qpure: A tool to estimate tumor cellularity from genome-wide single-nucleotide polymorphism profiles

PLoS One. 2012;7(9):e45835. doi: 10.1371/journal.pone.0045835. Epub 2012 Sep 25.

Abstract

Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 ([Formula: see text]-value=0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 ([Formula: see text]-value [Formula: see text] 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 ([Formula: see text]-value=0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cell Line, Tumor
  • Computational Biology / methods*
  • Exons
  • Gene Expression Regulation
  • Gene Frequency
  • Genome-Wide Association Study
  • Humans
  • Loss of Heterozygosity
  • Models, Genetic
  • Models, Statistical
  • Mutation
  • Pancreatic Neoplasms / genetics
  • Pancreatic Neoplasms / metabolism
  • Polymorphism, Single Nucleotide*
  • Regression Analysis
  • Sequence Analysis, DNA
  • Software

Grants and funding

This research has been supported by the National Health and Medical Research Council of Australia (NHMRC; 631701, 535903, 427601); the Australian Government: Department of Innovation, Industry, Science and Research; the Australian Cancer Research Foundation; the Queensland Government (National and International Research Alliances Program); the University of Queensland; the Cancer Council New South Wales (NSW): (SRP06-01); the Cancer Institute NSW: (06/ECF/1-24; 09/CDF/2-40; 07/CDF/1-03; 10/CRF/1-01, 08/RSA/1-15, 07/CDF/1-28, 10/CDF/2-26,10/FRL/2-03, 06/RSA/1-05, 09/RIG/1-02, 10/TPG/1-04, 11/REG/1-10, 11/CDF/3-26); the Garvan Institute of Medical Research; the Avner Nahmani Pancreatic Cancer Research Foundation; the R.T. Hall Trust; the Petre Foundation; the Gastroenterological Society of Australia; the American Association for Cancer Research Landon Foundation INNOVATOR Award; the Royal Australasian College of Surgeons; the Royal Australasian College of Physicians; and the Royal College of Pathologists of Australasia. SG is a recipient of a NHMRC Principal Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.