Whole genome DNA copy number changes identified by high density oligonucleotide arrays

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.

Traditional Prenatal Diagnosis: Past to Present

In the nearly 60 years since prenatal diagnosis for genetic disease was first offered, the field of prenatal diagnosis has progressed far past rudimentary uterine puncture to provide fetal material to assess gender and interpret risk. Concurrent with the improvements in invasive fetal sampling came technological advances in cytogenetics and molecular biology that widened both the scope of genetic disorders that could be diagnosed and also the resolution at which the human genome could be interrogated. Nowadays, routine blood work available to all pregnant women can determine the risk for common chromosome abnormalities; chorionic villus sampling (CVS) and amniocentesis can be used to diagnose nearly all conditions with a known genetic cause; and the genome and/or exome of a fetus with multiple anomalies can be sequenced in an attempt to determine the underlying etiology. This chapter will discuss some of the major advances in prenatal sampling and prenatal diagnostic laboratory techniques that have occurred over the past six decades.

Computational Methods for the Analysis of Array Comparative Genomic Hybridization

Array comparative genomic hybridization (array CGH) is a technique for assaying the copy number status of cancer genomes. The widespread use of this technology has lead to a rapid accumulation of high throughput data, which in turn has prompted the development of computational strategies for the analysis of array CGH data. Here we explain the principles behind array image processing, data visualization and genomic profile analysis, review currently available software packages, and raise considerations for future software development.

Copy Number Variants in Alzheimer's Disease

Alzheimer's disease (AD) is a devastating disease mainly afflicting elderly people, characterized by decreased cognition, loss of memory, and eventually death. Although risk and deterministic genes are known, major genetics research programs are underway to gain further insights into the inheritance of AD. In the last years, in particular, new developments in genome-wide scanning methodologies have enabled the association of a number of previously uncharacterized copy number variants (CNVs, gain or loss of DNA) in AD. Because of the exceedingly large number of studies performed, it has become difficult for geneticists as well as clinicians to systematically follow, evaluate, and interpret the growing number of (sometime conflicting) CNVs implicated in AD. In this review, after a brief introduction of this type of structural variation, and a description of available databases, computational analyses, and technologies involved, we provide a systematic review of all published data showing statistical and scientific significance of pathogenic CNVs and discuss the role they might play in AD.

Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential

BACKGROUND

Development of human cancer can proceed through the accumulation of different genetic changes affecting the structure and function of the genome. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in cancer. The integrative methods that are used to investigate these data involve different fields, including biology, bioinformatics, and statistics.

RESULTS

These methodologies are presented in this review, and their implementation in breast cancer is discussed with a focus on integration strategies. We report current applications, recent studies and interesting results leading to the identification of candidate biomarkers for diagnosis, prognosis, and therapy in breast cancer by using both individual and combined analyses.

CONCLUSION

This review presents a state of art of the role of different technologies in breast cancer based on the integration of genetics and epigenetics, and shares some issues related to the new opportunities and challenges offered by the application of such integrative approaches.

LOHAS: loss-of-heterozygosity analysis suite

Detection of loss of heterozygosity (LOH) plays an important role in genetic, genomic and cancer research. We develop computational methods to estimate the proportion of homozygous SNP calls, identify samples with structural alterations and/or unusual genotypic patterns, cluster samples with close LOH structures and map the genomic segments bearing LOH by analyzing data of genome-wide SNP arrays or customized SNP arrays. In addition to cancer genetics/genomics, we also apply the methods to study long contiguous stretches of homozygosity (LCSH) in general populations. The LCSH analysis aids in the identification of samples with complex LCSH patterns indicative of nonrandom mating and/or meiotic recombination cold spots, separation of samples with different genetic backgrounds and sex, and mapping of regions of LCSH. Affymetrix Human Mapping 500K Set SNP data from an acute lymphoblastic leukemia study containing 304 cancer patients and 50 normal controls and from the HapMap Project containing 30 African trios, 30 Caucasian trios and 90 independent Asian samples were analyzed. We identified common gene regions of LOH, e.g., ETV6 and CDKN1B, and identified frequent regions of LCSH, e.g., the region that encompasses the centromeric gene desert region of chromosome 16. Unsupervised analysis separated cancer subtypes and ethnic subpopulations by patterns of LOH/LCSH. Simulation studies considering LOH width, effect size and heterozygous interference fraction were performed, and the results show that the proposed LOH association test has good test power and controls type 1 error well. The developed algorithms are packaged into LOHAS written in R and R GUI.

BCRgt: a Bayesian cluster regression-based genotyping algorithm for the samples with copy number alterations

Background

Accurate genotype calling is a pre-requisite of a successful Genome-Wide Association Study (GWAS). Although most genotyping algorithms can achieve an accuracy rate greater than 99% for genotyping DNA samples without copy number alterations (CNAs), almost all of these algorithms are not designed for genotyping tumor samples that are known to have large regions of CNAs.

Results

This study aims to develop a statistical method that can accurately genotype tumor samples with CNAs. The proposed method adds a Bayesian layer to a cluster regression model and is termed a Bayesian Cluster Regression-based genotyping algorithm (BCRgt). We demonstrate that high concordance rates with HapMap calls can be achieved without using reference/training samples, when CNAs do not exist. By adding a training step, we have obtained higher genotyping concordance rates, without requiring large sample sizes. When CNAs exist in the samples, accuracy can be dramatically improved in regions with DNA copy loss and slightly improved in regions with copy number gain, comparing with the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM).

Conclusions

In conclusion, we have demonstrated that BCRgt can provide accurate genotyping calls for tumor samples with CNAs.

GAP-Seq: a method for identification of DNA palindromes

BACKGROUND

Closely spaced long inverted repeats, also known as DNA palindromes, can undergo intrastrand annealing to form DNA hairpins. The ability to form these hairpins results in genome instability, difficulties in maintaining clones in Escherichia coli and major problems for most DNA sequencing approaches. Because of their role in genomic instability and gene amplification in some human cancers, it is important to develop systematic approaches to detect and characterize DNA palindromes.

RESULTS

We developed a new protocol to identify palindromes that couples the S1 nuclease treated Cot0 DNA (GAPF) with high-throughput sequencing (GAP-Seq). Unlike earlier protocols, it does not involve restriction enzymatic digestion prior to DNA snap-back thereby preserving longer DNA sequences. It also indicates the location of the novel junction, which can then be recovered. Using MCF-7 breast cancer cell line as the proof-of-principle analysis, we have identified 35 palindrome candidates and physically characterized the top 5 candidates and their junctions. Because this protocol eliminates many of the false positives that plague earlier techniques, we have improved palindrome identification.

CONCLUSIONS

The GAP-Seq approach underscores the importance of developing new tools for identifying and characterizing palindromes, and provides a new strategy to systematically assess palindromes in genomes. It will be useful for studying human cancers and other diseases associated with palindromes.

SNP rs10248565 in HDAC9 as a novel genomic aberration biomarker of lung adenocarcinoma in non-smoking women

BACKGROUND

Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Although cigarette smoking is the primary risk factor for lung cancer, only 7% of female lung cancer patients in Taiwan have a history of smoking. Since cancer results from progressive accumulation of genetic aberrations, genomic rearrangements may be early events in carcinogenesis.

RESULTS

In order to identify biomarkers of early-stage adenocarcinoma, the genome-wide DNA aberrations of 60 pairs of lung adenocarcinoma and adjacent normal lung tissue in non-smoking women were examined using Affymetrix Genome-Wide Human SNP 6.0 arrays. Common copy number variation (CNV) regions were identified by ≥30% of patients with copy number beyond 2 ± 0.5 of copy numbers for each single nucleotide polymorphism (SNP) and at least 100 continuous SNP variant loci. SNPs associated with lung adenocarcinoma were identified by McNemar's test. Loss of heterozygosity (LOH) SNPs were identified in ≥18% of patients with LOH in the locus. Aberration of SNP rs10248565 at HDAC9 in chromosome 7p21.1 was identified from concurrent analyses of CNVs, SNPs, and LOH.

CONCLUSION

The results elucidate the genetic etiology of lung adenocarcinoma by demonstrating that SNP rs10248565 may be a potential biomarker of cancer susceptibility.

Application of SNP microarrays to the genome-wide analysis of chromosomal instability in premalignant airway lesions

Chromosomal instability is central to the process of carcinogenesis. The genome-wide detection of somatic chromosomal alterations (SCA) in small premalignant lesions remains challenging because sample heterogeneity dilutes the aberrant cell information. To overcome this hurdle, we focused on the B allele frequency data from single-nucleotide polymorphism microarrays (SNP arrays). The difference of allelic fractions between paired tumor and normal samples from the same patient (delta-θ) provides a simple but sensitive detection of SCA in the affected tissue. We applied the delta-θ approach to small, heterogeneous clinical specimens, including endobronchial biopsies and brushings. Regions identified by delta-θ were validated by FISH and quantitative PCR in heterogeneous samples. Distinctive genomic variations were successfully detected across the whole genome in all invasive cancer cases (6 of 6), carcinoma in situ (3 of 3), and high-grade dysplasia (severe or moderate; 3 of 11). Not only well-described SCAs in lung squamous cell carcinoma, but also several novel chromosomal alterations were frequently found across the preinvasive dysplastic cases. Within these novel regions, losses of putative tumor suppressors (RNF20 and SSBP2) and an amplification of RASGRP3 gene with oncogenic activity were observed. Widespread sampling of the airway during bronchoscopy demonstrated that field cancerization reflected by SCAs at multiple sites was detectable. SNP arrays combined with delta-θ analysis can detect SCAs in heterogeneous clinical sample and expand our ability to assess genomic instability in the airway epithelium as a biomarker of lung cancer risk.

Association between mutation of the NF2 gene and monosomy 22 in menopausal women with sporadic meningiomas

BACKGROUND

Meningioma was the first solid tumor shown to contain a recurrent genetic alteration e.g. monosomy 22/del(22q), NF2 being the most relevant gene involved. Although monosomy 22/del(22q) is present in half of all meningiomas, and meningiomas frequently carry NF2 mutations, no study has been reported so far in which both alterations are simultaneously assessed and correlated with the features of the disease.

METHODS

Here, we analyzed the frequency of both copy number changes involving chromosome 22 and NF2 mutations in 20 sporadic meningiomas using high-density SNP-arrays, interphase-FISH and PCR techniques.

RESULTS

Our results show a significant frequency of NF2 mutations (6/20 patients, 30%), most of which (5/6) had not been previously reported in sporadic meningiomas. NF2 mutations involved five different exons and led to a truncated protein (p.Leu163CysfsX46, p.Phe62LeufsX61, p.Asp281MetfsX15, p.Phe285LeufsX11, p.Gln389ArgfsX37) and an in frame deletion of Phe119. Interestingly, all NF2 mutated cases were menopausal women with monosomy 22 but not del(22q).

CONCLUSIONS

These results confirm and extend on previous observations about the high frequency and heterogeneity of NF2 mutations in sporadic meningiomas and indicate they could be restricted to a well-defined cytogenetic and clinical subgroup of menopausal women. Further studies in large series of patients are required to confirm our observations.

Comprehensive high-resolution genomic profiling and cytogenetics of two pediatric and one adult medulloblastoma

Medulloblastoma (WHO grade IV) is a rare, malignant, invasive, embryonal tumor which mainly occurs in children and represents less than 1% of all adult brain tumors. Systematic comprehensive genetic analyses on medulloblastomas are rare but necessary to provide more detailed information. Therefore, we performed comprehensive cytogenetic analyses (blood and tissue) of two pediatric and one adult medulloblastoma, using trypsin-Giemsa staining, spectral karyotyping (tissues only), SNP-arrays, and gene expression analyses. We confirmed frequently detected chromosomal aberrations in medulloblastoma, such as +7q, -8p/q, -9q, -11q, -12q, and +17q and identified novel genetic events. Applying SNP-array, we identified constitutional de novo losses 5q21.1, 15q11.2, 17q21.31, 19p12 (pediatric medulloblastoma), 9p21.1, 19p12, 19q13.3, 21q11.2 (adult medulloblastoma) and gains 16p11.1-16p11.2, 18p11.32, Yq11.223-Yq11.23 (pediatric medulloblastoma), Xp22.31 (adult medulloblastoma) possibly representing inherited causal events for medulloblastoma formation. We show evidence for somatic segmental uniparental disomy in regions 1p36, 6q16.3, 6q24.1, 14q21.2, 17p13.3, and 17q22 not previously described for primary medulloblastoma. Gene expression analysis supported classification of the adult medulloblastoma to the WNT-subgroup and classification of pediatric medulloblastomas to group 3 tumors. Analyses of tumors and matched normal tissues (blood) with a combination of complementary techniques will help to further elucidate potentially causal genetic events for medulloblastomas.

Genome-wide detection of copy number variations using high-density SNP genotyping platforms in Holsteins

Background

Copy number variations (CNVs) are widespread in the human or animal genome and are a significant source of genetic variation, which has been demonstrated to play an important role in phenotypic diversity. Advances in technology have allowed for identification of a large number of CNVs in cattle. Comprehensive explore novel CNVs in the bovine genome would provide valuable information for functional analyses of genome structural variation and facilitating follow-up association studies between complex traits and genetic variants.

Results

In this study, we performed a genome-wide CNV detection based on high-density SNP genotyping data of 96 Chinese Holstein cattle. A total of 367 CNV regions (CNVRs) across the genome were identified, which cover 42.74Mb of the cattle genome and correspond to 1.61% of the genome sequence. The length of the CNVRs on autosomes range from 10.76 to 2,806.42 Kb with an average of 96.23 Kb. 218 out of these CNVRs contain 610 annotated genes, which possess a wide spectrum of molecular functions. To confirm these findings, quantitative PCR (qPCR) was performed for 17 CNVRs and 13(76.5%) of them were successfully validated.

Conclusions

Our study demonstrates the high density SNP array can significantly improve the accuracy and sensitivity of CNV calling. Integration of different platforms can enhance the detection of genomic structure variants. Our results provide a significant replenishment for the high resolution map of copy number variation in the bovine genome and valuable information for investigation of genomic structural variation underlying traits of interest in cattle.

Genome-wide DNA profiling of HIV-related B-cell lymphomas

Non-Hodgkin lymphomas represent a frequent complication of human immunodeficiency virus (HIV) infection, occurring at higher frequency than in immunocompetent individuals, and causing morbidity and mortality. Here, we present the method we have followed to analyze the genomic lesions in HIV-related and in other immunodeficiency-related lymphomas, as well in diffuse large B-cell lymphoma (DLBCL) samples derived from immunocompetent hosts. The technology we have used is represented by the GeneChip Human Mapping 250K NspI arrays (Affymetrix, Santa Clara, CA, USA), arrays based on 25mer oligonucleotides initially designed for large-scale genotyping, that is, the detection of thousands of single-nucleotide polymorphisms (SNPs), then shown to be applicable for the detection of cancer alterations. The protocol is shown in all its steps with suggestions and tips. Applications of the technology and obtained results are also briefly summarized.

Sample processing considerations for detecting copy number changes in formalin-fixed, paraffin-embedded tissues

The Whole Genome Sampling Analysis (WGSA) assay in combination with Affymetrix GeneChip Mapping Arrays is used for copy number analysis of high-quality DNA samples (i.e., samples that have been collected from blood, fresh or frozen tissue, or cell lines). Formalin-fixed, paraffin-embedded (FFPE) samples, however, represent the most prevalent form of archived clinical samples, but they provide additional challenges for molecular assays. FFPE processing usually results in the degradation of FFPE DNA and in the contamination and chemical modification of these DNA samples. Because of these issues, FFPE DNA is not suitable for all molecular assays designed for high-quality DNA samples. Strategies recommended for processing FFPE DNA samples through WGSA and to the Mapping arrays are described here.

Data analysis considerations for detecting copy number changes in formalin-fixed, paraffin-embedded tissues

The Whole Genome Sampling Analysis (WGSA) assay in combination with Affymetrix GeneChip Mapping Arrays is used for copy number analysis of high-quality DNA samples (i.e., samples that have been collected from blood, fresh or frozen tissue, or cell lines). Formalin-fixed, paraffin-embedded (FFPE) samples, however, represent the most prevalent form of archived clinical samples, but they provide additional challenges for molecular assays. FFPE processing usually results in the degradation of FFPE DNA and in the contamination and chemical modification of these DNA samples. In this article, we describe the steps needed to obtain reliable copy number predictions from degraded and contaminated FFPE samples.

An improved allele-specific PCR primer design method for SNP marker analysis and its application

BACKGROUND

Although Single Nucleotide Polymorphism (SNP) marker is an invaluable tool for positional cloning, association study and evolutionary analysis, low SNP detection efficiency by Allele-Specific PCR (AS-PCR) still restricts its application as molecular marker like other markers such as Simple Sequence Repeat (SSR). To overcome this problem, primers with a single nucleotide artificial mismatch introduced within the three bases closest to the 3'end (SNP site) have been used in AS-PCR. However, for one SNP site, nine possible mismatches can be generated among the three bases and how to select the right one to increase primer specificity is still a challenge.

RESULTS

In this study, different from the previous reports which used a limited quantity of primers randomly (several or dozen pairs), we systematically investigated the effects of mismatch base pairs, mismatch sites and SNP types on primer specificity with 2071 primer pairs, which were designed based on SNPs from Brassica oleracea 01-88 and 02-12. According to the statistical results, we (1) found that the primers designed with SNP (A/T), in which the mismatch (CA) in the 3rd nucleotide from the 3' end, had the highest allele-specificity (81.9%). This information could be used when designing primers from a large quantity of SNP sites; (2) performed the primer design principle which forms the one and only best primer for every SNP type. This is never reported in previous studies. Additionally, we further identified its availability in rapeseed (Brassica napus L.) and sesame (Sesamum indicum). High polymorphism percent (75%) of the designed primers indicated it is a general method and can be applied in other species.

CONCLUSION

The method provided in this study can generate primers more effectively for every SNP site compared to other AS-PCR primer design methods. The high allele-specific efficiency of the SNP primer allows the feasibility for low- to moderate- throughput SNP analyses and is much suitable for gene mapping, map-based cloning, and marker-assisted selection in crops.

Rapid visualisation of microarray copy number data for the detection of structural variations linked to a disease phenotype

Whilst the majority of inherited diseases have been found to be caused by single base substitutions, small insertions or deletions (<1Kb), a significant proportion of genetic variability is due to copy number variation (CNV). The possible role of CNV in monogenic and complex diseases has recently attracted considerable interest. However, until the development of whole genome, oligonucleotide micro-arrays, designed specifically to detect the presence of copy number variation, it was not easy to screen an individual for the presence of unknown deletions or duplications with sizes below the level of sensitivity of optical microscopy (3-5 Mb). Now that currently available oligonucleotide micro-arrays have in excess of a million probes, the problem of copy number analysis has moved from one of data production to that of data analysis. We have developed CNViewer, to identify copy number variation that co-segregates with a disease phenotype in small nuclear families, from genome-wide oligonucleotide micro-array data. This freely available program should constitute a useful addition to the diagnostic armamentarium of clinical geneticists.

Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort

BACKGROUND

Genotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets.

RESULTS

Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs.

CONCLUSION

Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.

Characterising chromosome rearrangements: recent technical advances in molecular cytogenetics

Genomic rearrangements can result in losses, amplifications, translocations and inversions of DNA fragments thereby modifying genome architecture, and potentially having clinical consequences. Many genomic disorders caused by structural variation have initially been uncovered by early cytogenetic methods. The last decade has seen significant progression in molecular cytogenetic techniques, allowing rapid and precise detection of structural rearrangements on a whole-genome scale. The high resolution attainable with these recently developed techniques has also uncovered the role of structural variants in normal genetic variation alongside single-nucleotide polymorphisms (SNPs). We describe how array-based comparative genomic hybridisation, SNP arrays, array painting and next-generation sequencing analytical methods (read depth, read pair and split read) allow the extensive characterisation of chromosome rearrangements in human genomes.

Microdeletions and microduplications in patients with congenital heart disease and multiple congenital anomalies

OBJECTIVE

Multiple genetic syndromes are caused by recurrent chromosomal microdeletions or microduplications. The increasing use of high-resolution microarrays in clinical analysis has allowed the identification of previously undetectable submicroscopic copy number variants (CNVs) associated with genetic disorders. We hypothesized that patients with congenital heart disease and additional dysmorphic features or other anomalies would be likely to harbor previously undetected CNVs, which might identify new disease loci or disease-related genes for various cardiac defects.

DESIGN

Copy number analysis with single nucleotide polymorphism-based, oligonucleotide microarrays was performed on 58 patients with congenital heart disease and other dysmorphic features and/or other anomalies. The observed CNVs were validated using independent techniques and validated CNVs were further analyzed using computational algorithms and comparison with available control CNV datasets in order to assess their pathogenic potential.

RESULTS

Potentially pathogenic CNVs were detected in twelve of 58 patients (20.7%), ranging in size from 240 Kb to 9.6 Mb. These CNVs contained between 1 and 55 genes, including NRP1, NTRK3, MESP1, ADAM19, and HAND1, all of which are known to participate in cardiac development.

CONCLUSIONS

Genome-wide analysis in patients with congenital heart disease and additional phenotypes has identified potentially pathogenic CNVs affecting genes involved in cardiac development. The identified variant loci and the genes within them warrant further evaluation in similarly syndromic and nonsyndromic cardiac cohorts.

Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus

Both obesity and being underweight have been associated with increased mortality. Underweight, defined as a body mass index (BMI) ≤ 18.5 kg per m(2) in adults and ≤ -2 standard deviations from the mean in children, is the main sign of a series of heterogeneous clinical conditions including failure to thrive, feeding and eating disorder and/or anorexia nervosa. In contrast to obesity, few genetic variants underlying these clinical conditions have been reported. We previously showed that hemizygosity of a ∼600-kilobase (kb) region on the short arm of chromosome 16 causes a highly penetrant form of obesity that is often associated with hyperphagia and intellectual disabilities. Here we show that the corresponding reciprocal duplication is associated with being underweight. We identified 138 duplication carriers (including 132 novel cases and 108 unrelated carriers) from individuals clinically referred for developmental or intellectual disabilities (DD/ID) or psychiatric disorders, or recruited from population-based cohorts. These carriers show significantly reduced postnatal weight and BMI. Half of the boys younger than five years are underweight with a probable diagnosis of failure to thrive, whereas adult duplication carriers have an 8.3-fold increased risk of being clinically underweight. We observe a trend towards increased severity in males, as well as a depletion of male carriers among non-medically ascertained cases. These features are associated with an unusually high frequency of selective and restrictive eating behaviours and a significant reduction in head circumference. Each of the observed phenotypes is the converse of one reported in carriers of deletions at this locus. The phenotypes correlate with changes in transcript levels for genes mapping within the duplication but not in flanking regions. The reciprocal impact of these 16p11.2 copy-number variants indicates that severe obesity and being underweight could have mirror aetiologies, possibly through contrasting effects on energy balance.

SNP and gene networks construction and analysis from classification of copy number variations data

BACKGROUND

Detection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. It is interesting to identify and represent relevant CNVs from a genome-wide data due to high data volume and the complexity of interactions.

RESULTS

In this paper, we incorporate the DNA copy number variation data derived from SNP arrays into a computational shrunken model and formalize the detection of copy number variations as a case-control classification problem. More than 80% accuracy can be obtained using our classification model and by shrinkage, the number of relevant CNVs to disease can be determined. In order to understand relevant CNVs, we study their corresponding SNPs in the genome and a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and identify SNP networks based on their P-values. Our selected SNP networks are statistically significant compared with random SNP networks and play a role in the biological process. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that have similarity values being greater than a threshold are selected to construct gene networks. A gene enrichment analysis show that our gene networks are functionally important.Experimental results demonstrate that our selected SNP and gene networks based on the selected CNVs contain some functional relationships directly or indirectly to disease study.

CONCLUSIONS

Two datasets are given to demonstrate the effectiveness of the introduced method. Some statistical and biological analysis show that this shrunken classification model is effective in identifying CNVs from genome-wide data and our proposed framework has a potential to become a useful analysis tool for SNP data sets.

Identification of possible genetic alterations in the breast cancer cell line MCF-7 using high-density SNP genotyping microarray

Context:

Cancer cell lines are used extensively in various research. Knowledge of genetic alterations in these lines is important for understanding mechanisms underlying their biology. However, since paired normal tissues are usually unavailable for comparison, precisely determining genetic alterations in cancer cell lines is difficult. To address this issue, a highly efficient and reliable method is developed. Aims: Establishing a highly efficient and reliable experimental system for genetic profiling of cell lines.

Materials and Methods:

A widely used breast cancer cell line, MCF-7, was genetically profiled with 4,396 single nucleotide polymorphisms (SNPs) spanning 11 whole chromosomes and two other small regions using a newly developed high-throughput multiplex genotyping approach.

Results:

The fractions of homozygous SNPs in MCF-7 (13.3%) were significantly lower than those in the control cell line and in 24 normal human individuals (25.1% and 27.4%, respectively). Homozygous SNPs in MCF-7 were found in clusters. The sizes of these clusters were significantly larger than the expected based on random allelic combination. Fourteen such regions were found on chromosomes 1p, 1q, 2q, 6q, 13, 15q, 16q, 17q and 18p in MCF-7 and two in the small regions.

Conclusions:

These results are generally concordant with those obtained using different approaches but are better in defining their chromosomal positions. The used approach provides a reliable way to detecting possible genetic alterations in cancer cell lines without paired normal tissues.

The application of single nucleotide polymorphism microarrays in cancer research

The development of microarray technology has had a significant impact on the genetic analysis of human disease. The recently developed single nucleotide polymorphism (SNP) array can be used to measure both DNA polymorphism and dosage changes. Our laboratory has applied SNP microarray analysis to uncover frequent uniparental disomies and sub-microscopic genomic copy number gains and losses in different cancers. This review will focus on the wide range of applications of SNP microarray analysis to cancer research. SNP array genotyping can determine loss of heterozygosity, genomic copy number changes and DNA methylation alterations of cancer cells. The same technology can also be used to investigate allelic association in cancers. Therefore, it can be applied to the identification of cancer predisposition genes, oncogenes and tumor suppressor genes in specific types of tumors. As a consequence, they have potential in cancer risk assessment, diagnosis, prognosis and treatment selection.

Allelic imbalances in radiation-associated acute myeloid leukemia

Acute myeloid leukemia (AML) can develop as a secondary malignancy following radiotherapy, but also following low-dose environmental or occupational radiation exposure. Therapy-related AML frequently carries deletions of chromosome 5q and/or 7, but for low-dose exposure associated AML this has not been described. For the present study we performed genome-wide screens for loss-of-heterozygosity (LOH) in a set of 19 AML cases that developed after radiation-exposure following the Chernobyl accident. Using Affymetrix SNP arrays we found large regions of LOH in 16 of the cases. Eight cases (42%) demonstrated LOH at 5q and/or 7, which is a known marker of complex karyotypic changes and poor prognosis. We could show here for the first time that exposure to low-dose ionizing radiation induces AML with molecular alterations similar to those seen in therapy-related cases.

Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants

We have systematically compared copy number variant (CNV) detection on eleven microarrays to evaluate data quality and CNV calling, reproducibility, concordance across array platforms and laboratory sites, breakpoint accuracy and analysis tool variability. Different analytic tools applied to the same raw data typically yield CNV calls with <50% concordance. Moreover, reproducibility in replicate experiments is <70% for most platforms. Nevertheless, these findings should not preclude detection of large CNVs for clinical diagnostic purposes because large CNVs with poor reproducibility are found primarily in complex genomic regions and would typically be removed by standard clinical data curation. The striking differences between CNV calls from different platforms and analytic tools highlight the importance of careful assessment of experimental design in discovery and association studies and of strict data curation and filtering in diagnostics. The CNV resource presented here allows independent data evaluation and provides a means to benchmark new algorithms.

SNP array-based karyotyping: differences and similarities between aplastic anemia and hypocellular myelodysplastic syndromes

In aplastic anemia (AA), contraction of the stem cell pool may result in oligoclonality, while in myelodysplastic syndromes (MDS) a single hematopoietic clone often characterized by chromosomal aberrations expands and outcompetes normal stem cells. We analyzed patients with AA (N = 93) and hypocellular MDS (hMDS, N = 24) using single nucleotide polymorphism arrays (SNP-A) complementing routine cytogenetics. We hypothesized that clinically important cryptic clonal aberrations may exist in some patients with BM failure. Combined metaphase and SNP-A karyotyping improved detection of chromosomal lesions: 19% and 54% of AA and hMDS cases harbored clonal abnormalities including copy-neutral loss of heterozygosity (UPD, 7%). Remarkably, lesions involving the HLA locus suggestive of clonal immune escape were found in 3 of 93 patients with AA. In hMDS, additional clonal lesions were detected in 5 (36%) of 14 patients with normal/noninformative routine cytogenetics. In a subset of AA patients studied at presentation, persistent chromosomal genomic lesions were found in 10 of 33, suggesting that the initial diagnosis may have been hMDS. Similarly, using SNP-A, earlier clonal evolution was found in 4 of 7 AA patients followed serially. In sum, our results indicate that SNP-A identify cryptic clonal genomic aberrations in AA and hMDS leading to improved distinction of these disease entities.

SAQC: SNP array quality control

BACKGROUND

Genome-wide single-nucleotide polymorphism (SNP) arrays containing hundreds of thousands of SNPs from the human genome have proven useful for studying important human genome questions. Data quality of SNP arrays plays a key role in the accuracy and precision of downstream data analyses. However, good indices for assessing data quality of SNP arrays have not yet been developed.

RESULTS

We developed new quality indices to measure the quality of SNP arrays and/or DNA samples and investigated their statistical properties. The indices quantify a departure of estimated individual-level allele frequencies (AFs) from expected frequencies via standardized distances. The proposed quality indices followed lognormal distributions in several large genomic studies that we empirically evaluated. AF reference data and quality index reference data for different SNP array platforms were established based on samples from various reference populations. Furthermore, a confidence interval method based on the underlying empirical distributions of quality indices was developed to identify poor-quality SNP arrays and/or DNA samples. Analyses of authentic biological data and simulated data show that this new method is sensitive and specific for the detection of poor-quality SNP arrays and/or DNA samples.

CONCLUSIONS

This study introduces new quality indices, establishes references for AFs and quality indices, and develops a detection method for poor-quality SNP arrays and/or DNA samples. We have developed a new computer program that utilizes these methods called SNP Array Quality Control (SAQC). SAQC software is written in R and R-GUI and was developed as a user-friendly tool for the visualization and evaluation of data quality of genome-wide SNP arrays. The program is available online (http://www.stat.sinica.edu.tw/hsinchou/genetics/quality/SAQC.htm).

Genome-wide analysis of loss of heterozygosity and copy number amplification in uterine leiomyomas using the 100K single nucleotide polymorphism array

PURPOSE

Uterine leiomyomas (fibroids) are benign smooth muscle tumors commonly found among reproductive-aged women. Though benign, these tumors are the leading indication for hysterectomies in the United States and cause significant morbidity. Despite the importance of this tumor in women's health, relatively little is known about the molecular etiology.

METHODS

In this study, we used the Affymetrix 100K single nucleotide polymorphism (SNP) chip to assess whether the pattern and frequency of genome-wide loss of heterozygosity (LOH) and copy number amplifications is associated with clinical heterogeneity.

RESULTS

Thirty-seven tumors with varying sizes and histology from eleven patients were analyzed. LOH was observed in 4/37 tumors (10.8%) and significantly associated with large-sized tumors (p<0.0014). Two tumors revealed hemizygosity on chromosome 7q, a region that has been consistently reported to have LOH. Additionally, we detected one novel region of LOH, 16p13.11 in one tumor (2.7%). Copy number amplifications were observed on all chromosomes; however, most were low-level amplifications and only detected in a single tumor. One region of amplification at 3p26.3 was detected in four tumors.

CONCLUSIONS

Despite the use of a high-density SNP platform, our results suggest that genome-wide LOH and copy number amplifications are infrequent events and generally do not determine clinical and histologic characteristics of this disease.

Accuracy of CNV Detection from GWAS Data

Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites—Birdsuite, Partek, HelixTree, and PennCNV-Affy—in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two “gold standards,” the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a “gold standard” for detection of CNVs remains to be established.

Single nucleotide polymorphism microarray analysis of genetic alterations in cancer

The identification of structural genetic alterations, including DNA amplifications, deletions, and loss of heterozygosity (LOH), using single nucleotide polymorphism (SNP) microarrays has provided important insights into the pathogenesis of a number of hematologic malignancies. Currently available SNP arrays comprise over a million SNP and copy number oligonucleotide probes that interrogate the genome at sub-kilobase resolution. The accurate detection of DNA copy number abnormalities and LOH is critically dependent on the use of high-quality DNA, the use of matched reference samples wherever possible, optimal normalization of raw microarray data, and computational algorithms to detect copy number alterations sensitively and robustly. This chapter provides methods and guidelines for preparing samples, processing and analyzing data, and validation of novel lesions. Specific examples are provided for Affymetrix SNP arrays in acute lymphoblastic leukemia.

Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays

Background

The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.

Results

The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.

Conclusions

We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.

Genome wide analysis of DNA copy number neutral loss of heterozygosity (CNNLOH) and its relation to gene expression in esophageal squamous cell carcinoma

Background

Genomic instability plays an important role in human cancers. We previously characterized genomic instability in esophageal squamous cell carcinomas (ESCC) in terms of loss of heterozygosity (LOH) and copy number (CN) changes in tumors using the Affymetrix GeneChip Human Mapping 500K array in 30 cases from a high-risk region of China. In the current study we focused on copy number neutral (CN = 2) LOH (CNNLOH) and its relation to gene expression in ESCC.

Results

Overall we found that 70% of all LOH observed was CNNLOH. Ninety percent of ESCCs showed CNNLOH (median frequency in cases = 60%) and this was the most common type of LOH in two-thirds of cases. CNNLOH occurred on all 39 autosomal chromosome arms, with highest frequencies on 19p (100%), 5p (96%), 2p (95%), and 20q (95%). In contrast, LOH with CN loss represented 19% of all LOH, occurred in just half of ESCCs (median frequency in cases = 0%), and was most frequent on 3p (56%), 5q (47%), and 21q (41%). LOH with CN gain was 11% of all LOH, occurred in 93% of ESCCs (median frequency in cases = 13%), and was most common on 20p (82%), 8q (74%), and 3q (42%). To examine the effect of genomic instability on gene expression, we evaluated RNA profiles from 17 pairs of matched normal and tumor samples (a subset of the 30 ESCCs) using Affymetrix U133A 2.0 arrays. In CN neutral regions, expression of 168 genes (containing 1976 SNPs) differed significantly in tumors with LOH versus tumors without LOH, including 101 genes that were up-regulated and 67 that were down-regulated.

Conclusion

Our results indicate that CNNLOH has a profound impact on gene expression in ESCC, which in turn may affect tumor development.

Genetics of medulloblastoma: clues for novel therapies

Medulloblastoma is the most common malignant brain tumor in children. Current medulloblastoma therapy entails surgery, radiation and chemotherapy. The 5-year survival rate for patients ranges from 40 to 70%, with most survivors suffering from serious long-term treatment-related sequelae. Additional research on the molecular biology and genetics of medulloblastoma is needed to identify robust prognostic markers for disease-risk stratification, to improve current treatment regimes and to discover novel and more effective molecular-targeted therapies. Recent advances in molecular biology have led to the development of powerful tools for the study of medulloblastoma tumorigenesis, which have revealed new insights into the molecular underpinnings of this disease. Here we discuss the signaling pathway alterations implicated in medulloblastoma pathogenesis, the techniques used in molecular profiling of these tumors and recent molecular subclassification schemes. Particular emphasis is given to the identification of novel molecular targets for less toxic, patient-tailored therapeutic approaches.

Integrated profiling reveals a global correlation between epigenetic and genetic alterations in mesothelioma

Development of mesothelioma is linked mainly to asbestos exposure, but the combined contributions of genetic and epigenetic alterations are unclear. We investigated the potential relationships between gene copy number (CN) alterations and DNA methylation profiles in a case series of pleural mesotheliomas (n = 23). There were no instances of significantly correlated CN alteration and methylation at probed loci, whereas averaging loci over their associated genes revealed only two genes with significantly correlated CN and methylation alterations. In contrast to the lack of discrete correlations, the overall extent of tumor CN alteration was significantly associated with DNA methylation profile when comparing CN alteration extent among methylation profile classes. Further, there was evidence that this association was partially attributable to prevalent allele loss at the DNA methyltransferase gene DNMT1. Our findings define a strong association between global genetic and global epigenetic dysregulation in mesothelioma, rather than a discrete, local coordination of gene inactivation.

An integrated Bayesian analysis of LOH and copy number data

Background

Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events.

Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events.

Results

To better identify genomic aberrations, we propose a method (called gBPCR) which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the homozygosity status of the SNPs, resulting from an altered CN level. Namely, we model the distributions of the detected genotype, given a specific genomic alteration and we estimate the parameters involved on public reference datasets. The estimation is performed similarly to the modified Bayesian Piecewise Constant Regression, but with improved estimators for the detection of the breakpoints.

Using artificial and real data, we evaluate the quality of the estimation of gBPCR and we also show that it outperforms other well-known methods for LOH estimation.

Conclusions

We propose a method (gBPCR) for the estimation of both LOH and CN aberrations, improving their estimation by integrating both types of data and accounting for their relationships. Moreover, gBPCR performed very well in comparison with other methods for LOH estimation and the estimated CN lesions on real data have been validated with another technique.

Background adjustment for DNA microarrays using a database of microarray experiments

DNA microarrays have become an indispensable technique in biomedical research. The raw measurements from microarrays undergo a number of preprocessing steps before the data are converted to the genomic level for further analysis. Background adjustment is an important step in preprocessing. Estimating background noise has been challenging because background levels vary a lot from probe to probe, yet there are limited observations on each probe. Most current methods have used the empirical Bayes approach to borrow information across probes on the same array. These approaches shrink the background estimate for either the entire sample or probes sharing similar sequence structures. In this article, we present a solution that is truly probe specific by using a database of large number of microarray experiments. Information is borrowed across samples and background noise is estimated for each probe individually. The ability to obtain probe specific background distributions allows us to extend the dynamic range of gene expression levels. We illustrate the improvement in detecting gene expression variation on two datasets: a Latin Square spike-in experiment from Affymetrix and an Estrogen Receptor experiment with biological replicates. An R package dbRMA implementing our method can be obtained from the authors.

Genetic and epigenetic somatic alterations in head and neck squamous cell carcinomas are globally coordinated but not locally targeted

Background

Solid tumors, including head and neck squamous cell carcinomas (HNSCC), arise as a result of genetic and epigenetic alterations in a sustained stress environment. Little work has been done that simultaneously examines the spectrum of both types of changes in human tumors on a genome-wide scale and results so far have been limited and mixed. Since it has been hypothesized that epigenetic alterations may act by providing the second carcinogenic hit in gene silencing, we sought to identify genome-wide DNA copy number alterations and CpG dinucleotide methylation events and examine the global/local relationships between these types of alterations in HNSCC.

Methodology/Principal Findings

We have extended a prior analysis of 1,413 cancer-associated loci for epigenetic changes in HNSCC by integrating DNA copy number alterations, measured at 500,000 polymorphic loci, in a case series of 19 primary HNSCC tumors. We have previously demonstrated that local copy number does not bias methylation measurements in this array platform. Importantly, we found that the global pattern of copy number alterations in these tumors was significantly associated with tumor methylation profiles (p<0.002). However at the local level, gene promoter regions did not exhibit a correlation between copy number and methylation (lowest q = 0.3), and the spectrum of genes affected by each type of alteration was unique.

Conclusion/Significance

This work, using a novel and robust statistical approach demonstrates that, although a “second hit” mechanism is not likely the predominant mode of action for epigenetic dysregulation in cancer, the patterns of methylation events are associated with the patterns of allele loss. Our work further highlights the utility of integrative genomics approaches in exploring the driving somatic alterations in solid tumors.

A new highly penetrant form of obesity due to deletions on chromosome 16p11.2

Obesity has become a major worldwide challenge to public health, owing to an interaction between the Western 'obesogenic' environment and a strong genetic contribution. Recent extensive genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms associated with obesity, but these loci together account for only a small fraction of the known heritable component. Thus, the 'common disease, common variant' hypothesis is increasingly coming under challenge. Here we report a highly penetrant form of obesity, initially observed in 31 subjects who were heterozygous for deletions of at least 593 kilobases at 16p11.2 and whose ascertainment included cognitive deficits. Nineteen similar deletions were identified from GWAS data in 16,053 individuals from eight European cohorts. These deletions were absent from healthy non-obese controls and accounted for 0.7% of our morbid obesity cases (body mass index (BMI) >or= 40 kg m(-2) or BMI standard deviation score >or= 4; P = 6.4 x 10(-8), odds ratio 43.0), demonstrating the potential importance in common disease of rare variants with strong effects. This highlights a promising strategy for identifying missing heritability in obesity and other complex traits: cohorts with extreme phenotypes are likely to be enriched for rare variants, thereby improving power for their discovery. Subsequent analysis of the loci so identified may well reveal additional rare variants that further contribute to the missing heritability, as recently reported for SIM1 (ref. 3). The most productive approach may therefore be to combine the 'power of the extreme' in small, well-phenotyped cohorts, with targeted follow-up in case-control and population cohorts.

Array-based karyotyping for prognostic assessment in chronic lymphocytic leukemia: performance comparison of Affymetrix 10K2.0, 250K Nsp, and SNP6.0 arrays

Specific chromosomal alterations are recognized as important prognostic factors in chronic lymphocytic leukemia (CLL). Array-based karyotyping is gaining acceptance as an alternative to the standard fluorescence in situ hybridization (FISH) panel for detecting these aberrations. This study explores the optimum single nucleotide polymorphism (SNP) array probe density for routine clinical use, presents clinical validation results for the 250K Nsp Affymetrix SNP array, and highlights clinically actionable genetic lesions missed by FISH and conventional cytogenetics. CLL samples were processed on low (10K2.0), medium (250K Nsp), and high (SNP6.0) probe density Affymetrix SNP arrays. Break point definition and detection rates for clinically relevant genetic lesions were compared. The 250K Nsp array was subsequently validated for routine clinical use and demonstrated 98.5% concordance with the standard CLL FISH panel. SNP array karyotyping detected genomic complexity and/or acquired uniparental disomy not detected by the FISH panel. In particular, a region of acquired uniparental disomy on 17p was shown to harbor two mutated copies of TP53 that would have gone undetected by FISH, conventional cytogenetics, or array comparative genomic hybridization. SNP array karyotyping allows genome-wide, high resolution detection of copy number and uniparental disomy at genomic regions with established prognostic significance in CLL, detects lesions missed by FISH, and provides insight into gene dosage at these loci.

Increased expression of cellular retinol-binding protein 1 in laryngeal squamous cell carcinoma

PURPOSE

To investigate the genomic alterations in larynx carcinomas (LaCa) tissues and its prognostics values in predicting survival.

METHODS

To analyse the aberrations in the genome of LaCa patients, we used array comparative genomic hybridization in 19 human laryngeal tumour samples. DNA samples were also subjected to detect human papillomavirus (HPV) sequences by polymerase chain reaction (PCR). Copy number gain was confirmed by real-time PCR. The cellular retinol-binding protein 1 (CRBP-1) gene expression was also confirmed by immunohistochemistry assay on LaCa tissues. To identify prognostic feature, CRBP-1 gene gain was correlated to patient survival.

RESULTS

The most common gains were detected for CRBP-1 and EGFR genes, while DNA lost in RAF-1 gene. Immunohistochemistry assay was revealed strong expression of CRBP1 protein in those cases with CRBP-1 gene gain. The CRBP-1 gene gain and its expression correlated significantly with survival (P = 0.003). Cox regression analysis indicated that CRBP-1 expression level was a factor of survival (P = 0.008). HPV sequences were detected in 42% of the samples, and did not show any relationship with specific gene alterations.

CONCLUSION

Our data shows that CRBP-1 gene gain can be determined by immunohistochemistry on routinely processed tissue specimens, and could support as a potential novel marker for long-term survival in laryngeal squamous cell carcinoma.

Genomics of medulloblastoma: from Giemsa-banding to next-generation sequencing in 20 years

Advances in the field of genomics have recently enabled the unprecedented characterization of the cancer genome, providing novel insight into the molecular mechanisms underlying malignancies in humans. The application of high-resolution microarray platforms to the study of medulloblastoma has revealed new oncogenes and tumor suppressors and has implicated changes in DNA copy number, gene expression, and methylation state in its etiology. Additionally, the integration of medulloblastoma genomics with patient clinical data has confirmed molecular markers of prognostic significance and highlighted the potential utility of molecular disease stratification. The advent of next-generation sequencing technologies promises to greatly transform our understanding of medulloblastoma pathogenesis in the next few years, permitting comprehensive analyses of all aspects of the genome and increasing the likelihood that genomic medicine will become part of the routine diagnosis and treatment of medulloblastoma.

Somatic mutations in p85alpha promote tumorigenesis through class IA PI3K activation

Members of the mammalian phosphoinositide-3-OH kinase (PI3K) family of proteins are critical regulators of various cellular process including cell survival, growth, proliferation, and motility. Oncogenic activating mutations in the p110alpha catalytic subunit of the heterodimeric p110/p85 PI3K enzyme are frequent in human cancers. Here we show the presence of frequent mutations in p85alpha in colon cancer, a majority of which occurs in the inter-Src homology-2 (iSH2) domain. These mutations uncouple and retain p85alpha's p110-stabilizing activity, while abrogating its p110-inhibitory activity. The p85alpha mutants promote cell survival, AKT activation, anchorage-independent cell growth, and oncogenesis in a p110-dependent manner.