How to minimise the effect of tumour cell content in detection of aberrant genetic markers in neuroblastoma

Background:Clinical heterogeneity reflects the complexity of genetic events associated with neuroblastoma (NB). To identify the status of all described genetic loci with possible prognostic interest, high-throughput approaches have been used, but only with tumour cell content >60%. In some tumours, necrotic, haemorrhagic and/or calcification areas influence the low amount of neuroblasts. We evaluated the effect of tumour cell content in the detection of relevant aberrant genetic markers (AGM) diagnosed by fluorescence in situ hybridisation (FISH) on tissue microarrays (TMA) in NB.Methods:Two hundred and thirty-three MYCN non-amplified primary NB included in 12 TMAs were analysed.Results:Presence of AGM reduced event-free survival (EFS) (P=0.004) as well as overall survival (OS) (P=0.004) of patients in the whole cohort. There were no differences in prognostic impact of presence of AGM according to tumour cell content.Conclusion:We propose the use of FISH to diagnose AGM of all NB samples having the above-mentioned areas to determine patient risk

Cytogenetic analysis of an exposed-referent study: perchloroethylene-exposed dry cleaners compared to unexposed laundry workers

<p>Abstract</p> <p>Background</p> <p>Significant numbers of people are exposed to tetrachloroethylene (perchloroethylene, PCE) every year, including workers in the dry cleaning industry. Adverse health effects have been associated with PCE exposure. However, investigations of possible cumulative cytogenetic damage resulting from PCE exposure are lacking.</p> <p>Methods</p> <p>Eighteen dry cleaning workers and 18 laundry workers (unexposed controls) provided a peripheral blood sample for cytogenetic analysis by whole chromosome painting. Pre-shift exhaled air on these same participants was collected and analyzed for PCE levels. The laundry workers were matched to the dry cleaners on race, age, and smoking status. The relationships between levels of cytological damage and exposures (including PCE levels in the shop and in workers' blood, packyears, cumulative alcohol consumption, and age) were compared with correlation coefficients and t-tests. Multiple linear regressions considered blood PCE, packyears, alcohol, and age.</p> <p>Results</p> <p>There were no significant differences between the PCE-exposed dry cleaners and the laundry workers for chromosome translocation frequencies, but PCE levels were significantly correlated with percentage of cells with acentric fragments (R²= 0.488, p < 0.026).</p> <p>Conclusions</p> <p>There does not appear to be a strong effect in these dry cleaning workers of PCE exposure on persistent chromosome damage as measured by translocations. However, the correlation between frequencies of acentric fragments and PCE exposure level suggests that recent exposures to PCE may induce transient genetic damage. More heavily exposed participants and a larger sample size will be needed to determine whether PCE exposure induces significant levels of persistent chromosome damage.</p

Recent Trends in Cytogenetic Studies

Recent Trends in Cytogenetic Studies - Methodologies and Applications deals with recent trends in cytogenetics with minute details of methodologies that can be adopted in clinical laboratories. The chapters deal with basic methods of primary cultures, cell lines and their applications; microtechnologies and automations; array CGH for the diagnosis of fetal conditions; approaches to acute lymphoblastic and myeloblastic leukemias in patients and survivors of atomic bomb exposure; use of digital image technology and using chromosomes as tools to discover biodiversity. While concentrating on the advanced methodologies in cytogenetic studies and their applications, authors have pointed out the need to develop cytogenetic labs with modern tools to facilitate precise and effective diagnosis to benefit the patient population

Biomarkers of Lung Cancer Risk and Progression

Lung cancer causes high mortality because most people present late with advanced disease that is not amenable to curative treatment. Screening high-risk groups with low dose CT imaging of the thorax has been shown to reduce lung cancer mortality by 20%, but at the cost of a high false positive rate. Population stratification with molecular biomarkers could improve the cost-benefit of lung cancer screening programmes and reduce false positives. Tumour cells shed DNA into the blood, enabling tumour-derived genetic alterations to be detected non-invasively by analysing circulating cell-free DNA (cfDNA). The aim of this study was to determine the screening and prognostic potential of total cfDNA levels and two genomic instability scores based on the detection of copy number aberrations in cfDNA samples of lung cancer cases and controls collected in the ReSoLuCENT study (A Resource for the Study of Lung Cancer Epidemiology in North Trent). Controls were identified as low or high risk for the development of lung cancer over five years using the Liverpool Lung Project risk model. CfDNA was extracted from the plasma of 52 untreated lung cancer cases, 32 high risk controls and 10 low risk controls and quantified total cfDNA levels by SYBR green real-time qPCR. Low coverage whole genome sequencing with Illumina HiSeq 2500 was completed for a subset of cases (N=62) and controls (N=40). Two published genomic instability scores were adapted and tested; the plasma genomic abnormality (PGA2) and the copy number aberration (CNA) score. Screening potential was evaluated by performing Receiver Operating Characteristic (ROC) curves to assess the ability of the test to discriminate between lung cancer cases and controls by calculating area under the curve (AUC). Logistic regression was used to further assess the ability of total cfDNA levels and genomic instability scores to predict case or control status. Prognostic value was determined by Kaplan Meir and Cox regression survival analyses. In this preliminary study, there was no difference in total cfDNA levels between early stage lung cancer cases and high risk controls. The PGA2 score was higher in high risk controls compared to lung cancer cases and was not further evaluated. In comparison, the CNA score had good discriminatory ability for high risk controls compared to all lung cancer cases (stage I-IV) with an AUC of 0.74 but poorer discriminatory ability for early stage cases (I-IIIA) with an AUC of 0.60. Although total cfDNA levels and CNA scores above the median value were associated with poor survival, both were statistically significant in univariable but not multivariable cox survival regression analyses. Therefore, total cfDNA levels and the CNA score had limited prognostic value when other factors were taken into account. Total cfDNA levels are not recommended as a screening tool because total levels lack specificity for cancer. The screening performance of the CNA score may be improved by targeting recurrent copy number aberrations and by combining the score with alternative tumour-derived genetic alterations in cfDNA such as point mutations or methylation changes

Identification of somatic mutations in cancer through Bayesian-based analysis of sequenced genome pairs

abstract: BACKGROUND: The field of cancer genomics has rapidly adopted next-generation sequencing (NGS) in order to study and characterize malignant tumors with unprecedented resolution. In particular for cancer, one is often trying to identify somatic mutations--changes specific to a tumor and not within an individual's germline. However, false positive and false negative detections often result from lack of sufficient variant evidence, contamination of the biopsy by stromal tissue, sequencing errors, and the erroneous classification of germline variation as tumor-specific. RESULTS: We have developed a generalized Bayesian analysis framework for matched tumor/normal samples with the purpose of identifying tumor-specific alterations such as single nucleotide mutations, small insertions/deletions, and structural variation. We describe our methodology, and discuss its application to other types of paired-tissue analysis such as the detection of loss of heterozygosity as well as allelic imbalance. We also demonstrate the high level of sensitivity and specificity in discovering simulated somatic mutations, for various combinations of a) genomic coverage and b) emulated heterogeneity. CONCLUSION: We present a Java-based implementation of our methods named Seurat, which is made available for free academic use. We have demonstrated and reported on the discovery of different types of somatic change by applying Seurat to an experimentally-derived cancer dataset using our methods; and have discussed considerations and practices regarding the accurate detection of somatic events in cancer genomes. Seurat is available at https://sites.google.com/site/seuratsomatic.View the article as published at: http://www.biomedcentral.com/1471-2164/14/30

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

BackgroundA key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison.ResultsIn this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues).ConclusionsIn this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN

Estimation of Parent Specific DNA Copy Number in Tumors using High-Density Genotyping Arrays

Chromosomal gains and losses comprise an important type of genetic change in tumors, and can now be assayed using microarray hybridization-based experiments. Most current statistical models for DNA copy number estimate total copy number, which do not distinguish between the underlying quantities of the two inherited chromosomes. This latter information, sometimes called parent specific copy number, is important for identifying allele-specific amplifications and deletions, for quantifying normal cell contamination, and for giving a more complete molecular portrait of the tumor. We propose a stochastic segmentation model for parent-specific DNA copy number in tumor samples, and give an estimation procedure that is computationally efficient and can be applied to data from the current high density genotyping platforms. The proposed method does not require matched normal samples, and can estimate the unknown genotypes simultaneously with the parent specific copy number. The new method is used to analyze 223 glioblastoma samples from the Cancer Genome Atlas (TCGA) project, giving a more comprehensive summary of the copy number events in these samples. Detailed case studies on these samples reveal the additional insights that can be gained from an allele-specific copy number analysis, such as the quantification of fractional gains and losses, the identification of copy neutral loss of heterozygosity, and the characterization of regions of simultaneous changes of both inherited chromosomes

Discovering Tumor Suppressor Genes Through Genome-Wide Copy Number Analysis

Classical tumor suppressor gene discovery has largely involved linkage analysis and loss-of-heterozygosity (LOH) screens, followed by detailed mapping of relatively large chromosomal regions. Subsequent efforts made use of genome-wide PCR-based methods to detect rare homozygous deletions. More recently, high-resolution genomic arrays have been applied to cancer gene discovery. However, accurate characterization of regions of genomic loss is particularly challenging due to sample heterogeneity, the small size of deleted regions and the high frequency of germline copy number polymorphisms. Here, we review the application of genome-wide copy number analysis to the specific problem of identifying tumor suppressor genes