Radiation exposure determination in a secure, cloud-based online environment.

Rapid sample processing and interpretation of estimated exposures will be critical for triaging exposed individuals after a major radiation incident. The dicentric chromosome (DC) assay assesses absorbed radiation using metaphase cells from blood. The Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI) identifies DCs and determines radiation doses. This study aimed to broaden accessibility and speed of this system, while protecting data and software integrity. ADCI Online is a secure web-streaming platform accessible worldwide from local servers. Cloud-based systems containing data and software are separated until they are linked for radiation exposure estimation. Dose estimates are identical to ADCI on dedicated computer hardware. Image processing and selection, calibration curve generation, and dose estimation of 9 test samples completed in < 2 days. ADCI Online has the capacity to alleviate analytic bottlenecks in intermediate-to-large radiation incidents. Multiple cloned software instances configured on different cloud environments accelerated dose estimation to within clinically relevant time frames.

Differentially accessible, single copy sequences form contiguous domains along metaphase chromosomes that are conserved among multiple tissues.

BACKGROUND: During mitosis, chromatin engages in a dynamic cycle of condensation and decondensation. Condensation into distinct units to ensure high fidelity segregation is followed by rapid and reproducible decondensation to produce functional daughter cells. Factors contributing to the reproducibility of chromatin structure between cell generations are not well understood. We investigated local metaphase chromosome condensation along mitotic chromosomes within genomic intervals showing differential accessibility (DA) between homologs. DA was originally identified using short sequence-defined single copy (sc) DNA probes of < 5 kb in length by fluorescence in situ hybridization (scFISH) in peripheral lymphocytes. These structural differences between metaphase homologs are non-random, stable, and heritable epigenetic marks which have led to the proposed function of DA as a marker of chromatin memory. Here, we characterize the organization of DA intervals into chromosomal domains by identifying multiple DA loci in close proximity to each other and examine the conservation of DA between tissues. RESULTS: We evaluated multiple adjacent scFISH probes at 6 different DA loci from chromosomal regions 2p23, 3p24, 12p12, 15q22, 15q24 and 20q13 within peripheral blood T-lymphocytes. DA was organized within domains that extend beyond the defined boundaries of individual scFISH probes. Based on hybridizations of 2 to 4 scFISH probes per domain, domains ranged in length from 16.0 kb to 129.6 kb. Transcriptionally inert chromosomal DA regions in T-lymphocytes also demonstrated conservation of DA in bone marrow and fibroblast cells. CONCLUSIONS: We identified novel chromosomal regions with allelic differences in metaphase chromosome accessibility and demonstrated that these accessibility differences appear to be aggregated into contiguous domains extending beyond individual scFISH probes. These domains are encompassed by previously established topologically associated domain (TAD) boundaries. DA appears to be a conserved feature of human metaphase chromosomes across different stages of lymphocyte differentiation and germ cell origin, consistent with its proposed role in maintenance of intergenerational cellular chromosome memory.

De novo STXBP1 Mutations in Two Patients With Developmental Delay With or Without Epileptic Seizures.

Objectives: Mutations in the STXBP1 gene have been associated with epileptic encephalopathy. Previous studies from in vitro neuroblastoma 2A cells showed that haploinsufficiency of STXBP1 is the mechanism for epileptic encephalopathy. In this ex vivo study, STXPB1 DNA mutations and RNA expression were assessed from two patients to help understand the impact of STXBP1 mutations on the disease etiology and mechanism. Methods: Microarray analysis and DNA sequencing were performed on two children with development delay, one with and one without infantile spasms. Different pathogenic mutations of STXBP1 were identified in the patients and RNA expression of STXPB1 was then performed by RT-Q-PCR on RNA extracted from blood samples of each patient. Results: Pathogenic deletion [of exons 13-20 and 3' downstream of STXBP1] and nonsense mutation [c.1663G>T (p.Glu555X) in exon 18 of STXBP1] were detected from the two patients, respectively. RNA analysis showed that 1) the deletion mediated RNA decay, and that 2) no RNA decay was identified for the nonsense mutation at codon 555 which predicts a truncated STXBP1 protein. Significance: Our RNA expression analyses from the patient blood samples are the first ex vivo studies to support that both haploinsufficiency and truncation of STXBP1 protein (either dominant negative or haploinsufficiency) are causative mechanisms for epileptic encephalopathies, intellectual disability and developmental delay. The RNA assay also suggests that escape from nonsense-mediated RNA decay is possible when the nonsense mutation resides <50 nucleotides upstream of the last coding exon-exon junction even in the presence of additional non-coding exons that are 3' downstream of the last coding exon.

Estimating partial-body ionizing radiation exposure by automated cytogenetic biodosimetry.

PURPOSE: Inhomogeneous exposures to ionizing radiation can be detected and quantified with the dicentric chromosome assay (DCA) of metaphase cells. Complete automation of interpretation of the DCA for whole-body irradiation has significantly improved throughput without compromising accuracy, however, low levels of residual false positive dicentric chromosomes (DCs) have confounded its application for partial-body exposure determination. MATERIALS AND METHODS: We describe a method of estimating and correcting for false positive DCs in digitally processed images of metaphase cells. Nearly all DCs detected in unirradiated calibration samples are introduced by digital image processing. DC frequencies of irradiated calibration samples and those exposed to unknown radiation levels are corrected subtracting this false positive fraction from each. In partial-body exposures, the fraction of cells exposed, and radiation dose can be quantified after applying this modification of the contaminated Poisson method. RESULTS: Dose estimates of three partially irradiated samples diverged 0.2-2.5 Gy from physical doses and irradiated cell fractions deviated by 2.3%-15.8% from the known levels. Synthetic partial-body samples comprised of unirradiated and 3 Gy samples from 4 laboratories were correctly discriminated as inhomogeneous by multiple criteria. Root mean squared errors of these dose estimates ranged from 0.52 to 1.14 Gy2 and from 8.1 to 33.3%2 for the fraction of cells irradiated. CONCLUSIONS: Automated DCA can differentiate whole- from partial-body radiation exposures and provides timely quantification of estimated whole-body equivalent dose.

Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling.

BACKGROUND: Accurate radiation dose estimates are critical for determining eligibility for therapies by timely triaging of exposed individuals after large-scale radiation events. However, the universal assessment of a large population subjected to a nuclear spill incident or detonation is not feasible. Even with high-throughput dosimetry analysis, test volumes far exceed the capacities of first responders to measure radiation exposures directly, or to acquire and process samples for follow-on biodosimetry testing. AIM: To significantly reduce data acquisition and processing requirements for triaging of treatment-eligible exposures in population-scale radiation incidents. METHODS: Physical radiation plumes modelled nuclear detonation scenarios of simulated exposures at 22 US locations. Models assumed only location of the epicenter and historical, prevailing wind directions/speeds. The spatial boundaries of graduated radiation exposures were determined by targeted, multistep geostatistical analysis of small population samples. Initially, locations proximate to these sites were randomly sampled (generally 0.1% of population). Empirical Bayesian kriging established radiation dose contour levels circumscribing these sites. Densification of each plume identified critical locations for additional sampling. After repeated kriging and densification, overlapping grids between each pair of contours of successive plumes were compared based on their diagonal Bray-Curtis distances and root-mean-square deviations, which provided criteria (<10% difference) to discontinue sampling. RESULTS/CONCLUSIONS: We modeled 30 scenarios, including 22 urban/high-density and 2 rural/low-density scenarios under various weather conditions. Multiple (3-10) rounds of sampling and kriging were required for the dosimetry maps to converge, requiring between 58 and 347 samples for different scenarios. On average, 70±10% of locations where populations are expected to receive an exposure ≥2Gy were identified. Under sub-optimal sampling conditions, the number of iterations and samples were increased, and accuracy was reduced. Geostatistical mapping limits the number of required dose assessments, the time required, and radiation exposure to first responders. Geostatistical analysis will expedite triaging of acute radiation exposure in population-scale nuclear events.

RADIATION DOSE ESTIMATION BY COMPLETELY AUTOMATED INTERPRETATION OF THE DICENTRIC CHROMOSOME ASSAY.

Accuracy of the automated dicentric chromosome (DC) assay relies on metaphase image selection. This study validates a software framework to find the best image selection models that mitigate inter-sample variability. Evaluation methods to determine model quality include the Poisson goodness-of-fit of DC distributions for each sample, residuals after calibration curve fitting and leave-one-out dose estimation errors. The process iteratively searches a pool of selection model candidates by modifying statistical and filter cut-offs to rank the best candidates according to their respective evaluation scores. Evaluation scores minimize the sum of squared errors relative to the actual radiation dose of the calibration samples. For one laboratory, the minimum score for the curve fit residual method was 0.0475 Gy2, compared to 1.1975 Gy2 without image selection. Application of optimal selection models using samples of unknown exposure produced estimated doses within 0.5 Gy of physical dose. Model optimization standardizes image selection among samples and provides relief from manual DC scoring, improving accuracy and consistency of dose estimation.

Whole-Gene Duplication of PCSK9 as a Novel Genetic Mechanism for Severe Familial Hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) is a common genetic disorder of severely elevated low-density lipoprotein (LDL) cholesterol, characterized by premature atherosclerotic cardiovascular disease. Although copy number variations (CNVs) are a large-scale mutation-type capable of explaining FH cases, they have been, to date, assessed only in the LDLR gene. Here, we performed novel CNV screening in additional FH-associated genes using a next-generation sequencing-based approach. METHODS: In 704 patients with FH, we sequenced FH-associated genes APOB, PCSK9, LDLRAP1, APOE, STAP1, LIPA, and ABCG5/8 using our LipidSeq targeted next-generation sequencing panel. Bioinformatic tools were applied to LipidSeq data for CNV screening, and identified CNVs were validated using whole-exome sequencing and microarray-based copy number analyses. RESULTS: We identified a whole-gene duplication of PCSK9 in 2 unrelated Canadian FH index cases; this PCSK9 CNV was also found to cosegregate with affected status in family members. Features in affected individuals included severely elevated LDL cholesterol levels that were refractory to intensive statin therapy, pronounced clinical stigmata, premature cardiovascular events, and a plasma PCSK9 of approximately 5000 ng/mL in 1 index case. We found no CNVs in APOB, LDLRAP1, APOE, STAP1, LIPA, and ABCG5/8 in our cohort of 704 FH individuals. CONCLUSIONS: Here, we report the first description of a CNV affecting the PCSK9 gene in FH. This finding is associated with a profound FH phenotype and the highest known plasma PCSK9 level reported in a human. This finding also has therapeutic relevance, as elevated PCSK9 levels may limit the efficacy of high-dose statin therapy and also PCSK9 inhibition.

Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection.

Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs arising primarily from sister chromatid separation, chromosome fragmentation, and cellular debris. This reduced FPs by an average of 55% and was highly specific to these abnormal structures (≥97.7%) in three samples. Additional filters selectively removed images with incomplete, highly overlapped, or missing metaphase cells, or with poor overall chromosome morphologies that increased FP rates. Image selection is optimized and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Applying the same image segmentation filtering procedures to both calibration and test samples reduced the average dose estimation error from 0.4 Gy to <0.2 Gy, obviating the need to first manually review these images. This reliable and scalable solution enables batch processing for multiple samples of unknown dose, and meets current requirements for triage radiation biodosimetry of high quality metaphase cell preparations.

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation.

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation.

Radiation Dose Estimation by Automated Cytogenetic Biodosimetry.

The dose from ionizing radiation exposure can be interpolated from a calibration curve fit to the frequency of dicentric chromosomes (DCs) at multiple doses. As DC counts are manually determined, there is an acute need for accurate, fully automated biodosimetry calibration curve generation and analysis of exposed samples. Software, the Automated Dicentric Chromosome Identifier (ADCI), is presented which detects and discriminates DCs from monocentric chromosomes, computes biodosimetry calibration curves and estimates radiation dose. Images of metaphase cells from samples, exposed at 1.4-3.4 Gy, that had been manually scored by two reference laboratories were reanalyzed with ADCI. This resulted in estimated exposures within 0.4-1.1 Gy of the physical dose. Therefore, ADCI can determine radiation dose with accuracies comparable to standard triage biodosimetry. Calibration curves were generated from metaphase images in ~10 h, and dose estimations required ~0.8 h per 500 image sample. Running multiple instances of ADCI may be an effective response to a mass casualty radiation event.

Clinical Next-Generation Sequencing Pipeline Outperforms a Combined Approach Using Sanger Sequencing and Multiplex Ligation-Dependent Probe Amplification in Targeted Gene Panel Analysis.

Advances in next-generation sequencing (NGS) have facilitated parallel analysis of multiple genes enabling the implementation of cost-effective, rapid, and high-throughput methods for the molecular diagnosis of multiple genetic conditions, including the identification of BRCA1 and BRCA2 mutations in high-risk patients for hereditary breast and ovarian cancer. We clinically validated a NGS pipeline designed to replace Sanger sequencing and multiplex ligation-dependent probe amplification analysis and to facilitate detection of sequence and copy number alterations in a single test focusing on a BRCA1/BRCA2 gene analysis panel. Our custom capture library covers 46 exons, including BRCA1 exons 2, 3, and 5 to 24 and BRCA2 exons 2 to 27, with 20 nucleotides of intronic regions both 5' and 3' of each exon. We analyzed 402 retrospective patients, with previous Sanger sequencing and multiplex ligation-dependent probe amplification results, and 240 clinical prospective patients. One-hundred eighty-three unique variants, including sequence and copy number variants, were detected in the retrospective (n = 95) and prospective (n = 88) cohorts. This standardized NGS pipeline demonstrated 100% sensitivity and 100% specificity, uniformity, and high-depth nucleotide coverage per sample (approximately 7000 reads per nucleotide). Subsequently, the NGS pipeline was applied to the analysis of larger gene panels, which have shown similar uniformity, sample-to-sample reproducibility in coverage distribution, and sensitivity and specificity for detection of sequence and copy number variants.

A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer.

BACKGROUND: Sequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants have also been proven to significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT) and prioritizing patients with large intragenic deletions. METHODS: We captured and enriched for coding and non-coding variants in genes known to harbor mutations that increase HBOC risk. Custom oligonucleotide baits spanning the complete coding, non-coding, and intergenic regions 10 kb up- and downstream of ATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2, and TP53 were synthesized for solution hybridization enrichment. Unique and divergent repetitive sequences were sequenced in 102 high-risk, anonymized patients without identified mutations in BRCA1/2. Aside from protein coding and copy number changes, IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be recognized by proteins or protein complexes involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. This approach was supplemented by in silico and laboratory analysis of UTR structure. RESULTS: 15,311 unique variants were identified, of which 245 occurred in coding regions. With the unified IT-framework, 132 variants were identified and 87 functionally significant VUS were further prioritized. An intragenic 32.1 kb interval in BRCA2 that was likely hemizygous was detected in one patient. We also identified 4 stop-gain variants and 3 reading-frame altering exonic insertions/deletions (indels). CONCLUSIONS: We have presented a strategy for complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression. This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes.

Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing.

Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ = 1.3, TPR = 0.52 and PPV = 0.83, while at σ = 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and σ = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures.

Prioritizing Variants in Complete Hereditary Breast and Ovarian Cancer Genes in Patients Lacking Known BRCA Mutations.

BRCA1 and BRCA2 testing for hereditary breast and ovarian cancer (HBOC) does not identify all pathogenic variants. Sequencing of 20 complete genes in HBOC patients with uninformative test results (N = 287), including noncoding and flanking sequences of ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, EPCAM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD51B, STK11, TP53, and XRCC2, identified 38,372 unique variants. We apply information theory (IT) to predict and prioritize noncoding variants of uncertain significance in regulatory, coding, and intronic regions based on changes in binding sites in these genes. Besides mRNA splicing, IT provides a common framework to evaluate potential affinity changes in transcription factor (TFBSs), splicing regulatory (SRBSs), and RNA-binding protein (RBBSs) binding sites following mutation. We prioritized variants affecting the strengths of 10 splice sites (four natural, six cryptic), 148 SRBS, 36 TFBS, and 31 RBBS. Three variants were also prioritized based on their predicted effects on mRNA secondary (2°) structure and 17 for pseudoexon activation. Additionally, four frameshift, two in-frame deletions, and five stop-gain mutations were identified. When combined with pedigree information, complete gene sequence analysis can focus attention on a limited set of variants in a wide spectrum of functional mutation types for downstream functional and co-segregation analysis.

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.

Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients. Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n = 31 genes) or gemcitabine (n = 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models. These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.

Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomes.

BACKGROUND: Chromatin-modifying reagents that alter histone associating proteins, DNA conformation or its sequence are well established strategies for studying chromatin structure in interphase (G1, S, G2). Little is known about how these compounds act during metaphase. We assessed the effects of these reagents at genomic loci that show reproducible, non-random differences in accessibility to chromatin that distinguish homologous targets by single copy DNA probe fluorescence in situ hybridization (scFISH). By super-resolution 3-D structured illumination microscopy (3D-SIM) and other criteria, the differences correspond to 'differential accessibility' (DA) to these chromosomal regions. At these chromosomal loci, DA of the same homologous chromosome is stable and epigenetic hallmarks of less accessible interphase chromatin are present. RESULTS: To understand the basis for DA, we investigate the impact of epigenetic modifiers on these allelic differences in chromatin accessibility between metaphase homologs in lymphoblastoid cell lines. Allelic differences in metaphase chromosome accessibility represent a stable chromatin mark on mitotic metaphase chromosomes. Inhibition of the topoisomerase IIα-DNA cleavage complex reversed DA. Inter-homolog probe fluorescence intensity ratios between chromosomes treated with ICRF-193 were significantly lower than untreated controls. 3D-SIM demonstrated that differences in hybridized probe volume and depth between allelic targets were equalized by this treatment. By contrast, DA was impervious to chromosome decondensation treatments targeting histone modifying enzymes, cytosine methylation, as well as in cells with regulatory defects in chromatid cohesion. These data altogether suggest that DA is a reflection of allelic differences in metaphase chromosome compaction, dictated by the localized catenation state of the chromosome, rather than by other epigenetic marks. CONCLUSIONS: Inhibition of the topoisomerase IIα-DNA cleavage complex mitigated DA by decreasing DNA superhelicity and axial metaphase chromosome condensation. This has potential implications for the mechanism of preservation of cellular phenotypes that enables the same chromatin structure to be correctly reestablished in progeny cells of the same tissue or individual.

Canadian anaplastic lymphoma kinase study: a model for multicenter standardization and optimization of ALK testing in lung cancer.

INTRODUCTION: Fluorescence in situ hybridization (FISH) is currently the standard for diagnosing anaplastic lymphoma kinase (ALK)-rearranged (ALK+) lung cancers for ALK inhibitor therapies. ALK immunohistochemistry (IHC) may serve as a screening and alternative diagnostic method. The Canadian ALK (CALK) study was initiated to implement a multicenter optimization and standardization of laboratory developed ALK IHC and FISH tests across 14 hospitals. METHODS: Twenty-eight lung adenocarcinomas with known ALK status were used as blinded study samples. Thirteen laboratories performed IHC using locally developed staining protocols for 5A4, ALK1, or D5F3 antibodies; results were assessed by H-score. Twelve centers conducted FISH using protocols based on Vysis' ALK break-apart FISH kit. Initial IHC results were used to optimize local IHC protocols, followed by a repeat IHC study to assess the results of standardization. Three laboratories conducted a prospective parallel IHC and FISH analysis on 411 consecutive clinical samples using post-validation optimized assays. RESULTS: Among study samples, FISH demonstrated 22 consensus ALK+ and six ALK wild type tumors. Preoptimization IHC scores from 12 centers with 5A4 and the percent abnormal cells by FISH from 12 centers showed intraclass correlation coefficients of 0.83 and 0.68, respectively. IHC optimization improved the intraclass correlation coefficients to 0.94. Factors affecting FISH scoring and outliers were identified. Post-optimization concurrent IHC/FISH testing in 373 informative cases revealed 100% sensitivity and specificity for IHC versus FISH. CONCLUSIONS: Multicenter standardization study may accelerate the implementation of ALK testing protocols across a country/region. Our data support the use of an appropriately validated IHC assay to screen for ALK+ lung cancers.

Automating dicentric chromosome detection from cytogenetic biodosimetry data.

We present a prototype software system with sufficient capacity and speed to estimate radiation exposures in a mass casualty event by counting dicentric chromosomes (DCs) in metaphase cells from many individuals. Top-ranked metaphase cell images are segmented by classifying and defining chromosomes with an active contour gradient vector field (GVF) and by determining centromere locations along the centreline. The centreline is extracted by discrete curve evolution (DCE) skeleton branch pruning and curve interpolation. Centromere detection minimises the global width and DAPI-staining intensity profiles along the centreline. A second centromere is identified by reapplying this procedure after masking the first. Dicentrics can be identified from features that capture width and intensity profile characteristics as well as local shape features of the object contour at candidate pixel locations. The correct location of the centromere is also refined in chromosomes with sister chromatid separation. The overall algorithm has both high sensitivity (85 %) and specificity (94 %). Results are independent of the shape and structure of chromosomes in different cells, or the laboratory preparation protocol followed. The prototype software was recoded in C++/OpenCV; image processing was accelerated by data and task parallelisation with Message Passaging Interface and Intel Threading Building Blocks and an asynchronous non-blocking I/O strategy. Relative to a serial process, metaphase ranking, GVF and DCE are, respectively, 100 and 300-fold faster on an 8-core desktop and 64-core cluster computers. The software was then ported to a 1024-core supercomputer, which processed 200 metaphase images each from 1025 specimens in 1.4 h.

Intensity integrated Laplacian-based thickness measurement for detecting human metaphase chromosome centromere location.

Accurate detection of the human metaphase chromosome centromere is an important step in many chromosome analysis and medical diagnosis algorithms. The centromere location can be utilized to derive information such as the chromosome type, polarity assignment, etc. Methods available in the literature yield unreliable results mainly due to high variability of morphology in metaphase chromosomes and boundary noise present in the image. In this paper, we have proposed a multistaged algorithm which includes the use of discrete curve evolution, gradient vector flow active contours, functional approximation of curve segments, and support vector machine classification. The standard Laplacian thickness measurement algorithm was enhanced to incorporate both contour information as well as intensity information to obtain a more accurate centromere location. In addition to segmentation and width profile measurement, the proposed algorithm can also correct for sister chromatid separation in cell images. The proposed method was observed to be more accurate and statistically significant as compared to a centerline-based method when tested with 226 human metaphase chromosomes.

Expanding probe repertoire and improving reproducibility in human genomic hybridization.

Diagnostic DNA hybridization relies on probes composed of single copy (sc) genomic sequences. Sc sequences in probe design ensure high specificity and avoid cross-hybridization to other regions of the genome, which could lead to ambiguous results that are difficult to interpret. We examine how the distribution and composition of repetitive sequences in the genome affects sc probe performance. A divide and conquer algorithm was implemented to design sc probes. With this approach, sc probes can include divergent repetitive elements, which hybridize to unique genomic targets under higher stringency experimental conditions. Genome-wide custom probe sets were created for fluorescent in situ hybridization (FISH) and microarray genomic hybridization. The scFISH probes were developed for detection of copy number changes within small tumour suppressor genes and oncogenes. The microarrays demonstrated increased reproducibility by eliminating cross-hybridization to repetitive sequences adjacent to probe targets. The genome-wide microarrays exhibited lower median coefficients of variation (17.8%) for two HapMap family trios. The coefficients of variations of commercial probes within 300 nt of a repetitive element were 48.3% higher than the nearest custom probe. Furthermore, the custom microarray called a chromosome 15q11.2q13 deletion more consistently. This method for sc probe design increases probe coverage for FISH and lowers variability in genomic microarrays.