Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing.

As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.

Immunohistochemical Detection of Synuclein Pathology in Skin in Idiopathic Rapid Eye Movement Sleep Behavior Disorder and Parkinsonism.

BACKGROUND: Recent studies reported abnormal alpha-synuclein deposition in biopsy-accessible sites of the peripheral nervous system in Parkinson's disease (PD). This has considerable implications for clinical diagnosis. Moreover, if deposition occurs early, it may enable tissue diagnosis of prodromal PD. OBJECTIVE: The aim of this study was to develop and test an automated bright-field immunohistochemical assay of cutaneous pathological alpha-synuclein deposition in patients with idiopathic rapid eye movement sleep behavior disorder, PD, and atypical parkinsonism and in control subjects. METHODS: For assay development, postmortem skin biopsies were taken from 28 patients with autopsy-confirmed Lewy body disease and 23 control subjects. Biopsies were stained for pathological alpha-synuclein in automated stainers using a novel dual-immunohistochemical assay for serine 129-phosphorylated alpha-synuclein and pan-neuronal marker protein gene product 9.5. After validation, single 3-mm punch skin biopsies were taken from the cervical 8 paravertebral area from 79 subjects (28 idiopathic rapid eye movement sleep behavior disorder, 20 PD, 10 atypical parkinsonism, and 21 control subjects). Raters blinded to clinical diagnosis assessed the biopsies. RESULTS: The immunohistochemistry assay differentiated alpha-synuclein pathology from nonpathological-appearing alpha-synuclein using combined phosphatase and protease treatments. Among autopsy samples, 26 of 28 Lewy body samples and none of the 23 controls were positive. Among living subjects, punch biopsies were positive in 23 (82%) subjects with idiopathic rapid eye movement sleep behavior disorder, 14 (70%) subjects with PD, 2 (20%) subjects with atypical parkinsonism, and none (0%) of the control subjects. After a 3-year follow-up, eight idiopathic rapid eye movement sleep behavior disorder subjects phenoconverted to defined neurodegenerative syndromes, in accordance with baseline biopsy results. CONCLUSION: Even with a single 3-mm punch biopsy, there is considerable promise for using pathological alpha-synuclein deposition in skin to diagnose both clinical and prodromal PD. © 2020 International Parkinson and Movement Disorder Society.

Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol.

INTRODUCTION: Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks. METHODS AND ANALYSIS: We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km×5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices' populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case-control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland.Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype. ETHICS AND DISSEMINATION: The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.