PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data

GLO1-A novel amplified gene in human cancer

To identify a novel amplified cancer gene a systematic screen of 975 human cancer DNA samples, 750 cell lines and 225 primary tumors, using the Affymetrix 10K SNP microarray was undertaken. The screen identified 193 amplicons. A previously uncharacterized amplicon located on 6p21.2 whose 1 Mb minimal common amplified region contained eight genes (GLO1, DNAH8, GLP1R, C6orf64, KCNK5, KCNK17, KCNK16, and C6orf102) was further investigated to determine which gene(s) are the biological targets of this amplicon. Real time quantitative PCR (qPCR) analysis of all amplicon 6p21.2 genes in 618 human cancer cell lines identified GLO1, encoding glyoxalase 1, to be the most frequently amplified gene [twofold or greater amplification in 8.4% (49/536) of cancers]. Also the association between amplification and overexpression was greatest for GLO1. RNAi knockdown of GLO1 had the greatest and most consistent impact on cell accumulation and apoptosis. Cell lines with GLO1 amplification were more sensitive to inhibition of GLO1 by bromobenzylglutathione cyclopentyl diester (BBGC). Subsequent qPCR of 520 primary tumor samples identified twofold and greater amplification of GLO1 in 8/37 (22%) of breast, 12/71 (17%) of sarcomas, 6/53 (11.3%) of nonsmall cell lung, 2/23 (8.7%) of bladder, 6/93 (6.5%) of renal and 5/83 (6%) of gastric cancers. Amplification of GLO1 was rare in colon cancer (1/35) and glioma (1/94). Collectively the results indicate that GLO1 is at least one of the targets of gene amplification on 6p21.2 and may represent a useful target for therapy in cancers with GLO1 amplification.

Protocol matters: which methylome are you actually studying?

The field of epigenetics is now capitalizing on the vast number of emerging technologies, largely based on second-generation sequencing, which interrogate DNA methylation status and histone modifications genome-wide. However, getting an exhaustive and unbiased view of a methylome at a reasonable cost is proving to be a significant challenge. In this article, we take a closer look at the impact of the DNA sequence and bias effects introduced to datasets by genome-wide DNA methylation technologies and where possible, explore the bioinformatics tools that deconvolve them. There remains much to be learned about the performance of genome-wide technologies, the data we mine from these assays and how it reflects the actual biology. While there are several methods to interrogate the DNA methylation status genome-wide, our opinion is that no single technique suitably covers the minimum criteria of high coverage and, high resolution at a reasonable cost. In fact, the fraction of the methylome that is studied currently depends entirely on the inherent biases of the protocol employed. There is promise for this to change, as the third generation of sequencing technologies is expected to again 'revolutionize' the way that we study genomes and epigenomes.

Genetic inactivation of the Fanconi anemia gene FANCC identified in the hepatocellular carcinoma cell line HuH-7 confers sensitivity towards DNA-interstrand crosslinking agents

Background

Inactivation of the Fanconi anemia (FA) pathway through defects in one of 13 FA genes occurs at low frequency in various solid cancer entities among the general population. As FA pathway inactivation confers a distinct hypersensitivity towards DNA interstrand-crosslinking (ICL)-agents, FA defects represent rational targets for individualized therapeutic strategies. Except for pancreatic cancer, however, the prevalence of FA defects in gastrointestinal (GI) tumors has not yet been systematically explored.

Results

A panel of GI cancer cell lines was screened for FA pathway inactivation applying FANCD2 monoubiquitination and FANCD2/RAD51 nuclear focus formation and a newly identified FA pathway-deficient cell line was functionally characterized. The hepatocellular carcinoma (HCC) line HuH-7 was defective in FANCD2 monoubiquitination and FANCD2 nuclear focus formation but proficient in RAD51 focus formation. Gene complementation studies revealed that this proximal FA pathway inactivation was attributable to defective FANCC function in HuH-7 cells. Accordingly, a homozygous inactivating FANCC nonsense mutation (c.553C > T, p.R185X) was identified in HuH-7, resulting in partial transcriptional skipping of exon 6 and leading to the classic cellular FA hypersensitivity phenotype; HuH-7 cells exhibited a strongly reduced proliferation rate and a pronounced G2 cell cycle arrest at distinctly lower concentrations of ICL-agents than a panel of non-isogenic, FA pathway-proficient HCC cell lines. Upon retroviral transduction of HuH-7 cells with FANCC cDNA, FA pathway functions were restored and ICL-hypersensitivity abrogated. Analyses of 18 surgical HCC specimens yielded no further examples for genetic or epigenetic inactivation of FANCC, FANCF, or FANCG in HCC, suggesting a low prevalence of proximal FA pathway inactivation in this tumor type.

Conclusions

As the majority of HCC are chemoresistant, assessment of FA pathway function in HCC could identify small subpopulations of patients expected to predictably benefit from individualized treatment protocols using ICL-agents.

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/).

CNAnova: a new approach for finding recurrent copy number abnormalities in cancer SNP microarray data

MOTIVATION

The current generation of single nucleotide polymorphism (SNP) arrays allows measurement of copy number aberrations (CNAs) in cancer at more than one million locations in the genome in hundreds of tumour samples. Most research has focused on single-sample CNA discovery, the so-called segmentation problem. The availability of high-density, large sample-size SNP array datasets makes the identification of recurrent copy number changes in cancer, an important issue that can be addressed using the cross-sample information.

RESULTS

We present a novel approach for finding regions of recurrent copy number aberrations, called CNAnova, from Affymetrix SNP 6.0 array data. The method derives its statistical properties from a control dataset composed of normal samples and, in contrast to previous methods, does not require segmentation and permutation steps. For rigorous testing of the algorithm and comparison to existing methods, we developed a simulation scheme that uses the noise distribution present in Affymetrix arrays. Application of the method to 128 acute lymphoblastic leukaemia samples shows that CNAnova achieves lower error rate than a popular alternative approach. We also describe an extension of the CNAnova framework to identify recurrent CNA regions with intra-tumour heterogeneity, present in either primary or relapsed samples from the same patients.

AVAILABILITY

The CNAnova package and synthetic datasets are available at http://www.compbio.group.cam.ac.uk/software.html.

DNA copy number, including telomeres and mitochondria, assayed using next-generation sequencing

Background

DNA copy number variations occur within populations and aberrations can cause disease. We sought to develop an improved lab-automatable, cost-efficient, accurate platform to profile DNA copy number.

Results

We developed a sequencing-based assay of nuclear, mitochondrial, and telomeric DNA copy number that draws on the unbiased nature of next-generation sequencing and incorporates techniques developed for RNA expression profiling. To demonstrate this platform, we assayed UMC-11 cells using 5 million 33 nt reads and found tremendous copy number variation, including regions of single and homogeneous deletions and amplifications to 29 copies; 5 times more mitochondria and 4 times less telomeric sequence than a pool of non-diseased, blood-derived DNA; and that UMC-11 was derived from a male individual.

Conclusion

The described assay outputs absolute copy number, outputs an error estimate (p-value), and is more accurate than array-based platforms at high copy number. The platform enables profiling of mitochondrial levels and telomeric length. The assay is lab-automatable and has a genomic resolution and cost that are tunable based on the number of sequence reads.

Signatures of mutation and selection in the cancer genome

The cancer genome is moulded by the dual processes of somatic mutation and selection. Homozygous deletions in cancer genomes occur over recessive cancer genes, where they can confer selective growth advantage, and over fragile sites, where they are thought to reflect an increased local rate of DNA breakage. However, most homozygous deletions in cancer genomes are unexplained. Here we identified 2,428 somatic homozygous deletions in 746 cancer cell lines. These overlie 11% of protein-coding genes that, therefore, are not mandatory for survival of human cells. We derived structural signatures that distinguish between homozygous deletions over recessive cancer genes and fragile sites. Application to clusters of unexplained homozygous deletions suggests that many are in regions of inherent fragility, whereas a small subset overlies recessive cancer genes. The results illustrate how structural signatures can be used to distinguish between the influences of mutation and selection in cancer genomes. The extensive copy number, genotyping, sequence and expression data available for this large series of publicly available cancer cell lines renders them informative reagents for future studies of cancer biology and drug discovery.