A workflow for clinical profiling of BRCA genes in Chilean breast cancer patients via targeted sequencing

A workflow for clinical profiling of BRCA genes in Chilean breast cancer patients via targeted sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A workflow for clinical profiling of BRCA genes in Chilean breast cancer patients via targeted sequencing Evelin González, Rodrigo Moreno Salinas, Manuel Muñoz, Soledad Lantadilla Herrera, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5284910/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Breast cancer (BC) is the leading cause of cancer-related deaths among women globally and in Chile. Mutations in the tumor-suppressor genes BRCA1 and BRCA2 significantly increase the risk of developing cancer, with the probability rising by more than 50%. Identifying pathogenic variants in BRCA1 and BRCA2 is crucial for both diagnosis and treatment. Targeted panels, which focus on medically relevant subsets of genes, have become essential tools in precision oncology. Beyond technical and human resource factors, standardized bioinformatics workflows are essential for the accurate interpretation of results. We developed a robust bioinformatics pipeline, implemented with Nextflow, to process sequencing data from targeted panels to identify germline variants. Results : We developed an automated and reproducible pipeline using Nextflow for the targeted sequencing of BRCA1/2 genes. The pipeline incorporates two variant callers, Strelka and DeepVariant, both of which have demonstrated high performance in detecting germline SNVs and indels. The runtime is efficient, with a median execution time of less than 3 minutes per task. We sequenced and processed 16 samples from breast cancer patients. In our analysis, we identified 8 nonsynonymous mutations in BRCA1 and 9 in BRCA2 . Of the total reported germline mutations, 97% were classified as benign, 1% as pathogenic, 1% as of uncertain significance, and 1% as unknown. The allelic frequencies observed in our cohort closely resemble those of Admixed American and South Asian populations, with the greatest divergence observed in comparison to African individuals. Conclusion : We successfully analyzed the BRCA1 and BRCA2 genes in 16 breast cancer patients at a public hospital in Chile. A custom Nextflow pipeline was developed to process the sequencing data and evaluate the pathological significance of the identified genetic variants. By employing multiple variant-calling methodologies, we were able to detect and mitigate potential false positives, thereby enhancing the accuracy and reliability of variant detection through cross-verification. A pathogenic variant was identified in one patient, while benign or likely benign variants were found in the remaining 15. Expanding the number of oncogenes sequenced per patient could improve the detection of actionable variants. Breast cancer target sequencing BRCA1 BRCA2 Bioinformatic workflow Chile Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Breast cancer is a significant public health issue and the most common oncological pathology among women [ 1 ]. In Chile the incidence of breast cancer was 210 cases per 100,000 women, higher than the reported by GLOBOCAN for 2020 [ 2 ]. In 2023, a total of 7,503 women insured by the public health system were confirmed to have breast cancer, with 77% of them being over 50 years old [ 3 ]. Breast cancer is characterized as a heterogeneous group of tumors that can be classified based on histopathological features, genetic alterations, and gene-expression profiles [ 4 ]. These tumors develop from aberrant gene expression caused by mutations in the DNA. While environmental and lifestyle factors play a crucial role in DNA mutations, genetic factors can also increase the risk of developing cancer. For example, mutations in the breast cancer genes BRCA1 and BRCA2 raise the risks for breast, ovarian, fallopian tube, and peritoneal cancer [ 5 ]. Although the incidence rate varies by ethnicity and geographic regions [ 6 ], between 10 to 50% of patients with hereditary breast cancer carry deleterious mutations at BRCA1 or BRCA2 , which increase their risk of developing breast cancer in more than 50% [ 7 – 9 ]. In Chile, it has been estimated that 15 to 20% of patients with hereditary breast cancer carry deleterious mutations in BRCA1 or BRCA2 [ 10 – 12 ]. Identifying pathogenic variants in BRCA1 and BRCA2 is crucial for determining genetic predisposition and assessing the risk of potentially developing breast cancer. Furthermore, identifying pathogenic variants is essential for prevention strategies, treatment planning, and the development of therapeutic drugs [ 13 , 14 ]. For example, in 2018 the Food and Drug Administration (FDA) approved the use of olaparib for the treatment of patients with HR-positive/HER2-negative metastatic breast cancer who have a germline BRCA mutation [ 15 ]. Targeted panels that sequence medically relevant subsets of genes have become a fundamental component of precision oncology [ 16 ]. In Chile, there have been advances in precision oncology focused on BRCA1/2 genes. In 2006, the first genetic study of BRCA gene mutations was published [ 12 ]. Among high-risk breast and/or ovarian cancer families, 10.9% were found to carry BRCA1 mutations, while 4.7% carried BRCA2 germline mutations [ 12 ]. A 2017 study screened 453 Chilean patients with hereditary breast cancer for mutations in BRCA1/2 , finding a total of 25 mutations (6 novel) in 71 index patients, with nine mutations present exclusively in Chilean patients [ 10 ]. The Fundación Arturo López Pérez (FALP) conducted the latest NGS sequencing study on breast cancer in Chile [ 17 ]. A multigene panel profiling on 722 patients with breast or ovarian cancer identified pathogenic variants in the BRCA1/2 genes in 103 (14%) individuals, including seven previously unreported BRCA1 variants [ 17 ]. Current breast cancer (BC) guidelines in Chile lack defined strategies for germline genetic testing. A 2023 study found that only 15% of BC patients who meet NCCN criteria for germline testing actually receive it [ 5 ]. This percentage is even lower in public hospitals. Extrapolating these figures to the entire country suggests that fewer than 1 in 10 individuals who meet the NCCN criteria have access to testing within the public health system [ 5 ]. Technological advances have made genetic sequencing increasingly accessible at a lower cost. In Chile, certain institutions have the capacity and expertise to implement NGS protocols within their facilities. These capabilities can serve as a foundation for integrating these technologies into genetic diagnostics [ 18 ]. In addition to the need for equipment, reproducible bioinformatics methodologies and workflows are essential. These should encompass data processing, quality control, variant calling, annotation, visualization, and ultimately reporting in a format that is readily interpretable for clinical decision-making and genetic counseling [ 19 ]. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes depend [ 16 ]. Additionally, employing standardized workflows that are publicly available to the community could reduce costs. Here, we performed targeted sequencing of the BRCA1/2 genes in 16 breast cancer patients at a public hospital in Chile. Utilizing a custom-designed Nextflow pipeline, we processed the sequencing data to identify, annotate, and assess the impact of genetic variants in the BRCA1/2 genes. Our findings are intended to help medical practitioners guide more targeted interventions for at-risk breast cancer patients and demonstrate that NGS technologies can be effectively implemented in public hospitals in Chile. Results 1. A Nextflow Workflow for BRCA1/2 variant profiling. An NGS workflow for paired-end Illumina libraries was developed to identify germline variants for targeted sequencing using the AmpliSeq for Illumina BRCA Panel [ 20 ]. This workflow, built on the Nextflow platform, enables an autonomous, reproducible, and scalable scientific pipeline [ 21 ]. The integrated tools are executed through containers, and the entire workflow is available on the GitHub repository ( https://github.com/digenoma-lab/BRCA ). The BRCA workflow (Fig. 1 ) starts by mapping FASTQ files to the human hg38 genome reference using the Burrows-Wheeler Alignment tool (BWA) [ 22 ] and performs read quality control with FastQC, which allows for the identification of potential problems in the generated reads. Metrics of aligned reads and exon coverage are computed with Qualimap [ 23 ]. After aligning reads with BWA during data preprocessing, the reads are processed to ensure consistency and correct mate information in BAM files using Samtools fixmate, and reads are sorted using the Samtools sort [ 24 ]. In parallel to variant calling, the outputs from FastQC, Qualimap, and Samtools are consolidated by MultiQC [ 25 ] to create an HTML report with the metrics of processed samples. The preprocessed BAM files are used as input for Variant calling that is performed using two callers: Strelka [ 26 ] and DeepVariant [ 27 , 28 ]. Both have demonstrated high performance for detecting germline SNVs and Indels. These callers were executed in two modes: single-sample mode (S), which calls variants for each sample individually, and multisample mode (MS), which performs joint variant calling and genotyping, considering all samples simultaneously. Finally, the functional annotation of the identified variants is performed using ANNOVAR [ 29 ], integrating databases such as gnomAD v4, dbSNP v150, ICGC v28, ClinVar, InterVar, and REVEL (Fig. 1 ). REVEL is an ensemble method for predicting the pathogenicity of missense variants based on 18 individual tools, including conservation and functional scores [ 30 ] and InterVar is a tool designed for the clinical interpretation of genetic variants according to the ACMG/AMP 2015 guidelines [ 31 ]. We used REVEL and InterVar to understand the functional significance of the genetic variants and their potential effects on BRCA1/2 genes. This pipeline design ensures comprehensive variant detection and annotation, providing a robust and reliable tool for clinical and research applications. 2. The BRCA pipeline enables rapid and reproducible variant profiling for clinical practice. Sixteen breast cancer patients were sequenced using the AmpliSeq for Illumina BRCA Panel, which includes all exonic regions of the BRCA1 and BRCA2 genes and flanking intronic sequences, covering a total of 22 Kb [ 20 ]. To ensure reproducibility, we executed the BRCA Nextflow pipeline 10 times using data from 16 breast cancer patients. The results showed high reproducibility, with 100% concordance of the variants reported by Strelka and DeepVariant in both single-sample and multi-sample modes. Additionally, we used the Fastq Shuffle tool [ 32 ] to create new FASTA files by randomly reordering the reads per sample for three independent runs, as well as altering the sample names in one instance. We observed a 100% concordance of the variants reported in Strelka and DeepVariant in both modes. Finally, all tasks showed a median execution time of less than 3 minutes (Supplementary Fig. 1) and with an average total execution of 13 minutes for all 16 samples. The most time-consuming tasks were annotation with ANNOVAR, which had a median of 2 minutes and 43 seconds, and BWA-MEM, with a median of 1 minute and 31 seconds. Among the variant callers, Strelka had a median execution time of 50 seconds in single-sample mode and 1 minute and 35 seconds in multi-sample mode. In comparison, DeepVariant had a median execution time of 51 seconds, and GLnexus took only 4.5 seconds per run (Supplementary Fig. 1). Overall, the BRCA Nextflow pipeline demonstrated exceptional performance, with rapid execution times and high reproducibility, making it a reliable and efficient tool for clinical practice. 3. High-quality sequencing and comprehensive variant report for BRCA1/2 genes We obtained high-quality reads for all 16 samples, with an average of 478,730 raw reads per sample (Supplementary Fig. 2). All the reads passed quality control. The average GC content was 38%, with 99% of reads mapped to the target regions per sample, achieving a mean coverage of 1,905X (Supplementary Fig. 2). We identified a total of 70 unique mutations in the BRCA1/2 genes across all patients, of which 4 are UTR, 39 intronic, and 27 exonic. Among the exonic mutations, 10 are silent, 16 are missense, and one is a frameshift insertion (Fig. 2 ). The most frequent mutation changes are T > C (n = 193), followed by C > T (n = 81) and T > G (n = 26). Strelka and DeepVariant identified a median of 13 mutations in exonic regions per patient. Patients HR06 and HR12 had a higher number of mutations (n = 9), while patients HR14 had 3 mutations and HR02 had 2 mutations. (Fig. 2 ). We examined coverage variation across the target regions of the panel and observed a high degree of uniformity (Supplementary Fig. 3). We detected 8 nonsynonymous mutations in BRCA1 and 9 in BRCA2 . Among these, one is a frameshift insertion, and the remaining are missense mutations. Strelka reported one variant more than DeepVariant in 16 BC patients (Fig. 3 ). With a median of 6.5 nonsynonymous variants per sample for both varcallers. Non-synonymous mutations were detected in 81.25% (n = 13) and 100% (n = 16) in BRCA1 and BRCA2 with Strelka and DeepVariant, respectively (Fig. 3 ). 4. Comparison of Single and Multi-Sample Modes in Variant Detection for BRCA1 and BRCA2 Using Strelka and DeepVariant. In Strelka, both single and multi-sample modes found 8 nonsynonymous variants in BRCA1 . These were 1 frameshift insertion and 7 missense mutations. Moreover, 9 missense mutations in BRCA2 are also found by both strelka modes. The E1390G variant in BRCA1 was identified in one patient in single mode and in three patients in multisample mode. This suggests it could be a novel mutation or a sequencing artifact (Supplementary Fig. 4). Visual inspection of this variant in IGV supports the former rather than the latter possibility (variant phred quality 49, Supplementary Fig. 6). On the other hand, DeepVariant, in both single and multi-sample modes, reported identical exonic nonsynonymous variants. We observed no differences in variant calling between the two modes of DeepVariant (Supplementary Fig. 5). 5. Frequency and Impact of BRCA1/2 Mutations in Chilean Patients Diagnosed with Breast Cancer The 16 sequenced patients were Chilean women, up to 40 years old, diagnosed with breast cancer via histopathological confirmation between January 2015 and December 2021. None of the participants were selected based on a family history of cancer. Of the germline mutations detected on this cohort, 97% are benign, 1% are pathogenic, 1% are of uncertain significance, and 1% are unknown. We identified a pathogenic variant in one breast cancer patient (HR06). Both Strelka and DeepVariant reported the p.L655Ffs*10 frameshift insertion in exon 9 of BRCA1 in single and multisample modes (Figs. 2 and 4 ). This variant, with rs80357880, is classified as pathogenic in ClinVar and has been associated with hereditary breast and ovarian cancer. According to the OncoKB database [ 33 ], this variant is classified as likely oncogenic with a Level 1 evidence classification, indicating that it is an FDA-recognized biomarker predictive of response to several FDA-approved drugs, including PARP inhibitors. In patient HR10, we identified a BRCA2 mutation of uncertain significance, p.K797Q, reported by both Strelka and DeepVariant in both modes. According to ClinVar, this mutation has been associated with hereditary breast and ovarian cancer syndrome. Predictors SIFT, PROVEAN, and M-CAP suggest the variant could be deleterious. However, FATHMM, REVEL (score of 0.281), and InterVar indicate that it could be benign. Additionally, this mutation is not present in gnomAD v4.0. We identified one variant that has not been detected in other repositories. The E1390G mutation in BRCA1 was reported by Strelka in single mode for one patient and in multisample mode for three patients. DeepVariant did not report this variant. This mutation does not have a REVEL score; however, predictors MutationTaster, MetaRNN, and FATHMM-MKL classify it as benign. The E1390G mutation in BRCA1 was visually inspected using IGV for the three patients reporting this variant. The mutation is located in reads flanking one of the target regions, showing low read coverage (greater than 8). However, it has a Strelka Phred quality score of 47, suggesting a low probability of being an artifact (Supplementary Fig. 6). The mutations with the highest REVEL scores are A2951T in BRCA2 and S993N in BRCA1 , with scores of 0.39 and 0.37, respectively. We did not identify any variants with a REVEL score over 0.5 (Fig. 4 ), which is the minimum required to classify variants as pathogenic. We identified 14 mutations in BRCA1/2 that are present in gnomAD. The most frequent mutations in our cohort for BRCA1 are K1136R, E991G, P824L, and S104G, each reported in 13 patients, with an allele frequency of 0.5 in our cohort (Fig. 4 ). For BRCA2 , the most common mutations are I2490T, N372H, and V2466A, found in 5, 9, and 16 patients, respectively, with allele frequencies of 0.15, 0.34, and 1.0 in our cohort (Fig. 4 ). In the gnomAD Latino/Admixed American (AMR) subpopulation, the frequencies for these mutations are 0.07, 0.30, and 0.99, respectively. To evaluate the frequency of these mutations, we compared the allele frequencies (AF) in our Chilean cohort (CHI) with those of the subpopulations reported in gnomAD. We subsequently performed a PCA analysis based on the allele frequencies of the mutations in our cohort and gnomAD, followed by hierarchical clustering. We observed that the first principal component accounted for 44.9% of the variance in allele frequencies, while the second explained 18%. Using the first two components (62.9% variance), we identified three distinct clusters. This analysis indicates that the allele frequencies identified in our study are more closely aligned with those reported for South Asians and Admixed Americans (clustered in blue in Fig. 5 ). The mutations contributing most to the variance of dimension 1 were E991G, K1136R, and S104G with contributions of 12.8%, 12,6%, and 12,6%, respectively (Fig. 5 ). Discussion Breast cancer continues to be one of the leading causes of cancer-related mortality among women worldwide, including in Chile. Genetic predisposition, particularly through mutations in the BRCA1 and BRCA2 genes, plays a pivotal role in increasing the risk of breast and other related cancers. Our study aimed to develop an automated and reproducible pipeline for identifying pathogenic variants in the BRCA1 and BRCA2 genes among Chilean breast cancer patients, with the goal of improving genetic testing at a public hospital in Chile. Mutations in BRCA1 and BRCA2 genes are well-documented as major contributors to hereditary breast cancer, with incidence variations observed based on ethnicity and geography. In Chile, approximately 15–20% of patients with hereditary breast cancer carry deleterious mutations in these genes. Our findings are consistent with these statistics, reinforcing the importance of genetic testing and counseling within our population [ 5 ]. Despite advancements in technology making genetic sequencing more accessible and affordable, challenges remain in integrating these tools into routine clinical practice in Chile. Consequently, fewer than 10% of individuals who meet the NCCN criteria have access to testing in the public health system. The latest study by Fundación Arturo López Pérez (FALP) highlights the potential of next-generation sequencing (NGS) for identifying pathogenic variants in breast cancer patients [ 17 ]. However, broader implementation of sequencing technologies requires addressing challenges related to infrastructure, reproducible bioinformatics workflows, and clinical interpretation of genetic data [ 34 ]. We developed a custom and robust Nextflow NGS workflow for processing the AmpliSeq Illumina BRCA Panel data. Our pipeline ensures reproducibility, demonstrated by 100% concordance in variant calling across multiple runs and perturbation experiments (read shuffling and label rename). The integration of tools such as Strelka and DeepVariant for variant calling, along with ANNOVAR for annotation, enhances the sensitivity and reliability in identifying and interpreting genetic variants. We identified a total of 70 unique mutations in BRCA1/2 genes, including novel and known pathogenic variants. Notably, the p.L655Ffs*10 frameshift insertion in BRCA1 , classified as pathogenic, underscores the critical need for genetic testing to guide clinical decision-making. Additionally, our study identified a variant of uncertain significance, emphasizing the complexity of interpreting genetic data and the necessity for continuous updates in local genetic databases and predictive tools. Significantly, our sequencing was performed at a public regional hospital, demonstrating for the first time that comprehensive BRCA testing is feasible in such settings. This opens the door to expanding genetic testing to whole-genome sequencing, which is particularly needed to address patients who test negative for BRCA mutations but may have other genetic predispositions to breast cancer. Our principal component analysis revealed that the allele frequencies of BRCA1/2 mutations in our cohort are closer to those reported for South Asians and Admixed Americans in gnomAD. This finding highlights the importance of considering population-specific genetic variations in developing tailored prevention and treatment strategies. To enhance the impact of genetic testing in Chile, several measures can be employed: first, update national breast cancer guidelines to include mandatory genetic testing for high-risk individuals as our cohort (women with breast cancer before 40 of age), strengthen the technological capacity of local institutions to implement NGS protocols and bioinformatics workflows, promote training programs for healthcare professionals in genetic counseling and the interpretation of genetic data, and encourage collaborative research efforts to continuously update and validate genetic databases with Chilean specific data. Our workflow is a step towards the use of open-source software in clinical practice, which allows for minimizing costs per patient. For variant calling, best practices in clinical sequencing recommend incorporating 2 or 3 tools for each class of variant to maximize detection sensitivity [ 16 ]. We implemented two high-accuracy varcallers to detect SNPs and indels. Strelka2, in the PrecisionFDA Consistency and Truth Challenge, improved the indel F-score of the best submission by 0.11% [ 26 ] and DeepVariant won the highest performance award for SNPs at the PrecisionFDA Truth Challenge 2016 [ 27 ] The discordance between the implemented callers allows us to identify potential artifacts that could be erroneously reported as variants. The E1390G mutation in BRCA1 was the only discordant variant between both variant callers. A subsequent visual inspection with IGV identified it as a potential variant (Supplementary Fig. 6). However, analytical validation is necessary to adhere to the standards and guidelines for the NGS bioinformatics pipeline. Improperly developed, validated, and/or monitored pipelines may generate inaccurate results that could have negative consequences for patient care [ 35 ]. In summary, our study demonstrates the feasibility and importance of integrating advanced genetic testing into clinical practice for breast cancer in Chile. By addressing existing challenges and leveraging technological advancements, we can significantly improve early detection, prevention, and personalized treatment strategies, ultimately enhancing patient outcomes. Material and Methods Patients and sample collection We analyzed the DNA of 16 breast cancer patients from a cohort study in the O'Higgins region of Chile, treated at the Hospital Regional in Rancagua. Inclusion criteria were Chilean patients, males of all ages and females up to 40 years old, diagnosed with breast cancer with histopathological confirmation between January 2015 and December 2021. None of the participants were selected based on a family history of cancer. This study represents the first geographically based cancer study in Chile. The Comité Ético Científico del Servicio de Salud Metropolitano Sur (Ethics Committee of the South Metropolitan Health Service) reviewed and approved this study. Informed written consent was obtained from all participants, who also received pre- and post-genetic counseling in accordance with international recommendations, as well as follow-up by geneticists. Swabs samples were collected from each patient, and DNA was extracted using standard protocols. DNA Extraction and Sequencing Genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen) following the manufacturer's instructions. The quality and quantity of the extracted DNA were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and a Qubit 3.0 Fluorometer (Invitrogen). Next-generation exome sequencing (NGS) of the BRCA1 and BRCA2 genes was performed using the Illumina iSeq 100 platform at the Molecular Laboratory of the Pathological Anatomy Service at the Hospital Regional de Rancagua, following the recommended protocols for the Illumina AmpliSeq BRCA Panel. Briefly, 10 ng of genomic DNA from each sample was used for library construction, followed by target amplification, library purification, and quantification. Sequencing was performed on iSeq100 platform, generating paired-end reads of 150 bp. Bioinformatics Workflow A custom Nextflow pipeline was developed to process the sequencing data, ensuring reproducibility and scalability. The workflow comprised the following steps: 1. Quality Control: Raw sequencing reads were subjected to quality control using FastQC to identify potential issues in the data. 2. Alignment: Reads were aligned to the human reference genome (hg38) using the Burrows-Wheeler Aligner (BWA-MEM2). 3. Post-alignment Processing: Aligned reads were processed to ensure consistency and correct mate information using Samtools fixmate and sorted with Samtools sort. 4. Variant Calling: Variants were called using two tools, Strelka and DeepVariant, in both single-sample and multisample modes. GLnexus was utilized to consolidate variant calls from GVCFs DeepVariants files. 5. Mapping Quality: Metrics of aligned reads and coverage were analyzed with Qualimap, and consolidated reports were generated using MultiQC. 6. Variant Annotation: Identified variants were annotated using ANNOVAR, integrating multiple databases such as gnomAD, dbSNP, ICGC, ClinVar, and REVEL. Reproducibility and Performance Analysis To ensure the reproducibility of our pipeline, we performed ten independent runs using the same data from the 16 breast cancer patients. Additionally, the Fastq Shuffle tool was used to create new FASTA files by randomly reordering reads per sample for three independent runs, with one instance involving the alteration of sample names. Execution times for each task were recorded using trace information provided by Nextflow. The bcftools isec was employed to check the reproducibility of results. Quality Control Metrics To verify the quality of sequencing, we collect multiple metrics throughout the workflow using FASTQC, Qualimap, and Samtools. FASTQC reports the total number of sequencing reads, duplicated reads, and the quality scores (in Phred scale) for both forward and reverse reads. Qualimap provides metrics such as percentage of GC content, insert size, percentage of reads with at least 10x-50x coverage, mean coverage, and total reads per sample. Samtools indicates the number of mapped reads. All these metrics are summarized and visualized in an HTML report generated by MultiQC. We evaluated the quality of sequencing reads, coverage, and variant calling performance. High-quality reads were obtained for all samples, with an average of 478,730 raw reads per sample and a mean coverage of 1,905X. Variants were classified into silent, missense, frameshift insertions, and intronic mutations. The functional significance of identified variants was assessed using REVEL and in silico predictors. Statistical Analysis Principal component analysis (PCA) and clustering was conducted to compare allele frequencies of BRCA1/2 mutations in our cohort with those in the gnomAD database. The first two principal components were analyzed to explain the total variance, and contributions of individual mutations to these dimensions were calculated. Data Visualization Data visualization was performed using various tools: Integrative Genomics Viewer (IGV) was used for visual inspection of alignments for clinically relevant variants. R Statistical analysis and plots, such as bar plots, lollipop plots, and heatmaps, were generated using R programming language. For processing variants and their functional information, annotation files were transformed into MAF format using the maftools library in R [ 36 ]. This library was also utilized to summarize, analyze, and visualize the data, including generating lollipop plots for BRCA1/2 and a summary of mutations. Validation and Benchmarking We implemented the use of two tools for variant calling, both of which have been reported to have high accuracy for identifying SNVs and indels in germline variants. The results were validated through visual inspection using IGV to remove potential sequencing artifacts. Declarations Ethical Considerations The study was conducted in accordance with the ethical standards of the institutional research committee. All participants provided written informed consent before participation in the study. Software availability The workflow, including the configurations and tools, is publicly available on the GitHub repository: https://github.com/digenoma-lab/BRCA . Competing interests The authors declare no competing interests. Funding This work was supported by ANID FONDECYT Regular 1221029, ANID SA77210017, Center for Mathematical Modeling, and Centro UOH de Bioingenieria (CUBI). Author Contribution R.M.S. and A.D.G. contributed to the conceptualization of the study. The methodology was developed by E.V, R.M.S., S.L.H., M.C.M., and A.D.G. Software was developed by E.V., M.M., C.M., and A.D.G. Validation was carried out by E.V. and A.D.G. Formal analysis was performed by E.V., P.J., C.M., and A.D.G. Investigation was conducted by E.V., R.M.S., S.L.H., M.C.M., and A.D.G. Resources were provided by R.M.S., S.L.H., M.C.M.., W.E.D., G.V.S., J.A.R., C.M., and A.D.G. Data curation was handled by E.V., R.M.S., J.F.M., L.J., and A.D.G. The original draft was written by E.V. and A.D.G., while review and editing were contributed by E.V., R.M.S., P.J., W.E.D., G.V.S., J.A.R., J.F.M., L.J., C.M., and A.D.G. Visualization was managed by E.V., P.J., and A.D.G. Supervision, project administration, and funding acquisition were handled by A.D.G. Acknowledgement The supercomputing infrastructure of the High-Performance Computing UOH laboratory (FIC 40059065-0) of the University of O’Higgins and The supercomputing infrastructure of the NLHPC (ECM-02); Data Availability Data is provided within the manuscript or supplementary information files and GitHub. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. 10.3322/caac.21834 . Villalobos C, Ferrer-Rosende P, Cavallera C, Cavada G, Manríquez M, Quirland C, et al. Distribución geográfica de la incidencia de cáncer de beneficiarios de un convenio de atención oncológica en Chile. Rev méd Chile. 2023;151:340–8. 10.4067/s0034-98872023000300340 . Merino-Pereira G. Puesta al día en cáncer de mama en Chile. Rev Chil Obstet Ginecol. 2023;88:261–2. 10.24875/rechog.m23000063 . Kreike B, van Kouwenhove M, Horlings H, Weigelt B, Peterse H, Bartelink H, et al. Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas. Breast Cancer Res. 2007;9:R65. 10.1186/bcr1771 . Acevedo F, Walbaum B, Camus M, Manzor M, Muñiz S, Medina L, et al. Access disparities and underutilization of germline genetic testing in Chilean breast cancer patients. Breast Cancer Res Treat. 2023;199:363–70. 10.1007/s10549-023-06909-z . Fackenthal JD, Olopade OI. Breast cancer risk associated with BRCA1 and BRCA2 in diverse populations. Nat Rev Cancer. 2007;7:937–48. 10.1038/nrc2054 . Antoniou A, Pharoah PDP, Narod S, Risch HA, Eyfjord JE, Hopper JL, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet. 2003;72:1117–30. 10.1086/375033 . Chen S, Parmigiani G. Meta-analysis of BRCA1 and BRCA2 penetrance. J Clin Oncol. 2007;25:1329–33. 10.1200/JCO.2006.09.1066 . Kuchenbaecker KB, Hopper JL, Barnes DR, Phillips K-A, Mooij TM, Roos-Blom M-J, et al. Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers. JAMA. 2017;317:2402–16. 10.1001/jama.2017.7112 . Alvarez C, Tapia T, Perez-Moreno E, Gajardo-Meneses P, Ruiz C, Rios M, et al. BRCA1 and BRCA2 founder mutations account for 78% of germline carriers among hereditary breast cancer families in Chile. Oncotarget. 2017;8:74233–43. 10.18632/oncotarget.18815 . Gallardo M, Silva A, Rubio L, Alvarez C, Torrealba C, Salinas M, et al. Incidence of BRCA1 and BRCA2 mutations in 54 Chilean families with breast/ovarian cancer, genotype-phenotype correlations. Breast Cancer Res Treat. 2006;95:81–7. 10.1007/s10549-005-9047-1 . Jara L, Ampuero S, Santibáñez E, Seccia L, Rodríguez J, Bustamante M, et al. BRCA1 and BRCA2 mutations in a South American population. Cancer Genet Cytogenet. 2006;166:36–45. 10.1016/j.cancergencyto.2005.08.019 . Lee A, Moon BI, Kim TH. / Pathogenic Variant Breast Cancer: Treatment and Prevention Strategies. Ann Lab Med. 2020;40:114–21. 10.3343/alm.2020.40.2.114 . Tung NM, Boughey JC, Pierce LJ, Robson ME, Bedrosian I, Dietz JR, et al. Management of Hereditary Breast Cancer: American Society of Clinical Oncology, American Society for Radiation Oncology, and Society of Surgical Oncology Guideline. J Clin Oncol. 2020;38:2080–106. 10.1200/JCO.20.00299 . Schwartzberg LS, Kiedrowski LA. Olaparib in hormone receptor-positive, HER2-negative metastatic breast cancer with a somatic BRCA2 mutation. Ther Adv Med Oncol. 2021;13:17588359211006962. 10.1177/17588359211006962 . Koboldt DC. Best practices for variant calling in clinical sequencing. Genome Med. 2020;12:91. 10.1186/s13073-020-00791-w . Martin FJ, Saffie IM, Hurtado MA, Avila-Jaque D, Lagos RA, Selman CA, et al. Variants in BRCA1/2 in a hospital-based cohort in Chile and national literature review. Ecancermedicalscience. 2024;18:1683. 10.3332/ecancer.2024.1683 . Marcelain {katherine, Selman-Bravo C, García B, Bustamante E, Gaete JFF, Moyano L et al. Avances y desafíos locales en el diagnóstico molecular de tumores sólidos: una mirada sanitaria hacia la oncología de precisión en Chile. Rev Med Chile. 2019. Jäger N. Bioinformatics workflows for clinical applications in precision oncology. Semin Cancer Biol. 2022;84:103–12. 10.1016/j.semcancer.2020.12.020 . Tariq H, Gul A, Khadim T, Ud-Din H, Tipu HN, Asif M, et al. Next generation sequencing-based germline Panel testing for breast and ovarian cancers in Pakistan. Asian Pac J Cancer Prev. 2021;22:719–24. 10.31557/APJCP.2021.22.3.719 . Patel Y, Zhu C, Yamaguchi TN, Bugh YZ, Tian M, Holmes A, et al. NFTest: automated testing of Nextflow pipelines. Bioinformatics. 2024;40. 10.1093/bioinformatics/btae081 . Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60. 10.1093/bioinformatics/btp324 . García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28:2678–9. 10.1093/bioinformatics/bts503 . Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10. 10.1093/gigascience/giab008 . Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8. 10.1093/bioinformatics/btw354 . Kim S, Scheffler K, Halpern AL, Bekritsky MA, Noh E, Källberg M, et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods. 2018;15:591–4. 10.1038/s41592-018-0051-x . Poplin R, Chang P-C, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36:983–7. 10.1038/nbt.4235 . Yun T, Li H, Chang P-C, Lin MF, Carroll A, McLean CY. Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics. 2021;36:5582–9. 10.1093/bioinformatics/btaa1081 . Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164. 10.1093/nar/gkq603 . Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 2016;99:877–85. 10.1016/j.ajhg.2016.08.016 . Li Q, Wang K, InterVar. Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. Am J Hum Genet. 2017;100:267–80. 10.1016/j.ajhg.2017.01.004 . Sanders P. Random permutations on distributed, external and hierarchical memory. Inf Process Lett. 1998;67:305–9. 10.1016/S0020-0190(98)00127-6 . Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J et al. OncoKB: A precision oncology knowledge base. JCO Precis Oncol. 2017;2017. 10.1200/PO.17.00011 Yadav D, Patil-Takbhate B, Khandagale A, Bhawalkar J, Tripathy S, Khopkar-Kale P. Next-Generation sequencing transforming clinical practice and precision medicine. Clin Chim Acta. 2023;551:117568. 10.1016/j.cca.2023.117568 . Roy S, Coldren C, Karunamurthy A, Kip NS, Klee EW, Lincoln SE, et al. Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines: A Joint Recommendation of the Association for Molecular Pathology and the College of American Pathologists. J Mol Diagn. 2018;20:4–27. 10.1016/j.jmoldx.2017.11.003 . Mayakonda A, Lin D-C, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28:1747–56. 10.1101/gr.239244.118 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial2.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5284910","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367456722,"identity":"1293cb24-0be9-44fa-b999-b0035b59f7a0","order_by":0,"name":"Evelin González","email":"","orcid":"","institution":"Universidad de O’Higgins","correspondingAuthor":false,"prefix":"","firstName":"Evelin","middleName":"","lastName":"González","suffix":""},{"id":367456723,"identity":"53054ee4-a84b-466f-985f-e274a556293d","order_by":1,"name":"Rodrigo Moreno Salinas","email":"","orcid":"","institution":"Hospital Dr Franco Ravera Zunino (HDFRZ)","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"Moreno","lastName":"Salinas","suffix":""},{"id":367456724,"identity":"dc73a668-e82b-43da-8032-1bfa6c091b44","order_by":2,"name":"Manuel Muñoz","email":"","orcid":"","institution":"Universidad de O’Higgins","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Muñoz","suffix":""},{"id":367456725,"identity":"0687c481-e8bc-41c6-926b-8f6e086cafd6","order_by":3,"name":"Soledad Lantadilla Herrera","email":"","orcid":"","institution":"Hospital Dr Franco Ravera Zunino (HDFRZ)","correspondingAuthor":false,"prefix":"","firstName":"Soledad","middleName":"Lantadilla","lastName":"Herrera","suffix":""},{"id":367456726,"identity":"3f5e068d-39ef-4a71-b027-ad7678e5f63e","order_by":4,"name":"Mylene Cabrera Morales","email":"","orcid":"","institution":"Hospital Dr Franco Ravera Zunino (HDFRZ)","correspondingAuthor":false,"prefix":"","firstName":"Mylene","middleName":"Cabrera","lastName":"Morales","suffix":""},{"id":367456727,"identity":"cd26062e-e276-4954-b530-6f3e10caa791","order_by":5,"name":"Pastor Jullian","email":"","orcid":"","institution":"Universidad de O’Higgins","correspondingAuthor":false,"prefix":"","firstName":"Pastor","middleName":"","lastName":"Jullian","suffix":""},{"id":367456728,"identity":"e1409472-6b4b-4310-869f-1215714fd0b8","order_by":6,"name":"Waleska Ebner Durrels","email":"","orcid":"","institution":"SEREMI Salud Región O’Higgins","correspondingAuthor":false,"prefix":"","firstName":"Waleska","middleName":"Ebner","lastName":"Durrels","suffix":""},{"id":367456729,"identity":"4ee08295-7aac-4131-a89e-e73b8783ed00","order_by":7,"name":"Gonzalo Vigueras Stari","email":"","orcid":"","institution":"Hospital Dr Franco Ravera Zunino (HDFRZ)","correspondingAuthor":false,"prefix":"","firstName":"Gonzalo","middleName":"Vigueras","lastName":"Stari","suffix":""},{"id":367456730,"identity":"bd5b993a-3490-4c11-a5f1-5649ed620437","order_by":8,"name":"Javier Anabalón Ramos","email":"","orcid":"","institution":"Hospital Dr Franco Ravera Zunino (HDFRZ)","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"Anabalón","lastName":"Ramos","suffix":""},{"id":367456731,"identity":"38f619d7-0d1b-4bc9-ac18-04803aa35cea","order_by":9,"name":"Juan Francisco Miquel","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Francisco","lastName":"Miquel","suffix":""},{"id":367456732,"identity":"7a77c76e-e8d0-4379-b5b5-34d2d526e03a","order_by":10,"name":"Lilian Jara","email":"","orcid":"","institution":"Universidad de Chile","correspondingAuthor":false,"prefix":"","firstName":"Lilian","middleName":"","lastName":"Jara","suffix":""},{"id":367456733,"identity":"0d00d24f-8ba4-4b67-8fd6-d9a60d9b3ead","order_by":11,"name":"Carol Moraga","email":"","orcid":"","institution":"Universidad de O’Higgins","correspondingAuthor":false,"prefix":"","firstName":"Carol","middleName":"","lastName":"Moraga","suffix":""},{"id":367456734,"identity":"ba997ae0-1f2c-48c1-a942-1cf43ec289b5","order_by":12,"name":"Alex Genova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACdhDBJgHlVTAwGDAwMB7Aq4UZRcsZsBYGYrRAOYxtRGjhb2Y+JsFQZmHXL918TOLnvMNy5gzMD/BqkTjMlmzAcE4ieeacY2mSvdsOG1s2sBngd9hhHsMHjG0SyQY3cowNeLcdTtxwgAe/w+QP8384ANJiD9Ri+HcOEVoMDvMwgmyxM5DIMXzM20CEFsPDbMYGCeckEiRupCU+ljmWbmxwmIBf5I43P5P4UFZnzz8j+cDBNzXWcgbHmx8+wKcFDBIYGBIb4DxmguohwJ5IdaNgFIyCUTASAQDOd0ktOIS/jgAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad de O’Higgins","correspondingAuthor":true,"prefix":"","firstName":"Alex","middleName":"","lastName":"Genova","suffix":""}],"badges":[],"createdAt":"2024-10-17 19:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5284910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5284910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67731564,"identity":"f9bf36da-0963-49ea-b428-6855c8585183","added_by":"auto","created_at":"2024-10-29 07:18:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNextflow pipeline for detecting SNVs and Indels in BRCA1/2 genes. \u003c/strong\u003eThe workflow identifies germline variants from paired-end sequencing data and includes four main processes: preprocessing, quality reporting, variant calling, and annotation. Subprocesses are shown in rectangles, with input and output files in green.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5284910/v1/f8ed4574575c7ea7094d1c09.png"},{"id":67731565,"identity":"eabc63b6-0dad-4290-9a87-1e8bf66c6505","added_by":"auto","created_at":"2024-10-29 07:18:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of germline mutations found in BC patients. \u003c/strong\u003eThese panels present the classification, type, and distribution of variants in 16 breast cancer patients, including Silent, Missense Mutations, and Frame Shift Insertions. Details SNV base changes, and shows a median of 13 variants per sample. \u003cem\u003eBRCA2\u003c/em\u003e and \u003cem\u003eBRCA1\u003c/em\u003e have mutation rates of 100% and 81%, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5284910/v1/440830e647250c5e13d5ee09.png"},{"id":67730872,"identity":"1cdd918c-c51e-4cd6-aa5f-c0ae2d688dc5","added_by":"auto","created_at":"2024-10-29 07:10:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLollipop Plots of variants in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e detected by Strelka and DeepVariant callers.\u003c/strong\u003e Graphical representation of the \u003cem\u003eBRCA1\u003c/em\u003eand \u003cem\u003eBRCA2\u003c/em\u003e genes, comparing the mutations obtained by Strelka and Deepvariant. The Y-axis shows the number of patients with missense mutations (green) and frameshift insertions (purple). The top section represents the results from Strelka, and the bottom section represents the results from DeepVariant of each gene. The domains are in the labels. The results correspond to the variants obtained by both variant callers in single mode.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5284910/v1/925531f66bea3ec38b8e9b32.jpeg"},{"id":67730875,"identity":"43fde1d2-2c4e-4ca4-929b-f384cda1dd9c","added_by":"auto","created_at":"2024-10-29 07:10:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":433730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of BRCA1/2 mutations reported in 16 breast cancer patients using DeepVariant and Strelka, both in single and multisample mode.\u003c/strong\u003e The Y-axis shows the amino acid changes (right) in single and pooled modes (left), and the X-axis shows the 16 patients (top) across the two varcallers used (bottom). The color indicates the potential pathogenicity as given by the REVEL score. Variants without REVEL score are in gray. Single-sample mode (S), Multisample mode (MS).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5284910/v1/a3944d7646033d29aea73b9d.png"},{"id":67730871,"identity":"f197ccb9-6340-46d2-b1e6-a1b0a80b725d","added_by":"auto","created_at":"2024-10-29 07:10:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":230156,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHierarchical clustering and variants contribution on principal components. \u003c/strong\u003eA) Clustering of PCA dimensions 1 and 2 B) Contribution of mutations to dimensions 1 and 2. A dashed line shown corresponds to the expected value if the contribution were uniform. Subpopulations: Africans (AFR), Admixed Americans (AMR), Amish (AMI), Ashkenazi Jewish (ASJ), East Asians (EAS), South Asians (SAS), Finns (FIN), Middle Easterners (MID), Non-Finnish Europeans (NFE), and women (XX).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5284910/v1/9c57ee8c2c85e40256b6e411.png"},{"id":72809117,"identity":"f8e188ff-3c94-47d2-8be7-45de8c50502a","added_by":"auto","created_at":"2025-01-02 10:54:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1806292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5284910/v1/70340922-bf42-41e8-8ea8-ba2e56f4e50b.pdf"},{"id":67730877,"identity":"c25b3f70-7666-4981-9d03-5582ff9a6ff3","added_by":"auto","created_at":"2024-10-29 07:10:12","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2564120,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5284910/v1/fd74de3008ac2c30e86e7e73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A workflow for clinical profiling of BRCA genes in Chilean breast cancer patients via targeted sequencing","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer is a significant public health issue and the most common oncological pathology among women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Chile the incidence of breast cancer was 210 cases per 100,000 women, higher than the reported by GLOBOCAN for 2020 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2023, a total of 7,503 women insured by the public health system were confirmed to have breast cancer, with 77% of them being over 50 years old [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBreast cancer is characterized as a heterogeneous group of tumors that can be classified based on histopathological features, genetic alterations, and gene-expression profiles [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These tumors develop from aberrant gene expression caused by mutations in the DNA. While environmental and lifestyle factors play a crucial role in DNA mutations, genetic factors can also increase the risk of developing cancer. For example, mutations in the breast cancer genes \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e raise the risks for breast, ovarian, fallopian tube, and peritoneal cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the incidence rate varies by ethnicity and geographic regions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], between 10 to 50% of patients with hereditary breast cancer carry deleterious mutations at \u003cem\u003eBRCA1\u003c/em\u003e or \u003cem\u003eBRCA2\u003c/em\u003e, which increase their risk of developing breast cancer in more than 50% [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Chile, it has been estimated that 15 to 20% of patients with hereditary breast cancer carry deleterious mutations in \u003cem\u003eBRCA1\u003c/em\u003e or \u003cem\u003eBRCA2\u003c/em\u003e [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Identifying pathogenic variants in \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e is crucial for determining genetic predisposition and assessing the risk of potentially developing breast cancer. Furthermore, identifying pathogenic variants is essential for prevention strategies, treatment planning, and the development of therapeutic drugs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For example, in 2018 the Food and Drug Administration (FDA) approved the use of olaparib for the treatment of patients with HR-positive/HER2-negative metastatic breast cancer who have a germline \u003cem\u003eBRCA\u003c/em\u003e mutation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Targeted panels that sequence medically relevant subsets of genes have become a fundamental component of precision oncology [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Chile, there have been advances in precision oncology focused on \u003cem\u003eBRCA1/2\u003c/em\u003e genes. In 2006, the first genetic study of \u003cem\u003eBRCA\u003c/em\u003e gene mutations was published [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Among high-risk breast and/or ovarian cancer families, 10.9% were found to carry BRCA1 mutations, while 4.7% carried \u003cem\u003eBRCA2\u003c/em\u003e germline mutations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A 2017 study screened 453 Chilean patients with hereditary breast cancer for mutations in \u003cem\u003eBRCA1/2\u003c/em\u003e, finding a total of 25 mutations (6 novel) in 71 index patients, with nine mutations present exclusively in Chilean patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The Fundaci\u0026oacute;n Arturo L\u0026oacute;pez P\u0026eacute;rez (FALP) conducted the latest NGS sequencing study on breast cancer in Chile [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A multigene panel profiling on 722 patients with breast or ovarian cancer identified pathogenic variants in the \u003cem\u003eBRCA1/2\u003c/em\u003e genes in 103 (14%) individuals, including seven previously unreported \u003cem\u003eBRCA1\u003c/em\u003e variants [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Current breast cancer (BC) guidelines in Chile lack defined strategies for germline genetic testing. A 2023 study found that only 15% of BC patients who meet NCCN criteria for germline testing actually receive it [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This percentage is even lower in public hospitals. Extrapolating these figures to the entire country suggests that fewer than 1 in 10 individuals who meet the NCCN criteria have access to testing within the public health system [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTechnological advances have made genetic sequencing increasingly accessible at a lower cost. In Chile, certain institutions have the capacity and expertise to implement NGS protocols within their facilities. These capabilities can serve as a foundation for integrating these technologies into genetic diagnostics [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition to the need for equipment, reproducible bioinformatics methodologies and workflows are essential. These should encompass data processing, quality control, variant calling, annotation, visualization, and ultimately reporting in a format that is readily interpretable for clinical decision-making and genetic counseling [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes depend [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, employing standardized workflows that are publicly available to the community could reduce costs. Here, we performed targeted sequencing of the \u003cem\u003eBRCA1/2\u003c/em\u003e genes in 16 breast cancer patients at a public hospital in Chile. Utilizing a custom-designed Nextflow pipeline, we processed the sequencing data to identify, annotate, and assess the impact of genetic variants in the BRCA1/2 genes. Our findings are intended to help medical practitioners guide more targeted interventions for at-risk breast cancer patients and demonstrate that NGS technologies can be effectively implemented in public hospitals in Chile.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. A Nextflow Workflow for BRCA1/2 variant profiling.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn NGS workflow for paired-end Illumina libraries was developed to identify germline variants for targeted sequencing using the AmpliSeq for Illumina BRCA Panel [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. This workflow, built on the Nextflow platform, enables an autonomous, reproducible, and scalable scientific pipeline [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The integrated tools are executed through containers, and the entire workflow is available on the GitHub repository ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/digenoma-lab/BRCA\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"Underline\"\u003e).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe BRCA workflow (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) starts by mapping FASTQ files to the human hg38 genome reference using the Burrows-Wheeler Alignment tool (BWA) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] and performs read quality control with FastQC, which allows for the identification of potential problems in the generated reads. Metrics of aligned reads and exon coverage are computed with Qualimap [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. After aligning reads with BWA during data preprocessing, the reads are processed to ensure consistency and correct mate information in BAM files using Samtools fixmate, and reads are sorted using the Samtools sort [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. In parallel to variant calling, the outputs from FastQC, Qualimap, and Samtools are consolidated by MultiQC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] to create an HTML report with the metrics of processed samples. The preprocessed BAM files are used as input for Variant calling that is performed using two callers: Strelka [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] and DeepVariant [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Both have demonstrated high performance for detecting germline SNVs and Indels. These callers were executed in two modes: single-sample mode (S), which calls variants for each sample individually, and multisample mode (MS), which performs joint variant calling and genotyping, considering all samples simultaneously. Finally, the functional annotation of the identified variants is performed using ANNOVAR [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], integrating databases such as gnomAD v4, dbSNP v150, ICGC v28, ClinVar, InterVar, and REVEL (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). REVEL is an ensemble method for predicting the pathogenicity of missense variants based on 18 individual tools, including conservation and functional scores [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] and InterVar is a tool designed for the clinical interpretation of genetic variants according to the ACMG/AMP 2015 guidelines [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. We used REVEL and InterVar to understand the functional significance of the genetic variants and their potential effects on BRCA1/2 genes. This pipeline design ensures comprehensive variant detection and annotation, providing a robust and reliable tool for clinical and research applications.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp;\u003cstrong\u003eThe BRCA pipeline enables rapid and reproducible variant profiling for clinical practice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSixteen breast cancer patients were sequenced using the AmpliSeq for Illumina BRCA Panel, which includes all exonic regions of the \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e genes and flanking intronic sequences, covering a total of 22 Kb [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTo ensure reproducibility, we executed the BRCA Nextflow pipeline 10 times using data from 16 breast cancer patients. The results showed high reproducibility, with 100% concordance of the variants reported by Strelka and DeepVariant in both single-sample and multi-sample modes. Additionally, we used the Fastq Shuffle tool [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] to create new FASTA files by randomly reordering the reads per sample for three independent runs, as well as altering the sample names in one instance. We observed a 100% concordance of the variants reported in Strelka and DeepVariant in both modes. Finally, all tasks showed a median execution time of less than 3 minutes (Supplementary Fig.\u0026nbsp;1) and with an average total execution of 13 minutes for all 16 samples. The most time-consuming tasks were annotation with ANNOVAR, which had a median of 2 minutes and 43 seconds, and BWA-MEM, with a median of 1 minute and 31 seconds. Among the variant callers, Strelka had a median execution time of 50 seconds in single-sample mode and 1 minute and 35 seconds in multi-sample mode. In comparison, DeepVariant had a median execution time of 51 seconds, and GLnexus took only 4.5 seconds per run (Supplementary Fig.\u0026nbsp;1). Overall, the BRCA Nextflow pipeline demonstrated exceptional performance, with rapid execution times and high reproducibility, making it a reliable and efficient tool for clinical practice.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e3. High-quality sequencing and comprehensive variant report for BRCA1/2 genes\u003c/h2\u003e\n\u003cp\u003eWe obtained high-quality reads for all 16 samples, with an average of 478,730 raw reads per sample (Supplementary Fig.\u0026nbsp;2). All the reads passed quality control. The average GC content was 38%, with 99% of reads mapped to the target regions per sample, achieving a mean coverage of 1,905X (Supplementary Fig.\u0026nbsp;2). We identified a total of 70 unique mutations in the BRCA1/2 genes across all patients, of which 4 are UTR, 39 intronic, and 27 exonic. Among the exonic mutations, 10 are silent, 16 are missense, and one is a frameshift insertion (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The most frequent mutation changes are T\u0026thinsp;\u0026gt;\u0026thinsp;C (n\u0026thinsp;=\u0026thinsp;193), followed by C\u0026thinsp;\u0026gt;\u0026thinsp;T (n\u0026thinsp;=\u0026thinsp;81) and T\u0026thinsp;\u0026gt;\u0026thinsp;G (n\u0026thinsp;=\u0026thinsp;26). Strelka and DeepVariant identified a median of 13 mutations in exonic regions per patient. Patients HR06 and HR12 had a higher number of mutations (n\u0026thinsp;=\u0026thinsp;9), while patients HR14 had 3 mutations and HR02 had 2 mutations. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). We examined coverage variation across the target regions of the panel and observed a high degree of uniformity (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003eWe detected 8 nonsynonymous mutations in \u003cem\u003eBRCA1\u003c/em\u003e and 9 in \u003cem\u003eBRCA2\u003c/em\u003e. Among these, one is a frameshift insertion, and the remaining are missense mutations. Strelka reported one variant more than DeepVariant in 16 BC patients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). With a median of 6.5 nonsynonymous variants per sample for both varcallers. Non-synonymous mutations were detected in 81.25% (n\u0026thinsp;=\u0026thinsp;13) and 100% (n\u0026thinsp;=\u0026thinsp;16) in \u003cem\u003eBRCA1 and BRCA2\u003c/em\u003e with Strelka and DeepVariant, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Comparison of Single and Multi-Sample Modes in Variant Detection for BRCA1 and BRCA2 Using Strelka and DeepVariant.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Strelka, both single and multi-sample modes found 8 nonsynonymous variants in \u003cem\u003eBRCA1\u003c/em\u003e. These were 1 frameshift insertion and 7 missense mutations. Moreover, 9 missense mutations in \u003cem\u003eBRCA2\u003c/em\u003e are also found by both strelka modes. The E1390G variant in \u003cem\u003eBRCA1\u003c/em\u003e was identified in one patient in single mode and in three patients in multisample mode. This suggests it could be a novel mutation or a sequencing artifact (Supplementary Fig.\u0026nbsp;4). Visual inspection of this variant in IGV supports the former rather than the latter possibility (variant phred quality 49, Supplementary Fig.\u0026nbsp;6).\u003c/p\u003e\n\u003cp\u003eOn the other hand, DeepVariant, in both single and multi-sample modes, reported identical exonic nonsynonymous variants. We observed no differences in variant calling between the two modes of DeepVariant (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e5. Frequency and Impact of BRCA1/2 Mutations in Chilean Patients Diagnosed with Breast Cancer\u003c/h3\u003e\n\u003cp\u003eThe 16 sequenced patients were Chilean women, up to 40 years old, diagnosed with breast cancer via histopathological confirmation between January 2015 and December 2021. None of the participants were selected based on a family history of cancer. Of the germline mutations detected on this cohort, 97% are benign, 1% are pathogenic, 1% are of uncertain significance, and 1% are unknown. We identified a pathogenic variant in one breast cancer patient (HR06). Both Strelka and DeepVariant reported the p.L655Ffs*10 frameshift insertion in exon 9 of \u003cem\u003eBRCA1\u003c/em\u003e in single and multisample modes (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This variant, with rs80357880, is classified as pathogenic in ClinVar and has been associated with hereditary breast and ovarian cancer. According to the OncoKB database [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], this variant is classified as likely oncogenic with a Level 1 evidence classification, indicating that it is an FDA-recognized biomarker predictive of response to several FDA-approved drugs, including PARP inhibitors.\u003c/p\u003e\n\u003cp\u003eIn patient HR10, we identified a \u003cem\u003eBRCA2\u003c/em\u003e mutation of uncertain significance, p.K797Q, reported by both Strelka and DeepVariant in both modes. According to ClinVar, this mutation has been associated with hereditary breast and ovarian cancer syndrome. Predictors SIFT, PROVEAN, and M-CAP suggest the variant could be deleterious. However, FATHMM, REVEL (score of 0.281), and InterVar indicate that it could be benign. Additionally, this mutation is not present in gnomAD v4.0. We identified one variant that has not been detected in other repositories. The E1390G mutation in \u003cem\u003eBRCA1\u003c/em\u003e was reported by Strelka in single mode for one patient and in multisample mode for three patients. DeepVariant did not report this variant. This mutation does not have a REVEL score; however, predictors MutationTaster, MetaRNN, and FATHMM-MKL classify it as benign. The E1390G mutation in \u003cem\u003eBRCA1\u003c/em\u003e was visually inspected using IGV for the three patients reporting this variant. The mutation is located in reads flanking one of the target regions, showing low read coverage (greater than 8). However, it has a Strelka Phred quality score of 47, suggesting a low probability of being an artifact (Supplementary Fig.\u0026nbsp;6). The mutations with the highest REVEL scores are A2951T in \u003cem\u003eBRCA2\u003c/em\u003e and S993N in \u003cem\u003eBRCA1\u003c/em\u003e, with scores of 0.39 and 0.37, respectively. We did not identify any variants with a REVEL score over 0.5 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), which is the minimum required to classify variants as pathogenic.\u003c/p\u003e\n\u003cp\u003eWe identified 14 mutations in BRCA1/2 that are present in gnomAD. The most frequent mutations in our cohort for \u003cem\u003eBRCA1\u003c/em\u003e are K1136R, E991G, P824L, and S104G, each reported in 13 patients, with an allele frequency of 0.5 in our cohort (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). For \u003cem\u003eBRCA2\u003c/em\u003e, the most common mutations are I2490T, N372H, and V2466A, found in 5, 9, and 16 patients, respectively, with allele frequencies of 0.15, 0.34, and 1.0 in our cohort (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In the gnomAD Latino/Admixed American (AMR) subpopulation, the frequencies for these mutations are 0.07, 0.30, and 0.99, respectively.\u003c/p\u003e\n\u003cp\u003eTo evaluate the frequency of these mutations, we compared the allele frequencies (AF) in our Chilean cohort (CHI) with those of the subpopulations reported in gnomAD. We subsequently performed a PCA analysis based on the allele frequencies of the mutations in our cohort and gnomAD, followed by hierarchical clustering. We observed that the first principal component accounted for 44.9% of the variance in allele frequencies, while the second explained 18%. Using the first two components (62.9% variance), we identified three distinct clusters. This analysis indicates that the allele frequencies identified in our study are more closely aligned with those reported for South Asians and Admixed Americans (clustered in blue in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The mutations contributing most to the variance of dimension 1 were E991G, K1136R, and S104G with contributions of 12.8%, 12,6%, and 12,6%, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBreast cancer continues to be one of the leading causes of cancer-related mortality among women worldwide, including in Chile. Genetic predisposition, particularly through mutations in the \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e genes, plays a pivotal role in increasing the risk of breast and other related cancers. Our study aimed to develop an automated and reproducible pipeline for identifying pathogenic variants in the \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e genes among Chilean breast cancer patients, with the goal of improving genetic testing at a public hospital in Chile.\u003c/p\u003e \u003cp\u003eMutations in \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e genes are well-documented as major contributors to hereditary breast cancer, with incidence variations observed based on ethnicity and geography. In Chile, approximately 15\u0026ndash;20% of patients with hereditary breast cancer carry deleterious mutations in these genes. Our findings are consistent with these statistics, reinforcing the importance of genetic testing and counseling within our population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite advancements in technology making genetic sequencing more accessible and affordable, challenges remain in integrating these tools into routine clinical practice in Chile. Consequently, fewer than 10% of individuals who meet the NCCN criteria have access to testing in the public health system. The latest study by Fundaci\u0026oacute;n Arturo L\u0026oacute;pez P\u0026eacute;rez (FALP) highlights the potential of next-generation sequencing (NGS) for identifying pathogenic variants in breast cancer patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, broader implementation of sequencing technologies requires addressing challenges related to infrastructure, reproducible bioinformatics workflows, and clinical interpretation of genetic data [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe developed a custom and robust Nextflow NGS workflow for processing the AmpliSeq Illumina BRCA Panel data. Our pipeline ensures reproducibility, demonstrated by 100% concordance in variant calling across multiple runs and perturbation experiments (read shuffling and label rename). The integration of tools such as Strelka and DeepVariant for variant calling, along with ANNOVAR for annotation, enhances the sensitivity and reliability in identifying and interpreting genetic variants. We identified a total of 70 unique mutations in \u003cem\u003eBRCA1/2\u003c/em\u003e genes, including novel and known pathogenic variants. Notably, the p.L655Ffs*10 frameshift insertion in \u003cem\u003eBRCA1\u003c/em\u003e, classified as pathogenic, underscores the critical need for genetic testing to guide clinical decision-making. Additionally, our study identified a variant of uncertain significance, emphasizing the complexity of interpreting genetic data and the necessity for continuous updates in local genetic databases and predictive tools.\u003c/p\u003e \u003cp\u003eSignificantly, our sequencing was performed at a public regional hospital, demonstrating for the first time that comprehensive \u003cem\u003eBRCA\u003c/em\u003e testing is feasible in such settings. This opens the door to expanding genetic testing to whole-genome sequencing, which is particularly needed to address patients who test negative for \u003cem\u003eBRCA\u003c/em\u003e mutations but may have other genetic predispositions to breast cancer. Our principal component analysis revealed that the allele frequencies of \u003cem\u003eBRCA1/2\u003c/em\u003e mutations in our cohort are closer to those reported for South Asians and Admixed Americans in gnomAD. This finding highlights the importance of considering population-specific genetic variations in developing tailored prevention and treatment strategies.\u003c/p\u003e \u003cp\u003e To enhance the impact of genetic testing in Chile, several measures can be employed: first, update national breast cancer guidelines to include mandatory genetic testing for high-risk individuals as our cohort (women with breast cancer before 40 of age), strengthen the technological capacity of local institutions to implement NGS protocols and bioinformatics workflows, promote training programs for healthcare professionals in genetic counseling and the interpretation of genetic data, and encourage collaborative research efforts to continuously update and validate genetic databases with Chilean specific data.\u003c/p\u003e \u003cp\u003eOur workflow is a step towards the use of open-source software in clinical practice, which allows for minimizing costs per patient. For variant calling, best practices in clinical sequencing recommend incorporating 2 or 3 tools for each class of variant to maximize detection sensitivity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. We implemented two high-accuracy varcallers to detect SNPs and indels. Strelka2, in the PrecisionFDA Consistency and Truth Challenge, improved the indel F-score of the best submission by 0.11% [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and DeepVariant won the highest performance award for SNPs at the PrecisionFDA Truth Challenge 2016 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe discordance between the implemented callers allows us to identify potential artifacts that could be erroneously reported as variants. The E1390G mutation in \u003cem\u003eBRCA1\u003c/em\u003e was the only discordant variant between both variant callers. A subsequent visual inspection with IGV identified it as a potential variant (Supplementary Fig.\u0026nbsp;6). However, analytical validation is necessary to adhere to the standards and guidelines for the NGS bioinformatics pipeline. Improperly developed, validated, and/or monitored pipelines may generate inaccurate results that could have negative consequences for patient care [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, our study demonstrates the feasibility and importance of integrating advanced genetic testing into clinical practice for breast cancer in Chile. By addressing existing challenges and leveraging technological advancements, we can significantly improve early detection, prevention, and personalized treatment strategies, ultimately enhancing patient outcomes.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatients and sample collection\u003c/h2\u003e \u003cp\u003e We analyzed the DNA of 16 breast cancer patients from a cohort study in the O'Higgins region of Chile, treated at the Hospital Regional in Rancagua. Inclusion criteria were Chilean patients, males of all ages and females up to 40 years old, diagnosed with breast cancer with histopathological confirmation between January 2015 and December 2021. None of the participants were selected based on a family history of cancer. This study represents the first geographically based cancer study in Chile. The Comit\u0026eacute; \u0026Eacute;tico Cient\u0026iacute;fico del Servicio de Salud Metropolitano Sur (Ethics Committee of the South Metropolitan Health Service) reviewed and approved this study. Informed written consent was obtained from all participants, who also received pre- and post-genetic counseling in accordance with international recommendations, as well as follow-up by geneticists. Swabs samples were collected from each patient, and DNA was extracted using standard protocols.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDNA Extraction and Sequencing\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen) following the manufacturer's instructions. The quality and quantity of the extracted DNA were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and a Qubit 3.0 Fluorometer (Invitrogen). Next-generation exome sequencing (NGS) of the \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e genes was performed using the Illumina iSeq 100 platform at the Molecular Laboratory of the Pathological Anatomy Service at the Hospital Regional de Rancagua, following the recommended protocols for the Illumina AmpliSeq BRCA Panel. Briefly, 10 ng of genomic DNA from each sample was used for library construction, followed by target amplification, library purification, and quantification. Sequencing was performed on iSeq100 platform, generating paired-end reads of 150 bp.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBioinformatics Workflow\u003c/h3\u003e\n\u003cp\u003eA custom Nextflow pipeline was developed to process the sequencing data, ensuring reproducibility and scalability. The workflow comprised the following steps: 1. Quality Control: Raw sequencing reads were subjected to quality control using FastQC to identify potential issues in the data. 2. Alignment: Reads were aligned to the human reference genome (hg38) using the Burrows-Wheeler Aligner (BWA-MEM2). 3. Post-alignment Processing: Aligned reads were processed to ensure consistency and correct mate information using Samtools fixmate and sorted with Samtools sort. 4. Variant Calling: Variants were called using two tools, Strelka and DeepVariant, in both single-sample and multisample modes. GLnexus was utilized to consolidate variant calls from GVCFs DeepVariants files. 5. Mapping Quality: Metrics of aligned reads and coverage were analyzed with Qualimap, and consolidated reports were generated using MultiQC. 6. Variant Annotation: Identified variants were annotated using ANNOVAR, integrating multiple databases such as gnomAD, dbSNP, ICGC, ClinVar, and REVEL.\u003c/p\u003e\n\u003ch3\u003eReproducibility and Performance Analysis\u003c/h3\u003e\n\u003cp\u003eTo ensure the reproducibility of our pipeline, we performed ten independent runs using the same data from the 16 breast cancer patients. Additionally, the Fastq Shuffle tool was used to create new FASTA files by randomly reordering reads per sample for three independent runs, with one instance involving the alteration of sample names. Execution times for each task were recorded using trace information provided by Nextflow. The bcftools isec was employed to check the reproducibility of results.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQuality Control Metrics\u003c/h2\u003e \u003cp\u003eTo verify the quality of sequencing, we collect multiple metrics throughout the workflow using FASTQC, Qualimap, and Samtools. FASTQC reports the total number of sequencing reads, duplicated reads, and the quality scores (in Phred scale) for both forward and reverse reads. Qualimap provides metrics such as percentage of GC content, insert size, percentage of reads with at least 10x-50x coverage, mean coverage, and total reads per sample. Samtools indicates the number of mapped reads. All these metrics are summarized and visualized in an HTML report generated by MultiQC.\u003c/p\u003e \u003cp\u003eWe evaluated the quality of sequencing reads, coverage, and variant calling performance. High-quality reads were obtained for all samples, with an average of 478,730 raw reads per sample and a mean coverage of 1,905X. Variants were classified into silent, missense, frameshift insertions, and intronic mutations. The functional significance of identified variants was assessed using REVEL and \u003cem\u003ein silico\u003c/em\u003e predictors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) and clustering was conducted to compare allele frequencies of \u003cem\u003eBRCA1/2\u003c/em\u003e mutations in our cohort with those in the gnomAD database. The first two principal components were analyzed to explain the total variance, and contributions of individual mutations to these dimensions were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData Visualization\u003c/h2\u003e \u003cp\u003eData visualization was performed using various tools: Integrative Genomics Viewer (IGV) was used for visual inspection of alignments for clinically relevant variants. R Statistical analysis and plots, such as bar plots, lollipop plots, and heatmaps, were generated using R programming language. For processing variants and their functional information, annotation files were transformed into MAF format using the maftools library in R [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This library was also utilized to summarize, analyze, and visualize the data, including generating lollipop plots for \u003cem\u003eBRCA1/2\u003c/em\u003e and a summary of mutations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation and Benchmarking\u003c/h2\u003e \u003cp\u003eWe implemented the use of two tools for variant calling, both of which have been reported to have high accuracy for identifying SNVs and indels in germline variants. The results were validated through visual inspection using IGV to remove potential sequencing artifacts.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the ethical standards of the institutional research committee. All participants provided written informed consent before participation in the study.\u003c/p\u003e \u003cp\u003eSoftware availability\u003c/p\u003e \u003cp\u003eThe workflow, including the configurations and tools, is publicly available on the GitHub repository: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/digenoma-lab/BRCA\u003c/span\u003e\u003cspan address=\"https://github.com/digenoma-lab/BRCA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by ANID FONDECYT Regular 1221029, ANID SA77210017, Center for Mathematical Modeling, and Centro UOH de Bioingenieria (CUBI).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.M.S. and A.D.G. contributed to the conceptualization of the study. The methodology was developed by E.V, R.M.S., S.L.H., M.C.M., and A.D.G. Software was developed by E.V., M.M., C.M., and A.D.G. Validation was carried out by E.V. and A.D.G. Formal analysis was performed by E.V., P.J., C.M., and A.D.G. Investigation was conducted by E.V., R.M.S., S.L.H., M.C.M., and A.D.G. Resources were provided by R.M.S., S.L.H., M.C.M.., W.E.D., G.V.S., J.A.R., C.M., and A.D.G. Data curation was handled by E.V., R.M.S., J.F.M., L.J., and A.D.G. The original draft was written by E.V. and A.D.G., while review and editing were contributed by E.V., R.M.S., P.J., W.E.D., G.V.S., J.A.R., J.F.M., L.J., C.M., and A.D.G. Visualization was managed by E.V., P.J., and A.D.G. Supervision, project administration, and funding acquisition were handled by A.D.G.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe supercomputing infrastructure of the High-Performance Computing UOH laboratory (FIC 40059065-0) of the University of O\u0026rsquo;Higgins and The supercomputing infrastructure of the NLHPC (ECM-02);\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files and GitHub.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21834\u003c/span\u003e\u003cspan address=\"10.3322/caac.21834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillalobos C, Ferrer-Rosende P, Cavallera C, Cavada G, Manr\u0026iacute;quez M, Quirland C, et al. Distribuci\u0026oacute;n geogr\u0026aacute;fica de la incidencia de c\u0026aacute;ncer de beneficiarios de un convenio de atenci\u0026oacute;n oncol\u0026oacute;gica en Chile. Rev m\u0026eacute;d Chile. 2023;151:340\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4067/s0034-98872023000300340\u003c/span\u003e\u003cspan address=\"10.4067/s0034-98872023000300340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerino-Pereira G. Puesta al d\u0026iacute;a en c\u0026aacute;ncer de mama en Chile. Rev Chil Obstet Ginecol. 2023;88:261\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.24875/rechog.m23000063\u003c/span\u003e\u003cspan address=\"10.24875/rechog.m23000063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKreike B, van Kouwenhove M, Horlings H, Weigelt B, Peterse H, Bartelink H, et al. Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas. Breast Cancer Res. 2007;9:R65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/bcr1771\u003c/span\u003e\u003cspan address=\"10.1186/bcr1771\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcevedo F, Walbaum B, Camus M, Manzor M, Mu\u0026ntilde;iz S, Medina L, et al. Access disparities and underutilization of germline genetic testing in Chilean breast cancer patients. Breast Cancer Res Treat. 2023;199:363\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10549-023-06909-z\u003c/span\u003e\u003cspan address=\"10.1007/s10549-023-06909-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFackenthal JD, Olopade OI. Breast cancer risk associated with BRCA1 and BRCA2 in diverse populations. Nat Rev Cancer. 2007;7:937\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrc2054\u003c/span\u003e\u003cspan address=\"10.1038/nrc2054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntoniou A, Pharoah PDP, Narod S, Risch HA, Eyfjord JE, Hopper JL, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet. 2003;72:1117\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1086/375033\u003c/span\u003e\u003cspan address=\"10.1086/375033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Parmigiani G. Meta-analysis of BRCA1 and BRCA2 penetrance. J Clin Oncol. 2007;25:1329\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2006.09.1066\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2006.09.1066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuchenbaecker KB, Hopper JL, Barnes DR, Phillips K-A, Mooij TM, Roos-Blom M-J, et al. Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers. JAMA. 2017;317:2402\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2017.7112\u003c/span\u003e\u003cspan address=\"10.1001/jama.2017.7112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlvarez C, Tapia T, Perez-Moreno E, Gajardo-Meneses P, Ruiz C, Rios M, et al. BRCA1 and BRCA2 founder mutations account for 78% of germline carriers among hereditary breast cancer families in Chile. Oncotarget. 2017;8:74233\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.18815\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.18815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallardo M, Silva A, Rubio L, Alvarez C, Torrealba C, Salinas M, et al. Incidence of BRCA1 and BRCA2 mutations in 54 Chilean families with breast/ovarian cancer, genotype-phenotype correlations. Breast Cancer Res Treat. 2006;95:81\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10549-005-9047-1\u003c/span\u003e\u003cspan address=\"10.1007/s10549-005-9047-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJara L, Ampuero S, Santib\u0026aacute;\u0026ntilde;ez E, Seccia L, Rodr\u0026iacute;guez J, Bustamante M, et al. BRCA1 and BRCA2 mutations in a South American population. Cancer Genet Cytogenet. 2006;166:36\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cancergencyto.2005.08.019\u003c/span\u003e\u003cspan address=\"10.1016/j.cancergencyto.2005.08.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee A, Moon BI, Kim TH. / Pathogenic Variant Breast Cancer: Treatment and Prevention Strategies. Ann Lab Med. 2020;40:114\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3343/alm.2020.40.2.114\u003c/span\u003e\u003cspan address=\"10.3343/alm.2020.40.2.114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTung NM, Boughey JC, Pierce LJ, Robson ME, Bedrosian I, Dietz JR, et al. Management of Hereditary Breast Cancer: American Society of Clinical Oncology, American Society for Radiation Oncology, and Society of Surgical Oncology Guideline. J Clin Oncol. 2020;38:2080\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.20.00299\u003c/span\u003e\u003cspan address=\"10.1200/JCO.20.00299\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartzberg LS, Kiedrowski LA. Olaparib in hormone receptor-positive, HER2-negative metastatic breast cancer with a somatic BRCA2 mutation. Ther Adv Med Oncol. 2021;13:17588359211006962. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/17588359211006962\u003c/span\u003e\u003cspan address=\"10.1177/17588359211006962\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoboldt DC. Best practices for variant calling in clinical sequencing. Genome Med. 2020;12:91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13073-020-00791-w\u003c/span\u003e\u003cspan address=\"10.1186/s13073-020-00791-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin FJ, Saffie IM, Hurtado MA, Avila-Jaque D, Lagos RA, Selman CA, et al. Variants in BRCA1/2 in a hospital-based cohort in Chile and national literature review. Ecancermedicalscience. 2024;18:1683. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3332/ecancer.2024.1683\u003c/span\u003e\u003cspan address=\"10.3332/ecancer.2024.1683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarcelain {katherine, Selman-Bravo C, Garc\u0026iacute;a B, Bustamante E, Gaete JFF, Moyano L et al. Avances y desaf\u0026iacute;os locales en el diagn\u0026oacute;stico molecular de tumores s\u0026oacute;lidos: una mirada sanitaria hacia la oncolog\u0026iacute;a de precisi\u0026oacute;n en Chile. Rev Med Chile. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ\u0026auml;ger N. Bioinformatics workflows for clinical applications in precision oncology. Semin Cancer Biol. 2022;84:103\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.semcancer.2020.12.020\u003c/span\u003e\u003cspan address=\"10.1016/j.semcancer.2020.12.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTariq H, Gul A, Khadim T, Ud-Din H, Tipu HN, Asif M, et al. Next generation sequencing-based germline Panel testing for breast and ovarian cancers in Pakistan. Asian Pac J Cancer Prev. 2021;22:719\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31557/APJCP.2021.22.3.719\u003c/span\u003e\u003cspan address=\"10.31557/APJCP.2021.22.3.719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel Y, Zhu C, Yamaguchi TN, Bugh YZ, Tian M, Holmes A, et al. NFTest: automated testing of Nextflow pipelines. Bioinformatics. 2024;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btae081\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btae081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btp324\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btp324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, G\u0026ouml;tz S, Tarazona S, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28:2678\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/bts503\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bts503\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gigascience/giab008\u003c/span\u003e\u003cspan address=\"10.1093/gigascience/giab008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEwels P, Magnusson M, Lundin S, K\u0026auml;ller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btw354\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btw354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim S, Scheffler K, Halpern AL, Bekritsky MA, Noh E, K\u0026auml;llberg M, et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods. 2018;15:591\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41592-018-0051-x\u003c/span\u003e\u003cspan address=\"10.1038/s41592-018-0051-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoplin R, Chang P-C, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36:983\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nbt.4235\u003c/span\u003e\u003cspan address=\"10.1038/nbt.4235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun T, Li H, Chang P-C, Lin MF, Carroll A, McLean CY. Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics. 2021;36:5582\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btaa1081\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btaa1081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkq603\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkq603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIoannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 2016;99:877\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajhg.2016.08.016\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2016.08.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Wang K, InterVar. Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. Am J Hum Genet. 2017;100:267\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajhg.2017.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2017.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanders P. Random permutations on distributed, external and hierarchical memory. Inf Process Lett. 1998;67:305\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0020-0190(98)00127-6\u003c/span\u003e\u003cspan address=\"10.1016/S0020-0190(98)00127-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J et al. OncoKB: A precision oncology knowledge base. JCO Precis Oncol. 2017;2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/PO.17.00011\u003c/span\u003e\u003cspan address=\"10.1200/PO.17.00011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav D, Patil-Takbhate B, Khandagale A, Bhawalkar J, Tripathy S, Khopkar-Kale P. Next-Generation sequencing transforming clinical practice and precision medicine. Clin Chim Acta. 2023;551:117568. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cca.2023.117568\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2023.117568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy S, Coldren C, Karunamurthy A, Kip NS, Klee EW, Lincoln SE, et al. Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines: A Joint Recommendation of the Association for Molecular Pathology and the College of American Pathologists. J Mol Diagn. 2018;20:4\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmoldx.2017.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jmoldx.2017.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayakonda A, Lin D-C, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28:1747\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/gr.239244.118\u003c/span\u003e\u003cspan address=\"10.1101/gr.239244.118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, target sequencing, BRCA1, BRCA2, Bioinformatic workflow, Chile","lastPublishedDoi":"10.21203/rs.3.rs-5284910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5284910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Breast cancer (BC) is the leading cause of cancer-related deaths among women globally and in Chile. Mutations in the tumor-suppressor genes \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e significantly increase the risk of developing cancer, with the probability rising by more than 50%. Identifying pathogenic variants in \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e is crucial for both diagnosis and treatment. Targeted panels, which focus on medically relevant subsets of genes, have become essential tools in precision oncology. Beyond technical and human resource factors, standardized bioinformatics workflows are essential for the accurate interpretation of results. We developed a robust bioinformatics pipeline, implemented with Nextflow, to process sequencing data from targeted panels to identify germline variants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: We developed an automated and reproducible pipeline using Nextflow for the targeted sequencing of \u003cem\u003eBRCA1/2\u003c/em\u003egenes. The pipeline incorporates two variant callers, Strelka and DeepVariant, both of which have demonstrated high performance in detecting germline SNVs and indels. The runtime is efficient, with a median execution time of less than 3 minutes per task. We sequenced and processed 16 samples from breast cancer patients. In our analysis, we identified 8 nonsynonymous mutations in \u003cem\u003eBRCA1\u003c/em\u003e and 9 in \u003cem\u003eBRCA2\u003c/em\u003e. Of the total reported germline mutations, 97% were classified as benign, 1% as pathogenic, 1% as of uncertain significance, and 1% as unknown. The allelic frequencies observed in our cohort closely resemble those of Admixed American and South Asian populations, with the greatest divergence observed in comparison to African individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: We successfully analyzed the \u003cem\u003eBRCA1\u003c/em\u003eand \u003cem\u003eBRCA2\u003c/em\u003e genes in 16 breast cancer patients at a public hospital in Chile. A custom Nextflow pipeline was developed to process the sequencing data and evaluate the pathological significance of the identified genetic variants. By employing multiple variant-calling methodologies, we were able to detect and mitigate potential false positives, thereby enhancing the accuracy and reliability of variant detection through cross-verification. A pathogenic variant was identified in one patient, while benign or likely benign variants were found in the remaining 15. Expanding the number of oncogenes sequenced per patient could improve the detection of actionable variants.\u003c/p\u003e","manuscriptTitle":"A workflow for clinical profiling of BRCA genes in Chilean breast cancer patients via targeted sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-29 07:10:07","doi":"10.21203/rs.3.rs-5284910/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"26158c89-1733-4353-87f6-ef08c708c68d","owner":[],"postedDate":"October 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T10:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-29 07:10:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5284910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5284910","identity":"rs-5284910","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}