1
|
Cogno N, Axenie C, Bauer R, Vavourakis V. Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation. Cancer Biol Ther 2024; 25:2344600. [PMID: 38678381 PMCID: PMC11057625 DOI: 10.1080/15384047.2024.2344600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.
Collapse
Affiliation(s)
- Nicolò Cogno
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Institute for Condensed Matter Physics, Technische Universit¨at Darmstadt, Darmstadt, Germany
| | - Cristian Axenie
- Computer Science Department and Center for Artificial Intelligence, Technische Hochschule Nürnberg Georg Simon Ohm, Nuremberg, Germany
| | - Roman Bauer
- Nature Inspired Computing and Engineering Research Group, Computer Science Research Centre, University of Surrey, Guildford, UK
| | - Vasileios Vavourakis
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| |
Collapse
|
2
|
Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
Collapse
Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
| |
Collapse
|
3
|
Zhao R, Xi Z, Liu H, Jian X, Zhang J, Zhang Z, Li S. MIST: Multi-instance selective transformer for histopathological subtype prediction. Med Image Anal 2024; 97:103251. [PMID: 38954942 DOI: 10.1016/j.media.2024.103251] [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: 04/19/2023] [Revised: 01/24/2024] [Accepted: 06/21/2024] [Indexed: 07/04/2024]
Abstract
Accurate histopathological subtype prediction is clinically significant for cancer diagnosis and tumor microenvironment analysis. However, achieving accurate histopathological subtype prediction is a challenging task due to (1) instance-level discrimination of histopathological images, (2) low inter-class and large intra-class variances among histopathological images in their shape and chromatin texture, and (3) heterogeneous feature distribution over different images. In this paper, we formulate subtype prediction as fine-grained representation learning and propose a novel multi-instance selective transformer (MIST) framework, effectively achieving accurate histopathological subtype prediction. The proposed MIST designs an effective selective self-attention mechanism with multi-instance learning (MIL) and vision transformer (ViT) to adaptive identify informative instances for fine-grained representation. Innovatively, the MIST entrusts each instance with different contributions to the bag representation based on its interactions with instances and bags. Specifically, a SiT module with selective multi-head self-attention (S-MSA) is well-designed to identify the representative instances by modeling the instance-to-instance interactions. On the contrary, a MIFD module with the information bottleneck is proposed to learn the discriminative fine-grained representation for histopathological images by modeling instance-to-bag interactions with the selected instances. Substantial experiments on five clinical benchmarks demonstrate that the MIST achieves accurate histopathological subtype prediction and obtains state-of-the-art performance with an accuracy of 0.936. The MIST shows great potential to handle fine-grained medical image analysis, such as histopathological subtype prediction in clinical applications.
Collapse
Affiliation(s)
- Rongchang Zhao
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Zijun Xi
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Huanchi Liu
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Xiangkun Jian
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Jian Zhang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Zijian Zhang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Shuo Li
- School of Computer Science and Engineering, Central South University, Changsha, China; Department of Computer and Data Science and Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA.
| |
Collapse
|
4
|
Luque LM, Carlevaro CM, Rodriguez-Lomba E, Lomba E. In silico study of heterogeneous tumour-derived organoid response to CAR T-cell therapy. Sci Rep 2024; 14:12307. [PMID: 38811838 PMCID: PMC11137006 DOI: 10.1038/s41598-024-63125-5] [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: 02/19/2024] [Accepted: 05/24/2024] [Indexed: 05/31/2024] Open
Abstract
Chimeric antigen receptor (CAR) T-cell therapy is a promising immunotherapy for treating cancers. This method consists in modifying the patients' T-cells to directly target antigen-presenting cancer cells. One of the barriers to the development of this type of therapies, is target antigen heterogeneity. It is thought that intratumour heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model (ABM), built upon a previous ABM, to rationalise the outcomes of different CAR T-cells therapies strategies over heterogeneous tumour-derived organoids. We found that one dose of CAR T-cell therapy should be expected to reduce the tumour size as well as its growth rate, however it may not be enough to completely eliminate it. Moreover, the amount of free CAR T-cells (i.e. CAR T-cells that did not kill any cancer cell) increases as we increase the dosage, and so does the risk of side effects. We tested different strategies to enhance smaller dosages, such as enhancing the CAR T-cells long-term persistence and multiple dosing. For both approaches an appropriate dosimetry strategy is necessary to produce "effective yet safe" therapeutic results. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low antigen expression. This shield turns out to protect cells with high antigen expression. Finally we tested a multi-antigen recognition therapy to overcome antigen escape and heterogeneity. Our studies suggest that larger dosages can completely eliminate the organoid, however the multi-antigen recognition increases the risk of side effects. Therefore, an appropriate small dosages dosimetry strategy is necessary to improve the outcomes. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and to explore the characteristics of successful and unsuccessful treatments.
Collapse
Affiliation(s)
- Luciana Melina Luque
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, EH16 4UU, UK.
| | - Carlos Manuel Carlevaro
- Instituto de Física de Líquidos y Sistemas Biológicos, Consejo Nacional de Investigaciones Científicas y Técnicas, 1900, La Plata, Argentina
- Departamento de Ingeniería Mecánica, Universidad Tecnológica Nacional, Facultad Regional La Plata, 1900, La Plata, Argentina
| | | | - Enrique Lomba
- Instituto de Química Física Blas Cabrera, Consejo Superior de Investigaciones Científicas, 28006, Madrid, Spain
| |
Collapse
|
5
|
Bergman DR, Wang Y, Trujillo E, Fernald AA, Li L, Pearson AT, Sweis RF, Jackson TL. Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model. Front Immunol 2024; 15:1358019. [PMID: 38515743 PMCID: PMC10954792 DOI: 10.3389/fimmu.2024.1358019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 02/21/2024] [Indexed: 03/23/2024] Open
Abstract
Bladder cancer is an increasingly prevalent global disease that continues to cause morbidity and mortality despite recent advances in treatment. Immune checkpoint inhibitors (ICI) and fibroblast growth factor receptor (FGFR)-targeted therapeutics have had modest success in bladder cancer when used as monotherapy. Emerging data suggests that the combination of these two therapies could lead to improved clinical outcomes, but the optimal strategy for combining these agents remains uncertain. Mathematical models, specifically agent-based models (ABMs), have shown recent successes in uncovering the multiscale dynamics that shape the trajectory of cancer. They have enabled the optimization of treatment methods and the identification of novel therapeutic strategies. To assess the combined effects of anti-PD-1 and anti-FGFR3 small molecule inhibitors (SMI) on tumor growth and the immune response, we built an ABM that captures key facets of tumor heterogeneity and CD8+ T cell phenotypes, their spatial interactions, and their response to therapeutic pressures. Our model quantifies how tumor antigenicity and FGFR3 activating mutations impact disease trajectory and response to anti-PD-1 antibodies and anti-FGFR3 SMI. We find that even a small population of weakly antigenic tumor cells bearing an FGFR3 mutation can render the tumor resistant to combination therapy. However, highly antigenic tumors can overcome therapeutic resistance mediated by FGFR3 mutation. The optimal therapy depends on the strength of the FGFR3 signaling pathway. Under certain conditions, ICI alone is optimal; in others, ICI followed by anti-FGFR3 therapy is best. These results indicate the need to quantify FGFR3 signaling and the fitness advantage conferred on bladder cancer cells harboring this mutation. This ABM approach may enable rationally designed treatment plans to improve clinical outcomes.
Collapse
Affiliation(s)
- Daniel R. Bergman
- Department of Mathematics, University of Michigan, Ann Arbor, MI, United States
| | - Yixuan Wang
- Department of Mathematics, University of Michigan, Ann Arbor, MI, United States
| | - Erica Trujillo
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, United States
| | - Anthony A. Fernald
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, United States
| | - Lie Li
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, United States
| | - Alexander T. Pearson
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, United States
| | - Randy F. Sweis
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, United States
| | | |
Collapse
|
6
|
van Genderen MNG, Kneppers J, Zaalberg A, Bekers EM, Bergman AM, Zwart W, Eduati F. Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties. NPJ Syst Biol Appl 2024; 10:20. [PMID: 38383542 PMCID: PMC10881528 DOI: 10.1038/s41540-024-00344-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024] Open
Abstract
Inhibiting androgen receptor (AR) signaling through androgen deprivation therapy (ADT) reduces prostate cancer (PCa) growth in virtually all patients, but response may be temporary, in which case resistance develops, ultimately leading to lethal castration-resistant prostate cancer (CRPC). The tumor microenvironment (TME) plays an important role in the development and progression of PCa. In addition to tumor cells, TME-resident macrophages and fibroblasts express AR and are therefore also affected by ADT. However, the interplay of different TME cell types in the development of CRPC remains largely unexplored. To understand the complex stochastic nature of cell-cell interactions, we created a PCa-specific agent-based model (PCABM) based on in vitro cell proliferation data. PCa cells, fibroblasts, "pro-inflammatory" M1-like and "pro-tumor" M2-like polarized macrophages are modeled as agents from a simple set of validated base assumptions. PCABM allows us to simulate the effect of ADT on the interplay between various prostate TME cell types. The resulting in vitro growth patterns mimic human PCa. Our PCABM can effectively model hormonal perturbations by ADT, in which PCABM suggests that CRPC arises in clusters of resistant cells, as is observed in multifocal PCa. In addition, fibroblasts compete for cellular space in the TME while simultaneously creating niches for tumor cells to proliferate in. Finally, PCABM predicts that ADT has immunomodulatory effects on macrophages that may enhance tumor survival. Taken together, these results suggest that AR plays a critical role in the cellular interplay and stochastic interactions in the TME that influence tumor cell behavior and CRPC development.
Collapse
Affiliation(s)
- Maisa N G van Genderen
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Jeroen Kneppers
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Anniek Zaalberg
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Elise M Bekers
- Division of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Andries M Bergman
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
| | - Wilbert Zwart
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
| |
Collapse
|
7
|
El Nahhas OSM, Loeffler CML, Carrero ZI, van Treeck M, Kolbinger FR, Hewitt KJ, Muti HS, Graziani M, Zeng Q, Calderaro J, Ortiz-Brüchle N, Yuan T, Hoffmeister M, Brenner H, Brobeil A, Reis-Filho JS, Kather JN. Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nat Commun 2024; 15:1253. [PMID: 38341402 PMCID: PMC10858881 DOI: 10.1038/s41467-024-45589-1] [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: 04/11/2023] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
Collapse
Grants
- P30 CA008748 NCI NIH HHS
- JNK is supported by the German Federal Ministry of Health (DEEP LIVER, ZMVI1-2520DAT111) and the Max-Eder-Programme of the German Cancer Aid (grant #70113864), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (Transplant.KI, 01VSF21048) the European Union (ODELIA, 101057091; GENIAL, 101096312) and the National Institute for Health and Care Research (NIHR, NIHR213331) Leeds Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.
Collapse
Affiliation(s)
- Omar S M El Nahhas
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Chiara M L Loeffler
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Zunamys I Carrero
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Marko van Treeck
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Fiona R Kolbinger
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Katherine J Hewitt
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Mara Graziani
- University of Applied Sciences of Western Switzerland (HES-SO Valais), Rue du Technopole 3, 3960, Sierre, Valais, Switzerland
| | - Qinghe Zeng
- Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, Paris, France
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Département de Pathologie, CHU Henri Mondor, F-94000, Créteil, France
| | - Nadina Ortiz-Brüchle
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Cologne, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, 69120, Heidelberg, Germany
- Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Jorge S Reis-Filho
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
| |
Collapse
|
8
|
Leschiera E, Al-Hity G, Flint MS, Venkataraman C, Lorenzi T, Almeida L, Audebert C. An individual-based model to explore the impact of psychological stress on immune infiltration into tumour spheroids. Phys Biol 2024; 21:026003. [PMID: 38266283 DOI: 10.1088/1478-3975/ad221a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/24/2024] [Indexed: 01/26/2024]
Abstract
In recentin vitroexperiments on co-culture between breast tumour spheroids and activated immune cells, it was observed that the introduction of the stress hormone cortisol resulted in a decreased immune cell infiltration into the spheroids. Moreover, the presence of cortisol deregulated the normal levels of the pro- and anti-inflammatory cytokines IFN-γand IL-10. We present an individual-based model to explore the interaction dynamics between tumour and immune cells under psychological stress conditions. With our model, we explore the processes underlying the emergence of different levels of immune infiltration, with particular focus on the biological mechanisms regulated by IFN-γand IL-10. The set-up of numerical simulations is defined to mimic the scenarios considered in the experimental study. Similarly to the experimental quantitative analysis, we compute a score that quantifies the level of immune cell infiltration into the tumour. The results of numerical simulations indicate that the motility of immune cells, their capability to infiltrate through tumour cells, their growth rate and the interplay between these cell parameters can affect the level of immune cell infiltration in different ways. Ultimately, numerical simulations of this model support a deeper understanding of the impact of biological stress-induced mechanisms on immune infiltration.
Collapse
Affiliation(s)
- Emma Leschiera
- Léonard de Vinci Pôle Universitaire, Research Center, 92 916 Paris, La Défense, France
- Univ. Bordeaux, CNRS, INRIA, Bordeaux INP, IMB, UMR 5251, F-33400 Talence, France
| | - Gheed Al-Hity
- School of Applied Sciences, University of Brighton, Centre for Stress and Age-related Diseases, Moulsecoomb, Brighton BN2 4GJ, United Kingdom
| | - Melanie S Flint
- School of Applied Sciences, University of Brighton, Centre for Stress and Age-related Diseases, Moulsecoomb, Brighton BN2 4GJ, United Kingdom
| | - Chandrasekhar Venkataraman
- School of Mathematical and Physical Sciences, University of Sussex, Department of Mathematics, Falmer, Brighton BN1 9QH, United Kingdom
| | - Tommaso Lorenzi
- Department of Mathematical Sciences 'G. L. Lagrange', Politecnico di Torino, 10129 Torino, Italy
| | - Luis Almeida
- Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions UMR 7598, 75005 Paris, France
| | - Chloe Audebert
- Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions UMR 7598, 75005 Paris, France
- Sorbonne Université, CNRS, Institut de biologie Paris-Seine (IBPS), Laboratoire de Biologie Computationnelle et Quantitative UMR 7238, 75005 Paris, France
| |
Collapse
|
9
|
Das S, Dey MK, Devireddy R, Gartia MR. Biomarkers in Cancer Detection, Diagnosis, and Prognosis. SENSORS (BASEL, SWITZERLAND) 2023; 24:37. [PMID: 38202898 PMCID: PMC10780704 DOI: 10.3390/s24010037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/27/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024]
Abstract
Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).
Collapse
Affiliation(s)
| | | | | | - Manas Ranjan Gartia
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (S.D.); (M.K.D.); (R.D.)
| |
Collapse
|
10
|
Eftimie R, Rolin G, Adebayo OE, Urcun S, Chouly F, Bordas SPA. Modelling Keloids Dynamics: A Brief Review and New Mathematical Perspectives. Bull Math Biol 2023; 85:117. [PMID: 37855947 DOI: 10.1007/s11538-023-01222-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023]
Abstract
Keloids are fibroproliferative disorders described by excessive growth of fibrotic tissue, which also invades adjacent areas (beyond the original wound borders). Since these disorders are specific to humans (no other animal species naturally develop keloid-like tissue), experimental in vivo/in vitro research has not led to significant advances in this field. One possible approach could be to combine in vitro human models with calibrated in silico mathematical approaches (i.e., models and simulations) to generate new testable biological hypotheses related to biological mechanisms and improved treatments. Because these combined approaches do not really exist for keloid disorders, in this brief review we start by summarising the biology of these disorders, then present various types of mathematical and computational approaches used for related disorders (i.e., wound healing and solid tumours), followed by a discussion of the very few mathematical and computational models published so far to study various inflammatory and mechanical aspects of keloids. We conclude this review by discussing some open problems and mathematical opportunities offered in the context of keloid disorders by such combined in vitro/in silico approaches, and the need for multi-disciplinary research to enable clinical progress.
Collapse
Affiliation(s)
- R Eftimie
- Laboratoire de Mathématiques de Besançon, Université de Franche-Comté, 25000, Besançon, France.
| | - G Rolin
- INSERM CIC-1431, CHU Besançon, F-25000, Besançon, France
- EFS, INSERM, UMR 1098 RIGHT, Université de Franche-Comté, F-25000, Besançon, France
| | - O E Adebayo
- Laboratoire de Mathématiques de Besançon, Université de Franche-Comté, 25000, Besançon, France
| | - S Urcun
- Institute for Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - F Chouly
- Institut de Mathématiques de Bourgogne, Université de Franche-Comté, 21078, Dijon, France
- Center for Mathematical Modelling and Department of Mathematical Engineering, University of Chile and IRL 2807 - CNRS, Santiago, Chile
- Departamento de Ingeniería Matemática, CI2MA, Universidad de Concepción, Casilla 160-C, Concepción, Chile
| | - S P A Bordas
- Institute for Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| |
Collapse
|
11
|
Passier M, van Genderen MN, Zaalberg A, Kneppers J, Bekers EM, Bergman AM, Zwart W, Eduati F. Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model. CANCER RESEARCH COMMUNICATIONS 2023; 3:1473-1485. [PMID: 37554550 PMCID: PMC10405859 DOI: 10.1158/2767-9764.crc-23-0097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/15/2023] [Accepted: 07/10/2023] [Indexed: 08/10/2023]
Abstract
Over 10% of men will be diagnosed with prostate cancer during their lifetime. Arising from luminal cells of the prostatic acinus, prostate cancer is influenced by multiple cells in its microenvironment. To expand our knowledge and explore means to prevent and treat the disease, it is important to understand what drives the onset and early stages of prostate cancer. In this study, we developed an agent-based model of a prostatic acinus including its microenvironment, to allow for in silico studying of prostate cancer development. The model was based on prior reports and in-house data of tumor cells cocultured with cancer-associated fibroblasts (CAF) and protumor and/or antitumor macrophages. Growth patterns depicted by the model were pathologically validated on hematoxylin and eosin slide images of human prostate cancer specimens. We identified that stochasticity of interactions between macrophages and tumor cells at early stages strongly affect tumor development. In addition, we discovered that more systematic deviations in tumor development result from a combinatorial effect of the probability of acquiring mutations and the tumor-promoting abilities of CAFs and macrophages. In silico modeled tumors were then compared with 494 patients with cancer with matching characteristics, showing strong association between predicted tumor load and patients' clinical outcome. Our findings suggest that the likelihood of tumor formation depends on a combination of stochastic events and systematic characteristics. While stochasticity cannot be controlled, information on systematic effects may aid the development of prevention strategies tailored to the molecular characteristics of an individual patient. Significance We developed a computational model to study which factors of the tumor microenvironment drive prostate cancer development, with potential to aid the development of new prevention strategies.
Collapse
Affiliation(s)
- Margot Passier
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Maisa N.G. van Genderen
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Anniek Zaalberg
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| | - Jeroen Kneppers
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| | - Elise M. Bekers
- Division of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Andries M. Bergman
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| | - Wilbert Zwart
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| |
Collapse
|
12
|
Zhang Y, Wang K, Du Y, Yang H, Jia G, Huang D, Chen W, Shan Y. Computational Modeling to Determine the Effect of Phenotypic Heterogeneity in Tumors on the Collective Tumor-Immune Interactions. Bull Math Biol 2023; 85:51. [PMID: 37142885 DOI: 10.1007/s11538-023-01158-z] [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: 08/02/2022] [Accepted: 04/12/2023] [Indexed: 05/06/2023]
Abstract
Tumor immunotherapy aims to maintain or enhance the killing capability of CD8+ T cells to clear tumor cells. The tumor-immune interactions affect the function of CD8+ T cells. However, the effect of phenotype heterogeneity of a tumor mass on the collective tumor-immune interactions is insufficiently investigated. We developed the cellular-level computational model based on the principle of cellular Potts model to solve the case mentioned above. We considered how asymmetric division and glucose distribution jointly regulated the transient changes in the proportion of proliferating/quiescent tumor cells in a solid tumor mass. The evolution of a tumor mass in contact with T cells was explored and validated by comparing it with previous studies. Our modeling exhibited that proliferating/quiescent tumor cells, exhibiting distinct anti-apoptotic and suppressive behaviors, redistributed within the domain accompanied by the evolution of a tumor mass. Collectively, a tumor mass prone to a quiescent state weakened the collective suppressive functions of a tumor mass on cytotoxic T cells and triggered a decline of apoptosis of tumor cells. Although quiescent tumor cells did not sufficiently do their inhibitory functions, the possibility of long-term survival was improved due to their interior location within a mass. Overall, the proposed model provides a useful framework to investigate collective-targeted strategies for improving the efficiency of immunotherapy.
Collapse
Affiliation(s)
- Yuyuan Zhang
- Department of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Kaiqun Wang
- Department of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Yaoyao Du
- Department of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Huiyuan Yang
- Department of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Guanjie Jia
- Department of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Di Huang
- Department of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Weiyi Chen
- Department of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Yanhu Shan
- School of Instrument and Electronics, North University of China, Taiyuan, 030051, China.
| |
Collapse
|
13
|
Cess CG, Finley SD. Calibrating agent-based models to tumor images using representation learning. PLoS Comput Biol 2023; 19:e1011070. [PMID: 37083821 PMCID: PMC10156003 DOI: 10.1371/journal.pcbi.1011070] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/03/2023] [Accepted: 04/03/2023] [Indexed: 04/22/2023] Open
Abstract
Agent-based models (ABMs) have enabled great advances in the study of tumor development and therapeutic response, allowing researchers to explore the spatiotemporal evolution of the tumor and its microenvironment. However, these models face serious drawbacks in the realm of parameterization-ABM parameters are typically set individually based on various data and literature sources, rather than through a rigorous parameter estimation approach. While ABMs can be fit to simple time-course data (such as tumor volume), that type of data loses the spatial information that is a defining feature of ABMs. Tumor images provide spatial information; however, such images only represent individual timepoints, limiting their utility in calibrating the tumor dynamics predicted by ABMs. Furthermore, it is exceedingly difficult to compare tumor images to ABM simulations beyond a qualitative visual comparison. Without a quantitative method of comparing the similarity of tumor images to ABM simulations, a rigorous parameter fitting is not possible. Here, we present a novel approach that applies neural networks to represent both tumor images and ABM simulations as low dimensional points, with the distance between points acting as a quantitative measure of difference between the two. This enables a quantitative comparison of tumor images and ABM simulations, where the distance between simulated and experimental images can be minimized using standard parameter-fitting algorithms. Here, we describe this method and present two examples to demonstrate the application of the approach to estimate parameters for two distinct ABMs. Overall, we provide a novel method to robustly estimate ABM parameters.
Collapse
Affiliation(s)
- Colin G Cess
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
| | - Stacey D Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California, United States of America
| |
Collapse
|
14
|
Grajek J, Kather JN, Poleszczuk J. An in silico model to study the impact of carbonic anhydrase IX expression on tumour growth and anti-PD-1 therapy. J R Soc Interface 2023; 20:20220654. [PMID: 36695125 PMCID: PMC9874981 DOI: 10.1098/rsif.2022.0654] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/04/2023] [Indexed: 01/26/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) are revolutionary cancer treatments. However, the mechanisms behind their effectiveness are not yet fully understood. Here, we aimed to investigate the role of the pH-regulatory enzyme carbonic anhydrase IX (CAIX) in ICI success. Consequently, we developed an in silico model of the tumour microenvironment. The hybrid model consists of an agent-based model of tumour-immune cell interactions, coupled with a set of diffusion-reaction equations describing substances in the environment. It is calibrated with data from the literature, enabling the study of its qualitative behaviour. In our model, CAIX-expressing tumours acidified their neighbourhood, thereby reducing immune infiltration by 90% (p < 0.001) and resulting in a 25% increase in tumour burden (p < 0.001). Moreover, suppression of CAIX improved the response to anti-PD-1 (23% tumour reduction in CAIX knockouts and 6% in CAIX-expressing tumours, p < 0.001), independently of initial PD-L1 expression. Our simulations suggest that patients with CAIX-expressing tumours could respond favourably to combining ICIs with CAIX suppression, even in the absence of pre-treatment PD-L1 expression. Furthermore, when calibrated with tumour-type-specific data, our model could serve as a high-throughput tool for testing the effectiveness of such a combinatorial approach.
Collapse
Affiliation(s)
- Julia Grajek
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw 02-109, Poland
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden 01309, Germany
| | - Jan Poleszczuk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw 02-109, Poland
| |
Collapse
|
15
|
Yavuz A, Alpsoy A, Gedik EO, Celik MY, Bassorgun CI, Unal B, Elpek GO. Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology. Artif Intell Gastroenterol 2022; 3:142-162. [DOI: 10.35712/aig.v3.i5.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/25/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022] Open
Abstract
Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.
Collapse
Affiliation(s)
- Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Elif Ocak Gedik
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | | | | | - Betul Unal
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| |
Collapse
|
16
|
Cesaro G, Milia M, Baruzzo G, Finco G, Morandini F, Lazzarini A, Alotto P, da Cunha Carvalho de Miranda NF, Trajanoski Z, Finotello F, Di Camillo B. MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach. BIOINFORMATICS ADVANCES 2022; 2:vbac092. [PMID: 36699399 PMCID: PMC9744439 DOI: 10.1093/bioadv/vbac092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/03/2022] [Indexed: 12/10/2022]
Abstract
Motivation Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario. Results We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor-immune dynamics starting from high-throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model.The application to real data of human colorectal cancer tissue investigating the spatio-temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach. Availability and implementation MAST, implemented in Python language, is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast), and a Docker image is provided to ease its deployment. The submitted software version and test data are available in Zenodo at https://dx.doi.org/10.5281/zenodo.7267745. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Collapse
Affiliation(s)
| | | | - Giacomo Baruzzo
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Giovanni Finco
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Francesco Morandini
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Alessio Lazzarini
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Piergiorgio Alotto
- Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
| | | | - Zlatko Trajanoski
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Francesca Finotello
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria,Institute of Molecular Biology, University Innsbruck, 6020 Innsbruck, Austria,Digital Science Center (DiSC), University Innsbruck, 6020 Innsbruck, Austria
| | | |
Collapse
|
17
|
A Hybrid Discrete–Continuum Modelling Approach to Explore the Impact of T-Cell Infiltration on Anti-tumour Immune Response. Bull Math Biol 2022; 84:141. [DOI: 10.1007/s11538-022-01095-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/06/2022] [Indexed: 11/02/2022]
|
18
|
Haddad AF, Young JS, Gill S, Aghi MK. Resistance to immune checkpoint blockade: Mechanisms, counter-acting approaches, and future directions. Semin Cancer Biol 2022; 86:532-541. [PMID: 35276342 PMCID: PMC9458771 DOI: 10.1016/j.semcancer.2022.02.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 02/01/2022] [Accepted: 02/16/2022] [Indexed: 01/27/2023]
Abstract
Immunotherapies seek to unleash the immune system against cancer cells. While a variety of immunotherapies exist, one of the most commonly used is immune checkpoint blockade, which refers to the use of antibodies to interfere with immunosuppressive signaling through immune checkpoint molecules. Therapies against various checkpoints have had success in the clinic across cancer types. However, the efficacy of checkpoint inhibitors has varied across different cancer types and non-responsive patient populations have emerged. Non-responders to these therapies have highlighted the importance of understanding underlying mechanisms of resistance in order to predict which patients will respond and to tailor individual treatment paradigms. In this review we discuss the literature surrounding tumor mediated mechanisms of immune checkpoint resistance. We also describe efforts to overcome resistance and combine checkpoint inhibitors with additional immunotherapies. Finally, we provide insight into the future of immune checkpoint blockade, including the need for improved preclinical modeling and predictive biomarkers to facilitate personalized cancer treatments for patients.
Collapse
Affiliation(s)
| | | | | | - Manish K. Aghi
- Corresponding author at: Department of Neurological Surgery, University of California, San Francisco, 505 Parnassus Ave, M-779, San Francisco, CA 94143-0112, USA. (M.K. Aghi)
| |
Collapse
|
19
|
Giorgadze T, Fischel H, Tessier A, Norton KA. Investigating Two Modes of Cancer-Associated Antigen Heterogeneity in an Agent-Based Model of Chimeric Antigen Receptor T-Cell Therapy. Cells 2022; 11:cells11193165. [PMID: 36231127 PMCID: PMC9561977 DOI: 10.3390/cells11193165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 11/27/2022] Open
Abstract
Simple Summary Chimeric antigen receptor (CAR) T-cell therapy has shown much promise in liquid tumors but often fails in solid tumors. This work uses a computational model to examine under what conditions this therapy might fail or be successful. The model includes interactions between cancer cells, CAR T-cells (treatment), and vascular cells (that feed and support tumor growth). From our results, we determined specific tumor conditions in which CAR T-cell therapy is predicted to fail and suggest a combination treatment that might improve the efficacy of the treatment. Abstract Chimeric antigen receptor (CAR) T-cell therapy has been successful in treating liquid tumors but has had limited success in solid tumors. This work examines unanswered questions regarding CAR T-cell therapy using computational modeling, such as, what percentage of the tumor must express cancer-associated antigens for treatment to be successful? The model includes cancer cell and vascular and CAR T-cell modules that interact with each other. We compare two different models of antigen expression on tumor cells, binary (in which cancer cells are either susceptible or are immune to CAR T-cell therapy) and gradated (where each cancer cell has a probability of being killed by a CAR T-cell). We vary the antigen expression levels within the tumor and determine how effective each treatment is for the two models. The simulations show that the gradated antigen model eliminates the tumor under more parameter values than the binary model. Under both models, shielding, in which the low/non-antigen-expressing cells protect high antigen-expressing cells, reduced the efficacy of CAR T-cell therapy. One prediction is that a combination of CAR T-cell therapies that targets the general population of cells as well as one that specifically targets cancer stem cells should increase its efficacy.
Collapse
|
20
|
Hutchinson LG, Grimm O. Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy. NPJ Digit Med 2022; 5:92. [PMID: 35821064 PMCID: PMC9276679 DOI: 10.1038/s41746-022-00636-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
In oncology clinical trials, on-treatment biopsy samples are taken to confirm the mode of action of new molecules, among other reasons. Yet, the time point of sample collection is typically scheduled according to 'Expert Best Guess'. We have developed an approach integrating digital pathology and mathematical modelling to provide clinical teams with quantitative information to support this decision. Using digitised biopsies from an ongoing clinical trial as the input to an agent-based mathematical model, we have quantitatively optimised and validated the model demonstrating that it accurately recapitulates observed biopsy samples. Furthermore, the validated model can be used to predict the dynamics of simulated biopsies, with applications from protocol design for phase 1–2 studies to the conception of combination therapies, to personalised healthcare.
Collapse
Affiliation(s)
- L G Hutchinson
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - O Grimm
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| |
Collapse
|
21
|
Target-Based Small Molecule Drug Discovery for Colorectal Cancer: A Review of Molecular Pathways and In Silico Studies. Biomolecules 2022; 12:biom12070878. [PMID: 35883434 PMCID: PMC9312989 DOI: 10.3390/biom12070878] [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: 03/21/2022] [Revised: 06/05/2022] [Accepted: 06/17/2022] [Indexed: 01/27/2023] Open
Abstract
Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.
Collapse
|
22
|
Bergman D, Sweis RF, Pearson AT, Nazari F, Jackson TL. A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues. iScience 2022; 25:104387. [PMID: 35637730 PMCID: PMC9142654 DOI: 10.1016/j.isci.2022.104387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/30/2022] [Accepted: 05/05/2022] [Indexed: 11/18/2022] Open
Abstract
Agent-based models (ABMs) are a natural platform for capturing the multiple time and spatial scales in biological processes. However, these models are computationally expensive, especially when including molecular-level effects. The traditional approach to simulating this type of multiscale ABM is to solve a system of ordinary differential equations for the molecular events per cell. This significantly adds to the computational cost of simulations as the number of agents grows, which contributes to many ABMs being limited to around10 5 cells. We propose an approach that requires the same computational time independent of the number of agents. This speeds up the entire simulation by orders of magnitude, allowing for more thorough explorations of ABMs with even larger numbers of agents. We use two systems to show that the new method strongly agrees with the traditionally used approach. This computational strategy can be applied to a wide range of biological investigations.
Collapse
Affiliation(s)
- Daniel Bergman
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Randy F. Sweis
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, 5841 S Maryland Avenue, MC 2115, Chicago, IL 60605, USA
| | - Alexander T. Pearson
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, 5841 S Maryland Avenue, MC 2115, Chicago, IL 60605, USA
| | | | | |
Collapse
|
23
|
Qaiser T, Lee CY, Vandenberghe M, Yeh J, Gavrielides MA, Hipp J, Scott M, Reischl J. Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials. NPJ Precis Oncol 2022; 6:37. [PMID: 35705792 PMCID: PMC9200764 DOI: 10.1038/s41698-022-00275-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 04/27/2022] [Indexed: 11/24/2022] Open
Abstract
Understanding factors that impact prognosis for cancer patients have high clinical relevance for treatment decisions and monitoring of the disease outcome. Advances in artificial intelligence (AI) and digital pathology offer an exciting opportunity to capitalize on the use of whole slide images (WSIs) of hematoxylin and eosin (H&E) stained tumor tissue for objective prognosis and prediction of response to targeted therapies. AI models often require hand-delineated annotations for effective training which may not be readily available for larger data sets. In this study, we investigated whether AI models can be trained without region-level annotations and solely on patient-level survival data. We present a weakly supervised survival convolutional neural network (WSS-CNN) approach equipped with a visual attention mechanism for predicting overall survival. The inclusion of visual attention provides insights into regions of the tumor microenvironment with the pathological interpretation which may improve our understanding of the disease pathomechanism. We performed this analysis on two independent, multi-center patient data sets of lung (which is publicly available data) and bladder urothelial carcinoma. We perform univariable and multivariable analysis and show that WSS-CNN features are prognostic of overall survival in both tumor indications. The presented results highlight the significance of computational pathology algorithms for predicting prognosis using H&E stained images alone and underpin the use of computational methods to improve the efficiency of clinical trial studies.
Collapse
Affiliation(s)
- Talha Qaiser
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK.
| | | | | | - Joe Yeh
- AetherAI, Taipei City, Taiwan
| | | | - Jason Hipp
- Early Oncology, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Marietta Scott
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Joachim Reischl
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK
| |
Collapse
|
24
|
Chen X, Song E. The theory of tumor ecosystem. Cancer Commun (Lond) 2022; 42:587-608. [PMID: 35642770 PMCID: PMC9257988 DOI: 10.1002/cac2.12316] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/08/2022] [Accepted: 05/25/2022] [Indexed: 02/07/2023] Open
Abstract
Cancer cells can be conceived as “living organisms” interacting with cellular or non‐cellular components in the host internal environment, not only the local tumor microenvironment but also the distant organ niches, as well as the immune, nervous and endocrine systems, to construct a self‐sustainable tumor ecosystem. With increasing evidence for the systemic tumor‐host interplay, we predict that a new era of cancer therapy targeting the ecosystemic vulnerability of human malignancies has come. Revolving around the tumor ecosystem scoped as different hierarchies of primary, regional, distal and systemic onco‐spheres, we comprehensively review the tumor‐host interaction among cancer cells and their local microenvironment, distant organ niches, immune, nervous and endocrine systems, highlighting material and energy flow with tumor ecological homeostasis as an internal driving force. We also substantiate the knowledge of visualizing, modelling and subtyping this dynamically intertwined network with recent technological advances, and discuss ecologically rational strategies for more effective cancer therapies.
Collapse
Affiliation(s)
- Xueman Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China
| |
Collapse
|
25
|
Chen S, Zhang M, Wang J, Xu M, Hu W, Wee L, Dekker A, Sheng W, Zhang Z. Automatic Tumor Grading on Colorectal Cancer Whole-Slide Images: Semi-Quantitative Gland Formation Percentage and New Indicator Exploration. Front Oncol 2022; 12:833978. [PMID: 35646672 PMCID: PMC9130480 DOI: 10.3389/fonc.2022.833978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 03/21/2022] [Indexed: 01/14/2023] Open
Abstract
Tumor grading is an essential factor for cancer staging and survival prognostication. The widely used the WHO grading system defines the histological grade of CRC adenocarcinoma based on the density of glandular formation on whole-slide images (WSIs). We developed a fully automated approach for stratifying colorectal cancer (CRC) patients' risk of mortality directly from histology WSI relating to gland formation. A tissue classifier was trained to categorize regions on WSI as glands, stroma, immune cells, background, and other tissues. A gland formation classifier was trained on expert annotations to categorize regions as different degrees of tumor gland formation versus normal tissues. The glandular formation density can thus be estimated using the aforementioned tissue categorization and gland formation information. This estimation was called a semi-quantitative gland formation ratio (SGFR), which was used as a prognostic factor in survival analysis. We evaluated gland formation percentage and validated it by comparing it against the WHO cutoff point. Survival data and gland formation maps were then used to train a spatial pyramid pooling survival network (SPPSN) as a deep survival model. We compared the survival prediction performance of estimated gland formation percentage and the SPPSN deep survival grade and found that the deep survival grade had improved discrimination. A univariable Cox model for survival yielded moderate discrimination with SGFR (c-index 0.62) and deep survival grade (c-index 0.64) in an independent institutional test set. Deep survival grade also showed better discrimination performance in multivariable Cox regression. The deep survival grade significantly increased the c-index of the baseline Cox model in both validation set and external test set, but the inclusion of SGFR can only improve the Cox model less in external test and is unable to improve the Cox model in the validation set.
Collapse
Affiliation(s)
- Shenlun Chen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- MAASTRO (Department of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Meng Zhang
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Midie Xu
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weigang Hu
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Leonard Wee
- MAASTRO (Department of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Andre Dekker
- MAASTRO (Department of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Weiqi Sheng
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhen Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| |
Collapse
|
26
|
T cell therapy against cancer: a predictive diffuse-interface mathematical model informed by pre-clinical studies. J Theor Biol 2022; 547:111172. [DOI: 10.1016/j.jtbi.2022.111172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 11/18/2022]
|
27
|
Classical mathematical models for prediction of response to chemotherapy and immunotherapy. PLoS Comput Biol 2022; 18:e1009822. [PMID: 35120124 PMCID: PMC8903251 DOI: 10.1371/journal.pcbi.1009822] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 03/08/2022] [Accepted: 01/10/2022] [Indexed: 01/02/2023] Open
Abstract
Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, even the simplest textbook models have been barely validated in real world-data of human patients. In this study, we fitted a range of differential equation models to tumor volume measurements of patients undergoing chemotherapy or cancer immunotherapy for solid tumors. We used a large dataset of 1472 patients with three or more measurements per target lesion, of which 652 patients had six or more data points. We show that the early treatment response shows only moderate correlation with the final treatment response, demonstrating the need for nuanced models. We then perform a head-to-head comparison of six classical models which are widely used in the field: the Exponential, Logistic, Classic Bertalanffy, General Bertalanffy, Classic Gompertz and General Gompertz model. Several models provide a good fit to tumor volume measurements, with the Gompertz model providing the best balance between goodness of fit and number of parameters. Similarly, when fitting to early treatment data, the general Bertalanffy and Gompertz models yield the lowest mean absolute error to forecasted data, indicating that these models could potentially be effective at predicting treatment outcome. In summary, we provide a quantitative benchmark for classical textbook models and state-of-the art models of human tumor growth. We publicly release an anonymized version of our original data, providing the first benchmark set of human tumor growth data for evaluation of mathematical models. Mathematical oncology uses quantitative models for prediction of tumor growth and treatment response. The theoretical foundation of mathematical oncology is provided by six classical mathematical models: the Exponential, Logistic, Classic Bertalanffy, General Bertalanffy, Classic Gompertz and General Gompertz model. These models have been introduced decades ago, have been used in thousands of scientific articles and are part of textbooks and curricula in mathematical oncology. However, these models have not been systematically tested in clinical data from actual patients. In this study, we have collected quantitative tumor volume measurements from thousands of patients in five large clinical trials of cancer immunotherapy. We use this dataset to systematically investigate how accurately mathematical models can describe tumor growth, showing that there are pronounced differences between models. In addition, we show that two of these models can predict tumor response to immunotherapy and chemotherapy at later time points when trained on early tumor growth dynamics. Thus, our article closes a conceptual gap in the literature and at the same time provides a simple tool to predict response to chemotherapy and immunotherapy on the level of individual patients.
Collapse
|
28
|
A mathematical model to study the impact of intra-tumour heterogeneity on anti-tumour CD8+ T cell immune response. J Theor Biol 2022; 538:111028. [DOI: 10.1016/j.jtbi.2022.111028] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 12/13/2022]
|
29
|
Bergman D, Marazzi L, Chowkwale M, Maheshvare M D, Bidanta S, Mapder T, Li J. PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell. GIGABYTE 2022; 2022:gigabyte72. [PMID: 36950142 PMCID: PMC10027063 DOI: 10.46471/gigabyte.72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/10/2022] [Indexed: 12/05/2022] Open
Abstract
Pharmacokinetics and pharmacodynamics (PKPD) are key considerations in any study of molecular therapies. It is thus imperative to factor their effects into any in silico model of biological tissue involving such therapies. Furthermore, creating a standardized and flexible framework will benefit the community by increasing access to such modules and enhancing their communicability. PhysiCell is an open-source physics-based cell simulator, i.e., a platform for modeling biological tissue, that is quickly being adopted and utilized by the mathematical biology community. We present here PhysiPKPD, an open-source PhysiCell-based package that allows users to include PKPD in PhysiCell models. Availability & Implementation The source code for PhysiPKPD is located here: https://github.com/drbergman/PhysiPKPD.
Collapse
Affiliation(s)
- Daniel Bergman
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
- Corresponding author. E-mail:
| | - Lauren Marazzi
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Mukti Chowkwale
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Deepa Maheshvare M
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru 560012, India
| | | | - Tarunendu Mapder
- Quantitative Systems Pharmacology, Bristol-Myers Squibb, Princeton Lawrenceville, NJ, USA
| | - Jialun Li
- McGill University, Montréal, QC H3A 0G4, Canada
| |
Collapse
|
30
|
Haslund-Vinding J, Møller JR, Ziebell M, Vilhardt F, Mathiesen T. The role of systemic inflammatory cells in meningiomas. Neurosurg Rev 2021; 45:1205-1215. [PMID: 34716512 DOI: 10.1007/s10143-021-01642-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/27/2021] [Accepted: 09/05/2021] [Indexed: 12/16/2022]
Abstract
The aim of this review is to describe the inflammatory systemic cell infiltrate and its role in pathophysiology and prognostic implications of meningiomas. Articles from PubMed describing inflammation and immune cells in meningioma were systematically selected and reviewed. Infiltrating inflammatory cells are common in meningiomas and correlate with tumor behavior and peritumoral edema. The immune cell infiltrate mainly comprised macrophages, CD4 + T cells of the Th1 and Th2 subtype, CD8 + cytotoxic T cells, mast cells, and to a lesser degree B cells. The polarization of macrophages to M1 or M2 states, as well as the differentiation of T-helper cells to Th1 or Th2 subsets, is of prognostic value, but whether or not the presence of macrophages is associated with the degree of malignancy of the tumor is controversial. The best documented immunosuppressive and tumor-promoting mechanism is the expression of programmed cell death protein 1 (PD-1/PD-1L) which is found on both tumor cells and tumor-infiltrating immune cells. The immune cell infiltration varies between different meningiomas. It contributes to a microenvironment with potential contradictory effects on tumor growth and edema. The immune mechanisms are potential therapeutic targets provided that their effects can be comprehensively understood.
Collapse
Affiliation(s)
- Jeppe Haslund-Vinding
- Department of Neurosurgery, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
| | - Jens Riis Møller
- Department of Neurosurgery, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Morten Ziebell
- Department of Neurosurgery, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Frederik Vilhardt
- Department of Cellular and Molecular Medicine, Panum Institute, University of Copenhagen, Copenhagen, Denmark
| | - Tiit Mathiesen
- Department of Neurosurgery, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Clinical Neuroscience, Section for Neurosurgery, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
31
|
Improving cancer treatments via dynamical biophysical models. Phys Life Rev 2021; 39:1-48. [PMID: 34688561 DOI: 10.1016/j.plrev.2021.10.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/13/2021] [Indexed: 12/17/2022]
Abstract
Despite significant advances in oncological research, cancer nowadays remains one of the main causes of mortality and morbidity worldwide. New treatment techniques, as a rule, have limited efficacy, target only a narrow range of oncological diseases, and have limited availability to the general public due their high cost. An important goal in oncology is thus the modification of the types of antitumor therapy and their combinations, that are already introduced into clinical practice, with the goal of increasing the overall treatment efficacy. One option to achieve this goal is optimization of the schedules of drugs administration or performing other medical actions. Several factors complicate such tasks: the adverse effects of treatments on healthy cell populations, which must be kept tolerable; the emergence of drug resistance due to the intrinsic plasticity of heterogeneous cancer cell populations; the interplay between different types of therapies administered simultaneously. Mathematical modeling, in which a tumor and its microenvironment are considered as a single complex system, can address this complexity and can indicate potentially effective protocols, that would require experimental verification. In this review, we consider classical methods, current trends and future prospects in the field of mathematical modeling of tumor growth and treatment. In particular, methods of treatment optimization are discussed with several examples of specific problems related to different types of treatment.
Collapse
|
32
|
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.
Collapse
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.
| |
Collapse
|
33
|
Application of smart nanoparticles as a potential platform for effective colorectal cancer therapy. Coord Chem Rev 2021. [DOI: 10.1016/j.ccr.2021.213949] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
34
|
Truong VT, Baverel PG, Lythe GD, Vicini P, Yates JWT, Dubois VFS. Step-by-step comparison of ordinary differential equation and agent-based approaches to pharmacokinetic-pharmacodynamic models. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 11:133-148. [PMID: 34399036 PMCID: PMC8846629 DOI: 10.1002/psp4.12703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/28/2021] [Accepted: 05/14/2021] [Indexed: 12/03/2022]
Abstract
Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time‐course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent‐based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent‐based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach.
Collapse
Affiliation(s)
- Van Thuy Truong
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Department of Applied Mathematics, University of Leeds, Leeds, United Kingdom
| | - Paul G Baverel
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Roche Pharma Research and Early Development, Clinical Pharmacology, Pharmaceutical Sciences, Roche Innovation Center Basel F. Hoffmann-La Roche Ltd, Switzerland
| | - Grant D Lythe
- Department of Applied Mathematics, University of Leeds, Leeds, United Kingdom
| | - Paolo Vicini
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Confo Therapeutics, Technologiepark 94, 9052, Ghent (Zwijnaarde), Belgium
| | | | - Vincent F S Dubois
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK
| |
Collapse
|
35
|
Gong C, Ruiz-Martinez A, Kimko H, Popel AS. A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor-Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy. Cancers (Basel) 2021; 13:3751. [PMID: 34359653 PMCID: PMC8345161 DOI: 10.3390/cancers13153751] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/09/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022] Open
Abstract
Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer-immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials.
Collapse
Affiliation(s)
- Chang Gong
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
| | - Alvaro Ruiz-Martinez
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD 20878, USA;
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
- Sidney Kimmel Comprehensive Cancer Center, Department of Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| |
Collapse
|
36
|
Heinrich MA, Mostafa AMRH, Morton JP, Hawinkels LJAC, Prakash J. Translating complexity and heterogeneity of pancreatic tumor: 3D in vitro to in vivo models. Adv Drug Deliv Rev 2021; 174:265-293. [PMID: 33895214 DOI: 10.1016/j.addr.2021.04.018] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 02/08/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive type of cancer with an overall survival rate of less than 7-8%, emphasizing the need for novel effective therapeutics against PDAC. However only a fraction of therapeutics which seemed promising in the laboratory environment will eventually reach the clinic. One of the main reasons behind this low success rate is the complex tumor microenvironment (TME) of PDAC, a highly fibrotic and dense stroma surrounding tumor cells, which supports tumor progression as well as increases the resistance against the treatment. In particular, the growing understanding of the PDAC TME points out a different challenge in the development of efficient therapeutics - a lack of biologically relevant in vitro and in vivo models that resemble the complexity and heterogeneity of PDAC observed in patients. The purpose and scope of this review is to provide an overview of the recent developments in different in vitro and in vivo models, which aim to recapitulate the complexity of PDAC in a laboratory environment, as well to describe how 3D in vitro models can be integrated into drug development pipelines that are already including sophisticated in vivo models. Hereby a special focus will be given on the complexity of in vivo models and the challenges in vitro models face to reach the same levels of complexity in a controllable manner. First, a brief introduction of novel developments in two dimensional (2D) models and ex vivo models is provided. Next, recent developments in three dimensional (3D) in vitro models are described ranging from spheroids, organoids, scaffold models, bioprinted models to organ-on-chip models including a discussion on advantages and limitations for each model. Furthermore, we will provide a detailed overview on the current PDAC in vivo models including chemically-induced models, syngeneic and xenogeneic models, highlighting hetero- and orthotopic, patient-derived tissues (PDX) models, and genetically engineered mouse models. Finally, we will provide a discussion on overall limitations of both, in vitro and in vivo models, and discuss necessary steps to overcome these limitations to reach an efficient drug development pipeline, as well as discuss possibilities to include novel in silico models in the process.
Collapse
Affiliation(s)
- Marcel A Heinrich
- Department of Biomaterials Science and Technology, Section Targeted Therapeutics, Technical Medical Centre, University of Twente, 7500AE Enschede, the Netherlands
| | - Ahmed M R H Mostafa
- Department of Biomaterials Science and Technology, Section Targeted Therapeutics, Technical Medical Centre, University of Twente, 7500AE Enschede, the Netherlands
| | - Jennifer P Morton
- Cancer Research UK, Beatson Institute, Garscube Estate, Switchback Rd, Glasgow G61 1BD, UK; Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Rd, Glasgow G61 1QH, UK
| | - Lukas J A C Hawinkels
- Department of Gastroenterology-Hepatology, Leiden University Medical Centre, PO-box 9600, 2300 RC Leiden, the Netherlands
| | - Jai Prakash
- Department of Biomaterials Science and Technology, Section Targeted Therapeutics, Technical Medical Centre, University of Twente, 7500AE Enschede, the Netherlands.
| |
Collapse
|
37
|
Fu T, Dai LJ, Wu SY, Xiao Y, Ma D, Jiang YZ, Shao ZM. Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response. J Hematol Oncol 2021; 14:98. [PMID: 34172088 PMCID: PMC8234625 DOI: 10.1186/s13045-021-01103-4] [Citation(s) in RCA: 172] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 06/03/2021] [Indexed: 02/08/2023] Open
Abstract
Tumors are not only aggregates of malignant cells but also well-organized complex ecosystems. The immunological components within tumors, termed the tumor immune microenvironment (TIME), have long been shown to be strongly related to tumor development, recurrence and metastasis. However, conventional studies that underestimate the potential value of the spatial architecture of the TIME are unable to completely elucidate its complexity. As innovative high-flux and high-dimensional technologies emerge, researchers can more feasibly and accurately detect and depict the spatial architecture of the TIME. These findings have improved our understanding of the complexity and role of the TIME in tumor biology. In this review, we first epitomized some representative emerging technologies in the study of the spatial architecture of the TIME and categorized the description methods used to characterize these structures. Then, we determined the functions of the spatial architecture of the TIME in tumor biology and the effects of the gradient of extracellular nonspecific chemicals (ENSCs) on the TIME. We also discussed the potential clinical value of our understanding of the spatial architectures of the TIME, as well as current limitations and future prospects in this novel field. This review will bring spatial architectures of the TIME, an emerging dimension of tumor ecosystem research, to the attention of more researchers and promote its application in tumor research and clinical practice.
Collapse
Affiliation(s)
- Tong Fu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Lei-Jie Dai
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Song-Yang Wu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Yi Xiao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Ding Ma
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Yi-Zhou Jiang
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Zhi-Ming Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| |
Collapse
|
38
|
Budithi A, Su S, Kirshtein A, Shahriyari L. Data Driven Mathematical Model of FOLFIRI Treatment for Colon Cancer. Cancers (Basel) 2021; 13:2632. [PMID: 34071939 PMCID: PMC8198096 DOI: 10.3390/cancers13112632] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 12/12/2022] Open
Abstract
Many colon cancer patients show resistance to their treatments. Therefore, it is important to consider unique characteristic of each tumor to find the best treatment options for each patient. In this study, we develop a data driven mathematical model for interaction between the tumor microenvironment and FOLFIRI drug agents in colon cancer. Patients are divided into five distinct clusters based on their estimated immune cell fractions obtained from their primary tumors' gene expression data. We then analyze the effects of drugs on cancer cells and immune cells in each group, and we observe different responses to the FOLFIRI drugs between patients in different immune groups. For instance, patients in cluster 3 with the highest T-reg/T-helper ratio respond better to the FOLFIRI treatment, while patients in cluster 2 with the lowest T-reg/T-helper ratio resist the treatment. Moreover, we use ROC curve to validate the model using the tumor status of the patients at their follow up, and the model predicts well for the earlier follow up days.
Collapse
Affiliation(s)
- Aparajita Budithi
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (A.B.); (S.S.)
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (A.B.); (S.S.)
| | - Arkadz Kirshtein
- Department of Mathematics, Tufts University, Medford, MA 02155, USA;
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (A.B.); (S.S.)
| |
Collapse
|
39
|
Macnamara CK. Biomechanical modelling of cancer: Agent‐based force‐based models of solid tumours within the context of the tumour microenvironment. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2021. [DOI: 10.1002/cso2.1018] [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)
- Cicely K. Macnamara
- School of Mathematics and Statistics Mathematical Institute University of St Andrews St Andrews Fife UK
| |
Collapse
|
40
|
Le T, Su S, Kirshtein A, Shahriyari L. Data-Driven Mathematical Model of Osteosarcoma. Cancers (Basel) 2021; 13:cancers13102367. [PMID: 34068946 PMCID: PMC8156666 DOI: 10.3390/cancers13102367] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/22/2022] Open
Abstract
As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-γ, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.
Collapse
Affiliation(s)
- Trang Le
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (T.L.); (S.S.)
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (T.L.); (S.S.)
| | - Arkadz Kirshtein
- Department of Mathematics, Tufts University, Medford, MA 02155, USA;
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (T.L.); (S.S.)
- Correspondence:
| |
Collapse
|
41
|
Stutz TC, Landeros A, Xu J, Sinsheimer JS, Sehl M, Lange K. Stochastic simulation algorithms for Interacting Particle Systems. PLoS One 2021; 16:e0247046. [PMID: 33651796 PMCID: PMC7924777 DOI: 10.1371/journal.pone.0247046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 01/29/2021] [Indexed: 11/19/2022] Open
Abstract
Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source programming language Julia. We also apply our algorithms to several complex spatial stochastic phenomena. including a rock-paper-scissors game, cancer growth in response to immunotherapy, and lipid oxidation dynamics. Our approach aids in standardizing mathematical models and in generating hypotheses based on concrete mechanistic behavior across a wide range of observed spatial phenomena.
Collapse
Affiliation(s)
- Timothy C. Stutz
- Department of Computational Medicine, University of California, Los Angeles, CA, United States of America
| | - Alfonso Landeros
- Department of Computational Medicine, University of California, Los Angeles, CA, United States of America
| | - Jason Xu
- Department of Statistical Sciences, Duke University, Durham, NC, United States of America
| | - Janet S. Sinsheimer
- Department of Computational Medicine, University of California, Los Angeles, CA, United States of America
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, CA, United States of America
| | - Mary Sehl
- Department of Computational Medicine, University of California, Los Angeles, CA, United States of America
- Division of Hematology-Oncology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America
| | - Kenneth Lange
- Department of Computational Medicine, University of California, Los Angeles, CA, United States of America
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America
- Department of Statistics, University of California, Los Angeles, CA, United States of America
- * E-mail:
| |
Collapse
|
42
|
Cess CG, Finley SD. Multi-scale modeling of macrophage-T cell interactions within the tumor microenvironment. PLoS Comput Biol 2020; 16:e1008519. [PMID: 33362239 PMCID: PMC7790427 DOI: 10.1371/journal.pcbi.1008519] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 01/07/2021] [Accepted: 11/11/2020] [Indexed: 02/06/2023] Open
Abstract
Within the tumor microenvironment, macrophages exist in an immunosuppressive state, preventing T cells from eliminating the tumor. Due to this, research is focusing on immunotherapies that specifically target macrophages in order to reduce their immunosuppressive capabilities and promote T cell function. In this study, we develop an agent-based model consisting of the interactions between macrophages, T cells, and tumor cells to determine how the immune response changes due to three macrophage-based immunotherapeutic strategies: macrophage depletion, recruitment inhibition, and macrophage reeducation. We find that reeducation, which converts the macrophages into an immune-promoting phenotype, is the most effective strategy and that the macrophage recruitment rate and tumor proliferation rate (tumor-specific properties) have large impacts on therapy efficacy. We also employ a novel method of using a neural network to reduce the computational complexity of an intracellular signaling mechanistic model.
Collapse
Affiliation(s)
- Colin G. Cess
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
| | - Stacey D. Finley
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
- Department of Quantitative and Biological Sciences, University of Southern California, Los Angeles, California, United States of America
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California, United States of America
| |
Collapse
|
43
|
Valous NA, Moraleda RR, Jäger D, Zörnig I, Halama N. Interrogating the microenvironmental landscape of tumors with computational image analysis approaches. Semin Immunol 2020; 48:101411. [PMID: 33168423 DOI: 10.1016/j.smim.2020.101411] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/13/2020] [Accepted: 09/04/2020] [Indexed: 02/07/2023]
Abstract
The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain.
Collapse
Affiliation(s)
- Nektarios A Valous
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany.
| | - Rodrigo Rojas Moraleda
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany.
| | - Dirk Jäger
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Inka Zörnig
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Niels Halama
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Division of Translational Immunotherapy, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
| |
Collapse
|
44
|
Mi H, Gong C, Sulam J, Fertig EJ, Szalay AS, Jaffee EM, Stearns V, Emens LA, Cimino-Mathews AM, Popel AS. Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer. Front Physiol 2020; 11:583333. [PMID: 33192595 PMCID: PMC7604437 DOI: 10.3389/fphys.2020.583333] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/24/2020] [Indexed: 12/17/2022] Open
Abstract
Overwhelming evidence has shown the significant role of the tumor microenvironment (TME) in governing the triple-negative breast cancer (TNBC) progression. Digital pathology can provide key information about the spatial heterogeneity within the TME using image analysis and spatial statistics. These analyses have been applied to CD8+ T cells, but quantitative analyses of other important markers and their correlations are limited. In this study, a digital pathology computational workflow is formulated for characterizing the spatial distributions of five immune markers (CD3, CD4, CD8, CD20, and FoxP3) and then the functionality is tested on whole slide images from patients with TNBC. The workflow is initiated by digital image processing to extract and colocalize immune marker-labeled cells and then convert this information to point patterns. Afterward invasive front (IF), central tumor (CT), and normal tissue (N) are characterized. For each region, we examine the intra-tumoral heterogeneity. The workflow is then repeated for all specimens to capture inter-tumoral heterogeneity. In this study, both intra- and inter-tumoral heterogeneities are observed for all five markers across all specimens. Among all regions, IF tends to have higher densities of immune cells and overall larger variations in spatial model fitting parameters and higher density in cell clusters and hotspots compared to CT and N. Results suggest a distinct role of IF in the tumor immuno-architecture. Though the sample size is limited in the study, the computational workflow could be readily reproduced and scaled due to its automatic nature. Importantly, the value of the workflow also lies in its potential to be linked to treatment outcomes and identification of predictive biomarkers for responders/non-responders, and its application to parameterization and validation of computational immuno-oncology models.
Collapse
Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Chang Gong
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Johns Hopkins Mathematical Institute for Data Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States
| | - Alexander S Szalay
- Henry A. Rowland Department of Physics and Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, United States.,Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States.,The Bloomberg∼Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Vered Stearns
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States
| | - Leisha A Emens
- Department of Medicine/Hematology-Oncology, Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Ashley M Cimino-Mathews
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States.,Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States
| |
Collapse
|
45
|
Schürch CM, Bhate SS, Barlow GL, Phillips DJ, Noti L, Zlobec I, Chu P, Black S, Demeter J, McIlwain DR, Kinoshita S, Samusik N, Goltsev Y, Nolan GP. Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front. Cell 2020; 182:1341-1359.e19. [PMID: 32763154 PMCID: PMC7479520 DOI: 10.1016/j.cell.2020.07.005] [Citation(s) in RCA: 348] [Impact Index Per Article: 87.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 04/22/2020] [Accepted: 07/08/2020] [Indexed: 12/21/2022]
Abstract
Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We re-engineered co-detection by indexing (CODEX) for paraffin-embedded tissue microarrays, enabling simultaneous profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers. We identified nine conserved, distinct cellular neighborhoods (CNs)-a collection of components characteristic of the CRC iTME. Enrichment of PD-1+CD4+ T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating how complex biological processes, such as antitumoral immunity, occur through concerted actions of cells and spatial domains.
Collapse
Affiliation(s)
- Christian M Schürch
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
| | - Salil S Bhate
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Graham L Barlow
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Darci J Phillips
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Dermatology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Luca Noti
- Institute of Pathology, University of Bern, 3008 Bern, Switzerland
| | - Inti Zlobec
- Institute of Pathology, University of Bern, 3008 Bern, Switzerland
| | - Pauline Chu
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sarah Black
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Janos Demeter
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David R McIlwain
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shigemi Kinoshita
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Nikolay Samusik
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yury Goltsev
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Garry P Nolan
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
| |
Collapse
|
46
|
Halama N, Haberkorn U. The Unmet Needs of the Diagnosis, Staging, and Treatment of Gastrointestinal Tumors. Semin Nucl Med 2020; 50:389-398. [PMID: 32768003 DOI: 10.1053/j.semnuclmed.2020.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
New scientific insights in cancer biology and immunobiology have changed the clinical practice of medical oncology in recent years. The molecular stratification of solid tumors has led to improved clinical outcomes and is a key part in the diagnostic workup. Beyond mutational spectra (like Rat sarcoma [RAS] mutations or tumor mutational burden), the investigation of the immunological microenvironment has attracted more efforts. Especially as immunotherapies have changed the standard treatment for some solid tumors dramatically and have become an important part of routine oncology, also for gastrointestinal tumors. Still only a subgroup of patients benefits from immunotherapy in gastrointestinal tumors with prominent examples from colorectal, pancreatic or gastric cancer. Not only microsatellite instability as a marker for response to immunotherapy has shown its utility, there plenty of other approaches currently being investigated to better stratify and understand the microenvironment. But these insights have not translated into clinical utility. Reasons for this are limited technical capabilities for stratification and for coping with heterogeneity of tumor cells and the microenvironment as such. So the situation for gastrointestinal tumors has shown mainly progress for a subgroup of immunotherapy-receptive tumors (eg, microsatellite instability), but advances for the remaining majority have been in the area of stratification and combinatorial therapies, including approaches without chemotherapy. Molecular stratification (eg, B-Rapidly Accelerated Fibrosarcoma [BRAF] V600E mutation in colorectal cancer or NRG1 fusions in Kirsten-rat sarcoma (KRAS) Wild-Type Pancreatic Cancer) has clearly improved the possibilities for directed therapies, but there is a plethora of clinical situations where further developments are needed to improve patient care. Finding these areas and identifying the technical approach to unravel the complexities is the next decisive step. Here the recent advances are summarized and an outlook on possible diagnostic and treatment options in areas of unmet need is given with the context of new molecular imaging possibilities and cutting edge advances in nuclear medicine.
Collapse
Affiliation(s)
- Niels Halama
- German Cancer Research Center (DKFZ), Department of Translational Immunotherapy, German Cancer Research Center (DKFZ), Germany; Helmholtz-Institute for Translational Oncology Mainz (HI-TRON Mainz), Germany; Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases (NCT), Heidelberg, Germany; Institute for Immunology, University Hospital Heidelberg, University Heidelberg.
| | - Uwe Haberkorn
- Department of Nuclear Medicine, University Hospital Heidelberg, Germany; Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| |
Collapse
|
47
|
Campenni M, May AN, Boddy A, Harris V, Nedelcu AM. Agent-based modelling reveals strategies to reduce the fitness and metastatic potential of circulating tumour cell clusters. Evol Appl 2020; 13:1635-1650. [PMID: 32821275 PMCID: PMC7428819 DOI: 10.1111/eva.12943] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 02/14/2020] [Accepted: 02/20/2020] [Indexed: 12/11/2022] Open
Abstract
Metastasis-the ability of cancer cells to disperse throughout the body and establish new tumours at distant locations-is responsible for most cancer-related deaths. Although both single and clusters of circulating tumour cells (CTCs) have been isolated from cancer patients, CTC clusters are generally associated with higher metastatic potential and worse prognosis. From an evolutionary perspective, being part of a cluster can provide cells with several benefits both in terms of survival (e.g. protection) and reproduction (group dispersal). Thus, strategies aimed at inducing cluster dissociation could decrease the metastatic potential of CTCs. However, finding agents or conditions that induce the dissociation of CTC clusters is hampered by the fact that their detection, isolation and propagation remain challenging. Here, we used a mechanistic agent-based model to (a) investigate the response of CTC clusters of various sizes and densities to different challenges-in terms of cell survival and cluster stability, and (b) make predictions as to the combination of factors and parameter values that could decrease the fitness and metastatic potential of CTC clusters. Our model shows that the resilience and stability of CTC clusters are dependent on both their size and density. Also, CTC clusters of distinct sizes and densities respond differently to changes in resource availability, with high-density clusters being least affected. In terms of responses to microenvironmental threats (such as drugs), increasing their intensity is, generally, least effective on high-density clusters. Lastly, we found that combining various levels of resource availability and threat intensity can be more effective at decreasing the survival of CTC clusters than each factor alone. We suggest that the complex effects that cluster density and size showed on both the resilience and stability of the CTC clusters are likely to have significant consequences for their metastatic potential and responses to therapies.
Collapse
Affiliation(s)
- Marco Campenni
- BiosciencesUniversity of ExeterPenrynUK
- Department of PsychologyArizona State UniversityTempeAZUSA
| | - Alexander N. May
- Research Casting InternationalQuinte WestONCanada
- Biodesign InstituteArizona State UniversityTempeAZUSA
| | - Amy Boddy
- Biodesign InstituteArizona State UniversityTempeAZUSA
- Department of AnthropologyUniversity of California Santa BarbaraSanta BarbaraCAUSA
| | | | | |
Collapse
|
48
|
Aguilar B, Gibbs DL, Reiss DJ, McConnell M, Danziger SA, Dervan A, Trotter M, Bassett D, Hershberg R, Ratushny AV, Shmulevich I. A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma. Gigascience 2020; 9:giaa075. [PMID: 32696951 PMCID: PMC7374045 DOI: 10.1093/gigascience/giaa075] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 02/14/2020] [Accepted: 06/21/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. RESULTS By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
Collapse
Affiliation(s)
- Boris Aguilar
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David L Gibbs
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David J Reiss
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Mark McConnell
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Samuel A Danziger
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Andrew Dervan
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Matthew Trotter
- BMS Center for Innovation and Translational Research Europe (CITRE), Pabellon de Italia, Calle Isaac Newton 4, Sevilla 41092, Spain
| | - Douglas Bassett
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Robert Hershberg
- Formerly Celgene Corporation, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Alexander V Ratushny
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Ilya Shmulevich
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| |
Collapse
|
49
|
Lapuente-Santana Ó, Eduati F. Toward Systems Biomarkers of Response to Immune Checkpoint Blockers. Front Oncol 2020; 10:1027. [PMID: 32670886 PMCID: PMC7326813 DOI: 10.3389/fonc.2020.01027] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 05/22/2020] [Indexed: 12/13/2022] Open
Abstract
Immunotherapy with checkpoint blockers (ICBs), aimed at unleashing the immune response toward tumor cells, has shown a great improvement in overall patient survival compared to standard therapy, but only in a subset of patients. While a number of recent studies have significantly improved our understanding of mechanisms playing an important role in the tumor microenvironment (TME), we still have an incomplete view of how the TME works as a whole. This hampers our ability to effectively predict the large heterogeneity of patients' response to ICBs. Systems approaches could overcome this limitation by adopting a holistic perspective to analyze the complexity of tumors. In this Mini Review, we focus on how an integrative view of the increasingly available multi-omics experimental data and computational approaches enables the definition of new systems-based predictive biomarkers. In particular, we will focus on three facets of the TME toward the definition of new systems biomarkers. First, we will review how different types of immune cells influence the efficacy of ICBs, not only in terms of their quantification, but also considering their localization and functional state. Second, we will focus on how different cells in the TME interact, analyzing how inter- and intra-cellular networks play an important role in shaping the immune response and are responsible for resistance to immunotherapy. Finally, we will describe the potential of looking at these networks as dynamic systems and how mathematical models can be used to study the rewiring of the complex interactions taking place in the TME.
Collapse
Affiliation(s)
- Óscar Lapuente-Santana
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
| |
Collapse
|
50
|
Metzcar J, Wang Y, Heiland R, Macklin P. A Review of Cell-Based Computational Modeling in Cancer Biology. JCO Clin Cancer Inform 2020; 3:1-13. [PMID: 30715927 PMCID: PMC6584763 DOI: 10.1200/cci.18.00069] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.
Collapse
|