MOTIVATION: Clonal heterogeneity is common in many types of cancer, including chronic lymphocytic leukemia (CLL). Previous research suggests that the presence of multiple distinct cancer clones is associated with clinical outcome. Detection of clonal heterogeneity from high throughput data, such as sequencing or single nucleotide polymorphism (SNP) array data, is important for gaining a better understanding of cancer and may improve prediction of clinical outcome or response to treatment. Here, we present a new method, CloneSeeker, for inferring clinical heterogeneity from sequencing data, SNP array data, or both. RESULTS: We generated simulated SNP array and sequencing data and applied CloneSeeker along with two other methods. We demonstrate that CloneSeeker is more accurate than existing algorithms at determining the number of clones, distribution of cancer cells among clones, and mutation and/or copy numbers belonging to each clone. Next, we applied CloneSeeker to SNP array data from samples of 258 previously untreated CLL patients to gain a better understanding of the characteristics of CLL tumors and to elucidate the relationship between clonal heterogeneity and clinical outcome. We found that a significant majority of CLL patients appear to have multiple clones distinguished by copy number alterations alone. We also found that the presence of multiple clones corresponded with significantly worse survival among CLL patients. These findings may prove useful for improving the accuracy of prognosis and design of treatment strategies. AVAILABILITY AND IMPLEMENTATION: Code available on R-Forge: https://r-forge.r-project.org/projects/CloneSeeker/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2019) doi: 10.1093/bioinformatics/btz057 In the original article, under heading 2.4 ‘Performance metrics’, the equations under points 2, 3 and 4 were incorrect. These have now been corrected as below.

Abstract Motivation: A major focus of current cancer research is to identify genes that can be used as markers for prognosis and diagnosis, and as targets for therapy. Microarray technology has been applied extensively for this purpose, even though it has been reported that the agreement between microarray platforms is poor. A critical question is: how can we best combine the measurements of matched genes across microarray platforms to develop diagnostic and prognostic tools related to the underlying biology? Results: We introduce a statistical approach within a Bayesian framework to combine the microarray data on matched genes from three investigations of gene expression profiling of B-cell chronic lymphocytic leukemia (CLL) and normal B cells (NBC) using three different microarray platforms, oligonucleotide arrays, cDNA arrays printed on glass slides and cDNA arrays printed on nylon membranes. Using this approach, we identified a number of genes that were consistently differentially expressed between CLL and NBC samples. Availability: Glass slide cDNA array data are available through the public archives of Stanford University at http://cmgm.stanford.edu/pbrown/. Oligonucleotide array data are available in the supplemental material from Klein ηl. Nylon membrane cDNA microarray data are freely available at our website, http://bioinformatics.mdanderson.org/pubdata.html. Software to batch-process gene annotations (GeneLink) is available at http://bioinformatics.mdanderson.org/GeneLink.html. Image quantification software (ScanAlyze) is available at http://rana.lbl.gov/downloads/ScanAlyze.zip. Statistical software (S-Plus 2000) is available commercially from Insightful Corp., Seattle, WA. Software to determine the co-occurrence of terms in PubMed abstracts (PDQ_MED) is commercially available from Inpharmix Inc., Greenwood, IN.