Overview

CNAqc Input/Output
CNAqc Input/Output

Summary

CNAqc requires in input

  • read counts from somatic mutations such as single-nucleotide variants (SNVs) or insertion-deletions (indels);
  • allele-specific copy number segments (CNAs) for clonal segments and, optionally, for subclonal segments;
  • a tumor purity estimate.

CNAqc uses chromosome coordinates the to map mutations to segments. The conversion of relative to absolute genome coordinates requires to fix a reference genome build; supported references are GRCh38/hg17 and hg19/GRCh37, but custom references can also be built.

The tool can elaborate a number of analysis to assess the consistency among mutations, CNAs and tumour purity (see Articles).

CNAqc can be used to:

  1. QC concordance across the input mutations, CNAs and purity;
  2. select among alternative tumour segmentations and purity/ ploidy estimates;
  3. optimize CNA calling with automatic QC procedures leveraging the Sequenza copy number caller;
  4. estimate CCF values of the input variants, and estimate their uncertainty;
  5. identify patterns of over-fragmentation of chromosome arms;
  6. smooth and subset segments with various filters;
  7. annotate putative driver mutations among the input variants, using VariantAnnotation.

The model

The following concepts are used to develop CNAqc.

VAF peaks

CNAqc Input/Output
Expected VAF peaks for mutations mapped to diploid heterozygous and triploid clonal CNAs (at purity \(\pi=1\)).

Clonal CNAs

Consider:

  • mutations present in a percentage \(0<c<1\) of tumour cells, sitting on a segment \(nA:nB\);
  • tumour purity \(\pi\);
  • a healthy diploid normal;

Since the proportion of all reads from the tumour is \(\pi(n_A+n_B)\), and from the normal is \(2(1-\pi)\). Then, muations present in \(m\) copies of the tumour genome should peak at VAF value \[ v_m(c) = \dfrac{m \pi c}{ 2 (1 - \pi) + \pi (n_A+n_B) } \, . \]

Subclonal CNAs

Consider a mixture of 2 subclones with segments \(n_{A,1}:n_{B,1}\) and \(n_{A,2}:n_{B,2}\), proportions \(\rho_1\) and \(\rho_2\) (\(\rho_1+\rho_2=1\)).

The expected peak for a shared mutation with multiplicity \(m_1\)/ \(m_2\) in the first/ second subclone is \[ v_{m_1,m_2} = \dfrac{ (m_1\rho_1 + m_2\rho_2) \pi } { 2 (1 - \pi) + \pi [(n_{A,1}+n_{B,1})\rho_1 + (n_{A,2}+n_{B,2})\rho_2] } \, . \]

The expected peak for a private mutation with multiplicity \(m\) in subclone \(i\) is \[ v_{m_i} = \dfrac{ m\rho_i \pi } { 2 (1 - \pi) + \pi [(n_{A,1}+n_{B,1})\rho_1 + (n_{A,2}+n_{B,2})\rho_2] } \, . \]

Cancer Cell Fractions (CCFs)

Given VAF, tumour purity and CNAs, CCF values can be computed as

\[ c = \dfrac{ v[ (n_a+n_B - 2)\pi + 2 ] } { m \pi } \]

Therefore the problem of computing CCFs is essentially linked to computing m, mutation multiplicity. This type of computation can be done by “phasing” the VAFs against the multiplicity.