👤 David A Gutman

Machine learning enables scalable quantification of neuropathology, offering deeper phenotyping of Alzheimer's disease (AD). In this validation study, we quantified amyloid-beta (Aβ) deposits, evaluating multiple brain regions across institutions, and evaluated associations with clinical, demographic, and genetic factors in persons pathologically diagnosed with AD. All linear models were adjusted for sex, age of death, ethnicity, and center. We analyzed densities (#/mm2) of cored plaques, diffuse plaques, and cerebral amyloid angiopathy (CAA) in 273 individuals from 3 Alzheimer's Disease Research Centers. Formalin-fixed paraffin-embedded sections of frontal, temporal, and parietal cortices were immunostained and digitized, generating 799 whole-slide images (WSIs). Following log transformation, mixed-effects modeling revealed the parietal cortex had the highest cored plaque densities (P < .001); the temporal cortex had the highest diffuse plaque (P < .001); CAA showed no regional differences. Wilcoxon rank-sum test, and covariates adjusted linear models showed ApoE ε4- status was associated with higher cored plaque densities in the temporal lobe (P = .04). ApoE ε4+ status was associated with diffuse plaques in the temporal lobe (P = .001), and CAA in the frontal lobe (P = .004). These findings provide further validation and provide exploratory associations advancing deeper phenotyping of AD. Show less

The Cancer Genome Atlas Project (TCGA) has produced an extensive collection of '-omic' data on glioblastoma (GBM), resulting in several key insights on expression signatures. Despite the richness of TCGA GBM data, the absence of lower grade gliomas in this data set prevents analysis genes related to progression and the uncovering of predictive signatures. A complementary dataset exists in the form of the NCI Repository for Molecular Brain Neoplasia Data (Rembrandt), which contains molecular and clinical data for diffuse gliomas across the full spectrum of histologic class and grade. Here we present an investigation of the significance of the TCGA consortium's expression classification when applied to Rembrandt gliomas. We demonstrate that the proneural signature predicts improved clinical outcome among 176 Rembrandt gliomas that includes all histologies and grades, including GBMs (log rank test p = 1.16e-6), but also among 75 grade II and grade III samples (p  =  2.65e-4). This gene expression signature was enriched in tumors with oligodendroglioma histology and also predicted improved survival in this tumor type (n =  43, p  =  1.25e-4). Thus, expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for lower grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy. Integrated DNA and RNA analysis of low-grade and high-grade proneural gliomas identified increased expression and gene amplification of several genes including GLIS3, TGFB2, TNC, AURKA, and VEGFA in proneural GBMs, with corresponding loss of DLL3 and HEY2. Pathway analysis highlights the importance of the Notch and Hedgehog pathways in the proneural subtype. This demonstrates that the expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for low-grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy. Show less