In cancer, clonal evolution is characterized based on single nucleotide variants and copy number alterations. Nonetheless, previous methods failed to combine information from both sources to accurately reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previous methods accepted as input all variants predicted by variant-callers, regardless of differences in dispersion of variant allele frequencies (VAFs) due to uneven depth of coverage and possible presence of strand bias, prohibiting accurate inference of clonal architecture. We present a general framework for assignment of functional mutations to specific cancer clones, which is based on distinction between passenger variants with expected low dispersion of VAF versus putative functional variants, which may not be used for the reconstruction of cancer clonal architecture but can be assigned to inferred clones at the final stage. The key element of our framework is QuantumClone, a method to cluster variants into clones, which we have thoroughly tested on simulated data. QuantumClone takes into account VAFs and genotypes of corresponding regions together with information about normal cell contamination. We applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We discovered specific pathways recurrently altered by deleterious mutations in different clonal populations. Some such pathways were previously reported (e.g., MAPK and neuritogenesis) while some were novel (e.g., epithelial–mesenchymal transition, cell survival and DNA repair). Most pathways and their modules had more mutations at relapse compared to diagnosis.

Clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction