1
|
Hickey JW, Agmon E, Horowitz N, Tan TK, Lamore M, Sunwoo JB, Covert MW, Nolan GP. Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy. Cell Syst 2024; 15:322-338.e5. [PMID: 38636457 PMCID: PMC11030795 DOI: 10.1016/j.cels.2024.03.004] [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: 12/14/2022] [Revised: 03/07/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- John W Hickey
- 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 Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Center for Cell Analysis and Modeling, University of Connecticut Health, Farmington, CT 06032, USA
| | - Nina Horowitz
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Tze-Kai Tan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew Lamore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - John B Sunwoo
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Otolaryngology, Head and Neck Surgery, Stanford Cancer Institute Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
| |
Collapse
|
2
|
Cain JY, Evarts JI, Yu JS, Bagheri N. Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae131. [PMID: 38444088 PMCID: PMC10957516 DOI: 10.1093/bioinformatics/btae131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 02/08/2024] [Accepted: 03/01/2024] [Indexed: 03/07/2024]
Abstract
MOTIVATION Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. RESULTS Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. AVAILABILITY AND IMPLEMENTATION All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.
Collapse
Affiliation(s)
- Jason Y Cain
- Department of Chemical Engineering, University of Washington, Seattle, WA 98195, United States
| | - Jacob I Evarts
- Department of Biology, University of Washington, Seattle, WA 98195, United States
| | - Jessica S Yu
- Department of Biology, University of Washington, Seattle, WA 98195, United States
| | - Neda Bagheri
- Department of Chemical Engineering, University of Washington, Seattle, WA 98195, United States
- Department of Biology, University of Washington, Seattle, WA 98195, United States
| |
Collapse
|
3
|
Yu JS, Bagheri N. Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions. PLoS Comput Biol 2024; 20:e1011917. [PMID: 38457450 PMCID: PMC10954156 DOI: 10.1371/journal.pcbi.1011917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 03/20/2024] [Accepted: 02/12/2024] [Indexed: 03/10/2024] Open
Abstract
Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels-system, cell, and environment-within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.
Collapse
Affiliation(s)
- Jessica S. Yu
- Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
- Biology, University of Washington, Seattle, Washington, United States of America
| | - Neda Bagheri
- Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
- Biology, University of Washington, Seattle, Washington, United States of America
- Chemical Engineering, University of Washington, Seattle, Washington, United States of America
| |
Collapse
|
4
|
Hardman D, Hennig K, Gomes ER, Roman W, Bernabeu MO. An in vitro agent-based modelling approach to optimization of culture medium for generating muscle cells. J R Soc Interface 2024; 21:20230603. [PMID: 38228184 PMCID: PMC10791523 DOI: 10.1098/rsif.2023.0603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/11/2023] [Indexed: 01/18/2024] Open
Abstract
Methodologies for culturing muscle tissue are currently lacking in terms of quality and quantity of mature cells produced. We analyse images from in vitro experiments to quantify the effects of culture media composition on mouse-derived myoblast behaviour and myotube quality. Metrics of early indicators of cell quality were defined. Images of muscle cell differentiation reveal that altering culture media significantly affects quality indicators and myoblast migratory behaviours. To study the effects of early-stage cell behaviours on mature cell quality, metrics drawn from experimental images or inferred by approximate Bayesian computation (ABC) were applied as inputs to an agent-based model (ABM) of skeletal muscle cell differentiation with quality indicator metrics as outputs. Computational modelling was used to inform further in vitro experiments to predict the optimum media composition for culturing muscle cells. Our results suggest that myonuclei production in myotubes is inversely related to early-stage nuclei fusion index and that myonuclei density and spatial distribution are correlated with residence time of fusing myoblasts, the age at which myotube-myotube fusion ends and the repulsion force between myonuclei. Culture media with 5% serum was found to produce the optimum cell quality and to make muscle cells cultured in a neuron differentiation medium viable.
Collapse
Affiliation(s)
- David Hardman
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh EH16 4UX, UK
| | - Katharina Hennig
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal
| | - Edgar R. Gomes
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal
| | - William Roman
- Australian Regenerative Medicine Institute, Monash University, Clayton, Australia
| | - Miguel O. Bernabeu
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh EH16 4UX, UK
- The Bayes Centre, University of Edinburgh, Edinburgh EH8 9BT, UK
| |
Collapse
|
5
|
Iwasa JH, Lyons B, Johnson GT. The dawn of interoperating spatial models in cell biology. Curr Opin Biotechnol 2022; 78:102838. [PMID: 36402095 DOI: 10.1016/j.copbio.2022.102838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 06/01/2022] [Accepted: 10/07/2022] [Indexed: 11/18/2022]
Abstract
Spatial simulations are becoming an increasingly ubiquitous component in the cycle of discovery, experimentation, and communication across the sciences. In cell biology, many researchers share a vision of developing multiscale models that recapitulate observable behaviors spanning from atoms to cells to tissues. For this dream to become a reality, however, simulation technologies must provide a means for integration and interoperability as they advance. Already, the field has developed numerous methods that span scales of length, time, and complexity to create an extensive body of effective simulation approaches, and although these approaches rarely interoperate, they collectively cover a large spectrum of knowledge that future models may handle in a more unified manner. Here, we discuss the importance of making the data, workflows, and outputs of spatial simulations shareable and interoperable; and how democratization could encourage diverse biologists to participate more easily in developing models to advance our understanding of biological systems.
Collapse
Affiliation(s)
| | - Blair Lyons
- Visualization & Data Integration, Allen Institute for Cell Science, USA
| | - Graham T Johnson
- Visualization & Data Integration, Allen Institute for Cell Science, USA.
| |
Collapse
|
6
|
Prybutok AN, Yu JS, Leonard JN, Bagheri N. Mapping CAR T-Cell Design Space Using Agent-Based Models. Front Mol Biosci 2022; 9:849363. [PMID: 35903149 PMCID: PMC9315201 DOI: 10.3389/fmolb.2022.849363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 05/23/2022] [Indexed: 12/15/2022] Open
Abstract
Chimeric antigen receptor (CAR) T-cell therapy shows promise for treating liquid cancers and increasingly for solid tumors as well. While potential design strategies exist to address translational challenges, including the lack of unique tumor antigens and the presence of an immunosuppressive tumor microenvironment, testing all possible design choices in vitro and in vivo is prohibitively expensive, time consuming, and laborious. To address this gap, we extended the modeling framework ARCADE (Agent-based Representation of Cells And Dynamic Environments) to include CAR T-cell agents (CAR T-cell ARCADE, or CARCADE). We conducted in silico experiments to investigate how clinically relevant design choices and inherent tumor features—CAR T-cell dose, CD4+:CD8+ CAR T-cell ratio, CAR-antigen affinity, cancer and healthy cell antigen expression—individually and collectively impact treatment outcomes. Our analysis revealed that tuning CAR affinity modulates IL-2 production by balancing CAR T-cell proliferation and effector function. It also identified a novel multi-feature tuned treatment strategy for balancing selectivity and efficacy and provided insights into how spatial effects can impact relative treatment performance in different contexts. CARCADE facilitates deeper biological understanding of treatment design and could ultimately enable identification of promising treatment strategies to accelerate solid tumor CAR T-cell design-build-test cycles.
Collapse
Affiliation(s)
- Alexis N. Prybutok
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Jessica S. Yu
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
- Department of Biology, University of Washington, Seattle, WA, United States
| | - Joshua N. Leonard
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
- Center for Synthetic Biology, Northwestern University, Evanston, IL, United States
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, United States
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, IL, United States
- *Correspondence: Neda Bagheri, ; Joshua N. Leonard,
| | - Neda Bagheri
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
- Department of Biology, University of Washington, Seattle, WA, United States
- Center for Synthetic Biology, Northwestern University, Evanston, IL, United States
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, United States
- Department of Chemical Engineering, University of Washington, Seattle, WA, United States
- *Correspondence: Neda Bagheri, ; Joshua N. Leonard,
| |
Collapse
|
7
|
Prybutok AN, Cain JY, Leonard JN, Bagheri N. Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems. Curr Opin Biotechnol 2022; 75:102704. [DOI: 10.1016/j.copbio.2022.102704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/27/2022] [Accepted: 02/06/2022] [Indexed: 11/03/2022]
|
8
|
Jafari Nivlouei S, Soltani M, Shirani E, Salimpour MR, Travasso R, Carvalho J. A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach. Cell Prolif 2022; 55:e13187. [PMID: 35132721 PMCID: PMC8891571 DOI: 10.1111/cpr.13187] [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: 08/13/2021] [Revised: 12/09/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVES Computational modeling of biological systems is a powerful tool to clarify diverse processes contributing to cancer. The aim is to clarify the complex biochemical and mechanical interactions between cells, the relevance of intracellular signaling pathways in tumor progression and related events to the cancer treatments, which are largely ignored in previous studies. MATERIALS AND METHODS A three-dimensional multiscale cell-based model is developed, covering multiple time and spatial scales, including intracellular, cellular, and extracellular processes. The model generates a realistic representation of the processes involved from an implementation of the signaling transduction network. RESULTS Considering a benign tumor development, results are in good agreement with the experimental ones, which identify three different phases in tumor growth. Simulating tumor vascular growth, results predict a highly vascularized tumor morphology in a lobulated form, a consequence of cells' motile behavior. A novel systematic study of chemotherapy intervention, in combination with targeted therapy, is presented to address the capability of the model to evaluate typical clinical protocols. The model also performs a dose comparison study in order to optimize treatment efficacy and surveys the effect of chemotherapy initiation delays and different regimens. CONCLUSIONS Results not only provide detailed insights into tumor progression, but also support suggestions for clinical implementation. This is a major step toward the goal of predicting the effects of not only traditional chemotherapy but also tumor-targeted therapies.
Collapse
Affiliation(s)
- Sahar Jafari Nivlouei
- Department of Mechanical Engineering, Isfahan University of Technology, Isafahan, Iran.,Department of Physics, CFisUC, University of Coimbra, Coimbra, Portugal
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.,Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada.,Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran.,Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Ebrahim Shirani
- Department of Mechanical Engineering, Isfahan University of Technology, Isafahan, Iran.,Department of Mechanical Engineering, Foolad Institute of Technology, Fooladshahr, Iran
| | | | - Rui Travasso
- Department of Physics, CFisUC, University of Coimbra, Coimbra, Portugal
| | - João Carvalho
- Department of Physics, CFisUC, University of Coimbra, Coimbra, Portugal
| |
Collapse
|