Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid ($< 90$ seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma ($n=153$) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of $93.3\pm 1.6\%$. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.Comment: Paper published in Nature Medicin

BRAF V600E Mutations Are Common in Pleomorphic Xanthoastrocytoma: Diagnostic and Therapeutic Implications

Pleomorphic xanthoastrocytoma (PXA) is low-grade glial neoplasm principally affecting children and young adults. Approximately 40% of PXA are reported to recur within 10 years of primary resection. Upon recurrence, patients receive radiation therapy and conventional chemotherapeutics designed for high-grade gliomas. Genetic changes that can be targeted by selective therapeutics have not been extensively evaluated in PXA and ancillary diagnostic tests to help discriminate PXA from other pleomorphic and often more aggressive astrocytic malignancies are limited. In this study, we apply the SNaPshot multiplexed targeted sequencing platform in the analysis of brain tumors to interrogate 60 genetic loci that are frequently mutated in 15 cancer genes. In our analysis we detect BRAF V600E mutations in 12 of 20 (60%) WHO grade II PXA, in 1 of 6 (17%) PXA with anaplasia and in 1 glioblastoma arising in a PXA. Phospho-ERK was detected in all tumors independent of the BRAF mutation status. BRAF duplication was not detected in any of the PXA cases. BRAF V600E mutations were identified in only 2 of 71 (2.8%) glioblastoma (GBM) analyzed, including 1 of 9 (11.1%) giant cell GBM (gcGBM). The finding that BRAF V600E mutations are common in the majority of PXA has important therapeutic implications and may help in differentiating less aggressive PXAs from lethal gcGBMs and GBMs

BRAF V600E Mutations Are Common in Pleomorphic Xanthoastrocytoma: Diagnostic and Therapeutic Implications

Pleomorphic xanthoastrocytoma (PXA) is low-grade glial neoplasm principally affecting children and young adults. Approximately 40% of PXA are reported to recur within 10 years of primary resection. Upon recurrence, patients receive radiation therapy and conventional chemotherapeutics designed for high-grade gliomas. Genetic changes that can be targeted by selective therapeutics have not been extensively evaluated in PXA and ancillary diagnostic tests to help discriminate PXA from other pleomorphic and often more aggressive astrocytic malignancies are limited. In this study, we apply the SNaPshot multiplexed targeted sequencing platform in the analysis of brain tumors to interrogate 60 genetic loci that are frequently mutated in 15 cancer genes. In our analysis we detect BRAF V600E mutations in 12 of 20 (60%) WHO grade II PXA, in 1 of 6 (17%) PXA with anaplasia and in 1 glioblastoma arising in a PXA. Phospho-ERK was detected in all tumors independent of the BRAF mutation status. BRAF duplication was not detected in any of the PXA cases. BRAF V600E mutations were identified in only 2 of 71 (2.8%) glioblastoma (GBM) analyzed, including 1 of 9 (11.1%) giant cell GBM (gcGBM). The finding that BRAF V600E mutations are common in the majority of PXA has important therapeutic implications and may help in differentiating less aggressive PXAs from lethal gcGBMs and GBMs

An unusual association of deletion of SMARCB1 in a patient with intracranial yolk sac tumor: A case-report

Background: Deletion of SMARCB1/loss of INI is a well-known association in atypical rhabdoid teratoid tumors (ATRT) in the brain, rhabdoid tumors in the kidney, and less common tumors, including sinonasal INI1 deficient carcinoma, gastric undifferentiated carcinoma, undifferentiated uterine sarcomas, and poorly differentiated chordomas. Case report: We describe homozygous deletion of the SMARCB1 gene in a patient diagnosed with pineal yolk sac tumor, which is a rare entity. The association highlights the importance of INI1 staining when the clinical course is not progressing as expected and raises a critical management question: should this rare entity be treated aggressively, like ATRT, versus the conventional approach to intracranial yolk sac tumor? Conclusion: This case highlights the importance of INI1 staining in pediatric primitive central nervous system tumors as some germ cell markers are expressed in rhabdoid tumors at the stem cell level, implicating the germ cell origin of ATRT, which can complicate the diagnosis

Automated histologic diagnosis of CNS tumors with machine learning

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often established when a small specimen is sent for immediate interpretation by a neuropathologist. Access to neuropathologists may be limited in resource-poor settings, which has prompted several groups to develop machine learning algorithms for automated interpretation. Most attempts have focused on fixed histopathology specimens, which do not apply in the intraoperative setting. The greatest potential for clinical impact probably lies in the automated diagnosis of intraoperative specimens. Successful future studies may use machine learning to automatically classify whole-slide intraoperative specimens among a wide array of potential diagnoses

Mechanisms of Glioma Formation: Iterative Perivascular Glioma Growth and Invasion Leads to Tumor Progression, VEGF-Independent Vascularization, and Resistance to Antiangiogenic Therapy

As glioma cells infiltrate the brain they become associated with various microanatomic brain structures such as blood vessels, white matter tracts, and brain parenchyma. How these distinct invasion patterns coordinate tumor growth and influence clinical outcomes remain poorly understood. We have investigated how perivascular growth affects glioma growth patterning and response to antiangiogenic therapy within the highly vascularized brain. Orthotopically implanted rodent and human glioma cells are shown to commonly invade and proliferate within brain perivascular space. This form of brain tumor growth and invasion is also shown to characterize de novo generated endogenous mouse brain tumors, biopsies of primary human glioblastoma (GBM), and peripheral cancer metastasis to the human brain. Perivascularly invading brain tumors become vascularized by normal brain microvessels as individual glioma cells use perivascular space as a conduit for tumor invasion. Agent-based computational modeling recapitulated biological perivascular glioma growth without the need for neoangiogenesis. We tested the requirement for neoangiogenesis in perivascular glioma by treating animals with angiogenesis inhibitors bevacizumab and DC101. These inhibitors induced the expected vessel normalization, yet failed to reduce tumor growth or improve survival of mice bearing orthotopic or endogenous gliomas while exacerbating brain tumor invasion. Our results provide compelling experimental evidence in support of the recently described failure of clinically used antiangiogenics to extend the overall survival of human GBM patients

SNO 2020 diversity survey: defining demographics, racial biases, career success metrics and a path forward for the field of neuro-oncology

Abstract Background Neuro-oncology has grown tremendously since 2010, marked by increasing society membership, specialized clinical expertise, and new journals. Yet, modest improvement in racial/ethnic diversity amongst clinical trial participants, researchers, and clinicians led us to conduct a survey to identify opportunities to enhance diversity and inclusiveness amongst neuro-oncology professionals. Methods In summer 2020, the Women and Diversity Committee of the Society for Neuro-Oncology (SNO) distributed an anonymous online survey to members and affiliates including the European Association of Neuro-Oncology (EANO), Asian Society for Neuro-Oncology (ASNO), Society for Neuro-Oncology Latin America (SNOLA) and Society for Neuro-Oncology Sub-Saharan Africa (SNOSSA). The survey captured personal and professional characteristics, biases, effective mentorship qualities, career service metrics, and suggested field/society changes. Results were analyzed by geography, profession, age, racial/ethnic, and sexual identity. Standard descriptive statistics characterized the study population. Results The 386 respondents were predominantly female (58%) with a median age range of 40–49 years (31%), White (65%), and SNO members (97%). Most worked in North America (77%) in a research profession (67%). A majority of White respondents reported never experiencing biases (64%), while the majority of non-White respondents reported unconscious biases/microaggressions, followed by a lack of/limited mentorship. Qualitative assessments showcased that personal/professional success metrics were linked to needed improvements in diversity and inclusion efforts within the neuro-oncology field. Conclusions The prevalence of racial/ethnic biases and poor mentorship rates amongst underrepresented groups in neuro-oncology is high and potentially linked to the limited diverse representation amongst members and affiliates. These findings warrant a swift implementation of equity and inclusion practices within the neuro-oncology field