Rare copy number variants contribute to congenital left-sided heart disease

Left-sided congenital heart disease (CHD) encompasses a spectrum of malformations that range from bicuspid aortic valve to hypoplastic left heart syndrome. It contributes significantly to infant mortality and has serious implications in adult cardiology. Although left-sided CHD is known to be highly heritable, the underlying genetic determinants are largely unidentified. In this study, we sought to determine the impact of structural genomic variation on left-sided CHD and compared multiplex families (464 individuals with 174 affecteds (37.5%) in 59 multiplex families and 8 trios) to 1,582 well-phenotyped controls. 73 unique inherited or de novo CNVs in 54 individuals were identified in the left-sided CHD cohort. After stringent filtering, our gene inventory reveals 25 new candidates for LS-CHD pathogenesis, such as SMC1A, MFAP4, and CTHRC1, and overlaps with several known syndromic loci. Conservative estimation examining the overlap of the prioritized gene content with CNVs present only in affected individuals in our cohort implies a strong effect for unique CNVs in at least 10% of left-sided CHD cases. Enrichment testing of gene content in all identified CNVs showed a significant association with angiogenesis. In this first family-based CNV study of left-sided CHD, we found that both co-segregating and de novo events associate with disease in a complex fashion at structural genomic level. Often viewed as an anatomically circumscript disease, a subset of left-sided CHD may in fact reflect more general genetic perturbations of angiogenesis and/or vascular biology

Suboptimal herd performance amplifies the spread of infectious disease in the cattle industry

Farms that purchase replacement breeding cattle are at increased risk of introducing many economically important diseases. The objectives of this analysis were to determine whether the total number of replacement breeding cattle purchased by individual farms could be reduced by improving herd performance and to quantify the effects of such reductions on the industry-level transmission dynamics of infectious cattle diseases. Detailed information on the performance and contact patterns of British cattle herds was extracted from the national cattle movement database as a case example. Approximately 69% of beef herds and 59% of dairy herds with an average of at least 20 recorded calvings per year purchased at least one replacement breeding animal. Results from zero-inflated negative binomial regression models revealed that herds with high average ages at first calving, prolonged calving intervals, abnormally high or low culling rates, and high calf mortality rates were generally more likely to be open herds and to purchase greater numbers of replacement breeding cattle. If all herds achieved the same level of performance as the top 20% of herds, the total number of replacement beef and dairy cattle purchased could be reduced by an estimated 34% and 51%, respectively. Although these purchases accounted for only 13% of between-herd contacts in the industry trade network, they were found to have a disproportionately strong influence on disease transmission dynamics. These findings suggest that targeting extension services at herds with suboptimal performance may be an effective strategy for controlling endemic cattle diseases while simultaneously improving industry productivity

The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer

<div><p>Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson’s r = 0.909) and between users (Pearson’s r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.</p></div

Assessment of correlation between hot-spot Ki67 index and Oncotype DX scores.

<p>(A) Plot showing association between Ki67 index and Oncotype DX low-risk (blue), intermediate-risk (green) and high-risk (red) groupings. Pearson’s r = 0.5533; P<0.001. (B) Plot showing association between Ki67 and Oncotype DX low- and high-risk groupings. Pearson’s r = 0.684; P<0.001.</p

Assessment of interaction between Ki67 indices and clinico-pathological variables.

<p>Assessment of interaction between Ki67 indices and clinico-pathological variables.</p

Contribution of individual variables to the accuracy of the respective Random Forest models, as assessed by increases in mean squared error for models created without each variable.

<p>Graphs represent models after 1,000 cycles of validation trained with both clinico-pathological data and Oncotype DX expression data for ER, PgR and HER2 (A) or with clinico-pathological data alone (B). Error bars represent standard deviation from the mean. ER intensity, estrogen receptor staining intensity; ER score, estrogen receptor expression score (immunohistochemistry); PR intensity, progesterone receptor staining intensity; PR score, progesterone receptor expression score (immunohistochemistry); ODX ER, Oncotype DX estrogen receptor gene expression score; ODX HER2, Oncotype DX HER2 expression score; ODX PR, Oncotype DX progesterone receptor gene expression score; Tumor_arch, tumor differentiation score; Tumor_nuc_grade, tumor nuclear grade.</p

Outline of Random Forest training and evaluation workflow.

<p>Outline of Random Forest training and evaluation workflow.</p

Summary performance of the Random Forest models predicting Oncotype DX risk groups.

<p>Recurrence Scores were predicted using the clinico-pathological variables listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188983#pone.0188983.s004" target="_blank">S2 Table</a> alone (pRS), or using the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188983#pone.0188983.s004" target="_blank">S2 Table</a> variables in addition to gene expression scores for ER, PgR and HER2 that were included in the official Oncotype DX reports (pRS_odx). Evaluation of performance of the Random Forest models was based on the extent to which the models correctly predicted, or failed to predict, each patient’s actual low- or high-risk Oncotype DX category. Values represent the mean outcomes ± standard deviations over 1,000 testing iterations.</p