Faculty Bibliography

Background/UNASSIGNED:Glioma is a family of primary brain malignancies with limited treatment options and in need of novel therapies. We previously demonstrated that the adhesion G protein-coupled receptor GPR133 (ADGRD1) is necessary for tumor growth in adult glioblastoma, the most advanced malignancy within the glioma family. However, the expression pattern of GPR133 in other types of adult glioma is unknown. Methods/UNASSIGNED:We used immunohistochemistry in tumor specimens and non-neoplastic cadaveric brain tissue to profile GPR133 expression in adult gliomas. Results/UNASSIGNED:We show that GPR133 expression increases as a function of WHO grade and peaks in glioblastoma, where all tumors ubiquitously express it. Importantly, GPR133 is expressed within the tumor bulk, as well as in the brain-infiltrating tumor margin. Furthermore, GPR133 is expressed in both isocitrate dehydrogenase (IDH) wild-type and mutant gliomas, albeit at higher levels in IDH wild-type tumors. Conclusion/UNASSIGNED:The fact that GPR133 is absent from non-neoplastic brain tissue but de novo expressed in glioma suggests that it may be exploited therapeutically.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery¹. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive^2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce⁴. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)^5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)². In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

PURPOSE/OBJECTIVE:Molecular subgroups of pediatric brain tumors associated with divergent biological, clinical, and prognostic features have been identified. However, data regarding the impact of subgroup affiliation on the outcome of children with malignant brain tumors treated with radiation-sparing protocol is limited. We report long-term clinical outcomes and the molecular subgroups of malignant brain tumors in young children whose first-line treatment was high-dose chemotherapy without irradiation. METHODS:Tumor subclassification was performed using the Illumina HumanMethylation450 BeadChip (450k) genome-wide methylation array profiling platform. Clinical information was obtained from chart review. RESULTS:Methylation array profiling yielded information on molecular subgroups in 22 children. Median age at surgery was 26 months (range 1-119 months). Among medulloblastomas (MB), all 6 children in the infant sonic hedgehog (SHH) subgroup were long-term survivors, whereas all 4 children in subgroup 3 MB died. There was one long-term survivor in subgroup 4 MB. One out of five children with ependymoma was a long-term survivor (RELPOS). Both children with primitive neuroectodermal tumors died. One child with ATRT TYR and one child with choroid plexus carcinoma were long-term survivors. CONCLUSIONS:The efficacy of high-dose chemotherapy radiation-sparing treatment appears to be confined to favorable molecular subgroups of pediatric brain tumors, such as infant SHH MB. Identification of molecular subgroups that benefit from radiation-sparing therapy will aid in the design of prospective, "precision medicine"-driven clinical trials.

Congenital tumors account for 2% to 4% of all pediatric central nervous system tumors. Glioblastoma multiforme (GBM) represents a small subset of these tumors. Despite harboring histologic features similar to older patients, infants with GBM exhibit improved survival and respond more favorably to surgery and chemotherapy. To highlight this tumor's unique behavior, we report the case of a survivor of infantile GBM who developed a recurrent tumor in the surgical bed 6 months after diagnosis. The tumor was ultimately resected and was a ganglioglioma. This case shows both a favorable clinical outcome to an infantile GBM and this tumor's natural history.

Medulloblastoma (MB) is the most common childhood malignant brain tumor and is a leading cause of cancer-related death in children. DNA methylation profiling has rapidly advanced our understanding of MB pathogenesis at the molecular level, but assessments in Saudi Arabian (SA)-MB cases are sparse. MBs can be sub-grouped according to methylation patterns from FPPE samples into Wingless (WNT-MB), Sonic Hedgehog (SHH-MB), Group 3 (G3), and Group 4 (G4) tumors. The WNT-MB and SHH-MB subgroups are characterized by gain-of function mutations that activate oncogenic cell signaling, whilst G3/G4 tumors show recurrent chromosomal alterations. Given that each subgroup has distinct clinical outcomes, the ability to subgroup SA-FPPE samples holds significant prognostic and therapeutic value. Here, we performed the first assessment of MB-DNA methylation patterns in an SA cohort using archival biopsy material (FPPE n = 49). Of the 41 materials available for methylation assessments, 39 could be classified into the major DNA methylation subgroups (SHH, WNT, G3, and G4). Furthermore, methylation analysis was able to reclassify tumors that could not be sub-grouped through next-generation sequencing, highlighting its superior accuracy for MB molecular classifications. Independent assessments demonstrated known clinical relationships of the subgroups, exemplified by the high survival rates observed for WNT tumors. Surprisingly, the G4 subgroup did not conform to previously identified phenotypes, with a high prevalence in females, high metastatic rates, and a large number of tumor-associated deaths. Taking our results together, we demonstrate that DNA methylation profiling enables the robust sub-classification of four disease sub-groups in archival FFPE biopsy material from SA-MB patients. Moreover, we show that the incorporation of DNA methylation biomarkers can significantly improve current disease-risk stratification schemes, particularly concerning the identification of aggressive G4 tumors. These findings have important implications for future clinical disease management in MB cases across the Arab world.