Alteration of the immune microenvironment in the axillary metastatic lymph nodes of luminal A breast cancer patients.

BACKGROUND: The alteration of the immune microenvironment in the axillary metastatic lymph nodes of luminal A breast cancer patients is still unclear. METHODS: Postsurgical tissues from the enrolled luminal A BCs were divided into five categories: primary BC lesion at stage N0 (PL1), primary BC lesion at stage N1 (PL2), negative axillary lymph node at stage N0 BC (LN1), negative axillary lymph node at stage N1 BC (LN2), and positive axillary lymph node at stage N1 BC (LN3). The frequencies of positive immune markers (CD4, CD8, PD1, PD-L1, T-cell immunoglobulin and mucin domain 3 (TIM3), and forkhead box protein 3 (Foxp3)) in the above tissues were quantified by AKOYA Opal Polaris 7 Color Manual IHC Detection Kit. RESULTS: A total of 50 female patients with luminal A BC were enrolled in this study. Among these patients, 23 had stage N1 disease, and 27 had stage N0 disease. Compared with that in the PL2 subgroup, the frequency of PD-1-positive cells was significantly greater in the PL1 subgroup, whether at the stromal or intratumoral level (P value < 0.05). Both the frequency of CD8 + T cells in LN1 and that in LN2 were significantly greater than that in LN3 (P value < 0.05). The frequency of TIM3 + T cells in LN1 was significantly greater than that in PL1 (P value < 0.05). The frequency of CD8 + TIM3 + T cells was significantly greater in both the LN2 and LN3 groups than in the PL2 group (P value < 0.05). The frequency of CD4 + Foxp3 + T cells was significantly greater in LN1 than in PL1 (P value < 0.05), which was the same for both LN3 and PL2 (P value < 0.05). CONCLUSION: Increased frequencies of CD8 + PD1+, CD8 + TIM3 + and CD4 + Foxp3 + T cells might inhibit the immune microenvironment of axillary metastatic lymph nodes in luminal A breast cancer patients and subsequently promote lymph node metastasis.

CMA mediates resistance in breast cancer models.

BACKGROUND: Breast cancer (BC) is the most common malignancy in women and the second leading cause of cancer-related death; chemoresistance is still a clinical challenge mainly because of the different molecular features of this kind of tumour. Doxorubicin (Doxo) is widely used despite its adverse effects and the common onset of resistance. Chaperone-Mediated Autophagy (CMA) has been identified as an important mechanism through which chemotherapeutics can exert their cytotoxic effects and, in this context, LAMP-2A, the key player of CMA, can be a useful biomarker. METHODS: A cohort of patients and breast cancer cells have been screened for Doxo effect and CMA activation by analysing the LAMP-2A level. Molecular silencing has been used to clarify CMA role in BC responsiveness to treatments. Low Doxo doses were combined with other drugs (TMZ or PX-478, a HIF-1α inhibitor) to evaluate their cytotoxic ability and their role in modulating CMA. RESULTS: In this paper, we showed that CMA is an important mechanism mediating the responsiveness of breast cancer cell to different treatments (Doxo and TMZ, as suggested by triple negative cells that are TMZ-resistant and fails to activate CMA). The LAMP-2A expression level was specific for different cell lines and patient-derived tumour subtypes, and was also useful in discriminating patients for their survival rates. Moreover, molecular silencing or pharmacological blockage of HIF-1α activity reverted BC resistance to TMZ. The combination of low-dose Doxo with TMZ or PX-478 showed that the drug associations have synergistic behaviours. CONCLUSION: Here, we demonstrated that CMA activity exerts a fundamental role in the responsiveness to different treatments, and LAMP-2A can be proposed as a reliable prognostic biomarker in breast cancer. In this context, HIF-1α, a potential target of CMA, can also be assessed as a valuable therapeutic target in BC in view of identifying new, more efficient and less toxic therapeutic drug combinations. Moreover, the possibility to combine Doxo with other drugs acting on different but coherent molecular targets could help overcome resistance and open the way to a decrease in the dose of the single drugs.

In Vitro Effects of Combining Genistein with Aromatase Inhibitors: Concerns Regarding Its Consumption during Breast Cancer Treatment.

INTRODUCTION: The third-generation of aromatase inhibitors (AIs)-Exemestane (Exe), Letrozole (Let), and Anastrozole (Ana)-is the main therapeutic approach applied for estrogen receptor-positive (ER+) breast cancer (BC), the most common neoplasm in women worldwide. Despite their success, the development of resistance limits their efficacy. Genistein (G), a phytoestrogen present in soybean, has promising anticancer properties in ER+ BC cells, even when combined with anticancer drugs. Thus, the potential beneficial effects of combining G with AIs were investigated in sensitive (MCF7-aro) and resistant (LTEDaro) BC cells. METHODS: The effects on cell proliferation and expression of aromatase, ERα/ERß, and AR receptors were evaluated. RESULTS: Unlike the combination of G with Ana or Let, which negatively affects the Ais' therapeutic efficacy, G enhanced the anticancer properties of the steroidal AI Exe, increasing the antiproliferative effect and apoptosis relative to Exe. The hormone targets studied were not affected by this combination when compared with Exe. CONCLUSIONS: This is the first in vitro study that highlights the potential benefit of G as an adjuvant therapy with Exe, emphasizing, however, that soy derivatives widely used in the diet or applied as auxiliary medicines may increase the risk of adverse interactions with nonsteroidal AIs used in therapy.

Associations between quantitative measures of TDLU involution and breast tumor molecular subtypes among breast cancer cases in the Black Women's Health Study: a case-case analysis.

BACKGROUND: Terminal duct lobular units (TDLUs) are the structures in the breast that give rise to most breast cancers. Previous work has shown that TDLU involution is inversely associated with TDLU metrics, such as TDLU count/100mm2, TDLU span (µm), and number of acini/TDLU, and that these metrics may be elevated in the normal breast tissue of women diagnosed with triple-negative (TN) compared with luminal A breast tumors. It is unknown whether this relationship exists in Black women, who have the highest incidence of TN breast cancer and the highest overall breast cancer mortality rate. We examined relationships between TDLU metrics and breast cancer molecular subtype among breast cancer cases in the Black Women's Health Study (BWHS). METHODS: We assessed quantitative TDLU metrics (TDLU count/100mm2, TDLU span (µm), and number of acini/TDLU) in digitized 247 hematoxylin and eosin-stained adjacent normal tissue sections from 223 BWHS breast cancer cases, including 65 triple negative (TN) cancers (estrogen receptor (ER) negative, progesterone receptor (PR) negative, human epidermal growth factor-2 (HER2) negative) and 158 luminal A cancers (ER positive, HER2 negative). We evaluated associations of least square mean TDLU metrics adjusted for age and body mass index (BMI) with patient and clinical characteristics. In logistic regression models, we evaluated associations between TDLU metrics and breast cancer subtype, adjusting for age, BMI, and tumor size. RESULTS: Older age and higher BMI were associated with lower TDLU metrics and larger tumor size and lymph node invasion with higher TDLU metrics. The odds of TN compared with luminal A breast cancer increased with increasing tertiles of TDLU metrics, with odds ratios (95% confidence intervals) for tertile 3 versus tertile 1 of 2.18 (0.99, 4.79), 2.77 (1.07, 7.16), and 1.77 (0.79, 3.98) for TDLU count, TDLU span, and acini count/TDLU, respectively. CONCLUSION: Associations of TDLU metrics with breast cancer subtypes in the BWHS are consistent with previous studies of White and Asian women, demonstrating reduced TDLU involution in TN compared with luminal A breast cancers. Further investigation is needed to understand the factors that influence TDLU involution and the mechanisms that mediate TDLU involution and breast cancer subtype.

Identification of a 5-gene-risk score model for predicting luminal A-invasive lobular breast cancer survival.

Breast cancer is a devastating malignancy, among which the luminal A (LumA) breast cancer is the most common subtype. In the present study, we used a comprehensive bioinformatics approach in the hope of identifying novel prognostic biomarkers for LumA breast cancer patients. Transcriptomic profiling of 611 LumA breast cancer patients was downloaded from TCGA database. Differentially expressed genes (DEGs) between tumor samples and controls were first identified by differential expression analysis, before being used for the weighted gene co-expression network analysis. The subsequent univariate Cox regression and LASSO algorithm were used to uncover key prognostic genes for constructing multivariate Cox regression model. Patients were stratified into high-risk and low-risk groups according to the risk score, and subjected to multiple downstream analyses including survival analysis, gene set enrichment analysis (GSEA), inference on immune cell infiltration and analysis of mutation burden. Receiving operator curve analysis was also performed. A total of 7071 DEGs were first identified by edgeR package, pink module was found significantly associated with invasive lobular carcinoma (ILC). 105 prognostic genes and 9 predictors were identified, allowing the identification of a 5-key prognostic genes (LRRC77P, CA3, BAMBI, CABP1, ATP8A2) after intersection. These 5 genes, and the resulting Cox model, displayed good prognostic performance. Furthermore, distinct differences existed between two risk-score stratified groups at various levels. The identified 5-gene prognostic model will help deepen the understanding of the molecular and immunological mechanisms that affect the survival of LumA-ILC patients and guide and proper monitoring of these patients.

ZEB2 regulates endocrine therapy sensitivity and metastasis in luminal a breast cancer cells through a non-canonical mechanism.

PURPOSE: The transcription factors ZEB1 and ZEB2 mediate epithelial-to-mesenchymal transition (EMT) and metastatic progression in numerous malignancies including breast cancer. ZEB1 and ZEB2 drive EMT through transcriptional repression of cell-cell junction proteins and members of the tumor suppressive miR200 family. However, in estrogen receptor positive (ER +) breast cancer, the role of ZEB2 as an independent driver of metastasis has not been fully investigated. METHODS: In the current study, we induced exogenous expression of ZEB2 in ER + MCF-7 and ZR-75-1 breast cancer cell lines and examined EMT gene expression and metastasis using dose-response qRT-PCR, transwell migration assays, proliferation assays with immunofluorescence of Ki-67 staining. We used RNA sequencing to identify pathways and genes affected by ZEB2 overexpression. Finally, we treated ZEB2-overexpressing cells with 17ß-estradiol (E2) or ICI 182,780 to evaluate how ZEB2 affects estrogen response. RESULTS: Contrary to expectation, we found that ZEB2 did not increase canonical epithelial nor decrease mesenchymal gene expressions. Furthermore, ZEB2 overexpression did not promote a mesenchymal cell morphology. However, ZEB1 and ZEB2 protein expression induced significant migration of MCF-7 and ZR-75-1 breast cancer cells in vitro and MCF-7 xenograft metastasis in vivo. Transcriptomic (RNA sequencing) pathway analysis revealed alterations in estrogen signaling regulators and pathways, suggesting a role for ZEB2 in endocrine sensitivity in luminal A breast cancer. Expression of ZEB2 was negatively correlated with estrogen receptor complex genes in luminal A patient tumors. Furthermore, treatment with 17ß-estradiol (E2) or the estrogen receptor antagonist ICI 182,780 had no effect on growth of ZEB2-overexpressing cells. CONCLUSION: ZEB2 is a multi-functional regulator of drug sensitivity, cell migration, and metastasis in ER + breast cancer and functions through non-canonical mechanisms.

Expression profiles and functional prediction of long non-coding RNAs LINC01133, ZEB1-AS1 and ABHD11-AS1 in the luminal subtype of breast cancer.

BACKGROUND: Luminal breast cancer (BC) is the most frequent subtype accounting for more than 70% of BC. LncRNAs, a class of non-coding RNAs with more than 200 nucleotides, are involved in a variety of cellular processes and biological functions. Abberant expression is related to the development of various cancers, such as breast cancer. LINC01133, ZEB1-AS1, and ABHD11-AS1 were reported to be dysregulated in different cancers. However, their expression level in luminal BC remains poorly known. The aim of the present study was to evaluate the potential roles of these lncRNAs in BC, especially in luminal subtypes. METHODS: A comprehensive analysis was performed using the Lnc2Cancer database to identify novel cancer-associated lncRNA candidates. After conducting a literature review, three novel lncRNAs named LINC01133, ZEB1-AS1, and ABHD11-AS1 were chosen as target genes of the present study. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to evaluate the expression level of the mentioned lncRNAs in both luminal BC tissues and cell lines. Then, the correlation of the three mentioned lncRNAs expression with clinicopathological characteristics of the patients was studied. Moreover, several datasets were used to discover the potential roles and functions of LINC01133, ZEB1-AS1 and ABHD11-AS1 in luminal subtype of BC. RESULTS: According to the qRT-PCR assay, the expression levels of LINC01133 and ZEB1-AS1 were decreased in luminal BC tissues and cell lines. On the other hand, ABHD11-AS1 was upregulated in the above-mentioned samples. The expression levels of LINC01133, ZEB1-AS1, and ABHD11-AS1 were not associated with any of the clinical features. Also, the results obtained from the bioinformatics analyses were consistent with qRT-PCR data. Functional annotation of the co-expressed genes with the target lncRNAs, protein-protein interactions and significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways across luminal BC were also obtained using bioinformatics analysis. CONCLUSIONS: Taken together, our findings disclosed the dysregulation of LINC01133, ZEB1-AS1, and ABHD11-AS1 in luminal BC. It was revealed that LINC01133 and ZEB1-AS1 expression was significantly downregulated in luminal BC tissues and cell lines, while ABHD11-AS1 was upregulated considerably in the mentioned tissues and cell lines. Also, bioinformatics and systems biology analyses have helped to identify the possible role of these lncRNAs in luminal BC. However, further analysis is needed to confirm the current findings.

Nomograms predict survival of patients with lymph node-positive, luminal a breast cancer.

BACKGROUND: To develop nomograms for the prediction of the 1-, 3-, and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) for patients with lymph node positive, luminal A breast cancer. METHODS: Thirty-nine thousand fifty-one patients from The Surveillance, Epidemiology, and End Results (SEER) database were included in our study and were set into a training group (n = 19,526) and a validation group (n = 19,525). Univariate analysis and Cox proportional hazards analysis were used to select variables and set up nomogram models on the basis of the training group. Kaplan-Meier curves and the log-rank test were adopted in the survival analysis and curves plotting. C-index, calibration plots and ROC curves were used to performed internal and external validation on the training group and validation group. RESULTS: Following independent factors were included in our nomograms: Age, marital status, grade, ethnic group, T stage, positive lymph nodes numbers, Metastasis, surgery, radiotherapy, chemotherapy. In both the training group and testing group, the calibration plots show that the actual and nomogram-predicted survival probabilities are consistent greatly. The C-index values of the nomograms in the training and validation cohorts were 0.782 and 0.806 for OS and 0.783 and 0.804 for BCSS, respectively. The ROC curves show that our nomograms have good discrimination. CONCLUSIONS: The nomograms may assist clinicians predict the 1-, 3-, and 5-year OS and BCSS of patients with lymph node positive, luminal A breast cancer.

Distinct genetic landscape and a low response to doxorubicin in a luminal-A breast cancer cell line of Pakistani origin.

BACKGROUND: Breast cancers exhibit genetic heterogeneity which causes differential responses to various chemotherapy agents. Given the unique demographic and genomic background in South Asia, genetic architecture in breast cancers is not fully explored. METHODS AND RESULTS: In this study, we determined the genetic landscape of our previously established luminal-A subtype breast cancer cell line (BC-PAK1), and compared it with a Caucasian origin MCF7 breast cancer cell line of the same molecular subtype. Deep whole-exome sequencing (100X) was performed from early passages of the primary cancer cells using the Illumina NextSeq500. Data analysis with in silico tools showed novel non-silent somatic mutations previously not described in breast cancers, including a frameshift insertion (p.Ala1591AlafsTer28) in CIC, and a frameshift deletion (p.Lys333LysfsTer21) in PABPC1. Five genes CDC27, PIK3CG, ARAP3, RAPGEF1, and EFNA3, related with cell cycle pathway (hsa04110), ErbB signaling pathway (hsa04012), Ras signaling pathway (hsa04014), and Rap1 signaling pathway (hsa04015) were found to have recurrent non-silent somatic mutations. Further, the major contribution of COSMIC signatures 3 (failure of DNA double-strand break repair by homologous recombination), and 12 (transcriptional strand-bias for T>C substitutions) was observed. Also, the somatic mutations landscape in BC-PAK1 was found to be different as compared to the MCF7 cell line. The unique genetic landscape of BC-PAK1 might be responsible for significantly reduced response to doxorubicin than the MCF7 cell line. CONCLUSION: This study presents a distinct genetic architecture in luminal-A breast cancer potentially responsible for differential response to chemotherapy. Further studies on large cohorts of breast cancer patients are suggested for implementation in personalized medicine.

Concordance of the molecular subtype classification between core needle biopsy and surgical specimen in primary breast cancer.

BACKGROUND: Molecular profiling of breast cancer (BC) classifies several intrinsic subtypes based on different patterns of gene expression. Multigene assays estimate the risk of recurrence and help to select high-risk patients for adjuvant chemotherapy. However, these tests are associated with significant costs. Immunohistochemistry (IHC) offers a surrogate classification for molecular subtypes by determining estrogen (ER) and progesterone receptors (PR), human epidermal growth factor (Her2neu), as well as the proliferation marker Ki67. Core needle biopsy (CNB) is well established in BC diagnosis and allows a pre-operative assessment of biomarkers. The aim of this study was to analyze the concordance of these markers between CNB and surgical specimens to assess whether re-testing of the surgical specimen is mandatory. MATERIALS AND METHODS: Within a 3-year period, patients with primary BC and paired samples of CNB and surgical specimens were analyzed retrospectively. Concordance rates of ER, PR, Her2neu, Ki67, and the surrogate classification for molecular subtypes were calculated using the Landis and Koch agreement grades. RESULTS: Out of 2254 patients with primary breast cancer, 1307 paired specimens without pre-operative treatment were available for analysis Concordance rates for ER, PR, Her2neu, and Ki67 status showed substantial-to-almost perfect agreement grades (κ = 0.91, 0.75, 0.89, and 0.61, respectively). Though substantial concordance was also found for the subtype classification (κ = 0.70), the molecular subtype changed in 18.5% of patients based on the testing of the surgical specimen, mainly from luminal A-like to luminal B-like. CONCLUSIONS: Though the concordance rates for single markers were convincing, a significant proportion of the molecular subtypes differed between CNB and the surgical specimen. Re-testing of PR and Ki67 is mandatory to ensure optimal treatment decisions. Further research is necessary to define safe, efficient, and cost-effective predictive models in adjuvant breast cancer therapy.

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells.

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

Evaluation of the potential role of long non-coding RNA LINC00961 in luminal breast cancer: a case-control and systems biology study.

BACKGROUND: Luminal subtype is the most common subgroup of breast cancer (BC), accounting for more than 70% of this cancer. Long non-coding RNAs (lncRNAs) are a group of RNAs which play critical roles in diverse cellular processes. It is proved that dysregulation of them can contribute to the development of various cancers, including BC. LINC00961 was reported to be downregulated in several cancers, however, its expression level in BC remains largely unknown. The purpose of the present study was to investigate the possible role of LINC00961 in luminal A and B subtypes of BC. METHODS: To obtain novel lncRNAs associated with different cancers and differentially expressed lncRNAs (DElncRNAs) between BC tumor and normal tissues, Lnc2Cancer and GDC databases were used, respectively. After performing literature review, the expression level of the selected lncRNA (LINC00961) was evaluated in 79 luminal A and B BC specimens and adjacent non-cancerous tissues by Quantitative Reverse Transcription PCR (qRT-PCR). LINC00961 expression was also evaluated in two luminal A BC cell lines, compared to a normal breast cell line. The comparison of the differences between tumor and adjacent non-tumor samples was performed by paired sample t-test. Moreover, correlation analysis between LINC00961 expression and clinicopathological features was performed using the chi-square, fisher exact, and independent t-test. In order to investigate the possible roles of LINC00961 in luminal A and B BC, different bioinformatics analyses such as functional annotation of the LINC00961 co-expressed genes and protein-protein interaction (PPI) networks construction were also performed. RESULTS: LINC00961 was selected as a significant DElncRNA which had not been studied in BC. According to q-RT PCR assay, LINC00961 was downregulated in luminal BC tissues and cell lines. Its expression was correlated with smoking status and the age of menarche in luminal BC patients. Also, the results of the bioinformatics analysis were consistent with the data obtained from q-RT PCR assay. The final results indicated that LINC00961 might be involved in multiple cancer-associated pathways such as chemokine, Ras and PI3K-Akt signaling pathways, GPCR ligand binding, and signal transduction in luminal subtypes of BC. CDH5, GNG11, GNG8, SELL, S1PR1, CCL19, FYN, ACAN, CD3E, ACVRL1, CAV1, and PPARGC1A were identified as the top hub genes of the PPI networks across luminal subgroup. CONCLUSION: Our findings suggested that LINC00961 was significantly downregulated in luminal A and B subtypes of BC. Moreover, bioinformatics analysis provided a basis for better identification of the potential role of LINC00961 in luminal subtype of BC.

Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis.

BACKGROUND: Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. METHODS: We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein-protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. RESULTS: We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein-protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. CONCLUSIONS: NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

Efficacy of chemotherapy for lymph node-positive luminal A subtype breast cancer patients: an updated meta-analysis.

OBJECTIVE: To assess the association between chemotherapy and prognosis of patients with breast cancer of luminal A subtype and lymph node-positive, luminal A subtype breast cancer METHODS: Articles published between January 1, 2010, and May 1, 2020, were collected from PubMed, Embase, and Web of Science databases. On the basis of a test for heterogeneity, we selected the random effects model or fixed effects model for meta-analysis. Article quality was evaluated by sensitivity analysis, and Begg's and Egger's tests were used to measure publication bias. RESULTS: Six eligible articles were identified. The hazard ratio of overall survival of luminal A breast cancer patients who received both chemotherapy and endocrine therapy was 1.73 (95% CI 1.23, 2.43). The hazard ratio of overall survival for lymph node-positive, luminal A breast cancer patients who received chemotherapy and endocrine therapy was 1.86 and 95% CI 1.26, 2.81. The hazard ratio of relapse-free survival to disease-free survival was 1.30 (95% CI 0.85, 1.77). Tumor size, vascular invasion, and age did not show significant correlations with breast cancer prognosis. CONCLUSION: Compared with endocrine therapy alone, the addition of chemotherapy did not improve the prognosis of patients with luminal type A and lymph node positive cancer; instead, side effects of the additional chemotherapy may have negatively affected prognosis. Prospective studies are needed to determine whether the number of positive lymph nodes also correlates with efficacy of chemotherapy of luminal type A breast cancer.

miRNA Expression Profiles in Luminal A Breast Cancer-Implications in Biology, Prognosis, and Prediction of Response to Hormonal Treatment.

Breast cancer, which is the most common malignancy in women, does not form a uniform nosological unit but represents a group of malignant diseases with specific clinical, histopathological, and molecular characteristics. The increasing knowledge of the complex pathophysiological web of processes connected with breast cancercarcinogenesis allows the development of predictive and prognostic gene expressionand molecular classification systems with improved risk assessment, which could be used for individualized treatment. In our review article, we present the up-to-date knowledge about the role of miRNAs and their prognostic and predictive value in luminal A breast cancer. Indeed, an altered expression profile of miRNAs can distinguish not only between cancer and healthy samples, but they can classify specific molecular subtypes of breast cancer including HER2, Luminal A, Luminal B, and TNBC. Early identification and classification of breast cancer subtypes using miRNA expression profilescharacterize a promising approach in the field of personalized medicine. A detection of sensitive and specific biomarkers to distinguish between healthy and early breast cancer patients can be achieved by an evaluation of the different expression of several miRNAs. Consequently, miRNAs represent a potential as good diagnostic, prognostic, predictive, and therapeutic biomarkers for patients with luminal A in the early stage of BC.

NR2F2 Orphan Nuclear Receptor is Involved in Estrogen Receptor Alpha-Mediated Transcriptional Regulation in Luminal A Breast Cancer Cells.

Nuclear Receptor Subfamily 2 Group F Member 2 (NR2F2) is a member of the steroid/thyroid hormone receptor superfamily with a crucial role in organogenesis, angiogenesis, cardiovascular development and tumorigenesis. However, there is limited knowledge about the cistrome and transcriptome of NR2F2 in breast cancer. In this study, we mapped the regulatory mechanism by NR2F2 using functional genomic methods. To investigate the clinical significance of NR2F2 in breast cancer, The Cancer Genome Atlas (TCGA) data were used. These results show that a high NR2F2 is associated with better survival of a specific subset of patients, namely those with luminal A breast cancer. Therefore, genome-wide NR2F2 and estrogen receptor alpha (ERα) binding sites were mapped in luminal A breast cancer cells using chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq), revealing that most NR2F2 overlap with ERα that are co-occupied by forkhead box A1 (FOXA1) and GATA binding protein 3 (GATA3) in active enhancer regions. NR2F2 overlaps with highly frequent ERα chromatin interactions, which are essential for the formation of ERα-bound super-enhancers. In the process of the transcriptome profiling of NR2F2-depleted breast cancer cells such differentially expressed genes have been identified that are involved in endocrine therapy resistance and are also ERα target genes. Overall, these findings demonstrate that the NR2F2 nuclear receptor has a key role in ERα-mediated transcription and it can offer a potential therapeutic target in patients with luminal A breast cancer.

Significance of Targeting VEGFR-2 and Cyclin D1 in Luminal-A Breast Cancer.

The hormonal luminal-A is the most pre-dominant sub type of breast cancer (BC), and it is associated with a high level of cyclin D1 in Saudi patients. Tamoxifen is the golden therapy for hormonal BC, but resistance of cancer cells to tamoxifen contributes to the recurrence of BC due to many reasons, including high levels of AIB1 and cyclin D1. Overcoming drug resistance could be achieved by exploring alternative targetable therapeutic pathways and new drugs or combinations. The objective of this study was to determine the differentially enriched pathways in 12 samples of Saudi women diagnosed with luminal-A using the PamChip peptide microarray-based kinase activity profiling, and to compare the activity of HAA2020 and dinaciclib with tamoxifen in singles and combinations in the MCF7 luminal-A cell line. Our results of network and pathway analysis of the 12 samples highlighted the importance of VEGFR and CDKs in promoting luminal-A breast cancer. The activation of VEGF signaling via VEGFR-2 leads to activation of PI3K/AKT kinases and an increase of cell survival, and leads to activation of Hsp90, which induces the phosphorylation of FAK1, resulting in cytoskeleton remodeling. PLC-gamma 1 is also activated, leading to FAK-2 and PKC activation. Notably, the G1/S cell cycle phases and phosphorylation processes contribute to the top seven tumorigenesis processes in the 12 samples. Further, the MTT combination of HAA2020 and dinaciclib showed the best combination index (CI), was more clonogenic against MCF7 cells compared to the other combinations, and it also showed the best selectivity index (SI) in normal MRC5 cells. Interestingly, HAA2020 and dinaciclib showed a synergistic apoptotic and G1 cell cycle effect in MCF7 cells, which was supported by their synergistic CDK2, cyclin D1, and PCNA inhibition activities. Additionally, the combination showed VEGFR-2 and Hsp90 inhibition activities in MCF7 cells. The results show the significance of targeting VEGFR-2 and cyclin D1 in Saudi luminal-A breast cancer patients, and the effect of combining HAA2020 and dinaciclib on those targets in the MCF7 model. It also warrants further preclinical and in vivo investigations for the combination of HAA2020 and dinaciclib as a possible future second-line treatment for luminal-A breast cancers.

The long-term prognostic and predictive capacity of cyclin D1 gene amplification in 2305 breast tumours.

BACKGROUND: Use of cyclin D1 (CCND1) gene amplification as a breast cancer biomarker has been hampered by conflicting assessments of the relationship between cyclin D1 protein levels and patient survival. Here, we aimed to clarify its prognostic and treatment predictive potential through comprehensive long-term survival analyses. METHODS: CCND1 amplification was assessed using SNP arrays from two cohorts of 1965 and 340 patients with matching gene expression array and clinical follow-up data of over 15 years. Kaplan-Meier and multivariable Cox regression analyses were used to determine survival differences between CCND1 amplified vs. non-amplified tumours in clinically relevant patient sets, within PAM50 subtypes and within treatment-specific subgroups. Boxplots and differential gene expression analyses were performed to assess differences between amplified vs. non-amplified tumours within PAM50 subtypes. RESULTS: When combining both cohorts, worse survival was found for patients with CCND1-amplified tumours in luminal A (HR = 1.68; 95% CI, 1.15-2.46), luminal B (1.37; 1.01-1.86) and ER+/LN-/HER2- (1.66; 1.14-2.41) subgroups. In gene expression analysis, CCND1-amplified luminal A tumours showed increased proliferation (P < 0.001) and decreased progesterone (P = 0.002) levels along with a large overlap in differentially expressed genes when comparing luminal A and B-amplified vs. non-amplified tumours. CONCLUSIONS: Our results indicate that CCND1 amplification is associated with worse 15-year survival in ER+/LN-/HER2-, luminal A and luminal B patients. Moreover, luminal A CCND1-amplified tumours display gene expression changes consistent with a more aggressive phenotype. These novel findings highlight the potential of CCND1 to identify patients that could benefit from long-term treatment strategies.

Ki-67 (30-9) scoring and differentiation of Luminal A- and Luminal B-like breast cancer subtypes.

INTRODUCTION: Ki-67 labeling index assessed by immunohistochemical assays has been shown useful in assessing the risk of recurrence for estrogen receptor (ER)-positive HER2-negative breast cancers (BC) and distinguishing Luminal A-like from Luminal B-like tumors. We aimed to assess the performance of the Ventana CONFIRM anti-Ki-67 (30-9) Rabbit Monoclonal Primary Antibody. METHODS: We constructed a case-cohort design based on a random sample (n = 679) of all patients operated on for a first primary, non-metastatic, ER-positive, HER2-negative BC at the European Institute of Oncology (IEO) Milan, Italy during 1998-2002 and all additional patients (n = 303) operated during the same period, who developed an event (metastasis in distant organs or death due to BC as primary event) and were not included in the previous subset. Multivariable Cox proportional hazards regression with inverse subcohort sampling probability weighting was used to evaluate the risk of event according to Ki-67 (30-9) and derived intrinsic molecular subtype, using previously defined cutoff values, i.e., respectively 14% and 20%. RESULTS: Ki-67 was < 14% in 318 patients (32.4%), comprised between 14 and 19% in 245 patients (24.9%) and ≥ 20 in 419 patients (42.7%). At multivariable analysis, the risk of developing distant disease was 1.88 (95% CI 1.20-2.93; P = 0.006) for those with Ki-67 comprised between 14 and 19%, and 3.06 (95% CI 1.93-4.84; P < 0.0001) for those with Ki-67 ≥ 20% compared to those with Ki-67 < 14%. Patients with Luminal B-like BC had an approximate twofold risk of developing distant disease (HR = 1.91; 95% CI 1.35-2.71; P = 0.0003) than patients with Luminal A-like BC defined using Ki-67 (30-9). CONCLUSIONS: Ki-67 evaluation using the 30-9 rabbit monoclonal primary antibody was able to stratify patients with ER-positive HER2-negative BC into prognostically distinct groups. Ki-67 assessment, with strict adherence to the international recommendations, should be included among the clinically useful biological parameters for the best treatment of patients with BC.

Quantification of intrinsic subtype ambiguity in Luminal A breast cancer and its relationship to clinical outcomes.

BACKGROUND: PAM50 gene profiling assigns each cancer to a single intrinsic subtype. However, individual cancers vary in their adherence to a prototype, and due to bulk tissue sampling, some may exhibit expression patterns that indicate intra-tumor admixture of multiple subtypes. Our objective was to develop admixture metrics from PAM50 gene expression profiles in order to stratify Luminal A (LumA) cases according to their degree of subtype admixture, and then relate such admixture to clinical and molecular variables. METHODS: We re-constructed scaled, normalized PAM50 profiles for 1980 cases (674 LumA) in the METABRIC cohort and for each case computed its Mahalanobis (M-) distance from its assigned centroid and M-distance from all other centroids. We used t-SNE plots to visualize overlaps in subtype clustering. With Normal-like cases excluded, we developed two metrics: Median Distance Criteria (MDC) classified pure cases as those located within the 50th percentile of the LumA centroid and > =50th percentile from any other centroid. Distance Ratio Criteria (DRC) was computed as the ratio of M-distances from the LumA centroid to the nearest non-assigned centroid. Pure and admixed LumA cases were compared on clinical/molecular traits. TCGA LumA cases (n = 509) provided independent validation. RESULTS: Compared to pure cases in METABRIC, admixed ones had older age at diagnosis, larger tumor size, and higher grade and stage. These associations were stronger for the DRC metric compared to MDC. Admixed cases were associated with HER2 gain, high proliferation, higher PAM50 recurrence scores, more frequent TP53 mutation, and less frequent PIK3CA mutation. Similar results were observed in the TCGA validation cohort, which also showed a positive association between admixture and number of clonal populations estimated by PyClone. LumA-LumB confusion predominated, but other combinations were also present. Degree of admixture was associated with overall survival in both cohorts, as was disease-free survival in TCGA, independent of age, grade and stage (HR = 2.85, Tertile 3 vs.1). CONCLUSIONS: Luminal A breast cancers subgrouped based on PAM50 subtype purity support the hypothesis that admixed cases have worse clinical features and survival. Future analyses will explore more extensive genomic metrics for admixture and their spatial significance within a single tumor.