Race-associated biological differences among luminal A and basal-like breast cancers in the Carolina Breast Cancer Study

Abstract Background We examined racial differences in the expression of eight genes and their associations with risk of recurrence among 478 white and 495 black women who participated in the Carolina Breast Cancer Study Phase 3. Methods Breast tumor samples were analyzed for PAM50 subtype and for eight genes previously found to be differentially expressed by race and associated with breast cancer survival: ACOX2, MUC1, FAM177A1, GSTT2, PSPH, PSPHL, SQLE, and TYMS. The expression of these genes according to race was assessed using linear regression and each gene was evaluated in association with recurrence using Cox regression. Results Compared to white women, black women had lower expression of MUC1, a suspected good prognosis gene, and higher expression of GSTT2, PSPHL, SQLE, and TYMS, suspected poor prognosis genes, after adjustment for age and PAM50 subtype. High expression (greater than median versus less than or equal to median) of FAM177A1 and PSPH was associated with a 63% increase (hazard ratio (HR) = 1.63, 95% confidence interval (CI) = 1.09–2.46) and 76% increase (HR = 1.76, 95% CI = 1.15–2.68), respectively, in risk of recurrence after adjustment for age, race, PAM50 subtype, and ROR-PT score. Log2-transformed SQLE expression was associated with a 20% increase (HR = 1.20, 95% CI = 1.03–1.41) in recurrence risk after adjustment. A continuous multi-gene score comprised of eight genes was also associated with increased risk of recurrence among all women (HR = 1.11, 95% CI = 1.04–1.19) and among white (HR = 1.14, 95% CI = 1.03–1.27) and black (HR = 1.11, 95% CI = 1.02–1.20) women. Conclusions Racial differences in gene expression may contribute to the survival disparity observed between black and white women diagnosed with breast cancer

Race-associated biological differences among luminal A and basal-like breast cancers in the Carolina Breast Cancer Study

Abstract Background We examined racial differences in the expression of eight genes and their associations with risk of recurrence among 478 white and 495 black women who participated in the Carolina Breast Cancer Study Phase 3. Methods Breast tumor samples were analyzed for PAM50 subtype and for eight genes previously found to be differentially expressed by race and associated with breast cancer survival: ACOX2, MUC1, FAM177A1, GSTT2, PSPH, PSPHL, SQLE, and TYMS. The expression of these genes according to race was assessed using linear regression and each gene was evaluated in association with recurrence using Cox regression. Results Compared to white women, black women had lower expression of MUC1, a suspected good prognosis gene, and higher expression of GSTT2, PSPHL, SQLE, and TYMS, suspected poor prognosis genes, after adjustment for age and PAM50 subtype. High expression (greater than median versus less than or equal to median) of FAM177A1 and PSPH was associated with a 63% increase (hazard ratio (HR) = 1.63, 95% confidence interval (CI) = 1.09–2.46) and 76% increase (HR = 1.76, 95% CI = 1.15–2.68), respectively, in risk of recurrence after adjustment for age, race, PAM50 subtype, and ROR-PT score. Log2-transformed SQLE expression was associated with a 20% increase (HR = 1.20, 95% CI = 1.03–1.41) in recurrence risk after adjustment. A continuous multi-gene score comprised of eight genes was also associated with increased risk of recurrence among all women (HR = 1.11, 95% CI = 1.04–1.19) and among white (HR = 1.14, 95% CI = 1.03–1.27) and black (HR = 1.11, 95% CI = 1.02–1.20) women. Conclusions Racial differences in gene expression may contribute to the survival disparity observed between black and white women diagnosed with breast cancer

Data-driven asthma endotypes defined from blood biomarker and gene expression data.

The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes) driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels

Four distinct asthma endotypes identified by data-driven integration of blood gene expression and clinical biomarkers.

<p>Underlying mechanistic information is suggestive of metabolic syndrome and potential biomarkers.</p

Mechanistic interpretation of the decision tree.

<p>Cellular drivers were determined by using linear regression as described in the methods and summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s010" target="_blank">S1 Table</a>. The results are summarized in green boxes. Gene expression changes were interpreted using Ingenuity Pathway Analysis (IPA). The top networks from IPA are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s011" target="_blank">S2 Table</a> along with their significance scores. All networks that were considered as part of the functional interpretation are included as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s002" target="_blank">S2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s009" target="_blank">S9</a> Figs. The final functional summaries from this analysis are shown in blue boxes. Clinical biomarkers (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s012" target="_blank">S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s013" target="_blank">S4</a> Tables) correlated with the key genes from each metagene are shown in the purple boxes; atopy is based on allergen-specific IgE levels (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s012" target="_blank">S3 Table</a>, Phadiatop) and IgE represents total serum IgE. (A) K-PC1, no IPA network (B) B-PC2, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s003" target="_blank">S3 Fig</a>. (C) C-PC2, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s004" target="_blank">S4 Fig</a>. (D) B-PC1, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s002" target="_blank">S2 Fig</a>. (E) J-PC2, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s008" target="_blank">S8</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s009" target="_blank">S9</a> Figs. (F) F-PC2, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s007" target="_blank">S7 Fig</a>. (G) E-PC2, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s005" target="_blank">S5</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117445#pone.0117445.s006" target="_blank">S6</a> Figs.</p

Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium.

Pre-pregnancy maternal obesity is associated with adverse offspring outcomes at birth and later in life. Individual studies have shown that epigenetic modifications such as DNA methylation could contribute. Within the Pregnancy and Childhood Epigenetics (PACE) Consortium, we meta-analysed the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs). We attempted to infer causality by comparing the effects of maternal versus paternal BMI and incorporating genetic variation. In four additional cohorts (1,817 mother-child pairs), we meta-analysed the association between maternal BMI at the start of pregnancy and blood methylation in adolescents. In newborns, maternal BMI was associated with small (&lt;0.2% per BMI unit (1 kg/m2), P &lt; 1.06 × 10-7) methylation variation at 9,044 sites throughout the genome. Adjustment for estimated cell proportions greatly attenuated the number of significant CpGs to 104, including 86 sites common to the unadjusted model. At 72/86 sites, the direction of the association was the same in newborns and adolescents, suggesting persistence of signals. However, we found evidence for acausal intrauterine effect of maternal BMI on newborn methylation at just 8/86 sites. In conclusion, this well-powered analysis identified robust associations between maternal adiposity and variations in newborn blood DNA methylation, but these small effects may be better explained by genetic or lifestyle factors than a causal intrauterine mechanism. This highlights the need for large-scale collaborative approaches and the application of causal inference techniques in epigenetic epidemiology