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

Development and validation of a molecular tool to predict pathologic complete response in esophageal adenocarcinoma

Pathologic complete response (pCR) to neoadjuvant chemoradiation for locally advanced esophageal adenocarcinoma (EAC) confers significantly improved survival. The ability to infer pCR may spare esophagectomy in some patients. Currently, there are no validated biomarkers of pCR. This study sought to evaluate whether a distinct signature of DNA copy number alterations (CNA) can be predictive of pCR in EAC. Pretreatment biopsies from 38 patients with locally advanced EAC (19 with pCR and 19 with pathologic partial/poor response) were assessed for CNA using OncoScan assay. A novel technique was employed where within every cytogenetic band, the quantity of bases gained by each sample was computed as the sum of gained genomic segment lengths weighted by the surplus copy number of each segment. A threefold cross-validation was used to assess association with pCR or pathologic partial/poor response. Forty patients with locally advanced EAC from The Cancer Genome Atlas (TCGA) constituted an independent validation cohort. Gains in the chromosomal loci 14q11 and 17p11 were preferentially associated with pCR. Average area under the receiver operating characteristic curve (AUC) for predicting pCR was 0.80 among the threefold cross-validation test sets. Using 0.3 megabases as the cutoff that optimizes trade-off between sensitivity (63%) and specificity (89%) in the discovery cohort, similar prediction performance for clinical and radiographic response was demonstrated in the validation cohort from TCGA (sensitivity 61%, specificity 82%). Copy number gains in the 14q11 and 17p11 loci may be useful for prediction of pCR, and, potentially, personalization of esophagectomy in EAC.

Genetic Aberration Analysis in Thai Colorectal Adenoma and Early-Stage Adenocarcinoma Patients by Whole-Exome Sequencing

Colorectal adenomas are precursor lesions of colorectal adenocarcinoma. The transition from adenoma to carcinoma in patients with colorectal cancer (CRC) has been associated with an accumulation of genetic aberrations. However, criteria that can screen adenoma progression to adenocarcinoma are still lacking. This present study is the first attempt to identify genetic aberrations, such as the somatic mutations, copy number variations (CNVs), and high-frequency mutated genes, found in Thai patients. In this study, we identified the genomic abnormality of two sample groups. In the first group, five cases matched normal-colorectal adenoma-colorectal adenocarcinoma. In the second group, six cases matched normal-colorectal adenomas. For both groups, whole-exome sequencing was performed. We compared the genetic aberration of the two sample groups. In both normal tissues compared with colorectal adenoma and colorectal adenocarcinoma analyses, somatic mutations were observed in the tumor suppressor gene APC (Adenomatous polyposis coli) in eight out of ten patients. In the group of normal tissue comparison with colorectal adenoma tissue, somatic mutations were also detected in Catenin Beta 1 (CTNNB1), Family With Sequence Similarity 123B (FAM123B), F-Box And WD Repeat Domain Containing 7 (FBXW7), Sex-Determining Region Y-Box 9 (SOX9), Low-Density Lipoprotein Receptor-Related Protein 5 (LRP5), Frizzled Class Receptor 10 (FZD10), and AT-Rich Interaction Domain 1A (ARID1A) genes, which are involved in the Wingless-related integration site (Wnt) signaling pathway. In the normal tissue comparison with colorectal adenocarcinoma tissue, Kirsten retrovirus-associated DNA sequences (KRAS), Tumor Protein 53 (TP53), and Ataxia-Telangiectasia Mutated (ATM) genes are found in the receptor tyrosine kinase-RAS (RTK-RAS) signaling pathway and p53 signaling pathway, respectively. These results suggest that APC and TP53 may act as a potential screening marker for colorectal adenoma and early-stage CRC. This preliminary study may help identify patients with adenoma and early-stage CRC and may aid in establishing prevention and surveillance strategies to reduce the incidence of CRC.

Constitutive function of the Ikaros transcription factor in primary leukemia cells from pediatric newly diagnosed high-risk and relapsed B-precursor ALL patients

We examined the constitutive function of the Ikaros (IK) transcription factor in blast cells from pediatric B-precursor acute lymphoblastic leukemia (BPL) patients using multiple assay platforms and bioinformatics tools. We found no evidence of diminished IK expression or function for primary cells from high-risk BPL patients including a Philadelphia chromosome (Ph)⁺ subset. Relapse clones as well as very aggressive in vivo clonogenic leukemic B-cell precursors isolated from spleens of xenografted NOD/SCID mice that developed overt leukemia after inoculation with primary leukemic cells of patients with BPL invariably and abundantly expressed intact IK protein. These results demonstrate that a lost or diminished IK function is not a characteristic feature of leukemic cells in Ph⁺ or Ph^- high-risk BPL.

Finding common regions of alteration in copy number data

In this chapter, we review some recent methods designed for detecting recurrent copy number regions, that is, genomic regions that show evidence of being altered in a set of samples. We analyze Affymetrix SNP6 data from 87 Her2-type breast tumors from a recent study using three different methods, showing different definitions and features of common regions: studying heterogeneity in copy number profiles, refining candidates for driver oncogenes, and consolidating broad amplifications.

Copy number variation signature to predict human ancestry

BACKGROUND

Copy number variations (CNVs) are genomic structural variants that are found in healthy populations and have been observed to be associated with disease susceptibility. Existing methods for CNV detection are often performed on a sample-by-sample basis, which is not ideal for large datasets where common CNVs must be estimated by comparing the frequency of CNVs in the individual samples. Here we describe a simple and novel approach to locate genome-wide CNVs common to a specific population, using human ancestry as the phenotype.

RESULTS

We utilized our previously published Genome Alteration Detection Analysis (GADA) algorithm to identify common ancestry CNVs (caCNVs) and built a caCNV model to predict population structure. We identified a 73 caCNV signature using a training set of 225 healthy individuals from European, Asian, and African ancestry. The signature was validated on an independent test set of 300 individuals with similar ancestral background. The error rate in predicting ancestry in this test set was 2% using the 73 caCNV signature. Among the caCNVs identified, several were previously confirmed experimentally to vary by ancestry. Our signature also contains a caCNV region with a single microRNA (MIR270), which represents the first reported variation of microRNA by ancestry.

CONCLUSIONS

We developed a new methodology to identify common CNVs and demonstrated its performance by building a caCNV signature to predict human ancestry with high accuracy. The utility of our approach could be extended to large case-control studies to identify CNV signatures for other phenotypes such as disease susceptibility and drug response.

Genome-wide identification of significant aberrations in cancer genome

BACKGROUND

Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme.

RESULTS

We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies.

CONCLUSIONS

Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open-source and platform-independent SAIC software is implemented using C++, together with R scripts for data formatting and Perl scripts for user interfacing, and it is easy to install and efficient to use. The source code and documentation are freely available at http://www.cbil.ece.vt.edu/software.htm.

Finding recurrent copy number alterations preserving within-sample homogeneity

MOTIVATION

Copy number alterations (CNAs) represent an important component of genetic variation and play a significant role in many human diseases. Development of array comparative genomic hybridization (aCGH) technology has made it possible to identify CNAs. Identification of recurrent CNAs represents the first fundamental step to provide a list of genomic regions which form the basis for further biological investigations. The main problem in recurrent CNAs discovery is related to the need to distinguish between functional changes and random events without pathological relevance. Within-sample homogeneity represents a common feature of copy number profile in cancer, so it can be used as additional source of information to increase the accuracy of the results. Although several algorithms aimed at the identification of recurrent CNAs have been proposed, no attempt of a comprehensive comparison of different approaches has yet been published.

RESULTS

We propose a new approach, called Genomic Analysis of Important Alterations (GAIA), to find recurrent CNAs where a statistical hypothesis framework is extended to take into account within-sample homogeneity. Statistical significance and within-sample homogeneity are combined into an iterative procedure to extract the regions that likely are involved in functional changes. Results show that GAIA represents a valid alternative to other proposed approaches. In addition, we perform an accurate comparison by using two real aCGH datasets and a carefully planned simulation study.

AVAILABILITY

GAIA has been implemented as R/Bioconductor package. It can be downloaded from the following page http://bioinformatics.biogem.it/download/gaia.

CONTACT

ceccarelli@unisannio.it; morganella@unisannio.it.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.