Abstract
A well-established approach for detecting genes involved in tumorigenesis due to copy number alterations (CNAs) is to assess the recurrence of the alteration across multiple samples. Expression data can be used to filter this list of candidates by assessing whether the gene expression significantly differs between tumors depending on the copy number status. A drawback of this approach is that it may fail to detect low-recurrent drivers. Furthermore, this analysis does not provide information about expression changes for each gene as compared to the whole data set and does not take into consideration the expression of normal samples. Here we describe a novel method (Oncodrive-CIS) aimed at ranking genes according to the expression impact caused by the CNAs. The rationale of Oncodrive-CIS is based on the hypothesis that genes involved in cancer due to copy number changes are more biased towards misregulation than are bystanders. Moreover, to gain insight into the expression changes caused by gene dosage, the expression of samples with CNAs is compared to that of tumor samples with diploid genotype and also to that of normal samples. Oncodrive-CIS demonstrated better performance in detecting putative associations between copy-number and expression in simulated data sets as compared to other methods aimed to this purpose, and picked up genes likely to be related with tumorigenesis when applied to real cancer samples. In summary, Oncodrive-CIS provides a statistical framework to evaluate the in cis effect of CNAs that may be useful to elucidate the role of these aberrations in driving oncogenesis. An implementation of this method and the corresponding user guide are freely available at http://bg.upf.edu/oncodrivecis.
Highlights
Interpreting the role of copy number alterations (CNAs) in cancer is challenging because it requires unraveling causative aberrations from passenger ones
Oncodrive-CIS Overview The rationale of the method is based on two hypotheses: first, a gene driving oncogenesis through copy number changes is more prone to be biased towards overexpression, compared to bystanders; second, the effect of CNAs is better assessed by observing expression changes among tumors and taking into account normal samples data
Since the magnitude of expression changes measured in gene deletions was lower than that of multicopy amplifications, Oncodrive-CIS carried out these analyses separately to obtain a fair estimation of their impact
Summary
Interpreting the role of copy number alterations (CNAs) in cancer is challenging because it requires unraveling causative aberrations from passenger ones. A currently well-established approach for identifying genes with alterations involved in the disease is to evaluate whether they are recurrently amplified or deleted across multiple tumor samples, and thereafter to use expression data to further refine the evaluation of the potential drivers: the expression of key genes may be regulated by other mechanisms, an amplification or deletion that does not modify the expression of the altered gene is unlikely to be tumorigenic [1] This may be performed by comparing the expression of amplified or deleted tumor samples to their diploid counterparts to check whether they show consistent expression changes [2]. Even small expression changes can reach significance if the sample size is large enough ( this may overestimate the number of genes to include), and two-groups comparison tests tend to not reach significance when the group of samples with CNAs is small, and this may further impair the detection of less-recurrent drivers
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