TrackSigFreq: subclonal reconstructions based on mutation signatures and allele frequencies

Abstract

Mutational signatures are patterns of mutation types, many of which are linked to known mutagenic processes. Signature activity represents the proportion of mutations a signature generates. In cancer, cells may gain advantageous phenotypes through mutation accumulation, causing rapid growth of that subpopulation within the tumour. The presence of many subclones can make cancers harder to treat and have other clinical implications. Reconstructing changes in signature activities can give insight into the evolution of cells within a tumour. Recently, we introduced a new method, TrackSig, to detect changes in signature activities across time from single bulk tumour sample. By design, TrackSig is unable to identify mutation populations with different frequencies but little to no difference in signature activity. Here we present an extension of this method, TrackSigFreq, which enables trajectory reconstruction based on both observed density of mutation frequencies and changes in mutational signature activities. TrackSigFreq preserves the advantages of TrackSig, namely optimal and rapid mutation clustering through segmentation, while extending it so that it can identify distinct mutation populations that share similar signature activities.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

“…Score_sig_1B is the Euclidean distance between simulated and estimated signature profiles (weighted sum over all clones). This is closer to the objective of CloneSig, TrackSig [18] and TrackSigFreq [19].…”

“…This is the objective function of most signature reconstruction approaches (including deconstructSigs[13] and Palimpsest [17]). Score_sig_1B is the Euclidean distance between simulated and estimated signature profiles (weighted sum over all clones). This is closer to the objective of CloneSig, TrackSig [18] and TrackSigFreq [19]. Score_sig_1C measures the ability of each method to correctly identify present signatures.…”

“…A segmentation algorithm is then applied to determine the number of breakpoints, from which we obtain subclones with different mutational processes. A recent extension integrating CCF in the segmentation algorithm to also perform subclonal reconstruction, TrackSigFreq [19] is also considered. Because of this rationale, the authors recommend to have at least 600 observed mutations to apply TrackSig or TrackSigFreq.…”

“…This is an important limitation given the popularity of whole exome sequencing (WES) to characterize tumors, particularly in the clinical setting. Recently, an extension has been proposed to better account for SNV frequencies in the change point detection, TrackSigFreq [19], but is still limited to WGS samples.…”

DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution

“…While recent single-cell DNA sequencing technologies enable high-resolution measurements of tumor heterogeneity [5][6][7][8][9][10][11] , the vast majority of cancer studies in research and clinical settings [12][13][14] heterogeneity from SNVs is the Cancer Cell Fraction (CCF) -also known as the cellular prevalence or the mutation cellularity -which is the proportion of cancer cells that contain the SNV. CCFs form the basis for many cancer analyses including: studying tumor heterogeneity 13,15,16 , reconstructing clonal evolution and metastatic progression 13,[17][18][19] , identifying selection [20][21][22] , and analyzing changes in mutational processes over time [23][24][25] . In these and other studies, the underlying assumption is that groups of SNVs with the same CCF are likely to be present in the same cancer cells and thus occurred on the same branch of the phylogenetic tree that describes the evolution of the tumor (Figure 1a).…”