Information theoretic sub-network mining characterizes breast cancer subtypes in terms of cancer core mechanisms

A breast cancer subtype classification scheme, PAM50, based on genetic information is widely accepted for clinical applications. On the other hands, experimental cancer biology studies have been successful in revealing the mechanisms of breast cancer and now the hallmarks of cancer have been determined to explain the core mechanisms of tumorigenesis. Thus, it is important to understand how the breast cancer subtypes are related to the cancer core mechanisms, but multiple studies are yet to address the hallmarks of breast cancer subtypes. Therefore, a new approach that can explain the differences among breast cancer subtypes in terms of cancer hallmarks is needed. We developed an information theoretic sub-network mining algorithm, differentially expressed sub-network and pathway analysis (DeSPA), that retrieves tumor-related genes by mining a gene regulatory network (GRN) of transcription factors and miRNAs. With extensive experiments of the cancer genome atlas (TCGA) breast cancer sequencing data, we showed that our approach was able to select genes that belong to cancer core pathways such as DNA replication, cell cycle, p53 pathways while keeping the accuracy of breast cancer subtype classification comparable to that of PAM50. In addition, our method produces a regulatory network of TF, miRNA, and their target genes that distinguish breast cancer subtypes, which is confirmed by experimental studies in the literature.

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Hubness weighted svm ensemble for prediction of breast cancer subtypes

Technology and Health Care ◽

10.3233/thc-212825 ◽

2021 ◽

pp. 1-14

Author(s):

S. Raja Sree ◽

A. Kunthavai

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Support Vector Machine ◽

Breast Cancer Subtype ◽

Breast Cancer Subtypes ◽

High Dimensional ◽

Support Vector ◽

Gene Expressions ◽

Cancer Subtypes ◽

Cancer Subtype

BACKGROUND: Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Cancer subtype prediction from gene expression data can provide more information for cancer treatment decisions. OBJECTIVE: Gene expressions are complex for analysis due to its high dimensional nature. Machine learning algorithms such as k-Nearest Neighbors, Support Vector Machine (SVM) and Random Forest are used with selection of features for prediction of breast cancer subtypes. Prediction accuracy of the existing methods are affected due to high dimensional nature of gene expressions. The objective of the work is to propose an efficient algorithm for the prediction of breast cancer subtypes from gene expression. METHODS: For subtype prediction, a novel Hubness Weighted Support Vector machine algorithm (HWSVM) using bad hubness score as a weight measure to handle the outliers in the data has been proposed. Based on the various subtypes, features are projected into seven different feature sets and Ensemble based Hubness Aware Weighted Support Vector Machine (HWSVMEns) is implemented for breast cancer subtype prediction. RESULTS: The proposed algorithms have been compared with the classical SVM and other traditional algorithms such as Random Forest, k-Nearest Neighbor algorithms and also with various gene selection methods. CONCLUSIONS: Experimental results show that the proposed HWSVM outperforms other algorithms in terms of accuracy, precision, recall and F1 score due to the hubness weightage scheme and the ensemble approach. The experiments have shown an average accuracy of 92% across various gene expression datasets.

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Population-Based Study on Cancer Subtypes, Guideline-Concordant Adjuvant Therapy, and Survival Among Women With Stage I–III Breast Cancer

Journal of the National Comprehensive Cancer Network ◽

10.6004/jnccn.2018.7272 ◽

2019 ◽

Vol 17 (6) ◽

pp. 676-686 ◽

Cited By ~ 2

Author(s):

Mei-Chin Hsieh ◽

Lu Zhang ◽

Xiao-Cheng Wu ◽

Mary B. Davidson ◽

Michelle Loch ◽

...

Keyword(s):

Breast Cancer ◽

Adjuvant Therapy ◽

Breast Cancer Subtype ◽

Population Based ◽

Survival Outcome ◽

Stage I ◽

Breast Cancer Subtypes ◽

Population Based Study ◽

Cancer Subtypes ◽

Cancer Subtype

Background: Breast cancer subtype is a key determinant in treatment decision-making, and also effects survival outcome. In this population-based study, in-depth analyses were performed to examine the impact that breast cancer subtype and receipt of guideline-concordant adjuvant systemic therapy (AST) have on survival using a population-based cancer registry’s data. Methods: Women aged ≥20 years with microscopically confirmed stage I–III breast cancer diagnosed in 2011 were identified from the Louisiana Tumor Registry. Breast cancer subtypes were categorized based on hormone receptor (HR) and HER2 status. Guideline-concordant treatment was defined using the NCCN Guidelines for Breast Cancer. Logistic regression was applied to identify factors associated with guideline-concordant AST receipt. Kaplan-Meier survival curves were generated to compare survival among subtypes by AST receipt status, and a semiparametric additive hazard model was used to verify the factors impacting survival outcome. Results: Of 2,214 eligible patients, most (70.8%) were HR+/HER2– followed by HR–/HER2– (14.4%), and 78.6% received guideline-concordant AST. Compared with patients with the HR+/HER2+ subtype, women with other subtypes were more likely to be guideline-concordant after adjusting for sociodemographic and clinical variables. Women with the HR–/HER2+ or HR–/HER2– subtype had a higher risk of any-cause and breast cancer–specific death than those with the HR+/HER2+ subtype. Those who did not receive AST had an additional adjusted hazard of 0.0191 (P=.0001) in overall survival and 0.0126 (P=.0011) in cause-specific survival compared with those who received AST. Conclusions: Most patients received guideline-concordant AST, except for those with the HR+/HER2+ subtype. Patients receiving guideline-adherent adjuvant therapy had better survival outcomes across all breast cancer subtypes.

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Differences in Breast Cancer Subtypes Among Racial/Ethnic Groups

10.21203/rs.3.rs-734284/v1 ◽

2021 ◽

Author(s):

Surbhi Bansil ◽

Anthony Silva ◽

Corinne Jones ◽

Elena Hidalgo ◽

Ian Pagano ◽

...

Keyword(s):

Breast Cancer ◽

Ethnic Groups ◽

Breast Cancer Subtype ◽

Cancer Prognosis ◽

Breast Cancer Subtypes ◽

Her2 Receptor ◽

Cancer Subtypes ◽

Her2 Breast Cancer ◽

Cancer Subtype ◽

Racial Ethnic

Abstract PurposeDifferences in incidence of breast cancer subtypes among racial/ethnic groups have been evaluated as a contributing factor in the disparities seen in breast cancer prognosis. We evaluated new breast cancer cases in Hawaii to determine if there were subtype differences according to race/ethnicity that may contribute to known disparities.MethodsWe reviewed 4,318 cases of women diagnosed with breast cancer from two large tumor registries between 2013-2020. We evaluated the new breast cancer cases according to age at diagnosis, self-reported race, and breast cancer subtype (ER, PR, and HER2 receptor status).ResultsWe found both premenopausal and postmenopausal Native Hawaiian women were less likely to be diagnosed with triple negative breast cancer (OR=0.33, P=0.009; OR=0.62, P=0.03 respectively). Premenopausal Japanese women were 71% less likely to be diagnosed with triple positive (ER+/PR+/HER2+) breast cancer (OR=0.29, P=0.0003). Postmenopausal Filipino women were 89% more likely to be diagnosed with ER-/PR-/HER2+ breast cancer (OR=1.89, P=0.02). ConclusionsResults of our study support that there are racial/ethnic differences in breast cancer subtypes among our population which may contribute to the differences in outcome seen. Further evaluation of other clinical and pathological features in each breast cancer subtype may inform potential mechanisms for outcome disparities seen among different racial/ethnic groups.

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The association between volumetric breast density and breast cancer subtypes among women newly diagnosed with breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e13115 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e13115-e13115 ◽

Cited By ~ 1

Author(s):

Wintana A. Balema ◽

Tanya W. Moseley ◽

Olena Weaver ◽

Kenneth R. Hess ◽

Abenaa M. Brewster

Keyword(s):

Breast Cancer ◽

Risk Factor ◽

Breast Density ◽

Breast Cancer Subtype ◽

Breast Cancer Subtypes ◽

Her2 Positive ◽

Cancer Subtypes ◽

Volumetric Density ◽

Increased Risk ◽

Cancer Subtype

e13115 Background: Increased breast density is a strong risk factor for breast cancer, women with high breast density have a four to six-fold increased risk of breast cancer compared to those with low density. This study explores breast density as a risk factor for specific breast cancer subtypes in order to improve risk assessment and screening recommendations for the general population. Methods: 790 women ≥ 18 years with breast cancer were evaluated who had volumetric percent density and volumetric density grade (VDG) assessed from diagnostic mammograms obtained within 9 months of diagnosis. Breast cancer subtypes were approximated based on the estrogen receptor (ER), progesterone (PR) and Her2neu status; ER and/or PR positive/Her2 negative or positive (HR+), ER and PR negative and Her2 positive (Her2-positive) and ER, PR and Her2 negative (TN). A linear model on a log scale was conducted to evaluate the associations between percentage volumetric breast density and VDG and breast cancer subtypes and race. Results: 36% of women were < 50 years and 64% ≥50 years, 76% were white, 12% Black and 12% other race. There was no significant association between breast cancer subtype with age ( P = 0.068), BMI ( P = 0.81) or race ( P = 0.11). Women with VDG 1 or 2 were more likely to have HR+ (81.3%) than Her2-positive (5.1%) or TN subtypes (13.6%) (P = 0.024). There was no significant association between the percent volumetric breast density and breast tumor subtype or race. Conclusions: We found a significant association between lower breast density measured using VDG and the HR+ breast cancer subtype. This suggests a potential opportunity for assessing volumetric density grade for the development of individualized risk prediction models and for the identification of women who may benefit from preventive therapy to reduce HR+ breast cancer risk.

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A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

Mathematics ◽

10.3390/math9131574 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1574

Author(s):

Ala’a El-Nabawy ◽

Nahla A. Belal ◽

Nashwa El-Bendary

Keyword(s):

Breast Cancer ◽

Breast Cancer Subtype ◽

Biological Data ◽

Cancer Subtypes ◽

Cancer Data ◽

Subtype Classification ◽

Computational Performance ◽

Cancer Subtype ◽

Deep Forest ◽

Single Data

Automated diagnosis systems aim to reduce the cost of diagnosis while maintaining the same efficiency. Many methods have been used for breast cancer subtype classification. Some use single data source, while others integrate many data sources, the case that results in reduced computational performance as opposed to accuracy. Breast cancer data, especially biological data, is known for its imbalance, with lack of extensive amounts of histopathological images as biological data. Recent studies have shown that cascade Deep Forest ensemble model achieves a competitive classification accuracy compared with other alternatives, such as the general ensemble learning methods and the conventional deep neural networks (DNNs), especially for imbalanced training sets, through learning hyper-representations through using cascade ensemble decision trees. In this work, a cascade Deep Forest is employed to classify breast cancer subtypes, IntClust and Pam50, using multi-omics datasets and different configurations. The results obtained recorded an accuracy of 83.45% for 5 subtypes and 77.55% for 10 subtypes. The significance of this work is that it is shown that using gene expression data alone with the cascade Deep Forest classifier achieves comparable accuracy to other techniques with higher computational performance, where the time recorded is about 5 s for 10 subtypes, and 7 s for 5 subtypes.

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Identifying Breast Cancer Subtype Related miRNAs from Two Constructed miRNAs Interaction Networks in Silico Method

BioMed Research International ◽

10.1155/2013/798912 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Lin Hua ◽

Lin Li ◽

Ping Zhou

Keyword(s):

Breast Cancer ◽

Expression Profiling ◽

Breast Cancer Subtype ◽

Estimation Method ◽

Interaction Networks ◽

Breast Cancer Subtypes ◽

Topological Properties ◽

Cancer Subtypes ◽

Cancer Subtype ◽

Dual Expression

Background. It has been known that microRNAs (miRNAs) regulate the expression of multiple proteins and therefore are likely to emerge as more effective targets of selective therapeutic modalities for breast cancer. Although recent lines of evidence have approved that miRNAs are associated with the most common molecular breast cancer subtypes, the studies to breast cancer subtypes have not been well characterized.Objectives. In this study, we propose a silico method to identify breast cancer subtype related miRNAs based on two constructed miRNAs interaction networks using miRNA-mRNA dual expression profiling data arising from the same samples.Methods. Firstly, we used a new mutual information estimation method to construct two miRNAs interaction networks based on miRNA-mRNA dual expression profiling data. Secondly, we compared and analyzed the topological properties of these two networks. Finally, miRNAs showing the outstanding topological properties in both of the two networks were identified.Results. Further functional analysis and literature evidence confirm that the identified potential breast cancer subtype related miRNAs are essential to unraveling their biological function.Conclusions. This study provides a new silico method to predict candidate miRNAs of breast cancer subtype from a system biology level and can help exploit for functional studies of important breast cancer subtype related miRNAs.

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Epigenetic aspects of triple-negative in patients with breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.1041 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 1041-1041

Author(s):

Joaquina Martínez-Galan ◽

Sandra Rios ◽

Juan Ramon Delgado ◽

Blanca Torres-Torres ◽

Jesus Lopez-Peñalver ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Dna Methylation ◽

Triple Negative ◽

Breast Cancer Subtype ◽

Regulation Of Gene Expression ◽

Breast Cancer Subtypes ◽

Luminal A ◽

Cancer Subtypes ◽

Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

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Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes

PeerJ ◽

10.7717/peerj.8049 ◽

2019 ◽

Vol 7 ◽

pp. e8049 ◽

Cited By ~ 4

Author(s):

Dana Aisina ◽

Raigul Niyazova ◽

Shara Atambayeva ◽

Anatoliy Ivashchenko

Keyword(s):

Breast Cancer ◽

Candidate Genes ◽

Binding Sites ◽

Target Genes ◽

Breast Cancer Subtype ◽

Luminal A ◽

Cancer Subtypes ◽

Nucleotide Binding Sites ◽

Cancer Subtype ◽

Gene Mrna

The development of breast cancer (BC) subtypes is controlled by distinct sets of candidate genes, and the expression of these genes is regulated by the binding of their mRNAs with miRNAs. Predicting miRNA associations and target genes is thus essential when studying breast cancer. The MirTarget program identifies the initiation of miRNA binding to mRNA, the localization of miRNA binding sites in mRNA regions, and the free energy from the binding of all miRNA nucleotides with mRNA. Candidate gene mRNAs have clusters (miRNA binding sites with overlapping nucleotide sequences). mRNAs of EPOR, MAZ and NISCH candidate genes of the HER2 subtype have clusters, and there are four clusters in mRNAs of MAZ, BRCA2 and CDK6 genes. Candidate genes of the triple-negative subtype are targets for multiple miRNAs. There are 11 sites in CBL mRNA, five sites in MMP2 mRNA, and RAB5A mRNA contains two clusters in each of the three sites. In SFN mRNA, there are two clusters in three sites, and one cluster in 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in FOXA1 mRNA and 15 sites in HMGA2 mRNA. There are clusters of five sites in mRNAs of ITGB1 and SOX4 genes. Clusters of eight sites and 10 sites are identified in mRNAs of SMAD3 and TGFB1 genes, respectively. Organizing miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in mRNAs. This overlapping of miRNA binding sites creates a competition among miRNAs for a binding site. From 6,272 miRNAs studied, only 29 miRNAs from miRBase and 88 novel miRNAs had binding sites in clusters of target gene mRNA in breast cancer. We propose using associations of miRNAs and their target genes as markers in breast cancer subtype diagnosis.

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Evaluation of FGFR targeting in breast cancer through interrogation of patient-derived models

Breast Cancer Research ◽

10.1186/s13058-021-01461-4 ◽

2021 ◽

Vol 23 (1) ◽

Author(s):

Nicole J. Chew ◽

Terry C. C. Lim Kam Sian ◽

Elizabeth V. Nguyen ◽

Sung-Young Shin ◽

Jessica Yang ◽

...

Keyword(s):

Breast Cancer ◽

Therapeutic Target ◽

Breast Cancer Subtype ◽

Breast Cancer Subtypes ◽

Cancer Tissue ◽

Breast Cancers ◽

Luminal A ◽

Tissue Samples ◽

Cancer Subtypes ◽

Luminal B

Abstract Background Particular breast cancer subtypes pose a clinical challenge due to limited targeted therapeutic options and/or poor responses to the existing targeted therapies. While cell lines provide useful pre-clinical models, patient-derived xenografts (PDX) and organoids (PDO) provide significant advantages, including maintenance of genetic and phenotypic heterogeneity, 3D architecture and for PDX, tumor–stroma interactions. In this study, we applied an integrated multi-omic approach across panels of breast cancer PDXs and PDOs in order to identify candidate therapeutic targets, with a major focus on specific FGFRs. Methods MS-based phosphoproteomics, RNAseq, WES and Western blotting were used to characterize aberrantly activated protein kinases and effects of specific FGFR inhibitors. PDX and PDO were treated with the selective tyrosine kinase inhibitors AZD4547 (FGFR1-3) and BLU9931 (FGFR4). FGFR4 expression in cancer tissue samples and PDOs was assessed by immunohistochemistry. METABRIC and TCGA datasets were interrogated to identify specific FGFR alterations and their association with breast cancer subtype and patient survival. Results Phosphoproteomic profiling across 18 triple-negative breast cancers (TNBC) and 1 luminal B PDX revealed considerable heterogeneity in kinase activation, but 1/3 of PDX exhibited enhanced phosphorylation of FGFR1, FGFR2 or FGFR4. One TNBC PDX with high FGFR2 activation was exquisitely sensitive to AZD4547. Integrated ‘omic analysis revealed a novel FGFR2-SKI fusion that comprised the majority of FGFR2 joined to the C-terminal region of SKI containing the coiled-coil domains. High FGFR4 phosphorylation characterized a luminal B PDX model and treatment with BLU9931 significantly decreased tumor growth. Phosphoproteomic and transcriptomic analyses confirmed on-target action of the two anti-FGFR drugs and also revealed novel effects on the spliceosome, metabolism and extracellular matrix (AZD4547) and RIG-I-like and NOD-like receptor signaling (BLU9931). Interrogation of public datasets revealed FGFR2 amplification, fusion or mutation in TNBC and other breast cancer subtypes, while FGFR4 overexpression and amplification occurred in all breast cancer subtypes and were associated with poor prognosis. Characterization of a PDO panel identified a luminal A PDO with high FGFR4 expression that was sensitive to BLU9931 treatment, further highlighting FGFR4 as a potential therapeutic target. Conclusions This work highlights how patient-derived models of human breast cancer provide powerful platforms for therapeutic target identification and analysis of drug action, and also the potential of specific FGFRs, including FGFR4, as targets for precision treatment.

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