The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer

Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., ~140–160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual’s CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with CRC before the question of the potential utility of germline genomic profiling can be definitively answered

Changes in bone turnover markers with HIV seroconversion and ART initiation.

BackgroundOsteoporosis is common among HIV-infected persons and contributes to risk of fragility fracture. While ART initiation is associated with decreases in bone mineral density and increases in bone turnover, the impact of HIV on bone metabolism is unclear.MethodsWe identified men at the Chicago site of the Multicenter AIDS Cohort Study who HIV seroconverted while under observation. Concentrations of 25-OH vitamin D, bone turnover markers [procollagen type 1 N terminal propeptide (P1NP), osteocalcin (OC), C-telopeptide (CTX)] and sclerostin were measured from stored serum obtained at pre-HIV infection, pre-ART and post-ART initiation timepoints. Mixed models, with each biomarker as an outcome, were fitted. Timepoint, age, CD4 count (cells/mm 3 ), HIV-viral suppression, season and an age by timepoint interaction term were considered as fixed effects.ResultsData from 52 participants revealed that median duration between HIV seroconversion and ART initiation was 8.7 years (IQR 3.7-11.6). Median CD4 and plasma HIV-RNA concentrations were 445 (IQR 298.5-689) and 20 184 copies/mL (IQR 6237-64 340), respectively, at the pre-ART timepoint. Multivariate analyses demonstrated pre-HIV infection levels of OC that were higher than pre-ART levels (6.8 versus 5.7 ng/mL, P  =   0.04); and pre-ART levels of sclerostin that were higher than post-ART levels (0.033 versus 0.02 ng/mL, P  <0.001). No changes in P1NP, CTX and 25-OH vitamin D levels were detected.ConclusionsHIV seroconversion was associated with decreased OC levels while ART initiation was associated with decreases in sclerostin, a negative regulator of bone formation. Our results suggest that both HIV infection and ART have an impact on bone metabolism in white men

Longitudinal multivariate normative comparisons

Motivated by the Multicenter AIDS Cohort Study (MACS), we develop classification procedures for cognitive impairment based on longitudinal measures. To control family-wise error, we adapt the cross-sectional multivariate normative comparisons (MNC) method to the longitudinal setting. The cross-sectional MNC was proposed to control family-wise error by measuring the distance between multiple domain scores of a participant and the norms of healthy controls and specifically accounting for intercorrelations among all domain scores. However, in a longitudinal setting where domain scores are recorded multiple times, applying the cross-sectional MNC at each visit will still have inflated family-wise error rate due to multiple testing over repeated visits. Thus, we propose longitudinal MNC procedures that are constructed based on multivariate mixed effects models. A χ2 test procedure is adapted from the cross-sectional MNC to classify impairment on longitudinal multivariate normal data. Meanwhile, a permutation procedure is proposed to handle skewed data. Through simulations we show that our methods can effectively control family-wise error at a predetermined level. A dataset from a neuropsychological substudy of the MACS is used to illustrate the applications of our proposed classification procedures