Research Papers

The Mechanism of the Initiation and Progression of Glioma

[+] Author and Article Information
Ishwar K. Puri

N. Waldo Harrison Professor
Fellow ASME
Department of Engineering Sciences
and Mechanics,
Virginia Tech.,
Blacksburg, VA 24061

Subbiah Elankumaran

Assistant Professor
Department of Biomedical Sciences
and Pathobiology,
Virginia Tech.,
Blacksburg, VA 24061

Moanaro Biswas

Postdoctoral Associate
Institute for Critical Technology
and Applied Science,
Virginia Tech.,
Blacksburg, VA 24061

Liwu Li

Department of Biological Sciences,
Virginia Tech.,
Blacksburg, VA 24061

1Corresponding author.

Manuscript received March 22, 2012; final manuscript received December 15, 2012; accepted manuscript posted January 22, 2013; published online July 19, 2013. Assoc. Editor: Martin Ostoja-Starzewski.

J. Appl. Mech 80(5), 050901 (Jul 19, 2013) (7 pages) Paper No: JAM-12-1112; doi: 10.1115/1.4023472 History: Received March 22, 2012; Revised December 15, 2012; Accepted January 22, 2013

The fate of malignant glioma (MG) is governed by a multifaceted and dynamic circuit that involves the surrounding cellular and molecular tumor microenvironment. Despite extensive experimental studies, a complete understanding of the complex interactions among the constituents of this microenvironment remains elusive. To clarify this, we introduce a biologically based mathematical model that examines the dynamic modulation of glioma cancer stem cells (GSC) by different immune cell types and intracellular signaling pathways. It simulates the proliferation of glioma stem cells due to macrophage-induced inflammation, particularly involving two microglia phenotypes. The model can be used to regulate therapies by monitoring the GSC self-renewal rates that determine tumor progression. We observe that the GSC population is most sensitive to its own proliferation rate and the relative levels of the activating natural killer (NK) cell stimulatory receptors (NKG2D) versus killer inhibitory receptors (KIR) on NK cells that influence the proliferation or demise of the GSC population. Thus, the two most important factors involved in tumorigenesis or tumor regression are (1) GSC proliferation and (2) the functional status of NK cells. Therefore, strategies aimed at blocking proliferation and enhancing NKG2D and KIR signals should have a potentially beneficial impact for treating malignant gliomas.

Copyright © 2013 by ASME
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Fig. 1

Schematic of the cellular network controlling glial stem cell (Gsc) proliferation that result by considering the mechanism of cancer. Positive as well as negative feedbacks connecting Gsc and other cells in the microenvironment are depicted. The weights associated with the molecular signaling pathways 1–9 are included in Table 1. Arrows “→” imply positive influences whereas the symbol ⊥ denotes inhibition.

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Fig. 2

These images from experiments by our group show that GBM cells (U87MG) differentiate in the presence of serum and express (a) the embryonic stem cell marker nestin (light contrast), (b) the neuronal differentiation marker β Tubulin III (light), and (c) the glial fibrillary acidic protein (GFAP) (light). When they are grown in the absence of serum in stem cell medium as neurospheres, they enrich for (d) nestin (light) but lose the expression of (e) β tubulin III and (f) GFAP. This shows how the fate of GSCs is driven towards stemness in a stem cell microenvironment, i.e., how the TME drives this fate.

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Fig. 3

Variations in the Gsc populations over 9 months when the values of α9 are varied, i.e., αp = 10−1× (top), 1× (middle, baseline), and 10× (lowermost) of the value reported in Table 1. As α9 increases, the glial stem cell population that drives tumor progression decreases.

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Fig. 4

Variations in the M2 populations over 9 months when the values of α3 and α4 are varied, i.e., α3,4 = 10−1× (lowermost), 1× (middle, baseline), and 10× (top) of their values reported in Table 1. As α3,4 increase, the population of proinflammatory M2 microglia decreases. These macrophages drive inflammation by directly and indirectly (through Th1 lineage T cells) inhibiting the influence of NK cells and thus enhance tumor progression.

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Fig. 5

Variation in the Gsc population over 9 months as a result of decreasing αp to a tenth of its base value or when it is doubled, i.e., αp = 10−1× (lowermost, baseline), 1× (middle), and 2× (top) of the value reported in Table 1. As αp increases, so does Gsc and hence tumor progression.

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Fig. 6

Variation in the Nk population over 9 months as a result of decreasing α6 to a tenth of its base value or when it is halved, i.e., α6 = 10−1× (top, baseline), 0.5× (middle), and 1× (lowermost) of the value reported in Table 1. As α6 increases, the Nk population falls.




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