CLONET: CLONality Estimate in Tumors
Cancer arises from initiating cells (clones) that undergo intense evolutionary selection during disease progression and can be widely altered during treatment. The tumor cell evolutionary process may lead to subclonal divergence resulting in genetic and molecular heterogeneity. Computational approaches to establish maps of cancer evolution would assist in determining the progression status of each patient tumor and possibly inform treatment strategies. Technical challenges related to tumor DNA purity and cancer cell ploidy have been addressed but critical aspects remain for minimally aberrant or highly heterogeneous tumors.
Available tools all apply a global use of the genome data to infer tumor DNA purity and tumor ploidy. Global approaches are well-suited for tumor samples with fairly homogenous genomic aberrations (high ratio of clonal versus subclonal lesions). In the clinical setting where tumor samples might exhibit heterogeneity due to progression or subsequent to multiple lines of treatment and/or for tumor types that undergo complex structural changes, global approaches may prove sub-optimal as they undermine the genomic diversity.
CLONET belongs to a second generation of tools based on local (in contrast to global) optimization where estimates of purity and ploidy are derived from few clonal events. CLONET exploits individuals’ genetic background by using the abundant germline heterozygous SNP (called informative SNPs) genotype data provided by whole genome sequence coverage to quantify the percentage of reads supporting the considered aberration. A closed-form solution relates aberrant reads with clonality status and allows propagating uncertainty due to sequencing. CLONET computes the clonality of somatic copy number changes, point mutations, and rearrangements in a coherent mathematical model enabling comparison across tumor types of the same aberration class and across different aberrations within the same tumor type. Finally, the temporal path along which the somatic aberrations originated is inferred from the composite frequencies at which they are observed to be clonal or subclonal in a single sample. CLONET allows harnessing NGS data, including whole genome, whole exome, and targeted sequencing, to determine the percentage of tumor cells harboring each mutation and to draft evolution charts.
Prandi et al.: Unraveling the clonal hierarchy of somatic genomic aberrations. Genome Biology 2014, 15:439.
CLONET is a collection of R scripts that allows:
CLONET scripts have the following common syntaxt:
Folders are organized as follows:
CLONET -> CLONET.R -> Docs -> Examples -> Functions -> Tools
CLONET.R is the main R script required to compute global DNA admixture, ploidy and clonality of segmented data.
The Docs folder contains this document.
The Examples folder contains a folder Small that includes a complete run of all the CLONET scripts.
The Functions folder contains all the functions used by CLONET.
The Tools folder contains R scripts to perform point mutations (PM) analysis, structural rearrangement (RR) analysis and tumor evolution path analysis.
CLONET requires Linux kernel >= 2.6.15. CLONET requires R >= 2.7 and the following packages, parallel, dgof, sets, and pso, igraph, reshape2. CLONET requires global folder names. CLONET recommends ASEQ tool to generate initial pileup analysis (binaries provided).
Code by Davide Prandi
Laboratory of Computational Oncology (F. Demichelis)
Centre for Integrative Biology, University of Trento, Italy
email contacts: firstname.lastname@example.org; email@example.com.
CLONET is distributed under the MIT Licence.