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Table of Contents
Year : 2018  |  Volume : 1  |  Issue : 1  |  Page : 25-33

Understanding the brain tumor microenvironment: Considerations to applying systems biology and immunotherapy

1 Department of Translational Neuroscience and Neurotherapeutics, John Wayne Cancer Institute; Pacific Neuroscience Institute, Santa Monica, USA
2 Department of Translational Molecular Medicine, John Wayne Cancer Institute, Santa Monica, USA
3 Institute of Systems Biology, Seattle, WA, USA
4 Department of Translational Neuroscience and Neurotherapeutics, John Wayne Cancer Institute; Pacific Neuroscience Institute, Santa Monica; Institute of Systems Biology, Seattle, WA, USA

Date of Submission22-Oct-2018
Date of Acceptance23-Oct-2018
Date of Web Publication14-Nov-2018

Correspondence Address:
Dr. Santosh Kesari
Department of Translational Neurosciences and Neurotherapeutics, Pacific Neuroscience Institute and John Wayne Cancer Institute at Providence Saint John's Health Center, 2200 Santa Monica Blvd., Santa Monica, CA 90404
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/IJNO.IJNO_11_18

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Patients with malignant brain cancers such as glioblastoma and brain metastases (BM) represent a population with a large unmet medical need, and a multitude of drugs have failed over decades. The current treatment modalities include surgery, radiation, and chemotherapy; yet, the median survival of patients with gliomas and BM remains abysmally low at 15 months and 2–14 months, respectively. In addition, standard treatments cause debilitating motor and neurological deficits. The paucity of effective therapies, despite intense investigation over the past several decades, represents inherent challenges to treating brain cancer and the critical knowledge gap in understanding tumor sensitivity, drug delivery, and microenvironmental shifts. Recently, immunotherapy has shown tremendous efficacy in melanoma and other cancers but has yet to revolutionize the treatment of brain cancers. However, as immunotherapy holds the promise of specifically targeting and eliminating tumor cells while sparing normal brain cells, innovative methods for investigating immunotherapy for brain cancer are essential for optimizing patient response. In this review, we will summarize the key issues and how a systems biology approach can help decipher this complexity and lead to better understanding and therapeutic targeting of the brain cancers.

Keywords: Brain cancer, glioma, immunotherapy, systems biology

How to cite this article:
Juarez TM, Carrillo JA, Achrol AA, Salomon MP, Marzese DM, Park JH, Baliga NS, Kesari S. Understanding the brain tumor microenvironment: Considerations to applying systems biology and immunotherapy. Int J Neurooncol 2018;1:25-33

How to cite this URL:
Juarez TM, Carrillo JA, Achrol AA, Salomon MP, Marzese DM, Park JH, Baliga NS, Kesari S. Understanding the brain tumor microenvironment: Considerations to applying systems biology and immunotherapy. Int J Neurooncol [serial online] 2018 [cited 2023 May 31];1:25-33. Available from: https://www.Internationaljneurooncology.com/text.asp?2018/1/1/25/245358

  Introduction Top

Patients with malignant brain tumors such as glioblastoma (GBM) and brain metastases (BM) represent a population with a large unmet medical need as several drugs have failed to be effective enough to change standard medical practice. While GBM is the most common primary malignant brain cancer, BM from melanoma, lung cancer, and breast cancer account for nearly 75% of patients with intracranial metastases.[1] The median survival of patients with gliomas and BM is 15 months and 2–14 months, respectively, and there is an ~90% chance of recurrence in gliomas such as GBM.[2],[3],[4],[5] In addition to poor response, standard treatments can cause debilitating motor and neurological deficits when normal brain tissue is damaged. The paucity of effective therapies despite intense investigation over the past several decades represents an inherent challenge to treating brain cancer, as well as the critical knowledge gap in understanding tumor sensitivity, drug delivery, and microenvironmental shifts. Using the immune system to specifically target brain tumors is not a new concept, and several approaches have been tried over the past two decades including vaccines, antibodies, viruses, checkpoint inhibitors, and more recently CAR-T cells.[6],[7],[8],[9],[10],[11],[12],[13],[14],[15] However, no immunotherapy is yet approved for brain tumors; recent attempts have resulted in either too little immune modulation to impact overall survival or too much immune stimulation that leads to detrimental toxicity.[16],[17]

Tumor molecular profiling has demonstrated promising results to guide treatment in various cancers,[18],[19],[20],[21],[22],[23] and has revealed intratumoral and interpatient heterogeneity, with unique molecular characteristics present in most tumors.[24],[25] Furthermore, the selective pressures of treatment can alter tumor and microenvironment profiles over time.[26] Clinical trials of targeted agents have largely been unsuccessful in unselected GBM patients, which emphasizes the need for ways of selecting patients who are more likely to respond to individualized treatments. It is increasingly apparent that this molecular diversity must be considered in clinical practice and clinical trial design, as well as the longitudinal changes that occur in tumors before, during, and after treatment. Articles referenced in this review were identified by literature search of relevant papers in PubMed using various terms including gliomas, microenvironment, and selected based on relevance to narrow focus of this review.

  Critical Barriers to Progress Top

Tumor microenvironment

The brain tumor microenvironment (TME) is a critical regulator of the response and resistance to therapies in both primary and metastatic brain malignancies.[27] A comprehensive understanding of the TME landscape, including tumor heterogeneity, blood–brain barrier (BBB) dynamics, and immunosuppression, is needed to identify new therapeutic strategies and understand the mechanisms of treatment resistance. In addition, tumor cell invasion and treatment-related neurotoxicity need better management.

Tumor heterogeneity

Genomic analyses performed on a large data cohort from the Cancer Genome  Atlas More Details (TCGA) revealed the biological complexity of somatic alterations associated with GBM.[28],[29] RNA sequencing, DNA copy number, transcriptomics, DNA methylation aberrations, and proteomic profiling have all been used to characterize and stratify subclasses of GBM that drive clinical prognoses,[30],[31],[32],[33] and efforts continue to identify molecular subtypes that affect the sensitivity of individual tumors to treatment and subsequent clinical outcome.[34],[35],[36] Not only do varied genetic drivers of GBM exist, but also oftentimes the involvement of multiple or compensatory molecular pathways leads to the development of drug resistance.[37] Expanded tumor profiling has revealed intratumor heterogeneity as well as interpatient heterogeneity, with most patients having tumors with unique molecular alterations.[24],[25] Individual efforts have generated molecular profiles of BM, mostly involving gene sequencing,[38],[39],[40] gene expression,[41] DNA methylation,[42] and combinations of these methods,[43],[44],[45],[46] and additional large-scale projects are needed that can advance our understanding of the BM landscape.

Blood–brain barrier

The BBB is a highly selective, semipermeable structure along the cerebral microvasculature that is responsible for maintaining brain homeostasis and protecting the central nervous system (CNS) from harmful substances in circulating blood. Drug delivery to treat brain cancer has been hindered by the BBB's physical barrier and efflux pumps that actively transport substances out of the brain. Several strategies are being developed to circumvent this barrier, but an important question to address is whether immunotherapies that boost adaptive immunity must penetrate the BBB to exert their therapeutic effect or if activity in peripheral components of the immune system such as the lymph nodes is sufficient.


Evading immune surveillance is one of the hallmarks of cancers; it develops out of an intricate cross-talk between the immune system, cancer cells, and normal stromal cells that can both inhibit and enhance tumor growth.[47] GBM and BM produce immunosuppressive cytokines inhibit effector T-cell recognition, proliferation, and function; increase T-cell apoptosis; activate regulatory T cells; and polarize microglia and tumor-associated macrophages toward an immunosuppressive phenotype.[48],[49],[50],[51],[52] Malignant brain tumors also subvert the immune system by downregulating major histocompatibility complex expression and antigen presentation, as well as capitalizing on immune checkpoint pathways to dampen antitumor activities.[53],[54],[55] Successful immunotherapy will depend on targeting these strategies for immune evasion and elucidating changes in the molecular state of the TME that occur due to different treatments, with the ultimate goal of enabling the rational design of combination therapy to target complementary pathways.[56]

Patients with brain cancer are frequently prescribed corticosteroids to address clinical symptoms of peritumoral edema and to minimize side effects from radiation therapy.[57] However, the long-term use of corticosteroids is associated with side effects such as myopathy, gastrointestinal bleeding, osteoporosis, mood disturbance, and immunosuppression. Dampening the immune system's ability to mount an inflammatory response puts patients at risk of potentially serious opportunistic infections and will hinder immunologic approaches to treat brain tumors. For example, the most commonly used corticosteroid, dexamethasone, influences lymphocyte cytokine production to shift an immune response from a Th1 cellular response towards a Th2 humoral response.[58] Moreover, dexamethasone has a proapoptotic effect on T lymphocytes and reduces the number of splenic and lymph node B cells while attenuating the proliferation of early B-cell progenitors.[59],[60],[61]


The vital nature of normal brain tissue creates a two-fold problem with invading tumors-neurological symptoms and deficits caused by compressed areas in the brain and challenges for complete surgical resection. Even with gross total resections, diffuse dissemination of tumor cells can leave microscopic residual disease not detectable by radiographic imaging, as evidenced by tumors tending to recur around the resection margin.[62],[63],[64]

Radiation- and chemotherapy-induced neurotoxicity and transformation

Although the benefits of radiation and chemotherapy far outweigh the risk for most people at this time, treatment can lead to temporary or permanent cognitive decline and decreased quality of life.[65],[66],[67] Radiotherapy is indiscriminate in damaging tumors, tumor/peritumoral microvasculature, normal brain white matter, the hippocampus, and prefrontal cortex.[66],[68] Over half of the patients who receive brain radiation and survive beyond 6 months exhibit progressive cognitive impairment and other symptoms.[62],[66],[68],[69],[70],[71] Not only does radiation therapy adversely affect normal brain function but it can also promote tumor recurrence with a more aggressive phenotype.[72],[73] Finding alternate treatments to delay or circumvent radiation therapy would significantly impact patients' quality of life.

  How a Systems Biology Approach Can Address These Challenges Top

Current standard treatments are not good enough, and new approaches are needed to improve clinical outcomes

While there is no doubt that radiation and temozolomide have conferred a survival advantage and has been the cornerstone of therapies for most malignant brain cancers, the fact remains that significant improvements in patient survival and quality of life are still desperately needed. In addition to developing more efficacious treatments, a deeper understanding of the pharmacodynamic and pharmacogenetic interplay between treatments and tumors is needed to help physicians make informed treatment decisions throughout a patient's disease.[74] Despite the past 20 years of focused efforts on brain cancers, from basic biology to clinical trials, substantial progress has not been made in improving patient outcomes. The reasons are varied, but one central reason is that clinical trials should be performed earlier in the disease course where novel treatments, especially immunological approaches, can have the maximal benefit before chemotherapy or radiation negatively impact the immune system and TME. Preirradiation treatment (neoadjuvant therapy) is also advantageous to treat micrometastatic lesions much earlier than conventional adjuvant therapy, as well as to identify truly active or ineffective therapeutic regimens early.

Blood-based biomarkers for brain cancers

With the development of new approaches to treat early stage-brain tumors, it becomes more important to have better early detection methods that can be carried out, repetitively. The early detection of small lesions allows the implementation of endoscopic surgery, focused radiotherapies, and new therapeutics such as small molecule inhibitors and checkpoint inhibitors. Liquid (blood) biopsy for circulating biomarkers offers an important tool not only to detect brain tumors early but also to monitor progression and treatment responses in real time. To date, there has been little progress in this field primarily due to limited molecular biomarkers and multiomics analysis. Since brain tumors such as BMs GBMs have a limited number of mutations, changes in other cell-free nucleic acids, proteins, and metabolites must be profiled to obtain a comprehensive and sensitive detection system. However, a holistic approach is required to analyze these complex, heterogeneous multiomic datasets to discover indicators of perturbed networks that can be mined for biomarkers together with other clinical demographics. A systems biology approach of integrating new analytical tools and computational modeling for innovative solutions is novel and elevates the traditional single biomarker and single platform assay analysis such as that found in the standard development of liquid biopsies.

Interpatient and intratumoral heterogeneity

In addition to varied genetic drivers of GBM, nongenetic heterogeneity caused by processes such as epigenetic modifications, differentiation gene-expression programs, and stochastic gene expression can also drive cells into distinct functional states. For instance, Shaffer et al. demonstrated that transcriptional variability within an isogenic cell population supports the presence of rare cell types that are susceptible to drug-induced transcriptional reprogramming, which results in distinct, stable, acquired drug resistance.[75] While distinct in nature, these genetic and nongenetic factors are not mutually exclusive. CpG island methylator phenotype-associated silencing of DNA repair genes can cause genetic changes. Conversely, translocation and mutations can lead to epigenetic disruption.[76] Ultimately, these genetic and nongenetic factors, as well as the interplay among them, drives tumor cells into the distinct functional states that underlie distinct biological and clinical phenotypes. Consequently, the differences underlying intratumoral heterogeneity also drive differences across tumors (e.g., interpatient heterogeneity). In addition to molecular and cellular differences, regional differences (e.g., TME), and temporal changes (e.g., recurrent tumors) also contribute to the heterogeneity observed within tumors (e.g., multifocal GBM) and among patients.[77] This interpatient and intratumor heterogeneity necessitates a more individualized approach to treatment.[78],[79]

Several approaches have been proposed to reduce recurrence rates and drug resistance, such as targeting stem-like cells, dual therapy for long-term disease control, and therapy with synergistic drug combinations.[80],[81],[82],[83],[84],[85],[86],[87],[88],[89] Therefore, a growing number of clinical trials are investigating multiple drug combinations for the treatment of GBM (e.g., ClinicalTrials.gov identifier: NCT01430351). However, the number of possible drug combinations is too large to screen effectively. Moreover, not all drug combinations are likely to be effective against all patient tumors. A gene network-based strategy, which utilizes information regarding the complex mechanisms underlying tumor biology elucidated by systems-type analyses of tumors, is necessary to accelerate the discovery of drug combinations that have a higher likelihood of acting synergistically against the unique vulnerabilities of a specific patient(s) tumor.

For example, we have performed systems genetics network analysis [SYGNAL; [Figure 1]a and [Figure 1]b of multiomics GBM data from TCGA to generate an omic-scale predictive network model (gbmSYGNAL) that delineates how specific genetic and non-genetic mechanisms mechanistically alter gene networks governing oncogenic processes across tumors.[90] For example, using gbmSYGNAL, we have identified a putative mechanism in which GBM tumors with NF1 and PIK3C mutations have increased lymphocyte infiltration [Figure 2].[91] Similarly, we have demonstrated how gbmSYGNAL can be used to infer molecular targets (e.g., miRNA targets) that can be perturbed to increase drug efficacy [Figure 1]c and [Figure 1]d. This model, together with multiscale computational and experimental models that evaluate events occurring over distinct spatial and temporal levels, will uncover multiple vulnerabilities across heterogeneous tumor populations to discover combination therapies effective against the unique characteristics of a patient's tumor and the cell subpopulations within an individual tumor.
Figure 1: SYGNAL pipeline and network analysis. (a) Input data, computational steps, and output of SYGNAL pipeline. (b) Left: A disease-relevant gene module discovered by SYGNAL. glioblastoma tumors are rank-ordered based on the median expression of 16 coregulated genes in module. Individual bars represent interquartile range of expression of module genes within a glioblastoma tumor. High variance in expression of these genes within samples to the right of the red vertical line resulted in their exclusion from the bicluster. Right: Statistical enrichment of glioblastoma tumor subtypes in quintiles. The color code is same as in left panel. (c) microRNA miR-486-3p predicted as a putative regulator of HDAC5 (gene belonging to bicluster TargetScan 474), motivating the hypothesis that the combination of romidepsin and miR-486-3p would have a synergistic effect on inhibiting HDAC5. (d) Left: Dose-response curve for a miR-486-3p mimic on the relative proliferation of the U251 glioma cell line shows an IC50 of 4.6 nM. Middle: The HDAC inhibitor romidepsin has an IC50 of 1.1 nM (both red dashed lines). Right: Dose-response matrix for the effect of miR-486-3p and romidepsin combinations on relative cellular proliferation. Combinations having synergistic anti-proliferative effects on relative cellular proliferation are outlined in cyan (synergy score ≥2.3)

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Figure 2: SYGNAL identifies putative, causal, mechanistic interactions affecting lymphocyte infiltration. An independent analysis of the Cancer Genome Atlas samples utilizing histologic and clinical correlates of tumor-infiltrating lymphocytes showed an enrichment of the mesenchymal subtype in tumors having moderate (1+) to high (2+) scoring of lymphocyte infiltration, along with a strong association with mutations in NF1. The middle-upper table summarizes the breakdown of tumors having either no lymphocyte infiltration (0) or moderate/high lymphocyte infiltration according to glioblastoma subtype. While this analysis statistically associates tumor lymphocyte infiltration to a somatic mutation/transcriptional glioblastoma subtype, causal, and mechanistic information explaining these associations are missing. To address this knowledge-gap, a separate SYGNAL analysis of the Cancer Genome Atlas-glioblastoma data identified a mechanistic pathway in which somatic mutations in NF1 (and PIK3C) affect transcription factor IRF1, which regulates a group of genes (i.e., bicluster PITA_282) associated with cancer hallmarks of immune response evasion and inflammation (right panel flow diagram). Closer examination of PITA_282 reveals that expression of genes in this bicluster stratifies glioblastoma subtypes (middle rank-ordered expression plot) with the ME subtype being enriched in the upper quantile of samples (middle-bottom subtype enrichment plot). The stratification of tumor subtypes along with the causal, mechanistic interactions identified by SYGNAL “fills in the gaps,” outlining a causal flow of information from somatic mutations previously observed interactions between glioblastoma and the microenvironment. glioblastoma subtype abbreviations: NB: Normal brain, GC: G-CIMP, PN: Proneural, NE: Neural, CL: Classical, ME: Mesenchymal

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Immunotherapy could offer specificity for invasive disease

Although genomic studies have improved our understanding of gliomas and BM, extensive clinical strides have not been made using immunotherapy. Using the immune system to specifically target brain tumors have been tried over the past two decades including vaccines, antibodies, viruses, checkpoint inhibitors, and more recently CAR-T cells,[6],[7],[8],[9],[10],[11],[12],[13],[14],[15] with the recent failure of nivolumab to increase overall survival in patients with recurrent GBM.[92] One confounding issue is that most clinical trials are conducted in the recurrent disease setting, where the immune system is already damaged and has less chance of responding.[93] Understanding the polar responses to immunotherapy, where either too little immune modulation (e.g. vaccines) or too much (e.g. checkpoint inhibitors) occurs, and identifying a middle ground, as well as managing CNS side effects without compromising the tumor-specific immune response, will be key to the future success of immune-based therapies for brain cancers.[92],[93],[94],[95],[96],[97] Furthermore, the realization that the tumor and its microenvironment are moving targets that evolve over time necessitates longitudinal studies in patients.

Tumor responses with immunotherapies have been observed in patients with BM from melanoma and lung cancer.[98],[99],[100],[101] Results from the Phase II Checkmate-204 clinical trial of nivolumab plus ipilimumab for patients with asymptomatic melanoma brain metastases (MBM) showed a favorable safety profile and high intracranial antitumor activity, with clinical benefit of 57% including complete responses in 26% of patients.[101],[102] A separate Phase II study of ipilimumab monotherapy in patients with MBM identified differences in the intracranial disease control rate between asymptomatic patients without corticosteroids (25%) and symptomatic patients requiring corticosteroids (10%).[98]

The durable response seen in cases of recurrent GBM arising from biallelic mismatch repair deficiency treated with nivolumab is also encouraging for hypermutant cancers.[94] Initial results from the Phase II Checkmate-143 clinical trial of nivolumab for patients with recurrent GBM encouraged continued testing on a Phase III cohort.[95] Although the study did not culminate with significantly increased survival, the larger implication is that immunotherapy has the potential to deliver responses to some patients with brain cancer and identifying those determinants is imperative.

These successes and failures of immune checkpoint inhibition in patients with primary and secondary brain cancer suggest underlying characteristics predisposing each tumor to immunotherapy responsiveness. Much remains unknown of what these individual tumor characteristics are; therefore, it is critical to identify the determinants of response that can help develop and select the most effective therapies for each individual patient, leading to a new type of trial, an n = 1 trial.

The concept of n = 1 trials is misleading as by definition it is statistically underpowered. However, by leveraging multiomics data from large cohorts, it is feasible to reverse engineer the ensemble of networks that are perturbed in brain cancer and identify what subnetworks may be enriched across different patient subgroups, as we have done using the gbmSYGNAL network [Figure 1]b. Thereafter, multiomics profiles of an individual patient can be mapped on to this network to identify the unique set of genetic and nongenetic mechanisms that have become dysfunctional to drive oncogenic processes in their tumors. In other words, the network-based approach enables the mapping of patients to complex, dysfunctional networks delineated by the gbmSYGNAL network model, which enables the identification of potential regulatory mechanisms that may be targeted to specific therapies that have a higher likelihood of being effective. In fact, screens using patient-derived GBM cell lines can then be used to find drug combinations that might synergize only in the context of the unique dysfunctional mechanisms of that patient's tumor, thereby motivating an n = 1 type trial.

  Conclusions Top

To better serve patients with brain cancer, new clinical trial designs that incorporate advanced genomics and other methodologies to interrogate tumor biology and the TME could enable rationally designed, customized treatment combinations early in the treatment course. The provision of biological specimens centered on treatments administered before radiation would lead to a more undisturbed, comprehensive profile evaluation to address questions regarding the individualistic biological effects of treatment, how many signals or pathways need to be targeted, and what possible mechanisms of drug resistance and lack of durable response emerge.

In the era of genomics and immune-oncology, precision and systems medicine is clearly needed to optimize treatments for each patient (n = 1). We need to learn from every patient-treatment pairing to improve our understanding of the underlying causes of the variability in patient response. We need to carefully look at the impact of immune therapy before the immune system/TME is altered by radiation and chemotherapy and do so in a longitudinal fashion. Not only will there be a broader and deeper understanding of the TME, but this will also help us will help us design new therapeutic strategies to transform the outcomes of brain cancers.

Methods used for locating, selecting, extracting, and synthesizing data

Articles referenced in this review were identified by literature search of relevant papers in PubMed using various terms including gliomas, GBM, immunotherapy, checkpoint inhibitors, BM, TME, and others selected based on relevance to narrow focus of this review.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

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