Site-saturation functional screens identify PALB2 missense variants associated with increased breast cancer risk

Domain: biology
Published: 2026-01-19
Editorial outcome: major_revision

Abstract
Loss-of-function variants in PALB2 give rise to defects in DNA damage repair by homologous recombination (HR), increasing the risk of breast cancer in female carriers. However, genetic testing frequently reveals missense variants of uncertain significance (VUS) for which the impact on protein function and cancer risk are unclear. Here we assay 84% of all possible missense variants in 11 out of 13 PALB2 exons using site-saturation functional screens with PARP inhibitor sensitivity as a readout for HR. These exons encode the coiled-coil and WD40 domains, which we identify as the minimal regions required for HR. Furthermore, we reveal the functional impact of 6718 missense variants, classifying 3904 variants as functional (58%), 2422 as intermediate (36%), and 392 as damaging (6%). A burden-type analysis shows that damaging missense variants in PALB2 are associated with a significantly increased risk of breast cancer, similar to that observed for truncating variants. These results will be valuable for the classification of PALB2 missense VUS and clinical management of carriers.

Introduction
Germline protein-truncating variants (PTV) in PALB2 are associated with high breast and pancreatic cancer risk and moderate ovarian cancer risk1. However, genetic testing has identified numerous PALB2 missense variants for which the cancer risk is unclear. To improve the clinical interpretation of these variants of uncertain significance (VUS), complementary approaches that assess the molecular effects of such variants are needed. The American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) have proposed variant interpretation guidelines that incorporate different types of evidence at various levels of strength. These guidelines provide rules for combining the different types of evidence for clinical classification of variants as benign, likely benign, uncertain significance, likely pathogenic, or pathogenic2,3. The reliable classification of variants depends heavily on genetic and clinical data which is lacking for the majority of the rare missense variants in PALB2. This indicates the urgent need for additional means to classify VUS in PALB2.

Functional analysis has been shown to provide accurate information on the impact of VUS on PALB2 protein function. Given that PALB2 plays a pivotal role in DNA double-strand break repair by homologous recombination (HR), a process that is key to tumor suppression, most assays measure the functional effect of PALB2 variants on HR4–6. These assays are mostly based on the use of well-established HR reporters such as DR-GFP, or measure cellular sensitivity to treatment with poly(ADP-ribosyl) polymerase (PARP) inhibitor. Generally, PALB2-deficient cells that ectopically express PALB2 cDNA with a particular variant were used in these assays. This way >150 different PALB2 variants, comprising >120 missense VUS, were functionally characterized7–9. VUS with an impact on protein function (>50% reduced HR activity compared to WT) were all located in the coiled-coil (CC) or WD40 domain of PALB2, highlighting the importance of these domains for PALB2’s role in HR. Indeed, PALB2 interacts with BRCA1 and BRCA2 through these domains, respectively. Both BRCA1 and BRCA2, similar to PALB2, function as tumour suppressors by playing essential roles in HR. Thus, the functional analysis of missense variants in PALB2 has the potential to identify damaging variants that may confer increased cancer risk, thereby aiding in the clinical classification of such variants.

The functional assays that have been used thus far for the analysis of missense variants in PALB2 are often time and resource intensive7–9, and the results may not be publicly available for years after the variant is encountered in carriers. These assays will therefore not be able to cope with the pace and scale at which genetic testing reveals single nucleotide variants (SNVs) in PALB2 that are classified as VUS (as of June 30, 2025, >2,900 SNVs in PALB2 were listed as VUS in ClinVar). Moreover, given that PALB2 variants are very rare, variant frequencies from extremely large breast cancer patient cohorts and functional data for large sets of variants are needed to link the functional impact of variants with cancer risk. Here, we develop and apply a cDNA-based site-saturation screen in Palb2-deficient mouse embryonic stem (mES) cells to functionally characterize all possible missense variants in the CC and WD40 domains of human PALB2, which appear to be the sole domains required for its function in HR. Using this approach, more than 7,000 variants in PALB2 are functionally interrogated by determining their impact on cellular PARPi sensitivity, identifying 392 damaging missense variants. By combining these results with case-control association studies involving more than 485,000 breast cancer cases and nearly 600,000 non-cancer female controls, the functional impact of PALB2 missense variants can be linked to breast cancer risk. Importantly, damaging missense variants in PALB2 are associated with a similar high risk for breast cancer as observed for protein truncating variants10,11. Thus, the site-saturation functional screens presented here are a valuable source for the clinical classification of variants in PALB2, thereby improving clinical management of carriers.

Results
The region between the CC and WD40 domains of PALB2 is dispensable for HR
No missense variants outside of the N-terminal CC and C-terminal WD40 domains of PALB2 have been identified as damaging in functional assays that use HR as a read-out7–9. However, whether the region between the CC and WD40 domains, which includes the Chromatin Association Motif (ChAM) and MRG15-binding motif, is required for HR is not fully understood, with previous work implicating ChAM in promoting loading of the core-HR factor RAD51 onto damaged chromatin and in conferring resistance to DNA inter-strand crosslinks12. We therefore set out to examine the importance of this region for HR. We generated five PALB2 cDNA deletion constructs lacking either the ChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6) (Fig. 1a). All exon deletions were generated in-frame. We did not examine exon 3 as deletion largely disrupts the CC domain. The constructs were introduced site-specifically by recombination-mediated cassette exchange (RMCE) at the Rosa26 locus in Palb2KO mES cells carrying the DR-GFP reporter, which allows flow cytometry-based detection of HR after I-SceI endonuclease-induced DNA double-strand break formation7. The stable expression of these PALB2 deletion constructs rescued the HR defect of Palb2KO cells to levels observed in cells expressing wild-type PALB2 (Fig. 1b, c). Consistently, the expression of these deletion constructs also rescued the sensitivity of Palb2KO cells to treatment with PARPi (Fig. 1d). These data suggest that these regions are dispensable for the PALB2 function in HR, reducing the likelihood that missense variants therein are capable of impacting HR. We therefore focused on classifying all possible missense variants in the CC and WD40 domains of PALB2 by assessing the impact on HR en masse.

Site-saturation functional screens of missense variants in the CC and WD40 domains of PALB2
To comprehensively evaluate the functional impact of PALB2 variants, we conducted site-saturation screens, analysing all possible missense variants in the PALB2 CC and WD40 domains. In these screens, PARPi inhibitor sensitivity was used as a readout for HR efficiency (Fig. 2a). We employed 6 libraries based on human PALB2 cDNA: one for the CC domain (amino acid 9-43) and 5 libraries that together cover the entire WD40 domain (amino acid 856-1,186). The libraries, which together contained >95% of all possible missense and nonsense variants, as well as 49 synonymous variants (Supplementary Fig. 1), were introduced in the Palb2KO mES cells by RMCE (6 and 3 biological replicates for the CC and WD40 domain, respectively). Clones expressing the PALB2 variants were selected, pooled and expanded. PARPi sensitivity assays were performed on these cells using non-treated cells as control conditions (3 technical replicates for the libraries of both the CC and WD40 domain). Following DNA isolation, the PALB2 cDNA region that encodes the CC domain and the 5 regions spanning the WD40 domain were amplified by PCR and sequenced. For each variant, depletion scores and standard errors (SE) were calculated using Enrich2 software (Fig. 2a)13,14. The depletion scores are based on the ratio of variant frequencies before and after PARPi treatment. SE were based on replicate measurements and used to filter out variants that could not be confidently scored (SE>0.5). Finally, depletion scores were normalized to that of wild-type PALB2 (present in each library), which was set to ‘0’, and to the average score of all nonsense variants (of each library), which was set to ‘-1’. Biological replicates generally showed a moderate but significant correlation between depletion scores of variants in both the CC (Supplementary Fig. 2) and WD40 domain (Supplementary Fig. 3) based on Pearson correlation analysis (see Methods). Using these depletion scores, functional maps for variants in the CC and WD40 domain were generated (Figs. 2b and 3). Altogether, we obtained depletion scores for 6,718 missense variants, which comprises 97% of all possible missense variants in the CC and WD40 domains of PALB2 (609 for the CC domain, which comprises 92% of all possible variants, and 6,109 for the WD40 domain, which comprises 97% of all possible variants) (Supplementary Data 1). The median depletion score for the nonsense variants was -1.011 (n=339, standard deviation = 0.413), while for the synonymous variants it was -0.096 (n=49, standard deviation = 0.306). Importantly, depletion scores of nonsense and synonymous variants were bimodally distributed (Fig. 4a and Supplementary Fig. 4a, b), allowing the classification of missense variants as ‘damaging’, ‘intermediate’ or ‘functional’ by fitting a two-component mixture model. Using this mixture model analysis (Fig. 4b), thresholds were established to achieve 98% sensitivity and specificity, such that 2% of nonsense variants scored as functional and 2% of synonymous variants as damaging. Applying these thresholds (Fig. 4b), we classified 270 out of 339 nonsense variants (80%) as damaging and 18 out of 42 unique synonymous variants (43%) as functional. Among the missense variants, 392 (6%) were categorized as damaging, 2422 (36%) as intermediate, and 3904 (58%) as functional (Fig. 4c and Supplementary Data 1).

Validation of site-saturation functional screens of missense variants in the CC and WD40 domains of PALB2
To validate our site-saturation functional screens, we measured the relative PARPi resistance levels for a panel of missense variants (n=140). Of these variants, 96 were newly tested, while 44 had been previously tested7,15. When comparing PARPi resistance levels to the depletion scores from the screens (Supplementary Data 1), we observed a statistically significant correlation of good to very good strength for variants in the CC domain. (n=43, r=0.8055, p<0.0001), WD40 (n=97, r=0.6500, p<0.0001) and CC+WD40 domains (n=140, r=0.6494, p<0.0001) (Fig. 4d and Supplementary Fig. 4c, d). Consistently, we also observed a significant correlation, ranging from good to very good, between the outcome of the site-saturation screens and those from DR-GFP reporter-based assays for the same variants in the CC (n=43, r=0.8715, p<0.0001), WD40 (n=97, r=0.7131, p<0.0001) and CC+WD40 domains (n=140, r=0.7086, p<0.0001) (Fig. 4e and Supplementary Fig. 4e, f). Moreover, our site-saturation screens correctly categorized previously reported missense variants with no functional impact (e.g., p.E19V, p.E42K, p.G998E), a moderate impact (e.g., p.Y28C, p.R37H, p.L1070P), or a detrimental impact (comparable to that of truncating variants, e.g., p.L35P, p.W912G, p.I944N) as functional, intermediate or damaging, respectively (Supplementary Data 1)7,15.

Next we aimed at validating the outcome of our site-saturation screens in human cells using saturation genome editing (SGE)16. To this end, we performed a deep mutational scan of SNVs in exon 10 encoding p.G1000 to p.I1037 of the WD40 domain of PALB2 using a cell survival assay in haploid human HAP1 cells (Fig. 4f). Since PALB2 is essential in HAP1 cells17, damaging variants in this gene reduce cell survival. CRISPR/Cas9-based saturation genome editing was used in combination with a variant library to introduce all unique SNVs in the PALB2 exon 10 region. Cells were cultured to allow genome-editing and harvested 5 and 20 days later. DNA was extracted and targeted sequencing of the exon 10 region was performed. Sequencing data were processed to quantify the abundance of each variant and calculate a SGE score using the frequency difference of reads between day 20 and 5. Functional variants are abundant and will have high SGE scores, whereas damaging variants show low abundance due to depletion from the cell culture and will have low SGE scores (Fig. 4f). We obtained SGE scores for 276 SNVs, leading to 9 nonsense and 68 synonymous changes, as well as 199 amino acid substitutions. The nonsense and synonymous variants combined, as well as the missense variants showed an asymmetric distribution that is skewed towards low SGE scores (Supplementary Fig. 4g, h). Among these variants, we identified 218 unique nonsense (n=9), synonymous (n=31) and missense (n=178) changes (see Fig. 4g in Source Data file). Importantly, we obtained depletion scores for 179 of these variants, comprising 170 missense and 9 nonsense variants, in the PARPi sensitivity-based site-saturation screens (Fig. 4g and Supplementary Data 1), allowing comparison of depletion scores and SGE scores. We observed a good and significant correlation between these scores (n=179, r=0.6439, p<0.0001; Fig. 4g), indicating concordance between the outcomes of our functional analysis of PALB2 variants in mES and human HAP1 cells.

Finally, we examined the relationship between the functional categorization of variants and frequencies in large-scale variant databases, including gnomAD, FLOSSIES and BRAVO. GnomAD contains whole-genome and whole-exome sequencing data from more than 140,000 individuals18, including 186 missense, 9 nonsense and 8 synonymous PALB2 variants that were functionally assessed in the PARPi sensitivity-based site-saturation screens. Functional missense and synonymous variants (i.e. those with high depletion scores) tend to exhibit higher allele frequencies than damaging missense and nonsense variants (Supplementary Fig. 5a; Supplementary Data 2). A similar trend was observed in the BRAVO dataset (Supplementary Fig. 5b; Supplementary Data 2), which includes genome sequencing data from over 145,000 non-cancer individuals. The FLOSSIES dataset, composed of genome data form ~10,000 elderly, healthy women, lacks damaging missense and nonsense variants. Nevertheless, functional missense variants in FLOSSIES tend to show higher allele frequencies compared to intermediate variants (Supplementary Fig. 5c; Supplementary Data 2). Collectively, the concordance with PARPi-sensitivity and DR-GFP assays in mES cells, SGE in human HAP1 cells, and large-scale human variant databases supports the validity of the site-saturation functional screens.

Variants in the CC domain disrupt PALB2’s interaction with BRCA1 and its recruitment to DNA damage sites
We and others have previously shown that damaging missense variants in the PALB2 CC-domain have an impact on the interaction with BRCA1, as well as on the BRCA1-dependent recruitment of PALB2 to sites of DNA damage (e.g., p.L24S, p.L35P or p.R37H)7,9,19. To further validate findings from the site-saturation functional screens, we selected variants that scored as damaging (p.L21S, p.A22P, p.Y28D, p.T31P, p.A33P, p.R34P, p.Q36P), intermediate (p.S10T, p.C11W, p.E13A, p.L17S, p.K25E, p.E27G, p.Y28S, p.S29G, p.K30E) or functional (p.E19V, p.R26K) (Supplementary Data 1). The previously reported damaging p.L35P variant was included as a control. To examine the impact on the PALB2-BRCA1 interaction, we transiently expressed YFP-tagged PALB2 carrying these individual variants in U2OS cells and performed pull-downs using GFP Trap beads. The two functional variants p.E19V and p.R26K, as well as the three intermediate variants p.S10T, p.C11W and p.E13A, efficiently precipitated endogenous BRCA1 to similar levels as observed for wild-type PALB2 (Fig. 5a), whereas the other intermediate variants p.L17S, p.K25E, p.E27G, p.Y28S, p.S29G and p.K30E partially affected the co-precipitation of BRCA1 (Fig. 5a). In contrast, the damaging variants p.L21S, p.A22P, p.Y28D, p.T31P, p.A33P, p.R34P and p.Q36P, similar to p.L35P, failed to co-precipitate any endogenous BRCA1 (Fig. 5a).

Next, we examined for some of these variants whether they have an impact on the BRCA1-dependent localization of PALB2 to sites of DNA damage. To this end, YFP-tagged PALB2 carrying these variants was transiently expressed in U2OS cells and examined for localization at DNA damage-containing tracks generated by UV-A laser micro-irradiation. The localization of DNA damage sensor protein NBS1, which was tagged with mCherry and co-expressed in these cells, was used to control for equal DNA damage levels. We found that the intermediate variants p.S10T and p.E13A, similar to the functional variants p.E19V and p.R26K, did not impact the accumulation of PALB2 at sites of DNA damage, whereas p.L17S and p.E27G had a moderate impact when compared to that of wild-type PALB2 (Supplementary Fig. 6a, b). The damaging variants p.L21S and p.L35P had the strongest impact on the accumulation of PALB2 at sites of DNA damage. The accumulation of mCherry-NBS1 was unaffected in these cells (Supplementary Fig. 6a, b). The effect of these variants on the PALB2-BRCA1 interaction and on PALB2 localization at sites of DNA damage, despite the variability and non-significant differences, was consistent with the observed HR defect in DR-GFP assays, PARPi sensitivity assays and site-saturation functional screens (Figs. 2, 4d, e and Supplementary Data 1). Taken together, these analyses identified p.L17S, p.L21S, p.A22P, p.K25E, p.E27G, p.Y28D, p.Y28S, p.S29G, p.K30E, p.T31P, p.A33P, p.R34P and p.Q36P as new variants that impair PALB2 function in HR by impacting the interaction with BRCA1, which in turn affects PALB2 recruitment to DNA damage sites.

Variants in the PALB2 WD40, but not CC domain, affect protein stability
Prior studies, including ours, have demonstrated that damaging missense variants in the PALB2 WD40 domain impact the expression of PALB27,9,19. Reverse transcription-quantitative (RT-q) PCR analysis indicated that these variants did not affect expression at the mRNA level, but rather affected protein folding and/or stability, as shown in inhibition assays of translation and protein degradation using cycloheximide and proteasome inhibitor, respectively7,15. To further validate findings from the site-saturation functional screens, we selected 61 variants in the WD40 domain that were scored as damaging (25), intermediate (18) or functional (18), and examined effects on PALB2 expression by western blot analysis (Fig. 5b and Supplementary Data 1). We also included 33 variants in the CC domain that were scored as damaging (10), intermediate (21) or functional (2) (Supplementary Fig. 6c and Supplementary Data 1). For all functional variants in the WD40 domain, PALB2 expression was comparable to that of wild type. However, damaging variants generally showed very low levels of expression (e.g. 0.22 for p.W912S; Fig. 5b), whereas intermediate variants often partially reduced PALB2 expression (e.g. 0.70 for p.R976G; Fig. 5b). In contrast, variants in the CC domain generally showed near wild-type levels of PALB2 expression regardless of their functional classification, except for p.K20I, which showed reduced expression levels (0.31; Supplementary Fig. 6c).

WD40 variants showing reduced expression have been shown to mislocalise, likely as a result of protein instability and subsequent proteasomal degradation in the cytoplasm8,15. Consistent with these findings we found that 5 WD40 variants, which showed reduced expression (p.I887S, p.V895F, p.C933R, p.G971D, p.F1016C; Fig. 5b), were mislocalised when introduced in YFP-tagged PALB2 and transiently expressed in U2OS cells (Fig. 5c), an effect that was similar to that of the previously reported mislocalised p.L1027R variant15. In contrast, the WD40 variant p.E1083K, which was functional and expressed to WT levels (Fig. 5b and Supplementary Data 1), and several variants in the CC domain (p.S10T, p.E13A, p.L17S, p.Y28S, p.A22P, and p.Y28D), all of which showed near wild-type expression levels (Supplementary Fig. 6c), did not grossly mislocalise in the cytoplasm (Fig. 5c and Supplementary Fig. 6d).

Given the correlation between protein instability and loss of functionality for missense variants in the WD40 domain, we asked whether predicting the effect of WD40 missense variants on protein stability may constitute a step toward understanding the functional impact of these variants. Several methods have been developed to predict changes in the Gibbs free energy of unfolding (ΔΔG) between wild-type and variant proteins using sequence and structure information. We used the FoldX BuildModel command to determine the ΔΔG values for all possible missense variants in the structured regions of the WD40 domain20. ΔΔG values were obtained for 5,596 missense variants in this domain. While highly elevated ΔΔG values were observed for several missense variants, a vast majority of variants showed moderately increased ΔΔG values, indicative of a mild protein destabilisation (Supplementary Fig. 7a). This suggests that only substitutions of a subset of residues may lead to severe protein destabilisation and loss of functionality. In agreement, we found that ΔΔG values only correlated weakly with depletion scores obtained for missense variants in the WD40 domain (Supplementary Fig. 7a; n=5,596, r=-0.2137, p<0.0001) (see Methods). A similar trend was observed when ΔΔG values were obtained using the deep-learning based method RaSP (Supplementary Fig. 7b; n=6,071, r=-0.2460, p<0.0001)21. Taken together, our results suggest that missense variants in the WD40 domain, but not in the CC domain, can lead to protein instability and subsequent loss of function, the latter of which is only poorly predicted by ΔΔG-based in silico models.

In silico tools show limited specificity in predicting the functional impact of PALB2 variants
We previously showed that the outcomes of in silico prediction tools such as PolyPhen22, SIFT23, Align-GVGD24 and REVEL25 generally show little to no correlation with those from PARPi sensitivity and DR-GFP assays7. We sought to examine this further by comparing the predictive performance of four more recently developed tools, Helix, Alpha-Missense, EVE, and BayesDel, against the outcome of the PARPi sensitivity-based site-saturation screens for 6718 missense variants in the CC and WD40 domains26–29. All tools generated predictions for 1347 variants, showing moderate correlations with depletion scores for Helix (r=-0.4384), AlphaMissense (r=-0.4484), and BayesDel (r=-0.3541), while EVE showed a weaker correlation (r=-0.2154), all with p<0.0001 (Supplementary Fig. 8). Notably, although all tools tended to overestimate the damaging impact of variants with high depletion scores, potentially leading to false positives, they were more accurate for variants with low depletion scores. Extending the analysis to all 6718 variants, Helix and AlphaMissense showed strong correlation for variants in the CC domain (Helix: r=-0.6509; AlphaMissense: r=-0.3408; n=609), moderate to weak correlation for those in the WD40 domain (Helix: r=-0.2873; AlphaMissense: r=-0.2266; n=6,109), and moderate correlation across the combined domains (Helix: r=-0.2882; AlphaMissense: r=-0.2527; n=6,718), all with p<0.0001 (Supplementary Fig. 9a–f). Again, both tools overestimated the damaging effects of variants with high depletion scores.

To further assess the performance of the in silico prediction tools, receiver operating characteristic (ROC) analysis was performed using missense variants classified as damaging (HR<30%; n=41) or functional (HR>70%; n=105) based on DR-GFP assays from both the current study (Fig. 4e) and previous research (Supplementary Fig. 9g)7. The analysis revealed that Helix, BayesDel and EVE performed moderately well (AUC values 0.846, 0.781 and 0.645, respectively). In contrast, AlphaMissense showed relatively better performance (AUC value 0.946), with good sensitivity (1) but limited specificity (0.8) (Supplementary Fig. 9g–i). This aligns with the Pearson correlation data, showing that AlphaMissense frequently overestimate the deleteriousness of variants with functional impact in our site-saturation screens (Supplementary Figs. 8 and 9a–f). In conclusion, while Helix and AlphaMissense may offer reliable predictions for missense variants within the CC domain, overall Helix, AlphaMissense, EVE, and BayesDel, like other in silico models previously evaluated7, generally lack sufficient accuracy in predicting the functional impact of PALB2 variants. Notably, these models tend to lack specificity, often overestimating the damaging impact of variants with high depletion scores.

Site-saturation functional screens aid in the clinical reclassification of missense VUS in PALB2
Genetic testing has led to a tremendous increase in the number of reported VUS in PALB2. As of June 30, 2025, 3014 SNVs in PALB2 were listed in ClinVar, 2925 of which encode 2800 unique missense VUS (Fig. 6a and Supplementary Data 3). We next investigated whether the results of our site-saturation functional screens could assist in the classification of VUS in the CC and WD40 domain. The screens produced depletion scores for 872 VUS in these domains, representing 31.1% of those reported in ClinVar, which we categorized as functional (n=518), intermediate (n=304), or damaging (n=50). For only 41 of the VUS (1.5%) in the CC and WD40 domains no depletion scores were available (Fig. 6a). Of note, for 21 ClinVar-reported missense VUS located between the start and CC domain of PALB2 (pre-CC), no depletion scores were available as this region was not subjected to functional analysis (Fig. 6a and Supplementary Data 3). Thus, our site-saturation functional screens can aid in the categorization of 872 (31.1%) of the VUS in the CC and WD40 domains listed in ClinVar.

We previously showed that the region between the CC and WD40 domain (encoding p.I44 to p.L855) is dispensable for PALB2 function in HR, suggesting it is unlikely that damaging missense variants will be identified therein (Fig. 1). ClinVar listed 1866 missense VUS in this region (Fig. 6a). To provide additional support that these variants may be functional rather than damaging, we randomly chose 26 variants, along with 5 synthetic missense variants in this region (Fig. 6b), and expressed them in the Palb2KO mES cells to determine the impact on HR (Supplementary Fig. 10). Expression of all 26 missense VUS rescued HR in the DR-GFP reporter to levels comparable to that in cells expressing wild-type PALB2 (Fig. 6c). This result was validated in PARPi assays, which demonstrated for 12 of these variants that expression rendered Palb2KO cells resistant to PARPi treatment (Fig. 6d). Corroborating these findings, 16 of the missense VUS exhibited near wild-type expression, whereas a synthetic variant causing a frameshift (p.S201fs) markedly reduced PALB2 expression, as expected (Supplementary Fig. 10). These findings further indicate that the 1,866 missense VUS located in region p.I44 to p.L855 are likely functional. Therefore, our functional data may also contribute to the reclassification of missense VUS located in the middle region of PALB2, including those reported in ClinVar.

Damaging missense variants in PALB2 are associated with increased breast cancer risk
Having categorized missense variants in PALB2 as functional, intermediate or damaging (Fig. 4c and Supplementary Data 1), we next asked if their functional impact correlated with increased breast cancer risk. The risks of breast cancer for individuals carrying PALB2 functional (depletion scores >-0.0759) and damaging (depletion scores <-0.7037) missense variants were estimated in case-control association analyses. Burden analysis of damaging missense variants in the BRIDGES, CARRIERS and CZECANCA case-control studies combined yielded a strong association with breast cancer (odds ratio (OR)=4.67; 95% CI: 1.36–18.82; p=0.009) (Table 1), consistent with previously reported results for protein truncating variants11,30. In contrast, functional variants were not associated with increased risk (OR=1.07; 95% CI: 0.93–1.23; p=0.333). Likewise, intermediate variants with functional scores between -0.7037 and -0.0759 were not associated with increased risk of disease (OR=1.28; 95% CI: 1.00–1.64; p=0.050) (Table 1).

To validate these findings, association studies for functionally characterized missense variants were conducted using breast cancer cases and controls from UK Biobank (see Methods and Table 1). Damaging missense variants were once more associated with an increased risk of breast cancer (OR=4.94; 95% CI: 1.07 – 20.68; p=0.043) whereas functional variants (OR=0.99; 95% CI: 0.75–1.30; p=1.000) and intermediate variants (OR=1.63; 95% CI: 1.12 – 2.34; p=0.013) were not. These findings were further confirmed for damaging (OR=3.25; 95% CI: 1.58–7.51; p=7.83×10-4), intermediate (OR=1.00; 95% CI: 0.86–1.16; p=1.000), and functional variants (OR=1.10; 95% CI: 0.94–1.27; p=0.228) using a clinical cohort and non-cancer female gnomAD controls, as well as for damaging (OR=2.73; 95% CI: 1.16 – 6.53; p=0.013), intermediate (OR=0.87; 95% CI: 0.73 – 1.03; p=0.096), and functional variants (OR=1.09; 95% CI: 0.93 – 1.28; p=0.285) from the same clinical cohort using controls from the All of Us research program (see Methods and Table 1)18. Importantly all associations for damaging variants were significantly different from functional and intermediate variants, indicating that the damaging missense variants identified in the site-saturation functional screens represent a group of variants that are likely associated with a high risk of developing breast cancer.

Methods
Cell culture and generation of Trp53KO/Palb2KO mES cells with DR-GFP and RMCE
Trp53KO/Palb2KO mES cells carrying the DR-GFP reporter and RMCE system at the Pim1 and Rosa26 locus, respectively, were generated previously7 and cultured as previously described40. HAP1 cells (Horizon Discovery) were maintained in IMDM with 10% fetal bovine serum and 1% Penicillin/Streptomycin. U2OS cells were cultured in 5% CO2 at 37°C in DMEM (Dulbecco’s modified Eagle’s medium) supplemented with 10% fetal calf serum and 1% Penicillin/Streptomycin. Human HAP1 and U2OS cells were authenticated using Short Tandem Repeat (STR) analysis by ATCC services (100% match). Cells were used only when confirmed free of mycoplasma contamination using the MycoAlert™ detection kit (LONZA).

Introducing variants and deletions into human PALB2 cDNA
The RMCE vector (pRNA-251-MCS-RMCE) (TaconicArtemis GmbH) containing human PALB2 cDNA driven by an Ef1α promotor was generated previously7. PALB2 variants, as well as ChAM (c.1183-c.1338) and MRG15 (c.1831 - c.2292) deletions, were introduced by site-directed mutagenesis using the Quick-Change Lightning protocol (Agilent Technologies). Exon deletions (exon 4, c.212-c.1684; exon 5, c.1684-c.2514, includes the last nucleotide of exon 4 to generate an in-frame deletion; exon 6, c.2515–c.2586) were generated using primer pairs that flank the exons. Primer phosphorylation by T4 PNK (NEB) was followed by PCR with Phusion Polymerase (NEB), DpnI (NEB) digestion and ligation with T4 DNA ligase (NEB). All constructs were verified by Sanger sequencing and used for downstream mES cell-based assays.

PARPi sensitivity assays
Functional analysis of single PALB2 variants using proliferation-based PARPi (Olaparib; Selleckchem, S1060) sensitivity assays was performed for selected PALB2 missense variants as previously described7. Briefly, cells were exposed to various concentrations of PARPi for two days. Thereafter, cells were incubated for one more day in drug free media, after which viability was measured using flow cytometry (using only forward scatter and side scatter).

HR reporter assays
HR assays using 2×106 Trp53KO/Palb2KO mES cells carrying the DR-GFP reporter and RMCE system were performed as previously described7. Briefly, cells that were complemented with human PALB2 cDNA with or without a variant (or an empty vector) and were treated with neomycin to select for cells with integrated PALB2 variant cDNA. Two days after transfection of an I-Scel and mCherry co-expression vector41, GFP expression was measured using flow cytometry.

Generation of site-saturation variant libraries for CC and WD40 domains
Using the RMCE vector (pRNA-251-MCS-RMCE) (TaconicArtemis GmbH) containing human PALB2 cDNA7, a variant library specific for the CC region of PALB2 was generated by Ranomics Incorporated in Toronto, Canada, by using a series of in-house computational tools and multiplex PCR assembly reactions. The library was validated by the Leiden Genome Technology Center (LGTC) of the Leiden University Medical center (LUMC), and was shown to contain ~95% of all possible missense and nonsense variants in the CC region of (p.L9-p.K43) PALB2 (Supplementary Fig. 1).

Five WD40 variant libraries were generated by TWIST Bioscience using the RMCE vector (pRNA-251-MCS-RMCE) (TaconicArtemis GmbH) containing wild-type PALB2 cDNA. Altogether, the five variant libraries cover the entire WD40 domain of PALB2; WD40 library 1 (p.Q856-p.Q921), WD40 library 2 (p.I922-p.Q987), WD40 library 3 (p.Q988-p.D1053), WD40 library 4 (p.S1054-p.L1119), WD40 library 5 (p.E1120-p.S1186). For each variant library, TWIST supplied a double stranded linear fragment containing the entire WD40 region with the same added flanking sequences resulting in a final product of 1,071 bp for each library. For cloning, the RMCE vector containing wild-type PALB2 cDNA was used to amplify the entire vector lacking only the WD40 region of PALB2, using the site-directed mutagenesis kit and Quick-Change Lightning protocol (Agilent Technologies) and the following primers; Fw: 5’-CTTAATTAAGGCCAGGGATCTTCAAGC-3’, Rv: 5’-ATGCTATCAGAAGCAGGAAGCTCTG-3’. The reaction mix was subjected to DpnI digestion for 1 hour at 37°C and the amplified vector was gel extracted. Each WD40 library was then cloned (in a separate reaction) into the amplified vector by Gibson Assembly (NEB) for 40 min at 50°C using 50 ng amplified vector (RMCE vector backbone containing the entire PALB2 cDNA except the WD40 region) and 29 ng of WD40 library. The entire ligation mix was transformed into DH5α and grown o/n in 50 ml lysogeny broth in the presence of ampicillin. After midiprep of each WD40 library, all five libraries were validated by the Leiden Genome Technology Center (LGTC) of the Leiden University Medical center (LUMC), and were shown to contain >90% of all possible missense and nonsense variants in the WD40 region of PALB2 (Supplementary Fig. 1).

Integration of site-saturation variant libraries for CC and WD40 domains
The PALB2 variant libraries were integrated in 100×106 Trp53KO/Palb2KO mES cells (six times the CC library and three times each WD40 library, representing biological replicates). Cells were divided in fractions of 10 × 106 cells for which each fraction was subjected to co-transfection of 1μg FlpO expression vector (pCAGGs-FlpO-IRES-puro)42 with 1μg RMCE exchange vector containing the variant libraries, as previously described7. Transfected cells were divided over twenty 10 cm tissue-culture plates and treated one day later with 50μg/ml neomycin/G418 sulphate (Thermo Fisher, 10131035) for 6-7 days. Resistant colonies expressing PALB2 variant cDNAs were pooled (50-100 × 103 colonies per variant library integration), mixed well and plated over three 10 cm tissue-culture plates containing neomycin. Two plates were trypsinized and stored at -80°C as backup and one plate was used for the PARPi sensitivity assay.

High-throughput PARPi sensitivity assays
PARPi sensitivity after PALB2 variant library integration was assessed in triplo (representing technical replicates) by using 0.57×106 cells seeded on 6 cm tissue-culture plates. One day after seeding, cells were treated with 0.5µM PARPi (Olaparib; Selleckchem, S1060) for two days, after which the medium was refreshed with drug-free medium and cells were cultured for one more day. A non-treated plate was taken along as a control at the start of seeding. DNA was eventually isolated from the surviving cells and subjected to next-generation sequencing.

PALB2 cDNA amplification and next-generation sequencing
The variant containing region of the integrated human PALB2 cDNA was amplified from 100 ng genomic DNA. Reactions contained 2* Kapa HiFi Master Mix polymerase (KR0370) and primers specific for either the CC region (Fw: 5’-GATGTGTATAAGAGACAGCGAGCTCGGATCCACTAGTAACG-3’; Rv: ’-CGTGTGCTCTTCCGATCTCTGAGTGTTTTAGCTGCGGTGAG-3’), WD40 region 1 (Fw: 5’- GATGTGTATAAGAGACAGCG AACAGACTGAAACAGCAGAGC-3’; Rv 5’- CGTGTGCTCTTCCGATCT- CAAAGCTACACACACGAGATTATACAC-3’), WD40 region 2 (Fw: 5’- GATGTGTATAAGAGACAGCTTTATACCTGGCACTTCGCAGAG-3’; Rv 5’- CGTGTGCTCTTCCGATCTGGTTTTCTTTGCCTCCTCCATCTTC-3’), WD 40 region 3 (Fw: 5’- GATGTGTATAAGAGACAGGCCTGACAAAGAGGAGGCTAGTTAG-3’; Rv 5’- CGTGTGCTCTTCCGATCTGGATGACTCAG GACAATAAAGAGAAGCC-3’), WD40 region 4 (Fw: 5’- GATGTGTATAA GAGACAGGCTCTGCTTGGTACTACTATTATGAACAAC-3’; Rv 5’- CGTG TGCTCTTCCGATCTGGCAATTGTTCCAGAAGTCAAGATTGC-3’), or WD40 region 5 (Fw: 5’- GATGTGTATAAGAGACAGGGTGTGATGC TGTACTGTCTTCCTC-3’; Rv 5’- CGTGTGCTCTTCCGATCTCTGTAGGT CTGCTTGAAGATCCCTG-3’). PCR was performed under the following conditions; 98°C for 1 minute; 18 cycles of 98°C for 20 seconds, 65°C for 30 seconds, and 72°C for 30 seconds; and 72°C for 2 minutes. The reactions produced amplicons specific for each of the integrated human PALB2 variant libraries. After clean up with Ampure XP beads (Beckman Coulter) the PCR product was checked on a Agilent Bio-Analyzer 2100 HS chip. A second PCR with Illumina index primers was performed under the following conditions; 98°C for 1 minute; 10 cycles of 98°C for 20 seconds, 60°C for 30 seconds, and 72°C for 30 seconds; and 72°C for 2 minutes. The resulting PCR products were equimolarly pooled. All samples were sequenced on an Illumina MiSeq.

Variant scoring and analysis
FASTQ files from each sample were analysed using the Enrich2 software package13. Enrich2 grouped and counted identical amino acid changes even if they arose from different nucleotide changes. Reads containing insertions, deletions or multiple amino acid substitutions were removed from the analysis. The number of sequencing reads supporting each amino acid variant was then calculated. Variants that showed noisy or unreliable results across replicates, based on a standard error (SE) greater than 0.5, were filtered out. Depletion scores were calculated for each remaining variant. These scores reflect the ratio of a variant’s frequency in PARPi-treated samples compared to untreated samples, normalized to the abundance of wild-type PALB2 (set to ‘0’). Only variants that passed the SE filter and were detected in all replicate experiments (six for the CC library and three for each WD40 library) were included in the final analysis. Each library included a varying number of nonsense variants. An average depletion score was calculated for the nonsense variants in each integration experiment. All variant scores were normalized by setting the average nonsense score to ‘-1’, using the following Eq. (1):

0Norm: Enrich2 score 0 = ‘Enrich2 score’ - ‘Mean nonsense score (as neg: value)’ / ‘Mean nonsense score (as pos: value)’ - ‘Mean nonsense score (as neg: value)’ - 1 (1)

A final depletion score for each variant was calculated as the mean of its normalized depletion scores across all replicate library integration experiments (six for the CC library and three for each WD40 library). The SE for each variant was determined by dividing the standard deviation of its normalized scores by the square root of the number of replicates. Final depletion scores were visualized in a heatmap using the matrix analysis software MORPHEUS (https://software.broadinstitute.org/morpheus; see Supplementary Data 1).

Mixture modelling
We fitted a two-component mixture model to Enrich2 scores of the combined data. Since the data were heavier-tailed than normal, each of the components was assumed to have a scaled and shifted t-distribution. The scores of synonymous variants were all assumed to come from mixture component 1, those from nonsense variants from component 2, and the missense were assumed to be a true mixture. The resulting seven-parameter model was fitted by maximum likelihood. Mixture modelling was applied to estimate the probability that each variant is damaging (Pd), based on its average depletion score across replicates. Classification thresholds were defined as follows: variants with Pd < 0.02 were classified as ‘functional’, those with Pd > 0.98 as ‘damaging’, and variants with 0.02 < Pd < 0.98 were assigned to the ‘intermediate’ category. ROC curves and AUC were calculated for the raw data and for the model. For the data-based AUC a confidence interval was calculated using a previously reported method43.

Generation of site-saturation mutagenesis libraries and Cas9/sgRNA plasmids for SGE
PALB2 exon 10 and adjacent upstream and downstream 10 nucleotide introns flanking the exon (GRCh38 chr.16: 23,621,352-23,621,488 (137 nucleotides)) were selected for SGE. A single guide RNA (sgRNA) (protospacer sequence CCAATTTTTGATGCCCCCTG) was designed by Benchling design tool. sgRNA annealed oligos were ligated into pSpCas9(BB)-2A-Puro(PX459v2.0) (Addgene; 62988) following BbsI (New England Biolabs, R0539L) digestion to create Cas9-sgRNA co-expression construct for SGE. For the SGE, 600-1000 bp homologous arms upstream and downstream of the target region were amplified from WT HAP1 genomic DNA and cloned into BamHI-HF digested pUC19 vector using the NEBuilder HiFi DNA assembly Cloning Kit. Cloned plasmid backbones were subject to site-saturation mutagenesis by inverse PCR using mutagenized codon “NNN” primers for all possible nucleotide changes at each amino acid position. A Protospacer Protection Edit (PPE) encoding a synonymous mutation (c.3030G>A; p.Glu1010Glu) was introduced by site-directed mutagenesis into the protospacer adjacent motif (PAM) site of the target region to prevent re-cutting by the Cas9/sgRNA after successful editing. This variant received a maximum delta score of zero across SpliceAI predictions for donor and acceptor gain and loss, indicating no predicted impact on RNA splicing. Furthermore, a 3-nucleotide mutation was introduced into the intronic region of the homologous arm to strengthen reamplification of the DNA within that target region.

Saturation genome editing
In the SGE experiment, 5 million haploid-sorted HAP1 cells were co-transfected with 4μg target-specific variant library and 16μg Cas9/sgRNA targeting construct using Turbofectin 8.0 (Origene). Cells were selected in puromycin (1μg/ml) for 3 days. Cells were harvested at day 5 (24 hrs after puromycin selection) and day 20 post-transfection and genomic DNA (gDNA) was extracted using Monarch Genomic DNA Purification Kit (New England Biolabs, T3010L). The target region was amplified by PCR to add barcodes for multiplexing. All PCR reactions were performed in 50 μL reactions using Q5 High-Fidelity 2X Master Mix (New England Biolabs, M0492L). Primers for genomic DNA amplification are included in Supplementary Data 6). All reactions were cleaned and concentrated using Ampure XP beads prior to sequencing for 150 cycles on an Illumina MiSeq (approximately 5 million reads per run).

Sequence analysis after SGE
FASTQ files of sequenced samples from Illumina MiSeq were trimmed for adapter sequences using cutadapt (v3.5). SeqPrep (v1.2) converted the paired-end reads into single reads. The single reads were aligned to the human reference genome (GRCh38) utilizing bwa-mem (v0.7.17). Following alignment, a custom-developed tool ‘CountReads‘ was used for mutation identification and characterization. ‘CountReads‘ included the preparation of reference amino acid and DNA sequences, validation of sequencing data integrity, and precise trimming of reads to relevant regions. The method also differentiated between various variant types and confirmed the presence of specific variants and aggregated and reported variant data. ‘CountReads‘ produced a VCF (Variant Call Format) file which was annotated with CAVA. The log2 ratio between the frequency of day 20 and day 5 read counts was used to measure the depletion/enrichment effect for each variant. Variants with under-represented read counts (<10) in the library and day 5 were excluded from further analysis. Log2 ratios of variants were linearly scaled relative to median values for synonymous and nonsense SNVs using within exon normalization. SGE was performed in duplicate. Replicates of individual SNVs with Log2 ratios >1 relative to mean replicate values were excluded. Mean SGE scores were calculated if the difference between replicate SGE scores was between -1.5 and 1.5 (Supplementary Data 1).

Pulldown assays
Pulldown assays were performed as previously described7. Briefly, 20μg of pGFP-NLS or pYFP-PALB2 plasmid12 was transfected into ~5×106 U2OS cells on a 15 cm dish using Lipofectamine 2000. The next day cells were trypsinized, and lysed in 1 ml EBC buffer (50 mM Tris pH 7.3, 150 mM NaCl, 0.5% NP-40, 2.5 mM MgCl2) containing 1 tablet protease inhibitor (Roche) per 10 ml buffer. Lysates were incubated with benzonase and centrifuged. The supernatant was then added to 25μl of pre-washed GFP-trap beads (ChromoTek) and incubated for 1.5 hours at 4°C on a rotating wheel. The beads were washed 5–6 times with EBC buffer and eventually resuspended in 25μl Laemmli buffer after which about half of each sample was analysed by western blot analysis using an antibodies against human BRCA1 (1:1,000, MS110, Merck, cat. nr. OP92) and GFP (1:10,000, Abcam, cat nr. ab290).

Western blot analysis
Expression of all PALB2 variants was examined by Western blot analysis as previously described7. Two different primary rabbit polyclonal antibodies directed against the N-terminus of human PALB2 (1:1,000, kindly provided by Cell Signalling Technology prior to commercialization) were used. Wild-type human PALB2 and empty vector (Ev) were used as controls on the blot, while Tubulin (1:10,000, Sigma, T6199 clone DM1A) was used as loading control. For protein stability and degradation assays, cells were treated with 100μg/ml cycloheximide (Sigma, C7698-1G) for up to 3 hours, or 0.5 or 3μM MG-132 (Selleckchem, S2619) for 24 hours, after which western blot samples were collected and analysed.

Laser micro-irradiation and PALB2 recruitment
U2OS cells were grown on 18-mm coverslips and sensitized with 10µM 5′-bromo-2-deoxyuridine (BrdU) for 24h before micro-irradiation. Cells were co-transfected with 1µg YFP-PALB2, with or without a variant, and 0.5µg mCherry-NBS1 expression vector using lipofectamine 2000 (Invitrogen). For micro-irradiation, cells were placed in a live-cell imaging chamber set to 37°C in CO2-independent Leibovitz’s L15 medium supplemented with 10% FCS and penicillin–streptomycin (Invitrogen). Micro-irradiation experiments were carried out with a Zeiss Axio Observer microscope driven by ZEN software using a 63x/1.4 oil immersion objective coupled to a 355 nm pulsed DPSS UV-laser (Rapp OptoElectronic). To monitor the recruitment of YFP-PALB2 to laser-induced DNA damage sites, cells were imaged before and 360s after laser irradiation. The fluorescence intensity of YFP-PALB2 and mCherry-NBS1 at DNA damage sites relative to that in an unirradiated region of the nucleus was quantified and plotted over time. Kinetic curves were obtained by averaging the relative fluorescence intensity of cells displaying positive recruitment (n > 30 cells per condition).

Cellular localization assay
Quantification of YFP-PALB2 subcellular localization was based on transient expression in U2OS cells that were fixed using 4% formaldehyde and permeabilized using Triton X-100. Cells were immunostained with anti-GFP and DAPI prior to immunofluorescence analysis and quantification (based on ~100 cells per condition per replicate). Images were acquired on a Zeiss Axio Imager M2 wide-field fluorescence microscope with 63x PLAN APO (1.4 NA) oil-immersion objectives, running ZEN 2012 blue edition v1.1.0.0 (Zeiss). Assays were conducted in triplicate and average values and SEM were calculated to generate the respective plots.

Destabilization energy analysis for variants in the WD40 domain
Structural analysis of WD40 domain variants was performed using a previously reported X-ray crystal structure from the Protein Data Bank (https://www.rcsb.org/: 2W18)44. The structure was prepared using the FoldX RepairPDB command to identify and repair clashes and bad torsion angles in the side-chains, keeping the backbone of the structure fixed. Destabilization energies (kcal/mol) for variants identified with deep mutational scanning were computed using the FoldX BuildModel command, taking the average of triplicate runs20. Alternatively, a list of destabilization energies for variants the WD40 domain, which was prepared using a structure from the Alpha-fold Protein Structure Database (https://alphafold.ebi.ac.uk/: Q86YC2) in combination with the deep learning-based RaSP method21, was obtained from the Electronic Data Research Archive (https://sid.erda.dk/public/).

Cancer risk analysis
Associations between pooled PALB2 variants and breast cancer risk were assessed using two-sided Fisher’s Exact tests, based on data from breast cancer cases and age-matched female controls without cancer from the population-based BRIDGES, CARRIERS, and CZECANCA studies11,30,45. Association testing by logistic regression for pooled variants was also conducted using female breast cancer cases compared to non-cancer female controls from UK Biobank (adjusted for age and ancestry); for female breast cancer cases receiving cancer genetic testing by Ambry Genetics compared to the gnomAD v.4.1. non-UK Biobank female controls (adjusted for ancestry), and All of Us research program non-cancer female controls (adjusted for ancestry and for age of diagnosis of cases and age at enrolment for controls). Analyses were conducted for pooled functional, intermediate and damaging variants and also for variants in the middle region of PALB2 using Firth logistic regression, adjusting for age and ancestry and deriving P-values from the likelihood ratio of the Firth logistic regression. All variants with minor allele frequencies >0.001 were excluded to avoid dilution of associations by recurrent variants (Supplementary Data 1).

Data analysis
Missense, nonsense, and synonymous variants were selected by filtering prior to download. Variants were aligned with our functional dataset following the conversion of the variant Id (based on genomic location) into HGVS-compliant variant descriptions using the Variant Validator tool available at: https://variantvalidator.org/service/validate. Throughout this study, NCBI transcript NM_024675.4 and NP_078951.2 were used to indicate nucleotide and amino acid positions in PALB2, respectively. Frequency data derived from gnomAD18, BRAVO, FLOSSIES and classification data from the ClinVar database were harmonized with the 7106 PALB2 variants for which depletion scores were available (Supplementary Data 2 and 3). For 57 codons, multiple nucleotide variants resulted in the same amino acid alteration (Supplementary Data 1). As data analysis and correlation with functional data were performed at the amino acid level, we merged the available information for distinct nucleotide variants, yielding a unified entry for a specific protein variant.

Pearson’s correlation coefficients (r) were used to assess the strength of linear relationships between variables. Positive correlations were classified as weak (r<0.25), moderate (0.25 ≤ r<0.50), good (0.50≤r<0.75) and very good (r≥0.75), while negative correlations were classified as weak (r>-0.25), moderate (-0.25≥r>-0.50), good (-0.50≥r>-0.75) and very good (r≤-0.75). Statistical significance was determined using corresponding P-values.

ROC analysis of in silico prediction outcomes was performed using a set of missense variants that were individually tested via the DR-GFP assay in both the current study and previous work7. Variants exhibiting <30% HR activity were used as proxies for damaging variants, while those with >70% HR activity were considered functional. Although these thresholds are arbitrary, they provide a practical framework for comparative analysis in the absence of definitive clinical classifications for PALB2 missense variants. Based on these analysis, key performance metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy, were calculated to evaluate the predictive power. Finally, 2 × 2 contingency tables were generated to compare the predictions of each in silico tool with the experimentally determined functional impact.

Regarding frequency data, we computed the mean frequency for nucleotide variants giving rise to identical protein variants. For ClinVar submissions, we joined the clinical significance classifications for variants and subsequently performed a majority voting approach to attribute the protein variant to a singular category (akin to handling variants with conflicting interpretations, outlined below). ClinVar employs the term “conflicting interpretations” to denote variants for which varying clinical significance classifications have been submitted. The presence of these “conflicting” variants added complexity to the breakdown of variant classes, as they would be tallied threefold (i.e., counted in VUS, (L)B, and the “conflicting” category). To resolve this, a majority voting strategy was applied, assigning each variant to either VUS, (L)B or (L)P, based on the most frequent classification (see Fig. 6a in Source Data file). Finally, splice site prediction was performed using SpliceAI version 1.3.1. (https://github.com/Illumina/SpliceAI)37. SpliceAI was run locally with maximum distance of 10k nucleotides (4999 nucleotides on either site from the variant of interest) with masked scores. SpliceAI cut points to predict spliceogenicity were based on the maximum delta score of donor/acceptor loss and donor/acceptor gain >0.5 for predicted splicing impact (Supplementary Data 4). All statistical analysis was performed using Graphpad Prism (version 10.2.3).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The data and work presented in the manuscript are highly relevant and deserve to be published, as they will have a significant downstream impact on the classification of missense variants in PALB2 with unknown clinical significance. This will greatly benefit cancer patients carrying these variants. The study makes meaningful contributions to the field and is the first to provide functional evidence for more than 6,000 variants. Additionally, the work presents evidence that damaging missense variants are associated with a clinically relevant increased risk of breast cancer.\nHowever, the authors need to address a few flaws in data interpretation, correct minor mistakes and provide more detail in the methods.\nGENERAL REMARKS\n-Throughout the text the authors alternatively use wildtype or wild type. Only one form should be used. Please review.\n-Please check that PALB2 be stated in italics when it refers to the gene name. For instance:\ni) in the Introduction, in the sentence “Importantly, several damaging missense variants in PALB2”, PALB2 should be in italics.\nii) in the section of Data analysis of Materials and Methods, in the sentence “Frequency data derived from gnomAD 18, BRAVO, FLOSSIES and classification data from the ClinVar database were harmonized with the 7,106 PALB2 variants for which depletion scores were available (Supplementary Tables 2 and 3)”, PALB2 should be in italics.\nSPECIFIC REMARKS\nABSTRACT\n“These exons encode the Coiled Coil and WD40 domains, which we found to be minimally required for HR.”\nThis statement may be misleading by interpreting it that the two domains are modestly required for HR. A slightly reworded sentence may be more much less ambiguous:\nThese exons encode the Coiled Coil and WD40 domains, which we found to be the minimal essential components required for HR.\nRESULTS\nSECTION “The region between the coiled-coil (CC) and WD40 domains of PALB2 is dispensable for HR”\n-“We generated five PALB2 cDNA deletion constructs lacking either the ChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6) (Fig. 1a)”\nThe mention of the Figure 1 here should state both 1a and 1b. Please review.\n-“We generated five PALB2 cDNA deletion constructs lacking either the ChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6) (Fig. 1a). All exon deletions were generated in-frame.”\nGiven that exon 5 is out of frame and exon 6 encodes amino acids 856 to 862 of the WD40 domain, it would be useful to provide the specific cDNA coordinates of the deletions to understand the sequence of the resulting protein. In fact, it would be useful to include in Materials and Methods section the cDNA coordinates for all analysed cDNA deletion constructs:\nChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6).\n- “The constructs were introduced site-specifically by recombination-mediated cassette exchange (RMCE) at the Rosa26 locus in Palb2KO mES cells carrying the DR-GFP reporter, which allows flow cytometry-based detection of HR after I-SceI endonuclease-induced DNA double-strand break formation (Fig. 1b).”\nIt is Figure 1c. Please review.\nSection: Site-saturation functional screens of missense variants in the CC and WD40 domains of PALB2\n-“PARPi sensitivity assays were performed in triplicate on these cells using non-treated cells as control conditions”\nAccording to Materials and Methods as well as to Source data file, PARP sensitivity was done with six independent CC-library and three independent WD40-library integration experiments. Please clarify it.\n-“Using Receiver Operating Curve (ROC) analysis (Fig. 4b), we categorized 392 (6%) of the missense variants as damaging, 2,422 (36%) as intermediate and 3,904 (58%) as functional with 98% sensitivity and specificity (Fig. 4c and Supplementary Table 1).”\nAccording to Supplementary Table 1, 62 out of 339 nonsense variants were categorised as intermediate and seven nonsense variants as functional. Based on this data, is it possible to hypothesise that these nonsense variants are associated with a lower cancer risk? Please consider adding these findings and discuss their potential implications in conferring a different cancer risk.\nSECTION: Validation of site-saturation functional screens of missense variants in the CC and WD40 domains of PALB2\n- “To validate our site-saturation functional screens, we measured the relative PARPi resistance levels for a panel of variants (n=140), and correlated these levels to the depletion scores from the screens (Supplementary Table 1) 7,15.”\nIt is not clear if the authors generated this data in the context of the present work or if there were taken from references 7 and 15. Please clarify it in Results and Materials and Methods sections.\n- “To validate our site-saturation functional screens, we measured the relative PARPi resistance levels for a panel of variants (n=140), and correlated these levels to the depletion scores from the screens (Supplementary Table 1) 7,15.”\nAccording to Supplementary Table 1, there were 140 missense variants and 7 nonsense variants assayed. For the sake of clarity, please specify that the panel consists of 140 missense variants.\n-This showed a good and significant correlation for variants in the CC (n=43, r=0.8055, p<0.0001), WD40 (n=97, r=0.6500, p<0.0001) and CC + WD40 domains (n=140, r=0.6494, p<0.0001) (Fig. 4d and Extended Data Fig. 2a, b). Consistently, we observed a similarly good correlation between the outcome of the site saturation screens and those from DR-GFP reporter-based assays for the same variants in the CC (n=43, r=0.8715, p<0.0001), WD40 (n=97, r=0.7131, p<0.0001) and CC + WD40 domains (n=140, r=0.7086, p<0.0001) (Fig. 4e and Extended Data Fig. 2c, d).\nWhat type of correlation method was used? i.e., Spearman, Pearson, etc?\nIn fact, this information is missing for all correlations performed throughout the entire manuscript.\n- “Moreover, our site-saturation screens correctly categorized previously reported missense variants with no functional impact (e.g., p.K18R, p.E42K, p.G998E)”\nAccording to Supplementary Table 1, the p.K18R is intermediate.\nPlease correct it.\n- “We obtained MAVE scores for 276 single nucleotide variants, leading to 9 nonsense and 68 synonymous changes, as well as 199 amino acid substitutions”\nTo help readers find these data, could you please specify that the respective data are available in the Source Data file?\n-“The MAVE data furthermore included scores for 195 unique changes consisting of 9 nonsense variants and 186 missense variants (Fig. 4g).”\nAccording to Source Data file for Fig. 4g, these numbers are not correct. In the column T to W which collect MAVE data for unique amino acid changes, all synonym variants are incorrectly stated as missense. Therefore, the correct numbers of unique variants are: 177 missense, 32 synonym and nine nonsense variants.\nPlease review and correct it.\nAdditionally, in the Source Data file for Fig. 4g, the columns Y to AB show MAVE data for unique amino acid changes with depletion scores. The total number in the column names is incorrectly listed as 218. It should be stated as 179 variants.\nPlease review it.\n-“Importantly, we obtained depletion scores for 179 of these variants in the PARPi sensitivity-based site saturation screens (Fig. 2, Fig. 3 and Supplementary Table 1), allowing comparison of depletion scores and MAVE scores.\nTo ensure clear understanding of the downstream analysis, it would be helpful to specify that the 179 variants consist of 170 missense variants and 9 nonsense variants.\n-“Importantly, we obtained depletion scores for 179 of these variants in the PARPi sensitivity-based site saturation screens (Fig. 2, Fig. 3 and Supplementary Table 1), allowing comparison of depletion scores and MAVE scores. We observed a good and significant correlation between these scores (n=179, r=0.6439, p<0.0001; Fig. 4g), indicating concordance between the outcomes of our functional analysis of PALB2 variants in mouse and human cells.”\nIf the number of MAVE-analyzed variants is sufficient for a robust analysis, it would be more informative to categorize the variants as functional, intermediate, and damaging. This categorization would allow you to compare whether the two assays consistently classify the variants. The authors could refer to Figure 1 in Brinch et al. (2022) https://doi.org/10.1186/s13073-022-01052-8 for an approach on how to define these thresholds.\nSECTION: “Variants in the CC domain disrupt PALB2’s interaction with BRCA1 and its recruitment to DNA damage sites”\n- “To further validate findings from the site-saturation functional screens, we selected variants that scored as damaging (p.L21S, p.A22P, p.Y28D, p.T31P, p.A33P, p.R34P, p.Q36P), intermediate (p.S10T, p.C11W, p.E13A, p.L17S, p.K25E, p.E27G, p.Y28S, p.S29G, p.K30E) or functional (p.E19V, p.R26K) (Supplementary Table 1).”\nWhat were the criteria for selecting the variants? Understanding the reasons for variant selection would help clarify why only two functional variants were included compared to the seven damaging and nine intermediate variants.\n-“We found that the intermediate variants p.S10T and p.E13A, similar to the functional variants p.E19V and p.R26K, did not impact the accumulation of PALB2 at sites of DNA damage, whereas p.L17S and p.E27G had a moderate impact when compared to that of wildtype PALB2 (Extended Data Fig. 4a, b). The damaging variants p.L21S and p.L35P had the strongest impact on the accumulation of PALB2 at sites of DNA damage.”\nThis statement is not entirely accurate. According to Fig. 4b in the extended data, only the p.L35P variant showed a statistically significant decrease in PALB2 accumulation at sites of DNA damage. It would be beneficial to normalize PALB2 accumulation relative to the levels of NBS1 (the DNA damage biomarker). This normalization would help more accurately assess the recruited levels of PALB2 by accounting for potential variations in DNA damage levels. Please review it.\n-“Taken together, these analyses identified p.L17S, p.L21S, p.A22P, p.K25E, p.E27G, p.Y28D, p.Y28S, p.S29G, p.K30E, p.T31P, p.A33P, p.R34P and p.Q36P as new variants that impair PALB2 function in HR by impacting the interaction with BRCA1, which in turn affects PALB2 recruitment to DNA damage sites.”\nThe statement that the impact on the interaction with BRCA1 caused by the listed PALB2 variants affects PALB2 recruitment to DNA damage sites is not entirely accurate, as it is based on weak evidence. Firstly, the results shown in Fig. 4b of the extended data indicate that only the p.L35P variant achieved statistical significance (as noted in the previous comment). Secondly, only three out of the 13 variants that affected the co-precipitation of BRCA1 were assessed for their impact on PALB2 recruitment to DNA damage sites.\nSECTION: “Variants in the PALB2 WD40, but not CC domain, affect protein stability”\n-“For all functional variants in the WD40 domain, PALB2 expression was comparable to that of wildtype. However, damaging variants generally showed very low levels of expression (e.g. p.W912S; Fig. 5b), whereas intermediate variants often partially reduced PALB2 expression (e.g. p.R976G; Fig. 5b). In contrast, variants in the CC domain generally showed near wildtype levels of PALB2 expression regardless the functional classification, except for p.K20I, which showed reduced expression levels (Extended Data Fig. 4c).”\nThe analysis of the expression of PALB2 in the western blots is based on visual inspection which is hardly interpretable.\nCould the authors assess western blots outcomes using some kind of numerical values to avoid a subjective interpretation?\nFor example, in Fig. 5b and considering tubulin expression, the functional variants S873R, L1150R, G937E seem to express less PALB2 than WT.\n-“In contrast, the WD40 variant p.E1083K, which was functional and expressed to WT levels (Fig. 5b and Supplementary Table 1), and several variants in the CC domain (p.S10T, p. E13A, p.L17S, p.Y28S, p.A22P, p.Y28D, and p.L35P), all of which showed near wildtype expression levels (Extended Data Fig. 4c), did not grossly mislocalize in the cytoplasm (Fig. 5c and Extended Data Fig. 4d).”\nThe p.L35P variant is not in the Extended Data Fig. 4c. Please review it.\nRegarding the data included in the Extended Data Fig. 4d, although the p.S10T, p. E13A, p.L17S, p.Y28S, p.A22P, p.Y28D, and p.L35P variants were not mislocalize in the cytoplasm, they exhibited a statistically significant low levels of cellular localization in the nucleus (and in both) compared to WT. Should it be related to a low PALB2 expression in the western blots of figure 4d (extended data)? However, the corresponding western blots do not show a low expression of PALB2.\nCould you include in the text a potential explanation for these apparent conflicting findings?\n-“ Taken together, our results suggests that missense variants in the WD40, but not the CC domain, can cause protein instability and subsequent loss of functionality, the latter of which can be predicted only moderately well by ΔΔG-based in silico prediction models.”\nAn additional analysis that could reinforce the finding that missense variants in the PALB2 WD40 domain, but not the CC domain, affect protein stability would be to compare protein stability predictions between damaging missense variants in the WD40 domain and those in the CC domain. This could be done using methods such as FoldX BuildModel and RaSP. Could this analysis be performed?\nSECTION: In silico models poorly predict the functional impact of variants in PALB2\n-“While we observed a reasonably good correlation between these outcomes for missense variants in the CC domain when using Helix (n=193, r=-0.5499, p<0.0001; Extended Data Fig. 6a) or BayesDel (n=193, r=-0.4660, p<0.0001; Extended Data Fig. 6d), the correlation was somewhat poor for these variants in the WD40 alone (Helix: n=1,881, r=-0.2391, p<0.0001; BayesDel: n=1,881, r=-0.2223, p<0.0001; Extended Data Fig. 6b, e), or combined with those in the CC domain (Helix: n=2,074, r=-0.2313, p<0.0001; BayesDel: n=2,074, r=-0.2544, p<0.0001; Extended Data Fig. 6c, f).”\nand\n- “However, similar to Helix, BayesDel and EVE, AlphaMissense correctly predicted a damaging effect for variants with low depletion scores, yet classified a large proportion of the variants with high depletion scores as damaging. Consequently, its predictions overall also poorly correlated with results from the PARPi sensitivity-based site-saturation screens (CC n=609, r=-0.3408, <0,0001; WD40 n=6,109, r=-0.2266, <0,0001; CC+WD40 n=6,718, r=-0.2527, <0,0001; Extended Data Fig. 7d-f).”\nWhy for Helix, BayesDel and EVE the number of total analysed missense variants is 2,074 while for AlphaMissense is 6,718? Please explain it.\nSECTION: “Site-saturation functional screens aid in the clinical reclassification of missense VUS in PALB2”\n-“As of May 2023, 2,444 missense variants in PALB2 were listed in ClinVar”\nAs of May 2024, there are 2,786 missense variants in PALB2 listed in ClinVar. It is possible that more VUS in the CC and WD40 domains are now included in ClinVar compared to the 2,367 mentioned in the current version of the manuscript.\nConsider updating the number of included VUS.\n-“Thus, our site-saturation functional screens can aid in the reclassification of 741 (31,3%) of the VUS in the CC and WD40 domains listed in ClinVar.”\nThis statement is not entirely accurate, as the application of an intermediate functional result (for 261 variants) for VUS reclassification remains uncertain. How should this intermediate functional finding be applied as the PS3 criterion using the ACMG/AMP sequence variant interpretation framework?\n-“We previously showed that the region between the CC and WD40 domain (encoding p.I44 to p.L855) is dispensable for PALB2 function in HR, suggesting it is unlikely that damaging missense variants will be identified therein (Fig. 1b, c).”\nThe mention of the Fig 1 should be 1b, c and d.\n-“To provide additional support that these variants may be functional rather than damaging, we randomly chose 26 variants, along with 5 synthetic missense variants in this region (Fig. 6b), and expressed them in the Palb2KO mES cells to determine the impact on HR (Extended Data Fig. 8).”\nIn the extended figure 8 there are only 16 variants depicted. Please review it.\n-“These findings further indicate that the 1,596 missense VUS located in region p.I44 to p.L855 are likely functional. In conclusion, our functional data will be of significance in the reclassification of 99% of the missense VUS reported in ClinVar.”\nThis statement is based on two pieces of evidence provided by the authors’ work:\ni) The region between the CC and WD40 domains (encoding p.I44 to p.L855) is suggested to be dispensable for PALB2 function in HR, as indicated by testing the functional effect of constructs containing different deletions of the entire region (ΔChAM, ΔMRG15, ΔExon4, ΔExon5, and ΔExon6).\nii) 31 missense variants out of 1,596 (1.6%) located in the region p.I44 to p.L855 and reported in ClinVar rescued homologous recombination efficiency and rendered Palb2KO cells resistant to PARPi treatment.\nWhile these findings are suggestive, they are not sufficient to claim that the entire ClinVar cohort of 1,596 VUS located between the CC and WD40 domains would not affect PALB2 function. The authors have not assayed the vast majority of the ClinVar VUS located between CC and WD40 domains, providing data on only 31 variants. Therefore, it is not accurate to claim that the authors’ functional data will be of significance in the reclassification of 99% of the missense VUS reported in ClinVar.\nPlease review it.\nSECTION: “Damaging missense variants in PALB2 are associated with increased breast cancer risk”\n-“These findings were further confirmed for damaging (OR = 6.45; 95%CI: 2.58 – 16.19; p = 6.04 x 10-9), intermediate (OR = 2.39; 95%CI: 1.94 – 2.94; p = 3.82 x 10-17), and functional variants (OR = 1.49; 95%CI: 1.31 – 1.70; p = 1.58 x 10-9) using UK Biobank controls along with clinical testing cases (Table 1) .”\nThe p-values for functional (p = 1.58 x 10-9) and intermediated variants (p = 3.82 x 10-17) are highly significant. Are these p-values, correct? Please review these data and if they are correct explain why functional and intermediate variants would be associated with increased risk.\n-“Importantly all associations for damaging variants were significantly different from functional and intermediate variants, indicating that the damaging missense variants identified in the site saturation functional screens represent a group of variants that are likely associated with a high risk of breast cancer.”\nPlease review this statement if needed after addressing the previous comment.\nDISCUSSION\n-The authors provide convincing evidence about the existence of a set of missense variants with an intermediate functional effect. The authors could discuss if these intermediate missense variants would confer an intermediate cancer risk.\n-“Importantly, the screen data are expected to play an important role in the reclassification of 99% of the missense VUS reported in ClinVar, by showing that 1,594 (67.3%) VUS located in region p.I44 to p.L855 are likely functional,… ”\nThe authors have not assayed the vast majority of the ClinVar VUS located between CC and WD40 domains, providing data on only 31 variants out of 1,594 VUS located in region p.I44 to p.L855. Therefore, it is not accurate to claim that the authors’ functional data will be of significance in the reclassification of 99% of the missense VUS reported in ClinVar.\n-“However, in silico analysis using SpliceAI suggests that only 11 of the 6,718 missense variants (0.16%) tested in our site-saturation functional screens are encoded by nucleotide variants predicted to have an impact on PALB2 mRNA splicing (SpliceAI threshold >0.8 for predicted donor loss (DL), acceptor loss (AL), donor gain (DG) and acceptor gain (AG) at canonical splice sites; Supplementary Table 4), indicating that they mostly impair PALB2 functionality at the protein level 36. Of note, only 12 of the 4,758 nucleotide variants (0.25%), encoding missense variants in the region between the CC and WD40 encoding regions, are predicted to impact mRNA splicing (SpliceAI threshold >0.8; Supplementary Table 4), which is in line with our conclusion that few damaging variants will be identified in this region of PALB2.”\n-Why was a 0.8 threshold used? After calibration analysis, a threshold of ≥0.2 has been recommended for applying SpliceAI as pathogenic supporting evidence (PP3) and ≤0.1 for benignity evidence (BP4) according to the ACMG/AMP guidelines for classifying variants (PMID: 37352859).\n-“The findings presented in this study demonstrate the fulfilment of these prerequisites for our assay, allowing the incorporation of the functional data using ACMG/AMP-based variant classification guidelines.”\nThe work provides both robust functional data and strong evidence of that damaging missense variants are associated with a clinically relevant increased risk of breast cancer. Therefore, the authors could propose how the functional evidence provided in their work can be used for variant classification applying PS3/BS3 evidence?\nMATERIALS AND METHODS\nSECTION: “Cell culture and generation of Trp53KO/Palb2KO mES cells with DR-GFP and RMCE”\n-UOS2 cell culture conditions are missing.\nSECTION: “Site-directed mutagenesis to introduce variants into human PALB2 cDNA”\n-it would be useful to have cDNA coordinates for cDNA deletion constructs lacking either the ChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6) (results shown in Fig. 1b).\nSECTION: “Integration of site-saturation variant libraries for CC and WD40 domains”\n-Two plates were trypsinized and stored at -80°C as backup and one plate was used for three replicate PARPi sensitivity assays.\nIt is stated in the results of the manuscript and in the “Variant scoring and analysis” section of Materials and Methods that six independent CC-library and three independent WD40-library integration experiments were performed. Here it is only mentioned three replicate PARPi sensitivity assays. Please clarify it.\nSECTION: “Variant scoring and analysis”\nThe entire section and how the final depletion score for each variant is calculated is hard to understand. Please consider rewording for better clarity and readability.\nSECTION: “Mixture modelling”\n-“Two thresholds were determined from the model: a upper threshold such that exactly 98% of the synonymous variants were above the threshold, and an lower threshold such that 98% of the nonsense variants were below that threshold.”\nWhat are these thresholds? If the thresholds referred to are those defined in the Results section (functional variants with depletion scores > -0.0759 and damaging variants with depletion scores < -0.7037), based on Supplementary Table 1 and the Source data file, it is noted that 41% of synonymous variants are above the threshold of > -0.0759, while 80% of nonsense variants are below the threshold of < -0.7037. Please clarify it.\nSECTION: “Generation of site-saturation mutagenesis libraries and Cas9/sgRNA plasmids for saturation genome editing (SGE)”\n-“A Protospacer Protection Edit (PPE) encoding a synonymous mutation was introduced by site-directed mutagenesis into the protospacer adjacent motif (PAM) site of the target region to prevent recutting by the Cas9/sgRNA after successful editing.”\nPlease add which exact synonymous mutation was introduced (cDNA nomenclature).\nDid you rule out if this synonymous mutation induce a potential splicing alteration?\nSECTION “Cellular localization assay”\n- “Quantification of EGFP-PALB2 subcellular localization was based on transient expression in HeLa cells that were fixed using 4% formaldehyde and permeabilized using Triton X-100.”\nIn Results and figures is stated that the used cells for these experiments were U2OS cells. Please clarify it.\nPlease specify the type of microscope used and provide details about its visualization characteristics for evaluating the images.\nSECTION “Cancer risk analysis”\n-“Breast cancer cases were females with breast cancer receiving cancer genetic testing by Ambry Genetics, whereas control populations were noncancer females from gnomAD Genome (v3.1.2) and gnomAD Exome (v2.1.1) or from noncancer European ancestry females from UK Biobank.”\nRegarding gnomAD data, what populations where included?\nFinish, Amish, Ashkenazi jewish shouldn't be considered because allele frequency of some variants could be higher in these populations.\nSECTION “Data analysis”\n-“As data analysis and correlation with functional data were performed at the amino acid level, we merged the available information for distinct nucleotide variants, yielding a unified entry for a specific protein variant.”\nIt is unclear if merging available information for different nucleotide variants encoding the same amino acid is the best approach. One nucleotide variant might be pathogenic due to splicing alteration, while another variant might not alter splicing but result in the same amino acid change, which could be benign because the amino acid change is not damaging.\nA better alternative might be to assess the nucleotide variants leading to the same missense change with a SpliceAI predictor and exclude those with a positive splicing-altering prediction.\n-“ SpliceAI cutpoints to predict spliceogenicity were based on the maximum delta score of donor/acceptor loss and donor/acceptor gain >0.8 for predicted splicing impact (Supplementary Table 4).”\nDetails about how SpliceAI was used such as version, window, masked/raw are missing. Did the authors use SpliceAI via https://github.com/ Illumina/SpliceAI—version 1.3.1?.\nSECTION “Data availability”\n“In silico prediction scores for PALB2 missense variants were obtained for CADD (through dbNSFP at https://sites.google.com/site/jpopgen/dbNSFP; version 1.6), Helix 28, BayesDel 29, EVE 26,27 and AlphaMissense 30.”\nWas the analysis with BayesDel done including maximum and minor allele frequencies across populations or without? This predictor provides two outcomes: those computed integrating allele frequencies or those without allele frequencies.\nFIGURES\n“Fig. 2 Site-saturation functional screens of PALB2 variants in the CC domain. a Schematic flow of the site-saturation functional screens performed in this study. NGS = Next-Generation Sequencing. AA = amino acid. b Amino acid function map of the CC domain of PALB2 spanning 35 amino acid residues from p.L9 to p.K43) (top). The map shows depletion scores for 667 PALB2 missense and nonsense variants as generated by Enrich2, n = 6 biological replicates. Amino acid characteristics for all variants are indicated (left). Dark red squares represent variants that were depleted in PARPi-treated conditions versus untreated conditions. Blue squares represent variants that were enriched. Grey squares represent variants that were either missing in the library or filtered out during the analysis. Orange dots represent the original (wild type) amino acids. Data for all variants were normalized to WT PALB2, which was set to ‘0, and to the average of the nonsense variants, which was set to ‘-1’.”\n-The mentioned orange dots are not orange, they are grey. This should be corrected in the all other related figures.\n-According to Supplementary Table 1 there are 638 missense and nonsense variants.\n“Fig. 3 Site-saturation functional screens of PALB2 variants in the WD40 domain”\n-According to Supplementary Table 1 there are 6419 missense and nonsense variants.\n“Fig. 5 Molecular validation of the impact of PALB2 missense variants in site-saturation functional screens. a YPF/GFP pulldowns of the indicated PALB2 CC variant proteins following transient expression in U2OS cells. PALB2 CC variants are indicated in three colours reflecting their functional outcome in the site-saturation screens in ‘Fig. 2b’; green is functional, orange is intermediate, red is damaging. GFP-NLS and YFP-PALB2-L35P served as negative controls. Western blot analysis was performed using antibodies against GFP and BRCA1. b Western blot analysis of the expression of human PALB2 WD40 variants in Trp53KO/Palb2KO mES cells using an antibody directed against PALB2. The empty vector (Ev) served as a negative control on each blot. Tubulin was used as a loading control. * indicates a non-specific band. PALB2 variants are indicated in colour as in ‘a’. c Fluorescence microscopy analysis (left) and quantification of the cellular distribution (right) of human YFP-PALB2 WD40 variants following transient expression in U2OS cells. Coloring of variants is as indicated in ‘a’. Data represent the mean ±s.e.m. n = 3 biological replicates, dots represent individual data points, two-way ANOVA with Dunnet’s multiple comparison test. *P<0.05, **P < 0.01, ***P < 0.001,NS, not significant.”\n-The left-hand block of images showed in figure 5a of the Source Data file do not correspond to the image included in the manuscript. The images in the Source Data File are inverted comparing to the figure included in the manuscript. Please review it.\n-GFP-NLS is not referenced in Material and Methods\nSUPPLEMENTARY TABLE 1\n-Column A, labelled \"Amino acid nr,\" incorrectly annotates the number of amino acids for all synonym changes. The number depicted in the column is the cDNA position and not the correct amino acid number. Please correct it.\n-The predictions for BayesDel are missing. Please add them.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "Thank you for asking me to review this manuscript which I have studied in detail. I should point out that I am not a cell biologist; therefore I would recommend that the technical aspects are reviewed in detail my co-reviewers who may be more experienced in these aspects than me. I have, however, studied all the experimental findings and, to my mind, the blots/bar charts / fluorescence microscopy results are all appropriately displayed with the appropriate controls included, and appropriate conclusions drawn from the findings presented. The supplementary excel spreadsheets with the raw data given for each possible variant are appropriate, and the results are easily accessible to the reader. The rationale for this work, the results and their interpretation are clearly presented.\nThe work draws and builds upon the group’s previously published functional assays with the addition of bringing in the comparisons with large clinical/population datasets. Comparing the ‘wet lab’ functional analyses with the ‘dry’ case control data in the same study is of particular value.\nThe classification of PALB2 VUS is a real challenge in clinical cancer genetics and having this robust high throughput functional analyses of the missense variants is extremely helpful and will certainly aid accurate variant interpretation which will then be of immense benefit to healthcare professionals and patients and families.\nI have some minor comments, in no particular order:\n-Extended data figure 4, there is a typo in the legend YPF instead of YFP\n- ‘These exons encode the Coiled Coil and WD40 domains, which we found to be minimally required for HR’.\nThe phrase ‘minimally required for HR’ referring to the CC and WD40 domains, is in the abstract and discussion. This is a grammatical point and slightly pedantic but, to my mind ‘minimally required’ means indispensable/can do without. Whereas the authors really mean are required as a minimum/crucial for HR function.\n-In the discussion ‘Moreover, PALB2 VUS are very rare in carriers, complicating cancer risk analysis’. I know what the authors are driving at, as in PALB2 missense VUS are not detected that often (unlike BRCA1/2) which adds to the challenge of variant interpretation as often there is a lack of clinical/functional data to draw upon; however, I would re-word to make clearer as the current wording sounds a little as though (i) such a rare event, that it isn’t really an issue (and we know it is a challenge) and (ii) PALB2 carriers are common, just the VUS that are rare. Thank you.\n-understandably the discussion and scene setting in the introduction is very much focussed toward breast cancer given breast cancer accounts for the majority of PALB2 diagnostic testing. However, there ought be some comment made re the role of PALB2 PVs in pancreatic/ovarian cancer and, although detected much less frequently in these scenarios, the clinical challenges are the same and PALB2 is on these NHS England panels.\n- ‘The findings presented in this study demonstrate the fulfilment of these prerequisites for our assay, allowing the incorporation of the functional data using ACMG/AMP-based variant classification guidelines.’\nI would tone this down a little, to eg facilitate incorporation. The results are extremely helpful but is for ACMG/AMP and national groups to decide what data / assays can be used in variant interpretation.", "reviewer_4": "Summary:\nBoonen et al. first identify functional domains in PALB2 needed for HR and PARPi sensitivity, using mouse ES PALB2-KO cells and a recombinase system to insert deletion alleles. They proceed by performing a MAVE to assess ~7,000 PALB2 variants for effects on PARPi sensitivity across the coiled-coil and WD40 domains. The assay identifies a small but meaningful fraction of missense variants to be damaging, and is generally well validated through a wealth of additional functional data, including individual PARPi sensitivity and homologous recombination assays, a saturation genome editing assay, and others. The authors next show that damaging missense variants in PALB2 confer higher cancer risk as a group, validating the clinical relevance of the data set.\nImpressions:\nOverall, the work represents a big step forward for the study of variants in PALB2 in that many variants are functionally annotated for the first time. The newly identified loss-of-function variants, though not particularly numerous, may tangibly improve patient care. However, the assay also has some technical limitations regarding score precision. Therefore, while this is a large amount of functional evidence that should be made publicly available relatively soon, my general enthusiasm is somewhat weakened by the overall quality of the main data set. My main concerns mostly involve transparency regarding the technical quality of the data and limitations inherent to scoring a large number of variants as intermediate. Several points need to be addressed.\nMajor Comments:\n1. Overall, more needs to be done to show how reproducible and robust individual variant scores are.\n- The number of technical and biological replicates for the large data set is not completely clear between the figure legends (n = 3/6) and text (“triplicate”). Please define both types of replicates clearly (e.g. separate transfections into cells, separate populations for sequencing, etc.) and state how many times each replicate was performed, and how scores were averaged.\n- Please include supplemental plots showing the correlation of scores across independent biological replicates (prior to removing variants with high variance) and include scores from all individual replicates in supplementary table 1.\n- The plot of score distributions for nonsense, synonymous, and missense needs to be more clear, as too many synonymous variants are obscured (Fig. 4a). Please plot these separately, or stack instead of overlaying.\n- Upon checking the supplementary table, it seems more synonymous variants were deemed “intermediate” than “functional”. Please report this in text.\n- Some variants seem to have large scores in the positive direction (dark blue on heat map). Are these plausibly biologically meaningful, or are these more likely noisy scores?\n- The description of the mixture modeling / determination of thresholds for functional categories is unclear and needs to make more intuitive sense in both methods and results. What were the 7 parameters included in the model that are referenced in methods? What data is the basis of the ROC curve shown – is it nonsense vs. synonymous classification? It seems many more than 2% of the synonymous variants score below the threshold for neutral, despite the fact that the threshold is reported as the line above which 98% of synonymous variants score – what explains this discrepancy?\n2. Related to the point above, the intermediate risk category includes many variants and it is unclear whether such scores reflect technical noise or truly hypomorphic variants possibly of intermediate cancer risk. While the authors do cite this as a limitation, more could be done to quantitatively explore this category.\n- Please clarify the sensitivity and specificity of the main assay for detecting nonsense variants as damaging and synonymous variants as neutral in the main text.\n- 36% of variants are classed intermediate, far more than are called damaging. Provide some estimate of how many damaging variants / cancer-risk variants may be missed in this category.\n- What do the orthologous validation studies and computational predictors tell us about intermediate risk variants overall? Sometimes the results of the intermediate risk variants look bimodal (the individual PARP inhibitor assay, the HR assay, possibly the SGE data). When evidence is contradictory across assays, which assay should be trusted? Do the computational predictors suggest the intermediate risk variants are more deleterious than functional variants?\nMinor Comments:\n1. The authors mention the assay’s inability to detect splicing variants as a potential caveat. A few comments on this:\n- 0.8 is a very high spliceAI threshold to use. It is important to know how many variants may be misclassified using lower thresholds, for instance, 0.20 and 0.50. Please clarify.\n- Are there any variants that score lowly when edited into the genome via CRISPR but not when introduced via recombination? If so, are these splice variants?\n- Include depletion scores for variants with high spliceAI scores in the second tab of Supp. Table 4 to make it clear how these variants scored in the assay.\n2. Are there any significant domain-specific differences in OR for damaging missense variants? It is okay if there is no power to see this, but worth looking at and commenting on if so, especially considering the observed differences in stability effects across domains.\n3. This is one of the few cases where a deep mutational scan and saturation genome editing have been directly compared for the same gene, which is of interest. The assays measure different phenotypes – PARPi resistance in mESCs and cell growth in HAP1. Does the relationship between scores tell us anything about the assays, i.e. is one assay more or less sensitive than the other? Is it clear whether the cells are dying in the SGE assay due to an HR defect?\n4. The data presented in EDF4a/b does not lead to new statistically significant results. The paper doesn’t hinge on these data points, and the general trends are as expected for validation, but the text in the results must make it more clear that the effects being described were not statistically significant, or perhaps the data can be plotted to show a significant correlation across the variants assayed instead of pairwise comparisons?\n5. Regarding the analysis of EVE and AlphaMissense data, considering these are genome-wide models it is worth exploring precision-recall or AUROC analyzes to ask what sensitivity and specificity can be achieved for identifying deleterious PALB2 variants scoring as hits in the assay. Given there are many variants that remain in the intermediate category, it would make the paper stronger to also explore to what extent a combination of computational predictor score and functional data can help delineate which intermediate variants might increase cancer risk.\n6. The MAVE terminology is used in a way that leads to confusion and should be fixed. MAVEs are a broad class of high-throughput assays, including both deep mutational scans and saturation genome editing. The authors have performed two types of MAVEs. The first is referred to as “site-saturation functional screens”. This seems to refer to a “deep mutational scan”. The other MAVE involving CRISPR-introduction of SNVs endogenously is commonly called “saturation genome editing”. For clarity:\n- Would recommend calling the “site-saturation” experiments “deep mutational scans”, as many sites in the protein are all being saturated, not a single residue.\n- Refer to the exon 10 endogenous editing experiment as a “Saturation Genome Editing” experiment and not a “Deep Mutational Scan”\n- Refrain from using “MAVE score”, but instead “SGE score”, as “MAVE score” will be ambiguous to readers.\n7. It was not immediately clear whether the 140 missense variants measured independently have already been functionally characterized (references 7 and 15) or whether these were newly tested in this study for validation? Generally, these experiments could be introduced in a way that is more clear with regards to how it differs from the high-throughput experiments that are described previously.\n8. Regarding the 741 VUS / 31.3% – please clarify whether 31.3% is of VUS in the CC or WD40 domains, or whether it is of all VUS in the gene – from the chart it looks the latter, but the sentences in text are confusing. More importantly, it is important to state what fraction of the VUS were deemed either “functional” or “damaging” and what fraction were deemed “intermediate”.\n9. “In conclusion, our functional data will be of significance in the reclassification of 99% of the missense VUS reported in ClinVar.” This sentence goes too far for what has been shown. Is there a clinical classification framework that explicitly permits domain-level evidence for some variants as functional data for other variants? If so, please cite this. Regardless, this seems more appropriate as a discussion point rather than a main results claim.\n10. Revise this sentence to be more clear:\n“All nonsense variants scored below 0.532 (n = 339, median = -1,011), whereas synonymous variants scored above -0.613 (n = 49, median = - 0.096).” The max and min numbers are confusing. For instance, 0.532 doesn’t make sense on a scale of -1 to 0. Would instead highlight the standard deviations of nonsense and synonymous variant measurements to go with the medians, and (as said above), it is important to say how many of each were classified as expected using the cut-offs applied to the whole data set.\n11. Fix the Table 1 typo:\n“Burden-type analysis of odds ratios for functional, intermediate and functional missense variants”\n12. In methods – ‘mg’ is used in some instances where the correct unit is likely micrograms (ug), for instance, DNA transfection quantities."}, "author_response": "We thank the reviewers for their constructive comments and useful suggestions. Based on these we have adapted the manuscript textually, performed several additional analyses, and included multiple new figure panels. These changes have solidified the main conclusions. We have addressed all comments point-by-point below.\nReviewer #1, expertise in PALB2/PARPi sensitivity assays and breast cancer therapy\n(Remarks to the Author):\nThe data and work presented in the manuscript are highly relevant and deserve to be published, as they will have a significant downstream impact on the classification of missense variants in PALB2 with unknown clinical significance. This will greatly benefit cancer patients carrying these variants. The study makes meaningful contributions to the field and is the first to provide functional evidence for more than 6,000 variants. Additionally, the work presents evidence that damaging missense variants are associated with a clinically relevant increased risk of breast cancer. However, the authors need to address a few flaws in data interpretation, correct minor mistakes and provide more detail in the methods.\nGENERAL REMARKS\n-Throughout the text the authors alternatively use wildtype or wild type. Only one form should be used. Please review.\n‘Wild type' is a noun and 'wild-type' is an adjective. We have changed the text accordingly.\n-Please check that PALB2 be stated in italics when it refers to the gene name. For instance:\ni) in the Introduction, in the sentence “Importantly, several damaging missense variants in PALB2, PALB2 should be in italics.\nWe have changed the text as requested.\nii) in the section of Data analysis of Materials and Methods, in the sentence “Frequency data derived from gnomAD 18, BRAVO, FLOSSIES and classification data from the ClinVar database were harmonized with the 7,106 PALB2 variants for which depletion scores were available (Supplementary Tables 2 and 3), PALB2 should be in italics.\nWe have changed this accordingly.\nSPECIFIC REMARKS\nABSTRACT\n“These exons encode the Coiled Coil and WD40 domains, which we found to be minimally required for HR.” This statement may be misleading by interpreting it that the two domains are modestly required for HR. A slightly reworded sentence may be more much less ambiguous:\nTo avoid any confusion, we have changed this sentence to “These exons encode the Coiled Coil and WD40 domains, which we identified as the minimal regions required for HR” in both the Abstract and Discussion (first paragraph).\nRESULTS SECTION: “The region between the coiled-coil (CC) and WD40 domains of PALB2 is dispensable for HR\n-“We generated five PALB2 cDNA deletion constructs lacking either the ChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6) (Fig. 1a)”. The mention of the Figure 1 here should state both 1a and 1b. Please review.\nThe citation of Figure 1a is correct. Figure 1b shows the expression of the deletion constructs.\nWe have therefore cited this figure following the sentence in which we refer to their expression in cells: “The stable expression of these PALB2 deletion constructs rescued the HR defect of Palb2 KO cells to levels observed in cells expressing wild-type PALB2 (Fig. 1b, c).” See first paragraph of the Results section.\n-“We generated five PALB2 cDNA deletion constructs lacking either the ChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6) (Fig. 1a). All exon deletions were generated in-frame.”\nGiven that exon 5 is out of frame and exon 6 encodes amino acids 856 to 862 of the WD40 domain, it would be useful to provide the specific cDNA coordinates of the deletions to understand the sequence of the resulting protein. In fact, it would be useful to include in Materials and Methods section the cDNA coordinates for all analysed cDNA deletion constructs: ChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6).\nWe have now provided a full description of how the ChAM, MRG15, exon 4, exon 5 and exon 6 deletions were generated, and included the cDNA coordinates of the deletions. We also mention that the deletion of exon 5 was made in-frame by deleting the last nucleotide of exon 4 as well. Please see subsection “Introducing variants and deletions into human PALB2 cDNA” of the Materials and Methods.\n- “The constructs were introduced site-specifically by recombination-mediated cassette exchange (RMCE) at the Rosa26 locus in Palb2KO mES cells carrying the DR-GFP reporter, which allows flow cytometry-based detection of HR after I-SceI endonuclease-induced DNA double-strand break formation (Fig. 1b).”It is Figure 1c. Please review.\nFigure 1b shows the expression of the deletion constructs in cells, while Figure 1c shows the results of the cytometry-based detection of HR. We have therefore cited these figure panels following the sentence in which we refer to their expression and impact on HR in cells: “The stable expression of these PALB2 deletion constructs rescued the HR defect of Palb2KO cells to levels observed in cells expressing wild-type PALB2 (Fig. 1b, c).” See first paragraph of the Results section.\n-“PARPi sensitivity assays were performed in triplicate on these cells using non-treated cells as control conditions”. According to Materials and Methods as well as to Source data file, PARP sensitivity was done with six independent CC-library and three independent WD40-library integration experiments. Please clarify it.\nWhile variants in the WD40 library showed relatively equal abundance, those in the CC library were more unevenly distributed (Extended Data Fig. 1b). This unevenness reduced the read numbers after NGS, negatively impacting coverage for certain CC library variants.\nConsequently, greater variability in PARPi sensitivity was observed for these variants (Supplementary Table 1 and Fig. 2b in Source Data). These issues could be mitigated by including additional replicates for the CC library.\n-“Using Receiver Operating Curve (ROC) analysis (Fig. 4b), we categorized 392 (6%) of the missense variants as damaging, 2,422 (36%) as intermediate and 3,904 (58%) as functional with 98% sensitivity and specificity (Fig. 4c and Supplementary Table 1).”\nAccording to Supplementary Table 1, 62 out of 339 nonsense variants were categorised as intermediate and seven nonsense variants as functional. Based on this data, is it possible to hypothesise that these nonsense variants are associated with a lower cancer risk? Please consider adding these findings and discuss their potential implications in conferring a different cancer risk.\nIndividual association of the 62 intermediate and 7 functional nonsense variants and breast cancer risk could not be conducted due to the limited numbers within the cohorts used for cancer risk analysis. Associations based on fewer than 5 cases or 5 controls are inherently unstable. Therefore, we choose not to present such data or to speculate on whether these variants might confer a reduced cancer risk.\nSECTION: Validation of site-saturation functional screens of missense variants in the CC and WD40 domains of PALB2\n- “To validate our site-saturation functional screens, we measured the relative PARPi resistance levels for a panel of variants (n=140), and correlated these levels to the depletion scores from the screens (Supplementary Table 1) 7,15.”\nIt is not clear if the authors generated this data in the context of the present work or if there were taken from references 7 and 15. Please clarify it in Results and Materials and Methods sections.\nWe have added an additional column to the tabs in the Source Data file containing validation data for the 140 missense variants assessed in the DR-GFP and PARPi sensitivity assays.\nThis new column references the two publications in which some of these variants were previously analyzed. Of the 140 variants 44 variants had been previously tested in these publications, while 96 were newly tested. This is now also mentioned in the Results.\n3\n- “To validate our site-saturation functional screens, we measured the relative PARPi resistance levels for a panel of variants (n=140), and correlated these levels to the depletion scores from the screens (Supplementary Table 1) 7,15.”\nAccording to Supplementary Table 1, there were 140 missense variants and 7 nonsense variants assayed. For the sake of clarity, please specify that the panel consists of 140 missense variants.\nWe now mention in the text that a panel of 140 missense variants was assayed. See first sentence of the subsection “Validation of site-saturation functional screens of missense variants in the CC and WD40 domains of PALB2” of the Results.\n-“This showed a good and significant correlation for variants in the CC (n=43, r=0.8055, p<0.0001), WD40 (n=97, r=0.6500, p<0.0001) and CC + WD40 domains (n=140, r=0.6494, p<0.0001) (Fig. 4d and Extended Data Fig. 2a, b). Consistently, we observed a similarly good correlation between the outcome of the site saturation screens and those from DR-GFP reporter-based assays for the same variants in the CC (n=43, r=0.8715, p<0.0001), WD40 (n=97, r=0.7131, p<0.0001) and CC + WD40 domains (n=140, r=0.7086, p<0.0001) (Fig. 4e and Extended Data Fig. 2c, d).”\nWhat type of correlation method was used? i.e., Spearman, Pearson, etc? In fact, this information is missing for all correlations performed throughout the entire manuscript.\nWe now mention in the figure legends “r = Pearson correlation coefficient” to indicate that Pearson correlation analyses were performed.\n- “Moreover, our site-saturation screens correctly categorized previously reported missense variants with no functional impact (e.g., p.K18R, p.E42K, p.G998E)”According to Supplementary Table 1, the p.K18R is intermediate. Please correct it.\nWe have corrected this mistake by replacing p.K18R with p.E19V, which is a functional missense variant.\n- “We obtained MAVE scores for 276 single nucleotide variants, leading to 9 nonsense and 68 synonymous changes, as well as 199 amino acid substitutions. To help readers find these data, could you please specify that the respective data are available in the Source Data file?\nAll data presented in the manuscript are available in the Source Data file. To better indicate, this we have now added the sentence “Source data are provided as a Source Data file” to each figure legend (as per the journal’s style).\n-“The MAVE data furthermore included scores for 195 unique changes consisting of 9 nonsense variants and 186 missense variants (Fig. 4g).”\nAccording to Source Data file for Fig. 4g, these numbers are not correct. In the column T to W which collect MAVE data for unique amino acid changes, all synonym variants are incorrectly stated as missense. Therefore, the correct numbers of unique variants are: 177 missense, 32 synonym and nine nonsense variants. Please review and correct it.\nWe have checked and corrected these numbers in column T to W in the Source Data file, which should be: 31 synonymous, 9 nonsense and 178 missense variants (total = 218 variants. Finally, we have changed the title of these columns to “SGE data for unique amino acid positions (n=218)”.\nAdditionally, in the Source Data file for Fig. 4g, the columns Y to AB show MAVE data for unique amino acid changes with depletion scores. The total number in the column names is incorrectly listed as 218. It should be stated as 179 variants. Please review it.\nIn column Y – AB the total number of variants has been corrected to 179 instead of 218. We have also changed the title of these columns to “SGE data for unique amino acid positions (n=179)”.\n-“Importantly, we obtained depletion scores for 179 of these variants in the PARPi sensitivity-based site saturation screens (Fig. 2, Fig. 3 and Supplementary Table 1), allowing comparison of depletion scores and MAVE scores.”\nTo ensure clear understanding of the downstream analysis, it would be helpful to specify that the 179 variants consist of 170 missense variants and 9 nonsense variants.\nWe have now clarified in the main text that the set of 179 variants for which we obtained MAVE scores consists of 170 missense variants and 9 nonsense variants.\n-“Importantly, we obtained depletion scores for 179 of these variants in the PARPi sensitivity-based site saturation screens (Fig. 2, Fig. 3 and Supplementary Table 1), allowing comparison of depletion scores and MAVE scores. We observed a good and significant correlation between these scores (n=179, r=0.6439, p<0.0001; Fig. 4g), indicating concordance between the outcomes of our functional analysis of PALB2 variants in mouse and human cells.”\nIf the number of MAVE-analyzed variants is sufficient for a robust analysis, it would be more informative to categorize the variants as functional, intermediate, and damaging. This categorization would allow you to compare whether the two assays consistently classify the variants. The authors could refer to Figure 1 in Brinch et al. (2022) for an approach on how to define these thresholds.\nWe agree that comparing the categorization of variants between the two assays would be interesting. However, the relatively modest number of MAVE-analyzed synonymous and nonsense variants makes it challenging to reliably establish precise thresholds for distinguishing damaging and functional variants. Therefore, we decided to present a correlation plot to illustrate the concordance between the functional analysis outcomes of variants in mouse and human cells.\nSECTION: “Variants in the CC domain disrupt PALB2’s interaction with BRCA1 and its recruitment to DNA damage sites”\n5\n- “To further validate findings from the site-saturation functional screens, we selected variants that scored as damaging (p.L21S, p.A22P, p.Y28D, p.T31P, p.A33P, p.R34P, p.Q36P), intermediate (p.S10T, p.C11W, p.E13A, p.L17S, p.K25E, p.E27G, p.Y28S, p.S29G, p.K30E) or functional (p.E19V, p.R26K) (Supplementary Table 1).”\nWhat were the criteria for selecting the variants? Understanding the reasons for variant selection would help clarify why only two functional variants were included compared to the seven damaging and nine intermediate variants.\nMostly damaging and intermediate variants were randomly selected, as these may confer cancer risk and are therefore most relevant. Two randomly chosen functional variants served as negative controls.\n-“We found that the intermediate variants p.S10T and p.E13A, similar to the functional variants p.E19V and p.R26K, did not impact the accumulation of PALB2 at sites of DNA damage, whereas p.L17S and p.E27G had a moderate impact when compared to that of wildtype PALB2 (Extended Data Fig. 4a, b). The damaging variants p.L21S and p.L35P had the strongest impact on the accumulation of PALB2 at sites of DNA damage.”\nThis statement is not entirely accurate. According to Fig. 4b in the extended data, only the p.L35P variant showed a statistically significant decrease in PALB2 accumulation at sites of DNA damage. It would be beneficial to normalize PALB2 accumulation relative to the levels of NBS1 (the DNA damage biomarker). This normalization would help more accurately assess the recruited levels of PALB2 by accounting for potential variations in DNA damage levels. Please review it.\nWe have tried to be careful with the wording when describing the accumulation of PALB2 variants. Given the rather low levels of PALP2 accumulation and the variability in accumulation levels of both PALB2 and NBS1, we believe that normalization the data would lead to artefacts. Hence, we believe the assay provides qualitative rather than quantitative data for PALB2 recruitment. Our statement in the text complies with this and therefore we want to keep the text as is.\n-“Taken together, these analyses identified p.L17S, p.L21S, p.A22P, p.K25E, p.E27G, p.Y28D, p.Y28S, p.S29G, p.K30E, p.T31P, p.A33P, p.R34P and p.Q36P as new variants that impair PALB2 function in HR by impacting the interaction with BRCA1, which in turn affects PALB2 recruitment to DNA damage sites.”\nThe statement that the impact on the interaction with BRCA1 caused by the listed PALB2 variants affects PALB2 recruitment to DNA damage sites is not entirely accurate, as it is based on weak evidence. Firstly, the results shown in Fig. 4b of the extended data indicate that only the p.L35P variant achieved statistical significance (as noted in the previous comment). Secondly, only three out of the 13 variants that affected the co-precipitation of BRCA1 were assessed for their impact on PALB2 recruitment to DNA damage sites.\nThe laser micro-irradiation experiments assessing the impact of missense variants on PALB2 recruitment to DNA damage sites showed considerable variability, largely due to the low 6\naccumulation of PALB2 in damage tracks relative to the background nuclear signal. Among the tested variants, only the positive control, p.L35P, demonstrated a statistically significant difference compared to wild-type PALB2. Although variants with reduced PALB2 recruitment generally showed impaired BRCA1 co-immunoprecipitation, we have toned down our conclusions regarding the impact of these variants on PALB2 recruitment in the Results.\nSECTION: “Variants in the PALB2 WD40, but not CC domain, affect protein stability”\n-“For all functional variants in the WD40 domain, PALB2 expression was comparable to that of wildtype. However, damaging variants generally showed very low levels of expression (e.g. p.W912S; Fig. 5b), whereas intermediate variants often partially reduced PALB2 expression (e.g. p.R976G; Fig. 5b). In contrast, variants in the CC domain generally showed near wildtype levels of PALB2 expression regardless the functional classification, except for p.K20I, which showed reduced expression levels (Extended Data Fig. 4c).”\nThe analysis of the expression of PALB2 in the western blots is based on visual inspection which is hardly interpretable. Could the authors assess western blots outcomes using some kind of numerical values to avoid a subjective interpretation? For example, in Fig. 5b and considering tubulin expression, the functional variants S873R, L1150R, G937E seem to express less PALB2 than WT.\nWe have quantified the expressions levels of all PALB2 variants relative to that of Tubulin (see Figure 5b, Extended Data Figures 4c and 8). While S873R was expressed to a somewhat higher levels compared to wild-type (rel. expression level 1.26), the expression of L1150R and G937E was modestly impaired (rel. expression 0.88 and 0.86, respectively).\n-“In contrast, the WD40 variant p.E1083K, which was functional and expressed to WT levels (Fig. 5b and Supplementary Table 1), and several variants in the CC domain (p.S10T, p. E13A, p.L17S, p.Y28S, p.A22P, p.Y28D, and p.L35P), all of which showed near wildtype expression levels (Extended Data Fig. 4c), did not grossly mislocalize in the cytoplasm (Fig. 5c and Extended Data Fig. 4d).”\nThe quantification confirms near wild-type expression for these variants. Thus, these variants are stable and do not mislocalize in the cytoplasm.\nThe p.L35P variant is not in the Extended Data Fig. 4c. Please review it.\np.L35P was tested in Boonen et al., Nat. Commun. (2019), hence we have removed this variant from the text in the Results.\nRegarding the data included in the Extended Data Fig. 4d, although the p.S10T, p. E13A, p.L17S, p.Y28S, p.A22P, p.Y28D, and p.L35P variants were not mislocalize in the cytoplasm, they exhibited a statistically significant low levels of cellular localization in the nucleus (and in both) compared to WT. Should it be related to a low PALB2 expression in the western blots of figure 4d (extended data)? However, the corresponding western blots do not show a low expression of PALB2. Could you include in the text a potential explanation for these apparent conflicting findings?\np.L35P expression seemed increased, but this was assessed in a non-quantitative manner in Boonen et al. (Nat. Commun., 2019). The expression of these variants led to an increase in cells with both nuclear and cytoplasmic staining (at the expense of cells showing nuclear localization only), suggesting this phenotype does not correlate with reduced but with increased expression. We hypothesize that this phenotype may relate to altered PALB2-BRCA1 interactions or PALB2 self-interactions (Buisson and Masson, Nucleic. Acids Res., 2012). The exact mechanism needs further research and is currently highly speculative.\nTherefore, we decided not to mention this in the main text.\n-“ Taken together, our results suggests that missense variants in the WD40, but not the CC domain, can cause protein instability and subsequent loss of functionality, the latter of which can be predicted only moderately well by ΔΔG-based in silico prediction models.”\nAn additional analysis that could reinforce the finding that missense variants in the PALB2 WD40 domain, but not the CC domain, affect protein stability would be to compare protein stability predictions between damaging missense variants in the WD40 domain and those in the CC domain. This could be done using methods such as FoldX BuildModel and RaSP. Could this analysis be performed?\nWhile there are published structures of the PALB2 coiled-coil domain in the homodimeric state (PDB: 6E4H) and heterodimeric state with BRCA1 (PDB: 7K3S), we opted not to use them for stability calculations for two reasons. First, they are of mouse PALB2 and would require homology modelling, and second, they are NMR structures, unlike the native X-ray crystal structure of the WD40 domain used in our analysis. These two aspects of the structures are likely to degrade the quality of any stability calculations, since FoldX is notoriously sensitive to the quality of the structure (Kepp et al., Biochim. Biophys. Acta, 2015; Buß et al,. Comput. Struct. Biotechnol. J., 2018). Furthermore, due in part to this sensitivity, directly comparing raw FoldX-computed stability scores from different structures can produce spurious results; while computing FoldX stability scores on two structures of the same domain is unlikely to provide identical values, the overall trend of scores within each structure is often retained (e.g. one variant being more destabilizing than another). Since there is no structure of the full PALB2 protein, one way to theoretically circumvent this issue is to use a predicted structure, such as one created by AlphaFold. However, the accuracy of FoldX scores computed using these structures remains uncertain (Mehmet et al., Nat. Struct. Mol. Biol., 2022). While we agree that the reviewer's suggestion is valuable, we concluded that focusing our stability calculations solely on the structure of the WD40 domain provided better data quality and consistency. A similar analysis of the coiled-coil domain may be more appropriate for future work, once either new structural data becomes available or the reliability of FoldX analysis on predicted structures is established.\nSECTION: In silico models poorly predict the functional impact of variants in PALB2\n-“While we observed a reasonably good correlation between these outcomes for missense variants in the CC domain when using Helix (n=193, r=-0.5499, p<0.0001; Extended Data Fig. 8\n6a) or BayesDel (n=193, r=-0.4660, p<0.0001; Extended Data Fig. 6d), the correlation was somewhat poor for these variants in the WD40 alone (Helix: n=1,881, r=-0.2391, p<0.0001; BayesDel: n=1,881, r=-0.2223, p<0.0001; Extended Data Fig. 6b, e), or combined with those in the CC domain (Helix: n=2,074, r=-0.2313, p<0.0001; BayesDel: n=2,074, r=-0.2544, p<0.0001; Extended Data Fig. 6c, f).” and “However, similar to Helix, BayesDel and EVE, AlphaMissense correctly predicted a damaging effect for variants with low depletion scores, yet classified a large proportion of the variants with high depletion scores as damaging. Consequently, its predictions overall also poorly correlated with results from the PARPi sensitivity-based site-saturation screens (CC n=609, r=-0.3408, <0,0001; WD40 n=6,109, r=-0.2266, <0,0001; CC+WD40 n=6,718, r=-0.2527, <0,0001; Extended Data Fig. 7d-f).”\nWhy for Helix, BayesDel and EVE the number of total analysed missense variants is 2,074 while for AlphaMissense is 6,718? Please explain it.\nThe number of variants for which predictions were available varied across the models. Both Helix and AlphaMissense generated scores for all 6,718 variants. In contrast, EVE does not cover the entire PALB2 protein, providing predictions for 4,363 variants, while BayesDel is limited to single nucleotide substitutions, resulting in predictions for 2,078 variants. Overall, there are 1,347 variants for which predictions are available from all models. We have now provided the correlations between functional data and in silico predictions from Helix and AlphaMissense, showing the highest possible coverage (6,718 variants) (see new Extended Data Fig. 7). Moreover, we also show correlations for the 1,347 variants with predictions from all 4 tools for a better comparison of the in silico predictions (see new Extended Data Fig. 6).\nAlthough Helix may be effective in predicting the functional impact of variants within the CC domain, the overarching conclusion remains: in silico tools tend to overestimate the damaging effects of variants with high depletion scores and, as a result, perform poorly in predicting the functional consequences of variants in PALB2.\nSECTION: “Site-saturation functional screens aid in the clinical reclassification of missense VUS in PALB2”\n-“As of May 2023, 2,444 missense variants in PALB2 were listed in ClinVar”As of May 2024, there are 2,786 missense variants in PALB2 listed in ClinVar. It is possible that more VUS in the CC and WD40 domains are now included in ClinVar compared to the 2,367 mentioned in the current version of the manuscript. Consider updating the number of included VUS.\nThe number of variants of uncertain significance (VUS) was updated using ClinVar data as of June 2025 (see new Figure 6a and Supplementary Table 3).\n-“Thus, our site-saturation functional screens can aid in the reclassification of 741 (31,3%) of the VUS in the CC and WD40 domains listed in ClinVar.”\nThis statement is not entirely accurate, as the application of an intermediate functional result (for 261 variants) for VUS reclassification remains uncertain. How should this intermediate functional finding be applied as the PS3 criterion using the ACMG/AMP sequence variant interpretation framework?\n-“We previously showed that the region between the CC and WD40 domain (encoding p.I44 to p.L855) is dispensable for PALB2 function in HR, suggesting it is unlikely that damaging missense variants will be identified therein (Fig. 1b, c).”\nThe mention of the Fig 1 should be 1b, c and d.\nWe have changed the figure citations to Fig. 1.\n-“To provide additional support that these variants may be functional rather than damaging, we randomly chose 26 variants, along with 5 synthetic missense variants in this region (Fig. 6b), and expressed them in the Palb2KO mES cells to determine the impact on HR (Extended Data Fig. 8).”\nIn the extended figure 8 there are only 16 variants depicted. Please review it.\nThe number of selected variants is correct. However, the figure citations are not. We have corrected this, now mentioning the selected variants in Figure 6b (n=31), and their functional analysis using HR as a readout in Figure 6c. For a subset of these variants functional analysis using PARPi sensitivity as a readout is shown in Figure 6d (n=12), while for another subset protein expression is presented in Extended Data Figure 8 (n=17).\n-“These findings further indicate that the 1,596 missense VUS located in region p.I44 to p.L855 are likely functional. In conclusion, our functional data will be of significance in the reclassification of 99% of the missense VUS reported in ClinVar.”\nThis statement is based on two pieces of evidence provided by the authors’ work:\ni) The region between the CC and WD40 domains (encoding p.I44 to p.L855) is suggested to be dispensable for PALB2 function in HR, as indicated by testing the functional effect of constructs containing different deletions of the entire region (ΔChAM, ΔMRG15, ΔExon4, ΔExon5, and ΔExon6).\nii) 31 missense variants out of 1,596 (1.6%) located in the region p.I44 to p.L855 and reported in ClinVar rescued homologous recombination efficiency and rendered Palb2KO cells resistant to PARPi treatment.\nWhile these findings are suggestive, they are not sufficient to claim that the entire ClinVar cohort of 1,596 VUS located between the CC and WD40 domains would not affect PALB2 function. The authors have not assayed the vast majority of the ClinVar VUS located between CC and WD40 domains, providing data on only 31 variants. Therefore, it is not accurate to claim that the authors’ functional data will be of significance in the reclassification of 99% of the missense VUS reported in ClinVar. Please review it.\nSECTION: “Damaging missense variants in PALB2 are associated with increased breast cancer risk”\n-“These findings were further confirmed for damaging (OR = 6.45; 95%CI: 2.58 – 16.19; p = 6.04 x 10-9), intermediate (OR = 2.39; 95%CI: 1.94 – 2.94; p = 3.82 x 10-17), and functional variants (OR = 1.49; 95%CI: 1.31 – 1.70; p = 1.58 x 10-9) using UK Biobank controls along with clinical testing cases (Table 1) .”\nThe p-values for functional (p = 1.58 x 10-9) and intermediated variants (p = 3.82 x 10-17) are highly significant. Are these p-values, correct? Please review these data and if they are correct explain why functional and intermediate variants would be associated with increased risk.\nWe now present an updated and more comprehensive cancer risk analysis in Table 1, incorporating additional breast cancer cases and controls from the CZECANCA consortium.\nFor the Clinical Cohort of breast cancer cases, we utilized female controls from gnomAD v4.1 (excluding UK Biobank participants and adjusted for ancestry) and from the All of Us research program (non-cancer female controls, adjusted for both ancestry and age—specifically, age at diagnosis for cases and age at enrollment for controls). All OR and p-values have been re-calculated, showing a significant association with breast cancer for damaging and nonsense, but not functional variants in PALB2. For intermediate variants there was no significant association in 3 out of 4 case-control association analyses.\n-“Importantly all associations for damaging variants were significantly different from functional and intermediate variants, indicating that the damaging missense variants identified in the site saturation functional screens represent a group of variants that are likely associated with a high risk of breast cancer.”\nPlease review this statement if needed after addressing the previous comment.\nSee previous point. We have carefully calculated and checked the p-values in our new analysis (see new results Table 1). Damaging variants remain significantly different from functional and intermediate variants, indicating that damaging variants are likely associated with a increased risk of breast cancer.\nDISCUSSION\n-The authors provide convincing evidence about the existence of a set of missense variants\n11\nwith an intermediate functional effect. The authors could discuss if these intermediate missense variants would confer an intermediate cancer risk.\nProtein function and associated risk are continuous measures. The cancer genetics field has generally grouped variants into damaging/functional or damaging/intermediate/functional categories because of limited numbers of variants in cases and controls and associated limited statistical power. Here we show that a subset of variants have intermediate or hypomorphic activity. However, our updated and more comprehensive cancer risk analysis (see new Table 1) revealed that intermediate variants did not show a significant association in three out of four large case-control analyses, suggesting that these variants may not confer an increased cancer risk.\n-“Importantly, the screen data are expected to play an important role in the reclassification of 99% of the missense VUS reported in ClinVar, by showing that 1,594 (67.3%) VUS located in region p.I44 to p.L855 are likely functional,…”\nThe authors have not assayed the vast majority of the ClinVar VUS located between CC and WD40 domains, providing data on only 31 variants out of 1,594 VUS located in region p.I44 to p.L855. Therefore, it is not accurate to claim that the authors’ functional data will be of significance in the reclassification of 99% of the missense VUS reported in ClinVar.\nSee previous points on this issue above.\n-“However, in silico analysis using SpliceAI suggests that only 11 of the 6,718 missense variants (0.16%) tested in our site-saturation functional screens are encoded by nucleotide variants predicted to have an impact on PALB2 mRNA splicing (SpliceAI threshold >0.8 for predicted donor loss (DL), acceptor loss (AL), donor gain (DG) and acceptor gain (AG) at canonical splice sites; Supplementary Table 4), indicating that they mostly impair PALB2 functionality at the protein level 36. Of note, only 12 of the 4,758 nucleotide variants (0.25%), encoding missense variants in the region between the CC and WD40 encoding regions, are predicted to impact mRNA splicing (SpliceAI threshold >0.8; Supplementary Table 4), which is in line with our conclusion that few damaging variants conferring an increased cancer risk will be identified in this region of PALB2. Supporting this notion, ClinVar-reported missense variants in the region between the CC and WD40 domains were not associated with an increased risk of breast cancer (Supplementary Table 5).”\nWhy was a 0.8 threshold used? After calibration analysis, a threshold of ≥0.2 has been recommended for applying SpliceAI as pathogenic supporting evidence (PP3) and ≤0.1 for benignity evidence (BP4) according to the ACMG/AMP guidelines for classifying variants (PMID: 37352859).\nIndeed, a lower SpliceAI threshold is used to apply a PP3 or BP4 score in the ACMG/AMP-based classification. In our study, however, we wanted to identify those variants that are highly likely to affect RNA splicing and thereby will be damaging. Variants with scores >0,8 are most likely to be true splicing variants (Walker et al., Am. J. Hum. Genet., 2023). In addition, we have now also included functional variants with spliceAI scores >0,5. This new analysis revealed only 22 of the missense variants (0.33%) are encoded by SNVs that may possibly impact splicing, and hence protein functionality (see new Supplementary Table 4). This suggests a minimal damaging impact of variants on splicing, if at all.\n-“The findings presented in this study demonstrate the fulfilment of these prerequisites for our\n12\nassay, allowing the incorporation of the functional data using ACMG/AMP-based variant classification guidelines.”\nThe work provides both robust functional data and strong evidence of that damaging missense variants are associated with a clinically relevant increased risk of breast cancer. Therefore, the authors could propose how the functional evidence provided in their work can be used for variant classification applying PS3/BS3 evidence?\nCurrently, the PALB2 Variant Curation Expert Panel\n(https://cspec.genome.network/cspec/ui/svi/doc/GN077?version=1.1.0) does not use data from functional studies to apply PS3 and BS3 due to the absence of well-established pathogenic missense variants. Consequently, we do not explicitly reference the use of these codes in the manuscript. We recognize the ongoing discussion in the field regarding alternative forms of evidence to validate functional studies such as the use of protein truncating variants and case-control studies, demonstrating an association between damaging variants and increased risk as mentioned in the last part of the discussion. If the VCEP acknowledges that this serves as validation of the assay, both PS3 and BS3 can be applied for damaging and functional variants respectively.\nMATERIALS AND METHODS\nSECTION: “Cell culture and generation of Trp53KO/Palb2KO mES cells with DR-GFP and RMCE”\n-UOS2 cell culture conditions are missing.\nCulture conditions for U2OS cells have now been described in the Materials and Methods.\nSECTION: “Site-directed mutagenesis to introduce variants into human PALB2 cDNA”\n-it would be useful to have cDNA coordinates for cDNA deletion constructs lacking either the ChAM (ΔChAM), MRG-binding motif (ΔMRG15), exon 4 (ΔExon4) exon 5 (ΔExon5), or exon 6 (ΔExon6) (results shown in Fig. 1b).\nThe coordinates have now been added to the respective section in the revised manuscript.\nSECTION: “Integration of site-saturation variant libraries for CC and WD40 domains”\n-Two plates were trypsinized and stored at -80°C as backup and one plate was used for three replicate PARPi sensitivity assays.\nIt is stated in the results of the manuscript and in the “Variant scoring and analysis” section of Materials and Methods that six independent CC-library and three independent WD40-library integration experiments were performed. Here it is only mentioned three replicate PARPi sensitivity assays. Please clarify it.\nWe have changed the text such that we do not mention the number of replicates for the PARP inhibitor assays in the “Integration of site-saturation variant libraries for CC and WD40 domains” section as this information was incomplete. We now only mention in the “Variant\n13\nscoring and analysis” section that six independent CC-library and three independent WD40-library integration experiments were performed.\nSECTION: “Variant scoring and analysis”\nThe entire section and how the final depletion score for each variant is calculated is hard to understand. Please consider rewording for better clarity and readability.\nWe have rephrased the entire section “Variant scoring and analysis” to improve readability and provide clarity.\nSECTION: “Mixture modelling”\n-“Two thresholds were determined from the model: a upper threshold such that exactly 98% of the synonymous variants were above the threshold, and an lower threshold such that 98% of the nonsense variants were below that threshold.”\nWhat are these thresholds? If the thresholds referred to are those defined in the Results section (functional variants with depletion scores > -0.0759 and damaging variants with depletion scores < -0.7037), based on Supplementary Table 1 and the Source data file, it is noted that 41% of synonymous variants are above the threshold of > -0.0759, while 80% of nonsense variants are below the threshold of < -0.7037. Please clarify it.\nWe have clarified this by changing the text in the respective Materials and Methods section as follows: “Mixture modelling was applied to estimate the probability that each variant is damaging (P ), based on its average depletion score across replicates. Classification d\nthresholds were defined as follows: variants with P < 0.02 were classified as ‘functional’, those d\nwith P > 0.98 as ‘damaging’, and variants with 0.02 < P < 0.98 were assigned to the d d\n‘intermediate’ category.” Thus, 2% of the variants above the threshold of > -0.0759 may be (incorrectly) classified as damaging, while 2% of those below the threshold of < -0.7037 may be (incorrectly) classified as benign.\nSECTION: “Generation of site-saturation mutagenesis libraries and Cas9/sgRNA plasmids for saturation genome editing (SGE)”\n-“A Protospacer Protection Edit (PPE) encoding a synonymous mutation was introduced by site-directed mutagenesis into the protospacer adjacent motif (PAM) site of the target region to prevent recutting by the Cas9/sgRNA after successful editing.”\nPlease add which exact synonymous mutation was introduced (cDNA nomenclature).\nDid you rule out if this synonymous mutation induce a potential splicing alteration?\nA single synonymous nucleotide alteration (c.3030G>A; p.Glu1010Glu) was introduced into the PAM site to prevent recutting at the target site. This variant received a maximum delta score of zero across SpliceAI predictions for donor and acceptor gain and loss, indicating no predicted impact on RNA splicing. This information has been added to the Materials and methods.\n- “Quantification of EGFP-PALB2 subcellular localization was based on transient expression in HeLa cells that were fixed using 4% formaldehyde and permeabilized using Triton X-100.”\nIn Results and figures is stated that the used cells for these experiments were U2OS cells. Please clarify it.\nThe experiments were done in U2OS cells. This has been corrected in the section “Cellular localization assay” of the Materials and Methods.\nPlease specify the type of microscope used and provide details about its visualization characteristics for evaluating the images.\nImages were acquired on a Zeiss AxioImager M2 wide-field fluorescence microscope with 63x PLAN APO (1.4 NA) oil-immersion objectives, running ZEN 2012 blue edition v1.1.0.0 (Zeiss). This information has been added to the section “Cellular localization assay” of the Materials and Methods.\nSECTION “Cancer risk analysis”\n-“Breast cancer cases were females with breast cancer receiving cancer genetic testing by Ambry Genetics, whereas control populations were noncancer females from gnomAD Genome (v3.1.2) and gnomAD Exome (v2.1.1) or from noncancer European ancestry females from UK Biobank.”\nRegarding gnomAD data, what populations where included?\nFinish, Amish, Ashkenazi jewish shouldn't be considered because allele frequency of some variants could be higher in these populations.\nAll races and ethnicities were included in the gnomAD data in order to make the results generalizable. We acknowledge that certain populations may be enriched for founder variants. For this reason, we have excluded all variants with minor allele frequencies >0.001 to avoid dilution of associations by recurrent variants.\nSECTION “Data analysis”\n-“As data analysis and correlation with functional data were performed at the amino acid level, we merged the available information for distinct nucleotide variants, yielding a unified entry for a specific protein variant.”\nIt is unclear if merging available information for different nucleotide variants encoding the same amino acid is the best approach. One nucleotide variant might be pathogenic due to splicing alteration, while another variant might not alter splicing but result in the same amino acid change, which could be benign because the amino acid change is not damaging.\nA better alternative might be to assess the nucleotide variants leading to the same missense change with a SpliceAI predictor and exclude those with a positive splicing-altering prediction.\n-“ SpliceAI cutpoints to predict spliceogenicity were based on the maximum delta score of donor/acceptor loss and donor/acceptor gain >0.8 for predicted splicing impact (Supplementary Table 4).”Details about how SpliceAI was used such as version, window, masked/raw are missing. Did the authors use SpliceAI via https://github.com/ Illumina/SpliceAI—version 1.3.1?.\nSpliceAI scores were indeed obtained using version 1.3.1.\n(https://github.com/Illumina/SpliceAI—version 1.3.1), which was run locally with maximum distance of 10k nucleotides (4999 nucleotides on either site from the variant of interest) with masked scores. This information was added to the section “Data analysis” of the Materials and Methods.\nSECTION “Data availability”\n“In silico prediction scores for PALB2 missense variants were obtained for CADD (through dbNSFP at https://sites.google.com/site/jpopgen/dbNSFP; version 1.6), Helix 28, BayesDel 29, EVE 26,27 and AlphaMissense 30.”\nWas the analysis with BayesDel done including maximum and minor allele frequencies across populations or without? This predictor provides two outcomes: those computed integrating allele frequencies or those without allele frequencies.\nThe analysis was done without allele frequencies (BayesDel_noAF). We have added this information to the Method section.\nFIGURES\n“Fig. 2 Site-saturation functional screens of PALB2 variants in the CC domain. a Schematic flow of the site-saturation functional screens performed in this study. NGS = Next-Generation Sequencing. AA = amino acid. b Amino acid function map of the CC domain of PALB2 spanning 35 amino acid residues from p.L9 to p.K43) (top). The map shows depletion scores for 667 PALB2 missense and nonsense variants as generated by Enrich2, n = 6 biological replicates. Amino acid characteristics for all variants are indicated (left). Dark red squares represent variants that were depleted in PARPi-treated conditions versus untreated conditions. Blue squares represent variants that were enriched. Grey squares represent variants that were either missing in the library or filtered out during the analysis. Orange dots represent the original (wild type) amino acids. Data for all variants were normalized to WT PALB2, which was set to ‘0, and to the average of the nonsense variants, which was set to ‘-1’.”\n16\n-The mentioned orange dots are not orange, they are grey. This should be corrected in the all other related figures.\nWe have corrected this mistake in the legends of Figure 2 and 3.\n-According to Supplementary Table 1 there are 638 missense and nonsense variants.\nWe have corrected this mistake in the legend of Figure 2.\n“Fig. 3 Site-saturation functional screens of PALB2 variants in the WD40 domain”\n-According to Supplementary Table 1 there are 6419 missense and nonsense variants.\nWe have corrected this mistake in the legend of Figure 3.\n“Fig. 5 Molecular validation of the impact of PALB2 missense variants in site-saturation functional screens. a YPF/GFP pulldowns of the indicated PALB2 CC variant proteins following transient expression in U2OS cells. PALB2 CC variants are indicated in three colours reflecting their functional outcome in the site-saturation screens in ‘Fig. 2b’; green is functional, orange is intermediate, red is damaging. GFP-NLS and YFP-PALB2-L35P served as negative controls. Western blot analysis was performed using antibodies against GFP and BRCA1. b Western blot analysis of the expression of human PALB2 WD40 variants in Trp53KO/Palb2KO mES cells using an antibody directed against PALB2. The empty vector (Ev) served as a negative control on each blot. Tubulin was used as a loading control. * indicates a non-specific band. PALB2 variants are indicated in colour as in ‘a’. c Fluorescence microscopy analysis (left) and quantification of the cellular distribution (right) of human YFP-PALB2 WD40 variants following transient expression in U2OS cells. Coloring of variants is as indicated in ‘a’. Data represent the mean ±s.e.m. n = 3 biological replicates, dots represent individual data points, two-way ANOVA with Dunnet’s multiple comparison test. *P<0.05, **P < 0.01, ***P < 0.001,NS, not significant.”\n-The left-hand block of images showed in figure 5a of the Source Data file do not correspond to the image included in the manuscript. The images in the Source Data File are inverted comparing to the figure included in the manuscript. Please review it.\nWe have corrected this mistake by swapping the IP blots with anti-GFP and anti-BRCA1.\n-GFP-NLS is not referenced in Material and Methods\nWe have now referenced the GFP-NLS plasmid in the Materials and Methods.\nSUPPLEMENTARY TABLE 1\n-Column A, labelled \"Amino acid nr,\" incorrectly annotates the number of amino acids for all synonym changes. The number depicted in the column is the cDNA position and not the correct amino acid number. Please correct it.\n-The predictions for BayesDel are missing. Please add them.\nThe BayesDel predictions have been added to Supplementary Table 1.\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nAuthor):\nThank you for asking me to review this manuscript which I have studied in detail. I should point\nout that I am not a cell biologist; therefore I would recommend that the technical aspects are\nreviewed in detail my co-reviewers who may be more experienced in these aspects than me.\nI have, however, studied all the experimental findings and, to my mind, the blots/bar charts /\nfluorescence microscopy results are all appropriately displayed with the appropriate controls\nincluded, and appropriate conclusions drawn from the findings presented. The supplementary\nexcel spreadsheets with the raw data given for each possible variant are appropriate, and the\nresults are easily accessible to the reader. The rationale for this work, the results and their\ninterpretation are clearly presented.\nThe work draws and builds upon the group’s previously published functional assays with the\naddition of bringing in the comparisons with large clinical/population datasets. Comparing the\n‘wet lab’ functional analyses with the ‘dry’ case control data in the same study is of particular\nvalue.\nThe classification of PALB2 VUS is a real challenge in clinical cancer genetics and having this\nrobust high throughput functional analyses of the missense variants is extremely helpful and\nwill certainly aid accurate variant interpretation which will then be of immense benefit to\nhealthcare professionals and patients and families.\nI have some minor comments, in no particular order:\n-Extended data figure 4, there is a typo in the legend YPF instead of YFP.\nWe have corrected this mistake.\n- ‘These exons encode the Coiled Coil and WD40 domains, which we found to be minimally\nrequired for HR’. The phrase ‘minimally required for HR’ referring to the CC and WD40\ndomains, is in the abstract and discussion. This is a grammatical point and slightly pedantic\nbut, to my mind ‘minimally required’ means indispensable/can do without. Whereas the authors\nreally mean are required as a minimum/crucial for HR function.\nWe have changed this sentence in both the Abstract and Discussion to: “These exons encode\nthe Coiled Coil and WD40 domains, which we identified as the minimal regions required for\nHR.”\n-In the discussion ‘Moreover, PALB2 VUS are very rare in carriers, complicating cancer risk analysis’. I know what the authors are driving at, as in PALB2 missense VUS are not detected that often (unlike BRCA1/2) which adds to the challenge of variant interpretation as often there\nis a lack of clinical/functional data to draw upon; however, I would re-word to make clearer as\nthe current wording sounds a little as though (i) such a rare event, that it isn’t really an issue\n(and we know it is a challenge) and (ii) PALB2 carriers are common, just the VUS that are rare. Thank you.\nWe have changed this sentence in the Discussion to: “Moreover, PALB2 VUS are very rare,\ncomplicating cancer risk analysis.”\n20\n-understandably the discussion and scene setting in the introduction is very much focussed\ntoward breast cancer given breast cancer accounts for the majority of PALB2 diagnostic\ntesting. However, there ought be some comment made re the role of PALB2 PVs in\npancreatic/ovarian cancer and, although detected much less frequently in these scenarios, the\nclinical challenges are the same and PALB2 is on these NHS England panels.\nThe first sentence of the Introduction already mentioned this: “Germline protein-truncating\nvariants (PTV) in PALB2 are associated with high breast and pancreatic cancer risk and\nmoderate ovarian cancer risk”. We have now also added this information to the first sentence\nof the Discussion.\n- ‘The findings presented in this study demonstrate the fulfilment of these prerequisites for our\nassay, allowing the incorporation of the functional data using ACMG/AMP-based variant\nclassification guidelines.’\nI would tone this down a little, to eg facilitate incorporation. The results are extremely helpful\nbut is for ACMG/AMP and national groups to decide what data / assays can be used in variant\ninterpretation.\nWe have changed this sentence to: “The findings presented in this study confirm that our assay\nmeets the necessary prerequisites, thereby facilitating the use of our functional data for\nACMG/AMP-based variant classification.”\nmutational analysis (Remarks to the Author):\nSummary:\nBoonen et al. first identify functional domains in PALB2 needed for HR and PARPi sensitivity,\nusing mouse ES PALB2-KO cells and a recombinase system to insert deletion alleles. They\nproceed by performing a MAVE to assess ~7,000 PALB2 variants for effects on PARPi\nsensitivity across the coiled-coil and WD40 domains. The assay identifies a small but\nmeaningful fraction of missense variants to be damaging, and is generally well validated\nthrough a wealth of additional functional data, including individual PARPi sensitivity and\nhomologous recombination assays, a saturation genome editing assay, and others. The\nauthors next show that damaging missense variants in PALB2 confer higher cancer risk as a\ngroup, validating the clinical relevance of the data set.\nImpressions:\nOverall, the work represents a big step forward for the study of variants in PALB2 in that many\nvariants are functionally annotated for the first time. The newly identified loss-of-function\nvariants, though not particularly numerous, may tangibly improve patient care. However, the\nassay also has some technical limitations regarding score precision. Therefore, while this is a\nlarge amount of functional evidence that should be made publicly available relatively soon, my\ngeneral enthusiasm is somewhat weakened by the overall quality of the main data set. My\nmain concerns mostly involve transparency regarding the technical quality of the data and\nlimitations inherent to scoring a large number of variants as intermediate. Several points need\nto be addressed.\nMajor Comments:\n1. Overall, more needs to be done to show how reproducible and robust individual variant\nscores are.\n- The number of technical and biological replicates for the large data set is not completely clear\nbetween the figure legends (n = 3/6) and text (“triplicate”). Please define both types of\nreplicates clearly (e.g. separate transfections into cells, separate populations for sequencing,\netc.) and state how many times each replicate was performed, and how scores were averaged.\nWe have provided additional details about the number of transfections (biological replicates),\nPARPi assays (technical replicates) and score analysis after sequencing of each of these\nreplicates in the section “Site-saturation functional screens of missense variants in the CC and\nWD40 domains of PALB2” of the Results, the sections “Integration of site-saturation variant\nlibraries for CC and WD40 domains” and “Variant scoring and analysis” of the Materials and\nMethods, and the legends of Figure 2 and 3. Additional details about how scores were\naveraged can also be found in the section “Variant scoring and analysis” of the Materials and\nMethods.\n- Please include supplemental plots showing the correlation of scores across independent\n22\nbiological replicates (prior to removing variants with high variance) and include scores from all\nindividual replicates in supplementary table 1.\nPlots showing good correlation between scores across independent biological replicates have\nbeen included in two new Supplementary Figures 1 (CC domain ) and 2 (WD40 domain), and\nhave been cited in the Results.\n- The plot of score distributions for nonsense, synonymous, and missense needs to be more\nclear, as too many synonymous variants are obscured (Fig. 4a). Please plot these separately,\nor stack instead of overlaying.\nWe have plotted the nonsense + synonymous and missense variants in separate plots, which\ncan be found in Extended Data Fig. 2a, b).\n- Upon checking the supplementary table, it seems more synonymous variants were deemed\n“intermediate” than “functional”. Please report this in text.\nWe now mention in the section “Site-saturation functional screens of missense variants in the\nCC and WD40 domains of PALB2” of the Results that 270 (80%) nonsense variants were\nscored as damaging, while 20 (43%) synonymous variants were scored as functional.\n- Some variants seem to have large scores in the positive direction (dark blue on heat map).\nAre these plausibly biologically meaningful, or are these more likely noisy scores?\nWe speculate that these variants may represent noisy scores rather than biologically\nmeaningful effects, although we cannot entirely exclude a potential protective impact (for\ninstance by improving PALB2 protein stability and function). Future research is needed to\nclarify their relevance, which is beyond the scope of the current manuscript.\n- The description of the mixture modeling / determination of thresholds for functional categories\nis unclear and needs to make more intuitive sense in both methods and results. What were the\n7 parameters included in the model that are referenced in methods? What data is the basis of\nthe ROC curve shown – is it nonsense vs. synonymous classification? It seems many more\nthan 2% of the synonymous variants score below the threshold for neutral, despite the fact that\nthe threshold is reported as the line above which 98% of synonymous variants score – what\nexplains this discrepancy?\nThe mixture modeling and threshold determination involve seven parameters: the location,\nscale, and degrees of freedom for each of the two components, as well as the mixture\nprobability for the missense variants.\nThe basis for the ROC curve is indeed the nonsense versus synonymous classification.\nWe have provided clarity on the use of thresholds for categorization of variants by changing\nthe text in the Materials and Methods section as follows: “Mixture modelling was applied to\nestimate the probability that each variant is damaging (Pd), based on its average depletion\nscore across replicates. Classification thresholds were defined as follows: variants with Pd <\n0.02 were classified as ‘functional’, those with Pd > 0.98 as ‘damaging’, and variants with 0.02\n23\n< Pd < 0.98 were assigned to the ‘intermediate’ category.” Thus, 2% of the variants above the\nthreshold of > -0.0759 may be (incorrectly) classified as damaging, while 2% of those below\nthe threshold of < -0.7037 may be (incorrectly) classified as functional.\n2. Related to the point above, the intermediate risk category includes many variants and it is\nunclear whether such scores reflect technical noise or truly hypomorphic variants possibly of\nintermediate cancer risk. While the authors do cite this as a limitation, more could be done to\nquantitatively explore this category.\nIt remains unclear how we could further investigate this quantitatively. For example, the results\nfrom in silico predictions show poor correlation with our high-throughput functional analyses,\noften overestimating the damaging effects of intermediate variants. Additionally, comparisons\nwith previously published single-variant datasets are limited by the small number of variants\ntested, which hinders the application of threshold-based functional categorization. To address\nthis, a comprehensive MAVE dataset for PALB2 would be essential, enabling the distinction\nbetween true intermediate or hypomorphic variants and technical noise. However, such a\ndataset has yet to be established for PALB2 missense variants.\n- Please clarify the sensitivity and specificity of the main assay for detecting nonsense variants\nas damaging and synonymous variants as neutral in the main text.\nWe have now explained this in the Results by stating: “ Using this mixture model analysis (Fig.\n4b), thresholds were established to achieve 98% sensitivity and specificity, such that 2% of\nnonsense variants scored as functional and 2% of synonymous variants as damaging.\nApplying these thresholds (Fig. 4b), we categorized 392 (6%) of the missense variants as\ndamaging, 2,422 (36%) as intermediate and 3,904 (58%) as functional (Fig. 4c and\nSupplementary Table 1).”\n- 36% of variants are classed intermediate, far more than are called damaging. Provide some\nestimate of how many damaging variants / cancer-risk variants may be missed in this category.\nIt is difficult to make a fair estimate of how many damaging variants may be missed due to\ntheir categorization as intermediate. This is because this is dependent on many screen\nparameters such as the thresholds and sensitivity/specificity for categorization, which in turn\nare affected by the number of controls (nonsense and synonymous variants) and sensitivity of\nthe PARPi sensitivity assays, among others. Ideally, a comparison to SGE data for of the entire\nPALB2 gene would better help to identify mis-categorized variants, but such a dataset is\ncurrently not available.\n- What do the orthologous validation studies and computational predictors tell us about\nintermediate risk variants overall? Sometimes the results of the intermediate risk variants look\nbimodal (the individual PARP inhibitor assay, the HR assay, possibly the SGE data). When\nevidence is contradictory across assays, which assay should be trusted? Do the computational\npredictors suggest the intermediate risk variants are more deleterious than functional variants?\nThe validation PARPi sensitivity and DR-GFP assays generally showed good correlation with\noutcomes of the PARPi sensitivity-based screens in mES cells and survival-based MAVE\nassays in human cells (Figure 4). In contrast, we previously already reported that the outcomes\n24\nof in silico prediction tools such as PolyPhen, SIFT, Align-GVGD and REVEL generally show\nlittle to no correlation with those from PARPi sensitivity and DR-GFP assays (Boonen et al.,\nNat. Commun. (2019). In the current manuscript we extended this analysis for all 6718\nmissense variants using Helix and AlphaMissense, and for 1347 missense variants using not\nonly Helix and AlphaMissense, but also EVE and BayesDel predictions. The conclusions\nremain similar to those in our previous publication, namely that the outcomes of these tools\ngenerally show little to no correlation with those from the PARPi sensitivity-based screens.\nHence the evidence across assays is not contradictory, but prediction tools are not very well\nsuited to predict the functional impact of missense variants in PALB2.\nMinor Comments:\n1. The authors mention the assay’s inability to detect splicing variants as a potential caveat. A\nfew comments on this:\n- 0.8 is a very high spliceAI threshold to use. It is important to know how many variants may\nbe misclassified using lower thresholds, for instance, 0.20 and 0.50. Please clarify.\nIndeed, a lower SpliceAI threshold is used to apply a PP3 or BP4 score in the ACMG/AMP-\nbased classification. In our study, however, we wanted to identify those variants that are highly\nlikely to affect RNA splicing and thereby will be damaging. Variants with scores ≥0,8 are most\nlikely to be true splicing variants (Walker et al., Am. J. Hum. Genet., 2023). We have now\nincluded variants with spliceAI scores >0,5. This new analysis revealed only 22 variants of the\nmissense variants (0.33%) are encoded by SNVs that may possibly impact splicing, and hence\nprotein functionality (see revised Supplementary Table 4). This suggests a minimal damaging\nimpact of variants on splicing, if at all.\n- Are there any variants that score lowly when edited into the genome via CRISPR but not\nwhen introduced via recombination? If so, are these splice variants?\nOur new spliceAI analysis using a cut-off score of >0.5 did not reveal any variants that scored\nlow in the MAVE, but high the PARPi sensitivity-based screens (new Supplementary Tables 1\nand 4), suggesting that these variants may not impact splicing.\n- Include depletion scores for variants with high spliceAI scores in the second tab of Supp.\nTable 4 to make it clear how these variants scored in the assay.\nDepletion scores for variants with spliceAI scores >0.5 have been added Supplementary Table\n4.\n2. Are there any significant domain-specific differences in OR for damaging missense variants?\nIt is okay if there is no power to see this, but worth looking at and commenting on if so,\nespecially considering the observed differences in stability effects across domains.\nWe cannot determine domain-specific ORs as the number of damaging missense variants in\nthe CC domain is too low in the cohorts.\n3. This is one of the few cases where a deep mutational scan and saturation genome editing\n25\nhave been directly compared for the same gene, which is of interest. The assays measure\ndifferent phenotypes – PARPi resistance in mESCs and cell growth in HAP1. Does the\nrelationship between scores tell us anything about the assays, i.e. is one assay more or less\nsensitive than the other? Is it clear whether the cells are dying in the SGE assay due to an HR\ndefect?\nUnfortunately, the MAVE dataset is too limited for such an analysis, but this is an area of great\ninterest. In particular, it would be valuable to assess not only the impact on HAP1 cell growth\nof variants in the CC and WD40 domains, but also of variants located between the CC and\nWD40 domains. Specifically, determining whether any missense variants (excluding those\naffecting splicing) influence cell survival in this region would be insightful. We eagerly await\nnew datasets to enable this analysis. Whether reduced survival is the consequence of impaired\nHR in the MAVE assay is unclear and remains to be established.\n4. The data presented in EDF4a/b does not lead to new statistically significant results. The\npaper doesn’t hinge on these data points, and the general trends are as expected for validation,\nbut the text in the results must make it more clear that the effects being described were not\nstatistically significant, or perhaps the data can be plotted to show a significant correlation\nacross the variants assayed instead of pairwise comparisons?\nGiven that the number of variants that was examined in Extended Data Fig. 4a-b is rather small\nand that their impact on PALB2 recruitment is somewhat variable, we believe the data is not\nstrong enough for a correlation analysis. Instead, we toned down the conclusion in the\nmanuscript by stating that the differences are not significant, but that the trend of their impact\nis as expected.\n5. Regarding the analysis of EVE and AlphaMissense data, considering these are genome-\nwide models it is worth exploring precision-recall or AUROC analyzes to ask what sensitivity\nand specificity can be achieved for identifying deleterious PALB2 variants scoring as hits in the\nassay. Given there are many variants that remain in the intermediate category, it would make\nthe paper stronger to also explore to what extent a combination of computational predictor\nscore and functional data can help delineate which intermediate variants might increase cancer\nrisk.\nAs said above, we previously already reported that the outcomes of in silico prediction tools\nsuch as PolyPhen, SIFT, Align-GVGD and REVEL generally show little to no correlation with\nthose from PARPi sensitivity and DR-GFP assays (Boonen et al., Nat. Commun. (2019). In the\ncurrent manuscript we extended this analysis for all 6718 missense variants using Helix and\nAlphaMissense, and for 1347 missense variants using not only Helix and AlphaMissense, but\nalso EVE and BayesDel predictions. The conclusions remain similar to those in our previous\npublication, namely that the outcomes of these tools generally show little to no correlation with\nthose from the PARPi sensitivity-based screens. Hence these prediction tools are not very well\nsuited to predict the functional impact of missense variants in PALB2. We would therefore also\nnot like to use these predictors to delineate cancer risk. Instead, we are currently employing a\nstatistical model called VarCall (Iversen et al., Cancer Epidemiol. Biomark. (2011) for inferring\ndisease relevance of VUS based on functional data. This work, which is in progress and\nbeyond the scope of the current manuscript, is in preparation for publication elsewhere.\n26\n6. The MAVE terminology is used in a way that leads to confusion and should be fixed. MAVEs\nare a broad class of high-throughput assays, including both deep mutational scans and\nsaturation genome editing. The authors have performed two types of MAVEs. The first is\nreferred to as “site-saturation functional screens”. This seems to refer to a “deep mutational\nscan”. The other MAVE involving CRISPR-introduction of SNVs endogenously is commonly\ncalled “saturation genome editing”. For clarity:\n- Would recommend calling the “site-saturation” experiments “deep mutational scans”, as\nmany sites in the protein are all being saturated, not a single residue.\nWe deem the term “site-saturation screen” correct as we have modified each individual amino\nacids of the CC and WD40 domains of PALB2 into all 19 possible alternative amino acids.\nHowever, we have changed “MAVE” into “SGE” or “saturation genome editing” across all text,\nfigures and tables.\n- Refer to the exon 10 endogenous editing experiment as a “Saturation Genome Editing”\nexperiment and not a “Deep Mutational Scan”\nWe also deem the term “deep mutational scan” correct as we have examined most, but not all\npossible SNVs in a region of exon 10 of PALB2.\n- Refrain from using “MAVE score”, but instead “SGE score”, as “MAVE score” will be\nambiguous to readers.\nSee previous related point where we indicated that “MAVE” has been changed to “SGE”.\nAccordingly, we have changed “MAVE score” to SGE score”.\n7. It was not immediately clear whether the 140 missense variants measured independently\nhave already been functionally characterized (references 7 and 15) or whether these were\nnewly tested in this study for validation? Generally, these experiments could be introduced in\na way that is more clear with regards to how it differs from the high-throughput experiments\nthat are described previously.\nWe have added an additional column to the tabs in the Source Data file containing validation\ndata for the 140 missense variants assessed in the DR-GFP and PARPi sensitivity assays.\nThis new column references the two publications in which a subset of these variants was\npreviously analyzed. Of the 140 variants, 44 variants had been tested in these publications,\nwhile the remaining 96 were newly tested. This is now also mentioned in the Results.\nDetails of the high-throughput and validation experiments are provided in the Materials and\nMethods section.\n8. Regarding the 741 VUS / 31.3% – please clarify whether 31.3% is of VUS in the CC or\nWD40 domains, or whether it is of all VUS in the gene – from the chart it looks the latter, but\nthe sentences in text are confusing. More importantly, it is important to state what fraction of\nthe VUS were deemed either “functional” or “damaging” and what fraction were deemed\n“intermediate”.\nlisted in ClinVar, encoding 2,799 unique missense variants of which 2,778 are located in the\nCC domain, middle region and WD40 domain of PALB2 (see new Fig. 6a-b and new\nSupplementary Table 3). We next investigated whether the results of our site-saturation\nfunctional screens could assist in the classification of these VUS in the CC and WD40 domain.\nThe screens produced depletion scores for 872 VUS in these domains, representing 31,4% of\nthose reported in ClinVar, which we categorized as functional (n = 518), intermediate (n = 304),\nor damaging (n = 50) (see new Fig. 6c).”\n9. “In conclusion, our functional data will be of significance in the reclassification of 99% of the\nmissense VUS reported in ClinVar.” This sentence goes too far for what has been shown. Is\nthere a clinical classification framework that explicitly permits domain-level evidence for some\nvariants as functional data for other variants? If so, please cite this. Regardless, this seems\nmore appropriate as a discussion point rather than a main results claim.\nWe indeed have to be careful with this statement as we did not functionally test all variants in\nthe middle regions. However, we provide multiple lines of evidence to support our claim:\n1. Deletion of all regions/domain in the middle region did not affect protein functionality\n(Fig. 1)\n2. Since our assay is cDNA-based, we may have missed functional defects caused by\naberrant splicing. However, SpliceAI did not predict any major splicing effects of VUS\nin the middle region, as only 0.42% had a score above the threshold (>0.5)\n(Supplementary Table 4).\n3. We have now also performed a burden-type analysis of all ClinVar-reported missense\nVUS in the middle region, revealing no increased cancer risk (mean OR = 1.03; see\nnew results Supplementary Table 5), which is in line with the observation that none of\nthe VUS tested in this region showed a functional defect (Fig. 6c-d). This has now\nbeen mentioned in the Results and Discussion.\nDespite these lines of evidence, we have toned down the text with regards to our claim.\n10. Revise this sentence to be more clear:“All nonsense variants scored below 0.532 (n = 339, median = -1,011), whereas synonymous variants scored above -0.613 (n = 49, median = - 0.096).” The max and min numbers are confusing. For instance, 0.532 doesn’t make sense on a scale of -1 to 0. Would instead highlight the standard deviations of nonsense and synonymous variant measurements to go with the medians, and (as said above), it is important to say how many of each were classified as expected using the cut-offs applied to the whole data set.\nWe have revised the sentence by adding standard deviations rather than max and min\nnumbers to the median depletion scores. In addition, we mention that thresholds were\nestablished to achieve 98% sensitivity and specificity, such that 2% of nonsense variants\nscored as functional and 2% of synonymous variants as damaging. Applying these thresholds\n(Fig. 4b), we categorized 392 (6%) of the missense variants as damaging, 2,422 (36%) as\nintermediate and 3,904 (58%) as functional (Fig. 4c and Supplementary Table 1).\n28\nwe have mentioned that 270 (80%) nonsense variants were scored as damaging, and 20 (43%)\nsynonymous variants as functional.\n11. Fix the Table 1 typo:“Burden-type analysis of odds ratios for functional, intermediate and\nfunctional missense variants”\nWe have changed “…functional, intermediate and functional missense variants…” to\n“…functional, intermediate and damaging missense variants…”\n12. In methods – ‘mg’ is used in some instances where the correct unit is likely micrograms\n(ug), for instance, DNA transfection quantities.\nWe have corrected micrograms to μg where appropriate to correct the mistakes.\nWe thank the reviewers for their constructive comments and useful suggestions. Based on\nthese we have adapted the manuscript textually, performed additional analyses, and made\nseveral changes to the figures, tables, and source data files. We have addressed all comments\npoint-by-point below.\nReviewer #1 (Remarks to the Author):\nWe appreciate that the authors have addressed each of our comments, and we acknowledge\nthat the manuscript has greatly benefited from their additional analyses, corrections and edits.\nWe have, however, some additional comments and minor remarks.\nINTRODUCTION\n- STATEMENT: “However, genetic testing has identified numerous PALB2 missense variants\nfor which the cancer risk assessment is unclear” The name of the gene PALB2 should be in\nitalics. Please review it also through entire manuscript, figures, tables, supplementary and\nextended data files.\nWe have corrected the formatting of the gene name where appropriate.\n-The STATEMENT “These assays will therefore not be able to cope with the pace and scale\nat which genetic testing reveals VUS in PALB2 (currently >2,900 VUS listed in ClinVar)”\nrequires clarification:\na. Please specify the ClinVar accession date for this count.\nWe have now specified the accession date, which is June 30, 2025, throughout the manuscript\nand tables.\nb. Could you clarify whether the reported number (>2,900 VUS) refers exclusively to missense\nvariants or to all variant types? If it refers only to missense variants, it would be helpful to state\nthis explicitly. In addition, Supplementary Table 3 lists 2,779 missense variants of uncertain\nsignificance, which does not appear to match the figure of >2,900. Please review this\ndiscrepancy.\nAs of June 30, 2025, ClinVar reports 3,014 SNVs in PALB2, 2,925 of which account for VUS\n(Figure 6a). Hence, we mention in the text that ClinVar lists >2,900 VUS. The 2,925 SNVs that\naccount for VUS, encode for 2,800 unique missense variants in PALB2, now also including\nthe region between the start codon and the first codon of the CC domain of PALB2 (pre-CC;\nSupplementary Table 3). We have now clarified this better in the Introduction, Results,\nDiscussion and Supplementary Table 3.\nc. According to Supplementary Table 3 (column G: ClinVar class, downloaded on 30-06-2025),\na total of 2,779 missense variants is reported in ClinVar with the following classifications:\nVariants of uncertain significance (VUS): 2,351 Variants with conflicting interpretations of\npathogenicity: 425 Likely benign variants: 3 In your analysis, based on the column H “ClinVar\n1\nclassification (majority-voting-based)”, the 3 likely benign variants were grouped as uncertain\nsignificance. However, in ClinVar, the majority-voting-based classification places them in the\nlikely benign category. Please review this point carefully and clarify the classification criteria\napplied.\nIn cases where two different SNVs result in the same amino acid substitution but have different\nClinVar classifications (i.e. one classified as likely benign and the other as uncertain\nsignificance), we consistently assigned the classification ‘uncertain significance’ to column H,\nas described in the Materials and Methods section. For three missense variants (i.e.\nLys908Asn; Phe948Leu; Met1032Ile), however, only the ClinVarClass ‘likely benign’,\ncorresponding to one of the SNVs, was inadvertently transferred to column G, while the\nclassification of the other SNV was not included. This, however, did not impact the results as\nall these variants were classified as VUS following majority-based voting.\nOf note, majority-voting based classification were also listed in the Source Data of Figure 6a.\nTo avoid further confusion we have therefore removed the classification from Supplementary\nTable 3 and only refer to Figure 6a in the Source Data file.\nRESULTS\nSECTION: “Site-saturation functional screens of missense variants in the CC and WD40\ndomains of PALB2”\n- STATEMENT: “Using this mixture model analysis (Fig. 4b), thresholds were established to\nachieve 98% sensitivity and specificity, such that 2% of nonsense variants scored as functional\nand 2% of synonymous variants as damaging.” In the “Source Data file,” we could not find the\nd the\ndata for the mixture model analysis. Without these data, it is difficult to understand and\ncorroborate the established thresholds. Please consider including them.\nIn the results we state “The median depletion score for the nonsense variants was -1.011 (n =\n339, standard deviation = 0.413), while for the synonymous variants it was -0.096 (n = 49,\nstandard deviation = 0.306). Importantly, depletion scores of nonsense and synonymous\nvariants were bimodally distributed (Fig. 4a and Extended Data Fig. 4a, b), allowing the\nclassification of missense variants as ‘damaging’, ‘intermediate’ or ‘functional’ by fitting a two-\ncomponent mixture model.”. Thus, 339 nonsense and 49 synonymous variants were used for\nthe mixture modelling. Depletion scores and distribution plots of these variants can be found\nin Fig. 4a and Extended Data Fig. 4a, as well as in the Source Data file.\nSECTION: “Validation of site-saturation functional screens of missense variants in the CC and\nWD40 domains of PALB2”\n- STATEMENT: “When comparing the PARPi resistance levels to the depletion scores from\nthe screens (Supplementary Table 1); a good and significant correlation for variants in the CC\n(n=43, r=0.8055, p<0.0001), WD40 (n=97, r=0.6500, p<0.0001) and CC + WD40 domains\n(n=140, r=0.6494, p<0.0001) was observed (Fig. 4d and Extended Data Fig. 2c, d)”.\nThe term “good” is subjective and not standard in scientific writing. Please consider replacing\n“a good and significant correlation” with “a significant positive correlation” to maintain precision\nand avoid subjective wording. There are additional uses of the word “good” throughout the\nmanuscript. Please also review and revise those accordingly.\nWe have added the following description of the correlation analysis to the Materials and\nMethods to clarify what we mean with terms such as “good”: ”Pearson’s correlation coefficients\n(r) were used to assess the strength of linear relationships between variables. Positive\ncorrelations were classified as weak (r < 0.25), moderate (0.25 ≤ r < 0.50), good (0.50 ≤ r <\n0.75) and very good (r ≥ 0.75), while negative correlations were classified as weak (r > -0.25),\nmoderate (-0.25 ≥ r > -0.50), good (-0.50 ≥ r > -0.75) and very good (r ≤ -0.75). Statistical\nsignificance was determined using corresponding p-values.”. We have adjusted the use of\nthese descriptive terms throughout the manuscript according to the r-values from the\nrespective correlation analyses, to ensure greater precision.\n-STATEMENT: “The SGE data furthermore included scores for 195 unique changes consisting\nof 9 nonsense variants and 186 missense variants (Fig. 4g)”.\na. In Fig. 4g, the 195 variants themselves are not shown. Rather, this figure presents the\ncorrelation between SGE scores and depletion scores for 179 unique variants (170 missense\nand 9 nonsense). It may be clearer to reference here the “Fig. 4g source data file” instead of\nFig. 4g.\nWe have now referenced Fig. 4g in the Source Data file.\nb. The reference to Fig. 4g would be more appropriately placed with the following statement:\n“Importantly, we obtained depletion scores for 179 of these variants, comprising 170 missense\nand 9 nonsense variants, in the PARPi sensitivity-based site-saturation screens (Fig. 2, Fig. 3\nand Supplementary Table 1), allowing comparison of depletion scores and SGE scores.” The\nreference to Fig. 4g could replace the current references to Fig. 2, Fig. 3, and Supplementary\nTable 1.\nWe now reference Fig. 4g and Supplementary Table 1, the latter of which lists both the\ndepletion scores and SGE scores.\n-STATEMENT: “The SGE data furthermore included scores for 195 unique changes consisting\nof 9 nonsense variants and 186 missense variants (Fig. 4g)”. According to the “Fig. 4g source\ndata file”, the number of unique missense variants is 178, not 186 as stated. Please review\nand correct all numbers in the sentence.\nThe source data for Fig. 4g indeed lists 178, and not 186 unique missense variants. We have\ncorrected this in the manuscript.\n-STATEMENT: “ We observed a good and significant correlation between these scores (n=179,\nr=0.6439, p<0.0001; Fig. 4g), indicating concordance between the outcomes of our functional\nanalysis of PALB2 variants in mouse and human cells.” Again, please replace “a good and\nsignificant correlation” with “a significant positive correlation.”\nWe have changed the description of the correlations throughout the manuscript. See our\nresponse above on this issue.\n3\n-STATEMENT: “GnomAD contains whole-genome and whole-exome sequencing data from\nmore than 140,000 individuals”\nVersion numbers, freeze numbers and/or download dates for population/clinical data from\ngnomAD, as well as BRAVO, Flossies and ClinVar, have been mentioned in the Data\nAvailability section of the Materials and Methods.\n-STATEMENT: “Functional missense and synonymous variants (i.e., those with high depletion\nscores) were found to have higher allele frequencies compared to damaging missense and\nnonsense variants (Wilcoxon Signed Rank Test, p<0.0001; Extended Data Fig. 3a and\nSupplementary Table 2). A similar trend was observed with the BRAVO and FLOSSIES\ndatabases (Wilcoxon Signed Rank Test, p<0.0001; Extended Data Fig. 3b, c and\nSupplementary Table 2), which contain genome sequencing data and variants from more than\n>145,000 and 10,000 noncancer patients, respectively.” The Wilcoxon Signed Rank Test is\ndesigned for paired or matched data. In this case, the functional and damaging groups are\nindependent, not paired:\no Functional group: 121 missense + 1 synonymous\no Damaging group: 5 missense + 7 nonsense.\nTherefore, the Wilcoxon Signed Rank Test is not appropriate for this comparison. The suitable\nalternative could be the Mann–Whitney U test (also known as Wilcoxon Rank-Sum test), which\nis designed for unpaired groups. Please consider revising the statistical analysis accordingly.\nA Mann–Whitney U Test for unpaired groups is indeed the more appropriate test to compare\nthe data. However, given the small group size for damaging missense and nonsense variants\nin gnomAD (n=14) and BRAVO (n=7) , we have performed a power analysis using a t-test\napproximation (commonly used for Mann–Whitney U), comparing allele frequencies of\ndamaging and nonsense variants to those of intermediate and functional variants in these\ndatasets (FLOSSIES does not contain any damaging missense or nonsense variants). The\nachieved power was very low (<0.3; desired power min. 0.8 with significance level 0.05) ,\nmeaning the Mann-Whitney U Test will not be powerful enough to reliably detect true\ndifferences. To this end, we have removed the statistical analysis from the paper and now\nsolely describe the observed trends.\n-STATEMENT: “A similar trend was observed with the BRAVO and FLOSSIES databases\n(Wilcoxon Signed Rank Test, p<0.0001; Extended Data Fig. 3b, c and Supplementary Table\n2), which contain genome sequencing data and variants from more than >145,000 and 10,000\nnoncancer patients, respectively.” From Extended Data Fig. 3c and Supplementary Table 2, it\nappears that the FLOSSIES database did not contain any damaging PALB2 variants. This\nseems consistent with the nature of FLOSSIES as a dataset of germline genomic variation in\nhealthy older women. However, it is unclear how the statistical comparison was performed in\nthis case without a “damaging” category. Please consider clarifying the text accordingly.\nFor the reasons outlined in the previous point, we have removed the statistical analysis from\nthe paper and revised the text, solely describing the observed trends.\n-STATEMENT: “Collectively, the good correlation with data from PARPi-sensitivity and DR-\nGFP assays in mouse cells and SGE in human cells, as well as with those from large-scale\n4\nvariant databases of human populations, validates results from the site saturation functional\nscreens.”\nReferring simply to “mouse cells” or “human cells” is too generic and may be misleading. The\nassays were specifically performed in mouse embryonic stem (mES) cells and haploid human\nHAP1 cells. Please consider replacing “mouse cells” with mES cells and “human cells” with\nHAP1 cells for accuracy. There are additional references to “mouse cells” or “human cells”\nthroughout the manuscript; please also review and revise those accordingly.\nWe have changed “mouse cells” to “mES cells” and ”human cells” to “human HAP1 cells”\nthroughout the manuscript.\nSECTION: “Variants in the PALB2 WD40, but not CC domain, affect protein stability”\n-STATEMENT under review: “For all functional variants in the WD40 domain, PALB2\nexpression was comparable to that of wildtype. However, damaging variants generally showed\nvery low levels of expression (e.g. p.W912S; Fig. 5b), whereas intermediate variants often\npartially reduced PALB2 expression (e.g. p.R976G; Fig. 5b). In contrast, variants in the CC\ndomain generally showed near wildtype levels of PALB2 expression regardless the functional\nclassification, except for p.K20I, which showed reduced expression levels (Extended Data Fig.\n4c).”\nOUR PREVIOUS COMMENT (ROUND 1): “The analysis of the expression of PALB2 in the\nwestern blots is based on visual inspection which is hardly interpretable. Could the authors\nassess western blots outcomes using some kind of numerical values to avoid a subjective\ninterpretation? For example, in Fig. 5b and considering tubulin expression, the functional\nvariants S873R, L1150R, G937E seem to express less PALB2 than WT.”\nAUTHORS’ RESPONSE: “We have quantified the expressions levels of all PALB2 variants\nrelative to that of Tubulin (see Figure 5b, Extended Data Figures 4c and 8). While S873R was\nexpressed to a somewhat higher levels compared to wild-type (rel. expression level 1.26), the\nexpression of L1150R and G937E was modestly impaired (rel. expression 0.88 and 0.86,\nrespectively).”\nOUR COMMENT TO THEIR RESPONSE: We thank authors their work for quantifying the\nexpression level. However, if we are not mistaken, the relative expression level for S873R is\n0.26 rather than 1.26 (see current version of the Fig.5b).\nThe reviewer is right. We have corrected the relative expression level for S873R from 0.26 to\n1.26. We checked all quantifications again and additionally corrected the relative expression\nlevel for C993Y from 0.11 to 1.11. All other expression values were correct.\nIn addition, we believe that including the relative expression values for the variants cited in the\nabove statement (p.W912S, p.R976G, p.K20I) would substantially improve clarity and help\nreaders better grasp the findings. Check and add expression values if possible. Why were\nthey not added in the first place?\nThe relative expression values were presented in Figure 5b (0.22 for p.W912S; 0.70 for\np.R976G) and Extended Data Figure 4c (0.31 for p.K20I) of our revised paper, but have now\nalso been mentioned in the Results. Of note, Extended Data Figure 4c has now become\nExtended Data Figure 6c.\nThe section compares functional characterization with in silico predictions using Pearson\ncorrelation. While informative, Pearson correlation alone can be insufficient. To strengthen the\nconclusions, please consider adding:\n-ROC analyses, which could indicate which tool is the best in silico predictor for PALB2\nmissense variants, and\n-calculate sensitivity, specificity, PPV, NPV and accuracy with 2×2 contingency tables to\nprovide numerical support for the trends observed in the Extended Data Figures 6 and 7.\nAn ideal ROC-based evaluation of the in silico prediction tools would require a well-defined\nground truth set of proven pathogenic and benign missense variants in PALB2. Unfortunately,\nsuch a comprehensive and validated dataset does not currently exist.\nAs an alternative, we employed a set of missense variants that were individually tested using\nthe DR-GFP assay (current study and Boonen et al., Nat. Commun., 2019), which measures\nhomologous recombination (HR) activity. Variants displaying <30% HR activity were used as\na proxy for damaging variants (n = 41), while those with >70% HR activity were considered\nfunctional (n = 105). We acknowledge that these thresholds are arbitrary, but they provide a\npractical framework for comparative analysis in the absence of definitive classifications.\nThe ROC and Pearson correlation analysis show that, while Helix and AlphaMissense may\noffer reliable predictions for missense variants, particularly within the CC domain, overall Helix,\nAlphaMissense, EVE, and BayesDel, like other in silico models previously evaluated⁷,\ngenerally lack sufficient accuracy in predicting the functional impact of PALB2 variants.\nNotably, these models tend to lack specificity, often overestimating the damaging impact of\nvariants with high depletion scores. The ROC analysis and 2 x 2 contingency tables, with\ncalculated sensitivity, specificity, PPV, NPV and accuracy, have been added to new Extended\nData Fig. 9g-i and the Source Data file, and are described in the Results.\nSECTION: “Site-saturation functional screens aid in the clinical reclassification of missense\nVUS in PALB2”\n-STATEMENT: “For only 41 of the 2,779 (1.5%) VUS in the CC and WD40 domains no\ndepletion scores were available (Fig. 6a).” The numbers presented in Fig. 6a are inconsistent\nwith this statement and with Supplementary Table 3.\no In Fig. 6a, the first radial bar chart shows 2,925 VUS, which does not match the stated total\nof 2,779 VUS.\nWe have now clarified these numbers in both the main text and Fig. 6a (including the legend).\nSpecifically, we corrected the number 2,778 to 2,779 and included the 21 missense VUS\nlocated between the start and CC domain of PALB2 (see comment below). Thus, the 2,925\nSNVs correspond to 2,779 unique missense VUS in the CC domain, middle region and WD40\ndomain, plus 21 variants located between the start and CC domain, resulting in 2,800 unique\nmissense VUS overall.\nWe have now clarified these numbers better in the text by stating “As of June 30, 2025, 3,014\nSNVs in PALB2 were listed in ClinVar, 2,925 of which encode 2,800 unique missense VUS in\nthe pre-CC region, CC domain, middle region and WD40 domain (Fig. 6a and Supplementary\nTable 3).”.\no In addition, the second 2D pie chart in Fig. 6a indicates a total of 2,778 variants.\nPlease review these discrepancies and clarify which counts are correct.\nAs explained above, this should be 2,779. We have corrected this mistake.\n-STATEMENT: “Of note, for 21 ClinVar-reported missense VUS located between the start and\nCC domain of PALB2, no depletion scores were available as this region was not subjected to\nfunctional analysis (Fig. 6a and Supplementary Table 3).”\no These 21 missense VUS are not visible in Fig. 6a or Supplementary Table 3.\nPlease clarify the source of this number and ensure consistency across the text, figure, and\ntable.\nWe clarified this in the text by stating: “Of note, for 21 ClinVar-reported missense VUS located\nbetween the start codon and first codon of the CC domain of PALB2 (pre-CC region), no\ndepletion scores were available as this region was not subjected to functional analysis (Fig.\n6a and Supplementary Table 3).” These 21 ClinVar-reported variants are now presented in an\nupdated version of Fig. 6a, and are listed in a new version of Supplementary Table 3.\n-STATEMENT: “ClinVar listed 1,866 missense VUS in this region (Fig. 6a).”\no In Fig. 6a, the number reported is 1,865 variants, not 1,866.\nPlease revise and harmonize the numbers.\nThis should be 1,866 as mentioned in the text and listed in Supplementary Table 3a. We have\ncorrected this mistake in Fig. 6a.\n-STATEMENT UNDER REVIEW: “To provide additional support that these variants may be\nfunctional rather than damaging, we randomly chose 26 variants, along with 5 synthetic\nmissense variants in this region (Fig. 6b), and expressed them in the Palb2KO mES cells to\ndetermine the impact on HR (Extended Data Fig. 8).”\nOUR PREVIOUS COMMENT (ROUND 1): In the extended figure 8 there are only 16 variants\ndepicted. Please review it.\nAUTHORS’ RESPONSE: “The number of selected variants is correct. However, the figure\ncitations are not. We have corrected this, now mentioning the selected variants in Figure 6b\n(n=31), and their functional analysis using HR as a readout in Figure 6c. For a subset of these\nvariants functional analysis using PARPi sensitivity as a readout is shown in Figure 6d (n=12),\nwhile for another subset protein expression is presented in Extended Data Figure 8 (n=17).”\nOUR COMMENT TO THEIR RESPONSE: We could not find the correction to the figure\ncitations that the authors mentioned.\nPlease review it.\nThis was mentioned in the Results, but we have now tried to clarify this better in the Results\nby stating: “To provide additional support that these variants may be functional rather than\ndamaging, we randomly chose 26 variants, along with 5 synthetic missense variants in this\nregion (Fig. 6b), and expressed them in the Palb2KO mES cells to determine the impact on HR\n(Extended Data Fig. 10). Expression of all 26 missense VUS rescued HR in the DR-GFP\nreporter to levels comparable to that in cells expressing wild-type PALB2 (Fig. 6c). This result\nwas validated in PARPi assays, which demonstrated for 12 of these variants that expression\nrendered Palb2KO cells resistant to PARPi treatment (Fig. 6d). Corroborating these findings,\n16 of the missense VUS exhibited near wild-type expression, whereas a synthetic variant\ncausing a frameshift (p.S201fs) markedly reduced PALB2 expression, as expected (Extended\nData Fig. 10).”\n-STATEMENT: “These findings further indicate that the 1,865 missense VUS located in region\np.I44 to p.L855 are likely functional.”\no Here again, the total alternates between 1,865 and 1,866 variants.\nThis should be 1,866 and has been corrected in the text.\no According to Supplementary Table 3, the correct number appears to be 1,866.\nPlease verify and unify the figure/table/text values.\nThe correct number is 1,866. This has been corrected in the text and in Figure 6a.\nDISCUSSION\n-STATEMENT: “However, genetic testing frequently reveals missense VUS in PALB2 for which\nthe impact on protein function and cancer risk are unclear (as of June 2025, >2,900 VUS in\nPALB2 were listed in ClinVar).”\nPlease ensure consistency with the discrepancies noted in our related comment on the\nIntroduction section: the Supplementary Table 3 has listed 2,779 missense variants as\nuncertain significance (VUS).\nWe have clarified this by stating in the Introduction: “genetic testing reveals single nucleotide\nvariants (SNVs) in PALB2 that are classified as VUS (as of June 30, 2025, >2,900 SNVs in\nPALB2 were listed as VUS in ClinVar)”. Moreover, we now also state in both the Results and\nDiscussion: “(As of June 30, 2025, 3,014 SNVs in PALB2 were listed in ClinVar, 2,925 of which\nencode 2,800 unique missense VUS (Fig. 6a and Supplementary Table 3).”\n-STATEMENT: “However, in silico analysis using SpliceAI (threshold >0.5) suggests that only\n22 of the 6,718 missense variants (0.33%) tested in our site-saturation functional screens are\nencoded by nucleotide variants predicted to have an impact on PALB2 mRNA splicing\n(Supplementary Table 4). This indicates that these variants mostly impair PALB2 functionality\nat the protein level”.\nAccording to Supplementary Table 4, SpliceAI predictions are available only for a subset of\nthe unique 6,718 missense variants. If we are correct, predictions exist for 2,078 unique single\nnucleotide variants, of which 22 are above the threshold (>0.5). This corresponds to 1.1%,\nnot 0.33%. Please review the denominator used in this calculation and clarify whether the\n6,718 or the 2,078 variants represent the correct reference set for the SpliceAI analysis.\nSpliceAI predictions exist for 2,078 missense variants encoded by SNVs. The reviewer is right\nthat this is the correct reference set and that 2,078 should have been the denominator used\nin the calculation. We have corrected this mistake by changing the text, which now states:\n“However, in silico analysis using SpliceAI (threshold >0.5) indicates that only 22 out of the\n2,078 missense variants (1.06%) assessed in our site-saturation functional screens are\nencoded by SNVs predicted to impact PALB2 mRNA splicing (see Supplementary Table 4).\nThis indicates that these variants mostly impair PALB2 functionality at the protein level”.\n-STATEMENT: “This indicates that these variants mostly impair PALB2 functionality at the\nprotein level (ref 35)”.\nReference 35 corresponds to SpliceAI, which does not align with this conclusion. Please\nconsider either removing the citation here or moving it to the previous sentence, e.g.:\n“However, in silico analysis using SpliceAI (threshold >0.5) (ref 35) …”\nWe have corrected this by moving the citation to the previous sentence.\n-STATEMENT: “Of note, only 20 of the 4,758 missense variants (0.42%) in the region between\nthe CC and WD40 domain are predicted to be encoded by nucleotide variants that impact\nmRNA splicing (SpliceAI threshold >0.5; Supplementary Table 4). This is in line with our\nconclusion that few damaging variants conferring an increased cancer risk will be identified in\nthis region of PALB2. Supporting this notion, ClinVar-reported missense variants in the region\nbetween the CC and WD40 domains were not associated with an increased risk of breast\ncancer (Supplementary Table 5).”\nThe current phrasing is hard to follow and may be misleading. The 20 variants predicted to\nimpact splicing could indeed be pathogenic due to splicing alterations. As written, the\nstatement appears to blur the distinction between potential spliceogenic effects and the\nconclusion that there are few damaging missense variants impairing protein function in this\nregion. Please consider rewording. For example: “Of note, SpliceAI (threshold >0.5) predicts\nthat only 20 of the 4,758 missense variants (0.42%) in the region between the CC and WD40\ndomains may impact mRNA splicing (Supplementary Table 4). These variants could be\npathogenic due to splicing alterations. However, outside of these few spliceogenic cases, our\ndata suggest that missense variants in this region rarely impair PALB2 protein functon(Fig 1,\nFig 6b c and d). Supporting this notion, ClinVar-reported missense variants in this region were\nnot associated with an increased risk of breast cancer (Supplementary Table 5).”\nWe have changed the wording in the text according to the reviewer’s suggestion.\n-STATEMENT: “In contrast, our study showed that damaging missense variants in PALB2 are\nassociated with an increased risk of breast cancer, similar to that observed for PTVs.”\nPlease add Table 1 at the end of this statement to facilitate reader understanding and highlight\nthe finding.\nWe have added a reference to Table 1 at the end of this sentence.\nMATERIALS AND METHODS\n-A statistical methods section appears to be missing. Please consider adding a brief\ndescription of the software used to perform the different statistical tests.\nThe type of statistical test used is specified in each figure legend, in accordance with the\njournal’s style. Additionally, we have included the following statement in the Data Analysis\nsection of the Materials and Methods: “All statistical analysis was performed using Graphpad\nPrism (version 10.2.3).”\nFIGURES\n-Fig.1a: Please replace “Nucleotide nr” with “cDNA nr”, or clarify in the figure legend that it\nrefers to cDNA coordinates. The term “Nucleotide” maybe confusing, as it could also be\ninterpreted as referring to a genomic position. Also review accordingly the “Extended Data Fig.\n1”.\nWe have changed “Nucleotide nr.” To “cDNA nr.” in Fig. 1a and the legend to Fig. 1a.\nAdditionally, we have updated “Nucleotide variant” to “Nucleotide variant (cDNA)” to clarify\nthat the listed changes refer specifically to cDNA alterations.\n-Legend of Fig. 3: “Grey dots represent the original (wild type) amino acids.” Wild-type is used\nas an adjective, so it should include a hyphen.\nWe have corrected this mistake.\n-Fig.6b: The missense variant L704P is incorrectly shown in blue in the schematic\nrepresentation of the PALB2 protein. It should be displayed using the colour assigned to\nsynthetic variants. Please correct this.\nThe L704P is correctly displayed using the light purple colour for synthetic variants.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nThank you for revising the manuscript. In my opinion, the reviewers' comments have been\naddressed appropriately.\nReviewer #4 (Remarks to the Author):\nThanks to the authors for submitting an improved manuscript. In general, my major concerns\nhave been partially addressed. I am withholding my support of publication until further\nimprovements are made. The minimal changes I find to be essential are included as bullets\nbelow, and I anticipate these should not take more than a day or two to complete.\nthe right direction. However, the new plots (SF1/SF2) reveal highly noisy replicates. Putting\nthese plots in as Supplementary Figures and not EDFs made these very hard for me to find\nas a reviewer. I imagine most readers will never see them, which feels deliberate. These\nfigures are highly important, as they show that technical noise is a major limitation of the study,\nwith implications for clinical interpretation. Therefore, I insist the cross-replicate reproducibility\ndata be represented in EDFs. What’s more, the degree of noise is still hard to assess fully,\nbecause no correlation coefficients are included with the plots.\n- Include EDFs showing the correlations between replicates with coefficients provided on the\nplots or as an additional figure panel displaying the coefficients between biological replicates\n(e.g. a heatmap).\nAll plots showing correlations between replicates for variants in the CC and WD40 domain are\nnow provided in Extended Data Figures 2 and 3, respectively. Pearson correlations are\nindicated at each plot. All correlates were significant with p-values equal or smaller than 0.0001\nas indicated in the legends of these Extended Data Figures.\nThe authors claim that: “Biological replicates generally showed good correlation between\ndepletion scores of variants in both the CC (Supplementary Fig. 1) and WD40 domain\n(Supplementary Fig. 2).”\n- This needs revision in light of the highly sub-optimal correlations plotted. Change to:\n‘Depletion scores from biological replicates were generally correlated between biological\nreplicates.’\nWe have added the following description of the correlation analysis to the Materials and\nMethods to clarify what we mean with terms such as “good”: ”Pearson’s correlation coefficients\n(r) were used to assess the strength of linear relationships between variables. Positive\ncorrelations were classified as weak (r < 0.25), moderate (0.25 ≤ r < 0.50), good (0.50 ≤ r <\n0.75) and very good (r ≥ 0.75), while negative correlations were classified as weak (r > -0.25),\nmoderate (-0.25 ≥ r > -0.50), good (-0.50 ≥ r > -0.75) and very good (r ≤ -0.75). Statistical\nsignificance was determined using corresponding p-values.”. We have adjusted the use of\nthese descriptive terms throughout the manuscript according to the r-values from the\nrespective correlation analyses, to ensure greater precision. Accordingly, we have changed\nthe sentence to: “Depletion scores from biological replicates were generally correlated\nbetween biological replicates.” to “Biological replicates generally showed a moderate but\nsignificant correlation between depletion scores of variants in both the CC (Extended Data\nFig. 2) and WD40 domain (Extended Data Fig. 3) based on Pearson correlation analysis (see\nMaterials and Methods).”\nThe stats mentioned in the rebuttal regarding 270 (80%) nonsense variants being damaging\nand 20 (43%) synonymous variants being scored functional are crucial in that they concretely\ndescribe the modest predictive power of the data. I do not see these numbers actually included\nin the revised Results section, as the authors have claimed them to be in their rebuttal. This\nis essential to include. For instance, the authors state that 58% of missense variants assayed\nwere deemed “functional”. It is important to understand that this is actually a greater fraction\n11\nof missense variants being deemed functional than synonymous variants, for which there is\nno explanation.\n- Please include these stats (on the 270 nonsense variants and 20 synonymous variants) in\nResults.\nThe numbers were obtained from Supplementary Table 1 by filtering for damaging variants\namong the nonsense variants. Specifically, 270 out of 339 nonsense variants were classified\nas damaging, corresponding to 80%. Similarly, by filtering for functional variants among the\nunique synonymous variants, we found that 18 out of 42 variants were classified as functional,\ncorresponding to 43%. We have added these numbers to the Results by stating: “Applying\nthese thresholds (Fig. 4b), we classified 270 out of 339 nonsense variants (80%) as damaging\nand 18 out of 42 unique synonymous variants (43%) as functional. Among the missense\nvariants, 392 (6%) were categorized as damaging, 2,422 (36%) as intermediate, and 3,904\n(58%) as functional (Fig. 4c and Supplementary Table 1).”\nThe mixture model is now described better in the text, but the figure caption for 4b remains\nunclear: “ROC curve showing 98% sensitivity and specificity for the classification of the\nvariants in ‘a’.” This is a key performance metric. As I understand from the rebuttal, this curve\nis only for nonsense and synonymous variants (so it is not actually for all variants in ‘a’, making\nthe figure legend inaccurate). This would be clear if the numbers of synonymous and\nnonsense variants were properly specified on the plot or in the legend.\n- Add these sample sizes as required in a revised figure legend.\nWe have added the sample sizes in the figure legend by saying: “To generate the ROC curve,\na two-component mixture model was fitted to the depletion scores of 339 nonsense and 49\nsynonymous variants.”\nThe use of “site-saturation screen” vs. “deep mutational scan” is still incorrect. ‘Site-saturation’\nrefers to changing one residue in a protein to all possible amino acids. Deep mutational scanning is when the same mutagenesis strategy is applied to many consecutive residues, or\nall residues of a protein. The authors have performed a deep mutational scan and should use\nthe correct terminology. A recent review nicely explains the origins and use of these\nterms: https://www.nature.com/articles/s41576-025-00870-x\n- “A deep mutational scan of single nucleotide changes” doesn’t make sense. “Deep mutational\nscan” must be taken out here as DMS (all possible amino acid substitutions) was not\nperformed. Replace with “we assayed nearly all possible single nucleotide changes in exon\n10…”\nA site-saturation screen is a high-throughput experimental technique to systematically assess\nthe functional impact of all possible amino acid substitutions at specific positions within a\nprotein or gene. Accordingly, this term is appropriately used to describe the screen we\nperformed in mES cells, in which we evaluated the functional consequences of all possible\namino acid substitutions in the CC and WD40 domains of PALB2.\nfunctional effects of a large number of mutations across a protein or gene. Given that we\nexamined the impact of a broad set of single-nucleotide variants (SNVs) in exon 10 of the\nPALB2 gene, we consider the use of this term to be accurate.\n- I would strongly suggest the authors replace “site-saturation screen” with deep mutational\nscan throughout the paper, but I leave this to editorial discretion.\nThis point has been addressed in our response to the preceding and related comment.\n13"}]

Decision letter