TrackSig: reconstructing evolutionary trajectories of mutations in cancer

Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Nil Sahin, Amit Deshwar, Quaid Morris, PCAWG Evolution and Heterogeneity Working Group, and PCAWG network

Dec 06, 2018

Received Date: 22nd November 18

We present a new method, TrackSig, to estimate the evolutionary trajectories of signatures of somatic mutational processes. TrackSig uses cancer cell fraction (CCF) corrected by copy number to infer an approximate order in which the somatic mutations accumulate. TrackSig segments mutation ordering by CCF and fits signature exposures (activities) as a piece-wise constant function of the mutation ordering. TrackSig uses optimal segmentation to find the points of change in signature activities. We assess TrackSig’s reconstruction accuracy using simulations. We find 2% median activity error on simulations with one to three change-points. The size and the direction of the signature change is consistent in 83% and 95% of cases respectively. There were an average of 0.02 missed change-points and 0.12 false positive change-points per sample. We provide a framework to estimate signature exposure trajectories across CCF scale as well as the way to determine active signatures. The code is available at

Read in full at bioRxiv.

This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

Nature Communications

Nature Research, Springer Nature