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Hoffmann K, Pelz A, Karg E, Gottschalk A, Zerjatke T, Schuster S, Böhme H, Glauche I, Roeder I. Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions. PLOS DIGITAL HEALTH 2023; 2:e0000140. [PMID: 37186586 PMCID: PMC10184916 DOI: 10.1371/journal.pdig.0000140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 03/30/2023] [Indexed: 05/17/2023]
Abstract
The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.
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Affiliation(s)
- Katja Hoffmann
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Anne Pelz
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Elena Karg
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Andrea Gottschalk
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Thomas Zerjatke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Silvio Schuster
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Heiko Böhme
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Ingmar Glauche
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ingo Roeder
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
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Rodriguez J, Iniguez A, Jena N, Tata P, Liu ZY, Lander AD, Lowengrub J, Van Etten RA. Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy. eLife 2023; 12:e84149. [PMID: 37115622 PMCID: PMC10212564 DOI: 10.7554/elife.84149] [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: 10/12/2022] [Accepted: 04/26/2023] [Indexed: 04/29/2023] Open
Abstract
Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.
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MESH Headings
- Mice
- Animals
- Tyrosine Kinase Inhibitors
- Protein Kinase Inhibitors/pharmacology
- Protein Kinase Inhibitors/therapeutic use
- Drug Resistance, Neoplasm
- Myelopoiesis
- Fusion Proteins, bcr-abl/genetics
- Fusion Proteins, bcr-abl/pharmacology
- Mice, Transgenic
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
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Affiliation(s)
- Jonathan Rodriguez
- Graduate Program in Mathematical, Computational and Systems Biology, University of California, IrvineIrvineUnited States
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
| | - Abdon Iniguez
- Graduate Program in Mathematical, Computational and Systems Biology, University of California, IrvineIrvineUnited States
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
| | - Nilamani Jena
- Department of Medicine, University of California, IrvineIrvineUnited States
| | - Prasanthi Tata
- Department of Medicine, University of California, IrvineIrvineUnited States
| | - Zhong-Ying Liu
- Department of Medicine, University of California, IrvineIrvineUnited States
| | - Arthur D Lander
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
- Department of Developmental and Cell Biology, University of California, IrvineIrvineUnited States
- Chao Family Comprehensive Cancer Center, University of California, IrvineIrvineUnited States
- Department of Biomedical Engineering, University of California, IrvineIrvineUnited States
| | - John Lowengrub
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
- Chao Family Comprehensive Cancer Center, University of California, IrvineIrvineUnited States
- Department of Biomedical Engineering, University of California, IrvineIrvineUnited States
- Department of Mathematics, University of California, IrvineIrvineUnited States
| | - Richard A Van Etten
- Center for Complex Biological Systems, University of California, IrvineIrvineUnited States
- Department of Medicine, University of California, IrvineIrvineUnited States
- Chao Family Comprehensive Cancer Center, University of California, IrvineIrvineUnited States
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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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A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration. Health Inf Sci Syst 2022; 10:22. [PMID: 36039096 PMCID: PMC9417071 DOI: 10.1007/s13755-022-00183-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/02/2022] [Indexed: 12/02/2022] Open
Abstract
Industry 4.0 era has witnessed that more and more high-tech and precise devices are applied into medical field to provide better services. Besides EMRs, medical data include a large amount of unstructured data such as X-rays, MRI scans, CT scans and PET scans, which is still continually increasing. These massive, heterogeneous multi-modal data bring the big challenge to finding valuable data sets for healthcare researchers and other users. The traditional data warehouses are able to integrate the data and support interactive data exploration through ETL process. However, they have high cost and are not real-time. Furthermore, they lack of the ability to deal with multi-modal data in two phases—data fusion and data exploration. In the data fusion phase, it is difficult to unify the multi-modal data under one data model. In the data exploration phase, it is challenging to explore the multi-modal data at the same time, which impedes the process of extracting the diverse information underlying multi-modal data. Therefore, in order to solve these problems, we propose a highly efficient data fusion framework supporting data exploration for heterogeneous multi-modal medical data based on data lake. This framework provides a novel and efficient method to fuse the fragmented multi-modal medical data and store their metadata in the data lake. It offers a user-friendly interface supporting hybrid graph queries to explore multi-modal data. Indexes are created to accelerate the hybrid data exploration. One prototype has been implemented and tested in a hospital, which demonstrates the effectiveness of our framework.
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de los Reyes AA, Kim Y. Optimal regulation of tumour-associated neutrophils in cancer progression. ROYAL SOCIETY OPEN SCIENCE 2022; 9:210705. [PMID: 35127110 PMCID: PMC8808100 DOI: 10.1098/rsos.210705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 11/19/2021] [Indexed: 06/14/2023]
Abstract
In a tumour microenvironment, tumour-associated neutrophils could display two opposing differential phenotypes: anti-tumour (N1) and pro-tumour (N2) effector cells. Converting N2 to N1 neutrophils provides innovative therapies for cancer treatment. In this study, a mathematical model for N1-N2 dynamics describing the cancer survival and immune inhibition in response to TGF-β and IFN-β is considered. The effects of exogenous intervention of TGF-β inhibitor and IFN-β are examined in order to enhance N1 recruitment to combat tumour progression. Our approach employs optimal control theory to determine drug infusion protocols that could minimize tumour volume with least administration cost possible. Four optimal control scenarios corresponding to different therapeutic strategies are explored, namely, TGF-β inhibitor control only, IFN-β control only, concomitant TGF-β inhibitor and IFN-β controls, and alternating TGF-β inhibitor and IFN-β controls. For each scheme, different initial conditions are varied to depict different pathophysiological condition of a cancer patient, leading to adaptive treatment schedule. TGF-β inhibitor and IFN-β drug dosages, total drug amount, infusion times and relative cost of drug administrations are obtained under various circumstances. The control strategies achieved could guide in designing individualized therapeutic protocols.
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Affiliation(s)
- Aurelio A. de los Reyes
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines
| | - Yangjin Kim
- Department of Mathematics, Konkuk University, Seoul 05029, Republic of Korea
- Mathematical Biosciences Institute, Columbus, OH 43210, USA
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Finding new edges: systems approaches to MTOR signaling. Biochem Soc Trans 2021; 49:41-54. [PMID: 33544134 PMCID: PMC7924996 DOI: 10.1042/bst20190730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/23/2020] [Accepted: 01/05/2021] [Indexed: 11/17/2022]
Abstract
Cells have evolved highly intertwined kinase networks to finely tune cellular homeostasis to the environment. The network converging on the mechanistic target of rapamycin (MTOR) kinase constitutes a central hub that integrates metabolic signals and adapts cellular metabolism and functions to nutritional changes and stress. Feedforward and feedback loops, crosstalks and a plethora of modulators finely balance MTOR-driven anabolic and catabolic processes. This complexity renders it difficult — if not impossible — to intuitively decipher signaling dynamics and network topology. Over the last two decades, systems approaches have emerged as powerful tools to simulate signaling network dynamics and responses. In this review, we discuss the contribution of systems studies to the discovery of novel edges and modulators in the MTOR network in healthy cells and in disease.
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Kheifetz Y, Scholz M. Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances. Br J Clin Pharmacol 2021; 87:3127-3138. [PMID: 33382112 DOI: 10.1111/bcp.14722] [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: 06/08/2020] [Revised: 12/02/2020] [Accepted: 12/20/2020] [Indexed: 11/30/2022] Open
Abstract
AIMS Thrombocytopenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual's thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, 2 dynamic mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semimechanistic model and a comprehensive mechanistic model. In this study, we assess the performance of these models with respect to existing thrombocytopenia grading schemes. METHODS We consider close-meshed individual time series data of 135 non-Hodgkin's lymphoma patients treated with 6 cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models' predictability are assessed by determining deviations of predicted and observed degrees of thrombocytopenia in the next cycles. RESULTS The mechanistic model results in better agreement of model prediction and individual time series data. Prediction accuracy of future cycle toxicities by the mechanistic model is higher even if the semimechanistic model is provided with data of more cycles for calibration. CONCLUSION We successfully established a quantitative and clinically relevant method for assessing prediction performances of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semimechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision-making.
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Affiliation(s)
- Yuri Kheifetz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
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Ngai LL, Kelder A, Janssen JJWM, Ossenkoppele GJ, Cloos J. MRD Tailored Therapy in AML: What We Have Learned So Far. Front Oncol 2021; 10:603636. [PMID: 33575214 PMCID: PMC7871983 DOI: 10.3389/fonc.2020.603636] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/16/2020] [Indexed: 12/22/2022] Open
Abstract
Acute myeloid leukemia (AML) is a heterogeneous clonal disease associated with a dismal survival, partly due to the frequent occurrence of relapse. Many patient- and leukemia-specific characteristics, such as age, cytogenetics, mutations, and measurable residual disease (MRD) after intensive chemotherapy, have shown to be valuable prognostic factors. MRD has become a rich field of research where many advances have been made regarding technical, biological, and clinical aspects, which will be the topic of this review. Since many laboratories involved in AML diagnostics have experience in immunophenotyping, multiparameter flow cytometry (MFC) based MRD is currently the most commonly used method. Although molecular, quantitative PCR based techniques may be more sensitive, their disadvantage is that they can only be applied in a subset of patients harboring the genetic aberration. Next-generation sequencing can assess and quantify mutations in many genes but currently does not offer highly sensitive MRD measurements on a routine basis. In order to provide reliable MRD results, MRD assay optimization and standardization is essential. Different techniques for MRD assessment are being evaluated, and combinations of the methods have shown promising results for improving its prognostic value. In this regard, the load of leukemic stem cells (LSC) has also been shown to add to the prognostic value of MFC-MRD. At this moment, MRD after intensive chemotherapy is most often used as a prognostic factor to help stratify patients, but also to select the most appropriate consolidation therapy. For example, to guide post-remission treatment for intermediate-risk patients where MRD positive patients receive allogeneic stem cell transplantation and MRD negative receive autologous stem cell transplantation. Other upcoming uses of MRD that are being investigated include: selecting the type of allogeneic stem cell transplantation therapy (donor, conditioning), monitoring after stem cell transplantation (to allow intervention), and determining drug efficacy for the use of a surrogate endpoint in clinical trials.
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Affiliation(s)
| | | | | | | | - Jacqueline Cloos
- Department of Hematology, Amsterdam UMC, Cancer Center Amsterdam, Vrije Universiteit, Amsterdam, Netherlands
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Lemmen C, Woopen C, Stock S. Systems medicine 2030: A Delphi study on implementation in the German healthcare system. Health Policy 2020; 125:104-114. [PMID: 33288301 DOI: 10.1016/j.healthpol.2020.11.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 11/06/2020] [Accepted: 11/11/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Systems medicine is based on approaches taken from systems biology, omics research, bio-informatics and network theory. It promises to facilitate a better understanding of the causes of diseases, detection at an earlier stage, and the use of tailor-made approaches to prevention and therapy. This study provides information on how systems medicine could be incorporated into the German healthcare system. METHODS In a Policy Delphi, consensus and divergence was elicited on whether experts believed systems medicine could be incorporated into the German healthcare system by 2030. Additionally, factors that could influence the implementation process were analysed. 11 theses on potential systems medicine developments and 193 arguments on influencing factors were evaluated. Experts from health and health-related fields were selected using "purposive sampling". RESULTS The experts interviewed expressed their trust in the provision of a legal-political framework, though they remained uncertain as to whether the necessary social discourse on the ethical and cultural questions surrounding systems medicine would occur. They do not (currently) expect systems medicine to be implemented by 2030. CONCLUSION Systems medicine is currently regarded as a visionary concept. As such, it would be premature to attempt to judge the success of the translation process at this stage. The results can help with the identification of the challenges involved in implementation, and the action required to achieve this aim.
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Affiliation(s)
- Clarissa Lemmen
- Institute for Health Economics and Clinical Epidemiology, University Hospital Cologne, Cologne, Germany.
| | - Christiane Woopen
- Research Unit Ethics, Institute for the History of Medicine and Medical Ethics, Faculty of Medicine, University of Cologne and University Hospital Cologne, Cologne, Germany; Ceres, Cologne Center for Ethics, Rights, Economics, and Social Sciences of Health, University of Cologne, Universitätsstr. 91, 50931 Cologne, Germany.
| | - Stephanie Stock
- Institute for Health Economics and Clinical Epidemiology, University Hospital Cologne, Cologne, Germany.
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