Beyond Tumors: Reduced Survival Linked to Pathogenic PIK3CA and TP53 Post-Zygotic Variants in the Uninvolved Breast Tissue of Recurrent Cancer Patients

Beyond Tumors: Reduced Survival Linked to Pathogenic PIK3CA and TP53 Post-Zygotic Variants in the Uninvolved Breast Tissue of Recurrent Cancer Patients | 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 Beyond Tumors: Reduced Survival Linked to Pathogenic PIK3CA and TP53 Post-Zygotic Variants in the Uninvolved Breast Tissue of Recurrent Cancer Patients Maria Andreou, Katarzyna Chojnowska, Natalia Filipowicz, Monika Horbacz, and 29 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7293078/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 Histologically normal mammary tissue from breast cancer patients can harbor significant genetic alterations that could precede visible tumor development and influence disease progression. Methods Whole-exome sequencing was performed on 408 samples from 77 breast cancer patients with poor prognosis, 49 patients recruited without prognosis-based selection, and 15 individuals undergoing non-cancer-related mammoplasty. Paired primary tumor and histologically normal mammary gland tissues were analyzed. Variant classification adhered to strict filtering criteria, incorporating allele frequency thresholds, multiple annotation databases, and in silico prediction tools. Duplex sequencing was employed to detect and confirm pathogenic PIK3CA and TP53 variants in normal mammary tissue samples from 11 breast cancer patients with unfavorable prognosis. Statistical analyses included hypergeometric testing, Kaplan–Meier survival analysis, and Cox proportional hazards modeling. Results Post-zygotic pathogenic variants in cancer-associated genes were significantly more prevalent in normal mammary tissue of poor-prognosis patients (29%) than in unselected patients (12.5%) (p = 0.0008578). Disease recurrence, significantly reduced survival rates, with poor-prognosis patients experiencing higher mortality within 24 months (p = 0.0088), were further worsened by the presence of pathogenic post-zygotic variants. Truncating variants were exclusive to poor-prognosis cases. Frequently altered genes included AKT1, PIK3CA, PTEN, TBX3 , and TP53 , with TP53 variants detected only in patients with adverse outcomes. Duplex sequencing confirmed the presence of low-frequency variants (as low as 1.34%) in regions of histologically normal breast tissue from patients with a poor prognosis. Notably, nearly one-quarter of all identified cases (24%, 12/49) harbored pathogenic variants in normal tissue that were not present in the corresponding primary tumor, indicating independent clonal evolution. Conlcusions Post-zygotic pathogenic variants in normal mammary tissue are associated with increased recurrence risk and reduced survival in breast cancer patients. These findings highlight the potential of integrating genetic screening of non-tumorous breast tissue into risk assessment strategies to better inform patient monitoring and management. Molecular Genetics Oncology breast cancer recurrence post-zygotic variants unfavorable outcome uninvolved mammary gland mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Breast cancer accounts for 12.5% of global annual cancer diagnoses, with the incidence rate increasing by 0.5% annually in recent years( 1 , 2 ). Despite an overall 42% reduction between 1989 and 2021, primarily attributed to increased awareness and early detection, breast cancer still constitutes one of the leading causes of death among women( 3 ). Notably, stage I, low-risk breast cancer cases still present a 15–20% chance of recurrence even two decades after the initial diagnosis( 4 ). While 5–10% of breast cancer cases are hereditary, with 25–30% of heritable breast cancer risk attributed to pathogenic variants in genes of high and moderate penetrance( 5 ), the majority of cases are considered sporadic( 6 , 7 ). Research has recently focused on the normal mammary gland within the affected breast for early detection of tumor formation at the molecular level, preceding changes on imaging or palpative screens( 8 ). Concurrently, breast-conserving surgery (BCS), which aims to remove the tumor and preserve the remaining healthy breast tissue, stands as the preferred approach( 9 , 10 ). However, mammary gland tissue from breast cancer patients, although appearing normal, has been found to harbor significant genomic and transcriptomic alterations( 11 – 14 ). In particular, non-tumorous tissue from patients undergoing BCS shows clearly pathogenic low-level post-zygotic alterations in the PIK3CA and TP53 genes, raising questions regarding its oncogenic potential ( 15 , 16 ). Nevertheless, the association between post-zygotic alterations in ostensibly normal mammary gland tissue of breast cancer patients and their clinical utility remains unclear. Hence, we screened paired uninvolved mammary gland (UM) and primary tumor (PT) samples of reportedly sporadic breast cancer patients with adverse outcomes within 10 years post-original surgery for the presence of post-zygotic alterations. We compared our findings with a second breast cancer cohort of reportedly sporadic patients recruited without specific prognosis criteria. Additionally, normal mammary gland samples from individuals with no personal or family history of cancer were included as controls (study overview in Additional File 1, Supplementary Fig. 1). Here, we demonstrate that pathogenic post-zygotic variants in cancer-associated genes are commonly found in histologically normal mammary tissue of breast cancer patients with adverse prognoses. These variants, often also found in corresponding primary tumors, are linked with patient survival, emphasizing the need for molecular screening to improve the clinical management of patients. METHODS Patient recruitment, sample collection, and DNA isolation We carried out Whole Exome Sequencing (WES) on 408 samples from three distinct groups: two cohorts of female breast cancer patients with differing prognostic outcomes and a control group composed of female individuals who underwent mammoplasty surgery for non-cancer-related reasons. The assignment of patients to the cohorts was determined by their clinical prognoses. The first cohort included 77 reportedly sporadic breast cancer patients with adverse outcomes. All individuals in this group experienced either recurrent disease, such as local recurrence/metastasis to the breast or secondary organs (n = 40), developed a second independent tumor (n = 18), or both (n = 8), and/or succumbed to the disease (n = 45) within the proceeding 10 years ( B reast C ancer A dverse P rognoses cohort, BCAP), (Table 1 ; Additional File 2:Supplementary Table 1). The second cohort included 49 individuals from the same ethnic population, diagnosed with reported sporadic breast cancer but recruited without specific criteria related to prognosis ( B reast C ancer U n- S elected cohort, BCUS). Within this group, 5 out of 49 patients experienced recurrence, and 3 of them died within 2 years post-surgery; however, the follow-up period for this cohort was considerably shorter compared to the BCAP cohort (Table 1 ; Additional File 2: Supplementary Table 1). The majority of BCAP and BCUS patients were treated with BCS (n = 63 and n = 31, respectively) versus mastectomy (n = 12 and n = 18, respectively) (Additional File 2: Supplementary Table 1) (data missing for 2 BCAP patients). All recruited individuals did not receive neoadjuvant therapy. The control group comprised 15 individuals who underwent reduction mammoplasty surgeries and had no personal or familial history of cancer ( R eduction M ammoplasty cohort, RM). Written informed consent was obtained from all enrolled individuals. The study was approved by the the Bioethical Committee at the Collegium Medicum, Nicolaus Copernicus University in Toruń (approval number KB509/2010) and by the Independent Bioethics Committee for Research at the Medical University of Gdańsk (approval number NKBBN/564/2018 with multiple amendments), recruited and enrolled all donors under informed and written consent, collected, and stored all tissue samples. A total of 415 samples, including PT, UM, blood (BL), or skin (SK) from all three cohorts were collected by the Oncology Centre in Bydgoszcz, Jagiellonian University Hospital in Cracow, and the University Clinical Centre in Gdańsk, with the necessary ethical approvals and written informed consent from participants and deposited in the biobank of our unit at the Medical University of Gdańsk, along with clinical data, including follow-up information (Table 1 ; Additional File 2: Supplementary Table 1). Distal UM samples (UMD, 1.5-3 cm from PT, median 2.35 cm), available for 7 BCAP patients, were collected and included in the downstream targeted confirmatory analysis; however, they were not initially sequenced. For the RM cohort, sets of UM and BL samples were included. UM samples were located at least 1 cm away from the corresponding PT. All collected samples were frozen at -80°C. Detailed tissue-collecting protocols were previously described by Filipowicz et al( 17 ). All fragments prepared for molecular analysis were histologically evaluated by expert pathologists to identify tumor fragments (PT) and confirm the normal histology of UM and SK samples. DNA isolation from tissue lysates and whole blood was performed as previously described( 17 ). Table 1 Summarized clinicopathological features of breast cancer patients included in the BCAP and BCUS cohorts. BCAP cohort BCUS cohort Number of individuals 77 49 Age (median, range) 62, 23–85 65, 37–84 p value = 0.082 Collected samples 238 147 Primary Tumor, PT 77 49 Uninvolved mammary gland, UM 77 49 Distal fragment of uninvolved mammary gland, UMD 7 - Reference sample (whole peripheral blood, BL or skin, SK) 77 49 Histology Invasive ductal carcinoma, IDC 59 40 Invasive lobular carcinoma, ILC 3 4 IDC - ILC 6 1 other 9 4 Receptors Estrogen, ER (positive / negative / not available) 57 / 20 43 / 5 / 1 Progesterone, PR (positive / negative / not available) 43 / 34 44 / 4 / 1 HER2 (positive / negative / not available) 16 / 56 / 5 5 / 43 / 1 Subtype Luminal A 14 22 Luminal B 37 21 HER-2 enriched 9 2 Triple-negative breast cancer, TNBC 11 1 Not available 6 3 Follow-up information Recurrence (yes / no) 50 / 27 5 / 44 Second cancer (yes / no) 26 / 51 0 / 49 Death* (yes / no) 45 / 31 3 / 46 Table 1. Matched and primary tumor (PT) and uninvolved mammary gland (UM, ≥1 cm ) samples were collected from two breast cancer cohorts, i.e., 77 individuals characterized with adverse outcomes (Breast Cancer Adverse Prognoses cohort, BCAP) and 49 individuals recruited without any pre-selection criteria related to prognosis (Breast Cancer Un-Selected cohort, BCUS ). Whole peripheral blood (BL) or skin (SK) samples (if BL was not available) were collected as reference samples to distinguish between post-zygotic and germline variants. Distal UM samples (UMD, 1.5-3 cm from PT, median 2.35 cm), available for 7 BCAP patients, were included. The detailed sampling design is described in Materials and Methods. An overview is also available in Figure 1. *Death status refers to patients who succumbed to the disease (patient with ID BCAP61 died from non-oncological reasons). Detailed clinicopathological information for BCAP and BCUS cohorts is provided in Additional File 2: Supplementary Table 1. Whole-exome sequencing, data analysis, variant detection, and validation with independent methods WES analyses were performed using the Agilent SureSelectXT Human All Exon V7 capture kit for sequencing library construction, followed by 150 bp paired-end sequencing on the HiSeq Illumina platform (Illumina, San Diego, CA), outsourced to Macrogen Europe (Amsterdam, The Netherlands). Sequencing coverage was 200x on average, with at least 100x on target. The sequencing coverage and quality statistics for each sample are summarized in Additional File 2: Supplementary Table 2A. FASTQ files were inspected and processed with Trim Galore! (v0.6.7) ( https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ ) to remove Illumina-specific adapter sequences and poor-quality reads when necessary. After converting FASTQ files to BAM format and extracting read groups from the raw data, reads were processed using GATK4 Best Practices (v4.0) ( https://github.com/gatk-workflows/seq-format-conversion , https://github.com/gatk-workflows/gatk4-data-processing ). The reads were mapped to the human genome (hg38) using the BWA-MEM tool ( http://bio-bwa.sourceforge.net ). Octopus (v0.7.4), in cancer mode, was used for variant calling. The cancer calling model can jointly genotype multiple samples from the same individual, using a reference sample (whole peripheral blood or skin when blood was not available) to distinguish between post-zygotic and germline variants ( https://luntergroup.github.io/octopus/ ). Frameshift insertions/deletions, nonsense, and missense variants located in exons were included in the analysis. A random forest filtering approach was implemented to minimize false calls. Variants in reads with poor mapping quality (< 30) and variants supported by high-quality bases (≥ 30) in fewer than five reads were excluded from the analysis. Variants were annotated using ANNOVAR ( https://annovar.openbioinformatics.org/ ) (last updated on 07.07.2020 and accessed between 06.2022–08.2022) and wANNOVAR ( https://wannovar.wglab.org/ ) (last accessed on 21.05.2024), using the MANE SELECT transcript for investigated genes ( https://www.ensembl.org/info/genome/genebuild/mane.html ). A brief overview of filtering strategies implemented for identifying post-zygotic and germline variants related to breast cancer, within BCAP, BCUS, and RM cohorts, and validation experiments for selected post-zygotic variants of BCAP patients are available in Additional File 1: Supplementary Fig. 2. Post-zygotic variants Only variants with sequencing depth ≥ 50 and tissue allele frequency ≥ 0.03 were included in the analysis. Variants were filtered based on their annotation in ClinVar and InterVar databases; variants reported as “pathogenic”, “likely pathogenic”, “uncertain significance”, or “conflicting interpretations of pathogenicity” were included. In parallel, variants described in the COSMIC database (Cosmic_95coding) were incorporated. Missense variants documented in the COSMIC database, but annotated as “benign” or “likely benign” in ClinVar or InterVar databases, were excluded. Variants in genomic regions with known read-through transcription between adjacent genes, or those spanning multiple genes (e.g., P2RY11; PPAN-P2RY11 and KIR2DL1; KIR2DS5; LOC112267881 ), were also excluded. The remaining variants were filtered by their frequency in the general population, retaining only those with a minor allele frequency (MAF) ≤ 0.001 across all gnomAD populations (“popmax”) or not listed in gnomAD (v2.1.1) (Additional File 2: Supplementary Table 3). Variants were further categorized as truncating (Additional File 2: Supplementary Table 4) or missense (Additional File 2: Supplementary Table 5). Truncating variants were considered pathogenic, regardless of their annotation in ClinVar or InterVar. For missense variants, in-silico analyses using the REVEL tool( 18 ) (with a threshold score of 0.75) were performed. In summary, truncating variants, variants classified as “pathogenic” or “likely pathogenic” in ClinVar, and missense variants with conflicting interpretations or annotated as “pathogenic” in ClinVar without assertion criteria, but having a REVEL score ≥ 0.75, were deemed pathogenic. To select post-zygotic variants potentially linked to breast cancer, we prioritized those annotated as “pathogenic” or “likely pathogenic” in the ClinVar database, reviewed by expert panels, or submitted by multiple parties without discordance (Additional File 2: Supplementary Table 6). Missense variants classified as of uncertain significance, pathogenic/likely pathogenic without assertion criteria, or with conflicting interpretations in ClinVar with a REVEL score ≥ 0.75 were also included. Truncating variants in known tumor-suppressor genes implicated in breast cancer, such as KMT2C ( 19 ), TBX3 ( 20 ), and TP53 ( 21 ), were included in the same table even if they were absent from the ClinVar database (Additional File 2: Supplementary Table 6). UM, UMD, PT, and SK samples from 8 BCAP patients were further investigated using Sanger sequencing or High-Resolution Melting to verify the presence of selected variants (Table 2 ; Additional File 2: Supplementary Table 7; Additional File 1: Supplementary Fig. 3). Table 2 Pathogenic post-zygotic variants within the uninvolved mammary tissue of BCAP patients, selected for further investigation/validation. Gene Variant a ClinVar b COSMIC ID c AVSNP150 d Individual ID and UM sample VAF Confirmation AKT1 c.49G > A (p.Glu17Lys) Pathogenic ID = COSV62571334 rs121434592 BCAP32 (0.6%), BCAP66* (0.7%) SS / HRM PIK3CA c.1624G > A (p.Glu542Lys) Pathogenic ID = COSV55873227 rs121913273 BCAP56 (0.7%), BCAP45 (12%) SS / HRM PIK3CA c.3140A > G (p.His1047Arg) Pathogenic ID = COSV55873195 rs121913279 BCAP15 (0.08%), BCAP31* (19%), BCAP36 (0.3%), BCAP53* (0.7%) SS / HRM / DS PIK3CA c.3140A > T (p.His1047Leu) Pathogenic ID = COSV55873401 rs121913279 BCAP54* (0.5%) DS PTEN c.388C > T (p.Arg130*) Pathogenic ID = COSV64288463 rs121909224 BCAP15 (0.7%) SS / HRM TBX3 c.371_372insTGGT (p.Ile125Profs*14) n.a. ID = COSV57471668 n.a. BCAP44 (12%) SS / HRM TP53 c.151G > T (p.Glu51*) Pathogenic ID = COSV52694020 n.a. BCAP58* (16%) SS / HRM TP53 c.227del (p.Ala76Aspfs*47) n.a. ID = COSV52728465 n.a. BCAP54 (0.5%) DS TP53 c.329G > C (p.Arg110His) Pathogenic ID = COSV52668419 rs11540654 BCAP45 (0.8%) DS TP53 c.637C > T (p.Arg213*) Pathogenic ID = COSV52665560 rs397516436 BCAP01* (0.8%), BCAP48* (0.6%) DS TP53 c.711G > A (p.Met237Ile) Pathogenic ID = COSV52661887 rs587782664 BCAP15 (0.8%) SS / HRM TP53 c.1024C > T (p.Arg342*) Pathogenic ID = COSV52665487 rs730882029 BCAP38 (0.5%), BCAP47 (0.7%) DS TP53 c.1025G > C (p.Arg342Pro) Pathogenic/Likely pathogenic ID = COSV52690857 rs375338359 BCAP57 (0.6%) DS Table 2. Presented variants, identified via Whole Exome sequencing in the uninvolved mammary (UM) samples of individuals characterized with adverse outcomes (Breast Cancer Adverse Prognoses cohort, BCAP), were corroborated with either Sanger sequencing/High-Resolution Melting, or Duplex sequencing. a Variant annotation provided for the basic isoform of the transcript. b Pathogenicity classification according to the ClinVar database. c ID of the variant in the COSMIC (Cosmic_95 coding) database. d rsIDs in dbSNP build 150. e Individual ID and Variant Allele Frequency (VAF) for UM samples. Detailed description of selected post-zygotic variants is provided in Additional File 2: Supplementary Table 6. Confirmation of post-zygotic variants by Sanger sequencing/High-Resolution Melting, or Duplex sequencing is provided in Additional File 1: Supplementary Figure and Additional File 2: Supplementary Tables 7 and 9, respectively. SS – Sanger sequencing. HRM – High-Resolution Melting. DS – Duplex sequencing. n.a.- not available. *variants were also detected in the distal uninvolved mammary gland sample (UMD) of selected patients. Germline variants For germline variant detection, only variants in high- and moderate-penetrance breast cancer susceptibility genes were included in the study, as defined by the NCCN Clinical Practice Guidelines in Oncology( 22 ) (Version 1.2023, September 7, 2022). The list of genes of interest includes: ATM (MIM *607585), BRCA1 (MIM *113705), BRCA2 (MIM *600185), BARD1 (MIM *601593), BRIP1 (MIM *605882), CHEK2 (MIM *604373), CDH1 (MIM *192090), PALB2 (MIM *610355), PTEN (MIM *601728), TP53 (MIM *191170), NF1 (MIM *613113), STK11 (MIM *602216), RAD50 (MIM *604040), RAD51C (MIM *602774), RAD51D (MIM *602954) and additionally PIK3CA (MIM *171834). Variants were filtered based on their frequency in the general population: variants with minor allele frequency (MAF) ≤ 0.01 across all gnomAD populations (“popmax”) or not noted in the database (gnomAD v2.1.1) were included. Evidence according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology recommendations( 23 ) was included to describe all germline pathogenic variants. Specifically, the evaluation of identified BRCA1 and BRCA2 variants was performed according to the Evidence-based Network for the Interpretation of Germline Mutation Alleles (ENIGMA) BRCA1 and BRCA2 Variant Curation Expert Panel( 24 ) (Version 1.1.0) (Clinical Genome Resource, https://www.clinicalgenome.org/affiliation/50087/ , https://cspec.genome.network/cspec/ui/svi/doc/GN092 , https://cspec.genome.network/cspec/ui/svi/doc/GN097 ). Pathogenic germline variants meeting the study’s criteria, identified within breast cancer patients of the BCAP and BCUS cohorts, are described in Additional File 2: Supplementary Table 8. Duplex sequencing UM samples from 11 BCAP patients were selected to investigate the presence of low-frequency PIK3CA and TP53 variants beyond the detection limits of Sanger sequencing and High-Resolution Melting and a single, higher-frequency TP53 variant, i.e., c.151G > T (p.Glu51*), located in a difficult GC-rich region. (Table 2 ; Additional File 2: Supplementary Table 9). Additionally, UMD samples, available for 6 of those patients, were included to explore further the distribution of selected variants in a more distant from the tumor, seemingly normal mammary tissue. Duplex sequencing was performed as previously described( 15 , 25 ). Duplex sequencing data analysis Raw duplex sequencing data were analyzed using the Snakemake-based Duplex-seq-Pipeline (v1.1.4) ( https://github.com/Kennedy-Lab-UW/Duplex-Seq-Pipeline ) as previously described( 26 ). The sequencing coverage and quality statistics for each sample are summarized in Additional File 2: Supplementary Table 2B. Statistical analysis All statistical analyses were carried out with in-house developed scripts using R Studio version 4.1.2 (2021-11-01). Packages pheatmap (version 1.0.12) and ggplot2 (version 3.4.1) were used for plotting. Statistical significance of differences between two or multiple groups was tested using the Mann–Whitney U test or the Kruskal–Wallis H test, respectively. Statistical significance of features between multiple groups was tested with the Hypergeometric test or Fisher’s exact test. Hazard Ratios were calculated using the coxph function from the package survival (version 3.5-5). Kaplan-Meier analysis was performed using the survfit and ggsurvplot functions from the survminer package (version 0.4.9), and groups were tested with the log-rank test. Differences were considered significant at p < 0.05. RESULTS Truncating post-zygotic variants in dosage-sensitive genes predominate in BCAP compared to BCUS and RM cohorts We examined UM and PT sample sets from all breast cancer patients to identify variants associated with breast cancer (Fig. 1 a). A BL or SK sample—used as a reference when blood was unavailable—helped distinguish post-zygotic from germline variants (Fig. 1 b). Details of post-zygotic variants in the BCAP, BCUS, and RM cohorts that met the study’s cut-off criteria (Methods; Additional File 1: Supplementary Fig. 2) are summarized in Additional File 2: Supplementary Table 3. A significant age difference was observed between BCAP, BCUS, and RM cohorts (Kruskal-Wallis test, p = 7.7e-05), with the BCAP and BCUS cohorts being significantly older than the control group (Kruskal–Wallis H test, p = 0.000034 and p = 0.00036 for BCAP and BCUS, respectively). However, no significant difference was observed between the BCAP (median age: 62, range: 23–85) and BCUS (median age: 65, range: 37–84) cohorts (Kruskal–Wallis H test, p = 0.082). Within the BCAP cohort, 167 distinct variants were identified in UM samples of 41 patients. The corresponding numbers for the BCUS were strikingly lower, i.e., 56 variants were identified in the UM samples of 24 patients. The RM cohort presented 10 distinct variants in seven unrelated individuals, but all were either variants of uncertain significance or not reported in the ClinVar/InterVar databases. Truncating variants—including nonsense (n = 25) and frameshift (n = 12) mutations, which lead to transcript elimination via nonsense-mediated mRNA decay ( 27 )—were found exclusively in the BCAP cohort (Additional File 2: Supplementary Table 4). In the BCAP cohort, 29% (49/167) of the identified variants were deemed pathogenic. This list included truncating variants (n = 37), missense variants annotated as pathogenic/likely pathogenic (n = 8), and missense variants reported as uncertain significance or pathogenic/likely pathogenic in ClinVar, lacking assertion criteria but showing evidence of pathogenicity according to in-silico analyses ( REVEL threshold set to 0.75 ( 18 ) ) (n = 4) (Additional File 2; Supplementary Tables 4 and 5). Notably, nearly one-quarter (24%, 12/49) of the pathogenic BCAP variants were detected only in the UM samples and were absent in the corresponding PTs. In comparison, the BCUS cohort had seven pathogenic variants, representing 13% of the total identified variants (n = 56). These included variants reported as pathogenic (n = 4), and missense variants classified as uncertain significance or pathogenic/likely pathogenic in ClinVar without assertion criteria, showing evidence of pathogenicity according to REVEL (n = 3) (Additional File 2: Supplementary Table 5). Nearly half (43%, 3/7) of the pathogenic BCUS variants were identified only in UM samples. The UM samples from the BCAP cohort were significantly enriched for pathogenic post-zygotic variants compared to those from the BCUS cohort (Hypergeometric test, p = 0.0008578). We then focused on post-zygotic variants potentially linked to breast cancer, including those classified as pathogenic or likely pathogenic in ClinVar, variants predicted to be deleterious (REVEL ≥ 0.75), and truncating variants in known tumor suppressor genes (see Methods). UM, UMD, PT, and SK samples of 16 BCAP patients were subjected to further investigation (Sanger sequencing or High-Resolution Melting, Duplex sequencing) to verify presence or explore spatial distribution of selected variants (Table 2 ; Additional File 2: Supplementary Tables 7 and 8; Additional File 1: Supplementary Fig. 3). We identified several variants affecting dosage-sensitive genes. These included deleterious variants in the tumor suppressors KMT2C ( 19 ), PTEN ( 27 ), PTCH1 ( 28 ), TBX3 ( 20 ), and TP53 ( 21 ) as well as activating variants in oncogenes AKT1 ( 29 ) and PIK3CA ( 30 ) identified in BCAP UM samples (Additional File 2: Supplementary Table 6). Oncogenes such as SF3B1 ( 31 ), HRAS ( 32 ), and GNAS ( 33 ), and genes with a dual role in cancer ( RUNX1 ( 34 )) were affected solely in the BCUS cohort, with only the latter two being dosage-sensitive (Additional File 2: Supplementary Table 6). PIK3CA was the only gene recurrently affected in both BCAP and BCUS cohorts. Recurrence, coexistence, spatial distribution in the breast, and validation of variants In the BCAP cohort, pathogenic variants in two driver genes, PIK3CA and TP53 , were predominant across all subtypes of invasive cancer. PIK3CA , which encodes the catalytically active p100alpha isoform, is a key regulator of cell proliferation and growth receptor signaling cascades( 35 ). We detected three distinct pathogenic post-zygotic PIK3CA variants in UM samples: c.1624G > A (p.Glu542Lys), c.3140A > G (p.His1047Arg), and c.3140A > T (p.His1047Leu), found in two, four, and one unrelated individuals, respectively (Additional File 2: Supplementary Table 5). Another PIK3CA variant, c.3012G > T (p.Met1004Ile), was found in the UM of a single BCAP cohort individual. However, this variant, reported only once as of uncertain significance in the ClinVar database and with a REVEL score of 0.437 suggesting it might be benign, was classified as a variant of uncertain significance due to limited evidence (Additional File 2: Supplementary Table 5). PIK3CA c.3140A > G (p.His1047Arg) and c.3140A > T (p.His1047Leu) variants, located at the commonly mutated PIK3CA site in breast cancer, were also observed in UMs of the BCUS cohort (Fig. 2 ; Additional File 2: Supplementary Table 5). TP53 is the most commonly mutated gene in various human cancers, and its normal protein function is frequently compromised in many types of malignancies( 37 ). We detected seven TP53 variants in the normal mammary gland samples of nine BCAP patients, including two recurrent variants: six pathogenic or likely pathogenic (c.151G > T [p.Glu51*], c.329G > C [p.Arg110His], c.637C > T [p.Arg213*], c.711G > A [p.Met237Ile], c.1024C > T [p.Arg342*], and c.1025G > C [p.Arg342Pro]), and one frameshift variant c.227del (p.Ala76Aspfs*47), not previously reported in the ClinVar database (Additional File 2: Supplementary Table 6). Importantly, no TP53 variants were observed in the normal mammary gland samples of the BCUS cohort (Fig. 2 ). Pathogenic variants in AKT1 , PIK3CA , PTEN , TBX3 , and TP53 in 16 BCAP patients were selected for validation with independent methods (Table 2 ; Additional File 1: Supplementary Fig. 2). The presence of PIK3CA variants c.3140A > G (p.His1047Arg) (in three patients), c.1624G > A (p.Glu542Lys) (in two patients) and TP53 c.711G > A (p.Met237Ile) (in a single patient) was confirmed by Sanger sequencing or High-Resolution Melting in the UM samples of six BCAP patients (Table 2 ; Additonal File 1, Supplementary Fig. 3; Additional File 2: Supplementary Table 7). Notably, the PIK3CA c.3140A > G (p.His1047Arg) and TP53 c.711G > A (p.Met237Ile) variants co-existed in the UM sample of patient BCAP15. Additionally, two pathogenic variants, c.49G > A (p.Glu17Lys) in AKT1 and c.388C > T (p.Arg130*) in PTEN , were identified in the UM samples of three BCAP patients and subsequently confirmed via Sanger sequencing or High-Resolution Melting (Table 2 ; Additional File 1: Supplementary Fig. 3; Additional File 2: Supplementary Table 7). The presence of the AKT1 variant was confirmed in the UM samples of two patients and the UMD of one of them, indicating a broad spatial distribution of this variant in that particular patient. Finally, the TBX3 c.371_372insTGGT (p.Ile125Profs*14) variant was confirmed in the UM sample of a single patient (Table 2 ; Additional File 1: Supplementary Fig. 3; Additional File 2: Supplementary Table 7). We further employed duplex sequencing to validate the presence of extremely low-frequency PIK3CA and TP53 variants and examine their tissue/spatial distribution. We selected 11 individuals: BCAP01, BCAP31, BCAP36, BCAP38, BCAP45, BCAP47, BCAP48, BCAP53, BCAP54, BCAP57, and BCAP58 based on the presence of PIK3CA and TP53 variants in proximal UM samples from WES data. UMD samples available for 6 BCAP patients, located at a greater distance from the PTs and not included in the original WES run, were also analyzed (Table 2 ). Ultra-deep targeted duplex sequencing with a mean coverage of 4789x confirmed the following low-frequency (as low as 1.34%) pathogenic variants in PIK3CA : c.3140A > G (p.His1047Arg) and c.3140A > T (p.His1047Leu) in the UM samples from two patients, and revealed the presence of c.3140A > G (p. His1047Arg) variant in the UMD samples of another two patients (Table 2 ; Additional File 2: Supplementary Table 9). Furthermore, duplex sequencing confirmed the presence of six TP53 variants (c.151G > T [p.Glu51*], c.227del [p.Ala76Aspfs*47], c.329G > C [p.Arg110His], c.637C > T [p.Arg213*], c.1024C > T [p.Arg342*], and c.1025G > C [p.Arg342Pro]) in UM tissues of eight patients, and revealed the presence of c.151G > T (p.Glu51*), c.227del (p.Ala76Aspfs*47), and c.637C > T (p.Arg213*) variants in the paired UMD tissues of four patients (Table 2 ; Additional File 2: Supplementary Table 9). An overview of validated variants in AKT1 , PIK3CA , PTEN , TBX3 , and TP53 genes, along with follow-up information for the corresponding patients, is provided in Fig. 3 . Spectrum of germline pathogenic variants in the two breast cancer cohorts All breast cancer cases included in our study were reported as sporadic based on the family history of the patient; however, genetic testing results were not available prior to recruitment. We analyzed BL or SK samples from each participant to screen for pathogenic or likely pathogenic germline variants across all cohorts (Methods; Additional File 1: Supplementary Fig. 2). In the BCAP cohort, 14 of 77 individuals (18%) carried germline pathogenic variants in known breast cancer-associated genes( 22 ). These included c.4186C > T (p.Gln1396*), c.4689C > G (p.Tyr1563*), c.5179A > T (p.Lys1727*), and c.5266dup (p.Gln1756Profs*74) in BRCA1 , c.5645C > A (p.Ser1882*), c.6591_6592del (p.Glu2198Asnfs*4), and c.9382C > T (p.Arg3128*) in BRCA2 , c.172_175del (p.Gln60Argfs*7) and c.1671_1674del (p.Ile558Lysfs*2) in PALB2 , and c.3233_3236del (p.Lys1079Valfs*28) in RAD50 . Only BRCA1 c.5266dup (p.Gln1756Profs*74) and PALB2 c.172_175del (p.Gln60Argfs*7) were recurrent, observed in four and two unrelated individuals, respectively. This incidence surpasses the rates from other studies, where up to approximately 10% of reportedly sporadic cases turn out hereditary after molecular testing and likely reflects the aggressive outcomes in these cases( 15 , 21 , 38 ). Four individuals (4/14, 29%) with germline pathogenic variants also carried pathogenic post-zygotic variants in known, curated cancer-related genes( 39 ) in their UM samples. The germline variants in these cases were found in BRCA1 (four cases) and RAD50 (one case). The corresponding post-zygotic variants were identified in PIK3CA or TP53 . BRCA1 c.5266dup (p.Gln1756Profs*74) was the only variant observed in a single patient from the BCUS cohort. In the control group, no individuals were found to carry germline pathogenic or likely pathogenic variants in genes associated with breast cancer. Pathogenic germline variants in high- and moderate-penetrance breast cancer susceptibility genes identified in the BCAP and BCUS cohorts are detailed in Additional File 2: Supplementary Table 8. Pathogenic post-zygotic variants in patients with recurrent disease affect the survival rate We used Kaplan-Meier plots to evaluate survival probabilities and compare patients with recurrence (n = 53) to those without recurrence (n = 72) in both the BCAP and BCUS cohorts. Overall, patients with recurrence had significantly lower survival probabilities (log-rank test, p = 0.00017) (Additional File 1: Supplementary Fig. 4A), with a hazard ratio of 2.44 (95% CI: 1.07–5.54, p = 0.0337), indicating more than twice the risk of death compared to the non-recurrence group. Due to the shorter follow-up period for the BCUS cohort (2 years) compared to the BCAP cohort (10 years), we focused on the first 24 months post-diagnosis. During this period, recurrence patients in both cohorts (n = 53) had significantly lower survival probabilities compared to non-recurrence patients (n = 71) (log-rank test, p < 0.0001) (Additional File 1: Supplementary Fig. 4B), with a hazard ratio of 4.85 (95% CI: 1.4-16.25, p = 0.0105), indicating more than four times the risk of death for the recurrence group. Within the first 24 months, BCAP patients experienced significantly more recurrence events than BCUS patients (Fisher’s exact test, p = 0.005488). In the BCAP cohort, patients with recurrence (n = 48) had lower survival probabilities throughout the follow-up period compared to those without recurrence (n = 28) (log-rank test, p = 0.015). This pattern was also observed in the first 24 months (n = 48 vs. n = 27) (log-rank test, p = 0.0088) (Additional File 1: Supplementary Figs. 4C and 4D). Additionally, we assessed the impact of pathogenic post-zygotic and germline variants on the survival of patients with recurrent disease. Figure 4 displays Kaplan-Meier curves based on the presence and type of pathogenic variants. Survival probabilities differ significantly across studied groups (log-rank test, p = 0.024), indicating that the combination of pathogenic post-zygotic variants, pathogenic germline variants, and recurrence status significantly impacts patient survival. Patients with pathogenic germline variants (green) had the shortest recurrence-free survival, with most recurrences occurring within the first 60 months. Notably, patients with pathogenic post-zygotic variants in breast cancer-specific genes (blue) also experienced recurrences, though less frequently and over a longer follow-up period, highlighting the impact of these variants on recurrence risk, albeit to a lesser extent than germline variants. Patients without pathogenic germline or post-zygotic variants (yellow) showed intermediate outcomes. In conclusion, while pathogenic germline variants have the most pronounced effect on survival rates, pathogenic post-zygotic variants also seem to play a notable role in influencing recurrence risk and patient outcomes, highlighting their importance in understanding disease progression. DISCUSSION The early detection and treatment of breast cancer, including its precursors, have shifted research focus from tumors to the normal mammary gland to deepen our understanding of the disease's origins( 40 ). Genetic and transcriptomic studies have revealed a wide spectrum of alterations in critical breast cancer driver genes within the normal mammary gland of patients who have undergone BCS or mastectomy, compared to control tissues( 11 – 15 , 41 ). Recognizing that histologically normal tissue may harbor early genetic changes, we screened for post-zygotic alterations in two similarly aged breast cancer cohorts (BCAP and BCUS, Kruskal–Wallis H test, p = 0.082) with differing survival outcomes. We also included a significantly younger control group of individuals treated surgically for non-cancerous reasons (RM cohort, Kruskal–Wallis H test, p = 0.000034 and p = 0.00036 for BCAP and BCUS cohorts, respectively). Truncating variants (nonsense and frameshift) were found exclusively in the UM samples of patients with adverse prognoses (BCAP cohort) (Additional File 2: Supplementary Table 4). In contrast, BCUS patients and RM controls showed only missense variants (Additional File 2: Supplementary Table 5). UM samples from BCAP patients were significantly enriched for pathogenic post-zygotic variants (Hypergeometric test, p = 0.0008578), affecting several known cancer-related genes ( 39 ), i.e., AKT1 , KMT2C , PIK3CA , PTCH1 , PTEN , TBX3 , and TP53 , and almost a quarter were found exclusively in UM samples, absent from the corresponding PTs, suggesting early tumorigenic processes. These findings indicate that the presence of pathogenic post-zygotic alterations in UM samples could signal a higher risk for aggressive cancer, preceding clinical symptoms. The high prevalence of PIK3CA and TP53 variants in BCAP patients' UM samples highlights their pivotal role in oncogenesis. The PIK3CA gene encodes the p110α catalytic subunit of phosphoinositide 3-kinase (PI3K), a key regulator of the PI3K/AKT signaling pathway, essential for cellular growth, proliferation, and survival and has a known oncogene function( 35 , 38 ). Missense variants in PIK3CA , especially in the accessory (p.Glu542Lys) and catalytic domains ([p.His1047Arg], [p.His1047Leu]), enhance kinase activity and promote oncogenic signaling. The recurrence of the p.His1047Arg variant, critical for PIK3CA function, underscores its significance in tumorigenesis. PIK3CA variants were common in both BCAP and BCUS cohorts, suggesting a fundamental role in breast cancer development. In contrast, TP53 variants were found exclusively in the BCAP cohort, underscoring the gene's crucial role in maintaining genomic integrity. TP53 encodes the tumor protein p53, a key tumor suppressor involved in DNA repair, apoptosis, and cell cycle regulation( 21 ). Loss-of-function variants in TP53 can inactivate its tumor-suppressing activity during oncogenesis( 42 – 44 ). The observed TP53 variants included truncating variants (nonsense and frameshift) as well as missense alterations affecting the DNA-binding domain ([p.Arg110His], [p.Met237Ile]) and the tetramerization motif (p.Arg342Pro), highlighting the various ways p53 function can be disrupted, potentially leading to malignancy. The exclusive presence of TP53 variants in the BCAP cohort may suggest a more aggressive disease phenotype and a worse prognosis associated with these variants. PIK3CA and TP53 variants co-occurred in three BCAP patients (BCAP15, BCAP45, BCAP54), suggesting a synergistic role in cancer progression. In particular, patient BCAP15 exhibited concurrent pathogenic variants in PIK3CA (p.His1047Arg), TP53 (p.Met237Ile), and PTEN (p.Arg130*). PTEN , another critical tumor suppressor, negatively regulates the PI3K/AKT pathway( 27 ). The presence of alterations in all three genes within a single patient emphasizes the complex interplay of multiple oncogenic and tumor-suppressive pathways in breast cancer pathogenesis. This confluence of variants likely contributes to a more aggressive clinical course, underscoring the importance of comprehensive genetic profiling in understanding individual tumor biology. While post-zygotic variants in TP53 or PIK3CA have been observed in breast tumors, their consequences in normal mammary tissue are less clear. Some studies suggest a benign effect( 45 , 46 ). Healthy breast tissue accumulates alterations with age at an accelerating rate( 47 ) and is influenced by hormonal stimuli, undergoing cycles of expansion during puberty, pregnancy, and lactation( 48 ). Estrogen and its metabolites can cause DNA damage, increasing cellular stress and the risk of genetic alterations and cancer( 48 ). However, the accumulation of alterations alone does not cause cancer; observed tissue-specific patterns and the "ground state" theory suggest that quiescent stem cells with oncogenic variants rarely transform unless activated by developmental, aging, or injury factors which normally resemble physiological mammary gland conditions( 49 ). The presence of such variants in ostensibly normal mammary gland tissue suggests they may represent early, pre-cancerous changes. Despite the mammary gland's inherent multi-layer protection system against clonal expansions, surviving mutant clones can lead to large fields of mutated cells, i.e., field cancerization, thereby increasing cancer risk( 50 ). A subset of the BCAP cohort (18%, n = 14/77) and one patient from the BCUS cohort (2%, n = 1/49) carried germline pathogenic variants in breast cancer genes (Additional File 2: Supplementary Table 8). Among the BCAP patients with these variants, four had concurrent pathogenic post-zygotic variants in curated cancer genes, whereas 14 had only post-zygotic alterations in curated cancer genes and genes implicated in breast cancer (Additional File 2: Supplementary Tables 6 and 8). Despite their differing genetic profiles, all BCAP patients experienced adverse outcomes within ten years of surgery, highlighting the impact of these genetic variations on prognosis. The interaction between germline and post-zygotic variants remains unclear, as recent research indicates that the influence of germline variants on tumor behavior can vary significantly based on factors such as penetrance and lineage, with some variants exhibiting minimal or transient effects on tumor development( 51 ). In the first 24 months post-diagnosis, the BCAP cohort had significantly more recurrence events than the BCUS cohort (Fisher’s exact test, p = 0.005488). Recurrence was associated with much lower survival probabilities across both cohorts. While pathogenic post-zygotic variants alone did not drastically alter survival rates, their impact became more severe when combined with disease recurrence, suggesting a link to disease aggressiveness. This highlights the importance of comprehensive genetic screening and vigilant monitoring of patients with pathogenic post-zygotic variants in disease-related genes to improve outcomes. Current diagnostics primarily focus on identifying germline pathogenic variants in known breast cancer-associated genes to assess breast cancer risk and guide personalized therapy( 22 ). However, over 80% of breast tumors are not caused by inherited alterations( 9 ). Our study reveals that pathogenic post-zygotic variants, such as PIK3CA and TP53 alterations, are often found in seemingly normal mammary gland tissue left behind after BCS, with allele frequencies ranging from 0.03 to 0.28. In some instances, these variants represent distinct clonal populations not present in the corresponding primary tumors (Additional File 2: Supplementary Table 6), indicating the independent evolution of cell lineages within the mammary tissue and suggesting they are unlikely to be micrometastases. The timing of their emergence relative to the primary tumor, whether during early tumor progression or later, remains uncertain, but in both scenarios, they may contribute to recurrence in the breast or metastasis to other organs( 52 ). Understanding these dynamics is essential for improving diagnostic approaches and tailoring effective treatments. Our study comes with certain limitations, particularly regarding the notable age differences between the breast cancer cohorts and individuals subjected to reduction mammoplasty surgeries. Recruiting age-matched control individuals poses a challenge, as those opting for cosmetic surgical treatments are typically younger. The incidence of breast cancer diagnosis among younger women is relatively infrequent, with only about one out of eight invasive breast cancers being diagnosed in women under the age of 45( 1 ). Another limitation arises in recruiting healthy control individuals, given that approximately 13% of women are expected to develop invasive breast cancer during their lifetime and the precise onset of carcinogenesis remains unclear( 1 , 2 ). Here, control normal mammary glands were sampled from individuals without a personal or familial history of cancer undergoing plastic surgery. Hence, these samples represent the most appropriate, available control samples from a biological standpoint. Our findings demonstrate that pathogenic post-zygotic variants in breast cancer-associated genes are significantly more prevalent in histologically normal mammary tissues of patients who experienced adverse outcomes, such as recurrence or metastasis, compared to those without prognosis-based selection or control individuals. Longitudinal monitoring of these patients over nearly a decade enabled us to link these variants to clinical trajectories, particularly early recurrence. These observations suggest that genetically altered yet morphologically normal tissue may act as a reservoir for aggressive disease, raising important questions about its role in early oncogenesis and revealing potential blind spots in tumor-centric surveillance strategies. CONCLUSIONS Our study reveals that histologically normal mammary tissue from breast cancer patients, particularly those with poor prognoses, frequently harbors pathogenic post-zygotic variants in key cancer-associated genes such as PIK3CA and TP53 . These alterations are often distinct from those found in the primary tumor, suggesting independent clonal evolution and potential early oncogenic activity. Their presence correlates with a higher risk of recurrence and decreased survival, especially within the first two years post-diagnosis. These findings underscore the importance of comprehensive genetic profiling not only of tumors but also of adjacent normal tissue. Integrating post-zygotic variant analysis into routine diagnostics could improve risk stratification, guide therapeutic decisions, and inform long-term surveillance strategies in breast cancer care. Abbreviations BCAP: Breast Cancer Adverse Prognoses BCS: Breast-conserving surgery BCUS: Breast Cancer Un-Selected BL: Whole Peripheral Blood PT: Primary Tumor RM: Reduction Mammoplasty SK: Skin UM: Uninvolved mammary gland UMD: Distal uninvolved mammary gland WES: Whole Exome Sequencing Declarations Ethics approval and consent to participate Tissue samples and patient histories were provided for this study by the Oncology Centre in Bydgoszcz, Jagiellonian University Hospital in Cracow, and the University Clinical Centre in Gdańsk, who, under a research protocol approved by the Bioethical Committee at the Collegium Medicum, Nicolaus Copernicus University in Toruń (approval number KB509/2010) and by the Independent Bioethics Committee for Research at the Medical University of Gdańsk (approval number NKBBN/564/2018 with multiple amendments), recruited and enrolled all donors under informed and written consent, collected, and stored all tissue samples. Consent for publication Not applicable. Availability of data and materials Raw duplex sequencing and WES data are available upon request in the EGA archive, under study IDs EGAS50000000538 and EGAS50000000539, respectively. Competing interests J.P.D. is a cofounder and shareholder in Cray Innovation AB. J.M. is a co-founder and shareholder of Genegoggle sp. z o.o. The remaining authors have declared that no competing interests exist. Funding This research was supported by the Foundation for Polish Science under the International Research Agendas Program financed from the Smart Growth Operational Program 2014–2020 (Grant Agreement No. MAB/2018/6) to A.P. and J.P.D. Parts of the study were supported by The Swedish Cancer Society (No. 20 0889 PjF) and Swedish Medical Research Council (No. 2020-02010) to J.P.D., by The National Science Centre Poland Miniatura 4 (Project No. 2020/04/X/NZ2/02084) to K.C., and by the Austrian Science Fund FWF (P30867000) and the European Regional Development Fund (REGGEN ATCZ207) to I.T-B. Authors’ contribution Conceptualization: M. A., M. Koczkowska, J. P. D., A. P.; Methodology: A. K., J. S., T. N., I. T.-B.; Software: M. H., P. M., M. Jąkalski, J. M.; Data curation: M. A., K. C., N. F., M. H., P. M., K. D., U. Ł., H. D., B. B.-O., M. Koszyński, K. D.-C., M. Jaśkiewicz, A. K., M. D.-K., M. N., M. L.-J., D. B., J. H., E. Ś., M. Jankowski, J. .J., D. H.-Z., J. S., Ł. S., W. Z., T. N., J. M., J. P. D., A. P.; Investigation: M. A., K. C., N. F., K. D., U. Ł., H. D., B. B.-O., M. Koszyński, K. D.-C., M. Jaśkiewicz, M. Jąkalski, A. K., E. Ś., Ł. S., M. Koczkowska, A. P.; Validation: M. A., K. C., K. D., U. Ł., H. D., B. B.-O., M. Koszyński, K. D.-C., M. Jaśkiewicz; Formal analysis: M. H., P. M., M. Jąkalski, J. M.; Supervision: J. M., M. Koczkowska, J. P. D., A. P.; Funding acquisition: K. C., I. T.-B., J. P. D., A. P.; Visualization: M. A., M. Koszyński, M. Jąkalski; Project administration: N. F., K. D.-C.; Resources: M. D.-K., M. N., M. L.-J., D. B., J. H., E. Ś., M. Jankowski, J. .J., D. H.-Z., J. S., Ł. S., W. Z., T. N., J. P. D.; Writing - original draft: M. A., P. G. B., A. P.; Writing - review & editing: M. A., N. F., M. H., K. D., U. Ł., M. Jaśkiewicz, A. K., P. G. B., I. T.-B., J. M., M. Koczkowska, J. P. D., A. P.. All authors have read and agreed to the published version of the manuscript. Acknowledgments The authors wish to thank Agata Wojdak for her help with administrative activities. We also would like to thank all the patients and volunteer control individuals for acceptance to participate in the study and sample contribution; hospital staff involved in the patient recruitment process in Oncology Center - Prof. Franciszek Łukaszczyk Memorial Hospital in Bydgoszcz, University Clinical Centre in Gdańsk and University Hospital in Cracow. We thank Dr. Leszek Kalinowski for the access to selected laboratory facilities. Figure 1 and Additional File 1, Supplementary Figure 1 were partly generated using Servier Medical Art, licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/). References Breast Cancer Facts & Figures 2022-2024. Cancer Facts & Figures 2023. 1930; Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). SEER Cancer Statistics Review, 1975-2018, National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2018/, based on November 2020 SEER data submission, posted to the SEER web site, April 2021. Pan H, Gray R, Braybrooke J, Davies C, Taylor C, McGale P, et al. 20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years. N Engl J Med. 2017 Nov 9;377(19):1836–46. Shiovitz S, Korde LA. Genetics of breast cancer: a topic in evolution. Ann Oncol Off J Eur Soc Med Oncol. 2015 Jul;26(7):1291–9. Coughlin SS. Epidemiology of Breast Cancer in Women. Adv Exp Med Biol. 2019;1152:9–29. Kleibl Z, Kristensen VN. Women at high risk of breast cancer: Molecular characteristics, clinical presentation and management. Breast Edinb Scotl. 2016 Aug;28:136–44. Danforth DN. Genomic Changes in Normal Breast Tissue in Women at Normal Risk or at High Risk for Breast Cancer. Breast Cancer Basic Clin Res. 2016 Jan;10:BCBCR.S39384. Loibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. Lancet Lond Engl. 2021 May 8;397(10286):1750–69. Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA. 2019 Jan 22;321(3):288–300. Gadaleta E, Fourgoux P, Pirró S, Thorn GJ, Nelan R, Ironside A, et al. Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer. NPJ Breast Cancer. 2020;6:38. Aran D, Camarda R, Odegaard J, Paik H, Oskotsky B, Krings G, et al. Comprehensive analysis of normal adjacent to tumor transcriptomes. Nat Commun. 2017 Oct 20;8(1):1077. Forsberg LA, Rasi C, Pekar G, Davies H, Piotrowski A, Absher D, et al. Signatures of post-zygotic structural genetic aberrations in the cells of histologically normal breast tissue that can predispose to sporadic breast cancer. Genome Res. 2015 Oct;25(10):1521–35. Ronowicz A, Janaszak-Jasiecka A, Skokowski J, Madanecki P, Bartoszewski R, Bałut M, et al. Concurrent DNA Copy-Number Alterations and Mutations in Genes Related to Maintenance of Genome Stability in Uninvolved Mammary Glandular Tissue from Breast Cancer Patients. Hum Mutat. 2015 Nov;36(11):1088–99. Kostecka A, Nowikiewicz T, Olszewski P, Koczkowska M, Horbacz M, Heinzl M, et al. High prevalence of somatic PIK3CA and TP53 pathogenic variants in the normal mammary gland tissue of sporadic breast cancer patients revealed by duplex sequencing. Npj Breast Cancer. 2022 Jun 29;8(1):1–10. Pipek O, Alpár D, Rusz O, Bödör C, Udvarnoki Z, Medgyes-Horváth A, et al. Genomic Landscape of Normal and Breast Cancer Tissues in a Hungarian Pilot Cohort. Int J Mol Sci. 2023 May 10;24(10):8553. Filipowicz N, Drężek K, Horbacz M, Wojdak A, Szymanowski J, Rychlicka-Buniowska E, et al. Comprehensive cancer-oriented biobanking resource of human samples for studies of post-zygotic genetic variation involved in cancer predisposition. Kim IY, editor. PLOS ONE. 2022 Apr 7;17(4):e0266111. 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 Oct 6;99(4):877–85. Rampias T, Karagiannis D, Avgeris M, Polyzos A, Kokkalis A, Kanaki Z, et al. The lysine‐specific methyltransferase KMT 2C/ MLL 3 regulates DNA repair components in cancer. EMBO Rep. 2019 Mar;20(3):e46821. Liang B, Zhou Y, Qian M, Xu M, Wang J, Zhang Y, et al. TBX3 functions as a tumor suppressor downstream of activated CTNNB1 mutants during hepatocarcinogenesis. J Hepatol. 2021 Jul;75(1):120–31. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012 Oct 4;490(7418):61–70. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic Version 1.2023 — September 7, 2022 NCCN.org. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med Off J Am Coll Med Genet. 2015 May;17(5):405–24. Walker LC, Hoya MDL, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet. 2023 Jul;110(7):1046–67. Salazar R, Arbeithuber B, Ivankovic M, Heinzl M, Moura S, Hartl I, et al. Discovery of an unusually high number of de novo mutations in sperm of older men using duplex sequencing. Genome Res. 2022 Mar;32(3):499–511. Sanchez-Contreras M, Sweetwyne MT, Kohrn BF, Tsantilas KA, Hipp MJ, Schmidt EK, et al. A replication-linked mutational gradient drives somatic mutation accumulation and influences germline polymorphisms and genome composition in mitochondrial DNA. Nucleic Acids Res. 2021 Nov 8;49(19):11103–18. Luongo F, Colonna F, Calapà F, Vitale S, Fiori ME, De Maria R. PTEN Tumor-Suppressor: The Dam of Stemness in Cancer. Cancers. 2019 Jul 30;11(8):1076. Gailani MR, Bale SJ, Leffell DJ, DiGiovanna JJ, Peck GL, Poliak S, et al. Developmental defects in Gorlin syndrome related to a putative tumor suppressor gene on chromosome 9. Cell. 1992 Apr 3;69(1):111–7. Staal SP. Molecular cloning of the akt oncogene and its human homologues AKT1 and AKT2: amplification of AKT1 in a primary human gastric adenocarcinoma. Proc Natl Acad Sci U S A. 1987 Jul;84(14):5034–7. Bader AG, Kang S, Zhao L, Vogt PK. Oncogenic PI3K deregulates transcription and translation. Nat Rev Cancer. 2005 Dec;5(12):921–9. Kesarwani AK, Ramirez O, Gupta AK, Yang X, Murthy T, Minella AC, et al. Cancer-associated SF3B1 mutants recognize otherwise inaccessible cryptic 3’ splice sites within RNA secondary structures. Oncogene. 2017 Feb 23;36(8):1123–33. Hobbs GA, Der CJ, Rossman KL. RAS isoforms and mutations in cancer at a glance. J Cell Sci. 2016 Apr 1;129(7):1287–92. He X, Zhang L, Chen Y, Remke M, Shih D, Lu F, et al. The G protein α subunit Gαs is a tumor suppressor in Sonic hedgehog-driven medulloblastoma. Nat Med. 2014 Sep;20(9):1035–42. Otalora‑Otalora B, Henriquez B, Lopez‑Kleine L, Rojas A. RUNX family: Oncogenes or tumor suppressors (Review). Oncol Rep [Internet]. 2019 May 6 [cited 2024 Jan 22]; Available from: http://www.spandidos-publications.com/10.3892/or.2019.7149 Martínez-Sáez O, Chic N, Pascual T, Adamo B, Vidal M, González-Farré B, et al. Frequency and spectrum of PIK3CA somatic mutations in breast cancer. Breast Cancer Res. 2020 Dec;22(1):45. Zhou X, Edmonson MN, Wilkinson MR, Patel A, Wu G, Liu Y, et al. Exploring genomic alteration in pediatric cancer using ProteinPaint. Nat Genet. 2016 Jan;48(1):4–6. Hainaut P, Pfeifer GP. Somatic TP53 Mutations in the Era of Genome Sequencing. Cold Spring Harb Perspect Med. 2016 Nov 1;6(11):a026179. Pereira B, Chin SF, Rueda OM, Vollan HKM, Provenzano E, Bardwell HA, et al. The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes. Nat Commun. 2016 May 10;7(1):11479. Sondka Z, Bamford S, Cole CG, Ward SA, Dunham I, Forbes SA. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat Rev Cancer. 2018 Nov;18(11):696–705. Crosby D, Bhatia S, Brindle KM, Coussens LM, Dive C, Emberton M, et al. Early detection of cancer. Science. 2022 Mar 18;375(6586):eaay9040. Troester MA, Hoadley KA, D’Arcy M, Cherniack AD, Stewart C, Koboldt DC, et al. DNA defects, epigenetics, and gene expression in cancer-adjacent breast: a study from The Cancer Genome Atlas. NPJ Breast Cancer. 2016;2:16007. Baugh EH, Ke H, Levine AJ, Bonneau RA, Chan CS. Why are there hotspot mutations in the TP53 gene in human cancers? Cell Death Differ. 2018 Jan;25(1):154–60. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW. Cancer Genome Landscapes. Science. 2013 Mar 29;339(6127):1546–58. The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Aaltonen LA, Abascal F, Abeshouse A, Aburatani H, Adams DJ, et al. Pan-cancer analysis of whole genomes. Nature. 2020 Feb 6;578(7793):82–93. Kandel R, Li SQ, Ozcelik H, Rohan T. p53 protein accumulation and mutations in normal and benign breast tissue. Int J Cancer. 2000 Jul 1;87(1):73–8. Soysal SD, Ng CKY, Costa L, Weber WP, Paradiso V, Piscuoglio S, et al. Genetic Alterations in Benign Breast Biopsies of Subsequent Breast Cancer Patients. Front Med. 2019 Jul 24;6:166. Cereser B, Yiu A, Tabassum N, Del Bel Belluz L, Zagorac S, Ancheta KRZ, et al. The mutational landscape of the adult healthy parous and nulliparous human breast. Nat Commun. 2023 Sep 6;14(1):5136. Macias H, Hinck L. Mammary gland development. Wiley Interdiscip Rev Dev Biol. 2012;1(4):533–57. Jassim A, Rahrmann EP, Simons BD, Gilbertson RJ. Cancers make their own luck: theories of cancer origins. Nat Rev Cancer. 2023 Oct;23(10):710–24. Ciwinska M, Messal HA, Hristova HR, Lutz C, Bornes L, Chalkiadakis T, et al. Mechanisms that clear mutations drive field cancerization in mammary tissue. Nature. 2024 Sep 5;633(8028):198–206. Srinivasan P, Bandlamudi C, Jonsson P, Kemel Y, Chavan SS, Richards AL, et al. The context-specific role of germline pathogenicity in tumorigenesis. Nat Genet. 2021 Nov;53(11):1577–85. Davis A, Gao R, Navin N. Tumor evolution: Linear, branching, neutral or punctuated? Biochim Biophys Acta BBA - Rev Cancer. 2017 Apr;1867(2):151–61. Additional Declarations The authors declare potential competing interests as follows: J.P.D. is a cofounder and shareholder in Cray Innovation AB. J.M. is a co-founder and shareholder of Genegoggle sp. z o.o. The remaining authors have declared that no competing interests exist. Supplementary Files AdditionalFile1.pdf AdditionalFile2.xlsx 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-7293078","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495530669,"identity":"7fcdf16f-81b6-4e19-9945-c5964e655e2f","order_by":0,"name":"Maria Andreou","email":"","orcid":"https://orcid.org/0000-0002-2197-597X","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Andreou","suffix":""},{"id":495530671,"identity":"3510f897-a953-4802-95ae-c0cdd13c967c","order_by":1,"name":"Katarzyna Chojnowska","email":"","orcid":"","institution":"Medical University 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UM samples were located at least 1 cm away from the corresponding PT. Control mammary gland samples were obtained from individuals who underwent reduction mammoplasty surgeries and had no history of cancer (Reduction Mammoplasty cohort, RM). (\u003cstrong\u003eb)\u003c/strong\u003e Matched peripheral blood (BL) or skin (SK) samples were collected for each individual as reference samples to distinguish between post-zygotic and germline variants. Post-zygotic variants were identified as those that were absent from the reference samples.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7293078/v1/dafe7bd48d3aa13f92e2a96f.png"},{"id":88425641,"identity":"f4116280-2317-4be0-9474-9dc116f0d027","added_by":"auto","created_at":"2025-08-06 09:56:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePost-zygotic (a) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePIK3CA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and (b) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTP53\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e variants detected in breast cancer patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathogenic post-zygotic \u003cem\u003ePIK3CA\u003c/em\u003e (a) and \u003cem\u003eTP53\u003c/em\u003e (b) variants were identified in the uninvolved mammary gland (UM) and primary tumor (PT) samples of breast cancer patients with adverse prognoses and patients recruited without any prognoses bias (BCAP and BCUS cohorts, respectively). Lollipop plots represent post-zygotic variants of \u003cem\u003ePIK3CA\u003c/em\u003eand \u003cem\u003eTP53\u003c/em\u003e genes detected by Whole Exome Sequencing (WES). The upper panel represents variants detected in UM samples of BCAP patients. The lower panel represents variants detected in UM samples of BCUS patients. \u003cem\u003eTP53\u003c/em\u003evariants were found exclusively in the BCAP cohort. All identified variants have been reported in the COSMIC database (https://cancer.sanger.ac.uk/cosmic). Detailed descriptions of variants can be found in Additional File 2: Supplementary Table 6. p85 - p85 binding domain, RBD - Ras-binding domain, C2 - C2 domain, AD - accessory domain, CD - catalytic domain. TAD1, TAD2 - transcription activation domain 1 and 2, DBD - DNA-binding domain, DNA-binding sites are marked with green lines, TD - tetramerization domain. Lollipop plots were prepared based on the images generated with the Protein paint application(36). () indicates the number of patients in whom the variant was identified.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7293078/v1/83e4ec102497510a95f2a72b.png"},{"id":88423655,"identity":"2489eb49-41d4-4a55-8000-b0d5cbec08c6","added_by":"auto","created_at":"2025-08-06 09:40:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":293084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathogenic post-zygotic variants in breast cancer patients with adverse prognoses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariants were confirmed via Sanger sequencing/High-Resolution Melting or Duplex sequencing (Methods; Table 2; Additional File 1: Supplementary Figure 3; Additional File 2: Supplementary Tables 7 and 8). Variant presence in the ClinVar database and follow-up information for the corresponding patients are included. Detailed clinicopathological information for the presented patients is provided in Additional File 2: Supplementary Table 1. A full description of detected variants is provided in Additional File 2: Supplementary Table 6. PT – primary tumor. UMD – uninvolved mammary gland at a further distance from the corresponding PT.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7293078/v1/070606dd42abf2814a5aa957.png"},{"id":88423664,"identity":"4c4eb2e5-2b0e-46d0-b35e-d504be39833c","added_by":"auto","created_at":"2025-08-06 09:40:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves of breast cancer patients with pathogenic variants and recurrent disease.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe curves represent survival probabilities for different groups of patients from the BCAP cohort (breast cancer patients with adverse prognoses) and the BCUS cohort (breast cancer patients without specific prognosis criteria), stratified by the presence of recurrent disease and/or pathogenic germline or post-zygotic variants in breast cancer-specific genes. Survival time was measured from the date of diagnosis to death or the end of the follow-up period (10 years for BCAP and 2 years for BCUS). Detailed information on recurrence status and patient outcomes can be found in Additional File 2: Supplementary Table 1. The x-axis represents time in months, and the y-axis represents the probability of survival. Vertical ticks on the curves indicate censored death events.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7293078/v1/37af2acde621f31bd3ad8ba7.png"},{"id":88426927,"identity":"9b9937d7-7c5a-41f8-b093-a1a95c49f423","added_by":"auto","created_at":"2025-08-06 10:04:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2109881,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7293078/v1/0aa18e58-190e-46a3-beec-9b0fb936fa3e.pdf"},{"id":88423654,"identity":"fc1f10e2-4795-449e-aa7c-7901232f0976","added_by":"auto","created_at":"2025-08-06 09:40:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2418963,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7293078/v1/1ff984f5d8ca3b76416e8be3.pdf"},{"id":88424591,"identity":"6b5b161a-33f5-4591-8de2-ebc1b8fbf75c","added_by":"auto","created_at":"2025-08-06 09:48:25","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":146769,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7293078/v1/e71643683064f8b28685596c.xlsx"}],"financialInterests":"The authors declare potential competing interests as follows: J.P.D. is a cofounder and shareholder in Cray Innovation AB. J.M. is a co-founder and shareholder of Genegoggle sp. z o.o. The remaining authors have declared that no competing interests exist.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBeyond Tumors: Reduced Survival Linked to Pathogenic \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePIK3CA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTP53\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Post-Zygotic Variants in the Uninvolved Breast Tissue of Recurrent Cancer Patients\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eBreast cancer accounts for 12.5% of global annual cancer diagnoses, with the incidence rate increasing by 0.5% annually in recent years(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite an overall 42% reduction between 1989 and 2021, primarily attributed to increased awareness and early detection, breast cancer still constitutes one of the leading causes of death among women(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Notably, stage I, low-risk breast cancer cases still present a 15–20% chance of recurrence even two decades after the initial diagnosis(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). While 5–10% of breast cancer cases are hereditary, with 25–30% of heritable breast cancer risk attributed to pathogenic variants in genes of high and moderate penetrance(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), the majority of cases are considered sporadic(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearch has recently focused on the normal mammary gland within the affected breast for early detection of tumor formation at the molecular level, preceding changes on imaging or palpative screens(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Concurrently, breast-conserving surgery (BCS), which aims to remove the tumor and preserve the remaining healthy breast tissue, stands as the preferred approach(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, mammary gland tissue from breast cancer patients, although appearing normal, has been found to harbor significant genomic and transcriptomic alterations(\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In particular, non-tumorous tissue from patients undergoing BCS shows clearly pathogenic low-level post-zygotic alterations in the \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e genes, raising questions regarding its oncogenic potential (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNevertheless, the association between post-zygotic alterations in ostensibly normal mammary gland tissue of breast cancer patients and their clinical utility remains unclear. Hence, we screened paired uninvolved mammary gland (UM) and primary tumor (PT) samples of reportedly sporadic breast cancer patients with adverse outcomes within 10 years post-original surgery for the presence of post-zygotic alterations. We compared our findings with a second breast cancer cohort of reportedly sporadic patients recruited without specific prognosis criteria. Additionally, normal mammary gland samples from individuals with no personal or family history of cancer were included as controls (study overview in Additional File 1, Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eHere, we demonstrate that pathogenic post-zygotic variants in cancer-associated genes are commonly found in histologically normal mammary tissue of breast cancer patients with adverse prognoses. These variants, often also found in corresponding primary tumors, are linked with patient survival, emphasizing the need for molecular screening to improve the clinical management of patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003ePatient recruitment, sample collection, and DNA isolation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe carried out Whole Exome Sequencing (WES) on 408 samples from three distinct groups: two cohorts of female breast cancer patients with differing prognostic outcomes and a control group composed of female individuals who underwent mammoplasty surgery for non-cancer-related reasons. The assignment of patients to the cohorts was determined by their clinical prognoses. The first cohort included 77 reportedly sporadic breast cancer patients with adverse outcomes. All individuals in this group experienced either recurrent disease, such as local recurrence/metastasis to the breast or secondary organs (n = 40), developed a second independent tumor (n = 18), or both (n = 8), and/or succumbed to the disease (n = 45) within the proceeding 10 years (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eB\u003c/span\u003ereast \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC\u003c/span\u003eancer \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003edverse \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eP\u003c/span\u003erognoses cohort, BCAP), (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Additional File 2:Supplementary Table\u0026nbsp;1). The second cohort included 49 individuals from the same ethnic population, diagnosed with reported sporadic breast cancer but recruited without specific criteria related to prognosis (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eB\u003c/span\u003ereast \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC\u003c/span\u003eancer \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eU\u003c/span\u003en-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eS\u003c/span\u003eelected cohort, BCUS). Within this group, 5 out of 49 patients experienced recurrence, and 3 of them died within 2 years post-surgery; however, the follow-up period for this cohort was considerably shorter compared to the BCAP cohort (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Additional File 2: Supplementary Table\u0026nbsp;1). The majority of BCAP and BCUS patients were treated with BCS (n = 63 and n = 31, respectively) versus mastectomy (n = 12 and n = 18, respectively) (Additional File 2: Supplementary Table\u0026nbsp;1) (data missing for 2 BCAP patients). All recruited individuals did not receive neoadjuvant therapy. The control group comprised 15 individuals who underwent reduction mammoplasty surgeries and had no personal or familial history of cancer (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eR\u003c/span\u003eeduction \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM\u003c/span\u003eammoplasty cohort, RM). Written informed consent was obtained from all enrolled individuals. The study was approved by the the Bioethical Committee at the Collegium Medicum, Nicolaus Copernicus University in Toruń (approval number KB509/2010) and by the Independent Bioethics Committee for Research at the Medical University of Gdańsk (approval number NKBBN/564/2018 with multiple amendments), recruited and enrolled all donors under informed and written consent, collected, and stored all tissue samples. A total of 415 samples, including PT, UM, blood (BL), or skin (SK) from all three cohorts were collected by the Oncology Centre in Bydgoszcz, Jagiellonian University Hospital in Cracow, and the University Clinical Centre in Gdańsk, with the necessary ethical approvals and written informed consent from participants and deposited in the biobank of our unit at the Medical University of Gdańsk, along with clinical data, including follow-up information (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Additional File 2: Supplementary Table\u0026nbsp;1). Distal UM samples (UMD, 1.5-3 cm from PT, median 2.35 cm), available for 7 BCAP patients, were collected and included in the downstream targeted confirmatory analysis; however, they were not initially sequenced. For the RM cohort, sets of UM and BL samples were included. UM samples were located at least 1 cm away from the corresponding PT. All collected samples were frozen at -80°C. Detailed tissue-collecting protocols were previously described by Filipowicz et al(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). All fragments prepared for molecular analysis were histologically evaluated by expert pathologists to identify tumor fragments (PT) and confirm the normal histology of UM and SK samples. DNA isolation from tissue lysates and whole blood was performed as previously described(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummarized clinicopathological features of breast cancer patients included in the BCAP and BCUS cohorts.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCAP cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBCUS cohort\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of individuals\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (median, range)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62, 23–85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65, 37–84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep value = 0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCollected samples\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Tumor, PT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUninvolved mammary gland, UM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistal fragment of uninvolved mammary gland, UMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReference sample\u003c/p\u003e\u003cp\u003e(whole peripheral blood, BL or skin, SK)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvasive ductal carcinoma, IDC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvasive lobular carcinoma, ILC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDC - ILC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReceptors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstrogen, ER (positive / negative / not available)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 / 20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 / 5 / 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProgesterone, PR (positive / negative / not available)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 / 34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 / 4 / 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER2 (positive / negative / not available)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 / 56 / 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 / 43 / 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubtype\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER-2 enriched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriple-negative breast cancer, TNBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up information\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecurrence (yes / no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 / 27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 / 44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond cancer (yes / no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 / 51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 / 49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath* (yes / no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 / 31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 / 46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eMatched and primary tumor (PT) and uninvolved mammary gland (UM, \u0026ge;1 cm ) samples were collected from two breast cancer cohorts, i.e., 77 individuals characterized with adverse outcomes (Breast Cancer Adverse Prognoses cohort,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBCAP) and 49 individuals recruited without any pre-selection criteria related to prognosis (Breast Cancer Un-Selected cohort, BCUS ). Whole peripheral blood (BL) or skin (SK) samples (if BL was not available) were collected as reference samples to distinguish between post-zygotic and germline variants. Distal UM samples (UMD, 1.5-3 cm from PT, median 2.35 cm), available for 7 BCAP patients, were included. The detailed sampling design is described in Materials and Methods. An overview is also available in Figure 1. *Death status refers to patients who succumbed to the disease (patient with ID BCAP61 died from non-oncological reasons). Detailed clinicopathological information for BCAP and BCUS cohorts is provided in Additional File 2: Supplementary Table 1.\u003c/p\u003e\u003cp\u003eWhole-exome sequencing, data analysis, variant detection, and validation with independent methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWES analyses were performed using the Agilent SureSelectXT Human All Exon V7 capture kit for sequencing library construction, followed by 150 bp paired-end sequencing on the HiSeq Illumina platform (Illumina, San Diego, CA), outsourced to Macrogen Europe (Amsterdam, The Netherlands). Sequencing coverage was 200x on average, with at least 100x on target. The sequencing coverage and quality statistics for each sample are summarized in Additional File 2: Supplementary Table\u0026nbsp;2A.\u003c/p\u003e\u003cp\u003eFASTQ files were inspected and processed with \u003cem\u003eTrim Galore!\u003c/em\u003e (v0.6.7) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/trim_galore/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to remove Illumina-specific adapter sequences and poor-quality reads when necessary. After converting FASTQ files to BAM format and extracting read groups from the raw data, reads were processed using GATK4 Best Practices (v4.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gatk-workflows/seq-format-conversion\u003c/span\u003e\u003cspan address=\"https://github.com/gatk-workflows/seq-format-conversion\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gatk-workflows/gatk4-data-processing\u003c/span\u003e\u003cspan address=\"https://github.com/gatk-workflows/gatk4-data-processing\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The reads were mapped to the human genome (hg38) using the BWA-MEM tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bio-bwa.sourceforge.net\u003c/span\u003e\u003cspan address=\"http://bio-bwa.sourceforge.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Octopus (v0.7.4), in cancer mode, was used for variant calling. The cancer calling model can jointly genotype multiple samples from the same individual, using a reference sample (whole peripheral blood or skin when blood was not available) to distinguish between post-zygotic and germline variants (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://luntergroup.github.io/octopus/\u003c/span\u003e\u003cspan address=\"https://luntergroup.github.io/octopus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Frameshift insertions/deletions, nonsense, and missense variants located in exons were included in the analysis. A random forest filtering approach was implemented to minimize false calls. Variants in reads with poor mapping quality (\u0026lt; 30) and variants supported by high-quality bases (≥ 30) in fewer than five reads were excluded from the analysis. Variants were annotated using ANNOVAR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://annovar.openbioinformatics.org/\u003c/span\u003e\u003cspan address=\"https://annovar.openbioinformatics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (last updated on 07.07.2020 and accessed between 06.2022–08.2022) and wANNOVAR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wannovar.wglab.org/\u003c/span\u003e\u003cspan address=\"https://wannovar.wglab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (last accessed on 21.05.2024), using the MANE SELECT transcript for investigated genes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ensembl.org/info/genome/genebuild/mane.html\u003c/span\u003e\u003cspan address=\"https://www.ensembl.org/info/genome/genebuild/mane.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A brief overview of filtering strategies implemented for identifying post-zygotic and germline variants related to breast cancer, within BCAP, BCUS, and RM cohorts, and validation experiments for selected post-zygotic variants of BCAP patients are available in Additional File 1: Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePost-zygotic variants\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOnly variants with sequencing depth ≥ 50 and tissue allele frequency ≥ 0.03 were included in the analysis. Variants were filtered based on their annotation in ClinVar and InterVar databases; variants reported as “pathogenic”, “likely pathogenic”, “uncertain significance”, or “conflicting interpretations of pathogenicity” were included. In parallel, variants described in the COSMIC database (Cosmic_95coding) were incorporated. Missense variants documented in the COSMIC database, but annotated as “benign” or “likely benign” in ClinVar or InterVar databases, were excluded. Variants in genomic regions with known read-through transcription between adjacent genes, or those spanning multiple genes (e.g., \u003cem\u003eP2RY11; PPAN-P2RY11\u003c/em\u003e and \u003cem\u003eKIR2DL1; KIR2DS5; LOC112267881\u003c/em\u003e), were also excluded.\u003c/p\u003e\u003cp\u003eThe remaining variants were filtered by their frequency in the general population, retaining only those with a minor allele frequency (MAF) ≤ 0.001 across all gnomAD populations (“popmax”) or not listed in gnomAD (v2.1.1) (Additional File 2: Supplementary Table\u0026nbsp;3). Variants were further categorized as truncating (Additional File 2: Supplementary Table\u0026nbsp;4) or missense (Additional File 2: Supplementary Table\u0026nbsp;5). Truncating variants were considered pathogenic, regardless of their annotation in ClinVar or InterVar. For missense variants, \u003cem\u003ein-silico\u003c/em\u003e analyses using the REVEL tool(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) (with a threshold score of 0.75) were performed. In summary, truncating variants, variants classified as “pathogenic” or “likely pathogenic” in ClinVar, and missense variants with conflicting interpretations or annotated as “pathogenic” in ClinVar without assertion criteria, but having a REVEL score ≥ 0.75, were deemed pathogenic.\u003c/p\u003e\u003cp\u003eTo select post-zygotic variants potentially linked to breast cancer, we prioritized those annotated as “pathogenic” or “likely pathogenic” in the ClinVar database, reviewed by expert panels, or submitted by multiple parties without discordance (Additional File 2: Supplementary Table\u0026nbsp;6). Missense variants classified as of uncertain significance, pathogenic/likely pathogenic without assertion criteria, or with conflicting interpretations in ClinVar with a REVEL score ≥ 0.75 were also included. Truncating variants in known tumor-suppressor genes implicated in breast cancer, such as \u003cem\u003eKMT2C\u003c/em\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), \u003cem\u003eTBX3\u003c/em\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), and \u003cem\u003eTP53\u003c/em\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), were included in the same table even if they were absent from the ClinVar database (Additional File 2: Supplementary Table\u0026nbsp;6). UM, UMD, PT, and SK samples from 8 BCAP patients were further investigated using Sanger sequencing or High-Resolution Melting to verify the presence of selected variants (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 2: Supplementary Table\u0026nbsp;7; Additional File 1: Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePathogenic post-zygotic variants within the uninvolved mammary tissue of BCAP patients, selected for further investigation/validation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariant\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClinVar\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCOSMIC ID\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAVSNP150\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIndividual ID and UM sample VAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eConfirmation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAKT1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.49G \u0026gt; A (p.Glu17Lys)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV62571334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers121434592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP32 (0.6%), BCAP66* (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSS / HRM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.1624G \u0026gt; A (p.Glu542Lys)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV55873227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers121913273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP56 (0.7%), BCAP45 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSS / HRM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.3140A \u0026gt; G (p.His1047Arg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV55873195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers121913279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP15 (0.08%), BCAP31* (19%), BCAP36 (0.3%), BCAP53* (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSS / HRM / DS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.3140A \u0026gt; T (p.His1047Leu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV55873401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers121913279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP54* (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.388C \u0026gt; T (p.Arg130*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV64288463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers121909224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP15 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSS / HRM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTBX3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.371_372insTGGT (p.Ile125Profs*14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV57471668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP44 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSS / HRM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.151G \u0026gt; T (p.Glu51*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV52694020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP58* (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSS / HRM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.227del (p.Ala76Aspfs*47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV52728465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en.a.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP54 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.329G \u0026gt; C (p.Arg110His)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV52668419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers11540654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP45 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.637C \u0026gt; T (p.Arg213*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV52665560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers397516436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP01* (0.8%), BCAP48* (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.711G \u0026gt; A (p.Met237Ile)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV52661887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers587782664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP15 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSS / HRM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.1024C \u0026gt; T (p.Arg342*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV52665487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers730882029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP38 (0.5%), BCAP47 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec.1025G \u0026gt; C (p.Arg342Pro)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathogenic/Likely pathogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eID = COSV52690857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ers375338359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBCAP57 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePresented variants, identified via Whole Exome sequencing in the uninvolved mammary (UM) samples of individuals characterized with adverse outcomes (Breast Cancer Adverse Prognoses cohort,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBCAP), were corroborated with either Sanger sequencing/High-Resolution Melting, or Duplex sequencing. \u003csup\u003ea\u003c/sup\u003eVariant annotation provided for the basic isoform of the transcript. \u003csup\u003eb\u003c/sup\u003ePathogenicity classification according to the ClinVar database. \u003csup\u003ec\u003c/sup\u003eID of the variant in the COSMIC (Cosmic_95 coding) database. \u003csup\u003ed\u003c/sup\u003ersIDs in dbSNP build 150. \u003csup\u003ee\u003c/sup\u003eIndividual ID and Variant Allele Frequency (VAF) for UM samples. Detailed description of selected post-zygotic variants is provided in Additional File 2: Supplementary Table 6. Confirmation of post-zygotic variants by Sanger sequencing/High-Resolution Melting, or Duplex sequencing is provided in Additional File 1: Supplementary Figure and Additional File 2: Supplementary Tables 7 and 9, respectively. SS \u0026ndash; Sanger sequencing. HRM \u0026ndash; High-Resolution Melting. DS \u0026ndash; Duplex sequencing. n.a.- not available. *variants were also detected in the distal uninvolved mammary gland sample (UMD) of selected patients.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGermline variants\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFor germline variant detection, only variants in high- and moderate-penetrance breast cancer susceptibility genes were included in the study, as defined by the NCCN Clinical Practice Guidelines in Oncology(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) (Version 1.2023, September 7, 2022). The list of genes of interest includes: \u003cem\u003eATM\u003c/em\u003e (MIM *607585), \u003cem\u003eBRCA1\u003c/em\u003e (MIM *113705), \u003cem\u003eBRCA2\u003c/em\u003e (MIM *600185), \u003cem\u003eBARD1\u003c/em\u003e (MIM *601593), \u003cem\u003eBRIP1\u003c/em\u003e (MIM *605882), \u003cem\u003eCHEK2\u003c/em\u003e (MIM *604373), \u003cem\u003eCDH1\u003c/em\u003e (MIM *192090), \u003cem\u003ePALB2\u003c/em\u003e (MIM *610355), \u003cem\u003ePTEN\u003c/em\u003e (MIM *601728), \u003cem\u003eTP53\u003c/em\u003e (MIM *191170), \u003cem\u003eNF1\u003c/em\u003e (MIM *613113), \u003cem\u003eSTK11\u003c/em\u003e (MIM *602216), \u003cem\u003eRAD50\u003c/em\u003e (MIM *604040), \u003cem\u003eRAD51C\u003c/em\u003e (MIM *602774), \u003cem\u003eRAD51D\u003c/em\u003e (MIM *602954) and additionally \u003cem\u003ePIK3CA\u003c/em\u003e (MIM *171834). Variants were filtered based on their frequency in the general population: variants with minor allele frequency (MAF) ≤ 0.01 across all gnomAD populations (“popmax”) or not noted in the database (gnomAD v2.1.1) were included. Evidence according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology recommendations(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) was included to describe all germline pathogenic variants. Specifically, the evaluation of identified \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e variants was performed according to the Evidence-based Network for the Interpretation of Germline Mutation Alleles (ENIGMA) \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e Variant Curation Expert Panel(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) (Version 1.1.0) (Clinical Genome Resource, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.clinicalgenome.org/affiliation/50087/\u003c/span\u003e\u003cspan address=\"https://www.clinicalgenome.org/affiliation/50087/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cspec.genome.network/cspec/ui/svi/doc/GN092\u003c/span\u003e\u003cspan address=\"https://cspec.genome.network/cspec/ui/svi/doc/GN092\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cspec.genome.network/cspec/ui/svi/doc/GN097\u003c/span\u003e\u003cspan address=\"https://cspec.genome.network/cspec/ui/svi/doc/GN097\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pathogenic germline variants meeting the study’s criteria, identified within breast cancer patients of the BCAP and BCUS cohorts, are described in Additional File 2: Supplementary Table\u0026nbsp;8.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDuplex sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUM samples from 11 BCAP patients were selected to investigate the presence of low-frequency \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e variants beyond the detection limits of Sanger sequencing and High-Resolution Melting and a single, higher-frequency \u003cem\u003eTP53\u003c/em\u003e variant, i.e., c.151G \u0026gt; T (p.Glu51*), located in a difficult GC-rich region. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 2: Supplementary Table\u0026nbsp;9). Additionally, UMD samples, available for 6 of those patients, were included to explore further the distribution of selected variants in a more distant from the tumor, seemingly normal mammary tissue. Duplex sequencing was performed as previously described(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDuplex sequencing data analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRaw duplex sequencing data were analyzed using the Snakemake-based Duplex-seq-Pipeline (v1.1.4) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Kennedy-Lab-UW/Duplex-Seq-Pipeline\u003c/span\u003e\u003cspan address=\"https://github.com/Kennedy-Lab-UW/Duplex-Seq-Pipeline\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e as previously described(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The sequencing coverage and quality statistics for each sample are summarized in Additional File 2: Supplementary Table\u0026nbsp;2B.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were carried out with in-house developed scripts using R Studio version 4.1.2 (2021-11-01). Packages \u003cem\u003epheatmap\u003c/em\u003e (version 1.0.12) and \u003cem\u003eggplot2\u003c/em\u003e (version 3.4.1) were used for plotting. Statistical significance of differences between two or multiple groups was tested using the Mann–Whitney U test or the Kruskal–Wallis H test, respectively. Statistical significance of features between multiple groups was tested with the Hypergeometric test or Fisher’s exact test. Hazard Ratios were calculated using the \u003cem\u003ecoxph\u003c/em\u003e function from the package \u003cem\u003esurvival\u003c/em\u003e (version 3.5-5). Kaplan-Meier analysis was performed using the \u003cem\u003esurvfit\u003c/em\u003e and \u003cem\u003eggsurvplot\u003c/em\u003e functions from the \u003cem\u003esurvminer\u003c/em\u003e package (version 0.4.9), and groups were tested with the log-rank test. Differences were considered significant at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eTruncating post-zygotic variants in dosage-sensitive genes predominate in BCAP compared to BCUS and RM cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined UM and PT sample sets from all breast cancer patients to identify variants associated with breast cancer (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). A BL or SK sample\u0026mdash;used as a reference when blood was unavailable\u0026mdash;helped distinguish post-zygotic from germline variants (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). Details of post-zygotic variants in the BCAP, BCUS, and RM cohorts that met the study\u0026rsquo;s cut-off criteria (Methods; Additional File 1: Supplementary Fig. 2) are summarized in Additional File 2: Supplementary Table 3. A significant age difference was observed between BCAP, BCUS, and RM cohorts (Kruskal-Wallis test, p\u0026thinsp;=\u0026thinsp;7.7e-05), with the BCAP and BCUS cohorts being significantly older than the control group (Kruskal\u0026ndash;Wallis H test, p\u0026thinsp;=\u0026thinsp;0.000034 and p\u0026thinsp;=\u0026thinsp;0.00036 for BCAP and BCUS, respectively). However, no significant difference was observed between the BCAP (median age: 62, range: 23\u0026ndash;85) and BCUS (median age: 65, range: 37\u0026ndash;84) cohorts (Kruskal\u0026ndash;Wallis H test, p\u0026thinsp;=\u0026thinsp;0.082).\u003c/p\u003e\n\u003cp\u003eWithin the BCAP cohort, 167 distinct variants were identified in UM samples of 41 patients. The corresponding numbers for the BCUS were strikingly lower, i.e., 56 variants were identified in the UM samples of 24 patients. The RM cohort presented 10 distinct variants in seven unrelated individuals, but all were either variants of uncertain significance or not reported in the ClinVar/InterVar databases. Truncating variants\u0026mdash;including nonsense (n\u0026thinsp;=\u0026thinsp;25) and frameshift (n\u0026thinsp;=\u0026thinsp;12) mutations, which lead to transcript elimination via nonsense-mediated mRNA decay (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e)\u0026mdash;were found exclusively in the BCAP cohort (Additional File 2: Supplementary Table\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003eIn the BCAP cohort, 29% (49/167) of the identified variants were deemed pathogenic. This list included truncating variants (n\u0026thinsp;=\u0026thinsp;37), missense variants annotated as pathogenic/likely pathogenic (n\u0026thinsp;=\u0026thinsp;8), and missense variants reported as uncertain significance or pathogenic/likely pathogenic in ClinVar, lacking assertion criteria but showing evidence of pathogenicity according to \u003cem\u003ein-silico\u003c/em\u003e analyses ( REVEL threshold set to 0.75 (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e) ) (n\u0026thinsp;=\u0026thinsp;4) (Additional File 2; Supplementary Tables\u0026nbsp;4 and 5). Notably, nearly one-quarter (24%, 12/49) of the pathogenic BCAP variants were detected only in the UM samples and were absent in the corresponding PTs. In comparison, the BCUS cohort had seven pathogenic variants, representing 13% of the total identified variants (n\u0026thinsp;=\u0026thinsp;56). These included variants reported as pathogenic (n\u0026thinsp;=\u0026thinsp;4), and missense variants classified as uncertain significance or pathogenic/likely pathogenic in ClinVar without assertion criteria, showing evidence of pathogenicity according to REVEL (n\u0026thinsp;=\u0026thinsp;3) (Additional File 2: Supplementary Table\u0026nbsp;5). Nearly half (43%, 3/7) of the pathogenic BCUS variants were identified only in UM samples. The UM samples from the BCAP cohort were significantly enriched for pathogenic post-zygotic variants compared to those from the BCUS cohort (Hypergeometric test, p\u0026thinsp;=\u0026thinsp;0.0008578).\u003c/p\u003e\n\u003cp\u003eWe then focused on post-zygotic variants potentially linked to breast cancer, including those classified as pathogenic or likely pathogenic in ClinVar, variants predicted to be deleterious (REVEL\u0026thinsp;\u0026ge;\u0026thinsp;0.75), and truncating variants in known tumor suppressor genes (see Methods). UM, UMD, PT, and SK samples of 16 BCAP patients were subjected to further investigation (Sanger sequencing or High-Resolution Melting, Duplex sequencing) to verify presence or explore spatial distribution of selected variants (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 2: Supplementary Tables\u0026nbsp;7 and 8; Additional File 1: Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003eWe identified several variants affecting dosage-sensitive genes. These included deleterious variants in the tumor suppressors \u003cem\u003eKMT2C\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e), \u003cem\u003ePTEN\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e), \u003cem\u003ePTCH1\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e), \u003cem\u003eTBX3\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e), and \u003cem\u003eTP53\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e) as well as activating variants in oncogenes \u003cem\u003eAKT1\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e) and \u003cem\u003ePIK3CA\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e) identified in BCAP UM samples (Additional File 2: Supplementary Table 6). Oncogenes such as \u003cem\u003eSF3B1\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e), \u003cem\u003eHRAS\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e), and \u003cem\u003eGNAS\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e), and genes with a dual role in cancer (\u003cem\u003eRUNX1\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e)) were affected solely in the BCUS cohort, with only the latter two being dosage-sensitive (Additional File 2: Supplementary Table 6). \u003cem\u003ePIK3CA\u003c/em\u003e was the only gene recurrently affected in both BCAP and BCUS cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecurrence, coexistence, spatial distribution in the breast, and validation of variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the BCAP cohort, pathogenic variants in two driver genes, \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e, were predominant across all subtypes of invasive cancer. \u003cem\u003ePIK3CA\u003c/em\u003e, which encodes the catalytically active p100alpha isoform, is a key regulator of cell proliferation and growth receptor signaling cascades(\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e). We detected three distinct pathogenic post-zygotic \u003cem\u003ePIK3CA\u003c/em\u003e variants in UM samples: c.1624G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Glu542Lys), c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;G (p.His1047Arg), and c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;T (p.His1047Leu), found in two, four, and one unrelated individuals, respectively (Additional File 2: Supplementary Table 5). Another \u003cem\u003ePIK3CA\u003c/em\u003e variant, c.3012G\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Met1004Ile), was found in the UM of a single BCAP cohort individual. However, this variant, reported only once as of uncertain significance in the ClinVar database and with a REVEL score of 0.437 suggesting it might be benign, was classified as a variant of uncertain significance due to limited evidence (Additional File 2: Supplementary Table 5). \u003cem\u003ePIK3CA\u003c/em\u003e c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;G (p.His1047Arg) and c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;T (p.His1047Leu) variants, located at the commonly mutated \u003cem\u003ePIK3CA\u003c/em\u003e site in breast cancer, were also observed in UMs of the BCUS cohort (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 2: Supplementary Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e is the most commonly mutated gene in various human cancers, and its normal protein function is frequently compromised in many types of malignancies(\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e). We detected seven \u003cem\u003eTP53\u003c/em\u003e variants in the normal mammary gland samples of nine BCAP patients, including two recurrent variants: six pathogenic or likely pathogenic (c.151G\u0026thinsp;\u0026gt;\u0026thinsp;T [p.Glu51*], c.329G\u0026thinsp;\u0026gt;\u0026thinsp;C [p.Arg110His], c.637C\u0026thinsp;\u0026gt;\u0026thinsp;T [p.Arg213*], c.711G\u0026thinsp;\u0026gt;\u0026thinsp;A [p.Met237Ile], c.1024C\u0026thinsp;\u0026gt;\u0026thinsp;T [p.Arg342*], and c.1025G\u0026thinsp;\u0026gt;\u0026thinsp;C [p.Arg342Pro]), and one frameshift variant c.227del (p.Ala76Aspfs*47), not previously reported in the ClinVar database (Additional File 2: Supplementary Table 6). Importantly, no \u003cem\u003eTP53\u003c/em\u003e variants were observed in the normal mammary gland samples of the BCUS cohort (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePathogenic variants in \u003cem\u003eAKT1\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eTBX3\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e in 16 BCAP patients were selected for validation with independent methods (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 1: Supplementary Fig. 2). The presence of \u003cem\u003ePIK3CA\u003c/em\u003e variants c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;G (p.His1047Arg) (in three patients), c.1624G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Glu542Lys) (in two patients) and \u003cem\u003eTP53\u003c/em\u003e c.711G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Met237Ile) (in a single patient) was confirmed by Sanger sequencing or High-Resolution Melting in the UM samples of six BCAP patients (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Additonal File 1, Supplementary Fig. 3; Additional File 2: Supplementary Table 7). Notably, the \u003cem\u003ePIK3CA\u003c/em\u003e c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;G (p.His1047Arg) and \u003cem\u003eTP53\u003c/em\u003e c.711G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Met237Ile) variants co-existed in the UM sample of patient BCAP15. Additionally, two pathogenic variants, c.49G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Glu17Lys) in \u003cem\u003eAKT1\u003c/em\u003e and c.388C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg130*) in \u003cem\u003ePTEN\u003c/em\u003e, were identified in the UM samples of three BCAP patients and subsequently confirmed via Sanger sequencing or High-Resolution Melting (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 1: Supplementary Fig. 3; Additional File 2: Supplementary Table 7). The presence of the \u003cem\u003eAKT1\u003c/em\u003e variant was confirmed in the UM samples of two patients and the UMD of one of them, indicating a broad spatial distribution of this variant in that particular patient. Finally, the \u003cem\u003eTBX3\u003c/em\u003e c.371_372insTGGT (p.Ile125Profs*14) variant was confirmed in the UM sample of a single patient (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 1: Supplementary Fig.\u0026nbsp;3; Additional File 2: Supplementary Table\u0026nbsp;7).\u003c/p\u003e\n\u003cp\u003eWe further employed duplex sequencing to validate the presence of extremely low-frequency \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e variants and examine their tissue/spatial distribution. We selected 11 individuals: BCAP01, BCAP31, BCAP36, BCAP38, BCAP45, BCAP47, BCAP48, BCAP53, BCAP54, BCAP57, and BCAP58 based on the presence of \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e variants in proximal UM samples from WES data. UMD samples available for 6 BCAP patients, located at a greater distance from the PTs and not included in the original WES run, were also analyzed (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Ultra-deep targeted duplex sequencing with a mean coverage of 4789x confirmed the following low-frequency (as low as 1.34%) pathogenic variants in \u003cem\u003ePIK3CA\u003c/em\u003e: c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;G (p.His1047Arg) and c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;T (p.His1047Leu) in the UM samples from two patients, and revealed the presence of c.3140A\u0026thinsp;\u0026gt;\u0026thinsp;G (p. His1047Arg) variant in the UMD samples of another two patients (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 2: Supplementary Table 9). Furthermore, duplex sequencing confirmed the presence of six \u003cem\u003eTP53\u003c/em\u003e variants (c.151G\u0026thinsp;\u0026gt;\u0026thinsp;T [p.Glu51*], c.227del [p.Ala76Aspfs*47], c.329G\u0026thinsp;\u0026gt;\u0026thinsp;C [p.Arg110His], c.637C\u0026thinsp;\u0026gt;\u0026thinsp;T [p.Arg213*], c.1024C\u0026thinsp;\u0026gt;\u0026thinsp;T [p.Arg342*], and c.1025G\u0026thinsp;\u0026gt;\u0026thinsp;C [p.Arg342Pro]) in UM tissues of eight patients, and revealed the presence of c.151G\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Glu51*), c.227del (p.Ala76Aspfs*47), and c.637C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg213*) variants in the paired UMD tissues of four patients (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional File 2: Supplementary Table 9). An overview of validated variants in \u003cem\u003eAKT1\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eTBX3\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e genes, along with follow-up information for the corresponding patients, is provided in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpectrum of germline pathogenic variants in the two breast cancer cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll breast cancer cases included in our study were reported as sporadic based on the family history of the patient; however, genetic testing results were not available prior to recruitment. We analyzed BL or SK samples from each participant to screen for pathogenic or likely pathogenic germline variants across all cohorts (Methods; Additional File 1: Supplementary Fig.\u0026nbsp;2). In the BCAP cohort, 14 of 77 individuals (18%) carried germline pathogenic variants in known breast cancer-associated genes(\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). These included c.4186C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Gln1396*), c.4689C\u0026thinsp;\u0026gt;\u0026thinsp;G (p.Tyr1563*), c.5179A\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Lys1727*), and c.5266dup (p.Gln1756Profs*74) in \u003cem\u003eBRCA1\u003c/em\u003e, c.5645C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Ser1882*), c.6591_6592del (p.Glu2198Asnfs*4), and c.9382C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg3128*) in \u003cem\u003eBRCA2\u003c/em\u003e, c.172_175del (p.Gln60Argfs*7) and c.1671_1674del (p.Ile558Lysfs*2) in \u003cem\u003ePALB2\u003c/em\u003e, and c.3233_3236del (p.Lys1079Valfs*28) in \u003cem\u003eRAD50\u003c/em\u003e. Only \u003cem\u003eBRCA1\u003c/em\u003e c.5266dup (p.Gln1756Profs*74) and \u003cem\u003ePALB2\u003c/em\u003e c.172_175del (p.Gln60Argfs*7) were recurrent, observed in four and two unrelated individuals, respectively. This incidence surpasses the rates from other studies, where up to approximately 10% of reportedly sporadic cases turn out hereditary after molecular testing and likely reflects the aggressive outcomes in these cases(\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e). Four individuals (4/14, 29%) with germline pathogenic variants also carried pathogenic post-zygotic variants in known, curated cancer-related genes(\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e) in their UM samples. The germline variants in these cases were found in \u003cem\u003eBRCA1\u003c/em\u003e (four cases) and \u003cem\u003eRAD50\u003c/em\u003e (one case). The corresponding post-zygotic variants were identified in \u003cem\u003ePIK3CA\u003c/em\u003e or \u003cem\u003eTP53\u003c/em\u003e. \u003cem\u003eBRCA1\u003c/em\u003e c.5266dup (p.Gln1756Profs*74) was the only variant observed in a single patient from the BCUS cohort. In the control group, no individuals were found to carry germline pathogenic or likely pathogenic variants in genes associated with breast cancer. Pathogenic germline variants in high- and moderate-penetrance breast cancer susceptibility genes identified in the BCAP and BCUS cohorts are detailed in Additional File 2: Supplementary Table 8.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathogenic post-zygotic variants in patients with recurrent disease affect the survival rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used Kaplan-Meier plots to evaluate survival probabilities and compare patients with recurrence (n\u0026thinsp;=\u0026thinsp;53) to those without recurrence (n\u0026thinsp;=\u0026thinsp;72) in both the BCAP and BCUS cohorts. Overall, patients with recurrence had significantly lower survival probabilities (log-rank test, p\u0026thinsp;=\u0026thinsp;0.00017) (Additional File 1: Supplementary Fig.\u0026nbsp;4A), with a hazard ratio of 2.44 (95% CI: 1.07\u0026ndash;5.54, p\u0026thinsp;=\u0026thinsp;0.0337), indicating more than twice the risk of death compared to the non-recurrence group.\u003c/p\u003e\n\u003cp\u003eDue to the shorter follow-up period for the BCUS cohort (2 years) compared to the BCAP cohort (10 years), we focused on the first 24 months post-diagnosis. During this period, recurrence patients in both cohorts (n\u0026thinsp;=\u0026thinsp;53) had significantly lower survival probabilities compared to non-recurrence patients (n\u0026thinsp;=\u0026thinsp;71) (log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Additional File 1: Supplementary Fig.\u0026nbsp;4B), with a hazard ratio of 4.85 (95% CI: 1.4-16.25, p\u0026thinsp;=\u0026thinsp;0.0105), indicating more than four times the risk of death for the recurrence group. Within the first 24 months, BCAP patients experienced significantly more recurrence events than BCUS patients (Fisher\u0026rsquo;s exact test, p\u0026thinsp;=\u0026thinsp;0.005488). In the BCAP cohort, patients with recurrence (n\u0026thinsp;=\u0026thinsp;48) had lower survival probabilities throughout the follow-up period compared to those without recurrence (n\u0026thinsp;=\u0026thinsp;28) (log-rank test, p\u0026thinsp;=\u0026thinsp;0.015). This pattern was also observed in the first 24 months (n\u0026thinsp;=\u0026thinsp;48 vs. n\u0026thinsp;=\u0026thinsp;27) (log-rank test, p\u0026thinsp;=\u0026thinsp;0.0088) (Additional File 1: Supplementary Figs.\u0026nbsp;4C and 4D).\u003c/p\u003e\n\u003cp\u003eAdditionally, we assessed the impact of pathogenic post-zygotic and germline variants on the survival of patients with recurrent disease. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e displays Kaplan-Meier curves based on the presence and type of pathogenic variants. Survival probabilities differ significantly across studied groups (log-rank test, p\u0026thinsp;=\u0026thinsp;0.024), indicating that the combination of pathogenic post-zygotic variants, pathogenic germline variants, and recurrence status significantly impacts patient survival. Patients with pathogenic germline variants (green) had the shortest recurrence-free survival, with most recurrences occurring within the first 60 months. Notably, patients with pathogenic post-zygotic variants in breast cancer-specific genes (blue) also experienced recurrences, though less frequently and over a longer follow-up period, highlighting the impact of these variants on recurrence risk, albeit to a lesser extent than germline variants. Patients without pathogenic germline or post-zygotic variants (yellow) showed intermediate outcomes. In conclusion, while pathogenic germline variants have the most pronounced effect on survival rates, pathogenic post-zygotic variants also seem to play a notable role in influencing recurrence risk and patient outcomes, highlighting their importance in understanding disease progression.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe early detection and treatment of breast cancer, including its precursors, have shifted research focus from tumors to the normal mammary gland to deepen our understanding of the disease's origins(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Genetic and transcriptomic studies have revealed a wide spectrum of alterations in critical breast cancer driver genes within the normal mammary gland of patients who have undergone BCS or mastectomy, compared to control tissues(\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecognizing that histologically normal tissue may harbor early genetic changes, we screened for post-zygotic alterations in two similarly aged breast cancer cohorts (BCAP and BCUS, Kruskal\u0026ndash;Wallis H test, p\u0026thinsp;=\u0026thinsp;0.082) with differing survival outcomes. We also included a significantly younger control group of individuals treated surgically for non-cancerous reasons (RM cohort, Kruskal\u0026ndash;Wallis H test, p\u0026thinsp;=\u0026thinsp;0.000034 and p\u0026thinsp;=\u0026thinsp;0.00036 for BCAP and BCUS cohorts, respectively).\u003c/p\u003e\u003cp\u003eTruncating variants (nonsense and frameshift) were found exclusively in the UM samples of patients with adverse prognoses (BCAP cohort) (Additional File 2: Supplementary Table\u0026nbsp;4). In contrast, BCUS patients and RM controls showed only missense variants (Additional File 2: Supplementary Table\u0026nbsp;5). UM samples from BCAP patients were significantly enriched for pathogenic post-zygotic variants (Hypergeometric test, p\u0026thinsp;=\u0026thinsp;0.0008578), affecting several known cancer-related genes (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), i.e., \u003cem\u003eAKT1\u003c/em\u003e, \u003cem\u003eKMT2C\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003ePTCH1\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eTBX3\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e, and almost a quarter were found exclusively in UM samples, absent from the corresponding PTs, suggesting early tumorigenic processes. These findings indicate that the presence of pathogenic post-zygotic alterations in UM samples could signal a higher risk for aggressive cancer, preceding clinical symptoms.\u003c/p\u003e\u003cp\u003eThe high prevalence of \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e variants in BCAP patients' UM samples highlights their pivotal role in oncogenesis. The \u003cem\u003ePIK3CA\u003c/em\u003e gene encodes the p110α catalytic subunit of phosphoinositide 3-kinase (PI3K), a key regulator of the PI3K/AKT signaling pathway, essential for cellular growth, proliferation, and survival and has a known oncogene function(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Missense variants in \u003cem\u003ePIK3CA\u003c/em\u003e, especially in the accessory (p.Glu542Lys) and catalytic domains ([p.His1047Arg], [p.His1047Leu]), enhance kinase activity and promote oncogenic signaling. The recurrence of the p.His1047Arg variant, critical for PIK3CA function, underscores its significance in tumorigenesis. \u003cem\u003ePIK3CA\u003c/em\u003e variants were common in both BCAP and BCUS cohorts, suggesting a fundamental role in breast cancer development. In contrast, \u003cem\u003eTP53\u003c/em\u003e variants were found exclusively in the BCAP cohort, underscoring the gene's crucial role in maintaining genomic integrity. \u003cem\u003eTP53\u003c/em\u003e encodes the tumor protein p53, a key tumor suppressor involved in DNA repair, apoptosis, and cell cycle regulation(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Loss-of-function variants in \u003cem\u003eTP53\u003c/em\u003e can inactivate its tumor-suppressing activity during oncogenesis(\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The observed \u003cem\u003eTP53\u003c/em\u003e variants included truncating variants (nonsense and frameshift) as well as missense alterations affecting the DNA-binding domain ([p.Arg110His], [p.Met237Ile]) and the tetramerization motif (p.Arg342Pro), highlighting the various ways p53 function can be disrupted, potentially leading to malignancy. The exclusive presence of \u003cem\u003eTP53\u003c/em\u003e variants in the BCAP cohort may suggest a more aggressive disease phenotype and a worse prognosis associated with these variants.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e variants co-occurred in three BCAP patients (BCAP15, BCAP45, BCAP54), suggesting a synergistic role in cancer progression. In particular, patient BCAP15 exhibited concurrent pathogenic variants in \u003cem\u003ePIK3CA\u003c/em\u003e (p.His1047Arg), \u003cem\u003eTP53\u003c/em\u003e (p.Met237Ile), and \u003cem\u003ePTEN\u003c/em\u003e (p.Arg130*). \u003cem\u003ePTEN\u003c/em\u003e, another critical tumor suppressor, negatively regulates the PI3K/AKT pathway(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The presence of alterations in all three genes within a single patient emphasizes the complex interplay of multiple oncogenic and tumor-suppressive pathways in breast cancer pathogenesis. This confluence of variants likely contributes to a more aggressive clinical course, underscoring the importance of comprehensive genetic profiling in understanding individual tumor biology.\u003c/p\u003e\u003cp\u003eWhile post-zygotic variants in \u003cem\u003eTP53\u003c/em\u003e or \u003cem\u003ePIK3CA\u003c/em\u003e have been observed in breast tumors, their consequences in normal mammary tissue are less clear. Some studies suggest a benign effect(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Healthy breast tissue accumulates alterations with age at an accelerating rate(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) and is influenced by hormonal stimuli, undergoing cycles of expansion during puberty, pregnancy, and lactation(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Estrogen and its metabolites can cause DNA damage, increasing cellular stress and the risk of genetic alterations and cancer(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). However, the accumulation of alterations alone does not cause cancer; observed tissue-specific patterns and the \"ground state\" theory suggest that quiescent stem cells with oncogenic variants rarely transform unless activated by developmental, aging, or injury factors which normally resemble physiological mammary gland conditions(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The presence of such variants in ostensibly normal mammary gland tissue suggests they may represent early, pre-cancerous changes. Despite the mammary gland's inherent multi-layer protection system against clonal expansions, surviving mutant clones can lead to large fields of mutated cells, i.e., field cancerization, thereby increasing cancer risk(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA subset of the BCAP cohort (18%, n\u0026thinsp;=\u0026thinsp;14/77) and one patient from the BCUS cohort (2%, n\u0026thinsp;=\u0026thinsp;1/49) carried germline pathogenic variants in breast cancer genes (Additional File 2: Supplementary Table\u0026nbsp;8). Among the BCAP patients with these variants, four had concurrent pathogenic post-zygotic variants in curated cancer genes, whereas 14 had only post-zygotic alterations in curated cancer genes and genes implicated in breast cancer (Additional File 2: Supplementary Tables\u0026nbsp;6 and 8). Despite their differing genetic profiles, all BCAP patients experienced adverse outcomes within ten years of surgery, highlighting the impact of these genetic variations on prognosis. The interaction between germline and post-zygotic variants remains unclear, as recent research indicates that the influence of germline variants on tumor behavior can vary significantly based on factors such as penetrance and lineage, with some variants exhibiting minimal or transient effects on tumor development(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the first 24 months post-diagnosis, the BCAP cohort had significantly more recurrence events than the BCUS cohort (Fisher\u0026rsquo;s exact test, p\u0026thinsp;=\u0026thinsp;0.005488). Recurrence was associated with much lower survival probabilities across both cohorts. While pathogenic post-zygotic variants alone did not drastically alter survival rates, their impact became more severe when combined with disease recurrence, suggesting a link to disease aggressiveness. This highlights the importance of comprehensive genetic screening and vigilant monitoring of patients with pathogenic post-zygotic variants in disease-related genes to improve outcomes.\u003c/p\u003e\u003cp\u003eCurrent diagnostics primarily focus on identifying germline pathogenic variants in known breast cancer-associated genes to assess breast cancer risk and guide personalized therapy(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, over 80% of breast tumors are not caused by inherited alterations(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Our study reveals that pathogenic post-zygotic variants, such as \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e alterations, are often found in seemingly normal mammary gland tissue left behind after BCS, with allele frequencies ranging from 0.03 to 0.28. In some instances, these variants represent distinct clonal populations not present in the corresponding primary tumors (Additional File 2: Supplementary Table\u0026nbsp;6), indicating the independent evolution of cell lineages within the mammary tissue and suggesting they are unlikely to be micrometastases. The timing of their emergence relative to the primary tumor, whether during early tumor progression or later, remains uncertain, but in both scenarios, they may contribute to recurrence in the breast or metastasis to other organs(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Understanding these dynamics is essential for improving diagnostic approaches and tailoring effective treatments.\u003c/p\u003e\u003cp\u003eOur study comes with certain limitations, particularly regarding the notable age differences between the breast cancer cohorts and individuals subjected to reduction mammoplasty surgeries. Recruiting age-matched control individuals poses a challenge, as those opting for cosmetic surgical treatments are typically younger. The incidence of breast cancer diagnosis among younger women is relatively infrequent, with only about one out of eight invasive breast cancers being diagnosed in women under the age of 45(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Another limitation arises in recruiting healthy control individuals, given that approximately 13% of women are expected to develop invasive breast cancer during their lifetime and the precise onset of carcinogenesis remains unclear(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Here, control normal mammary glands were sampled from individuals without a personal or familial history of cancer undergoing plastic surgery. Hence, these samples represent the most appropriate, available control samples from a biological standpoint.\u003c/p\u003e\u003cp\u003eOur findings demonstrate that pathogenic post-zygotic variants in breast cancer-associated genes are significantly more prevalent in histologically normal mammary tissues of patients who experienced adverse outcomes, such as recurrence or metastasis, compared to those without prognosis-based selection or control individuals. Longitudinal monitoring of these patients over nearly a decade enabled us to link these variants to clinical trajectories, particularly early recurrence. These observations suggest that genetically altered yet morphologically normal tissue may act as a reservoir for aggressive disease, raising important questions about its role in early oncogenesis and revealing potential blind spots in tumor-centric surveillance strategies.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOur study reveals that histologically normal mammary tissue from breast cancer patients, particularly those with poor prognoses, frequently harbors pathogenic post-zygotic variants in key cancer-associated genes such as \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e. These alterations are often distinct from those found in the primary tumor, suggesting independent clonal evolution and potential early oncogenic activity. Their presence correlates with a higher risk of recurrence and decreased survival, especially within the first two years post-diagnosis. These findings underscore the importance of comprehensive genetic profiling not only of tumors but also of adjacent normal tissue. Integrating post-zygotic variant analysis into routine diagnostics could improve risk stratification, guide therapeutic decisions, and inform long-term surveillance strategies in breast cancer care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCAP:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eBreast Cancer Adverse Prognoses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eBreast-conserving surgery\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCUS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eBreast Cancer Un-Selected\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBL:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eWhole Peripheral Blood\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePT:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ePrimary Tumor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRM:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eReduction Mammoplasty\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSK:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eSkin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUM:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eUninvolved mammary gland\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUMD:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eDistal uninvolved mammary gland\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWES:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eWhole Exome Sequencing\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTissue samples and patient histories were provided for this study by the Oncology Centre in Bydgoszcz, Jagiellonian University Hospital in Cracow, and the University Clinical Centre in Gdańsk, who, under a research protocol approved by the Bioethical Committee at the Collegium Medicum, Nicolaus Copernicus University in Toruń (approval number KB509/2010) and by the Independent Bioethics Committee for Research at the Medical University of Gdańsk (approval number NKBBN/564/2018 with multiple amendments), recruited and enrolled all donors under informed and written consent, collected, and stored all tissue samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw duplex sequencing and WES data are available upon request in the EGA archive, under study IDs EGAS50000000538 and EGAS50000000539, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.P.D. is a cofounder and shareholder in Cray Innovation AB. J.M. is a co-founder and shareholder of Genegoggle sp. z o.o. The remaining authors have declared that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Foundation for Polish Science under the International Research Agendas Program financed from the Smart Growth Operational Program 2014\u0026ndash;2020 (Grant Agreement No. MAB/2018/6) to A.P. and J.P.D. Parts of the study were supported by The Swedish Cancer Society (No. 20 0889 PjF) and Swedish Medical Research Council (No. 2020-02010) to J.P.D., by The National Science Centre Poland Miniatura 4 (Project No. 2020/04/X/NZ2/02084) to K.C., and by the Austrian Science Fund FWF (P30867000) and the European Regional Development Fund (REGGEN ATCZ207) to I.T-B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: M. A., M. Koczkowska, J. P. D., A. P.; Methodology: A. K., J. S., T. N., I. T.-B.; Software: M. H., P. M., M. Jąkalski, J. M.; Data curation: M. A., K. C., N. F., M. H., P. M., K. D., U. Ł., H. D., B. B.-O., M. Koszyński, K. D.-C., M. Jaśkiewicz, A. K., M. D.-K., M. N., M. L.-J., D. B., J. H., E. Ś., M. Jankowski, J. .J., D. H.-Z., J. S., Ł. S., W. Z., T. N., J. M., J. P. D., A. P.; Investigation: M. A., K. C., N. F., K. D., U. Ł., H. D., B. B.-O., M. Koszyński, K. D.-C., M. Jaśkiewicz, M. Jąkalski, A. K., E. Ś., Ł. S., M. Koczkowska, A. P.; Validation: M. A., K. C., K. D., U. Ł., H. D., B. B.-O., M. Koszyński, K. D.-C., M. Jaśkiewicz; Formal analysis: M. H., P. M., M. Jąkalski, J. M.; Supervision: J. M., M. Koczkowska, J. P. D., A. P.; Funding acquisition: K. C., I. T.-B., J. P. D., A. P.; Visualization: M. A., M. Koszyński, M. Jąkalski; Project administration: N. F., K. D.-C.; Resources: M. D.-K., M. N., M. L.-J., D. B., J. H., E. Ś., M. Jankowski, J. .J., D. H.-Z., J. S., Ł. S., W. Z., T. N., J. P. D.; Writing - original draft: M. A., P. G. B., A. P.; Writing - review \u0026amp; editing: M. A., N. F., M. H., K. D., U. Ł., M. Jaśkiewicz, A. K., P. G. B., I. T.-B., J. M., M. Koczkowska, J. P. D., A. P.. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank Agata Wojdak for her help with administrative activities. We also would like to thank all the patients and volunteer control individuals for acceptance to participate in the study and sample contribution; hospital staff involved in the patient recruitment process in Oncology Center - Prof. Franciszek Łukaszczyk Memorial Hospital in Bydgoszcz, University Clinical Centre in Gdańsk and University Hospital in Cracow. We thank Dr. Leszek Kalinowski for the access to selected laboratory facilities. Figure 1 and Additional File 1, Supplementary Figure 1 were partly generated using Servier Medical Art, \u0026nbsp;licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBreast Cancer Facts \u0026amp; Figures 2022-2024. \u003c/li\u003e\n\u003cli\u003eCancer Facts \u0026amp; Figures 2023. 1930; \u003c/li\u003e\n\u003cli\u003eHowlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). SEER Cancer Statistics Review, 1975-2018, National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2018/, based on November 2020 SEER data submission, posted to the SEER web site, April 2021. \u003c/li\u003e\n\u003cli\u003ePan H, Gray R, Braybrooke J, Davies C, Taylor C, McGale P, et al. 20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years. N Engl J Med. 2017 Nov 9;377(19):1836\u0026ndash;46. \u003c/li\u003e\n\u003cli\u003eShiovitz S, Korde LA. Genetics of breast cancer: a topic in evolution. Ann Oncol Off J Eur Soc Med Oncol. 2015 Jul;26(7):1291\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eCoughlin SS. Epidemiology of Breast Cancer in Women. Adv Exp Med Biol. 2019;1152:9\u0026ndash;29. \u003c/li\u003e\n\u003cli\u003eKleibl Z, Kristensen VN. Women at high risk of breast cancer: Molecular characteristics, clinical presentation and management. Breast Edinb Scotl. 2016 Aug;28:136\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eDanforth DN. Genomic Changes in Normal Breast Tissue in Women at Normal Risk or at High Risk for Breast Cancer. Breast Cancer Basic Clin Res. 2016 Jan;10:BCBCR.S39384. \u003c/li\u003e\n\u003cli\u003eLoibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. Lancet Lond Engl. 2021 May 8;397(10286):1750\u0026ndash;69. \u003c/li\u003e\n\u003cli\u003eWaks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA. 2019 Jan 22;321(3):288\u0026ndash;300. \u003c/li\u003e\n\u003cli\u003eGadaleta E, Fourgoux P, Pirr\u0026oacute; S, Thorn GJ, Nelan R, Ironside A, et al. Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer. NPJ Breast Cancer. 2020;6:38. \u003c/li\u003e\n\u003cli\u003eAran D, Camarda R, Odegaard J, Paik H, Oskotsky B, Krings G, et al. Comprehensive analysis of normal adjacent to tumor transcriptomes. Nat Commun. 2017 Oct 20;8(1):1077. \u003c/li\u003e\n\u003cli\u003eForsberg LA, Rasi C, Pekar G, Davies H, Piotrowski A, Absher D, et al. Signatures of post-zygotic structural genetic aberrations in the cells of histologically normal breast tissue that can predispose to sporadic breast cancer. Genome Res. 2015 Oct;25(10):1521\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eRonowicz A, Janaszak-Jasiecka A, Skokowski J, Madanecki P, Bartoszewski R, Bałut M, et al. Concurrent DNA Copy-Number Alterations and Mutations in Genes Related to Maintenance of Genome Stability in Uninvolved Mammary Glandular Tissue from Breast Cancer Patients. Hum Mutat. 2015 Nov;36(11):1088\u0026ndash;99. \u003c/li\u003e\n\u003cli\u003eKostecka A, Nowikiewicz T, Olszewski P, Koczkowska M, Horbacz M, Heinzl M, et al. High prevalence of somatic PIK3CA and TP53 pathogenic variants in the normal mammary gland tissue of sporadic breast cancer patients revealed by duplex sequencing. Npj Breast Cancer. 2022 Jun 29;8(1):1\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003ePipek O, Alp\u0026aacute;r D, Rusz O, B\u0026ouml;d\u0026ouml;r C, Udvarnoki Z, Medgyes-Horv\u0026aacute;th A, et al. Genomic Landscape of Normal and Breast Cancer Tissues in a Hungarian Pilot Cohort. Int J Mol Sci. 2023 May 10;24(10):8553. \u003c/li\u003e\n\u003cli\u003eFilipowicz N, Drężek K, Horbacz M, Wojdak A, Szymanowski J, Rychlicka-Buniowska E, et al. Comprehensive cancer-oriented biobanking resource of human samples for studies of post-zygotic genetic variation involved in cancer predisposition. Kim IY, editor. PLOS ONE. 2022 Apr 7;17(4):e0266111. \u003c/li\u003e\n\u003cli\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 Oct 6;99(4):877\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eRampias T, Karagiannis D, Avgeris M, Polyzos A, Kokkalis A, Kanaki Z, et al. The lysine‐specific methyltransferase KMT 2C/ MLL 3 regulates DNA repair components in cancer. EMBO Rep. 2019 Mar;20(3):e46821. \u003c/li\u003e\n\u003cli\u003eLiang B, Zhou Y, Qian M, Xu M, Wang J, Zhang Y, et al. TBX3 functions as a tumor suppressor downstream of activated CTNNB1 mutants during hepatocarcinogenesis. J Hepatol. 2021 Jul;75(1):120\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eCancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012 Oct 4;490(7418):61\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eNCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines\u0026reg;) Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic Version 1.2023 \u0026mdash; September 7, 2022 NCCN.org. \u003c/li\u003e\n\u003cli\u003eRichards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med Off J Am Coll Med Genet. 2015 May;17(5):405\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eWalker LC, Hoya MDL, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet. 2023 Jul;110(7):1046\u0026ndash;67. \u003c/li\u003e\n\u003cli\u003eSalazar R, Arbeithuber B, Ivankovic M, Heinzl M, Moura S, Hartl I, et al. Discovery of an unusually high number of de novo mutations in sperm of older men using duplex sequencing. Genome Res. 2022 Mar;32(3):499\u0026ndash;511. \u003c/li\u003e\n\u003cli\u003eSanchez-Contreras M, Sweetwyne MT, Kohrn BF, Tsantilas KA, Hipp MJ, Schmidt EK, et al. A replication-linked mutational gradient drives somatic mutation accumulation and influences germline polymorphisms and genome composition in mitochondrial DNA. Nucleic Acids Res. 2021 Nov 8;49(19):11103\u0026ndash;18. \u003c/li\u003e\n\u003cli\u003eLuongo F, Colonna F, Calap\u0026agrave; F, Vitale S, Fiori ME, De Maria R. PTEN Tumor-Suppressor: The Dam of Stemness in Cancer. Cancers. 2019 Jul 30;11(8):1076. \u003c/li\u003e\n\u003cli\u003eGailani MR, Bale SJ, Leffell DJ, DiGiovanna JJ, Peck GL, Poliak S, et al. Developmental defects in Gorlin syndrome related to a putative tumor suppressor gene on chromosome 9. Cell. 1992 Apr 3;69(1):111\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eStaal SP. Molecular cloning of the akt oncogene and its human homologues AKT1 and AKT2: amplification of AKT1 in a primary human gastric adenocarcinoma. Proc Natl Acad Sci U S A. 1987 Jul;84(14):5034\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eBader AG, Kang S, Zhao L, Vogt PK. Oncogenic PI3K deregulates transcription and translation. Nat Rev Cancer. 2005 Dec;5(12):921\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eKesarwani AK, Ramirez O, Gupta AK, Yang X, Murthy T, Minella AC, et al. Cancer-associated SF3B1 mutants recognize otherwise inaccessible cryptic 3\u0026rsquo; splice sites within RNA secondary structures. Oncogene. 2017 Feb 23;36(8):1123\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eHobbs GA, Der CJ, Rossman KL. RAS isoforms and mutations in cancer at a glance. J Cell Sci. 2016 Apr 1;129(7):1287\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eHe X, Zhang L, Chen Y, Remke M, Shih D, Lu F, et al. The G protein \u0026alpha; subunit G\u0026alpha;s is a tumor suppressor in Sonic hedgehog-driven medulloblastoma. Nat Med. 2014 Sep;20(9):1035\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eOtalora‑Otalora B, Henriquez B, Lopez‑Kleine L, Rojas A. RUNX family: Oncogenes or tumor suppressors (Review). Oncol Rep [Internet]. 2019 May 6 [cited 2024 Jan 22]; Available from: http://www.spandidos-publications.com/10.3892/or.2019.7149\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-S\u0026aacute;ez O, Chic N, Pascual T, Adamo B, Vidal M, Gonz\u0026aacute;lez-Farr\u0026eacute; B, et al. Frequency and spectrum of PIK3CA somatic mutations in breast cancer. Breast Cancer Res. 2020 Dec;22(1):45. \u003c/li\u003e\n\u003cli\u003eZhou X, Edmonson MN, Wilkinson MR, Patel A, Wu G, Liu Y, et al. Exploring genomic alteration in pediatric cancer using ProteinPaint. Nat Genet. 2016 Jan;48(1):4\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eHainaut P, Pfeifer GP. Somatic TP53 Mutations in the Era of Genome Sequencing. Cold Spring Harb Perspect Med. 2016 Nov 1;6(11):a026179. \u003c/li\u003e\n\u003cli\u003ePereira B, Chin SF, Rueda OM, Vollan HKM, Provenzano E, Bardwell HA, et al. The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes. Nat Commun. 2016 May 10;7(1):11479. \u003c/li\u003e\n\u003cli\u003eSondka Z, Bamford S, Cole CG, Ward SA, Dunham I, Forbes SA. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat Rev Cancer. 2018 Nov;18(11):696\u0026ndash;705. \u003c/li\u003e\n\u003cli\u003eCrosby D, Bhatia S, Brindle KM, Coussens LM, Dive C, Emberton M, et al. Early detection of cancer. Science. 2022 Mar 18;375(6586):eaay9040. \u003c/li\u003e\n\u003cli\u003eTroester MA, Hoadley KA, D\u0026rsquo;Arcy M, Cherniack AD, Stewart C, Koboldt DC, et al. DNA defects, epigenetics, and gene expression in cancer-adjacent breast: a study from The Cancer Genome Atlas. NPJ Breast Cancer. 2016;2:16007. \u003c/li\u003e\n\u003cli\u003eBaugh EH, Ke H, Levine AJ, Bonneau RA, Chan CS. Why are there hotspot mutations in the TP53 gene in human cancers? Cell Death Differ. 2018 Jan;25(1):154\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eVogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW. Cancer Genome Landscapes. Science. 2013 Mar 29;339(6127):1546\u0026ndash;58. \u003c/li\u003e\n\u003cli\u003eThe ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Aaltonen LA, Abascal F, Abeshouse A, Aburatani H, Adams DJ, et al. Pan-cancer analysis of whole genomes. Nature. 2020 Feb 6;578(7793):82\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eKandel R, Li SQ, Ozcelik H, Rohan T. p53 protein accumulation and mutations in normal and benign breast tissue. Int J Cancer. 2000 Jul 1;87(1):73\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eSoysal SD, Ng CKY, Costa L, Weber WP, Paradiso V, Piscuoglio S, et al. Genetic Alterations in Benign Breast Biopsies of Subsequent Breast Cancer Patients. Front Med. 2019 Jul 24;6:166. \u003c/li\u003e\n\u003cli\u003eCereser B, Yiu A, Tabassum N, Del Bel Belluz L, Zagorac S, Ancheta KRZ, et al. The mutational landscape of the adult healthy parous and nulliparous human breast. Nat Commun. 2023 Sep 6;14(1):5136. \u003c/li\u003e\n\u003cli\u003eMacias H, Hinck L. Mammary gland development. Wiley Interdiscip Rev Dev Biol. 2012;1(4):533\u0026ndash;57. \u003c/li\u003e\n\u003cli\u003eJassim A, Rahrmann EP, Simons BD, Gilbertson RJ. Cancers make their own luck: theories of cancer origins. Nat Rev Cancer. 2023 Oct;23(10):710\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eCiwinska M, Messal HA, Hristova HR, Lutz C, Bornes L, Chalkiadakis T, et al. Mechanisms that clear mutations drive field cancerization in mammary tissue. Nature. 2024 Sep 5;633(8028):198\u0026ndash;206. \u003c/li\u003e\n\u003cli\u003eSrinivasan P, Bandlamudi C, Jonsson P, Kemel Y, Chavan SS, Richards AL, et al. The context-specific role of germline pathogenicity in tumorigenesis. Nat Genet. 2021 Nov;53(11):1577\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eDavis A, Gao R, Navin N. Tumor evolution: Linear, branching, neutral or punctuated? Biochim Biophys Acta BBA - Rev Cancer. 2017 Apr;1867(2):151\u0026ndash;61. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Gdańsk Medical University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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, recurrence, post-zygotic variants, unfavorable outcome, uninvolved mammary gland, mortality","lastPublishedDoi":"10.21203/rs.3.rs-7293078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7293078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHistologically normal mammary tissue from breast cancer patients can harbor significant genetic alterations that could precede visible tumor development and influence disease progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWhole-exome sequencing was performed on 408 samples from 77 breast cancer patients with poor prognosis, 49 patients recruited without prognosis-based selection, and 15 individuals undergoing non-cancer-related mammoplasty. Paired primary tumor and histologically normal mammary gland tissues were analyzed. Variant classification adhered to strict filtering criteria, incorporating allele frequency thresholds, multiple annotation databases, and in silico prediction tools. Duplex sequencing was employed to detect and confirm pathogenic \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e variants in normal mammary tissue samples from 11 breast cancer patients with unfavorable prognosis. Statistical analyses included hypergeometric testing, Kaplan\u0026ndash;Meier survival analysis, and Cox proportional hazards modeling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePost-zygotic pathogenic variants in cancer-associated genes were significantly more prevalent in normal mammary tissue of poor-prognosis patients (29%) than in unselected patients (12.5%) (p\u0026thinsp;=\u0026thinsp;0.0008578). Disease recurrence, significantly reduced survival rates, with poor-prognosis patients experiencing higher mortality within 24 months (p\u0026thinsp;=\u0026thinsp;0.0088), were further worsened by the presence of pathogenic post-zygotic variants. Truncating variants were exclusive to poor-prognosis cases. Frequently altered genes included \u003cem\u003eAKT1, PIK3CA, PTEN, TBX3\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e, with \u003cem\u003eTP53\u003c/em\u003e variants detected only in patients with adverse outcomes. Duplex sequencing confirmed the presence of low-frequency variants (as low as 1.34%) in regions of histologically normal breast tissue from patients with a poor prognosis. Notably, nearly one-quarter of all identified cases (24%, 12/49) harbored pathogenic variants in normal tissue that were not present in the corresponding primary tumor, indicating independent clonal evolution.\u003c/p\u003e\u003ch2\u003eConlcusions\u003c/h2\u003e\u003cp\u003ePost-zygotic pathogenic variants in normal mammary tissue are associated with increased recurrence risk and reduced survival in breast cancer patients. These findings highlight the potential of integrating genetic screening of non-tumorous breast tissue into risk assessment strategies to better inform patient monitoring and management.\u003c/p\u003e","manuscriptTitle":"Beyond Tumors: Reduced Survival Linked to Pathogenic PIK3CA and TP53 Post-Zygotic Variants in the Uninvolved Breast Tissue of Recurrent Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 09:40:20","doi":"10.21203/rs.3.rs-7293078/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":"ac9265c4-021e-48f7-a2c9-c25dea796bc4","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52626186,"name":"Molecular Genetics"},{"id":52626187,"name":"Oncology"}],"tags":[],"updatedAt":"2025-08-06T09:40:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 09:40:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7293078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7293078","identity":"rs-7293078","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}