Abstract
Sequencing-based genetic tests have uncovered a vast array of BRCA2 sequence variants1. Owing to limited clinical, familial and epidemiological data, thousands of variants are considered to be variants of uncertain significance2,3,4 (VUS). Here we have utilized CRISPR–Cas9-based saturation genome editing in a humanized mouse embryonic stem cell line to determine the functional effect of VUS. We have categorized nearly all possible single nucleotide variants (SNVs) in the region that encodes the carboxylate-terminal DNA-binding domain of BRCA2. We have generated function scores for 6,551 SNVs, covering 96.4% of possible SNVs in exons 15–26 spanning BRCA2 residues 2479–3216. These variants include 1,282 SNVs that are categorized as missense VUS in the clinical variant database ClinVar, with 77.2% of these classified as benign and 20.4% classified as pathogenic using our functional score. Our assay provides evidence that 3,384 of the SNVs in the region are benign and 776 are pathogenic. Our classification aligns closely with pathogenicity data from ClinVar, orthogonal functional assays and computational meta predictors. We have integrated our embryonic stem cell-based BRCA2-saturation genome editing dataset with other available evidence and utilized the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines for clinical classification of all possible SNVs. This classification is available as a sequence–function map and serves as a valuable resource for interpreting unidentified variants in the population and for physicians and genetic counsellors to assess BRCA2 VUS in patients.
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Data availability
All data generated or analysed during this study are included in the manuscript and in the supplementary file. The raw sequencing reads were deposited to the Gene Expression Omnibus (GEO) under accession GSE248438 and the reads were aligned to GRCh38 human reference genome. The MAVISp dataset for BRCA2 was deposited in the MAVISp database (https://services.healthtech.dtu.dk/services/MAVISp-1.0/). The interactive interface for BRCA2 variant exploration is available at https://appshare.cancer.gov/BRCA2_SGE/.
Code availability
The code used for ES cell-based BRCA2 SGE is available at https://github.com/MelissaGall/AVENGERS.
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Acknowledgements
The authors thank K. Biswas, R. Sharan, S. Modak, D. Chakraborty, S. Acharya and members of our laboratory for helpful discussions and suggestions on this project; J. Carrel and M. Karwan; and E. Conner. The ACMG/AMP codes were derived from a paid subscription to Varsome (Saphetor). We obtained segregation and frequency data for SNVs in exons 15–26 from the BRCA2 gene variant database of the Global Variome shared LOVD installation. The S.K.S. laboratory is funded by the Intramural Research Program, Center for Cancer Research, National Cancer Institute, US National Institutes of Health. S.S. is supported by a K99 grant from National Cancer Institute, US National Institutes of Health (K99CA279648). R.C. is funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN261201500003I. The E.P. laboratory is supported by Danmarks Grundforskningsfond (DNRF125), Novo Nordisk Fonden Bioscience and Basic Biomedicine (NNF20OC0065262) and Hartmanns Fond (R241-A33877). M.A. is supported by a PhD Fellowship from the Danish Data Science Academy (DDSA).
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S.S. and S.K.S. conceived the study and designed the experimental approach. M.G. performed the bioinformatics analysis. S.S., E.S., D.C. and T.S. performed the experiments. J.G. performed cloning of sgRNA. M.A. and E.P. performed MAVISp analyses. R.C. helped in genome editing experiments. K.M. and M.Z. helped in analysing breast cancer datasets. S.S. and S.K.S. wrote the manuscript, and all authors were involved during the discussion and editing.
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Nature thanks Yongsub Kim, Sean Tavtigian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.
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Extended data figures and tables
Extended Data Fig. 1 Generation of all possible SNVs using CRISPR-Cas9 based SGE.
a, Heatmap showing the total number of expected and recovered variants across 51 experimental pools to saturate the C-terminal DNA binding domain. The value in the boxes represents the number of variants expected/recovered ranging from 80 SNVs (light blue) to 234 SNVs (dark blue). A total of 96.4% of all possible SNVs were recovered (6551 out of 6796 SNVs). b,c, Bar plot showing the distribution of (b) indel and (c) HDR rates calculated for each experimental pool. HDR rates are calculated based on the percentage of reads for SNVs with fixed PAM modification in each experimental pool. d, Pearson correlation between the read counts of all SNVs recovered after HDR at a frequency of 1 in 105 reads in both replicates across the experimental pools.
Extended Data Fig. 2 Function scores based on cell fitness and drug response enhances classification accuracy of ESC-based BRCA2 SGE.
a, The Pearson correlation of Function Scores (FS) for 6551SNVs between cell fitness (DMSO treated) and response to drugs (cisplatin treated). Additionally, we assessed the correlation between cell fitness and olaparib treatment, as well as the correlation between cisplatin and olaparib responses. b, Schematic showing our filtering strategy to integrate classification scores based on cell fitness and drug response data to accurately categorize SNVs. 6551 SNVs were categorized using Mixture-modelling based on their survival at day 14, and integrated FS values were computed by combining cell fitness and drug response scores. c, By employing GMM, we categorized variants based on the integrated values, revealing that 6209 (94.7%) SNVs exhibited strong concordance, while only 353 (5.4%) SNVs showed an opposite classification with cell fitness data. d, Heatmap represents the percentage of variant recovered and then finally classified by integrating the scores and excluding the uncertain variants. The value in the boxes represent the percentages ranging from 82% (light blue) to 100% (dark blue).
Extended Data Fig. 3 SGE experiments reveal temporal depletion of Cas9-induced indels.
Histogram showing the distribution of indels identified in each experimental pool, along with their corresponding function scores. In this analysis, we considered indels that met specific criteria: they had to align with the reference genome, displaying a single insertion or deletion within 50 base pairs of the anticipated Cas9 cleavage site defined by the sgRNAs used in each experimental pool. These indels were only counted if their frequency was at least 1 in 106 reads in both replicates, and in-frame indels were marked when their size was divisible by 3. Function scores were determined by comparing the frequency of indels at day 14 relative to day 3. We observed depletion of frameshifting indels in the pool, while a few in-frame indels were occasionally tolerated.
Extended Data Fig. 4 Mixture modelling of function scores to classify BRCA2 SNVs.
a, Correlation of function scores of all SNVs between individual replicates demonstrate the distribution of functional and nonfunctional thresholds. b, Correlation of function scores of all SNVs between individual replicates for individual exons. The dashed line represents the thresholds for functional and nonfunctional thresholds derived from GMM. c–e, Swarm plots demonstrating the distribution of (c) synonymous and nonsense SNVs (n = 1648), (d) ClinVar-reported SNVs (n = 783) and (e) all classified variants (n = 6198). The dashed line represents the median of the nonsense SNVs (red) and synonymous SNVs (blue).
Extended Data Fig. 5 ESC-based BRCA2 SGE outperforms computational meta predictors.
a, ROC curves indicate the performance of computational models at categorizing ClinVar-reported missense variants in the BRCA2 CTDB domain. ROC shows an AUC value of 0.96 in classifying ClinVar variants and AUC value of 0.96 in accurately categorizing nonsense and synonymous SNVs. We observed a moderate concordance to other computational predictors like CADD, BayesDel, REVEL, AlignGV-GD grade, PRIOR scores, EVE and AlphaMissense. b, Correlation between SGE-derived function scores and computational metrics in determining ClinVar-reported BRCA2 SNVs. The colour code represents the benign, likely benign, pathogenic, and likely pathogenic class reported in ClinVar.
Extended Data Fig. 6 ESC-based BRCA2 SGE map for key functional domains reveal strong concordance to AlphaMissense prediction.
a–d, 3D structural plots derived from BRCA2 CTDB domain from Alphafold depicting the helical domain, OB1, OB2 and OB3. The color code of the 3D structure plot is derived from AlphaFold and correlated with the pLDDT score, a score to provide information about the confidence of the prediction of a specific residue, in particular regions with a pLDDT score <50 are coloured in orange, regions with a pLDDT score between 50 and 70 are coloured in yellow, regions with a pLDDT score between 70 and 90 are coloured in light blue, regions with a pLDDT score > 90 are coloured in blue. In MAVISP we used structures or portion of structures with a pLDDT score > 70. Sequence-function map displaying the concordance of functional and non-functional categorization of individual amino acid changes across (a) helical domain residues 2479-2668, (b) OB1 residues from 2682-2794, (c) OB2 residues from 2804-3054 and (d) OB3 domain residues from 3073-3167 of the BRCA2 CTDB domain. The first box represents ESC-based BRCA2 SGE dataset and their concordance with MAVISP saturation scan and Alpha-Missense prediction. The box colour signifies “functional (blue)”, “non-functional (red)”, and “uncertain (turquoise blue)” class. The wildtype amino acid is shown as a circle, while white boxes indicate excluded amino acids. The Y-axis denotes the alternative amino acid changes. The magnified image of the 3D ribbon plot depicts the position of amino acid residues where clusters of functional/non-functional SNVs are reported across individual domains.
Extended Data Fig. 7 Correlation of SNV classification by ESC-based BRCA2 SGE and homology directed repair (HDR) assay.
Robust alignment between our ESC-based BRCA2 SGE-based classification and HDR score, particularly for 440 germline BRCA2 missense variants distributed across crucial functional regions (Helical domain and OB1) within the BRCA2 C-terminal DNA binding domain. The box colour represents light blue as benign, red as pathogenic, pooled from strong, moderate and supporting categories. The light red denotes the uncertain class of SNVs. The HR score for individual SNVs were available from Hu et al.49.
Extended Data Fig. 8 ESC-based BRCA2 SGE-based classification show concordance with orthogonal functional assay.
a, Concordance between HDR score and SGE-based classification for OB2 and OB3 domain SNVs. Notably, SNVs in the Tower domain exhibit a high HDR score, consistent with the functional nature of our SGE-based classification. b, Sankey plot representing the overall distribution of HDR-normal and abnormal SNVS and their concordance with different tiers of pathogenicity and benignity in the functional assay. c, Comparison of SGE-based classification with CRISPR-independent, BAC recombineering-based method. d, Strong concordance of SGE-based classification with CRISPR-based prime editing.
Extended Data Fig. 9 Categorizing 149 uncertain SNVs as potential hypomorphic variants.
a, Comparison of ESC-based BRCA2 SGE-based classification of SNVs previously classified using the MANO-B assay based on their responses to various PARP inhibitors (olaparib, rucaparib, niraparib) and carboplatin. b, Experimental strategy demonstrating the filtering of “uncertain” class SNVs with conflicting classifications between cell fitness and drug response data to identify potential hypomorphs (n = 302 SNVs). The uncertain variants were filtered to identify SNVs that fall in the uncertain and pathogenic categories and further identified if they represent pathogenic class in both cisplatin and olaparib dataset. c, Heatmap showing potential hypomorphic variants that survive in the DMSO pool but are sensitive to DNA damaging agents (n = 158 SNVs).
Extended Data Fig. 10 Comparison of ACMG/AMP classification of BRCA2 SNVs by ESC-based BRCA2 SGE with VarSome and HAP1 cell line-based MAVE classification.
a, Sankey plot representing the concordance between ACMG/AMP classification performed by VarSome and ESC-based BRCA2 SGE. The number of VUSs also reduced by integrating our SGE functional assay data into PS3/BS3 evidence categories. b, Sankey plot represents the concordance between ESC-based BRCA2 SGE and cell viability-based MAVEs developed using human HAP1 cell line.
Supplementary information
Supplementary Table 1
Details on the oligonucleotide sequences used for sgRNA cloning and for amplicon sequencing.
Supplementary Table 2
Details on the oligonucleotide pool used for the ES cell-based BRCA2 SGE.
Supplementary Table 3
Percentage of variants recovered and classified across individual experimental pools.
Supplementary Table 4
Case–control, family history, pathology and segregation dataset on BRCA2 variants used for calibration.
Supplementary Table 5
Function scores for 6,551 SNVs across exons 15 to 26 encoding the CTDB of BRCA2.
Supplementary Table 6
Points-based ACMG/AMP clinical classification of 6,551 BRCA2 SNVs.
Supplementary Table 7
Validation of the ES cell-based BRCA2 SGE and concordance with orthogonal functional assays.
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Sahu, S., Galloux, M., Southon, E. et al. Saturation genome editing-based clinical classification of BRCA2 variants. Nature 638, 538–545 (2025). https://doi.org/10.1038/s41586-024-08349-1
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DOI: https://doi.org/10.1038/s41586-024-08349-1
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