Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.

Activation of the insulin-like growth factor 1 receptor may underlie clinical resistance to inhibition of the anaplastic lymphoma receptor kinase in lung cancer. Crizotinib, a selective tyrosine kinase inhibitor (TKI), shows marked activity in patients whose lung cancers harbor fusions in the gene encoding anaplastic lymphoma receptor tyrosine kinase (ALK), but its efficacy is limited by variable primary responses and acquired resistance. In work arising from the clinical observation of a patient with ALK fusion–positive lung cancer who had an exceptional response to an insulin-like growth factor 1 receptor (IGF-1R)-specific antibody, we define a therapeutic synergism between ALK and IGF-1R inhibitors. Similar to IGF-1R, ALK fusion proteins bind to the adaptor insulin receptor substrate 1 (IRS-1), and IRS-1 knockdown enhances the antitumor effects of ALK inhibitors. In models of ALK TKI resistance, the IGF-1R pathway is activated, and combined ALK and IGF-1R inhibition improves therapeutic efficacy. Consistent with this finding, the levels of IGF-1R and IRS-1 are increased in biopsy samples from patients progressing on crizotinib monotherapy. Collectively these data support a role for the IGF-1R–IRS-1 pathway in both ALK TKI–sensitive and ALK TKI–resistant states and provide a biological rationale for further clinical development of dual ALK and IGF-1R inhibitors.

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data, such as the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA). While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources remains challenging, due to differences in sample and data processing. Here, we developed a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment, gene expression quantification, and batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from GTEx and TCGA and successfully corrected for study-specific biases, enabling comparative analysis between TCGA and GTEx. The normalized datasets are available for download on figshare.