Recurrent copy number alterations (CNAs) play an important role in cancer genesis. While a number of computational methods have been proposed for identifying such CNAs, their relative merits remain largely unknown in practice since very few efforts have been focused on comparative analysis of the methods. To facilitate studies of recurrent CNA identification in cancer genome, it is imperative to conduct a comprehensive comparison of performance and limitations among existing methods. In this paper, six representative methods proposed in the latest six years are compared. These include one-stage and two-stage approaches, working with raw intensity ratio data and discretized data respectively. They are based on various techniques such as kernel regression, correlation matrix diagonal segmentation, semi-parametric permutation and cyclic permutation schemes. We explore multiple criteria including type I error rate, detection power, Receiver Operating Characteristics (ROC) curve and the area under curve (AUC), and computational complexity, to evaluate performance of the methods under multiple simulation scenarios. We also characterize their abilities on applications to two real datasets obtained from cancers with lung adenocarcinoma and glioblastoma. This comparison study reveals general characteristics of the existing methods for identifying recurrent CNAs, and further provides new insights into their strengths and weaknesses. It is believed helpful to accelerate the development of novel and improved methods.

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.

This study explored using a novel diffuse correlation spectroscopy (DCS) flow-oximeter to noninvasively monitor blood flow and oxygenation changes in head and neck tumors during radiation delivery. A fiber-optic probe connected to the DCS flow-oximeter was placed on the surface of the radiologically/clinically involved cervical lymph node. The DCS flow-oximeter in the treatment room was remotely operated by a computer in the control room. From the early measurements, abnormal signals were observed when the optical device was placed in close proximity to the radiation beams. Through phantom tests, the artifacts were shown to be caused by scattered x rays and consequentially avoided by moving the optical device away from the x-ray beams. Eleven patients with head and neck tumors were continually measured once a week over a treatment period of seven weeks, although there were some missing data due to the patient related events. Large inter-patient variations in tumor hemodynamic responses were observed during radiation delivery. A significant increase in tumor blood flow was observed at the first week of treatment, which may be a physiologic response to hypoxia created by radiation oxygen consumption. Only small and insignificant changes were found in tumor blood oxygenation, suggesting that oxygen utilizations in tumors during the short period of fractional radiation deliveries were either minimal or balanced by other effects such as blood flow regulation. Further investigations in a large patient population are needed to correlate the individual hemodynamic responses with the clinical outcomes for determining the prognostic value of optical measurements.