Estrogen receptor negative/progesterone receptor positive breast cancer is not a reproducible subtype

Introduction: Estrogen receptor (ER) and progesterone receptor (PR) testing are performed in the evaluation of breast cancer. While the clinical utility of ER as a predictive biomarker to identify patients likely to benefit from hormonal therapy is well-established, the added value of PR is less well-defined. The primary goals of our study were to assess the distribution, inter-assay reproducibility, and prognostic significance of breast cancer subtypes defined by patterns of ER and PR expression. Methods: We integrated gene expression microarray (GEM) and clinico-pathologic data from 20 published studies to determine the frequency (n = 4,111) and inter-assay reproducibility (n = 1,752) of ER/PR subtypes (ER+/PR+, ER+/PR-, ER-/PR-, ER-/PR+). To extend our findings, we utilized a cohort of patients from the Nurses’ Health Study (NHS) with ER/PR data recorded in the medical record and assessed on tissue microarrays (n = 2,011). In both datasets, we assessed the association of ER and PR expression with survival. Results: In a genome-wide analysis, progesterone receptor was among the least variable genes in ER- breast cancer. The ER-/PR+ subtype was rare (approximately 1 to 4%) and showed no significant reproducibility (Kappa = 0.02 and 0.06, in the GEM and NHS datasets, respectively). The vast majority of patients classified as ER-/PR+ in the medical record (97% and 94%, in the GEM and NHS datasets) were re-classified by a second method. In the GEM dataset (n = 2,731), progesterone receptor mRNA expression was associated with prognosis in ER+ breast cancer (adjusted P <0.001), but not in ER- breast cancer (adjusted P = 0.21). PR protein expression did not contribute significant prognostic information to multivariate models considering ER and other standard clinico-pathologic features in the GEM or NHS datasets. Conclusion: ER-/PR+ breast cancer is not a reproducible subtype. PR expression is not associated with prognosis in ER- breast cancer, and PR does not contribute significant independent prognostic information to multivariate models considering ER and other standard clinico-pathologic factors. Given that PR provides no clinically actionable information in ER+ breast cancer, these findings question the utility of routine PR testing in breast cancer

Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth

While a genetic component of preterm birth (PTB) has long been recognized and recently mapped by genome-wide association studies (GWASs), the molecular determinants underlying PTB remain elusive. This stems in part from an incomplete availability of functional genomic annotations in human cell types relevant to pregnancy and PTB. We generated transcriptome (RNA-seq), epigenome (ChlP-seq of H3K27ac, H3K4mel, and H3K4me3 histone modifications), open chromatin (ATAC-seq), and chromatin interaction (promoter capture Hi-C) annotations of cultured primary decidua-derived mesenchymal stromal/stem cells and in vitro differentiated decidual stromal cells and developed a computational framework to integrate these functional annotations with results from a GWAS of gestational duration in 56,384 women. Using these resources, we uncovered additional loci associated with gestational duration and target genes of associated loci. Our strategy illustrates how functional annotations in pregnancy-relevant cell types aid in the experimental follow-up of GWAS for PTB and, likely, other pregnancy-related conditions.Peer reviewe

Meta-analysis of four new genome scans for lipid parameters and analysis of positional candidates in positive linkage regions

Lipid levels in plasma strongly influence the risk for coronary heart disease. To localise and subsequently identify genes affecting lipid levels, we performed four genome-wide linkage scans followed by combined linkage/association analysis. Genome-scans were performed in 701 dizygotic twin pairs from four samples with data on plasma levels of HDL- and LDL-cholesterol and their major protein constituents, apolipoprotein AI (ApoAI) and Apolipoprotein B (ApoB). To maximise power, the genome scans were analysed simultaneously using a well-established meta-analysis method that was newly applied to linkage analysis. Overall LOD scores were estimated using the means of the sample-specific quantitative trait locus (QTL) effects inversely weighted by the standard errors obtained using an inverse regression method. Possible heterogeneity was accounted for with a random effects model. Suggestive linkage for HDL-C was observed on 8p23.1 and 12q21.2 and for ApoAI on 1q21.3. For LDL-C and ApoB, linkage regions frequently coincided (2p24.1, 2q32.1, 19p13.2 and 19q13.31). Six of the putative QTLs replicated previous findings. After fine mapping, three maximum LOD scores mapped within 1cM of major candidate genes, namely APOB (LOD =2.1), LDLR (LOD =1.9) and APOE (LOD =1.7). APOB haplotypes explained 27% of the QTL effect observed for LDL-C on 2p24.1 and reduced the LOD-score by 0.82. Accounting for the effect of the LDLR and APOE haplotypes did not change the LOD score close to the LDLR gene but abolished the linkage signal at the APOE gene. In conclusion, application of a new meta-analysis approach maximised the power to detect QTLs for lipid levels and improved the precision of their location estimate. © 2005 Nature Publishing Group. All rights reserved

Sex Differences in Social Interaction Behavior Following Social Defeat Stress in the Monogamous California Mouse (Peromyscus californicus)

Stressful life experiences are known to be a precipitating factor for many mental disorders. The social defeat model induces behavioral responses in rodents (e.g. reduced social interaction) that are similar to behavioral patterns associated with mood disorders. The model has contributed to the discovery of novel mechanisms regulating behavioral responses to stress, but its utility has been largely limited to males. This is disadvantageous because most mood disorders have a higher incidence in women versus men. Male and female California mice (Peromyscus californicus) aggressively defend territories, which allowed us to observe the effects of social defeat in both sexes. In two experiments, mice were exposed to three social defeat or control episodes. Mice were then behaviorally phenotyped, and indirect markers of brain activity and corticosterone responses to a novel social stimulus were assessed. Sex differences in behavioral responses to social stress were long lasting (4 wks). Social defeat reduced social interaction responses in females but not males. In females, social defeat induced an increase in the number of phosphorylated CREB positive cells in the nucleus accumbens shell after exposure to a novel social stimulus. This effect of defeat was not observed in males. The effects of defeat in females were limited to social contexts, as there were no differences in exploratory behavior in the open field or light-dark box test. These data suggest that California mice could be a useful model for studying sex differences in behavioral responses to stress, particularly in neurobiological mechanisms that are involved with the regulation of social behavior

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci

On Estimating the Genetic Basis of Complex Traits and Their Molecular Intermediates

For a variant to have a causal effect on an organism-level trait, there must be a chain of causal events starting with the DNA-sequence, proceeding through one (or more often many) molecular intermediates in a functional pathway before it is observable at the organism level. The causal influence of a variant on a trait is, unfortunately, neither necessary nor sufficient for a variant to appear to be statistically associated with a trait in an association study, and spurious association of genotype and phenotype is not uncommon. It is for this reason that much of the hard work of a genetic association study begins after the association statistics have been generated. The true goal of the genetic association study is not simply to identify the genetic variation that most correlates with phenotype, but to try to identify the set of variants whose correlation with the trait of interest are driven by causal relationships, rather than by coincidence or confounding. In this dissertation I discuss three strategies for relating genotype to phenotype based on the results from genetic association studies. I first discuss the method FGEM, which combines the output from gene-basedassociation tests with gene-level annotation data to both estimate the enrichment of the annotations and re-prioritize genes based on those enrichment estimates. I find that FGEM's joint modeling of gene-level association data with gene-level annotation data is a powerful approach for identifying enriched pathways. Furthermore, I find that identification of enriched pathways can be used to identify additional causal genes. Next I describe my method for heritability estimation from summary statistics, RSSp. RSSp uses GWAS summary statistics and an estimate of pairwise Linkage Disequilibrium (LD) to estimate narrow-sense heritability. I find that RSSp estimates heritability in polygenic traits from GWAS summary statistics and a reference LD panel with accuracy comparable to in-sample methods. Finally, I discuss my efforts in discovering risk genes for preterm birth via fine-mapping GWAS summary statistics. I find that disease-relevant functional genomic annotations are useful for improving statistical fine-mapping. Using this approach I identified new genes not (directly) implicated from GWAS alone

Detailed modeling of positive selection improves detection of cancer driver genes

Identifying driver genes from somatic mutations is a central problem in cancer biology. Existing methods, however, either lack explicit statistical models, or use models based on simplistic assumptions. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a model-based approach to driver gene identification. This method explicitly models positive selection at the single-base level, as well as highly heterogeneous background mutational processes. In particular, the selection model captures elevated mutation rates in functionally important sites using multiple external annotations, and spatial clustering of mutations. Simulations under realistic evolutionary models demonstrate the increased power of driverMAPS over current approaches. Applying driverMAPS to TCGA data of 20 tumor types, we identified 159 new potential driver genes, including the mRNA methyltransferase METTL3-METTL14. We experimentally validated METTL3 as a tumor suppressor gene in bladder cancer, providing support to the important role mRNA modification plays in tumorigenesis