The natural history of EGFR and EGFRvIII in glioblastoma patients

BACKGROUND: The epidermal growth factor receptor (EGFR) is over expressed in approximately 50–60% of glioblastoma (GBM) tumors, and the most common EGFR mutant, EGFRvIII, is expressed in 24–67% of cases. This study was designed to address whether over expressed EGFR or EGFRvIII is an actual independent prognostic indicator of overall survival in a uniform body of patients in whom gross total surgical resection (GTR; ≥ 95% resection) was not attempted or achieved. METHODS: Biopsed or partially/subtotally resected GBM patients (N = 54) underwent adjuvant conformal radiation and chemotherapy. Their EGFR and EGFRvIII status was determined by immunohistochemistry and Kaplan-Meier estimates of overall survival were obtained. RESULTS: In our study of GBM patients with less than GTR, 42.6% (n = 23) failed to express EGFR, 25.9% (n = 14) had over expression of the wild-type EGFR only and 31.5 % (n = 17) expressed the EGFRvIII. Patients within groups expressing the EGFR, EGFRvIII, or lacking EGFR expression did not differ in age, Karnofsky Performance Scale (KPS) score, extent of tumor resection. They all had received postoperative radiation and chemotherapy. The median overall survival times for patients with tumors having no EGFR expression, over expressed EGFR only, or EGFRvIII were 12.3 (95% CI, 8.04–16.56), 11.03 (95% CI, 10.18–11.89) and 14.07 (95% CI, 7.39–20.74) months, respectively, log rank test p > 0.05). Patients with tumors that over expressed the EGFR and EGFRvIII were more likely to present with ependymal spread, 21.4% and 35.3% respectively, compared to those patients whose GBM failed to express either marker, 13.0%, although the difference was not statistically significant. There was no significant difference in multifocal disease or gliomatosis cerebri among EGFR expression groups. CONCLUSION: The over expressed wild-type EGFR and EGFRvIII are not independent predictors of median overall survival in the cohort of patients who did not undergo extensive tumor resection

Innate immune functions of microglia isolated from human glioma patients

BACKGROUND: Innate immunity is considered the first line of host defense and microglia presumably play a critical role in mediating potent innate immune responses to traumatic and infectious challenges in the human brain. Fundamental impairments of the adaptive immune system in glioma patients have been investigated; however, it is unknown whether microglia are capable of innate immunity and subsequent adaptive anti-tumor immune responses within the immunosuppressive tumor micro-environment of human glioma patients. We therefore undertook a novel characterization of the innate immune phenotype and function of freshly isolated human glioma-infiltrating microglia (GIM). METHODS: GIM were isolated by sequential Percoll purification from patient tumors immediately after surgical resection. Flow cytometry, phagocytosis and tumor cytotoxicity assays were used to analyze the phenotype and function of these cells. RESULTS: GIM expressed significant levels of Toll-like receptors (TLRs), however they do not secrete any of the cytokines (IL-1β, IL-6, TNF-α) critical in developing effective innate immune responses. Similar to innate macrophage functions, GIM can mediate phagocytosis and non-MHC restricted cytotoxicity. However, they were statistically less able to mediate tumor cytotoxicity compared to microglia isolated from normal brain. In addition, the expression of Fas ligand (FasL) was low to absent, indicating that apoptosis of the incoming lymphocyte population may not be a predominant mode of immunosuppression by microglia. CONCLUSION: We show for the first time that despite the immunosuppressive environment of human gliomas, GIM are capable of innate immune responses such as phagocytosis, cytotoxicity and TLR expression but yet are not competent in secreting key cytokines. Further understanding of these innate immune functions could play a critical role in understanding and developing effective immunotherapies to malignant human gliomas

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Modulation of Angiogenic and Inflammatory Response in Glioblastoma by Hypoxia

Glioblastoma are rapidly proliferating brain tumors in which hypoxia is readily recognizable, as indicated by focal or extensive necrosis and vascular proliferation, two independent diagnostic criteria for glioblastoma. Gene expression profiling of glioblastoma revealed a gene expression signature associated with hypoxia-regulated genes. The correlated gene set emerging from unsupervised analysis comprised known hypoxia-inducible genes involved in angiogenesis and inflammation such as VEGF and BIRC3, respectively. The relationship between hypoxia-modulated angiogenic genes and inflammatory genes was associated with outcome in our cohort of glioblastoma patients treated within prospective clinical trials of combined chemoradiotherapy. The hypoxia regulation of several new genes comprised in this cluster including ZNF395, TNFAIP3, and TREM1 was experimentally confirmed in glioma cell lines and primary monocytes exposed to hypoxia in vitro. Interestingly, the cluster seems to characterize differential response of tumor cells, stromal cells and the macrophage/microglia compartment to hypoxic conditions. Most genes classically associated with the inflammatory compartment are part of the NF-kappaB signaling pathway including TNFAIP3 and BIRC3 that have been shown to be involved in resistance to chemotherapy

cGAS-STING pathway targeted therapies and their applications in the treatment of high-grade glioma

Median survival of patients with glioblastoma (GBM) treated with standard of care which consists of maximal safe resection of the contrast-enhancing portion of the tumor followed by radiation therapy with concomitant adjuvant temozolomide (TMZ) remains 15 months. The tumor microenvironment (TME) is known to contain immune suppressive myeloid cells with minimal effector T cell infiltration. Stimulator of interferon genes (STING) is an important activator of immune response and results in production of Type 1 interferon and antigen presentation by myeloid cells. This review will discuss important developments in STING agonists, potential biomarkers for STING response, and new combinatorial therapeutic approaches in gliomas

Clinical management of supratentorial non-skull sase meningiomas

Supratentorial non-skull base meningiomas are the most common primary central nervous system tumor subtype. An understanding of their pathophysiology, imaging characteristics, and clinical management options will prove of substantial value to the multi-disciplinary team which may be involved in their care. Extensive review of the broad literature on the topic is conducted. Narrowing the scope to meningiomas located in the supratentorial non-skull base anatomic location highlights nuances specific to this tumor subtype. Advances in our understanding of the natural history of the disease and how findings from both molecular pathology and neuroimaging have impacted our understanding are discussed. Clinical management and the rationale underlying specific approaches including observation, surgery, radiation, and investigational systemic therapies is covered in detail. Future directions for probable advances in the near and intermediate term are reviewed

Primary CNS lymphoma commonly expresses immune response biomarkers.

Background: Primary central nervous system lymphoma (PCNSL) is rare and there is limited genomic and immunological information available. Incidental clinical and radiographic responses have been reported in PCNSL patients treated with immune checkpoint inhibitors. Materials and Methods: To genetically characterize and ascertain if the majority of PCNSL patients may potentially benefit from immune checkpoint inhibitors, we profiled 48 subjects with PCNSL from 2013 to 2018 with (1) next-generation sequencing to detect mutations, gene amplifications, and microsatellite instability (MSI); (2) RNA sequencing to detect gene fusions; and (3) immunohistochemistry to ascertain PD-1 and PD-L1 expression. Tumor mutational burden (TMB) was calculated using somatic nonsynonymous missense mutations. Results: High PD-L1 expression (\u3e5% staining) was seen in 18 patients (37.5%), and intermediate expression (1-5% staining) was noted in 14 patients (29.2%). Sixteen patients (33.3%) lacked PD-L1 expression. PD-1 expression (\u3e1 cell/high-power field) was seen in 12/14 tumors (85.7%), uncorrelated with PD-L1 expression. TMB of greater than or equal to 5 mutations per megabase (mt/Mb) occurred in 41/42 tumors, with 19% ( Conclusions: Based on TMB biomarker expression, over 90% of PCNSL patients may benefit from the use of immune checkpoint inhibitors

yuDetecting the percent of peripheral blood mononuclear cells displaying p-STAT-3 in malignant glioma patients

<p>Abstract</p> <p>Background</p> <p>The signal transducer and activator of transcription 3 (STAT-3) is frequently overexpressed in cancer cells, propagates tumorigenesis, and is a key regulator of immune suppression in cancer patients. The presence of phosphorylated STAT-3 (p-STAT-3) in the tumor can induce p-STAT-3 in tumor-associated immune cells that can return to the circulatory system. We hypothesized that the number of peripheral blood mononuclear cells (PBMCs) displaying p-STAT-3 would be increased in glioma patients, which would correlate with the extent of tumor-expressed p-STAT-3, and that higher p-STAT-3 levels in peripheral blood would correlate with a higher fraction of immune-suppressive regulatory T cells (Tregs).</p> <p>Methods</p> <p>We measured the percentage of PBMCs displaying p-STAT-3 in 19 healthy donors and 45 patients with primary brain tumors. The level of p-STAT-3 in tumor tissue was determined by immunohistochemistry. The degree of immune suppression was determined based on the fraction of Tregs in the CD4 compartment.</p> <p>Results</p> <p>Healthy donors had 4.8 ± 3.6% of PBMCs that expressed p-STAT-3, while the mean proportion of PBMCs displaying p-STAT-3 in patients with GBM was 11.8 ± 13.5% (<it>P </it>= 0.03). We did not observe a correlation by Spearman correlation between the degree of p-STAT-3 levels in the tumor and the percent of PBMCs displaying p-STAT-3. Furthermore, the percent of PBMCs displaying p-STAT-3 in glioma patients was not directly correlated with the fraction of Tregs in the CD4 compartment.</p> <p>Conclusion</p> <p>We conclude that the percent of PBMCs displaying p-STAT-3 may be increased in malignant glioma patients.</p