ARID1A mutations in endometriosis-associated ovarian carcinomas.

BACKGROUND: Ovarian clear-cell and endometrioid carcinomas may arise from endometriosis, but the molecular events involved in this transformation have not been described. METHODS: We sequenced the whole transcriptomes of 18 ovarian clear-cell carcinomas and 1 ovarian clear-cell carcinoma cell line and found somatic mutations in ARID1A (the AT-rich interactive domain 1A [SWI-like] gene) in 6 of the samples. ARID1A encodes BAF250a, a key component of the SWI­SNF chromatin remodeling complex. We sequenced ARID1A in an additional 210 ovarian carcinomas and a second ovarian clear-cell carcinoma cell line and measured BAF250a expression by means of immunohistochemical analysis in an additional 455 ovarian carcinomas. RESULTS: ARID1A mutations were seen in 55 of 119 ovarian clear-cell carcinomas (46%), 10 of 33 endometrioid carcinomas (30%), and none of the 76 high-grade serous ovarian carcinomas. Seventeen carcinomas had two somatic mutations each. Loss of the BAF250a protein correlated strongly with the ovarian clear-cell carcinoma and endometrioid carcinoma subtypes and the presence of ARID1A mutations. In two patients, ARID1A mutations and loss of BAF250a expression were evident in the tumor and contiguous atypical endometriosis but not in distant endometriotic lesions. CONCLUSIONS: These data implicate ARID1A as a tumor-suppressor gene frequently disrupted in ovarian clear-cell and endometrioid carcinomas. Since ARID1A mutation and loss of BAF250a can be seen in the preneoplastic lesions, we speculate that this is an early event in the transformation of endometriosis into cancer. (Funded by the British Columbia Cancer Foundation and the Vancouver General Hospital­University of British Columbia Hospital Foundation.).

Detection of pathogenic copy number variants in children with idiopathic intellectual disability using 500 K SNP array genomic hybridization.

BACKGROUND: Array genomic hybridization is being used clinically to detect pathogenic copy number variants in children with intellectual disability and other birth defects. However, there is no agreement regarding the kind of array, the distribution of probes across the genome, or the resolution that is most appropriate for clinical use. RESULTS: We performed 500 K Affymetrix GeneChip array genomic hybridization in 100 idiopathic intellectual disability trios, each comprised of a child with intellectual disability of unknown cause and both unaffected parents. We found pathogenic genomic imbalance in 16 of these 100 individuals with idiopathic intellectual disability. In comparison, we had found pathogenic genomic imbalance in 11 of 100 children with idiopathic intellectual disability in a previous cohort who had been studied by 100 K GeneChip array genomic hybridization. Among 54 intellectual disability trios selected from the previous cohort who were re-tested with 500 K GeneChip array genomic hybridization, we identified all 10 previously-detected pathogenic genomic alterations and at least one additional pathogenic copy number variant that had not been detected with 100 K GeneChip array genomic hybridization. Many benign copy number variants, including one that was de novo, were also detected with 500 K array genomic hybridization, but it was possible to distinguish the benign and pathogenic copy number variants with confidence in all but 3 (1.9%) of the 154 intellectual disability trios studied. CONCLUSION: Affymetrix GeneChip 500 K array genomic hybridization detected pathogenic genomic imbalance in 10 of 10 patients with idiopathic developmental disability in whom 100 K GeneChip array genomic hybridization had found genomic imbalance, 1 of 44 patients in whom 100 K GeneChip array genomic hybridization had found no abnormality, and 16 of 100 patients who had not previously been tested. Effective clinical interpretation of these studies requires considerable skill and experience.

Use of Affymetrix mapping arrays in the diagnosis of gene copy number variation.

Diagnosing constitutional pathogenic copy number variants requires measuring the submicroscopic segmental chromosomal imbalance. The Affymetrix GeneChip mapping array has been used to measure duplication and deletion of genetic material in DNA samples. This protocol describes the measurement and analysis processes, specifically the computational analyses that are involved in identifying pathogenic copy number variants.

Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data.

BACKGROUND: Genomic deletions and duplications are important in the pathogenesis of diseases, such as cancer and mental retardation, and have recently been shown to occur frequently in unaffected individuals as polymorphisms. Affymetrix GeneChip whole genome sampling analysis (WGSA) combined with 100 K single nucleotide polymorphism (SNP) genotyping arrays is one of several microarray-based approaches that are now being used to detect such structural genomic changes. The popularity of this technology and its associated open source data format have resulted in the development of an increasing number of software packages for the analysis of copy number changes using these SNP arrays. RESULTS: We evaluated four publicly available software packages for high throughput copy number analysis using synthetic and empirical 100 K SNP array data sets, the latter obtained from 107 mental retardation (MR) patients and their unaffected parents and siblings. We evaluated the software with regards to overall suitability for high-throughput 100 K SNP array data analysis, as well as effectiveness of normalization, scaling with various reference sets and feature extraction, as well as true and false positive rates of genomic copy number variant (CNV) detection. CONCLUSION: We observed considerable variation among the numbers and types of candidate CNVs detected by different analysis approaches, and found that multiple programs were needed to find all real aberrations in our test set. The frequency of false positive deletions was substantial, but could be greatly reduced by using the SNP genotype information to confirm loss of heterozygosity.

Novel deletions of 14q11.2 associated with developmental delay, cognitive impairment and similar minor anomalies in three children.

METHODS AND RESULTS: We identified de novo submicroscopic chromosome 14q11.2 deletions in two children with idiopathic developmental delay and cognitive impairment. Vancouver patient 5566 has a approximately 200 kb deletion and Vancouver patient 8326 has a approximately 1.6 Mb deletion. The Database of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources (DECIPHER) revealed a third patient with idiopathic developmental delay and cognitive impairment, DECIPHER patient 126, who has a approximately 1.1 Mb deletion of 14q11.2. The deletion of patient 5566 overlaps that of patient 126 and both of these deletions lie entirely within that of patient 8326. All three children have similar dysmorphic features, including widely-spaced eyes, short nose with flat nasal bridge, long philtrum, prominent Cupid's bow of the upper lip, full lower lip and similar auricular anomalies. CONCLUSION: The minimal common deletion region on chromosome 14q11.2 is only approximately 35 kb (from 20.897 to 20.932, University of California at Santa Cruz (UCSC) Genome Browser; build hg18, March 2006) and includes only two genes, SUPT16H and CHD8, which are good candidate genes for the phenotypes. The non-recurrent breakpoints of these patients, the presence of normal copy number variants in the region and the local genomic structure support the notion that this region has reduced stability.

Novel expressed sequences identified in a model of androgen independent prostate cancer.

BACKGROUND: Prostate cancer is the most frequently diagnosed cancer in American men, and few effective treatment options are available to patients who develop hormone-refractory prostate cancer. The molecular changes that occur to allow prostate cells to proliferate in the absence of androgens are not fully understood. RESULTS: Subtractive hybridization experiments performed with samples from an in vivo model of hormonal progression identified 25 expressed sequences representing novel human transcripts. Intriguingly, these 25 sequences have small open-reading frames and are not highly conserved through evolution, suggesting many of these novel expressed sequences may be derived from untranslated regions of novel transcripts or from non-coding transcripts. Examination of a large metalibrary of human Serial Analysis of Gene Expression (SAGE) tags demonstrated that only three of these novel sequences had been previously detected. RT-PCR experiments confirmed that the 6 sequences tested were expressed in specific human tissues, as well as in clinical samples of prostate cancer. Further RT-PCR experiments for five of these fragments indicated they originated from large untranslated regions of unannotated transcripts. CONCLUSION: This study underlines the value of using complementary techniques in the annotation of the human genome. The tissue-specific expression of 4 of the 6 clones tested indicates the expression of these novel transcripts is tightly regulated, and future work will determine the possible role(s) these novel transcripts may play in the progression of prostate cancer.

Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines.

We describe the details of a serial analysis of gene expression (SAGE) library construction and analysis platform that has enabled the generation of >298 high-quality SAGE libraries and >30 million SAGE tags primarily from sub-microgram amounts of total RNA purified from samples acquired by microdissection. Several RNA isolation methods were used to handle the diversity of samples processed, and various measures were applied to minimize ditag PCR carryover contamination. Modifications in the SAGE protocol resulted in improved cloning and DNA sequencing efficiencies. Bioinformatic measures to automatically assess DNA sequencing results were implemented to analyze the integrity of ditag structure, linker or cross-species ditag contamination, and yield of high-quality tags per sequence read. Our analysis of singleton tag errors resulted in a method for correcting such errors to statistically determine tag accuracy. From the libraries generated, we produced an essentially complete mapping of reliable 21-base-pair tags to the mouse reference genome sequence for a meta-library of approximately 5 million tags. Our analyses led us to reject the commonly held notion that duplicate ditags are artifacts. Rather than the usual practice of discarding such tags, we conclude that they should be retained to avoid introducing bias into the results and thereby maintain the quantitative nature of the data, which is a major theoretical advantage of SAGE as a tool for global transcriptional profiling.

Oligonucleotide microarray analysis of genomic imbalance in children with mental retardation.

The cause of mental retardation in one-third to one-half of all affected individuals is unknown. Microscopically detectable chromosomal abnormalities are the most frequently recognized cause, but gain or loss of chromosomal segments that are too small to be seen by conventional cytogenetic analysis has been found to be another important cause. Array-based methods offer a practical means of performing a high-resolution survey of the entire genome for submicroscopic copy-number variants. We studied 100 children with idiopathic mental retardation and normal results of standard chromosomal analysis, by use of whole-genome sampling analysis with Affymetrix GeneChip Human Mapping 100K arrays. We found de novo deletions as small as 178 kb in eight cases, de novo duplications as small as 1.1 Mb in two cases, and unsuspected mosaic trisomy 9 in another case. This technology can detect at least twice as many potentially pathogenic de novo copy-number variants as conventional cytogenetic analysis can in people with mental retardation.

Sequence biases in large scale gene expression profiling data.

We present the results of a simple, statistical assay that measures the G+C content sensitivity bias of gene expression experiments without the requirement of a duplicate experiment. We analyse five gene expression profiling methods: Affymetrix GeneChip, Long Serial Analysis of Gene Expression (LongSAGE), LongSAGELite, 'Classic' Massively Parallel Signature Sequencing (MPSS) and 'Signature' MPSS. We demonstrate the methods have systematic and random errors leading to a different G+C content sensitivity. The relationship between this experimental error and the G+C content of the probe set or tag that identifies each gene influences whether the gene is detected and, if detected, the level of gene expression measured. LongSAGE has the least bias, while Signature MPSS shows a strong bias to G+C rich tags and Affymetrix data show different bias depending on the data processing method (MAS 5.0, RMA or GC-RMA). The bias in the Affymetrix data primarily impacts genes expressed at lower levels. Despite the larger sampling of the MPSS library, SAGE identifies significantly more genes (60% more RefSeq genes in a single comparison).

A mouse atlas of gene expression: large-scale digital gene-expression profiles from precisely defined developing C57BL/6J mouse tissues and cells.

We analyzed 8.55 million LongSAGE tags generated from 72 libraries. Each LongSAGE library was prepared from a different mouse tissue. Analysis of the data revealed extensive overlap with existing gene data sets and evidence for the existence of approximately 24,000 previously undescribed genomic loci. The visual cortex, pancreas, mammary gland, preimplantation embryo, and placenta contain the largest number of differentially expressed transcripts, 25% of which are previously undescribed loci.