Low-cost and clinically applicable copy number profiling using repeat DNA.

BACKGROUND: Somatic copy number alterations (SCNAs) are an important class of genomic alteration in cancer. They are frequently observed in cancer samples, with studies showing that, on average, SCNAs affect 34% of a cancer cell's genome. Furthermore, SCNAs have been shown to be major drivers of tumour development and have been associated with response to therapy and prognosis. Large-scale cancer genome studies suggest that tumours are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Despite the frequency of SCNAs and their clinical relevance, the use of genomics assays in the clinic is biased towards targeted gene panels, which identify SNVs but provide limited scope to detect SCNAs throughout the genome. There is a need for a comparably low-cost and simple method for high-resolution SCNA profiling. RESULTS: We present conliga, a fully probabilistic method that infers SCNA profiles from a low-cost, simple, and clinically-relevant assay (FAST-SeqS). When applied to 11 high-purity oesophageal adenocarcinoma samples, we obtain good agreement (Spearman's rank correlation coefficient, rs=0.94) between conliga's inferred SCNA profiles using FAST-SeqS data (approximately £14 per sample) and those inferred by ASCAT using high-coverage WGS (gold-standard). We find that conliga outperforms CNVkit (rs=0.89), also applied to FAST-SeqS data, and is comparable to QDNAseq (rs=0.96) applied to low-coverage WGS, which is approximately four-fold more expensive, more laborious and less clinically-relevant. By performing an in silico dilution series experiment, we find that conliga is particularly suited to detecting SCNAs in low tumour purity samples. At two million reads per sample, conliga is able to detect SCNAs in all nine samples at 3% tumour purity and as low as 0.5% purity in one sample. Crucially, we show that conliga's hidden state information can be used to decide when a sample is abnormal or normal, whereas CNVkit and QDNAseq cannot provide this critical information. CONCLUSIONS: We show that conliga provides high-resolution SCNA profiles using a convenient, low-cost assay. We believe conliga makes FAST-SeqS a more clinically valuable assay as well as a useful research tool, enabling inexpensive and fast copy number profiling of pre-malignant and cancer samples.

Quantifying Microsatellite Mutation Rates from Intestinal Stem Cell Dynamics in Msh2-Deficient Murine Epithelium.

Microsatellite sequences have an enhanced susceptibility to mutation, and can act as sentinels indicating elevated mutation rates and increased risk of cancer. The probability of mutant fixation within the intestinal epithelium is dictated by a combination of stem cell dynamics and mutation rate. Here, we exploit this relationship to infer microsatellite mutation rates. First a sensitive, multiplexed, and quantitative method for detecting somatic changes in microsatellite length was developed that allowed the parallel detection of mutant [CA]n sequences from hundreds of low-input tissue samples at up to 14 loci. The method was applied to colonic crypts in Mus musculus, and enabled detection of mutant subclones down to 20% of the cellularity of the crypt (∼50 of 250 cells). By quantifying age-related increases in clone frequencies for multiple loci, microsatellite mutation rates in wild-type and Msh2-deficient epithelium were established. An average 388-fold increase in mutation per mitosis rate was observed in Msh2-deficient epithelium (2.4 × 10-2) compared to wild-type epithelium (6.2 × 10-5).