Quality Control Metrics Research Articles

Association of an inflammatory gene signature with CD8 expression by immunohistochemistry (IHC) in multiple tumor types.

2593 Background: A multiparameter tumor inflammation assay based on gene expression profiling (TIA-GEP) can extend the utility of IHC to interrogate the tumor microenvironment (TME). Using CD8 expression assessed by IHC (CD8-IHC) as a surrogate for inflammation, statistical modelling was used to develop a specific gene signature on the TIA-GEP panel to predict CD8-IHC. The correlation between TIA-GEP and CD8-IHC and the prevalence of inflammation were explored across multiple tumor types. Methods: Levels of inflammation were measured by CD8-IHC and TIA-GEP on 1778 procured samples across 12 tumor types. Quality control metrics involved sample input quality, technical errors, and inter-run variability. Generalized linear models were used to identify an inflammation score that predicts the CD8-IHC score in melanoma and SCCHN tissue. The predictive accuracy of this signature was also examined in 10 additional tumor types. Results: Assessment of TME inflammation by CD8-IHC was consistent with that observed by TIA-GEP in multiple tumor types. The range of inflammation varied across different tumor types, with relatively lower inflammation range and scores in SCLC, ovarian, and prostate cancers, and higher values in NSCLC, melanoma, SCCHN, and gastric cancers. R2 x 100 values reflecting percent variation in CD8-IHC associated with TIA-GEP ranged from 62.4% to 79.2% ( P < 0.0001) for all tumor types except prostate cancer (32.5%). Low correlation in prostate cancer may be a result of low prevalence of inflammation by CD8-IHC. Estimated linear regression slopes between CD8-IHC and TIA-GEP ranged from 0.74 in SCLC to 1.27 in gastric cancer. Conclusions: The results suggest that the inflammation signature is a robust potential diagnostic tool predicting inflammation in the TME. The inflammation signature not only correlates with CD8-IHC for multiple tumor types, but also leverages the alternative benefits associated with TIA-GEP, which include information related to tumor inflammation-associated biomarkers and flexibility in exploring the value of other genomic signatures.

Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics

The use of treatment plan characteristics to predict patient-specific quality assurance (QA) measurement results has recently been reported as a strategy to help facilitate automated pre-treatment verification workflows or to provide a virtual assessment of delivery quality. The goal of this work is to investigate the potential of using treatment plan characteristics and linac performance metrics (i.e. quality control test results) in combination with machine learning techniques to predict the results of VMAT patient-specific QA measurements.Using features that describe treatment plan complexity and linac performance metrics, we trained a linear support vector classifier (SVC) to classify the results of VMAT patient-specific QA measurements. The ‘targets’ in this model were simple classes representing median dose difference between measured and expected dose distributions—‘hot’ if the median dose deviation was >1%, ‘cold’ if it was <−1%, and ‘normal’ if it was within ±1%. A total of 1620 unique patient-specific QA measurements were available for model development and testing. 75% of the data were used to develop and cross-validate the model, and the remaining 25% were used for an independent assessment of model performance. For the model development phase, a recursive feature elimination (RFE) cross-validation technique was used to eliminate unimportant features. Model performance was assessed using receiver operator characteristic (ROC) curve metrics.Of the ten features found to be most predictive of patient-specific QA measurement results, half were derived from treatment plan characteristics and half from quality control (QC) metrics characterizing linac performance. The model achieved a micro-averaged area under the ROC curve of 0.93, and a macro-averaged area under the ROC curve of 0.88.This work demonstrates the potential of using both treatment plan characteristics and routine linac QC results in the development of machine learning models for VMAT patient-specific QA measurements.

Analytical Validation of a Highly Sensitive, Multiplexed Chronic Myeloid Leukemia Monitoring System Targeting BCR-ABL1 RNA

This study describes the analytical performance of the QuantideX qPCR BCR-ABL IS Kit, the first Food and Drug Administration–cleared assay designed to monitor breakpoint cluster region–Abelson tyrosine-protein kinase 1 (BCR-ABL1) fusion transcripts isolated from peripheral blood specimens from patients with chronic myeloid leukemia. This multiplex real-time quantitative RT-PCR assay amplifies both e13a2 and e14a2 Major BCR-ABL1 transcripts and the reference target ABL1. The test results are provided in international scale (IS) values by incorporating armored RNA-based calibrators that have defined IS values tied directly to the World Health Organization BCR-ABL1 Primary Reference Materials, without the necessity of determining and maintaining conversion factors. For each batch run, the integrated interpretive software evaluates run and specimen quality control metrics (including a sufficient amount of ABL1 control transcripts to ensure a minimal limit of detection) and calculates both molecular response (MR) and %IS values for each specimen. The test has a limit of detection of MR4.7 (0.002%IS) and a linear range from MR0.3 (50%IS) to MR4.7 (0.002%IS) for both Major transcripts. Single-site and multisite precision studies demonstrated a maximum SD of 0.13 MR (30% CV within the assay range between MR0.7 and MR3.7). The performance of this BCR-ABL1 monitoring test meets all of the clinical guideline recommendations for sensitivity and IS reporting for the management of chronic myeloid leukemia patients.

Targeted resequencing using the MinION long read sequencing platform

MinION nanopore DNA sequencing is an emerging paradigm in portable, long read, economic sequencing technology. Using a targeted resequencing approach of the rat osteopontin (Spp1) region in the stroke‐prone spontaneously hypertensive (SHRSPGla) and Wistar Kyoto (WKYGla) rats. We aim to develop and validate a variant calling pipeline based on long reads produced by the MinION platform and to corroborate with published SHRSPGla and WKYGla promotor variants using the Rat Genome Database (RGD) (Rnor6.0).Using the Phusion high fidelity DNA polymerase, seven PCR amplifications of defined length were amplified across a 15kb region of the rat Spp1 promoter and gene from liver samples from two individual SHRSPGla and WKYGla animals. The library for MinION sequencing was prepared using Oxford Nanopore Ligation Sequencing Kit and Native Barcoding Expansion 1–12. Purified PCR amplicons were repaired and end‐prepped with dA‐tailing. Barcodes and adapters were ligated to the prepared DNA fragments before loading to the MinION flow cell for sequencing. Two Oxford Nanopore SpotON Flow cells were primed and duplicate prepared samples were each loaded onto flow cells on two separate MinION. The MinION raw data was analysed in real time during sequencing by cloud‐based EPI2ME software (Metrichor). This enabled base calling of raw data to produce DNA sequence from output measurements of current, converting raw fast5 files to base called fastq format. Reads were separated by barcode to identify reads by strain and animal. Quality control metrics for each sample were obtained from the BAM files produced by Samtools Mpileup performed on the merged MinION fastq files mapping to the Spp1 region (Chromosome 14, 6,675,281–6,690,258). A BWA/Bcftools pipeline was used to call variants (Phred&gt;20). Variant calls from the SHRSPGla/WKYGla strains using our MinION pipeline were cross‐referenced with previously published Illumina BWA/GATK calls on RGD for this region (Rnor6.0).The MinION platform produced a mean read count of 1.49×104(± 6.21×103) per sample, with a mean mapping percentage of 49.9% (±10.1). The mean read length was 1130 bases (±132) with a mean quality of base calls of 12.83 (±0.16). Mean depth of coverage of each individual sample was 610x(±180). RGD describes 38 variants in this region of the osteopontin promotor. Across two sequencing runs, 27 of the 38 variants listed on the Rat Genome Database were called and identified several potential novel variants in the region.In conclusion, we confirm the MinION platform can be used as a targeted resequencing platform and variant calls can be made from the long reads produced, although read accuracy is dependent on read coverage.Support or Funding InformationBritish Heart Foundation and Stratified Medicine Scotland Innovation Centre is supported by the Scottish Funding Council, Scottish Enterprise and the Chief Scientist Office of ScotlandThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Three-dimensionally-printed anthropomorphic physical phantom for mammography and digital breast tomosynthesis with custom materials, lesions, and uniform quality control region.

Anthropomorphic breast phantoms mimic patient anatomy in order to evaluate clinical mammography and digital breast tomosynthesis system performance. Our goal is to create a modular phantom with an anthropomorphic region to allow for improved lesion and calcification detection as well as a uniform region to evaluate standard quality control (QC) metrics. Previous versions of this phantom used commercial photopolymer inkjet three-dimensional printers to recreate breast anatomy using four surfaces that were fabricated with commercial materials spanning only a limited breast density range of 36% to 64%. We use modified printers to create voxelized, dithered breast phantoms with continuous gradations between glandular and adipose tissues. Moreover, the new phantom replicates the low-end density (representing adipose tissue) using third party material, Jf Flexible, and increases the high-end density to the density of glandular tissue and beyond by either doping Jf Flexible with salts and nanoparticles or using a new commercial resin, VeroPureWhite. An insert design is utilized to add masses, calcifications, and iodinated objects into the phantom for increased utility. The uniform chest wall region provides a space for traditional QC objects such as line pair patterns for measuring resolution and scale bars for measuring printer linearity. Incorporating these distinct design modules enables us to create an improved, complete breast phantom to better evaluate clinical mammography systems for lesion and calcification detection and standard QC performance evaluation.

Towards Improving Embryo Prioritization: Parallel Next Generation Sequencing of DNA and RNA from a Single Trophectoderm Biopsy

Improved embryo prioritization is crucial in optimizing the results in assisted reproduction, especially in light of increasing utilization of elective single embryo transfers. Embryo prioritization is currently based on morphological criteria and in some cases incorporates preimplantation genetic testing for aneuploidy (PGT-A). Recent technological advances have enabled parallel genomic and transcriptomic assessment of a single cell. Adding transcriptomic analysis to PGT-A holds promise for better understanding early embryonic development and implantation, and for enhancing available embryo prioritization tools. Our aim was to develop a platform for parallel genomic and transcriptomic sequencing of a single trophectoderm (TE) biopsy, that could later be correlated with clinical outcomes. Twenty-five embryos donated for research were utilized; eight for initial development and optimization of our method, and seventeen to demonstrate clinical safety and reproducibility of this method. Our method achieved 100% concordance for ploidy status with that achieved by the classic PGT-A. All sequencing data exceeded quality control metrics. Transcriptomic sequencing data was sufficient for performing differential expression (DE) analysis. All biopsies expressed specific TE markers, further validating the accuracy of our method. Using PCA, samples clustered in euploid and aneuploid aggregates, highlighting the importance of controlling for ploidy in every transcriptomic assessment.

Standardization of Blood Collection and Processing for the Diagnostic Use of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane vesicles released by many types of cells in both health and disease. EVs can be found in most body fluids, carrying a plethora of biomolecules, including proteins, RNA, and DNA, that reflect the biomolecular composition of the tissue of origin. Parenchymal and stromal cells actively release EVs in the extracellular milieu and in circulation, providing valuable information that may be exploited for diagnostic applications. However, isolation of these EV subpopulations in circulation is extremely challenging as they are diluted within more abundant EV subpopulations derived from blood cells (red blood cells, platelets, and white blood cells). A number of preanalytical variables during blood collection and processing greatly impact the levels of blood-derived EVs, thus affecting sample quality. So far, lack of standard protocols for blood collection and processing as well as quality control metrics has limited the clinical validation and adoption of EV-based diagnostic assays. In this review, we describe the preanalytical variables that affect sample quality and suitability for EV-based diagnostic approaches. Furthermore, we suggest biochemical and molecular quality control (QC) metrics to minimize intra- and interstudy variability and improve data robustness and reproducibility.

Quantitative quality-control metrics for in vivo oximetry in small vessels by visible light optical coherence tomography angiography.

Biological functions rely on local microvasculature to deliver oxygen and nutrients and carry away metabolic waste. Alterations to local oxygenation levels are manifested in diseases including cancer, diabetes mellitus, etc. The ability to quantify oxygen saturation (sO2) within microvasculature in vivo to assess local tissue oxygenation and metabolic function is highly sought after. Visible light optical coherence tomography (vis-OCT) angiography has shown promise in reaching this goal. However, achieving reliable measurements in small vessels can be challenging due to the reduced contrast and requires data averaging to improve the spectral data quality. Therefore, a method for quality-control of the vis-OCT data from small vessels becomes essential to reject unreliable readings. In this work, we present a quantitative metrics to evaluate the spectral data for a reliable measurement of sO2, including angiography signal to noise ratio (SNR), spectral anomaly detection and discard, and theory-experiment correlation analysis. The thresholds for each quantity can be flexibly adjusted according to different applications and system performance. We used these metrics to measure sO2 of C57BL/6J mouse lower extremity microvasculature and validated it by introducing hyperoxia for expected sO2 changes. After validation, we applied this protocol on C57BL/6J mouse ear microvasculature to conduct in vivo small blood vessel OCT oximetry. This work seeks to standardize the data processing method for in vivo oximetry in small vessels by vis-OCT.

Bioinformatics Basics for High-Throughput Hybridization-Based Targeted DNA Sequencing from FFPE-Derived Tumor Specimens: From Reads to Variants.

The use of next-generation sequencing and hybridization-based capture for target enrichment have enabled the interrogation of coding regions of several clinically significant cancer genes in tumor specimens using both targeted panels of a few to hundreds of genes, to whole-exome panels encompassing coding regions of all genes in the genome. Next-generation sequencing (NGS)technologies produce millions of relatively short segments of sequences or reads that require bioinformatics tools to map reads back to a reference genome using various read alignment tools, as well as to determine differences between single bases (single nucleotide variants or SNVs) or multiple bases (insertions and deletions or indels) between the aligned reads and the reference genome to call variants. In addition to single nucleotide changes or small insertions and deletions, high copy gains and losses can also be gleaned from NGS data to call gene amplifications and deletions. Throughout these processes, numerous quality control metrics can be assessed at each step to ensure that the resulting called variants are of high quality and are accurate. In this chapter we review common tools used to generate reads from Illumina-derived sequence data, align reads, and call variants from hybridization-based targeted NGS panel data generated from tumor FFPE-derived DNA specimens as well as basic quality metrics to assess for each assayed specimen.

Optimization of ribosome profiling using low-input brain tissue from fragile X syndrome model mice.

Dysregulated protein synthesis is a major underlying cause of many neurodevelopmental diseases including fragile X syndrome. In order to capture subtle but biologically significant differences in translation in these disorders, a robust technique is required. One powerful tool to study translational control is ribosome profiling, which is based on deep sequencing of mRNA fragments protected from ribonuclease (RNase) digestion by ribosomes. However, this approach has been mainly applied to rapidly dividing cells where translation is active and large amounts of starting material are readily available. The application of ribosome profiling to low-input brain tissue where translation is modest and gene expression changes between genotypes are expected to be small has not been carefully evaluated. Using hippocampal tissue from wide type and fragile X mental retardation 1 (Fmr1) knockout mice, we show that variable RNase digestion can lead to significant sample batch effects. We also establish GC content and ribosome footprint length as quality control metrics for RNase digestion. We performed RNase titration experiments for low-input samples to identify optimal conditions for this critical step that is often improperly conducted. Our data reveal that optimal RNase digestion is essential to ensure high quality and reproducibility of ribosome profiling for low-input brain tissue.

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards.

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in the use of clinical biomarkers, cellular modeling, and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards that are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved mass spectrometry (MS) instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy-labeled peptides and flags those that fail quality-control metrics. Using four metrics, the tool detects problems with both individual SRM transitions and the collective group of transitions that monitor a single peptide. The program's speed and simplicity enable its use at the point of data acquisition and can be ideally run immediately upon the completion of a liquid chromatography-SRM-MS analysis.

Alfred: interactive multi-sample BAM alignment statistics, feature counting and feature annotation for long- and short-read sequencing.

SummaryHarmonizing quality control (QC) of large-scale second and third-generation sequencing datasets is key for enabling downstream computational and biological analyses. We present Alfred, an efficient and versatile command-line application that computes multi-sample QC metrics in a read-group aware manner, across a wide variety of sequencing assays and technologies. In addition to standard QC metrics such as GC bias, base composition, insert size and sequencing coverage distributions it supports haplotype-aware and allele-specific feature counting and feature annotation. The versatility of Alfred allows for easy pipeline integration in high-throughput settings, including DNA sequencing facilities and large-scale research initiatives, enabling continuous monitoring of sequence data quality and characteristics across samples. Alfred supports haplo-tagging of BAM/CRAM files to conduct haplotype-resolved analyses in conjunction with a variety of next-generation sequencing based assays. Alfred’s companion web application enables interactive exploration of results and comparison to public datasets.Availability and implementationAlfred is open-source and freely available at https://tobiasrausch.com/alfred/.Supplementary information Supplementary data are available at Bioinformatics online.

Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space.

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (−)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.

Immunoglobulin Lambda Translocations Identify Poor Outcome and IMiD Resistance in Multiple Myeloma and Co-Occur with Hyperdiploidy

▪Patients with the plasma cell malignancy multiple myeloma now benefit from treatments such as proteasome inhibitors, immunomodulatory imide drugs (IMiDs), autologous stem cell transplant, and monoclonal antibodies. However, 20% of patients still relapse or die within two years and are deemed ‘high risk’. Current markers fail to identify all high-risk patients resulting in misdiagnoses, therefore additional markers for this deadly form of the disease are required.To better understand and identify high-risk myeloma, we analyzed the structural variant landscape of 826 myelomas from newly-diagnosed patients using whole genome sequencing as part of the CoMMpass trial (NCT01454297). High-confidence somatic structural variants were determined using DELLY and quality control metrics to exclude regions with sequencing anomalies.Myeloma from newly diagnosed patients had a median of 21 somatic structural variants including 7 duplications, 2 deletions, 7 inversions, and 3 translocations. The number of deletions, duplications, and translocations corresponded to poor progression-free (PFS) and overall survival (OS), with translocations being the most significant (P <6.1x10-7). The two most common translocations occurred at the IgH (41%) and MYC (23%) loci, but did not correspond with differential outcome. However, the third most commonly translocated region (10%) occurred at the IgL locus and was indicative of poor PFS and OS with hazard ratios (HR) of 1.71 and 1.81, respectively (Figure 1). IgL-translocated myeloma did not correspond with distinct clinical features such as age, stage, gender, or b2M levels; and IgL-translocated patients were treated with similar therapeutic regimens as others. Additionally, IgL-translocated myeloma did not have a distinct mutational repertoire, gene expression subtype, and did not have many unique structural genetic elements. The notable exception is that 70% of IgL-translocated myeloma co-occurred with hyperdiploidy, a marker normally associated with better prognosis. However, patients with IgL-translocated and hyperdiploid myeloma experienced poor outcome with a median PFS of 23 months compared to 42.8 months for non-t(IgL) hyperdiploid myeloma (PFS HR=2.35; OS HR=2.41). This poor outcome is partially explained by the failure of patients with IgL-translocated myeloma to benefit from IMiDs. In fact, IMiDs provided no survival benefit to patients with IgL-translocated myeloma who experienced poor outcomes commensurate with patients that did not receive IMiDs (PFS HR=1.56; OS HR=1.49). This is in contrast to patients with myeloma that harbor other translocations, such as IgH translocation, who benefited from IMiDs (PFS HR=0.83, OS HR=0.61).These data identify IgL translocation as an independent marker of poor prognosis regardless of translocation partner, and suggest this may be due to the failure of this myeloma subtype to benefit from IMiDs. One potential mechanistic explanation is that the IgL enhancer is one of the most robust enhancers of gene expression and is therefore uniquely resistant to therapeutic inhibition. Indeed, the IgL enhancer is bound by several transcription factors at some of the highest levels in the B cell / myeloma epigenome, including BRD4, MED1, and IKZF1. This last factor is particularly interesting as IKZF1 is the target of IMiDs, and thus high-levels of IKZF1 occupancy at the IgL enhancer may be more difficult to fully deplete therapeutically than other loci. This may explain why patients with IgL-translocated myeloma do not benefit from IMiDs whereas patients with IgH- or IgK-translocated myeloma do. Finally, the co-occurrence of myeloma with IgL-translocation and hyperdiploidy is particularly unfortunate, as hyperdiploidy is routinely tested for clinically, whereas IgL-translocations are rarely diagnosed, likely resulting in their misclassification as standard risk.Figure: IgL translocations portend poor prognosis. a Circos plot showing the repertoire of IgL translocations in newly diagnosed myeloma where line thickness denotes frequency (key bottom left). b Kaplan-Meier analysis of IgL translocated [t(IgL)] patients (N=81) as compared to non-t(IgL) (N=745) for progression-free (PFS; top) and overall survival (OS; bottom). P-values were calculated using a Cox proportional hazards Wald's test or permutation based P-value with 1,000 permutations based on the hazard ratio. [Display omitted] DisclosuresNeri:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Bahlis:Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Nooka:Adaptive technologies: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Spectrum Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kaufman:Janssen: Consultancy; Roche: Consultancy; Karyopharm: Other: data monitoring committee; Abbvie: Consultancy; BMS: Consultancy. Lonial:Amgen: Research Funding. Boise:Abbvie: Consultancy; AstraZeneca: Honoraria.

Association between the Risk of Low/High-Count Monoclonal B-Cell Lymphocytosis (MBL) and the Chronic Lymphocytic Leukemia (CLL) Polygenic Risk Score (PRS)

BackgroundMBL is a precursor state to CLL, with a prevalence of ~5-8% in the general population and 15-18% in CLL families. To date, 41 single nucleotide polymorphisms (SNPs) have been found to be associated with CLL. We previously showed that a PRS of the weighted average of the number of risk alleles of these SNPs is associated with CLL risk using cases and controls from the International Lymphoma Epidemiology (InterLymph) Consortium. We validated this score in an independent sample of CLL cases and controls from the Genetic Epidemiology of CLL (GEC) Consortium, a cohort of families with ≥2 members with CLL. In the CLL families we also reported an association between the PRS and MBL risk, where 93% of MBLs were low-count MBL. Here we evaluate the PRS in an independent sample ascertained from the Mayo Clinic Biobank.MethodsThe Mayo Clinic Biobank is a large-scale bio-repository of adult patients assembled to provide a wide array of health-related research studies. Biobank participants (N=2530) were screened for MBL using a highly-sensitive, 8-color (CD38, CD45, Kappa, Lambda, CD19, CD23, CD5 and CD20) flow-cytometry assay on stored cryopreserved peripheral blood mononucleotide cells. Low-count and high-count MBL were defined as those individuals who had <85% and ≥85 clonal B-cells out of total B-cells, respectively. Individuals without MBL (“controls”) were frequency matched to MBLs based on age and sex, after excluding non-Caucasian and non-interpretable flow results. Participants were genotyped using the Illumina OmniExpress array. Standard genotyping quality-control metrics were applied. We computed the PRS as previously published and categorized it by quintiles based on values previously used with InterLymph controls. The middle quintile served as the reference category. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CI), adjusting for age at consent, sex, and socioeconomic status. In addition, we compared the median PRS by low-count MBL and high count MBL using the Mann-Whitney U test.ResultsWe identified 276 MBLs overall and 2109 controls; 97% (N=267) of MBLs had low-count MBL and only 3% (N=9) had high-count MBL. We then selected 844 controls frequency matched to the MBL cases on age and sex. Family history of leukemia/lymphoma was 11.3% among MBLs overall and 8.8% among controls. The median PRS was 7.88 and 7.46 (Figure) among MBLs overall and controls, respectively. The continuous PRS had a 1.75-fold increased risk for MBL overall (CI:1.5-2.1, P=2.65x10-12). Compared to the middle quintile, the highest quintile had 2.2-fold increased risk for MBL overall (CI:1.5-3.3, P=1.46x10-4) and the lowest quintile had 0.6-fold decreased risk (CI:0.4-0.96, P=0.03). The median PRS for low-count and high count MBL were 7.86 and 8.04, respectively. The continuous PRS had a 1.75-fold increased risk for low-count MBL (CI:1.5-2.1, P=4.81x10-12) and a 1.89-fold increased risk for high-count MBL (CI:0.8-4.2, P=0.13), however, the effect is not statistically significant between high-count and low-count MBL (P=0.89).ConclusionIn this independent sample, we validated our previous findings that the CLL PRS is associated with MBL risk. Although the sample size of high-count MBL was limited (N=9), there is evidence of a weak trend towards a higher PRS compared to low-count MBL and controls Larger sample size of high-count MBL is warranted in order to stratify MBL risk by low-count and high-count MBL. These results may help identify individuals at higher risk of MBL including low-count and high-count MBL, beyond the risk associated with age and family history of CLL.Figure: Polygenic risk score distribution by control and MBL status DisclosuresParikh:Janssen: Research Funding; Pharmacyclics: Honoraria, Research Funding; Gilead: Honoraria; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; MorphoSys: Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees. Shanafelt:Genentech: Research Funding; Pharmacyclics: Research Funding; GlaxoSmithKline: Research Funding; Jansen: Research Funding.

Stemformatics: visualize and download curated stem cell data.

Stemformatics is an established gene expression data portal containing over 420 public gene expression datasets derived from microarray, RNA sequencing and single cell profiling technologies. Developed for the stem cell community, it has a major focus on pluripotency, tissue stem cells, and staged differentiation. Stemformatics includes curated ‘collections’ of data relevant to cell reprogramming, as well as hematopoiesis and leukaemia. Rather than simply rehosting datasets as they appear in public repositories, Stemformatics uses a stringent set of quality control metrics and its own pipelines to process handpicked datasets from raw files. This means that about 30% of datasets processed by Stemformatics fail the quality control metrics and never make it to the portal, ensuring that Stemformatics data are of high quality and have been processed in a consistent manner. Stemformatics provides easy-to-use and intuitive tools for biologists to visually explore the data, including interactive gene expression profiles, principal component analysis plots and hierarchical clusters, among others. The addition of tools that facilitate cross-dataset comparisons provides users with snapshots of gene expression in multiple cell and tissues, assisting the identification of cell-type restricted genes, or potential housekeeping genes. Stemformatics is freely available at stemformatics.org.

A Further Comment on 'Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets' by Lam et al.

Lam et al. (2018) respond to a commentary of their paper entitled 'Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets' Lam et al. (2017). While Lam et al. (2018) have now provided the recommended quality control metrics for their paper, problems remain. Specifically, Lam et al. (2018) do not dispute that the results of their multi-trait analysis of genome-wide association study (MTAG) analysis has produced a phenotype with a genetic correlation of one with three measures of education, but do claim the associations found are specific to the trait of cognitive ability. In this brief paper, it is empirically demonstrated that the phenotype derived by Lam et al. (2017) is more genetically similar to education than cognitive ability. In addition, it is shown that of the genome-wide significant loci identified by Lam et al. (2017) are loci that are associated with education rather than with cognitive ability.

Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction

Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts.

"Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats".

Over the last few decades, deformable image registration (DIR) has gained popularity in image-guided radiation therapy for a number of applications, such as contour propagation, dose warping, and accumulation. Although this raises promising perspectives for the improvement of treatment outcomes and quality of radiotherapy clinical practice, the variety of proposed DIR algorithms, combined with the lack of an effective quantitative quality control metric of the registration, is slowing the transfer of DIR into the clinical routine. Recently, a task group (AAPM TG132) report was published outlining the essential aspects of DIR for image guidance in radiotherapy. However, an accurate and efficient patient-specific validation is not yet defined, and appropriate metrics should be identified to achieve the definition of both geometric and dosimetric accuracy. In this respect, the use of a dense set of anatomical landmarks, along with additional evaluations on contours or deformation field analysis, are likely to drive patient-specific DIR validation in clinical image-guided radiotherapy applications to account for geometric inaccuracies. Automatic and efficient strategies able to provide spatial information of DIR uncertainties and to evaluate monomodal and multimodal image registration, as well as to describe homogenous and un-contrasted regions are believed to represent the future direction in DIR validation. But especially in the case of DIR applications for dose mapping and accumulation, the need of accurate patient-specific validation is not only limited to the evaluation of geometric accuracy. In fact, the need to account for dosimetric inaccuracies due to DIR represents another important area in the field of adaptive treatments. Different approaches are currently being investigated to quantify the effect of DIR error on dose analysis, mainly relying on clinically relevant dose metrics, or on the study of deformation field properties for a voxel-by-voxel evaluation. However, novel research is required for the definition of dedicated and personalized measures capable to relate the geometric and dosimetric inaccuracies, thus bearing useful information for a safe use of DIR by clinical end users. In this paper we provide insights on DIR results evaluation on a patient-specific basis, facing the issues of both geometric and dosimetric paradigms. Challenges on DIR validation are overviewed and discussed, in order to push preliminary clinical guidelines forward on this fundamental topic and boost the implementation of more robust and reliable patient-specific evaluation metrics.

Audit of transvaginal sonography of normal postmenopausal ovaries by sonographers from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS)

Background: We report on a unique audit of seven sonographers self-reporting high visualization rates of normal postmenopausal ovaries in the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). This audit was ordered by the trial’s Ultrasound Management Subcommittee after an initiative taken in 2008 to improve the quality of scanning and the subsequent increase in the number of sonographers claiming very high ovary visualisation rates. Methods: Seven sonographers reporting high rates (>89%) of visualizing normal postmenopausal ovaries in examinations performed between 1 st January and 31 st December 2008 were identified. Eight experts in gynaecological scanning reviewed a random selection of exams performed by these sonographers and assessed whether visualization of both ovaries could be confirmed (cVR-Both) in the examinations. A random effects bivariate probit model was fitted to analyse the results. Results: The eight experts reviewed images from 357 examinations performed on 349 postmenopausal women (mean age 60.0 years, range 50.2-73.3) by the seven sonographers. The mean cVR-Both obtained from the model for these sonographers was 67.2% with a range of 47.6-86.5% (95%CI 63.9-70.5%). The range of cVR-Both between the experts was 47.3-88.3% and the intra-class correlation coefficient (ICC) for left and right ovary confirmation was 0.39. Conclusions: The audit suggests that self-reported visualization of postmenopausal ovaries is unreliable, as visualisation of both ovaries could not be confirmed in almost a third of examinations. The agreement for visualization of both ovaries based on review of a static image between experts and sonographers and between expert reviewers alone was only moderate. Further research is needed to develop reliable Quality Control metrics for transvaginal ultrasound.