Brain Tumors: An Encyclopedic Approach (3rd Edition)

Brain Tumors: An Encyclopedic Approach
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Cancer registries worldwide take great care when implementing changes because of issues of geographical comparability and trends over time Letter from Sharon Whelan, IARC, January 9, Because Consensus Conference II was restricted to discussions involving tumors of the brain and CNS, the collection of data on genetic syndromes related to CNS tumors will need to be addressed within the context of the collection of data on genetic syndromes related to all cancer sites. Consensus Conference II is part of a continuum of meetings concerning issues affecting cancer registration of intracranial and other CNS tumors.

Classification of these tumors is dynamic, and the registration and coding of these tumors will need to be continually reviewed. The interaction between the cancer registration community and the brain tumor clinical and research community as they worked together to implement the collection of benign brain tumors proved educational for both parties and continues to build on one of the recommendations of Consensus Conference I: to continue this joint effort between the surveillance and clinical communities.

We thank all of those individuals whose input was used in preparing the tables for discussion, especially Roger McClendon and Edward Laws, and those listed in the Appendix. The Society for Neuro-Oncology provided the location for this meeting. Europe PMC requires Javascript to function effectively. Recent Activity. These and other issues were addressed at the subsequent Consensus Conference on Cancer Registration of Brain and Central Nervous System Tumors, at which the Central Brain Tumor Registry of the United States facilitated a discussion among epidemiologists, neurosurgeons, and neuropathologists.

The snippet could not be located in the article text. This may be because the snippet appears in a figure legend, contains special characters or spans different sections of the article. Neuro Oncol. PMID: Bridget J. Received May 19; Accepted Oct This article has been cited by other articles in PMC. Rationale During the process of reaching consensus on a standard definition for brain tumor registration and formulating implementation guidelines, concerns surfaced about the rules that guide the collection and reporting of all primary brain tumors.

Methods A comprehensive review of ICDO-3 was conducted to identify brain-related cysts and tumor-like lesions currently collected by the central cancer registries Table 1 ; Fritz et al. See Rathke cleft cyst in Table 2. Open in a separate window. Recommendation 2 The collection of additional selected cysts and tumor-like lesions found in brain-related sites but currently lacking ICDO codes was recommended. However, no separate codes are available. Foramen magnum Meninges of the skull base Parasellar moved from C Recommendation 4 The collection of data on genetic syndromes in persons diagnosed with tumors of the brain and CNS was recommended Table 4.

Table 4 Genetic syndromes associated with brain and central nervous system tumors. Acknowledgments We thank all of those individuals whose input was used in preparing the tables for discussion, especially Roger McClendon and Edward Laws, and those listed in the Appendix. Contributors to the materials for Consensus Conference II. Berger, M. Bigner, M.

Black, M. Bondy, Ph. The University of Texas M. Bruner, M. Engelhard, M. University of Illinois at Chicago Howard A. Fine, M. National Cancer Institute James G. Gurney, Ph. University of Minnesota Fred H. Hochberg, M. Inskip, Sc. In addition to inter-tumoral and inter-patient heterogeneity, GBM also exhibits significant intra-tumoral heterogeneity down to the single-cell level [11] , [12]. First, glioma cells originate from a variety of dynamically evolving progenitor cells [13].

It has been demonstrated that GBM cells demarcated by the neural stem cell marker CD exhibit much enhanced competencies for self-renewal and tumor initiation [14] , [15]. Recent studies have also shown instances in which CDnegative cells were able to generate the same outcomes [16] , [17] , [18] , [19]. Second, glioma cells constantly interact with a variety of stromal cells.

Brain Tumors

There is evidence that glioma cells acquire the ability to recruit and subvert their untransformed neighbor microglia into active collaborators to facilitate tumorigenesis. Direct correlation has been reported between the grade of glioma and the level of resident tumor microglia [20] , suggesting the mutual paracrine stimulation between microglial cells and glioma cells [21] , [22] , [23] , [24].

Microglial cells recruited by glioma can promote tumor growth [25] , [26] , [27] , dictated by paracrine loops responsible for glioma initiation and progression e. The crosstalk between activated astroglial and glioma cells has also been documented, although the mechanism of their interactions has not been full revealed. In silico models of tumor microenvironment integrate information about the biological context in which cancers develop, and thus represent a multi-scale consideration of oncogenesis as it occurs within somatic tissues [42] , [43].

Multiple factors involved in the development of an intrinsically complex tumor microenvironment have been studied including extracellular biomolecules, a spatially intricate and dynamic vasculature, and the immune system. The latter describe the dynamics of individual interacting units, such as cancer cells, in small confined space; the former can be applied to a large tissue scale where agent-based modeling is computationally prohibitive. However, none of these methods have been integrated with a large cell-cell communication network in a complex tumor microenvironment.

Herein we integrate all the intercellular signaling pathways known to date for human glioblastoma and generate a dynamic cell-cell communication network associated with the glioma microenvironment. Then we apply evolutionary population dynamics and the Hill functions to interrogate this intercellular signaling network and execute an in silico tumor microenvironment development.

The observed results reveal a profound influence of the microenvironmental cues on tumor initiation and growth, and suggest new venues for glioblastoma treatment by targeting cells or soluble mediators in the tumor microenvironment. Although much is known about the identities and biochemical activities of signaling molecules in the glioma microenvironment [1] , [2] , [3] , [4] , [5] , [44] , [45] , how these mediators coordinate and function collectively at the systems level to regulate tumor development is insufficiently understood.

Then we derived a quantitative model using stochastic population dynamics and the Hill functions. Second, the temporal growth rate of each cell type is also modulated by soluble signaling mediators present in the tumor microenvironment; this process is quantitatively described by the Hill functions. All differential equations are described in Supporting Text S1 and the initial settings of all parameters are detailed in Supporting Table S1.

As an example, we present here the procedure on how to construct the model for glioma cell population. It has been suggested that glioma can originate from cells at multiple differentiation stages during glial cell development, whereas the progenitor cells appear to be more susceptible to neoplastic transformation compared with mature glial cells [46] , [47]. PGE2 can transiently prevent glioma cell proliferation in vitro. Similarly, the same algorithm was applied to derive population dynamics equations for other cells.

The network comprises 5 types of cells and a panel of 15 cytokines. The processes involving cytokine or chemokine mediation are described by solid lines, while the other processes representing changes of cell states are depicted by dashed lines. Supporting Video S1 is the complete video showing the one-year evolution. QSC, quiescent stem-like cell. ASC, activated stem-like cell. The change of cytokines associated with tumor microenvironment development is described as the production and consumption by all the cells and modulation by other cytokines as revealed by prior experiments.

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For example, glioma stem cells, glioma cells, and microglial cells secrete substantial amounts of VEGF. In the end, the temporal rate of growth and death of each cell population or the rate of production and decay of each cytokine is expressed as an ODE; a set of 20 inter-coupled ODEs were constructed to interrogate the dynamics of intercellular signaling network in a glioma microenvironment. Supporting Tables S1 and S2 and Methods give a complete description of all the signaling processes and summarize the input values for all differential equations.

We performed an in silico stochastic study of glioma microenvironment development in a 1-ml control volume over a period of 12 months and observed a non-linear, synergistic co-evolution of all five cell types Fig. The dynamics of glioma cells GC exhibit three distinct phases Fig.

Brain Tumors - Their Biology and Pathology | K.J. Zülch | Springer

The starting cell populations are astrocyte 2. The initial conditions only change the quantitative timeline of the dynamics but would not affect the general trends observed in our model that properly reflect the dynamics of human glioma see Supporting Fig. We observed that glioma stem cells are the major cell sources for glioma formation. At the early stage, QSCs upon stimulation are rapidly activated into activated stem cells ASC via a reversible process conferring self-renewal capability Fig.

This step proceeds to completion within the first month. Despite the rapid lineage conversion of stem cells occurring as early as in the first month, glioma cells remain at a silent state with cell density way below the clinically detectable threshold. During the growth of glioma cells within the space that astrocytes occupy, astrocytes strive to maintain their abundance as well as their functions until they are displaced by the glioma cells in the late stage.

The number of microglial cells follows a steady increase all the way from the pre-tumor to the malignant stage, with a small kink occurring at the onset of rapid tumor expansion Fig. This unique behavior is consistent with glioblastoma development observed in animal models [17]. The astrocyte population shrinks due to competitive selection pressure exerted by a microenvironment unfavorable to astrocyte proliferation or favorable to astrocyte apoptosis that decreases the fitness advantages over time and eventually causes the loss of dominance.

We examined the contribution of direct mutation of astrocyte and the differentiation of glioma stem cells to glioma growth. We observed that neoplastic transformation of astrocytes directly to glioma cells does result in the formation of small numbers of glioma cells in the pre-cancer phase, but contributes little to tumor development in rapid growth and expansion phases see Supporting Fig.

The total cell concentration experienced a significant expansion during the seventh month, suggesting a density-gradient-driven potential for the glioma cells to invade neighboring tissues Fig. The total cell density we observed in the tumor microenvironment is higher than that in normal tissue, which is quantitatively consistent with the results obtained using tissue histology examinations [53] , [54] , [55]. A three-dimensional 3D stochastic simulation Fig. Cytokine dynamics also exhibit multi-stage non-linear characteristics Fig.

Activated microglial cells were found to be an important source of cytokines in the early stage, yielding a steady increase of cytokine concentrations prior to the emergence of tumor.

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These cytokines participate in the modulation of rapid glioma cell expansion in the later stage, suggesting that microglial cells may play an important role in tumor initiation by priming glioma cells at very low concentrations. Glioma cells also secrete paracrine signaling factors that promote the proliferation and migration of microglia, and thus in turn benefit from the increase of microglia cells that reside in the vicinity of the glioma growth front.

The normalized dynamics curves Fig. IL10 and PGE2 show a monotonic increase across all the three phases. All the other cytokines exhibit a rapid concentration increase in the second phase and reach a quasi-steady state correlated with the glioma population dynamics. It shows three types of cytokine dynamics based upon the temporal traces.

IL10 and PGE2 show constant increase regardless the growth phases of glioma cells. Other cytokines are apparently correlated to the three-phase growth dynamics. We first designed a novel therapy by targeting the cellular components of the tumor microenvironment. According to cell population dynamics Fig. Therefore, we designed a cell-targeting therapy that eliminates microglial cells in the tumor microenvironment.

To examine the applicability of this therapy to patients with different biomolecular background and assess the effect of inter-patient heterogeneity on therapeutic response, three virtual patients with different profiles of initial parameters cytokine production rate, receptor expression level, etc. The results are compared as shown in Fig. Two interesting features were observed in the microglia depletion therapy experiments.

Their Biology and Pathology

First, all patients responded in a similar manner although the length of therapeutic benefit and the recurrence time varied from one patient to the other. Second, the efficacy strongly depends on how early the treatment was given to the patients Fig. Supporting Video S2 shows the full video. These results, obtained by unbiased integration of basic biochemical parameters and cell signaling processes, were found to appropriately reflect clinical and experimental observations.

There is a consensus that activated microglia promote glioma growth and promotion, which is consistent with our in silico glioma development experiments [20] , [39] , [40] , [41]. Recently, an animal model study indicated that clonal cooperation between different mutant cells can lead to tumor formation, whereas any single-cell type alone cannot develop into tumor [57].

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A brain tumor is an abnormal growth of tissue, either malignant cancerous or benign noncancerous , in the brain. In the end, we present the intercellular signaling network as a set of coupled ordinary differential equations in terms of population dynamics. Science 81— Anticancer Res — Secondary brain tumors are most apt to occur in patients who have melanoma, breast, colon, kidney, or lung cancer.

What is more interesting is that the second clone, once activated by the first clone presumably through cytokine signaling, becomes fully self-sustained and develops into tumor without the presence of the first clone, which is strikingly similar to the glioma-microglia interaction observed in our model, and thus may share commonalities in molecular and cellular mechanisms. Our study suggests that cells in the tumor microenvironment can be good targets for therapeutic intervention or control of tumor progression, pointing to new venues for anti-tumor drug design and development.

The results of microglia-depletion therapy indicate that patients do not show significant responses unless they are diagnosed at the very early stage — the time when no clinically detectable tumors have been formed. Thus, we turn to assess the possibility of combination therapy that directly targets a number of key cytokine signaling pathways, which is anticipated to give more focused and potent therapeutic effects. Due to inter-tumoral heterogeneity, the best therapeutic regimen must be an individually tailored combination of inhibitors that act on selected cytokines or their receptors optimized for the patient.

We performed a sensitivity analysis to assess the tumorigenic potential of each cytokine and find the primary targets that, once subjected to blockade or promotion, exhibit the most effective responses in therapeutic intervention. The Methods and section 3 in Supporting Text S1 describe the details of this analysis.

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Basically, it measures the length of time taken by glioma cells to grow from the threshold concentration e. Twenty-nine tests, each perturbing a cytokine production rate or a cytokine receptor expression level, were performed to give the sensitivity factor of each cytokine or its receptor with respect to patient survival probability. According to the results, forced activation of a signaling pathway with a positive sensitivity factor is expected to promote patient survival, and vice versa. To test this therapy, the same virtual patients patients 1, 2, and 3 that were randomly designed for microglia depletion experiments are examined here to generate sensitivity factor profiles for every patient Fig.

Supporting Table S7 summarizes all the parameters u1—u29 , surface receptor expression level. The combination therapy results in a striking synergistic effect to suppress tumor progression whereas any single target treatment does not show appreciable benefit. Supporting Video S3 is the complete video showing the one-year evolution.

Although each of the four cytokines has a large negative sensitivity factor for promoting tumorigenesis, therapies that inhibit only one of these cytokines can hardly alter the time course of tumor progress, due to the homeostatic robustness of the cytokine network and the resulting intrinsic resistance to perturbation. To overcome this issue, we further applied to virtual patient 1 a combination treatment that simultaneously inhibits all four cytokines, and we observed substantial therapeutic responses that cannot be simply explained by the additive effect Supporting Fig.

Second, the same therapy was given to patients 2 and 3, but did not yield positive therapeutic responses Fig. S5 ; patient 2 exhibited a modest benefit by one month and patient 3 almost did not respond at all. Figure 4d shows the results of a 3D stochastic simulation of cell population dynamics in response to combination therapy administered at different times. Considering that these treatments were administered at a middle to late stage when clinically detectable tumors had already developed, we conclude that the combination therapy tailored to match individual patients is more focused and can give better therapeutic benefit even when microglia depletion therapy fails in the middle to late stages, highlighting the critical need for molecular diagnosis and patient stratification prior to the design of a combination therapy that targets the tumor microenvironment.

To the best of our knowledge, this is the first study that attempts to integrate a variety of cells and their intercellular signaling pathways into a cell-cell communication network and assess how this network controls tumor initiation and progression at the systems level.

Through in silico experimentation of tumor microenvironment development, the dynamics of cells and cytokines correctly reflects general trends of tumorigenesis observed experimentally or clinically [17] , [51] , [58]. Trigeminal neuralgia related to cerebellopontine angle tumors. Neurosurg Rev. Jannetta PJ. Microsurgical management of trigeminal neuralgia.

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Arch Neurol. Arterial compression of the trigeminal nerve at the pons in patients with trigeminal neuralgia. Microvascular decompression as a surgical management for trigeminal neuralgia: a critical review of the literature. Neurol India. Percutaneous controlled radiofrequency trigeminal rhizotomy for the treatment of idiopathic trigeminal neuralgia: year experience with 1, patients.

Epidemiology and clinical features of idiopathic trigeminal neuralgia and glossopharyngeal neuralgia: similarities and differences, Rochester, Minnesota, — Frequency of cerebellopontine angle tumours in patients with trigeminal neuralgia. J Ayub Med Coll Abbottabad. Symptomatic trigeminal neuralgia caused by cerebellopontine angle tumors. J Craniofac Surg. Microvascular decompression for treatment of trigeminal neuralgia in a patient with a facial nerve schwannoma. Matthies C, Samii M.

Management of 1, vestibular schwannomas acoustic neuromas : clinical presentation. Merskey HM, Bogduk N. Classification of chronic pain: descriptions of chronic pain syndromes and definitions of pain terms. Natural history of elderly patients with asymptomatic meningiomas.

J Neurol Neurosurg Psychiatry. Atypical Trigeminal Neuralgia Secondary to Meningioma. Case Rep Dent Article. Stereotactic radiosurgery for trigeminal pain secondary to recurrent malignant skull base tumors. Modern meningioma imaging techniques. Trigeminal neuralgia caused by venous angioma: a case report and review of the literature.

Satyarthee GD, Kumar S. Professor Peter Joseph Jannetta. Secondary trigeminal neuralgia in cerebellopontine angle tumors.

Skull Base. Various surgical modalities for trigeminal neuralgia: literature study of respective long-term outcomes. Acta Neurochir. Microvascular decompression for primary trigeminal neuralgia: short-term follow-up results and prognostic factors. J Korean Neurosurg Soc. Epidemiology and etiology of meningioma. Download references. HBG transformed the draft text into an article. EE participated in this process as a supervisor. All authors read and approved the final manuscript. Correspondence to Hasan Burak Gunduz.

I confirm that ethical approval was not required for reporting this case and reviewing literature. Written informed consent was obtained from the patient for publication of this case report and accompanying images. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Search all SpringerOpen articles Search. Abstract Trigeminal neuralgia may be idiopathic or may involve other causes. Main text A year-old male patient admitted to polyclinic with severe facial pain.

Full size image. Photo of tumor after resection.