In recessive tumor predisposition syndromes, one allele is mutated in the zygote (or, rarely, in early embryogenesis), while the second allele is inactivated by subsequent somatic mutation (second hit; Fig.1a). Although a second hit would ordinarily be expected to lead to neoplasia, it is possible that some cells remain phenotypically normal in the presence of biallelic mutation, just as oncogenic mutations have been reported within healthy tissues. Recent studies of normal adult tissues have revealed bona fide cancer-causing (driver) mutations that accumulate with age and exposure to environmental mutagens, primarily in exposed epithelial tissues^1,2,3,4. The acquisition of mutations in normal tissues may be accelerated by germline mutations perturbing the fidelity of DNA replication, as seen in normal intestinal crypts of patients with a mutant DNA polymerase⁵. Furthermore, in children with malignant rhabdoid tumors (cancers driven by biallelic inactivation ofSMARCB1), we have observed normal tissues that share a genetic ancestor with the nearby tumor and harbor the same somaticSMARCB1 hit, without an elevated mutation rate⁶ (Fig.1b). We therefore speculated that second hits may occur in normal tissues of predisposed individuals that are unrelated to tumor lineages or affected by hypermutation, which we set out to investigate here (Fig.1c).

a, The second mutation in a recessive tumor predisposition syndrome is typically thought to lead to neoplasia.b, Some second hits may be found in the adjacent normal tissue to a childhood cancer, indicating that their presence is insufficient for neoplastic transformation.c, The possibility remains that second hits may be sustained in normal tissues that are independent of the cancer cell lineage.d, Histological images of three illustrative microdissected tissues are shown. The layers of the cerebellar cortex are annotated on the uppermost image. The light blue outlines with yellow arrowheads on the images are representative regions microdissected. Scale bars = 500, 250 and 250 µm (top to bottom).e, Experiment overview, detailing the number of bulk- and LCM-derived sequences generated per anatomical region per child. Please note that this includes all biopsies, irrespective of tumor involvement. A version of this table, limited to the tumor biopsies used in the high-grade glioma driver mutation identification, is provided as Supplementary Table2. ML, molecular layer; PL, Purkinje layer; GL, granular layer; WM, white matter; CNS, central nervous system; PNS, peripheral nervous system; LCM, laser capture microdissection.

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Neurofibromatosis type 1 is a complex multisystem disorder that predisposes to neoplasia. It is caused by germline mutation in theNF1 gene, a tumor suppressor gene that encodes neurofibromin, a negative regulator of intracellular RAS/MAPK signaling. The syndrome’s neoplastic phenotype is variable and tends to affect neuroectodermal lineages, although tissues derived from other germ layers also have an increased risk of cancer. An essential diagnostic feature of neurofibromatosis type 1 is the café au lait spot, a macroscopically visible clonal expansion of melanocytes^7,8. Other neoplastic manifestations, which exhibit variable penetrance, include neurofibromas, skeletal dysplasias, leukemias, malignant peripheral nerve sheath tumors and gliomas^{9,10,11,12,13}. In all these lesions,NF1, as a recessive cancer gene, exhibits a second mutation, not infrequently as the sole detected somatic driver event^11,12, consistent with Knudson’s two-hit hypothesis¹⁴.

We performed a postmortem study of three children aged <10 years old with high-grade midline gliomas—two (PD50297 and PD51123) with sporadic tumors (H3F3A K27M mutant) and one with neurofibromatosis type 1 with a pathogenic truncatingNF1 (c.3113 + 1G>A) germline mutation. Our key question was whether normal tissues across the body harbored driver events, in particular in the predisposed child. We extensively sampled normal tissues and neoplasms (Supplementary Tables1 and2), which, in the case of the child with neurofibromatosis, included a brain tumor, a subcutaneous spindle cell lesion (Extended Data Fig.1) and a café au lait spot. Guided by parental wishes, we surveyed central nervous system (CNS) tissues in all three children and extracranial tissues in two of them, including the predisposed child. None of the children had been pretreated with cytotoxic chemotherapy. Radiotherapy was given to the two children with sporadic tumors.

In total, we performed whole-genome sequencing (WGS) on 838 microdissected groups of cells (median coverage 28×), using an established approach that we and others have pursued in the study of normal tissue genomes¹⁵ (Fig.1d,e). We supplemented this with additional bulk tissue WGS (n = 71) and whole-exome sequencing (WES) of other microdissected tissues (n = 180; Fig.1e). We assembled catalogs of all classes of mutations (substitutions¹⁶, insertions and deletions (InDels), rearrangements and copy number changes; Supplementary Tables3–8 andSupplementary Note) using a validated variant calling pipeline (Methods;Supplementary Note). To exclude low-level tumor contamination of normal tissues, we quantified the extent of tumor infiltration in each sample (including samples distant from the tumor) by searching for the mutations assigned to the tumor’s phylogenetic trunk (Supplementary Note and Supplementary Tables3 and9).

We identified somatic driver variants in both neoplastic and normal tissues (Fig.2 and Supplementary Tables10–14). Gliomas exhibited a multitude of driver mutations in cancer genes known to operate in gliomagenesis^17,18. The normal tissues of the two nonpredisposed children bore comparatively few cancer-associated mutations, yielding only aCREBBP frameshift mutation (p.Q2199fs*99) within a single colonic crypt (PD50297g_lo0012) by our standard pipeline. Further inspection of the copy number data revealed one more putative driver, chromosome 11p loss of heterozygosity (LOH; Extended Data Fig.2) in a nerve (PD51123t_lo0028), a variant commonly reported in Wilms tumor, rhabdomyosarcoma and hepatoblastoma¹⁹.

Individual driver variants can be found in the lists of mutations provided in Supplementary Tables10–14. A single asterisk indicates that the identification of the LOH event was only possible because we could phase parental alleles (Methods). The low cell fraction of many of these meant that it was not possible to determine the breakpoint. When counting LOH events, those called from sequences derived from the same original bulk biopsy are treated as the same event, and those from different biopsies are treated as unique. These events should not be considered when comparing mutations against the other two children because their SNP alleles on chromosome 17 could not be phased. Double asterisks indicate that in sequences from the high-grade glioma, we only considered mutations in genes recognized in a large meta-analysis to be drivers of these neoplasms (Methods). Triple asterisks indicate that one additionalMSH6 frameshift mutation (p.F1088fs*2) was noted in the spinal cord (PD51122v_lo0008) and spleen (PD51122z_lo0017) of the child with neurofibromatosis type 1; this, however, remained heterozygous without evidence of hypermutation and did not co-occur with somaticNF1 mutation, making it of uncertain significance. Four asterisks indicate that chromosome 11p LOH was identified in a single sample after manual inspection of the copy number output, although it was too low a fraction to be detected by the copy number caller. Del, deletion; Inv, inversion.

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By contrast, in the child with neurofibromatosis type 1, we found bona fide somaticNF1 driver point mutations (Fig.2) that either truncated the gene (p.R304*, p.K233fs*48 and p.I679fs*21) or were likely oncogenic based on recurrence, in silico predictions, correlation between genotype and phenotype and functional studies (p.Y489C)^20,21. These mutations were detected by a combination of WGS and WES of microdissections or bulk tissues. They occurred in anatomically distant regions of the CNS (left parietal cortex, cerebellar hemisphere or spinal cord). The affected tissues appeared macroscopically and microscopically normal and were not correlated with focal areas of signal intensity on imaging (a feature often found in the brains of children with neurofibromatosis type 1 (ref.²²; Extended Data Figs.3 and4). The variant allele frequencies (VAFs) of secondNF1 hits indicated clone sizes as large as 56% of cells (clone size = 2 × VAF) in a microdissection (hundreds of cells) and 19% in a bulk tissue (a macroscopic piece of tissue).NF1 second hits were independent of those found in the clonal lesions (glioma, spindle cell lesion and café au lait spot; Fig.3a). In particular, as both copies ofNF1 in the glioma were already inactivated, there should be no selection pressure for further loss-of-function mutations inNF1 in tumor cells. Given this and our ability to detect tumor contamination accurately (Supplementary Note), theNF1 mutations in normal tissues are not the result of tumor cells infiltrating normal tissues. The mutation burden ofNF1 null normal tissues was inconsistent with a recent clonal expansion (Fig.3b;Methods). Like the spindle cell lesion and café au lait spot, no additional driver mutations were identified in theNF1 null histologically normal tissues.

a, Loss of the wild-typeNF1 allele is an independent event in each of the three neoplasms in PD51122. The allele frequency (y axis) represents a rolling window of 50 SNPs. Gridlines are present for matching to coordinates.b, The substitution count and median VAF in normal brain, color-coded by the presence of biallelicNF1 mutation.c, Schematic representation of the brain and spinal cord outlining the location of each somaticNF1 mutation in normal tissue from PD51122 discovered by different sequencing methods (before genotyping). The VAF of each mutation is shown in the table (2sf). The figure is created with BioRender.com.d, Distribution of somaticNF1 mutations across tissues from PD51122 (above the locus-specific error rate;Methods). The number within the box indicates the number of mutated samples from that tissue (red if >0).e, Pairwise comparison of the number of substitutions shared between whole-genome sequences of normal biopsies, according to whether they possess the sameNF1 second hit. The black line represents the interquartile range. The black dot is the median.P values were generated using one-sided permutation tests (Methods). ‘n’ refers to the number of pairwise comparisons, not samples. The CNS and MES groups exclude normal tissues with a secondNF1 hit.f, The dN/dS ratios for truncating variants, according to germ layer andNF1 germline mutation status. The dot represents the maximum likelihood estimate, and the lines represent the 95% credible interval. When the lower bound of the credible interval is above 1 (red line), there is a statistically significant positive selection. Credible intervals falling below the boundary of the plot are terminated with slanted double lines.g, Normal tissues from adults with neurofibromatosis type 1 are grouped by tissue type and evaluated for an excess of nonsynonymous variants inNF1 and compared with the index children. Top, dN/dS ratios for truncating mutations (the dot represents the maximum likelihood estimate, and the lines represent the 95% credible interval); middle, counts of variants inNF1; bottom, total duplex coverage (Methods;Supplementary Note) overNF1 in each group. R, right; L, left; MES, mesoderm; WT, wild type.

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To expand the breadth and depth ofNF1 mutations detected within the normal tissues of the predisposed child, we used two strategies. First, we re-examined all the child’s sequencing data for evidence of LOH of theNF1 locus (chromosome 17q). As this child’s glioma harbored complete LOH of chromosome 17 (Fig.3a), we were able to increase the sensitivity to detect allelic shift by obtaining definitive chromosome 17 haplotypes from phasing allele-specific single nucleotide polymorphisms (Methods; Supplementary Tables3 and4 and Extended Data Fig.5). This haplotype-resolved copy number calling revealed six instances of LOH in normal tissues (Fig.2), of which at least two were demarcated by distinct breakpoints consistent with independent events (Extended Data Fig.5). We captured LOH-drivenNF1 null clone sizes as small as 2% (PD51122b; cerebellum) and up to 13% (PD51122h_lo0012; occipital cortex), and no secondNF1 hit was related to a neoplasm. Curiously, while this approach did not yield anyNF1 null clones in any non-neuroectodermal lineage, it did identify rare examples where the germline mutant allele was lost in microdissections of tissues derived from other germ layers (bladder muscle (PD51122s_lo0012) and a renal tubule (PD51122u_lo0009); Extended Data Fig.6). AllNF1 second hits identified thus far are shown in Fig.3c.

Second, to deepen our search forNF1 null normal cells, we assessed all samples for evidence ofNF1 point mutations that had been previously called in normal tissues. Three variants (p.R304*, p.I679fs*21 and p.Y489C) were detectable in more than one biopsy above the locus-specific error rate (Methods; Fig.3d and Supplementary Table15). There were the following two possible explanations: either the shared variants arose before the seeding of the anatomical areas in which the mutations were found, or mutations appeared independently in different tissues. We could establish which scenario was more likely by comparing the total number of mutations across the genome that were shared between affected tissues—those with a common developmental root would possess more. ForNF1 mutations that spanned only regions of the brain (p.R304* and p.I679fs*21), affected tissues shared significantly more mutations with each other than unaffected brain regions did (P < 0.001 for both mutations, one-sided permutation test; Fig.3e;Methods), implicating a common ancestor in their development, although convergent evolution with shared selection pressures between developmentally related tissues is also possible. The same could not be said for theNF1 mutation found in both the brain and spleen when compared to normal tissues of the CNS and mesoderm (p.Y489C;P = 0.373, one-sided permutation test; Fig.3d), meaning that they likely developed independently.

Taken together, the multiple lines of inquiry we had pursued thus far pointed toward the enrichment ofNF1 nonsynonymous mutations within the normal tissues of a predisposed individual but not wild-type individuals, at least within the brain and spinal cord. It seems likely that this pattern ofNF1 mutation has emerged as a consequence of positive selective pressure, given the absence of the concomitant silent and intronicNF1 variants (Fig.2) that would be expected under a neutral model.

To establish statistically whether there is a positive selection for nonsynonymous variants inNF1 in predisposed normal tissues, we re-interrogated 60 normal tissue samples (21 from the predisposed child and 39 from the unaffected children) by duplex sequencing of theNF1 gene^23,24. Duplex sequencing, through barcode tagging of both strands of DNA molecules, enables highly sensitive and specific mutation calling, which may deliver sufficient variants for formal statistical assessment of selection through the nonsynonymous to synonymous variant ratio (dN/dS)¹⁷. Duplex sequencing yielded a total of 29 nonsynonymous (21 truncating; eight missense or in-frame) and two synonymousNF1 mutations in the predisposed child (Supplementary Table16). By contrast, in the normal tissues of the two children without neurofibromatosis, we detected nine nonsynonymous (four truncating; five missense or in-frame) and five synonymousNF1 mutations (Supplementary Table16). Calculation of the dN/dS ratio provided strong evidence of positive selection for truncatingNF1 variants in normal tissues from the child with a germline predisposition (Fig.3f). Interestingly, the spleen had a particularly high proportion of truncating variants, and, when analyzed separately from other tissues, it had the highest dN/dS ratio (Fig.3f). This finding was of interest given that neurofibromatosis type 1 predisposes to juvenile myelomonocytic leukemia, which always (as per the diagnostic definition) involves the spleen²⁵, whether through entrapment of leukemic cells or as their organ of origin.

Next, we extended our analysis into normal tissue from adults with neurofibromatosis type 1 (Supplementary Table17). We were able to obtain normal peripheral nerves, muscle tissue or blood from nine individuals who underwent extensive surgical resections for sarcoma. The principal cellular material of peripheral nerves is made up of Schwann cells that are derived from the neuroectoderm, whereas muscle and blood develop from mesoderm. Consistent with the pattern of mutation we observed in pediatric tissues, we found, by duplex sequencing, a stark excess of truncatingNF1 mutations in peripheral nerves, indicative of positive selection (Fig.3g).

In this study ofNF1 mutations in individuals with neurofibromatosis type 1, we observed independent secondNF1 hits in macroscopically and histologically normal pediatric and adult tissues. Multiple lines of evidence arrive at the same conclusion: in neurofibromatosis type 1, nonsynonymous second somatic mutations ofNF1 are selected for in histologically normal tissues. AlthoughNF1 is a ubiquitously expressed gene, the tissue distribution of neoplasms associated with neurofibromatosis type 1 is not random, showing a predilection for neuroectodermal lineages²⁶, which is mirrored in the distribution of secondNF1 hits we identified. Unlike our study, though, where these mutations pervaded the CNS, most brain tumors that arise in children with neurofibromatosis type 1 are localized to the optic pathway and brainstem^26,27. Our findings may thus explain some, but not all, of the cancer phenotypes associated with neurofibromatosis type 1.

Three factors, unrelated to the germline mutation status ofNF1, may also have contributed to the number of nonsynonymous mutations we observed. The first is age, as neutral mutations accrue with time, and the second is the size of theNF1 gene.NF1 has one of the largest footprints of any gene (8,520 bp coding sequence compared to a median length across human genes of 1,257 bp (ref.²⁸)), meaning there are simply more sites to mutate. Hypermutation is a third factor that might augment the rate of driver mutation acquisition, but our comprehensive study of the index case found no evidence to support that here. Notably, all these factors are accounted for by our model of selection (dN/dS), meaning that they cannot explain our data. We can assume, then, that the strength of the signal we see in the carriers compared to unaffected children is the result of selective pressure specifically for second hits. The fact that this is so readily apparent in the extensively studied child suggests that this occurs from an early age, possibly even during development (Fig.3be). Given the extent to which we observedNF1 loss-of-function variants in normal tissues, it seems reasonable to propose that although in certain contexts second hits may be sufficient to cause neoplasia^10,11, as suggested by our case’s café au lait spot and spindle cell lesion, transformation to a discernible tumor is an uncommon immediate outcome of biallelicNF1 loss.

This finding may represent a fundamental principle ofNF1 mutation in neurofibromatosis type 1, of which determining the precise nuances and clinical implications will require extensive surveys in human tissues²⁹. Consistent with our data, mouse models support a complex relationship between cellular genotype and phenotype in neurofibromatosis type 1—the genetic background of the mouse, the identity of the cell in whichNF1 is inactivated, the presence of cooperating somatic mutations and the status ofNF1 function in neighboring cells, all appear to affect tumor development^30,31. From a practical, clinical point of view, it is conceivable that the extent of secondNF1 hits in normal tissues represents a quantifiable link between germline genotype and cancer risk. In the broader context of recessive cancer predispositions, our findings call for systematic investigations to establish whether second hits occur commonly in such predispositions or delineate a particular group of syndromes.

Sample collection

This study complies with all relevant ethical regulations. Written and informed consent was given for all samples. The study of the discovery cohort of three children was approved by National Health Service (NHS) research ethics committees (PD50297—HRA East Midlands Derby REC, 08/H0405/22+5; PD51122 and PD51123—London Brent REC, 16/LO/0960). Each autopsy was undertaken at the child’s local neuropathology unit, with parental consent, 1–7 days following death. Samples were snap-frozen at the point of sampling, with adjacent brain tissue taken for immediate formalin fixation and processing in the local diagnostic laboratory. A full list of the samples taken is provided in Supplementary Table1.

The study of the validation cohort of adults with neurofibromatosis type 1 was approved by NHS research ethics committees (20/YH/0088, IRAS 272816, NHS Yorkshire and the Humber—Leeds East Research Ethics Committee). Patients with a diagnosis of neurofibromatosis type 1 who were undergoing resection for sarcoma consented to the use in research of normal tissue removed as part of the resection but distant from the lesion or of blood samples. Solid tissue samples were immediately frozen in liquid nitrogen. Blood samples were centrifuged, and plasma and cellular fractions were separated before freezing.

Statistics and reproducibility

The study was designed in two phases. In the first phase, a discovery cohort of three children, of whom one had neurofibromatosis type 1 and two did not, was investigated. In the second phase, a validation cohort of ten adults, all of whom had neurofibromatosis type 1, was investigated.

The sample size in each case was determined by tissue availability. No statistical method was used to determine sample size. Lethal high-grade midline gliomas are rare in children—we studied all cases at collaborating centers over the study period (of approximately 2 years) in which consent for research was given. Of these, only one had neurofibromatosis type 1. For our validation cohort, all cases of patients with neurofibromatosis type 1 and tissue available at our collaborating center were studied. One patient from the validation cohort was excluded as noNF1 germline variant was identified. The experiments were not randomized, and the investigators were not blinded to whether patients had neurofibromatosis type 1 during the experiments and outcome assessment.

Preparation of samples for sequencing: tissue processing and DNA extraction

A subset of the bulk samples in the discovery cohort underwent bulk DNA extraction using either the DNeasy Blood & Tissue Kit (Qiagen), AllPrep DNA/RNA Mini Kit (Qiagen) or the Gentra Puregene Blood Kit (Qiagen). The choice of samples to undergo bulk DNA extraction was guided by a prior understanding of the clonal architecture of the tissue. For example, intestinal biopsies were not subject to bulk DNA extraction. This is because their clonality meant that pseudo-single-cell genome readouts, rather than a single polyclonal amalgamation, could be achieved by microdissection of individual crypts instead². Similarly, bulk DNA extraction was not performed for samples taken from the interface of the tumor and normal as it was hoped microdissection would better isolate the tumor and normal tissue compartments.

The remaining tissue from solid organ biopsies in the discovery cohort was fixed in PAXgene (PreAnalytiX), according to the manufacturer’s instructions, and processed in preparation for laser capture microdissection using an established protocol¹⁵. To ensure correct feature labeling of nervous system structures during microdissection, reference slides were generated using 4-micron-thick sections mounted on SuperFrost Plus slides (VWR International) and reviewed by the neuropathologist who performed the autopsy. The sections subject to microdissection were 16 microns thick and mounted on polyethylene naphthalate membrane slides (Leica Microsystems). The microdissected tissue then underwent lysis and further DNA extraction¹⁵. For duplex sequencing of samples from the first three children, curls of paraffin-embedded tissues were deparaffinized with xylene and ethanol 100% washes, followed by lysis with Arcturus PicoPure (Thermo Fisher Scientific) and DNA extraction using the DNA Micro Kit (Qiagen) according to the manufacturer’s protocol save for a double elution and the use of EB as an elution buffer rather than AE.

DNA was extracted from solid tissues from the adult cohort using the DNeasy Blood & Tissue Kit (Qiagen) and from blood using the Gentra Puregene Blood Kit (Qiagen).

Preparation of samples for sequencing: library preparation for WGS and WES

In the discovery cohort, the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs) was used for the preparation of DNA extracted from the bulk samples, while the protocol for microdissected tissue used the NEBNext Ultra II DNA FS Library Prep Kit (New England Biolabs) instead. For the microdissected libraries subject to WES, the SureSelect Human All Exon V5 bait set (Agilent Technologies) was used. A full list of the successfully generated whole-genome and whole-exome sequences can be found in Supplementary Tables3 and4, respectively.

Preparation of samples for sequencing: library preparation for duplex sequencing of theNF1 gene

Libraries were prepared using a version of the protocol for nanorate sequencing²³ that has been adapted to be compatible with targeted sequencing²⁴. DNA was sheared to an average size of 450 bp by focused ultrasonication using the Covaris 644 LE220 instrument (Covaris) in 120 μl. It was purified using a 0.8× soluble-phase reversible immobilization (SPRI) bead ratio and eluted in 30 μl nuclease-free water (NFW). DNA fragments were blunted in a final reaction volume of 30 μl, including 3 μl (10×) mung bean buffer (Takara Bio, 2420A), 0.125 μl mung bean nuclease (Takara Bio, 2420A), 1.875 μl of NFW and 25 μl DNA. The reaction was incubated at 37 °C for 10 min with the lid tracking 5 °C above. Samples were purified using 2.5× SPRI beads and eluted in 15 μl NFW. In total, 10 μl was used as input into an A-tailing reaction, containing 1.5 μl T4 DNA ligase buffer (New England Biolabs, B0202S), 1.5 μl (1 mM) dATP/ddBTP (New England Biolabs, N0440S; GE HealthCare, 27204501), 1.5 μl Klenow fragment (3′–5′ exo-; New England Biolabs, M0212L) and 0.5 μl T4 polynucleotide kinase (New England Biolabs, M0201). The reaction was incubated at 37 °C for 30 min with the lid tracking 15 °C above. The whole sample of 15 μl was taken into the ligation reaction mix, which consisted of 30 μl Ultra II Ligation MM (New England Biolabs, E7595L), 1 μl Ultra II ligation enhancer (New England Biolabs, E7595L), 1.25 μl xGen Duplex Seq Adapters (Integrated DNA Technologies, 1080799) and 12.75 μl NFW. The reaction was incubated at 20 °C for 20 min, with the lid temperature off. Ligated DNA was cleaned up using SPRI beads and eluted in 40 μl NFW.

DNA was quantified by qPCR using a KAPA library quantification kit (Kapa Biosystems, KK4835). The supplied primer premix was first added to the supplied KAPA SYBR FAST master mix. In addition, 20 μl of 100 μM NanoqPCR1 primer (HPLC, 5′-ACACTCTTTCCCTACACGAC-3′) and 20 μl of 100 μM NanoqPCR2 primer (HPLC, 5′-GTGACTGGAGTTCAGACGTG-3′) were added to the KAPA SYBR FAST master mix. Samples were diluted 1:500 using NFW, and reactions were set up in a 10 μl reaction volume (6 μl master mix, 2 μl sample/standard and 2 μl water) in a 384-well plate. Samples were run on the Roche 480 LightCycler and analyzed using absolute quantification (second derivative maximum method) with the high-sensitivity algorithm. The concentration (nM (fmol μl⁻¹)) was determined as follows: (mean of sample concentration × dilution factor (500) × 452/573/1,000) × adjustment factor (1.5), where 452 represents the size of the standard in bp, 573 is the proxy for the average fragment length of the library in bp and 1,000 is a unit conversion factor. Samples were diluted to the desired fmol amount in 25 μl using NFW.

Libraries were subsequently PCR-amplified in a 50-μl reaction volume comprising 25 μl of sample, 25 μl NEBNext Ultra II Q5 Master Mix and a unique dual index containing PCR primers (dried). The reaction was cycled as follows: step 1, 98 °C 30 s; step 2, 98 °C 10 s; step 3, 65 °C 75 s; step 4, return to step 2 (13 times); step 5, 65 °C for 5 min; step 6, hold at 4 °C. The number of PCR cycles is dependent on the input (Supplementary Table18). The PCR product was subsequently cleaned up using two consecutive 0.7× AMPure XP clean-ups. Each sample was quantified using the AccuClear Ultra High Sensitivity dsDNA Quantification kit (Biotium). Hybrid capture was performed using TWIST hybe reagents. Samples were pooled for hybridization with 1–4 μg of PCR-amplified material per capture reaction.

DNA sequencing

All DNA sequences were generated on the Illumina NovaSeq sequencing platform, generating paired-end 150 bp sequences. Sequences were aligned to the GRCh38 human reference genome using the Burrows–Wheeler Aligner-MEM³². Details on the assessment of DNA sequencing quality and sample-to-sample concordance may be found in theSupplementary Note.

Variant calling and filtering

A detailed explanation of variant calling and filtering may be found in theSupplementary Note. In brief, for WGS and WES, substitutions were called using CaVEMAN algorithm (v.1.15.1)³³, small InDels with Pindel algorithm (cgpPindel v.3.5.0)³⁴, copy number with both Battenberg (cgpBattenberg v.3.5.3)³⁵ and ASCAT (AscatNGS v.4.3.2)³⁶ and structural variants with GRIDSS (v.2.9.4)³⁷. For duplex sequencing, mutations were detected by considering only mutations that were supported by reads from both strands that were not called in the normal sample^23,24. After filtering, mutations were analyzed using the package dNdScv¹⁷ (v.0.0.1.0). Code for this analysis can be found athttps://github.com/trwo/nf1_second_hit_normal_tissues.

Driver mutation identification and annotation (WGS and WES): substitutions and InDels

For substitutions and InDels, all mutations resulting in protein-coding changes in genes reported in the COSMIC (v.94) cancer gene census were initially considered³⁸. Driver mutation status was assessed before the application of the exact binomial filter (which determines germline status—see above). This circumvented the risk that true driver mutations might be eliminated at a subsequent filtering step, for example, germline driver events. The following two classes of mutations were considered to be candidate drivers: first, missense substitutions or in-frame InDels occurring at hotspots in dominant-acting genes, and second, mutations in recessive-acting cancer genes predicted to result in loss of function, such as nonsense, frameshift or essential splice site variants. Candidate mutations were assigned to tiers, according to their likelihood of acting as a driver. Tier 1 substitution/InDel drivers occurred within genes that were recurrently mutated in a recent meta-analysis of over 1,000 pediatric high-grade gliomas¹⁸. This list of genes includedACVR1,ASXL1,ATM,ATRX,BCOR,BRAF,CCND2,CDK4,CDK6,CDKN2A,CDKN2B,EGFR,FGFR1,H3F3A,HIST1H3B,HIST1H3C,HIST2H3C,ID2,KDM6B,KDR,KIT,KRAS,MET,MYC,MYCN,NF1,NTRK1,NTRK2,NTRK3,PDGFRA,PIK3CA,PIK3R1,PPM1D,PTEN,RB1,SETD2,TERT,TOP3A andTP53. Tier 2 mutations occurred in other supposed cancer genes from the COSMIC (v.94) cancer gene census list.

Mutations inNF1 itself were considered differently. Inactivating mutations were considered to be probable drivers. AlthoughNF1 is a recessive cancer gene, it does have residues that are mutated more frequently. Missense mutations that occurred in such loci, defined as >4 mutations of a given residue in COSMIC, were considered as probable driver mutations, and further support for their functional effect was sought from the literature and from predictors of mutational effect³⁹.

Driver mutation identification and annotation (WGS and WES): copy number changes

Copy number changes were determined to be driver events according to sample ploidy, the genes found on each segment and the segment length. Oncogenes were considered to be amplified if their total copy number was ≥5 when ploidy was <2.7 or ≥9 when ploidy was ≥2.7. For tumor suppressor genes, the total copy number had to equal 0 for <2.7 ploidy and ≤ (ploidy − 2.7) when ploidy was ≥2.7. Copy number aberrations passing these criteria were then annotated as putative tier 1 driver mutations if the oncogene(s) or tumor suppressor gene(s) they contained were found in the list of genes above, the segment width was ≤10 Mb wide and this was a recognized oncogenic event for that gene. For example, copy number changes in genes that mediate oncogenesis via fusion events alone were not considered tier 1 drivers. Tier 2 drivers did not meet the criteria outlined for tier 1 variants but had to be found on segments ≤1 Mb wide.

Driver mutation identification and annotation (WGS and WES): structural variants

For a structural variant, independent of copy number state, to be annotated as a driver, it had to either form a fusion gene recognized to be oncogenic, truncate the gene footprint of a tumor suppressor gene or activate an oncogene through intragenic deletion (for example,PDGFRA). Once again, tier 1 events occurred in the list of genes used for other variant classes, whereas tier 2 events were plausible drivers that fell outside of these.

Testing for recent clonal expansions associated withNF1 null status

A linear mixed effects model comparing the mutation burden derived from WGS ofNF1 null versusNF1-heterozygous histologically normal CNS biopsies/microbiopsies was fitted in R using the package nlme.NF1 null status, whether the sample was derived from bulk sequencing or laser capture microdissection, and coverage were included as fixed effects. The piece of tissue from which the sample was derived was used as a random effect (that is, two microbiopsies from the same piece of tissue should be correlated with one another). AlthoughNF1 null status was associated with a statistically significant effect, the effect size was only of seven additional mutations. Given that the postnatal somatic mutation rate of most tissues is 10–50 mutations per year (including a rate in glia of 27 substitutions per year⁴⁰ and a rate in neurons of 17 substitutions per year^23,40) and the prenatal rate is usually higher, a recent clonal expansion should result in a mutation burden on the order of 100 mutations even in a child; we, therefore, concluded thatNF1 null status was incompatible with a recent clonal expansion.

Detecting independentNF1 null clones in normal tissue: WGS and WES

Driver events withinNF1 were initially identified in the same manner as all other driver mutations. This included a germline essential splice siteNF1 mutation within PD51122 that accounted for their neurofibromatosis type 1. Second loss of functionNF1 mutations in this case were assumed to render the affected cells ‘NF1 null’.

Evidence for anyNF1 driver point mutation that had been identified in the child with neurofibromatosis type 1 was sought in the remaining two cases in the discovery cohort. Similar to the substitution filtering, this provided an approximation of the base sequencing error rate, above which we could determine anNF1 point mutation to be truly present in the index case. We performed a one-sided Fisher’s exact test using the summed variant and total read depth from the two children withoutNF1 mutation against those observed in each microdissection and bulk biopsy from the child with neurofibromatosis type 1. After a multiple hypothesis testing correction (Benjamini–Hochberg method), theNF1 point mutation was considered present in a sample ifq < 0.01. AllNF1 driver point mutations that were identified were found in at least one sample without any detectable tumor involvement. No copy number aberrations or structural variants involvingNF1 were detected in normal tissues using standard variant calling.

To increase our sensitivity to detect LOH events in the normal tissue of the child with neurofibromatosis type 1, we phased SNPs to each gene allele. The child’s tumor had LOH of the entirety of chromosome 17, leaving only copies of the allele bearing the germlineNF1 mutation. We could phase the heterozygous SNPs identified in the deeply sequenced blood sample (PD51122q) on chromosome 17 according to which allele had the greatest allele frequency in one of the purest bulk tumor samples (PD51122m). These phased SNPs were then profiled in all remaining samples. Only SNPs with ≥10× coverage in a sample were kept for its downstream analysis, as few SNPs in noncoding regions would be captured by WES.

To identify samples with possible independent LOH events inactivatingNF1 in both the WGS and WES data, a two-sided exact binomial test was performed. In this test, the number of trials was the sum of total coverage across the heterozygous SNPs found across the gene. The number of successes was the sum of the depth of the alleles that were only found in the tumor. The hypothesized probability of success was the expected aggregate allele fraction. TheNF1 locus was 2 + 0 in the tumor, while a normal cell would have one copy of each parental allele. The aggregate allele fraction therefore would equal tumor purity + ((1 − tumor purity) × 0.5).P values underwent multiple hypothesis corrections using the Benjamini–Hochberg method. To ensure confidence that we were truly detecting these in normal tissue, only samples whereq < 0.01, the median coverage was ≥30× and tumor purity was <1% were considered to possess a copy number change to theNF1 locus that could not be explained by tumor infiltration alone.

Two samples that had significant shifts in the proportion of eachNF1 allele unusually favored the wild-type allele (PD51122s_lo0012 and PD51122u_lo0009). These microdissections of non-neuroectodermal origin are interpreted as containing clones with LOH events that lost the mutantNF1 allele.

TheNF1 locus had not undergone LOH in the other two children, meaning that a similar analysis could not be performed.

Assessment of the genetic relationship between tissues that shared a somaticNF1 variant in PD51122

For a variant to be found in two tissues, either it must have been acquired from a shared ancestor or developed independently. Assuming a comparable rate of mutation acquisition, tissues with a more recent common ancestor will share a greater number of mutations than those that are more distantly related. To determine whether the tissues carrying the same somaticNF1 mutation were uniquely related, we first needed control to determine how related two tissues would be by chance in this child.

To construct our control data, we used the normal tissues (<1% estimated tumor contamination) without evidence of a secondNF1 hit. Separate comparisons of normal CNS versus normal CNS and normal CNS versus normal mesoderm were made to account for differences in the genetic architecture between germ layers and tissues. A mutation was determined to be shared between tissues if it was identified in both using a Shearwater-like approach (Supplementary Note), rather than relying on the calls from the variant caller alone. This improved our sensitivity for detecting low VAF variants and mitigated some of the risk that true shared variants would not be called or erroneously filtered in one sample by our pipeline. All samples in each group were then iteratively compared to the others.

The pairwise comparison was then repeated for tissues that shared a somaticNF1 variant, and the mean number of shared substitutions per pair was calculated for each mutation (test data). The same number of pairwise comparisons for each mutation were then drawn from the control data at random, without replacement, and the mean was calculated. This was repeated 1,000 times. TheP value was determined by the number of draws where the control data mean was greater than that observed in the test data (one-sided test).

Reporting summary

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

WGS and targeted sequencing data are deposited in the European Genome–Phenome Archive (https://www.ebi.ac.uk/ega/) with accession IDEGAD00001015398. Mutation calls are available in Supplementary Tables1–18. The complete catalog of substitutions identified by WGS has been deposited on Mendeley and can be accessed athttps://doi.org/10.17632/hfv45sg3c5.1 (ref.¹⁶).

Custom R scripts used to analyze the data can be found athttps://github.com/trwo/nf1_second_hit_normal_tissues (ref.⁴¹).

This study was funded by the Wellcome Trust (institutional grant; personal fellowships to T.R.W.O. and S.B.; grants 206194, 108413/A/15/D and 223135/Z/21/Z). T.S.J. is grateful to the Brain Tumour Charity (including the Everest Centre for Low-Grade Paediatric Brain Tumours (GN-000382 and GN-000707) and the INSTINCT program), Great Ormond Street Hospital Children’s Charity, Children with Cancer UK, the Olivia Hodson Cancer Fund, Cancer Research UK and the National Institute for Health Research for funding. This research was supported by the National Institute for Health and Care Research (NIHR) Great Ormond Street Hospital Biomedical Research Centre and the NIHR Biomedical Research Centre at The Royal Marsden and the Institute for Cancer Research. Additional funding was received from The Royal National Orthopaedic Research and Development Department (to A.M.F.) and The Bone Cancer Research Trust (to A.M.F.). We thank the Children’s Cancer and Leukaemia Group (CCLG) Tissue Bank, the CCLG centers and the Experimental Cancer Medicine Centres Paediatric Network for the collection and provision of tissue samples (project 2016 BS 05). The CCLG Tissue Bank is funded by Cancer Research UK and CCLG. A.M.F. is also separately supported by the National Institute for Health Research, Sarcoma UK, the UCLH Biomedical Research Centre and the UCL Experimental Cancer Centre. Funding from these institutions supported the work of the Biobank where the samples from the adult cohort were stored. H.L.-S. was supported by an NIHR Academic Clinical Fellowship and a Junior Research Fellowship from Trinity College, Cambridge, UK. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. We thank the clinical teams of Cambridge University Hospitals and Great Ormond Street Hospital, including the mortuary staff. V. Lee, L. Ward and O. Ogunbiyi from Great Ormond Street Hospital and A. Whyte from Addenbrooke’s Hospital helped facilitate the collection and transfer of samples for which we are grateful. We thank G. Caravagna (University of Trieste) for his assistance with copy number calling quality control and A. Maartens (science writer at the Wellcome Sanger Institute) for his critical review of the manuscript. We are indebted to the families who participated in this research.

1. These authors contributed equally: Thomas R. W. Oliver, Andrew R. J. Lawson, Henry Lee-Six.

2. These authors jointly supervised this work: Kieren Allinson, Inigo Martincorena, Thomas S. Jacques, Sam Behjati.

Authors and Affiliations

1. Wellcome Sanger Institute, Hinxton, UK

   Thomas R. W. Oliver, Andrew R. J. Lawson, Henry Lee-Six, Hyunchul Jung, Yvette Hooks, Rashesh Sanghvi, Matthew D. Young, Timothy M. Butler, Pantelis A. Nicola, Taryn D. Treger, Stefanie V. Lensing, Inigo Martincorena & Sam Behjati

2. Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

   Thomas R. W. Oliver, Henry Lee-Six, Taryn D. Treger, G. A. Amos Burke, Flora A. Jessop, Kieren Allinson & Sam Behjati

3. Research Department of Pathology, University College London, London, UK

   Anna Tollit & Adrienne M. Flanagan

4. Department of Paediatrics, University of Cambridge, Cambridge, UK

   Taryn D. Treger & Sam Behjati

5. Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK

   G. A. Amos Burke

6. Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK

   Kristian Aquilina, Ulrike Löbel, Darren Hargrave, Mette Jorgensen & Thomas S. Jacques

7. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK

   Isidro Cortes-Ciriano

8. UCL Great Ormond Street Institute of Child Health, London, UK

   Darren Hargrave & Thomas S. Jacques

9. Broad Institute of MIT and Harvard, Cambridge, MA, USA

   Tim H. H. Coorens

10. Department of Histopathology, Royal National Orthopaedic Hospital NHS Trust, Middlesex, UK

    Adrienne M. Flanagan

Contributions

S.B. and T.R.W.O. designed the experiment and wrote the manuscript. T.S.J. and K. Allinson conducted the autopsies and provided neuropathological histology expertise. T.R.W.O. performed microdissection, with laboratory support provided by Y.H. and P.A.N., and A.T. provided further technical support. T.R.W.O., A.R.J.L. and H.L.-S. analyzed data, with the assistance of R.S., M.D.Y., T.H.H.C., H.J., T.M.B., S.V.L. and I.C.-C. M.D.Y. provided statistical expertise. G.A.A.B., K. Aquilina, D.H., M.J. and T.D.T. provided clinical expertise and contributed to discussions. U.L. provided radiological expertise. F.A.J. and A.M.F. provided pathological expertise. S.B., T.S.J., I.M. and K. Allinson directed the study.

Corresponding authors

Correspondence toKieren Allinson,Inigo Martincorena,Thomas S. Jacques orSam Behjati.

Competing interests

I.M. is a cofounder and consultant of Quotient Therapeutics. D.H. provides consultancy to AstraZeneca/MedImmune, Alexion Pharmaceuticals, Bayer, Biodexa, Roche/Genentech and Novartis, as well as expert testimony to AstraZeneca and Novartis, and his expenses are covered by Alexion Pharmaceuticals, Boehringer Ingelheim, Roche/Genentech and Novartis. The other authors declare no competing interests.

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