Array-based comparative genomic hybridization and copy number variation in cancer research

Genomic aspects in reproductive medicine

Infertility is a complex disease characterized by extreme genetic heterogeneity, compounded by various environmental factors. While there are exceptions, individual genetic and genomic variations related to infertility are typically rare, often family-specific, and may serve as susceptibility factors rather than direct causes of the disease. Consequently, identifying the cause of infertility and developing prevention and treatment strategies based on these factors remain challenging tasks, even in the modern genomic era. In this review, we first examine the genetic and genomic variations associated with infertility, and subsequently summarize the concepts and methods of preimplantation genetic testing in light of advances in genome analysis technology.

Preimplantation genetic testing: A remarkable history of pioneering, technical challenges, innovations, and ethical considerations

Preimplantation genetic testing (PGT) has emerged as a powerful companion to assisted reproduction technologies. The origins and history of PGT are reviewed here, along with descriptions of advances in molecular assays and sampling methods, their capabilities, and their applications in preventing genetic diseases and enhancing pregnancy outcomes. Additionally, the potential for increasing accuracy and genome coverage is considered, as well as some of the emerging ethical and legislative considerations related to the expanding capabilities of PGT.

DeepCNV: a deep learning approach for authenticating copy number variations

Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably high false positive rate. A common practice is to have human experts manually review original CNV calls for filtering false positives before further downstream analysis or experimental validation. Here, we propose DeepCNV, a deep learning-based tool, intended to replace human experts when validating CNV calls, focusing on the calls made by one of the most accurate CNV callers, PennCNV. The sophistication of the deep neural network algorithm is enriched with over 10 000 expert-scored samples that are split into training and testing sets. Variant confidence, especially for CNVs, is a main roadblock impeding the progress of linking CNVs with the disease. We show that DeepCNV adds to the confidence of the CNV calls with an optimal area under the receiver operating characteristic curve of 0.909, exceeding other machine learning methods. The superiority of DeepCNV was also benchmarked and confirmed using an experimental wet-lab validation dataset. We conclude that the improvement obtained by DeepCNV results in significantly fewer false positive results and failures to replicate the CNV association results.

Comprehensive Next-Generation Sequencing Unambiguously Distinguishes Separate Primary Lung Carcinomas From Intrapulmonary Metastases: Comparison with Standard Histopathologic Approach

PURPOSE

In patients with >1 non-small cell lung carcinoma (NSCLC), the distinction between separate primary lung carcinomas (SPLCs) and intrapulmonary metastases (IPMs) is a common diagnostic dilemma with critical staging implications. Here, we compared the performance of comprehensive next-generation sequencing (NGS) with standard histopathologic approaches for distinguishing NSCLC clonal relationships in clinical practice.

EXPERIMENTAL DESIGN

We queried 4,119 NSCLCs analyzed by 341-468 gene MSK-IMPACT NGS assay for patients with >1 surgically resected tumor profiled by NGS. Tumor relatedness predicted by prospective histopathologic assessment was contrasted with comparative genomic profiling by subsequent NGS.

RESULTS

Sixty patients with NGS performed on >1 NSCLCs were identified, yielding 76 tumor pairs. NGS classified tumor pairs into 51 definite SPLCs (median, 14; up to 72 unique somatic mutations per pair), and 25 IPMs (24 definite, one high probability; median, 5; up to 16 shared somatic mutations per pair). Prospective histologic prediction was discordant with NGS in 17 cases (22%), particularly in the prediction of IPMs (44% discordant). Retrospective review highlighted several histologic challenges, including morphologic progression in some IPMs. We subsampled MSK-IMPACT data to model the performance of less comprehensive assays, and identified several clinicopathologic differences between NGS-defined tumor pairs, including increased risk of subsequent recurrence for IPMs.

CONCLUSIONS

Comprehensive NGS allows unambiguous delineation of clonal relationship among NSCLCs. In comparison, standard histopathologic approach is adequate in most cases, but has notable limitations in the recognition of IPMs. Our results support the adoption of broad panel NGS to supplement histology for robust discrimination of NSCLC clonal relationships in clinical practice.

Multiple primary lung cancer: a rising challenge

With the use of high-resolution chest imaging system and lung cancer screening program, patients with multiple primary lung cancers (MPLCs) are becoming a growing population in clinical practice worldwide. The diagnostic criteria for MPLCs has been established and modified by three major lung cancer research institutes. However, due to the fact that the differential diagnosis between MPLCs and a recurrence, metastatic, or satellite lesion arising from the original lesion remains ambiguous and confusing, there is still insufficient evidence to support a uniform guideline. Newly developed molecular and genomic methods have the potential to better define the relationship among multiple lesions and bring the possibility of targeted therapy. Surgical resection remains the first choice for the treatment of MPLCs and detailed strategy should be carefully planned taking characteristics of the tumor and status of patients into consideration. For those who are intolerant to surgery, a new technology called stereotactic body radiation therapy (SBRT) is now an optional therapeutic strategy. Furthermore, multiple GGOs are unique MPLCs that need special attentions in the clinical practice.

Nucks1 synergizes with Trp53 to promote radiation lymphomagenesis in mice

NUCKS1 is a 27 kD vertebrate-specific protein, with a role in the DNA damage response. Here, we show that after 4 Gy total-body X-irradiation, Trp53+/− Nucks1+/− mice more rapidly developed tumors, particularly thymic lymphoma (TL), than Trp53+/− mice. TLs in both cohorts showed loss of heterozygosity (LOH) of the Trp53+ allele in essentially all cases. In contrast, LOH of the Nucks1+ allele was rare. Nucks1 expression correlated well with Nucks1 gene dosage in normal thymi, but was increased in the majority of TLs from Trp53+/− Nucks1+/− mice, suggesting that elevated Nucks1 message may be associated with progression towards malignancy in vivo. Trp53+/− Nucks1+/− mice frequently succumbed to CD4- CD8- TLs harboring translocations involving Igh but not Tcra/d, indicating TLs in Trp53+/− Nucks1+/− mice mostly originated prior to the double positive stage and at earlier lineage than TLs in Trp53+/- mice. Monoclonal rearrangements at Tcrb were more prevalent in TLs from Trp53+/− Nucks1+/− mice, as was infiltration of primary TL cells to distant organs (liver, kidney and spleen). We propose that, in the context of Trp53 deficiency, wild type levels of Nucks1 are required to suppress radiation-induced TL, likely through the role of the NUCKS1 protein in the DNA damage response.

Chromosome-scale mega-haplotypes enable digital karyotyping of cancer aneuploidy

Genomic instability is a frequently occurring feature of cancer that involves large-scale structural alterations. These somatic changes in chromosome structure include duplication of entire chromosome arms and aneuploidy where chromosomes are duplicated beyond normal diploid content. However, the accurate determination of aneuploidy events in cancer genomes is a challenge. Recent advances in sequencing technology allow the characterization of haplotypes that extend megabases along the human genome using high molecular weight (HMW) DNA. For this study, we employed a library preparation method in which sequence reads have barcodes linked to single HMW DNA molecules. Barcode-linked reads are used to generate extended haplotypes on the order of megabases. We developed a method that leverages haplotypes to identify chromosomal segmental alterations in cancer and uses this information to join haplotypes together, thus extending the range of phased variants. With this approach, we identified mega-haplotypes that encompass entire chromosome arms. We characterized the chromosomal arm changes and aneuploidy events in a manner that offers similar information as a traditional karyotype but with the benefit of DNA sequence resolution. We applied this approach to characterize aneuploidy and chromosomal alterations from a series of primary colorectal cancers.

Pyruvate Kinase and Fcγ Receptor Gene Copy Numbers Associated With Malaria Phenotypes

Genetic factors are associated with susceptibility to many infectious diseases and may be determinants of clinical progression. Gene copy number variation (CNV) has been shown to be associated with phenotypes of numerous diseases, including malaria. We quantified gene copy numbers of the pyruvate kinase, liver, and red blood cell (PKLR) gene as well as of the Fcγ receptor 2A and Fcγ receptor 2C (FCGR2A, FCGR2C) and Fcγ receptor 3 (FCGR3) genes using real-time quantitative polymerase chain reaction (RT-qPCR) assays in Gabonese children with severe (n = 184) or and mild (n = 189) malaria and in healthy Gabonese and white individuals (n = 76 each). The means of PKLR, FCGR2A, FCGR2C, and FCGR3 copy numbers were significantly higher among children with severe malaria compared to those with mild malaria (P < .002), indicating that a surplus of copies of those genes is significantly associated with malaria severity. Copy numbers of the FCGR2A and FCGR2C genes were significantly lower (P = .005) in Gabonese individuals compared with white individuals. In conclusion, CNV of the PKLR, FCGR2A, FCGR2C, and FCGR3 genes is associated with malaria severity, and our results provide evidence for a role of CNV in host responses to malaria.

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings.

EML4-ALK translocation in both metachronous second primary lung sarcomatoid carcinoma and lung adenocarcinoma: a case report

The EML4-ALK gene translocation was described in a non small cell lung cancer (NSCLC) subset, with a potent oncogenic activity. It represents one of the newest molecular targets in NSCLC. We report on the case of a metachronous second primary lung sarcomatoid carcinoma after resection of lung adenocarcinoma both with ALK translocation, in a non-smoking patient. EML4-ALK rearrangement was detected with immunohistochemistry and confirmed with fluorescent in situ hybridization (FISH). To assess the clonal relationship between the two tumors, both adenocarcinoma and sarcomatoid carcinoma were analyzed by array comparative genomic hybridization (aCGH). We observed different genomic profiles suggesting that the tumors arose independently and were thus multiple primaries. To the best of our knowledge, this is the first report concerning the presence of the EML4-ALK fusion gene in a sarcomatoid carcinoma of the lung. Crizotinib, the ALK tyrosine kinase inhibitor, is highly effective in ALK-rearranged NSCLC; therefore, it may be imperative to identify all NSCLC that harbor ALK translocations in the near future. Starting from our evidence, tumors with sarcomatoid histology may need to be screened for the presence of EML4-ALK rearrangement.

Common polymorphisms in angiogenesis

A wide variety of diseases have a significant genetic component, including major causes of morbidity and mortality in the western world. Many of these diseases are also angiogenesis dependent. In humans, common polymorphisms, although more subtle in effect than rare mutations that cause Mendelian disease, are expected to have greater overall effects on human disease. Thus, common polymorphisms in angiogenesis-regulating genes may affect the response to an angiogenic stimulus and thereby affect susceptibility to or progression of such diseases. Candidate gene studies have identified several associations between angiogenesis gene polymorphisms and disease. Similarly, emerging pharmacogenomic evidence indicates that several angiogenesis-regulating polymorphisms may predict response to therapy. In contrast, genome-wide association studies have identified only a few risk alleles in obvious angiogenesis genes. As in other traits, regulatory polymorphisms appear to dominate the landscape of angiogenic responsiveness. Rodent assays, including the mouse corneal micropocket assay, tumor models, and a macular degeneration model have allowed the identification and comparison of loci that directly affect the trait. Complementarity between human and animal approaches will allow increased understanding of the genetic basis for angiogenesis-dependent disease.

Bioinformatics/biostatistics: microarray analysis

The quantity and complexity of the molecular-level data generated in both research and clinical settings require the use of sophisticated, powerful computational interpretation techniques. It is for this reason that bioinformatic analysis of complex molecular profiling data has become a fundamental technology in the development of personalized medicine. This chapter provides a high-level overview of the field of bioinformatics and outlines several, classic bioinformatic approaches. The highlighted approaches can be aptly applied to nearly any sort of high-dimensional genomic, proteomic, or metabolomic experiments. Reviewed technologies in this chapter include traditional clustering analysis, the Gene Expression Dynamics Inspector (GEDI), GoMiner (GoMiner), Gene Set Enrichment Analysis (GSEA), and the Learner of Functional Enrichment (LeFE).

Genetic variation in phosphodiesterase (PDE) 7B in chronic lymphocytic leukemia: overview of genetic variants of cyclic nucleotide PDEs in human disease

Expression of cyclic adenosine monophosphate-specific phosphodiesterase 7B (PDE7B) mRNA is increased in patients with chronic lymphocytic leukemia (CLL), thus suggesting that variation may occur in the PDE7B gene in CLL. As genetic variation in other PDE family members has been shown to associate with numerous clinical disorders (reviewed in this manuscript), we sought to identify single-nucleotide polymorphisms (SNPs) in the PDE7B gene promoter and coding region of 93 control subjects and 154 CLL patients. We found that the PDE7B gene has a 5' non-coding region SNP -347C>T that occurs with similar frequency in CLL patients (1.9%) and controls (2.7%). Tested in vitro, -347C>T has less promoter activity than a wild-type construct. The low frequency of this 5' untranslated region variant indicates that it does not explain the higher PDE7B expression in patients with CLL but it has the potential to influence other settings that involve a role for PDE7B.

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.

Copy number increase of HER-2 in colorectal cancers

HER-2 is involved in genetic instability and is overexpressed in a number of human carcinomas, including colorectal cancer (CRC). The choromosomal locus of HER-2, 17q21, is frequently amplified in breast cancer, but the correlation between copy-number variations and HER-2 overexpression in CRC has yet to be elucidated. The functional impact of such regions requires extensive investigation in large numbers of CRC samples. Case-matched tissues of colorectal adenocarcinomas and adjacent normal epithelia (n=134) were included in this study. Quantitative PCR was performed to examine the copy number and mRNA expression of HER-2 in CRC. The results showed that copy number gains of HER-2 were detected in a relatively high percentage of CRC samples (35.1%, 47 out of 134). A positive correlation was noted between the copy number increase of HER-2 and tumor progression. Furthermore, copy number gains of HER-2 showed a positive correlation with mRNA overexpression in CRC. However, the expression levels of HER-2 mRNA were also enhanced in the group of CRC samples with unaltered copy numbers. In conclusion, the findings suggest that a copy number increase of HER-2 is a potential diagnostic indicator for CRC; whether alone or in combination with other markers.

A comprehensive genetic analysis of candidate genes regulating response to Trypanosoma congolense infection in mice

Background

African trypanosomes are protozoan parasites that cause “sleeping sickness” in humans and a similar disease in livestock. Trypanosomes also infect laboratory mice and three major quantitative trait loci (QTL) that regulate survival time after infection with T. congolense have been identified in two independent crosses between susceptible A/J and BALB/c mice, and the resistant C57BL/6. These were designated Tir1, Tir2 and Tir3 for Trypanosoma infection response, and range in size from 0.9–12 cM.

Principal Findings

Mapping loci regulating survival time after T. congolense infection in an additional cross revealed that susceptible C3H/HeJ mice have alleles that reduce survival time after infection at Tir1 and Tir3 QTL, but not at Tir2. Next-generation resequencing of a 6.2 Mbp region of mouse chromosome 17, which includes Tir1, identified 1,632 common single nucleotide polymorphisms (SNP) including a probably damaging non-synonymous SNP in Pram1 (PML-RAR alpha-regulated adaptor molecule 1), which was the most plausible candidate QTL gene in Tir1. Genome-wide comparative genomic hybridisation identified 12 loci with copy number variants (CNV) that correlate with differential gene expression, including Cd244 (natural killer cell receptor 2B4), which lies close to the peak of Tir3c and has gene expression that correlates with CNV and phenotype, making it a strong candidate QTL gene at this locus.

Conclusions

By systematically combining next-generation DNA capture and sequencing, array-based comparative genomic hybridisation (aCGH), gene expression data and SNP annotation we have developed a strategy that can generate a short list of polymorphisms in candidate QTL genes that can be functionally tested.

About one-third of cattle in sub-Saharan Africa are at risk of contracting “Nagana”—a disease caused by Trypanosoma parasites similar to those that cause human “Sleeping Sickness.” Laboratory mice can also be infected by trypanosomes, and different mouse breeds show varying levels of susceptibility to infection, similar to what is seen between different breeds of cattle. Survival time after infection is controlled by the underlying genetics of the mouse breed, and previous studies have localised three genomic regions that regulate this trait. These three “Quantitative Trait Loci” (QTL), which have been called Tir1, Tir2 and Tir3 (for Trypanosoma Infection Response 1–3) are well defined, but nevertheless still contain over one thousand genes, any number of which may be influencing survival. This study has aimed to identify the specific differences associated with genes that are controlling mouse survival after T. congolense infection. We have applied a series of analyses to existing datasets, and combined them with novel sequencing, and other genetic data to create short lists of genes that share polymorphisms across susceptible mouse breeds, including two promising “candidate genes”: Pram1 at Tir1 and Cd244 at Tir3. These genes can now be tested to confirm their effect on response to trypanosome infection.

Detecting copy number variations from array CGH data based on a conditional random field model

Array comparative genomic hybridization (aCGH) allows identification of copy number alterations across genomes. The key computational challenge in analyzing copy number variations (CNVs) using aCGH data or other similar data generated by a variety of array technologies is the detection of segment boundaries of copy number changes and inference of the copy number state for each segment. We have developed a novel statistical model based on the framework of conditional random fields (CRFs) that can effectively combine data smoothing, segmentation and copy number state decoding into one unified framework. Our approach (termed CRF-CNV) provides great flexibilities in defining meaningful feature functions. Therefore, it can effectively integrate local spatial information of arbitrary sizes into the model. For model parameter estimations, we have adopted the conjugate gradient (CG) method for likelihood optimization and developed efficient forward/backward algorithms within the CG framework. The method is evaluated using real data with known copy numbers as well as simulated data with realistic assumptions, and compared with two popular publicly available programs. Experimental results have demonstrated that CRF-CNV outperforms a Bayesian Hidden Markov Model-based approach on both datasets in terms of copy number assignments. Comparing to a non-parametric approach, CRF-CNV has achieved much greater precision while maintaining the same level of recall on the real data, and their performance on the simulated data is comparable.

A classification model for distinguishing copy number variants from cancer-related alterations

BACKGROUND

Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.

RESULTS

We develop a prediction model that distinguishes CNVs from CNAs based on the information contained in the DGV and several other variables, including segment's length, height, closeness to a telomere or centromere and occurrence in other patients. The models are fitted on data from glioblastoma and their corresponding normal samples that were collected as part of The Cancer Genome Atlas project and hybridized to Agilent 244 K arrays.

CONCLUSIONS

Using the DGV alone CNVs in the test set can be correctly identified with about 85% accuracy if the outliers are removed before segmentation and with 72% accuracy if the outliers are included, and additional variables improve the prediction by about 2-3% and 12%, respectively. Final models applied to data from ovarian tumors have about 90% accuracy with all the variables and 86% accuracy with the DGV alone.

New trends in molecular biomarker discovery for breast cancer

Breast cancer is one of the most common and leading causes of cancer death in women. Early diagnosis, selection of appropriate therapeutic strategies, and efficient follow-up play an important role in reducing mortality. Recently, HER-2/neu in breast cancer has been routinely used to guide treatment of using Trastuzumab in less than 25-30% of patients. More new biomarkers will be still expected in the future to tailor treatments. However, there are still many obstacles in developing clinically useful biomarker tests for clinical practice. A lack of specificity of tumor markers and lack of sensitivity of testing systems have been noticed, which limit their clinical use. Finding biomarkers for breast cancer could allow physicians to identify individuals who are susceptible to certain types and stages of cancer to tailor preventive and therapeutic modalities based on the genotype and phenotype information. These biomarkers should be cancer specific, and sensitively detectable in a wide range of specimen(s) containing cancer-derived materials, including body fluids (plasma, serum, urine, saliva, etc.), tissues, and cell lines. This review highlights the new trends and approaches in breast cancer biomarker discovery, which could be potentially used for early diagnosis, development of new therapeutic approaches, and follow-up of patients.

Genomic and mutational profiling to assess clonal relationships between multiple non-small cell lung cancers

PURPOSE

In cases of multiple non-small cell lung cancer, clinicians must decide whether patients have independent tumors or metastases and tailor treatment accordingly. Decisions are currently made using the Martini and Melamed criteria, which are mostly based on tumor location and histologic type. New genomic tools could improve the ability to assess tumor clonality.

EXPERIMENTAL DESIGN

We obtained fresh-frozen tumors specimens from patients who underwent surgery on at least two occasions for presumptively independent NSCLC. We did array comparative genomic hybridization (aCGH), mutational profiling of select genes, and detailed clinicopathologic review.

RESULTS

We analyzed a total of 42 tumors from 20 patients (6 patients with synchronous tumors, 14 patients with metachronous tumors, 24 potential tumor pair comparisons); 22 tumor pairs were evaluable by aCGH. Surprisingly, classification based on genomic profiling contradicted the clinicopathologic diagnosis in four (18%) of the comparisons, identifying independent primaries in one case diagnosed as metastasis and metastases in three cases diagnosed as independent primaries. Matching somatic point mutations were observed in these latter three cases. Another four tumor pairings were assigned an "equivocal" result based on aCGH; however, matching somatic point mutations were also found in these tumor pairs. None of the tumor pairs deemed independent primaries by aCGH harbored matching mutations.

CONCLUSION

Genomic analysis can help distinguish clonal tumors from independent primaries. The development of rapid, inexpensive, and reliable molecular tools may allow for refinement of clinicopathologic criteria currently used in this setting.

Complex genetics of obesity in mouse models

Traits related to energy balance and obesity are exceptionally complex, with varying contributions of genetic susceptibility and interacting environmental factors. The use of mouse models has been a powerful driving force in understanding the genetic architecture of polygenic traits such as obesity. However, the use of mouse models for analysis of complex traits is at an important crossroad. Genome-wide association studies in humans are now leading to direct identification of obesity genes. In this review, we focus on three areas representing the current and future roles of mouse models regarding genetics of complex obesity. First, we summarize increasingly powerful ways to harness the strength of mouse models for discovery of genes affecting polygenic obesity. Second, we examine the status of using a systems biology approach to dissect the genetic architecture of obesity. And third, we explore the effects of recent findings indicating increasing levels of complexity in the nature of variation underlying, and the heritability of, complex traits such as obesity.

Whole-genome scanning by array comparative genomic hybridization as a clinical tool for risk assessment in chronic lymphocytic leukemia

Array-based comparative genomic hybridization (array CGH) provides a powerful method for simultaneous genome-wide scanning and prognostic marker assessment in chronic lymphocytic leukemia (CLL). In the current study, commercially available bacterial artificial chromosome and oligonucleotide array CGH platforms were used to identify chromosomal alterations of prognostic significance in 174 CLL cases. Tumor genomes were initially analyzed by bacterial artificial chromosome array CGH followed by confirmation and breakpoint mapping using oligonucleotide arrays. Genomic changes involving loci currently interrogated by fluorescence in situ hybridization (FISH) panels were detected in 155 cases (89%) at expected frequencies: 13q14 loss (47%), trisomy 12 (13%), 11q loss (11%), 6q loss (7.5%), and 17p loss (4.6%). Genomic instability was the second most commonly identified alteration of prognostic significance with three or more alterations involving loci not interrogated by FISH panels identified in 37 CLL cases (21%). A subset of 48 CLL cases analyzed by six-probe FISH panels (288 total hybridizations) was concordant with array CGH results for 275 hybridizations (95.5%); 13 hybridizations (4.5%) were discordant because of clonal populations that comprised less than 30% of the sample. Array CGH is a powerful, cost-effective tool for genome-wide risk assessment in the clinical evaluation of CLL.

Analysis of changes in DNA copy number in radiation-induced thymic lymphomas of susceptible C57BL/6, resistant C3H and hybrid F1 Mice

Radiation-induced thymic lymphoma in mice is a useful model for studying both the mechanism of radiation carcinogenesis and genetic susceptibility to tumor development. Using array-comparative genomic hybridization, we analyzed genome-wide changes in DNA copy numbers in radiation-induced thymic lymphomas that had developed in susceptible C57BL/6 and resistant C3H mice and their hybrids, C3B6F1 and B6C3F1 mice. Besides aberrations at known relevant genetic loci including Ikaros and Bcl11b and trisomy of chromosome 15, we identified strain-associated genomic imbalances on chromosomes 5, 10 and 16 and strain-unassociated trisomy of chromosome 14 as frequent aberrations. In addition, biallelic rearrangements at Tcrb were detected more frequently in tumors from C57BL/6 mice than in those from C3H mice, suggesting aberrant V(D)J recombination and a possible link with tumor susceptibility. The frequency and spectrum of these copy-number changes in lymphomas from C3B6F1 and B6C3F1 mice were similar to those in C57BL/6 mice. Furthermore, the loss of heterozygosity analyses of tumors in F(1) mice indicated that allelic losses at Ikaros and Bcl11b were caused primarily by multilocus deletions, whereas those at the Cdkn2a/Cdkn2b and Pten loci were due mainly to uniparental disomy. These findings provide important clues to both the mechanisms for accumulation of aberrations during radiation-induced lymphomagenesis and the different susceptibilities of C57BL/6 and C3H mice.

Microarray-based global mapping of integration sites for the retrotransposon, intracisternal A-particle, in the mouse genome

Mammalian genomes contain numerous evolutionary harbored mobile elements, a part of which are still active and may cause genomic instability. Their movement and positional diversity occasionally result in phenotypic changes and variation by causing altered expression or disruption of neighboring host genes. Here, we describe a novel microarray-based method by which dispersed genomic locations of a type of retrotransposon in a mammalian genome can be identified. Using this method, we mapped the DNA elements for a mouse retrotransposon, intracisternal A-particle (IAP), within genomes of C3H/He and C57BL/6J inbred mouse strains; consequently we detected hundreds of probable IAP cDNA-integrated genomic regions, in which a considerable number of strain-specific putative insertions were included. In addition, by comparing genomic DNAs from radiation-induced myeloid leukemia cells and its reference normal tissue, we detected three genomic regions around which an IAP element was integrated. These results demonstrate the first successful genome-wide mapping of a retrotransposon type in a mammalian genome.

Genome-wide association studies of cancer

Genome-wide association studies provide a new and powerful approach to investigate the effect of inherited genetic variation on the risk of human disease. These studies rely on high throughput DNA microarray technology to genotype hundreds of thousands of genetic variants across the human genome. The first genome-wide association studies have identified previously unknown genetic risk factors that influence a range of diseases, including prostate cancer, breast cancer, myocardial infarction, age-related macular degeneration, diabetes, Crohn's disease and obesity. Many more studies are currently underway, including a number that will focus on other cancers (e.g., colorectal). Here we discuss the major issues involved in conducting genome-wide association studies and how these studies can be used to examine cancer phenotypes.

Integrative analysis of genomic aberrations associated with prostate cancer progression

Integrative analysis of genomic aberrations in the context of trancriptomic alterations will lead to a more comprehensive perspective on prostate cancer progression. Genome-wide copy number changes were monitored using array comparative genomic hybridization of laser-capture microdissected prostate cancer samples spanning stages of prostate cancer progression, including precursor lesions, clinically localized disease, and metastatic disease. A total of 62 specific cell populations from 38 patients were profiled. Minimal common regions (MCR) of alterations were defined for each sample type, and metastatic samples displayed the most number of alterations. Clinically localized prostate cancer samples with high Gleason grade resembled metastatic samples with respect to the size of altered regions and number of affected genes. A total of 9 out of 13 MCRs in the putative precursor lesion, high-grade prostatic intraepithelial neoplasia (PIN), showed an overlap with prostate cancer cases (amplifications in 3q29, 5q31.3-q32, 6q27, and 8q24.3 and deletions in 6q22.31, 16p12.2, 17q21.2, and 17q21.31), whereas postatrophic hyperplasia (PAH) did not exhibit this overlap. Interestingly, prostate cancers that do not overexpress ETS family members (i.e., gene fusion-negative prostate cancers) harbor differential aberrations in 1q23, 6q16, 6q21, 10q23, and 10q24. Integrative analysis with matched mRNA profiles identified genetic alterations in several proposed candidate genes implicated in prostate cancer progression.

Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability

A considerable and unanticipated plasticity of the human genome, manifested as inter-individual copy number variation, has been discovered. These structural changes constitute a major source of inter-individual genetic variation that could explain variable penetrance of inherited (Mendelian and polygenic) diseases and variation in the phenotypic expression of aneuploidies and sporadic traits, and might represent a major factor in the aetiology of complex, multifactorial traits. For these reasons, an effort should be made to discover all common and rare copy number variants (CNVs) in the human population. This will also enable systematic exploration of both SNPs and CNVs in association studies to identify the genomic contributors to the common disorders and complex traits.