RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues

Abstract

How somatic mutations accumulate in normal cells is poorly understood. A comprehensive analysis of RNA sequencing data from ~6700 samples across 29 normal tissues revealed multiple somatic variants, demonstrating that macroscopic clones can be found in many normal tissues. We found that sun-exposed skin, esophagus, and lung have a higher mutation burden than other tested tissues, which suggests that environmental factors can promote somatic mosaicism. Mutation burden was associated with both age and tissue-specific cell proliferation rate, highlighting that mutations accumulate over both time and number of cell divisions. Finally, normal tissues were found to harbor mutations in known cancer genes and hotspots. This study provides a broad view of macroscopic clonal expansion in human tissues, thus serving as a foundation for associating clonal expansion with environmental factors, aging, and risk of disease.

Fig. 1:. Validation of RNA-MuTect in TCGA samples.

(A) Total number of mutations detected before filtering in DNA (red) and RNA (blue) across samples in each TCGA cohort. (B) Sensitivity and precision of sufficiently covered sites, across training and validation samples. Box plots show median, 25^th and 75^th percentiles. The whiskers extend to the most extreme data points not considered outliers, and the outliers are represented as dots. (C) Co-mutation plot with mutations across the 243 TCGA samples, overall frequencies, allele fractions, and significance levels of candidate cancer genes (Q value < 0.05) identified by applying MutSig2CV (25) on the mutations detected in the RNA. Genes marked with a red arrow were also identified as significantly mutated in the DNA. (D) Mutational signatures identified by SignatureAnalyzer (26) on the basis of mutations detected in the RNA. The mutational signatures identified are: a mixture of smoking and nucleotide-excision repair signatures (W1, combination of COSMIC signatures 4 and 5, cosine similarities of 0.7 and 0.75, respectively); UV (W3, COSMIC signature 5, cosine similarity = 0.95); APOBEC (W4, COSMIC signature 13, cosine similarity = 0.9); Aging (W5, COSMIC signature 1, cosine similarity = 0.9); POLE (W6, COSMIC signature 10, cosine similarity = 0.88), and MSI (W7, COSMIC signature 15, cosine similarity = 0.8).

Fig. 2:. Somatic clonal expansion in normal tissues.

(A) An illustration of the composition of bulk RNA extracted from a normal human tissue. The biopsy consists of three different cell types that express different transcripts (marked in blue, green, and yellow) at different levels. Blue cells represent cells with a higher probability to form clones. Two clones, small and large, are shown denoted by purple- and red-dashed outlines, respectively. Mutated reads are marked with an “x”. The allele fractions of the mutations in the blue and green genes are the same (0.25; 2/8 and 4/16 reads, respectively), despite the different clone sizes. Additionally, the allele fraction of the mutation in the yellow gene is higher than the allele fractions of the mutations in the blue and green genes (0.33; 2/6 reads), despite the fact that the yellow mutation is supported by the same (or smaller) number of reads. These scenarios illustrate the challenge of identifying somatic mutations in bulk normal tissue due to a mixture of cell types and the relatively small clones. Moreover, inferring clone size is limited due to different cell types that exist in different proportions and express transcripts at different levels. (B) Number of mutations detected in the RNA-seq of all studied tissues. Each sample is represented with a circle. The black horizontal line represents the mean number of mutations in each tissue type. A confidence level from our estimation of false positives in the validation data is indicated in the right y-axis. Specifically, this confidence level is computed as the x^th percentile on the number of false positive calls (RNA-only mutations in DNA-powered sites) found in the validation set. “n” values represent the total number of samples analyzed in each tissue; “n_z” values represent the number of samples in which no mutations were detected; and “n_80” values represent the number of samples in which more than 13 mutations were found (equivalent to a confidence level of 80%). (C) Left panel: Distribution of allele fraction across all samples in which somatic mutations were detected. Inset: mutations with allele fraction ≤ 0.2. Right panel: Allele fraction as a function of the log₁₀(coverage) for all detected mutations.

Fig. 3:. Mutation load is associated with age and tissue-specific proliferation rate.

(A) Top panels: Differences in the average number of aging-related and total number of mutations before and after the age of 45 (left and right panels, respectively). Bottom panels: Differences in mutation number in esophagus and skin samples before and after the age of 45 (left and right panel, respectively). Box plots show median, 25^th, and 75 percentiles in each group. Red crosses represent the outliers, and black crosses represent the mean. (B) Mean expression of the proliferation marker MKI67 vs. the average number of mutations found in each tissue. (C) Left panel: Number of mutations associated with the UV signature in sun-exposed and non–sun exposed skin samples. Middle panel: Number of mutations found in sun-exposed and non–sun exposed skin samples taken from individuals of European ancestry. Right panel: Number of mutations found in sun-exposed and non–sun exposed skin samples taken from individuals of African-American ancestry.

Fig. 4:. Mutations in cancer genes across normal tissues.

(A) Genes in which hotspot mutations were detected. Left panel: Number of hotspot mutations detected in each gene, and number of silent and non-silent mutations that are not in hotspots. Right panel: Normal tissues in which the hotspot mutations were detected. All hotspot mutations except two (FAT1 p.E4454K; FGFR3 p.K650E) were annotated as pathogenic. (B) Occurences of each hotspot mutation found in different TCGA cohorts. (C) Co-mutation plot for genes significantly mutated in a pan-normal analysis, ordered by their significance level (by MutSig2CV); data show 93 of 6707 samples with at least one mutation in these genes and the overall frequency among samples with at least one mutation. The distribution of allele fraction of mutations appears at the bottom. (D) Allelic imbalance in chromosme 9q of a normal esophagus sample. Top panel: Allele fraction of heterozygous sites based on DNA from a matched-blood sample. Bottom panel: Allele fraction of heterozygous sites based on RNA from the esophagus sample. The black horizonal lines indicate the mean allele fraction per chromosomal arm of sites with allele fraction smaller or greater than 0.5.