Evaluation of statistical approaches for association testing in noisy drug screening data.

BACKGROUND: Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS: To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS: We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

Dual T-cell constant ß chain (TRBC)1 and TRBC2 staining for the identification of T-cell neoplasms by flow cytometry.

The diagnosis of leukemic T-cell malignancies is often challenging, due to overlapping features with reactive T-cells and limitations of currently available T-cell clonality assays. Recently developed therapeutic antibodies specific for the mutually exclusive T-cell receptor constant ß chain (TRBC)1 and TRBC2 isoforms provide a unique opportunity to assess for TRBC-restriction as a surrogate of clonality in the flow cytometric analysis of T-cell neoplasms. To demonstrate the diagnostic utility of this approach, we studied 164 clinical specimens with (60) or without (104) T-cell neoplasia, in addition to 39 blood samples from healthy donors. Dual TRBC1 and TRBC2 expression was studied within a comprehensive T-cell panel, in a fashion similar to the routine evaluation of kappa and lambda immunoglobulin light chains for the detection of clonal B-cells. Polytypic TRBC expression was demonstrated on total, CD4+ and CD8+ T-cells from all healthy donors; and by intracellular staining on benign T-cell precursors. All neoplastic T-cells were TRBC-restricted, except for 8 cases (13%) lacking TRBC expression. T-cell clones of uncertain significance were identified in 17 samples without T-cell malignancy (13%) and accounted for smaller subsets than neoplastic clones (median: 4.7 vs. 69% of lymphocytes, p < 0.0001). Single staining for TRBC1 produced spurious TRBC1-dim subsets in 24 clinical specimens (15%), all of which resolved with dual TRBC1/2 staining. Assessment of TRBC restriction by flow cytometry provides a rapid diagnostic method to detect clonal T-cells, and to accurately determine the targetable TRBC isoform expressed by T-cell malignancies.

Correlates of career satisfaction in internationally educated nurses: A cross-sectional survey-based study.

BACKGROUND: More and more high-income countries hire internationally educated nurses as part of their workforce. While the factors that push and pull internationally educated nurses to migrate and influence their workforce integration have been widely reported in the literature, little is known about internationally educated nurses' career development and whether they are satisfied with their nursing career in Canada. OBJECTIVE: This study aims to identify the main correlates of internationally educated nurses' career satisfaction. METHODS: A cross-sectional analysis of data from a pan-Canadian survey sample of 1,951 internationally educated nurses, including registered nurses, licensed practical nurses and registered psychiatric nurses, was conducted. Measures of career satisfaction included individual, job and career characteristics as well as organizational-related and integration process factors. Non-parametric Mann-Whitney U and Spearman rank correlation tests were used to examine the association of the various factors with career satisfaction. RESULTS: Overall, internationally educated nurses showed a high degree of career satisfaction. At the same time, career satisfaction varied greatly depending on sociodemographic characteristics, organizational setting, and geographic location. Older and more experienced internationally educated nurses tended to be more satisfied with their career than their younger or less experienced colleagues were. Furthermore, male were inclined to be less satisfied than their female counterparts, and having children tended to make all three groups (men, women and overall) more satisfied. The higher the level of education prior to immigrating the lower the career satisfaction. Internationally educated nurses who identified as White or Asian had the highest level of career satisfaction, whereas those who identified as Black tended to be the least satisfied. Career satisfaction was the highest among those who live in the Prairie Provinces (Alberta, Saskatchewan and Manitoba), and Ontario, the lowest in the Atlantic Provinces (New Brunswick, Nova Scotia, Prince Edward Island, Newfoundland and Labrador). As for organizational characteristics, full-time nurses were more satisfied than those working part-time or with occasional employment. Finally, internationally educated nurses who thought they had achieved their career goals were more satisfied, while those who experienced discrimination were less satisfied with their career. CONCLUSION: Our findings highlight the need for organizations to ensure a healthy work environment for internationally educated nurses, free of discrimination, where they can attain their career goals. Tweetable abstract: More and more countries rely on internationally educated nurses to ease their nursing shortages. This study aims to identify the main correlates of internationally educated nurses' career satisfaction, using non-parametric Mann-Whitney U and Spearman rank correlation tests on data from a pan-Canadian survey sample of 1,951 internationally educated nurses, including registered nurses, licensed practical nurses and registered psychiatric nurses. Overall, internationally educated nurses showed a high degree of career satisfaction. At the same time, career satisfaction varied greatly depending on the internationally educated nurses' sociodemographic characteristics, organizational settings and geographic location. Finally, internationally educated nurses who thought they had achieved their career goals were more satisfied, while those who experienced discrimination were less satisfied with their career. Our findings highlight the need for organizations to ensure environment free of discrimination, where internationally educated nurses can attain their career goals.

Assessing therapy response in patient-derived xenografts.

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, yet remains a challenging task. To be able to translate the results of the experiments more readily, a preferred measure to quantify this response should take into account more of the available experimental data, including both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. We test and compare KuLGaP to four widely used response measures using 329 patient-derived xenograft (PDX) models. Our results show that KuLGaP is more selective than currently existing measures, reduces the risk of false-positive calls, and improves translation of the laboratory results to clinical practice. We also show that outcomes of human treatment better align with the results of the KuLGaP measure than other response measures. KuLGaP has the potential to become a measure of choice for quantifying drug treatment in mouse models as it can be easily used via the kulgap.ca website.

Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation.

INTRODUCTION: Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition segmenting 6-month-old infant magnetic resonance images, but neonatal cerebral tissue type identification is challenging given its uniquely inverted tissue contrasts. The current study aims to evaluate the two architectures to segment neonatal brain tissue types at term equivalent age. METHODS: Both networks were retrained over 24 pairs of neonatal T1 and T2 data from the Developing Human Connectome Project public data set and validated on another eight pairs against ground truth. We then reported the best-performing model from training and its performance by computing the Dice similarity coefficient (DSC) for each tissue type against eight test subjects. RESULTS: During the testing phase, among the segmentation approaches tested, the dual-modality HyperDense-Net achieved the best statistically significantly test mean DSC values, obtaining 0.94/0.95/0.92 for the tissue types and took 80 h to train and 10 min to segment, including preprocessing. The single-modality LiviaNET was better at processing T2-weighted images than processing T1-weighted images across all tissue types, achieving mean DSC values of 0.90/0.90/0.88 for gray matter, white matter, and cerebrospinal fluid, respectively, while requiring 30 h to train and 8 min to segment each brain, including preprocessing. DISCUSSION: Our evaluation demonstrates that both neural networks can segment neonatal brains, achieving previously reported performance. Both networks will be continuously retrained over an increasingly larger repertoire of neonatal brain data and be made available through the Canadian Neonatal Brain Platform to better serve the neonatal brain imaging research community.

Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts.

Identifying robust biomarkers of drug response constitutes a key challenge in precision medicine. Patient-derived tumor xenografts (PDX) have emerged as reliable preclinical models that more accurately recapitulate tumor response to chemo- and targeted therapies. However, the lack of computational tools makes it difficult to analyze high-throughput molecular and pharmacologic profiles of PDX. We have developed Xenograft Visualization & Analysis (Xeva), an open-source software package for in vivo pharmacogenomic datasets that allows for quantification of variability in gene expression and pathway activity across PDX passages. We found that only a few genes and pathways exhibited passage-specific alterations and were therefore not suitable for biomarker discovery. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR < 0.05). We found novel biomarkers based on gene expressions, copy number aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology. SIGNIFICANCE: A computational platform for PDX data analysis reveals consistent gene and pathway activity across passages and confirms drug response prediction biomarkers in PDX.See related commentary by Meehan, p. 4324.