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Lapin A, Perfahl H, Jain HV, Reuss M. Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth. Sci Rep 2022; 12:12373. [PMID: 35858953 PMCID: PMC9300625 DOI: 10.1038/s41598-022-15767-6] [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: 03/01/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.
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Affiliation(s)
- Alexey Lapin
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.,Institute of Chemical Process Engineering, University Stuttgart, Stuttgart, Germany
| | - Holger Perfahl
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
| | - Harsh Vardhan Jain
- Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN, USA
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.
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Ha R, Chin C, Karcich J, Liu MZ, Chang P, Mutasa S, Pascual Van Sant E, Wynn RT, Connolly E, Jambawalikar S. Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset. J Digit Imaging 2020; 32:693-701. [PMID: 30361936 DOI: 10.1007/s10278-018-0144-1] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review of our database from January 2009 to June 2016 identified 141 locally advanced breast cancer patients who (1) underwent breast MRI prior to the initiation of NAC, (2) successfully completed adriamycin/taxane-based NAC, and (3) underwent surgical resection with available final surgical pathology data. Patients were classified into three groups based on their NAC response confirmed on final surgical pathology: complete (group 1), partial (group 2), and no response/progression (group 3). A total of 3107 volumetric slices of 141 tumors were evaluated. Breast tumor was identified on first T1 postcontrast dynamic images and underwent 3D segmentation. CNN consisted of ten convolutional layers, four max-pooling layers, and dropout of 50% after a fully connected layer. Dropout, augmentation, and L2 regularization were implemented to prevent overfitting of data. Non-linear functions were modeled by a rectified linear unit (ReLU). Batch normalization was used between the convolutional and ReLU layers to limit drift of layer activations during training. A three-class neoadjuvant prediction model was evaluated (group 1, group 2, or group 3). The CNN achieved an overall accuracy of 88% in three-class prediction of neoadjuvant treatment response. Three-class prediction discriminating one group from the other two was analyzed. Group 1 had a specificity of 95.1% ± 3.1%, sensitivity of 73.9% ± 4.5%, and accuracy of 87.7% ± 0.6%. Group 2 (partial response) had a specificity of 91.6% ± 1.3%, sensitivity of 82.4% ± 2.7%, and accuracy of 87.7% ± 0.6%. Group 3 (no response/progression) had a specificity of 93.4% ± 2.9%, sensitivity of 76.8% ± 5.7%, and accuracy of 87.8% ± 0.6%. It is feasible for current deep CNN architectures to be trained to predict NAC treatment response using a breast MRI dataset obtained prior to initiation of chemotherapy. Larger dataset will likely improve our prediction model.
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Affiliation(s)
- Richard Ha
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA.
| | - Christine Chin
- Division of Radiation Oncology, Columbia University Medical Center, Presbyterian Hospital Building, 622 West 168th Street, Level B, New York, NY, 10032, USA
| | - Jenika Karcich
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Michael Z Liu
- Department of Medical Physics, Columbia University Medical Center, 177 Ft. Washington Ave., Milstein Bldg Room 3-124B, New York, NY, 10032-3784, USA
| | - Peter Chang
- Department of Radiology, UC San Francisco Medical Center, 505 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Simukayi Mutasa
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Eduardo Pascual Van Sant
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Ralph T Wynn
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Eileen Connolly
- Division of Radiation Oncology, Columbia University Medical Center, Presbyterian Hospital Building, 622 West 168th Street, Level B, New York, NY, 10032, USA
| | - Sachin Jambawalikar
- Department of Medical Physics, Columbia University Medical Center, 177 Ft. Washington Ave., Milstein Bldg Room 3-124B, New York, NY, 10032-3784, USA
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Lo Gullo R, Eskreis-Winkler S, Morris EA, Pinker K. Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy. Breast 2020; 49:115-122. [PMID: 31786416 PMCID: PMC7375548 DOI: 10.1016/j.breast.2019.11.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 11/14/2019] [Accepted: 11/17/2019] [Indexed: 12/16/2022] Open
Abstract
In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patient's tumor on multiparametric MRI is insufficient to predict that patient's response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.
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Oraiopoulou ME, Tzamali E, Tzedakis G, Liapis E, Zacharakis G, Vakis A, Papamatheakis J, Sakkalis V. Integrating in vitro experiments with in silico approaches for Glioblastoma invasion: the role of cell-to-cell adhesion heterogeneity. Sci Rep 2018; 8:16200. [PMID: 30385804 PMCID: PMC6212459 DOI: 10.1038/s41598-018-34521-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 10/01/2018] [Indexed: 01/08/2023] Open
Abstract
Glioblastoma cells adopt migration strategies to invade into the brain parenchyma ranging from individual to collective mechanisms, whose role and dynamics are not yet fully understood. In this work, we explore Glioblastoma heterogeneity and recapitulate its invasive patterns both in vitro, by utilizing primary cells along with the U87MG cell line, and in silico, by adopting discrete, individual cell-based mathematics. Glioblastoma cells are cultured three-dimensionally in an ECM-like substrate. The primary Glioblastoma spheroids adopt a novel cohesive pattern, mimicking perivascular invasion in the brain, while the U87MG adopt a typical, starburst invasive pattern under the same experimental setup. Mathematically, we focus on the role of the intrinsic heterogeneity with respect to cell-to-cell adhesion. Our proposed mathematical approach mimics the invasive morphologies observed in vitro and predicts the dynamics of tumour expansion. The role of the proliferation and migration is also explored showing that their effect on tumour morphology is different per cell type. The proposed model suggests that allowing cell-to-cell adhesive heterogeneity within the tumour population is sufficient for variable invasive morphologies to emerge which remain originally undetectable by conventional imaging, indicating that exploration in pathological samples is needed to improve our understanding and reveal potential patient-specific therapeutic targets.
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Affiliation(s)
- M-E Oraiopoulou
- Department of Medicine, University of Crete, Heraklion, Crete, Greece
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
| | - E Tzamali
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
| | - G Tzedakis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
| | - E Liapis
- Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - G Zacharakis
- Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
| | - A Vakis
- Department of Medicine, University of Crete, Heraklion, Crete, Greece
- Neurosurgery Clinic, University General Hospital of Heraklion, Crete, Greece
| | - J Papamatheakis
- Gene Expression Laboratory, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
- Department of Biology, University of Crete, Heraklion, Crete, Greece
| | - V Sakkalis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece.
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Weis JA, Miga MI, Yankeelov TE. Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2017; 314:494-512. [PMID: 28042181 PMCID: PMC5193147 DOI: 10.1016/j.cma.2016.08.024] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data-driven framework for prediction of residual tumor burden following neoadjuvant therapy in breast cancer that uses a biophysical mathematical model combining reaction-diffusion growth/therapy dynamics and biomechanical effects driven by early time point imaging data. Whereas early work had been based on a limited dimensionality reduction (two-dimensional planar modeling analysis) to simplify the numerical implementation, in this work, we extend our framework to a fully volumetric, three-dimensional biophysical mathematical modeling approach in which parameter estimates are generated by an inverse problem based on the adjoint state method for numerical efficiency. In an in silico performance study, we show accurate parameter estimation with error less than 3% as compared to ground truth. We apply the approach to patient data from a patient with pathological complete response and a patient with residual tumor burden and demonstrate technical feasibility and predictive potential with direct comparisons between imaging data observation and model predictions of tumor cellularity and volume. Comparisons to our previous two-dimensional modeling framework reflect enhanced model prediction of residual tumor burden through the inclusion of additional imaging slices of patient-specific data.
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Affiliation(s)
- Jared A. Weis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Michael I. Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurosurgery, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Thomas E. Yankeelov
- Institute for Computational Engineering and Sciences, and Departments of Biomedical Engineering and Internal Medicine, The University of Texas, Austin, TX, USA
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Grassberger C, Paganetti H. Methodologies in the modeling of combined chemo-radiation treatments. Phys Med Biol 2016; 61:R344-R367. [DOI: 10.1088/0031-9155/61/21/r344] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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