Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis.
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ABSTRACT:
SUBMITTER: Pang H
PROVIDER: S-EPMC3042882 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:High-dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k-nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage-based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.
| S-EPMC2794982 | biostudies-literature
Project description:Genome-wide association studies (GWAS) and candidate gene analyses have led to the discovery of several dozen genetic polymorphisms associated with breast cancer susceptibility, many of which are considered well-established risk factors for the disease. Despite attempts to replicate these same variant-disease associations in African Americans, the evaluable populations are often too small to produce precise or consistent results. We estimated the associations between 83 previously identified single nucleotide polymorphisms (SNPs) and breast cancer among Carolina Breast Cancer Study (1993-2001) participants using maximum likelihood, Bayesian, and hierarchical methods. The selected SNPs were previous GWAS hits (n = 22), near-hits (n = 19), otherwise well-established risk loci (n = 5), or located in the same genes as selected variants (n = 37). We successfully replicated 18 GWAS-identified SNPs in whites (n = 2,352) and 10 in African Americans (n = 1,447). SNPs in the fibroblast growth factor receptor 2 gene (FGFR2) and the TOC high mobility group box family member 3 gene (TOX3) were strongly associated with breast cancer in both races. SNPs in the mitochondrial ribosomal protein S30 gene (MRPS30), mitogen-activated protein kinase kinase kinase 1 gene (MAP3K1), zinc finger, MIZ-type containing 1 gene (ZMIZ1), and H19, imprinted maternally expressed transcript gene (H19) were associated with breast cancer in whites, and SNPs in the estrogen receptor 1 gene (ESR1) and H19 gene were associated with breast cancer in African Americans. We provide precise and well-informed race-stratified odds ratios for key breast cancer-related SNPs. Our results demonstrate the utility of Bayesian methods in genetic epidemiology and provide support for their application in small, etiologically driven investigations.
| S-EPMC3895095 | biostudies-literature
Project description:BACKGROUND:Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. RESULTS:In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. CONCLUSION:This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.
| S-EPMC2459189 | biostudies-literature
Project description:Background:African American men (AAM) are at higher risk of being diagnosed with prostate cancer (PCa) and are at higher risk of dying from the disease compared to European American men (EAM). We sought to better understand PCa molecular diversity that may be underlying these disparities. We ran RNA-sequencing data analysis on high-grade PCa to identify genes showing differential tumor versus normal adjacent tissue expression patterns unique to AAM or EAM.
2018-05-18 | GSE104131 | GEO
Project description:Genome-wide association studies (GWAS) have revealed 19 common genetic variants that are associated with breast cancer risk. Testing of the index signals found through GWAS and fine-mapping of each locus in diverse populations will be necessary for characterizing the role of these risk regions in contributing to inherited susceptibility. In this large study of breast cancer in African-American women (3016 cases and 2745 controls), we tested the 19 known risk variants identified by GWAS and replicated associations (P < 0.05) with only 4 variants. Through fine-mapping, we identified markers in four regions that better capture the association with breast cancer risk in African Americans as defined by the index signal (2q35, 5q11, 10q26 and 19p13). We also identified statistically significant associations with markers in four separate regions (8q24, 10q22, 11q13 and 16q12) that are independent of the index signals and may represent putative novel risk variants. In aggregate, the more informative markers found in the study enhance the association of these risk regions with breast cancer in African Americans [per allele odds ratio (OR) = 1.18, P = 2.8 × 10(-24) versus OR = 1.04, P = 6.1 × 10(-5)]. In this detailed analysis of the known breast cancer risk loci, we have validated and improved upon markers of risk that better characterize their association with breast cancer in women of African ancestry.
| S-EPMC3196889 | biostudies-literature
Project description:While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations.
| S-EPMC3080860 | biostudies-literature
Project description:Translational Relevance Historically, African Americans have been underrepresented in clinical cancer research. Diversity helps to ensure equal access to new cancer therapies and better treatment for everyone. Cancer research is increasingly focused on classifying patients according to molecular profiles for particular groups. We provide a detailed molecular analysis from paired NSCLC tissues that identified differential coding and noncoding RNA expression in NSCLC from African Americans (AA) and European Americans (EA). Similar to other tumor types, we determined that race-enriched gene and microRNA expression signatures suggest a more aggressive disease in African Americans. Based on predicted drug resistance to adjuvant chemotherapies, AA may not equally benefit from the same range of clinical drugs as EA. Our findings provide a rationale for integrating coding and noncoding transcriptome profiles, along with clinical, demographic, and genomic data, when determining treatment options. Abstract Purpose: To determine if racial differences in gene and microRNA expression translates to differences in lung tumor biology with clinical relevance in African Americans (AA) and European Americans (EA). Experimental Design: The NCI-Maryland Case Control Study includes seven Baltimore City hospitals and is overrepresented with AA patients (~40%). Patients that underwent curative NSCLC surgery between 1998 and 2014 were enrolled. Comparative molecular profiling used mRNA (n = 22 AAs and n = 19 EAs) and microRNA (n = 42 AAs and n = 55 EAs) expression arrays to track differences in paired fresh frozen normal tissues and lung tumor specimens from AA and EA. Pathway enrichment, predicted drug response, tumor microenvironment infiltration, cancer immunotherapy antigen profiling, and microRNA target enrichment were assessed. Results: AA-enriched differential gene expression was characterized by stem-cell and invasion pathways. Differential gene expression in lung tumors from EA were primarily characterized by cell proliferation pathways. Population-specific gene expression was partly driven by population-specific miRNA expression profiles. Drug susceptibility predictions revealed a strong inverse correlation between AA resistance and EA sensitivity to the same panel of drugs. Statistically significant differences in M1 and M2 macrophage infiltration was observed in AA (P <0.05), however, PD-L1, PD-L2 expression was similar between both. Conclusions: Comparative transcriptomic profiling revealed clear differences in lung tumor biology between AA and EA. Increased participation by AA in lung cancer clinical trials are needed to integrate, and leverage, transcriptomic differences with other clinical information to maximize therapeutic benefit in both AA and EA. GSEA, Connectivity Map, CIBERSORT, cancer immunotherapy antigen profiling, and hypergeometric testing for overlapping miRNA targets were performed.
2017-07-28 | GSE101929 | GEO
Project description:Translational Relevance Historically, African Americans have been underrepresented in clinical cancer research. Diversity helps to ensure equal access to new cancer therapies and better treatment for everyone. Cancer research is increasingly focused on classifying patients according to molecular profiles for particular groups. We provide a detailed molecular analysis from paired NSCLC tissues that identified differential coding and noncoding RNA expression in NSCLC from African Americans (AA) and European Americans (EA). Similar to other tumor types, we determined that race-enriched gene and microRNA expression signatures suggest a more aggressive disease in African Americans. Based on predicted drug resistance to adjuvant chemotherapies, AA may not equally benefit from the same range of clinical drugs as EA. Our findings provide a rationale for integrating coding and noncoding transcriptome profiles, along with clinical, demographic, and genomic data, when determining treatment options. Abstract Purpose: To determine if racial differences in gene and microRNA expression translates to differences in lung tumor biology with clinical relevance in African Americans (AA) and European Americans (EA). Experimental Design: The NCI-Maryland Case Control Study includes seven Baltimore City hospitals and is overrepresented with AA patients (~40%). Patients that underwent curative NSCLC surgery between 1998 and 2014 were enrolled. Comparative molecular profiling used mRNA (n = 22 AAs and n = 19 EAs) and microRNA (n = 42 AAs and n = 55 EAs) expression arrays to track differences in paired fresh frozen normal tissues and lung tumor specimens from AA and EA. Pathway enrichment, predicted drug response, tumor microenvironment infiltration, cancer immunotherapy antigen profiling, and microRNA target enrichment were assessed. Results: AA-enriched differential gene expression was characterized by stem-cell and invasion pathways. Differential gene expression in lung tumors from EA were primarily characterized by cell proliferation pathways. Population-specific gene expression was partly driven by population-specific miRNA expression profiles. Drug susceptibility predictions revealed a strong inverse correlation between AA resistance and EA sensitivity to the same panel of drugs. Statistically significant differences in M1 and M2 macrophage infiltration was observed in AA (P <0.05), however, PD-L1, PD-L2 expression was similar between both. Conclusions: Comparative transcriptomic profiling revealed clear differences in lung tumor biology between AA and EA. Increased participation by AA in lung cancer clinical trials are needed to integrate, and leverage, transcriptomic differences with other clinical information to maximize therapeutic benefit in both AA and EA.
2017-08-04 | GSE102286 | GEO
Project description:BackgroundMicroarrays offer great potential as a platform for molecular diagnostics, testing clinical samples for the presence of numerous biomarkers in highly multiplexed assays. In this study applied to infectious diseases, data from a microarray designed for molecular serotyping of Streptococcus pneumoniae was used, identifying the presence of any one of 91 known pneumococcal serotypes from DNA extracts. This microarray incorporated oligonucleotide probes for all known capsular polysaccharide synthesis genes and required a statistical analysis of the microarray intensity data to determine which serotype, or combination of serotypes, were present within a sample based on the combination of genes detected.ResultsWe propose an empirical Bayesian model for calculating the probabilities of combinations of serotypes from the microarray data. The model takes into consideration the dependencies between serotypes, induced by genes they have in common, and by homologous genes which, although not identical, are similar to each other in sequence. For serotypes which are very similar in capsular gene composition, extra probes are included on the microarray, providing additional information which is integrated into the Bayesian model. For each serotype combination with high probability, a second model, a Bayesian random effects model is applied to determine the relative abundance of each serotype.ConclusionsTo assess the accuracy of the proposed analysis we applied our methods to experimental data from samples containing individual serotypes and samples containing combinations of serotypes with known levels of abundance. All but two of the known serotypes of S. pneumoniae that were tested as individual samples could be uniquely determined by the Bayesian model. The model also enabled the presence of combinations of serotypes within samples to be determined. Serotypes with very low abundance within a combination of serotypes can be detected (down to 2% abundance in this study). As well as detecting the presence of serotype combinations, an approximate measure of the percentage abundance of the serotypes within the combination can be obtained.
| S-EPMC3076268 | biostudies-literature
Project description:IntroductionToll-like receptor 9 (TLR9) is an innate immune system DNA-receptor that regulates tumor invasion and immunity in vitro. Low tumor TLR9 expression has been associated with poor survival in Caucasian patients with triple negative breast cancer (TNBC). African American (AA) patients with TNBC have worse prognosis than Caucasians but whether this is due to differences in tumor biology remains controversial. We studied the prognostic significance of tumor Toll like receptor-9 (TLR9) protein expression among African American (AA) triple negative breast cancer (TNBC) patients. Germline TLR9 variants in European Americans (EAs) and AAs were investigated, to determine their contribution to AA breast cancer risk.MethodsTLR9 expression was studied with immunohistochemistry in archival tumors. Exome Variant Server and The Cancer Genome Atlas were used to determine the genetic variation in the general EA and AA populations, and AA breast cancer cases. Minor allele frequencies (MAFs) were compared between EAs (n = 4300), AAs (n = 2203), and/or AA breast cancer cases (n = 131).ResultsThirty-two TLR9 variants had a statistically significant MAF difference between general EAs and AAs. Twenty-one of them affect a CpG site. Rs352140, a variant previously associated with protection from breast cancer, is more common in EAs than AAs (p = 2.20E-16). EAs had more synonymous alleles, while AAs had more rare coding alleles. Similar analyses comparing AA breast cancer cases with AA controls did not reveal any variant class differences; however, three previously unreported TLR9 variants were associated with late onset breast cancer. Although not statistically significant, rs352140 was observed less frequently in AA cases compared to controls. Tumor TLR9 protein expression was not associated with prognosis.ConclusionsTumor TLR9 expression is not associated with prognosis in AA TNBC. Significant differences were detected in TLR9 variant MAFs between EAs and AAs. They may affect TLR9 expression and function. Rs352140, which may protect from breast cancer, is 1.6 X more common among EAs. These findings call for a detailed analysis of the contribution of TLR9 to breast cancer pathophysiology and health disparities.
| S-EPMC5590816 | biostudies-literature