The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical. Changes in the imaging workflow can also lead to compromised diagnoses and patient safety risks. We evaluated whether physical color calibration of scanners can standardize WSI appearance and enable robust AI performance. We employed a color calibration slide in four different laboratories and evaluated its impact on the performance of an AI system for prostate cancer diagnosis on 1,161 WSIs. Color standardization resulted in consistently improved AI model calibration and significant improvements in Gleason grading performance. The study demonstrates that physical color calibration provides a potential solution to the variation introduced by different scanners, making AI-based cancer diagnostics more reliable and applicable in clinical settings.

Abstract A significant proportion of oestrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients are categorised as intermediate risk based on classic clinicopathological variables, thus providing limited information to guide treatment decisions. The Prosigna assay is one of the established prognostic multigene assays in clinical practice for risk profiling. Stratipath Breast is a novel deep learning-based image analysis tool that utilises haematoxylin and eosin (HE)-stained histopathological images for risk profiling. In this study, we aimed to evaluate the Stratipath Breast tool for image-based risk profiling and compare it with the Prosigna assay. In a real-world breast cancer case series comprising 234 invasive tumours from patients with early ER+/HER2- breast cancer, clinically intermediate risk and eligible for chemotherapy, clinicopathological data including Prosigna results and corresponding HE-stained tissue slides were retrieved. The digitised HE slides were analysed by Stratipath Breast. Our findings showed that the Stratipath Breast analysis identified 49.6% of the clinically intermediate tumours as low risk and 50.4% as high risk. The Prosigna assay classified 32.5%, 47.0% and 20.5% tumours as low, intermediate and high risk, respectively. Among Prosigna intermediate-risk tumours, 47.3% were stratified as Stratipath low risk and 52.7% as high risk. In addition, 89.7% of Stratipath low-risk cases were classified as Prosigna low/intermediate risk. The overall agreement between the two tests for low-risk and high-risk groups was 71.0%, with a Cohen’s kappa of 0.42. For both risk profiling tests, grade and Ki67 differed significantly between risk groups. In conclusion, for the first time, we here present the results from a clinical evaluation of image-based risk stratification and show a considerable agreement to an established gene expression assay in routine breast pathology. The findings demonstrate that image-based risk profiling may aid in the identification of low-risk patients who could potentially be spared adjuvant chemotherapy.

Background Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance. Methods This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. Results We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18–5.70 95%CI, p -value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07–4.82, p -value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 ( p -value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 ( p -value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 ( p -value = 0.030), and 4.07 ( p -value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant ( p -value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models ( p -value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. Conclusion Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

Abstract Background In breast cancer, several gene expression assays have been developed to provide a more personalised treatment. This study focuses on the prediction of two molecular proliferation signatures: an 11-gene proliferation score and the MKI67 proliferation marker gene. The aim was to assess whether these could be predicted from digital whole slide images (WSIs) using deep learning models. Methods WSIs and RNA-sequencing data from 819 invasive breast cancer patients were included for training, and models were evaluated on an internal test set of 172 cases as well as on 997 cases from a fully independent external test set. Two deep Convolutional Neural Network (CNN) models were optimised using WSIs and gene expression readouts from RNA-sequencing data of either the proliferation signature or the proliferation marker, and assessed using Spearman correlation (r). Prognostic performance was assessed through Cox proportional hazard modelling, estimating hazard ratios (HR). Results Optimised CNNs successfully predicted the proliferation score and proliferation marker on the unseen internal test set (ρ = 0.691(p < 0.001) with R2 = 0.438, and ρ = 0.564 (p < 0.001) with R2 = 0.251 respectively) and on the external test set (ρ = 0.502 (p < 0.001) with R2 = 0.319, and ρ = 0.403 (p < 0.001) with R2 = 0.222 respectively). Patients with a high proliferation score or marker were significantly associated with a higher risk of recurrence or death in the external test set (HR = 1.65 (95% CI: 1.05–2.61) and HR = 1.84 (95% CI: 1.17–2.89), respectively). Conclusions The results from this study suggest that gene expression levels of proliferation scores can be predicted directly from breast cancer morphology in WSIs using CNNs and that the predictions provide prognostic information that could be used in research as well as in the clinical setting.

Background Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts. Methods This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint. Results In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups. Conclusion The results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

Abstract Background: Among oestrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer, a significant proportion of patients are categorised as intermediate risk, based on classic clinico-pathological variables, thus providing limited information to guide adjuvant chemotherapy decisions. Prognostic risk profiling is an integrated part of modern breast cancer diagnostics to provide additional risk information for this patient group. Among the established prognostic assays based on gene expression, the Prosigna assay is widely used and provides an individual risk of recurrence (ROR) score and identifies intrinsic subtypes with associated survival outcomes. Pioneering artificial intelligence-based precision diagnostics, Stratipath Breast, is a deep learning-based image analysis tool that utilises digitised histopathological whole slide images to stratify intermediate risk patients in terms of risk of recurrence. Materials and methods: The study included 234 invasive breast tumours from patients with early ER-positive HER2-negative breast cancer, clinically assessed as intermediate risk tumours and eligible for chemotherapy. All tumours had therefore previously been analysed by the Prosigna assay in clinical routine at point of diagnosis between 2020 and 2022 at the Karolinska University Hospital and Södersjukhuset, Stockholm, Sweden. Clinicopathological data including Prosigna results (ROR score, risk group and intrinsic subtype) were extracted from medical records, along with the corresponding archived haematoxylin and eosin (HE)-stained formalin-fixed paraffin-embedded tissue slides. The HE slides were subsequently digitised and analysed by the Stratipath Breast tool. The agreement between the two tests for risk stratification was evaluated in this real-world breast cancer case series. Results: The Prosigna assay classified 76 (32.5%), 110 (47.0%) and 48 (20.5%) tumours as low, intermediate and high risk, respectively. The Stratipath Breast analysis identified 116 (49.6%) tumours as low risk and 118 (50.4%) as high risk. Among Prosigna intermediate risk tumours, 52 (47.3%) were stratified as low risk and 58 (52.7%) as high risk by Stratipath Breast. The overall agreement between the two tests for low risk and high risk groups was 71.0%, with a Cohen’s linear kappa of 0.42. Twelve of the 48 Prosigna high risk cases were classified as Stratipath low risk. ROR scores were higher in the Stratipath high risk group compared to the low risk group (p < 0.001), across all cases as well as in the Prosigna intermediate group. Among the 176 histological grade (NHG) 2 tumours, 97 (55.1%) and 79 (44.9%) were stratified as Stratipath low risk and high risk, respectively, whereas 66 (37.5%), 83 (47.2%) and 27 (15.3%) were stratified as Prosigna low, intermediate and high risk, respectively. The majority of NHG1 (10 of 12) and NHG3 (37 of 46) tumours were stratified as Stratipath low risk and high risk, respectively. For both risk profiling tests, NHG and Ki67 proliferation index differed between risk groups. Conclusions: In this study of clinically assessed intermediate risk ER-positive HER2-negative breast cancer, we observed a moderate agreement between Prosigna and Stratipath Breast for low risk and high risk groups. In addition, image-based risk profiling stratified more of the NHG2 tumours as high risk. Citation Format: Stephanie Robertson, Yinxi Wang, Wenwen Sun, Emelie Karlsson, Sandy Kang Lövgren, Mattias Rantalainen, Johan Hartman. Clinical evaluation of image-based risk profiling in breast cancer histopathology and comparison to an established gene expression assay [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-05.

Background Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens.Methods A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis.Results Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79).Conclusions DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

A bstract Using (27 . 12 ± 0 . 14) × 10 8 ψ (3686) events collected with the BESIII detector at BEPCII, the decay of ψ (3686) → $$ {\varOmega}^{-}{K}^{+}{\overline{\Xi}}^0 $$ Ω − K + Ξ ¯ 0 + c . c . is observed for the first time. The branching fraction of this decay is measured to be $$ {\mathcal{B}}_{\psi (3686)\to {\varOmega}^{-}{K}^{+}{\overline{\Xi}}^0+\textrm{c}.\textrm{c}.} $$ B ψ 3686 → Ω − K + Ξ ¯ 0 + c . c . = (2 . 78 ± 0 . 40 ± 0 . 18) × 10 − 6 , where the first uncertainty is statistical and the second is systematic. Possible baryon excited states are searched for in this decay, but no evident intermediate state is observed with the current sample size.

BACKGROUND:Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer. METHODS:Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262). RESULTS:We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm). CONCLUSION:We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer. SIGNIFICANCE:Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.

Molecular profiling is central in cancer precision medicine but remains costly and is only based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for the prediction of molecular phenotypes. Transcriptome-wide expression morphology (EMO) analysis with deep convolutional neural networks (CNN) enables the prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer. The NanoString GeoMx® Digital Spatial Profiler (DSP) platform has capabilities of associating crucial spatial information with intratumor variabilities of gene expression, serving as an ideal tool to characterize and validate the prediction of intratumor heterogeneity. The GeoMx platform also provides functionalities of overlaying and aligning hematoxylin and eosin (H&E) whole slide scans on serial tissue sections with morphology marker staining to facilitate region of interest (ROI) selection to match relevant image tiles from the model input, which is a key step to ensure accuracy for the evaluation of the model predictions. The GeoMx RNA Immune Pathways Panel contains 84 gene targets including key genes involved in immune pathways and tumorigenesis and was used to validate the prediction of mRNA expression output from deep CNN models. Citation Format: Kathy Ton, Yinxi Wang, Liuliu Pan, Kimmo Kartasalo, Balazs Acs, Philippe Weitz, Liang Zhang, Yan Liang, Johan Hartman, Masi Volkonen, Christer Larsson, Pekka Ruusuvuori, Joseph Beechem, Mattias Rantalainen. Validation of spatial gene expression patterns predicted by deep convolutional neural networks from breast cancer histopathology images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5432.

Abstract Motivation Molecular phenotyping by gene expression profiling is central in contemporary cancer research and in molecular diagnostics but remains resource intense to implement. Changes in gene expression occurring in tumours cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes from routine haematoxylin and eosin-stained whole slide images (WSIs) using convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach to model relationships between morphology and gene expression. Results We conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates from WSIs for 370 patients from the TCGA PRAD study. Out of 15 586 protein coding transcripts, 6618 had predicted expression significantly associated with RNA-seq estimates (FDR-adjusted P-value <1×10−4) in a cross-validation and 5419 (81.9%) of these associations were subsequently validated in a held-out test set. We furthermore predicted the prognostic cell-cycle progression score directly from WSIs. These findings suggest that contemporary computer vision models offer an inexpensive and scalable solution for prediction of gene expression phenotypes directly from WSIs, providing opportunity for cost-effective large-scale research studies and molecular diagnostics. Availability and implementation A self-contained example is available from http://github.com/phiwei/prostate_coexpression. Model predictions and metrics are available from doi.org/10.5281/zenodo.4739097. Supplementary information Supplementary data are available at Bioinformatics online.

Routine transrectal ultrasound-guided systematic prostate biopsy only samples a small volume of the prostate and tumors between biopsy cores can be missed, leading to low sensitivity to detect clinically relevant prostate cancers (PCa). Deep learning may enable detection of PCa despite benign biopsies. We included 14,354 hematoxylin-eosin stained benign prostate biopsies from 1,508 men in two groups: men without established PCa diagnosis and men with at least one core biopsy diagnosed with PCa. A 10-Convolutional Neural Network ensemble was optimized to distinguish benign biopsies from benign men or patients with PCa. Area under the receiver operating characteristic curve was estimated at 0.739 (bootstrap 95% CI:0.682-0.796) on man level in the held-out test set. At the specificity of 0.90, the model sensitivity was 0.348. The proposed model can detect men with risk of missed PCa and has the potential to reduce false negatives and to indicate men who could benefit from rebiopsies.

Abstract Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression–morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin–stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images. Significance: Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.

Analysis of whole-slide-images (WSIs) of histopathology tissue sections remains challenging due to the gigapixel scale of these images, which often necessitates their division into smaller image tiles. Recently, attention mechanisms have been successfully applied to alleviate the tile-to-slide challenges for classification tasks based on WSIs. In this study, we explore the potential of attention mechanisms in regression settings, by comparing four modelling approaches, two of which use attention mechanisms. We evaluate these models both in a simulated experiment using the MNIST data set, and in real histopathology data sets focused on prediction of gene expression levels from WSIs, including an analysis of the local prediction performance using spatial transcriptomics. The MNIST simulation demonstrates that if only a small proportion of instances in a set of images contribute to the set-level regression label, attention mechanisms may be preferable to commonly applied weakly supervised models. When predicting gene expression from WSIs, the differences in performance between the models that we investigated were small. Nevertheless, we found some evidence that attention mechanisms may be more sensitive to domain shifts. In the regression-based task of gene expression prediction, the prediction performance in the present study appears to be limited by other factors rather than by the choice of modelling approach. Nevertheless, attention mechanisms appear promising for regression objectives and warrant further investigation.

BACKGROUND:The Nottingham histological grade (NHG) is a well-established prognostic factor for breast cancer that is broadly used in clinical decision making. However, ∼50% of patients are classified as grade 2, an intermediate risk group with low clinical value. To improve risk stratification of NHG 2 breast cancer patients, we developed and validated a novel histological grade model (DeepGrade) based on digital whole-slide histopathology images (WSIs) and deep learning. PATIENTS AND METHODS:In this observational retrospective study, routine WSIs stained with haematoxylin and eosin from 1567 patients were utilised for model optimisation and validation. Model generalisability was further evaluated in an external test set with 1262 patients. NHG 2 cases were stratified into two groups, DG2-high and DG2-low, and the prognostic value was assessed. The main outcome was recurrence-free survival. RESULTS:DeepGrade provides independent prognostic information for stratification of NHG 2 cases in the internal test set, where DG2-high showed an increased risk for recurrence (hazard ratio [HR] 2.94, 95% confidence interval [CI] 1.24-6.97, P = 0.015) compared with the DG2-low group after adjusting for established risk factors (independent test data). DG2-low also shared phenotypic similarities with NHG 1, and DG2-high with NHG 3, suggesting that the model identifies morphological patterns in NHG 2 that are associated with more aggressive tumours. The prognostic value of DeepGrade was further assessed in the external test set, confirming an increased risk for recurrence in DG2-high (HR 1.91, 95% CI 1.11-3.29, P = 0.019). CONCLUSIONS:The proposed model-based stratification of patients with NHG 2 tumours is prognostic and adds clinically relevant information over routine histological grading. The methodology offers a cost-effective alternative to molecular profiling to extract information relevant for clinical decisions.

Background Exposure to benzo(a)pyrene (BaP) was associated with cognitive impairments and some Alzheimer’s disease (AD)-like pathological changes. However, it is largely unknown whether BaP exposure participates in the disease progression of AD. Objectives To investigate the effect of BaP exposure on AD progression and its underlying mechanisms. Methods BaP or vehicle was administered to 4-month-old APPswe/PS1dE9 transgenic (APP/PS1) mice and wildtype (WT) mice for 2 months. Learning and memory ability and exploratory behaviors were evaluated 1 month after the initiation/termination of BaP exposure. AD-like pathological and biochemical alterations were examined 1 month after 2-month BaP exposure. Levels of soluble beta-amyloid (Aβ) oligomers and the number of Aβ plaques in the cortex and the hippocampus were quantified. Gene expression profiling was used to evaluate alternation of genes/pathways associated with AD onset and progression. Immunohistochemistry and Western blot were used to demonstrate neuronal loss and neuroinflammation in the cortex and the hippocampus. Treatment of primary neuron-glia cultures with aged Aβ (a mixture of monomers, oligomers, and fibrils) and/or BaP was used to investigate mechanisms by which BaP enhanced Aβ-induced neurodegeneration. Results BaP exposure induced progressive decline in spatial learning/memory and exploratory behaviors in APP/PS1 mice and WT mice, and APP/PS1 mice showed severer behavioral deficits than WT mice. Moreover, BaP exposure promoted neuronal loss, Aβ burden and Aβ plaque formation in APP/PS1 mice, but not in WT mice. Gene expression profiling showed most robust alteration in genes and pathways related to inflammation and immunoregulatory process, Aβ secretion and degradation, and synaptic formation in WT and APP/PS1 mice after BaP exposure. Consistently, the cortex and the hippocampus of WT and APP/PS1 mice displayed activation of microglia and astroglia and upregulation of inducible nitric oxide synthase (iNOS), glial fibrillary acidic protein (GFAP), and NADPH oxidase (three widely used neuroinflammatory markers) after BaP exposure. Furthermore, BaP exposure aggravated neurodegeneration induced by aged Aβ peptide in primary neuron-glia cultures through enhancing NADPH oxidase-derived oxidative stress. Conclusion Our study showed that chronic exposure to environmental pollutant BaP induced, accelerated, and exacerbated the progression of AD, in which elevated neuroinflammation and NADPH oxidase-derived oxidative insults were key pathogenic events.

Background: The underlying mechanism of viral infection as a risk factor for Parkinson's disease (PD), the second most common neurodegenerative disease, remains unclear. Objective: We used Mac-1(-/-) and gp91(phox-/-) transgene animal models to investigate the mechanisms by which poly I:C, a mimic of virus double-stranded RNA, induces PD neurodegeneration. Method: Poly I:C was stereotaxically injected into the substantia nigra (SN) of wild-type (WT), Mac-1-knockout (Mac-1(-/-)) and gp91 (phox)-knockout (gp91(phox-/-)) mice (10 mu g/mu l), and nigral dopaminergic neurodegeneration, alpha-synuclein accumulation and neuroinflammation were evaluated. Result: Dopaminergic neurons in the nigra and striatum were markedly reduced in WT mice after administration of poly I:C together with abundant microglial activation in the SN, and the expression of alpha-synuclein was also elevated. However, these pathological changes were greatly dampened in Mac-1(-/-) and gp91(phox-/-) mice. Conclusions: Our findings demonstrated that viral infection could result in the activation of microglia as well as NADPH oxidase, which may lead to neuron loss and the development of Parkinson's-like symptoms. Mac-1 is a key receptor during this process. (C) 2020 Elsevier Inc. All rights reserved.

Abstract Breast cancer histologic grade is a well-established prognostic factor utilized in clinical decision making. In current clinical practice grading is conducted manually by pathologists and this procedure is associated with a substantial inter-observer variability. Furthermore, patients classified as histologic grade 2 has been reported to exhibit an intermediate recurrence risk, resulting in less prognostic value. With the aim of improving patient stratification, we have developed a model-based approach for histological grading using deep convolutional neural networks (CNNs). In this study, we developed a CNN model for improved histologic grading, with a focus on further stratification of grade 2 patients into high and low risk groups. Histopathology images from the Clinseq breast cancer study and the Cancer Genome Atlas (TCGA) containing 730 patients were scanned at 40X magnification and tiled into patches at 20X. In total, we obtained 5.3 million patches of size 299 × 299 pixels. Using image annotations of invasive cancer regions in Clinseq study, we trained a deep CNN model to segment cancer regions in TCGA data. Subsequently, the cancer regions from both Clinseq and TCGA were used to optimize a second deep CNN model for classification of grade 1 and 3, this model was applied to re-classify 291 patients with histologic grade 2 into low and high risk. The CNN model classified cancer and non-cancer with area under the receiver operating characteristic curve (AUC) of 0.941. The second CNN model achieved an AUC of 0.910 in terms of classification of grade 1 and 3 tumors (cross-validation). For grade 2 tumors (independent test data), 184 were re-classified into the lower risk group whereas 107 were classified as high risk. The risk of recurrence for the grade 2 higher risk group is significantly higher than that in lower risk group, with an estimated hazard ratio of 2.86 (95% confidence interval: 1.21-6.69) after adjusting for age, tumor size, estrogen receptor status and lymph node status. In conclusion, we found that the deep CNN model demonstrated a high capability to distinguish between breast cancer with histologic grade 1 and 3. We also found that re-stratification of Grade 2 patients into high and low risk groups was significantly associated with risk of recurrence. Improved histological grading, and further risk stratification of grade 2 patients, by deep CNN models could contribute towards a reduction of both over- and under-treatment of breast cancer patients. Citation Format: Mattias Rantalainen, Yinxi Wang, Balázs Ácz, Stephanie Robertson, Johan Hartman. Improved histologic grading of breast cancer by a novel deep learning-based model [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-02-06.

BACKGROUND:Atmospheric ultrafine particles (UFPs) and pesticide rotenone were considered as potential environmental risk factors for Parkinson's disease (PD). However, whether and how UFPs alone and in combination with rotenone affect the pathogenesis of PD remains largely unknown. METHODS:Ultrafine carbon black (ufCB, a surrogate of UFPs) and rotenone were used individually or in combination to determine their roles in chronic dopaminergic (DA) loss in neuron-glia, and neuron-enriched, mix-glia cultures. Immunochemistry using antibody against tyrosine hydroxylase was performed to detect DA neuronal loss. Measurement of extracellular superoxide and intracellular reactive oxygen species (ROS) were performed to examine activation of NADPH oxidase. Genetic deletion and pharmacological inhibition of NADPH oxidase and MAC-1 receptor in microglia were employed to examine their role in DA neuronal loss triggered by ufCB and rotenone. RESULTS:In rodent midbrain neuron-glia cultures, ufCB and rotenone alone caused neuronal death in a dose-dependent manner. In particularly, ufCB at doses of 50 and 100μg/cm2 induced significant loss of DA neurons. More importantly, nontoxic doses of ufCB (10μg/cm2) and rotenone (2nM) induced synergistic toxicity to DA neurons. Microglial activation was essential in this process. Furthermore, superoxide production from microglial NADPH oxidase was critical in ufCB/rotenone-induced neurotoxicity. Studies in mix-glia cultures showed that ufCB treatment activated microglial NADPH oxidase to induce superoxide production. Firstly, ufCB enhanced the expression of NADPH oxidase subunits (gp91phox, p47phox and p40phox); secondly, ufCB was recognized by microglial surface MAC-1 receptor and consequently promoted rotenone-induced p47phox and p67phox translocation assembling active NADPH oxidase. CONCLUSION:ufCB and rotenone worked in synergy to activate NADPH oxidase in microglia, leading to oxidative damage to DA neurons. Our findings delineated the potential role of ultrafine particles alone and in combination with pesticide rotenone in the pathogenesis of PD.