Multi-Omics after O-GlcNAc Alteration Identifies Cellular Processes Working Synergistically to Promote Aneuploidy.

Pharmacologic or genetic manipulation of O-GlcNAcylation, an intracellular, single sugar post-translational modification, are difficult to interpret due to the pleotropic nature of O-GlcNAc and the vast signaling pathways it regulates. To address this issue, we employed either OGT (O-GlcNAc transferase), OGA (O-GlcNAcase) liver knockouts, or pharmacological inhibition of OGA coupled with multi-Omics analysis and bioinformatics. We identified numerous genes, proteins, phospho-proteins, or metabolites that were either inversely or equivalently changed between conditions. Moreover, we identified pathways in OGT knockout samples associated with increased aneuploidy. To test and validate these pathways, we induced liver growth in OGT knockouts by partial hepatectomy. OGT knockout livers showed a robust aneuploidy phenotype with disruptions in mitosis, nutrient sensing, protein metabolism/amino acid metabolism, stress response, and HIPPO signaling demonstrating how OGT is essential in controlling aneuploidy pathways. Moreover, these data show how a multi-Omics platform can discern how OGT can synergistically fine-tune multiple cellular pathways.

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.

Urologist Access for the Male Population: An 18-Year Study on Population Trends.

INTRODUCTION: Our study examines the factors associated with urologist availability for younger and older men across the country over a period of 18 years from 2000 to 2018. METHODS: The Area Health Resource Files and US Census Data were analyzed from 2000, 2010, and 2018. The younger male population was defined as men aged 20 to 49, and the older male population was defined as ages 50 to 79. Urologist availability was determined by county at all time points. Logistic regression analysis and geographically weighted regression was completed. RESULTS: Over an 18-year period, overall urologist availability decreased for men by 19.6%. Access to urologist availability for men in metropolitan and rural counties decreased by 9.4% and 29.5%, respectively. Among the younger male cohort, urologist availability increased in metropolitan counties by 4%, but decreased by 16% in rural counties. There was an overall decrease in urologist availability of 28% and 43% in metropolitan and rural counties in the older male population. Multiple logistic regression analysis demonstrated that metropolitan status was the most significant factor associated with urologist availability for both male populations. The odds of each independent factor predicting urologist availability for the younger and older male population is dependent on geography. CONCLUSIONS: The majority of the male population has seen a decline in urologist availability. This is especially true for the older male residing in a rural county. Predictors of urologist availability depend on geographical regions, and understanding these regional drivers may allow us to better address disparities in urological care.

The rural mortality penalty in U.S. hospital patients with COVID-19.

Background: The COVID-19 pandemic brought greater focus to the rural mortality penalty in the U.S., which describes the greater mortality rate in rural compared to urban areas. Although it is understood that issues such as access to care, age structure of the population, and differences in behavior are likely drivers of the rural mortality penalty, it is critical to try and understand these factors to enable more effective public health policy. Methods: We performed a cross-sectional analysis of a population of patients with COVID-19 who were admitted to hospitals in the United States between 3/1/2020 and 2/26/2023 to better understand factors leading to outcome disparities amongst groups that all had some level of access to hospital care, hypothesizing that deteriorated patient condition at admission likely explained some of the observed difference in mortality between rural and urban populations. Results: Our results supported our hypothesis, showing that the rural mortality penalty persists in this population and that by multiple measures, rural patients were likely to be admitted in worse condition, had worse overall health, and were older. Conclusions: Although the pandemic threw the rural mortality penalty into sharp relief, it is important to remember that it existed prior to the pandemic and will continue to exist until effective interventions are implemented. This study demonstrates the critical need to address the underlying factors that resulted in rural-dwelling patients being admitted to the hospital in worse condition than their urban-dwelling counterparts during the COVID-19 pandemic, which likely affected other healthcare outcomes as well.

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.

AMEND: active module identification using experimental data and network diffusion.

BACKGROUND: Molecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein-protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions. RESULTS: To address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets. CONCLUSION: The AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND .

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.

pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples.

Batch effect Reduction of mIcroarray data with Dependent samples usinG Empirical Bayes (BRIDGE) is a recently developed statistical method to address the issue of batch effect correction in batch-confounded microarray studies with dependent samples. The key component of the BRIDGE methodology is the use of samples run as technical replicates in two or more batches, "bridging samples", to inform batch effect correction/attenuation. While previously published results indicate a relationship between the number of bridging samples, M, and the statistical power of downstream statistical testing on the batch-corrected data, there is of yet no formal statistical framework or user-friendly software, for estimating M to achieve a specific statistical power for hypothesis tests conducted on the batch-corrected data. To fill this gap, we developed pwrBRIDGE, a simulation-based approach to estimate the bridging sample size, M, in batch-confounded longitudinal microarray studies. To illustrate the use of pwrBRIDGE, we consider a hypothetical, longitudinal batch-confounded study whose goal is to identify Alzheimer's disease (AD) progression-associated genes from amnestic mild cognitive impairment (aMCI) to AD in human blood after a 5-year follow-up. pwrBRIDGE helps researchers design and plan batch-confounded microarray studies with dependent samples to avoid over- or under-powered studies.

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.

A Novel Framework for the Identification of Reference DNA Methylation Libraries for Reference-Based Deconvolution of Cellular Mixtures.

Reference-based deconvolution methods use reference libraries of cell-specific DNA methylation (DNAm) measurements as a means toward deconvoluting cell proportions in heterogeneous biospecimens (e.g., whole-blood). As the accuracy of such methods depends highly on the CpG loci comprising the reference library, recent research efforts have focused on the selection of libraries to optimize deconvolution accuracy. While existing approaches for library selection work extremely well, the best performing approaches require a training data set consisting of both DNAm profiles over a heterogeneous cell population and gold-standard measurements of cell composition (e.g., flow cytometry) in the same samples. Here, we present a framework for reference library selection without a training dataset (RESET) and benchmark it against the Legacy method (minfi:pickCompProbes), where libraries are constructed based on a pre-specified number of cell-specific differentially methylated loci (DML). RESET uses a modified version of the Dispersion Separability Criteria (DSC) for comparing different libraries and has four main steps: (1) identify a candidate set of cell-specific DMLs, (2) randomly sample DMLs from the candidate set, (3) compute the Modified DSC of the selected DMLs, and (4) update the selection probabilities of DMLs based on their contribution to the Modified DSC. Steps 2-4 are repeated many times and the library with the largest Modified DSC is selected for subsequent reference-based deconvolution. We evaluated RESET using several publicly available datasets consisting of whole-blood DNAm measurements with corresponding measurements of cell composition. We computed the RMSE and R 2 between the predicted cell proportions and their measured values. RESET outperformed the Legacy approach in selecting libraries that improve the accuracy of deconvolution estimates. Additionally, reference libraries constructed using RESET resulted in cellular composition estimates that explained more variation in DNAm as compared to the Legacy approach when evaluated in the context of epigenome-wide association studies (EWAS) of several publicly available data sets. This finding has implications for the statistical power of EWAS. RESET combats potential challenges associated with existing approaches for reference library assembly and thus, may serve as a viable strategy for library construction in the absence of a training data set.

18-Year Population Trends Determine Factors Associated with Future Access to Urologists.

INTRODUCTION: Our study evaluated urologist availability by United States county since 2000 relative to regional changes in the general population to identify factors associated with access to care. METHODS: County-level data from 2000, 2010 and 2018 from the Department of Health and Human Services, U.S. Census and American Community Survey were analyzed. Availability of urologists by county was defined as urologists per 10,000 adults. Multiple logistic and geographically weighted regression were performed. A predictive model was formulated with tenfold cross-validation (AUC=0.75). RESULTS: Despite a 6.95% increase in urologists over 18 years, local urologist availability declined 13% (-0.03 urologists/10,000 individuals, 95% CI 0.02-0.04, p <0.0001). On multiple logistic regression, metropolitan status was the greatest predictor of urologist availability (OR 1.86, 95% CI 1.47-2.34), followed by prior urologist presence (OR 1.49, 95% CI 1.16-1.89), defined as a higher number of urologists in 2000. The predictive weight of these factors varied by U.S. region. All regions experienced worsening overall urologist availability, with rural areas suffering the most. Large population shifts away from the Northeast to the West and South were outpaced by urologists leaving the Northeast, the only region with a decreasing number of total urologists (-1.36%). CONCLUSIONS: Urologist availability declined in every region over nearly 2 decades likely due to an increasing general population and inequitable regional migration. Predictors of urologist availability differed by region, and thus it will be necessary to investigate regional drivers influencing population shifts and urologist concentration to prevent worsening disparities in care.

Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE).

Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinGEmpirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called "bridge samples", to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinalComBat. Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.

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.