A probe-density-based analysis method for array CGH data: simulation, normalization and centralization

An Integrated Approach for RNA-seq Data Normalization

BACKGROUND

DNA copy number alteration is common in many cancers. Studies have shown that insertion or deletion of DNA sequences can directly alter gene expression, and significant correlation exists between DNA copy number and gene expression. Data normalization is a critical step in the analysis of gene expression generated by RNA-seq technology. Successful normalization reduces/removes unwanted nonbiological variations in the data, while keeping meaningful information intact. However, as far as we know, no attempt has been made to adjust for the variation due to DNA copy number changes in RNA-seq data normalization.

RESULTS

In this article, we propose an integrated approach for RNA-seq data normalization. Comparisons show that the proposed normalization can improve power for downstream differentially expressed gene detection and generate more biologically meaningful results in gene profiling. In addition, our findings show that due to the effects of copy number changes, some housekeeping genes are not always suitable internal controls for studying gene expression.

CONCLUSIONS

Using information from DNA copy number, integrated approach is successful in reducing noises due to both biological and nonbiological causes in RNA-seq data, thus increasing the accuracy of gene profiling.

Identifying Human Genome-Wide CNV, LOH and UPD by Targeted Sequencing of Selected Regions

Copy-number variations (CNV), loss of heterozygosity (LOH), and uniparental disomy (UPD) are large genomic aberrations leading to many common inherited diseases, cancers, and other complex diseases. An integrated tool to identify these aberrations is essential in understanding diseases and in designing clinical interventions. Previous discovery methods based on whole-genome sequencing (WGS) require very high depth of coverage on the whole genome scale, and are cost-wise inefficient. Another approach, whole exome genome sequencing (WEGS), is limited to discovering variations within exons. Thus, we are lacking efficient methods to detect genomic aberrations on the whole genome scale using next-generation sequencing technology. Here we present a method to identify genome-wide CNV, LOH and UPD for the human genome via selectively sequencing a small portion of genome termed Selected Target Regions (SeTRs). In our experiments, the SeTRs are covered by 99.73%~99.95% with sufficient depth. Our developed bioinformatics pipeline calls genome-wide CNVs with high confidence, revealing 8 credible events of LOH and 3 UPD events larger than 5M from 15 individual samples. We demonstrate that genome-wide CNV, LOH and UPD can be detected using a cost-effective SeTRs sequencing approach, and that LOH and UPD can be identified using just a sample grouping technique, without using a matched sample or familial information.

Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology

BACKGROUND

Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may profoundly affect CGH array results and therefore may adversely affect outcomes in the precision medicine context.

PATIENTS AND METHODS

The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia (CCLE) panel, and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n = 283, MOSCATO-01 n = 309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively.

RESULTS

Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (P < 0.05). Using the CCLE dataset, selected genes (ERBB2, EGFR) were better or equally correlated to the IC50 of their companion drug (lapatinib, erlotinib), when applying the alternative centralization. Finally, focusing on 24 actionable genes, we observed as many as 7.1% (SAFIR-01) and 6.8% (MOSCATO-01) of patients originally not oriented to a specific treatment, but who could have been proposed a treatment based on the alternative peak centralization method.

CONCLUSION

The centralization method substantially affects the call detection of CGH profiles and may thus impact precision medicine approaches. Among the three methods described, the alternative peak method addresses limitations associated with existing approaches.

Comparative genomic hybridisation array and DNA sequencing to direct treatment of metastatic breast cancer: a multicentre, prospective trial (SAFIR01/UNICANCER)

BACKGROUND

Breast cancer is characterised by genomic alterations. We did a multicentre molecular screening study to identify abnormalities in individual patients with the aim of providing targeted therapy matched to individuals' genomic alterations.

METHODS

From June 16, 2011, to July 30, 2012, we recruited patients who had breast cancer with a metastasis accessible for biopsy in 18 centres in France. Comparative genomic hybridisation (CGH) array and Sanger sequencing on PIK3CA (exon 10 and 21) and AKT1 (exon 4) were used to assess metastatic biopsy samples in five centres. Therapeutic targets were decided on the basis of identified genomic alterations. The primary objective was to include 30% of patients in clinical trials testing a targeted therapy and, therefore, the primary outcome was the proportion of patients to whom a targeted therapy could be offered. For the primary endpoint, the analyses were done on the overall population registered for the trial. This trial is registered with ClinicalTrials.gov, number NCT01414933.

FINDINGS

423 patients were included, and biopsy samples were obtained from 407 (metastatic breast cancer was not found in four). CGH array and Sanger sequencing were feasible in 283 (67%) and 297 (70%) patients, respectively. A targetable genomic alteration was identified in 195 (46%) patients, most frequently in PIK3CA (74 [25%] of 297 identified genomic alterations), CCND1 (53 [19%]), and FGFR1 (36 [13%]). 117 (39%) of 297 patients with genomic tests available presented with rare genomic alterations (defined as occurring in less than 5% of the general population), including AKT1 mutations, and EGFR, MDM2, FGFR2, AKT2, IGF1R, and MET high-level amplifications. Therapy could be personalised in 55 (13%) of 423 patients. Of the 43 patients who were assessable and received targeted therapy, four (9%) had an objective response, and nine others (21%) had stable disease for more than 16 weeks. Serious (grade 3 or higher) adverse events related to biopsy were reported in four (1%) of enrolled patients, including pneumothorax (grade 3, one patient), pain (grade 3, one patient), haematoma (grade 3, one patient), and haemorrhagic shock (grade 3, one patient).

INTERPRETATION

Personalisation of medicine for metastatic breast cancer is feasible, including for rare genomic alterations.

FUNDING

French National Cancer Institute, Breast Cancer Research Foundation, Odyssea, Operation Parrains Chercheurs.

The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation

Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.

Functional performance of aCGH design for clinical cytogenetics

Array-comparative genomic hybridization (aCGH) technology enables rapid, high-resolution analysis of genomic rearrangements. With the use of it, genome copy number changes and rearrangement breakpoints can be detected and analyzed at resolutions down to a few kilobases. An exon array CGH approach proposed recently accurately measures copy-number changes of individual exons in the human genome. The crucial and highly non-trivial starting task is the design of an array, i.e. the choice of appropriate (multi)set of oligos. The success of the whole high-level analysis depends on the quality of the design. Also, the comparison of several alternative designs of array CGH constitutes an important step in development of new diagnostic chip. In this paper, we deal with these two often neglected issues. We propose a new approach to measure the quality of array CGH designs. Our measures reflect the robustness of rearrangements detection to the noise (mostly experimental measurement error). The method is parametrized by the segmentation algorithm used to identify aberrations. We implemented the efficient Monte Carlo method for testing noise robustness within DNAcopy procedure. Developed framework has been applied to evaluation of functional quality of several optimized array designs.

Array comparative genomic hybridization: an overview of protocols, applications, and technology trends

From the earliest observations of human chromosomes in the late 1800s to modern day next generation sequencing technologies, much has been learned about human cancers by the vigorous application of the techniques of the day. In general, resolution has improved tremendously, and correspondingly the size of the datasets generated has grown exponentially such that computational methods required to handle massive datasets have had to be devised. This chapter provides a brief synopsis of the evolution of such techniques as an introduction to the subsequent chapters that provide methods and applications, relevant to research, and clinical diagnostics.

PAIR: paired allelic log-intensity-ratio-based normalization method for SNP-CGH arrays

MOTIVATION

Normalization is critical in DNA copy number analysis. We propose a new method to correctly identify two-copy probes from the genome to obtain representative references for normalization in single nucleotide polymorphism arrays. The method is based on a two-state Hidden Markov Model. Unlike most currently available methods in the literature, the proposed method does not need to assume that the percentage of two-copy state probes is dominant in the genome, as long as there do exist two-copy probes.

RESULTS

The real data analysis and simulation study show that the proposed algorithm is successful in that (i) it performs as well as the current methods (e.g. CGHnormaliter and popLowess) for samples with dominant two-copy states and outperforms these methods for samples with less dominant two-copy states; (ii) it can identify the copy-neutral loss of heterozygosity; and (iii) it is efficient in terms of the computational time used.

AVAILABILITY

R scripts are available at http://publichealth.lsuhsc.edu/PAIR.html.

Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data

MOTIVATION

Comparison of read depths from next-generation sequencing between cancer and normal cells makes the estimation of copy number alteration (CNA) possible, even at very low coverage. However, estimating CNA from patients' tumour samples poses considerable challenges due to infiltration with normal cells and aneuploid cancer genomes. Here we provide a method that corrects contamination with normal cells and adjusts for genomes of different sizes so that the actual copy number of each region can be estimated.

RESULTS

The procedure consists of several steps. First, we identify the multi-modality of the distribution of smoothed ratios. Then we use the estimates of the mean (modes) to identify underlying ploidy and the contamination level, and finally we perform the correction. The results indicate that the method works properly to estimate genomic regions with gains and losses in a range of simulated data as well as in two datasets from lung cancer patients. It also proves a powerful tool when analysing publicly available data from two cell lines (HCC1143 and COLO829).

AVAILABILITY

An R package, called CNAnorm, is available at http://www.precancer.leeds.ac.uk/cnanorm or from Bioconductor.

CONTACT

a.gusnanto@leeds.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A computational index derived from whole-genome copy number analysis is a novel tool for prognosis in early stage lung squamous cell carcinoma

Squamous cell carcinoma of the lung is remarkable for the extent to which the same chromosomal abnormalities are detected in individual tumours. We have used next generation sequencing at low coverage to produce high resolution copy number karyograms of a series of 89 non-small cell lung tumours specifically of the squamous cell subtype. Because this methodology is able to create karyograms from formalin-fixed paraffin-embedded material, we were able to use archival stored samples for which survival data were available and correlate frequently occurring copy number changes with disease outcome. No single region of genomic change showed significant correlation with survival. However, adopting a whole-genome approach, we devised an algorithm that relates to total genomic damage, specifically the relative ratios of copy number states across the genome. This algorithm generated a novel index, which is an independent prognostic indicator in early stage squamous cell carcinoma of the lung.

Network-guided analysis of genes with altered somatic copy number and gene expression reveals pathways commonly perturbed in metastatic melanoma

Cancer genomes frequently contain somatic copy number alterations (SCNA) that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression ('SCNA-genes') in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer.

Genomic diversity of Lactobacillus salivarius

Strains of Lactobacillus salivarius are increasingly employed as probiotic agents for humans or animals. Despite the diversity of environmental sources from which they have been isolated, the genomic diversity of L. salivarius has been poorly characterized, and the implications of this diversity for strain selection have not been examined. To tackle this, we applied comparative genomic hybridization (CGH) and multilocus sequence typing (MLST) to 33 strains derived from humans, animals, or food. The CGH, based on total genome content, including small plasmids, identified 18 major regions of genomic variation, or hot spots for variation. Three major divisions were thus identified, with only a subset of the human isolates constituting an ecologically discernible group. Omission of the small plasmids from the CGH or analysis by MLST provided broadly concordant fine divisions and separated human-derived and animal-derived strains more clearly. The two gene clusters for exopolysaccharide (EPS) biosynthesis corresponded to regions of significant genomic diversity. The CGH-based groupings of these regions did not correlate with levels of production of bound or released EPS. Furthermore, EPS production was significantly modulated by available carbohydrate. In addition to proving difficult to predict from the gene content, EPS production levels correlated inversely with production of biofilms, a trait considered desirable in probiotic commensals. L. salivarius displays a high level of genomic diversity, and while selection of L. salivarius strains for probiotic use can be informed by CGH or MLST, it also requires pragmatic experimental validation of desired phenotypic traits.

CGHnormaliter: a Bioconductor package for normalization of array CGH data with many CNAs

SUMMARY

CGHnormaliter is a package for normalization of array comparative genomic hybridization (aCGH) data. It uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). CGHnormaliter is integrated in the Bioconductor environment allowing a smooth link to visualization tools and further data analysis.

AVAILABILITY AND IMPLEMENTATION

The CGHnormaliter package is implemented in R and under GPL 3.0 license available at Bioconductor: http://www.bioconductor.org

CONTACT

heringa@few.vu.nl

Preprocessing and downstream analysis of microarray DNA copy number profiles

Analysis of DNA copy number profiles requires methods tailored to the specific nature of these data. The number of available data analysis methods has grown enormously in the last 5 years. We discuss the typical characteristics of DNA copy number data, as measured by microarray technology and review the extensive literature on preprocessing methods such as segmentation and calling. Subsequently, the focus narrows to applications of DNA copy number in cancer, in particular, several downstream analyses of multi-sample data sets such as testing, clustering and classification. Finally, we look ahead: what should we prepare for and which methodology-related topics may deserve attention in the near future?

Zoom-in array comparative genomic hybridization (aCGH) to detect germline rearrangements in cancer susceptibility genes

Disease predisposing germline mutations in cancer susceptibility genes may consist of large genomic rearrangements, including deletions or duplications that are challenging, to detect and characterize using standard PCR-based mutation screening methods. Such rearrangements range from single exons up to hundreds of kilobases of sequence in size. Array-based comparative genomic hybridization (aCGH) has evolved as a powerful technique to detect copy number alterations on a genome-wide scale. However, the conventional genome-wide approach of aCGH still provides only limited information about copy number status for individual exons. Custom-designed aCGH arrays focused on only a few target regions (zoom-in aCGH) may circumvent this drawback. Benefits of zoom-in aCGH include the possibility to target almost any region in the genome, and an unbiased coverage of exonic and intronic sequence facilitating convenient design of primers for sequence determination of the breakpoints. Furthermore, zoom-in aCGH can be streamlined for a particular application, for example, focusing on breast cancer susceptibility genes, with increased capacity using multiformat design.

CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations

BACKGROUND

Array comparative genomic hybridization (aCGH) is a popular technique for detection of genomic copy number imbalances. These play a critical role in the onset of various types of cancer. In the analysis of aCGH data, normalization is deemed a critical pre-processing step. In general, aCGH normalization approaches are similar to those used for gene expression data, albeit both data-types differ inherently. A particular problem with aCGH data is that imbalanced copy numbers lead to improper normalization using conventional methods.

RESULTS

In this study we present a novel method, called CGHnormaliter, which addresses this issue by means of an iterative normalization procedure. First, provisory balanced copy numbers are identified and subsequently used for normalization. These two steps are then iterated to refine the normalization. We tested our method on three well-studied tumor-related aCGH datasets with experimentally confirmed copy numbers. Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies. Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers.

CONCLUSION

We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers. This also leads to a more reliable assessment of aberrations. An R-package containing the implementation of CGHnormaliter is available at http://www.ibi.vu.nl/programs/cghnormaliterwww.