Passenger mutations accurately classify human tumors

Abstract

Determining the cancer type and molecular subtype has important clinical implications. The primary site is however unknown for some malignancies discovered in the metastatic stage. Moreover liquid biopsies may be used to screen for tumoral DNA, which upon detection needs to be assigned to a site-of-origin. Classifiers based on genomic features are a promising approach to prioritize the tumor anatomical site, type and subtype. We examined the predictive ability of causal (driver) somatic mutations in this task, comparing it against global patterns of non-selected (passenger) mutations, including features based on regional mutation density (RMD). In the task of distinguishing 18 cancer types, the driver mutations–mutated oncogenes or tumor suppressors, pathways and hotspots–classified 36% of the patients to the correct cancer type. In contrast, the features based on passenger mutations did so at 92% accuracy, with similar contribution from the RMD and the trinucleotide mutation spectra. The RMD and the spectra covered distinct sets of patients with predictions. In particular, introducing the RMD features into a combined classification model increased the fraction of diagnosed patients by 50 percentage points (at 20% FDR). Furthermore, RMD was able to discriminate molecular subtypes and/or anatomical site of six major cancers. The advantage of passenger mutations was upheld under high rates of false negative mutation calls and with exome sequencing, even though overall accuracy decreased. We suggest whole genome sequencing is valuable for classifying tumors because it captures global patterns emanating from mutational processes, which are informative of the underlying tumor biology.

Matching cell lines with cancer type and subtype of origin via mutational, epigenomic and transcriptomic patterns

“…In this validation setting, we applied such genomic classifiers to a problem of ‘one-versus-one’ classification, where we contrasted the originally assigned cancer type versus the newly-proposed cancer type for each reassignment. We found that such one-versus-one classifiers based on genomic data had satisfactory accuracy with our whole-exome sequencing data sets (Fig S4; our past work (24) suggests whole genome sequences are more powerful). Finally, we included an additional classifier based on copy number alteration (CNA) profiles, which were also shown to yield accurate predictive models of tissue specificity (24,25).…”

“…We next turned to support individual examples of cell lines with reassigned tissue identity by analyzing independent data. In particular, we used genomic sequence-based classifiers, which are able to predict the tissue of origin based on mutation patterns (24,25). As in our recent work (24), we used the trinucleotide mutation spectra and the oncogenic mutations.…”

“…In particular, we used genomic sequence-based classifiers, which are able to predict the tissue of origin based on mutation patterns (24,25). As in our recent work (24), we used the trinucleotide mutation spectra and the oncogenic mutations. In this validation setting, we applied such genomic classifiers to a problem of ‘one-versus-one’ classification, where we contrasted the originally assigned cancer type versus the newly-proposed cancer type for each reassignment.…”

“…The genomic classifiers we employed here were based on whole-exome sequences and were overall less powerful than the transcriptome/DNA methylation classifiers in our data (Fig S4). Recent work by us and others (24,25) suggests that analyzing whole-genome sequences of these cell lines would permit use of additional, highly predictive features based on regional mutation density of chromosomal domains. This may provide further genomic evidence for the identity of the cell-of-origin for the 35 suspected cell lines.…”

“…Finally, using the filtered somatic mutations we calculated three set of mutational features: Regional Mutation Density (RMD), Mutation Spectra (MS96) and Oncogenic Mutations (OGM) as described in Salvadores et. al (24). RMD features did not exhibit high accuracy when applied to exome-sequencing data and so were not considered further in this analysis.…”