WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing.

MOTIVATION: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data. RESULTS: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data. AVAILABILITY AND IMPLEMENTATION: Source code and executables are available at https://github.com/WaveCNV. The segmentation algorithm is implemented in MATLAB, and copy number assignment is implemented Perl. CONTACT: lakshmi.muthuswamy@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Expression profiling during mammary epithelial cell three-dimensional morphogenesis identifies PTPRO as a novel regulator of morphogenesis and ErbB2-mediated transformation.

Identification of genes that are upregulated during mammary epithelial cell morphogenesis may reveal novel regulators of tumorigenesis. We have demonstrated that gene expression programs in mammary epithelial cells grown in monolayer cultures differ significantly from those in three-dimensional (3D) cultures. We identify a protein tyrosine phosphate, PTPRO, that was upregulated in mature MCF-10A mammary epithelial 3D structures but had low to undetectable levels in monolayer cultures. Downregulation of PTPRO by RNA interference inhibited proliferation arrest during morphogenesis. Low levels of PTPRO expression correlated with reduced survival for breast cancer patients, suggesting a tumor suppressor function. Furthermore, we showed that the receptor tyrosine kinase ErbB2/HER2 is a direct substrate of PTPRO and that loss of PTPRO increased ErbB2-induced cell proliferation and transformation, together with tyrosine phosphorylation of ErbB2. Moreover, in patients with ErbB2-positive breast tumors, low PTPRO expression correlated with poor clinical prognosis compared to ErbB2-positive patients with high levels of PTPRO. Thus, PTPRO is a novel regulator of ErbB2 signaling, a potential tumor suppressor, and a novel prognostic marker for patients with ErbB2-positive breast cancers. We have identified the protein tyrosine phosphatase PTPRO as a regulator of three-dimensional epithelial morphogenesis of mammary epithelial cells and as a regulator of ErbB2-mediated transformation. In addition, we demonstrated that ErbB2 is a direct substrate of PTPRO and that decreased expression of PTPRO predicts poor prognosis for ErbB2-positive breast cancer patients. Thus, our results identify PTPRO as a novel regulator of mammary epithelial transformation, a potential tumor suppressor, and a predictive biomarker for breast cancer.

Predictive modeling in case-control single-nucleotide polymorphism studies in the presence of population stratification: a case study using Genetic Analysis Workshop 16 Problem 1 dataset.

In this paper, we apply the gradient-boosting machine predictive model to the rheumatoid arthritis data for predicting the case-control status. QQ-plot suggests severe population stratification. In univariate genome-wide association studies, a correction factor for ethnicity confounding can be derived. Here we propose a novel strategy to deal with population stratification in the context of multivariate predictive modeling. We address the problem by clustering the subjects on the axes of genetic variations, and building a predictive model separately in each cluster. This allows us to control ethnicity without explicitly including it in the model, which could marginalize the genetic signal we are trying to discover. Clustering not only leads to more similar ethnicity groups but also, as our results show, increases the accuracy of our model when compared to the non-clustered approach. The highest accuracy is achieved with the model adjusted for population stratification, when the genetic axes of variation are included among the set of predictors, although this may be misleading given the confounding effects.