Simulated reads for subclonal reconstruction

Tumor DNA sequencing data can be interpreted by computational methods that analyse genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating tumor phylogenies are lacking. To address this need, we systematically assess methods for reconstructing tumor sub-clonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. We benchmark 580 tumor reconstructions, varying tumor read-depth, tumour type, and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.