Harrison, W.T.A.

Abstract Post-translational modifications (PTM) of tau are implicated in Alzheimer disease (AD) progression and are established biomarkers in cerebrospinal fluid and plasma; however, the labor-intensive nature of conventional proteomics limits their investigation in histology-specific contexts. We describe the findings of an artificial intelligence-guided laser capture microdissection (LCM) pipeline for harvesting neurofibrillary tangles (NFTs) by maturation level into pretangles (pre-NFTs), mature tangles (iNFTs), and ghost tangles (eNFTs) using 38 cases obtained from 2 independent biobanks. We evaluated the performance characteristics of proprietary machine learning algorithms for the subclassification of these NFT categories in anti-pTau217-stained whole slide images of entorhinal cortex, hippocampus, frontal, and parietal cortex sections. Overall precision/recall/F1 scores were highest for Classifier A (0.6/0.46/0.5). The best performing algorithm was used to guide LCM capture and inform NFT enrichment. Targeted proteomics on tau signature peptides (pTau181, pTau217, and TauMTBR) was performed on approximately 1250 NFT collections. The results demonstrated that their abundance increased from pretangles to mature tangles (∼2-11-fold increase), and that this was followed by a sharp reduction in ghost tangles (∼3-116-fold decrease), with pTau217 showing the most drastic change. Pathologist-trained NFT classifiers represent an objective albeit imperfect means to enrich specific morphologic forms permitting coupled LCM-MS (mass spectrometry) to investigate AD-associated PTM.

Abstract Histopathology is the gold standard for distinguishing tumor recurrence from treatment-induced necrosis but differentiating pseudoprogression from true recurrence is challenging in tumors with ambiguous morphological features. Prior research reported a 36% inter-rater agreement during suspected recurrence (Holdhoff et al., 2019). While serial radiographic and laboratory assessments are common, serial histopathological assessments are not. This study evaluated agreement and factors influencing assessment of active and inactive tumors through single and multiple time-point histopathological assessments. Patients who underwent initial and subsequent reoperation were identified, and their available histology slides were examined retrospectively. Four independent neuropathologists conducted two types of reviews: single time-point reviews using most recent biopsy/surgery slides, and multiple time-point reviews comparing images from initial and repeat biopsies taken 1-3 months apart. Neuropathologists completed a questionnaire assessing various tumor characteristics. Assignments were categorized as "active tumor," "treatment effect," or "unable to classify." 324 assessments were conducted (108 single time-point, 216 multiple time-point). Intraclass correlation coefficients for single vs. multiple time-point assessments were 0.72-0.93 for active tumors and 0.57-0.86 for inactive tumors. Pathologists' assessment of % inactive tumor was influenced by mitotic activity (p= 0.0028), cellularity (p< 0.0001), hyalinized vessels (p= 0.0019), and necrosis (p= 0.0046). Evaluation of % active tumor was influenced by hyalinized vessels (p= 0.0006), mitotic activity (p< 0.0001), cellularity (p< 0.0001), and microvascular proliferation (p= 0.0093), with the latter three factors specifically affecting final sign-out pathology. Serial histopathological assessment didn’t affect determination of % active or inactive tumor. No association between % inactive tumor and fibrinoid necrosis for single time-point (p= 0.34) but a strong negative association was seen for serial assessment (p= 0.0006). Inter-rater agreement was higher for identifying active tumors than inactive tumors in both case reviews. Changes in necrosis between tissue samples in single vs. multiple timepoint assessments influenced pathologists' assessment of % inactive tumor.