Disparities in Clinical Trial Participation: A Cross-Sectional Survey of Cancer Patients at a Midwest Academic Medical Center.

Research conducted on homogenous populations can lead to biased and misleading findings, impeding the development of effective interventions and treatments for diverse populations. Low participation among minority groups further leads to disparities in access to innovative cancer care and treatment outcomes associated with trial participation. To better understand cancer patients' attitudes and willingness to participate in clinical trials, solid tumor patients attending their clinic visits were invited to complete a survey. The survey included questions on demographics, previous trial participation, and future trial interest. Responses were analyzed with frequency tables and chi-square tests. Of 300 participants, only 96 (32%) were asked to participate in a clinical trial. Of these, 81 (84%) chose to participate and 15 (16%) did not. There were notable differences by race but not gender or education level. Of the 204 who had never been asked to participate, 70% indicated that they would be willing to participate in future, and there was a strong sex-race interaction. Non-White males were the most hesitant group. Of 204, 99% indicated that they would participate to access new treatments, and 57% would participate to contribute to research overall. This study shows that many solid tumor patients undergoing treatment are not offered clinical trials. Racial differences in attitudes toward trial participation are evident. Nonetheless, many patients are willing to participate in trials to access innovative treatments and to support research. Culturally relevant outreach to build trust with minority groups is needed to increase overall participation in clinical trials.

PTPN11 Mutation Clonal Hierarchy in Acute Myeloid Leukemia.

Mutations in protein tyrosine phosphatase non-receptor type 11 ( PTPN11 ) have been considered late acquired mutations in acute myeloid leukemia (AML) development. To interrogate the ontogeny of PTPN11 mutations, we utilized single-cell DNA sequencing and identified that PTPN11 mutations can occur as initiating events in some AML patients when accompanied by strong oncogenic drivers, commonly NPM1 mutations. The co-driver role of PTPN11 mutations was confirmed in a novel murine model that exhibits an AML phenotype with early expansion of a diverse set of variably differentiated myeloid cells that engrafted into immunodeficient and immunocompetent mice. This immune diversity was reconstituted from early precursor cells when engrafted into immunodeficient mice. Moreover, immune diversity was also observed in the blast component of patient samples with NPM1 and PTPN11 mutations, providing novel antigen targets for immune based approaches in this subset of AML that is resistant to multiple targeted therapies.

Estimating Transition Intensity Rate on Interval-censored Data Using Semi-parametric with EM Algorithm Approach.

Phase IV clinical trials are designed to monitor long-term side effects of medical treatment. For instance, childhood cancer survivors treated with chest radiation and/or anthracycline are often at risk of developing cardiotoxicity during their adulthood. Often the primary focus of a study could be on estimating the cumulative incidence of a particular outcome of interest such as cardiotoxicity. However, it is challenging to evaluate patients continuously and usually, this information is collected through cross-sectional surveys by following patients longitudinally. This leads to interval-censored data since the exact time of the onset of the toxicity is unknown. Rai et al. computed the transition intensity rate using a parametric model and estimated parameters using maximum likelihood approach in an illness-death model. However, such approach may not be suitable if the underlying parametric assumptions do not hold. This manuscript proposes a semi-parametric model, with a logit relationship for the treatment intensities in two groups, to estimate the transition intensity rates within the context of an illness-death model. The estimation of the parameters is done using an EM algorithm with profile likelihood. Results from the simulation studies suggest that the proposed approach is easy to implement and yields comparable results to the parametric model.

Refining Surveillance Guidelines after Stereotactic Body Radiation Therapy for Early-Stage Lung Cancer.

INTRODUCTION: Stereotactic body radiation therapy (SBRT) is a treatment for patients with early-stage non-small cell lung cancer (ES-NSCLC). Surveillance guidelines vary after treatment. While patients are more likely to locally recur within 2 years of treatment, there remains a paucity of data on the benefit of frequent and long-term surveillance. We evaluated a cohort of NSCLC patients to evaluate surveillance patterns and outcomes. MATERIALS AND METHODS: Patients with ES-NSCLC treated with SBRT were retrospectively evaluated. Imaging was reviewed after SBRT for evidence of recurrence or new malignancy. The median scan interval (MSI) was calculated as the median number of months between surveillance scans. The MSI between patients with or without new disease was compared by t-test. New disease development and survival between patients with =T2 disease and with or without prior malignancy was compared using χ², Kaplan-Meier analysis, and Gray's test. RESULTS: A cohort of 168 patients with median follow up of 23.4 months met criteria for review with 50% developing new disease. MSI did not differ between patients with or without new disease. Patients with >=cT2 tumors had worse overall survival and trended towards higher incidence of new disease. New disease continued to occur, even 5 years after treatment. CONCLUSION: Increased scan frequency did not increase detection of new disease. Patients continued to fail 5 years after treatment. Larger tumors trended toward more frequent failures and those patients experienced worse OS. Surveillance guidelines should be optimized to prevent over surveillance after treatment and to continue long-term surveillance.

The effect of treatment package time on locally advanced oral cavity cancer outcomes.

OBJECTIVE(S): To assess the influence of treatment package time (TPT) on overall survival (OS) and event free survival (EFS) in oral cavity cancer (OCC) patients treated with surgery and adjuvant radiation therapy (RT) with or without concurrent chemotherapy (CHT). MATERIALS/METHODS: 354 adult OCC patients treated at a single, high-volume center between 2012-2022 with various pathologic risk features were included. TPT was defined as days from surgery to RT completion. Kaplan-Meier estimates, log-rank p-values, univariable (UVA) and multivariable (MVA) Cox regression analyses were performed to determine the impact of TPT on OS and EFS, and the optimal TPT cutoff. RESULTS: The optimal TPT cutoff was 105 days. TPT < 105 days was significantly associated with improved OS and EFS (p = 0.002 and p = 0.027, respectively) compared to TPT ≥ 105 days. On UVA, factors significantly associated with OS were TPT < 105 days, former/current smoker status, pathologic stage IV, positive perineural invasion (PNI), and extranodal extension (ENE) (all p < 0.05). On MVA for OS, TPT < 105 days, former/current smoker status, pathologic stage IV, and positive PNI (all p < 0.05) remained significant. Factors significantly associated with EFS on UVA were TPT < 105 days, former/current smoker status, pathologic stage IV, positive PNI or ENE, and concurrent CHT (all p < 0.05). On MVA, TPT < 105 days, pathologic stage IV, and positive PNI (all p < 0.05) remained significant. CONCLUSIONS: In a large, homogenous cohort of OCCs, optimal TPT was <105 days, with TPT ≥ 105 days significantly associated with worse OS and EFS. Multidisciplinary coordination should analyze factors potentially contributing to treatment delay.

Disparities Associated with Decision to Undergo Oncologic Surgery: A Prospective Mixed-Methods Analysis.

BACKGROUND: Underrepresented minority patients with surgical malignancies experience disparities in outcomes. The impact of provider-based factors, including communication, trust, and cultural competency, on outcomes is not well understood. This study examines modifiable provider-based barriers to care experienced by patients with surgical malignancies. METHODS: A parallel, prospective, mixed-methods study enrolled patients with lung or gastrointestinal malignancies undergoing surgical consultation. Surveys assessed patients' social needs and patient-physician relationship. Semi-structured interviews ascertained patient experiences and were iteratively analyzed, identifying key themes. RESULTS: The cohort included 24 patients (age 62 years; 63% White and 38% Black/African American). The most common cancers were lung (n = 18, 75%) and gastroesophageal (n = 3, 13%). Survey results indicated that food insecurity (n = 5, 21%), lack of reliable transportation (n = 4, 17%), and housing instability (n = 2, 8%) were common. Lack of trust in their physician (n = 3, 13%) and their physician's treatment recommendation (n = 3, 13%) were identified. Patients reported a lack of empathy (n = 3, 13%), lack of cultural competence (n = 3, 13%), and inadequate communication (n = 2, 8%) from physicians. Qualitative analysis identified five major themes regarding the decision to undergo surgery: communication, trust, health literacy, patient fears, and decision-making strategies. Five patients (21%) declined the recommended surgery and were more likely Black (100% vs. 21%), lower income (100% vs. 16%), and reported poor patient-physician relationship (40% vs. 5%; all p < 0.05). CONCLUSIONS: Factors associated with declining recommended cancer surgery were underrepresented minority race and poor patient-physician relationships. Interventions are needed to improve these barriers to care and racial disparities.

Pyrimidine depletion enhances targeted and immune therapy combinations in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a fatal disease characterized by the accumulation of undifferentiated myeloblasts, and agents that promote differentiation have been effective in this disease but are not curative. Dihydroorotate dehydrogenase inhibitors (DHODHi) have the ability to promote AML differentiation and target aberrant malignant myelopoiesis. We introduce HOSU-53, a DHODHi with significant monotherapy activity, which is further enhanced when combined with other standard-of-care therapeutics. We further discovered that DHODHi modulated surface expression of CD38 and CD47, prompting the evaluation of HOSU-53 combined with anti-CD38 and anti-CD47 therapies, where we identified a compelling curative potential in an aggressive AML model with CD47 targeting. Finally, we explored using plasma dihydroorotate (DHO) levels to monitor HOSU-53 safety and found that the level of DHO accumulation could predict HOSU-53 intolerability, suggesting the clinical use of plasma DHO to determine safe DHODHi doses. Collectively, our data support the clinical translation of HOSU-53 in AML, particularly to augment immune therapies. Potent DHODHi to date have been limited by their therapeutic index; however, we introduce pharmacodynamic monitoring to predict tolerability while preserving antitumor activity. We additionally suggest that DHODHi is effective at lower doses with select immune therapies, widening the therapeutic index.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression.

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Inhibition of Enhancer of Zeste Homolog 2 Induces Blast Differentiation, Impairs Engraftment and Prolongs Survival in Murine Models of Acute Myeloid Leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is the malignant proliferation of immature myeloid cells characterized by a block in differentiation. As such, novel therapeutic strategies to promote the differentiation of immature myeloid cells have been successful in AML, although these agents are targeted to a specific mutation that is only present in a subset of AML patients. In the current study, we show that targeting the epigenetic modifier enhancer of zeste homolog 2 (EZH2) can induce the differentiation of immature blast cells into a more mature myeloid phenotype and promote survival in AML murine models. METHODS: The EZH2 inhibitor EPZ011989 (EPZ) was studied in AML cell lines, primary in AML cells and normal CD34+ stem cells. A pharmacodynamic assessment of H3K27me3; studies of differentiation, cell growth, and colony formation; and in vivo therapeutic studies including the influence on primary AML cell engraftment were also conducted. RESULTS: EPZ inhibited H3K27me3 in AML cell lines and primary AML samples in vitro. EZH2 inhibition reduced colony formation in multiple AML cell lines and primary AML samples, while exhibiting no effect on colony formation in normal CD34+ stem cells. In AML cells, EPZ promoted phenotypic evidence of differentiation. Finally, the pretreatment of primary AML cells with EPZ significantly delayed engraftment and prolonged the overall survival when engrafted into immunodeficient mice. CONCLUSIONS: Despite evidence that EZH2 silencing in MDS/MPN can promote AML pathogenesis, our data demonstrate that the therapeutic inhibition of EZH2 in established AML has the potential to improve survival.

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach.

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

Exposure to Volatile Organic Compounds Is Associated with Hypertension in Black Adults: The Jackson Heart Study.

BACKGROUND: The prevalence of hypertension is higher among Black adults than among White and Hispanic adults. Nevertheless, reasons underlying the higher rates of hypertension in the Black population remain unclear but may relate to exposure to environmental chemicals such as volatile organic compounds (VOCs). METHODS: We evaluated the associations of blood pressure (BP) and hypertension with VOC exposure in non-smokers and smokers in a subgroup of the Jackson Heart Study (JHS), consisting of 778 never smokers and 416 age- and sex-matched current smokers. We measured urinary metabolites of 17 VOCs by mass spectrometry. RESULTS: After adjusting for covariates, we found that amoong non-smokers, metabolites of acrolein and crotonaldehyde were associated with a 1.6 mm Hg (95%CI: 0.4, 2.7; p = 0.007) and a 0.8 mm Hg (95%CI: 0.01, 1.6; p = 0.049) higher systolic BP, and the styrene metabolite was associated with a 0.4 mm Hg (95%CI: 0.09, 0.8, p = 0.02) higher diastolic BP. Current smokers had 2.8 mm Hg (95% CI 0.5, 5.1) higher systolic BP. They were at higher risk of hypertension (relative risk = 1.2; 95% CI, 1.1, 1.4), and had higher urinary levels of several VOC metabolites. Individuals who smoke had higher levels of the urinary metabolites of acrolein, 1,3-butadiene, and crotonaldehyde and were associated with higher systolic BP. The associations were stronger among participants who were <60 years of age and male. Using Bayesian kernel machine regression to assess the effects of multiple VOC exposures, we found that the relationship between VOCs and hypertension among non-smokers was driven primarily by acrolein and styrene in non-smokers, and crotonaldehyde in smokers. CONCLUSIONS: Hypertension in Black individuals may be attributed, in part, to VOC exposure from the environment or tobacco smoke.

Metallomics in pulmonary arterial hypertension patients.

Pulmonary arterial hypertension (PAH) prevalence is increasing worldwide, and the prognosis is poor with 5-year survival < 50% in high risk patients. The relationship between metal exposure/essential metal dyshomeostasis and PAH/right ventricular dysfunction is less investigated. The aim of this study is to investigate vegetable consumptions and metal levels between PAH patients and controls. This was a prospective, single center pilot study. Questionnaires were completed by all study subjects (20 PAH patients and 10 healthy controls) on smoking, metal exposure risks, metal supplements, and vegetable consumptions. Blood and urine samples were collected to measure 25 metal levels in blood, plasma, and urine using an X Series II quadrupole inductively coupled plasma mass spectrometry. Statistical analysis was conducted using SAS 9.5 and results with p value < 0.05 were considered significant. Vegetables consumptions (broccoli risk ratio [RR] = 0.4, CI = (0.2, 0.9)], cabbage [RR = 0.2, CI = (0.1, 0.8)], and brussel sprouts [RR = 0.2, CI = (0.1, 0.5)]) are associated with less risks of PAH. In the plasma samples, silver (p < 0.001), and copper (p = 0.002) levels were significantly higher in PAH patients. There was significant positive correlation between cardiac output and cardiac index with plasma levels of silver (r = 0.665, p = 0.001 and r = 0.678 p = 0.001), respectively. There was significant correlation between mixed venous saturation, 6-min walk distance, and last BNP with plasma levels of chromium (r = -0.520, p = 0.022; r = -0.55, p = 0.014; r = 0.463, p = 0.039), respectively. In conclusion, there are significant differences between PAH and control groups in terms of vegetable consumptions and metal concentrations. Silver and chromium levels are correlated with clinical indicators of PAH severities.

Associations between residential volatile organic compound exposures and liver injury markers: The role of biological sex and race.

While occupational exposures to volatile organic compounds (VOCs) have been linked to steatohepatitis and liver cancer in industrial workers, recent findings have also positively correlated low-dose, residential VOC exposures with liver injury markers. VOC sources are numerous; factors including biological make up (sex), socio-cultural constructs (gender, race) and lifestyle (smoking) can influence both VOC exposure levels and disease outcomes. Therefore, the current study's objective is to investigate how sex and race influence associations between residential VOC exposures and liver injury markers particularly in smokers vs. nonsmokers. Subjects (n = 663) were recruited from residential neighborhoods; informed consent was obtained. Exposure biomarkers included 16 urinary VOC metabolites. Serological disease biomarkers included liver enzymes, direct bilirubin, and hepatocyte death markers (cytokeratin K18). Pearson correlations and generalized linear models were conducted. Models were adjusted for common liver-related confounders and interaction terms. The study population constituted approximately 60% females (n = 401) and 40% males (n = 262), and a higher percent of males were smokers and/or frequent drinkers. Both sexes had a higher percent of White (75% females, 82% males) vs. Black individuals. Positive associations were identified for metabolites of acrolein, acrylamide, acrylonitrile, butadiene, crotonaldehyde, and styrene with alkaline phosphatase (ALP), a biomarker for cholestatic injury; and for the benzene metabolite with bilirubin; only in females. These associations were retained in female smokers. Similar associations were also observed between these metabolites and ALP only in White individuals (n = 514). In Black individuals (n = 114), the styrene metabolite was positively associated with aspartate transaminase. Interaction models indicated that positive associations for acrylamide/crotonaldehyde metabolites with ALP in females were dose-dependent. Most VOC associations with K18 markers were negative in this residential population. Overall, the findings demonstrated that biological sex, race, and smoking status influence VOC effects on liver injury and underscored the role of biological-social-lifestyle factor(s) interactions when addressing air pollution-related health disparities.

Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.

Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.

Estimation of Preclinical State Onset Age and Sojourn Time for Heavy Smokers in Lung Cancer.

Estimation of the three key parameters: onset age of the preclinical state, sojourn time and screening sensitivity is critical in cancer screening, since all other terms are functions of the three. A novel link function to connect sensitivity with time in the preclinical state and the likelihood method were used in this project; since sensitivity depends on how long one has entered the preclinical state relative to the total sojourn time. Simulations using Markov Chain Monte Carlo and maximum likelihood estimate were carried out to estimate the key parameters for male and female heavy smokers separately in the low-dose computed tomography group of the National Lung Screening Trial. Sensitivity for male and female heavy smokers were 0.883 and 0.915 respectively at the onset of the preclinical state, and increased to 0.972 and 0.981 at the end. The mean age to make the transition into the preclinical state was 70.94 or 71.15 for male and female heavy smokers respectively, and 90% of heavy smokers at risk for lung cancer would enter the preclinical state in age interval (55.7, 85.8) for males and (54.2, 87.7) for females, and the transition peaked around age 69 for both genders. The mean sojourn time in the preclinical state was 1.43 and 1.49 years, and the 99% credible intervals for the sojourn time were (0.21, 2.96) and (0.37, 2.69) years for male and female heavy smokers correspondingly. Based on the result, low-dose CT should be started at age 55 and ended before 85 for heavy smokers. This provided important information to policy makers.

Cervical Cancer Screening and Associated Barriers among Women in India: A Generalized Structural Equation Modeling Approach.

Exploring the barriers and facilitators of cervical cancer screening (CCS) is essential to reduce the incidence and mortality, particularly in low and middle-income countries. The present study investigates the direct, indirect, and total effects of the barriers and facilitators on CCS in India through the generalized structural equation modeling using data from women files of the fourth round of the National Family Health Survey (NFHS-4). Generalized structural equation models were used to quantify the hypothetical pathway via fitting a series of regression equations. Age, body mass index, religion, years of schooling, awareness of sexually transmitted infection, contraception use, lifetime number of sex partners, number of children, and wealth index were shown to have significant direct effects on the CCS. Older women had 1.16 times the odds of getting screened for cervical cancer as compared to their younger counterpart. The odds of CCS among the women in richest wealth quintile is 2.50 times compared to the poorest. Those who are aware of STIs have 1.39 times the odds of getting screened for cervical cancer. Wealth index, years of schooling, and religion have a substantial indirect and total impact on the CCS. The findings will aid in policy formulations for enhancing the CCS in India.

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival.

INTRODUCTION: Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive. OBJECTIVES: This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS. METHODS: Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events. RESULTS: Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates). CONCLUSION: Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

Applications of Bayesian shrinkage prior models in clinical research with categorical responses.

BACKGROUND: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions such as diabetes, colon cancer, and Alzheimer's disease. Developing accurate prediction and classification methods benefits personalized medicine. Building an excellent predictive model involves selecting the features that are most significantly associated with the outcome. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. This work discusses variable selection with three shrinkage priors and illustrates its application to clinical data such as Pima Indians Diabetes, Colon cancer, ADNI, and OASIS Alzheimer's real-world data. METHODS: A unified Bayesian hierarchical framework that implements and compares shrinkage priors in binary and multinomial logistic regression models is presented. The key feature is the representation of the likelihood by a Polya-Gamma data augmentation, which admits a natural integration with a family of shrinkage priors, specifically focusing on Horseshoe, Dirichlet Laplace, and Double Pareto priors. Extensive simulation studies are conducted to assess the performances under different data dimensions and parameter settings. Measures of accuracy, AUC, brier score, L1 error, cross-entropy, and ROC surface plots are used as evaluation criteria comparing the priors with frequentist methods as Lasso, Elastic-Net, and Ridge regression. RESULTS: All three priors can be used for robust prediction on significant metrics, irrespective of their categorical response model choices. Simulation studies could achieve the mean prediction accuracy of 91.6% (95% CI: 88.5, 94.7) and 76.5% (95% CI: 69.3, 83.8) for logistic regression and multinomial logistic models, respectively. The model can identify significant variables for disease risk prediction and is computationally efficient. CONCLUSIONS: The models are robust enough to conduct both variable selection and prediction because of their high shrinkage properties and applicability to a broad range of classification problems.

Environmental exposure to volatile organic compounds is associated with endothelial injury.

OBJECTIVE: Volatile organic compounds (VOCs) are airborne toxicants abundant in outdoor and indoor air. High levels of VOCs are also present at various Superfund and other hazardous waste sites; however, little is known about the cardiovascular effects of VOCs. We hypothesized that ambient exposure to VOCs exacerbate cardiovascular disease (CVD) risk by depleting circulating angiogenic cells (CACs). APPROACH AND RESULTS: In this cross-sectional study, we recruited 603 participants with low-to-high CVD risk and measured 15 subpopulations of CACs by flow cytometry and 16 urinary metabolites of 12 VOCs by LC/MS/MS. Associations between CAC and VOC metabolite levels were examined using generalized linear models in the total sample, and separately in non-smokers. In single pollutant models, metabolites of ethylbenzene/styrene and xylene, were negatively associated with CAC levels in both the total sample, and in non-smokers. The metabolite of acrylonitrile was negatively associated with CD45dim/CD146+/CD34+/AC133+ cells and CD45+/CD146+/AC133+, and the toluene metabolite with AC133+ cells. In analysis of non-smokers (n = 375), multipollutant models showed a negative association with metabolites of ethylbenzene/styrene, benzene, and xylene with CD45dim/CD146+/CD34+ cells, independent of other VOC metabolite levels. Cumulative VOC risk score showed a strong negative association with CD45dim/CD146+/CD34+ cells, suggesting that total VOC exposure has a cumulative effect on pro-angiogenic cells. We found a non-linear relationship for benzene, which showed an increase in CAC levels at low, but depletion at higher levels of exposure. Sex and race, hypertension, and diabetes significantly modified VOC associated CAC depletion. CONCLUSION: Low-level ambient exposure to VOCs is associated with CAC depletion, which could compromise endothelial repair and angiogenesis, and exacerbate CVD risk.

Circulating MicroRNAs, Polychlorinated Biphenyls, and Environmental Liver Disease in the Anniston Community Health Survey.

BACKGROUND: Polychlorinated biphenyl (PCB) exposures have been associated with liver injury in human cohorts, and steatohepatitis with liver necrosis in model systems. MicroRNAs (miRs) maintain cellular homeostasis and may regulate the response to environmental stress. OBJECTIVES: We tested the hypothesis that specific miRs are associated with liver disease and PCB exposures in a residential cohort. METHODS: Sixty-eight targeted hepatotoxicity miRs were measured in archived serum from 734 PCB-exposed participants in the cross-sectional Anniston Community Health Survey. Necrotic and other liver disease categories were defined by serum keratin 18 (K18) biomarkers. Associations were determined between exposure biomarkers (35 ortho-substituted PCB congeners) and disease biomarkers (highly expressed miRs or previously measured cytokines), and Ingenuity Pathway Analysis was performed. RESULTS: The necrotic liver disease category was associated with four up-regulated miRs (miR-99a-5p, miR-122-5p, miR-192-5p, and miR-320a) and five down-regulated miRs (let-7d-5p, miR-17-5p, miR-24-3p, miR-197-3p, and miR-221-3p). Twenty-two miRs were associated with the other liver disease category or with K18 measurements. Eleven miRs were associated with 24 PCBs, most commonly congeners with anti-estrogenic activities. Most of the exposure-associated miRs were associated with at least one serum hepatocyte death, pro-inflammatory cytokine or insulin resistance bioarker, or with both. Within each biomarker category, associations were strongest for the liver-specific miR-122-5p. Pathways of liver toxicity that were identified included inflammation/hepatitis, hyperplasia/hyperproliferation, cirrhosis, and hepatocellular carcinoma. Tumor protein p53 and tumor necrosis factor α were well integrated within the top identified networks. DISCUSSION: These results support the human hepatotoxicity of environmental PCB exposures while elucidating potential modes of PCB action. The MiR-derived liquid liver biopsy represents a promising new technique for environmental hepatology cohort studies. https://doi.org/10.1289/EHP9467.