Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Using Artificial Intelligence in Predicting Ischemic Stroke Events After Percutaneous Coronary Intervention.

BACKGROUND: Ischemic stroke (IS) is an uncommon but severe complication in patients undergoing percutaneous coronary intervention (PCI). Despite significant morbidity and economic cost associated with post PCI IS, a validated risk prediction model is not currently available. AIMS: We aim to develop a machine learning model that predicts IS after PCI. METHODS: We analyzed data from Mayo Clinic CathPCI registry from 2003 to 2018. Baseline clinical and demographic data, electrocardiography (ECG), intra/post-procedural data, and echocardiographic variables were abstracted. A random forest (RF) machine learning model and a logistic regression (LR) model were developed. The receiver operator characteristic (ROC) analysis was used to assess model performance in predicting IS at 6-month, 1-, 2-, and 5-years post-PCI. RESULTS: A total of 17,356 patients were included in the final analysis. The mean age of this cohort was 66.9 ± 12.5 years, and 70.7% were male. Post-PCI IS was noted in 109 patients (.6%) at 6 months, 132 patients (.8%) at 1 year, 175 patients (1%) at 2 years, and 264 patients (1.5%) at 5 years. The area under the curve of the RF model was superior to the LR model in predicting ischemic stroke at 6 months, 1-, 2-, and 5-years. Periprocedural stroke was the strongest predictor of IS post discharge. CONCLUSIONS: The RF model accurately predicts short- and long-term risk of IS and outperforms logistic regression analysis in patients undergoing PCI. Patients with periprocedural stroke may benefit from aggressive management to reduce the future risk of IS.

Performance comparisons of methylation and structural variants from low-input whole-genome methylation sequencing.

Aim: Whole-genome methylation sequencing carries both DNA methylation and structural variant information (single nucleotide variant [SNV]; copy number variant [CNV]); however, limited data is available on the reliability of obtaining this information simultaneously from low-input DNA using various library preparation and sequencing protocols. Methods: A HapMap NA12878 sample was sequenced with three protocols (EM-sequencing, QIA-sequencing and Swift-sequencing) and their performance was compared on CpG methylation measurement and SNV and CNV detection. Results: At low DNA input (10-25 ng), EM-sequencing was superior in almost all metrics except CNV detection where all protocols were similar. EM-sequencing captured the highest number of CpGs and true SNVs. Conclusion: EM-sequencing is suitable to detect methylation, SNVs and CNVs from single sequencing with low-input DNA.

A New Insight Into Nonbacterial Thrombotic Endocarditis: A Systematic Review of Cases.

BACKGROUND: Nonbacterial thrombotic endocarditis is characterized by the presence of organized thrombi on cardiac valves, often associated with hypercoagulable states. There is a paucity of data regarding the predictors of mortality in patients with nonbacterial thrombotic endocarditis. Our primary aim was to identify predictors of in-hospital mortality in patients with nonbacterial thrombotic endocarditis. METHODS: A systematic literature review of all published cases and case series was performed until May 2018 according to Preferred Reporting Items for Systematic Review and Meta-analyses statement guidelines. We applied random forest machine learning model to identify predictors of in-patient mortality in patients with nonbacterial thrombotic endocarditis. RESULTS: Our search generated a total of 163 patients (mean age, 46 ± 17 years; women, 69%) with newly diagnosed nonbacterial thrombotic endocarditis. The in-hospital mortality rate in the study cohort was 30%. Among the patients who died in the hospital, initial presentation of pulmonary embolism (12.2 vs. 2.6%), splenic (38.7 vs. 10.5%), and renal (40.8 vs. 9.6%) infarcts were higher compared to patients alive at the time of discharge. Higher rates of malignancy (71.4 vs. 39.4%, P = .0003) and lower rates of antiphospholipid syndrome (8.1 vs. 48.2%, P = .0001) were noted in deceased patients. Random forest machine learning analysis showed that older age, presence of antiphospholipid syndrome, splenic infarct, renal infarct, peripheral thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality. CONCLUSION: Patients admitted with nonbacterial thrombotic endocarditis have a high rate of in-hospital mortality. Factors including older age, presence of antiphospholipid syndrome, splenic/renal infarct, lower limb thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.

ω-3PUFA supplementation ameliorates adipose tissue inflammation and insulin-stimulated glucose disposal in subjects with obesity: a potential role for apolipoprotein E.

BACKGROUND: Long chain omega-3 polyunsaturated fatty acids (ω-3PUFA) supplementation in animal models of diet-induced obesity has consistently shown to improve insulin sensitivity. The same is not always reported in human studies with insulin resistant (IR) subjects with obesity. OBJECTIVE: We studied whether high-dose ω-3PUFA supplementation for 3 months improves insulin sensitivity and adipose tissue (AT) inflammation in IR subjects with obesity. METHODS: Thirteen subjects (BMI = 39.3 ± 1.6 kg/m2) underwent 80 mU/m2·min euglycemic-hyperinsulinemic clamp with subcutaneous (Sc) AT biopsy before and after 3 months of ω-3PUFA (DHA and EPA, 4 g/daily) supplementation. Cytoadipokine plasma profiles were assessed before and after ω-3PUFA. AT-specific inflammatory gene expression was evaluated on Sc fat biopsies. Microarray analysis was performed on the fat biopsies collected during the program. RESULTS: Palmitic and stearic acid plasma levels were significantly reduced (P < 0.05) after ω-3PUFA. Gene expression of pro-inflammatory markers and adipokines were improved after ω-3PUFA (P < 0.05). Systemic inflammation was decreased after ω-3PUFA, as shown by cytokine assessment (P < 0.05). These changes were associated with a 25% increase in insulin-stimulated glucose disposal (4.7 ± 0.6 mg/kg ffm•min vs. 5.9 ± 0.9 mg/kg ffm•min) despite no change in body weight. Microarray analysis identified 53 probe sets significantly altered post- ω-3PUFA, with Apolipoprotein E (APOE) being one of the most upregulated genes. CONCLUSION: High dose of long chain ω-3PUFA supplementation modulates significant changes in plasma fatty acid profile, AT, and systemic inflammation. These findings are associated with significant improvement of insulin-stimulated glucose disposal. Unbiased microarray analysis of Sc fat biopsy identified APOE as among the most differentially regulated gene after ω-3PUFA supplementation. We speculate that ω-3PUFA increases macrophage-derived APOE mRNA levels with anti-inflammatory properties.

National Cardiovascular Data Registry Model Predicts Long-Term Mortality in Patients Undergoing Percutaneous Coronary Interventions.

National Cardiovascular Data Registry (NCDR)-based logistic regression model is available for clinicians to predict in-hospital all-cause mortality after a percutaneous coronary intervention (PCI). However, this model has never been used to predict long-term all-cause mortality after PCI. Therefore, we sought to test the ability of the NCDR model to predict the short- and long-term risk of all-cause mortality in patients undergoing PCI. All patients undergoing PCI in the Mayo Clinic Health System were enrolled in the Mayo Clinic CathPCI registry. Patient-level demographic, clinical, and angiographic data from January 2006 to December 2017 were extracted from the registry. Patients who underwent coronary artery bypass graft surgery (CABG) were excluded. The area under the receiver operator characteristic curve (AUC) was calculated to assess the ability of the NCDR model to predict outcomes of interest (6-month, 1-year, 2-year, and 5-year all-cause mortality) after PCI. A total of 17,356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery. The mean age was 66.9 ± 12.5 years, and 71% were men. The 6-month, 1-year, 2-year, and 5-year all-cause mortality rates were 4.2% (n = 737), 5.8% (n = 1,005), 8.06% (n = 1,399), and 14.2% (n = 2,472), respectively. The AUCs of the NCDR model to predict 6-month, 1-year, 2-year, and 5-year all-cause mortality were 0.84 (95% CI: 0.82-0.86), 0.82 (95% CI: 0.80-0.84), 0.80 (95% CI: 0.79-0.81), and 0.78 (95% CI: 0.77-0.79), respectively. The NCDR model was able to accurately predict both short- and long-term all-cause mortality after PCI.

Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma.

Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model.

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics-the study of gene expression-also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

Comparison of echocardiographic parameters with cardiac magnetic resonance imaging in the assessment of right ventricular function.

INTRODUCTION: The right ventricle (RV) strain measured by speckle tracking (RVS) is an echocardiographic parameter used to assess RV function. We compared RVS to RV fractional area change (FAC%), tricuspid annular plane systolic excursion (TAPSE) and Doppler tissue imaging-derived peak systolic velocity (S') in the assessment of right ventricular (RV) systolic function measured using cardiac magnetic resonance imaging (MRI). METHODS: We enrolled consecutive patients who underwent cardiac MRI between Jan 2012 and Dec 2017 and a transthoracic echocardiogram (TTE) within 1 month of the MRI with no interval event. Baseline clinical characteristics and MRI parameters were extracted from chart review. Echocardiographic parameters were measured prospectively. TTE parameters including RVS, TAPSE, S', and FAC% were tested for accuracy to identify impaired RV EF (EF < 45% & <30%) using receiver operator curves. RESULTS: The study cohort included 500 patients with mean age 55 years ± 18 and peak tricuspid regurgitation velocity 2.7 ± 1.4 m/s. The area under ROC for RVS was 0.69 (95% CI 0.63-0.75) and 0.78 (95% CI 0.70-0.88) to predict RVEF < 45% & RVEF < 30%, respectively. The RV FAC% had second highest accuracy of predicting RVEF among all the TTE parameters tested in study. CONCLUSION: Right ventricular strain is the most accurate echocardiographic method to detect impaired right ventricular systolic function when using MRI as the gold standard.

Ultrafast and scalable variant annotation and prioritization with big functional genomics data.

The advances of large-scale genomics studies have enabled compilation of cell type-specific, genome-wide DNA functional elements at high resolution. With the growing volume of functional annotation data and sequencing variants, existing variant annotation algorithms lack the efficiency and scalability to process big genomic data, particularly when annotating whole-genome sequencing variants against a huge database with billions of genomic features. Here, we develop VarNote to rapidly annotate genome-scale variants in large and complex functional annotation resources. Equipped with a novel index system and a parallel random-sweep searching algorithm, VarNote shows substantial performance improvements (two to three orders of magnitude) over existing algorithms at different scales. It supports both region-based and allele-specific annotations and introduces advanced functions for the flexible extraction of annotations. By integrating massive base-wise and context-dependent annotations in the VarNote framework, we introduce three efficient and accurate pipelines to prioritize the causal regulatory variants for common diseases, Mendelian disorders, and cancers.

Does a Gradient-Adjusted Cardiac Power Index Improve Prediction of Post-Transcatheter Aortic Valve Replacement Survival Over Cardiac Power Index?

PURPOSE: Cardiac power (CP) index is a product of mean arterial pressure (MAP) and cardiac output (CO). In aortic stenosis, however, MAP is not reflective of true left ventricular (LV) afterload. We evaluated the utility of a gradient-adjusted CP (GCP) index in predicting survival after transcatheter aortic valve replacement (TAVR), compared to CP alone. MATERIALS AND METHODS: We included 975 patients who underwent TAVR with 1 year of follow-up. CP was calculated as (CO×MAP)/[451×body surface area (BSA)] (W/m²). GCP was calculated using augmented MAP by adding aortic valve mean gradient (AVMG) to systolic blood pressure (CP1), adding aortic valve maximal instantaneous gradient to systolic blood pressure (CP2), and adding AVMG to MAP (CP3). A multivariate Cox regression analysis was performed adjusting for baseline covariates. Receiver operator curves (ROC) for CP and GCP were calculated to predict survival after TAVR. RESULTS: The mortality rate at 1 year was 16%. The mean age and AVMG of the survivors were 81±9 years and 43±4 mm Hg versus 80±9 years and 42±13 mm Hg in the deceased group. The proportions of female patients were similar in both groups (p=0.7). Both CP and GCP were independently associated with survival at 1 year. The area under ROCs for CP, CP1, CP2, and CP3 were 0.67 [95% confidence interval (CI), 0.62-0.72], 0.65 (95% CI, 0.60-0.70), 0.66 (95% CI, 0.61-0.71), and 0.63 (95% CI 0.58-0.68), respectively. CONCLUSION: GCP did not improve the accuracy of predicting survival post TAVR at 1 year, compared to CP alone.

"Direct to Drug" screening as a precision medicine tool in multiple myeloma.

Seventy-six FDA-approved oncology drugs and emerging therapeutics were evaluated in 25 multiple myeloma (MM) and 15 non-Hodgkin's lymphoma cell lines and in 113 primary MM samples. Ex vivo drug sensitivities were mined for associations with clinical phenotype, cytogenetic, genetic mutation, and transcriptional profiles. In primary MM samples, proteasome inhibitors, dinaciclib, selinexor, venetoclax, auranofin, and histone deacetylating agents had the broadest cytotoxicity. Of interest, newly diagnosed patient samples were globally less sensitive especially to bromodomain inhibitors, inhibitors of receptor tyrosine kinases or non-receptor kinases, and DNA synthesis inhibitors. Clustering demonstrated six broad groupings of drug sensitivity linked with genomic biomarkers and clinical outcomes. For example, our findings mimic clinical observations of increased venetoclax responsiveness in t(11;14) patients but also identify an increased sensitivity profile in untreated patients, standard genetic risk, low plasma cell S-Phase, and in the absence of Gain(1q) and t(4;14). In contrast, increased ex vivo responsiveness to selinexor was associated with biomarkers of poor prognosis and later relapse patients. This "direct to drug" screening resource, paired with functional genomics, has the potential to successfully direct appropriate individualized therapeutic approaches in MM and to enrich clinical trials for likely responders.

Resting Cardiac Efficiency Affects Survival Following Transcatheter Aortic Valve Replacement.

OBJECTIVE: Cardiac power to left ventricular mass (LVM) ratio, also termed cardiac efficiency (CE), reflects the rate of cardiac work delivered to the potential energy stored in LVM. We sought to assess the association between baseline resting CE and survival post transcatheter aortic valve replacement (TAVR). METHODS: We retrospectively extracted data of patients who received TAVR in the Mayo Clinic Foundation with follow up data available at 1 year. Cardiac output was measured using Doppler echocardiography at baseline. CE was calculated using the formula, (cardiac output × mean arterial blood pressure)/(451 × LVM × 100) W/100 g. Survival score analysis was performed to identify cut off value for CE to identify the maximum difference in mortality in the study cohort. Patients were subsequently divided into 2 groups CE < 0.38 W/100 g and CE ≥ 0.38 W/100 g. Survival was determined using Kaplan-Meier method. RESULTS: We included 954 patients in the final analysis. CE in group1 vs group 2 was 0.31 ± 0.05 W/100 g vs 0.59 ± 0.18 W/100 g. Patients in group1 were more likely to be male, had a higher prevalence of atrial fibrillation, prior myocardial infarction, mitral and tricuspid regurgitation. They also had a higher STS risk score, NYHA functional class, and lower aortic valve area. The remainder of the baseline characteristics was similar in both groups. A lower CE was associated with higher 1-year mortality following TAVR based on multivariate analysis. (Group1: 22.18% vs Group 2: 9.89%, p < .0001). CONCLUSION: In our cohort, a low baseline CE (<0.38 W/100 g) conferred higher mortality risk following TAVR.

Does Resting Cardiac Power Index Affect Survival Post Transcatheter Aortic Valve Replacement?

OBJECTIVE: Cardiac power index (CPI) is an integrative hemodynamic measure of cardiac pumping capability and is the product of the simultaneously measured mean arterial pressure and the cardiac output. We assessed the association between baseline resting CPI and survival post transcatheter aortic valve replacement (TAVR). METHODS AND RESULTS: We retrospectively abstracted data of patients who underwent TAVR at the Mayo Clinic Foundation with follow-up data available at 1 year. Baseline demographic, clinical, and echocardiographic data were abstracted. CPI was calculated using the formula, (cardiac output x mean arterial blood pressure) / (451 x body surface area) W/m². Patients were divided into CPI <0.48 W/m² (group 1) and CPI ≥0.48 W/m² (group 2). Survival according to CPI was determined using Kaplan-Meier method. Multivariate Cox regression analysis was performed to adjust for covariates. Nine hundred and seventy-five patients were included in the final analysis. CPI in group 1 vs group 2 was 0.41 ± 0.05 W/m² vs 0.66 ± 0.14 W/m², respectively (P<.001, two-sided t-test). Patients in group 1 were more likely to be male and to have a prior history of myocardial infarction, coronary revascularization, peripheral arterial disease, diabetes mellitus, transient ischemic attack, carotid artery disease, atrial fibrillation, lower left ventricular ejection fraction, and moderate to severe mitral and tricuspid regurgitation. After adjusting for baseline covariates, a lower CPI was associated with higher 1-year mortality among patients undergoing TAVR (24.39% in group 1 vs 8.28% in group 2; P<.001). CONCLUSION: Low baseline CPI (<0.48 W/m²) confers higher mortality risk among patients undergoing TAVR and provides additional prognostic information, which can help risk-stratify patients.

Integrated RNA-seq and ChIP-seq analysis reveals a feed-forward loop regulating H3K9ac and key labor drivers in human placenta.

BACKGROUND: Chromatin alterations are important mediators of gene expression changes. We have recently shown that activated non-canonical NF-κB signaling (RelB/p52) recruits histone acetyltransferase CBP and deacetylase HDAC1 to selectively acetylate H3K9 (H3K9ac) to induce expression of corticotropin-releasing hormone (CRH) and prostaglandin-endoperoxide synthase-2 (PTGS2) in the human placenta. Both of these genes play a role in initiating parturition in human pregnancy. METHODS: We performed chromatin immunoprecipitation followed by gene sequencing (ChIP-seq) in primary term human cytotrophoblast (CTB) with use of antibodies to RelB, CBP, HDAC1 and H3K9ac. We further associated these chromatin alterations with gene expression changes from mid-trimester to term in CTB by RNA sequencing (RNA-seq). RESULTS: We detected a genome-wide differential gene enrichment between mid-trimester and term human placenta. Pathway analysis identified that cytokine-cytokine receptor interaction, NF-κB, and TNF are the leading pathways enriched in term placenta and associated with these chromatin alterations. DISCUSSIONS: Our analysis has provided the first-time characterization of the key players of human placental origin with molecular changes resulting from chromatin modifications, which could drive human labor.

GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits.

Genome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.

Colonoscopy surveillance for high risk polyps does not always prevent colorectal cancer.

AIM: To determine the frequency and risk factors for colorectal cancer (CRC) development among individuals with resected advanced adenoma (AA)/traditional serrated adenoma (TSA)/advanced sessile serrated adenoma (ASSA). METHODS: Data was collected from medical records of 14663 subjects found to have AA, TSA, or ASSA at screening or surveillance colonoscopy. Patients with inflammatory bowel disease or known genetic predisposition for CRC were excluded from the study. Factors associated with CRC developing after endoscopic management of high risk polyps were calculated in 4610 such patients who had at least one surveillance colonoscopy within 10 years following the original polypectomy of the incident advanced polyp. RESULTS: 84/4610 (1.8%) patients developed CRC at the polypectomy site within a median of 4.2 years (mean 4.89 years), and 1.2% (54/4610) developed CRC in a region distinct from the AA/TSA/ASSA resection site within a median of 5.1 years (mean 6.67 years). Approximately, 30% (25/84) of patients who developed CRC at the AA/TSA/ASSA site and 27.8% (15/54) of patients who developed CRC at another site had colonoscopy at recommended surveillance intervals. Increasing age; polyp size; male sex; right-sided location; high degree of dysplasia; higher number of polyps resected; and piecemeal removal were associated with an increased risk for CRC development at the same site as the index polyp. Increasing age; right-sided location; higher number of polyps resected and sessile endoscopic appearance of the index AA/TSA/ASSA were significantly associated with an increased risk for CRC development at a different site. CONCLUSION: Recognition that CRC may develop following AA/TSA/ASSA removal is one step toward improving our practice efficiency and preventing a portion of CRC related morbidity and mortality.

Molecular characterization of colorectal adenomas with and without malignancy reveals distinguishing genome, transcriptome and methylome alterations.

The majority of colorectal cancer (CRC) arises from precursor lesions known as polyps. The molecular determinants that distinguish benign from malignant polyps remain unclear. To molecularly characterize polyps, we utilized Cancer Adjacent Polyp (CAP) and Cancer Free Polyp (CFP) patients. CAPs had tissues from the residual polyp of origin and contiguous cancer; CFPs had polyp tissues matched to CAPs based on polyp size, histology and dysplasia. To determine whether molecular features distinguish CAPs and CFPs, we conducted Whole Genome Sequencing, RNA-seq, and RRBS on over 90 tissues from 31 patients. CAPs had significantly more mutations, altered expression and hypermethylation compared to CFPs. APC was significantly mutated in both polyp groups, but mutations in TP53, FBXW7, PIK3CA, KIAA1804 and SMAD2 were exclusive to CAPs. We found significant expression changes between CAPs and CFPs in GREM1, IGF2, CTGF, and PLAU, and both expression and methylation alterations in FES and HES1. Integrative analyses revealed 124 genes with alterations in at least two platforms, and ERBB3 and E2F8 showed aberrations specific to CAPs across all platforms. These findings provide a resource of molecular distinctions between polyps with and without cancer, which have the potential to enhance the diagnosis, risk assessment and management of polyps.

Discovery of Surface Target Proteins Linking Drugs, Molecular Markers, Gene Regulation, Protein Networks, and Disease by Using a Web-Based Platform Targets-search.

Integration and analysis of high content omics data have been critical to the investigation of molecule interactions (e.g., DNA-protein, protein-protein, chemical-protein) in biological systems. Human proteomic strategies that provide enriched information on cell surface proteins can be utilized for repurposing of drug targets and discovery of disease biomarkers. Although several published resources have proved useful to the analysis of these interactions, our newly developed web-based platform Targets-search has the capability of integrating multiple types of omics data to unravel their association with diverse molecule interactions and disease. Here, we describe how to use Targets-search, for the integrated and systemic exploitation of surface proteins to identify potential drug targets, which can further be used to analyze gene regulation, protein networks, and possible biomarkers for diseases and cancers. To illustrate this process, we have taken data from Ewing's sarcoma to identify surface proteins differentially expressed in Ewing's sarcoma cells. These surface proteins were then analyzed to determine which ones were known drug targets. The information suggested putative targets for drug repurposing and subsequent analyses illustrated their regulation by the transcription factor EWSR1.