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Househam J, Heide T, Cresswell GD, Spiteri I, Kimberley C, Zapata L, Lynn C, James C, Mossner M, Fernandez-Mateos J, Vinceti A, Baker AM, Gabbutt C, Berner A, Schmidt M, Chen B, Lakatos E, Gunasri V, Nichol D, Costa H, Mitchinson M, Ramazzotti D, Werner B, Iorio F, Jansen M, Caravagna G, Barnes CP, Shibata D, Bridgewater J, Rodriguez-Justo M, Magnani L, Sottoriva A, Graham TA. Phenotypic plasticity and genetic control in colorectal cancer evolution. Nature 2022; 611:744-753. [PMID: 36289336 PMCID: PMC9684078 DOI: 10.1038/s41586-022-05311-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 09/01/2022] [Indexed: 12/12/2022]
Abstract
Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.
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Affiliation(s)
- Jacob Househam
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Timon Heide
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - George D Cresswell
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Inmaculada Spiteri
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Chris Kimberley
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Luis Zapata
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Claire Lynn
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Chela James
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Maximilian Mossner
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | | | | | - Ann-Marie Baker
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Calum Gabbutt
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Alison Berner
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Melissa Schmidt
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Bingjie Chen
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Eszter Lakatos
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Vinaya Gunasri
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Daniel Nichol
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Helena Costa
- UCL Cancer Institute, University College London, London, UK
| | - Miriam Mitchinson
- Histopathology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Daniele Ramazzotti
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Benjamin Werner
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Francesco Iorio
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Marnix Jansen
- UCL Cancer Institute, University College London, London, UK
| | - Giulio Caravagna
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Darryl Shibata
- Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | | | | | - Luca Magnani
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Computational Biology Research Centre, Human Technopole, Milan, Italy.
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.
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Sové RJ, Verma BK, Wang H, Ho WJ, Yarchoan M, Popel AS. Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model. J Immunother Cancer 2022; 10:e005414. [PMID: 36323435 PMCID: PMC9639136 DOI: 10.1136/jitc-2022-005414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is the third-leading cause of cancer-related death worldwide. Most patients with HCC are diagnosed at an advanced stage, and the median survival for patients with advanced HCC treated with modern systemic therapy is less than 2 years. This leaves the advanced stage patients with limited treatment options. Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or its ligand, are widely used in the treatment of HCC and are associated with durable responses in a subset of patients. ICIs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) also have clinical activity in HCC. Combination therapy of nivolumab (anti-PD-1) and ipilimumab (anti-CTLA-4) is the first treatment option for HCC to be approved by Food and Drug Administration that targets more than one immune checkpoints. METHODS In this study, we used the framework of quantitative systems pharmacology (QSP) to perform a virtual clinical trial for nivolumab and ipilimumab in HCC patients. Our model incorporates detailed biological mechanisms of interactions of immune cells and cancer cells leading to antitumor response. To conduct virtual clinical trial, we generate virtual patient from a cohort of 5,000 proposed patients by extending recent algorithms from literature. The model was calibrated using the data of the clinical trial CheckMate 040 (ClinicalTrials.gov number, NCT01658878). RESULTS Retrospective analyses were performed for different immune checkpoint therapies as performed in CheckMate 040. Using machine learning approach, we predict the importance of potential biomarkers for immune blockade therapies. CONCLUSIONS This is the first QSP model for HCC with ICIs and the predictions are consistent with clinically observed outcomes. This study demonstrates that using a mechanistic understanding of the underlying pathophysiology, QSP models can facilitate patient selection and design clinical trials with improved success.
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Affiliation(s)
- Richard J Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Babita K Verma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Won Jin Ho
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mark Yarchoan
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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3
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Lan W, Zhang H, Yang B. Preliminary Study on the Therapeutic Effect of Doxorubicin-Loaded Targeting Nanoparticles on Glioma. Appl Bionics Biomech 2022; 2022:6405400. [PMID: 35386209 PMCID: PMC8979730 DOI: 10.1155/2022/6405400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/05/2022] [Accepted: 01/10/2022] [Indexed: 11/27/2022] Open
Abstract
Doxorubicin (DOX) is an anthracycline anticancer drug, which is often associated with drug resistance and cytotoxicity. More unfortunately, the biological barrier in the human environment can weaken the efficacy of DOX, such as the blood-brain barrier (BBB). This work attempts to make efforts to solve this problem. We used polyethylene glycol distearoylphosphatidylethanolamine (PEG-DSPE) as a nanocarrier and DOX as a model drug to construct a composite nanodrug (TF-PEG-DSPE/DOX NPs) by coupling transferrin (TF). The results of glioma experiments show that the nanodrug can effectively penetrate BBB to achieve an antitumor effect.
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Affiliation(s)
- Weitu Lan
- Department of Neurosurgery, Cangzhou People's Hospital, Cangzhou, 061000 Hebei, China
| | - Hongguang Zhang
- Department of Neurosurgery, Gaotang People's Hospital, Liaocheng, 252800 Shandong, China
| | - Bo Yang
- Department of Neurosurgery, Zibo Central Hospital, Zibo, 255000 Shandong, China
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Glioma invasion along white matter tracts: A dilemma for neurosurgeons. Cancer Lett 2022; 526:103-111. [PMID: 34808285 DOI: 10.1016/j.canlet.2021.11.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 12/15/2022]
Abstract
Invasive growth along white matter (WM) tracts is one of the most prominent clinicopathological features of glioma and is also an important reason for surgical treatment failure in glioma patients. A full understanding of relevant clinical features and mechanisms is of great significance for finding new therapeutic targets and developing new treatment regimens and strategies. Herein, we review the imaging and histological characteristics of glioma patients with WM tracts invasion and summarize the possible molecular mechanism. On this basis, we further discuss the correlation between glioma molecular typing, radiotherapy and tumor treating fields (TTFields) and the invasion of glioma along WM tracts.
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A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type. J Digit Imaging 2021; 33:439-446. [PMID: 31654174 DOI: 10.1007/s10278-019-00282-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The explosion of medical imaging data along with the advent of big data analytics has launched an exciting era for clinical research. One factor affecting the ability to aggregate large medical image collections for research is the lack of infrastructure for automated data annotation. Among all imaging modalities, annotation of magnetic resonance (MR) images is particularly challenging due to the non-standard labeling of MR image types. In this work, we aimed to train a deep neural network to annotate MR image sequence type for scans of brain tumor patients. We focused on the four most common MR sequence types within neuroimaging: T1-weighted (T1W), T1-weighted post-gadolinium contrast (T1Gd), T2-weighted (T2W), and T2-weighted fluid-attenuated inversion recovery (FLAIR). Our repository contains images acquired using a variety of pulse sequences, sequence parameters, field strengths, and scanner manufacturers. Image selection was agnostic to patient demographics, diagnosis, and the presence of tumor in the imaging field of view. We used a total of 14,400 two-dimensional images, each visualizing a different part of the brain. Data was split into train, validation, and test sets (9600, 2400, and 2400 images, respectively) and sets consisted of equal-sized groups of image types. Overall, the model reached an accuracy of 99% on the test set. Our results showed excellent performance of deep learning techniques in predicting sequence types for brain tumor MR images. We conclude deep learning models can serve as tools to support clinical research and facilitate efficient database management.
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Li Z, Yang F, Xu C, Ma C, Zhao Y. Clinical value of neuronavigation combined with a fluorescent staining technique during microsurgery for treating supratentorial glioma: A case-control observational study. GLIOMA 2021. [DOI: 10.4103/glioma.glioma_27_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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7
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Lentiviral Vector Induced Modeling of High-Grade Spinal Cord Glioma in Minipigs. Sci Rep 2020; 10:5291. [PMID: 32210315 PMCID: PMC7093438 DOI: 10.1038/s41598-020-62167-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/09/2020] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Prior studies have applied driver mutations targeting the RTK/RAS/PI3K and p53 pathways to induce the formation of high-grade gliomas in rodent models. In the present study, we report the production of a high-grade spinal cord glioma model in pigs using lentiviral gene transfer. METHODS Six Gottingen Minipigs received thoracolumbar (T14-L1) lateral white matter injections of a combination of lentiviral vectors, expressing platelet-derived growth factor beta (PDGF-B), constitutive HRAS, and shRNA-p53 respectively. All animals received injection of control vectors into the contralateral cord. Animals underwent baseline and endpoint magnetic resonance imaging (MRI) and were evaluated daily for clinical deficits. Hematoxylin and eosin (H&E) and immunohistochemical analysis was conducted. Data are presented using descriptive statistics including relative frequencies, mean, standard deviation, and range. RESULTS 100% of animals (n = 6/6) developed clinical motor deficits ipsilateral to the oncogenic lentiviral injections by a three-week endpoint. MRI scans at endpoint demonstrated contrast enhancing mass lesions at the site of oncogenic lentiviral injection and not at the site of control injections. Immunohistochemistry demonstrated positive staining for GFAP, Olig2, and a high Ki-67 proliferative index. Histopathologic features demonstrate consistent and reproducible growth of a high-grade glioma in all animals. CONCLUSIONS Lentiviral gene transfer represents a feasible pathway to glioma modeling in higher order species. The present model is the first lentiviral vector induced pig model of high-grade spinal cord glioma and may potentially be used in preclinical therapeutic development programs.
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Gallaher JA, Massey SC, Hawkins-Daarud A, Noticewala SS, Rockne RC, Johnston SK, Gonzalez-Cuyar L, Juliano J, Gil O, Swanson KR, Canoll P, Anderson ARA. From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response. PLoS Comput Biol 2020; 16:e1007672. [PMID: 32101537 PMCID: PMC7062288 DOI: 10.1371/journal.pcbi.1007672] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 03/09/2020] [Accepted: 01/21/2020] [Indexed: 11/18/2022] Open
Abstract
Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens.
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Affiliation(s)
- Jill A. Gallaher
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Susan C. Massey
- Precision NeuroTherapeutics Innovation Program, Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Precision NeuroTherapeutics Innovation Program, Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Sonal S. Noticewala
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, United States of America
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Russell C. Rockne
- Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, California, United States of America
| | - Sandra K. Johnston
- Precision NeuroTherapeutics Innovation Program, Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiology, University of Washington, Seattle, Washington, United States of America
| | - Luis Gonzalez-Cuyar
- Department of Pathology, University of Washington, Seattle, Washington, United States of America
| | - Joseph Juliano
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Orlando Gil
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, United States of America
- Department of Biology, Hunter College, City University of New York, New York, New York, United States of America
| | - Kristin R. Swanson
- Precision NeuroTherapeutics Innovation Program, Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Peter Canoll
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, United States of America
| | - Alexander R. A. Anderson
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
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9
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Hormuth DA, Jarrett AM, Yankeelov TE. Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling. Radiat Oncol 2020; 15:4. [PMID: 31898514 PMCID: PMC6941255 DOI: 10.1186/s13014-019-1446-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/12/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Intra-and inter-tumoral heterogeneity in growth dynamics and vascularity influence tumor response to radiation therapy. Quantitative imaging techniques capture these dynamics non-invasively, and these data can initialize and constrain predictive models of response on an individual basis. METHODS We have developed a family of 10 biologically-based mathematical models describing the spatiotemporal dynamics of tumor volume fraction, blood volume fraction, and response to radiation therapy. To evaluate this family of models, rats (n = 13) with C6 gliomas were imaged with magnetic resonance imaging (MRI) three times before, and four times following a single fraction of 20 Gy or 40 Gy whole brain irradiation. The first five 3D time series data of tumor volume fraction, estimated from diffusion-weighted (DW-) MRI, and blood volume fraction, estimated from dynamic contrast-enhanced (DCE-) MRI, were used to calibrate tumor-specific model parameters. The most parsimonious and well calibrated of the 10 models, selected using the Akaike information criterion, was then utilized to predict future growth and response at the final two imaging time points. Model predictions were compared at the global level (percent error in tumor volume, and Dice coefficient) as well as at the local or voxel level (concordance correlation coefficient). RESULT The selected model resulted in < 12% error in tumor volume predictions, strong spatial agreement between predicted and observed tumor volumes (Dice coefficient > 0.74), and high level of agreement at the voxel level between the predicted and observed tumor volume fraction and blood volume fraction (concordance correlation coefficient > 0.77 and > 0.65, respectively). CONCLUSIONS This study demonstrates that serial quantitative MRI data collected before and following radiation therapy can be used to accurately predict tumor and vasculature response with a biologically-based mathematical model that is calibrated on an individual basis. To the best of our knowledge, this is the first effort to characterize the tumor and vasculature response to radiation therapy temporally and spatially using imaging-driven mathematical models.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA.
| | - Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
- Departments of Oncology, The University of Texas at Austin, Austin, TX, USA
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10
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Chkhaidze K, Heide T, Werner B, Williams MJ, Huang W, Caravagna G, Graham TA, Sottoriva A. Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. PLoS Comput Biol 2019; 15:e1007243. [PMID: 31356595 PMCID: PMC6687187 DOI: 10.1371/journal.pcbi.1007243] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 08/08/2019] [Accepted: 07/05/2019] [Indexed: 12/19/2022] Open
Abstract
Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constraints, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors remains challenging. However, mechanistic model-based approaches have the potential to capture the sources of noise and better interpret the data.
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Affiliation(s)
- Ketevan Chkhaidze
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Timon Heide
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Benjamin Werner
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Marc J. Williams
- Evolution and Cancer Lab, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Weini Huang
- Evolution and Cancer Lab, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Giulio Caravagna
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Trevor A. Graham
- Evolution and Cancer Lab, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
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11
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Zhao J, Tian JP. Spatial Model for Oncolytic Virotherapy with Lytic Cycle Delay. Bull Math Biol 2019; 81:2396-2427. [PMID: 31089864 DOI: 10.1007/s11538-019-00611-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 05/07/2019] [Indexed: 01/18/2023]
Abstract
We formulate a mathematical model of functional partial differential equations for oncolytic virotherapy which incorporates virus diffusivity, tumor cell diffusion, and the viral lytic cycle based on a basic oncolytic virus dynamics model. We conduct a detailed analysis for the dynamics of the model and carry out numerical simulations to demonstrate our analytic results. Particularly, we establish the positive invariant domain for the [Formula: see text] limit set of the system and show that the model has three spatially homogenous equilibriums solutions. We prove that the spatially uniform virus-free steady state is globally asymptotically stable for any viral lytic period delay and diffusion coefficients of tumor cells and viruses when the viral burst size is smaller than a critical value. We obtain the conditions, for example the ratio of virus diffusion coefficient to that of tumor cells is greater than a value and the viral lytic cycle, is greater than a critical value, under which the spatially uniform positive steady state is locally asymptotically stable. We also obtain conditions under which the system undergoes Hopf bifurcations, and stable periodic solutions occur. We point out medical implications of our results which are difficult to obtain from models without combining diffusive properties of viruses and tumor cells with viral lytic cycles.
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Affiliation(s)
- Jiantao Zhao
- Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM, 88001, USA.,School of Mathematical Sciences, Heilongjiang University, Harbin, 150080, Heilongjiang, People's Republic of China
| | - Jianjun Paul Tian
- Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM, 88001, USA. .,School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, 723000, Shaanxi, People's Republic of China.
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12
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Affiliation(s)
- Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford, UK.
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13
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Massey SC, Hawkins-Daarud A, Gallaher J, Anderson ARA, Canoll P, Swanson KR. Lesion Dynamics Under Varying Paracrine PDGF Signaling in Brain Tissue. Bull Math Biol 2019; 81:1645-1664. [PMID: 30796683 DOI: 10.1007/s11538-019-00587-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 02/12/2019] [Indexed: 01/08/2023]
Abstract
Paracrine PDGF signaling is involved in many processes in the body, both normal and pathological, including embryonic development, angiogenesis, and wound healing as well as liver fibrosis, atherosclerosis, and cancers. We explored this seemingly dual (normal and pathological) role of PDGF mathematically by modeling the release of PDGF in brain tissue and then varying the dynamics of this release. Resulting simulations show that by varying the dynamics of a PDGF source, our model predicts three possible outcomes for PDGF-driven cellular recruitment and lesion growth: (1) localized, short duration of growth, (2) localized, chronic growth, and (3) widespread chronic growth. Further, our model predicts that the type of response is much more sensitive to the duration of PDGF exposure than the maximum level of that exposure. This suggests that extended duration of paracrine PDGF signal during otherwise normal processes could potentially lead to lesions having a phenotype consistent with pathologic conditions.
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Affiliation(s)
- Susan Christine Massey
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
| | - Andrea Hawkins-Daarud
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Jill Gallaher
- Integrative Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Peter Canoll
- Division of Neuropathology, Department of Pathology and Cell Biology, Columbia University School of Medicine, New York, NY, USA
| | - Kristin R Swanson
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA
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14
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Norton KA, Gong C, Jamalian S, Popel AS. Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment. Processes (Basel) 2019; 7:37. [PMID: 30701168 PMCID: PMC6349239 DOI: 10.3390/pr7010037] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.
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Affiliation(s)
- Kerri-Ann Norton
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Computer Science Program, Department of Science, Mathematics, and Computing, Bard College, Annandale-on-Hudson, NY 12504, USA
| | - Chang Gong
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Samira Jamalian
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
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15
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Chen S, Le T, Harley BAC, Imoukhuede PI. Characterizing Glioblastoma Heterogeneity via Single-Cell Receptor Quantification. Front Bioeng Biotechnol 2018; 6:92. [PMID: 30050899 PMCID: PMC6050407 DOI: 10.3389/fbioe.2018.00092] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 06/21/2018] [Indexed: 01/09/2023] Open
Abstract
Dysregulation of tyrosine kinase receptor (RTK) signaling pathways play important roles in glioblastoma (GBM). However, therapies targeting these signaling pathways have not been successful, partially because of drug resistance. Increasing evidence suggests that tumor heterogeneity, more specifically, GBM-associated stem and endothelial cell heterogeneity, may contribute to drug resistance. In this perspective article, we introduce a high-throughput, quantitative approach to profile plasma membrane RTKs on single cells. First, we review the roles of RTKs in cancer. Then, we discuss the sources of cell heterogeneity in GBM, providing context to the key cells directing resistance to drugs. Finally, we present our provisionally patented qFlow cytometry approach, and report results of a "proof of concept" patient-derived xenograft GBM study.
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Affiliation(s)
- Si Chen
- Department of Bioengineering, University of Illinois at Urbana–Champaign, Champaign, IL, United States
| | - Thien Le
- Department of Mathematics and Department of Computer Science, University of Illinois at Urbana–Champaign, Champaign, IL, United States
| | - Brendan A. C. Harley
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana–Champaign, Urbana, IL, United States
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana Champaign, Urbana, IL, United States
| | - P. I. Imoukhuede
- Department of Bioengineering, University of Illinois at Urbana–Champaign, Champaign, IL, United States
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana Champaign, Urbana, IL, United States
- Department of Biomedical Engineering, Washington University, St. Louis, MO, United States
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16
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Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients. PLoS Comput Biol 2018; 14:e1005924. [PMID: 29293494 PMCID: PMC5766249 DOI: 10.1371/journal.pcbi.1005924] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2017] [Revised: 01/12/2018] [Accepted: 12/12/2017] [Indexed: 12/15/2022] Open
Abstract
Human primary glioblastomas (GBM) often harbor mutations within the epidermal growth factor receptor (EGFR). Treatment of EGFR-mutant GBM cell lines with the EGFR/HER2 tyrosine kinase inhibitor lapatinib can effectively induce cell death in these models. However, EGFR inhibitors have shown little efficacy in the clinic, partly because of inappropriate dosing. Here, we developed a computational approach to model the in vitro cellular dynamics of the EGFR-mutant cell line SF268 in response to different lapatinib concentrations and dosing schedules. We then used this approach to identify an effective treatment strategy within the clinical toxicity limits of lapatinib, and developed a partial differential equation modeling approach to study the in vivo GBM treatment response by taking into account the heterogeneous and diffusive nature of the disease. Despite the inability of lapatinib to induce tumor regressions with a continuous daily schedule, our modeling approach consistently predicts that continuous dosing remains the best clinically feasible strategy for slowing down tumor growth and lowering overall tumor burden, compared to pulsatile schedules currently known to be tolerated, even when considering drug resistance, reduced lapatinib tumor concentrations due to the blood brain barrier, and the phenotypic switch from proliferative to migratory cell phenotypes that occurs in hypoxic microenvironments. Our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment schedules in search for optimal dosing strategies for glioblastoma and other cancer types.
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