A fast variational algorithm to detect the clonal copy number substructure of tumors from single-cell data

Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clonal substructure of tumors from single cell data. It uses a multichannel segmentation algorithm exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN can automatically and accurately discriminate between malignant and non-malignant cells, resulting in a practical framework to analyze tumors and their microenvironment. We apply SCEVAN to several datasets encompassing 106 samples and 93,322 cells from different tumors types and technologies. We demonstrate its application to characterize the intratumor heterogeneity and geographic evolution of malignant brain tumors.

Association Tests Using Copy Number Profile Curves (CONCUR) Enhances Power in Rare Copy Number Variant Analysis

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV association, kernel-based methods have been shown to be a powerful tool because their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their ability to accommodate effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., Copy Number profile Curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype association by comparing individuals’ copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of “copy number profile curves” to describe the CNV profile of an individual, and the “common area under the curve (cAUC) kernel” to model the multi-feature CNV effects. Compared to existing methods, CONCUR captures the effects of CNV dosage and length, accounts for the continuous nature of copy number values, and accommodates between- and within-locus etiological heterogeneities without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable and improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in gene pathways associated with phenotypes using data from the Swedish Schizophrenia Study and the Taiwan Biobank.Author summaryCopy number variants comprise a large proportion of variation in human genomes. Large rare CNVs, especially those disrupting genes or changing the dosages of genes, can carry relatively strong risks for neurodevelopmental and neuropsychiatric disorders. Kernel-based association methods have been developed for the analysis of rare CNVs and shown to be a valuable tool. Kernel methods model the collective effect of rare CNVs using flexible kernel functions that capture the characteristics of CNVs and measure CNV similarity of individual pairs. Typically kernels are created by summarizing similarity within an artificially defined “CNV locus” and then collapsing across all loci. In this work, we propose a new kernel-based test, CONCUR, that is based on the CNV location information contained in standard processing of the variants and removes the need for any arbitrarily defined CNV loci. CONCUR quantifies similarity between individual pairs as the common area under their copy number profile curves and is designed to detect CNV dosage, length and dosage-length interaction effects. In simulation studies and real data analysis, we demonstrate the ability of CONCUR test to detect CNV effects under diverse CNV architectures with power and robustness over existing methods.

Profiling copy number alterations in cell-free tumour DNA using a single-reference

BackgroundThe accurate detection of copy number alterations from the analysis of circulating cell free tumour DNA (ctDNA) in blood is essential to realising the potential of liquid biopsies. However, currently available approaches require a large number of plasma samples from healthy individuals, sequenced using the same platform and protocols to act as a reference panel. Obtaining this reference panel can be challenging, prohibitively expensive and limits the ability to migrate to improved sequencing platforms and improved protocols.MethodsWe developed qCNV and sCNA-seq, two distinct tools that together provide a new approach for profiling somatic copy number alterations (sCNA) through the analysis of cell free DNA (cfDNA) without a reference panel. Our approach was designed to identify sCNA from cfDNA through the analysis of a single plasma sample and a matched normal DNA sample -both of which can be obtained from the same blood draw. qCNV is an efficient method for extracting read-depth from BAM files and sCNA-seq is a method that uses a probabilistic model of read depth to infer the copy number segmentation of the tumour. We compared the results from our pipeline to the established copy number profile of a cell-line, as well as the results from the plasma-Seq analysis of cfDNA-like mixtures and real, clinical data-sets.ResultsWith a single, unmatched, germline reference sample, our pipeline recapitulated the known copy number profile of a cell-line and demonstrated similar results to those obtained from plasma-Seq. With less than 1X genome coverage, our approach identified clinically relevant sCNA in samples with as little as 20 % tumour DNA. When applied to plasma samples from cancer patients, our pipeline identified clinically significant mutations.ConclusionsThese results show it is possible to identify therapeutically-relevant copy number mutations from plasma samples without the need to generate a reference panel from a large number of healthy individuals. Together with the range of sequencing platforms supported by our qCNV+sCNA-Seq pipeline, as well as the Galaxy implementation of this solution, this pipeline makes cfDNA profiling more accessible and makes it easier to identify sCNA from the plasma of cancer patients.