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Developmental venous anomalies are a genetic primer for cerebral cavernous malformations

Abstract

Cerebral cavernous malformations (CCMs) are a neurovascular anomaly that may occur sporadically or be inherited due to autosomal dominant mutations in KRIT1, CCM2 or PDCD10 (refs. 1,2,3,4). Individual lesions are caused by somatic mutations that have been identified in KRIT1, CCM2, PDCD10, MAP3K3 and PIK3CA5,6,7,8,9,10,11. However, the interactions between mutations and their relative contributions to sporadic versus familial cases are unclear. We show that mutations in KRIT1, CCM2, PDCD10 and MAP3K3 are mutually exclusive but may co-occur with mutations in PIK3CA. We also find that MAP3K3 mutations may cause sporadic but not familial CCM. Furthermore, we find identical PIK3CA mutations in CCMs and adjacent developmental venous anomalies (DVAs), a common vascular malformation frequently found in the vicinity of sporadic CCMs12,13,14. However, somatic mutations in MAP3K3 are found only in the CCM. This suggests that sporadic CCMs are derived from cells of the DVA that have acquired an additional mutation in MAP3K3.

Main

CCMs are prone to hemorrhage often leading to stroke, seizures and disability. The inherited form of CCM disease is characterized by numerous lesions throughout the brain and spinal cord and is caused by an autosomal dominant loss of function (LOF) mutation in the genes encoding components of the CCM signaling complex: KRIT1 (refs. 1,2), CCM2 (ref. 3) or PDCD10 (ref. 4). In contrast, sporadic CCMs typically occur as solitary lesions and form in the absence of an inherited germ line mutation. Previous studies have established that somatic mutations in genes of the CCM complex cause biallelic LOF5,6,7,8; however, it is unclear how the recently identified mutations in MAP3K3 and PIK3CA fit into this existing model of pathogenesis.

The presence of multiple somatic mutations in CCMs also raises the question of how these mutations arise, especially in sporadic cases where none of the mutations are inherited. It has long been appreciated that sporadic CCMs often form in the vicinity of DVAs, but the underlying cause has remained a long-standing mystery. DVAs are the most common vascular malformation present in 6–14% of the adult population15,16,17 with most developing before the age of 20 (ref. 15). When assessed by magnetic resonance imaging, an adjacent DVA is identified in 24–32% of sporadic CCM cases12,13,14; an even greater fraction of sporadic CCMs are associated with a DVA at surgery12,14. One study focused on DVAs reported an adjacent sporadic CCM in 6.9% of all DVAs in a general population (116 out of 1,689)15. These studies highlight the association between DVAs and sporadic CCMs. By contrast, familial CCM lesions have not been associated with DVAs18. These combined data suggest that a DVA is not required for CCM formation but may be a predisposing factor in sporadic cases.

Results

To evaluate whether sporadic and familial CCMs have distinct somatic mutation spectra, we identified somatic mutations present in 71 CCMs (20 familial CCMs and 51 sporadic/presumed sporadic CCMs). Familial and sporadic CCM were identified by clinical and genetic characteristics (Methods), whereas cases lacking information concerning family history (for example, deidentified biobank samples) were classified as unknown. Mutations in KRIT1, CCM2, PDCD10 and PIK3CA were detected by targeted sequencing and/or droplet digital PCR (ddPCR) as described previously9. The common gain of function (GOF) mutation in MAP3K3 (hg38 chr17:63,691,212, NM_002401.3, c.1323 C>G; NP_002392, p.I441M) was detected by ddPCR (Supplementary Table 1).

The p.I441M mutation in MAP3K3 was identified in 15 out of 51 sporadic CCMs and 0 out of 20 familial CCMs (Fig. 1a). We also screened for MAP3K3 p.I441M in eight blood samples for which we were previously unable to identify a germ line mutation in KRIT1, CCM2 and PDCD10. None of the eight blood samples harbored MAP3K3 p.I441M. Notably 11 out of 51 sporadic CCMs harbored at least 1 somatic mutation in KRIT1, CCM2 or PDCD10; however, none of these CCMs also had a mutation in MAP3K3 indicating that both a mutual loss of the CCM complex and GOF in MEKK3 (the protein product of MAP3K3) are not required for CCM formation. Since the CCM complex is a direct inhibitor of MEKK3 activity19, these data strongly suggest identical functional consequences of these mutations.

Fig. 1: Mutations in MAP3K3 are mutually exclusive with CCM gene mutations and occur in the same cells as PIK3CA mutations.
figure 1

a, Mutations present in 71 CCMs. Disease type denotes whether the sample was familial (F), sporadic (S) or unknown (blank). The presence of somatic mutations in PIK3CA and MAP3K3 are denoted by black and purple bars, respectively. Germ line and somatic mutations (green and blue, respectively) in KRIT1, CCM2 or PDCD10 are shown in CCM Mut 1 with the second-hit mutation shown in CCM Mut 2 if present. bd, Nuclei genotypes determined by snDNA-seq. The left and right circles in each Venn diagram show the number of nuclei with the PIK3CA or MAP3K3 mutations while the overlap shows nuclei harboring both mutations. ***P < 0.0001, P = 4.3 × 10−27 (b), P = 9.4 × 10−33 (c), P = 1.3 × 10−5 (d). e, Summary of data presented in bd, including P values determined by chi-squared test of the observed number of double-mutant nuclei to the expected value derived from a Poisson distribution as done previously9.

Most CCMs and verrucous venous malformations with a mutation in MAP3K3 harbor the p.I441M variant10,11,20; however, an alternative variant p.Y544H has also been identified in a venous malformation21. While ddPCR provides superior sensitivity and specificity compared to targeted sequencing, it is restricted to detecting a single mutation per assay. To determine whether other mutations contribute to CCM pathogenesis—either MAP3K3 mutations besides p.I441M or mutations in yet undiscovered genes—we performed whole-exome sequencing (mean depth 133×) on 8 sporadic CCMs for which no somatic mutations in KRIT1, CCM2, PDCD10 or MAP3K3 were found. No additional mutations in MAP3K3 were identified and no candidate variants in other genes passed the quality control filters (Methods).

While somatic mutations in KRIT1, CCM2, PDCD10 and MAP3K3 are mutually exclusive, somatic GOF mutations in PIK3CA may co-occur with any other mutation (Fig. 1a). We have previously shown that co-occurring mutations in KRIT1/CCM2 and PIK3CA occur in the same clonal population of cells9. To determine whether MAP3K3 and PIK3CA mutations coexist in the same cells, we performed single-nucleus DNA sequencing (snDNA-seq) on frozen tissue from three surgically resected CCMs determined to harbor both mutations (Fig. 1b–d).

In CCMs 5002 and 5030, the vast majority of mutant nuclei harbored both mutations in MAP3K3 and PIK3CA indicating that these mutations coexist in the same cells. In CCM 5032, 37% (19 out of 51) of mutant nuclei harbored both mutations. While this is a far lower fraction compared to other samples, it is significantly higher than may be expected by chance when sampling from 1,405 total nuclei (P = 1.3 × 10−5; Fig. 1e). In bulk genetic analysis, the allele frequencies of the PIK3CA and MAP3K3 mutations detected in CCM 5002 were 19% and 13%, respectively. In snDNA-seq, the allele frequencies of these mutations increased to 28.7% and 30.9%, respectively. This difference likely reflects the mosaic nature of CCMs. Since snDNA-seq requires nuclei collected from frozen tissue, we sampled a new area of the frozen lesion than was sampled for bulk sequencing. Sampling from different sites of the same lesion often results in minor changes in allele frequency; however, the large increase in allele frequency we found in CCM 5002 suggests either that our initial sample of the lesion for bulk sequencing contained largely non-lesion tissue, or an uneven distribution of mutant cells in the lesion.

All three samples support the coexistence of MAP3K3 and PIK3CA somatic mutations in single cells; however, it is worth noting that in each sample we also observed singly mutated nuclei representing each possible genotype. This arrangement of mutations is biologically unlikely because it would require a somatic mutation in one gene, followed by reversion of the mutation in the other gene. Instead, the observed singly mutated genotypes are likely the result of ‘allelic dropout’, a common technical artifact in single-cell DNA-seq methods22. Because each allele is present in a single copy per cell, the inability to consistently amplify both alleles (for example, due to incomplete nuclear lysis) leads to occasional, random dropout of an allele and misrepresentation of genotypes. Allelic dropout prevented us from accurately identifying the small populations of cells that acquired the first mutation before acquiring the second mutation.

To determine whether DVAs and CCMs originate from a shared mutation, we collected three sporadic CCMs and sampled a portion of the associated DVAs obtained during surgery (Fig. 2a–c). Assays for mutations via ddPCR revealed that all three CCMs had a somatic activating mutation in PIK3CA and that the same mutation was present within the paired DVA samples at lower frequency (Fig. 2d). Furthermore, ddPCR revealed that two of the CCMs harbored a mutation in MAP3K3 in addition to the previously noted mutation in PIK3CA. However, unlike the PIK3CA mutation, the MAP3K3 mutation was entirely absent from both DVA samples (Fig. 2e). The presence of the PIK3CA, but not the MAP3K3, mutation in the DVA confirmed that the PIK3CA mutation in the DVA did not arise via cross-sample contamination. The presence of multiple somatic mutations in these CCMs allows us to infer the developmental history of the lesion. The cancer field commonly uses the presence or absence of somatic mutations in clonal populations to track the evolutionary history of a tumor. Recent studies have expanded on this approach to use somatic mutations as endogenous barcodes to track embryonic development23. Using this same approach, we inferred that the DVA was the first lesion to develop and that the associated CCM is derived from cells of the DVA after a somatic mutation in MAP3K3. The one CCM sample where we found a mutation in PIK3CA but not MAP3K3 or CCM complex genes supports the role of PIK3CA in DVA development but cannot be used to infer the temporal sequence of mutations. Notably, the lack of an MAP3K3/CCM complex mutation in 1 of 3 samples (33.3%) is consistent with our observations from bulk sequencing data where we did not identify MAP3K3/CCM complex mutations in 25 out of 71 samples (35.2%).

Fig. 2: Associated CCMs and DVAs harbor identical somatic mutations in PIK3CA.
figure 2

ac, Axial magnetic resonance susceptibility weighted images acquired at 3 Tesla showing CCM (blue circle) and associated DVA branches sampled during surgery (red arrow) in individuals with CCM 5080 (a) or 5081 (b) or 5082 (c) (scale bars, 15 mm). The inset red box in c shows the region expanded to the right with the CCM and DVA marked (scale bar, 5 mm). d,e, Somatic mutations in PIK3CA (d) and MAP3K3 (e) in the CCM (top) and associated DVA (bottom) from samples 5080, 5081 and 5082. Mutations were detected by ddPCR and are shown as the fluorescence of the reference probe on the x axis and the mutant probe on the y axis. Droplets containing the reference allele, mutant allele, both or neither are colored in green, blue, orange and black, respectively. The percentage inset into each graph shows the variant allele frequency for the displayed mutation. If the mutation was determined to be present, the percentage is shown in blue, otherwise the percentage is shown in red.

In addition to assaying the presence of PIK3CA mutations in DVAs associated with CCMs, we would ideally also assay DVAs that are not associated with CCMs. Unfortunately, DVAs are benign malformations and are not resected unless associated with an additional pathology. This has precluded the direct assessment of PIK3CA mutations in DVAs without CCMs. To address this limitation, we sought another source of tissue that could be assayed noninvasively for indirect evidence of PIK3CA activation. Thus, we collected plasma from individuals with DVAs without CCMs and measured circulating microRNAs that might serve as biomarkers reflecting PIK3CA activity24.

We sequenced the plasma miRNomes of 12 individuals with a sporadic CCM associated with a DVA (CCM + DVA), 6 individuals with DVA-only and 7 healthy controls. Three plasma miRNAs were differentially expressed in the DVA-only group when compared to healthy controls (P < 0.05; false discovery rate (FDR)-corrected). One of the differentially expressed miRNAs, miR-134-5p (log2 fold change = −3.30) was downregulated and has been shown to inhibit PI3K/Akt signaling25 (Supplementary Table 2).

In addition, 18 plasma miRNAs were differentially expressed in patients with DVAs only when compared to CCM + DVA (P < 0.05; FDR-corrected). One of these 18 differentially expressed miRNAs, let-7c-5p (log2 fold change = −3.66) was downregulated and is known to target PIK3CA26,27 (Supplementary Table 2). Of interest, let-7c-5p also targets COL1A1 (ref. 28), a differentially expressed gene within the transcriptome of human sporadic CCM lesions (Supplementary Information).

Additionally, 28 differentially expressed plasma miRNAs were identified between CCM + DVA and healthy controls (P < 0.05; FDR-corrected). Four of these miRNAs putatively target PIK3CA: miR-148a-3p (log2 fold change = 1.71), miR-148b-3p (log2 fold change = 1.4), miR-128-3p (log2 fold change = 1.35) and let-7c-5p (log2 fold change = 2.07) (Supplementary Table 2)26,27,29,30,31.

Downregulation of a miRNA may lead to upregulation of the targeted gene32. Even though these associations cannot be validated by somatic mutation analysis due to the lack of surgical tissue for these patients, the results of the circulating miRNome may reflect biomarkers of PIK3CA activation in patients harboring a DVA.

Discussion

In this study, we further interrogated the relationship between somatic mutations in KRIT1, CCM2, PDCD10, MAP3K3 and PIK3CA, which contribute to the pathogenesis of CCM. We found that somatic mutations in MAP3K3 were not present in CCMs from individuals with familial CCM, which is consistent with a recent study10. We found that sporadic CCMs may harbor mutations in MAP3K3, KRIT1, CCM2 or PDCD10, but that the lesion will only have mutations in one of these genes. This implies that mutations in any of MAP3K3, KRIT1, CCM2 or PDCD10 are sufficient for CCM formation without the need for mutations in a second gene. Since the CCM complex is a direct inhibitor of MAP3K3 activity19, this pathway may be activated by either CCM complex LOF or by MAP3K3 GOF; however, the mutual exclusivity of mutations in these genes suggests that only one of these events is necessary for lesion formation.

CCMs often develop as the result of multiple somatic mutations that coexist within the same cells as we showed with snDNA-seq. Although several somatic mutations occur in every cell division, the specificity of the mutations in CCM translates to a very low chance of acquiring these mutations within a single cell. This is especially true of somatic mutations in MAP3K3 and PIK3CA, both of which have very narrow spectra of activating mutations. Despite this improbability, the accumulation of these mutations in CCMs seems to occur frequently. We propose that after an initial somatic mutation, the singly mutated cell undergoes clonal expansion to form an intermediate lesion. In this study, we identified 7 CCMs with either biallelic LOF in a CCM complex gene or MAP3K3 GOF in the absence a PIK3CA mutation, suggesting that PIK3CA activation is not required for CCM formation. Furthermore, previous work in mouse models showed that loss of a CCM complex gene (with wild-type Pik3ca) leads to clonal expansion of the mutant cells33,34. Because of this clonal expansion, the probability of creating a double-mutant cell increases by a factor of the clonal population size as there are more cells where the second mutation may occur. The data presented in this study suggest that DVAs function similarly; they develop from a PIK3CA mutation that expands clonally, increasing the number of cells where a second mutation may occur.

Plasma miRNA analysis of individuals with DVA-associated CCMs and DVAs without CCMs revealed that both groups exhibit differentially expressed miRNAs that putatively target PI3K/Akt signaling. Notably, it is unclear if the circulating differentially expressed plasma miRNAs identified in this study affect their predicted gene targets and associated biological pathways within the lesions35. While DVA-only versus healthy controls revealed one differentially expressed miRNA that putatively targets PI3K/Akt signaling, DVA + CCM versus healthy controls revealed three differentially expressed miRNAs targeting PI3K/Akt. This may reflect the synergistic effects of the CCM signaling pathway with PIK3CA mutation to drive PI3K/Akt signaling as reported previously9. One major limitation of this exploratory miRNA study is the limited sample sizes of the cohorts. While further studies will be required to understand the effects of DVAs and CCMs on the circulating miRNome and identify biomarkers of PIK3CA activation, these data are thus far consistent with our observation of PIK3CA GOF mutations in DVAs associated with CCMs. Furthermore, these data motivate further studies to identify circulating plasma miRNAs that may be a valuable clinical tool to assay PIK3CA activation noninvasively.

The presence of PIK3CA mutations in DVAs suggests that DVAs act as genetic precursors to CCMs, which would account for the strong association between sporadic CCMs and DVAs (Fig. 3). Likewise, DVAs are not associated with familial CCMs because the presence of an inherited germ line mutation in a CCM gene biases probability toward a CCM gene somatic mutation occurring first, since there are many different mutations that may cause LOF but far fewer that would cause GOF in PIK3CA.

Fig. 3: Genetic model of CCM pathogenesis.
figure 3

Genetic trajectories that underpin familial and sporadic CCM pathogenesis. Familial CCMs already harbor a predisposing germ line mutation in the CCM complex (KRIT1, CCM2, PDCD10) and are therefore most likely to develop without requiring association with a DVA (top). In contrast, individuals without familial CCM, but who have a PIK3CA-mutant DVA, are predisposed to sporadic CCM formation adjacent to the DVA since one genetic ‘hit’ is already present (bottom). However, sporadic CCMs could also develop in the absence of DVAs (top), depending on the temporal sequence of acquisition of somatic mutations.

Collecting tissue from CCM-associated DVAs is challenging; however, collecting tissue from DVAs not associated with CCMs is yet more challenging because DVAs are considered benign and are therefore not resected. We have attempted to address this limitation by studying biomarkers of PI3K activity that can be assayed noninvasively in blood plasma. Assaying the presence of PIK3CA mutations in DVAs not associated with CCMs will be the domain of future studies but the data we present in this study demonstrate a clear link between DVAs and PIK3CA and suggest a model that explains the long recognized, but poorly understood, association between CCMs and DVAs.

While we are unable to address the presence of PIK3CA mutations in DVAs not associated with CCMs, it is worth noting that DVAs have been associated with other PI3K-related disorders36,37,38,39,40, including some cancers and neurological malformations, suggesting that DVAs may have a role, possibly even as a genetic primer, in these other phenotypes.

Methods

Sample collection

Surgically resected CCMs were obtained from consenting participants at the University of Chicago, the Barrow Neurological Institute and the Angioma Alliance biobank. Additional DVA tissue was discretely dissected from the lesion during surgical resection of the associated CCM at the University of Chicago. This study was approved by each institution’s respective institutional review board.

Familial and sporadic diagnosis

Familial CCM patients harbored multiple lesions throughout the brain on magnetic resonance susceptibility weighted imaging, a documented KRIT1, CCM2 or PDCD10 germ line mutation and/or first-degree relative with a history of CCM. Sporadic/solitary patients typically harbored a single lesion on susceptibility weighted imaging or a CCM associated with a developmental venous anomaly41. Cases without clear information about family history, for example, deidentified samples acquired from tissue biobanks, were classified as unknown.

DNA extraction

DNA from CCM and DVA samples was extracted using the DNeasy Blood & Tissue Kit (catalog no. 69504; QIAGEN) according to the manufacturer’s protocol. DNA purity was determined by NanoDrop and concentration was determined using the Qubit dsDNA HS and BR Assay Kit (catalog no. Q32850; Invitrogen) according to the manufacturer’s protocol.

ddPCR

Detection of MAP3K3 p.I441M was performed via ddPCR using a previously published probe set20 detailed and synthesized by Integrated DNA Technologies: forward primer: 5′-TGCAGTACTATGGCTGTCTG-3′; reverse primer: 5′-GTCTCACATGCATTCAAGG-3′; reference allele probe: 5′-HEX-CCTGACCATcTTCATGGAGTACA-IBlk-3′; alternate allele probe: 5′-FAM-CCTGACCATgTTCATGGAGTACA-IBlk-3′.

Assays were performed using 30–100 nanograms of DNA with the QX200 AutoDG ddPCR System (Bio-Rad Laboratories) and quantified with the QX200 Droplet Reader (Bio-Rad Laboratories). Analysis was performed with the QuantaSoft software (Bio-Rad Laboratories, v. 1.7.8.0917).

Sequencing

Eight sporadic CCMs with no identified mutation in KRIT1, CCM2, PDCD10 or MAP3K3 (5001, 5005, 5006, 5022, 5024, 5036, 5078 and 5081) were used for whole-exome sequencing prepared using the SureSelect Human All Exon V7 probe set (design ID S31285117; Agilent Technologies) according to the manufacturer’s protocol. Prepared libraries were sequenced on one lane of a NovaSeq 6000 S4 Flow Cell for a mean depth of 133×.

Sequence analysis

Sequencing data was processed using the Genome Analysis Toolkit (GATK; Broad Institute, v. 4.1.3.0) while following the GATK best practice for somatic short variant discovery using Mutect2 (GATK v. 4.1.3.0). Secondary variant detection was performed with gonomics (github.com/vertgenlab/gonomics) and bcftools mpileup (v. 1.14) to manually examine KRIT1, CCM2, PDCD10 and MAP3K3 for somatic variants. Putative variants were annotated using Funcotator (GATK v. 4.1.3.0), the Catalogue Of Somatic Mutation in Cancer (COSMIC, v94) and the Genome Aggregation Database (gnomAD, v. 3.1). Putative variants were filtered according to the following criteria: greater than 50× total coverage, less than 90% strand specificity, >5 reads supporting the alternate allele, >1% alternate allele frequency, less than 1% population allele frequency and predicted protein/splicing change.

snDNA-seq

Nuclear isolates were prepared via Dounce homogenization of frozen tissue in Nuclei EZ Lysis Buffer (Sigma-Aldrich) and sorted to a single-nucleus suspension with a FACSAria II (BD Biosciences) (70-μm nozzle, 70 pound-force per square inch, 4-Way Purity, chiller). Sequencing libraries from individual nuclei were prepared using the Tapestri platform (Mission Bio) using a custom panel targeting KRIT1, CCM2, PDCD10, MAP3K3 and PIK3CA. Libraries were pooled and sequenced with a NextSeq Mid-Output 2 × 150 base pair kit (Illumina). Data processing and quality control were performed with the Mission Bio cloud analysis pipeline v.1.10.0. P values for mutation co-occurrence was determined by chi-squared test of observed and expected genotype counts as determined by a Poisson distribution9.

miRNA extraction and sequencing

Total plasma RNA was extracted from the plasma of 12 individuals with a sporadic CCM and associated DVA (CCM + DVA), 6 individuals with DVA and without a CCM (DVA-only) and 7 healthy controls using the miRNeasy Serum/Plasma Kit (QIAGEN) according to the manufacturer’s isolation protocol. Diagnosis of CCM with an associated DVA, and DVA without a CCM lesion, was confirmed on susceptibility weighted magnetic resonance imaging. Illumina small RNA-seq kits (Clontech) were then used to generate complementary DNA libraries; sequencing was completed with the Illumina HiSeq 4000 platform, with single-end 50-bp reads, at the University of Chicago Genomics Core. Differential miRNA analyses were completed between (1) CCM + DVA to DVA-only and then (2) DVA-only to healthy controls. The differentially expressed miRNAs were identified as having P < 0.05, FDR-corrected. All analyses were completed using the sRNAToolbox (2019 version with Vienna 2.0) and DESeq2 R packages (v. 1.30.1, R version v. 4.0.5)42,43.

Identification of putative targets

miRWalk 3.0 was queried to identify the putative gene targets of each of the differentially expressed miRNAs, using a random forest tree algorithm with a bonding prediction probability higher than 95% on the 3 different gene locations (3′-untranslated region (UTR), 5′-UTR and coding sequence)44. Putative gene targets of the differentially expressed miRNAs were identified in at least two of the three databases. Differentially expressed miRNAs between (1) CCM + DVA and DVA-only and (2) DVA-only and healthy controls were then analyzed for potential targeting of the PI3K signaling pathway.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Data not included in this paper can be accessed through the National Center for Biotechnology Information (DNA sequencing, BioProject accession no. PRJNA802805) or Gene Expression Omnibus (RNA sequencing, accession no. GSE195732). The public datasets used in this study are available at the COSMIC (cancer.sanger.ac.uk/cosmic), dbSNP (ncbi.nlm.nih.gov/snp), 1000 Genomes Project (internationalgenome.org), gnomAD (gnomad.broadinstitute.org), miRWalk 3.0 (mirwalk.umm.uni-heidelberg.de) and DAVID databases (david.ncifcrf.gov).

Code availability

The variant calling software was implemented as part of gonomics, an ongoing effort to develop an open-source genomics platform in the Go programming language.

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Acknowledgements

We thank the patients who donated tissue for this study. We thank the Angioma Alliance, Barrow Neurological Institute and University of Chicago for patient enrollment and sample collection. Nucleus sorting was performed in the Duke Human Vaccine Institute Research Flow Cytometry Shared Resource Facility. We thank Duke University School of Medicine for the use of the Sequencing and Genomic Technologies Shared Resource for library preparation and sequencing. These studies were supported by National Institutes of Health grant nos. P01NS092521 (D.A.M., I.A.A., M.L.K.) and F31HL152738 (D.A.S.).

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D.A.S. designed and performed the genetic studies of human CCM lesions and wrote the manuscript. I.A.A. performed the surgical resection of the CCM and DVA samples used in this study. R.G., R.L., A.S., S.R., Y.L., C.C. and I.A.A. performed the plasma miRNA sequencing and analysis. A.A.R. and M.L.K. assisted with experimental design. R.G., I.A.A. and D.A.M. designed the experiments and wrote the manuscript.

Corresponding authors

Correspondence to Issam A. Awad or Douglas A. Marchuk.

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I.A.A. is Chairman of the scientific advisory board for the Angioma Alliance and provides expert opinions related to clinical care of CCMs. The other authors declare no competing interests.

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Nature Cardiovascular Research thanks Matteo Malinverno, Murat Gunel and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Snellings, D.A., Girard, R., Lightle, R. et al. Developmental venous anomalies are a genetic primer for cerebral cavernous malformations. Nat Cardiovasc Res 1, 246–252 (2022). https://doi.org/10.1038/s44161-022-00035-7

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  • DOI: https://doi.org/10.1038/s44161-022-00035-7

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