Analysis of somatic microsatellite indels identifies driver events in human tumors.
Yosef E MaruvkaKent W MouwRosa KarlicPrasanna ParasuramanAtanas KamburovPaz PolakNicholas J HaradhvalaJulian M HessEsther RheinbayYehuda BrodyAmnon KorenLior Z BraunsteinAlan D'AndreaMichael S LawrenceAdam BassAndre BernardsFranziska MichorGad A GetzPublished in: Nature biotechnology (2017)
Microsatellites (MSs) are tracts of variable-length repeats of short DNA motifs that exhibit high rates of mutation in the form of insertions or deletions (indels) of the repeated motif. Despite their prevalence, the contribution of somatic MS indels to cancer has been largely unexplored, owing to difficulties in detecting them in short-read sequencing data. Here we present two tools: MSMuTect, for accurate detection of somatic MS indels, and MSMutSig, for identification of genes containing MS indels at a higher frequency than expected by chance. Applying MSMuTect to whole-exome data from 6,747 human tumors representing 20 tumor types, we identified >1,000 previously undescribed MS indels in cancer genes. Additionally, we demonstrate that the number and pattern of MS indels can accurately distinguish microsatellite-stable tumors from tumors with microsatellite instability, thus potentially improving classification of clinically relevant subgroups. Finally, we identified seven MS indel driver hotspots: four in known cancer genes (ACVR2A, RNF43, JAK1, and MSH3) and three in genes not previously implicated as cancer drivers (ESRP1, PRDM2, and DOCK3).
Keyphrases
- mass spectrometry
- papillary thyroid
- multiple sclerosis
- ms ms
- genome wide
- squamous cell
- endothelial cells
- copy number
- bioinformatics analysis
- machine learning
- dna methylation
- genome wide identification
- deep learning
- risk factors
- high resolution
- gene expression
- young adults
- childhood cancer
- transcription factor
- induced pluripotent stem cells
- artificial intelligence
- nucleic acid