scholarly journals Differences in Breast Cancer Subtypes Among Racial/Ethnic Groups

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.

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

2016 ◽  
Vol 14 (05) ◽  
pp. 1644002 ◽  
Author(s):  
Jinwoo Park ◽  
Benjamin Hur ◽  
Sungmin Rhee ◽  
Sangsoo Lim ◽  
Min-Su Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regulatory Network ◽  
Cancer Biology ◽  
Breast Cancer Subtype ◽  
Breast Cancer Subtypes ◽  
Information Theoretic ◽  
Cancer Subtypes ◽  
Network Mining ◽  
Subtype Classification ◽  
Cancer Subtype

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.

Hubness weighted svm ensemble for prediction of breast cancer subtypes

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.

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

2019 ◽  
Vol 17 (6) ◽  
pp. 676-686 ◽  
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.

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

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13115-e13115 ◽  
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.

scholarly journals The Influence of Socioeconomic Status on Racial/Ethnic Disparities among the ER/PR/HER2 Breast Cancer Subtypes

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Carol A. Parise ◽  
Vincent Caggiano
Keyword(s):  
Breast Cancer ◽  
American Indians ◽  
White Women ◽  
Ethnic Disparities ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Her2 Breast Cancer ◽  
Risk Of Mortality ◽  
Race Ethnicity ◽  
Racial Ethnic

Background. The eight ER/PR/HER2 breast cancer subtypes vary widely in demographic and clinicopathologic characteristics and survival. This study assesses the contribution of SES to the risk of mortality for blacks, Hispanics, Asian/Pacific Islanders, and American Indians when compared with white women for each ER/PR/HER2 subtype.Methods. We identified 143,184 cases of first primary female invasive breast cancer from the California Cancer Registry between 2000 and 2012. The risk of mortality was computed for each race/ethnicity within each ER/PR/HER2 subtype. Models were adjusted for tumor grade, year of diagnosis, and age. SES was added to a second set of models. Analyses were conducted separately for each stage.Results. Race/ethnicity did not contribute to the risk of mortality for any subtype in stage 1 when adjusted for SES. In stages 2, 3, and 4, race/ethnicity was associated with risk of mortality and adjustment for SES changed the risk only in some subtypes. SES reduced the risk of mortality by over 45% for American Indians with stage 2 ER+/PR+/HER2− cancer, but it decreased the risk of mortality for blacks with stage 2 triple negative cancer by less than 4%.Conclusions. Racial/ethnic disparities do not exist in all ER/PR/HER2 subtypes and, in general, SES modestly alters these disparities.

scholarly journals Lifetime risks of specific breast cancer subtypes among women in four racial/ethnic groups

2010 ◽  
Vol 12 (6) ◽  
Author(s):  
Allison W Kurian ◽  
Kari Fish ◽  
Sarah J Shema ◽  
Christina A Clarke
Keyword(s):  
Breast Cancer ◽  
Ethnic Groups ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Racial Ethnic

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

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
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.

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

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.

Breast cancer subtypes in Chinese Americans: A study from the Mount Sinai Health System.

2014 ◽  
Vol 32 (26_suppl) ◽  
pp. 39-39
Author(s):  
Camila Masias ◽  
Theresa H. Shao
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Chinese Americans ◽  
Breast Cancer Subtypes ◽  
Age At Diagnosis ◽  
Chinese Patients ◽  
Breast Cancers ◽  
Cancer Subtypes ◽  
Her2 Breast Cancer ◽  
Mount Sinai

39 Background: Breast cancer has been increasing in many Asian countries, as well as among Asian Americans. While many studies have examined breast cancer subtypes in African American and Caucasian populations, few have looked at tumor subtypes in the Asian population. We aimed to examine breast cancer subtypes in Chinese Americans. Methods: We identified all Chinese patients diagnosed with invasive breast cancers between 2005 and 2012 from the Cancer Registry of Mount Sinai Beth Israel, Mount Sinai St. Luke’s, and Roosevelt Hospitals. The following clinical data were collected for each patient: age at diagnosis, year of diagnosis, largest tumor size (cm), lymph node status, estrogen receptor (ER), progesterone receptor (PR) and HER2 status. Based on ER, PR, and HER2, patients were categorized into three molecular subtypes: 1) Hormone receptor (HR)+ (ER and/or PR positive, HER2 negative), HER2+ and triple negative (TN) (ER, PR, and HER2 negative). Descriptive variables were analyzed using one-way Anova test. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated from logistic regression models. Results: There were 175 Chinese patients diagnosed with invasive breast cancers from 2005 to 2012. Median age at diagnosis was 54 (range 27-90). One hundred fourteen (65%) were HR+, 41 (23%) were HER2+, and 20 (11%) were TN. There were 59 (34%) patients diagnosed at age ≤ 50 and twelve patients (7%) were diagnosed at age < 40. There were more HER2+ and TN breast cancers diagnosed in women age ≤ 50 compared to age > 50, but the difference was not statistically significant. Women in the HR+ group were diagnosed at an older age compared to the other two subgroups (57 ± 12, 52 ± 8, and 52 ± 10 for HR+, HER2+, and TN, respectively, p = 0.036). Patients with TN breast cancers were more likely to have lymph node involvement compared to HR+ or HER2+ patients (p = 0.02). There was a trend of increasing prevalence of HER2+ breast cancer observed in recent years: 18.5% in 2005-2006, 23.8% in 2007-2008, 18.4% in 2009-2010, and 29.8% in 2011-2012. Conclusions: We observed a high proportion of breast cancer among young Chinese Americans as well as an increasing prevalence of HER2+ breast cancer in this population in recent years. Further studies are warranted.

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