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.
Highlights
The rapid development of genomic techniques has brought considerable advances to the diagnosis and treatment of cancer
We examined the predictive utility of these existing features and evaluated a novel set of genomic features, based on the global patterns of passenger mutations: the regional mutation density (RMD)
We systematically evaluated whether cancer types can be classified using features describing regional mutation density (RMD), which are the normalized mutation counts across 2655 megabase-sized chromosomal domains
Summary
The rapid development of genomic techniques has brought considerable advances to the diagnosis and treatment of cancer. Genetic variants in cancer genes can serve as markers for targeted therapeutics, in certain cases resulting in an impressive clinical response Such cases are still not prevalent [1,2], and the cancer tissue-of-origin is a major factor in deciding on therapeutic approaches [3]. Genomic classifiers of cancer type are relevant for liquid biopsies, where cell-free DNA or circulating tumor cells are retrieved from blood and DNA sequencing is performed. This approach was shown to hold much potential for screening the general population, or persons at risk for cancer [14,15]. It is increasingly appreciated that integrating diverse omics data types leads to more robust subtyping [6,16], motivating research into how somatic mutation data may complement transcriptome, DNA methylation or proteome data for assigning a clinically relevant subtype to each tumor [17]
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