Computational protocol: Copy number alterations detected by whole-exome and whole-genome sequencing of esophageal adenocarcinoma

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Protocol publication

[…] The NGS data, including both WGS and WES data, were generated in [] and stored in the database of Genotypes and Phenotypes (dbGaP) (study accession: phs000598.v1.p1). The dataset is comprised of 145 matched tumor-normal samples. Among them, 15 samples both have WGS and WES data, and the rest 130 samples have only WES data. The EA samples include those from the gastric-esophageal junction, not treated with chemotherapy or radiation before surgery. The tumor samples were examined by a board-certified pathologist and ensured that their carcinoma content >70 %. The samples were sequenced on multiple Illumina HiSeq flow cells to have the average target exome coverage of ~80× in WES data, and sequenced on the Illumina Genome Analyzer Iix or the Illumina HiSeq sequencer with an average of ~30× coverage depth in WGS data. The details of the sample collection, DNA extraction, and sequencing procedures can be found in [].The raw sequence data were extracted from dbGaP using the NCBI SRA Toolkit; the sequences were aligned to the NCBI build 37 (hg19) reference using BWA [] and processed following GATK best practices. The base score re-calibrated bam files were used for CNA detection. [...] Control-FREEC was applied in this study on both WGS and WES data. It divided the genome into small contiguous regions using sliding windows. The read count profiles in each region for normal and tumor samples were computed and normalized accounting for GC-content and mappability. The read count ratios of tumors to matched normal samples were calculated and used as the proxy of the copy number ratios. A LASSO-based algorithm was used to segment the data. LASSO is a widely used generalized linear regression method that involves penalizing the absolute size of its regression coefficients []. Using LASSO, a piecewise constant smoothed step profile was used to model the copy number ratios, and the positions with nonzero coefficients were considered as change points. For WES data, the window size was set to 500, and the step size was set to 250, which were recommended by the authors. For WGS data, those parameters were set as 2000 and 1000, respectively. Control-FREEC estimates the normal cell contamination in tumor samples by comparing the observed and predicted copy numbers. It uses the Kolmogorov-Smirnov test to assess the false-positive rate of each detected CNA. Control-FREEC can predict absolute copy numbers if the ploidy information is provided. We used ABSOLUTE [] to estimate the ploidy of the 15 EA samples using WES data, and the results are listed in the supplement. In this study we classified the identified CNAs based on their status (amplification or deletion) instead of their absolute copy numbers. Control-FREEC ignored genomic regions with mappability less than 0.85 by default, and hence, we did not consider the effect of unmappable regions in this study.GISTIC2.0 was used to identify regions with a statistically high frequency of copy number aberrations over background aberrations. It evaluated both the frequency and the significance to identify regions of interest. The G score measured both the frequency of aberrations, and the magnitude of the copy number changes (log ratio intensity) in each sample. Each location was scored separately for gains and losses. Then locations in each sample were permuted to simulate random aberrations. This random distribution was compared to the observed statistic to identify scores that are statistically significant. A false discovery rate (FDR) multiple testing correction was applied to calculate a q-bound significance score. Within each statistically significant region, a peak region was identified so that the region with a maximal G score and a minimal q value is most likely to contain affected genes. In addition to the q value, it also computed the residual q value, which measured the q value of a peak region after removing events that overlap with other more significant peak regions in the same chromosome. The 145 WES data were segmented using circular binary segmentation (CBS) algorithm [] and combined to form the segmentation file, while the 15 WGS data were segmented using Control-FREEC as described above. The parameter settings were as follows: amplification threshold = 0.1, deletion threshold = 0.1, broad length cutoff = 0.98, remove X-chromosome = 0, and confidence level = 0.95.Whenever possible, default parameters and recommended settings were used in the implementation of these tools. […]

Pipeline specifications

Software tools BWA, GATK, Control-FREEC
Databases dbGaP SRA
Applications WGS analysis, WES analysis
Diseases Adenocarcinoma, Barrett Esophagus, Neoplasms