Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study.

AIMS: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. METHODS: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. RESULTS: For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. CONCLUSIONS: Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.

Using Machine Learning Multiclass Classification Technique to Detect IoT Attacks in Real Time.

This paper presents a real-time intrusion detection system (IDS) aimed at detecting the Internet of Things (IoT) attacks using multiclass classification models within the PySpark architecture. The research objective is to enhance detection accuracy while reducing the prediction time. Various machine learning algorithms are employed using the OneVsRest (OVR) technique. The proposed method utilizes the IoT-23 dataset, which consists of network traffic from smart home IoT devices, for model development. Data preprocessing techniques, such as data cleaning, transformation, scaling, and the synthetic minority oversampling technique (SMOTE), are applied to prepare the dataset. Additionally, feature selection methods are employed to identify the most relevant features for classification. The performance of the classifiers is evaluated using metrics such as accuracy, precision, recall, and F1 score. The results indicate that among the evaluated algorithms, extreme gradient boosting achieves a high accuracy of 98.89%, while random forest demonstrates the most efficient training and prediction times, with a prediction time of only 0.0311 s. The proposed method demonstrates high accuracy in real-time intrusion detection of IoT attacks, outperforming existing approaches.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments.

The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS). Our proposed approach includes the development of a binary balanced MQTT dataset with an effective feature engineering and machine learning framework to enhance the security of MQTT traffic. Our feature selection analysis and comparison demonstrates that selecting a 10-feature model provides the highest effectiveness, as it shows significant advantages in terms of constant accuracy and superior training and testing times across all models. The results of this study show that the framework has the capability to enhance the efficiency of an IDS for MQTT traffic, with more than 96% accuracy, precision, recall, F1-score, and ROC, and it outperformed the most recent study that used the same dataset.

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.

This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%.

Investigating genomic, proteomic, and post-transcriptional regulation profiles in colorectal cancer: a comparative study between primary tumors and associated metastases.

INTRODUCTION: Approximately 50% of patients with primary colorectal carcinoma develop liver metastases. This study investigates the possible molecular discrepancies between primary colorectal cancer (pCRC) and their respective metastases. METHODS: A total of 22 pairs of pCRC and metastases were tested. Mutation profiling of 26 cancer-associated genes was undertaken in 22/22primary-metastasis tumour pairs using next-generation sequencing, whilst the expression of a panel of six microRNAs (miRNAs) was investigated using qPCRin 21/22 pairs and 22 protein biomarkers was tested using Reverse Phase Protein Array (RPPA)in 20/22 patients' tumour pairs. RESULTS: Among the primary and metastatic tumours the mutation rates for the individual genes are as follows:TP53 (86%), APC (44%), KRAS (36%), PIK3CA (9%), SMAD4 (9%), NRAS (9%) and 4% for FBXW7, BRAF, GNAS and CDH1. The primary-metastasis tumour mutation status was identical in 54/60 (90%) loci. However, there was discordance in heterogeneity status in 40/58 genetic loci (z-score = 6.246, difference = 0.3793, P < 0.0001). Furthermore, there was loss of concordance in miRNA expression status between primary and metastatic tumours, and 57.14-80.95% of the primary-metastases tumour pairs showed altered primary-metastasis relative expression in all the miRNAs tested. Moreover, 16 of 20 (80%) tumour pairs showed alteration in at least 3 of 6 (50%) of the protein biomarker pathways analysed. CONCLUSION: The molecular alterations of primary colorectal tumours differ significantly from those of their matched metastases. These differences have profound implications for patients' prognoses and response to therapy.

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things' Devices Security.

The Internet of Things (IoT) comprises a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Studies have shown that these protocols pose a severe threat (Cyber-attacks) to the security of data transmitted due to their ease of exploitation. In this research, we aim to contribute to the literature by improving the Intrusion Detection System (IDS) detection efficiency. In order to improve the efficiency of the IDS, a binary classification of normal and abnormal IoT traffic is constructed to enhance the IDS performance. Our method employs various supervised ML algorithms and ensemble classifiers. The proposed model was trained on TON-IoT network traffic datasets. Four of the trained ML-supervised models have achieved the highest accurate outcomes; Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor. These four classifiers are fed to two ensemble approaches: voting and stacking. The ensemble approaches were evaluated using the evaluation metrics and compared for their efficacy on this classification problem. The accuracy of the ensemble classifiers was higher than that of the individual models. This improvement can be attributed to ensemble learning strategies that leverage diverse learning mechanisms with varying capabilities. By combining these strategies, we were able to enhance the reliability of our predictions while reducing the occurrence of classification errors. The experimental results show that the framework can improve the efficiency of the Intrusion Detection System, achieving an accuracy rate of 0.9863.

Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning.

Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. Epileptic siezure prediction systems face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model, which is able to achieve enhanced capability by utilizing a significant number of seizure patterns from globally distributed patients while maintaining data privacy. The determination of the preictal state is influenced by global and local model-assisted decision making by modeling the two-level edge layer. The Spiking Encoder (SE), integrated with the Graph Convolutional Neural Network (Spiking-GCNN), works as the local model trained using a bi-timescale approach. Each local model utilizes the aggregated seizure knowledge obtained from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. Thus, the proposed approach achieved 96.33% sensitivity and 96.14% specificity when tested on the CHB-MIT EEG dataset when modeling was performed using the bi-timescale approach and Spiking-GCNN-based epileptic pattern learning. Moreover, the adoption of federated learning greatly assists the proposed system, yielding a 96.28% higher accuracy as a result of addressing data scarcity.

Deep Learning Technology to Recognize American Sign Language Alphabet.

Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet. The primary objective of this study was to leverage contemporary technology to bridge the communication gap between hearing-impaired individuals and individuals with no hearing impairment. The models utilized in this research include AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer were trained and tested using an extensive dataset comprising over 87,000 images of the ASL alphabet hand gestures. Numerous experiments were conducted, involving modifications to the architectural design parameters of the models to obtain maximum recognition accuracy. The experimental results of our study revealed that ResNet-50 achieved an exceptional accuracy rate of 99.98%, the highest among all models. EfficientNet attained an accuracy rate of 99.95%, ConvNeXt achieved 99.51% accuracy, AlexNet attained 99.50% accuracy, while VisionTransformer yielded the lowest accuracy of 88.59%.

BZR proteins: identification, evolutionary and expression analysis under various exogenous growth regulators in plants.

BACKGROUNDS: The BRASSINAZOLE-RESISTANT (BZR) family of transcription factors affects a variety of developmental and physiological processes and plays a key role in multiple stress-resistance functions in plants. However, the evolutionary relationship and individual expression patterns of the BZR genes are unknown in various crop plants. METHODS AND RESULTS: In this study, we performed a genome-wide analysis of the BZR genes family in wheat and rice. Here, we found a total of 16 and 6 proteins containing the BZR domain in wheat and rice respectively. The phylogenetic analysis divided the identified BZR proteins from several plants into five subfamilies. The intron/exon structural patterns and conserved motifs distribution revealed that BZR proteins exhibite high specificities in each subfamily. Moreover, the co-expression and protein-protein interaction analysis suggested that BZR proteins may interact/co-expressed with several other proteins to perform various functions in plants. The presence of different stresses, hormones and light-responsive cis-elements in promoter regions of BZR genes imply its diverse functions in plants. The expression patterns indicated that many BZR genes regulate organ development and differentiation. BZR genes significantly respond to exogenous application of brassinosteroids, melatonin and abiotic stresses, demonstrating its key role in various developmental and physiological processes. CONCLUSION: The present study establishes the foundation for future functional genomics studies of BZR genes through reverse genetics and to further explore the potential of BZR genes in mitigating the stress tolerance in crop plants.

Marker controlled superpixel nuclei segmentation and automatic counting on immunohistochemistry staining images.

MOTIVATION: For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation. RESULTS: This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30 000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R2 = 0.901 (P < 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006. AVAILABILITY AND IMPLEMENTATION: The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Guidelines for the management of hereditary colorectal cancer from the British Society of Gastroenterology (BSG)/Association of Coloproctology of Great Britain and Ireland (ACPGBI)/United Kingdom Cancer Genetics Group (UKCGG).

Heritable factors account for approximately 35% of colorectal cancer (CRC) risk, and almost 30% of the population in the UK have a family history of CRC. The quantification of an individual's lifetime risk of gastrointestinal cancer may incorporate clinical and molecular data, and depends on accurate phenotypic assessment and genetic diagnosis. In turn this may facilitate targeted risk-reducing interventions, including endoscopic surveillance, preventative surgery and chemoprophylaxis, which provide opportunities for cancer prevention. This guideline is an update from the 2010 British Society of Gastroenterology/Association of Coloproctology of Great Britain and Ireland (BSG/ACPGBI) guidelines for colorectal screening and surveillance in moderate and high-risk groups; however, this guideline is concerned specifically with people who have increased lifetime risk of CRC due to hereditary factors, including those with Lynch syndrome, polyposis or a family history of CRC. On this occasion we invited the UK Cancer Genetics Group (UKCGG), a subgroup within the British Society of Genetic Medicine (BSGM), as a partner to BSG and ACPGBI in the multidisciplinary guideline development process. We also invited external review through the Delphi process by members of the public as well as the steering committees of the European Hereditary Tumour Group (EHTG) and the European Society of Gastrointestinal Endoscopy (ESGE). A systematic review of 10 189 publications was undertaken to develop 67 evidence and expert opinion-based recommendations for the management of hereditary CRC risk. Ten research recommendations are also prioritised to inform clinical management of people at hereditary CRC risk.

Tensin4 (TNS4) is upregulated by Wnt signalling in adenomas in multiple intestinal neoplasia (Min) mice.

ApcMin/+ mice are regarded as a standard animal model of colorectal cancer (CRC). Tensin4 (TNS4 or Cten) is a putative oncogene conferring features of stemness and promoting motility. Our objective was to assess TNS4 expression in intestinal adenomas and determine whether TNS4 is upregulated by Wnt signalling. ApcMin/+ mice (n = 11) were sacrificed at approximately 120 days old at the onset of anaemia signs. Small intestines were harvested, and Swiss roll preparations were tested for TNS4 expression by immunohistochemistry (IHC). Individual polyps were also separately collected (n = 14) and tested for TNS4 mRNA expression and Kras mutation. The relationship between Wnt signalling and TNS4 expression was tested by Western blotting in the human CRC cell line HCT116 after inhibition of ß-catenin activity with MSAB or its increase by transfection with a Flag ß-catenin expression vector. Overall, 135/148 (91.2%) of the total intestinal polyps were positive for TNS4 expression by IHC, whilst adjacent normal areas were negative. RT-qPCR analysis showed approximately 5-fold upregulation of TNS4 mRNA in the polyps compared to adjacent normal tissue and no Kras mutations were detected. In HCT116, ß-catenin inhibition resulted in reduced TNS4 expression, and conversely, ß-catenin overexpression resulted in increased TNS4 expression. In conclusion, this is the first report linking aberrant Wnt signalling to upregulation of TNS4 both during initiation of intestinal adenomas in mice and in in vitro models. The exact contribution of TNS4 to adenoma development remains to be investigated, but the ApcMin/+ mouse represents a good model to study this.

Tumour parcellation and quantification (TuPaQ): a tool for refining biomarker analysis through rapid and automated segmentation of tumour epithelium.

BACKGROUND AND AIMS: Immunohistochemistry (IHC) is an essential component of biomarker research in cancer. Automated biomarker quantification is hampered by the failure of computational algorithms to discriminate 'negative' tumour cells from 'negative' stromal cells. We sought to develop an algorithm for segmentation of tumour epithelium in colorectal cancer (CRC), irrespective of the biomarker expression in the cells. METHODS AND RESULTS: We developed tumour parcellation and quantification (TuPaQ) to segment tumour epithelium and parcellate sections into 'epithelium' and 'non-epithelium'. TuPaQ comprises image pre-processing, extraction of regions of interest (ROIs) and quantification of tumour epithelium (total area occupied by epithelium and number of nuclei in the occupied area). A total of 286 TMA cores from CRC were manually annotated and analysed using the commercial halo software to provide ground truth. The performance of TuPaQ was evaluated against the ground truth using a variety of metrics. The image size of each core was 7000 × 7000 pixels and each core was analysed in a matter of seconds. Pixel × pixel analysis showed a sensitivity of 84% and specificity of 95% in detecting epithelium. The mean tumour area obtained by TuPaQ was very close to the area quantified after manual annotation (r = 0.956, P < 0.001). Moreover, quantification of tumour nuclei by TuPaQ correlated very strongly with that of halo (r = 0.891, P < 0.001). CONCLUSION: TuPaQ is a very rapid and accurate method of separating the epithelial and stromal compartments of colorectal tumours. This will allow more accurate and objective analysis of immunohistochemistry.

Cten promotes Epithelial-Mesenchymal Transition (EMT) in colorectal cancer through stabilisation of Src.

Cten is an oncogene promoting EMT in many signaling pathways, namely through Snail. We investigated whether Cten function could be mediated through Src. Cten levels were modulated by forced expression in HCT116 and gene knockdown in SW620 CRC (colorectal cancer) cell lines. In all cell lines, Cten was a positive regulator of Src expression. The functional importance of Src was tested by simultaneous Cten overexpression and Src knockdown. This resulted in abrogation of Cten motility-inducing activity and reduction of colony formation ability together with failure to induce Cten targets. In SW620ΔCten reduced Src expression increased following restoration of Cten, also leading to increased cell motility and colony formation, which were lost if Src was concomitantly knocked down. By qRT-PCR we showed modulation of Cten had no effect on Src mRNA. However, a CHX pulse chase assay demonstrated stabilization of Src protein by Cten. Finally, expression of Cten and Src was tested in a series of 84 primary CRCs and there was a significant correlation between them (P = 0.001). We conclude that Src is a novel and functionally important target of the Cten signaling pathway and that Cten protein causes post-transcriptional stabilization of Src in promoting EMT and possibly metastasis in CRC.

TGFß1-induced cell motility but not cell proliferation is mediated through Cten in colorectal cancer.

Cten (C-terminal tensin-like) is a member of the tensin protein family found in complex with integrins at focal adhesions. It promotes epithelial-mesenchymal transition (EMT) and cell motility. The precise mechanisms regulating Cten are unknown, although we and others have shown that Cten could be under the regulation of several cytokines and growth factors. Since transforming growth factor beta 1 (TGF-ß1) regulates integrin function and promotes EMT/cell motility, we were prompted to investigate whether TGF-ß1 induces EMT and cell motility through Cten signalling in colorectal cancer. TGF-ß1 signalling was modulated by either stimulation with TGF-ß1 or knockdown of TGF-ß1 in the CRC cell lines SW620 and HCT116. The effect of this modulation on expression of Cten, EMT markers and on cellular function was tested. The role of Cten as a direct mediator of TGF-ß1 signalling was investigated in a CRC cell line in which the Cten gene had been deleted (SW620ΔCten ). When TGF-ß1 was stimulated or inhibited, this resulted in, respectively, upregulation and downregulation of Cten expression and EMT markers (Snail, Rock, N-cadherin, Src). Cell migration and cell invasion were significantly increased following TGF-ß1 stimulation and lost by TGF-ß1 knockdown. TGF-ß1 stimulation of the SW620ΔCten cell line resulted in selective loss of the effect of TGF-ß1 signalling pathway on EMT and cell motility while the stimulatory effect on cell proliferation was retained. These data suggested Cten may play an essential role in mediating TGF-ß1-induced EMT and cell motility and may therefore play a role in metastasis in CRC.

HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues.

AIMS: Evaluating expression of the human epidermal growth factor receptor 2 (HER2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognized importance as a predictive and prognostic marker in clinical practice. However, visual scoring of HER2 is subjective, and consequently prone to interobserver variability. Given the prognostic and therapeutic implications of HER2 scoring, a more objective method is required. In this paper, we report on a recent automated HER2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art artificial intelligence (AI)-based automated methods for HER2 scoring. METHODS AND RESULTS: The contest data set comprised digitized whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both haematoxylin and eosin (H&E) and IHC for HER2. The contesting algorithms predicted scores of the IHC slides automatically for an unseen subset of the data set and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple 'Man versus Machine' contest for the scoring of HER2 and show that the automated methods could beat the pathology experts on this contest data set. CONCLUSIONS: This paper presents a benchmark for comparing the performance of automated algorithms for scoring of HER2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.

Tumour-draining axillary lymph nodes in patients with large and locally advanced breast cancers undergoing neoadjuvant chemotherapy (NAC): the crucial contribution of immune cells (effector, regulatory) and cytokines (Th1, Th2) to immune-mediated tumour cell death induced by NAC.

BACKGROUND: The tumour microenvironment consists of malignant cells, stroma and immune cells. In women with large and locally advanced breast cancers (LLABCs) undergoing neoadjuvant chemotherapy (NAC), tumour-infiltrating lymphocytes (TILs), various subsets (effector, regulatory) and cytokines in the primary tumour play a key role in the induction of tumour cell death and a pathological complete response (pCR) with NAC. Their contribution to a pCR in nodal metastases, however, is poorly studied and was investigated. METHODS: Axillary lymph nodes (ALNs) (24 with and 9 without metastases) from women with LLABCs undergoing NAC were immunohistochemically assessed for TILs, T effector and regulatory cell subsets, NK cells and cytokine expression using labelled antibodies, employing established semi-quantitative methods. IBM SPSS statistical package (21v) was used. Non-parametric (paired and unpaired) statistical analyses were performed. Univariate and multivariate regression analyses were carried out to establish the prediction of a pCR and Spearman's Correlation Coefficient was used to determine the correlation of immune cell infiltrates in ALN metastatic and primary breast tumours. RESULTS: In ALN metastases high levels of TILs, CD4+ and CD8+ T and CD56+ NK cells were significantly associated with pCRs.. Significantly higher levels of Tregs (FOXP3+, CTLA-4+) and CD56+ NK cells were documented in ALN metastases than in the corresponding primary breast tumours. CD8+ T and CD56+ NK cells showed a positive correlation between metastatic and primary tumours. A high % CD8+ and low % FOXP3+ T cells and high CD8+: FOXP3+ ratio in metastatic ALNs (tumour-free para-cortex) were associated with pCRs. Metastatic ALNs expressed high IL-10, low IL-2 and IFN-ϒ. CONCLUSIONS: Our study has provided new data characterising the possible contribution of T effector and regulatory cells and NK cells and T helper1 and 2 cytokines to tumour cell death associated with NAC in ALNs. TRIAL REGISTRATION: The Trial was retrospectively registered. Study Registration Number is ISRCTN00407556 .

CD10 inhibits cell motility but expression is associated with advanced stage disease in colorectal cancer.

INTRODUCTION: CD10 is a cell membrane-bound endopeptidase which is expressed in normal small bowel but not in normal colon. It is aberrantly expressed in a small proportion of colorectal cancers (CRC) and this has been associated with liver metastasis and poor prognosis. We sought to investigate the mechanism of CD10 activity and its association with clinicopathological features. MATERIAL AND METHODS: CD10 was stably knocked down by lentiviral shRNA transduction in the CRC cell lines SW480 and SW620 which are derived from a primary tumour and its corresponding metastasis respectively. Expression of epithelial - mesenchymal transition (EMT) markers was tested as well as the effect of knockdown on cell viability, migration and invasion assays. In addition, immunohistochemical expression of CD10 in primary colorectal tumours (N = 84) in a tissue microarray was digitally quantified and analysed for associations with clinicopathological variables. RESULTS: Knockdown of CD10 did not alter cell viability in SW480, but migration and invasion levels increased (P < 0.001 for each) and this was associated with a cadherin switch. In SW620, CD10 knockdown caused a reduction in cell viability after 72 h (P = 0.0018) but it had no effect on cell migration and invasion. Expression of epithelial CD10 in primary tumours was associated with presence of lymph node invasion (P = 0.001) and advanced Duke's stage (P = 0.001). CONCLUSIONS: Our results suggest that the function of CD10 may change during tumour evolution. It may inhibit cell motility in early-stage disease whilst promoting cell viability in late-stage disease. It has a complex role and further studies are needed to elucidate the suitability of CD10 as a prognostic marker or therapeutic target.