Three somatic mutation analysis categories including early-stage, late-stage, and tumor mutations

The cohorts consisted of multiple organs, including brain (n = 301), blood (n = 100), liver (n = 60), heart (n = 13), and other peripheral tissues (n = 24). The 190 individuals from whom tissues were obtained included patients with ‘non-tumor’ neurological disorders (n = 133), brain tumors (glioblastoma and ganglioglioma, n = 19), and non-diseased controls (n = 38) (Fig 1A and S1 Table).

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Fig 1. Detection of early and late-stage somatic variants in the brain and matched peripheral tissues.

(A) Schematic flow showing the bioinformatics pipelines for the analysis of 301 brain tissues and 197 peripheral tissues from 190 individuals. Tumor mutations were identified from primary or recurrent tumor specimens, and confirmed by histologic examination. To identify somatic variants, Mutect2 and RePlow/NeuSomatic were used for reciprocal mutation calling by all-pairs testing. First, to detect somatic mutation in tissue “A”, tissues “A” and “B” were used as a case and control, respectively, and tissue “A” was further used as a control to identify somatic mutations in tissue “B”. For tumor tissues, the same analysis was performed as in other samples. Subsequently, post-call filtering was used. After strict filtration and tests for organ specificity using one-sample proportion tests, the shared and organ-specific mutations were classified. The colored bar indicates read distribution, and the red and blue-colored bars indicate the reference and altered reads, respectively (lower right panel). (B), (C) Early-stage, late-stage, and tumor mutations were classified with a highly accurate precision rate (89.47%, early-stage; and 90.24% in late-stage and tumor mutations). We determined organ specificity using a one-sample proportion test. (D) Correlation of VAFs from two matched tissues and the WES and TASeq data. VAFs were highly concordant between paired tissues (r = 0.84; P < 0.0001), and the WES and TASeq data (r = 0.61; P < 0.0001).

https://doi.org/10.1371/journal.pgen.1010404.g001

We defined and used three different categories in the analysis: early-stage, late-stage, and tumor mutations (Fig 1B). Early-stage mutations were defined as shared mutations in multiple organs that may occur during early embryonic development before gastrulation. Late-stage mutations are restricted to a single organ and are more likely to be induced by clone-forming cells containing late embryonic (post-gastrulation) or post-natal somatic mutations, although a few mutations may be present during the pre-gastrulation stage. Tumor specimens were acquired from the primary or recurrent tumor, and microscopic tumor analysis via histological evaluation was performed by a neuropathologist using the World Health Organization (WHO) 2016 grading criteria [17].

Based on the above definitions, somatic mutation identification was performed using an ensemble of robust variant calling algorithms such as Mutect2 [18], RePlow [19], and NeuSomatic [20] for the 1034 potential sample pair combinations. After strict filtration and tests for organ specificity using one-sample proportion tests (Fig 1A and Materials and Methods), we detected 103 early and 997 late-stage mutations, as well as 583 tumor mutations. To validate the identified mutations, 114 randomly selected single nucleotide variants (SNVs; ~10% of non-tumor mutations) were sequenced using Sanger sequencing and via targeted amplicon sequencing (TASeq) to obtain an ultra-high depth (average: 507,856× reads). Our call set showed high accuracy for the identification of the early-stage (89.47%, 17/19) and late-stage and tumor mutations (90.24%, 74/82) (Fig 1C and S2 Table). A high concordance in VAFs across tissues (Pearson’s correlation r = 0.84; P = 1.00×10⁻⁴²) and between the WES and TASeq data (r = 0.61; P = 1.17×10⁻⁴⁸) indicated the accuracy of the calls (Fig 1D).

Additionally, we compared the quantitative traits of the mutations including the number and allele frequency at different stages. On average, we identified 0.18 early and 2.8 late-stage somatic mutations per individual (Fig 2A). The number of mutations in normal tissues was substantially lower than that in tumors (8.12 per individual). The overall number of late-stage and tumor mutations was positively correlated with age (Pearson’s r: late-stage, 0.45; and tumor, 0.36) (Fig 2B). However, the VAF of late-stage mutations was inversely correlated with age, probably due to the accumulation of late-occurring somatic mutations in blood cells with a low allelic fraction. Conversely, we found no correlation between early-stage mutations and age, indicating that these mutations are confined to early developmental stages. The VAFs of the mutations were higher in the early (6.17 ± 3.32%) relative to the late stage (1.50 ± 3.29%) of development (Figs 2C and S1A and S1B).

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Fig 2. Basic descriptive statistics for somatic mutations.

(A) The number of somatic mutations per individual in the early-stage, late-stage, and tumor mutation groups. (B) Correlation of age with somatic mutation counts in the groups. (C) Average VAFs between the three mutation groups.

https://doi.org/10.1371/journal.pgen.1010404.g002

Three distinct mutation spectra identified by mutational signature analysis

Next, we performed a mutation profiling analysis to investigate the underlying mutagenic processes in somatic cells (Fig 3A–3D). De novo signature extraction of the 1494 somatic SNVs (94 early, 880 late stage, and 520 tumor SNVs) helped identify three novel signatures (Fig 3A), including signatures A, B1, and B2, all of which exhibited C>T as the major base substitution, and showed an additional T>C enrichment in signature A. Despite the overall similarity in the mutational spectrum, especially between B1 and B2 (cosine similarity: 0.95), a clear distinction was observed in the relative contribution of the mutations to the sample groups, indicating the uniqueness of the signatures (i.e., signatures A, B1, and B2 predominantly contributed to the early, late-stage, and tumor SNVs, respectively) (Fig 3B).

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Fig 3. Mutational profiles and functional analysis.

(A) De novo extraction of somatic mutations by non-negative matrix factorization. (B) Each group was classified according to the three signatures (A, B1, and B2). (C) The relative contribution of the common clock-like signatures (SBS1, SBS5, and SBS40 for single-base substitutions, and ID1, ID2, ID5, and ID8 for small insertion-deletions) from PCAWG signatures. Mutations from the three groups were extracted as three signatures by NMF. Mutational contributions were obtained by fitting the extracted signatures to the mutations in each group. (D) The dN/dS score ratios, (E) proportion of trinucleotides with atypical mutability, and (F) pLI score for gnomAD Exome and each group are shown.

https://doi.org/10.1371/journal.pgen.1010404.g003

Mapping of the three signatures to COSMIC Mutational Signatures (v3.1; June 2020) [21] identified clock-like SNVs (SBS1, SBS5, and SBS40), and small insertion-deletion (indels) signatures (ID1, ID2, ID5, and ID8) as major components of the mutational profiles (Fig 3C). We noted that the relative contributions of the two well-known age-related mutational signatures (SBS1 and SBS5) were altered in early to late-stage SNVs (SBS1: 19% to 29%; SBS5: 78% to 49%).

Differences in functional tolerance based on mutation timing

We identified functional differences between the early and late-stage mutations. In particular, early-stage mutations showed a lower ratio of non-synonymous to synonymous substitutions (quantified as the dN/dS ratio by maximum-likelihood methods) (0.79) than did late-stage mutations (0.94), tumor mutations (0.94), and common germ-line coding variants (0.90; gnomAD Exome) (Fig 3D), indicating a stronger negative selection [22]. Additionally, early-stage mutations were located in trinucleotides less frequently (2.1%) with atypical mutability [23, 24] than late-stage mutations (8.0%), tumor mutations (8.2%), and common germ-line coding variants (9.9%) (Fig 3E). Sites with atypical mutability are more highly mutated in cancer than is expected to occur randomly, indicating their functional significance and drive in cancer [24, 25]. Furthermore, genes that harbor early-stage mutations showed a lower probability of loss-of-function (LoF) intolerance (pLI score) [26] (Fig 3F), indicating that early-stage mutations are more enriched in LoF-tolerant genes.

Organ-specific characteristics of late-stage mutations

Next, we investigated the characteristics of late-stage mutations, with a particular focus on diversity among different organs and cell types. The numbers of mutations varied substantially by organ, with a smaller number in the brain (0.67 per individual) and a higher number in the blood (9.23 per individual) relative to other peripheral organs (average: 1.12 per individual) (Fig 4A). However, the average value of VAFs was inversely proportional in these groups, with the highest number in the brain (7.32%) and the lowest in the blood (0.50%) (Fig 4B). The number of late-stage mutations and the age of individuals showed a significant positive correlation in the blood (r = 0.45; p = 2.99×10⁻¹⁰; Fig 4C), and this phenotype has been observed in post-natal clonal hematopoiesis [27, 28].

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Fig 4. Analysis of late-stage mutations based on the mutation source for the various organs (brain, blood, and other organs).

(A), (B) Number of mutations per individual and VAF distribution. (C) Age correlation with mutation counts. (D) Unsupervised hierarchical clustering of late-stage mutations. Late brain somatic mutations were fitted to signatures A and B2, whereas those in the blood were fitted to signatures B1 and B2. (E) The VAFs of three different cell types [neuronal (NeuN+), oligogenic (Olig2+), and others (negative)] for early and late-stage mutations in the brain. (F) Signature distribution of late brain somatic variants that were divided among temporal and non-temporal brain areas, or according to brain-disease status. (G) Mutational-strand asymmetry. Late-onset blood and tumor mutations are noted as having strand bias with T>C.

https://doi.org/10.1371/journal.pgen.1010404.g004

Unsupervised hierarchical clustering of the three signatures (A, B1, and B2) in late-stage mutations showed that mutations in the brain primarily comprised signatures A (early-stage) and B2 (tumor), whereas blood mutations harbored signatures B1 (late-stage) and B2 (tumor) (Fig 4D).

We assessed the cell-type specificity of the somatic mutations in the brain by evaluating two brain samples. One (NLE-P-0150) contained early-stage mutations (5.47% VAFs) whereas the other (NLE-P-0225) contained five late-stage mutations (average: 8.00% VAF) (Fig 4E), each of which was sorted by fluorescence-activated nuclei sorting (FANS) to isolate three different cell types: neuronal (NeuN⁺), oligogenic (Olig2⁺), and others (negative). TASeq of the separated cell populations showed that both early and late-stage mutations were present in multiple cell lineages, but a large asymmetry in mutation frequencies was present among cell types with late-stage mutations (Fig 4E).

We further classified the late-stage brain mutations into temporal and non-temporal areas, and analyzed area-specific mutational signatures (Fig 4F). As reported previously, signatures A and B2 contributed to mutations in both areas; however, signature B2 had a higher contribution in the temporal lobe (70.3%) than non-temporal tissue (25.7%), indicating that the characteristics of somatic mutations in the temporal lobe are more similar to those of tumor mutations.

Furthermore, late-stage mutations in the blood and tumor mutations showed enrichment for T>C mutations on transcribed strands (Fig 4G). Transcription-coupled repair occurs more frequently at higher transcription levels, and this bias is increased in actively replicating templates [29, 30]. We found that clonal expansion-derived somatic mutations were present in the blood, and were similar to those in tumors [31].

Ethics statement

The study was performed with written/verbal consent was obtained from the Participants, Patients or Parents according to the protocols approved by the Institutional Review Boards of Severance Hospital and Korea Advanced Institute of Science and Technology (KAIST), as well as the Committee on Human research. The developmental assessment was performed one month before surgery for child participants, and informed consent according to developmental age was obtained from the patient or patient/parent, then anonymized.

Patient samples

The freshly frozen brain and peripheral samples acquired from 24 autism spectrum disorder (ASD) and five non-ASD cases from the National Institute of Child Health & Human Development (Bethesda, MD, USA) included various brain regions, such as the frontal, temporal, occipital, and cerebellar areas. Paired samples with other organs were derived from 13 ASD and five non-ASD cases, and brain samples were obtained from 11 ASD cases. The Stanley Medical Research Institute (Rockville, MD, USA) provided genomic DNA for brain tissue and other matched organs from 25 non-schizophrenic and 26 schizophrenic cases. Additionally, the institute provided genomic DNA for the brain and matched liver tissues from patients with major depressive disorders. Fresh frozen brain samples from patients with Alzheimer’s disease (AD) were provided from the Netherlands Brain Bank (project number Lee-835) for 96 brain and matched blood samples for AD and non-demented control cases. Additionally, 15 samples from patients with AD and non-demented control cases were obtained from the Human Brain and Spinal Fluid Resource Center (West Los Angeles Healthcare Center, Los Angeles, CA, USA), which is sponsored by NINDS/NIMH (Bethesda, MD, USA), the National Multiple Sclerosis Society (Raleigh, NC, USA), and the US Department of Veterans Affairs (Bethesda, MD, USA). Fresh frozen samples of lumbosacral lipoma were obtained from the Severance Children’s Hospital of Yonsei University College of Medicine (Seoul, Republic of Korea). Bone tissues of patients with non-syndromic craniosynostosis were provided from the Severance Hospital of Yonsei University College of Medicine. Individuals with refractory epilepsy, including focal cortical dysplasia and non-lesional epilepsy, and who had undergone epilepsy surgery, were enrolled through the Severance Children’s Hospital of Yonsei University College of Medicine. Patients with glioblastoma and ganglioglioma were enrolled at the Severance Hospital of Yonsei University College of Medicine, and satisfied diagnostic criteria described in the 2016 World Health Organization Classification of Tumors of the Central Nervous System [17]. Tumor specimens were collected from the primary or recurrent tumor, and we evaluated tumor involvement via histological analysis, with normal brain or blood used as a control sample.

Deep WES

Genomic DNA was extracted with either the QIAamp mini DNA kit (Qiagen, Hilden, Germany) from freshly frozen brain tissues or the Wizard genomic DNA purification kit (Promega, Madison, WI, USA) from blood according to manufacturer instructions. Each sample was prepared according to Agilent library preparation protocols (Human All Exon 50 Mb kit; Agilent Technologies, Santa Clara, CA, USA). Libraries were paired-end sequenced on Illumina Hiseq 2000 and 2500 instruments (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions, and we used a confidence-mapping quality (mapping quality score ≥ 20; base quality score ≥ 20).

Data processing and reciprocal somatic variant calling

We checked the quality of the raw sequencing reads using the FastQC [44] (v.0.11.7) software. The FASTQ-formatted reads from each sample that passed the quality checks were aligned to the human reference genome (build 38; NCBI, Bethesda, MD, USA) using the BWA-MEM [45] algorithm and converted into a BAM file. The initial BAM file was updated with the read groups, and duplicate information was excluded as the analysis progressed using Picard [46] and GATK [47]. Additionally, we performed local realignment and base-quality recalibration with GATK tools for each exome. The processed BAM files were used to analyze contamination between samples, with the probability of swapping assessed using the NGSCheckMate [48] software and cross-contamination tested using GATK tools. The Vecuum [49] software was used to check for vector contamination during library construction, and Depth of Coverage (GATK) was used to analyze sequencing depth. Processes that are not described in detail were performed based on the GATK best-practice pipeline.

Two or more tissue samples from each individual were analyzed using all-pairs testing. First, to detect somatic mutations in tissue “A”, tissue “A” and “B” were used as a case and control, respectively, and further, tissue “A” was used as a control to identify somatic mutations in tissue “B”. We used the somatic mutation-detection pipeline (paired mode) with sample pairs as inputs with a three somatic variant caller Mutect2 [18] and excluding the panel of normal creation (single nucleotide variants and small insertion-deletions), RePlow [19] (single nucleotide variants), and NeuSomatic [20] with the control of the false detection rate control performed by Varlociraptor [50] (small insertion-deletions).

Systematic variant filtering and variant type discrimination

All mutations that met the following criteria were removed from the initial variant-calling results in the VCF format: oxoG-induced errors based on the method described by Costello et al. [51], common single-nucleotide polymorphisms by NCBI dbSNP [52] (build 153), segmental duplication and simple repeat regions according to the UCSC database [53], a mappability score under 0.8 by Umap [54], and the presence of off-target regions [55], whole genome data without exomes, and untranslated regions. After the removal of artifacts, somatic mutations showing “PASS” results for both Mutect2 and the filters of other callers (RePlow/NeuSomatic) were considered organ-specific mutations. We determined allelic frequency concordance using a one-sample proportion test. The one-sample proportion test indicates individual differences based on a binomial proportion analysis, which is used for testing a hypothesized proportion compared to a given theoretical proportion of the population. Thus, we tested whether there is a difference between the individual’s allelic frequency for somatic mutations relative to the population.

To determine whether multiple-organs shared mutations, variants that were present in matched sample pairs alone were included. In the filtering process, paired-mode somatic calling by Mutect2 was used. The probability of the candidates as multiple-organ shared mutations was identified using the combinations of the following identities: no correspondence to the error filter of other callers (RePlow/NeuSomatic), concordance of nucleotide substitution type and genomic location in matched pair samples, and statistical validity using the one-sample proportion test.

Because differences in the number of samples analyzed can lead to biases, we corrected the data by dividing the number of detected mutations by the number of tissues.

Validation sequencing using deep-targeted amplicon or Sanger sequencing

We performed validation sequencing by randomly selecting multi-organ shared and organ-specific mutations. For validation, we used deep-targeted amplicon sequencing or Sanger sequencing of PCR-amplified DNA, with the same genomic DNA used for deep WES. Primers for PCR amplification were designed using the Primer3 software (http://bioinfo.ut.ee/primer3-0.4.0/) [56]. Target regions were amplified by PCR using specific primer sets and high-fidelity PrimeSTAR GXL DNA polymerase (Takara, Shiga, Japan). Sanger sequencing was performed using BigDye Terminator reactions, and samples were loaded onto a 3730xl DNA analyzer (Applied Biosystems, San Francisco, CA, USA).

Bioinformatics analysis

All somatic mutations with false positives excluded by validation sequencing were annotated using VEP [57] (v.99.0) with “-everything -plugin ExACpLI” options. The results were evaluated using an in-house script to analyze the descriptive statistics of the properties of the basic mutations, the effect of each gene, and potential correlations with patient demographics (age, disease, etc.). Non-negative matrix factorization-based novel signature extraction (NMF, 200 iterations) and transcriptional strand-bias analysis were performed using the Mutational Patterns program (R package) [58]. The signature and 96 variant context types were fitted to the clockwise Pan-Cancer Analysis of Whole Genomes (PCAWG) single-base substitution and small insertions and deletions signatures by deconstructSigs [59], Mutalisk [60] (date of use: March 2020), and YAPSA [61]. The maximum-likelihood dN/dS method was implemented using dNdScv (Wellcome Sanger Institute, Cambridge, UK) [22]. Mutability was calculated using NCBI MutaGene [23, 24] (v.0.9.1.0) which is distributed as a Python package.

Nuclei extraction and fluorescence-activated nuclei sorting

Frozen brain samples were minced using pre-chilled razor blades and one or two drops of lysis buffer [0.2% Triton X-100, 1× protease inhibitor, and 1 mM DTT in 2% bovine serum albumin (BSA) in phosphate-buffered saline]. Lysis buffer (1 mL) was added to the homogenate and mixed by pipetting, after which the lysate was fixed in 1% paraformaldehyde at room temperature for 10 min. The fixed lysate was quenched with 0.125 M glycine at room temperature for 5 min. The homogenate was washed with suspension buffer (1 mM EDTA and 2% BSA), and filtered with a 40 μM cell strainer. The sample was incubated with anti-NeuN (mature neuronal marker; 1:1000 dilution, MAB377, Millipore) and anti-Olig2 (oligodendrocyte lineage marker; 1:500 dilution, ab10983, abcam) antibodies overnight at 4°C, followed by washing with suspension buffer and staining with the secondary antibody for 1 h at 4°C. After washing with suspension buffer, nuclei were passed through a 40 μM cell strainer and stained with 1 μg of 4′,6-diamidino-2-phenylindole. Nuclei used to isolate each cell type were analyzed and sorted using a MoFlo Astrios EQ cell sorter (Beckman Coulter, Brea, CA, USA). Nuclei pellets were centrifuged for 10 min at 1500 ×g and processed immediately for gDNA extraction using a QIAamp DNA micro kit (Qiagen) according to manufacturer instructions.