A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma

Burkitt lymphoma (BL) is the most common B-cell lymphoma in children. Within the International Cancer Genome Consortium (ICGC), we performed whole genome and transcriptome sequencing of 39 sporadic BL. Here, we unravel interaction of structural, mutational, and transcriptional changes, which contribute to MYC oncogene dysregulation together with the pathognomonic IG-MYC translocation. Moreover, by mapping IGH translocation breakpoints, we provide evidence that the precursor of at least a subset of BL is a B-cell poised to express IGHA. We describe the landscape of mutations, structural variants, and mutational processes, and identified a series of driver genes in the pathogenesis of BL, which can be targeted by various mechanisms, including IG-non MYC translocations, germline and somatic mutations, fusion transcripts, and alternative splicing

DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control

Although Burkitt lymphomas and follicular lymphomas both have features of germinal center B cells, they are biologically and clinically quite distinct. Here we performed whole-genome bisulfite, genome and transcriptome sequencing in 13 IG-MYC translocation-positive Burkitt lymphoma, nine BCL2 translocation-positive follicular lymphoma and four normal germinal center B cell samples. Comparison of Burkitt and follicular lymphoma samples showed differential methylation of intragenic regions that strongly correlated with expression of associated genes, for example, genes active in germinal center dark-zone and light-zone B cells. Integrative pathway analyses of regions differentially methylated in Burkitt and follicular lymphomas implicated DNA methylation as cooperating with somatic mutation of sphingosine phosphate signaling, as well as the TCF3-ID3 and SWI/SNF complexes, in a large fraction of Burkitt lymphomas. Taken together, our results demonstrate a tight connection between somatic mutation, DNA methylation and transcriptional control in key B cell pathways deregulated differentially in Burkitt lymphoma and other germinal center B cell lymphomas

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Evaluation of respiratory liver and kidney movements for MRI navigator gating

Purpose To determine the tracking factor by studying the relationship between kidney and diaphragm motions and to compare the efficiency of the gating-and-following and gating-only algorithms in reducing motion artifacts in navigator-gated scans. Materials and Methods Diaphragm and kidney motions were measured by using real-time TrueFISP sequences from 10 healthy human volunteers to determine tracking factors at different acceptance windows. Mean tracking factors were used to calculate mean residual errors and improvement factors for the gating-and-following and gating-only algorithms. Results Mean tracking factors for ±4, ±6, ±8 mm and full acceptance windows ranged from 0.6 to 0.7, with large interindividual variations. Acceptance rates increased as the size of the acceptance window increased (acceptance rate for a 4 mm window ∼ 50%). There was a greater reduction of motion errors by gating-and-following (maximum of 1.86 mm) than gating-only (maximum of 7.05 mm). Conclusion Mean tracking factors obtained in this study can be used as a guideline for using the gating-and-following algorithm in navigator-gated kidney scans. The gating-and-following and gating-only algorithms were quantitatively compared, and it was found that the former is more effective in reducing motion errors. © 2010 Wiley-Liss, Inc

Quantitative Susceptibility Mapping Using a Multispectral Autoregressive Moving Average Model to Assess Hepatic Iron Overload

Background: R2*-MRI is clinically used to noninvasively assess hepatic iron content (HIC) to guide potential iron chelation therapy. However, coexisting pathologies, such as fibrosis and steatosis, affect R2* measurements and may thus confound HIC estimations. Purpose: To evaluate whether a multispectral auto regressive moving average (ARMA) model can be used in conjunction with quantitative susceptibility mapping (QSM) to measure magnetic susceptibility as a confounder-free predictor of HIC. Study Type: Phantom study and in vivo cohort. Subjects: Nine iron phantoms covering clinically relevant R2* range (20–1200/second) and 48 patients (22 male, 26 female, median age 18 years). Field Strength/Sequence: Three-dimensional (3D) and two-dimensional (2D) multi-echo gradient echo (GRE) at 1.5 T. Assessment: ARMA-QSM modeling was performed on the complex 3D GRE signal to estimate R2*, fat fraction (FF), and susceptibility measurements. R2*-based dry clinical HIC values were calculated from the 2D GRE acquisition using a published R2*-HIC calibration curve as reference standard. Statistical Tests: Linear regression analysis was performed to compare ARMA R2* and susceptibility-based estimates to iron concentrations and dry clinical HIC values in phantoms and patients, respectively. Results: In phantoms, the ARMA R2* and susceptibility values strongly correlated with iron concentrations (R2 ≥ 0.9). In patients, the ARMA R2* values highly correlated (R2 = 0.97) with clinical HIC values with slope = 0.026, and the susceptibility values showed good correlation (R2 = 0.82) with clinical dry HIC values with slope = 3.3 and produced a dry-to-wet HIC ratio of 4.8. Data Conclusion: This study shows the feasibility that ARMA-QSM can simultaneously estimate susceptibility-based wet HIC, R2*-based dry HIC and FFs from a single multi-echo GRE acquisition. Our results demonstrate that both, R2* and susceptibility-based wet HIC values estimated with ARMA-QSM showed good association with clinical dry HIC values with slopes similar to published R2*-biopsy HIC calibration and dry-to-wet tissue weight ratio, respectively. Hence, our study shows that ARMA-QSM can provide potentially confounder-free assessment of hepatic iron overload. Level of Evidence: 3. Technical Efficacy: Stage 2

Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by R2*-MRI

Background: Extraction of liver parenchyma is an important step in the evaluation of R *2 -based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole-liver contouring and T *2 -thresholding to exclude hepatic vessels. However, the vessel exclusion process is iterative, time-consuming, and susceptible to interreviewer variability. Purpose: To implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate assessment of R *2 -based HIC. Study Type: Retrospective analysis of clinical data. Subjects: Data from 511 MRI exams performed on 257 patients were analyzed. Field Strength/Sequence: All patients were scanned on a 1.5T scanner using a multiecho gradient echo sequence for clinical monitoring of HIC. Assessment: An automated method based on a multiscale vessel enhancement filter was investigated for three input data types—contrast-optimized composite image, T *2 map, and R *2 map—to segment blood vessels and extract liver tissue for R *2 -based HIC assessment. Segmentation and R *2 results obtained using this automated technique were compared with those from a reference T *2 -thresholding technique performed by a radiologist. Statistical Tests: The Dice similarity coefficient was used to compare the segmentation results between the extracted parenchymas, and linear regression and Bland-Altman analyses were performed to compare the R *2 results, obtained with the automated and reference techniques. Results: Mean liver R *2 values estimated from all three filter-based methods showed excellent agreement with the reference method (slopes 1.04–1.05, R 2 \u3e 0.99, P \u3c 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87–88%. The T *2 -thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (\u3c5%) in R *2 values compared to the automated method. Data Conclusion: The excellent agreement between reference and automated hepatic vessel segmentation methods confirms the accuracy and robustness of the proposed method. This automated approach might improve the radiologist\u27s workflow by reducing the interpretation time and operator dependence for assessing HIC, an important clinical parameter that guides iron overload management. Level of Evidence: 3. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;47:1542–1551

Autoregressive moving average modeling for hepatic iron quantification in the presence of fat

Background: Measuring hepatic R2* by fitting a monoexponential model to the signal decay of a multigradient-echo (mGRE) sequence noninvasively determines hepatic iron content (HIC). Concurrent hepatic steatosis introduces signal oscillations and confounds R2* quantification with standard monoexponential models. Purpose: To evaluate an autoregressive moving average (ARMA) model for accurate quantification of HIC in the presence of fat using biopsy as the reference. Study Type: Phantom study and in vivo cohort. Population: Twenty iron–fat phantoms covering clinically relevant R2* (30–800 s-1) and fat fraction (FF) ranges (0–40%), and 10 patients (four male, six female, mean age 18.8 years). Field Strength/Sequence: 2D mGRE acquisitions at 1.5 T and 3 T. Assessment: Phantoms were scanned at both field strengths. In vivo data were analyzed using the ARMA model to determine R2* and FF values, and compared with biopsy results. Statistical Tests: Linear regression analysis was used to compare ARMA R2* and FF results with those obtained using a conventional monoexponential model, complex-domain nonlinear least squares (NLSQ) fat–water model, and biopsy. Results: In phantoms and in vivo, all models produced R2* and FF values consistent with expected values in low iron and low/high fat conditions. For high iron and no fat phantoms, monoexponential and ARMA models performed excellently (slopes: 0.89–1.07), but NLSQ overestimated R2* (slopes: 1.14–1.36) and produced false FFs (12–17%) at 1.5 T; in high iron and fat phantoms, NLSQ (slopes: 1.02–1.16) outperformed monoexponential and ARMA models (slopes: 1.23–1.88). The results with NLSQ and ARMA improved in phantoms at 3 T (slopes: 0.96–1.04). In patients, mean R2*-HIC estimates for monoexponential and ARMA models were close to biopsy-HIC values (slopes: 0.90–0.95), whereas NLSQ substantially overestimated HIC (slope 1.4) and produced false FF values (4–28%) with very high SDs (15–222%) in patients with high iron overload and no steatosis. Data Conclusion: ARMA is superior in quantifying R2* and FF under high iron and no fat conditions, whereas NLSQ is superior for high iron and concurrent fat at 1.5 T. Both models give improved R2* and FF results at 3 T. Level of Evidence: 2. Technical Efficacy Stage: 2. J. Magn. Reson. Imaging 2019;50:1620–1632

Autoregressive moving average modeling for hepatic iron quantification in the presence of fat

Background: Measuring hepatic R2* by fitting a monoexponential model to the signal decay of a multigradient-echo (mGRE) sequence noninvasively determines hepatic iron content (HIC). Concurrent hepatic steatosis introduces signal oscillations and confounds R2* quantification with standard monoexponential models. Purpose: To evaluate an autoregressive moving average (ARMA) model for accurate quantification of HIC in the presence of fat using biopsy as the reference. Study Type: Phantom study and in vivo cohort. Population: Twenty iron–fat phantoms covering clinically relevant R2* (30–800 s-1) and fat fraction (FF) ranges (0–40%), and 10 patients (four male, six female, mean age 18.8 years). Field Strength/Sequence: 2D mGRE acquisitions at 1.5 T and 3 T. Assessment: Phantoms were scanned at both field strengths. In vivo data were analyzed using the ARMA model to determine R2* and FF values, and compared with biopsy results. Statistical Tests: Linear regression analysis was used to compare ARMA R2* and FF results with those obtained using a conventional monoexponential model, complex-domain nonlinear least squares (NLSQ) fat–water model, and biopsy. Results: In phantoms and in vivo, all models produced R2* and FF values consistent with expected values in low iron and low/high fat conditions. For high iron and no fat phantoms, monoexponential and ARMA models performed excellently (slopes: 0.89–1.07), but NLSQ overestimated R2* (slopes: 1.14–1.36) and produced false FFs (12–17%) at 1.5 T; in high iron and fat phantoms, NLSQ (slopes: 1.02–1.16) outperformed monoexponential and ARMA models (slopes: 1.23–1.88). The results with NLSQ and ARMA improved in phantoms at 3 T (slopes: 0.96–1.04). In patients, mean R2*-HIC estimates for monoexponential and ARMA models were close to biopsy-HIC values (slopes: 0.90–0.95), whereas NLSQ substantially overestimated HIC (slope 1.4) and produced false FF values (4–28%) with very high SDs (15–222%) in patients with high iron overload and no steatosis. Data Conclusion: ARMA is superior in quantifying R2* and FF under high iron and no fat conditions, whereas NLSQ is superior for high iron and concurrent fat at 1.5 T. Both models give improved R2* and FF results at 3 T. Level of Evidence: 2. Technical Efficacy Stage: 2. J. Magn. Reson. Imaging 2019;50:1620–1632

Comparison of whole liver and small region-of-interest measurements of MRI liver R2* in children with iron overload

Background: Measurement of liver MRI T2* and R2* is emerging as a reliable alternative to liver biopsy for the quantitation of liver iron content. A systematic investigation of the influence of the region-of-interest size and placement has not been conducted. Objective: To compare small and whole liver region-of-interest (ROI) MRI R2* values to each other and to biopsy liver iron content in patients with iron overload. Materials and methods: Forty-one iron-overloaded patients, ages 7-35 years, underwent biopsy for liver iron content quantitation and MRI for liver R2* measurement within 30 days. Three reviewers independently used small and whole liver ROIs to measure R2*. Inter-reviewer agreement was assessed with the intra-class correlation coefficient (ICC). Associations between R2* and liver iron content were investigated using Spearman\u27s rank-order correlation and Monte Carlo estimated exact P values. Results: Biopsy liver iron content and small and whole liver ROI R2* measurements were strongly associated for all reviewers (all P \u3c 0.0001). Although inter-reviewer agreement was excellent for both ROI methods (ICC = 0.98-0.99), the small ROI technique more frequently led to inter-reviewer differences larger than 75 Hz, slightly higher R2* values, larger standard errors and greater range in values. Conclusion: Small and whole liver ROI techniques are strongly associated with biopsy liver iron content. We found slightly greater inter-reviewer variability in R2* values using the small ROI technique. Because such variability could adversely impact patient management when R2* values are near a threshold of iron chelation therapy, we recommend using a whole liver ROI. © 2010 Springer-Verlag