The Prognostic Role of Androgen Receptor in Patients with Early-Stage Breast Cancer: A Meta-analysis of Clinical and Gene Expression Data

Abstract In leukemia, the integrity of the transcriptome is altered by chromosomal translocations, deletions, duplications, as well as by epigenetic changes in chromatin structure. By targeting mRNAs for translational repression or RNase-dependent hydrolysis (AU-rich miRNAs or shRNA-like effects), the micro RNA (miRNA) component of the transcriptome is estimated to regulate expression of up to 30% of all proteins. Yet the causes and role of deregulated miRNA expression in malignancy are largely unknown, in part because promoter events are not characterized. Since more than one-third of all known mammalian miRNA genes are encoded in the introns of protein-coding genes they may be regulated by the same promoter events that regulate host-gene mRNA expression. To provide experimental validation for coordinated expression of miRNAs and their host genes we compared Affymetrix U133A gene expression data for the promyelocytic NB4 and acute myelogenous leukemia AML2 cell lines with the expression of miRNA precursors. We found similar patterns of host gene expression in the two cell lines and a good correlation with the expression of miRNA precursors in NB4 cells (r=0.464, N=30 miRNAs, p<0.016). To further demonstrate that host gene mRNAs and miRNAs are expressed from common transcripts, we activated promoter events by enforcing the expression of Lyl1 a basic helix-loop-helix transcription factor that is often over-expressed in AML. This resulted in a greater than 2-fold increase in hsa-mir-126-1, 032-2, 107-1, 026a, -023b, -103-2, and 009-3-1 intronic miRNA precursors and a corresponding increase in host gene expression. Meta-analysis of microarray data across many experiments and platforms (available through Oncomine.org) has been used to study the cancer transcriptome. To help determine if intronic miRNAs play a substantial role in malignancy, we correlated host gene expression data with the expression of predicted miRNA targets. Less than 20% of all differentially expressed genes in leukemia and lymphoma were predicted targets, compared to 68% in breast cancer. Since the Gene Ontology term “ion binding” is most commonly associated with miRNA host genes, the data suggest that this cancer module is relatively inactive in leukemia and lymphoma, compared to breast cancer. Gene cluster analysis of a leukemia data set using only miRNA host gene expression was able to classify patients into similar (but not identical) subsets as did an analysis based on over 20,000 transcripts. To further demonstrate that miRNAs and their host genes are expressed from the same transcription unit, we correlated the expression of miRNA targets with that of genes that are either hosts for miRNAs or are situated several kilobases downstream of a miRNA, and thus belong to different transcription units. We applied this analysis to a subset of 81 AML patients that presented a normal karyotype and found significant negative correlations (p<0.01) between the levels of host genes for hsa-mir-15b, -103-1, and -128 and the expression ranks of their predicted gene targets, but no statistically significant correlation between non-host genes and targets for up-stream miRNAs. These data demonstrate co-regulated expression of host genes and intronic miRNAs and the usefulness of intronic miRNAs in cancer profiling.

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Comparing alternative pipelines for cross-platform microarray gene expression data integration with RNA-seq data in breast cancer

10.1101/059600 ◽

2016 ◽

Cited By ~ 2

Author(s):

Alina Frolova ◽

Vladyslav Bondarenko ◽

Maria Obolenska

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Data Integration ◽

Gene Expression Data ◽

Statistical Power ◽

Meta Analysis ◽

Expression Data ◽

Rna Seq ◽

Microarray Gene Expression ◽

Cross Platform

AbstractBackgroundAccording to major public repositories statistics an overwhelming majority of the existing and newly uploaded data originates from microarray experiments. Unfortunately, the potential of this data to bring new insights is limited by the effects of individual study-specific biases due to small number of biological samples. Increasing sample size by direct microarray data integration increases the statistical power to obtain a more precise estimate of gene expression in a population of individuals resulting in lower false discovery rates. However, despite numerous recommendations for gene expression data integration, there is a lack of a systematic comparison of different processing approaches aimed to asses microarray platforms diversity and ambiguous probesets to genes correspondence, leading to low number of studies applying integration.ResultsHere, we investigated five different approaches of the microarrays data processing in comparison with RNA-seq data on breast cancer samples. We aimed to evaluate different probesets annotations as well as different procedures of choosing between probesets mapped to the same gene. We show that pipelines rankings are mostly preserved across Affymetrix and Illumina platforms. BrainArray approach based on updated annotation and redesigned probesets definition and choosing probeset with the maximum average signal across the samples have best correlation with RNA-seq, while averaging probesets signals as well as scoring the quality of probes sequences mapping to the transcripts of the targeted gene have worse correlation. Finally, randomly selecting probeset among probesets mapped to the same gene significantly decreases the correlation with RNA-seq.ConclusionWe show that methods, which rely on actual probesets signal intensities, are advantageous to methods considering biological characteristics of the probes sequences only and that cross-platform integration of datasets improves correlation with the RNA-seq data. We consider the results obtained in this paper contributive to the integrative analysis as a worthwhile alternative to the classical meta-analysis of the multiple gene expression datasets.

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The prognostic role of androgen receptor in early-stage breast cancer patients: A meta-analysis.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.528 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 528-528 ◽

Cited By ~ 1

Author(s):

Ivana Bozovic-Spasojevic ◽

Dimitrios Zardavas ◽

Evandro De Azambuja ◽

Lieveke Ameye ◽

Christos Sotiriou ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Androgen Receptor ◽

Cancer Patients ◽

Early Stage ◽

Meta Analysis ◽

Prognostic Impact ◽

Breast Cancer Patients ◽

Prognostic Role ◽

The Impact

528 Background: Androgen receptor (AR) expression has been observed in ~70% of breast cancer (BC) patients, but its prognostic role is not established yet. To assess this we performed a meta-analysis of studies that evaluated the impact of AR on disease free survival (DFS) and/or on overall survival (OS) in early stage BC. Methods: Published studies were identified by an electronic search on PubMed using the MeSH terms "breast neoplasm" and "androgen receptor" (up to June 2012). Identified studies were assessed against the following criteria for inclusion in the analysis: early stage BC and reported results of AR status in correlation with clinical outcome. We report combined HRs with 95% confidence intervals (CI) using AR negative patients as reference. Results: Twenty studies were eligible for the meta-analysis out of 493 initially identified and 12 among them, including 6,525 patients, were considered as evaluable (i.e., reporting enough information to allow aggregation of results). AR positivity was associated with lower risk of relapse in all breast cancer patients, and better overall survival in both univariate (U) and multivariate (M) analysis. AR prognostic impact in different subtypes was also assessed (see Table). Conclusions: Our analysis demonstrated that AR delivers prognostic information overall, serving as a positive prognostic factor in early stage BC. Further studies are needed to delineate its prognostic impact within the different subtypes of the disease. [Table: see text]

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Evaluation of Gene Expression Data From Cybrids and Tumours Highlights Elevated NDRG1-Driven Proliferation in Triple-Negative Breast Cancer

Breast Cancer Basic and Clinical Research ◽

10.1177/1178223420934447 ◽

2020 ◽

Vol 14 ◽

pp. 117822342093444

Author(s):

Akanksha Mishra ◽

Maria Bonello ◽

Adam Byron ◽

Simon P Langdon ◽

Andrew H Sims

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Gene Expression Data ◽

Triple Negative Breast Cancer ◽

Breast Cancer Cells ◽

Triple Negative ◽

Donor Cell ◽

Expression Data ◽

Donor Cells

Background: Triple-negative breast cancer is an aggressive type of breast cancer with high risk of recurrence. It is still poorly understood and lacks any targeted therapy, which makes it difficult to treat. Thus, it is important to understand the underlying mechanisms and pathways that are dysregulated in triple-negative breast cancer. Methods: To investigate the role of mitochondria in triple-negative breast cancer progression, we analysed previously reported gene expression data from triple-negative breast cancer cybrids with SUM-159 as the nuclear donor cell and SUM-159 or A1N4 (c-SUM-159, c-A1N4) as the mitochondrial donor cells and with 143B as the nuclear donor cell and MCF-10A or MDA-MB-231 (c-MCF-10A, c-MDA-MB-231) as the mitochondrial donor cells. The role of potential biomarkers in cell proliferation and migration was examined in SUM-159 and MDA-MB-231 cells using sulforhodamine B and wound healing assays. Results: Rank product analysis of cybrid gene expression data identified 149 genes which were significantly up-regulated in the cybrids with mitochondria from the cancer cell line. Analysis of previously reported breast tumour gene expression datasets confirmed 9 of the 149 genes were amplified, up-regulated, or down-regulated in more than 10% of the patients. The genes included NDRG1, PVT1, and EXT1, which are co-located in cytoband 8q24, which is frequently amplified in breast cancer. NDRG1 showed the largest down-regulation in the cybrids with benign mitochondria and was associated with poor prognosis in a breast cancer clinical dataset. Knockdown of NDRG1 expression significantly decreased proliferation of SUM-159 triple-negative breast cancer cells. Conclusions: These results indicate that mitochondria-regulated nuclear gene expression helps breast cancer cells survive and proliferate, consistent with previous work focusing on an Src gene signature which is mitochondria regulated and drives malignancy in breast cancer cybrids. This is the first study to show that mitochondria in triple-negative breast cancer mediate significant up-regulation of a number of genes, and silencing of NDRG1 leads to significant reduction in proliferation.

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Role of androgen receptor expression in early stage ER+/PgR−/HER2– breast cancer

Therapeutic Advances in Medical Oncology ◽

10.1177/1758835920958355 ◽

2020 ◽

Vol 12 ◽

pp. 175883592095835 ◽

Cited By ~ 1

Author(s):

Barbara Tagliaferri ◽

Erica Quaquarini ◽

Raffaella Palumbo ◽

Emanuela Balletti ◽

Daniele Presti ◽

...

Keyword(s):

Breast Cancer ◽

Androgen Receptor ◽

Early Stage ◽

Tumor Biology ◽

Growth Factor Receptor ◽

Primary Objective ◽

Receptor Expression ◽

Prognostic Role ◽

Her2 Breast Cancer

Background: Progesterone receptor (PgR) negative breast cancer (BC) is an aggressive subtype with poor prognosis and reduced response to endocrine treatments. Several studies have suggested that androgen receptor (AR) expression is associated with a favorable tumor biology, longer recurrence free survival (RFS), and overall survival. In the literature no data exist regarding the role of AR expression in early stage estrogen receptor (ER)+/PgR– BCs. The aim of this study was to evaluate the prognostic role of AR expression in this setting. Patients and methods: This is a monocentric retrospective study in which 208 patients who underwent surgical intervention for ER+/PgR−/Human Epidermal growth factor Receptor 2 (HER2)– BC were included. The primary objective was to analyze the relationship between AR expression and RFS. Results: At a median follow-up of 77 months, 75 patients (36%) had a disease relapse (all sites included). AR expression was significantly higher in patients who did not relapse compared with those who relapsed with an impact on RFS (hazard ratio [HR] = 0.99, p = 0.025). Patients with AR expression ⩾80% had a lower risk of relapse compared with those with AR <80% (HR = 0.53, p = 0.008). In addition, breast tumors with higher AR expression had good biological features (low ki67 and nuclear grade) compared with BCs with lower AR expression, at least partly explaining the different outcome. Conclusions: The results of this study support the potential prognostic role of AR in patients with ER+/PgR− BCs and may contribute to the identification of subgroups of high-risk patients.

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Genomic Machine Learning Model Predicts Radiation Therapy Benefit in Early-Stage Breast Cancer Patients with High Accuracy

10.21203/rs.3.rs-1209026/v1 ◽

2022 ◽

Author(s):

Kimberly Badal ◽

Jerome E. Foster ◽

Rajini Haraksingh ◽

Melford John

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Machine Learning ◽

Gene Expression Data ◽

Early Stage ◽

High Accuracy ◽

Expression Data ◽

Genome Wide ◽

Number Of Genes ◽

Genome Wide Gene Expression

Abstract BackgroundRadiation therapy (RT) is frequently recommended for post-surgery treatment of early-stage breast cancer (BC) patients, though not all benefit. Clinical factors currently guide RT treatment decisions. At present, models to predict RT-benefit predominantly use statistical methods with modest performance. In this paper we present a high-accuracy genomic Machine Learning (ML) model to predict RT-benefit in early-stage BC patients. We also present a novel method for selecting genomic features for training ML algorithms. MethodsGene expression data from 463 early-stage BC patients treated with surgery and RT from the METABRIC cohort were obtained. Wilcoxon Rank Sum (Wilcoxon RS) test and Cox Proportional Hazards (Cox PH) were used to reduce the number of genes used to train eight ML algorithms. ML algorithms were trained on 80% of data using 10-fold cross validation and tested on 20% of data to assess performance in predicting relapse status. Results Genome-wide gene expression data was reduced by 96% using Wilcoxon RS and Cox PH to a 1,596 gene set and a 977 gene set. These gene sets were used to train eight ML algorithms resulting in models that ranged in performance accuracies from 54.01% to 95.6%. Highest accuracies were obtained using Support Vector Machine (SVM977–93.41%, SVM1596–95.6%) and Neural Networks algorithms (NN977 – 92.31%, NN1596 – 93.41%). In RT-untreated patients, accuracies of all models were 30% to 40% lower compared to RT-treated patients. SVM977 had the highest sensitivity of 91.09%. Members of the 977 set were enriched with genes involved in cell cycle and differentiation as well as genes associated with radiosensitivity and radioresistance. Conclusion This study presents a novel genomic feature selection approach that used Wilcoxon RS followed by Cox PH to reduce the number of genes from genome-wide gene expression data used for training ML algorithms by 96%. This approach led to an SVM model that used the expression values of 977 genes to predict RT-benefit in early-stage BC patients with 93.41% accuracy. This work demonstrates that ML models can be clinically useful for predicting cancer patient outcomes.

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