SNP arrays in heterogeneous tissue: highly accurate collection of both germline and somatic genetic information from unpaired single tumor samples

Detection of DNA copy number alterations by matrix-assisted laser desorption/ionization time-of-flight mass spectrometric analysis of single nucleotide polymorphisms

OBJECTIVES

Copy number alterations (CNAs) are frequently found in malignant tissues. Different approaches have been used for CNA detection. However, it is not easy to detect a large panel of CNA targets in heterogenous tumors.

METHODS

We have developed a CNAs detection approach through quantitatively analyzed allelic imbalance by allelotyping single nucleotide polymorphisms (SNPs) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Furthermore, the copy number changes were quantified by real-competitive PCR (rcPCR) to distinguish loss of heterozygosity (LOH) and genomic amplification. The approach was used to validate the CNA regions detected by next generation sequencing (NGS) in early-stage lung carcinoma.

RESULTS

CNAs were detected in heterogeneous DNA samples where tumor DNA is present at only 10% through the SNP based allelotyping. In addition, two different types of CNAs (loss of heterozygosity and chromosome amplification) were able to be distinguished quantitatively by rcPCR. Validation on a total of 41 SNPs from the selected CNA regions showed that copy number changes did occur, and the tissues from early-stage lung carcinoma were distinguished from normal.

CONCLUSIONS

CNA detection by MALDI-TOF MS can be used for validating potentially interesting genomic regions identified from next generation sequencing, and for detecting CNAs in tumor tissues consisting of a mixture of neoplastic and normal cells.

Elucidation of the developmental mechanism of ovarian mature cystic teratomas using B allele-frequency plots of single nucleotide polymorphism array data

Ovarian mature cystic teratomas (MCTs) originate from post-meiotic germ cells. Conventional methods such as karyotyping or short tandem repeat-polymorphism analysis may be used to better classify MCTs, although such data would be insufficient. The aim of this study was to elucidate the origin of ovarian MCTs using B allele-frequency (BAF) plots of single nucleotide polymorphism array data. MCTs can be classified in terms of the zygosity of the centromeres and distal chromosome regions. We evaluated the zygosity of all chromosomes from 38 MCT specimens using BAF plot data. BAF plots were used to determine the homozygous and heterozygous regions over the whole genome. Theoretically, MCTs originated from the fusion of two ova (previously referred to as type V MCTs) should have a mixed pattern of centromeric zygosity, that is, a combination of heterozygous and homozygous regions in the centromeric regions. However, no MCTs in this study met this criterion. We identified 13 type I MCTs, 14 type II MCTs, and 11 type III MCTs. In addition, BAF plots facilitated the construction of recombination maps at the whole-genome level for type I and II MCTs. No crossover, especially in the short arms, contributed to the failure of meiosis I, resulting in type I MCTs. Crossover in all arms might assure the normal progress of meiosis in human oocytes. In conclusion, our findings indicate that BAF plots can elucidate the developmental mechanism of MCTs, and further serve as useful analytical tools for analyzing human oocyte meiosis, and related aberrations.

Chromosomal instability analysis and regional tumor heterogeneity in colon cancer

Chromosomal instability (CIN) is classically defined as an increase in the rate at which numerical or structural chromosomal aberrations are acquired in a cancer cell. The number of somatic copy number abnormalities (CNAs) revealed by high resolution genomic array can be considered as a surrogate marker for CIN, but several points, related to sample processing and data analysis, need to be standardized. In this work we analyzed 51 CRC samples and matched normal mucosae by whole genome SNP arrays and compared different bioinformatics tools in order to identify broad (>25% of a chromosomal arm) and focal somatic copy number abnormalities (BCNAs and FCNAs respectively). In 15 tumors, two samples, separated by at least 1 cm, were taken from the same tumor mass (double-sampling pairs) in order to evaluate differences in detection of chromosomal abnormalities between distant regions of the same tumor and their influence on CIN quantitative and qualitative analysis. Our data show a high degree of correlation of the quantitative CIN index (somatic BCNA number) between distant tumor regions. On the contrary, a lower correlation is observed in terms of chromosomal distribution of BCNAs, as summarized by a simplified cytogenetic table. Quantitative or qualitative analysis of FCNAs, including homozygous deletions and high level amplifications, did not add further information on the CIN status. The use of the index "somatic BCNA number" can be proposed for a robust classification of tumors as CIN positive or negative even in the presence of a significant tumor regional heterogeneity.

Early Events in the Molecular Pathogenesis of Lung Cancer

The majority of cancer-related deaths in the United States and worldwide are attributed to lung cancer. There are more than 90 million smokers in the United States who represent a significant population at elevated risk for lung malignancy. In other epithelial tumors, it has been shown that if neoplastic lesions can be detected and treated at their intraepithelial stage, patient prognosis is significantly improved. Thus, new strategies to detect and treat lung preinvasive lesions are urgently needed in order to decrease the overwhelming public health burden of lung cancer. Limiting these advances is a poor knowledge of the earliest events that underlie lung cancer development and that would constitute markers and targets for early detection and prevention. This review summarizes the state of knowledge of human lung cancer pathogenesis and the molecular pathology of premalignant lung lesions, with a focus on the molecular premalignant field that associates with lung cancer development. Lastly, we highlight new approaches and models to study genome-wide alterations in human lung premalignancy in order to facilitate the discovery of new markers for early detection and prevention of this fatal disease. Cancer Prev Res; 9(7); 518-27. ©2016 AACR.

Allele-specific copy number profiling by next-generation DNA sequencing

The progression and clonal development of tumors often involve amplifications and deletions of genomic DNA. Estimation of allele-specific copy number, which quantifies the number of copies of each allele at each variant loci rather than the total number of chromosome copies, is an important step in the characterization of tumor genomes and the inference of their clonal history. We describe a new method, falcon, for finding somatic allele-specific copy number changes by next generation sequencing of tumors with matched normals. falcon is based on a change-point model on a bivariate mixed Binomial process, which explicitly models the copy numbers of the two chromosome haplotypes and corrects for local allele-specific coverage biases. By using the Binomial distribution rather than a normal approximation, falcon more effectively pools evidence from sites with low coverage. A modified Bayesian information criterion is used to guide model selection for determining the number of copy number events. Falcon is evaluated on in silico spike-in data and applied to the analysis of a pre-malignant colon tumor sample and late-stage colorectal adenocarcinoma from the same individual. The allele-specific copy number estimates obtained by falcon allows us to draw detailed conclusions regarding the clonal history of the individual's colon cancer.

Deciphering clonality in aneuploid breast tumors using SNP array and sequencing data

Intra-tumor heterogeneity concerns the existence of genetically different subclones within the same tumor. Single sample quantification of heterogeneity relies on precise determination of chromosomal copy numbers throughout the genome, and an assessment of whether identified mutation variant allele fractions match clonal or subclonal copy numbers. We discuss these issues using data from SNP arrays, whole exome sequencing and pathologist purity estimates on several breast cancers characterized by ERBB2 amplification. We show that chromosomal copy numbers can only be estimated from SNP array signals or sequencing depths for subclonal tumor samples with simple subclonal architectures under certain assumptions.

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.

ARMC5 mutations in macronodular adrenal hyperplasia with Cushing's syndrome

BACKGROUND

Corticotropin-independent macronodular adrenal hyperplasia may be an incidental finding or it may be identified during evaluation for Cushing's syndrome. Reports of familial cases and the involvement of both adrenal glands suggest a genetic origin of this condition.

METHODS

We genotyped blood and tumor DNA obtained from 33 patients with corticotropin-independent macronodular adrenal hyperplasia (12 men and 21 women who were 30 to 73 years of age), using single-nucleotide polymorphism arrays, microsatellite markers, and whole-genome and Sanger sequencing. The effects of armadillo repeat containing 5 (ARMC5) inactivation and overexpression were tested in cell-culture models.

RESULTS

The most frequent somatic chromosome alteration was loss of heterozygosity at 16p (in 8 of 33 patients for whom data were available [24%]). The most frequent mutation identified by means of whole-genome sequencing was in ARMC5, located at 16p11.2. ARMC5 mutations were detected in tumors obtained from 18 of 33 patients (55%). In all cases, both alleles of ARMC5 carried mutations: one germline and the other somatic. In 4 patients with a germline ARMC5 mutation, different nodules from the affected adrenals harbored different secondary ARMC5 alterations. Transcriptome-based classification of corticotropin-independent macronodular adrenal hyperplasia indicated that ARMC5 mutations influenced gene expression, since all cases with mutations clustered together. ARMC5 inactivation decreased steroidogenesis in vitro, and its overexpression altered cell survival.

CONCLUSIONS

Some cases of corticotropin-independent macronodular adrenal hyperplasia appear to be genetic, most often with inactivating mutations of ARMC5, a putative tumor-suppressor gene. Genetic testing for this condition, which often has a long and insidious prediagnostic course, might result in earlier identification and better management. (Funded by Agence Nationale de la Recherche and others.).

Genetic architecture of skewed X inactivation in the laboratory mouse

X chromosome inactivation (XCI) is the mammalian mechanism of dosage compensation that balances X-linked gene expression between the sexes. Early during female development, each cell of the embryo proper independently inactivates one of its two parental X-chromosomes. In mice, the choice of which X chromosome is inactivated is affected by the genotype of a cis-acting locus, the X-chromosome controlling element (Xce). Xce has been localized to a 1.9 Mb interval within the X-inactivation center (Xic), yet its molecular identity and mechanism of action remain unknown. We combined genotype and sequence data for mouse stocks with detailed phenotyping of ten inbred strains and with the development of a statistical model that incorporates phenotyping data from multiple sources to disentangle sources of XCI phenotypic variance in natural female populations on X inactivation. We have reduced the Xce candidate 10-fold to a 176 kb region located approximately 500 kb proximal to Xist. We propose that structural variation in this interval explains the presence of multiple functional Xce alleles in the genus Mus. We have identified a new allele, Xce^e present in Mus musculus and a possible sixth functional allele in Mus spicilegus. We have also confirmed a parent-of-origin effect on X inactivation choice and provide evidence that maternal inheritance magnifies the skewing associated with strong Xce alleles. Based on the phylogenetic analysis of 155 laboratory strains and wild mice we conclude that Xce^a is either a derived allele that arose concurrently with the domestication of fancy mice but prior the derivation of most classical inbred strains or a rare allele in the wild. Furthermore, we have found that despite the presence of multiple haplotypes in the wild Mus musculus domesticus has only one functional Xce allele, Xce^b. Lastly, we conclude that each mouse taxa examined has a different functional Xce allele.

Although mammalian females have two X chromosomes in each cell, only one is functional, while gene expression from the other is silenced through a process called X chromosome inactivation. Little is known about the early stages of this process including how one parental X chromosome is inactivated over the other on a cell-by-cell basis. It has been shown, however, that certain inbred mouse strains are functionally different at a locus that controls this choice that provides an opportunity to identify the locus and determine its molecular mechanism. This has been the goal of many researchers over the past 40 years with incremental success. Here we took advantage of new mouse genotype and whole genome sequencing data to pinpoint the locus controlling choice. Our results identified a smaller region on the X chromosome that contains large duplicated sequences. We propose an explanation for multiple functional alleles in mouse and provide insight into the possible molecular mechanism of X chromosome inactivation choice. Our evolutionary analysis reveals why functional diversity at this locus appears to be common in laboratory mice and offers an explanation as to why we do not see this level of diversity in humans.

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling

Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.

THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.

cAMP/PKA signaling defects in tumors: genetics and tissue-specific pluripotential cell-derived lesions in human and mouse

In the last few years, bench and clinical studies led to significant new insight into how cyclic adenosine monophosphate (cAMP) signaling, the molecular pathway that had been identified in the early 2000s as the one involved in most benign cortisol-producing adrenal hyperplasias, affects adrenocortical growth and development, as well as tumor formation. A major discovery was the identification of tissue-specific pluripotential cells (TSPCs) as the culprit behind tumor formation not only in the adrenal, but also in bone. Discoveries in animal studies complemented a number of clinical observations in patients. Gene identification continued in parallel with mouse and other studies on the cAMP signaling and other pathways.

Genetic instability and intratumoral heterogeneity in neuroblastoma with MYCN amplification plus 11q deletion

Background/Aim

Genetic analysis in neuroblastoma has identified the profound influence of MYCN amplification and 11q deletion in patients’ prognosis. These two features of high-risk neuroblastoma usually occur as mutually exclusive genetic markers, although in rare cases both are present in the same tumor. The purpose of this study was to characterize the genetic profile of these uncommon neuroblastomas harboring both these high-risk features.

Methods

We selected 18 neuroblastomas with MNA plus 11q loss detected by FISH. Chromosomal aberrations were analyzed using Multiplex Ligation-dependent Probe Amplification and Single Nucleotide Polymorphism array techniques.

Results and Conclusion

This group of tumors has approximately the same high frequency of aberrations as found earlier for 11q deleted tumors. In some cases, DNA instability generates genetic heterogeneity, and must be taken into account in routine genetic diagnosis.

Nonfamilial breast cancer subtypes

Over the last decade, our knowledge in somatic genetic events related to breast cancer has increased -enormously. Through usage of various genome-wide molecular approaches, it has become increasingly clear that breast cancer is a vastly heterogeneous disease. Microarray-based gene expression profiling has divided breast cancer into five distinct intrinsic subtypes termed basal-like, HER2-enriched, normal-like, luminal A, and luminal B. Importantly, these subtypes are closely correlated to clinical variables as well as different outcomes, with luminal A tumors as the good prognostic group. Initial studies using genome-wide DNA copy number data broadly partitioned breast cancers into three types, complex, amplifier, and simple, and moreover associated distinct copy number changes with the intrinsic subtypes defined by gene expression profiles. More recently, this genomic classification was refined into six genomic subtypes demonstrating strong resemblance to the intrinsic gene expression classification. Additionally, inherited BRCA1- and BRCA2-mutated tumors were significantly correlated to specific subtypes. In this chapter, we will review the current status regarding genomic subtypes of nonfamilial breast cancer.

Haplotype-based profiling of subtle allelic imbalance with SNP arrays

Due to limitations of surgical dissection and tumor heterogeneity, tumor samples collected for cancer genomics studies are often heavily diluted with normal tissue or contain subpopulations of cells harboring important aberrations. Methods for profiling tumor-associated allelic imbalance in such scenarios break down at aberrant cell proportions of 10%-15% and below. Here, we present an approach that offers a vast improvement for detection of subtle allelic imbalance, or low proportions of cells harboring aberrant allelic ratio among nonaberrant cells, in unpaired tumor samples using SNP microarrays. We leverage the expected pattern of allele-specific intensity ratios determined by an individual's germline haplotypes, information that has been ignored in existing approaches. We demonstrate our method on real and simulated data from the CRL-2324 breast cancer cell line genotyped on the Illumina 370K array. Assuming a 5 million SNP array, we can detect the presence of aberrant cells in proportions lower than 0.25% in the breast cancer sample, approaching the sensitivity of some minimal residual disease assays. Further, we apply a hidden Markov model to identify copy-neutral LOH (loss of heterozygosity) events as short as 11 Mb in mixtures of only 4% tumor using 370K data. We anticipate our approach will offer a new paradigm for genomic profiling of heterogeneous samples.

Change-point analysis of paired allele-specific copy number variation data

The recent genome-wide allele-specific copy number variation data enable us to explore two types of genomic information including chromosomal genotype variations as well as DNA copy number variations. For a cancer study, it is common to collect data for paired normal and tumor samples. Then, two types of paired data can be obtained to study a disease subject. However, there is a lack of methods for a simultaneous analysis of these four sequences of data. In this study, we propose a statistical framework based on the change-point analysis approach. The validity and usefulness of our proposed statistical framework are demonstrated through the simulation studies and applications based on an experimental data set.

qpure: A tool to estimate tumor cellularity from genome-wide single-nucleotide polymorphism profiles

Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 (-value = 0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 (-value 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 (-value = 0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/.

Mutational signatures of de-differentiation in functional non-coding regions of melanoma genomes

Much emphasis has been placed on the identification, functional characterization, and therapeutic potential of somatic variants in tumor genomes. However, the majority of somatic variants lie outside coding regions and their role in cancer progression remains to be determined. In order to establish a system to test the functional importance of non-coding somatic variants in cancer, we created a low-passage cell culture of a metastatic melanoma tumor sample. As a foundation for interpreting functional assays, we performed whole-genome sequencing and analysis of this cell culture, the metastatic tumor from which it was derived, and the patient-matched normal genomes. When comparing somatic mutations identified in the cell culture and tissue genomes, we observe concordance at the majority of single nucleotide variants, whereas copy number changes are more variable. To understand the functional impact of non-coding somatic variation, we leveraged functional data generated by the ENCODE Project Consortium. We analyzed regulatory regions derived from multiple different cell types and found that melanocyte-specific regions are among the most depleted for somatic mutation accumulation. Significant depletion in other cell types suggests the metastatic melanoma cells de-differentiated to a more basal regulatory state. Experimental identification of genome-wide regulatory sites in two different melanoma samples supports this observation. Together, these results show that mutation accumulation in metastatic melanoma is nonrandom across the genome and that a de-differentiated regulatory architecture is common among different samples. Our findings enable identification of the underlying genetic components of melanoma and define the differences between a tissue-derived tumor sample and the cell culture created from it. Such information helps establish a broader mechanistic understanding of the linkage between non-coding genomic variations and the cellular evolution of cancer.

Here we investigate the relationship between somatic variants and non-coding regulatory regions. To do this, we develop a new algorithm for identifying single nucleotide somatic variants in whole-genome sequencing data and apply it to a metastatic melanoma sample and a cell culture derived from this sample. Our results show that the two genomes are similar at the level of single nucleotide changes and more variable at larger copy number changes. We further observe that patterns of somatic mutation accumulation in non-coding regulatory regions suggests that the metastatic melanoma cells de-differentiated into a more basal regulatory state. That is, by simply looking at mutation accumulation across cell-type-specific non-coding functional regions, one can clearly see patterns that are indicative of cell state de-differentiation. Results from genome-wide functional regulatory region experimental mapping support this observation.

Ras-driven transcriptome analysis identifies aurora kinase A as a potential malignant peripheral nerve sheath tumor therapeutic target

PURPOSE

Patients with neurofibromatosis type 1 (NF1) develop malignant peripheral nerve sheath tumors (MPNST), which are often inoperable and do not respond well to current chemotherapies or radiation. The goal of this study was to use comprehensive gene expression analysis to identify novel therapeutic targets.

EXPERIMENTAL DESIGN

Nerve Schwann cells and/or their precursors are the tumorigenic cell types in MPNST because of the loss of the NF1 gene, which encodes the RasGAP protein neurofibromin. Therefore, we created a transgenic mouse model, CNP-HRas12V, expressing constitutively active HRas in Schwann cells and defined a Ras-induced gene expression signature to drive a Bayesian factor regression model analysis of differentially expressed genes in mouse and human neurofibromas and MPNSTs. We tested functional significance of Aurora kinase overexpression in MPNST in vitro and in vivo using Aurora kinase short hairpin RNAs (shRNA) and compounds that inhibit Aurora kinase.

RESULTS

We identified 2,000 genes with probability of linkage to nerve Ras signaling of which 339 were significantly differentially expressed in mouse and human NF1-related tumor samples relative to normal nerves, including Aurora kinase A (AURKA). AURKA was dramatically overexpressed and genomically amplified in MPNSTs but not neurofibromas. Aurora kinase shRNAs and Aurora kinase inhibitors blocked MPNST cell growth in vitro. Furthermore, an AURKA selective inhibitor, MLN8237, stabilized tumor volume and significantly increased survival of mice with MPNST xenografts.

CONCLUSION

Integrative cross-species transcriptome analyses combined with preclinical testing has provided an effective method for identifying candidates for molecular-targeted therapeutics. Blocking Aurora kinases may be a viable treatment platform for MPNST.

Enhanced RAD21 cohesin expression confers poor prognosis in BRCA2 and BRCAX, but not BRCA1 familial breast cancers

INTRODUCTION

The RAD21 gene encodes a key component of the cohesin complex, which is essential for chromosome segregation, and together with BRCA1 and BRCA2, for high-fidelity DNA repair by homologous recombination. Although its expression correlates with early relapse and treatment resistance in sporadic breast cancers, it is unclear whether familial breast cancers behave in a similar manner.

METHODS

We performed an immunohistochemical analysis of RAD21 expression in a cohort of 94 familial breast cancers (28 BRCA1, 27 BRCA2, and 39 BRCAX) and correlated these data with genotype and clinicopathologic parameters, including survival. In these cancers, we also correlated RAD21 expression with genomic expression profiling and gene copy-number changes and miRNAs predicted to target RAD21.

RESULTS

No significant differences in nuclear RAD21 expression were observed between BRCA1 (12 (43%) of 28), BRCA2 (12 (44%) of 27), and BRCAX cancers (12 (33%) of 39 (p = 0.598). No correlation was found between RAD21 expression and grade, size, or lymph node, ER, or HER2 status (all P > 0.05). As for sporadic breast cancers, RAD21 expression correlated with shorter survival in grade 3 (P = 0.009) and but not in grade 1 (P = 0.065) or 2 cancers (P = 0.090). Expression of RAD21 correlated with poorer survival in patients treated with chemotherapy (P = 0.036) but not with hormonal therapy (P = 0.881). RAD21 expression correlated with shorter survival in BRCA2 (P = 0.006) and BRCAX (P = 0.008), but not BRCA1 cancers (P = 0.713). Changes in RAD21 mRNA were reflected by genomic changes in DNA copy number (P < 0.001) and by RAD21 protein expression, as assessed with immunohistochemistry (P = 0.047). High RAD21 expression was associated with genomic instability, as assessed by the total number of base pairs affected by genomic change (P = 0.048). Of 15 miRNAs predicted to target RAD21, mir-299-5p inversely correlated with RAD21 expression (P = 0.002).

CONCLUSIONS

Potential use of RAD21 as a predictive and prognostic marker in familial breast cancers is hence feasible and may therefore take into account the patient's BRCA1/2 mutation status.

Germline copy number variants are not associated with globally acquired copy number changes in familial breast tumours

A characteristic of sporadic and familial breast tumours is genomic instability, resulting from either inherited mutations in genes that control genome integrity or mutations that are acquired in somatic cells during development. It is well established that abnormal chromosome number and structural changes to chromosomes play an important role in the cause and progression of breast cancer. Familial BRCA1 breast tumours are characterised by basal-like phenotype and high-histological grade which are typically associated with increased genomic instability. Consistent with previous studies, the genomes with the greatest number of base pairs covered by copy number change were typically found in basal-like and/or high-histological grade breast tumours within our cohort. Moreover, we show that luminal A tumours that are high grade had significantly less copy number variant (CNV) coverage than the more clinically aggressive high-grade luminal B tumours, suggesting that chromosomal instability rather than cellular differentiation contributes to the aggressive nature of luminal B tumours. It has previously been proposed that germline CNVs may contribute to somatically acquired chromosome changes in the tumour, but this is the first study to address this idea in breast cancer. By comparing germline CNVs and tumour-specific CNVs in matched breast tumour and normal tissue using data from the Illumina Human CNV370 duo beadarray, we provide evidence that germline CNVs do not tend to act as a foundation on which larger chromosome copy number aberrations develop in tumour cells. Further studies are required with increased sequence resolution that will detect smaller CNVs and define CNV breakpoints to comprehensively assess the relationship between inherited genomic variation and genome evolution in breast cancer.

Genotyping techniques to address diversity in tumors

Array-based genotyping platforms have during recent years been established as a valuable tool for the characterization of genomic alterations in cancer. The analysis of tumor samples, however, presents challenges for data analysis and interpretation. For example, tumor samples are often admixed with nonaberrant cells that define the tumor microenvironment, such as infiltrating lymphocytes and fibroblasts, or vasculature. Furthermore, tumors often comprise subclones harboring divergent aberrations that are acquired subsequent to the tumor-initiating event. The combined analysis of both genotype and copy number status obtained by array-based genotyping platforms provide opportunities to address these challenges. In this chapter, we present the basic principles for current array-based genotyping platforms and how they can be used to infer genotype and copy number for acquired genomic alterations. We describe how these techniques can be used to resolve tumor ploidy, normal cell admixture, and subclonality. We also exemplify how genotyping techniques can be applied in tumor studies to elucidate the hierarchy among tumor clones, and thus, provide means to study clonal expansion and tumor evolution.

Dissecting loss of heterozygosity (LOH) in neurofibromatosis type 1-associated neurofibromas: Importance of copy neutral LOH

Dermal neurofibromas (dNFs) are benign tumors of the peripheral nervous system typically associated with Neurofibromatosis type 1 (NF1) patients. Genes controlling the integrity of the DNA are likely to influence the number of neurofibromas developed because dNFs are caused by somatic mutational inactivation of the NF1 gene, frequently evidenced by loss of heterozygosity (LOH). We performed a comprehensive analysis of the prevalence and mechanisms of LOH in dNFs. Our study included 518 dNFs from 113 patients. LOH was detected in 25% of the dNFs (N = 129). The most frequent mechanism causing LOH was mitotic recombination, which was observed in 62% of LOH-tumors (N = 80), and which does not reduce the number of NF1 gene copies. All events were generated by a single crossover located between the centromere and the NF1 gene, resulting in isodisomy of 17q. LOH due to the loss of the NF1 gene accounted for a 38% of dNFs with LOH (N = 49), with deletions ranging in size from ∼80 kb to ∼8 Mb within 17q. In one tumor we identified the first example of a neurofibroma-associated second-hit type-2 NF1 deletion. Analysis of the prevalence of mechanisms causing LOH in dNFs in individual patients (possibly under genetic control) will elucidate whether there exist interindividual variation.

Chromosome 3 anomalies investigated by genome wide SNP analysis of benign, low malignant potential and low grade ovarian serous tumours

Ovarian carcinomas exhibit extensive heterogeneity, and their etiology remains unknown. Histological and genetic evidence has led to the proposal that low grade ovarian serous carcinomas (LGOSC) have a different etiology than high grade carcinomas (HGOSC), arising from serous tumours of low malignant potential (LMP). Common regions of chromosome (chr) 3 loss have been observed in all types of serous ovarian tumours, including benign, suggesting that these regions contain genes important in the development of all ovarian serous carcinomas. A high-density genome-wide genotyping bead array technology, which assayed >600,000 markers, was applied to a panel of serous benign and LMP tumours and a small set of LGOSC, to characterize somatic events associated with the most indolent forms of ovarian disease. The genomic patterns inferred were related to TP53, KRAS and BRAF mutations. An increasing frequency of genomic anomalies was observed with pathology of disease: 3/22 (13.6%) benign cases, 40/53 (75.5%) LMP cases and 10/11 (90.9%) LGOSC cases. Low frequencies of chr3 anomalies occurred in all tumour types. Runs of homozygosity were most commonly observed on chr3, with the 3p12-p11 candidate tumour suppressor region the most frequently homozygous region in the genome. An LMP harboured a homozygous deletion on chr6 which created a GOPC-ROS1 fusion gene, previously reported as oncogenic in other cancer types. Somatic TP53, KRAS and BRAF mutations were not observed in benign tumours. KRAS-mutation positive LMP cases displayed significantly more chromosomal aberrations than BRAF-mutation positive or KRAS and BRAF mutation negative cases. Gain of 12p, which harbours the KRAS gene, was particularly evident. A pathology review reclassified all TP53-mutation positive LGOSC cases, some of which acquired a HGOSC status. Taken together, our results support the view that LGOSC could arise from serous benign and LMP tumours, but does not exclude the possibility that HGOSC may derive from LMP tumours.

Parent-specific copy number in paired tumor-normal studies using circular binary segmentation

MOTIVATION

High-throughput techniques facilitate the simultaneous measurement of DNA copy number at hundreds of thousands of sites on a genome. Older techniques allow measurement only of total copy number, the sum of the copy number contributions from the two parental chromosomes. Newer single nucleotide polymorphism (SNP) techniques can in addition enable quantifying parent-specific copy number (PSCN). The raw data from such experiments are two-dimensional, but are unphased. Consequently, inference based on them necessitates development of new analytic methods.

METHODS

We have adapted and enhanced the circular binary segmentation (CBS) algorithm for this purpose with focus on paired test and reference samples. The essence of paired parent-specific CBS (Paired PSCBS) is to utilize the original CBS algorithm to identify regions of equal total copy number and then to further segment these regions where there have been changes in PSCN. For the final set of regions, calls are made of equal parental copy number and loss of heterozygosity (LOH). PSCN estimates are computed both before and after calling.

RESULTS

The methodology is evaluated by simulation and on glioblastoma data. In the simulation, PSCBS compares favorably to established methods. On the glioblastoma data, PSCBS identifies interesting genomic regions, such as copy-neutral LOH.

AVAILABILITY

The Paired PSCBS method is implemented in an open-source R package named PSCBS, available on CRAN (http://cran.r-project.org/).

BACOM: in silico detection of genomic deletion types and correction of normal cell contamination in copy number data

MOTIVATION

Identification of somatic DNA copy number alterations (CNAs) and significant consensus events (SCEs) in cancer genomes is a main task in discovering potential cancer-driving genes such as oncogenes and tumor suppressors. The recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale with high resolution. However, existing copy number analysis methods are oblivious to normal cell contamination and cannot distinguish between contributions of cancerous and normal cells to the measured copy number signals. This contamination could significantly confound downstream analysis of CNAs and affect the power to detect SCEs in clinical samples.

RESULTS

We report here a statistically principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately estimate genomic deletion type and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. We tested the proposed method on two simulated datasets, two prostate cancer datasets and The Cancer Genome Atlas high-grade ovarian dataset, and obtained very promising results supported by the ground truth and biological plausibility. Moreover, based on a large number of comparative simulation studies, the proposed method gives significantly improved power to detect SCEs after in silico correction of normal tissue contamination. We develop a cross-platform open-source Java application that implements the whole pipeline of copy number analysis of heterogeneous cancer tissues including relevant processing steps. We also provide an R interface, bacomR, for running BACOM within the R environment, making it straightforward to include in existing data pipelines.

AVAILABILITY

The cross-platform, stand-alone Java application, BACOM, the R interface, bacomR, all source code and the simulation data used in this article are freely available at authors' web site: http://www.cbil.ece.vt.edu/software.htm.

GPHMM: an integrated hidden Markov model for identification of copy number alteration and loss of heterozygosity in complex tumor samples using whole genome SNP arrays

There is an increasing interest in using single nucleotide polymorphism (SNP) genotyping arrays for profiling chromosomal rearrangements in tumors, as they allow simultaneous detection of copy number and loss of heterozygosity with high resolution. Critical issues such as signal baseline shift due to aneuploidy, normal cell contamination, and the presence of GC content bias have been reported to dramatically alter SNP array signals and complicate accurate identification of aberrations in cancer genomes. To address these issues, we propose a novel Global Parameter Hidden Markov Model (GPHMM) to unravel tangled genotyping data generated from tumor samples. In contrast to other HMM methods, a distinct feature of GPHMM is that the issues mentioned above are quantitatively modeled by global parameters and integrated within the statistical framework. We developed an efficient EM algorithm for parameter estimation. We evaluated performance on three data sets and show that GPHMM can correctly identify chromosomal aberrations in tumor samples containing as few as 10% cancer cells. Furthermore, we demonstrated that the estimation of global parameters in GPHMM provides information about the biological characteristics of tumor samples and the quality of genotyping signal from SNP array experiments, which is helpful for data quality control and outlier detection in cohort studies.

Estimation of parent specific DNA copy number in tumors using high-density genotyping arrays

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

Many genetic diseases are related to copy number aberrations of some regions of the genome. As we know, each chromosome normally has two copies. However, under some circumstances, for some regions, either one or both of the chromosomes change. Genotyping microarray data provides the copy number of the two alleles of polymorphic sites along the chromosomes, which make the inference of the copy number aberrations of the chromosome feasible. One difficulty is that genotyping microarray data cannot provide the haplotype of the two copies of a chromosome. In this paper, we model the copy number along the chromosome as a two-dimensional Markov Chain. Using the observed copy number of both alleles of all the sites, we can determine the parent specific copy number along the chromosome as well as infer the haplotypes of the two copies of the inherited chromosomes in regions where there is allelic imbalance. Simulation results show high sensitivity and specificity of the method. Applying this method to glioblastoma samples from the Cancer Genome Atlas data illustrate the insights gained from allele-specific copy number analysis.

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.

MixHMM: inferring copy number variation and allelic imbalance using SNP arrays and tumor samples mixed with stromal cells

Background

Genotyping platforms such as single nucleotide polymorphism (SNP) arrays are powerful tools to study genomic aberrations in cancer samples. Allele specific information from SNP arrays provides valuable information for interpreting copy number variation (CNV) and allelic imbalance including loss-of-heterozygosity (LOH) beyond that obtained from the total DNA signal available from array comparative genomic hybridization (aCGH) platforms. Several algorithms based on hidden Markov models (HMMs) have been designed to detect copy number changes and copy-neutral LOH making use of the allele information on SNP arrays. However heterogeneity in clinical samples, due to stromal contamination and somatic alterations, complicates analysis and interpretation of these data.

Methods

We have developed MixHMM, a novel hidden Markov model using hidden states based on chromosomal structural aberrations. MixHMM allows CNV detection for copy numbers up to 7 and allows more complete and accurate description of other forms of allelic imbalance, such as increased copy number LOH or imbalanced amplifications. MixHMM also incorporates a novel sample mixing model that allows detection of tumor CNV events in heterogeneous tumor samples, where cancer cells are mixed with a proportion of stromal cells.

Conclusions

We validate MixHMM and demonstrate its advantages with simulated samples, clinical tumor samples and a dilution series of mixed samples. We have shown that the CNVs of cancer cells in a tumor sample contaminated with up to 80% of stromal cells can be detected accurately using Illumina BeadChip and MixHMM.

Availability

The MixHMM is available as a Python package provided with some other useful tools at http://genecube.med.yale.edu:8080/MixHMM.

TumorBoost: normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays

Background

High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses.

Results

We propose a method, TumorBoost, for normalizing allelic estimates of one tumor sample based on estimates from a single matched normal. The method applies to any paired tumor-normal estimates from any microarray-based technology, combined with any preprocessing method. We demonstrate that it increases the signal-to-noise ratio of allelic signals, making it significantly easier to detect allelic imbalances.

Conclusions

TumorBoost increases the power to detect somatic copy-number events (including copy-neutral LOH) in the tumor from allelic signals of Affymetrix or Illumina origin. We also conclude that high-precision allelic estimates can be obtained from a single pair of tumor-normal hybridizations, if TumorBoost is combined with single-array preprocessing methods such as (allele-specific) CRMA v2 for Affymetrix or BeadStudio's (proprietary) XY-normalization method for Illumina. A bounded-memory implementation is available in the open-source and cross-platform R package aroma.cn, which is part of the Aroma Project (http://www.aroma-project.org/).

Conditional random pattern model for copy number aberration detection

Background

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.

Results

This paper presents a conditional random pattern (CRP) model for CNA detection where much contextual cues are explored to suppress the noise and improve CNA detection accuracy. Both simulated and the real data are used to evaluate the proposed model, and the validation results show that the CRP model is more robust and reliable in the presence of noise for CNA detection using high density SNP array data, compared to a number of widely used software packages.

Conclusions

The proposed conditional random pattern (CRP) model could effectively detect the CNA regions in the presence of noise.

Subtypes of familial breast tumours revealed by expression and copy number profiling

Extensive expression profiling studies have shown that sporadic breast cancer is composed of five clinically relevant molecular subtypes. However, although BRCA1-related tumours are known to be predominantly basal-like, there are few published data on other classes of familial breast tumours. We analysed a cohort of 75 BRCA1, BRCA2 and non-BRCA1/2 breast tumours by gene expression profiling and found that 74% BRCA1 tumours were basal-like, 73% of BRCA2 tumours were luminal A or B, and 52% non-BRCA1/2 tumours were luminal A. Thirty-four tumours were also analysed by single nucleotide polymorphism-comparative genomic hybridization (SNP-CGH) arrays. Copy number data could predict whether a tumour was basal-like or luminal with high accuracy, but could not predict its mutation class. Basal-like BRCA1 and basal-like non-BRCA1 tumours were very similar, and contained the highest number of chromosome aberrations. We identified regions of frequent gain containing potential driver genes in the basal (8q and 12p) and luminal A tumours (1q and 17q). Regions of homozygous loss associated with decreased expression of potential tumour suppressor genes were also detected, including in basal tumours (5q and 9p), and basal and luminal tumours (10q). This study highlights the heterogeneity of familial tumours and the clinical consequences for treatment and prognosis.

Genome Alteration Print (GAP): a tool to visualize and mine complex cancer genomic profiles obtained by SNP arrays

We describe a method for automatic detection of absolute segmental copy numbers and genotype status in complex cancer genome profiles measured with single-nucleotide polymorphism (SNP) arrays. The method is based on pattern recognition of segmented and smoothed copy number and allelic imbalance profiles. Assignments were verified by DNA indexes of primary tumors and karyotypes of cell lines. The method performs well even for poor-quality data, low tumor content, and highly rearranged tumor genomes.

Translation of an STR-based biomarker into a clinically compatible SNP-based platform for loss of heterozygosity

Loss of heterozygosity (LOH) has been shown to be a promising biomarker of cancer risk in patients with premalignant conditions. In this study we describe analytical validation in clinical biopsy samples of a SNP-based pyrosequencing panel targeting regions of LOH on chromosomes 17p and 9p including TP53 and CDKN2A tumor suppressor genes. Assays were tested for analytic specificity, sensitivity, efficiency, and reproducibility. Accuracy was evaluated by comparing SNP-based LOH results to those obtained by previously well-studied short tandem repeat polymorphisms (STRs) in DNA derived from different tissue sources including fresh-frozen endoscopic biopsies, samples from surgical resections, and formalin-fixed paraffin-embedded sections. A 17p/9p LOH panel comprised of 43 SNPs was designed to amplify with universal assay conditions in a two-step PCR and sequence-by-synthesis reaction that can be completed in two hours and 10 minutes. The methods presented can be a model for developing a SNP-based LOH approach targeted to any chromosomal region of interest for other premalignant conditions and this panel could be incorporated as part of a biomarker for cancer risk prediction, early detection, or as entry criteria for randomized trials.

Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances

Array manufacturers originally designed single nucleotide polymorphism (SNP) arrays to genotype human DNA at thousands of SNPs across the genome simultaneously. In the decade since their initial development, the platform's applications have expanded to include the detection and characterization of copy number variation—whether somatic, inherited, or de novo—as well as loss-of-heterozygosity in cancer cells. The technology's impressive contributions to insights in population and molecular genetics have been fueled by advances in computational methodology, and indeed these insights and methodologies have spurred developments in the arrays themselves. This review describes the most commonly used SNP array platforms, surveys the computational methodologies used to convert the raw data into inferences at the DNA level, and details the broad range of applications. Although the long-term future of SNP arrays is unclear, cost considerations ensure their relevance for at least the next several years. Even as emerging technologies seem poised to take over for at least some applications, researchers working with these new sources of data are adopting the computational approaches originally developed for SNP arrays.

Quantification of normal cell fraction and copy number neutral LOH in clinical lung cancer samples using SNP array data

BACKGROUND

Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Since tumor tissue invariably contains genetically normal stromal cells, this may lead to a failure to detect aberrations in the tumor cells.

PRINCIPAL FINDING

Using SNP array data from 44 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples. The proportion of normal cells in combination with SNP array data can be used to detect and quantify copy number neutral loss-of-heterozygosity (CNNLOH) in the tumor cells both in crude tumor tissue and in samples enriched for tumor cells by laser capture microdissection.

CONCLUSION

Genome-wide quantitative analysis of CNNLOH using the CNNLOH Quantifier method can help to identify recurrent aberrations contributing to tumor development in clinical tumor samples. In addition, SNP-array based analysis of CNNLOH may become important for detection of aberrations that can be used for diagnostic and prognostic purposes.

Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios

Background

Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples.

Results

We demonstrate an asymmetry in the detection of the two alleles for each SNP, which deleteriously influences both allelic proportions and copy number estimates. The asymmetry is caused by a remaining bias between the two dyes used in the Infinium II assay after using the normalization method in Illumina's proprietary software (BeadStudio). We propose a quantile normalization strategy for correction of this dye bias. We tested the normalization strategy using 535 individual hybridizations from 10 data sets from the analysis of cancer genomes and normal blood samples generated on Illumina Infinium II 300 k version 1 and 2, 370 k and 550 k BeadChips. We show that the proposed normalization strategy successfully removes asymmetry in estimates of both allelic proportions and copy numbers. Additionally, the normalization strategy reduces the technical variation for copy number estimates while retaining the response to copy number alterations.

Conclusion

The proposed normalization strategy represents a valuable tool that improves the quality of data obtained from Illumina Infinium arrays, in particular when used for LOH and copy number variation studies.

Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays

A strategy is presented for detection of loss-of-heterozygosity and allelic imbalance in cancer cells from whole genome SNP genotyping data.

We present a strategy for detection of loss-of-heterozygosity and allelic imbalance in cancer cells from whole genome single nucleotide polymorphism genotyping data. Using a dilution series of a tumor cell line mixed with its paired normal cell line and data generated on Affymetrix and Illumina platforms, including paired tumor-normal samples and tumors characterized by fluorescent in situ hybridization, we demonstrate a high sensitivity and specificity of the strategy for detecting both minute and gross allelic imbalances in heterogeneous tumor samples.