Dynamic impact of liver allocation policy change on donor utilization.

Liver allocation policy was changed to reduce variance in median MELD scores at transplant (MMaT) in February 2020. "Acuity circles" replaced local allocation. Understanding the impact of policy change on donor utilization is important. Ideal (I), standard (S), and non-ideal (NI) donors were defined. NI donors include older, higher BMI donors with elevated transaminases or bilirubin, history of hepatitis B or C, and all DCD donors. Utilization of I, S, and NI donors was established before and after allocation change and compared between low MELD (LM) centers (MMaT ≤ 28 before allocation change) and high MELD (HM) centers (MMaT > 28). Following reallocation, transplant volume increased nationally (67 transplants/center/year pre, 74 post, p .0006) and increased for both HM and LM centers. LM centers significantly increased use of NI donors and HM centers significantly increased use of I and S donors. Centers further stratify based on donor utilization phenotype. A subset of centers increased transplant volume despite rising MMaT by broadening organ acceptance criteria, increasing use of all donor types including DCD donors (98% increase), increasing living donation, and transplanting more frequently for alcohol associated liver disease. Variance in donor utilization can undermine intended effects of allocation policy change.

Psychological Symptom Trajectories and Non-Small Cell Lung Cancer Survival: A Joint Model Analysis.

OBJECTIVE: Lung cancer remains the number one cause of cancer-related mortality worldwide, but less known is that lung cancer patients are among the most psychologically disabled of all cancer groups. Patients with stage IV non-small cell lung cancer (NSCLC) were studied to test the hypothesis that trajectories of depression and/or anxiety symptoms after diagnosis would show an adverse relationship with survival, beyond relevant controls. METHODS: Patients with stage IV NSCLC (n = 157) were enrolled (ClinicalTrials.gov Identifier: NCT03199651) at diagnosis and completed validated measures for depressive symptoms (Patient Health Questionnaire-9) and anxiety symptoms (Generalized Anxiety Disorder-7). Patients were reassessed every 1 to 2 months through 24 months (16 assessments; 80% average completion rate) and survival monitored. Joint statistical models provided simultaneous modeling of longitudinal (psychological) and time-to-event (survival) processes. Control variables were age, sex, marital status, education, smoking status, cancer type, and treatment received. RESULTS: Depression and anxiety symptoms significantly decreased with time since diagnosis. The 2-year trajectory of depressive symptoms was significantly associated with cancer survival after adjustment for covariates (hazard ratio = 1.09 per unit increase in the Patient Health Questionnaire-9, 95% confidence interval = 1.03-1.15, p = .002). Anxiety was marginally significant in the unadjusted (p = .053) but not the adjusted (p = .39) model. CONCLUSIONS: For the first time, joint model analyses test the interaction of a longitudinal trajectory of psychological symptoms, assessed from diagnosis to 24 months, and cancer survival. New data show the continuation of depressive and anxiety symptoms through treatment and thereafter. Immunotherapy and targeted therapies have dramatically improved survival for patients with advanced NSCLC; however, novel data suggest their benefit may be constrained by depressive symptoms.

Surgical resection rates after neoadjuvant therapy for localized pancreatic ductal adenocarcinoma: meta-analysis.

BACKGROUND: Neoadjuvant therapy is increasingly being used before surgery for localized pancreatic cancer. Given the importance of completing multimodal therapy, the aim of this study was to characterize surgical resection rates after neoadjuvant therapy as well as the reasons for, and long-term prognostic impact of, not undergoing resection. METHODS: A systematic review and meta-analysis of prospective trials and high-quality retrospective studies since 2010 was performed to calculate pooled resection rates using a generalized random-effects model for potentially resectable, borderline resectable, and locally advanced pancreatic cancer. Median survival times were calculated using random-effects models for patients who did and did not undergo resection. RESULTS: In 125 studies that met the inclusion criteria, neoadjuvant therapy consisted of chemotherapy (36.8 per cent), chemoradiation (15.2 per cent), or chemotherapy and radiation (48.0 per cent). Among 11 713 patients, the pooled resection rates were 77.4 (95 per cent c.i. 71.3 to 82.5), 60.6 (54.8 to 66.1), and 22.2 (16.7 to 29.0) per cent for potentially resectable, borderline resectable, and locally advanced pancreatic cancer respectively. The most common reasons for not undergoing resection were distant progression for resectable and borderline resectable cancers, and local unresectability for locally advanced disease. Among 42 studies with survival data available, achieving surgical resection after neoadjuvant therapy was associated with improved survival for patients with potentially resectable (median 38.5 versus 13.3 months), borderline resectable (32.3 versus 13.9 months), and locally advanced (30.0 versus 14.6 months) pancreatic cancer (P < 0.001 for all). CONCLUSION: Although rates of surgical resection after neoadjuvant therapy vary based on anatomical stage, surgery is associated with improved survival for all patients with localized pancreatic cancer. These pooled resection and survival rates may inform patient-provider decision-making and serve as important benchmarks for future prospective trials.

Exposure to coal ash and depression in children aged 6-14 years old.

BACKGROUND: When coal is burned for energy, coal ash, a hazardous waste product, is generated. Throughout the world, over 1 billion tons of coal ash is produced yearly. In the United States, over 78 million tons of coal ash was produced in 2019. Fly ash, the main component of coal ash contains neurotoxic metal (loid)s that may affect children's neurodevelopment and mental health. The objective of this study was to investigate the association between fly ash and depressive problems in children aged 6-14 years old. METHODS: Children and their parents/guardians were recruited from 2015 to 2020. Tobit regression and logistic regression were used to assess the association between coal fly ash and depressive problems. To determine fly ash presence, Scanning Electron Microscopy was conducted on polycarbonate filters containing PM10 from the homes of the study participants. Depressive problems in children were measured using the Depressive Problems DSM and withdrawn/depressed syndromic problem scales of the Child Behavior Checklist. RESULTS: In covariate-adjusted Tobit regression models, children with fly ash on the filter had higher scores on the DSM Depressive Problems (3.13 points; 95% CI = 0.39, 5.88) compared with children who did not have fly ash on the filter. Logistic regression supported these findings. CONCLUSION: Coal ash is one of the largest waste streams in the U.S, but it is not classified as a hazardous waste by the Environmental Protection Agency. To our knowledge, no studies have assessed the impact of coal ash on children's mental health. This study highlights the need for further research into the effects of coal ash exposure on children's mental health, and improved regulations on release and storage of coal ash.

Nail Samples of Children Living near Coal Ash Storage Facilities Suggest Fly Ash Exposure and Elevated Concentrations of Metal(loid)s.

Children who live near coal-fired power plants are exposed to coal fly ash, which is stored in landfills and surface impoundments near residential communities. Fly ash has the potential to be released as fugitive dust. Using data collected from 263 children living within 10 miles of coal ash storage facilities in Jefferson and Bullitt Counties, Kentucky, USA, we quantified the elements found in nail samples. Furthermore, using principal component analysis (PCA), we investigated whether metal(loid)s that are predominately found in fly ash loaded together to indicate potential exposure to fly ash. Concentrations of several neurotoxic metal(loid)s, such as chromium, manganese, and zinc, were higher than concentrations reported in other studies of both healthy and environmentally exposed children. From PCA, it was determined that iron, aluminum, and silicon in fly ash were found to load together in the nails of children living near coal ash storage facilities. These metal(loid)s were also highly correlated with each other. Last, results of geospatial analyses partially validated our hypothesis that children's proximity to power plants was associated with elevated levels of concentrations of fly ash metal(loid)s in nails. Taken together, nail samples may be a powerful tool in detecting exposure to fly ash.

Dual-specificity phosphatase (DUSP) genetic variants predict pulmonary hypertension in patients with bronchopulmonary dysplasia.

BACKGROUND: Pulmonary hypertension (PH) in patients with bronchopulmonary dysplasia (BPD) results from vasoconstriction and/or vascular remodeling, which can be regulated by mitogen-activated protein kinases (MAPKs). MAPKs are deactivated by dual-specificity phosphatases (DUSPs). We hypothesized that single-nucleotide polymorphisms (SNPs) in DUSP genes could be used to predict PH in BPD. METHODS: Preterm infants diagnosed with BPD (n = 188) were studied. PH was defined by echocardiographic criteria. Genomic DNA isolated from patient blood samples was analyzed for 31 SNPs in DUSP genes. Clinical characteristics and minor allele frequencies were compared between BPD-PH (cases) and BPD-without PH (control) groups. Biomarker models to predict PH in BPD using clinical and SNP data were tested by calculations of area under the ROC curve. RESULTS: In our BPD cohort, 32% (n = 61) had PH. Of the DUSP SNPs evaluated, DUSP1 SNP rs322351 was less common, and DUSP5 SNPs rs1042606 and rs3793892 were more common in cases than in controls. The best fit biomarker model combines clinical and DUSP genetic data with an area under the ROC curve of 0.76. CONCLUSION: We identified three DUSP SNPs as potential BPD-PH biomarkers. Combining clinical and DUSP genetic data yields the most robust predictor for PH in BPD.

"Early" and "Late" Hospital readmissions in the first year after kidney transplant at a single center.

BACKGROUND: Hospital readmission (HR) after surgery is considered a quality metric. METHODS: Data on 2371 first-time adult kidney transplant (KT) recipients were collected to analyze the "early" (≤30 days) and "late" (31-365 days) HR patterns after KT at a single center over a 12-year time span (2002-2013). RESULTS: 30-day, 90-day, and 1-year HR were 31%, 41%, and 53%, respectively. Risk factors for HR included age >50, female sex, black race, BMI >30, transplant LOS >5 days, and pre-transplant time on dialysis >765 days. Indications for early (n = 749) and late (n = 508) HR were similar. Early HR (OR: 3.80, P = .007) and black race (OR: 2.38, P = .009) were associated with higher odds of 1-year graft failure while frequency (1-2, 3-4, 5+) of HR (ORs: 4.68, 8.36, 9.44, P < .001) and age > 50 (OR: 2.11, P = .007) were associated with higher odds of 1-year mortality. Transplant LOS > 5 days increased both odds of 1-year graft failure (OR: 3.51, P = .001) and mortality (OR: 2.05, P = .006). One-year graft and recipient survival were 96.7% and 94.8%, respectively. CONCLUSIONS: Hospital readmission was associated with reduced graft and patient survival; however, despite a relatively high and consistent HR rate after KT, overall 1-year graft and patient survival was high.

BayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach.

BACKGROUND: With the rise of metabolomics, the development of methods to address analytical challenges in the analysis of metabolomics data is of great importance. Missing values (MVs) are pervasive, yet the treatment of MVs can have a substantial impact on downstream statistical analyses. The MVs problem in metabolomics is quite challenging and can arise because the metabolite is not biologically present in the sample, or is present in the sample but at a concentration below the lower limit of detection (LOD), or is present in the sample but undetected due to technical issues related to sample pre-processing steps. The former is considered missing not at random (MNAR) while the latter is an example of missing at random (MAR). Typically, such MVs are substituted by a minimum value, which may lead to severely biased results in downstream analyses. RESULTS: We develop a Bayesian model, called BayesMetab, that systematically accounts for missing values based on a Markov chain Monte Carlo (MCMC) algorithm that incorporates data augmentation by allowing MVs to be due to either truncation below the LOD or other technical reasons unrelated to its abundance. Based on a variety of performance metrics (power for detecting differential abundance, area under the curve, bias and MSE for parameter estimates), our simulation results indicate that BayesMetab outperformed other imputation algorithms when there is a mixture of missingness due to MAR and MNAR. Further, our approach was competitive with other methods tailored specifically to MNAR in situations where missing data were completely MNAR. Applying our approach to an analysis of metabolomics data from a mouse myocardial infarction revealed several statistically significant metabolites not previously identified that were of direct biological relevance to the study. CONCLUSIONS: Our findings demonstrate that BayesMetab has improved performance in imputing the missing values and performing statistical inference compared to other current methods when missing values are due to a mixture of MNAR and MAR. Analysis of real metabolomics data strongly suggests this mixture is likely to occur in practice, and thus, it is important to consider an imputation model that accounts for a mixture of missing data types.

Degree of MDM2 Amplification Affects Clinical Outcomes in Dedifferentiated Liposarcoma.

BACKGROUND: Dedifferentiated liposarcomas (DDLPS) are mesenchymal tumors associated with universally poor response to treatment. Genomic amplification of murine double minute 2 (MDM2) is used as a diagnostic biomarker; however, no established biomarkers exist to guide DDLPS treatment. In the largest study of its kind, we report that the extent of MDM2 amplification, not simply the presence of MDM2 amplification, may be biologically important to the actions of DDLPS. PATIENTS AND METHODS: The distribution of MDM2 amplification in DDLPS was assessed using data from a commercial sequencing laboratory (n = 642) and The Cancer Genome Atlas (n = 57). Data from two retrospective clinical trials (n = 15, n = 16) and one prospective clinical trial (n = 25) were used to test MDM2's utility as a clinical biomarker. in vitro and in vivo assessments were conducted in DDLPS cell lines. RESULTS: Genomic MDM2 amplification follows a highly reproducible log-normal distribution. In patients with DDLPS treated with complete tumor resection, elevated MDM2 was associated with shortened time to recurrence as measured by genomic amplification (p = .003) and mRNA expression (p = .04). In patients requiring systemic therapy, higher MDM2 amplification was associated with reduced overall survival (p = .04). Doxorubicin treatment of DDLPS cells in vitro demonstrated variable sensitivity based on baseline MDM2 levels, and doxorubicin treatment elevated MDM2 expression. In vivo, treatment with doxorubicin followed by an MDM2 inhibitor improved doxorubicin sensitivity. CONCLUSION: MDM2 amplification levels in DDLPS follow a reproducible distribution and are associated with clinical outcomes and drug sensitivity. These results suggest that a prospective study of MDM2 as a predictive biomarker in DDLPS is warranted. IMPLICATIONS FOR PRACTICE: No validated biomarkers exist for treatment selection in dedifferentiated liposarcoma (DDLPS). Although murine double minute 2 (MDM2) is currently used for diagnosis, the clinical relevance of MDM2 amplification has yet to be fully assessed. This study found that MDM2 amplification follows a predictable distribution in DDLPS and correlates with clinical and biological outcomes. These data suggests that MDM2 amplification may be a useful biomarker in DDLPS.

Exploring genetic counselors' perceptions of usefulness and intentions to use refined risk models in clinical care based on the Technology Acceptance Model (TAM).

Pathogenic germline mutations in the BRCA1 or BRCA2 genes are associated with an elevated lifetime risk for breast (50%-85% risk) and ovarian cancer (20%-40% risk). Genome-wide association studies have identified over 100 genetic variants associated with modified breast and/or ovarian cancer risk in BRCA1 and BRCA2 carriers. Risk models generated based on these variants have shown that these genetic modifiers strongly influence absolute risk of developing breast or ovarian cancer in BRCA mutation carriers. There is a lack of understanding, however, about the clinical applicability and utility of these risk models. To investigate this gap, we collected survey data from 274 cancer genetic counselors (GCs) through the National Society of Genetic Counselors Cancer Special Interest Group. Questions assessed perceptions of usefulness and intentions of genetic counselors to use these refined risk models in clinical care based on the Technology Acceptance Model (TAM). We found that GCs' reactions to the estimates were largely positive, though they thought the possibility of changing management based on results was unlikely. Additionally, we found that more experienced GCs were more likely to consider refined risk estimates in clinic. Support also was provided for core predictions within the TAM, whereby the perceived usefulness (indirect effect est. = 0.08, 95% CI: [0.04, 0.13]) and perceived ease of use (indirect effect est. = 0.078, 95% CI: [0.04, 0.13]) of refined risk estimates were indirectly associated with intentions to use via attitudes.

Recruitment strategies and challenges: Lessons learned from a coal ash and children's health study.

The purpose of this paper is to describe the approaches and recruitment strategies of a study focused on the impact of coal fly ash on neurobehavioral performance among children living in proximity to coal-burning power plants. Challenges encountered with each recruitment approach are highlighted as well as solutions used to overcome those challenges and ultimately enroll children and one of their parents or guardians. To ensure participants were distributed throughout the study area, geographical information systems were used to guide recruitment and achieve the target sample size (N = 300). Several approaches were employed to recruit the number of needed participants, including "shoe leather" or door-to-door recruitment, placement of flyers and brochures in public spaces, mailings to targeted addresses, media announcements, and local government outreach. Since September 2015, 265 participants have been enrolled in the study using a combination of the described recruitment approaches. Even with a well-designed plan, it is important to re-examine strategies at every step to maximize recruitment efforts. Researcher flexibility in adapting to new strategies is vital in facilitating recruitment efforts, and the recruitment of participants in the study remains a dynamic and evolving process.

The hepatic "matrisome" responds dynamically to injury: Characterization of transitional changes to the extracellular matrix in mice.

The extracellular matrix (ECM) consists of diverse components that work bidirectionally with surrounding cells to create a responsive microenvironment. In some contexts (e.g., hepatic fibrosis), changes to the ECM are well recognized and understood. However, it is becoming increasingly accepted that the hepatic ECM proteome (i.e., matrisome) responds dynamically to stress well before fibrosis. The term "transitional tissue remodeling" describes qualitative and quantitative ECM changes in response to injury that do not alter the overall architecture of the organ; these changes in ECM may contribute to early disease initiation and/or progression. The nature and magnitude of these changes to the ECM in liver injury are poorly understood. The goals of this work were to validate analysis of the ECM proteome and compare the impact of 6 weeks of ethanol diet and/or acute lipopolysaccharide (LPS). Liver sections were processed in a series of increasingly rigorous extraction buffers to separate proteins by solubility. Extracted proteins were identified using liquid chromatography/tandem mass spectrometry (LC-MS/MS). Both ethanol and LPS dramatically increased the number of matrisome proteins ∼25%. The enhancement of LPS-induced liver damage by ethanol preexposure was associated with unique protein changes. CONCLUSION: An extraction method to enrich the hepatic ECM was characterized. The results demonstrate that the hepatic matrisome responds dynamically to both acute (LPS) and chronic (ethanol) stresses, long before more-dramatic fibrotic changes to the liver occur. The changes to the mastrisome may contribute, at least in part, to the pathological responses to these stresses. It is also interesting that several ECM proteins responded similarly to both stresses, suggesting a common mechanism in both models. Nevertheless, there were responses that were unique to the individual and combined exposures. (Hepatology 2017;65:969-982).

Thermal liquid biopsy for monitoring melanoma patients under surveillance during treatment: A pilot study.

BACKGROUND: Differential Scanning Calorimetry (DSC) is a technique traditionally used to study thermally induced macromolecular transitions, and it has recently been proposed as a novel approach for diagnosis and monitoring of several diseases. We report a pilot study applying Thermal Liquid Biopsy (TLB, DSC thermograms of plasma samples) as a new clinical approach for diagnostic assessment of melanoma patients. METHODS: Multiparametric analysis of DSC thermograms of patient plasma samples collected during treatment and surveillance (63 samples from 10 patients) were compared with clinical and diagnostic imaging assessment to determine the utility of thermograms for diagnostic assessment in melanoma. Nine of the ten patients were stage 2 or 3 melanoma subjects receiving adjuvant therapy after surgical resection of their melanomas. The other patient had unresectable stage 4 melanoma and was treated with immunotherapy. Two reference groups were used: (A) 36 healthy subjects and (B) 13 samples from 8 melanoma patients who had completed successful surgical management of their disease and were determined by continued clinical assessment to have no evidence of disease. RESULTS: Plasma thermogram analysis applied to melanoma patients generally agrees with clinical evaluation determined by physical assessment or diagnostic imaging (~80% agreement). No false negatives were obtained from DSC thermograms. Importantly, this methodology was able to detect changes in disease status before it was identified clinically. CONCLUSIONS: Thermal Liquid Biopsy could be used in combination with current clinical assessment for the earlier detection of melanoma recurrence and metastasis. GENERAL SIGNIFICANCE: TLB offers advantages over current diagnostic techniques (PET/CT imaging), limited in frequency by radiation burden and expense, in providing a minimally-invasive, low-risk, low-cost clinical test for more frequent personalized patient monitoring to assess recurrence and facilitate clinical decision-making.

An evaluation of veterinary allergen extract content and resultant canine intradermal threshold concentrations.

BACKGROUND: Limited information is available for dogs on threshold concentrations (TCs), and the protein composition of common allergenic extracts produced by different manufacturers. HYPOTHESIS/OBJECTIVES: To characterize the protein heterogeneity of tree, grass, weed and mite allergens from different lots of allergenic extracts, and to determine intradermal TCs for healthy dogs using extracts from two manufacturers. ANIMALS: Twenty five privately owned, clinically healthy dogs and ten purpose-bred beagle dogs. METHODS AND MATERIALS: Protein concentration and heterogeneity of 11 allergens from two manufacturers were evaluated using a Bradford-style assay and SDS-PAGE. Intradermal testing was performed with 11 allergens from each company at four dilutions. Immediate reactions were subjectively scored (0 to 4+), and objectively measured (mm) and their percentage concordance evaluated. Model-based TCs were determined by fitting positive reactions (≥2+) at 15 min to generalized estimating equations. RESULTS: Allergen extract protein quantity and composition varied within and between manufacturers despite sharing the same PNU/mL values. Model-based TCs of one weed, five trees, two grasses and a house dust mite were determined for extracts from Manufacturer 1 (M1), and for extracts of three weeds, three trees and two grasses from Manufacturer 2 (M2). Receiver operating characteristic curve analyses determined a percentage concordance of the objective and subjective measurements of 77.3% for M1 and 75% for M2 allergens. CONCLUSIONS AND CLINICAL IMPORTANCE: Veterinary allergen extracts labelled as the same species and PNU/mL are not standardized; they show heterogeneity in composition and potency within and between manufacturers. Variability in extract content may require adjustment of intradermal testing concentrations.

Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies.

BACKGROUND: High throughput metabolomics makes it possible to measure the relative abundances of numerous metabolites in biological samples, which is useful to many areas of biomedical research. However, missing values (MVs) in metabolomics datasets are common and can arise due to both technical and biological reasons. Typically, such MVs are substituted by a minimum value, which may lead to different results in downstream analyses. RESULTS: Here we present a modified version of the K-nearest neighbor (KNN) approach which accounts for truncation at the minimum value, i.e., KNN truncation (KNN-TN). We compare imputation results based on KNN-TN with results from other KNN approaches such as KNN based on correlation (KNN-CR) and KNN based on Euclidean distance (KNN-EU). Our approach assumes that the data follow a truncated normal distribution with the truncation point at the detection limit (LOD). The effectiveness of each approach was analyzed by the root mean square error (RMSE) measure as well as the metabolite list concordance index (MLCI) for influence on downstream statistical testing. Through extensive simulation studies and application to three real data sets, we show that KNN-TN has lower RMSE values compared to the other two KNN procedures as well as simpler imputation methods based on substituting missing values with the metabolite mean, zero values, or the LOD. MLCI values between KNN-TN and KNN-EU were roughly equivalent, and superior to the other four methods in most cases. CONCLUSION: Our findings demonstrate that KNN-TN generally has improved performance in imputing the missing values of the different datasets compared to KNN-CR and KNN-EU when there is missingness due to missing at random combined with an LOD. The results shown in this study are in the field of metabolomics but this method could be applicable with any high throughput technology which has missing due to LOD.

Differential scanning calorimetry as a complementary diagnostic tool for the evaluation of biological samples.

BACKGROUND: Differential scanning calorimetry (DSC) is a tool for measuring the thermal stability profiles of complex molecular interactions in biological fluids. DSC profiles (thermograms) of biofluids provide specific signatures which are being utilized as a new diagnostic approach for characterizing disease but the development of these approaches is still in its infancy. METHODS: This article evaluates several approaches for the analysis of thermograms which could increase the utility of DSC for clinical application. Thermograms were analyzed using localized thermogram features and principal components (PCs). The performance of these methods was evaluated alongside six models for the classification of a data set comprised of 300 systemic lupus erythematosus (SLE) patients and 300 control subjects obtained from the Lupus Family Registry and Repository (LFRR). RESULTS: Classification performance was substantially higher using the penalized algorithms relative to localized features/PCs alone. The models were grouped into two sets, the first having smoother solution vectors but lower classification accuracies than the second with seemingly noisier solution vectors. CONCLUSIONS: Coupling thermogram technology with modern classification algorithms provides a powerful diagnostic approach for analysis of biological samples. The solution vectors from the models may reflect important information from the thermogram profiles for discriminating between clinical groups. GENERAL SIGNIFICANCE: DSC thermograms show sensitivity to changes in the bulk plasma proteome that correlate with clinical status. To move this technology towards clinical application the development of new approaches is needed to extract discriminatory parameters from DSC profiles for the comparison and diagnostic classification of patients.

Clinical outcomes linked to expression of gene subsets for protein hormones and their cognate receptors from LCM-procured breast carcinoma cells.

PURPOSE: Certain peptide hormones and/or their cognate receptors influencing normal cellular pathways also have been detected in breast cancers. The hypothesis is that gene subsets of these regulatory molecules predict risk of breast carcinoma recurrence in patients with primary disease. METHODS: Gene expression levels of 61 hormones and 81 receptors were determined by microarray with LCM-procured carcinoma cells of 247 de-identified biopsies. Univariable and multivariable Cox regressions were determined using expression levels of each hormone/receptor gene, individually or as a pair. RESULTS: Molecular signatures for ER+/PR+, ER-/PR-, and ER- carcinoma cells deciphered by LASSO were externally validated at HRs (CI) of 2.8 (1.84-4.4), 1.53 (1.01-2.3), and 1.72 (1.15-2.56), respectively. Using LCM-procured breast carcinoma cells, a 16-gene molecular signature was derived for ER+/PR+ biopsies, whereas a 10-gene signature was deciphered for ER-/PR- cancers. Four genes, POMC, CALCR, AVPR1A, and GH1, of this 10-gene signature were identified in a 6-gene molecular signature for ER- specimens. CONCLUSIONS: Applying these signatures, Kaplan-Meier plots definitively identified a cohort of patients with either ER-/PR- or ER- carcinomas that exhibited low risk of recurrence. In contrast, the ER+/PR+ signature identified a cohort of patients with high risk of breast cancer recurrence. Each of the three molecular signatures predicted clinical outcomes of breast cancer patients with greater accuracy than observed with either single-gene analysis or by ER/PR protein content alone. Collectively, our results suggest that gene expression profiles of breast carcinomas with suspected poor prognosis (ER-/PR-) have identified a subset of patients with decreased risk of recurrence.

Comparison of two equivalent model for end-stage liver disease scores for hepatocellular carcinoma patients using data from the United Network for Organ Sharing liver transplant waiting list registry.

Patients with hepatocellular carcinoma (HCC) have been advantaged on the liver transplant waiting list within the United States, and a 6-month delay and exception point cap have recently been implemented to address this disparity. An alternative approach to prioritization is an HCC-specific scoring model such as the MELD Equivalent (MELDEQ ) and the mixed new deMELD. Using data on adult patients added to the UNOS waitlist between 30 September 2009 and 30 June 2014, we compared projected dropout and transplant probabilities for patients with HCC under these two models. Both scores matched actual non-HCC dropout in groups with scores <22 and improved equity with non-HCC transplant probabilities overall. However, neither score matched non-HCC dropout accurately for scores of 25-40 and projected dropout increased beyond non-HCC probabilities for scores <16. The main differences between the two scores were as follows: (i) the MELDEQ assigns 6.85 more points after 6 months on the waitlist and (ii) the deMELD gives greater weight to tumor size and laboratory MELD. Post-transplant survival was lower for patients with scores in the 22-30 range compared with those with scores <16 (P = 0.007, MELDEQ ; P = 0.015, deMELD). While both scores result in better equity of waitlist outcomes compared with scheduled progression, continued development and calibration is recommended.

Projected outcomes of 6-month delay in exception points versus an equivalent Model for End-Stage Liver Disease score for hepatocellular carcinoma liver transplant candidates.

The United Network for Organ Sharing (UNOS) recently implemented a 6-month delay before granting exception points to liver transplantation candidates with hepatocellular carcinoma (HCC) to address disparity in transplantation access between HCC and non-HCC patients. An HCC-specific scoring scheme, the Model for End-Stage Liver Disease equivalent (MELDEQ ), has also been developed. We compared projected dropout and transplant probabilities and posttransplant survival for HCC and non-HCC patients under the 6-month delay and the MELDEQ using UNOS data from October 1, 2009, to June 30, 2014, and multistate modeling. Overall (combined HCC and non-HCC) wait-list dropout was similar under both schemes and slightly improved (though not statistically significant) compared to actual data. Projected HCC wait-list dropout was similar between the MELDEQ and 6-month delay at 6 months but thereafter started to differ, with the 6-month delay eventually favoring HCC patients (3-year dropout 10.0% [9.0%-11.0%] for HCC versus 14.1% [13.6%-14.6%]) for non-HCC) and the MELDEQ favoring non-HCC patients (3-year dropout 16.0% [13.2%-18.8%] for HCC versus 12.3% [11.9%-12.7%] for non-HCC). Projected transplant probabilities for HCC patients were substantially lower under the MELDEQ compared to the 6-month delay (26.6% versus 83.8% by 3 years, respectively). Projected HCC posttransplant survival under the 6-month delay was similar to actual, but slightly worse under the MELDEQ (2-year survival 82.9% [81.7%-84.2%] versus actual of 85.5% [84.3%-86.7%]). In conclusion, although the 6-month delay improves equity in transplant and dropout between HCC and non-HCC candidates, disparity between the 2 groups may still exist after 6 months of wait-list time. Projections under the MELDEQ , however, appear to disadvantage HCC patients. Therefore, modification to the exception point progression or refinement of an HCC prioritization score may be warranted. Liver Transplantation 22 1343-1355 2016 AASLD.

Coordinated histone H3 methylation and acetylation regulate physiologic and pathologic fas ligand gene expression in human CD4+ T cells.

Activation-induced Fas ligand (FasL) mRNA expression in CD4+ T cells is mainly controlled at transcriptional initiation. To elucidate the epigenetic mechanisms regulating physiologic and pathologic FasL transcription, TCR stimulation-responsive promoter histone modifications in normal and alcohol-exposed primary human CD4+ T cells were examined. TCR stimulation of normal and alcohol-exposed cells led to discernible changes in promoter histone H3 lysine trimethylation, as documented by an increase in the levels of transcriptionally permissive histone 3 lysine 4 trimethylation and a concomitant decrease in the repressive histone 3 lysine 9 trimethylation. Moreover, acetylation of histone 3 lysine 9 (H3K9), a critical feature of the active promoter state that is opposed by histone 3 lysine 9 trimethylation, was significantly increased and was essentially mediated by the p300-histone acetyltransferase. Notably, the degree of these coordinated histone modifications and subsequent recruitment of transcription factors and RNA polymerase II were significantly enhanced in alcohol-exposed CD4+ T cells and were commensurate with the pathologic increase in the levels of FasL mRNA. The clinical relevance of these findings is further supported by CD4+ T cells obtained from individuals with a history of heavy alcohol consumption, which demonstrate significantly greater p300-dependent H3K9 acetylation and FasL expression. Overall, these data show that, in human CD4+ T cells, TCR stimulation induces a distinct promoter histone profile involving a coordinated cross-talk between histone 3 lysine 4 and H3K9 methylation and acetylation that dictates the transcriptional activation of FasL under physiologic, as well as pathologic, conditions of alcohol exposure.