Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.

Introduction: Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance. Methods: The Bayesian hierarchical model uses Poisson models to describe the accrual performance of individual cancer clinical trials and a hierarchical component to describe the variation in performance across studies. Additionally, this model evaluates the impacts of the C3OD and the COVID-19 pandemic using posterior probabilities across evaluation years. The performance metric is the ratio of the observed accrual rate to the target accrual rate. Results: Posterior medians of the annual accrual performance at the KUCC from 2018 to 2023 are 0.233, 0.246, 0.197, 0.150, 0.254, and 0.340. The COVID-19 pandemic partly explains the drop in performance in 2020 and 2021. The posterior probability that annual accrual performance is better with C3OD in 2023 than pre-pandemic (2019) is 0.935. Conclusions: This study comprehensively evaluates the annual performance of clinical trial accrual at the KUCC, revealing a negative impact of COVID-19 and an ongoing positive impact of C3OD implementation. Two sensitivity analyses further validate the robustness of our model. Evaluating annual accrual performance across clinical trials is essential for a cancer center. The performance evaluation tools described in this paper are highly recommended for monitoring clinical trial accrual.

An R package for Survival-based Gene Set Enrichment Analysis.

Functional enrichment analysis is usually used to assess the effects of experimental differences. However, researchers sometimes want to understand the relationship between transcriptomic variation and health outcomes like survival. Therefore, we suggest the use of Survival-based Gene Set Enrichment Analysis (SGSEA) to help determine biological functions associated with a disease's survival. We developed an R package and corresponding Shiny App called SGSEA for this analysis and presented a study of kidney renal clear cell carcinoma (KIRC) to demonstrate the approach. In Gene Set Enrichment Analysis (GSEA), the log-fold change in expression between treatments is used to rank genes, to determine if a biological function has a non-random distribution of altered gene expression. SGSEA is a variation of GSEA using the hazard ratio instead of a log fold change. Our study shows that pathways enriched with genes whose increased transcription is associated with mortality (NES > 0, adjusted p-value < 0.15) have previously been linked to KIRC survival, helping to demonstrate the value of this approach. This approach allows researchers to quickly identify disease variant pathways for further research and provides supplementary information to standard GSEA, all within a single R package or through using the convenient app.

On the rocks: can urologists identify stone composition based on endoscopic images alone? A worldwide survey of urologists.

PURPOSE: As part of the management of nephrolithiasis, determination of chemical composition of stones is important. Our objective in this study is to assess urologists' accuracy in making visual, intraoperative determinations of stone composition. MATERIALS AND METHODS: We conducted a REDCap survey asking urologists to predict stone composition based on intraoperative images of 10 different pure-composition kidney stones of 7 different types: calcium oxalate monohydrate (COM), calcium oxalate dihydrate (COD), calcium phosphate (CP) apatite, CP brushite, uric acid (UA), struvite (ST) and cystine (CY). To evaluate experience, we examined specific endourologic training, years of experience, and number of ureteroscopy (URS) cases/week. A self-assessment of ability to identify stone composition was also required. RESULTS: With a response rate of 26% (366 completed surveys out of 1,370 deliveries), the overall accuracy of our cohort was 44%. COM, ST, and COD obtained the most successful identification rates (65.9%, 55.7%, and 52.0%, respectively). The most frequent misidentified stones were CP apatite (10.7%) and CY (14.2%). Predictors of increased overall accuracy included self-perceived ability to determine composition and number of ureteroscopies per week, while years of experience did not show a positive correlation. CONCLUSIONS: Although endoscopic stone recognition can be an important tool for surgeons, it is not reliable enough to be utilized as a single method for stone identification, suggesting that urologists need to refine their ability to successfully recognize specific stone compositions intraoperatively.

Image Distortion During Flexible Ureteroscopy: A Laboratory Model Comparing Super Pulsed Thulium Fiber Laser vs High-Power Ho:YAG Laser.

Purpose: Digital ureteroscopes employ "chip-on-the-tip" technology that allows for significant improvement in image resolution. However, image distortion often occurs during laser lithotripsy owing to acoustic wave production. We sought to compare image distortion using different laser power settings and distances from the laser fiber tip to the scope for the Super Pulsed Thulium Fiber (SPTF) laser and high-power Holmium:YAG (Ho:YAG) laser. Materials and Methods: Ureteroscopy was simulated using a silicon kidney-ureter-bladder model fitted with a 12F/14F access sheath and the Lithovue™ (Boston Scientific), disposable digital flexible ureteroscope. At defined laser parameters (10, 20, 30 and 40 W, short pulse), a 200-µm laser fiber was slowly retracted toward the tip of the ureteroscope during laser activation. Image distortion was identified, and distance from the laser tip to the scope tip was determined. Data from the two lasers were compared utilizing t-tests. Results: After controlling for frequency, power, and laser mode, utilizing 1.0 J of energy was significantly associated with less feedback than 0.5 J (-0.091 mm, p ≤ 0.05). Increased power was associated with larger feedback distance (0.016 mm, p ≤ 0.05); however, increase in frequency did not have a significant effect (-0.001 mm, p = 0.39). The SPFT laser had significantly less feedback when compared with all Holmium laser modes. Conclusions: Increased total power results in image distortion occurring at greater distances from the tip of the ureteroscope during laser activation. Image distortion occurs further from the ureteroscope with Ho:YAG laser than with SPTF fibers at the same laser settings. In clinical practice, the tip of the laser fiber should be kept further away from the tip of the scope during ureteroscopy as the power increases as well as when utilizing the Ho:YAG system compared with the SPTF laser platform. The SPTF laser may have a better safety profile in terms of potential scope damage.

Comprehensive exploration of JQ1 and GSK2801 targets in breast cancer using network pharmacology and molecular modeling approaches.

JQ1 and GSK2801 are bromo domain inhibitors (BDI) known to exhibit enhanced anti-cancer activity when combined with other agents. However, the underlying molecular mechanisms behind such enhanced activity remain unclear. We used network-pharmacology approaches to understand the shared molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 when used together to treat breast cancer (BC). The gene targets of JQ1 and GSK2801 were intersected with known BC-targets and their putative targets against BC were derived. The key genes were explored through gene-ontology-enrichment, Protein-Protein-Interaction (PPI) networking, survival analysis, and molecular modeling simulations. The genes, CTSB, MAPK14, MET, PSEN2 and STAT3, were found to be common targets for both drugs. In total, 49 biological processes, five molecular functions and 61 metabolic pathways were similarly enriched for JQ1 and GSK2801 BC targets among which several terms are related to cancer: IL-17, TNF and JAK-STAT signaling pathways. Survival analyses revealed that all five putative synergistic targets are significantly associated with survival in BC (log-rank p < 0.05). Molecular modeling studies showed stable binding of JQ1 and GSK2801 against their targets. In conclusion, this study explored and illuminated the possible molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 against BC and suggests synergistic action through their similar BC-targets and gene-ontologies.

Assessing equivalent and inverse change in genes between diverse experiments.

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

Rural-Urban Disparities in Health Access Factors Over Time: Implications for Cancer Prevention and Health Equity in the Midwest.

Purpose: Population-level environmental and socioeconomic factors may influence cancer burden within communities, particularly in rural and urban areas that may be differentially impacted by factors related to health care access. Methods: The University of Kansas (KU) Cancer Center serves a geographically large diverse region with 75% of its 123 counties classified as rural. Using County Health Rankings data and joinpoint regression, we examined trends in four factors related to the socioeconomic environment and health care access from 2009 to 2017 in rural and urban counties across the KU Cancer Center catchment area. Findings: The adult health uninsurance rate declined significantly in rural and urban counties across the catchment area (rural annual percent change [APC]=-5.96; 95% CI=[-7.71 to -4.17]; urban APC=-5.72; 95% CI=[-8.03 to -3.35]). Childhood poverty significantly decreased in rural counties over time (APC=-2.94; 95% CI=[-4.52 to -1.33]); in contrast, urban childhood poverty rates did not significantly change before 2012 (APC=3.68; 95% CI=[-15.12 to 26.65]), after which rates declined (APC=-5.89; 95% CI=[-10.01 to -1.58]). The number of primary care providers increased slightly but significantly in both rural and urban counties (APC=0.54; 95% CI=[0.28 to 0.80]), although urban counties had more primary care providers than rural areas (76.1 per 100K population vs. 57.1 per 100K population, respectively; p=0.009). Unemployment declined significantly faster in urban counties (APC=-10.33; 95% CI=[-12.16 to -8.47]) compared with rural counties (APC=-6.71; 95% CI=[-8.22 to -5.18]) (p=0.02). Conclusion: Our findings reveal potential disparities in systemic factors that may contribute to differences in cancer prevention, care, and survivorship in rural and urban regions.

Geographic differences in community oncology provider and practice location characteristics in the central United States.

PURPOSE: How care delivery influences urban-rural disparities in cancer outcomes is unclear. We sought to understand community oncologists' practice settings to inform cancer care delivery interventions. METHODS: We conducted secondary analysis of a national dataset of providers billing Medicare from June 1, 2019 to May 31, 2020 in 13 states in the central United States. We used Kruskal-Wallis rank and Fisher's exact tests to compare physician characteristics and practice settings among rural and urban community oncologists. FINDINGS: We identified 1,963 oncologists practicing in 1,492 community locations; 67.5% practiced in exclusively urban locations, 11.3% in exclusively rural locations, and 21.1% in both rural and urban locations. Rural-only, urban-only, and urban-rural spanning oncologists practice in an average of 1.6, 2.4, and 5.1 different locations, respectively. A higher proportion of rural community sites were solo practices (11.7% vs 4.0%, P<.001) or single specialty practices (16.4% vs 9.4%, P<.001); and had less diversity in training environments (86.5% vs 67.8% with <2 medical schools represented, P<.001) than urban community sites. Rural multispecialty group sites were less likely to include other cancer specialists. CONCLUSIONS: We identified 2 potentially distinct styles of care delivery in rural communities, which may require distinct interventions: (1) innovation-isolated rural oncologists, who are more likely to be solo providers, provide care at few locations, and practice with doctors with similar training experiences; and (2) urban-rural spanning oncologists who provide care at a high number of locations and have potential to spread innovation, but may face high complexity and limited opportunity for care standardization.

The need to study rural cancer outcome disparities at the local level: a retrospective cohort study in Kansas and Missouri.

BACKGROUND: Rural residence is commonly thought to be a risk factor for poor cancer outcomes. However, a number of studies have reported seemingly conflicting information regarding cancer outcome disparities with respect to rural residence, with some suggesting that the disparity is not present and others providing inconsistent evidence that either urban or rural residence is associated with poorer outcomes. We suggest a simple explanation for these seeming contradictions: namely that rural cancer outcome disparities are related to factors that occur differentially at a local level, such as environmental exposures, lack of access to care or screening, and socioeconomic factors, which differ by type of cancer. METHODS: We conducted a retrospective cohort study examining ten cancers treated at the University of Kansas Medical Center from 2011 to 2018, with individuals from either rural or urban residences. We defined urban residences as those in a county with a U.S. Department of Agriculture Urban Influence Code (UIC) of 1 or 2, with all other residences defines a rural. Inverse probability of treatment weighting was used to create a pseudo-sample balanced for covariates deemed likely to affect the outcomes modeled with cumulative link and weighted Cox-proportional hazards models. RESULTS: We found that rural residence is not a simple risk factor but rather appears to play a complex role in cancer outcome disparities. Specifically, rural residence is associated with higher stage at diagnosis and increased survival hazards for colon cancer but decreased risk for lung cancer compared to urban residence. CONCLUSION: Many cancers are affected by unique social and environmental factors that may vary between rural and urban residents, such as access to care, diet, and lifestyle. Our results show that rurality can increase or decrease risk, depending on cancer site, which suggests the need to consider the factors connected to rurality that influence this complex pattern. Thus, we argue that such disparities must be studied at the local level to identify and design appropriate interventions to improve cancer outcomes.

Ureteroscopic Performance of High Power Super Pulse Thulium Fiber Laser for the Treatment of Urolithiasis: Results of the First Case Series in North America.

OBJECTIVE: To report the first case series of ureterorenoscopy in North America using the High Power Super Pulse Thulium Fiber Laser for the treatment of upper urinary tract stones. METHODS: After Institutional Review Board approval, a multicentric retrospective chart review of patients treated with the High Power Super Pulse Thulium Fiber Laser from October 2019 to March 2020 was conducted. Basic demographic information, pre-operative, and peri-operative data were recorded. RESULTS: Seventy-six patients were included with a mean age of 60.9 ± 13.3 years. 118 stones were treated including 32 within the ureter, 49 in the lower pole, 37 in mid or upper poles. Dusting technique was commonly used (67.1%) with pulse frequencies up to 2400 Hz. Mean operative time was 59.4 ± 31.5 minutes. Mean laser time and total laser energy were 10.8 ± 14.1 minutes and 12.5 ± 19.1 KJ, respectively. Intraoperative complications were limited to 7 grade 1, 3 grade 2, and 1 grade 3 ureteral injuries and one case of renal collecting system bleeding that was adequately managed with laser coagulation settings (1J-20Hz). CONCLUSION: This initial case series in North America of the High Power Super Pulse Thulium Fiber Laser is promising for the treatment of urolithiasis. Sub-200 µm fibers and dusting settings up to 2400 Hz were utilized successfully. No specific complications related to use of the laser were seen.

Utilization of MOSES Modulated Pulse Mode Results in Improved Efficiency in Holmium:YAG Laser Ablation of the Prostate.

OBJECTIVE: To determine if MOSES technology improves efficiency and short-term outcomes in holmium laser ablation of the prostate (HoLAP). METHODS: A retrospective review of patients who underwent HoLAP between August 2016 and November 2019 was conducted. All procedures before and after the implementation of MOSES technology at our institution were evaluated. Preoperative patient characteristics and intraoperative data were collected. Postoperative International Prostate Symptom Score, quality of life, and postvoid residual measurements at 6 weeks and 3 months postoperatively were analyzed. RESULTS: This cohort included 65 males who underwent HoLAP, 32 without and 33 with MOSES. Patients in the MOSES group were slightly older, but no other differences in baseline characteristics were observed between the two groups. Ablation time was similar at 49.6 ± 26.1 minutes without and 40.7 ± 41.2 minutes with MOSES (P = .38). However, HoLAP with MOSES had significantly higher ablation efficiency (0.59 ± 0.24 g/min without vs 0.86 0.5 g/min with MOSES, P = .01). On multivariable regression modeling, HoLAP without MOSES added 12 minutes to operating time (estimate 12.3, standard error 3.44, P < .01) after controlling for prostate size and laser energy usage. Duration of catheterization, urinary incontinence and need for reoperation within 3 months were similar. There were no differences between groups in International Prostate Symptom Score, quality of life, or postvoid residual at 3 months postoperatively. CONCLUSION: Utilization of MOSES technology resulted in improved efficiency in HoLAP, translating into time savings in the operating room. Postoperative outcomes out to 3 months were similar among patients who underwent the procedure utilizing either laser pulse mode. Further studies are needed to investigate long-term outcomes as the use of MOSES is likely to become more commonly utilized.

Equivalent change enrichment analysis: assessing equivalent and inverse change in biological pathways between diverse experiments.

BACKGROUND: In silico functional genomics have become a driving force in the way we interpret and use gene expression data, enabling researchers to understand which biological pathways are likely to be affected by the treatments or conditions being studied. There are many approaches to functional genomics, but a number of popular methods determine if a set of modified genes has a higher than expected overlap with genes known to function as part of a pathway (functional enrichment testing). Recently, researchers have started to apply such analyses in a new way: to ask if the data they are collecting show similar disruptions to biological functions compared to reference data. Examples include studying whether similar pathways are perturbed in smokers vs. users of e-cigarettes, or whether a new mouse model of schizophrenia is justified, based on its similarity in cytokine expression to a previously published model. However, there is a dearth of robust statistical methods for testing hypotheses related to these questions and most researchers resort to ad hoc approaches. The goal of this work is to develop a statistical approach to identifying gene pathways that are equivalently (or inversely) changed across two experimental conditions. RESULTS: We developed Equivalent Change Enrichment Analysis (ECEA). This is a new type of gene enrichment analysis based on a statistic that we call the equivalent change index (ECI). An ECI of 1 represents a gene that was over or under-expressed (compared to control) to the same degree across two experiments. Using this statistic, we present an approach to identifying pathways that are changed in similar or opposing ways across experiments. We compare our approach to current methods on simulated data and show that ECEA is able to recover pathways exhibiting such changes even when they exhibit complex patterns of regulation, which other approaches are unable to do. On biological data, our approach recovered pathways that appear directly connected to the condition being studied. CONCLUSIONS: ECEA provides a new way to perform gene enrichment analysis that allows researchers to compare their data to existing datasets and determine if a treatment will cause similar or opposing genomic perturbations.

Improving survival prediction using a novel feature selection and feature reduction framework based on the integration of clinical and molecular data.

The accurate prediction of a cancer patient's risk of progression or death can guide clinicians in the selection of treatment and help patients in planning personal affairs. Predictive models based on patient-level data represent a tool for determining risk. Ideally, predictive models will use multiple sources of data (e.g., clinical, demographic, molecular, etc.). However, there are many challenges associated with data integration, such as overfitting and redundant features. In this paper we aim to address those challenges through the development of a novel feature selection and feature reduction framework that can handle correlated data. Our method begins by computing a survival distance score for gene expression, which in combination with a score for clinical independence, results in the selection of highly predictive genes that are non-redundant with clinical features. The survival distance score is a measure of variation of gene expression over time, weighted by the variance of the gene expression over all patients. Selected genes, in combination with clinical data, are used to build a predictive model for survival. We benchmark our approach against commonly used methods, namely lasso- as well as ridge-penalized Cox proportional hazards models, using three publicly available cancer data sets: kidney cancer (521 samples), lung cancer (454 samples) and bladder cancer (335 samples). Across all data sets, our approach built on the training set outperformed the clinical data alone in the test set in terms of predictive power with a c.Index of 0.773 vs 0.755 for kidney cancer, 0.695 vs 0.664 for lung cancer and 0.648 vs 0.636 for bladder cancer. Further, we were able to show increased predictive performance of our method compared to lasso-penalized models fit to both gene expression and clinical data, which had a c.Index of 0.767, 0.677, and 0.645, as well as increased or comparable predictive power compared to ridge models, which had a c.Index of 0.773, 0.668 and 0.650 for the kidney, lung, and bladder cancer data sets, respectively. Therefore, our score for clinical independence improves prognostic performance as compared to modeling approaches that do not consider combining non-redundant data. Future work will concentrate on optimizing the survival distance score in order to achieve improved results for all types of cancer.

A Bayesian framework for identifying consistent patterns of microbial abundance between body sites.

Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.

Perception, knowledge, and interest of urologic surgery: a medical student survey.

BACKGROUND: Although only a limited number of medical schools require a formal educational rotation in urologic surgery, urology as a medical specialty continues to attract a large number of students into the match each year. The purpose of this study was to describe medical student awareness, perception, and knowledge of urology, to determine factors influencing students' consideration of urology as a career, and to determine if prior urology clerkship experience is associated with differences in these variables. METHODS: In this cross-sectional study, medical students were electronically surveyed in 07/2016. Self-reported and question-based knowledge of urology were determined. A total of 25 factors were assessed with a five-point Likert scale to determine their influence on students' consideration of urology as a career. Data analysis was performed using R. RESULTS: The survey was completed by 114 students (13.5% of all medical students). A total of 11(9.65%)students had previously participated in a urology clerkship. All students reported awareness of urology; however, only 74 students (64.9%) correctly identified the training pathway and job duties of urologists. Self-perceived knowledge of urology was poor but improved with increased medical school training. Question-based assessment also demonstrated increased knowledge with advanced medical school training (27% per year; p < 0.01). Prior urology clerkship experience appeared to be associated with increased urologic knowledge; however, this was confounded by year in medical school training. When assessing factors impacting students' consideration of a career in urology, 'combination of medicine and surgery' was the most positively influential and 'competitiveness of the specialty' was the most negatively influential. CONCLUSIONS: Although medical students are aware of urology as a specialty, they perceive their knowledge of urology as poor. However, knowledge of urology increases throughout medical school training. Multiple factors influence students' consideration of urology as a career choice. Additional studies are needed to further explore how participation in a formal urology experience alters students' perceptions and influences their consideration of urology as a career choice. TRIAL REGISTRATION: Retrospectively registered.

pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS).

BACKGROUND: When designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed pwrEWAS. RESULTS: The current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (Δß), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user settings. CONCLUSION: Both under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in the design and planning of EWAS. AVAILABILITY: The web interface is written in the R statistical programming language using Shiny (RStudio Inc., 2016) and is available at https://biostats-shinyr.kumc.edu/pwrEWAS/ . The R package for pwrEWAS is publicly available at GitHub ( https://github.com/stefangraw/pwrEWAS ).

Pan-Cancer Analysis Reveals Differential Susceptibility of Bidirectional Gene Promoters to DNA Methylation, Somatic Mutations, and Copy Number Alterations.

Bidirectional gene promoters affect the transcription of two genes, leading to the hypothesis that they should exhibit protection against genetic or epigenetic changes in cancer. Therefore, they provide an excellent opportunity to learn about promoter susceptibility to somatic alteration in tumors. We tested this hypothesis using data from genome-scale DNA methylation (14 cancer types), simple somatic mutation (10 cancer types), and copy number variation profiling (14 cancer types). For DNA methylation, the difference in rank differential methylation between tumor and tumor-adjacent normal matched samples based on promoter type was tested by the Wilcoxon rank sum test. Logistic regression was used to compare differences in simple somatic mutations. For copy number alteration, a mixed effects logistic regression model was used. The change in methylation between non-diseased tissues and their tumor counterparts was significantly greater in single compared to bidirectional promoters across all 14 cancer types examined. Similarly, the extent of copy number alteration was greater in single gene compared to bidirectional promoters for all 14 cancer types. Furthermore, among 10 cancer types with available simple somatic mutation data, bidirectional promoters were slightly more susceptible. These results suggest that selective pressures related with specific functional impacts during carcinogenesis drive the susceptibility of promoter regions to somatic alteration.

Methylation-to-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, and Pathologic Complete Response in Multiple Cohorts.

Prognostic biomarkers serve a variety of purposes in cancer treatment and research, such as prediction of cancer progression, and treatment eligibility. Despite growing interest in multi-omic data integration for defining prognostic biomarkers, validated methods have been slow to emerge. Given that breast cancer has been the focus of intense research, it is amenable to studying the benefits of multi-omic prognostic models due to the availability of datasets. Thus, we examined the efficacy of our methylation-to-expression feature model (M2EFM) approach to combining molecular and clinical predictors to create risk scores for overall survival, distant metastasis, and chemosensitivity in breast cancer. Gene expression, DNA methylation, and clinical variables were integrated via M2EFM to build models of overall survival using 1028 breast tumor samples and applied to validation cohorts of 61 and 327 samples. Models of distant recurrence-free survival and pathologic complete response were built using 306 samples and validated on 182 samples. Despite different populations and assays, M2EFM models validated with good accuracy (C-index or AUC ≥ 0.7) for all outcomes and had the most consistent performance compared to other methods. Finally, we demonstrated that M2EFM identifies functionally relevant genes, which could be useful in translating an M2EFM biomarker to the clinic.

National Estimates of and Risk Factors for Inpatient Revision Surgeries for Orofacial Clefts.

OBJECTIVE: To provide national estimates of the number and cost of primary and revision cleft lip and palate surgeries in the U.S. and to determine patient and hospital characteristics associated with disproportionate use of revision surgery. DESIGN: Retrospective cross-sectional study using data obtained from the 2003, 2006, and 2009 Kids' Inpatient Database. SETTING: Inpatient. PATIENTS: Children with CL, CP, or CLP undergoing inpatient cleft lip and/or palate surgery. INTERVENTIONS: Inpatient cleft lip and/or palate surgery. MAIN OUTCOME MEASURES: Orofacial cleft surgery estimates, estimates of primary versus revision surgeries, and estimated inflation-adjusted hospitalization costs. RESULTS: In 2009, there were a total of 2824 and 5431 hospitalizations for cleft lip and palate surgeries, respectively. Revision surgery accounted for 24.2% of cleft lip surgeries and 36.8% of cleft palate surgeries. Children with CLP (OR 1.87, 95% CI: 1.48-2.38), a syndromic diagnosis (OR 1.47, 95% CI: 1.16-1.87), or private insurance (OR 1.71, 95% CI: 1.41-2.09) were more likely to undergo cleft lip revision surgery. Similar risk factors were found for children undergoing cleft palate revision. Mean cost per hospitalization ranged from $7564 to $8393 in 2009, depending on surgery type, and did not change significantly (in 2009 U.S. $) between 2003 and 2009. CONCLUSIONS: Interventions to reduce revision surgery by improving results of primary surgery should be targeted in the population of identified high-risk (e.g., syndromic) patients. In addition, the association of health insurance status with revision surgery highlights the need to understand and address the impact of economic disparities on cleft care delivery.

A METHYLATION-TO-EXPRESSION FEATURE MODEL FOR GENERATING ACCURATE PROGNOSTIC RISK SCORES AND IDENTIFYING DISEASE TARGETS IN CLEAR CELL KIDNEY CANCER.

Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration captures relationships in the data that represent highly disease-relevant functions.