Features of cardiovascular magnetic resonance native T1 mapping in maintenance hemodialysis patients and their related factors.

PURPOSE: Increased myocardial T1 values on cardiovascular MRI (CMRI) have been shown to be a surrogate marker for myocardial fibrosis. The use of CMRI in patients on hemodialysis (HD) remains limited. This research aimed to explore the characteristics of native T1 values in HD patients and identify factors related to T1 values. METHODS: A total of thirty-two patients on HD and fourteen healthy controls were included in this study. All participants underwent CMRI. Using modified Look-Locker inversion recovery (MOLLI) sequence, native T1 mapping was achieved. Native CMRI T1 values were compared between the two groups. In order to analyze the relationship between T1 values and clinical parameters, correlation analysis was performed in patients on HD. RESULTS: Patients on HD exhibited elevated global native T1 values compared to control subjects. In the HD group, the global native T1 value correlated positively with intact parathyroid hormone (iPTH) (r = 0.418, p = 0.017) and negatively with triglycerides (r= -0.366, p = 0.039). Moreover, the global native T1 value exhibited a positive correlation with the left ventricular end-diastolic volume indexed to body surface area (BSA; r = 0.528, p = 0.014), left ventricular end-systolic volume indexed to BSA (r = 0.506, p = 0.019), and left ventricular mass indexed to BSA (r = 0.600, p = 0.005). A negative correlation was observed between the global native T1 value and ejection fraction (r = 0.-0.551, p = 0.010). CONCLUSION: The global native T1 value was prolonged in HD patients compared with controls. In the HD group, the global T1 value correlated strongly with iPTH, triglycerides, and cardiac structural and functional parameters.

Insights from incorporating quantum computing into drug design workflows.

MOTIVATION: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene Expression Scoring of Immune Activity Levels for Precision Use of Hydrocortisone in Vasodilatory Shock.

PURPOSE: Among patients with vasodilatory shock, gene expression scores may identify different immune states. We aimed to test whether such scores are robust in identifying patients' immune state and predicting response to hydrocortisone treatment in vasodilatory shock. MATERIALS AND METHODS: We selected genes to generate continuous scores to define previously established subclasses of sepsis. We used these scores to identify a patient's immune state. We evaluated the potential for these states to assess the differential effect of hydrocortisone in two randomized clinical trials of hydrocortisone versus placebo in vasodilatory shock. RESULTS: We initially identified genes associated with immune-adaptive, immune-innate, immune-coagulant functions. From these genes, 15 were most relevant to generate expression scores related to each of the functions. These scores were used to identify patients as immune-adaptive prevalent (IA-P) and immune-innate prevalent (IN-P). In IA-P patients, hydrocortisone therapy increased 28-day mortality in both trials (43.3% vs 14.7%, P = 0.028) and (57.1% vs 0.0%, P = 0.99). In IN-P patients, this effect was numerically reversed. CONCLUSIONS: Gene expression scores identified the immune state of vasodilatory shock patients, one of which (IA-P) identified those who may be harmed by hydrocortisone. Gene expression scores may help advance the field of personalized medicine.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

Plasma-based longitudinal mutation monitoring as a potential predictor of disease progression in subjects with adenocarcinoma in advanced non-small cell lung cancer.

BACKGROUND: Identifying and tracking somatic mutations in cell-free DNA (cfDNA) by next-generation sequencing (NGS) has the potential to transform the clinical management of subjects with advanced non-small cell lung cancer (NSCLC). METHODS: Baseline tumor tissue (n = 47) and longitudinal plasma (n = 445) were collected from 71 NSCLC subjects treated with chemotherapy. cfDNA was enriched using a targeted-capture NGS kit containing 197 genes. Clinical responses to treatment were determined using RECIST v1.1 and correlations between changes in plasma somatic variant allele frequencies and disease progression were assessed. RESULTS: Somatic variants were detected in 89.4% (42/47) of tissue and 91.5% (407/445) of plasma samples. The most commonly mutated genes in tissue were TP53 (42.6%), KRAS (25.5%), and KEAP1 (19.1%). In some subjects, the allele frequencies of mutations detected in plasma increased 3-5 months prior to disease progression. In other cases, the allele frequencies of detected mutations declined or decreased to undetectable levels, indicating clinical response. Subjects with circulating tumor DNA (ctDNA) levels above background had significantly shorter progression-free survival (median: 5.6 vs 8.9 months, respectively; log-rank p = 0.0183). CONCLUSION: Longitudinal monitoring of mutational changes in plasma has the potential to predict disease progression early. The presence of ctDNA mutations during first-line treatment is a risk factor for earlier disease progression in advanced NSCLC.

Disease Monitoring Using Post-induction Circulating Tumor DNA Analysis Following First-Line Therapy in Patients with Metastatic Colorectal Cancer.

PURPOSE: We assessed plasma circulating tumor DNA (ctDNA) level as a prognostic marker for progression-free survival (PFS) following first-line metastatic colorectal cancer (mCRC) therapy. EXPERIMENTAL DESIGN: The Sequencing Triplet With Avastin and Maintenance (STEAM) was a randomized, phase II trial investigating efficacy of bevacizumab (BEV) plus 5-fluorouracil/leucovorin/oxaliplatin (FOLFOX) and 5-fluorouracil/leucovorin/irinotecan (FOLFIRI), administered concurrently or sequentially, versus FOLFOX-BEV in first-line mCRC. Evaluation of biomarkers associated with treatment outcomes was an exploratory endpoint. Patients in the biomarker-evaluable population (BEP) had 1 tissue sample, 1 pre-induction plasma sample, and 1 post-induction plasma sample collected ≤60 days of induction from last drug date. RESULTS: Among the 280 patients enrolled in STEAM, 183 had sequenced and evaluable tumor tissue, 118 had matched pre-induction plasma, and 54 (BEP) had ctDNA-evaluable sequencing data for pre- and post-induction plasma. The most common somatic variants in tumor tissue and pre-induction plasma were TP53, APC, and KRAS. Patients with lower-than-median versus higher-than-median post-induction mean allele fraction (mAF) levels had longer median PFS (17.7 vs. 7.5 months, HR, 0.33; 95% confidence interval, 0.17-0.63). Higher levels of post-induction mAF and post-induction mean mutant molecules per milliliter (mMMPM), and changes in ctDNA (stratified by a 10-fold or 100-fold reduction in mAF between pre- and post-induction plasma), were associated with shorter PFS. Post-induction mAF and mMMPM generally correlated with each other (ρ = 0.987, P < 0.0001). CONCLUSIONS: ctDNA quantification in post-induction plasma may serve as a prognostic biomarker for mCRC post-treatment outcomes.

ecTMB: a robust method to estimate and classify tumor mutational burden.

Tumor Mutational Burden (TMB) is a measure of the abundance of somatic mutations in a tumor, which has been shown to be an emerging biomarker for both anti-PD-(L)1 treatment and prognosis; however, multiple challenges still hinder the adoption of TMB as a biomarker. The key challenges are the inconsistency of tumor mutational burden measurement among assays and the lack of a meaningful threshold for TMB classification. Here we describe a new method, ecTMB (Estimation and Classification of TMB), which uses an explicit background mutation model to predict TMB robustly and to classify samples into biologically meaningful subtypes defined by tumor mutational burden.

Concordance of Genomic Alterations by Next-Generation Sequencing in Tumor Tissue versus Cell-Free DNA in Stage I-IV Non-Small Cell Lung Cancer.

Molecular biomarkers hold promise for personalization of cancer treatment. However, a typical tumor biopsy may be difficult to acquire and may not capture genetic variations within or across tumors. Given these limitations, tumor genotyping using next-generation sequencing of plasma-derived circulating tumor (ct)-DNA has the potential to transform non-small cell lung cancer (NSCLC) management. Importantly, mutations detected in biopsied tissue must also be detected in plasma-derived ctDNA at different disease stages. Using the AVENIO ctDNA Surveillance kit (research use only), mutations in ctDNA from NSCLC subjects were compared with those identified in matched tumor tissue samples, retrospectively. Plasma and tissue samples were collected from 141 treatment-naïve NSCLC subjects (stage I, n = 48; stage II, n = 37; stage III, n = 33; stage IV, n = 23). In plasma samples, the median numbers of variants per subject were 4, 6, 8, and 9 in those with stage I, II, III, and IV disease, respectively. The corresponding values in tissue samples were 5, 5, 6, and 4. The overall tissue-plasma concordance of stage II through IV was 62.2% by AVENIO software call. On multivariate analysis, concordance was positively and significantly associated with tumor size and cancer stage. Next-generation sequencing-based analyses with the AVENIO ctDNA Surveillance kit could be an alternative approach to detecting genetic variations in plasma-derived ctDNA isolated from NSCLC subjects.

ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles.

MOTIVATION: DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. RESULTS: We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer. AVAILABILITY AND IMPLEMENTATION: ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of activated enhancers and linked transcription factors in breast, prostate, and kidney tumors by tracing enhancer networks using epigenetic traits.

BACKGROUND: Although technological advances now allow increased tumor profiling, a detailed understanding of the mechanisms leading to the development of different cancers remains elusive. Our approach toward understanding the molecular events that lead to cancer is to characterize changes in transcriptional regulatory networks between normal and tumor tissue. Because enhancer activity is thought to be critical in regulating cell fate decisions, we have focused our studies on distal regulatory elements and transcription factors that bind to these elements. RESULTS: Using DNA methylation data, we identified more than 25,000 enhancers that are differentially activated in breast, prostate, and kidney tumor tissues, as compared to normal tissues. We then developed an analytical approach called Tracing Enhancer Networks using Epigenetic Traits that correlates DNA methylation levels at enhancers with gene expression to identify more than 800,000 genome-wide links from enhancers to genes and from genes to enhancers. We found more than 1200 transcription factors to be involved in these tumor-specific enhancer networks. We further characterized several transcription factors linked to a large number of enhancers in each tumor type, including GATA3 in non-basal breast tumors, HOXC6 and DLX1 in prostate tumors, and ZNF395 in kidney tumors. We showed that HOXC6 and DLX1 are associated with different clusters of prostate tumor-specific enhancers and confer distinct transcriptomic changes upon knockdown in C42B prostate cancer cells. We also discovered de novo motifs enriched in enhancers linked to ZNF395 in kidney tumors. CONCLUSIONS: Our studies characterized tumor-specific enhancers and revealed key transcription factors involved in enhancer networks for specific tumor types and subgroups. Our findings, which include a large set of identified enhancers and transcription factors linked to those enhancers in breast, prostate, and kidney cancers, will facilitate understanding of enhancer networks and mechanisms leading to the development of these cancers.

Glucose-regulated protein 94 deficiency induces squamous cell metaplasia and suppresses PTEN-null driven endometrial epithelial tumor development.

Endometrial carcinoma is the most prevalent gynecologic cancer in the United States. The tumor suppressor gene Pten (phosphatase and tensin homolog) is commonly mutated in the more common type 1 (endometrioid) subtype. The glucose-regulated protein 94 (GRP94) is emerging as a novel regulator for cancer development. Here we report that expression profiles from the Cancer Genome Atlas (TCGA) showed significantly increased Grp94 mRNA levels in endometrial tumor versus normal tissues, correlating with highly elevated GRP94 protein expression in patient samples and the requirement of GRP94 for maintaining viability of human endometrioid adenocarcinoma (EAC) cell lines. Through generation of uterus-specific knockout mouse models with deletion of Grp94 alone (c94f/f) or in combination with Pten (cPf/f94f/f), we discovered that c94f/f uteri induced squamous cell metaplasia (SCM) and reduced active nuclear ß-catenin. The cPf/f94f/f uteri showed accelerated SCM and suppression of PTEN-null driven EAC, with reduced cellular proliferation, attenuated ß-catenin signaling and decreased AKT/S6 activation in the SCM. In contrast to single PTEN knockout uteri (cPf/f), cPf/f94f/f uteri showed no decrease in E-cadherin level and no invasive lesion. Collectively, our study implies that GRP94 downregulation induces SCM in EAC and suppresses AKT/S6 signaling, providing a novel mechanism for suppressing EAC progression.

Effects on the transcriptome upon deletion of a distal element cannot be predicted by the size of the H3K27Ac peak in human cells.

Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased risk for colorectal cancer (CRC). A molecular understanding of the functional consequences of this genetic variation is complicated because most GWAS SNPs are located in non-coding regions. We used epigenomic information to identify H3K27Ac peaks in HCT116 colon cancer cells that harbor SNPs associated with an increased risk for CRC. Employing CRISPR/Cas9 nucleases, we deleted a CRC risk-associated H3K27Ac peak from HCT116 cells and observed large-scale changes in gene expression, resulting in decreased expression of many nearby genes. As a comparison, we showed that deletion of a robust H3K27Ac peak not associated with CRC had minimal effects on the transcriptome. Interestingly, although there is no H3K27Ac peak in HEK293 cells in the E7 region, deletion of this region in HEK293 cells decreased expression of several of the same genes that were downregulated in HCT116 cells, including the MYC oncogene. Accordingly, deletion of E7 causes changes in cell culture assays in HCT116 and HEK293 cells. In summary, we show that effects on the transcriptome upon deletion of a distal regulatory element cannot be predicted by the size or presence of an H3K27Ac peak.

Demystifying the secret mission of enhancers: linking distal regulatory elements to target genes.

Enhancers are short regulatory sequences bound by sequence-specific transcription factors and play a major role in the spatiotemporal specificity of gene expression patterns in development and disease. While it is now possible to identify enhancer regions genomewide in both cultured cells and primary tissues using epigenomic approaches, it has been more challenging to develop methods to understand the function of individual enhancers because enhancers are located far from the gene(s) that they regulate. However, it is essential to identify target genes of enhancers not only so that we can understand the role of enhancers in disease but also because this information will assist in the development of future therapeutic options. After reviewing models of enhancer function, we discuss recent methods for identifying target genes of enhancers. First, we describe chromatin structure-based approaches for directly mapping interactions between enhancers and promoters. Second, we describe the use of correlation-based approaches to link enhancer state with the activity of nearby promoters and/or gene expression. Third, we describe how to test the function of specific enhancers experimentally by perturbing enhancer-target relationships using high-throughput reporter assays and genome editing. Finally, we conclude by discussing as yet unanswered questions concerning how enhancers function, how target genes can be identified, and how to distinguish direct from indirect changes in gene expression mediated by individual enhancers.

Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.

Recent studies indicate that DNA methylation can be used to identify transcriptional enhancers, but no systematic approach has been developed for genome-wide identification and analysis of enhancers based on DNA methylation. We describe ELMER (Enhancer Linking by Methylation/Expression Relationships), an R-based tool that uses DNA methylation to identify enhancers and correlates enhancer state with expression of nearby genes to identify transcriptional targets. Transcription factor motif analysis of enhancers is coupled with expression analysis of transcription factors to infer upstream regulators. Using ELMER, we investigated more than 2,000 tumor samples from The Cancer Genome Atlas. We identified networks regulated by known cancer drivers such as GATA3 and FOXA1 (breast cancer), SOX17 and FOXA2 (endometrial cancer), and NFE2L2, SOX2, and TP63 (squamous cell lung cancer). We also identified novel networks with prognostic associations, including RUNX1 in kidney cancer. We propose ELMER as a powerful new paradigm for understanding the cis-regulatory interface between cancer-associated transcription factors and their functional target genes.

Functional annotation of colon cancer risk SNPs.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths in the United States. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased risk for CRC. A molecular understanding of the functional consequences of this genetic variation has been complicated because each GWAS SNP is a surrogate for hundreds of other SNPs, most of which are located in non-coding regions. Here we use genomic and epigenomic information to test the hypothesis that the GWAS SNPs and/or correlated SNPs are in elements that regulate gene expression, and identify 23 promoters and 28 enhancers. Using gene expression data from normal and tumour cells, we identify 66 putative target genes of the risk-associated enhancers (10 of which were also identified by promoter SNPs). Employing CRISPR nucleases, we delete one risk-associated enhancer and identify genes showing altered expression. We suggest that similar studies be performed to characterize all CRC risk-associated enhancers.

Global loss of DNA methylation uncovers intronic enhancers in genes showing expression changes.

BACKGROUND: Gene expression is epigenetically regulated by a combination of histone modifications and methylation of CpG dinucleotides in promoters. In normal cells, CpG-rich promoters are typically unmethylated, marked with histone modifications such as H3K4me3, and are highly active. During neoplastic transformation, CpG dinucleotides of CG-rich promoters become aberrantly methylated, corresponding with the removal of active histone modifications and transcriptional silencing. Outside of promoter regions, distal enhancers play a major role in the cell type-specific regulation of gene expression. Enhancers, which function by bringing activating complexes to promoters through chromosomal looping, are also modulated by a combination of DNA methylation and histone modifications. RESULTS: Here we use HCT116 colorectal cancer cells with and without mutations in DNA methyltransferases, the latter of which results in a 95% reduction in global DNA methylation levels. These cells are used to study the relationship between DNA methylation, histone modifications, and gene expression. We find that the loss of DNA methylation is not sufficient to reactivate most of the silenced promoters. In contrast, the removal of DNA methylation results in the activation of a large number of enhancer regions as determined by the acquisition of active histone marks. CONCLUSIONS: Although the transcriptome is largely unaffected by the loss of DNA methylation, we identify two distinct mechanisms resulting in the upregulation of distinct sets of genes. One is a direct result of DNA methylation loss at a set of promoter regions and the other is due to the presence of new intragenic enhancers.

ZBTB33 binds unmethylated regions of the genome associated with actively expressed genes.

BACKGROUND: DNA methylation and repressive histone modifications cooperate to silence promoters. One mechanism by which regions of methylated DNA could acquire repressive histone modifications is via methyl DNA-binding transcription factors. The zinc finger protein ZBTB33 (also known as Kaiso) has been shown in vitro to bind preferentially to methylated DNA and to interact with the SMRT/NCoR histone deacetylase complexes. We have performed bioinformatic analyses of Kaiso ChIP-seq and DNA methylation datasets to test a model whereby binding of Kaiso to methylated CpGs leads to loss of acetylated histones at target promoters. RESULTS: Our results suggest that, contrary to expectations, Kaiso does not bind to methylated DNA in vivo but instead binds to highly active promoters that are marked with high levels of acetylated histones. In addition, our studies suggest that DNA methylation and nucleosome occupancy patterns restrict access of Kaiso to potential binding sites and influence cell type-specific binding. CONCLUSIONS: We propose a new model for the genome-wide binding and function of Kaiso whereby Kaiso binds to unmethylated regulatory regions and contributes to the active state of target promoters.

Cell type-specific binding patterns reveal that TCF7L2 can be tethered to the genome by association with GATA3.

BACKGROUND: The TCF7L2 transcription factor is linked to a variety of human diseases, including type 2 diabetes and cancer. One mechanism by which TCF7L2 could influence expression of genes involved in diverse diseases is by binding to distinct regulatory regions in different tissues. To test this hypothesis, we performed ChIP-seq for TCF7L2 in six human cell lines. RESULTS: We identified 116,000 non-redundant TCF7L2 binding sites, with only 1,864 sites common to the six cell lines. Using ChIP-seq, we showed that many genomic regions that are marked by both H3K4me1 and H3K27Ac are also bound by TCF7L2, suggesting that TCF7L2 plays a critical role in enhancer activity. Bioinformatic analysis of the cell type-specific TCF7L2 binding sites revealed enrichment for multiple transcription factors, including HNF4alpha and FOXA2 motifs in HepG2 cells and the GATA3 motif in MCF7 cells. ChIP-seq analysis revealed that TCF7L2 co-localizes with HNF4alpha and FOXA2 in HepG2 cells and with GATA3 in MCF7 cells. Interestingly, in MCF7 cells the TCF7L2 motif is enriched in most TCF7L2 sites but is not enriched in the sites bound by both GATA3 and TCF7L2. This analysis suggested that GATA3 might tether TCF7L2 to the genome at these sites. To test this hypothesis, we depleted GATA3 in MCF7 cells and showed that TCF7L2 binding was lost at a subset of sites. RNA-seq analysis suggested that TCF7L2 represses transcription when tethered to the genome via GATA3. CONCLUSIONS: Our studies demonstrate a novel relationship between GATA3 and TCF7L2, and reveal important insights into TCF7L2-mediated gene regulation.

Human mast cell subtypes in conjunctiva of patients with atopic keratoconjunctivitis, ocular cicatricial pemphigoid and Stevens-Johnson syndrome.

PURPOSE: Investigate mast cell (MC(S)) subtypes in atopic keratoconjunctivitis (AKC), ocular cicatrical pemphigoid (OCP), and Stevens-Johnson Syndrome (SJS). METHODS: MC(S) subtypes were determined by immunohistochemistry of conjunctiva (obtained from 34 patients--9 AKC, 9 OCP, 9 SJS and 7 normal) using monoclonal antibodies directed against chymase (MC(C)) and tryptase (MC(T)). Double staining was used to distinguish MC(S) as positive for both chymase and tryptase (MC(TC)). RESULTS: The number of MC(S) was significantly increased in AKC, OCP and SJS patients, compared to normal subjects. MC( C) were especially high in AKC, and moderately high in OCP. MC(T ) and MC(TC) were similar in each disease group. CONCLUSIONS: While the AKC findings were not surprising, the result in OCP and SJS suggests that MC(S) play an underappreciated role in the inflammatory process of these disorders. Disparate proportions of MC(S) subtypes in these diseases may imply differential functions of MC(S) in these disorders.