Molecular Characterization of Hereditary Breast and Ovarian Cancer Patients from a Public Precision Medicine Service in the Southeast Brazilian Population

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The program was built around the development of a patient care flowchart to guide follow-up strategies, employing a highly specialized team that included genetic counseling, psychological support, and advanced molecular diagnostic techniques, targeting patients within the public health system. Methods: Genetic screening of genes associated with the syndrome was conducted on 210 Brazilian patients using Sanger sequencing and NGS technology. Results: Pathogenic or likely pathogenic mutations were identified in 33.3% (70/210) of patients, with 14.3% (30/210) harboring mutations in non- BRCA genes. The most prevalent pathogenic mutation identified was c.4829_4830delTG in the BRCA2 gene, with a prevalence of 8.5% among the identified mutations. This mutation seems to be very rare in other cohorts of Brazilian breast/ovarian patients. Additionally, 8.4% of the analyzed individuals exhibited P/LP variants in genes related to other hereditary cancers. Thirteen variants across nine genes, identified for the first time in Brazil, are presented here. Forty-one variants of uncertain significance were found, distributed across 12 different genes, with the majority (8/41, 19.5%) observed in the ATM gene. Conclusion: This study represents the first investigation focused on patients from the Brazilian public health system in the Southeast Brazilian population. By identifying pathogenic mutations in these patients, precision medicine care can be implemented, leading to improved care for patients and their families. Biological sciences/Cancer/Breast cancer Biological sciences/Genetics/Clinical genetics/Genetic testing HBOC in Brazil Hereditary breast and ovarian cancer Next-generation sequencing Figures Figure 1 Figure 2 Introduction The impact of cancer in the world, based on estimates from GLOBOCAN, indicated almost 20 million new cases worldwide (excluding cases of non-melanoma skin neoplasm) in 2022. Female breast cancer was the second most frequently diagnosed, responsible for nearly 2.3 million new occurrences (11.6%) [1]. In Brazil, breast cancer is the first cause of death in women, and the Southeast and South regions have the highest recorded death rates (12.43 and 12.69/100 thousand women, respectively). The incidence of ovarian cancer is lower compared to breast cancer. However, it ranks seventh among the most common cancers in Brazil, with a higher concentration rate in the South and Southeast regions. Additionally, ovarian cancer is known for its severity and difficulty in diagnosis [2]. Hereditary Breast and Ovarian Cancer syndrome (HBOC) is characterized by an increased risk in the development of breast and ovarian cancer due to the presence of inherited pathogenic mutations [3]. Although most cases of this syndrome are caused by mutations in the BRCA1/2 genes, other genes have also been associated with an increased risk. Recent studies have shown that around 12% of BRCA1 and BRCA2 negative patients with clinical criteria for HBOC harbor a pathogenic variant in another gene [4,5]. Therefore, it is essential to understand the genetic variations in specific ethnic groups related to the syndrome to identify high-risk individuals and implement appropriate population-based preventive measures. The Brazilian population is highly diverse, with ethnic composition varying across different states. The primary groups that contributed to the formation of the Brazilian population include Amerindians, Europeans, and Africans, alongside other specific groups. Understanding this diversity is crucial for analyzing the frequencies of genetic mutations, as it provides insights into regional profiles, heredity, genetic susceptibility, ancestry, and disease segregation [6]. In recent years, next-generation sequencing (NGS) has emerged as a powerful tool for analysing multiple genes, significantly enhancing the efficiency and accessibility of molecular tests. This technology enables the identification of individuals at high risk for breast and/or ovarian cancer and supports the molecular diagnosis of various other hereditary syndromes in affected individuals [7]. However, managing cancer within public health systems remains a challenge, particularly in middle- and low-income countries, where financial constraints and inadequate infrastructure may prevent effective diagnosis and care for cancer patients [8]. According to the 2019 National Health Survey (Pesquisa Nacional de Saúde, PNS), 71.5% of Brazilians, or more than 150 million people, rely exclusively on the public Unified Health System (Sistema Único de Saúde, SUS) for their healthcare. However, in a country of continental dimension such as Brazil, the SUS primary healthcare model under SUS often falls short due to the lack of molecular diagnostic centers and insufficient support in many states [9, 10, 11]. Despite the considerable allelic and locus heterogeneity in HBOC patients in Brazil, most studies have paid more attention to the BRCA1 , BRCA2 , and TP53 genes [12]. The first studies of patients meeting the criteria for HBOC using next-generation sequencing in Brazil were only published in 2016 [13, 14, 15]. This scenario has changed in the last years, although the number of studies is still low to show the broad spectrum of mutations in different regions of Brazil [12]. The state of Minas Gerais, in southeast Brazil, with its 22 million inhabitants, is demographically the second of the country. In the Midwest region of Minas Gerais, Brazil, we implemented a Hereditary Predisposition to Cancer Assessment and Family Monitoring Program to support patients at risk for hereditary cancer syndromes. This is a collaborative program between the Oncology Unit of Hospital São João de Deus, the Molecular Biology Laboratory of the Universidade Federal de São João del-Rei (UFSJ), and the Associação de Combate ao Câncer do Centro-Oeste Mineiro (ACOM). The program was based on the development of a patient care protocol to define follow-up strategies, employing a highly specialized team, which included genetic counseling, psychological support, and the use of advanced techniques for molecular diagnosis. The program provides care and monitoring for families affected by various hereditary cancer syndromes, with HBOC being the most prevalent. The entire care service was structured using resources from research projects funded by various funding agencies, as well as donations raised by ACOM. Collaborative research efforts with these institutions have focused on patients monitored by the public health system. In the initial years of the program, molecular characterization of the BRCA1 and BRCA2 genes was conducted on 44 patients using Sanger sequencing, along with the analysis of point mutations in the CHEK2 , TP53 , and PALB2 genes [16, 17]. Through the Minas Gerais Network for Population Genomics and Precision Medicine, funded by FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais), it was possible to expand the service, and a NGS panel for genes associated with HBOC was implemented to support the program. Despite all patients being assisted by the SUS, some are able to afford the test, which is conducted in private laboratories. Here, we present all these results collectively to ensure a more comprehensive understanding of the molecular profile in the state. Results Variants found This is the first study to evaluate mutations using an NGS panel (n=166) in patients assisted by the public health system with clinical criteria for HBOC in Minas Gerais. The results provide information on the mutation profile across more than 20 genes in the studied group, in conjunction with findings from Sanger sequencing (n=44). Pathogenic and likely pathogenic (P/LP) mutations have been found in 33.3% of patients (70/210), while in 16.7% of them (35/210), variants of uncertain significance (VUS) were found. Two pathogenic alterations were identified in the same individual: c.156_157insAlu ( BRCA2 ) and c.3802del ( ATM ). All pathogenic and likely pathogenic variants are presented below (Tables 1 and 2). It was observed that 71.8% (51/71) of the pathogenic and likely pathogenic variants were found in high penetrance genes 19 related to HBOC: 23 (32.4%) in BRCA2 ; 18 (25.4%) in BRCA1 ; 6 (8.4%) in TP53 , 4 (5.6%) in PALB2 . Genes with moderate penetrance 19 , such as ATM (3), CHEK2 (4), MSH2 (2), RAD51C (4), and RAD51D (1), represented 19.7% (14/71) of the mutations in the patients analyzed. It is worth mentioning that 8.4% of the probands presented a pathogenic or likely pathogenic variants in genes frequently associated with other types of hereditary cancer: BRIP1 (n = 1), CTC1 (n = 1), MITF (n = 1), PTCH1 (n = 1), RECQL4 (n = 1) and NTHL1 (n =1). The most frequently mutated gene in this study was BRCA2 (23/210; 11.0%). The distribution of pathogenic or likely pathogenic variants by genes and description of the most frequent pathogenic mutations found in Minas Gerais state can be seen in Figures 1 and 2. It’s worth noting that 13 variants across 9 genes presented here have been identified for the first time in Brazil: c.112_113del, c.*872_*873del, c.3328_3329del, c.4689_4694del and c.5072C>T in the BRCA1 gene; c.467G>A ( TP53 ); c.409C>T ( CHEK2 ); c.8438T>C ( ATM ); c.2990_2993del ( BRIP1 ); c.526-1G>A ( NTHL1 ); c.2831del ( CTC1 ); c.1511C>A ( PTCH1 ) and c.2412_2420del ( RECQL4 ). Variants of uncertain clinical significance Forty-one VUS were found in this study and distributed in 12 different genes. Most of these variants (8/41, 19.5%) were observed in the ATM gene(Supplementary Table S2). Studies related to this gene in patients with clinical criteria for HBOC began to be further explored after the advent of NGS technologies. In Brazil, few studies have evaluated this gene to date in the HBOC population and future studies are necessary to explore the pathogenicity of these variants better. The other genes that harbored VUS were BRCA1 , BRCA2 , CHEK2 , MSH2 , MLH1 , NF1 , RAD50 , PMS2 , RECQL4 , RAD51C and FAN1 . Patients and clinical characteristics A total of 210 patients who met the clinical criteria for HBOC as recommended by NCCN were included in this study. Among the patients with identified pathogenic mutations, 30 were diagnosed with primary breast cancer before the age of 40 (42.86%) (Table 3). However, a significant number of patients with benign variants were also diagnosed before the age of 40 (39 out of 105; 37.14%), and no statistically significant difference was observed between these groups (P = 0.2756). Furthermore, no statistically significant difference was found between the groups with respect to tumor type (Breast P = 0.1272; Ovarian P = 0.3090). The analysis of hormonal receptors revealed a high prevalence of triple-negative tumors, which are typically associated with BRCA1 gene mutations; however, in this study, such tumors were frequently observed in both groups. Discussion Studied Population Brazil is the seventh most inhabited country in the world, and it has a complex pattern of ethnic diversity. Among their five regions, the Southeast is the most populous with about 85 million people [20]. The majority of the HBOC molecular studies in Brazil are concentrated on the Southeast and South regions of the country. However, most patients came from the states of São Paulo, Rio de Janeiro, and Rio Grande do Sul, where the most extensive molecular diagnosis and care centers for cancer patients are located [12]. The literature review shows a lack of studies using NGS technology in patients with HBOC in Minas Gerais, the largest state in the Southeast in territorial extension (occupying 63% of the area) [21]. Minas Gerais has 20.539.989 inhabitants, the second most populous state in the Southeast region. Its territory was inhabited by indigenous people when the Portuguese arrived in Brazil. Therefore, most of the population of Minas Gerais are descendants of Portuguese settlers from northern Portugal and African slaves, mainly from West Africa. According to a study carried out on genetic ancestry, the composition of the Minas Gerais population is: 75.4% European, 18.3% African and 5.8% indigenous [22]. In a comparative study of Brazilian geographic regions, it was seen that the Northeast has the most significant African ancestry. In contrast, the Southeast/South of Brazil has the greatest European ancestry [23]. Furthermore, other populations arrived in Minas Gerais at different times, such as Italians, Spaniards, Japanese, Germans, Lebanese, Syrians, and others [24]. Few studies now have focused attention on HBOC in the Minas Gerais population. The first three studies in the state used screening mutations methodologies concentrated in specific genes or punctual mutations, while two of them are from our research group, and the results are synthesized here [16, 17, 25]. Two more recent studies from Belo Horizonte, capital of Minas Gerais state, described the mutational profile found in a medical service at a private genetic referral center in the city and in individuals with health insurance tested by private labs in MG, both by NGS panel [26, 27]. In the Carvalho et al. (2023) study, asymptomatic individuals with a familial history of cancer (169/382) were included, and it was not possible to know the real frequency of mutations as they included relatives in the research. However, we can see a more prevalent mutation in the BRCA1 gene, c.470_471delCT, identified in 5 different families in Carvalho´s study and not identified in the present work. Mutations as c.2808_2811del were more prevalent in Faria´s research but not identified by Carvalho et al. (2023) and in the present study. Other variants, such as c.4829_4830delTG and c.6405_6409del in BRCA2 gene were more prevalent in our cohort and not identified by Faria et al. (2024). Carvalho et al. (2023) did not identify any patients carrying the c.1010G>A mutation in the TP53 gene, one of the most prevalent mutations in the Brazilian population, and Faria et al. (2024) found this mutation in only one patient. In our study, however, it was the third most prevalent mutation identified. Therefore, it is clear that there are specific variants that seem to cluster in regions of a large state like Minas Gerais. Other mutations, such as c.5266dupC, c.2T>G, and c.156_157insAlu, were present in all studies [26, 27]. Mutational Profile A recent Brazilian review provides a comprehensive overview of the broad variability in molecular profiles related to hereditary breast and ovarian cancer in the country. Certain mutations stand out in the Brazilian population: c.5266dupC, c.156_157insAlu, and c.1010G>A in the BRCA1, BRCA2, and TP53 genes, respectively [12]. In the present work, pathogenic and likely pathogenic mutations have been found in 33.3% of patients (70/210), and a higher frequency of probands harboring the mutations in non- BRCA genes (30/210, 14.3%) was found when compared with others Brazilian research. Only ten studies in Brazil up to now evaluated breast cancer probands using multigene genetic panel tests, and the frequency of patients with pathogenic mutations in non- BRCA genes varied from 1.5% to 12.5% [26, 27, 28, 29, 30, 31, 32, 34, 35]. It is important to note that the panels used differ, which may contribute to the variation in frequency. Another recent study presents a geographical distribution of the most frequent BRCA1/2 mutations in Brazil, established by BRCA genetic testing results from 1267 unrelated individuals investigated routinely in a private laboratory, but it was not possible to identify the distribution of samples through the southeast states of Brazil [36]. Considering the 210 patients tested in Minas Gerais in the present work, it was seen that the frequency of pathogenic mutations in the BRCA2 gene was higher than in the BRCA1 gene, contrary to most studies published in the country [12]. This result is in accordance with the two previous studies from Minas Gerais [26, 27]. Four BRCA2 mutations have a significant impact on this data as they represent 78.3% of all BRCA2 mutations and 32.4% (23/71) of all P/LP mutations found in this study (Fig. 2). One of these mutations as c.4829_4830delTG, the most common mutation in our cohort, seems to be very rare in other cohorts of Brazilian breast/ovarian patients. The pathogenic frameshift variant c.4829_4830delTG results in the deletion of two nucleotides in exon 11 of the BRCA2 gene. Records of this mutation were identified in populations such as Korean, Pakistani, Moroccan, Israeli, and Ashkenazi Jewish [37, 38, 39, 40, 41]. Interestingly, immigration from the Middle East and Asia, where the mutation was most prevalent, comprised only 2% of the total immigration flow to Brazil until 1972 [22]. This variant was reported in the Brazilian population in just four other studies from the south and southeast regions but with only seven registers, where European and African ancestries are more evident due to European colonization and slavery [22, 26, 42, 43]. Mechanisms such as genetic drift and founder effect can partially explain the high frequency of this mutation in the Midwest region of MG, as we see many rural populations in this region of the state. The second most frequent mutation found in Minas Gerais state was c.5266dupC in the BRCA1 gene present in 2.3% (5/210) of patients. The frequency of this variant varies among regions of Brazil and had already been reported in a frequency of 11.6% (11/95) in the Carvalho et al ., study with lower frequencies in other works [28, 32, 34, 35, 44]. This is an ancestral mutation in the Ashkenazi Jewish population [45], and it was considered the most frequent mutation in Brazilian patients, representing 26.8% of all germline mutations found in the BRCA1 gene and detected in all geographic regions [12]. It was responsible for 7% of the germline pathogenic mutations found here. The c.1010G>A mutation was detected in five women, all diagnosed under 45 years old, and two of them had a family history of other cancers, such as prostate, pancreatic, esophageal, and leukemia, beyond breast cancer. The c.1010G>A variant is a Brazilian founder mutation and was found in 0.3% of the general population in southern Brazil [46]. This mutation is associated with Li-Fraumeni syndrome, an inherited cancer predisposition disease caused by a germline mutation in the TP53 gene. People with this syndrome have an increased risk for several types of cancer, such as childhood sarcoma, breast cancer, central nervous system tumors, leukemia, melanoma, prostate and pancreatic cancer [47]. It is very common to find Li-Fraumeni families filling clinical criteria for HBOC, which demonstrates that NGS panels give a precise molecular diagnosis, contributing to the follow-up of the patients. In Brazil, TP53 is the most mutated gene after BRCA in breast cancer patients, with the c.1010G>A variant representing more than 75% of the identified variants inside the gene. In the recent Brazilian review, it has been identified in several HBOC Brazilian studies with frequencies ranging from 0.8% to 7.1%. Considering all works that screened for this specific mutation, the frequency of c.1010G>A in patients who met clinical criteria for HBOC from Brazil was estimated in 1.83% (61/3336) [12]. In the present study, the c.1010G > A was found in 2.3% of patients from Minas Gerais, and it was responsible for 7% of the germline pathogenic mutations found. The c.156_157insAlu mutation accounted for 5.6% (4/71) of the pathogenic mutations identified in this study and warrants particular attention. This is a Portuguese founder mutation, and its high occurrence in Brazil is probably the result of Portuguese immigration during centuries of colonization [12]. It is frequently found in the predisposition genes BRCA2 for breast cancer and causes a jump in exon 3 that leads to splicing errors and, consequently, in the transcription and translation of the tumor suppressor protein. This mutation originated from families with cases of HBOC in the northeast and central regions of Portugal, representing 27-38% of all pathogenic BRCA2 mutations. Brazil is a country of Portuguese colonization, and until 1991, 2.2 million of these immigrants were received, which makes this mutation an interesting target of study [49, 50]. This rearrangement was reported most frequently in populations from the south and southeast regions [32, 49, 51] and has been seen with low frequency in HBOC families from the central-western region 1/224 (0,4%) [33]. To date, the Portuguese founding mutation BRCA2 c.156_157insAlu has not been identified in populations from the north and northeast of Brazil. This differential loading of Alu elements across the BRCA2 locus in many regions is likely due to differences in structure between populations. The c.2T>G mutation is also frequently reported in Portuguese families and has been previously described in Brazilian patients with hereditary breast and/or ovarian cancer, particularly in the Southeast and South regions of Brazil [13, 14, 28, 31]. Related to the variants of uncertain significance, the ATM gene had the highest variant frequency. This gene is associated not only with Ataxia-telangiectasia Syndrome and breast cancer but also with several other types of cancer, such as ductal adenocarcinoma of the pancreas, colorectal, prostate, endometrial, kidney, liver, ovarian, esophageal, salivary gland, gastric, thyroid and urinary tract [52, 53]. Among eight patients with VUS in this gene, four had family members with these cancers, especially prostate, breast, liver, endometrial, and pancreatic. Other VUS have been identified in the BRCA2 , CHEK2 , MLH1 , MSH2 , NF1 and RAD50 genes in patients who had a strong family history of several types of cancer. Future studies are essential to elucidate the pathogenicity of these variants. The Hereditary Predisposition to Cancer Assessment and Family Monitoring Program Minas Gerais, Brazil, has enabled access to comprehensive hereditary cancer services within the public health system. More than 250 family members of patients carrying pathogenic mutations have been attended through the program, with all services extended to them as well. This initiative facilitates the identification of individuals at high risk for cancer development, along with the implementation of preventive measures to reduce risks, enhance surveillance, enable early diagnosis, personalize patient prognoses, and explore the potential use of targeted therapies. Conclusions This study aimed to evaluate the molecular profile of hereditary breast and ovarian cancer in the state of Minas Gerais, marking the first research focused on patients from the Brazilian public health system. By identifying pathogenic mutations in individuals at high genetic risk for breast and/or ovarian cancer, precision medicine care can be implemented, providing better assistance to patients and their families. There is often overlap in clinical criteria among different hereditary syndromes, and next-generation sequencing technology in precision medicine is a valuable method for distinguishing between these conditions and ensuring appropriate clinical care for patients and family members. Methods Patients and Clinical Data Overall, 210 patients assisted by the Hereditary Predisposition to Cancer Assessment Program in Minas Gerais were tested. Despite all being assisted by the SUS, 84 patients were able to afford the test, which was performed in private laboratories. Meanwhile, 82 patients, who lacked the financial means to pay for the test, were tested at the UFSJ, following the methodologies described herein. It is important to note that the number of genes included in NGS panels from private laboratories differs from those covered in this study; however, all panels include at least the 22 genes presented here. All results from tests conducted in private laboratories, as well as those conducted in the University, are evaluated by the medical team for patient and family support. These results are presented together to ensure greater accuracy in the mutation frequency in the state. The mutations identified in the forty-four patients who had the BRCA1 and BRCA2 genes sequenced by Sanger sequencing during the initial years of the program are also included here for the same purpose [16, 17]. This study received approval from the Ethics and Research Committee of the São João de Deus Hospital (45662921.9.0000.5545). Written informed consent was obtained from all participants. All methods were performed in accordance with relevant guidelines and regulations. To be included in the study, the individuals had a prior breast or ovarian cancer diagnosis and fulfilled the ‘NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic’ (version 2022.1) [18]. Clinical data were collected from patient medical records. Pedigrees were constructed using the Progeny Pedigree Tool ( https://pedigree.progenygenetics.com/ ). DNA Samples Peripheral blood samples (3–5 mL) were collected in vacutainer tubes with EDTA. Genomic DNA was extracted through the Salting Out method and the Qiagen MiniAmp DNA Kit. The concentration and purity of the DNA samples obtained were analyzed through NanoDrop™ 2000/2000c Spectrophotometer and Qubit 4.0 fluorometer (Thermo Fisher) with the kit QubitTM 1X dsDNA HS Assay (Thermo Fisher). Genetic Screening A NGS multi-gene panel composed of 22 genes, ATM, BARD1, BRCA1, BRCA2, CHEK2, CDH1, EPCAM, MLH1, MSH2, MSH6, NBN, NF1, PALB2, PTEN, RAD50, RAD51, RAD51B, RAD51C, RAD51D, SMARCA4, STK11 and TP53 , was designed. The primers were designed through the Sequence Assay Designer Illumina tool, and two primer pools were created for the amplification of 803 amplicons with a 100% horizontal coverage of exons, 5’ and 3’ UTRs and splicing sites of all the 22 genes, targeting a total of 177.873 bp (Supplementary Table S1 ). The kit AmpliSeq for Illumina Custom DNA Panel was used to prepare the DNA libraries according to the procedures established in the protocol AmpliSeq for Illumina On-Demand, Custom, and Community Panels. The sequencing was performed on the MiSeq equipment using the kit MiSeq Regent v2 Micro (Illumina), with an input of 11 pM. This kit enables paired-end sequencing of 150 bp short reads, generating up to 1 GB of data and 6.6 million reads per run, with 96.7% of bases having a quality score above Q30. Bioinformatic Analysis The primary analysis was performed using FastQC ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) and MultiQC ( https://github.com/MultiQC/MultiQC ) to evaluate the quality of the data, and Trimmomatic ( http://www.usadellab.org/cms/?page=trimmomatic ) to trim reads. Two distinct bioinformatics strategies were adopted for the secondary analysis of the trimmed FASTQ files. The first pipeline utilized the DNA Amplicon tool (v2.1.1), available in Illumina's environment, while the second was developed based on the Genome Analysis Toolkit (GATK) best practices for data pre-processing and germline short variant discovery workflows ( https://gatk.broadinstitute.org/hc/en-us ). Quality control checkpoints were established throughout the analysis pipelines to monitor and control of the obtained data. To achieve this, tools such as Qualimap ( http://qualimap.conesalab.org/ ), Samtools ( https://www.htslib.org/ ), BEDtools ( https://bedtools.readthedocs.io/en/latest/ ), BCFtools ( https://samtools.github.io/bcftools/bcftools.html ) and VariantQC ( https://github.com/BimberLab/DISCVRSeq/ ), among others, were employed. The annotation process (tertiary analysis) of the VCF files generated by the DNA Amplicon pipeline was performed using the GEMINI framework, which employs databases such as RefSeq, ENCODE, OMIM, dbSNP, KEGG, and HPRD. In the GATK pipeline, annotation was conducted by the TAPES tool ( https://github.com/a-xavier/tapes ), based on the ANNOVAR tool, utilizing databases such as ClinVar, gnomAD, dbSNP, dbscSNV, among others. Variant Classification Quality values showed a GC content of 41%, an average coverage of approximately 230X, and 97.6% of the target regions covered at 20X, ensuring the quality of the generated files. The identified variants were filtered based on a minimum coverage of 20 reads (DP > 20) and an allele frequency greater than 30% for heterozygous variants. Only the variants detected by both pipelines were considered for the results. A visual analysis of the BAM and VCF files was also performed using the Integrative Genomics Viewer (IGV, https://igv.org/ ) tool. Variants considered as likely pathogenic, pathogenic, or of uncertain significance by the annotation process were verified by four independent researchers using the ClinVar ( https://www.ncbi.nlm.nih.gov/clinvar/ ) and Varsome ( https://varsome.com/ ) databases, in accordance with the guidelines of the American College of Medical Genetics (ACMG). Declarations Acknowledgments We sincerely thank all the patients for their participation in this study. We also extend our gratitude to the Oncology Unit of the Hospital São João de Deus for their collaboration and support. The authors are deeply grateful to Dr. Rennan G. Moreira from the Genomics Laboratory at the Multi-User Laboratory Center (CELAM), Institute of Biological Sciences, Federal University of Minas Gerais, for his invaluable expertise and technical assistance throughout this research. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Ethical approval and consent to participate This study received approval from the Ethics Committee of São João de Deus Hospital (approval number 45662921.9.0000.5545). All participants provided written informed consent prior to inclusion, and all procedures were carried out in compliance with applicable ethical standards and regulations. Consent for publication The authors have given their consent for the publication of this manuscript. All patients voluntarily agreed to participate in this study by signing an informed consent form, which was approved by the ethics committee of our institution. Funding sources Foundation for Research Support of the State of Minas Gerais (FAPEMIG, Grant Number, RED-00314-16 and RED-00089-23), Foundation for the Coordination of Higher Education Personnel Improvement (CAPES) and Federal University of São João del-Rei. References Bray, F., Laversanne, M., Cantou, H., Ferlay, J., Siegel, L.R., Soerjomataram, I., et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 74 . 229–263, 2024. INCA. 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Pathogenic and likely pathogenic variants identified in BRCA genes (n= 41) Variant (HGVS) Variant ID n ACMG Classification BRCA1 c.68_69del p.Glu23fs rs80357914 1 P c.112_113del p.Lys38fs rs80357949 1 P c.441+2T>A - rs397509173 1 P c.*872_*873del - rs59541324 1 P c.2037delGinsCC p.Lys679Asnfs rs397508932 1 P c.3328_3329del p.Lys1110AlafsTer4 - 1 LP c.3331_3334del p.Gln1111fs rs80357701 1 P c.3756_3759del p.Ser1253fs rs80357868 2 P c.4484G>T p.Arg1495Met rs80357389 1 P c.4689_4694del pTyr1563_Leu1564delinsTer - 2 P c.5072C>T p.Thr1691Ile rs2137522955 1 P c.5266dupC p.Gln1756fs rs80357906 5 P BRCA2 c.2T>C p.Met1Thr rs80358547 2 P c.2T>G p.Met1Arg rs80358547 4 P c.156_157insAlu p.Lys53Alafs*9 rs2138704192 4 P c.1310_1313delAAG p.Lys437fs rs80359277 1 P c.4829_4830del p.Val1610fs rs80359468 6 P c.5985delC p.Asn1995fs rs2137522955 1 P c.6405_6409del p.Asn2135fs rs80359584 4 P c.9154C>T p.Arg3052Trp rs45580035 1 P Table 2. Pathogenic and likely pathogenic variants identified in non-BRCA genes (n= 30) Gene Variant (HGVS) Variant ID n ACMG Classification TP53 c.1010G>A p.Arg337His rs121912664 5 P c.467G>A p.Arg156His rs371524413 1 LP PALB2 c.3027delT p.Glu1010fs rs876659378 4 P CHEK2 c.1036C>T p.Arg346Cys rs201206424 1 LP c.409C>T p.Arg137Ter rs730881701 2 P c.485A>G p.Asp162Gly rs587781652 1 LP RAD51C c.890_899del p.Leu297fs rs1555602141 3 P c.709C>T p.Arg237Ter rs770637624 1 P ATM c.8438T>C p.Phe2813Ser rs1555138027 1 LP c.3802del p.Glu1267_Val1268insTer rs587779834 2 P MSH2 c.2152C>T p.Gln718Ter rs587779139 2 P RAD51D c.571+4A>G - - 1 P BRIP1 c.2990_2993del p.Thr997fs rs771028677 1 P NTHL1 c.526-1G>A - rs779757251 1 LP MITF c.952G>A p.Glu318Lys rs149617956 1 P CTC1 c.2831del p.Pro944fs rs199473677 1 P PTCH1 c.1511C>A p.Pro504Gln rs1588598694 1 LP RECQL4 c.2412_2420del p.Ala805_Arg807del rs766312203 1 P Table 3. Clinical characteristics of patients harboring benign (n = 105), VUS (n = 35) and pathogenic variants (n = 70) Clinical features Patients with benign variants (n and %) Patients with pathogenic variants (n and %) X2 Patients with VUS (n and %) Age of diagnosis 20 - 40 n=39 (37.14%) n=30 (42.86%) n=17 (48.57%) 41 - 60 n=58 (55.24%) n=27 (38.57%) P >0.05 n=14 (40%) 61 - 80 n=8 (7.62%) n=9 (12.86%) n=2 (5.71%) NI n=0 (0%) n=4 (5.71%) P >0.05 n=2 (5.71%) Tumors Breast n=94 (89.52%) n=57 (81.43%) n=24 (68.57%) Ovarian n=5 (4.76%) n=6 (8.57%) n=2 (5.71%) Others n=6 (5.71%) n=2 (2.86%) n=2 (5.71%) NI n=0 (0%) n=5 (7.14%) P >0.05 n=7 (20%) Triple negative receptors n=21 (20%) n=15 (21.42%) n=6 (17.14%) Family History n=89 (84.76%) n=62 (88.57%) n=29 (82.86%) Additional Declarations No competing interests reported. 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P/PL, Pathogenic and Likely Pathogenic; nP, non-Pathogenic.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6457826/v1/d54ab7b3e65011dd268682e8.png"},{"id":83681099,"identity":"9fe642e0-099d-4b5d-9a71-cc1f7ba1b226","added_by":"auto","created_at":"2025-05-30 16:09:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73715,"visible":true,"origin":"","legend":"\u003cp\u003eMost frequently pathogenic variants found in patients with clinical criteria for HBOC in Minas Gerais state.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6457826/v1/95c934418a56cc57b9bd24b0.png"},{"id":92883911,"identity":"1e8255f2-0a7c-420d-89ae-f155efa6b0bb","added_by":"auto","created_at":"2025-10-06 16:10:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1175420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6457826/v1/f5ad3dfe-b7f9-4ef6-b63e-018a3787eea5.pdf"},{"id":83681096,"identity":"732f5e34-ab1b-4340-9c82-8e4f96de6fbf","added_by":"auto","created_at":"2025-05-30 16:09:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12234,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialMolecularCharacterizationofHBOCPatientsinBrazil.docx","url":"https://assets-eu.researchsquare.com/files/rs-6457826/v1/e7899ab8ee328d9352abb37b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular Characterization of Hereditary Breast and Ovarian Cancer Patients from a Public Precision Medicine Service in the Southeast Brazilian Population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe impact of cancer in the world, based on estimates from GLOBOCAN, indicated almost 20 million new cases worldwide (excluding cases of non-melanoma skin neoplasm) in 2022. Female breast cancer was the second most frequently diagnosed, responsible for nearly 2.3 million new occurrences (11.6%) [1]. In Brazil, breast cancer is the first cause of death in women, and the Southeast and South regions\u0026nbsp;have the highest recorded death rates (12.43 and 12.69/100 thousand women, respectively). The incidence of ovarian cancer is lower compared to breast cancer. However, it ranks seventh among the most common cancers in Brazil, with a higher concentration rate in the South and Southeast regions. Additionally, ovarian cancer is known for its severity and difficulty in diagnosis [2].\u003c/p\u003e\n\u003cp\u003eHereditary Breast and Ovarian Cancer syndrome (HBOC) is characterized by an increased risk in the development of breast and ovarian cancer due to the presence of inherited pathogenic mutations [3]. Although most cases of this syndrome are caused by mutations in the \u003cem\u003eBRCA1/2\u003c/em\u003e genes, other genes have also been associated with an increased risk. Recent studies have shown that around 12% of \u003cem\u003eBRCA1\u0026nbsp;\u003c/em\u003eand \u003cem\u003eBRCA2\u003c/em\u003e negative patients with clinical criteria for HBOC harbor a pathogenic variant in another gene [4,5]. Therefore, it is essential to understand the genetic variations in specific ethnic groups related to the syndrome to identify high-risk individuals and implement appropriate population-based preventive measures.\u003c/p\u003e\n\u003cp\u003eThe Brazilian population is highly diverse, with ethnic composition varying across different states. The primary groups that contributed to the formation of the Brazilian population include Amerindians, Europeans, and Africans, alongside other specific groups. Understanding this diversity is crucial for analyzing the frequencies of genetic mutations, as it provides insights into regional profiles, heredity, genetic susceptibility, ancestry, and disease segregation [6].\u003c/p\u003e\n\u003cp\u003eIn recent years, next-generation sequencing (NGS) has emerged as a powerful tool for analysing multiple genes, significantly enhancing the efficiency and accessibility of molecular tests. This technology enables the identification of individuals at high risk for breast and/or ovarian cancer and supports the molecular diagnosis of various other hereditary syndromes in affected individuals [7]. However, managing cancer within public health systems remains a challenge, particularly in middle- and low-income countries, where financial constraints and inadequate infrastructure may prevent effective diagnosis and care for cancer patients [8]. According to the 2019 National Health Survey (Pesquisa Nacional de Saúde, PNS), 71.5% of Brazilians, or more than 150 million people, rely exclusively on the public Unified Health System (Sistema Único de Saúde, SUS) for their healthcare. However, in a country of continental dimension such as Brazil, the SUS primary healthcare model under SUS often falls short due to the lack of molecular diagnostic centers and insufficient support in many states [9, 10, 11].\u003c/p\u003e\n\u003cp\u003eDespite the considerable allelic and locus heterogeneity in HBOC patients in Brazil, most studies have paid more attention to the \u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eBRCA2\u003c/em\u003e, and \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003egenes [12]. The first studies of patients meeting the criteria for HBOC using next-generation sequencing in Brazil were only published in 2016 [13, 14, 15]. This scenario has changed in the last years, although the number of studies is still low to show the broad spectrum of mutations in different regions of Brazil [12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe state of Minas Gerais, in southeast Brazil, with its 22 million inhabitants, is demographically the second of the country. In the Midwest region of Minas Gerais, Brazil, we implemented a Hereditary Predisposition to Cancer Assessment and Family Monitoring Program to support patients at risk for hereditary cancer syndromes. This is a collaborative program between the Oncology Unit of Hospital São João de Deus, the Molecular Biology Laboratory of the Universidade Federal de São João del-Rei (UFSJ), and the Associação de Combate ao Câncer do Centro-Oeste Mineiro (ACOM). The program was based on the development of a patient care protocol to define follow-up strategies, employing a highly specialized team, which included genetic counseling, psychological support, and the use of advanced techniques for molecular diagnosis. The program provides care and monitoring for families affected by various hereditary cancer syndromes, with HBOC being the most prevalent. The entire care service was structured using resources from research projects funded by various funding agencies, as well as donations raised by ACOM. Collaborative research efforts with these institutions have focused on patients monitored by the public health system. In the initial years of the program, molecular characterization of the \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e genes was conducted on 44 patients using Sanger sequencing, along with the analysis of point mutations in the \u003cem\u003eCHEK2\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, and \u003cem\u003ePALB2\u003c/em\u003e genes [16, 17]. Through the Minas Gerais Network for Population Genomics and Precision Medicine, funded by FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais), it was possible to expand the service, and a NGS panel for genes associated with HBOC was implemented to support the program. Despite all patients being assisted by the SUS, some are able to afford the test, which is conducted in private laboratories. Here, we present all these results collectively to ensure a more comprehensive understanding of the molecular profile in the state.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eVariants found\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is the first study to evaluate mutations using an NGS panel (n=166) in patients assisted by the public health system with clinical criteria for HBOC in Minas Gerais. The results provide information on the mutation profile across more than 20 genes in the studied group, in conjunction with findings from Sanger sequencing (n=44). Pathogenic and likely pathogenic (P/LP) mutations have been found in 33.3% of patients (70/210), while in 16.7% of them (35/210), variants of uncertain significance (VUS) were found. Two pathogenic alterations were identified in the same individual: c.156_157insAlu (\u003cem\u003eBRCA2\u003c/em\u003e) and c.3802del (\u003cem\u003eATM\u003c/em\u003e). All pathogenic and likely pathogenic variants are presented below (Tables 1 and 2). It was observed that 71.8% (51/71) of the pathogenic and likely pathogenic variants were found in high penetrance genes \u003csup\u003e19\u003c/sup\u003e related to HBOC: 23 (32.4%) in \u003cem\u003eBRCA2\u003c/em\u003e; 18 (25.4%) in \u003cem\u003eBRCA1\u003c/em\u003e; 6 (8.4%) in \u003cem\u003eTP53\u003c/em\u003e, 4 (5.6%) in \u003cem\u003ePALB2\u003c/em\u003e. Genes with moderate penetrance \u003csup\u003e19\u003c/sup\u003e , such as \u003cem\u003eATM\u003c/em\u003e (3), \u003cem\u003eCHEK2\u003c/em\u003e (4), \u003cem\u003eMSH2\u003c/em\u003e (2),\u003cem\u003e\u0026nbsp;RAD51C\u003c/em\u003e (4), and \u003cem\u003eRAD51D\u003c/em\u003e (1), represented 19.7% (14/71) of the mutations in the patients analyzed. It is worth mentioning that 8.4% of the probands presented a pathogenic or likely pathogenic variants in genes frequently associated with other types of hereditary cancer: \u003cem\u003eBRIP1\u003c/em\u003e (n = 1), \u003cem\u003eCTC1\u003c/em\u003e (n = 1), \u003cem\u003eMITF\u003c/em\u003e (n = 1),\u003cem\u003e\u0026nbsp;PTCH1\u0026nbsp;\u003c/em\u003e(n = 1), \u003cem\u003eRECQL4\u003c/em\u003e (n = 1) and \u003cem\u003eNTHL1\u003c/em\u003e (n =1). The most frequently mutated gene in this study was \u003cem\u003eBRCA2\u003c/em\u003e (23/210; 11.0%). The distribution of pathogenic or likely pathogenic variants by genes and description of the most frequent pathogenic mutations found in Minas Gerais state can be seen in Figures 1 and 2.\u003c/p\u003e\n\u003cp\u003eIt’s worth noting that 13 variants across 9 genes presented here have been identified for the first time in Brazil: c.112_113del, c.*872_*873del, c.3328_3329del, c.4689_4694del and c.5072C\u0026gt;T in the \u003cem\u003eBRCA1\u003c/em\u003e gene; c.467G\u0026gt;A (\u003cem\u003eTP53\u003c/em\u003e); c.409C\u0026gt;T (\u003cem\u003eCHEK2\u003c/em\u003e); c.8438T\u0026gt;C (\u003cem\u003eATM\u003c/em\u003e); c.2990_2993del (\u003cem\u003eBRIP1\u003c/em\u003e); c.526-1G\u0026gt;A (\u003cem\u003eNTHL1\u003c/em\u003e); c.2831del (\u003cem\u003eCTC1\u003c/em\u003e); c.1511C\u0026gt;A (\u003cem\u003ePTCH1\u003c/em\u003e) and c.2412_2420del (\u003cem\u003eRECQL4\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariants of uncertain clinical significance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForty-one VUS were found in this study and distributed in 12 different genes. Most of these variants (8/41, 19.5%) were observed in the \u003cem\u003eATM\u0026nbsp;\u003c/em\u003egene(Supplementary Table S2). Studies related to this gene in patients with clinical criteria for HBOC began to be further explored after the advent of NGS technologies. In Brazil, few studies have evaluated this gene to date in the HBOC population and future studies are necessary to explore the pathogenicity of these variants better. The other genes that harbored VUS were \u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eBRCA2\u003c/em\u003e, \u003cem\u003eCHEK2\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eNF1\u003c/em\u003e, \u003cem\u003eRAD50\u003c/em\u003e, \u003cem\u003ePMS2\u003c/em\u003e, \u003cem\u003eRECQL4\u003c/em\u003e, \u003cem\u003eRAD51C\u003c/em\u003e and \u003cem\u003eFAN1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients and clinical characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 210 patients who met the clinical criteria for HBOC as recommended by NCCN were included in this study. Among the patients with identified pathogenic mutations, 30 were diagnosed with primary breast cancer before the age of 40 (42.86%) (Table 3). However, a significant number of patients with benign variants were also diagnosed before the age of 40 (39 out of 105; 37.14%), and no statistically significant difference was observed between these groups (P = 0.2756). Furthermore, no statistically significant difference was found between the groups with respect to tumor type (Breast P = 0.1272; Ovarian P = 0.3090). The analysis of hormonal receptors revealed a high prevalence of triple-negative tumors, which are typically associated with \u003cem\u003eBRCA1\u003c/em\u003e gene mutations; however, in this study, such tumors were frequently observed in both groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eStudied Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrazil is the seventh\u0026nbsp;most inhabited country in the world, and it has a complex pattern of ethnic diversity. Among their five regions, the Southeast is the most populous with about 85 million people [20]. The majority of the HBOC molecular studies in Brazil are concentrated on the Southeast and South regions of the country. However, most patients came from the states of S\u0026atilde;o Paulo, Rio de Janeiro, and Rio Grande do Sul, where the most extensive molecular diagnosis and care centers for cancer patients are located [12]. The literature review shows a lack of studies using NGS technology in patients with HBOC in Minas Gerais, the largest state in the Southeast in territorial extension (occupying 63% of the area) [21].\u003c/p\u003e\n\u003cp\u003eMinas Gerais has 20.539.989 inhabitants, the second most populous state in the Southeast region. Its territory was inhabited by indigenous people when the Portuguese arrived in Brazil. Therefore, most of the population of Minas Gerais are descendants of Portuguese settlers from northern Portugal and African slaves, mainly from West Africa. According to a study carried out on genetic ancestry, the composition of the Minas Gerais population is: 75.4% European, 18.3% African and 5.8% indigenous [22]. In a comparative study of Brazilian geographic regions, it was seen that the Northeast has the most significant African ancestry. In contrast, the Southeast/South of Brazil has the greatest European ancestry [23]. Furthermore, other populations arrived in Minas Gerais at different times, such as Italians, Spaniards, Japanese, Germans, Lebanese, Syrians, and others [24].\u003c/p\u003e\n\u003cp\u003eFew studies now have focused attention on HBOC in the Minas Gerais population. The first three studies in the state used screening mutations methodologies concentrated in specific genes or punctual mutations, while two of them are from our research group, and the results are synthesized here [16, 17, 25]. Two more recent studies from Belo Horizonte, capital of Minas Gerais state, described the mutational profile found in a medical service at a private genetic referral center in the city and in individuals with health insurance tested by private labs in MG, both by NGS panel [26, 27]. In the Carvalho\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2023) study, asymptomatic individuals with a familial history of cancer (169/382) were included, and it was not possible to know the real frequency of mutations as they included relatives in the research. However, we can see a more prevalent mutation in the \u003cem\u003eBRCA1\u003c/em\u003e gene, c.470_471delCT, identified in 5 different families in Carvalho\u0026acute;s study and not identified in the present work. Mutations as c.2808_2811del were more prevalent in Faria\u0026acute;s research but not identified by Carvalho \u003cem\u003eet al.\u003c/em\u003e (2023) and in the present study. Other variants, such as c.4829_4830delTG and c.6405_6409del in \u003cem\u003eBRCA2\u003c/em\u003e gene were more prevalent in our cohort and not identified by Faria \u003cem\u003eet al.\u003c/em\u003e (2024). Carvalho\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e (2023) did not identify any patients carrying the c.1010G\u0026gt;A mutation in the TP53 gene, one of the most prevalent mutations in the Brazilian population, and Faria \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003e(2024) found this mutation in only one patient. In our study, however, it was the third most prevalent mutation identified. Therefore, it is clear that there are specific variants that seem to cluster in regions of a large state like Minas Gerais. Other mutations, such as c.5266dupC, c.2T\u0026gt;G, and c.156_157insAlu, were present in all studies [26, 27].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMutational Profile\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA recent Brazilian review provides a comprehensive overview of the broad variability in molecular profiles related to hereditary breast and ovarian cancer in the country. Certain mutations stand out in the Brazilian population: c.5266dupC, c.156_157insAlu, and c.1010G\u0026gt;A in the \u003cem\u003eBRCA1, BRCA2,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;TP53\u003c/em\u003e genes, respectively [12]. In the present work, pathogenic and likely pathogenic mutations have been found in 33.3% of patients (70/210), and a higher frequency of probands harboring the mutations in non-\u003cem\u003eBRCA\u003c/em\u003e genes (30/210, 14.3%) was found when compared with others Brazilian research. Only ten studies in Brazil up to now evaluated breast cancer probands using multigene genetic panel tests, and the frequency of patients with pathogenic mutations in non-\u003cem\u003eBRCA\u003c/em\u003e genes varied from 1.5% to 12.5% [26, 27, 28, 29, 30, 31, 32, 34, 35]. It is important to note that the panels used differ, which may contribute to the variation in frequency.\u003c/p\u003e\n\u003cp\u003eAnother recent study presents a geographical distribution of the most frequent \u003cem\u003eBRCA1/2\u003c/em\u003e mutations in Brazil, established by \u003cem\u003eBRCA\u003c/em\u003e genetic testing results from 1267 unrelated individuals investigated routinely in a private laboratory, but it was not possible to identify the distribution of samples through the southeast states of Brazil [36].\u003c/p\u003e\n\u003cp\u003eConsidering the 210 patients tested in Minas Gerais in the present work, it was seen that the frequency of pathogenic mutations in the \u003cem\u003eBRCA2\u0026nbsp;\u003c/em\u003egene was higher than in the \u003cem\u003eBRCA1\u003c/em\u003e gene, contrary to most studies published in the country [12]. This result is in accordance with the two previous studies from Minas Gerais [26, 27]. Four \u003cem\u003eBRCA2\u0026nbsp;\u003c/em\u003emutations have a significant impact on this data as they represent 78.3% of all \u003cem\u003eBRCA2\u003c/em\u003e mutations and 32.4% (23/71) of all P/LP mutations found in this study (Fig. 2). One of these mutations as c.4829_4830delTG, the most common mutation in our cohort, seems to be very rare in other cohorts of Brazilian breast/ovarian patients. The pathogenic frameshift variant c.4829_4830delTG results in the deletion of two nucleotides in exon 11 of the BRCA2 gene. Records of this mutation were identified in populations such as Korean, Pakistani, Moroccan, Israeli, and Ashkenazi Jewish [37, 38, 39, 40, 41]. Interestingly, immigration from the Middle East and Asia, where the mutation was most prevalent, comprised only 2% of the total immigration flow to Brazil until 1972 [22]. This variant was reported in the Brazilian population in just four other studies from the south and southeast regions but with only seven registers, where European and African ancestries are more evident due to European colonization and slavery [22, 26, 42, 43]. Mechanisms such as genetic drift and founder effect can partially explain the high frequency of this mutation in the Midwest region of MG, as we see many rural populations in this region of the state.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The second most frequent mutation found in Minas Gerais state was c.5266dupC in the \u003cem\u003eBRCA1\u003c/em\u003e gene present in 2.3% (5/210) of patients. The frequency of this variant varies among regions of Brazil and had already been reported in a frequency of 11.6% (11/95) in the Carvalho \u003cem\u003eet al\u003c/em\u003e., study with lower frequencies in other works [28, 32, 34, 35, 44]. \u0026nbsp;This is an ancestral mutation in the Ashkenazi Jewish population [45], and it was considered the most frequent mutation in Brazilian patients, representing 26.8% of all germline mutations found in the \u003cem\u003eBRCA1\u003c/em\u003e gene and detected in all geographic regions [12]. It was responsible for 7% of the germline pathogenic mutations found here.\u003c/p\u003e\n\u003cp\u003eThe c.1010G\u0026gt;A mutation was detected in five women, all diagnosed under 45 years old, and two of them had a family history of other cancers, such as prostate, pancreatic, esophageal, and leukemia, beyond breast cancer. The c.1010G\u0026gt;A variant is a Brazilian founder mutation and was found in 0.3% of the general population in southern Brazil [46]. This mutation is associated with Li-Fraumeni syndrome, an inherited cancer predisposition disease caused by a germline mutation in the \u003cem\u003eTP53\u003c/em\u003e gene. People with this syndrome have an increased risk for several types of cancer, such as childhood sarcoma, breast cancer, central nervous system tumors, leukemia, melanoma, prostate and pancreatic cancer [47]. It is very common to find Li-Fraumeni families filling clinical criteria for HBOC, which demonstrates that NGS panels give a precise molecular diagnosis, contributing to the follow-up of the patients. In Brazil, \u003cem\u003eTP53\u003c/em\u003e is the most mutated gene after \u003cem\u003eBRCA\u003c/em\u003e in breast cancer patients, with the c.1010G\u0026gt;A variant representing more than 75% of the identified variants inside the gene. In the recent Brazilian review, it has been identified in several HBOC Brazilian studies with frequencies ranging from 0.8% to 7.1%. Considering all works that screened for this specific mutation, the frequency of c.1010G\u0026gt;A in patients who met clinical criteria for HBOC from Brazil was estimated in 1.83% (61/3336) [12]. In the present study, the c.1010G \u0026gt; A was found in 2.3% of patients from Minas Gerais, and it was responsible for 7% of the germline pathogenic mutations found.\u003c/p\u003e\n\u003cp\u003eThe c.156_157insAlu mutation accounted for 5.6% (4/71) of the pathogenic mutations identified in this study and warrants particular attention. This is a Portuguese founder mutation, and its high occurrence in Brazil is probably the result of Portuguese immigration during centuries of colonization [12]. It is frequently found in the predisposition genes \u003cem\u003eBRCA2\u003c/em\u003e for breast cancer and causes a jump in exon 3 that leads to splicing errors and, consequently, in the transcription and translation of the tumor suppressor protein. This mutation originated from families with cases of HBOC in the northeast and central regions of Portugal, representing 27-38% of all pathogenic \u003cem\u003eBRCA2\u003c/em\u003e mutations. Brazil is a country of Portuguese colonization, and until 1991, 2.2 million of these immigrants were received, which makes this mutation an interesting target of study [49, 50]. This rearrangement was reported most frequently in populations from the south and southeast regions [32, 49, 51] and has been seen with low frequency in HBOC families from the central-western region 1/224 (0,4%) [33]. To date, the Portuguese founding mutation \u003cem\u003eBRCA2\u003c/em\u003e c.156_157insAlu has not been identified in populations from the north and northeast of Brazil. This differential loading of Alu elements across the \u003cem\u003eBRCA2\u003c/em\u003e locus in many regions is likely due to differences in structure between populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe c.2T\u0026gt;G mutation is also frequently reported in Portuguese families and has been previously described in Brazilian patients with hereditary breast and/or ovarian cancer, particularly in the Southeast and South regions of Brazil [13, 14, 28, 31].\u003c/p\u003e\n\u003cp\u003eRelated to the variants of uncertain significance, the \u003cem\u003eATM\u003c/em\u003e gene had the highest variant frequency. This gene is associated not only with Ataxia-telangiectasia Syndrome and breast cancer but also with several other types of cancer, such as ductal adenocarcinoma of the pancreas, colorectal, prostate, endometrial, kidney, liver, ovarian, esophageal, salivary gland, gastric, thyroid and urinary tract [52, 53]. Among eight patients with VUS in this gene, four had family members with these cancers, especially prostate, breast, liver, endometrial, and pancreatic. Other VUS have been identified in the \u003cem\u003eBRCA2\u003c/em\u003e, \u003cem\u003eCHEK2\u003c/em\u003e, \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eNF1\u003c/em\u003e and \u003cem\u003eRAD50\u003c/em\u003e genes in patients who had a strong family history of several types of cancer. Future studies are essential to elucidate the pathogenicity of these variants.\u003c/p\u003e\n\u003cp\u003eThe Hereditary Predisposition to Cancer Assessment and Family Monitoring Program Minas Gerais, Brazil, has enabled access to comprehensive hereditary cancer services within the public health system. More than 250 family members of patients carrying pathogenic mutations have been attended through the program, with all services extended to them as well. This initiative facilitates the identification of individuals at high risk for cancer development, along with the implementation of preventive measures to reduce risks, enhance surveillance, enable early diagnosis, personalize patient prognoses, and explore the potential use of targeted therapies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study aimed to evaluate the molecular profile of hereditary breast and ovarian cancer in the state of Minas Gerais, marking the first research focused on patients from the Brazilian public health system. By identifying pathogenic mutations in individuals at high genetic risk for breast and/or ovarian cancer, precision medicine care can be implemented, providing better assistance to patients and their families. There is often overlap in clinical criteria among different hereditary syndromes, and next-generation sequencing technology in precision medicine is a valuable method for distinguishing between these conditions and ensuring appropriate clinical care for patients and family members.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Clinical Data\u003c/h2\u003e \u003cp\u003eOverall, 210 patients assisted by the Hereditary Predisposition to Cancer Assessment Program in Minas Gerais were tested. Despite all being assisted by the SUS, 84 patients were able to afford the test, which was performed in private laboratories. Meanwhile, 82 patients, who lacked the financial means to pay for the test, were tested at the UFSJ, following the methodologies described herein. It is important to note that the number of genes included in NGS panels from private laboratories differs from those covered in this study; however, all panels include at least the 22 genes presented here. All results from tests conducted in private laboratories, as well as those conducted in the University, are evaluated by the medical team for patient and family support. These results are presented together to ensure greater accuracy in the mutation frequency in the state. The mutations identified in the forty-four patients who had the \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e genes sequenced by Sanger sequencing during the initial years of the program are also included here for the same purpose [16, 17]. This study received approval from the Ethics and Research Committee of the S\u0026atilde;o Jo\u0026atilde;o de Deus Hospital (45662921.9.0000.5545). Written informed consent was obtained from all participants. All methods were performed in accordance with relevant guidelines and regulations. To be included in the study, the individuals had a prior breast or ovarian cancer diagnosis and fulfilled the \u0026lsquo;NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines\u0026reg;) for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic\u0026rsquo; (version 2022.1) [18]. Clinical data were collected from patient medical records. Pedigrees were constructed using the Progeny Pedigree Tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pedigree.progenygenetics.com/\u003c/span\u003e\u003cspan address=\"https://pedigree.progenygenetics.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDNA Samples\u003c/h2\u003e \u003cp\u003ePeripheral blood samples (3\u0026ndash;5 mL) were collected in vacutainer tubes with EDTA. Genomic DNA was extracted through the Salting Out method and the Qiagen MiniAmp DNA Kit. The concentration and purity of the DNA samples obtained were analyzed through NanoDrop\u0026trade; 2000/2000c Spectrophotometer and Qubit 4.0 fluorometer (Thermo Fisher) with the kit QubitTM 1X dsDNA HS Assay (Thermo Fisher).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Screening\u003c/h2\u003e \u003cp\u003eA NGS multi-gene panel composed of 22 genes, \u003cem\u003eATM, BARD1, BRCA1, BRCA2, CHEK2, CDH1, EPCAM, MLH1, MSH2, MSH6, NBN, NF1, PALB2, PTEN, RAD50, RAD51, RAD51B, RAD51C, RAD51D, SMARCA4, STK11\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e, was designed. The primers were designed through the Sequence Assay Designer Illumina tool, and two primer pools were created for the amplification of 803 amplicons with a 100% horizontal coverage of exons, 5\u0026rsquo; and 3\u0026rsquo; UTRs and splicing sites of all the 22 genes, targeting a total of 177.873 bp (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe kit AmpliSeq for Illumina Custom DNA Panel was used to prepare the DNA libraries according to the procedures established in the protocol AmpliSeq for Illumina On-Demand, Custom, and Community Panels. The sequencing was performed on the MiSeq equipment using the kit MiSeq Regent v2 Micro (Illumina), with an input of 11 pM. This kit enables paired-end sequencing of 150 bp short reads, generating up to 1 GB of data and 6.6\u0026nbsp;million reads per run, with 96.7% of bases having a quality score above Q30.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatic Analysis\u003c/h2\u003e \u003cp\u003eThe primary analysis was performed using FastQC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and MultiQC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MultiQC/MultiQC\u003c/span\u003e\u003cspan address=\"https://github.com/MultiQC/MultiQC\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to evaluate the quality of the data, and Trimmomatic (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.usadellab.org/cms/?page=trimmomatic\u003c/span\u003e\u003cspan address=\"http://www.usadellab.org/cms/?page=trimmomatic\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to trim reads. Two distinct bioinformatics strategies were adopted for the secondary analysis of the trimmed FASTQ files. The first pipeline utilized the DNA Amplicon tool (v2.1.1), available in Illumina's environment, while the second was developed based on the Genome Analysis Toolkit (GATK) best practices for data pre-processing and germline short variant discovery workflows (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gatk.broadinstitute.org/hc/en-us\u003c/span\u003e\u003cspan address=\"https://gatk.broadinstitute.org/hc/en-us\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Quality control checkpoints were established throughout the analysis pipelines to monitor and control of the obtained data. To achieve this, tools such as Qualimap (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://qualimap.conesalab.org/\u003c/span\u003e\u003cspan address=\"http://qualimap.conesalab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Samtools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.htslib.org/\u003c/span\u003e\u003cspan address=\"https://www.htslib.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), BEDtools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bedtools.readthedocs.io/en/latest/\u003c/span\u003e\u003cspan address=\"https://bedtools.readthedocs.io/en/latest/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), BCFtools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://samtools.github.io/bcftools/bcftools.html\u003c/span\u003e\u003cspan address=\"https://samtools.github.io/bcftools/bcftools.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and VariantQC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/BimberLab/DISCVRSeq/\u003c/span\u003e\u003cspan address=\"https://github.com/BimberLab/DISCVRSeq/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), among others, were employed.\u003c/p\u003e \u003cp\u003eThe annotation process (tertiary analysis) of the VCF files generated by the DNA Amplicon pipeline was performed using the GEMINI framework, which employs databases such as RefSeq, ENCODE, OMIM, dbSNP, KEGG, and HPRD. In the GATK pipeline, annotation was conducted by the TAPES tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/a-xavier/tapes\u003c/span\u003e\u003cspan address=\"https://github.com/a-xavier/tapes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), based on the ANNOVAR tool, utilizing databases such as ClinVar, gnomAD, dbSNP, dbscSNV, among others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eVariant Classification\u003c/h2\u003e \u003cp\u003eQuality values showed a GC content of 41%, an average coverage of approximately 230X, and 97.6% of the target regions covered at 20X, ensuring the quality of the generated files. The identified variants were filtered based on a minimum coverage of 20 reads (DP\u0026thinsp;\u0026gt;\u0026thinsp;20) and an allele frequency greater than 30% for heterozygous variants. Only the variants detected by both pipelines were considered for the results. A visual analysis of the BAM and VCF files was also performed using the Integrative Genomics Viewer (IGV, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://igv.org/\u003c/span\u003e\u003cspan address=\"https://igv.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) tool. Variants considered as likely pathogenic, pathogenic, or of uncertain significance by the annotation process were verified by four independent researchers using the ClinVar (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/clinvar/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/clinvar/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Varsome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://varsome.com/\u003c/span\u003e\u003cspan address=\"https://varsome.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases, in accordance with the guidelines of the American College of Medical Genetics (ACMG).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all the patients for their participation in this study. We also extend our gratitude to the Oncology Unit of the Hospital São João de Deus for their collaboration and support. The authors are deeply grateful to Dr. Rennan G. Moreira from the Genomics Laboratory at the Multi-User Laboratory Center (CELAM), Institute of Biological Sciences, Federal University of Minas Gerais, for his invaluable expertise and technical assistance throughout this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Ethics Committee of São João de Deus Hospital (approval number 45662921.9.0000.5545). All participants provided written informed consent prior to inclusion, and all procedures were carried out in compliance with applicable ethical standards and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have given their consent for the publication of this manuscript. All patients voluntarily agreed to participate in this study by signing an informed consent form, which was approved by the ethics committee of our institution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFoundation for Research Support of the State of Minas Gerais (FAPEMIG, Grant Number, RED-00314-16 and RED-00089-23), Foundation for the Coordination of Higher Education Personnel Improvement (CAPES) and Federal University of São João del-Rei.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., Laversanne, M., Cantou, H., Ferlay, J., Siegel, L.R., Soerjomataram, I., et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA: A Cancer Journal for Clinicians.\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e. 229\u0026ndash;263, 2024.\u003c/li\u003e\n\u003cli\u003eINCA. 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The ATM Gene in Breast Cancer: Its Relevance in Clinical Practice. \u003cem\u003eGenes.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e. 727, 2021.\u003cbr\u003e \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Pathogenic and likely pathogenic variants identified in \u003cem\u003eBRCA\u003c/em\u003e genes (n= 41)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"659\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 344px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariant (HGVS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariant ID\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;ACMG Classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 344px;\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.68_69del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Glu23fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80357914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.112_113del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Lys38fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80357949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.441+2T\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers397509173 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.*872_*873del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers59541324 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.2037delGinsCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Lys679Asnfs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers397508932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.3328_3329del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Lys1110AlafsTer4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.3331_3334del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Gln1111fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80357701 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.3756_3759del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Ser1253fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80357868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.4484G\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Arg1495Met\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80357389 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.4689_4694del\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003epTyr1563_Leu1564delinsTer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.5072C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Thr1691Ile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers2137522955\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.5266dupC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Gln1756fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80357906 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 659px;\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.2T\u0026gt;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Met1Thr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80358547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.2T\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Met1Arg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80358547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.156_157insAlu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Lys53Alafs*9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;rs2138704192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.1310_1313delAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Lys437fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80359277 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.4829_4830del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Val1610fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80359468 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.5985delC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Asn1995fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers2137522955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.6405_6409del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Asn2135fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers80359584 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ec.9154C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ep.Arg3052Trp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ers45580035 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePathogenic and likely pathogenic variants identified in non-BRCA genes (n= 30)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 304px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariant (HGVS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariant ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACMG Classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.1010G\u0026gt;A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Arg337His\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers121912664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.467G\u0026gt;A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Arg156His\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers371524413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003ePALB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.3027delT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Glu1010fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers876659378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eCHEK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.1036C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Arg346Cys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers201206424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.409C\u0026gt;T\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Arg137Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers730881701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.485A\u0026gt;G\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Asp162Gly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers587781652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eRAD51C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.890_899del \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Leu297fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers1555602141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.709C\u0026gt;T\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Arg237Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers770637624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eATM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.8438T\u0026gt;C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Phe2813Ser\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers1555138027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.3802del\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Glu1267_Val1268insTer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers587779834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.2152C\u0026gt;T\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Gln718Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers587779139\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eRAD51D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.571+4A\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eBRIP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.2990_2993del\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Thr997fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers771028677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eNTHL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.526-1G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers779757251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eMITF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.952G\u0026gt;A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Glu318Lys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;rs149617956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eCTC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.2831del\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Pro944fs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers199473677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003ePTCH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.1511C\u0026gt;A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Pro504Gln\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers1588598694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003eRECQL4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ec.2412_2420del\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003ep.Ala805_Arg807del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ers766312203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Clinical characteristics of patients harboring benign (n = 105), VUS (n = 35) and pathogenic variants (n = 70)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with benign variants (n and %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with pathogenic variants (n and %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with VUS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n and %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eAge of diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e20 - 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=39 (37.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=30 (42.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=17 (48.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e41 - 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=58 (55.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=27 (38.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eP \u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=14 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e61 - 80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=8 (7.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=9 (12.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=2 (5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=4 (5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eP \u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=2 (5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eTumors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=94 (89.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=57 (81.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=24 (68.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eOvarian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=5 (4.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=6 (8.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=2 (5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=6 (5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=2 (2.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=2 (5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=5 (7.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eP \u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=7 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eTriple negative receptors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=21 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=15 (21.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=6 (17.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eFamily History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003en=89 (84.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en=62 (88.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003en=29 (82.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HBOC in Brazil, Hereditary breast and ovarian cancer, Next-generation sequencing","lastPublishedDoi":"10.21203/rs.3.rs-6457826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6457826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eIn the southeast of Brazil, we established a Hereditary Cancer Predisposition Assessment and Family Monitoring Program to support patients and families with hereditary cancer syndromes. The program was built around the development of a patient care flowchart to guide follow-up strategies, employing a highly specialized team that included genetic counseling, psychological support, and advanced molecular diagnostic techniques, targeting patients within the public health system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eGenetic screening of genes associated with the syndrome was conducted on 210 Brazilian patients using Sanger sequencing and NGS technology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003ePathogenic or likely pathogenic mutations were identified in 33.3% (70/210) of patients, with 14.3% (30/210) harboring mutations in non-\u003cem\u003eBRCA\u003c/em\u003e genes. The most prevalent pathogenic mutation identified was c.4829_4830delTG in the \u003cem\u003eBRCA2\u003c/em\u003e gene, with a prevalence of 8.5% among the identified mutations. This mutation seems to be very rare in other cohorts of Brazilian breast/ovarian patients. Additionally, 8.4% of the analyzed individuals exhibited P/LP variants in genes related to other hereditary cancers. Thirteen variants across nine genes, identified for the first time in Brazil, are presented here. Forty-one variants of uncertain significance were found, distributed across 12 different genes, with the majority (8/41, 19.5%) observed in the ATM gene.\u003cbr\u003e\n \u003cstrong\u003eConclusion:\u003c/strong\u003e This study represents the first investigation focused on patients from the Brazilian public health system in the Southeast Brazilian population. By identifying pathogenic mutations in these patients, precision medicine care can be implemented, leading to improved care for patients and their families.\u003c/p\u003e","manuscriptTitle":"Molecular Characterization of Hereditary Breast and Ovarian Cancer Patients from a Public Precision Medicine Service in the Southeast Brazilian Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 16:09:12","doi":"10.21203/rs.3.rs-6457826/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-30T09:37:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T18:03:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T06:32:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233102003186838687412237120586041524471","date":"2025-06-27T10:48:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149967059360610631846600087378832454760","date":"2025-06-26T07:42:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T20:50:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102381551209699192784768158546010937970","date":"2025-05-29T12:07:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191686671896471087413226695020777783294","date":"2025-05-27T15:09:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-27T09:49:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-15T05:36:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-28T10:56:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-28T10:55:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0bf2b477-8319-4d42-babf-e468aec4b2c0","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49120747,"name":"Biological sciences/Cancer/Breast cancer"},{"id":49120748,"name":"Biological sciences/Genetics/Clinical genetics/Genetic testing"}],"tags":[],"updatedAt":"2025-10-06T16:04:55+00:00","versionOfRecord":{"articleIdentity":"rs-6457826","link":"https://doi.org/10.1038/s41598-025-16870-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-29 15:58:16","publishedOnDateReadable":"September 29th, 2025"},"versionCreatedAt":"2025-05-30 16:09:12","video":"","vorDoi":"10.1038/s41598-025-16870-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-16870-0","workflowStages":[]},"version":"v1","identity":"rs-6457826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6457826","identity":"rs-6457826","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}