Total reads mapped in the baseline group does not change by DNA extraction type.

<p>GR UNG = GeneRead uracil-N-glycosylase, QA = QiaAmp FFPE kit.</p

Deamination events increase with sample age when not treated with UNG.

<p>GR UNG = GeneRead uracil-N-glycosylase, QA = QiaAmp FFPE kit.</p

Experimental workflow.

<p>DNA extracted from three patient groups was sequenced after treatment with and without UNG. The fixation group consisted of three patients from 2015 where paired tissue samples were snap frozen or put into formalin for a determined length of times. A baseline group consisting of 20 patients all from 2015/16 were fixed in formalin for an unknown amount of time. The block age group consisted of three patients from 1994, three from 2004 and three from 2014; all samples had an unknown fixation time. DNA was extracted from normal (N) and tumor (T) tissue when available.</p

Deamination mutations significantly increase at 48 hours fixation treatment time.

<p>Analysis of deamination events in the fixation group for all time points comparing UNG (uracil-N-glycosylase) and non-UNG effects. All variants representative of possible deamination events (C->T, G->A) are denoted by the orange crosses; linear regression line shown by the broken line and encompassed by dotted lines representing the 95% CI (CI regions colored). Similarly all other variants are represented by blue dots, shading and lines. After UNG treatment of the DNA, the deamination events nearly disappear (bottom row).</p

Fixation group patient metadata.

<p>Fixation group patient metadata.</p

Targeted regions of amplification for the NGS assay.

<p>Targeted regions of amplification for the NGS assay.</p

Number of deamination variants ranges widely amongst samples when fixation time is uncontrolled.

<p>Orange crosses represent number of deamination variants (y-axis; C->T/G->A). Blue dots represent all other possible variants. GR UNG = GeneRead uracil-N-glycosylase, QA = QiaAmp FFPE kit.</p

Read quality in the baseline group does not change by DNA extraction type.

<p>Read quality as measured by percent reads mapped. GR UNG = GeneRead uracil-N-glycosylase, QA = QiaAmp FFPE kit.</p

Sequencing read quality decreases over fixation time.

<p>Read quality as a measure of percent reads mapped (y-axis) decreases with a longer tissue fixation time (x-axis). Blue dots represent DNA without UNG treatment (extracted using QiaAmp FFPE kit; QA), orange crossed represent matched DNA treated with UNG (extraction using the GeneRead kit; GR UNG). Linear regression line depicted by the central broken line and encompassed by dotted lines representing the 95% confidence interval (CI region colored). Frozen samples used for ground truth are represented by green dots. GR UNG = GeneRead uracil-N-glycosylase, QA = QiaAmp FFPE kit.</p

Mutation Discovery in Regions of Segmental Cancer Genome Amplifications with CoNAn-SNV: A Mixture Model for Next Generation Sequencing of Tumors

<div><p>Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumor genome—in particular single nucleotide variants (SNVs). Most current computational and statistical models for analyzing next generation sequencing data, however, do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs)—which require special treatment of the data. Here we present CoNAn-SNV (<u>Co</u>py <u>N</u>umber <u>An</u>notated SNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended genotype space where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). We introduce the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to discover 21 experimentally revalidated somatic non-synonymous mutations in a lobular breast cancer genome that were not detected using copy number insensitive SNV detection algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. This was also supported by analysis of a recently published lymphoma genome with a relatively quiescent karyotype, where CoNAn-SNV showed similar results to other callers except in regions of copy number gain where increased sensitivity was conferred. Our results indicate that in genomically unstable tumors, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes.</p> </div