Integrating breast tumor homologous recombination deficiency status to aid germlineBRCA1andBRCA2variant classification

Integrating breast tumor homologous recombination deficiency status to aid germline BRCA1 and BRCA2 variant classification | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var 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, Heather Thorne , G Bruce Mann , Anita R Skandarajah , Lisa Devereux , Qihong Zhao , Dilanka L De Silva , Geoffrey J Lindeman , Paul A James , Ian Campbell , Amanda B Spurdle , Nicola Waddell doi: https://doi.org/10.1101/2025.06.12.25329237 Cristina Fortuno 1 QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jia Zhang 1 QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lambros T Koufariotis 1 QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Georgina Hollway 1 QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Scott Wood 1 QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site John V Pearson 1 QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter T Simpson 2 UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences, University of Queensland , Brisbane, QLD, Australia 3 School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural Sciences, University of Queensland , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sunil R Lakhani 2 UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences, University of Queensland , Brisbane, QLD, Australia 4 Pathology Queensland, Royal Brisbane and Women’s Hospital , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amy E McCart Reed 2 UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences, University of Queensland , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Heather Thorne 5 Sir Peter MacCallum Department of Oncology, The University of Melbourne , Melbourne, VIC, Australia 6 Peter MacCallum Cancer Centre , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site G Bruce Mann 6 Peter MacCallum Cancer Centre , Melbourne, VIC, Australia 7 The University of Melbourne , Melbourne, VIC, Australia 8 The Royal Melbourne Hospital , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anita R Skandarajah 6 Peter MacCallum Cancer Centre , Melbourne, VIC, Australia 7 The University of Melbourne , Melbourne, VIC, Australia 8 The Royal Melbourne Hospital , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lisa Devereux 5 Sir Peter MacCallum Department of Oncology, The University of Melbourne , Melbourne, VIC, Australia 6 Peter MacCallum Cancer Centre , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qihong Zhao 6 Peter MacCallum Cancer Centre , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dilanka L De Silva 7 The University of Melbourne , Melbourne, VIC, Australia 9 Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Geoffrey J Lindeman 6 Peter MacCallum Cancer Centre , Melbourne, VIC, Australia 7 The University of Melbourne , Melbourne, VIC, Australia 9 Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital , Melbourne, VIC, Australia 10 The Walter and Eliza Hall Institute of Medical Research , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul A James 5 Sir Peter MacCallum Department of Oncology, The University of Melbourne , Melbourne, VIC, Australia 6 Peter MacCallum Cancer Centre , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ian Campbell 5 Sir Peter MacCallum Department of Oncology, The University of Melbourne , Melbourne, VIC, Australia 6 Peter MacCallum Cancer Centre , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amanda B Spurdle 1 QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: nic.waddell{at}qimrb.edu.au amanda.spurdle{at}qimrb.edu.au Nicola Waddell 1 QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: nic.waddell{at}qimrb.edu.au amanda.spurdle{at}qimrb.edu.au Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Summary Pathogenic germline variants in certain genes are associated with somatic tumor mutation signatures. The use of somatic tumor mutation data has the potential to improve the identification of true pathogenic variants but remains underexplored. We investigated the integration of tumor homologous recombination (HR) deficiency status as a predictor of pathogenicity for germline BRCA1 and BRCA2 variants, building on the established link between HR deficiency and germline pathogenic variants in these genes. We analyzed breast tumor whole-genome sequence and matching germline data from 350 patients across four datasets: Familial Breast Cancer (N=77), The Cancer Genome Atlas (TCGA-BRCA, N=96), the MAGIC study (N=136), and Q-IMPROvE (N=41). A total of 13,364 germline variants (including structural variations) in BRCA1 , BRCA2 , and other DNA repair genes ( ATM , BARD1 , BRIP1 , CHEK2 , PALB2 , PTEN , RAD51C , RAD51D, TP53 ) underwent variant curation. Patients were categorized based on germline classification as BRCA1 positive (N=25), BRCA2 positive (N=21), and BRCA1/2 negative (N=149), excluding those with BRCA1/2 variants of uncertain significance (N=21) and pathogenic variants in other DNA repair genes (N=134). Somatic HR status (deficient or proficient) was predicted using three algorithms: HRDetect, CHORD, and HRDSum. HR-deficient and HR-proficient status were significant predictors of germline BRCA1/2 pathogenic variant status (positive and negative directions). The CHORD algorithm, which estimates BRCA1 and BRCA2 subtype specifically, added precision contributing evidence towards pathogenicity for the corresponding gene. Finally, we assessed CHORD HR predictions for variants of uncertain significance in BRCA1 and BRCA2 , and reported their tumor HR status for potential use as additional evidence in variant curation. Analysis across multiple tumor whole-genome sequencing datasets has shown that HR status prediction algorithms can separate profiles for BRCA1 and BRCA2 pathogenic variants and provide further evidence at increased weight to aid in the classification of germline BRCA1 and BRCA2 variants. Tumor sequencing offers a promising strategy for reducing the uncertainty in germline variant interpretation. Introduction Homologous recombination deficiency (HRD) impairs a cell’s ability to effectively repair DNA double-strand breaks and underlies tumorigenesis in a significant proportion of breast, ovarian, prostate, and pancreatic cancers. 1 Approximately 18-30% of breast cancers have been reported to exhibit HR deficiency. 2 – 4 Pathogenic germline variations in the hereditary breast and ovarian cancer susceptibility genes BRCA1 and BRCA2 , which are crucial for DNA repair through the HR repair pathway, are strongly associated with HRD at the tumor level. 5 However, HRD has also been associated to some degree with pathogenic germline variation in other DNA repair genes such as PALB2 6 , 7 and RAD51C. 8 Somatic mutation signatures linked to HRD in BRCA-associated tumors 9 , 10 , 11 have driven the development of several tools to predict HR status, including HRDSum, 12 HRDetect, 4 CHORD, 13 HRDCNA, 14 and HRProfiler. 15 Increasing evidence indicates that a tumor with HRD, regardless of germline pathogenic variant status of the patient, predicts response to poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitor therapy in breast, 16 ovarian 17 and prostate cancer 18 patients. As clinical trial evidence accumulates, tumor genomic profiling is expected to become central to the clinical management of patients at diagnosis. This represents a timely opportunity to evaluate a different clinical use of HRD to guide the management of cancer patients - the value and feasibility of using breast tumor tissue profiles generated at the time of diagnosis to aid BRCA1/2 germline variant classification. Despite the growing understanding of inherited genetic variations in cancer genes, and the establishment of ClinGen Variant Curation Expert Panels to develop gene-specific classification guidelines, the number of variants of uncertain significance (VUS) that are not clinically actionable, remains a substantial clinical challenge. The incorporation of many types of evidence in germline variant classification algorithms improves the classification of individual VUS. The use of tumor data to aid variant classification has already been investigated. For example, observing somatic variants that are commonly found in tumors can serve as evidence towards the pathogenicity of the same variant observed in the germline setting 19 . While, tumor histopathology features has been used to predict the pathogenicity of germline variants in several genes, including BRCA1/2 , TP53 and mismatch repair genes. 20 – 23 In this study, we utilize the known association between HRD and BRCA1/2 -associated breast cancer to explore the use of tumor HRD status in predicting BRCA1 and BRCA2 germline variant pathogenicity. Methods Cohorts of breast cancer patients The analysis included whole-genome sequencing (WGS) data of 350 primary breast tumor and blood DNA (germline) samples from patients within four study cohorts ( Supplementary Table 1 ): kConFab Familial Breast cohort (referred to as Familial Breast), comprising individuals with a personal or family history suggestive of hereditary breast cancer; 24 , 7 TCGA-BRCA, representing a diverse cohort of individuals with various clinical, genomic, and molecular characteristics of breast cancer; 25 the MAGIC study (MAGIC), including women with invasive or high grade in situ breast cancer and unknown germline status; 26 and Q-IMPROvE, a cohort of breast cancer samples that underwent WGS prior to treatment as part of a pilot study to test the value of using WGS in the neoadjuvant setting. 27 Case and sample de-identified IDs used were the same for the previously published studies (Familial Breast, TCGA-BRCA and Q-IMPROvE), while the identifiers for the MAGIC cohort underwent a two-step de-identification. All tumor samples consisted of fresh frozen tissue except for the MAGIC cohort, which comprised formalin-fixed paraffin-embedded (FFPE)-derived samples. Samples with less than 20x sequencing coverage in tumor or normal samples were excluded. These datasets were used to call somatic variants in tumors for HR prediction, and germline variants in known breast cancer predisposition genes ( BRCA1 , BRCA2 , ATM, BARD1 , BRIP1 , CHEK2 , PALB2 , PTEN , RAD51C , RAD51D, and TP53 ). Somatic variant calling WGS data from tumor and patient-matched germline samples were used to identify somatic single-nucleotide variants (SNVs) and insertions and deletions (indels). Specifically, short-read sequencing reads were aligned to the human genome assembly (GRCh38) using BWA-MEM. 28 Somatic SNVs were identified using a dual calling strategy using the intersection of the post-filtered output of qSNP 29 and GATK 30 . Short insertions and deletions (1-50 bp) were detected with GATK. Sequence alterations were annotated with qannotate to identify somatic-specific variants and to filter variants located within 6 base pairs of a homopolymer region, and with SnpEff 31 for gene consequence. Somatic copy number aberrations (CNA) were identified using the tool ascatNGS. 32 The copy number state of each gene was determined by annotation against known Ensembl genes (version 112). Structural variants (SVs) were determined using qSV; 33 https://github.com/AdamaJava/adamajava ) using both tumor and germline alignments and subsequent filtering to include high-confidence calls in the analysis. Single-base substitution signature analysis The Fit function of the R package signature.tools.lib (v2.4.5) was used to estimate the number of single-base-substitution COSMIC (v2) signatures based on the somatic SNV catalogues of each tumor sample with 1000 bootstraps. The proportions of each signature were calculated based on the total number of variants. Correction of the FFPE mutational signature in the MAGIC cohort Genomic analysis of DNA extracted from FFPE-derived samples can be problematic, as formalin fixation negatively impacts DNA quality and quantity compared to fresh frozen material. The MAGIC cohort tumor DNA was derived from FFPE samples, therefore FFPEsig ( https://github.com/QingliGuo/FFPEsig ) was used to correct the FFPE noise signatures from the observed mutational catalogues within these samples. This was performed using the unrepaired mode (without uracil DNA glycosylase). 34 The corrected profiles were then used in the signature analysis and for the HR prediction. Homologous recombination deficiency predictions The somatic tumor variant profiles were analyzed for each individual to predict HR status, classified as deficient (HRD) or proficient (HRP) using three prediction methods, CHORD, 13 HRDetect, 4 and HRDSum. 12 The CHORD pan-cancer HR predictor uses a random forest model trained with samples from different cancer types. 13 CHORD uses the somatic SNVs, indels and SVs as input, and primarily infers HRD from the relative proportions of microhomology-mediated deletions and 1-10kb duplications. Additionally, CHORD utilizes 1-100kb structural duplications to distinguish BRCA1 subtype HRD from BRCA2 subtype HRD. A score of >0.5 is considered to represent deficiency. 13 The HRDetect method uses the mutational signatures to predict BRCA1/2 deficiency as a surrogate for HR deficiency, 4 including base substitution signatures 3 and 8, indel patterns, SVs and the copy number-based score HRD loss of heterozygosity (LOH). The HRDetect_pipeline function of signature.tools.lib was used for the HRDetect implementation with 1000 bootstraps and “Breast” specific signatures. A score of >0.7 is considered to represent deficiency. 4 HRDsum score is a summary score previously used in clinical trials and is based on LOH, 35 large-scale state transitions 36 and the number of telomeric allelic imbalances 37 calculated from the somatic copy number profile. 12 The HRDsum score was estimated for each sample using the modified scripts of the R package scarHRD. 38 A cutoff point of 42 is currently considered an US Food and Drug Administration-approved biomarker to select ovarian cancer patients for PARP inhibition, 39 which we used to differentiate between deficiency and proficiency in this study. Germline variant calling and annotation The germline sequence data were processed with the GATK best practice workflow to detect germline SNVs and small indels. The nanno module of qannotate ( https://github.com/AdamaJava/adamajava ) was used to annotate SNVs against dbNSFP 40 (v4.1a), ClinVar 41 , and gnomAD 42 (v3.1.2). The annotations for the nine breast cancer susceptibility genes ( BRCA1 , BRCA2 , ATM, BARD1 , BRIP1 , CHEK2 , PALB2 , PTEN , RAD51C , RAD51D, and TP53 ) were extracted, and unique SNVs and indels were collected across 350 individuals. SVs in the germline genome were identified using DELLY (0.7.8) for each individual. SVs overlapping with target genes within a 100bp window were extracted for classification. A total of eight SVs in the related genes from six individuals, which were classified as pathogenic based on the Variant Effect Predictor consequence predicted null (i.e. frameshift, stop loss, coding sequence variants involving complex rearrangements in clinically relevant exons), were included for analyses as high-confidence germline variants. Germline variant curation and individual groupings All variants were reported and classified in relation to the MANE transcripts, as follows: NM_007294.4 ( BRCA1 ), NM_000059.4 ( BRCA2 ), NM_000051.4 ( ATM ), NM_000465.4 ( BARD1 ), NM_032043.3 ( BRIP1 ), NM_007194.4 ( CHEK2 ), NM_024675.4 ( PALB2 ), NM_000314.6 ( PTEN ), NM_058216.3 ( RAD51C ), NM_002878.4 ( RAD51D ), and NM_000546.6 ( TP53 ). Germline SNVs and indels were classified using a combination of ClinVar lookups and summary data review, filtering allele frequency (FAF) ≥0.0001 in the gnomAD database, variant effect, and bioinformatic prediction of variant impact using BayesDel and maximum SpliceAI delta score. Variants were collapsed into one of three groups – pathogenic/likely pathogenic (P/LP), benign/likely benign (B/LB) or variant of uncertain significance (VUS). Variants in BRCA1 and BRCA2 were additionally classified following the ENIGMA BRCA1 and BRCA2 Variant Curation Expert Panel (VCEP) specifications v1.1.0. 43 Each variant was assigned a category related to whether the variant was within BRCA1/2 or other genes ( ATM, BARD1 , BRIP1 , CHEK2 , PALB2 , PTEN , RAD51C , RAD51D, and TP53 ). The categories were as follows ( Supplementary Table 2 ): A: P/LP_Other genes (where suspicious VUS were also conservatively included), B: VUS_Other genes, C: B/LB = Other genes, D: P/LP_BRCA, E: VUS_BRCA, and F: B/LB_BRCA. The eight high-confidence pathogenic germline SVs identified using DELLY (three in BRCA1 , three in BRCA2 , one in PALB2 , and one in RAD51C ) were included in the relevant germline categories. Based on these categories and their combinations in the same individual, cases were grouped into four different groups for analysis ( Supplementary Table 3 ). These groups included BRCA1 and BRCA2 positive groups (individuals carrying a P/LP variant in BRCA1 or BRCA2 , respectively) and a BRCA1/2 negative group (individuals without a detectable BRCA1/2 P/LP variant). To avoid the confounding effect of pathogenic variants or VUS in the DNA repair genes that may be linked to tumorigenesis, two additional groups were created, termed Excluded (individuals with P/LP or suspicious VUS in other selected DNA repair genes), and BRCA1/2 VUS (individuals with VUS in BRCA1 or BRCA2, and with no other P/LP variants in any genes); both of these additional groups were excluded from the BRCA1/2 positive and negative groups used for main analyses. Likelihood ratio calculations Likelihood ratios (LRs) associated with each HR dichotomous status (HRP vs HRD) were calculated as predicted by each tool (HRDetect, CHORD, and HRDsum), using previously used methods; 20 this involved comparison of the proportion of HRD-predicted tumors observed for BRCA1/2 negative individuals compared to that observed for BRCA1 positive individuals, and separately for BRCA2 positive individuals. For CHORD-predicted, the LRs were estimated by stratifying HRD status further into BRCA1 and BRCA2 HRD subtypes. A sensitivity analysis was performed including individuals in the Excluded groups. The LRs were used to assign an ACMG/AMP evidence strength category and points using Bayesian conversions. 44 , 45 Correlation with histopathology data We collected tumor histopathology data comprising histological grade and hormone receptor status (ER, PR, HER2, and the combined triple-negative breast cancer, TNBC). Samples with missing histopathology data for a given variable were excluded from corresponding analysis. To examine the association between tumor pathological measures (grade and hormone receptor status), we performed chi-square tests. Effect size was estimated using Cramér’s V to assess the strength of association. Correlations between HR status, as predicted by the different tools, and tumor histopathology markers (grade and hormone receptor status) were analyzed using Cramér’s V to assess the significance of associations. In addition, for CHORD we compared the BRCA1-type HRD probability scores between TNBC and non-TNBC samples using the Wilcoxon rank-sum tests. Results WGS somatic and paired germline data from 350 breast cancer patients within four cohorts were processed (Supplementary Table 1) . Three of these cohorts (MAGIC, TCGA-BRCA and Q-IMPROVE) were comprised of unselected breast cancer patients, and the fourth cohort comprised patients with familial cancer. The average tumor purity of samples was 0.58 (Familial breast: 0.56, TCGA-BRCA: 0.60, MAGIC: 0.60 and Q-IMPROvE: 0.46) ( Supplementary Table 1 and Supplementary Figure 1A ). To harmonize the data, all data were re-processed with the same pipeline for somatic variant detection. Samples from the MAGIC cohort yielded fewer somatic variants, with a lower tumor mutation burden (TMB) compared to other cohorts ( Supplementary Figure 1B ) and fewer somatic SV events (p=2.2e-16) ( Supplementary Table 1, Supplementary Figure 1C ), likely due to the impact of FFPE-derived samples on DNA quality and lower read depth in these tumor samples. Classification of patients based on BRCA1/2 and other gene variants Across the 350 individuals, a total of 13,364 germline SNVs, indels, and SVs were detected in BRCA1 , BRCA2 and non- BRCA1/2 genes, including HR-related genes ( BARD1 , BRIP1 , PALB2, PTEN, RAD51C , RAD51D, and TP53) as well as ATM and CHEK2 . These germline variants were grouped into six categories as defined in Figure 1A and Supplementary Table 2 . Each individual person was then placed into a germline group based on germline pathogenic variant status for all genes: 25 individuals were classified as BRCA1 positive, 21 as BRCA2 positive, 149 as BRCA1/2 negative, 134 were excluded from analyses (Excluded) as they were identified to have P/LP variants in other non- BRCA1/2 genes, and 21 were considered for additional analyses ( BRCA1/2 VUS) ( Figure 1B and Figure 1C ). Download figure Open in new tab Figure 1. Germline variant classifications and individual allocation into a germline group based on the presence of germline variants. A) The total number of germline variants (including SNVs, indels and SVs) identified in the BRCA1 , BRCA2 , ATM , BARD1 , BRIP1 , CHEK2, PALB2, PTEN, RAD51C , RAD51D and TP53 genes. Each variant was classified into six categories (A to F) as defined in the boxes. B) The alluvial diagram illustrates the allocation of 350 individuals into five color-coded germline groups (dark blue: BRCA1 positive; red: BRCA2 positive; pink: Exclude; teal: BRCA1 or BRCA2 VUS; grey: BRCA1/2 negative). Individuals were classified into each group based on the four criteria displayed along the y-axis. C) The bar plot displays the individuals classified into five groups within each patient cohort (Familial Breast, TCGA-BRCA, MAGIC and Q-IMPROvE) HR profile according to cohort and prediction method Three methods for HR prediction, based on tumor genomic scars, were used to predict the tumor HR status of the 350 samples. We used the previously published thresholds to determine whether a sample was HRD: CHORD >0.5, 13 HRDetect >0.7, 4 and HRDSum >42. 12 The HRDetect and CHORD predicted HRD probabilities showed distinct separation at the selected thresholds for classifying HR status ( Figure 2A and 2B ), while HRDsum scores had no clear distinction between HRD and HRP breast tumors irrespective of threshold ( Figure 2C ). The two machine-learning-based approaches, HRDetect and CHORD, were highly concordant with each other and consistently identified 104 HRD and 241 HRP samples ( Figure 2D and Supplementary Figure 2 ). Only five samples were predicted differently by HRDetect and CHORD. One sample was predicted HRD by CHORD but proficient by HRDetect. This sample was in the Excluded group with a germline LP/P variant in PALB2 (category A). The other four samples were predicted as HRD by HRDetect but not CHORD: one in the BRCA1/2 negative group, and three samples in the Excluded group ( Figure 2D , Supplementary Table 4 ). The non-congruent HR classification for these samples may be attributed to a small number of SV events in one case (MAGIC37, N=8), or the presence of VUS in other selected DNA repair genes ( PALB2 , ATM or BRIP1 ) Supplementary Table 4 ). Download figure Open in new tab Figure 2. Comparison of HR status predictions using three approaches. The distribution of prediction scores or probabilities for all tumor samples using three methods: (A) CHORD, ( B ) HRDetect, and ( C ) HRDsum, with cutoff values indicated as grey dashed lines in the plots. D ) The comparison of HR prediction between CHORD and HRDetect. HRD samples are labelled as blue points and HRP samples as yellow points. Five samples with inconsistent predictions between CHORD and HRDetect are labelled in grey. E ) The number of samples predicted as HRD or HRP by the three tools indicated on the y-axis. The bars are colored by the proportion of individuals within each classification group (Dark blue: BRCA1 positive; red: BRCA2 positive; pink: Exclude; teal: BRCA1 or BRCA2 VUS; grey: BRCA1/2 negative) HRDsum identified more HRD samples than HRDetect and CHORD, predicting 40 samples in the BRCA1/2 negative group as HRD ( Figure 2E ). In comparison, HRDetect and CHORD only predicted 21 and 20 BRCA1/2 negative samples as HRD, respectively. This suggests that HRDsum may overcall HRD samples, particularly in those non-familial breast cohorts ( Supplementary Figure 3 ). All individuals carrying a pathogenic BRCA1 germline variant were consistently predicted as HRD by all three methods. However, HRDsum predicted two BRCA2 -positive individuals as HRP, while HRDetect and CHORD only predicted one of them as HRP ( Figure 2E ). Interestingly, this BRCA2 positive case predicted HRP using all tools did not show evidence of a “second hit” in the tumor, as previously reported, 7 whereas all other individuals with BRCA1 or BRCA2 pathogenic germline variants showed evidence of a somatic event leading to loss of the reference allele, or gain of the variant allele ( Supplementary Table 4 ). This suggests that the HR proficient tumor from the BRCA2 positive individual is likely unrelated to the BRCA2 germline variant, and due to another unknown mechanism of tumorigenesis. Overall, the proportion of HRD tumors in each of the four cohorts was consistent with the proportion of BRCA1/2 positive individuals included in that cohort, being highest in Familial breast where approximately 50% of the individuals carried BRCA1/2 germline pathogenic variants, and lowest in MAGIC where there was only one BRCA2 positive individual ( Figure 1C ). The MAGIC cohort consisted of sequence data from FFPE samples, therefore, we tested whether a signature correction for SNVs would alter the HR status prediction. Although there were minor differences in the score assigned, the prediction of HR status did not change after the signature correction for CHORD ( Supplementary Figure 4A ) or HRDetect ( Supplementary Figure 4B ). Prediction of BRCA1 and BRCA2 -associated HRD subtype using CHORD In addition to differentiating between HR deficient and proficient status, the CHORD tool can predict whether an HRD case is likely associated with BRCA1 or BRCA2 by providing an HRD subtype. CHORD subtype prediction of the 105 HRD samples identified 60 as BRCA1 type (32 in Familial breast, 17 in TCGA-BRCA, two in MAGIC, and nine in Q-IMPROvE), and 40 as BRCA2 type (21 in Familial Breast Cancer, 13 in TCGA-BRCA, five in MAGIC, and one in Q-IMPROvE) ( Figure 3A ). The remaining HRD samples (four in MAGIC and one in Q-IMPROvE), none falling in the BRCA1/2 positive germline groups, had an undetermined subtype as they could not be assigned to BRCA1 or BRCA2 subtype. While some of these undermined samples may be driven by a small number of somatic SV events detected in these samples, the presence of germline or somatic variation in other genes (e.g. PALB2 in MAGIC32, MAGIC37) may also contribute to the undetermined subtype categorization. Further training of CHORD on HRD cases associated with other genes (not BRCA1 or BRCA2 ) may improve the prediction of these cases. Download figure Open in new tab Figure 3. HRD subtype predicted by CHORD and samples from germline classification groups for different HR statuses. A) The number of HRD samples differentiated as BRCA1 and BRCA2 subtypes or undetermined (five samples). Samples are grouped by the study cohort (Familial breast, TCGA-BRCA, MAGIC and Q-IMPROvE). B) A mosaic diagram shows the proportion of HRD samples in the BRCA1/2 negative group and BRCA1 or BRCA2 positive groups. The color indicates the HR status predicted by CHORD (Dark blue HRD BRCA1 subtype, light blue HRD BRCA2 subtype, yellow HRP). C) Waffle plots indicate the number of individuals assigned to each germline group among the samples characterized as tumor HRD ( BRCA1 subtype, BRCA2 subtype, undetermined) and HRP. The Excluded group was further divided into those harboring P/LP variants or suspicious VUS in other DNA repair genes. The P/LP variants detected in non- BRCA1/2 genes and BRCA1/2 VUS of HRD samples are labelled in the plot All 25 BRCA1 positive samples were predicted to have a BRCA1 HRD subtype, and 18 of the 21 BRCA2 positive samples had a BRCA2 HRD subtype. For the 149 BRCA1/2 negative samples, the majority were predicted HRP (n=129, 87%), with the remainder predicted as HRD with an undetermined subtype (n=2), BRCA1 subtype (n=6), or BRCA2 subtype (n=12) ( Figure 3B ). A review of the somatic variants for these germline BRCA1/2 negative samples with BRCA1 or BRCA2 HRD subtype predictions revealed relevant somatic changes in these tumors ( Supplementary Table 4 ). The six BRCA1 subtype tumors contained somatic copy number events impacting BRCA1 (four with copy neutral LOH and two with gains). The 12 BRCA2 subtype tumors contained somatic copy number events impacting BRCA2 (two with homozygous loss, four with loss of one allele, four with copy neutral LOH and two with gains). Two of these samples also contained a somatic BRCA2 splice variant in one case and a somatic inframe PALB2 variant in another case ( Supplementary Table 4 ). Overall, samples predicted to have HRD BRCA1 or BRCA2 subtypes by CHORD, were enriched for individuals that were positive for a germline BRCA1 or BRCA2 pathogenic variant, respectively, while BRCA1/2 negative individuals accounted for more than 50% of the HRP samples ( Figure 3C ). Interestingly, of the 31 Excluded samples with P/LP variants in other DNA repair genes, nine had HRD BRCA1 or BRCA2 subtype tumors. These variants are specified in Figure 3C . HR status as evidence for or against the pathogenicity of BRCA1 and BRCA2 germline variants To determine if CHORD HR status is a significant predictor of BRCA1 and BRCA2 germline variant pathogenicity, the proportion of individuals with each HR status was compared for 149 BRCA1/2 negative individuals to the 25 BRCA1 positive or the 21 BRCA2 positive individuals, in order to calculate LRs towards pathogenicity. For BRCA1 , observation of an HRP breast tumor corresponded to benign strong evidence for that variant ( Table 1 ), while observation of an HRD breast tumor corresponded to pathogenic moderate evidence. For BRCA2 , the strength of HRP status as a predictor against pathogenicity was lower than for BRCA1 , i.e. benign moderate, while the evidence strength towards pathogenicity associated with CHORD-predicted HR status was the same as derived for BRCA1 i.e. pathogenic moderate. View this table: View inline View popup Download powerpoint Table 1. LR calculations associated with HR status predicted by CHORD The LR towards pathogenicity increased further for CHORD predictions stratified by the gene-specific HRD subtype ( Table 1 ), reaching pathogenic strong evidence for BRCA1 . Notably, the LR for the HRD subtype inconsistent with the gene being analyzed was not significant (based on the confidence intervals). Similar results were seen when HRDetect was used to predict HR status regardless of subtype ( Supplementary Table 5 ), although the LRs were of slightly lower magnitude in both benign and pathogenic directions using HRDetect (0.02 and 7.10 for BRCA1 , 0.06 and 6.76 for BRCA2 ) compared to CHORD (0.02 and 7.45 for BRCA1 , 0.06 and 7.10 for BRCA2 ). HRDSum also resulted in benign strong and moderate evidence for BRCA1 and BRCA2 , respectively, but with LRs of the lowest magnitude, while the evidence strength for BRCA1 and BRCA2 variants was equivalent only to pathogenic supporting evidence ( Supplementary Table 6 ). We also conducted a sensitivity analysis for the CHORD predictions considering the additional data for 134 individuals previously excluded (as they were identified to have P/LP variants in other non- BRCA1/2 genes). Among these, three were positive for BRCA1 , and another two were positive for BRCA2 . Reanalysis considering prediction of BRCA1 and BRCA2 positive status irrespective of presence of pathogenic variants in other genes revealed that LRs did not significantly change and the corresponding evidence categories remained the same, except that HRP status in BRCA2 increased in strength from benign moderate to benign strong ( Supplementary Table 7 ). Evaluation of HR predictions in individuals with a BRCA1/2 germline VUS There was a total of 21 breast cancer patients carrying 23 BRCA1 or BRCA2 germline variants classified as VUS that had been excluded from the reference sets used in the LR analyses. All variants were absent from gnomAD except NM_007294.4(BRCA1):c.-60C>T, with two alleles across gnomAD v2 and v3. Nine of the VUS (n=8 BRCA1 and n=1 BRCA2 ) identified in seven individuals demonstrated tumor HRD ( Supplementary Table 4 ) providing evidence in favor of pathogenicity ( Supplementary Table 8 ); however, two individuals each carried two VUS, making it unclear which specific variant could be contributing to the observed HRD. Of the individuals with a BRCA1 germline VUS with HRD, all had undergone somatic copy number events resulting in bi-allelic loss of BRCA1 (loss of one allele or copy neutral LOH), providing additional support. Twelve of the VUS (in 12 individuals) had HRP status providing evidence against pathogenicity ( Supplementary Table 8 ). The remaining two VUS had HRD subtypes contrasting with the gene in which they were identified (i.e., BRCA1 VUS with HRD BRCA2 subtype, and vice versa) – while, statistically, this observation provides no evidence for or against pathogenicity, it does suggest these variants are not associated with the tumorigenesis in these individuals. Correlation of predicted HR status with histopathological markers Breast tumor histological grade, ER status and TNBC status (negative for ER, PR and HER2) were previously shown as predictors of variant pathogenicity for classification of BRCA1 or BRCA2 germline variants in analysis of 4,477 BRCA1 pathogenic variant carriers, 2,565 BRCA2 pathogenic variant carriers, and 47,565 breast cancer cases without a known BRCA1 or BRCA2 variant. 46 While number of observations in the dataset analyzed for our study is considerably smaller than the previous study, 46 the overall trends in marker distribution were as expected. For example, based on the histopathology data available in our cohort ( Supplementary Table 4) , ER-negative status was enriched in BRCA1 positive individuals (22/25, 88%) compared to BRCA2 positive individuals (2/21,10%) and individuals in the BRCA1/2 negative group (40/149, 27%), which is comparable to trends reported previously for these groups ( BRCA1 , 76%; BRCA2 , 21%; non-carrier, 23%). 46 Similarly, the proportion of grade 3 tumors was highest for BRCA1 positive individuals (19/25, 76%), intermediate for BRCA2 positive individuals (12/21, 57%), and lowest for those in the BRCA1/2 negative group (39/149, 26%), comparable to trends reported previously ( BRCA1 , 77%; BRCA2 , 52%; non-carriers, 33%). 46 The HR status and histopathological markers for each case, as well as age group and sex were visualized ( Supplementary Figure 5A ). Using the combined dataset from this study, there was a significant correlation between HR status by all tools and each of the histopathological markers, except for HER2 ( Supplementary Figure 5B ). For both CHORD and HRDetect, of all pathology markers, correlation was highest for TNBC status (r = 0.51), and lowest for HER2 status (r = 0.16-0.17). Further, there was a significant difference between TNBC and non-TNBC tumors in the distribution of the CHORD-predicted HRD probability (Wilcoxon p < 2.2e-16, Supplementary Figure 5C ), and also BRCA1 subtype predicted probability (Wilcoxon p = 6.5e-08, Supplementary Figure 5D ). Discussion Despite the strong known association of tumor HRD with BRCA1/2 -associated hereditary cancer, 5 and increasing use of tumor HRD as a biomarker for cancer treatment at the time of cancer diagnosis, this evidence type is not routinely used in germline variant classification of BRCA1 and BRCA2 genes. Our study leveraged data from four different cohorts (Familial breast, TCGA-BRCA, MAGIC, and Q-IMPROvE) of breast tumors from 350 individuals. Using HR status predicted with three different algorithms (CHORD, HRDetect, and HRDSum), we estimated the strength of evidence of breast tumor HR profile for predicting pathogenicity of BRCA1 and BRCA2 germline variants. We found HR status provides statistical justification for the potential utility of tumor HR profiling as an additional data source for BRCA1 and BRCA2 variant classification within existing specifications. 43 Importantly, since different HR-calling algorithms are used in clinical practice, we investigated and demonstrated differences in the predictive capacity according to the HR testing method. We also assessed whether the prediction methods were robust for application to FFPE-derived samples. The tumor material source for the MAGIC cohort was FFPE-derived samples, for which HR status prediction was unchanged after applying a single base substitution signature noise correction. This suggests that both HRDetect and CHORD prediction methods are robust for application to FFPE-derived samples in our cohort. However, since large-scale genome alterations such as structural and copy-number variations are a feature of HR prediction, we cannot rule out that the lower number of somatic SVs detected in the FFPE-derived tumor material from MAGIC may adversely impact HR prediction for some sample sets. A particularly important outcome of this study is demonstrating the practical value of HR status for the assessment of BRCA1/2 variant pathogenicity within existing guidelines. In the current BRCA1/2 specifications, clinical data including breast tumor pathology status is currently captured within the ACMG/AMP PP4 and BP5 codes, 43 with code weights dependent on the combined LR across tumor observations. 47 To date, the breast tumor biomarkers used routinely in BRCA1/2 variant classification include grade, ER status and TNBC status status. 46 Calibrations from this previous study showed that most of the predictors were positively and negatively associated with BRCA1 with supporting evidence strength level, with a few exceptions reaching moderate strength, while all of the predictions associated with BRCA2 pathogenic germline variant status provided only pathogenic or benign supporting evidence for classification. Results from our study indicate that HR status is overall a stronger predictor of pathogenicity than other tumor pathology features currently used for both genes, 46 for both pathogenic and benign directions. Evidence strengths applicable to HRD compared to HRP status were generally consistent for CHORD and HRDdetect, with evidence weights higher than when using HRDSum. This highlights the importance of validating HR prediction approaches within testing laboratories. The evidence towards and against pathogenicity associated with CHORD-predicted HRD subtypes reached pathogenic and benign strong strength for BRCA1 and pathogenic and benign moderate strength for BRCA2 , demonstrating that HR status is a more useful predictor than previously used tumor markers for variant classification. We also demonstrated that previously used breast tumor pathology markers (grade, ER and TNBC status) are correlated with HR status. Together these observations highlight the importance of selecting only one source of evidence derived from a given tumor for use in variant classification, to avoid double-counting. At this point in time, it would be logical to apply the evidence type providing the greatest weight (that is, HRD over e.g., grade). Notably, separation by gene-specific HRD subtype predicted by CHORD can add another layer of precision, in that a VUS would not be assigned evidence towards pathogenicity if it was observed to have a tumor HRD profile with the opposite gene (e.g. a VUS in BRCA2 within a BRCA1 -like HRD tumor). This suggests that CHORD may provide more robust evidence towards pathogenicity for the classification of variants in BRCA1 or BRCA2 compared to other HR prediction algorithms. The gene-specific HRD subtype from CHORD invokes the exciting possibility that in the future, CHORD can be trained to predict non- BRCA1/2 subtypes, such as PALB2 or RAD51C . The ability of CHORD to predict which gene is associated with a HR deficient tumor will not only assist with classification of detected variants in these genes, but may also inform genetic analysis for patients with an undiagnosed germline cause of their cancer. For example, we hypothesize a patient with a HR deficient tumor predicted as BRCA1 -like by CHORD, but with no pathogenic variants identified within the gene, may have variants within regulatory regions or promoter methylation that perturbs BRCA1 and contributes to the HRD phenotype. 48 This study also provides evidence weight based on HR predictions for an additional group of 21 VUS in BRCA1/2 , and so may aid future classification of these variants. All except one variant were rare small indels or variants located in the 5’ or 3’ UTRs. These variant types are not well captured by existing classification guidelines, and thus this additional data could be beneficial to further inform classification. It is worth emphasizing the importance of assessing the performance of the HR predictors dataset by dataset, to ensure that the calibration of predictors provides reliable results tailored to the specific characteristics of each dataset. This will include accounting for factors such as differences in HR measurement and/or HR status distribution between different tumor types, and between histological subtypes for a given tumor type. Larger studies will be important to validate our HR-associated LRs in other datasets, including reanalyzing the evidence weight associated with HR status by different age groups, clinical and pathology features. In particular, it will be necessary to re-investigate the value of HR status in other tumor types, such as ovarian cancer, for predicting germline variant pathogenicity, since predictive capacity is related to the prevalence of the tumor feature in individuals without a pathogenic germline variant. For example, while up to 80% of BRCA1 or BRCA2- related ovarian cancers present with serous tumor histological subtype, this feature is not predictive of BRCA1 or BRCA2 variant pathogenicity since ∼70% of ovarian tumors without BRCA1 or BRCA2 pathogenic variants also present with this subtype. 20 In summary, this study shows that relatively simple calibration approaches can be used to compare and select HR-calling algorithms for use in predicting pathogenicity of germline BRCA1 and BRCA2 variants, and that any HR predictor that is sufficiently predictive can be used to provide an alternative form of tumor data for application in germline BRCA1 or BRCA2 variant classification. Importantly, we have shown by comparison of different tumor HR prediction methods, our results have relevance for accurate detection of HR status, of clinical significance in guiding breast cancer treatment, since HR status is the trigger to ensure that patients might benefit from PARP inhibitor therapy. Data and code availability The sequence data for the Familial breast cancers was previously deposited in the EGA under accession number EGAD00001004494 . The TCGA data was from TCGA-BRCA and accessed from TCGA ( https://portal.gdc.cancer.gov ). The MAGIC dataset contains de-identified patient information using pseudo-identifiers to protect privacy. Please contact the corresponding authors for data access. The Q-IMPROvE data are available upon reasonable request to the authors Code and approaches used in this study: https://github.com/AdamaJava/adamajava and https://github.com/QingliGuo/FFPEsig . Data Availability The sequence data for the Familial breast cancers was previously deposited in the EGA under accession number EGAD00001004494. The TCGA data was from TCGA-BRCA and accessed from TCGA ( https://portal.gdc.cancer.gov ). The MAGIC dataset contains de-identified patient information using pseudo-identifiers to protect privacy. Please contact the corresponding authors for data access. The Q-IMPROvE data are available upon reasonable request to the authors Code and approaches used in this study: https://github.com/AdamaJava/adamajava and https://github.com/QingliGuo/FFPEsig . Author contributions Cristina Fortuno: Conceptualization, Investigation, Formal Analysis, Methodology, Writing – Original Draft Preparation. Jia Zhang: Investigation, Formal Analysis, Methodology, Visualisation, Data Curation, Writing – Original Draft Preparation. Lambros T Koufariotis: Formal Analysis, Resources, Writing - Review and Editing. Georgina Hollway: Methodology, Writing - Review and Editing. Scott Wood: Resources, Writing - Review and Editing. John V Pearson: Resources, Supervision, Writing - Review and Editing. Peter Simpson: Formal Analysis, Resources, Writing - Review and Editing. Sunil R Lakhani: Resources, Writing - Review and Editing. Amy E McCart Reed: Resources, Writing - Review and Editing. Heather Thorne: Data Curation, Resources, Writing - Review and Editing. G Bruce Mann: Resources, Writing - Review and Editing. Anita R Skandarajah: Resources, Writing - Review and Editing. Lisa Devereux: Resources, Writing - Review and Editing. Qihong Zhao: Resources, Writing - Review and Editing. Dilanka L De Silva: Resources, Writing - Review and Editing. Geoffrey J Lindeman: Resources, Supervision, Writing - Review and Editing. Paul A James: Conceptualization, Resources, Writing - Review and Editing. Ian Campbell: Resources, Supervision, Writing - Review and Editing, Funding Acquisition. Amanda B Spurdle: Conceptualization, Supervision, Project Administration, Methodology, Resources, Data Curation, Writing - Review and Editing, Funding Acquisition. Nicola Waddell: Conceptualization, Supervision, Project Administration, Methodology, Resources, Data Curation, Writing - Review and Editing, Funding Acquisition. Funding This work was supported by a grant from the National Breast Cancer Foundation, Australia (IIRS-21-102). ABS and NW were supported by NHMRC Investigator Fellowships (APP177524 and APP2018244 respectively). GJL was supported by a NHMRC Investigator Fellowship (APP1175960 and APP2026004) and the Breast Cancer Research Foundation. The MAGIC cohort was supported by grants from the National Breast Cancer Foundation (IIRS-20-080). The Q-IMPROVE study was funded from Queensland Health through Queensland Genomics as an Innovation study, and the Medical Research Futures Fund, Genomics Health Futures Mission. This research was performed on QIMR Berghofer computing infrastructure supported by The Ian Potter Foundation and The Australian Cancer Research Foundation (ACRF). Declaration of interests JVP and NW are co-founders of genomiQa, GH is an employee within genomiQa. The remaining authors declare that there are no competing interests. Ethics statement No participants were recruited for this study, the study included data from previously published or approved studies. The Q-IMPROVE was approved by a research ethics committee (HREC/2021/QRBW/73637). The MAGIC study received multisite institutional ethics approval from the Peter MacCallum Cancer Centre Human Research Ethics Committee (19/224, HREC/58844/PMCC-2019) and Governance approval obtained from each hospital site. Access to data and the analysis work in this study was approved by the QIMR Berghofer human ethics research committee within project number P2095, P2802 and P352 Acknowledgements We are grateful to Prof Elgene Lim, St Vincent’s Sydney/Garvan; Dr Kate Cuff and Gillian Jagger, Princess Alexandra Hospital (PAH); Dr Kathryn Middleton, Mater Hospital South Brisbane; Dr Po-Ling Inglis and Dr Karin Steinke Royal Brisbane and Women’s Hospital (RBWH). We wish to thank Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics. kConFab is supported by a grant from the National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. We thank Dr Kate Cuff, Gillian Jagger, Dr Gorane Santamaria Hormaechea and the many staff across the RBWH, PAH and Mater hospitals and the Parkville Breast Unit and Familial Cancer Centre that helped facilitate this study. The results from the TCGA cohort are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga . We also wish to acknowledge the Brisbane Breast Tissue Bank. Thank you to the patients and their families. 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Share Integrating breast tumor homologous recombination deficiency status to aid germline BRCA1 and BRCA2 variant classification Cristina Fortuno , Jia Zhang , Lambros T Koufariotis , Georgina Hollway , Scott Wood , John V Pearson , Peter T Simpson , Sunil R Lakhani , Amy E McCart Reed , Heather Thorne , G Bruce Mann , Anita R Skandarajah , Lisa Devereux , Qihong Zhao , Dilanka L De Silva , Geoffrey J Lindeman , Paul A James , Ian Campbell , Amanda B Spurdle , Nicola Waddell medRxiv 2025.06.12.25329237; doi: https://doi.org/10.1101/2025.06.12.25329237 Share This Article: Copy Citation Tools Integrating breast tumor homologous recombination deficiency status to aid germline BRCA1 and BRCA2 variant classification Cristina Fortuno , Jia Zhang , Lambros T Koufariotis , Georgina Hollway , Scott Wood , John V Pearson , Peter T Simpson , Sunil R Lakhani , Amy E McCart Reed , Heather Thorne , G Bruce Mann , Anita R Skandarajah , Lisa Devereux , Qihong Zhao , Dilanka L De Silva , Geoffrey J Lindeman , Paul A James , Ian Campbell , Amanda B Spurdle , Nicola Waddell medRxiv 2025.06.12.25329237; doi: https://doi.org/10.1101/2025.06.12.25329237 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4538) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (541) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3333) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00d9ff31d1aad07',t:'MTc3OTYzOTU5Ng=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();