Deployment of a Machine Learning Algorithm in a Real-World Cohort for Quality Control Monitoring of Human Epidermal Growth Factor-2-Stained Clinical Specimens in Breast Cancer.

Context.—: Precise determination of biomarker status is necessary for clinical trial enrollment and endpoint analyses, as well as for optimal treatment determination in real-world practice. However, variabilities may be introduced into this process due to the processing of clinical specimens by different laboratories and assessment by distinct pathologists.

Impact of Decalcification, Cold Ischemia, and Deglycosylation on Performance of Programmed Cell Death Ligand-1 Antibodies With Different Binding Epitopes: Comparison of 7 Clones.

Programmed cell death ligand-1 (PD-L1) expression levels in patients' tumors have demonstrated clinical utility across many cancer types and are used to determine treatment eligibility. Several independently developed PD-L1 immunohistochemical (IHC) predictive assays are commercially available and have demonstrated different levels of staining between assays, generating interest in understanding the similarities and differences between assays. Previously, we identified epitopes in the internal and external domains of PD-L1, bound by antibodies in routine clinical use (SP263, SP142, 22C3, and 28-8).

Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials.

Understanding factors that impact prognosis for cancer patients have high clinical relevance for treatment decisions and monitoring of the disease outcome. Advances in artificial intelligence (AI) and digital pathology offer an exciting opportunity to capitalize on the use of whole slide images (WSIs) of hematoxylin and eosin (H&E) stained tumor tissue for objective prognosis and prediction of response to targeted therapies. AI models often require hand-delineated annotations for effective training which may not be readily available for larger data sets.

Impairment of Glycolysis-Derived l-Serine Production in Astrocytes Contributes to Cognitive Deficits in Alzheimer's Disease.

Alteration of brain aerobic glycolysis is often observed early in the course of Alzheimer's disease (AD). Whether and how such metabolic dysregulation contributes to both synaptic plasticity and behavioral deficits in AD is not known. Here, we show that the astrocytic l-serine biosynthesis pathway, which branches from glycolysis, is impaired in young AD mice and in AD patients.

Fast fluorescence in situ hybridisation for the enhanced detection of MET in non-small cell lung cancer.

The c-Met/hepatocyte growth factor receptor pathway is frequently dysregulated in multiple cancer types, including non-small cell lung cancer (NSCLC). MET amplification has been shown to develop as a resistance mechanism to treatment in NSCLC. The identification of increased MET copy number within tumour cells is increasingly important to stratify those tumours and patients which are susceptible to treatment targetting MET kinase inhibition.

Voxel-Based Statistical Analysis of 3D Immunostained Tissue Imaging.

Recently developed techniques to visualize immunostained tissues in 3D and in large samples have expanded the scope of microscopic investigations at the level of the whole brain. Here, we propose to adapt voxel-based statistical analysis to 3D high-resolution images of the immunostained rodent brain. The proposed approach was first validated with a simulation dataset with known cluster locations.

Tumour Heterogeneity of Breast Cancer: From Morphology to Personalised Medicine.

Breast cancer (BC) displays striking clinical, morphological, and behavioural diversity within a single tumour and between tumours. Currently, mounting evidence indicates that the morphological heterogeneity of BC reflects an underlying spectrum of genetic and epigenetic portraits that control BC behaviour. Further understanding of BC heterogeneity will have an impact, not only on the routine diagnostic practices but also on patients' management decisions.

A validation dataset for Macaque brain MRI segmentation.

Validation data for segmentation algorithms dedicated to preclinical images is fiercely lacking, especially when compared to the large number of databases of Human brain images and segmentations available to the academic community. Not only is such data essential for validating methods, it is also needed for objectively comparing concurrent algorithms and detect promising paths, as segmentation challenges have shown for clinical images. The dataset we present here is a first step in this direction.

Primatologist: A modular segmentation pipeline for macaque brain morphometry.

Because they bridge the genetic gap between rodents and humans, non-human primates (NHPs) play a major role in therapy development and evaluation for neurological disorders. However, translational research success from NHPs to patients requires an accurate phenotyping of the models. In patients, magnetic resonance imaging (MRI) combined with automated segmentation methods has offered the unique opportunity to assess in vivo brain morphological changes.

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer.

Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy.

ADAM30 Downregulates APP-Linked Defects Through Cathepsin D Activation in Alzheimer's Disease.

Although several ADAMs (A disintegrin-like and metalloproteases) have been shown to contribute to the amyloid precursor protein (APP) metabolism, the full spectrum of metalloproteases involved in this metabolism remains to be established. Transcriptomic analyses centred on metalloprotease genes unraveled a 50% decrease in ADAM30 expression that inversely correlates with amyloid load in Alzheimer's disease brains. Accordingly, in vitro down- or up-regulation of ADAM30 expression triggered an increase/decrease in Aβ peptides levels whereas expression of a biologically inactive ADAM30 (ADAM30(mut)) did not affect Aβ secretion.

In Vivo Detection of Amyloid Plaques by Gadolinium-Stained MRI Can Be Used to Demonstrate the Efficacy of an Anti-amyloid Immunotherapy.

Extracellular deposition of β amyloid plaques is an early event associated to Alzheimer's disease. Here, we have used in vivo gadolinium-stained high resolution (29(∗)29(∗)117 μm(3)) magnetic resonance imaging (MRI) to follow-up in a longitudinal way individual amyloid plaques in APP/PS1 mice and evaluate the efficacy of a new immunotherapy (SAR255952) directed against protofibrillar and fibrillary forms of Aβ. APP/PS1 mice were treated for 5 months between the age of 3.

High-throughput 3D whole-brain quantitative histopathology in rodents.

Histology is the gold standard to unveil microscopic brain structures and pathological alterations in humans and animal models of disease. However, due to tedious manual interventions, quantification of histopathological markers is classically performed on a few tissue sections, thus restricting measurements to limited portions of the brain. Recently developed 3D microscopic imaging techniques have allowed in-depth study of neuroanatomy.

Robust supervised segmentation of neuropathology whole-slide microscopy images.

Alzheimer's disease is characterized by brain pathological aggregates such as Aβ plaques and neurofibrillary tangles which trigger neuroinflammation and participate to neuronal loss. Quantification of these pathological markers on histological sections is widely performed to study the disease and to evaluate new therapies. However, segmentation of neuropathology images presents difficulties inherent to histology (presence of debris, tissue folding, non-specific staining) as well as specific challenges (sparse staining, irregular shape of the lesions).