Extended Multigene Panel Testing in a High-Risk Brazilian Public Health Cohort: Pathogenic Variant Prevalence and the Role of Self-Reported Ethnicity

Extended Multigene Panel Testing in a High-Risk Brazilian Public Health Cohort: Pathogenic Variant Prevalence and the Role of Self-Reported Ethnicity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Extended Multigene Panel Testing in a High-Risk Brazilian Public Health Cohort: Pathogenic Variant Prevalence and the Role of Self-Reported Ethnicity Christine Elisabete Rubio Alem, Bárbara Narciso Duarte, Ana Elisa Ribeiro da Silva Cabello, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9371542/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: Breast cancer remains the most common malignancy among women in Brazil, with a substantial proportion attributable to hereditary predisposition. Data regarding germline pathogenic variants in ethnically diverse, self-reported populations within public health settings remain limited. Objective: To evaluate the prevalence of pathogenic/likely pathogenic variants (PV/LPV) detected by extended multigene panel testing in a high-risk Brazilian public health cohort and to assess associations with clinical and demographic factors. Methods: This cross-sectional study included 364 women evaluated at a tertiary public cancer genetics clinic between November 2021 and October 2022. All participants met NCCN criteria for hereditary cancer risk assessment. Germline sequencing of 144 cancer-predisposition genes was performed using next-generation sequencing, including copy number variation analysis. Logistic regression models were used to evaluate factors associated with PV/LPV detection. Results: Pathogenic or likely pathogenic variants were identified in 29.7% of participants. Variants of uncertain significance were detected in 56.3%. The most frequently altered genes were BRCA1 , BRCA2 , TP53 , PALB2 , and MUTYH . In multivariate analysis, a personal history of breast cancer (OR 1.90, 95% CI 1.16–3.10, p = 0.011) and self-declared White ethnicity (OR 2.10, 95% CI 1.31–3.36, p = 0.002) were independently associated with PV/LPV detection. Conclusions: This high-risk Brazilian public health cohort demonstrated a substantial prevalence of pathogenic germline variants using extended multigene panel testing. The association between self-reported White ethnicity and pathogenic variant detection underscores the complexity of genetic risk assessment in diverse populations and highlights the need for greater representation of non-European ancestries in genomic variant databases. Figures Figure 1 Figure 2 1. Introduction Breast cancer is the most frequently diagnosed malignancy among women worldwide, excluding non-melanoma skin cancer, and remains a major public health challenge in Brazil. In 2021 alone, 18.139 deaths were attributed to breast cancer in the country, and approximately 73.610 new cases are projected for 2025 ( 1 ). Notably, Brazil faces a substantial burden of disease in younger women and in patients diagnosed at advanced stages, further emphasizing the need for improved risk stratification and preventive strategies. A proportion of breast cancer cases arises from hereditary susceptibility. Pathogenic germline variants (PVs) and likely pathogenic variants (LPVs) in cancer predisposition genes significantly increase lifetime risk and influence screening, prevention, and treatment decisions ( 2 ). Estimates suggest that approximately 5% to 10% of breast cancers are attributable to highly penetrant hereditary syndromes, although broader estimates—including moderate- and low-penetrance genes—range up to 30%–35%, depending on the population studied and testing strategy employed ( 3 – 5 ). High-penetrance genes such as BRCA1 and BRCA2 are responsible for a substantial proportion of hereditary breast and ovarian cancer cases, conferring lifetime breast cancer risks exceeding fourfold compared to the general population. Intermediate-penetrance genes—including ATM, BARD1, PALB2, and CHEK2—also contribute meaningfully to disease susceptibility, typically doubling or tripling risk ( 3 – 5 ). Despite these advances, a considerable fraction of hereditary risk remains unexplained, highlighting the complexity of polygenic inheritance and the potential role of additional susceptibility genes. The implementation of next-generation sequencing (NGS) technologies has facilitated the widespread adoption of multigene panel testing in clinical practice ( 6 ). Compared with single-gene testing, multigene panels enable simultaneous evaluation of multiple cancer predisposition genes, improving diagnostic yield. However, broader panels are also associated with higher detection rates of variants of uncertain significance (VUS), raising interpretative challenges. Most large-scale genomic studies of hereditary breast cancer have been conducted in populations of predominantly European ancestry ( 7 ). This imbalance may reflect differences in healthcare infrastructure, research funding, and access to genetic testing, but it also limits the generalizability of findings to ethnically diverse and admixed populations. Latin America represents one of the most genetically heterogeneous regions worldwide, shaped by complex historical admixture among European, African, and Indigenous ancestries ( 8 – 10 ). In Brazil specifically, substantial regional variation in ancestral composition exists, with higher European ancestry proportions in the South and more pronounced African and Indigenous contributions in other regions ( 9 , 10 ). Such diversity provides a unique opportunity to investigate the distribution of germline pathogenic variants within an admixed population. Despite this demographic complexity, studies evaluating germline variants in Brazilian breast cancer patients remain limited. In the largest national cohort reported to date, BRCA1 and BRCA2 accounted for nearly half of all pathogenic and likely pathogenic variants identified through multigene panel testing. Importantly, the use of extended panels nearly doubled the detection of non-BRCA susceptibility genes ( 11 ). However, that study did not comprehensively evaluate associations between variant detection and epidemiological variables. Given the growing implementation of multigene testing within the Brazilian public health system and the limited data available for admixed populations, further investigation is warranted. Therefore, the present study aimed to evaluate the prevalence of pathogenic and likely pathogenic germline variants in a high-risk Brazilian public health cohort undergoing extended multigene panel testing and to assess their association with clinical and demographic characteristics. 2. Materials and Methods The research was conducted at the Hospital da Mulher Prof. Dr. José Aristodemo Pinotti (CAISM), involving patients treated at the High-Risk Outpatient Clinic of the Department of Obstetrics and Gynecology at the State University of Campinas (UNICAMP), from November 2021 to October 2022. The cross-sectional study aimed to investigate the prevalence of pathogenic and likely pathogenic genetic variants in women at high risk for hereditary breast cancer. The sample consisted of 373 patients selected by convenience. Inclusion criteria included women with a personal history of luminal or HER2-positive breast cancer diagnosed before age 45 and at least one first- or second-degree relative with breast, ovarian, or prostate cancer; patients with a history of triple-negative breast cancer diagnosed before age 60 and at least one first- or second-degree relative with breast, ovarian, or prostate cancer; patients with breast cancer meeting NCCN high-risk criteria; and women without a diagnosis of breast or ovarian cancer but with a family history of at least one first- or second-degree relative diagnosed with breast or ovarian cancer before age 45 or meeting NCCN high-risk criteria ( 12 ). The project was approved by the Research Ethics Committee (CAAE: 54179621.1.0000.5404). Participants were invited to join the research during their consultation at the High-Risk Outpatient Clinic. The Informed Consent Form (ICF) for the CAISM Tissue and Tumor Bank – BIOBANK, as well as the ICF for the High-Risk Research Project, were read and clarified. Participants were included upon acceptance and signature. All data were entered into a database completed on the REDCAP online platform. Blood samples (20 mL in EDTA tubes) were collected and sent to the Genome Center at the Eurofins Scientific laboratory for next-generation sequencing (NGS), covering all coding regions and exon-adjacent flanking regions, including copy number variations (CNVs), of 144 genes related to hereditary cancer syndromes. After obtaining the results, patients were invited for in-person follow-ups, where they received feedback on genetic test results and genetic counseling, conducted exclusively by members of the study team, who were previously trained to provide appropriate guidance on results and follow-up. To characterize the sample profile concerning the analyzed variables, frequency tables were prepared for categorical variables, presenting absolute frequencies (n) and percentages (%). For numerical variables, descriptive statistics were calculated, including mean, standard deviation, minimum and maximum values, median, and quartiles. Comparisons between categorical variables were performed using the Chi-Square test or Fisher's exact test when expected values were less than 5. For numerical variables, the Mann-Whitney test was used for two-category comparisons, and the Kruskal-Wallis test for three or more categories, as the variable distributions were not normal. The analysis of factors associated with altered genetic test results was performed using simple and multiple logistic regression, employing the Stepwise criterion for variable selection. Odds ratios (ORs) and their respective 95% confidence intervals were calculated. The significance level adopted for all tests was 5% (P < 0.05). Statistical analysis was conducted using The SAS System for Windows, version 9.4 (SAS Institute Inc, 2002–2012, Cary, NC, USA) ( 13 – 18 ). 3. Results The study enrolled 373 women who underwent multigene germline panel testing. Among them, 188 were asymptomatic and 185 had a history of breast and/or ovarian cancer. Nine patients were excluded from the final analysis due to loss to follow-up, which prevented complete data collection after testing; all nine were from the asymptomatic group. The final analytic cohort comprised 364 women (179 with a personal history of cancer and 185 without). Among the 364 women analyzed, 51 (14%) had results classified as benign variants (BV) or likely benign variants (LBV), while 313 (85.9%) showed alterations in the test, including variants of uncertain significance (VUS), likely pathogenic variants (LPV), or pathogenic variants (PV). Of these, 108 (29.7%) had positive results (LPV or PV), with a higher percentage of positive results among patients with a cancer history (36.9%) compared to those without a history of the disease (22.7%). The VUS rate was 61.6% among patients without cancer, while it was 50.8% among patients with a previous history of breast cancer (p = 0.012) (Fig. 1 ). The five genes with the highest frequency of PV/LPV were BRCA1, BRCA2, TP53, PALB2, and MUTYH . Together, these genes accounted for the majority of clinically actionable findings in the cohort, consistent with their established contribution to hereditary breast cancer (Fig. 2 ). A total of 59.9% of patients self-identified as White. White women with a BMI < 29.9 and a personal history of breast cancer exhibited a higher frequency of pathogenic or likely pathogenic variants (PV/LPV) (Table 1 ). Table 1 Clinical and demographic characteristics of the study population. Variables PV/LPV n (%) BV/LBV/VUS N (%) TOTAL N (%) p 256 108 364 Age (years) 50 38 (35.19) 39 (36.11) 31 (28.70) 82 (32.03) 101 (39.45) 73 (28.52) 120 (32.96) 140 (38.46) 104 (28.57) 0.798 Race White Non-white 76 (70.37) 32 (29.63) 142 (55.47) 114 (44.53) 218 (59.89) 146 (40.10 0.008 BMI 30 10 (9.26) 28 (25.93) 48 (44.44) 22 (20.37) 9 (3.52) 58 (22.66) 103 (41.41) 56 (32.42) 19 (5.21) 86 (23.62) 154 (42.30) 105 (28.8) 0.028 Menopause Pre-menopause Post-menopause 59 (55.14) 48 (44.86) 157 (62.06) 96 (37.94) 216 (59.34) 144 (39.56) 0.221 Education Level Elementary Education High School Higher Education 22 (20.37) 45 (41.61) 41 (37.96) 54 (21.09) 116 (45.31) 86 (33.59) 73 (20.05) 161 (44.23) 127 (34.89) 0.718 Previous Pregnancy Yes No 24 (22.43) 83 (77.57) 39 (15.35) 215 (84.65) 63 (17.30) 298 (81.8) p = 0.106 Personal History of Breast Cancer No Yes 42 (38.89) 66 (61.11) 143 (55.86) 113 (44.14) 185 (50.82) 179 (49.17) 0.003 First-Degree Relatives with Cancer No Yes 35 (32.41) 73 (67.59) 106 (41.41) 150 (58.59) 141 (38.73) 223 (61.26) 0.107 Second-Degree Relatives with Cance No Yes 42 (39.62) 64 (60.38) 90 (32.29) 165 (64.71) 132 (36.26) 229 (62.91) 0.437 Mother with a History of Breast Cancer No Yes 17 (31.48) 37 (68.52) 28 (22.40) 97 (77.60) 45 (12.36) 134 (36.81) 0.199 In the multivariate model using stepwise variable selection, breast cancer status and ethnicity were identified as significant predictors of altered genetic test results (PV/LPV). Women with a personal history of breast cancer had a 1.9-fold increased likelihood of a positive genetic test result (OR 1.90, 95% CI 1.16–3.10), and those self-identified as White had a 2.1-fold increased likelihood (OR 2.10, 95% CI 1.31–3.36) (Table 2 ). Table 2 Univariate and multivariate analysis of factors associated with the presence of pathogenic (PV) or likely pathogenic variants (LPV) (n = 364) Variable Univariate analysis Multivariate analysis Categories Value-P O.R.* 95% CI O.R.* Value - O.R* 95% CI O.R.* Age < 40 years (ref.) 40–49 years ≥ 50 years --- 0.503 0.764 1.00 0.83 0.92 --- 0.49–1.42 0.52–1.62 Race Non-white (ref.) White --- 0.009 1.00 1.91 --- 1.18–3.08 -- 1.00 -- 0.002 2.10 1.31–3.36 Education Elementary (ref.) High School Higher Education/Postgraduate --- 0.874 0.619 1.00 0.95 1.17 --- 0.52–1.74 0.63–2.18 Age at Menarche Continuous variable (years) 0.300 1.073 0.939–1.225 Previous pregnancy No (ref.) Yes --- 0.108 1.00 0.63 --- 0.36–1.11 Menopause Pre-menopause (ref.) Post-menopause --- 0.222 1.00 1.33 --- 0.84–2.10 BMI < 20 20-24.9 25-29.9 ≥ 30 (ref.) 0.006 0.071 0.071 --- 4.19 1.81 1.71 1.00 1.52–11.58 0.95–3.49 0.96–3.05 --- Breast Cancer Group No (ref.) Yes --- 0.003 1.00 1.99 --- 1.26–3.15 -- 1.0 --- 0.011 1.90 1.16–3.10 Family history cancer 1st -degree relatives No (ref.) Yes --- 0.108 1.00 1.47 --- 0.92–2.37 Family history of breast cancer in mother No (ref.) Yes --- 0.201 1.00 0.63 --- 0.31–1.28 Family history of cancer 2nd -degree relatives No (ref.) Yes --- 0.437 1.00 0.83 --- 0.52–1.33 * OR (Odds Ratio) = Risk ratio for positive genetic test result; (n = 256 with BV/LBV/VUS and n = 108 with PV/LPV). 95% CI OR = 95% confidence interval for the odds ratio. Ref.: reference category. BV: benign variant; LBV: likely benign variant; VUS: variant of uncertain significance; PV: pathogenic variant; LPV: likely pathogenic variant. 4. Discussion In this high-risk Brazilian public health cohort, extended multigene panel testing identified pathogenic or likely pathogenic variants in nearly one-third of participants (29.7%), a prevalence consistent with previously reported data in selected hereditary breast cancer populations. The high overall rate of genetic alterations (including VUS) reflects both the enriched-risk selection criteria and the use of a broad 144-gene panel. The prevalence observed in our cohort aligns with findings from large-scale international consortia such as the CARRIERS study, which demonstrated clinically actionable pathogenic variants in a subset of breast cancer-associated genes among high-risk individuals ( 19 ). However, unlike predominantly European or North American cohorts, our population represents a highly admixed Brazilian group within the public health system, a setting historically underrepresented in genomic studies. The most frequently affected genes— BRCA1 , BRCA2 , TP53 , and PALB2 —are consistent with established hereditary breast cancer susceptibility genes. The presence of recurrent TP53 variants likely reflects the known Brazilian founder mutation (R337H), particularly prevalent in certain regions of the country. These findings reinforce the importance of maintaining TP53 testing within hereditary cancer panels in Brazil. The detection of pathogenic variants in MUTYH highlights the complexity of interpreting moderate-penetrance or recessive genes in multigene panels. Although monoallelic MUTYH variants have uncertain association with breast cancer risk, their identification underscores the interpretative challenges introduced by extended testing. A particularly noteworthy finding was the independent association between self-declared White ethnicity and PV/LPV detection. This observation should be interpreted with caution. Brazil is one of the most genetically admixed populations worldwide, and self-reported race does not necessarily correlate with genomic ancestry proportions. Several factors may explain this association: Historical overrepresentation of individuals of European ancestry in genomic databases, potentially increasing classification accuracy for variants identified in these individuals. Differences in variant interpretation due to reference bias. Residual confounding related to socioeconomic or healthcare access variables. Limitations of self-reported ethnicity as a proxy for ancestry. Previous studies have demonstrated that underrepresentation of non-European populations in genomic datasets may contribute to misclassification or higher rates of variants of uncertain significance. Therefore, our findings likely reflect both biological and structural components of genomic inequity. Patients with a personal history of breast cancer were nearly twice as likely to harbor PV/LPVs, consistent with current clinical guidelines recommending germline testing in affected individuals. This supports the continued implementation of multigene panel testing in high-risk patients within the public health system. The high VUS rate (56.3%) warrants consideration. Broad panels inevitably increase VUS detection, particularly in admixed populations. This finding reinforces the need for population-specific variant curation initiatives and functional validation studies. Limitations: This study has limitations. It was conducted at a single tertiary referral center using convenience sampling, which may limit generalizability. No genomic ancestry markers were used to quantify admixture proportions. The cross-sectional design precludes risk estimation. Additionally, the use of stepwise regression may introduce model selection bias. 5. Conclusion Extended multigene panel testing in this high-risk Brazilian public health cohort revealed a substantial burden of pathogenic germline variants. The association between self-reported White ethnicity and pathogenic variant detection reinforces the importance of hereditary cancer assessment in public health settings and highlights the need for greater genomic representation of ethnically diverse populations to enhance variant interpretation accuracy. Declarations Conflict of Interest The authors declare no conflicts of interest Funding This study did not receive specific funding from public, commercial, or not-for-profit funding agencies. Genetic testing was performed as part of routine clinical care within the Brazilian Unified Health System (SUS) at CAISM/UNICAMP. Author Contributions According to the CRediT (Contributor Roles Taxonomy): Conceptualization: C.E.R.A., B.N.D., C.C.; Methodology: C.E.R.A., B.N.D., S.R.C.T., T.G., C.C.; Data curation and formal analysis: A.E.R.S.C., M.L.S., A.L.V.; Investigation and data collection: C.E.R.A., B.N.D., A.E.R.S.C., S.R.C.T., D.I.S.B.C.S., A.L.V.; Writing – original draft: C.E.R.A., B.N.D.; Writing – review and editing: all authors; Supervision and project administration: C.C. All authors have read and approved the final manuscript. Data Availability Statement The datasets generated and analyzed during the current study are not publicly available due to patient privacy and ethical restrictions, but are available from the corresponding author upon reasonable request and subject to institutional review board approval. Ethics Statement This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University of Campinas (UNICAMP) under approval number 54179621.1.0000.5404. Informed consent was obtained from all participants involved in the study References Instituto Nacional de Câncer (INCA). Estimativa 2023: Incidência de Câncer no Brasil. Rio de Janeiro: INCA; 2022. Available from: https://www.gov.br/inca/pt-br/assuntos/cancer/numeros/ [cited 2022 Nov 1]. Green VL. Breast Cancer Risk Assessment and Management of the High-Risk Patient. Obstet Gynecol Clin North Am. 2022;49(1):87-116. Wendt C, Margolin S. Identifying breast cancer susceptibility genes - a review of the genetic background in familial breast cancer. Acta Oncol. 2019;58(2):135-46. Nelson HD, Huffman LH, Fu R, Harris EL. Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med. 2005;143(5):362-79. Duarte BN, Alem CER, da Silva Cabello AER, Teixeira SRC, Cabello C. Risk-reducing surgeries for breast cancer in Brazilian patients undergoing multigene germline panel: impact of results on decision making. Breast Cancer Res Treat. 2024. Robson M, Storm CD, Weitzel J, Shilman DS, Offit K; American Society of Clinical Oncology. American Society of Clinical Oncology Policy Statement Update: Genetic and Genomic Testing for Cancer Susceptibility. J Clin Oncol. 2015;33(31):3660-3667. Rebbeck TR, Friebel TM, Friedman E, Hamann U, Huo D, Kwong A, et al. Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Hum Mutat. 2018;39(5):593-620. Lynce F, Graves KD, Jandorf L, Ricker C, Castro E, Moreno L, et al. Genomic Disparities in Breast Cancer Among Latinas. Cancer Control. 2016;23(4):359-72. Chavarri-Guerra Y, Blazer KR, Weitzel JN. Genetic Cancer Risk Assessment for Breast Cancer in Latin America. Rev Invest Clin. 2017;69(2):94-102. Souza AM, Resende SS, Sousa TN, Brito CFA. A systematic scoping review of the genetic ancestry of the Brazilian population. Genet Mol Biol. 2019;42(3):495-508. Guindalini RSC, Viana DV, Kitajima J, Rocha VM, López RVM, Zheng Y, et al. Detection of germline variants in Brazilian breast cancer patients using multigene panel testing. Sci Rep. 2022;12(1):4190. National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology. Genetic/Familial High Risk Assessment: Breast and Ovarian [Internet]. 2022 [cited March 9, 2022]. Available from: https://www.nccn.org/professionals/physician_gls/pdf/genetics_bop.pdf. Tabachnick BG, Fidell LS. Using Multivariate Statistics. Boston: Allyn and Bacon. 4th ed. 2001. Conover WJ. Practical Nonparametric Statistics. New York: John Wiley & Sons. 3rd ed. 1999. Fletcher RH, W. FS. Epidemiologia Clínica: Elementos Essenciais. Porto Alegre: Artmed. 4th ed. 2006. Siegel S, Castellan Jr. NJ. Estatística Não-Paramétrica para Ciências do Comportamento. 2ª edição. 2006. Hosmer DW, Lemeshow SL. Applied Logistic Regression. New York: John Wiley & Sons. 1989. Fleiss JL, Levin B, Paik MC. Statistical Methods for Rates and Proportions. Hoboken, N.J: John Wiley & Sons. 3rd ed. 2003. Hu C, Hart SN, Gnanaolivu R, Huang H, Lee KY, Na J, et al. A Population-Based Study of Genes Previously Implicated in Breast Cancer. N Engl J Med. 2021;384(5):440-51. Dorling L, Carvalho S, Allen J, et al.; Breast Cancer Association Consortium. Breast cancer risk genes — association analysis in more than 113,000 women. N Engl J Med. 2021;384(5):428-439. de Freitas Ribeiro AA, Junior NMC, Dos Santos LL. Systematic review of the molecular basis of hereditary breast and ovarian cancer syndrome in Brazil: the current scenario. Eur J Med Res. 2024;29(1):187. Faria JP, Assumpção JG, de Oliveira Matos L, Soardi FC, Bretz GPM, Friedman E, et al. Spectrum of germline pathogenic variants in Brazilian hereditary breast/ovarian cancer cases. Breast Cancer Res Treat. 2024;207(3):615-24. Kuchenbaecker KB, Hopper JL, Barnes DR, Phillips KA, Mooij TM, Roos-Blom MJ, et al. Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers. Jama. 2017;317(23):2402-16. Palmero EI, Schüler-Faccini L, Caleffi M, Achatz MI, Olivier M, Martel-Planche G, et al. Detection of R337H, a germline TP53 mutation predisposing to multiple cancers, in asymptomatic women participating in a breast cancer screening program in Southern Brazil. Cancer Lett. 2008;261(1):21-5. Rahman N, Seal S, Thompson D, Kelly P, Renwick A, Elliott A, et al. PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nat Genet. 2007;39(2):165-7. Hong YR, Yadav S, Wang R, Vadaparampil S, Bian J, George TJ, et al. Genetic Testing for Cancer Risk and Perceived Importance of Genetic Information Among US Population by Race and Ethnicity: a Cross-sectional Study. J Racial Ethn Health Disparities. 2024;11(1):382-94. McQuillan MA, Zhang C, Tishkoff SA, Platt A. The importance of including ethnically diverse populations in studies of quantitative trait evolution. Curr Opin Genet Dev. 2020;62:30-5. Manrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA, Szolovits P, et al. Genetic Misdiagnoses and the Potential for Health Disparities. N Engl J Med. 2016;375(7):655-65. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Version 2.2023 — February 7, 2023. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9371542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630513921,"identity":"b09f4829-d732-4229-96b3-670f8eb39f18","order_by":0,"name":"Christine Elisabete Rubio Alem","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"Elisabete Rubio","lastName":"Alem","suffix":""},{"id":630513922,"identity":"eadc4652-6761-40f2-b6e3-4a09b213c178","order_by":1,"name":"Bárbara Narciso Duarte","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Bárbara","middleName":"Narciso","lastName":"Duarte","suffix":""},{"id":630513926,"identity":"35ecd82d-5ccd-4d1c-a8a4-fab05e93fff2","order_by":2,"name":"Ana Elisa Ribeiro da Silva Cabello","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Elisa Ribeiro da Silva","lastName":"Cabello","suffix":""},{"id":630513929,"identity":"aed5a78a-2e70-42cb-a6b0-2a1721c4134a","order_by":3,"name":"Sandra Regina Campos Teixeira","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"Regina Campos","lastName":"Teixeira","suffix":""},{"id":630513932,"identity":"6426befb-7ca6-47b4-995c-5d0b6b32323b","order_by":4,"name":"Thiago Gaspar","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Thiago","middleName":"","lastName":"Gaspar","suffix":""},{"id":630513934,"identity":"8d239a0c-8ec9-419b-b985-cb32de768979","order_by":5,"name":"Márcio Lopes de Souza","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Márcio","middleName":"Lopes","lastName":"de Souza","suffix":""},{"id":630513935,"identity":"3647c9cb-17e9-45c9-8a2f-bf2e57830947","order_by":6,"name":"Ana Lídia Viaro","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Lídia","lastName":"Viaro","suffix":""},{"id":630513939,"identity":"3c69e031-3be3-4159-9891-b3a863846909","order_by":7,"name":"Daniel Imbassahy de Sá Bittencourt Câmara e Silva","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Imbassahy de Sá Bittencourt Câmara e","lastName":"Silva","suffix":""},{"id":630513941,"identity":"b1d7299b-2b86-4359-886b-49ac406127d6","order_by":8,"name":"Cesar Cabello","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYFACHgaGBwUMcgwMjA1gvgFRWhIMGIxJ15LYAOMT1MLffvbggwQDm/QNt5sbP/zMucNgLn0AvxaJM3nJBgkGabkb7hxsluzd9ozBsi8BvxYDhhwziQSDw7kbbiS2MfBuO8xgcIaAwwz435j/AGpJNwBqYfxLlBaJHDOg9w8ngLQwE2WLxI13yUCHpRnOvJHYLC277RmPZQ8BLfz9uQc/fKiwkee7kf7w49ttd+TMeQhoQQcHSNUA1EKyjlEwCkbBKBj+AABNeUaJ6sJ1HwAAAABJRU5ErkJggg==","orcid":"","institution":"State University of Campinas","correspondingAuthor":true,"prefix":"","firstName":"Cesar","middleName":"","lastName":"Cabello","suffix":""}],"badges":[],"createdAt":"2026-04-09 17:53:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9371542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9371542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108385320,"identity":"8daf4652-e259-4d19-a777-d48f97d83d92","added_by":"auto","created_at":"2026-05-04 06:03:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27478,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of BV/LBV, VUS, and PV/LPV in patients with and without breast cancer (*Chi-square test: X2=8.78; df=2; p=0.012)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9371542/v1/496235666ec96e249557ae8c.png"},{"id":108385321,"identity":"b849f06d-997f-4af9-a748-f952bf14546d","added_by":"auto","created_at":"2026-05-04 06:03:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29907,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of altered genes according to personal history of cancer.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9371542/v1/57e4613829e92c7747f2cee5.png"},{"id":108492430,"identity":"1bbaac52-cda0-443c-8577-83f35c604ecd","added_by":"auto","created_at":"2026-05-05 09:57:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":344794,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9371542/v1/2dd78245-5fb8-44e7-8f6c-9476bdf33f74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Extended Multigene Panel Testing in a High-Risk Brazilian Public Health Cohort: Pathogenic Variant Prevalence and the Role of Self-Reported Ethnicity","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer is the most frequently diagnosed malignancy among women worldwide, excluding non-melanoma skin cancer, and remains a major public health challenge in Brazil. In 2021 alone, 18.139 deaths were attributed to breast cancer in the country, and approximately 73.610 new cases are projected for 2025 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Notably, Brazil faces a substantial burden of disease in younger women and in patients diagnosed at advanced stages, further emphasizing the need for improved risk stratification and preventive strategies.\u003c/p\u003e \u003cp\u003eA proportion of breast cancer cases arises from hereditary susceptibility. Pathogenic germline variants (PVs) and likely pathogenic variants (LPVs) in cancer predisposition genes significantly increase lifetime risk and influence screening, prevention, and treatment decisions (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Estimates suggest that approximately 5% to 10% of breast cancers are attributable to highly penetrant hereditary syndromes, although broader estimates\u0026mdash;including moderate- and low-penetrance genes\u0026mdash;range up to 30%\u0026ndash;35%, depending on the population studied and testing strategy employed (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh-penetrance genes such as BRCA1 and BRCA2 are responsible for a substantial proportion of hereditary breast and ovarian cancer cases, conferring lifetime breast cancer risks exceeding fourfold compared to the general population. Intermediate-penetrance genes\u0026mdash;including ATM, BARD1, PALB2, and CHEK2\u0026mdash;also contribute meaningfully to disease susceptibility, typically doubling or tripling risk (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Despite these advances, a considerable fraction of hereditary risk remains unexplained, highlighting the complexity of polygenic inheritance and the potential role of additional susceptibility genes.\u003c/p\u003e \u003cp\u003eThe implementation of next-generation sequencing (NGS) technologies has facilitated the widespread adoption of multigene panel testing in clinical practice (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Compared with single-gene testing, multigene panels enable simultaneous evaluation of multiple cancer predisposition genes, improving diagnostic yield. However, broader panels are also associated with higher detection rates of variants of uncertain significance (VUS), raising interpretative challenges.\u003c/p\u003e \u003cp\u003eMost large-scale genomic studies of hereditary breast cancer have been conducted in populations of predominantly European ancestry (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This imbalance may reflect differences in healthcare infrastructure, research funding, and access to genetic testing, but it also limits the generalizability of findings to ethnically diverse and admixed populations. Latin America represents one of the most genetically heterogeneous regions worldwide, shaped by complex historical admixture among European, African, and Indigenous ancestries (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In Brazil specifically, substantial regional variation in ancestral composition exists, with higher European ancestry proportions in the South and more pronounced African and Indigenous contributions in other regions (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Such diversity provides a unique opportunity to investigate the distribution of germline pathogenic variants within an admixed population.\u003c/p\u003e \u003cp\u003eDespite this demographic complexity, studies evaluating germline variants in Brazilian breast cancer patients remain limited. In the largest national cohort reported to date, BRCA1 and BRCA2 accounted for nearly half of all pathogenic and likely pathogenic variants identified through multigene panel testing. Importantly, the use of extended panels nearly doubled the detection of non-BRCA susceptibility genes (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, that study did not comprehensively evaluate associations between variant detection and epidemiological variables.\u003c/p\u003e \u003cp\u003eGiven the growing implementation of multigene testing within the Brazilian public health system and the limited data available for admixed populations, further investigation is warranted. Therefore, the present study aimed to evaluate the prevalence of pathogenic and likely pathogenic germline variants in a high-risk Brazilian public health cohort undergoing extended multigene panel testing and to assess their association with clinical and demographic characteristics.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThe research was conducted at the Hospital da Mulher Prof. Dr. Jos\u0026eacute; Aristodemo Pinotti (CAISM), involving patients treated at the High-Risk Outpatient Clinic of the Department of Obstetrics and Gynecology at the State University of Campinas (UNICAMP), from November 2021 to October 2022. The cross-sectional study aimed to investigate the prevalence of pathogenic and likely pathogenic genetic variants in women at high risk for hereditary breast cancer.\u003c/p\u003e \u003cp\u003eThe sample consisted of 373 patients selected by convenience. Inclusion criteria included women with a personal history of luminal or HER2-positive breast cancer diagnosed before age 45 and at least one first- or second-degree relative with breast, ovarian, or prostate cancer; patients with a history of triple-negative breast cancer diagnosed before age 60 and at least one first- or second-degree relative with breast, ovarian, or prostate cancer; patients with breast cancer meeting NCCN high-risk criteria; and women without a diagnosis of breast or ovarian cancer but with a family history of at least one first- or second-degree relative diagnosed with breast or ovarian cancer before age 45 or meeting NCCN high-risk criteria (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e The project was approved by the Research Ethics Committee (CAAE: 54179621.1.0000.5404). Participants were invited to join the research during their consultation at the High-Risk Outpatient Clinic. The Informed Consent Form (ICF) for the CAISM Tissue and Tumor Bank \u0026ndash; BIOBANK, as well as the ICF for the High-Risk Research Project, were read and clarified. Participants were included upon acceptance and signature. All data were entered into a database completed on the REDCAP online platform.\u003c/p\u003e \u003cp\u003eBlood samples (20 mL in EDTA tubes) were collected and sent to the Genome Center at the Eurofins Scientific laboratory for next-generation sequencing (NGS), covering all coding regions and exon-adjacent flanking regions, including copy number variations (CNVs), of 144 genes related to hereditary cancer syndromes.\u003c/p\u003e \u003cp\u003eAfter obtaining the results, patients were invited for in-person follow-ups, where they received feedback on genetic test results and genetic counseling, conducted exclusively by members of the study team, who were previously trained to provide appropriate guidance on results and follow-up.\u003c/p\u003e \u003cp\u003eTo characterize the sample profile concerning the analyzed variables, frequency tables were prepared for categorical variables, presenting absolute frequencies (n) and percentages (%). For numerical variables, descriptive statistics were calculated, including mean, standard deviation, minimum and maximum values, median, and quartiles. Comparisons between categorical variables were performed using the Chi-Square test or Fisher's exact test when expected values were less than 5. For numerical variables, the Mann-Whitney test was used for two-category comparisons, and the Kruskal-Wallis test for three or more categories, as the variable distributions were not normal.\u003c/p\u003e \u003cp\u003eThe analysis of factors associated with altered genetic test results was performed using simple and multiple logistic regression, employing the Stepwise criterion for variable selection. Odds ratios (ORs) and their respective 95% confidence intervals were calculated.\u003c/p\u003e \u003cp\u003eThe significance level adopted for all tests was 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Statistical analysis was conducted using The SAS System for Windows, version 9.4 (SAS Institute Inc, 2002\u0026ndash;2012, Cary, NC, USA) (\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe study enrolled 373 women who underwent multigene germline panel testing. Among them, 188 were asymptomatic and 185 had a history of breast and/or ovarian cancer. Nine patients were excluded from the final analysis due to loss to follow-up, which prevented complete data collection after testing; all nine were from the asymptomatic group. The final analytic cohort comprised 364 women (179 with a personal history of cancer and 185 without).\u003c/p\u003e \u003cp\u003eAmong the 364 women analyzed, 51 (14%) had results classified as benign variants (BV) or likely benign variants (LBV), while 313 (85.9%) showed alterations in the test, including variants of uncertain significance (VUS), likely pathogenic variants (LPV), or pathogenic variants (PV). Of these, 108 (29.7%) had positive results (LPV or PV), with a higher percentage of positive results among patients with a cancer history (36.9%) compared to those without a history of the disease (22.7%). The VUS rate was 61.6% among patients without cancer, while it was 50.8% among patients with a previous history of breast cancer (p\u0026thinsp;=\u0026thinsp;0.012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe five genes with the highest frequency of PV/LPV were \u003cem\u003eBRCA1, BRCA2, TP53, PALB2, and MUTYH\u003c/em\u003e. Together, these genes accounted for the majority of clinically actionable findings in the cohort, consistent with their established contribution to hereditary breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 59.9% of patients self-identified as White. White women with a BMI\u0026thinsp;\u0026lt;\u0026thinsp;29.9 and a personal history of breast cancer exhibited a higher frequency of pathogenic or likely pathogenic variants (PV/LPV) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and demographic characteristics of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePV/LPV\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBV/LBV/VUS\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (35.19)\u003c/p\u003e \u003cp\u003e39 (36.11)\u003c/p\u003e \u003cp\u003e31 (28.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (32.03)\u003c/p\u003e \u003cp\u003e101 (39.45)\u003c/p\u003e \u003cp\u003e73 (28.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 (32.96)\u003c/p\u003e \u003cp\u003e140 (38.46)\u003c/p\u003e \u003cp\u003e104 (28.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003cp\u003eNon-white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (70.37)\u003c/p\u003e \u003cp\u003e32 (29.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (55.47)\u003c/p\u003e \u003cp\u003e114 (44.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218 (59.89)\u003c/p\u003e \u003cp\u003e146 (40.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003cp\u003e20-24.9\u003c/p\u003e \u003cp\u003e25-29.9\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (9.26)\u003c/p\u003e \u003cp\u003e28 (25.93)\u003c/p\u003e \u003cp\u003e48 (44.44)\u003c/p\u003e \u003cp\u003e22 (20.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (3.52)\u003c/p\u003e \u003cp\u003e58 (22.66)\u003c/p\u003e \u003cp\u003e103 (41.41)\u003c/p\u003e \u003cp\u003e56 (32.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (5.21)\u003c/p\u003e \u003cp\u003e86 (23.62)\u003c/p\u003e \u003cp\u003e154 (42.30)\u003c/p\u003e \u003cp\u003e105 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-menopause\u003c/p\u003e \u003cp\u003ePost-menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (55.14)\u003c/p\u003e \u003cp\u003e48 (44.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157 (62.06)\u003c/p\u003e \u003cp\u003e96 (37.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216 (59.34)\u003c/p\u003e \u003cp\u003e144 (39.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary Education\u003c/p\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (20.37)\u003c/p\u003e \u003cp\u003e45 (41.61)\u003c/p\u003e \u003cp\u003e41 (37.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (21.09)\u003c/p\u003e \u003cp\u003e116 (45.31)\u003c/p\u003e \u003cp\u003e86 (33.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (20.05)\u003c/p\u003e \u003cp\u003e161 (44.23)\u003c/p\u003e \u003cp\u003e127 (34.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious Pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (22.43)\u003c/p\u003e \u003cp\u003e83 (77.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (15.35)\u003c/p\u003e \u003cp\u003e215 (84.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (17.30)\u003c/p\u003e \u003cp\u003e298 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal History of Breast Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (38.89)\u003c/p\u003e \u003cp\u003e66 (61.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143 (55.86)\u003c/p\u003e \u003cp\u003e113 (44.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185 (50.82)\u003c/p\u003e \u003cp\u003e179 (49.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-Degree Relatives with Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (32.41)\u003c/p\u003e \u003cp\u003e73 (67.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (41.41)\u003c/p\u003e \u003cp\u003e150 (58.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (38.73)\u003c/p\u003e \u003cp\u003e223 (61.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond-Degree Relatives with Cance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (39.62)\u003c/p\u003e \u003cp\u003e64 (60.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (32.29)\u003c/p\u003e \u003cp\u003e165 (64.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132 (36.26)\u003c/p\u003e \u003cp\u003e229 (62.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother with a History of Breast Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (31.48)\u003c/p\u003e \u003cp\u003e37 (68.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (22.40)\u003c/p\u003e \u003cp\u003e97 (77.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (12.36)\u003c/p\u003e \u003cp\u003e134 (36.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multivariate model using stepwise variable selection, breast cancer status and ethnicity were identified as significant predictors of altered genetic test results (PV/LPV). Women with a personal history of breast cancer had a 1.9-fold increased likelihood of a positive genetic test result (OR 1.90, 95% CI 1.16\u0026ndash;3.10), and those self-identified as White had a 2.1-fold increased likelihood (OR 2.10, 95% CI 1.31\u0026ndash;3.36) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of factors associated with the presence of pathogenic (PV) or likely pathogenic variants (LPV) (n\u0026thinsp;=\u0026thinsp;364)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue-P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO.R.*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI O.R.*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eValue - O.R* 95% CI O.R.*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40 years (ref.)\u003c/p\u003e \u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.503\u003c/p\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e0.83\u003c/p\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.49\u0026ndash;1.42\u003c/p\u003e \u003cp\u003e0.52\u0026ndash;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-white (ref.)\u003c/p\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e1.18\u0026ndash;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-- 1.00 --\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e 2.10 1.31\u0026ndash;3.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary (ref.)\u003c/p\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003cp\u003eHigher Education/Postgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.874\u003c/p\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.52\u0026ndash;1.74\u003c/p\u003e \u003cp\u003e0.63\u0026ndash;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at Menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous variable (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.939\u0026ndash;1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref.)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.36\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-menopause (ref.)\u003c/p\u003e \u003cp\u003ePost-menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.84\u0026ndash;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003cp\u003e20-24.9\u003c/p\u003e \u003cp\u003e25-29.9\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.071\u003c/p\u003e \u003cp\u003e0.071\u003c/p\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003cp\u003e1.81\u003c/p\u003e \u003cp\u003e1.71\u003c/p\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52\u0026ndash;11.58\u003c/p\u003e \u003cp\u003e0.95\u0026ndash;3.49\u003c/p\u003e \u003cp\u003e0.96\u0026ndash;3.05\u003c/p\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast Cancer Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref.)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e1.26\u0026ndash;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-- 1.0 ---\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e 1.90 1.16\u0026ndash;3.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history cancer\u003c/p\u003e \u003cp\u003e1st -degree relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref.)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.92\u0026ndash;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of breast cancer in mother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref.)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.31\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of cancer\u003c/p\u003e \u003cp\u003e2nd -degree relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref.)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e0.52\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* OR (Odds Ratio) = Risk ratio for positive genetic test result; (n\u0026thinsp;=\u0026thinsp;256 with BV/LBV/VUS and n\u0026thinsp;=\u0026thinsp;108 with PV/LPV). 95% CI OR\u0026thinsp;=\u0026thinsp;95% confidence interval for the odds ratio. Ref.: reference category. BV: benign variant; LBV: likely benign variant; VUS: variant of uncertain significance; PV: pathogenic variant; LPV: likely pathogenic variant.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this high-risk Brazilian public health cohort, extended multigene panel testing identified pathogenic or likely pathogenic variants in nearly one-third of participants (29.7%), a prevalence consistent with previously reported data in selected hereditary breast cancer populations. The high overall rate of genetic alterations (including VUS) reflects both the enriched-risk selection criteria and the use of a broad 144-gene panel.\u003c/p\u003e \u003cp\u003eThe prevalence observed in our cohort aligns with findings from large-scale international consortia such as the CARRIERS study, which demonstrated clinically actionable pathogenic variants in a subset of breast cancer-associated genes among high-risk individuals (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, unlike predominantly European or North American cohorts, our population represents a highly admixed Brazilian group within the public health system, a setting historically underrepresented in genomic studies.\u003c/p\u003e \u003cp\u003eThe most frequently affected genes\u0026mdash;\u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eBRCA2\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, and \u003cem\u003ePALB2\u003c/em\u003e\u0026mdash;are consistent with established hereditary breast cancer susceptibility genes. The presence of recurrent \u003cem\u003eTP53\u003c/em\u003e variants likely reflects the known Brazilian founder mutation (R337H), particularly prevalent in certain regions of the country. These findings reinforce the importance of maintaining TP53 testing within hereditary cancer panels in Brazil.\u003c/p\u003e \u003cp\u003eThe detection of pathogenic variants in \u003cem\u003eMUTYH\u003c/em\u003e highlights the complexity of interpreting moderate-penetrance or recessive genes in multigene panels. Although monoallelic \u003cem\u003eMUTYH\u003c/em\u003e variants have uncertain association with breast cancer risk, their identification underscores the interpretative challenges introduced by extended testing.\u003c/p\u003e \u003cp\u003eA particularly noteworthy finding was the independent association between self-declared White ethnicity and PV/LPV detection. This observation should be interpreted with caution. Brazil is one of the most genetically admixed populations worldwide, and self-reported race does not necessarily correlate with genomic ancestry proportions. Several factors may explain this association:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHistorical overrepresentation of individuals of European ancestry in genomic databases, potentially increasing classification accuracy for variants identified in these individuals.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDifferences in variant interpretation due to reference bias.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eResidual confounding related to socioeconomic or healthcare access variables.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLimitations of self-reported ethnicity as a proxy for ancestry.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that underrepresentation of non-European populations in genomic datasets may contribute to misclassification or higher rates of variants of uncertain significance. Therefore, our findings likely reflect both biological and structural components of genomic inequity.\u003c/p\u003e \u003cp\u003e Patients with a personal history of breast cancer were nearly twice as likely to harbor PV/LPVs, consistent with current clinical guidelines recommending germline testing in affected individuals. This supports the continued implementation of multigene panel testing in high-risk patients within the public health system.\u003c/p\u003e \u003cp\u003eThe high VUS rate (56.3%) warrants consideration. Broad panels inevitably increase VUS detection, particularly in admixed populations. This finding reinforces the need for population-specific variant curation initiatives and functional validation studies.\u003c/p\u003e \u003cp\u003eLimitations:\u003c/p\u003e \u003cp\u003eThis study has limitations. It was conducted at a single tertiary referral center using convenience sampling, which may limit generalizability. No genomic ancestry markers were used to quantify admixture proportions. The cross-sectional design precludes risk estimation. Additionally, the use of stepwise regression may introduce model selection bias.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eExtended multigene panel testing in this high-risk Brazilian public health cohort revealed a substantial burden of pathogenic germline variants. The association between self-reported White ethnicity and pathogenic variant detection reinforces the importance of hereditary cancer assessment in public health settings and highlights the need for greater genomic representation of ethnically diverse populations to enhance variant interpretation accuracy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive specific funding from public, commercial, or not-for-profit funding agencies. Genetic testing was performed as part of routine clinical care within the Brazilian Unified Health System (SUS) at CAISM/UNICAMP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the CRediT (Contributor Roles Taxonomy): Conceptualization: C.E.R.A., B.N.D., C.C.; Methodology: C.E.R.A., B.N.D., S.R.C.T., T.G., C.C.; Data curation and formal analysis: A.E.R.S.C., M.L.S., A.L.V.; Investigation and data collection: C.E.R.A., B.N.D., A.E.R.S.C., S.R.C.T., D.I.S.B.C.S., A.L.V.; Writing \u0026ndash; original draft: C.E.R.A., B.N.D.; Writing \u0026ndash; review and editing: all authors; Supervision and project administration: C.C. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to patient privacy and ethical restrictions, but are available from the corresponding author upon reasonable request and subject to institutional review board approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University of Campinas (UNICAMP) under approval number 54179621.1.0000.5404. Informed consent was obtained from all participants involved in the study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInstituto Nacional de C\u0026acirc;ncer (INCA). Estimativa 2023: Incid\u0026ecirc;ncia de C\u0026acirc;ncer no Brasil. Rio de Janeiro: INCA; 2022. Available from: https://www.gov.br/inca/pt-br/assuntos/cancer/numeros/ [cited 2022 Nov 1].\u003c/li\u003e\n\u003cli\u003eGreen VL. Breast Cancer Risk Assessment and Management of the High-Risk Patient. Obstet Gynecol Clin North Am. 2022;49(1):87-116.\u003c/li\u003e\n\u003cli\u003eWendt C, Margolin S. Identifying breast cancer susceptibility genes - a review of the genetic background in familial breast cancer. Acta Oncol. 2019;58(2):135-46.\u003c/li\u003e\n\u003cli\u003eNelson HD, Huffman LH, Fu R, Harris EL. Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med. 2005;143(5):362-79.\u003c/li\u003e\n\u003cli\u003eDuarte BN, Alem CER, da Silva Cabello AER, Teixeira SRC, Cabello C. Risk-reducing surgeries for breast cancer in Brazilian patients undergoing multigene germline panel: impact of results on decision making. Breast Cancer Res Treat. 2024.\u003c/li\u003e\n\u003cli\u003eRobson M, Storm CD, Weitzel J, Shilman DS, Offit K; American Society of Clinical Oncology. American Society of Clinical Oncology Policy Statement Update: Genetic and Genomic Testing for Cancer Susceptibility. J Clin Oncol. 2015;33(31):3660-3667.\u003c/li\u003e\n\u003cli\u003eRebbeck TR, Friebel TM, Friedman E, Hamann U, Huo D, Kwong A, et al. Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Hum Mutat. 2018;39(5):593-620.\u003c/li\u003e\n\u003cli\u003eLynce F, Graves KD, Jandorf L, Ricker C, Castro E, Moreno L, et al. Genomic Disparities in Breast Cancer Among Latinas. Cancer Control. 2016;23(4):359-72.\u003c/li\u003e\n\u003cli\u003eChavarri-Guerra Y, Blazer KR, Weitzel JN. Genetic Cancer Risk Assessment for Breast Cancer in Latin America. Rev Invest Clin. 2017;69(2):94-102.\u003c/li\u003e\n\u003cli\u003eSouza AM, Resende SS, Sousa TN, Brito CFA. A systematic scoping review of the genetic ancestry of the Brazilian population. Genet Mol Biol. 2019;42(3):495-508.\u003c/li\u003e\n\u003cli\u003eGuindalini RSC, Viana DV, Kitajima J, Rocha VM, L\u0026oacute;pez RVM, Zheng Y, et al. Detection of germline variants in Brazilian breast cancer patients using multigene panel testing. Sci Rep. 2022;12(1):4190.\u003c/li\u003e\n\u003cli\u003eNational Comprehensive Cancer Network Clinical Practice Guidelines in Oncology. Genetic/Familial High Risk Assessment: Breast and Ovarian [Internet]. 2022 [cited March 9, 2022]. Available from: https://www.nccn.org/professionals/physician_gls/pdf/genetics_bop.pdf.\u003c/li\u003e\n\u003cli\u003eTabachnick BG, Fidell LS. Using Multivariate Statistics. Boston: Allyn and Bacon. 4th ed. 2001.\u003c/li\u003e\n\u003cli\u003eConover WJ. Practical Nonparametric Statistics. New York: John Wiley \u0026amp; Sons. 3rd ed. 1999.\u003c/li\u003e\n\u003cli\u003eFletcher RH, W. FS. Epidemiologia Cl\u0026iacute;nica: Elementos Essenciais. Porto Alegre: Artmed. 4th ed. 2006.\u003c/li\u003e\n\u003cli\u003eSiegel S, Castellan Jr. NJ. Estat\u0026iacute;stica N\u0026atilde;o-Param\u0026eacute;trica para Ci\u0026ecirc;ncias do Comportamento. 2\u0026ordf; edi\u0026ccedil;\u0026atilde;o. 2006.\u003c/li\u003e\n\u003cli\u003eHosmer DW, Lemeshow SL. Applied Logistic Regression. New York: John Wiley \u0026amp; Sons. 1989.\u003c/li\u003e\n\u003cli\u003eFleiss JL, Levin B, Paik MC. Statistical Methods for Rates and Proportions. Hoboken, N.J: John Wiley \u0026amp; Sons. 3rd ed. 2003.\u003c/li\u003e\n\u003cli\u003eHu C, Hart SN, Gnanaolivu R, Huang H, Lee KY, Na J, et al. A Population-Based Study of Genes Previously Implicated in Breast Cancer. N Engl J Med. 2021;384(5):440-51.\u003c/li\u003e\n\u003cli\u003eDorling L, Carvalho S, Allen J, et al.; Breast Cancer Association Consortium. Breast cancer risk genes \u0026mdash; association analysis in more than 113,000 women. N Engl J Med. 2021;384(5):428-439.\u003c/li\u003e\n\u003cli\u003ede Freitas Ribeiro AA, Junior NMC, Dos Santos LL. Systematic review of the molecular basis of hereditary breast and ovarian cancer syndrome in Brazil: the current scenario. Eur J Med Res. 2024;29(1):187.\u003c/li\u003e\n\u003cli\u003eFaria JP, Assump\u0026ccedil;\u0026atilde;o JG, de Oliveira Matos L, Soardi FC, Bretz GPM, Friedman E, et al. Spectrum of germline pathogenic variants in Brazilian hereditary breast/ovarian cancer cases. Breast Cancer Res Treat. 2024;207(3):615-24.\u003c/li\u003e\n\u003cli\u003eKuchenbaecker KB, Hopper JL, Barnes DR, Phillips KA, Mooij TM, Roos-Blom MJ, et al. Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers. Jama. 2017;317(23):2402-16.\u003c/li\u003e\n\u003cli\u003ePalmero EI, Sch\u0026uuml;ler-Faccini L, Caleffi M, Achatz MI, Olivier M, Martel-Planche G, et al. Detection of R337H, a germline TP53 mutation predisposing to multiple cancers, in asymptomatic women participating in a breast cancer screening program in Southern Brazil. Cancer Lett. 2008;261(1):21-5.\u003c/li\u003e\n\u003cli\u003eRahman N, Seal S, Thompson D, Kelly P, Renwick A, Elliott A, et al. PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nat Genet. 2007;39(2):165-7.\u003c/li\u003e\n\u003cli\u003eHong YR, Yadav S, Wang R, Vadaparampil S, Bian J, George TJ, et al. Genetic Testing for Cancer Risk and Perceived Importance of Genetic Information Among US Population by Race and Ethnicity: a Cross-sectional Study. J Racial Ethn Health Disparities. 2024;11(1):382-94.\u003c/li\u003e\n\u003cli\u003eMcQuillan MA, Zhang C, Tishkoff SA, Platt A. The importance of including ethnically diverse populations in studies of quantitative trait evolution. Curr Opin Genet Dev. 2020;62:30-5.\u003c/li\u003e\n\u003cli\u003eManrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA, Szolovits P, et al. Genetic Misdiagnoses and the Potential for Health Disparities. N Engl J Med. 2016;375(7):655-65.\u003c/li\u003e\n\u003cli\u003eNCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines\u0026reg;) Version 2.2023 \u0026mdash; February 7, 2023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"hereditary-cancer-in-clinical-practice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hccp","sideBox":"Learn more about [Hereditary Cancer in Clinical Practice](http://jhoonline.biomedcentral.com)","snPcode":"13053","submissionUrl":"https://submission.nature.com/new-submission/13053/3","title":"Hereditary Cancer in Clinical Practice","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"snapp","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9371542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9371542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eBreast cancer remains the most common malignancy among women in Brazil, with a substantial proportion attributable to hereditary predisposition. Data regarding germline pathogenic variants in ethnically diverse, self-reported populations within public health settings remain limited.\u003c/p\u003e\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eTo evaluate the prevalence of pathogenic/likely pathogenic variants (PV/LPV) detected by extended multigene panel testing in a high-risk Brazilian public health cohort and to assess associations with clinical and demographic factors.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included 364 women evaluated at a tertiary public cancer genetics clinic between November 2021 and October 2022. All participants met NCCN criteria for hereditary cancer risk assessment. Germline sequencing of 144 cancer-predisposition genes was performed using next-generation sequencing, including copy number variation analysis. Logistic regression models were used to evaluate factors associated with PV/LPV detection.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003ePathogenic or likely pathogenic variants were identified in 29.7% of participants. Variants of uncertain significance were detected in 56.3%. The most frequently altered genes were \u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eBRCA2\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003ePALB2\u003c/em\u003e, and \u003cem\u003eMUTYH\u003c/em\u003e. In multivariate analysis, a personal history of breast cancer (OR 1.90, 95% CI 1.16\u0026ndash;3.10, p\u0026thinsp;=\u0026thinsp;0.011) and self-declared White ethnicity (OR 2.10, 95% CI 1.31\u0026ndash;3.36, p\u0026thinsp;=\u0026thinsp;0.002) were independently associated with PV/LPV detection.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThis high-risk Brazilian public health cohort demonstrated a substantial prevalence of pathogenic germline variants using extended multigene panel testing. The association between self-reported White ethnicity and pathogenic variant detection underscores the complexity of genetic risk assessment in diverse populations and highlights the need for greater representation of non-European ancestries in genomic variant databases.\u003c/p\u003e","manuscriptTitle":"Extended Multigene Panel Testing in a High-Risk Brazilian Public Health Cohort: Pathogenic Variant Prevalence and the Role of Self-Reported Ethnicity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:03:03","doi":"10.21203/rs.3.rs-9371542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-13T00:31:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15734691084513330567052923005596727514","date":"2026-04-23T01:02:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T09:15:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T08:16:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T18:28:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Hereditary Cancer in Clinical Practice","date":"2026-04-09T17:43:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"hereditary-cancer-in-clinical-practice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hccp","sideBox":"Learn more about [Hereditary Cancer in Clinical Practice](http://jhoonline.biomedcentral.com)","snPcode":"13053","submissionUrl":"https://submission.nature.com/new-submission/13053/3","title":"Hereditary Cancer in Clinical Practice","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"snapp","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29486405-5f36-4a2d-b4ca-0b5da6af868b","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-13T00:31:03+00:00","index":19,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:03:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:03:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9371542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9371542","identity":"rs-9371542","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}