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Somatic mosaicism in normal tissues Somatic cells can accumulate mutations over the course of an individual's lifetime. This generates cells that differ genetically at specific loci within the genome. To explore how this genetic diversity in individuals contributes to disease, Yizhak et al. developed a method to detect mutations from RNA sequencing data (see the Perspective by Tomasetti). Applying this method to Cancer Genome Atlas samples and normal samples from the Genotype-Tissue Expression (GTEx) project generated a tissue-specific study of mutation accumulation. Somatic mutations were detected in nearly all individuals and across many normal human tissues in genomic regions called cancer hotspots and in genes that play a role in cancer. Interestingly, the skin, lung, and esophagus exhibited the most mutations, suggesting that the environment generates many human mutations. Science , this issue p. eaaw0726 ; see also p. 938

Cancer genome studies have significantly advanced our knowledge of somatic mutations. However, how these mutations accumulate in normal cells and whether they promote pre-cancerous lesions remains poorly understood. Here we perform a comprehensive analysis of normal tissues by utilizing RNA sequencing data from ∼6,700 samples across 29 normal tissues collected as part of the Genotype-Tissue Expression (GTEx) project. We identify somatic mutations using a newly developed pipeline, RNA-MuTect, for calling somatic mutations directly from RNA-seq samples and their matched-normal DNA. When applied to the GTEx dataset, we detect multiple variants across different tissues and find that mutation burden is associated with both the age of the individual and tissue proliferation rate. We also detect hotspot cancer mutations that share tissue specificity with their matched cancer type. This study is the first to analyze a large number of samples across multiple normal tissues, identifying clones with genomic aberrations observed in cancer.

Cancer genome studies have significantly contributed to the discovery of somatic mutations and processes that drive cancer. However, how these mutations accumulate in normal cells and contribute to early cancer development remains poorly understood. Recent studies have addressed this question by studying normal blood and a small number of skin samples, discovering both driver mutations as well as mutational processes observed in cancer. Here we extend these studies by analyzing RNA from ~7,000 samples across 30 normal tissues from ~600 individuals compared to their germline DNA, collected as part of the GTEx project. To accomplish this goal we first developed a new pipeline termed RNA-MuTect for calling somatic mutations directly from RNA-seq samples and their matched-normal DNA. RNA-MuTect includes multiple filtering steps designed specifically for analyzing RNA-seq. We first validated RNA-MuTect by analyzing TCGA samples where both DNA and RNA data are available. Comparing the set of mutations detected by RNA-MuTect to those identified in the DNA, we show that whenever there is sufficient coverage to detect the mutations in RNA, we have a high sensitivity. Most importantly, RNA-MuTect has a very low false-positive rate with specificity >90%. We further demonstrate that we can discover most of the known driver genes in this cohort using the mutations detected based on the RNA data. Moreover, using the RNA data, we can detect the same mutational processes as identified in the DNA, including UV, aging, smoking and others. To study clonal expansion in normal tissues and investigate whether known cancer-related genes and processes can be identified, we applied RNA-MuTect to the GTEx dataset. As expected, multiple variants were detected across tissues, with skin, lung and esophagus having the highest number of somatic mutations. Overall, different cancer-related events were detected. Specifically: (1) we found 15 hotspot mutations in 6 different genes including TP53, KRAS and PIK3CA; (2) Mutated genes in 8 different tissues were found to be enriched with Cancer Gene Census genes; (3) Known cancer genes were found to be under positive selection in different tissues; (4) Known cancer-related mutational signatures were captured in normal tissues; (5) Cases of allelic imbalance were detected in various tissues. This study is the first to analyze a large number of samples across many tissues to explore the fundamental question of cancer initiation. Many cancer-related processes and events are discovered across different tissues, laying the foundation for studying the earliest stages of cancer development. Note: This abstract was not presented at the meeting. Citation Format: Keren Yizhak, Jaegil Kim, Francois Aguet, Julian Hess, Hailei Zhang, Eila Arich-Landkof, Noam Shoresh, Ayelet Segre, Chip Stewart, Dainel Rosebrock, Dimitri Livitz, Nicholas Haradhvala, Paz Polak, Tim Sullivan, Xiao Li, Kristin Ardlie, Gad Getz. Identifying cancer-related processes in normal tissues via RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-231. doi:10.1158/1538-7445.AM2017-LB-231