Intrinsic Subtypes and Androgen Receptor Gene Expression in Primary Breast Cancer. A Meta-Analysis.
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ABSTRACT:
SUBMITTER: Cruz-Tapias P
PROVIDER: S-EPMC8466727 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We studied androgen receptor (AR) gene expression in primary breast cancer (BC) to determine associations with clinical characteristics and outcomes in the I-SPY 1 study. AR was evaluated in I-SPY 1 (n = 149) using expression microarrays. Associations of AR with clinical and tumor features were determined using the Wilcoxon rank sum test (two-level factors) or the Kruskal-Wallis test (multi-level factors). We identified an optimal AR cut-point to maximize recurrence-free survival (RFS) differences between AR biomarker stratified groups, and assessed the association between the AR stratified groups and RFS using the Cox proportional hazard model. Pearson correlations between AR and selected genes were determined in I-SPY 1, METABRIC (n = 1992), and TCGA (n = 817). AR was lower in triple negative BC vs. hormone receptor positive (HR+)/HER2- and HER2+ disease (p < 0.00001), and lower in basal-like BC (p < 0.00001). AR was higher in grade I/II vs. III tumors (p < 0.00001), in patients >age 50 (p = 0.05), and in node negative disease (p = 0.006). Higher AR was associated with better RFS (p = 0.0007), which remained significant after receptor subtype adjustment (p = 0.01). AR correlated with expression of luminal, HER2, and steroid hormone genes. AR expression was related to clinicopathologic features, intrinsic subtype, and correlated with improved outcome.
| S-EPMC6904475 | biostudies-literature
Project description:Biological changes that occur during metastatic progression of breast cancer are still incompletely characterized. In this study, we compared intrinsic molecular subtypes and gene expression in 123 paired primary and metastatic tissues from breast cancer patients. Intrinsic subtype was identified using a PAM50 classifier and χ2 tests determined the differences in variable distribution. The rate of subtype conversion was 0% in basal-like tumors, 23.1% in HER2-enriched (HER2-E) tumors, 30.0% in luminal B tumors, and 55.3% in luminal A tumors. In 40.2% of cases, luminal A tumors converted to luminal B tumors, whereas in 14.3% of cases luminal A and B tumors converted to HER2-E tumors. We identified 47 genes that were expressed differentially in metastatic versus primary disease. Metastatic tumors were enriched for proliferation-related and migration-related genes and diminished for luminal-related genes. Expression of proliferation-related genes were better at predicting overall survival in metastatic disease (OSmet) when analyzed in metastatic tissue rather than primary tissue. In contrast, a basal-like gene expression signature was better at predicting OSmet in primary disease compared with metastatic tissue. We observed correlations between time to tumor relapse and the magnitude of changes of proliferation, luminal B, or HER2-E signatures in metastatic versus primary disease. Although the intrinsic subtype was largely maintained during metastatic progression, luminal/HER2-negative tumors acquired a luminal B or HER2-E profile during metastatic progression, likely reflecting tumor evolution or acquisition of estrogen independence. Overall, our analysis revealed the value of stratifying gene expression by both cancer subtype and tissue type, providing clinicians more refined tools to evaluate prognosis and treatment. Cancer Res; 77(9); 2213-21. ©2017 AACR.
| S-EPMC5822682 | biostudies-literature
Project description:PurposeTo determine intrinsic breast cancer subtypes represented within categories defined by quantitative hormone receptor (HR) and HER2 expression.MethodsWe merged 1,557 cases from three randomized phase III trials into a single data set. These breast tumors were centrally reviewed in each trial for quantitative ER, PR, and HER2 expression by immunohistochemistry (IHC) stain and by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), with intrinsic subtyping by research-based PAM50 RT-qPCR assay.ResultsAmong 283 HER2-negative tumors with <1% HR expression by IHC, 207 (73%) were basal-like; other subtypes, particularly HER2-enriched (48, 17%), were present. Among the 1,298 HER2-negative tumors, borderline HR (1%-9% staining) was uncommon (n = 39), and these tumors were heterogeneous: 17 (44%) luminal A/B, 12 (31%) HER2-enriched, and only 7 (18%) basal-like. Including them in the definition of triple-negative breast cancer significantly diminished enrichment for basal-like cancer (p < .05). Among 106 HER2-positive tumors with <1% HR expression by IHC, the HER2-enriched subtype was the most frequent (87, 82%), whereas among 127 HER2-positive tumors with strong HR (>10%) expression, only 69 (54%) were HER2-enriched and 55 (43%) were luminal (39 luminal B, 16 luminal A). Quantitative HR expression by RT-qPCR gave similar results. Regardless of methodology, basal-like cases seldom expressed ER/ESR1 or PR/PGR and were associated with the lowest expression level of HER2/ERBB2 relative to other subtypes.ConclusionSignificant discordance remains between clinical assay-defined subsets and intrinsic subtype. For identifying basal-like breast cancer, the optimal HR IHC cut point was <1%, matching the American Society of Clinical Oncology and College of American Pathologists guidelines. Tumors with borderline HR staining are molecularly diverse and may require additional assays to clarify underlying biology.
| S-EPMC4425383 | biostudies-literature
Project description:BackgroundIn hormone receptor-positive (HoR+) breast cancer (BC), gene expression analysis identifies luminal A (LumA), luminal B (LumB), human epidermal growth factor receptor 2 (HER2)-enriched (HER2-E), basal-like (BL) intrinsic subtypes and a normal-like group. This classification has an established prognostic value in early-stage HoR+ BC. Here, we carried out a trial-level meta-analysis to determine the prognostic ability of subtypes in metastatic BC (MBC).Materials and methodsWe systematically reviewed all the available prospective phase II/III trials in HoR+ MBC where subtype was assessed. The primary endpoint was progression-free survival (PFS)/time to progression (TTP) of the LumA subtype compared to non-LumA. Secondary endpoints were PFS/TTP of each individual subtype, according to treatment, menopausal and HER2 status and overall survival (OS). The random-effect model was applied, and heterogeneity assessed through Cochran's Q and I2. Threshold for significance was set at P < 0.05. The study was registered in PROSPERO (ID: CRD42021255769).ResultsSeven studies were included (2536 patients). Non-LumA represented 55.2% and was associated with worse PFS/TTP than LumA [hazard ratio (HR) 1.77, P < 0.001, I2 = 61%], independently of clinical HER2 status [Psubgroup difference (Psub) = 0.16], systemic treatment (Psub = 0.96) and menopausal status (Psub = 0.12). Non-LumA tumors also showed worse OS (HR 2.00, P < 0.001, I2 = 65%), with significantly different outcomes for LumB (PFS/TTP HR 1.46; OS HR 1.41), HER2-E (PFS/TTP HR 2.39; OS HR 2.08) and BL (PFS/TTP HR 2.67; OS HR 3.26), separately (PFS/TTP Psub = 0.01; OS Psub = 0.005). Sensitivity analyses supported the main result. No publication bias was observed.ConclusionsIn HoR+ MBC, non-LumA disease is associated with poorer PFS/TTP and OS than LumA, independently of HER2, treatment and menopausal status. Future trials in HoR+ MBC should consider this clinically relevant biological classification.
| S-EPMC10373919 | biostudies-literature
Project description:Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction.This paper describes an unsupervised context analysis to infer context-specific gene regulatory networks from 1,614 samples obtained from publicly available gene expression data, an extension of a previously published methodology. We use the context-specific gene regulatory networks to classify the tumors into clinically relevant subgroups, and provide candidates for a finer sub-grouping of the previously known intrinsic tumors with a focus on Basal-like tumors. Our analysis of pathway enrichment in the key contexts provides an insight into the biological mechanism underlying the identified subtypes of breast cancer.The use of context-specific gene regulatory networks to identify biological contexts from heterogenous breast cancer data set was able to identify genomic drivers for subgroups within the previously reported intrinsic subtypes. These subgroups (contexts) uphold the clinical relevant features for the intrinsic subtypes and were associated with increased survival differences compared to the intrinsic subtypes. We believe our computational approach led to the generation of novel rationalized hypotheses to explain mechanisms of disease progression within sub-contexts of breast cancer that could be therapeutically exploited once validated.
| S-EPMC3073183 | biostudies-literature
Project description:Interactions of cancer cells with the vasculature are essential for tumor growth and likely promote metastatic progression. Endothelial cell content and lympho-vascular invasion are generally associated with tumor aggressiveness, however, these features are generally not employed in the clinic. We aimed to determine if endothelial cell gene expression signatures could be utilized to better characterize breast tumor biology, and to establish if vascular cell-derived signatures could provide information to predict tumors likely to metastasize. Here we report on the identification of a gene signature for vascular endothelial cells, and a second for cancer-activated vasculature. Both signatures independently identify subsets of aggressive breast cancers. Interestingly, the vascular content signature and a previously identified hypoxia signature both provide prognostic information beyond currently utilized clinical parameters and intrinsic subtype classifications. In these studies, we also examined the relationship that the breast cancer subtypes have with vascular gene expression profiles, and found that claudin-low tumors and cell lines express vascular gene expression profiles and displayed endothelial-like tube formation when grown in three-dimensions. These findings are directly applicable to clinical care and therapeutic treatment design as they identify highly aggressive subsets of tumors with genetic and morphologic vascular properties. reference x sample
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Project description:PurposeThe recent development of multi-gene assays for gene expression profiling has contributed significantly to the understanding of the clinically and biologically heterogeneous breast cancer (BC) disease. PAM50 is one of these assays used to stratify BC patients and individualize treatment. The present study was conducted to characterize PAM50-based intrinsic subtypes among Ethiopian BC patients.Patients and methodsFormalin-fixed paraffin-embedded tissues were collected from 334 BC patients who attended five different Ethiopian health facilities. All samples were assessed using the PAM50 algorithm for intrinsic subtyping.ResultsThe tumor samples were classified into PAM50 intrinsic subtypes as follows: 104 samples (31.1%) were luminal A, 91 samples (27.2%) were luminal B, 62 samples (18.6%) were HER2-enriched and 77 samples (23.1%) were basal-like. The intrinsic subtypes were found to be associated with clinical and histopathological parameters such as steroid hormone receptor status, HER2 status, Ki-67 proliferation index and tumor differentiation, but not with age, tumor size or histological type. An immunohistochemistry-based classification of tumors (IHC groups) was found to correlate with intrinsic subtypes.ConclusionThe distribution of the intrinsic subtypes confirms previous immunohistochemistry-based studies from Ethiopia showing potentially endocrine-sensitive tumors in more than half of the patients. Health workers in primary or secondary level health care facilities can be trained to offer endocrine therapy to improve breast cancer care. Additionally, the findings indicate that PAM50-based classification offers a robust method for the molecular classification of tumors in the Ethiopian context.
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Project description:Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs), which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA )RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC) method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes) that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.
| S-EPMC5852561 | biostudies-literature
Project description:Molecular subtypes and gene expression signatures are widely used in early breast cancer but their role in metastatic disease is less explored. Two hundred-twenty patients diagnosed with primary breast cancer and subsequent relapse in Stockholm, Sweden between 1997 and 2006 were identified and their primary tumor was assessed for immunohistochemistry (IHC)- and PAM50-based subtypes, risk of recurrence (ROR-S) score, 21-gene and 70-gene signatures using research-based microarray expression profiles. Clinical and pathological data were retrospectively collected. Post-relapse survival within intrinsic subtypes and genomic signatures was investigated by Kaplan-Meier and Cox regression methods. ROR weighted for proliferation index (ROR-P) was explored and the prognostic contribution provided when combined to a clinical model estimated as change in LR- χ(2). IHC classified 27%, 24%, 36% and 13% of the tumors as luminal A, luminal B, HER2+ and triple negative, respectively. PAM50 categorized 22%, 24%, 26%, 22%, 6% of the tumors as luminal A, luminal B, HER2-enriched, basal-like and normal-like. Triple negative and basal tumors had a significantly shorter median post-relapse survival in comparison with luminal. Overall, neither IHC nor PAM50 subtypes, 21- and 70- gene profiles were prognostic in multivariable models. Low and medium ROR-S had a longer survival compared with the high-risk group (23 vs 10 months; p = 0.04). ROR-P independently correlated with post-relapse survival (p = 0.002) and provided the most significant prognostic information when added to a clinical model. ROR score from primary tumor represents an independent prognostic factor of post-relapse survival beyond classical clinical and pathological variables.
| S-EPMC5423143 | biostudies-literature
Project description:Recent studies have highlighted the role of androgen receptor (AR) as a prognostic biomarker of breast cancer. However, its predictive role in disease free survival (DFS) and overall survival (OS) still remains inconclusive. The present study aimed to retrospectively investigate the association between AR and survival outcomes in breast cancer and also identify this association by a meta-analysis of published researches. Clinical data from 109 patients with breast cancer, who underwent surgery at Ruijin Hospital, Shanghai, were retrospectively analyzed for immunohistochemical AR expression measured by tissue microarray. For meta-analysis, articles available in Pubmed on the relationship between AR and breast cancer outcomes were included. Data obtained from both were combined and analyzed. Women with AR positive tumors in the retrospective study had a significantly better DFS (HR 0.24, 95% CI 0.07-0.88) and OS (HR 0.19, 95% CI 0.04-0.85) than women with AR negative ones. Meta-analysis showed that AR expression in breast tumors was an indicator of better DFS (HR 0.52, 95% CI 0.43-0.64). In subgroup analysis, AR could predict DFS outcome in estrogen receptor (ER) positive (HR 0.45, 95% CI 0.34-0.59), ER negative (HR 0.42, 95% CI 0.26-0.67), and triple negative breast cancer (HR 0.40, 95% CI 0.23-0.69). Moreover, in ER positive breast cancer patients, the expression of AR could predict better OS (HR 0.39, 95% CI 0.19-0.82). The present analysis indicated that AR expression was associated with lower risk of recurrence in patients with all breast cancer types and better OS in cases with ER positive.
| S-EPMC3853592 | biostudies-literature