cell population versus a slow-growing persister population that is the precursor of acquired TKI resistance. We describe a general model of resistance development, including persister evolution and preexisting resistance, solely based on the macroscopic trajectory of tumor burden during treatment. We applied the model to 20 tumor volume trajectories of EGFR-mutant lung cancer patients treated with the TKI erlotinib. Under the assumption of only preexisting resistant cells or only persister evolution, it is not possible to explain the observed tumor trajectories with realistic parameter values. Assuming only persister evolution would require very high mutation induction rates, while only preexisting resistance would lead to very large preexisting populations of resistant cells at the initiation of treatment. However, combining preexisting resistance with persister populations can explain the observed tumor volume trajectories and yields an estimated preexisting resistant fraction varying from 10-4 to 10-1 at the time of treatment initiation for this study cohort. Our results also demonstrate that the growth rate of the resistant population is highly correlated to the time to tumor progression. These estimates of the size of the resistant and persistent tumor cell population during TKI treatment can inform combination treatment strategies such as multi-agent schedules or a combination of targeted agents and radiotherapy. SIGNIFICANCE: These findings quantify pre-existing resistance and persister cell populations, which are essential for the integration of targeted agents into the management of locally advanced disease and the timing of radiotherapy in metastatic patients.
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Affiliation(s)
- Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
| | - David McClatchy
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
| | - Changran Geng
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sophia C Kamran
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Florian Fintelmann
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yosef E Maruvka
- Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Broad Institute of the Massachusetts Institute of Technology (MIT) and Harvard University, Cambridge, Massachusetts
| | - Zofia Piotrowska
- Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Massachusetts General Hospital Cancer Center, Carlestown, Massachusetts
| | - Henning Willers
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Lecia V Sequist
- Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Massachusetts General Hospital Cancer Center, Carlestown, Massachusetts
| | - Aaron N Hata
- Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Massachusetts General Hospital Cancer Center, Carlestown, Massachusetts
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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26
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Spring BQ, Lang RT, Kercher EM, Rizvi I, Wenham RM, Conejo-Garcia JR, Hasan T, Gatenby RA, Enderling H. Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies. FRONTIERS IN PHYSICS 2019; 7:46. [PMID: 31123672 PMCID: PMC6529192 DOI: 10.3389/fphy.2019.00046] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cancer photomedicine offers unique mechanisms for inducing local tumor damage with the potential to stimulate local and systemic anti-tumor immunity. Optically-active nanomedicine offers these features as well as spatiotemporal control of tumor-focused drug release to realize synergistic combination therapies. Achieving quantitative dosimetry is a major challenge, and dosimetry is fundamental to photomedicine for personalizing and tailoring therapeutic regimens to specific patients and anatomical locations. The challenge of dosimetry is perhaps greater for photomedicine than many standard therapies given the complexity of light delivery and light-tissue interactions as well as the resulting photochemistry responsible for tumor damage and drug-release, in addition to the usual intricacies of therapeutic agent delivery. An emerging multidisciplinary approach in oncology utilizes mathematical and computational models to iteratively and quantitively analyze complex dosimetry, and biological response parameters. These models are parameterized by preclinical and clinical observations and then tested against previously unseen data. Such calibrated and validated models can be deployed to simulate treatment doses, protocols, and combinations that have not yet been experimentally or clinically evaluated and can provide testable optimal treatment outcomes in a practical workflow. Here, we foresee the utility of these computational approaches to guide adaptive therapy, and how mathematical models might be further developed and integrated as a novel methodology to guide precision photomedicine.
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Affiliation(s)
- Bryan Q. Spring
- Translational Biophotonics Cluster, Northeastern University, Boston, MA, United States
- Department of Physics, Northeastern University, Boston, MA, United States
- Department of Bioengineering, Northeastern University, Boston, MA, United States
| | - Ryan T. Lang
- Translational Biophotonics Cluster, Northeastern University, Boston, MA, United States
- Department of Physics, Northeastern University, Boston, MA, United States
| | - Eric M. Kercher
- Translational Biophotonics Cluster, Northeastern University, Boston, MA, United States
- Department of Physics, Northeastern University, Boston, MA, United States
| | - Imran Rizvi
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, United States
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Robert M. Wenham
- Department of Gynecologic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - José R. Conejo-Garcia
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Tayyaba Hasan
- Wellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Robert A. Gatenby
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
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27
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Sunassee ED, Tan D, Ji N, Brady R, Moros EG, Caudell JJ, Yartsev S, Enderling H. Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses. Int J Radiat Biol 2019; 95:1421-1426. [PMID: 30831050 DOI: 10.1080/09553002.2019.1589013] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Purpose: Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of non-identifiability and clinically unrealistic results. Materials and methods: We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients to predict patient-specific responses to subsequent radiation doses. Results: Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R2=0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index = 0.89). Conclusion: The PSI model may be suited to forecast treatment response for individual patients and offers actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.
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Affiliation(s)
- Enakshi D Sunassee
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Dean Tan
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Nathan Ji
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Renee Brady
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Eduardo G Moros
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA.,Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Jimmy J Caudell
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Slav Yartsev
- London Health Sciences Centre, London Regional Cancer Program , London , ON , Canada
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA.,Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
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28
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Mee T, Kirkby NF, Kirkby KJ. Mathematical Modelling for Patient Selection in Proton Therapy. Clin Oncol (R Coll Radiol) 2018; 30:299-306. [PMID: 29452724 DOI: 10.1016/j.clon.2018.01.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Accepted: 01/08/2018] [Indexed: 12/17/2022]
Abstract
Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality-adjusted life-years and Markov Chain models are all mathematical and statistical modelling techniques currently used but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.
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Affiliation(s)
- T Mee
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University, Manchester Academic Health Science Centre, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK.
| | - N F Kirkby
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University, Manchester Academic Health Science Centre, Manchester, UK
| | - K J Kirkby
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University, Manchester Academic Health Science Centre, Manchester, UK
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29
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Chou CY, Chang WI, Horng TL, Lin WL. Numerical modeling of nanodrug distribution in tumors with heterogeneous vasculature. PLoS One 2017; 12:e0189802. [PMID: 29287079 PMCID: PMC5747441 DOI: 10.1371/journal.pone.0189802] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 12/02/2017] [Indexed: 01/25/2023] Open
Abstract
The distribution and accumulation of nanoparticle dosage in a tumor are important in evaluating the effectiveness of cancer treatment. The cell survival rate can quantify the therapeutic effect, and the survival rates after multiple treatments are helpful to evaluate the efficacy of a chemotherapy plan. We developed a mathematical tumor model based on the governing equations describing the fluid flow and particle transport to investigate the drug transportation in a tumor and computed the resulting cumulative concentrations. The cell survival rate was calculated based on the cumulative concentration. The model was applied to a subcutaneous tumor with heterogeneous vascular distributions. Various sized dextrans and doxorubicin were respectively chosen as the nanodrug carrier and the traditional chemotherapeutic agent for comparison. The results showed that: 1) the largest nanoparticle drug in the current simulations yielded the highest cumulative concentration in the well vascular region, but second lowest in the surrounding normal tissues, which implies it has the best therapeutic effect to tumor and at the same time little harmful to normal tissue; 2) on the contrary, molecular chemotherapeutic agent produced the second lowest cumulative concentration in the well vascular tumor region, but highest in the surrounding normal tissue; 3) all drugs have very small cumulative concentrations in the tumor necrotic region, where drug transport is solely through diffusion. This might mean that it is hard to kill tumor stem cells hiding in it. The current model indicated that the effectiveness of the anti-tumor drug delivery was determined by the interplay of the vascular density and nanoparticle size, which governs the drug transport properties. The use of nanoparticles as anti-tumor drug carriers is generally a better choice than molecular chemotherapeutic agent because of its high treatment efficiency on tumor cells and less damage to normal tissues.
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Affiliation(s)
- Cheng-Ying Chou
- Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan
- * E-mail: (CYC); (TLH)
| | - Wan-I Chang
- Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Tzyy-Leng Horng
- Department of Applied Mathematics, Feng Chia University, Taichung, Taiwan
- * E-mail: (CYC); (TLH)
| | - Win-Li Lin
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli County, Taiwan
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