Dataset Information

Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling.

ABSTRACT: BACKGROUND:Genome-wide miRNA expression may be useful for predicting breast cancer risk and/or for the early detection of breast cancer. RESULTS:A 41-miRNA model distinguished breast cancer risk in the discovery study (accuracy of 83.3%), which was replicated in the independent study (accuracy = 63.4%, P=0.09). Among the 41 miRNA, 20 miRNAs were detectable in serum, and predicted breast cancer occurrence within 18 months of blood draw (accuracy 53%, P=0.06). These risk-related miRNAs were enriched for HER-2 and estrogen-dependent breast cancer signaling. MATERIALS AND METHODS:MiRNAs were assessed in two cross-sectional studies of women without breast cancer and a nested case-control study of breast cancer. Using breast tissues, a multivariate analysis was used to model women with high and low breast cancer risk (based upon Gail risk model) in a discovery study of women without breast cancer (n=90), and applied to an independent replication study (n=71). The model was then assessed using serum samples from the nested case-control study (n=410). CONCLUSIONS:Studying breast tissues of women without breast cancer revealed miRNAs correlated with breast cancer risk, which were then found to be altered in the serum of women who later developed breast cancer. These results serve as proof-of-principle that miRNAs in women without breast cancer may be useful for predicting breast cancer risk and/or as an adjunct for breast cancer early detection. The miRNAs identified herein may be involved in breast carcinogenic pathways because they were first identified in the breast tissues of healthy women.

SUBMITTER: Taslim C

PROVIDER: S-EPMC5349926 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling.

Taslim Cenny C Weng Daniel Y DY Brasky Theodore M TM Dumitrescu Ramona G RG Huang Kun K Kallakury Bhaskar V S BV Krishnan Shiva S Llanos Adana A AA Marian Catalin C McElroy Joseph J Schneider Sallie S SS Spear Scott L SL Troester Melissa A MA Freudenheim Jo L JL Geyer Susan S Shields Peter G PG

Oncotarget 20161201 52

<h4>Background</h4>Genome-wide miRNA expression may be useful for predicting breast cancer risk and/or for the early detection of breast cancer.<h4>Results</h4>A 41-miRNA model distinguished breast cancer risk in the discovery study (accuracy of 83.3%), which was replicated in the independent study (accuracy = 63.4%, P=0.09). Among the 41 miRNA, 20 miRNAs were detectable in serum, and predicted breast cancer occurrence within 18 months of blood draw (accuracy 53%, P=0.06). These risk-related miR ...[more]

PMID: 27833082

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Project description:MicroRNAs (miRNAs) are known to be deregulated in human breast cancer (BC). The purpose of the current study was to investigate the expression of miRNAs in different stages of BC to assess their biological value in BC progression. MiRNA expression was assessed in a series of BC patients (n=7) with distinct stages of tumour progression (Normal, in-situ (DCIS), primary invasive BC and nodal metastases) to evaluate miRNA differential expression. We used an Agilent miRNA microarray based platform which uses miRBase 16 to screen for 1205 Homo sapiens (hsa) and 144 human viral miRNA candidates. To validate the microarray data, the expression of two deregulated miRNAs was measured by TaqMan quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). This study was conducted Formalin Fixed and Paraffin Embedded (FFPE) specimens from 7 female patients. Twenty μm thick FFPE sections were cut and mounted on PALM Membrane Slides. Under direct microscopic visualization, tissues of interest were micro-dissected using PALM non-contact Laser catapulsion instrument (P.A.L.M. Microlaser Technologies, Carl Zeiss Ltd). Total RNA, including miRNAs, was extracted with the TRIzol reagent (Invitrogen, supplied by Fisher Scientific UK Ltd) according to the manufacturer’s instructions. Total RNA was quantified with Quant-iT RiboGreen RNA Quantitation Kit (Fisher Scientific UK Ltd), which is an accurate method of measuring total RNA with a detection limit of 0.001-1ng/µl. RNA purity and RNA integrity number (RIN) were determined on an Agilent 2100 Bioanalyzer and RNA 6000 NanoLabChip Kits (both Agilent Technologies, Santa Clara, USA), and the RIN of all the samples was >2 but <3. This is expected for FFPE breast samples and is acceptable by Agilent Technologies, Santa Clara, USA, for FFPE samples to undergo miRNA microarray analysis. MiRNAs extracted from laser microdissected tissue components were profiled using Agilent microRNA microarray profiling system (Agilent Technologies, Santa Clara, USA). Total RNA samples were spiked using the MicroRNA Spike-In Kit (Agilent Technologies, Santa Clara, USA) to evaluate efficiencies of labelling and hybridisation. Total RNA was treated with Calf Intestinal Alkaline Phosphatase (CIP), and then 100 ng of total RNA was used per sample to initiate a labelling reaction. Ligation master mix for T4 RNA ligase, which includes Cyanine 3-Cytidine biphosphate (Cyanine 3-pCp) (Complete miRNA Labelling and Hyb Kit, Agilent Technologies, Santa Clara, USA) was used to label the dephosphorylated RNA. Cyanine-3-labelled miRNA samples were hybridised to human miRNA microarrays (Release 16.0, 8x60K) (Agilent Technologies, Santa Clara, USA) at 55°C for 20 hrs. Microarray slides were washed with increasing stringency (Gene Expression Wash Buffers, Agilent Technologies, Santa Clara), and subsequently dried with acetonitrile (Sigma-Aldrich, St. Louis, USA). Fluorescent signal intensities were detected on an Agilent Microarray Scanner (Agilent Technologies, Santa Clara, USA) using the Scan Control A.8.4.1 Software (Agilent Technologies, Santa Clara, USA) and extracted from the images with the Feature Extraction 10.7.3.1 Software (Agilent Technologies, Santa Clara, USA).

Project description:BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

Dataset Information

Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling.

Publications

Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling.

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