Breast Cancer Subtype Classification Research Articles

Luminal androgen receptor subtype and tumor-infiltrating lymphocytes groups based on triple-negative breast cancer molecular subclassification.

In our previous study, we developed a triple-negative breast cancer (TNBC) subtype classification that correlated with the TNBC molecular subclassification. In this study, we aimed to evaluate the predictor variables of this subtype classification on the whole slide and to validate the model's performance by using an external test set. We explored the characteristics of this subtype classification and investigated genomic alterations, including genomic scar signature scores. First, TNBC was classified into the luminal androgen receptor (LAR) and non-luminal androgen receptor (non-LAR) subtypes based on the AR Allred score (≥ 6 and < 6, respectively). Then, the non-LAR subtype was further classified into the lymphocyte-predominant (LP), lymphocyte-intermediate (LI), and lymphocyte-depleted (LD) groups based on stromal tumor-infiltrating lymphocytes (TILs) (< 20%, > 20% but < 60%, and ≥ 60%, respectively). This classification showed fair agreement with the molecular classification in the test set. The LAR subtype was characterized by a high rate of PIK3CA mutation, CD274 (encodes PD-L1) and PDCD1LG2 (encodes PD-L2) deletion, and a low homologous recombination deficiency (HRD) score. The non-LAR LD TIL group was characterized by a high frequency of NOTCH2 and MYC amplification and a high HRD score.

The discerning influence of dynamic contrast-enhanced MRI in anticipating molecular subtypes of breast cancer through the artistry of artificial intelligence - a narrative review.

Radio genomics is an exciting new area that uses diagnostic imaging to discover genetic features of diseases. In this review, we carefully examined existing literature to evaluate the role of artificial intelligence (AI) and machine learning (ML) on dynamic contrastenhanced MRI (DCE-MRI) data to distinguish molecular subtypes of breast cancer (BC). Implications to noninvasive assessment of molecular subtype include reduction in procedure risks, tailored treatment approaches, ability to examine entire lesion, follow-up of tumour biology in response to treatment and evaluation of treatment resistance and failure secondary to tumour heterogeneity. Recent studies leverage radiomics and AI on DCE-MRI data for reliable, non-invasive breast cancer subtype classification. This review recognizes the potential of AI to predict the molecular subtypes of breast cancer non-invasively.

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning

BackgroundClassifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes.ResultsIn this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data.ConclusionsThe experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.

The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.

DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data

The precise classification of breast cancer subtypes is crucial for clinical diagnosis and treatment, yet early symptoms are often subtle. The use of multi-omics data from high-throughput sequencing can improve the classification accuracy. However, most research primarily focuses on the association between individual omics data and breast cancer, neglecting the interactions between different omics. This may fail to provide a comprehensive understanding of the biological processes of breast cancer. Here, we propose a novel framework called DiffRS-net for classifying breast cancer subtypes by identifying the association among different omics. DiffRS-net performs a differential analysis on each omics datum to identify differentially expressed genes (DE-genes) and adopts a robustness-aware Sparse Multi-View Canonical Correlation Analysis to detect multi-way association among DE-genes. These DE-genes with high levels of correlation are then used to train an attention learning network, thereby enhancing the prediction accuracy of breast cancer subtypes. The experimental results show that, by mining the associations between multi-omics data, DiffRS-net achieves a more accurate classification of breast cancer subtypes than the existing methods.

Abstract 3526: GAIN-BRCA: A graphical explainable AI-net framework for breast cancer subtype classification using multiomics data

Abstract Higher standards for precise distinction and classification among the four breast cancer subtypes, i.e., Luminal A. Luminal B, HER-2-enriched, and Basal, are essential to delivering customized treatment in precision oncology. The effective integration of readily accessible multi-omics datasets from the same patient may improve the accuracy of subtype prediction algorithms and help decipher reliable molecular features for the prediction and prognosis of breast cancer. Previous models for predicting breast cancer subtypes relied mostly on concatenation and autoencoder methods but rarely utilized graph-based integration to leverage the biological associations among omics datatypes. To overcome these limitations, we developed a graph-based multi-omics integration method using features from mRNA, DNA methylation, and miRNA data and synthesized features from their interactions. GAIN-BRCA computes weightage scores from miRNA-mRNA and DNA methylation-mRNA interactions to derive a new transformed feature vector without compromising biological information. The neural network architecture was designed and trained on the transformed features to classify breast cancer subtypes. Neural network prediction accuracies were compared using concatenated, autoencoder, and GAIN-BRCA-based integrated datasets. GAIN-BRCA outperforms the existing similar models MOGONET (Acc: 0.72) and moBRCA-net (Acc: 0.86) with an accuracy of 0.91. These transformed features were then processed using SHAP, and the top 357 features were selected for functional characterization and pathway analysis. We identified specific pathways and genes related to each breast cancer subtype separately. The most enriched pathways found in each subtype are ribonucleotide reductase signaling and glucocorticoid signaling pathways in luminal A, PFKB4 signaling pathway in luminal B, adipogenesis and DNA methylation and transcriptional signaling in HER2+, and the role of OCT4 in mammalian embryonic stem cell pluripotency in basal. Similarly, the most discriminated gene features are BRAF, ESR1, KRAS, MAPK1, SMARCE1, PPP3CA, and TGFBR2 in luminal A, TGFB3 and NCOA3 in luminal B, HNF4A, SIN3A, and SOX9 in HER2+, and POU5F1 and FOXA2 in basal subtypes. The GAIN-BRCA framework, combined with SHAP, weighs independent omics as cross-disciplinary rather than multidisciplinary and presents a distinctive picture for each subtype. As a result, the framework presents the importance of features about the corresponding subtype, which might lead to a greater understanding of the molecular basis of breast cancer subtypes. Citation Format: Jai Chand Patel, Sushil Kumar Shakyawar, Sahil Sethi, Chittibabu Guda. GAIN-BRCA: A graphical explainable AI-net framework for breast cancer subtype classification using multiomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3526.

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification

BackgroundIn recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature’s reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately.ResultsThis study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a “Few-Shot Genes Selection” method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature.ConclusionsThe identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

Identification of breast cancer subtypes based on graph convolutional network

Identification of molecular subtypes of malignant tumors plays a vital role in individualized diagnosis, personalized treatment, and prognosis prediction of cancer patients. The continuous improvement of comprehensive tumor genomics database and the ongoing breakthroughs in deep learning technology have driven further advancements in computer-aided tumor classification. Although the existing classification methods based on gene expression omnibus database take the complexity of cancer molecular classification into account, they ignore the internal correlation and synergism of genes. To solve this problem, we propose a multi-layer graph convolutional network model for breast cancer subtype classification combined with hierarchical attention network. This model constructs the graph embedding datasets of patients' genes, and develops a new end-to-end multi-classification model, which can effectively recognize molecular subtypes of breast cancer. A large number of test data prove the good performance of this new model in the classification of breast cancer subtypes. Compared to the original graph convolutional neural networks and two mainstream graph neural network classification algorithms, the new model has remarkable advantages. The accuracy, weight-F1-score, weight-recall, and weight-precision of our model in seven-category classification has reached 0.851 7, 0.823 5, 0.851 7 and 0.793 6 respectively. In the four-category classification, the results are 0.928 5, 0.894 9, 0.928 5 and 0.865 0 respectively. In addition, compared with the latest breast cancer subtype classification algorithms, the method proposed in this paper also achieved the highest classification accuracy. In summary, the model proposed in this paper may serve as an auxiliary diagnostic technology, providing a reliable option for precise classification of breast cancer subtypes in the future and laying the theoretical foundation for computer-aided tumor classification.

Breast Cancer Classification from Digital Pathology Images via Connectivity-aware Graph Transformer.

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Here the authors sought to use explainable artificial intelligence (XAI) to classify breast cancer subtypes within datasets of diagnosed patients. Following three trials they achieved a 95% success rate using the XGBoost model.

8P Precision classification of breast cancer subtypes using deep learning on multi-omics data: Towards personalized treatment strategies

8P Precision classification of breast cancer subtypes using deep learning on multi-omics data: Towards personalized treatment strategies

XAI-MethylMarker: Explainable AI approach for biomarker discovery for breast cancer subtype classification using methylation data

XAI-MethylMarker: Explainable AI approach for biomarker discovery for breast cancer subtype classification using methylation data

Prospective validation of the IRSN-23 model for personalized chemotherapy and proposal of new breast cancer subtype classification.

10 Background: The IRSN-23 model uses DNA microarray analysis of tumor tissue to stratify patients into highly sensitive (Gp-R) or less sensitive (Gp-NR) chemotherapy groups based on immune-related gene expression. In a previous report (n=901), Gp-R showed a 39% pCR rate, while Gp-NR had 11% (P=4.98E-23; Sota, Y., et al., Annals of Oncology 25.1 (2014): 100-106). This study aimed to evaluate the reproducibility of the IRSN-23 model in prospective independent validation cohorts and investigate its clinical significance and impact on breast cancer subtype classification. Methods: Tumor tissues were obtained from 143 breast cancer patients undergoing preoperative chemotherapy at Osaka University Hospital (OUH). Patients were classified into Gp-R or Gp-NR using the IRSN-23 model, and its ability to predict pathological complete response (pCR) was assessed. Results were validated with independent public datasets (n=965; breast cancer (n=919) and rectal cancer (n=46)). Results: In the OUH prospective cohort, the pCR rate was significantly higher in the Gp-R group (29.3% (11/41)) than in the Gp-NR group (1.4% (1/71)) without trastuzumab (P=1.70E-5). In all prospective validation cohorts without anti-HER2 therapy, the pCR rate in the Gp-R group was significantly higher than in the Gp-NR group. Pooled analysis of the prospective validation set revealed higher pCR rates in the Gp-R group than in the Gp-NR group, both without (Gp-R 41.9% (104/248) vs. Gp-NR 13.6% (50/367), P=4.0E-15) and with (Gp-R 48.3% (57/118) vs. Gp-NR 35.5% (66/186), P=0.03) anti-HER2 therapy (Table). Collaborative analyses of IRSN-23 Immune Score (IS) and OncotypeDx Recurrence Score (RS) suggested the identification of a highly sensitive chemotherapy group (pCR%; IS&gt;-25+RS&gt;25: 21.6% vs. IS ≤ 25 + RS &gt; 25: 2.9%, P=1.13E-04). PAM50 as an attack factor and IRSN-23 as a defense factor capture the tumor microenvironment, suggesting a new subtype classification of breast cancer. Conclusions: This study offers prospective validation of the IRSN-23 model in predicting chemotherapy efficacy, exhibiting high reproducibility. The findings indicate the IRSN-23 model is clinically valuable and propose new breast cancer subtype classification. These results hold significant implications for personalized treatment strategies.[Table: see text]

Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review.

Breast cancer (BC) is a prevalent and highly lethal malignancy affecting women worldwide. Immunotherapy has emerged as a promising therapeutic strategy for BC, offering potential improvements in patient survival. Neoadjuvant therapy (NAT) has also gained significant clinical traction. With the advancement of computer technology, Artificial Intelligence (AI) has been increasingly applied in pathology research, expanding and redefining the scope of the field. This narrative review aims to provide a comprehensive overview of the current literature on the application of computational pathology in BC, specifically focusing on diagnosis, immune microenvironment recognition, and the evaluation of immunotherapy and NAT response. A thorough examination of relevant literature was conducted, focusing on studies investigating the role of computational pathology in BC diagnosis, immune microenvironment recognition, and immunotherapy and NAT assessment. The application of computational pathology has shown significant potential in BC management. AI-based techniques enable improved diagnosis and classification of BC subtypes, enhance the identification and characterization of the immune microenvironment, and facilitate the evaluation of immunotherapy and NAT response. However, challenges related to data quality, standardization, and algorithm development still need to be addressed. The integration of computational pathology and AI has transformative implications for BC patient care. By leveraging AI-based technologies, clinicians can make more informed decisions in diagnosis, treatment planning, and therapeutic response assessment. Future research should focus on refining AI algorithms, addressing technical challenges, and conducting large-scale clinical validation studies to facilitate the translation of computational pathology into routine clinical practice for BC patients.

ACSL4: biomarker, mediator and target in quadruple negative breast cancer

Breast cancer is a heterogeneous disease for which effective treatment depends on correct categorization of its molecular subtype. For the last several decades this determination has relied on hormone receptor status for estrogen, progesterone and HER2. More recently, gene expression data have been generated that further stratify both receptor-positive and receptor-negative cancers. The fatty acid-activating enzyme, ACSL4, has been demonstrated to play a role in the malignant phenotype of a variety of cancers, including breast. This lipid metabolic enzyme is differentially expressed as a function of subtype in breast tumors, with highest expression observed in the mesenchymal (claudin low) and basal-like subtypes. Here we review data that support the potential of utilizing ACSL4 status as both a biomarker of molecular subtype and a predictor of response to a variety of targeted and non-targeted treatment regimens. Based on these findings, we suggest 3 expanded roles for ACSL4: 1. as a biomarker for classification of breast cancer subtypes; 2. as a predictor of sensitivity to hormone-based and certain other therapies; and 3. as a target for the development of new treatment modalities.

A3SOM, abstained explainable semi-supervised neural network based on self-organizing map.

In the sea of data generated daily, unlabeled samples greatly outnumber labeled ones. This is due to the fact that, in many application areas, labels are scarce or hard to obtain. In addition, unlabeled samples might belong to new classes that are not available in the label set associated with data. In this context, we propose A3SOM, an abstained explainable semi-supervised neural network that associates a self-organizing map to dense layers in order to classify samples. Abstained classification enables the detection of new classes and class overlaps. The use of a self-organizing map in A3SOM allows integrated visualization and makes the model explainable. Along with describing our approach, this paper shows that the method is competitive with other classifiers and demonstrates the benefits of including abstention rules. A use case is presented on breast cancer subtype classification and discovery to show the relevance of our method in real-world medical problems.

Multifocal and diffusely infiltrating breast cancers are highly fatal subgroups needing further improvement in diagnostic and therapeutic strategies

Physicians treating breast cancer patients often wonder why this dreaded disease is still fatal in some women despite our best diagnostic and therapeutic efforts. Our own studies on prospectively documented cases spanning several decades have given us new insights for approaching this problem. By using imaging biomarkers to classify breast cancer subtypes according to their apparent site of origin, we found that a majority of breast cancer deaths (71%) occur in a minority of breast cancers (45%). Breast cancer deaths are significantly more likely to occur in women with multifocal acinar adenocarcinoma of the breast, AAB (13.1%), diffusely invasive breast cancers of ductal origin, DAB (24 %) and breast malignancies of mesenchymal hybrid cell origin, BCMO (33.7%) compared with women having unifocal invasive breast cancers (6.1%). Preventing more of these fatal events will require a re-evaluation of the current imperfect histopathologic terminology of breast cancer with special attention to the diffuse breast cancer subtypes, intensification of multimodality imaging and multidisciplinary management, as well as application of image guided large format histopathology.

Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification.

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.

MoBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks

BackgroundBreast cancer is a highly heterogeneous disease that comprises multiple biological components. Owing its diversity, patients have different prognostic outcomes; hence, early diagnosis and accurate subtype prediction are critical for treatment. Standardized breast cancer subtyping systems, mainly based on single-omics datasets, have been developed to ensure proper treatment in a systematic manner. Recently, multi-omics data integration has attracted attention to provide a comprehensive view of patients but poses a challenge due to the high dimensionality. In recent years, deep learning-based approaches have been proposed, but they still present several limitations.ResultsIn this study, we describe moBRCA-net, an interpretable deep learning-based breast cancer subtype classification framework that uses multi-omics datasets. Three omics datasets comprising gene expression, DNA methylation and microRNA expression data were integrated while considering the biological relationships among them, and a self-attention module was applied to each omics dataset to capture the relative importance of each feature. The features were then transformed to new representations considering the respective learned importance, allowing moBRCA-net to predict the subtype.ConclusionsExperimental results confirmed that moBRCA-net has a significantly enhanced performance compared with other methods, and the effectiveness of multi-omics integration and omics-level attention were identified. moBRCA-net is publicly available at https://github.com/cbi-bioinfo/moBRCA-net.

SpongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.

Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. We show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. https://bioconductor.org/packages/devel/bioc/html/SPONGE.html. are available at Bioinformatics online.