Manchester Breast Center Mutation Protocol

Methodology for the NSG output, as described in Cheng et al 2015.

Genetic sequencing NGS

1. Demultiplexing and Read Alignment

BCL2FASTQ version 1.8.3 (Illumina) was used to demultiplex the base calls into individual FASTQ files using the following options: --force --no-eamss --fastq-cluster-count 0 --mismatches 1. Reads for which a matching index could not be identified were stored in a set of FASTQ files labeled Undetermined Indices. To monitor possible barcode contamination, a check was also performed to determine whether any known barcode indices were over-represented within the undetermined indices.

Vestigial adapter sequences were removed from the 3′ end of sequence reads before alignment using adaptor-trimming tools (TrimGalore and CutAdapt software version 0.2.5, http://www.bioinformatics.babraham.ac.uk/projects/trim_galore , last accessed October 1, 2013). Adaptor trimming was performed using the following options: 10% error and minimum of 3-bp match. Read pairs with insert size length <25 bp were discarded. Reads were aligned in paired-end mode to the hg19 b37 version of the human genome using BWA-MEM (Burrows-Wheeler Aligner software version 0.7.5a, http://arxiv.org/abs/1303.3997 , last accessed October 1, 2013). Aligned reads were written to a Sequence Alignment/Map file, which was converted into Binary Alignment/Map format using tools available in Picard (http://broadinstitute.github.io/picard , last accessed November 20, 2014). PCR duplicates were marked for exclusion in subsequent analysis using the MarkDuplicates tool in Picard. IndelRealigner (GATK software version 2.7.227) was used to perform a local multiple sequence realignment of reads in regions where indels were present; samples from the same patient (eg, tumor and normal) were jointly realigned. BaseRecalibrator (GATK27) was used to adjust the reported quality scores based on the following covariates: read group, reported quality score, cycle, and local sequence context. Recalibrated quality scores were subjected to a base quality threshold of 20, corresponding to a 1/100 chance of error. Both IndelRealigner and BaseRecalibrator steps were performed on intervals corresponding to the targeted regions only.

2. SNVs and Indels in Variant Calling

We performed paired-sample variant calling on tumor samples and their respective matched normal samples to identify point mutations/SNVs and small indels (<30 bp in length). In instances where a matched normal sample was unavailable, or where the matched normal sample was sequenced with low coverage (<50×), tumor samples were considered as unmatched samples, and variant calling was performed using a within-batch mixed normal control sample instead. MuTect29 (version 1.1.4) was used for SNV calling, and SomaticIndelDetector,27 a tool in GATK version 2.3.9, was used for detecting indel events. The following standard filters were applied to the raw MuTect and SomaticIndelDetector output as a first pass (with more rigorous filters being applied at a subsequent stage): variant frequency in tumor/variant frequency in normal >5×, Number of mutant allele reads in tumor sample >5, variant frequency in tumor sample >1%. Variants were annotated using Annovar30 (version 527), and the output was reformatted using a custom script to ensure annotations of the cDNA and protein primary sequence changes are compliant with HGVS31 standards. Dinucleotide and trinucleotide substitutions identified by the pipeline were annotated manually because this functionality was not supported by the version of Annovar used. Only variant annotations relative to the canonical transcript for each gene (derived from a list of known canonical transcripts obtained from the UCSC Genome Browser32) were reported. In cases where variant calling was performed using an unmatched normal sample, variants with minor allele frequency >1% in the 1000 Genomes cohort33 were also removed because they were more likely to be common population polymorphisms than somatic mutations.