Disparities within luminal breast cancer: Clinical and molecular features of African American and non-Hispanic white patients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1009-1009
Author(s):  
Kent Hoskins ◽  
Oana Cristina Danciu ◽  
Vijayakrishna K. Gadi ◽  
Yael Simons ◽  
Lisa Eileen Blumencranz ◽  
...  

1009 Background: African American breast cancer patients (AA) are diagnosed younger, have more high-risk features, and poorer clinical outcomes than non-Hispanic White patients (NHW), despite similar treatments. Although comorbidities such as obesity and metabolic syndrome may contribute to differences, ancestry-specific factors and effects of structural violence that disproportionately afflict AA individuals may influence tumor biology and outcomes. We previously reported differentially expressed genes (DEGs) associated with tumor aggressiveness in Basal tumors from AA compared with NHW (Sharma et al., 2020). Here, we compare DEGs in luminal tumors between AA and NHW. Methods: The prospective, observational FLEX study (NCT03053193) includes stage I-III breast cancer patients who receive 70-gene signature (MammaPrint/MP)/80-gene signature (BluePrint/BP) testing and consent to full transcriptome and clinical data collection. AA (n=364) and NHW (n=400, random selection) with BP luminal tumors, enrolled from 2017 to present, were included. Race/ethnicity was self-reported. AA were younger than NHW (mean, 59 vs. 62 years, p=0.001); thus, an age-matched subset (n= 360 AA, NHW) was compared. Differential gene expression analysis was performed with R limma package. Comparisons were made between AA and age-matched or randomly selected NHW in: (1) all, (2) luminal A, (3) luminal B, and (4) luminal B, obese. DEGs with FDR<0.05 were significant. Different fold change (FC) thresholds were evaluated. Results: Compared with age-matched NHW, AA were similar in menopausal status, T stage, grade, and tumor type; obesity, T2DM status, and nodal stage were significantly different ( p<0.01). Tumors from AA were more often MP high risk ( p<0.001), regardless of age matching. Luminal B AA vs. age-matched NHW comparison resulted in more DEGs (n=1070) than other comparisons; however, most were FC<2. Notably, 5/6 DEGs ( PSPH, NOTCH2NL, POLR1A, MAP1LC3P and RPS26P10) in basal tumors (Nunes et al. 2019) were also identified here. Of 9 DEGs (FC>1.7) in the luminal B age-matched comparison, 2 ( PSPH and LINC01139) were also found in the luminal B, obese subset. Consistently upregulated DEGs in AA were associated with metabolism, translation, and cellular stress response pathways. Conclusions: We found significant transcriptomic differences between luminal tumors from AA and NHW, when controlling for age, obesity, and genomic classification. A subset of DEGs in luminal B tumors were consistent with those in Basal tumors, suggesting that similar race-associated factors drive DEGs regardless of tumor subtype. DEGs that may be unique to AA luminal tumors were also found. This study suggests that some biological differences in breast tumors may result from patient ancestry or shared adverse socioeconomic exposures and underscores the need for inclusion of diverse patient groups in clinical trials. Clinical trial information: NCT03053193.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12526-e12526
Author(s):  
Xiaying Kuang ◽  
Du Cai ◽  
Ying Lin ◽  
Feng Gao

e12526 Background: Luminal B breast cancer is always routinely treated with chemotherapy and endocrine therapy but heterogeneous with respect to sensitivity to treatment, identification of patients who may most benefit remains a matter of controversy. Immune-related genes (IRGs) was found to be associated with the prognosis of breast cancer. The aim of this study is to evaluate the impact of IRGs in predicting the outcome of luminal B breast cancer patients. Methods: According to the Metabric microarray dataset also as a training cohort, 488 luminal B breast cancer patients were selected for generation of immune-related gene signature (IRGS). Another independent dataset (n=250) of patients with complete prognostic information was analyzed as a validation cohort. Prognostic analysis was assessed to test the predictive value of IRGS. Results: A model of prognostic IRGS containing 12 immune-related genes was developed. In both training and validation cohorts, IRGS significantly stratified luminal B breast cancer patients into immune low- and high-risk groups in terms of disease free survival (DFS, HR=4.95, 95% CI=3.22-7.62, P<0.001 in training cohort, HR=2.47, 95% CI=1.29-4.75, P<0.001 in validation cohort). Multivariate analysis revealed IRGS as an independent prognostic factor (HR=4.96, 95% CI=3.00-8.18, P<0.001 in training cohort, HR=2.56, 95% CI=1.28-5.09, P=0.007 in validation cohort). Furthermore, those 12 genes mostly related with response to chemical, and the expression levels of them were completely opposite in patients of immune low- and high-risk groups. Conclusions: The proposed IRGS is a satisfactory prognostic model for estimating DFS of luminal B breast cancer patients. Further studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for luminal B breast cancer patients. This work was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596).


BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ding Wang ◽  
Guodong Wei ◽  
Ju Ma ◽  
Shuai Cheng ◽  
Longyuan Jia ◽  
...  

Abstract Background Breast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women’s health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients’ survival. Methods Gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score. Results We constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001). Conclusion Our study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients’ prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.


2020 ◽  
Author(s):  
Jianing Tang ◽  
Gaosong Wu

Abstract Background Metabolic change is the hallmark of cancer. Even in the presence of oxygen, cancer cells reprogram their glucose metabolism to enhance glycolysis and reduce oxidative phosphorylation. In the present study, we aimed to develop a glycolysis-related gene signature to predict the prognosis of breast cancer patients.Methods Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO database. Univariate, Lasso-penalized, and multivariate Cox analysis were performed to construct the glycolysis-related gene signature.Results A four-gene based signature (ALDH2, PRKACB, STMN1 and ZNF292) was developed to separate patients into high-risk and low-risk groups. Kaplan-Meier survival analysis demonstrated that patients in low-risk group had significantly better prognosis than those in the high-risk group. Time-dependent ROC analysis demonstrated that the glycolysis-related gene signature had excellent prognostic accuracy. We further confirmed the expression of the four prognostic genes in breast cancer and paracancerous tissues samples using qRT-PCR analysis. Expression level of PRKACB was higher in paracancerous tissues, while STMN1 and ZNF292 were overexpressed in tumor samples. No difference was found in ALDH2 expression. The same results were observed in the IHC data from the human protein atlas. Global proteome data of 105 TCGA breast cancer samples obtained from the Clinical Proteomic Tumor Analysis Consortium were used to evaluate the prognostic value of their protein levels. Consistently, high expression of PRKACB protein level was associated with better prognosis, while high ZNF292 and STMN1 protein expression levels indicated poor prognosis.Conclusions The glycolysis-related gene signature might provide an effective prognostic predictor and a new view for individual treatment of breast cancer patients.


Author(s):  
Menha Swellam ◽  
Hekmat M EL Magdoub ◽  
May A Shawki ◽  
Marwa Adel ◽  
Mona M Hefny ◽  
...  

2009 ◽  
Vol 124 (5) ◽  
pp. 1213-1219 ◽  
Author(s):  
Dejana Braithwaite ◽  
C. Martin Tammemagi ◽  
Dan H. Moore ◽  
Elissa M. Ozanne ◽  
Robert A. Hiatt ◽  
...  

1996 ◽  
Vol 32 (1) ◽  
pp. 41-46 ◽  
Author(s):  
F. Perrone ◽  
C. Carlomagno ◽  
R. Lauria ◽  
M. De Laurentiis ◽  
A. Morabito ◽  
...  

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