PCA-Plus: Enhanced principal component analysis with illustrative applications to batch effects and their quantitation.

Background: Principal component analysis (PCA), a standard approach to analysis and visualization of large datasets, is commonly used in biomedical research for detecting similarities and differences among groups of samples. We initially used conventional PCA as a tool for critical quality control of batch and trend effects in multi-omic profiling data produced by The Cancer Genome Atlas (TCGA) project of the NCI. We found, however, that conventional PCA visualizations were often hard to interpret when inter-batch differences were moderate in comparison with intra-batch differences; it was also difficult to quantify batch effects objectively. We, therefore, sought enhancements to make the method more informative in those and analogous settings. Results: We have developed algorithms and a toolbox of enhancements to conventional PCA that improve the detection, diagnosis, and quantitation of differences between or among groups, e.g., groups of molecularly profiled biological samples. The enhancements include (i) computed group centroids; (ii) sample-dispersion rays; (iii) differential coloring of centroids, rays, and sample data points; (iii) trend trajectories; and (iv) a novel separation index (DSC) for quantitation of differences among groups. Conclusions: PCA-Plus has been our most useful single tool for analyzing, visualizing, and quantitating batch effects, trend effects, and class differences in molecular profiling data of many types: mRNA expression, microRNA expression, DNA methylation, and DNA copy number. An early version of PCA-Plus has been used as the central graphical visualization in our MBatch package for near-real-time surveillance of data for analysis working groups in more than 70 TCGA, PanCancer Atlas, PanCancer Analysis of Whole Genomes, and Genome Data Analysis Network projects of the NCI. The algorithms and software are generic, hence applicable more generally to other types of multivariate data as well. PCA-Plus is freely available in a down-loadable R package at our MBatch website.

RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data.

SUMMARY: Reverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting. AVAILABILITY AND IMPLEMENTATION: The standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study.

BACKGROUND: The delivery of standardized self-report assessments is essential for measurement-based care in mental health. Paper-based methods of measurement-based care data collection may result in transcription errors, missing data, and other data quality issues when entered into patient electronic health records (EHRs). OBJECTIVE: This study aims to help address these issues by using a dedicated instance of REDCap (Research Electronic Data Capture; Vanderbilt University)-a free, widely used electronic data capture platform-that was established to enable the deployment of digitized self-assessments in clinical care pathways to inform clinical decision making. METHODS: REDCap was integrated with the primary clinical information system to facilitate the real-time transfer of discrete data and PDF reports from REDCap into the EHR. Both technical and administrative components were required for complete implementation. A technology acceptance survey was also administered to capture physicians' and clinicians' attitudes toward the new system. RESULTS: The integration of REDCap with the EHR transitioned clinical workflows from paper-based methods of data collection to electronic data collection. This resulted in significant time savings, improved data quality, and valuable real-time information delivery. The digitization of self-report assessments at each appointment contributed to the clinic-wide implementation of the major depressive disorder integrated care pathway. This digital transformation facilitated a 4-fold increase in the physician adoption of this integrated care pathway workflow and a 3-fold increase in patient enrollment, resulting in an overall significant increase in major depressive disorder integrated care pathway capacity. Physicians' and clinicians' attitudes were overall positive, with almost all respondents agreeing that the system was useful to their work. CONCLUSIONS: REDCap provided an intuitive patient interface for collecting self-report measures and accessing results in real time to inform clinical decisions and an extensible backend for system integration. The approach scaled effectively and expanded to high-impact clinics throughout the hospital, allowing for the broad deployment of complex workflows and standardized assessments, which led to the accumulation of harmonized data across clinics and care pathways. REDCap is a flexible tool that can be effectively leveraged to facilitate the automatic transfer of self-report data to the EHR; however, thoughtful governance is required to complement the technical implementation to ensure that data standardization, data quality, patient safety, and privacy are maintained.

Qualitative socioecological factors of cervical cancer screening use among transgender men.

Lack of attendance to cervical cancer screening (CCS) services is the most attributable factor to the development of cervical cancer. Transgender men, individuals whose gender identity does match with their natal female sex, use CCS less often than the general female population. The underlying reasons for deficient CCS among transgender men relate mostly to their stigmatized identity, such as discrimination and unwelcoming healthcare environments. However, additional research is needed to expand our understanding of this complex issue. This exploratory qualitative research study aimed to identify the determinants of CCS from the perspective of transgender men. Twenty transgender men ages 21-65 were conveniently sampled to participate in a semi-structured interview in 2018. The data were analyzed using a deductive-inductive content analysis approach and the results were sorted into a socioecological framework (SEM). The participants were mostly non-Hispanic and white. The mean age was 33, and 55% of the sample had attended CCS in the last three years. Eight overarching factors were identified in the data. Each factor included descriptive sub-factors. At the institutional and interpersonal SEM levels, factors related to healthcare providers and healthcare organizations. At the individual level, factors related to past negative experiences, gender identity development, and socioeconomic status. To the investigators' knowledge, this is the first study to report the relationship between gender identity development and CCS behaviors. Gender identity development refers to the transition or coming-out process and gender dysphoria. This suggests that attendance to CCS services change as a transgender person's identity evolves.

The Influence of Legal Brothels on Illegal Sexual Service Purchasing Habits: The U.S. Context.

In this study, we use a survey of sex workers' clients to examine the relationship between having paid for services in legal brothels in Nevada and paying for criminalized sexual services among male clients. Using ordinary least squares (OLS) and generalized ordered logistic regression models, the use of legal brothels is found to be negatively related to reported purchasing of criminalized sexual services, regardless of criminal history, income, and most other demographic factors. When tested by criminalized purchase context, purchases made using the Internet, from public, outdoor contacts (such as the street) and indoor, public contacts (like bars), were less likely to occur with brothel experience. This study addresses a critical gap in scholarship on sex workers' clients purchasing choices with consideration to both market choice and frequency of purchase when having been exposed to a legal replacement for an illegal transaction. Implications and areas of further study are discussed.

Differential Sensitivity Analysis for Resistant Malignancies (DISARM) Identifies Common Candidate Therapies across Platinum-Resistant Cancers.

PURPOSE: Despite a growing arsenal of approved drugs, therapeutic resistance remains a formidable and, often, insurmountable challenge in cancer treatment. The mechanisms underlying therapeutic resistance remain largely unresolved and, thus, examples of effective combinatorial or sequential strategies to combat resistance are rare. Here, we present Differential Sensitivity Analysis for Resistant Malignancies (DISARM), a novel, integrated drug screen analysis tool designed to address this dilemma. EXPERIMENTAL DESIGN: DISARM, a software package and web-based application, analyzes drug response data to prioritize candidate therapies for models with resistance to a reference drug and to assess whether response to a reference drug can be utilized to predict future response to other agents. Using cisplatin as our reference drug, we applied DISARM to models from nine cancers commonly treated with first-line platinum chemotherapy including recalcitrant malignancies such as small cell lung cancer (SCLC) and pancreatic adenocarcinoma (PAAD). RESULTS: In cisplatin-resistant models, DISARM identified novel candidates including multiple inhibitors of PI3K, MEK, and BCL-2, among other classes, across unrelated malignancies. Additionally, DISARM facilitated the selection of predictive biomarkers of response and identification of unique molecular subtypes, such as contrasting ASCL1-low/cMYC-high SCLC targetable by AURKA inhibitors and ASCL1-high/cMYC-low SCLC targetable by BCL-2 inhibitors. Utilizing these predictions, we assessed several of DISARM's top candidates, including inhibitors of AURKA, BCL-2, and HSP90, to confirm their activity in cisplatin-resistant SCLC models. CONCLUSIONS: DISARM represents the first validated tool to analyze large-scale in vitro drug response data to statistically optimize candidate drug and biomarker selection aimed at overcoming candidate drug resistance.

Interactive Clustered Heat Map Builder: An easy web-based tool for creating sophisticated clustered heat maps.

Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology.  Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map.  Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps.  Preparation of the data and construction of a heat map is rarely a simple linear process.  Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches.  Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.

SoS Notebook: an interactive multi-language data analysis environment.

Motivation: Complex bioinformatic data analysis workflows involving multiple scripts in different languages can be difficult to consolidate, share and reproduce. An environment that streamlines the entire processes of data collection, analysis, visualization and reporting of such multi-language analyses is currently lacking. Results: We developed Script of Scripts (SoS) Notebook, a web-based notebook environment that allows the use of multiple scripting language in a single notebook, with data flowing freely within and across languages. SoS Notebook enables researchers to perform sophisticated bioinformatic analysis using the most suitable tools for different parts of the workflow, without the limitations of a particular language or complications of cross-language communications. Availability and implementation: SoS Notebook is hosted at http://vatlab.github.io/SoS/ and is distributed under a BSD license.

A Galaxy Implementation of Next-Generation Clustered Heatmaps for Interactive Exploration of Molecular Profiling Data.

Clustered heatmaps are the most frequently used graphics for visualization of molecular profiling data in biology. However, they are generally rendered as static, or only modestly interactive, images. We have now used recent advances in web technologies to produce interactive "next-generation" clustered heatmaps (NG-CHM) that enable extreme zooming and navigation without loss of resolution. NG-CHMs also provide link-outs to additional information sources and include other features that facilitate deep exploration of the biology behind the image. Here, we describe an implementation of the NG-CHM system in the Galaxy bioinformatics platform. We illustrate the algorithm and available computational tool using RNA-seq data from The Cancer Genome Atlas program's Kidney Clear Cell Carcinoma project. Cancer Res; 77(21); e23-26. ©2017 AACR.

Cross-validation of a Sequential Organ Failure Assessment score-based model to predict mortality in patients with cancer admitted to the intensive care unit.

PURPOSE: This study aims to validate the performance of the Sequential Organ Failure Assessment (SOFA) score to predict death of critically ill patients with cancer. MATERIAL AND METHODS: We conducted a retrospective observational study including adults admitted to the intensive care unit (ICU) between January 1, 2006, and December 31, 2008. We randomly selected training and validation samples in medical and surgical admissions to predict ICU and in-hospital mortality. By using logistic regression, we calculated the probabilities of death in the training samples and applied them to the validation samples to test the goodness-of-fit of the models, construct receiver operator characteristics curves, and calculate the areas under the curve (AUCs). RESULTS: In predicting mortality at discharge from the unit, the AUC from the validation group of medical admissions was 0.7851 (95% confidence interval [CI], 0.7437-0.8264), and the AUC from the surgical admissions was 0.7847 (95% CI, 0.6319-0.937). The AUCs of the SOFA score to predict mortality in the hospital after ICU admission were 0.7789 (95% CI, 0.74-0.8177) and 0.7572 (95% CI, 0.6719-0.8424) for the medical and surgical validations groups, respectively. CONCLUSIONS: The SOFA score had good discrimination to predict ICU and hospital mortality. However, the observed underestimation of ICU deaths and unsatisfactory goodness-of-fit test of the model in surgical patients to indicate calibration of the score to predict ICU mortality is advised in this group.

Costs and outcomes of acute kidney injury in critically ill patients with cancer.

BACKGROUND: Acute kidney injury (AKI) is a common complication in critically ill patients with cancer. The RIFLE criteria define three levels of AKI based on the percent increase in serum creatinine (Scr) from baseline: risk (> or = 50%), injury (> or = 100%), and failure (> or = 200% or requiring dialysis). The utility of the RIFLE criteria in critically ill patients with cancer is not known. OBJECTIVE: To examine the incidence, outcomes, and costs associated with AKI in critically ill patients with cancer. METHODS: We retrospectively analyzed all patients admitted to a single-center ICU over a 13-month period with a baseline Scr < or = 1.5 mg/dL (n = 2,398). Kaplan-Meier estimates for survival by RIFLE category were calculated. Logistic regression was used to determine the association of AKI on 60-day mortality. A log-linear regression model was used for economic analysis. Costs were assessed by hospital charges from the provider's perspective. RESULTS: For the risk, injury, and failure categories of AKI, incidence rates were 6%, 2.8%, and 3.7%; 60-day survival estimates were 62%, 45%, and 14%; and adjusted odds ratios for 60-day mortality were 2.3, 3, and 14.3, respectively (P < or = 0.001 compared to patients without AKI). Hematologic malignancy and hematopoietic cell transplant were not associated with mortality in the adjusted analysis. Hospital cost increased by 0.16% per 1% increase in creatinine and by 21% for patients requiring dialysis. LIMITATIONS: Retrospective analysis. Single-center study. No adjustment by cost-to-charge ratios. CONCLUSIONS: AKI is associated with higher mortality and costs in critically ill patients with cancer.

Cross-validation of a modified score to predict mortality in cancer patients admitted to the intensive care unit.

PURPOSE: The aim of this study was to cross-validate an automated and customized severity of illness score as a means of predicting death among adult cancer patients admitted to the intensive care unit (ICU). MATERIALS AND METHODS: We conducted a retrospective study of ICU discharges between January 1, 2001, and December 31, 2005, in a university comprehensive cancer center. We randomly selected training and validation samples in 2 ICU groups (medical and surgical patients). We used logistic regression to calculate the probabilities of death in the ICU and in-hospital death in training samples and applied these probabilities to the validation samples to calculate sensitivity and specificity, construct curves, and determined the areas under the receiver operating characteristic curve (AUC). RESULTS: We included 6880 patients. In predicting ICU mortality, the AUC was 0.77 (95% confidence interval [CI], 0.73-0.82) for the medical validation group and 0.8207 (95% CI, 0.7304-0.9109) for the surgical validation group. For in-hospital mortality, the AUCs for the groups of medical and surgical patients were 0.73 (95% CI, 0.69-0.76) and 0.77 (95% CI, 0.73-0.80), respectively. CONCLUSIONS: The modified Sequential Organ Failure Assessment score is a good and valid predictor of cancer patients' risk of dying in the ICU and/or hospital despite the modifications needed to automate the score using existing electronic data.

Automating and simplifying the SOFA score in critically ill patients with cancer.

The aim was to demonstrate the performance of a modified version of the Sequential Organ Failure Assessment (SOFA) score to predict mortality in medical and surgical patients with cancer. We performed an electronic retrospective review of databases. We included adult patients with cancer admitted into a 53-bed ICU over 28 months. We electronically calculated a modified SOFA (mSOFA) score at admission. A majority of the patients were admitted into the surgical ICU. Of 328 nonsurvivors, 85.1 per cent were medical patients and only 14.9 per cent surgical patients. The mean admission mSOFA scores for medical and surgical patients were 4.7 +/- 3.2 and 1.7 +/- 1.9, respectively. The overall area under the curve (AUC) of the mSOFA score was 0.84. The AUCs for medical and surgical patients were 0.72 and 0.78, respectively. Our results demonstrate that electronic assessment of mSOFA score has potential in resource allocation decisions as well as in critical care outreach programs.

Factors associated with anemia in patients with cancer admitted to an intensive care unit.

PURPOSE: The study aimed to evaluate the relative impact of clinical and demographic factors associated with the prevalence and incidence of anemia (hemoglobin [Hb] <12 g/dL) in critically ill patients with cancer. MATERIALS AND METHODS: We performed an electronic chart review for demographic and clinical data of adult patients with cancer with or without anemia admitted to the intensive care unit (ICU). Prevalence of anemia was determined at admission, and incidence determined if anemia developed during ICU stay. Anemia was classified as mild, moderate, or severe. The additive impact of clinical and demographic factors was evaluated by using a hierarchical linear regression model. RESULTS: A total of 4705 patients were included in the study. The prevalence and incidence of anemia were 68.0% and 46.6%, respectively. In prevalent cases, we found that the clinical covariates modified sequential organ failure assessment score, admission to the medical ICU, prior chemotherapy, diagnosis of hematologic cancer, and length of hospital stay before ICU admission explained 18.7% of the variance in the model, whereas the demographic covariates (age, sex, and race) explained only an additional 0.6%. The pattern was similar for incidence cases. CONCLUSIONS: Clinical factors are more influential than demographic factors in the observed rates of prevalence and incidence of anemia in the ICU; thus, protocols are needed to identify subgroups of patients with cancer who could benefit from novel management strategies.

Life-supportive therapy withdrawal and length of stay in a large oncologic intensive care unit at the end of life.

We evaluated the factors associated with life-supportive therapy withdrawal (LSTW) and length-of-stay (LOS) of adult patients with cancer who died while in the intensive care unit (ICU). We performed chart review of adult patients with cancer who died in a 53-bed ICU of a comprehensive cancer center and evaluated the relative impact of demographic and clinical factors by using logistic regression and linear regression. A total of 267 patients were included in the study. Multivariate analysis showed that white patients were 2.52 times more likely to have LSTW than patients of other ethnicities (95% confidence interval 1.23-5.15, p = 0.01). The mean LOS for patients with hematologic cancers was 7.7 days, compared to only 4.8 days for those diagnosed with nonhematologic cancers (p = 0.01). Having a hematologic cancer, LSTW, or admission into the surgical oncologic ICU independently predicted increased LOS for those who died in the ICU (p < 0.001 p = 0.001, and p < 0.001, respectively). Cultural differences in dealing with the end-of-life process rooted in religious beliefs or language barriers and reflected in the utilization rates of LSTW by non-whites and whites may partially explain our findings. The difficult transition from curative to palliative care in the ICU is reflected by the increased LOS of patients who received LSTW, were diagnosed with hematologic cancers, or were admitted into the surgical unit.

Stress-related mucosal bleeding in critically ill oncology patients.

OBJECTIVE: To determine the incidence of stress-related mucosal bleeding (SRMB) in a critically ill oncology population receiving stress ulcer prophylaxis (SUP) with either a histamine-2 receptor antagonist (H2RA) or proton pump inhibitor (PPI). DESIGN: Single-center, prospective, observational study. SETTING: Fifty-two bed medical-surgical intensive care unit of an academic oncology institution. PATIENTS: A convenience sample of 100 medical and surgical critically ill oncology patients who received intensive care for more than 24 hours and at least one dose of a H2RA or PPI for prevention of SRMB. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients were followed throughout their intensive care unit stay for the development of an overt and/or clinically significant gastrointestinal (GI) bleed. More patients received a PPI (n=81) in contrast to a H2RA (n=19) for SUP. Overall, 94 patients (94%) had at least one risk factor for a SRMB with four patients (4%) experiencing an event (overt bleed, n=3; clinically significant bleed, n=1). All cases of GI bleeding occurred in patients receiving a PPI. No ICU deaths were considered directly related to a GI bleed. CONCLUSIONS: The incidence of SRMB among high-risk critically ill oncology patients receiving SUP appears low; further, large-scale trials are needed to confirm this finding.