The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.

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.

The mechanistic target of rapamycin (mTOR) is a key regulator of cell growth that integrates growth factor signaling and nutrient availability and is a downstream effector of oncogenic receptor tyrosine kinases (RTKs) and PI3K/Akt signaling. Thus, activating mTOR mutations would be expected to enhance growth in many tumor types. However, tumor sequencing data has shown that mTOR mutations are enriched only in renal clear cell carcinoma, a clinically hypervascular tumor unlikely to be constrained by nutrient availability. To further define this cancer type-specific restriction, we studied activating mutations in mTOR. All mTOR mutants tested enhanced growth in a cell type agnostic manner under nutrient-replete conditions but were detrimental to cell survival in nutrient-poor conditions. Consistently, analysis of tumor data demonstrated that oncogenic mutations in the nutrient-sensing arm of the mTOR pathway display a similar phenotype and were exceedingly rare in human cancers of all types. Together, these data suggest that maintaining the ability to turn off mTOR signaling in response to changing nutrient availability is retained in most naturally occurring tumors.

In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional corrections to affiliations and author names have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper.

Supplementary Data from Activating mTOR Mutations Are Detrimental in Nutrient-Poor Conditions

Supplementary Data from Activating mTOR Mutations Are Detrimental in Nutrient-Poor Conditions

Supplementary Data from Activating mTOR Mutations Are Detrimental in Nutrient-Poor Conditions

Supplementary Data from Activating mTOR Mutations Are Detrimental in Nutrient-Poor Conditions