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.

Canadian ROS proto-oncogene 1 study (CROS) for multi-institutional implementation of ROS1 testing in non-small cell lung cancer.

Patients with non-small cell lung cancer (NSCLC) harboring ROS proto-oncogene 1 (ROS1) gene rearrangements show dramatic response to the tyrosine kinase inhibitor (TKI) crizotinib. Current best practice guidelines recommend that all advanced stage non-squamous NSCLC patients be also tested for ROS1 gene rearrangements. Several studies have suggested that ROS1 immunohistochemistry (IHC) using the D4D6 antibody may be used to screen for ROS1 fusion positive lung cancers, with assays showing high sensitivity but moderate to high specificity. A break apart fluorescence in situ hybridization (FISH) test is then used to confirm the presence of ROS1 gene rearrangement. The goal of Canadian ROS1 (CROS) study was to harmonize ROS1 laboratory developed testing (LDT) by using IHC and FISH assays to detect ROS1 rearranged lung cancers across Canadian pathology laboratories. Cell lines expressing different levels of ROS1 (high, low, none) were used to calibrate IHC protocols after which participating laboratories ran the calibrated protocols on a reference set of 24 NSCLC cases (9 ROS1 rearranged tumors and 15 ROS1 non-rearranged tumors as determined by FISH). Results were compared using a centralized readout. The stained slides were evaluated for the cellular localization of staining, intensity of staining, the presence of staining in non-tumor cells, the presence of non-specific staining (e.g. necrosis, extracellular mater, other) and the percent positive cells. H-score was also determined for each tumor. Analytical sensitivity and specificity harmonization was achieved by using low limit of detection (LOD) as either any positivity in the U118 cell line or H-score of 200 with the HCC78 cell line. An overall diagnostic sensitivity and specificity of up to 100% and 99% respectively was achieved for ROS1 IHC testing (relative to FISH) using an adjusted H-score readout on the reference cases. This study confirms that LDT ROS1 IHC assays can be highly sensitive and specific for detection of ROS1 rearrangements in NSCLC. As NSCLC can demonstrate ROS1 IHC positivity in FISH-negative cases, the degree of the specificity of the IHC assay, especially in highly sensitive protocols, is mostly dependent on the readout cut-off threshold. As ROS1 IHC is a screening assay for a rare rearrangements in NSCLC, we recommend adjustment of the readout threshold in order to balance specificity, rather than decreasing the overall analytical and diagnostic sensitivity of the protocols.

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.

Simplifying Detection of Copy-Number Variations in Maturity-Onset Diabetes of the Young.

OBJECTIVES: Copy-number variations (CNVs) are large-scale deletions or duplications of DNA that have required specialized detection methods, such as microarray-based genomic hybridization or multiplex ligation probe amplification. However, recent advances in bioinformatics have made it possible to detect CNVs from next-generation DNA sequencing (NGS) data. Maturity-onset diabetes of the young (MODY) 5 is a subtype of autosomal-dominant diabetes that is often caused by heterozygous deletions involving the HNF1B gene on chromosome 17q12. We evaluated the utility of bioinformatic processing of raw NGS data to detect chromosome 17q12 deletions in MODY5 patients. METHODS: NGS data from 57 patients clinically suspected to have MODY but who were negative for pathogenic mutations using a targeted panel were re-examined using a CNV calling tool (CNV Caller, VarSeq version 1.4.3). Potential CNVs for MODY5 were then confirmed using whole-exome sequencing, cytogenetic analysis and breakpoint analysis when possible. RESULTS: Whole-gene deletions in HNF1B, ranging from 1.46 to 1.85 million basepairs in size, were detected in 3 individuals with features of MODY5. These were confirmed by independent methods to be part of a more extensive 17q12 deletion syndrome. Two additional patients carrying a 17q12 deletion were subsequently diagnosed using this method. CONCLUSIONS: Large-scale deletions are the most common cause of MODY5 and can be detected directly from NGS data, without the need for additional methods.

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.

Implementation of an NGS-based sequencing and gene fusion panel for clinical screening of patients with suspected hematologic malignancies.

OBJECTIVES: The diagnosis of hematologic malignancies integrates multiple diagnostic and clinical disciplines. Historically, targeted (single-analyte) genetic testing has been used as reflex to initial prescreening by other diagnostic modalities including flow cytometry, anatomic pathology, and clinical cytogenetics. Given the wide range of mutations associated with hematologic malignancies a DNA/RNA-based NGS panel can provide a more effective and economical approach to comprehensive testing of patients as an initial, tier-1 screen. METHODS: Using a cohort of 380 patients, we performed clinical validation of a gene panel designed to assess 40 genes (DNA), and 29 fusion driver genes with over 600 gene fusion partners (RNA), including sample exchange data across three clinical laboratories, and correlation with cytogenetic testing results. RESULTS: The clinical validation of this technology demonstrated that its accuracy, sensitivity, and specificity are comparable to the majority of targeted single-gene approaches, while assessment of the initial patient cohort data demonstrated a high diagnostic yield of 50.5%. CONCLUSIONS: Implementation of a tier-1 NGS-based protocol for gene panel screening provides a comprehensive alternative to targeted molecular testing in patients with suspected hematologic malignancies, with increased diagnostic yield, scalability, reproducibility, and cost effectiveness, making it ideally suited for implementation in clinical laboratories.

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.

Large-scale deletions of the ABCA1 gene in patients with hypoalphalipoproteinemia.

Copy-number variations (CNVs) have been studied in the context of familial hypercholesterolemia but have not yet been evaluated in patients with extreme levels of HDL cholesterol. We evaluated targeted, next-generation sequencing data from patients with very low levels of HDL cholesterol (i.e., hypoalphalipoproteinemia) with the VarSeq-CNV® caller algorithm to screen for CNVs that disrupted the ABCA1, LCAT, or APOA1 genes. In four individuals, we found three unique deletions in ABCA1: a heterozygous deletion of exon 4, a heterozygous deletion that spanned exons 8 to 31, and a heterozygous deletion of the entire ABCA1 gene. Breakpoints were identified with Sanger sequencing, and the full-gene deletion was confirmed by using exome sequencing and the Affymetrix CytoScan HD array. Previously, large-scale deletions in candidate HDL genes had not been associated with hypoalphalipoproteinemia; our findings indicate that CNVs in ABCA1 may be a previously unappreciated genetic determinant of low levels of HDL cholesterol. By coupling bioinformatic analyses with next-generation sequencing data, we can successfully assess the spectrum of genetic determinants of many dyslipidemias, including hypoalphalipoproteinemia.

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.

Clinical Validation of Copy Number Variant Detection from Targeted Next-Generation Sequencing Panels.

Next-generation sequencing (NGS) technology has rapidly replaced Sanger sequencing in the assessment of sequence variations in clinical genetics laboratories. One major limitation of current NGS approaches is the ability to detect copy number variations (CNVs) approximately >50 bp. Because these represent a major mutational burden in many genetic disorders, parallel CNV assessment using alternate supplemental methods, along with the NGS analysis, is normally required, resulting in increased labor, costs, and turnaround times. The objective of this study was to clinically validate a novel CNV detection algorithm using targeted clinical NGS gene panel data. We have applied this approach in a retrospective cohort of 391 samples and a prospective cohort of 2375 samples and found a 100% sensitivity (95% CI, 89%-100%) for 37 unique events and a high degree of specificity to detect CNVs across nine distinct targeted NGS gene panels. This NGS CNV pipeline enables stand-alone first-tier assessment for CNV and sequence variants in a clinical laboratory setting, dispensing with the need for parallel CNV analysis using classic techniques, such as microarray, long-range PCR, or multiplex ligation-dependent probe amplification. This NGS CNV pipeline can also be applied to the assessment of complex genomic regions, including pseudogenic DNA sequences, such as the PMS2CL gene, and to mitochondrial genome heteroplasmy detection.

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.