scholarly journals Identification and transfer of spatial transcriptomics signatures for cancer diagnosis

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Niyaz Yoosuf ◽  
José Fernández Navarro ◽  
Fredrik Salmén ◽  
Patrik L. Ståhl ◽  
Carsten O. Daub
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Ductal Carcinoma ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Cancer Tissue ◽  
Diagnostic Challenge ◽  
Expression Signature ◽  
Tissue Sections

Abstract Background Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. Methods We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles. Results We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC. Conclusions This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future.

Role of Ag NORs in Improve Diagnosis of Breast Cancer with Different grades and Subtypes’ among Sudanese women

2021 ◽  
Vol 1 (4) ◽  
pp. 443-448
Author(s):  
Doaa Ibrahim Ahmed
Keyword(s):  
Breast Cancer ◽  
Invasive Carcinoma ◽  
Ductal Carcinoma ◽  
Lobular Carcinoma ◽  
Age Group ◽  
Lobular Carcinoma In Situ ◽  
Tissue Sections ◽  
In Situ Carcinoma

This study aimed to evaluate the role of Ag NORs in improves diagnosis of Breast cancer with different subtypes’ among Sudanese Patients. This study include tissue sections of breast cancer diagnosed women, they were 30, ductal and lobular invasive carcinoma were 10 for each, while ductal and lobular in-situ carcinoma were 5 each. Found correlation between subtypes of breast cancer and Ag NOR , Invasive ductal carcinoma had more NOR while the lobular carcinoma in situ was less one , Stage III most frequency than the other stage. Silver staining were performed and Ag-NOR were detected in ductal and lobular invasive carcinoma more than ductal and lobular in-situ carcinoma, grade III has more frequency of Ag-NOR than other stages, and no correlation found between Ag-NOR and age group

scholarly journals Sharp Downregulation of Hub Genes Associated With the Pathogenesis of Breast Cancer From Ductal Carcinoma In Situ to Invasive Ductal Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yao Wang ◽  
Faqing Liang ◽  
Yuting Zhou ◽  
Juanjuan Qiu ◽  
Qing Lv ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Protein Expression ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Cancer Tissue ◽  
Hub Genes ◽  
Expression Levels ◽  
Cancer Pathogenesis ◽  
Ductal Hyperplasia

IntroductionBreast atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are precursor stages of invasive ductal carcinoma (IDC). This study aimed to investigate the pathogenesis of breast cancer by dynamically analyzing expression changes of hub genes from normal mammary epithelium (NME) to simple ductal hyperplasia (SH), ADH, DCIS, and finally to IDC.MethodsLaser-capture microdissection (LCM) data for NME, SH, ADH, DCIS, and IDC cells were obtained. Weighted gene co-expression network analysis (WGCNA) was performed to dynamically analyze the gene modules and hub genes associated with the pathogenesis of breast cancer. Tissue microarray, immunohistochemical, and western blot analyses were performed to determine the protein expression trends of hub genes.ResultsTwo modules showed a trend of increasing expression during the development of breast disease from NME to DCIS, whereas a third module displayed a completely different trend. Interestingly, the three modules displayed inverse trends from DCIS to IDC compared with from NME to DCIS; that is, previously upregulated modules were subsequently downregulated and vice versa. We further analyzed the module that was most closely associated with DCIS (p=7e−07). Kyoto Gene and Genomic Gene Encyclopedia enrichment analysis revealed that the genes in this module were closely related to the cell cycle (p= 4.3e–12). WGCNA revealed eight hub genes in the module, namely, CDK1, NUSAP1, CEP55, TOP2A, MELK, PBK, RRM2, and MAD2L1. Subsequent analysis of these hub genes revealed that their expression levels were lower in IDC tissues than in DCIS tissues, consistent with the expression trend of the module. The protein expression levels of five of the hub genes gradually increased from NME to DCIS and then decreased in IDC. Survival analysis predicted poor survival among breast cancer patients if these hub genes were not downregulated from DCIS to IDC.ConclusionsFive hub genes, RRM2, TOP2A, PBK, MELK, and NUSAP1, which are associated with breast cancer pathogenesis, are gradually upregulated from NME to DCIS and then downregulated in IDC. If these hub genes are not downregulated from DCIS to IDC, patient survival is compromised. However, the underlying mechanisms warrant further elucidation in future studies.

Breast cancer prediction using an optimal machine learning technique for next generation sequences

2021 ◽  
pp. 1063293X2199180
Author(s):  
Babymol Kurian ◽  
VL Jyothi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Next Generation ◽  
Machine Learning Technique ◽  
Cancer Prediction ◽  
Learning Technique

A wide reach on cancer prediction and detection using Next Generation Sequencing (NGS) by the application of artificial intelligence is highly appreciated in the current scenario of the medical field. Next generation sequences were extracted from NCBI (National Centre for Biotechnology Information) gene repository. Sequences of normal Homo sapiens (Class 1), BRCA1 (Class 2) and BRCA2 (Class 3) were extracted for Machine Learning (ML) purpose. The total volume of datasets extracted for the process were 1580 in number under four categories of 50, 100, 150 and 200 sequences. The breast cancer prediction process was carried out in three major steps such as feature extraction, machine learning classification and performance evaluation. The features were extracted with sequences as input. Ten features of DNA sequences such as ORF (Open Reading Frame) count, individual nucleobase average count of A, T, C, G, AT and GC-content, AT/GC composition, G-quadruplex occurrence, MR (Mutation Rate) were extracted from three types of sequences for the classification process. The sequence type was also included as a target variable to the feature set with values 0, 1 and 2 for classes 1, 2 and 3 respectively. Nine various supervised machine learning techniques like LR (Logistic Regression statistical model), LDA (Linear Discriminant analysis model), k-NN (k nearest neighbours’ algorithm), DT (Decision tree technique), NB (Naive Bayes classifier), SVM (Support-Vector Machine algorithm), RF (Random Forest learning algorithm), AdaBoost (AB) and Gradient Boosting (GB) were employed on four various categories of datasets. Of all supervised models, decision tree machine learning technique performed most with maximum accuracy in classification of 94.03%. Classification model performance was evaluated using precision, recall, F1-score and support values wherein F1-score was most similar to the classification accuracy.

scholarly journals In-situ identification of material batches using machine learning for machining operations

2020 ◽  
Author(s):  
Benjamin Lutz ◽  
Dominik Kisskalt ◽  
Andreas Mayr ◽  
Daniel Regulin ◽  
Matteo Pantano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Machine Learning Model ◽  
Multiple Classification ◽  
Subtractive Manufacturing ◽  
Smart Service ◽  
Machining Operations

AbstractIn subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.

scholarly journals Epithelial cells captured from ductal carcinoma in situ reveal a gene expression signature associated with progression to invasive breast cancer

Oncotarget ◽  
2016 ◽  
Vol 7 (46) ◽  
pp. 75672-75684 ◽  
Author(s):  
Eliana Vanina Elias ◽  
Nadia Pereira de Castro ◽  
Paulo Henrique Baldan Pineda ◽  
Carolina Sens Abuázar ◽  
Cynthia Aparecida Bueno de Toledo Osorio ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Epithelial Cells ◽  
Ductal Carcinoma In Situ ◽  
Invasive Breast Cancer ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Gene Expression Signature ◽  
Expression Signature

scholarly journals A machine learning approach for single cell interphase cell cycle staging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hemaxi Narotamo ◽  
Maria Sofia Fernandes ◽  
Ana Margarida Moreira ◽  
Soraia Melo ◽  
Raquel Seruca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Function ◽  
Ground Truth ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Interphase Cell

AbstractThe cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited. It is therefore urgent to establish accurate strategies combining bioimaging with high-content image analysis for a bona fide classification. In this study we developed a supervised machine learning method for interphase cell cycle staging of individual adherent cells using in situ fluorescence images of nuclei stained with DAPI. A Support Vector Machine (SVM) classifier operated over normalized nuclear features using more than 3500 DAPI stained nuclei. Molecular ground truth labels were obtained by automatic image processing using fluorescent ubiquitination-based cell cycle indicator (Fucci) technology. An average F1-Score of 87.7% was achieved with this framework. Furthermore, the method was validated on distinct cell types reaching recall values higher than 89%. Our method is a robust approach to identify cells in G1 or S/G2 at the individual level, with implications in research and clinical applications.

scholarly journals A Support Vector Machine and Decision Tree Based Breast Cancer Prediction

2020 ◽  
Vol 9 (3) ◽  
pp. 2972-2976
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Misclassification Rate ◽  
Support Vector

The first step in diagnosis of a breast cancer is the identification of the disease. Early detection of the breast cancer is significant to reduce the mortality rate due to breast cancer. Machine learning algorithms can be used in identification of the breast cancer. The supervised machine learning algorithms such as Support Vector Machine (SVM) and the Decision Tree are widely used in classification problems, such as the identification of breast cancer. In this study, a machine learning model is proposed by employing learning algorithms namely, the support vector machine and decision tree. The kaggle data repository consisting of 569 observations of malignant and benign observations is used to develop the proposed model. Finally, the model is evaluated using accuracy, confusion matrix precision and recall as metrics for evaluation of performance on the test set. The analysis result showed that, the support vector machine (SVM) has better accuracy and less number of misclassification rate and better precision than the decision tree algorithm. The average accuracy of the support vector machine (SVM) is 91.92 % and that of the decision tree classification model is 87.12 %.

Analysis of Machine Learning Algorithms for Breast Cancer Detection

2020 ◽  
pp. 1-20
Author(s):  
Aswathy M. A. ◽  
Jagannath Mohan
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Diagnostic Techniques ◽  
Breast Cancer Detection ◽  
Machine Learning Algorithms ◽  
Cancer Prognosis ◽  
Breast Tissues

As per the latest health ministry registries of 2017-2018, breast cancer among women has ranked number one in India and number two in United States. Despite the fact that breast cancer affects men also, pervasiveness is lower in men than women. This is the reason breast cancer is such a vital concern among ladies. Roughly 80% of cancer malignancies emerge from epithelial cells inside breast tissues. In breast cancer spectrum, ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) are considered malignant cancers that need treatment and care. This chapter mainly deals with breast cancer and machine learning (ML) applications. All through this chapter, different issues related to breast cancer prognosis and early detection and diagnostic techniques using various ML algorithms are addressed.

scholarly journals Prediction of Breast Cancer Using AI-Based Methods

2021 ◽  
Author(s):  
Sanam Aamir ◽  
Aqsa Rahim ◽  
Sajid Bashir ◽  
Muddasar Naeem
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Effective Treatment ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Diagnostic Features ◽  
Wrong Diagnosis ◽  
Significant Health

Breast cancer has made its mark as the primary cause of female deaths and disability worldwide, making it a significant health problem. However, early diagnosis of breast cancer can lead to its effective treatment. The relevant diagnostic features available in the patient’s medical data may be used in an effective way to diagnose, categorize and classify breast cancer. Considering the importance of early detection of breast cancer in its effective treatment, it is important to accurately diagnose and classify breast cancer using diagnostic features present in available data. Automated techniques based on machine learning are an effective way to classify data for diagnosis. Various machine learning based automated techniques have been proposed by researches for early prediction/diagnosis of breast cancer. However, due to the inherent criticalities and the risks coupled with wrong diagnosis, there is a dire need that the accuracy of the predicted diagnosis must be improved. In this paper, we have introduced a novel supervised machine learning based approach that embodies Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network and Multilayer Perception methods. Experimental results show that the proposed framework has achieved an accuracy of 99.12%. Results obtained after the process of feature selection indicate that both preprocessing methods and feature selection increase the success of the classification system.

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