Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase

Prospective Evaluation of the Impact of Stress, Anxiety, and Depression on Household Income among Young Women with Early Breast Cancer from the Young and Strong Trial

Background: Young women with breast cancer tend to report lower quality of life and higher levels of stress than older women with breast cancer, and this may have implications for other psychosocial factors including finances. We sought to determine if stress, anxiety, and depression at diagnosis were associated with changes in household income over 12-months in young women with breast cancer in the United States. Methods: This study was a prospective, longitudinal cohort study comprised of women enrolled in the Young and Strong trial. Of the 467 women aged 18–45 newly diagnosed with early-stage breast cancer enrolled in the Young and Strong trial from 2012 to 2013, 356 (76%) answered income questions. Change in household income from baseline to 12 months was assessed and women were categorized as having lost, gained, maintained the same household income \u3c$100,000, or maintained household income ≥$100,000. Patient-reported stress, anxiety, and depression were assessed close to diagnosis at trial enrollment. Adjusted multinomial logistic regression models were used to compare women who lost, gained, or maintained household income ≥$100,000 to women who maintained the same household income \u3c$100,000. Results: Although most women maintained household income ≥$100,000 (37.1$100,000 (32.3%), 15.4% lost household income and 15.2% gained household income. Stress, anxiety, and depression were not associated with gaining or losing household income compared to women maintaining household incomes \u3c$100,000. Women with household incomes \u3c$50,000 had a higher risk of losing household income compared to women with household incomes ≥$50,000. Women who maintained household incomes ≥$100,000 were less likely to report financial or insurance problems. Among women who lost household income, 56% reported financial problems and 20% reported insurance problems at 12 months. Conclusions: Baseline stress, anxiety, and depression were not associated with household income changes for young women with breast cancer. However, lower baseline household income was associated with losing household income. Some young survivors encounter financial and insurance problems in the first year after diagnosis, and further support for these women should be considered

Survival analysis modeling with hidden censoring

There are well-established survival analysis methodologies for data sets that are complete, with accurate information on censoring. But what if they are not complete? In this article we consider how to analyze cases where “hidden censoring” occurs, where individuals have effectively left the study but the hospital is unaware of this. We develop a new Markov chain-based methodology for generating survival curves and hazard functions, and demonstrate this using a breast cancer data set from the Kurdistan region of Iraq

P04.74 Preclinical evaluation of combinations targeting the DNA damage response in 2D and 3D models of glioblastoma stem cells

Background Despite surgical resection followed by DNA-damaging adjuvant therapies, glioblastoma remain incurable. Increasing evidence demonstrates that aberrations within the DNA damage response (DDR) of cancer stem cells contribute to treatment resistance. We have previously shown that the Fanconi Anaemia (FA) pathway, a key DDR process, remains inactive in normal brain but is re-activated in glioblastoma, making it an appealing foundational target for cancer-specific combination therapies. Since intratumoural heterogeneity in glioblastoma and inherent capacity for functional redundancy within DDR networks are established concepts - we aimed to determine whether combined and hypothesis-driven targeting of the FA pathway along with interconnected DDR processes could form a basis for effective multimodal therapies. Material and Methods Bioinformatic analysis of mRNA expression data (REMBRANDT database) was used to confirm the relevance of FA pathway activity in glioma. Subsequently, immunofluorescence and cell viability assays were used to validate and establish the therapeutic potential of novel FA pathway inhibitors (nFAPi) and inhibition of related DDR targets in established cell models. Finally, combinations targeting the DDR were optimised using immunoblotting, and assessed using clonogenic survival in 2D and novel 3D patient-derived glioblastoma stem cell models. Results High expression of downstream FA pathway genes is strongly associated with poor survival (-17.1% 5-year OS, n=329, Log-rank, P Conclusion Simultaneously targeting the FA pathway and interconnected DDR processes in glioblastoma represents a promising therapeutic strategy. Early mechanistic studies suggest this approach augments DNA damage and enhances IR-induced cell cycle arrest in G2/M, however further preclinical evaluation is ongoing

Sex steroids, growth factors and mammographic density: a cross-sectional study of UK postmenopausal Caucasian and Afro-Caribbean women

INTRODUCTION: Sex steroids, insulin-like growth factors (IGFs) and prolactin are breast cancer risk factors but whether their effects are mediated through mammographic density, one of the strongest risk factors for breast cancer, is unknown. If such a hormonal basis of mammographic density exists, hormones may underlie ethnic differences in both mammographic density and breast cancer incidence rates. METHODS: In a cross-sectional study of 270 postmenopausal Caucasian and Afro-Caribbean women attending a population-based breast screening service in London, UK, we investigated whether plasma biomarkers (oestradiol, oestrone, sex hormone binding globulin (SHBG), testosterone, prolactin, leptin, IGF-I, IGF-II and IGF binding protein 3 (IGFBP3)) were related to and explained ethnic differences in mammographic percent density, dense area and nondense area, measured in Cumulus using the threshold method. RESULTS: Mean levels of oestrogens, leptin and IGF-I:IGFBP3 were higher whereas SHBG and IGF-II:IGFBP3 were lower in Afro-Caribbean women compared with Caucasian women after adjustment for higher mean body mass index (BMI) in the former group (by 3.2 kg/m(2) (95% confidence interval (CI): 1.8, 4.5)). Age-adjusted percent density was lower in Afro-Caribbean compared with Caucasian women by 5.4% (absolute difference), but was attenuated to 2.5% (95% CI: -0.2, 5.1) upon BMI adjustment. Despite ethnic differences in biomarkers and in percent density, strong ethnic-age-adjusted inverse associations of oestradiol, leptin and testosterone with percent density were completely attenuated upon adjustment for BMI. There were no associations of IGF-I, IGF-II or IGFBP3 with percent density or dense area. We found weak evidence that a twofold increase in prolactin and oestrone levels were associated, respectively, with an increase (by 1.7% (95% CI: -0.3, 3.7)) and a decrease (by 2.0% (95% CI: 0, 4.1)) in density after adjustment for BMI. CONCLUSIONS: These findings suggest that sex hormone and IGF levels are not associated with BMI-adjusted percent mammographic density in cross-sectional analyses of postmenopausal women and thus do not explain ethnic differences in density. Mammographic density may still, however, be influenced by much higher premenopausal hormone levels

Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies

Objective: To examine the interactions between genetic predisposition and consumption of fried food in relation to body mass index (BMI) and obesity. Design: Prospective cohort study. Setting: Health professionals in the United States. Participants: 9623 women from the Nurses’ Health Study, 6379 men from the Health Professionals Follow-up Study, and a replication cohort of 21 421 women from the Women’s Genome Health Study. Main outcome measure Repeated measurement of BMI over follow-up. Results: There was an interaction between fried food consumption and a genetic risk score based on 32 BMI-associated variants on BMI in both the Nurses’ Health Study and Health Professionals Follow-up Study (P≤0.001 for interaction). Among participants in the highest third of the genetic risk score, the differences in BMI between individuals who consumed fried foods four or more times a week and those who consumed fried foods less than once a week amounted to 1.0 (SE 0.2) in women and 0.7 (SE 0.2) in men, whereas the corresponding differences were 0.5 (SE 0.2) and 0.4 (SE 0.2) in the lowest third of the genetic risk score. The gene-diet interaction was replicated in the Women’s Genome Health Study (P<0.001 for interaction). Viewed differently, the genetic association with adiposity was strengthened with higher consumption of fried foods. In the combined three cohorts, the differences in BMI per 10 risk alleles were 1.1 (SE 0.2), 1.6 (SE 0.3), and 2.2 (SE 0.6) for fried food consumption less than once, one to three times, and four or more times a week (P<0.001 for interaction); and the odds ratios (95% confidence intervals) for obesity per 10 risk alleles were 1.61 (1.40 to 1.87), 2.12 (1.73 to 2.59), and 2.72 (2.12 to 3.48) across the three categories of consumption (P=0.002 for interaction). In addition, the variants in or near genes highly expressed or known to act in the central nervous system showed significant interactions with fried food consumption, with the FTO (fat mass and obesity associated) variant showing the strongest result (P<0.001 for interaction). Conclusion: Our findings suggest that consumption of fried food could interact with genetic background in relation to obesity, highlighting the particular importance of reducing fried food consumption in individuals genetically predisposed to obesity