Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology - PubMed (original) (raw)
Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology
Kristin R Swanson et al. Cancer Res. 2011.
Abstract
Gliomas are uniformly fatal forms of primary brain neoplasms that vary from low- to high-grade (glioblastoma). Whereas low-grade gliomas are weakly angiogenic, glioblastomas are among the most angiogenic tumors. Thus, interactions between glioma cells and their tissue microenvironment may play an important role in aggressive tumor formation and progression. To quantitatively explore how tumor cells interact with their tissue microenvironment, we incorporated the interactions of normoxic glioma cells, hypoxic glioma cells, vascular endothelial cells, diffusible angiogenic factors, and necrosis formation into a first-generation, biologically based mathematical model for glioma growth and invasion. Model simulations quantitatively described the spectrum of in vivo dynamics of gliomas visualized with medical imaging. Furthermore, we investigated how proliferation and dispersal of glioma cells combine to induce increasing degrees of cellularity, mitoses, hypoxia-induced neoangiogenesis and necrosis, features that characterize increasing degrees of "malignancy," and we found that changes in the net rates of proliferation (ρ) and invasion (D) are not always necessary for malignant progression. Thus, although other factors, including the accumulation of genetic mutations, can change cellular phenotype (e.g., proliferation and invasion rates), this study suggests that these are not required for malignant progression. Simulated results are placed in the context of the current clinical World Health Organization grading scheme for studying specific patient examples. This study suggests that through the application of the proposed model for tumor-microenvironment interactions, predictable patterns of dynamic changes in glioma histology distinct from changes in cellular phenotype (e.g., proliferation and invasion rates) may be identified, thus providing a powerful clinical tool.
Figures
Figure 1
Outline of the PIHNA mathematical model illustrating the interaction of glioma cells (normoxic and hypoxic) with the available vasculature which can be affected by the production of angiogenic factors.
Figure 2
Simulations illustrating each grade of glioma represented as a plot of density or concentration of the model variables (normoxic, hypoxic, and necrotic tissue, vasculature and VEGF) with respect to the distance from the center of the in silico tumor. Note the increasing cellularity, vasculature, hypoxia and necrosis with increasing grade.
Figure 3
In silico (survival) time from a typical diagnostic size of 1cm on T2 MRI to a typically fatal size of 4cm, as a function of D and ρ.
Figure 4
a) Maps of tumor grade as a function of tumor size (on T2 MRI) or of time. The blackened boxes indicate that the T2-visible portion of the simulated lesion has grown to a size sufficient to fill the whole brain. b) The * combination (high ρ, low D) produces a “primary” GBM (i.e., GBM from its first detectability), while comparatively c) the + combination (low ρ, high D – relative to *) produces a “secondary” GBM (i.e., “progressing” from lower grade).
Figure 5
A map of simulated “primary” (white) and “secondary” (grey) glioblastomas and gliomas that become fatal is size before malignant progression (black).
Figure 6
a) The total VEGF (integral amount of model TAF) normalized to the local glioma cell density for in silico Grade 3, sGBM and pGBM tumors showing increasing VEGF concentrations qualitatively consistent with the observations of (27). b) Increasing in silico expression of VEGF and hypoxia marker, HIF-1α, with increasing histological grade qualitatively consistent with the observations of (28, 29).
Figure 7
Connecting tissue level and imaging level model PIHNA model predictions of a) histological grade, b) fraction of cells that are hypoxic, and c) the radial size of central necrosis visible on T1-Gd MRI. Each grid cell represents a single virtual tumor with a specified combination of D and ρ, with in silico histologic grade, hypoxia and necrosis assessed within the virtual tumor and graphed separately. The PIHNA model accurately predicts relative amounts of hypoxia and necrosis as well as histologic grade for the 3 individual glioblastoma patients (stars). Each patient was approximately 2 cm in radius on T2 MRI with variable sizes on T1-Gd corresponding to the different D, ρ combinations graphed (quantified as in (25, 31)). Each patient was assessed for hypoxia quantified as the maximum HIF1α on immunohistochemistry (Patient 1: 30%, 2: 40%, 3: 40%) and a necrotic core radius on T1Gd MRI (Patient 1: 8mm, 2: 16mm, 3: 18mm).
Similar articles
- Expression of angiopoietin-2 in human glioma cells and its role for angiogenesis.
Koga K, Todaka T, Morioka M, Hamada J, Kai Y, Yano S, Okamura A, Takakura N, Suda T, Ushio Y. Koga K, et al. Cancer Res. 2001 Aug 15;61(16):6248-54. Cancer Res. 2001. PMID: 11507079 - The pro-migratory and pro-invasive role of the procoagulant tissue factor in malignant gliomas.
Dützmann S, Gessler F, Harter PN, Gerlach R, Mittelbronn M, Seifert V, Kögel D. Dützmann S, et al. Cell Adh Migr. 2010 Oct-Dec;4(4):515-22. doi: 10.4161/cam.4.4.12660. Cell Adh Migr. 2010. PMID: 20595809 Free PMC article. - Mechanisms of glioma formation: iterative perivascular glioma growth and invasion leads to tumor progression, VEGF-independent vascularization, and resistance to antiangiogenic therapy.
Baker GJ, Yadav VN, Motsch S, Koschmann C, Calinescu AA, Mineharu Y, Camelo-Piragua SI, Orringer D, Bannykh S, Nichols WS, deCarvalho AC, Mikkelsen T, Castro MG, Lowenstein PR. Baker GJ, et al. Neoplasia. 2014 Jul;16(7):543-61. doi: 10.1016/j.neo.2014.06.003. Neoplasia. 2014. PMID: 25117977 Free PMC article. - Angiogenesis and invasion in glioma.
Onishi M, Ichikawa T, Kurozumi K, Date I. Onishi M, et al. Brain Tumor Pathol. 2011 Feb;28(1):13-24. doi: 10.1007/s10014-010-0007-z. Epub 2011 Jan 8. Brain Tumor Pathol. 2011. PMID: 21221826 Review. - Invasion as limitation to anti-angiogenic glioma therapy.
Lamszus K, Kunkel P, Westphal M. Lamszus K, et al. Acta Neurochir Suppl. 2003;88:169-77. doi: 10.1007/978-3-7091-6090-9_23. Acta Neurochir Suppl. 2003. PMID: 14531575 Review.
Cited by
- Modeling of spatiotemporal dynamics of ligand-coated particle flow in targeted drug delivery processes.
Goraya SA, Ding S, Miller RC, Arif MK, Kong H, Masud A. Goraya SA, et al. Proc Natl Acad Sci U S A. 2024 May 28;121(22):e2314533121. doi: 10.1073/pnas.2314533121. Epub 2024 May 22. Proc Natl Acad Sci U S A. 2024. PMID: 38776373 - DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research.
Crouch SAW, Krause J, Dandekar T, Breitenbach T. Crouch SAW, et al. Comput Struct Biotechnol J. 2024 Apr 8;23:1755-1772. doi: 10.1016/j.csbj.2024.04.010. eCollection 2024 Dec. Comput Struct Biotechnol J. 2024. PMID: 38707537 Free PMC article. - Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning.
Guillevin R, Naudin M, Fayolle P, Giraud C, Le Guillou X, Thomas C, Herpe G, Miranville A, Fernandez-Maloigne C, Pellerin L, Guillevin C. Guillevin R, et al. J Clin Med. 2023 Dec 15;12(24):7706. doi: 10.3390/jcm12247706. J Clin Med. 2023. PMID: 38137775 Free PMC article. Review. - Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol.
Urcuyo JC, Curtin L, Langworthy JM, De Leon G, Anderies B, Singleton KW, Hawkins-Daarud A, Jackson PR, Bond KM, Ranjbar S, Lassiter-Morris Y, Clark-Swanson KR, Paulson LE, Sereduk C, Mrugala MM, Porter AB, Baxter L, Salomao M, Donev K, Hudson M, Meyer J, Zeeshan Q, Sattur M, Patra DP, Jones BA, Rahme RJ, Neal MT, Patel N, Kouloumberis P, Turkmani AH, Lyons M, Krishna C, Zimmerman RS, Bendok BR, Tran NL, Hu LS, Swanson KR. Urcuyo JC, et al. PLoS One. 2023 Dec 20;18(12):e0287767. doi: 10.1371/journal.pone.0287767. eCollection 2023. PLoS One. 2023. PMID: 38117803 Free PMC article. - Interactions between ploidy and resource availability shape clonal interference at initiation and recurrence of glioblastoma.
Nowicka Z, Rentzeperis F, Beck R, Tagal V, Pinto AF, Scanu E, Veith T, Cole J, Ilter D, Viqueira WD, Teer JK, Maksin K, Pasetto S, Abdalah MA, Fiandaca G, Prabhakaran S, Schultz A, Ojwang M, Barnholtz-Sloan JS, Farinhas JM, Gomes AP, Katira P, Andor N. Nowicka Z, et al. bioRxiv [Preprint]. 2023 Oct 20:2023.10.17.562670. doi: 10.1101/2023.10.17.562670. bioRxiv. 2023. PMID: 37905142 Free PMC article. Preprint.
References
- Brem S. The role of vascular proliferation in the growth of brain tumors. Clin Neurosurg. 1976;23:440–53. - PubMed
- Plate KH, Breier G, Weich HA, Mennel HD, Risau W. Vascular Endothelial Growth-Factor and Glioma Angiogenesis - Coordinate Induction of Vegf Receptors, Distribution of Vegf Protein and Possible in-Vivo Regulatory Mechanisms. Int J Cancer. 1994;59:520–9. - PubMed
- Harpold HL, Alvord EC, Jr, Swanson KR. The evolution of mathematical modeling of glioma proliferation and invasion. J Neuropathol Exp Neurol. 2007;66:1–9. - PubMed
- Swanson KR, Alvord EC, Jr, Murray JD. Virtual resection of gliomas: effects of location and extent of resection on recurrence. Mathematical and Computer Modeling. 2003;37:1177–90.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical