Abstract
Defining the chronology of molecular alterations may identify milestones in carcinogenesis. To unravel the temporal evolution of aberrations from clinical tumors, we developed CLONET, which upon estimation of tumor admixture and ploidy infers the clonal hierarchy of genomic aberrations. Comparative analysis across 100 sequenced genomes from prostate, melanoma, and lung cancers established diverse evolutionary hierarchies, demonstrating the early disruption of tumor-specific pathways. The analyses highlight the diversity of clonal evolution within and across tumor types that might be informative for risk stratification and patient selection for targeted therapies. CLONET addresses heterogeneous clinical samples seen in the setting of precision medicine.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0439-6) contains supplementary material, which is available to authorized users.
Highlights
Cancer arises from initiating cells that undergo intense evolutionary selection during disease progression and can be widely altered during treatment
Clonality assessment of aberrations from sequencing reads We reasoned that the reads mapped into a genomic window can be partitioned in two sets: one set includes reads that represent parental chromosomes; and the other set contains reads from only one parent chromosome
To aid in unraveling the critical temporal evolution of somatic aberrations in challenging clinical tumors, we developed CLONality Estimate in Tumors (CLONET), a computation tool that requires only few clonal events to precisely estimate tumor purity and ploidy and nominates the hierarchy of genomic aberrations
Summary
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 might inform clinical risk stratification and treatment strategies. Analysis strategies have been developed to address tumor DNA purity and cancer cell ploidy, but there remains a gap for the analysis of minimally aberrant or highly heterogeneous tumors. Several methods have been developed to quantify DNA admixture and ploidy from high density single-nucleotide polymorphism (SNP) array data [1,2,3,4] that utilize the relative abundance of specific allele signal (B allele frequency (BAF)) and the tumor over normal signal ratio (referred to as Log R) to measure the complexity of the underlying cellular population. Global optimization methods are applied to find the configuration of DNA admixture and ploidy that better
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