Targeted Next-Generation Sequencing of Circulating Tumor DNA Mutations among Metastatic Breast Cancer Patients

Targeted Next-Generation Sequencing of Circulating Tumor DNA Mutations among Metastatic Breast 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 Targeted Next-Generation Sequencing of Circulating Tumor DNA Mutations among Metastatic Breast Cancer Patients Minying Sun, Fangqin Lin, Lujia Chen, Hong Li, Weiquan Lin, Hongyan Du, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-494826/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jun, 2021 Read the published version in Current Oncology → Version 1 posted You are reading this latest preprint version Abstract Background Liquid biopsy through the detection of circulating tumor DNA (ctDNA) has potential advantages in cancer monitoring and prediction. However, most previous studies in this area were performed with a few hotspot genes, single time point detection, or insufficient sequencing depth. Methods In this study, we performed targeted next-generation sequencing (NGS) with a customized panel in metastatic breast cancer (MBC) patients. Fifty-four plasma samples were taken before chemotherapy and after the third course of treatment for detection and analysis. Paired lymphocytes were also included to eliminate clonal hematopoiesis (CH)-related alternatives. Results A total of 1182 nonsynonymous mutations on 419 genes were identified. More ctDNA mutations were detected in patients with tumors> 3cm ( P = 0.035) and HER2(−) patients ( P = 0.029). For a single gene, the distribution of ctDNA mutations was also correlated with clinical characteristics. Multivariate regression analysis revealed that HER2 status was significantly associated with mutation burden ( OR 0.02, 95% CI 0–0.62, P = 0.025). The profiles of ctDNA mutations exhibited marked discrepancies between two time points, and baseline ctDNA was more sensitive and specific than that after chemotherapy. Finally, elevated ctDNA mutation level was positively correlated with poor survival ( P < 0.001). Conclusion Mutations in ctDNA could serve as a potential biomarker for the evaluation and prediction, and guide the clinical management of MBC patients with chemotherapy. Molecular Biology Cancer Biology circulating tumor DNA mutation breast cancer targeted next-generation sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Breast cancer is the most common cancer among women in China as well as worldwide[ 1 ]. With progress in cancer management, most patients diagnosed in the early stages can be treated satisfactorily by surgical resection and adjuvant therapies. However, metastatic breast cancer (MBC) is still a challenge for clinicians due to the development of drug resistance, and a decreased overall survival rate is closely linked to the incidence of distant metastases[ 2 , 3 ]. MBC is a heterogeneous and dynamic disease with a range of biological characteristics, genomic alterations, and clinical outcomes[ 4 , 5 ]. It is important to monitor the clinical progression and therapy responsiveness during the course of personalized treatment[ 6 ]. Recent studies have demonstrated that circulating tumor DNA (ctDNA) released from tumor cells into the blood circulation and which contains a great deal of genetic and epigenetic information associated with cancer is a promising biomarker to assess cancer prognosis and the efficacy of treatment[ 7 – 10 ]. Monitoring of ctDNA can be performed more easily, repeatedly, and is non-invasive compared with tissue biopsy[ 11 ]. However, there are many scientific and logistical challenges, such as the low level of ctDNA in the “sea” of cell-free DNA (cfDNA) and the small number of validated ctDNA-based driver genes or mutations specific for MBC other than ER , HER2 , PIK3CA , and AKT1 [ 12 ]. In addition, most studies to date were performed with detection at only a single time point or with insufficient sequencing depth due to technical and cost limitations[ 13 ]. More data of high quality and comprehensive monitoring are essential to support and promote the application of ctDNA for the evaluation of MBC. In this prospective study, we designed a personalized target-capture region associated with breast cancer. Plasma samples obtained at two time points were examined by ultrasensitive high-throughput next-generation sequencing (NGS). Meanwhile, parallel sequencing from paired lymphocytes was performed to filter out interference by clonal hematopoiesis (CH) variants. Analyses and comparisons were performed to identify ctDNA mutations related to MBC progression and clinical outcome. The results will provide evidence regarding the clinical utility of ctDNA and potentially explore the molecular mechanism of MBC. Results Clinical characteristics of the patients and target-capture sequencing The main clinical characteristics of the 27 MBC patients included in the study are shown in Table 1 . The mean age at diagnosis was 51.30 years (range, 33–68 years). Most patients had infiltrating ductal carcinoma (clinical stage IV) with lymph node, bone, or hepatic metastasis. In total, 14 (51.8%) patients had a maximal tumor diameter> 3cm, and 11 (40.74%) patients had given birth three or more times. The proportions of ER(+), PR(+), and HER2(+)cases were 62.96%,59.26%, and 62.96%, respectively. Table 1 Clinical characteristics of MBC patients. Characteristics N (%) Diagnostic age (years) Mean (rang) 51.30 (33–68) Menarche age (years) Mean (rang) 14.33 (11–19) Stage III 1 (3.70%) IV 26 (96.30%) ER status ER(+) 17 (62.96%) PR status PR(+) 16 (59.26%) HER2 status HER2(+) 17 (62.96%) Menopause YES 11 (44.44%) Size of tumor ༞3cm 14 (51.85%) ≤ 3cm 13 (48.15%) Parturitions ≥ 3 11 (40.74%) ༜3 16 (59.26%) Therapeutic effect * PR/SD 16 (59.26%) PD 11 (40.74%) *PR: partial remission; SD: stable disease; PD: progressive disease After running an iterative algorithm with multiple databases and optimization by the NimbleGen Design portal, we selected a custom panel covering 119.20kb of the genome. The panel included 961 exons of 835 common driver genes distributed over all chromosomes. Details of the target-capture panel are presented in Table S1. DNA was successfully extracted from all 81 samples and qualified for target-capture sequencing. We obtained an average of 810.45 Mb (range 303.08–1424.64Mb) and 403.28Mb (range 183.51–789.27Mb) of high-quality data for the cfDNA and genomic DNA (gDNA) samples, respectively. The average sequencing depths for cfDNA and gDNA were 6799× (range 2543–11952×) and 3383× (range 1540–6621×), respectively. Identification of ctDNA mutations and related genes Some mutations originating from CH-related variants in lymphocytes can also be traced in cfDNA, which may interfere with the analysis of ctDNA. After the comparison and elimination of CH variants, we identified 1182 nonsynonymous mutations from all samples, including frameshift indels, stopgains, and single-nucleotide variants (SNVs). They were distributed in 419 genes on all chromosomes (Fig. 1 a). All patients had mutations in FRG1 , AQP7 , and DNAJC11 . Mutations were detected most frequently in FRG1 , and the highest mutation burden was seen in MUC16 with 58 mutations. The top 20 genes most frequently mutated in the ctDNA are shown in Fig. 1 b. Some typical cancer-related genes such as TP53 , PIK3CA , MAPK3K1 , KRAS , and PTEN were also included. The number of ctDNA mutations varied markedly between patients (range 38–171, mean 79.96). To evaluate the influence of biological features by ctDNA mutations, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted for the mutated genes from three subsets (overall, baseline, and after chemotherapy). The top ten subjects were selected for further comparison and analysis. The analysis revealed that two, seven, and six genes in all three subsets were related to biological process (BP), cellular component (CC), and molecular function (MF), respectively, including regulation of cellular component size, cation channel complex, and calmodulin binding. There were more discrepancies among the three subsets according to BP enrichment than CC and MF (Figure S1). On KEGG analysis, 96, 97, and 108 pathways were enriched in the baseline, after chemotherapy, and over all subsets, respectively. Among the top ten pathways, six were included in all three subsets (i.e., small cell lung cancer, EGFR tyrosine kinase inhibitor resistance, endometrial cancer, PI3K-Akt signaling pathway, endocrine resistance, and cholinergic synapse; Fig. 2 ). Most pathways were typical and indeed meaningful for cancer development, progression, or therapeutic response. The results revealed no significant discrepancies in overall enriched pathways among the three subsets. Distribution of gene mutations and clinical characteristics The patients were divided into different groups according to their clinical characteristics for further analysis. The results show that the ctDNA mutations were significantly associated with tumor size and HER2 status. Patients with tumors> 3cm carried more mutations than those with tumors≤ 3cm (92.29 vs. 66.69, respectively, P = 0.035), while HER2(+) patients carried fewer mutations than HER2(−) patients (68.65 vs. 99.20, respectively, P = 0.029). For single genes, the mutation profiles of ctDNA also exhibited discrepancies. We selected the top 20 genes for further analysis. The results show that mutations in AQP7 and PTEN were significantly increased in patients with a poor therapeutic effect showing progressive disease (PD) ( P 3cm ( P = 0.039). The same was also observed for DNAJC11 , MAP3K1 , and PGAP1 in patients with late menarche (≥ 14 years) and KRAS in older patients (age at diagnosis> 51 years, P < 0.05) (Table S2). A multivariate regression analysis was conducted to examine the relations between mutation burden and clinical parameters (Table 2). The results show that ctDNA mutations were significantly associated with HER2 status. Patients with HER2(+) carried fewer mutations than HER2(−) patients ( OR 0.02, 95% CI 0–0.62, P = 0.025). No significant associations were found for other characteristics. Table 2. Multivariate regression analysis of the relationship between ctDNA mutations and clinical parameters. Factors Coefficient (SE) Adjusted OR 95% CI P value Diagnostic age 0.12 0.92 0.73–1.16 0.475 Menarche age 0.41 1.57 0.70–3.55 0.274 ER status, ER(+) vs ER(-) 2.29 2.51 0.03-222.34 0.687 PR status, PR(+) vs PR(-) 2.07 0.07 0-4.30 0.210 HER2 status, HER2(+) vs HER2(-) 1.70 0.02 0-0.62 0.025 Menopause, YES vs NO 2.02 1.48 0.03–77.85 0.847 Size of tumor, ༞3cm vs ≤ 3cm 1.55 10.28 0.50-213.38 0.132 Parturitions NO. ≥3 vs ༜3 0.50 1.02 0.38–2.72 0.965 Therapeutic effect, PR/SD vs PD 1.33 4.26 0.31–58.24 0.277 Dynamics of ctDNA mutations during chemotherapy A total of 768 ctDNA mutations were detected in 302 genes at baseline, which decreased to 633 in 291 genes after three courses of treatment. The mean number of mutations for each patient also decreased after three courses of treatment (50.93 vs. 46.59, respectively). To examine the dynamic changes, we compared the distribution of ctDNA mutations in genes before and after chemotherapy (Fig. 3 ). The mutations reduced sharply after chemotherapy for MUC16 , NCOR1 , TTN , PIK3R1 , and TP53 . In contrast, more mutations were detected after chemotherapy for MYO6 , FLNC , FMN2 , CDH1 , and RHO . An analysis based on clinical information showed different mutation patterns in plasma DNA between baseline and after chemotherapy (Table S2). For baseline ctDNA, mutations in PTEN increased significantly in patients with PD compared to patients with a partial response/stable disease (PR/SD) ( P = 0.028). More mutations were detected in gene PIK3CA in patients with tumors> 3cm ( P = 0.0039), in MAP3K1 and PGAP1 in patients with late menarche (≥ 14 years, P = 0.006 and 0.008, respectively), and in KRAS in older patients (age > 51 years, P = 0.018). These associations were consistent in all samples, but not in samples obtained after treatment. In addition, fewer ctDNA mutations were found in AQP7 in PR(+) patients ( P = 0.023), and in KRAS and PIK3CA in HER2(+) patients ( P = 0.041 and 0.030, respectively). In ctDNA after chemotherapy, the number of mutations decreased significantly in TP53 in both HER2 (+) and PR(−) patients ( P = 0.011 and 0.024, respectively). Mutations in LUC7L2 were also strongly associated with HER2(−) status ( P = 0.020). ctDNA mutations and clinical outcomes A Kaplan–Meier analysis was performed to explore risk factors correlated with PFS, defined as the duration from sampling to first disease progression. The mean PFS was 537 days (range 324 to 823 days). All patients were stratified with the mean number of ctDNA mutations as the threshold. The results indicate that HER2 status, therapeutic effect, and number of ctDNA mutations exhibited significant associations with PFS (Fig. 4 ). HER2(+) patients had significantly longer PFS compared with HER2(−) patients (adjusted HR 0.26, P = 0.038), and PD was related to shorter PFS than PR/SD (adjusted HR 6.54, P = 0.038). Patients carrying more ctDNA mutations overall and in baseline plasma both had poor PFS rates ( P < 0.001). No associations were found for other clinical characteristics and mutations in ctDNA after chemotherapy. Discussion In clinical practice, the diagnosis and evaluation for breast cancer are based on tissue biopsy with immunohistochemical and cytogenetic tests, but it is invasive and difficult to perform multiple sampling[ 14 , 15 ]. Carcinoembryonic antigen (CEA) and cancer antigen 15 − 3 (CA15-3) are also used extensively as predictive markers. However, it has been demonstrated that only 7.2% and 12.3% of patients exhibited significantly elevated serum CEA and CA15-3 levels, respectively, among Chinese women[ 16 ]. Therefore, there is a need for more convenient and specific genetic markers of breast cancer. As ctDNA is double-stranded nucleic acid shed by tumor cells into the circulating blood, it should contain all of the genetic information present in tumor tissue, and could capture both spatial and temporal heterogeneity of tumors[ 17 , 18 ]. The application of ctDNA-based real-time liquid biopsy represents a noninvasive and highly sensitive biomarker for cancer diagnosis, early prediction, and therapeutic response assessment[ 7 , 19 – 22 ]. Although there have been many studies regarding ctDNA and breast cancer, some challenges and limitations remain for its clinical implementation, including the identification of specific driver mutations for the multiple demands of breast cancer management, the high costs of detecting the low proportion of ctDNA in the “sea” of normal DNA, and the development of standardized methods for data processing[ 12 , 23 , 24 ].The development of NGS and bioinformatics has provided new opportunities for the establishment and validation of specific panels of mutation biomarkers for routine clinical use in breast cancer management[ 25 , 26 ]. The prevalence of mutations in ctDNA was strikingly similar to matched tumor DNA, and they were more often detected in patients with advanced or metastatic disease[ 27 ]. However, most studies to date focused only on a limited repertoire of genes. They also varied in the quality of samples and sequencing data[ 28 ]. In this study, we performed target-capture NGS, which is more sensitive and specific than traditional sequencing, to detect and evaluate the ctDNA in MBC patients. In our cohort, 1182 nonsynonymous mutations in 419 genes were identified. More attention should be paid to some frequently mutated genes, including FRG1 , AQP7 , DNAJC11 , and MUC16 , in future studies. Mutations were detected in genes closely associated with cancer development, progression, or therapeutic response. Mutations in ctDNA could reflect intratumoral heterogeneity and disease processes[ 29 , 30 ]. The mutation burden was therefore associated with clinical parameters, representing the phenotype of cancer development[ 27 , 31 ]. We hypothesized that patients with “risk” clinical factors for breast cancer should harbor more mutations[ 32 , 33 ]. As predicted, our statistical analysis showed that an elevated ctDNA mutation burden was positively correlated with large primary tumor size, HER2(−) status, and poor survival outcome. These were all detrimental to cancer management. In addition, this study suggests that ctDNA mutations could be used as potential biomarkers for the evaluation and prediction of MBC to guide clinical management. Mutations in baseline ctDNA were more sensitive and specific than those after chemotherapy. The remarkable advantages of this study are the use of a custom-designed panel with broad coverage, standardized sequencing with high depth, sampling at two time points, and parallel sequencing to eliminate the interference of CH variants. However, this study also has some limitations. First, the sample size was small due to the lack of willingness among MBC patients to supply adequate amounts of blood. Some patients did not agree to participate in genomic testing even though it was provided without cost. Another limitation was the lack of paired tumor tissue samples, which could have been used to verify the mutations in ctDNA. These limitations restricted further analysis. Despite these limitations, our study contributes to the identification of MBC-related mutations and strengthens the evidence for the clinical applicability of ctDNA detection. Subsequent studies with larger numbers of participants and more complete information are already in progress in our laboratory. Conclusions Mutations in ctDNA were successfully detected in MBC patients by targeted NGS. The mutation burden was significantly associated with clinical factors such as age at diagnosis, tumor size, hormone receptor status, therapeutic effect, and survival. The results suggest that ctDNA could be used to predict the progression and treatment outcomes of MBC. Methods Study subjects and blood collection Patients with newly pathologically diagnosed MBC receiving consecutive gemcitabine and capecitabine treatment at Nanfang Hospital between February 2017 and October 2019 were enrolled in the study. The exclusion criteria were a previous cancer diagnosis within the last 5 years and an inability to provide adequate blood samples for NGS or undergo medical follow-up. A total of 27 Han Chinese women were included in the study population. Follow-up was performed to estimate the association between ctDNA profile and prognosis. Clinical data were obtained from the medical records and follow-up results. Therapeutic effects and prognosis were evaluated according to Response Evaluation Criteria in Solid Tumors guidelines. Peripheral blood samples were collected in 10-mL Streck tubes at two time points: before the initiation of chemotherapy and the end of the third course of treatment (63 ± 6days). Samples were shipped at 4–8°C to the laboratory and processed within 3 h. Plasma and lymphocytes were isolated by centrifugation (1600 × g , 4°C for 10min,and then 16000 × g , 4°C for 10min) immediately and stored at − 80°C. DNA extraction and assessment The cfDNA and gDNA were extracted from plasma and lymphocytes, respectively, using a QIAamp Circulating Nucleic Acid Kit (Qiagen, Valencia, CA, USA) and QIAamp DNA Blood Mini Kit (Qiagen) according to the manufacturer’s instructions. The DNA concentration was quantified using a Qubit 2.0 Fluorometer with Qubit dsDNA High Sensitivity (HS) Assay Kit (Fisher Scientific, Newark, DE, USA). An Agilent 2100 Bioanalyzer and DNA HS Kit (Agilent Technologies, Santa Clara, CA, USA) were used to assess the fragment length distribution. Only samples containing > 20ng of cfDNA and > 500ng of gDNA were processed for further analysis. All 81 DNA samples (54 cfDNA and 27 gDNA) were prepared and stored at − 20°C before library construction. Gene panel design and target-capture sequencing In this study, a personalized target panel was designed according to NCBI Build 37.1/GRCh37. Genomic exons that are frequently mutated in breast cancer or related to chemotherapy, immunotherapy, and targeted drugs were identified by searching the COSMIC database, The Cancer Genome Atlas, and published studies. An iterative algorithm was applied to maximize the number of mutations per patient while minimizing the region size. Target panel selection involved tradeoffs between sequencing cost, sensitivity, and coverage. Target-capture hybrid probes were custom designed through the NimbleGen Design portal (v1.2.R1, Roche, Basel, Switzerland). DNA libraries were prepared with a KAPA HyperPrep Kit (Kapa Biosystems, Woburn, MA, USA), including end repair and A-tailing, adapter ligation, post-ligation cleanup, library amplification, and post-amplification cleanup. The DNA polymerase displayed strong 3′→5′ exonuclease activity and a low error rate. Agencourt AMPure XP beads (Beckman Coulter, Fullerton, CA, USA) were employed for “with-bead” enzymatic and cleanup steps. Aliquots of 20–30 ng of plasma DNA were used directly for library construction. For gDNA, 500–1000ng DNA was sheared with a Covaris S2 instrument (Boston, MA, USA) set for 200-bp fragments and then used for library construction. Groups of four libraries were incorporated in a single capture pool. Hybridization and target capture were performed with custom probes from the SeqCap EZ Choice Library (Roche). The captured DNA products were amplified and purified with NimbleGen SeqCap Kits (Roche). After quality control of the DNA concentration and fragment distribution, we applied targeted NGS using 2 × 75bp or 2 × 150bp paired-end reads on the MiSeq Sequencing system (Illumina, San Diego, CA, USA) in accordance with the manufacturer’s recommendations. Data processing and statistical analysis Software fastQC (version 0.11.9) was used for sequencing data quality control. Low-quality data, including N bases≥ 50%, proportion of bases with a Phred quality score≤ 15 above 80%, or read length≤ 30bp, were excluded. The terminal adaptor sequences were removed with cutadapt (version 3.0). The reads were then mapped to the hg19 reference human genome using Burrows-Wheeler Aligner (BWA, version 0.7.17). Genome Analysis Toolkit (GATK, version 4.1.6), Picard (version 2.22.3), and SAMtools (version 1.10) were used to call small insertions/deletions (indels) and mutations according to the COSMIC and dbSNP databases. Annovar (2020Apr01) was used for annotation with multiple databases. Variations were filtered out if they had low depth< 2000×in cfDNA or< 1000×in gDNA, they were supported by less than five high-quality sequencing reads for cfDNA and two high-quality reads for gDNA, or if they were synonymous variants, including SNVs and indels. Variants detected in the plasma DNA that were wild type in the gDNA were considered ctDNA mutations and included for statistical analysis. GO and KEGG enrichment analyses of the mutated genes were performed using topGO_2.40.0 (clusterProfiler_3.16.1). The associations between ctDNA mutations and clinical characteristics were analyzed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA). The endpoint of clinical observation for the survival analysis was the last follow-up (Oct.31, 2019) or death. A progression-free survival (PFS) analysis was estimated by the Kaplan–Meier method using survival_3.2-7(survminer_0.4.9). Survival curves with a hazard ratio (HR) and 95% confidence interval (CI) were plotted with ggplot2_3.3.3 (circlize_0.4.12). The tests were two-sided, and P < 0.05 was taken to indicate statistical significance. Abbreviations MBC: Metastatic breast cancer; ctDNA: Circulating tumor DNA; cfDNA: Cell-free DNA; gDNA: Genomic DNA; NGS: Next-generation sequencing; PR: Partial remission; SD: Stable disease; PD: Progressive disease; GO: Gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; SNVs: single-nucleotide variants. Declarations Ethics approval and consent to participate This observational study was approved by the ethical committee of Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China. Consent for publication Not applicable Availability of data and materials The data of this study are available from the corresponding authors upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by The Key Project of Medicine Discipline of Guangzhou (No.2021-2023-12) and Basic Research Project of Key Laboratory of Guangzhou (21BRP004). Authors' contributions SM, YX and LM designed the study. CL enrolled the patients and performed follow-up. SM and LH performed the NGS and collected all the data. LF and LW analysed and interpreted the data. SM drafted the manuscript. DH revised the manuscript. YX and LM oversaw the work. All authors have read and approved the final manuscript. Acknowledgements We thank all the patients who participated in our study. Authors' information 1 Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, China. 2 GMU-GIBH Joint School of Life Science, Guangzhou Medical University, Guangzhou, 511436, China. 3 Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China. 4 Department of Primary Public Health, Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China. 5 Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China. The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: http://www.textcheck.com/certificate/fZ5yXi References Fahad Ullah M. Breast Cancer: Current Perspectives on the Disease Status. Adv Exp Med Biol 2019; 1152: 51-64. Peart O. Metastatic Breast Cancer. Radiol Technol 2017; 88: 519M-539M. 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Ma F, Guan Y, Yi Z et al. Assessing tumor heterogeneity using ctDNA to predict and monitor therapeutic response in metastatic breast cancer. Int J Cancer 2020; 146: 1359-1368. Romero D. Breast cancer: Tracking ctDNA to evaluate relapse risk. Nat Rev Clin Oncol 2015; 12: 624. Zhang Y, Yao Y, Xu Y et al. Pan-cancer circulating tumor DNA detection in over 10,000 Chinese patients. Nat Commun 2021; 12: 11. Fernandez-Garcia D, Hills A, Page K et al. Plasma cell-free DNA (cfDNA) as a predictive and prognostic marker in patients with metastatic breast cancer. Breast Cancer Res 2019; 21: 149. Cullinane C, Fleming C, O'Leary DP et al. Association of Circulating Tumor DNA With Disease-Free Survival in Breast Cancer: A Systematic Review and Meta-analysis. JAMA Netw Open 2020; 3: e2026921. Supplementary Files SupplementaryMaterialsSunmy2021.5.doc Cite Share Download PDF Status: Published Journal Publication published 24 Jun, 2021 Read the published version in Current Oncology → 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-494826","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":27733346,"identity":"a44f4bf8-b7a9-4ed3-9fef-7d1f53e6de26","order_by":0,"name":"Minying Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYNACAxs5NvbGxocfiNdSkGbMz3O42ViCeC0fDifOnJHeJsBDjGL5GblHN/MYMDNuuPmwjUGCwU5Ot4GAFoMbeWm3eQzYmA1uJ7Y9KGBINjY7QEiLRI4ZUAsPG1BLu4EEw4HEbYS0yM8Aa5HgMbh5sE2ChxgtDDfAWgwkJGcwEqnF4Mwbs5tzDBIM+HkSgYFsQIRf5NtzzG68+fO/vo39+MOHHyrs5AhqAQEmRHQYEKEcBBh/EKlwFIyCUTAKRigAAIBVQlve6S6oAAAAAElFTkSuQmCC","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Minying","middleName":"","lastName":"Sun","suffix":""},{"id":27733347,"identity":"548321d9-1311-42b3-9f47-1615f963456c","order_by":1,"name":"Fangqin Lin","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fangqin","middleName":"","lastName":"Lin","suffix":""},{"id":27733348,"identity":"39917203-504a-4c18-953a-ea75c7e1fb8d","order_by":2,"name":"Lujia Chen","email":"","orcid":"","institution":"Southern Medical University Nanfang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lujia","middleName":"","lastName":"Chen","suffix":""},{"id":27733349,"identity":"6fd4d79a-ab2a-4e1b-a4df-85b2c6fe1843","order_by":3,"name":"Hong Li","email":"","orcid":"","institution":"Southern Medical University Nanfang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Li","suffix":""},{"id":27733350,"identity":"d3476000-acc2-4ba3-88f5-d16a787f04ba","order_by":4,"name":"Weiquan Lin","email":"","orcid":"","institution":"Guangzhou Center for Disease Control and Prevention","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weiquan","middleName":"","lastName":"Lin","suffix":""},{"id":27733351,"identity":"6e0795f9-db09-4c0f-968f-75fc8fbde742","order_by":5,"name":"Hongyan Du","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Du","suffix":""},{"id":27733352,"identity":"ff4f6c0f-06d7-4917-9a60-675a29a225bd","order_by":6,"name":"Xuexi Yang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuexi","middleName":"","lastName":"Yang","suffix":""},{"id":27733353,"identity":"7c3b8fd0-7ead-4900-a6bf-12c4281f7473","order_by":7,"name":"Ming Li","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2021-05-05 08:28:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-494826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-494826/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/curroncol28040214","type":"published","date":"2021-06-24T16:04:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9355708,"identity":"f42391bb-b683-4bb9-9558-8167424f5cbe","added_by":"auto","created_at":"2021-05-19 19:29:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":437564,"visible":true,"origin":"","legend":"The profiles of ctDNA mutations identified. (a) Distribution of ctDNA mutations on all chromosomes. The height of each column represents the number of mutations. (b) Mutation burden of ctDNA in the top 20 genes for 27 MBC patients. The number of mutations in each gene and patients are listed to the right and top respectively.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-494826/v1/03546788bf20dfa74c753ca9.jpg"},{"id":9355460,"identity":"aea4a764-4d6e-4fff-981f-27741e7a1964","added_by":"auto","created_at":"2021-05-19 19:23:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":709890,"visible":true,"origin":"","legend":"KEGG analyses for the mutated genes from three subsets. (a) Enriched pathways for overall mutated genes. (b) Enriched pathways for baseline mutated genes. (c) Enriched pathways for mutated genes after chemotherapy. The top 10 pathways were shown. The length of each column indicates the number of enriched genes, and the colour of bars rep-resents statistical significance.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-494826/v1/a3cc53f9b42a22b3b900875e.jpg"},{"id":9355551,"identity":"016df523-f38e-4405-9a7e-7c7836d73963","added_by":"auto","created_at":"2021-05-19 19:26:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1510732,"visible":true,"origin":"","legend":"The distribution of ctDNA mutations before and after chemotherapy for all patients. The top 20 genes were shown. (a) ctDNA mutations at baseline. (b) ctDNA mutations after chemotherapy. The width of each part represents the number of mutations detected in corresponding patients or genes.","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-494826/v1/263d5e748719a416c5be99b8.jpg"},{"id":9355709,"identity":"a581b12a-4ee9-4cd2-9ab9-4c833be865fd","added_by":"auto","created_at":"2021-05-19 19:29:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":700991,"visible":true,"origin":"","legend":"Kaplan–Meier estimate of PFS. (a) Kaplan-Meier curves for HER2 status. (b) Kaplan-Meier curves for therapeutic effect. (c) Kaplan-Meier curves for mutations in overall ctDNA. (d) Kaplan-Meier curves for mutations in base-line ctDNA.","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-494826/v1/76a015ee714fb6f16a02101b.jpg"},{"id":60256093,"identity":"62297857-930c-41d6-ab32-71f1325af5cc","added_by":"auto","created_at":"2024-07-14 16:04:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3927839,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-494826/v1/01d30a3a-3795-4ba2-b265-35d37c6dbc0f.pdf"},{"id":9355553,"identity":"c3a982d3-e180-484f-af70-bcb9f281fa51","added_by":"auto","created_at":"2021-05-19 19:26:54","extension":"doc","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":585216,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsSunmy2021.5.doc","url":"https://assets-eu.researchsquare.com/files/rs-494826/v1/a4ec012baecb8075aca1b32c.doc"}],"financialInterests":"","formattedTitle":"Targeted Next-Generation Sequencing of Circulating Tumor DNA Mutations among Metastatic Breast Cancer Patients","fulltext":[{"header":"Background","content":" \u003cp\u003eBreast cancer is the most common cancer among women in China as well as worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With progress in cancer management, most patients diagnosed in the early stages can be treated satisfactorily by surgical resection and adjuvant therapies. However, metastatic breast cancer (MBC) is still a challenge for clinicians due to the development of drug resistance, and a decreased overall survival rate is closely linked to the incidence of distant metastases[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MBC is a heterogeneous and dynamic disease with a range of biological characteristics, genomic alterations, and clinical outcomes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is important to monitor the clinical progression and therapy responsiveness during the course of personalized treatment[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies have demonstrated that circulating tumor DNA (ctDNA) released from tumor cells into the blood circulation and which contains a great deal of genetic and epigenetic information associated with cancer is a promising biomarker to assess cancer prognosis and the efficacy of treatment[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMonitoring of ctDNA can be performed more easily, repeatedly, and is non-invasive compared with tissue biopsy[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, there are many scientific and logistical challenges, such as the low level of ctDNA in the \u0026ldquo;sea\u0026rdquo; of cell-free DNA (cfDNA) and the small number of validated ctDNA-based driver genes or mutations specific for MBC other than \u003cem\u003eER\u003c/em\u003e, \u003cem\u003eHER2\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, and \u003cem\u003eAKT1\u003c/em\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, most studies to date were performed with detection at only a single time point or with insufficient sequencing depth due to technical and cost limitations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. More data of high quality and comprehensive monitoring are essential to support and promote the application of ctDNA for the evaluation of MBC.\u003c/p\u003e \u003cp\u003eIn this prospective study, we designed a personalized target-capture region associated with breast cancer. Plasma samples obtained at two time points were examined by ultrasensitive high-throughput next-generation sequencing (NGS). Meanwhile, parallel sequencing from paired lymphocytes was performed to filter out interference by clonal hematopoiesis (CH) variants. Analyses and comparisons were performed to identify ctDNA mutations related to MBC progression and clinical outcome. The results will provide evidence regarding the clinical utility of ctDNA and potentially explore the molecular mechanism of MBC.\u003c/p\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the patients and target-capture sequencing\u003c/h2\u003e \u003cp\u003eThe main clinical characteristics of the 27 MBC patients included in the study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age at diagnosis was 51.30 years (range, 33\u0026ndash;68 years). Most patients had infiltrating ductal carcinoma (clinical stage IV) with lymph node, bone, or hepatic metastasis. In total, 14 (51.8%) patients had a maximal tumor diameter\u0026gt; 3cm, and 11 (40.74%) patients had given birth three or more times. The proportions of ER(+), PR(+), and HER2(+)cases were 62.96%,59.26%, and 62.96%, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of MBC patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (rang)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.30 (33\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenarche age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (rang)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.33 (11\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (3.70%)\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 \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (96.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (62.96%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (59.26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHER2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (62.96%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (44.44%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e༞3cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (51.85%)\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 \u003cp\u003e\u0026le;\u0026thinsp;3cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (48.15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParturitions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (40.74%)\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 \u003cp\u003e༜3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (59.26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapeutic effect\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR/SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (59.26%)\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 \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (40.74%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*PR: partial remission; SD: stable disease; PD: progressive disease\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter running an iterative algorithm with multiple databases and optimization by the NimbleGen Design portal, we selected a custom panel covering 119.20kb of the genome. The panel included 961 exons of 835 common driver genes distributed over all chromosomes. Details of the target-capture panel are presented in Table S1. DNA was successfully extracted from all 81 samples and qualified for target-capture sequencing. We obtained an average of 810.45 Mb (range 303.08\u0026ndash;1424.64Mb) and 403.28Mb (range 183.51\u0026ndash;789.27Mb) of high-quality data for the cfDNA and genomic DNA (gDNA) samples, respectively. The average sequencing depths for cfDNA and gDNA were 6799\u0026times; (range 2543\u0026ndash;11952\u0026times;) and 3383\u0026times; (range 1540\u0026ndash;6621\u0026times;), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of ctDNA mutations and related genes\u003c/h2\u003e \u003cp\u003eSome mutations originating from CH-related variants in lymphocytes can also be traced in cfDNA, which may interfere with the analysis of ctDNA. After the comparison and elimination of CH variants, we identified 1182 nonsynonymous mutations from all samples, including frameshift indels, stopgains, and single-nucleotide variants (SNVs). They were distributed in 419 genes on all chromosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). All patients had mutations in \u003cem\u003eFRG1\u003c/em\u003e, \u003cem\u003eAQP7\u003c/em\u003e, and \u003cem\u003eDNAJC11\u003c/em\u003e. Mutations were detected most frequently in \u003cem\u003eFRG1\u003c/em\u003e, and the highest mutation burden was seen in \u003cem\u003eMUC16\u003c/em\u003e with 58 mutations. The top 20 genes most frequently mutated in the ctDNA are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. Some typical cancer-related genes such as \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003eMAPK3K1\u003c/em\u003e, \u003cem\u003eKRAS\u003c/em\u003e, and \u003cem\u003ePTEN\u003c/em\u003e were also included. The number of ctDNA mutations varied markedly between patients (range 38\u0026ndash;171, mean 79.96).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the influence of biological features by ctDNA mutations, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted for the mutated genes from three subsets (overall, baseline, and after chemotherapy). The top ten subjects were selected for further comparison and analysis. The analysis revealed that two, seven, and six genes in all three subsets were related to biological process (BP), cellular component (CC), and molecular function (MF), respectively, including regulation of cellular component size, cation channel complex, and calmodulin binding. There were more discrepancies among the three subsets according to BP enrichment than CC and MF (Figure S1).\u003c/p\u003e \u003cp\u003eOn KEGG analysis, 96, 97, and 108 pathways were enriched in the baseline, after chemotherapy, and over all subsets, respectively. Among the top ten pathways, six were included in all three subsets (i.e., small cell lung cancer, EGFR tyrosine kinase inhibitor resistance, endometrial cancer, PI3K-Akt signaling pathway, endocrine resistance, and cholinergic synapse; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most pathways were typical and indeed meaningful for cancer development, progression, or therapeutic response. The results revealed no significant discrepancies in overall enriched pathways among the three subsets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of gene mutations and clinical characteristics\u003c/h2\u003e \u003cp\u003eThe patients were divided into different groups according to their clinical characteristics for further analysis. The results show that the ctDNA mutations were significantly associated with tumor size and HER2 status. Patients with tumors\u0026gt; 3cm carried more mutations than those with tumors\u0026le; 3cm (92.29 vs. 66.69, respectively, \u003cem\u003eP\u003c/em\u003e = 0.035), while HER2(+) patients carried fewer mutations than HER2(\u0026minus;) patients (68.65 vs. 99.20, respectively, \u003cem\u003eP\u003c/em\u003e = 0.029). For single genes, the mutation profiles of ctDNA also exhibited discrepancies. We selected the top 20 genes for further analysis. The results show that mutations in \u003cem\u003eAQP7\u003c/em\u003e and \u003cem\u003ePTEN\u003c/em\u003e were significantly increased in patients with a poor therapeutic effect showing progressive disease (PD) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cem\u003ePIK3CA\u003c/em\u003e mutations occurred more frequently in patients with tumors\u0026gt; 3cm (\u003cem\u003eP\u003c/em\u003e = 0.039). The same was also observed for \u003cem\u003eDNAJC11\u003c/em\u003e, \u003cem\u003eMAP3K1\u003c/em\u003e, and \u003cem\u003ePGAP1\u003c/em\u003e in patients with late menarche (\u0026ge; 14 years) and \u003cem\u003eKRAS\u003c/em\u003e in older patients (age at diagnosis\u0026gt; 51 years, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table S2).\u003c/p\u003e \u003cp\u003eA multivariate regression analysis was conducted to examine the relations between mutation burden and clinical parameters (Table\u0026nbsp;2). The results show that ctDNA mutations were significantly associated with HER2 status. Patients with HER2(+) carried fewer mutations than HER2(\u0026minus;) patients (\u003cem\u003eOR\u003c/em\u003e 0.02, 95% CI 0\u0026ndash;0.62, \u003cem\u003eP\u003c/em\u003e = 0.025). No significant associations were found for other characteristics.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;2. Multivariate regression analysis of the relationship between ctDNA mutations\u003c/p\u003e \u003cp\u003eand clinical parameters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted \u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenarche age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u0026ndash;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER status, ER(+) vs ER(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03-222.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR status, PR(+) vs PR(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0-4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 status, HER2(+) vs HER2(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause, YES vs NO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026ndash;77.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of tumor, ༞3cm vs\u0026thinsp;\u0026le;\u0026thinsp;3cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50-213.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParturitions NO. \u0026ge;3 vs ༜3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u0026ndash;2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapeutic effect, PR/SD vs PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u0026ndash;58.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDynamics of ctDNA mutations during chemotherapy\u003c/h2\u003e \u003cp\u003eA total of 768 ctDNA mutations were detected in 302 genes at baseline, which decreased to 633 in 291 genes after three courses of treatment. The mean number of mutations for each patient also decreased after three courses of treatment (50.93 vs. 46.59, respectively). To examine the dynamic changes, we compared the distribution of ctDNA mutations in genes before and after chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The mutations reduced sharply after chemotherapy for \u003cem\u003eMUC16\u003c/em\u003e, \u003cem\u003eNCOR1\u003c/em\u003e, \u003cem\u003eTTN\u003c/em\u003e, \u003cem\u003ePIK3R1\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e. In contrast, more mutations were detected after chemotherapy for \u003cem\u003eMYO6\u003c/em\u003e, \u003cem\u003eFLNC\u003c/em\u003e, \u003cem\u003eFMN2\u003c/em\u003e, \u003cem\u003eCDH1\u003c/em\u003e, and \u003cem\u003eRHO\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn analysis based on clinical information showed different mutation patterns in plasma DNA between baseline and after chemotherapy (Table S2). For baseline ctDNA, mutations in \u003cem\u003ePTEN\u003c/em\u003e increased significantly in patients with PD compared to patients with a partial response/stable disease (PR/SD) (\u003cem\u003eP\u003c/em\u003e = 0.028). More mutations were detected in gene \u003cem\u003ePIK3CA\u003c/em\u003e in patients with tumors\u0026gt; 3cm (\u003cem\u003eP\u003c/em\u003e = 0.0039), in \u003cem\u003eMAP3K1\u003c/em\u003e and \u003cem\u003ePGAP1\u003c/em\u003e in patients with late menarche (\u0026ge; 14 years, \u003cem\u003eP\u003c/em\u003e = 0.006 and 0.008, respectively), and in \u003cem\u003eKRAS\u003c/em\u003e in older patients (age \u0026gt; 51 years, \u003cem\u003eP\u003c/em\u003e = 0.018). These associations were consistent in all samples, but not in samples obtained after treatment. In addition, fewer ctDNA mutations were found in \u003cem\u003eAQP7\u003c/em\u003e in PR(+) patients (\u003cem\u003eP\u003c/em\u003e = 0.023), and in \u003cem\u003eKRAS\u003c/em\u003e and \u003cem\u003ePIK3CA\u003c/em\u003e in HER2(+) patients (\u003cem\u003eP\u003c/em\u003e = 0.041 and 0.030, respectively). In ctDNA after chemotherapy, the number of mutations decreased significantly in \u003cem\u003eTP53\u003c/em\u003e in both HER2 (+) and PR(\u0026minus;) patients (\u003cem\u003eP\u003c/em\u003e = 0.011 and 0.024, respectively). Mutations in \u003cem\u003eLUC7L2\u003c/em\u003e were also strongly associated with HER2(\u0026minus;) status (\u003cem\u003eP\u003c/em\u003e = 0.020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ectDNA mutations and clinical outcomes\u003c/h2\u003e \u003cp\u003eA Kaplan\u0026ndash;Meier analysis was performed to explore risk factors correlated with PFS, defined as the duration from sampling to first disease progression. The mean PFS was 537 days (range 324 to 823 days). All patients were stratified with the mean number of ctDNA mutations as the threshold. The results indicate that HER2 status, therapeutic effect, and number of ctDNA mutations exhibited significant associations with PFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). HER2(+) patients had significantly longer PFS compared with HER2(\u0026minus;) patients (adjusted HR 0.26, \u003cem\u003eP\u003c/em\u003e = 0.038), and PD was related to shorter PFS than PR/SD (adjusted HR 6.54, \u003cem\u003eP\u003c/em\u003e = 0.038). Patients carrying more ctDNA mutations overall and in baseline plasma both had poor PFS rates (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). No associations were found for other clinical characteristics and mutations in ctDNA after chemotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn clinical practice, the diagnosis and evaluation for breast cancer are based on tissue biopsy with immunohistochemical and cytogenetic tests, but it is invasive and difficult to perform multiple sampling[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Carcinoembryonic antigen (CEA) and cancer antigen 15\u0026thinsp;\u0026minus;\u0026thinsp;3 (CA15-3) are also used extensively as predictive markers. However, it has been demonstrated that only 7.2% and 12.3% of patients exhibited significantly elevated serum CEA and CA15-3 levels, respectively, among Chinese women[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, there is a need for more convenient and specific genetic markers of breast cancer. As ctDNA is double-stranded nucleic acid shed by tumor cells into the circulating blood, it should contain all of the genetic information present in tumor tissue, and could capture both spatial and temporal heterogeneity of tumors[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The application of ctDNA-based real-time liquid biopsy represents a noninvasive and highly sensitive biomarker for cancer diagnosis, early prediction, and therapeutic response assessment[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Although there have been many studies regarding ctDNA and breast cancer, some challenges and limitations remain for its clinical implementation, including the identification of specific driver mutations for the multiple demands of breast cancer management, the high costs of detecting the low proportion of ctDNA in the \u0026ldquo;sea\u0026rdquo; of normal DNA, and the development of standardized methods for data processing[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].The development of NGS and bioinformatics has provided new opportunities for the establishment and validation of specific panels of mutation biomarkers for routine clinical use in breast cancer management[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prevalence of mutations in ctDNA was strikingly similar to matched tumor DNA, and they were more often detected in patients with advanced or metastatic disease[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, most studies to date focused only on a limited repertoire of genes. They also varied in the quality of samples and sequencing data[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this study, we performed target-capture NGS, which is more sensitive and specific than traditional sequencing, to detect and evaluate the ctDNA in MBC patients. In our cohort, 1182 nonsynonymous mutations in 419 genes were identified. More attention should be paid to some frequently mutated genes, including \u003cem\u003eFRG1\u003c/em\u003e, \u003cem\u003eAQP7\u003c/em\u003e, \u003cem\u003eDNAJC11\u003c/em\u003e, and \u003cem\u003eMUC16\u003c/em\u003e, in future studies. Mutations were detected in genes closely associated with cancer development, progression, or therapeutic response.\u003c/p\u003e \u003cp\u003eMutations in ctDNA could reflect intratumoral heterogeneity and disease processes[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The mutation burden was therefore associated with clinical parameters, representing the phenotype of cancer development[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We hypothesized that patients with \u0026ldquo;risk\u0026rdquo; clinical factors for breast cancer should harbor more mutations[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As predicted, our statistical analysis showed that an elevated ctDNA mutation burden was positively correlated with large primary tumor size, HER2(\u0026minus;) status, and poor survival outcome. These were all detrimental to cancer management. In addition, this study suggests that ctDNA mutations could be used as potential biomarkers for the evaluation and prediction of MBC to guide clinical management. Mutations in baseline ctDNA were more sensitive and specific than those after chemotherapy.\u003c/p\u003e \u003cp\u003eThe remarkable advantages of this study are the use of a custom-designed panel with broad coverage, standardized sequencing with high depth, sampling at two time points, and parallel sequencing to eliminate the interference of CH variants. However, this study also has some limitations. First, the sample size was small due to the lack of willingness among MBC patients to supply adequate amounts of blood. Some patients did not agree to participate in genomic testing even though it was provided without cost. Another limitation was the lack of paired tumor tissue samples, which could have been used to verify the mutations in ctDNA. These limitations restricted further analysis. Despite these limitations, our study contributes to the identification of MBC-related mutations and strengthens the evidence for the clinical applicability of ctDNA detection. Subsequent studies with larger numbers of participants and more complete information are already in progress in our laboratory.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eMutations in ctDNA were successfully detected in MBC patients by targeted NGS. The mutation burden was significantly associated with clinical factors such as age at diagnosis, tumor size, hormone receptor status, therapeutic effect, and survival. The results suggest that ctDNA could be used to predict the progression and treatment outcomes of MBC.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects and blood collection\u003c/h2\u003e \u003cp\u003ePatients with newly pathologically diagnosed MBC receiving consecutive gemcitabine and capecitabine treatment at Nanfang Hospital between February 2017 and October 2019 were enrolled in the study. The exclusion criteria were a previous cancer diagnosis within the last 5 years and an inability to provide adequate blood samples for NGS or undergo medical follow-up. A total of 27 Han Chinese women were included in the study population. Follow-up was performed to estimate the association between ctDNA profile and prognosis. Clinical data were obtained from the medical records and follow-up results. Therapeutic effects and prognosis were evaluated according to Response Evaluation Criteria in Solid Tumors guidelines.\u003c/p\u003e \u003cp\u003ePeripheral blood samples were collected in 10-mL Streck tubes at two time points: before the initiation of chemotherapy and the end of the third course of treatment (63 \u0026plusmn; 6days). Samples were shipped at 4\u0026ndash;8\u0026deg;C to the laboratory and processed within 3 h. Plasma and lymphocytes were isolated by centrifugation (1600 \u0026times; \u003cem\u003eg\u003c/em\u003e, 4\u0026deg;C for 10min,and then 16000 \u0026times; \u003cem\u003eg\u003c/em\u003e, 4\u0026deg;C for 10min) immediately and stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and assessment\u003c/h2\u003e \u003cp\u003eThe cfDNA and gDNA were extracted from plasma and lymphocytes, respectively, using a QIAamp Circulating Nucleic Acid Kit (Qiagen, Valencia, CA, USA) and QIAamp DNA Blood Mini Kit (Qiagen) according to the manufacturer\u0026rsquo;s instructions. The DNA concentration was quantified using a Qubit 2.0 Fluorometer with Qubit dsDNA High Sensitivity (HS) Assay Kit (Fisher Scientific, Newark, DE, USA). An Agilent 2100 Bioanalyzer and DNA HS Kit (Agilent Technologies, Santa Clara, CA, USA) were used to assess the fragment length distribution. Only samples containing \u0026gt; 20ng of cfDNA and \u0026gt; 500ng of gDNA were processed for further analysis. All 81 DNA samples (54 cfDNA and 27 gDNA) were prepared and stored at \u0026minus;\u0026thinsp;20\u0026deg;C before library construction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGene panel design and target-capture sequencing\u003c/h2\u003e \u003cp\u003eIn this study, a personalized target panel was designed according to NCBI Build 37.1/GRCh37. Genomic exons that are frequently mutated in breast cancer or related to chemotherapy, immunotherapy, and targeted drugs were identified by searching the COSMIC database, The Cancer Genome Atlas, and published studies. An iterative algorithm was applied to maximize the number of mutations per patient while minimizing the region size. Target panel selection involved tradeoffs between sequencing cost, sensitivity, and coverage. Target-capture hybrid probes were custom designed through the NimbleGen Design portal (v1.2.R1, Roche, Basel, Switzerland).\u003c/p\u003e \u003cp\u003eDNA libraries were prepared with a KAPA HyperPrep Kit (Kapa Biosystems, Woburn, MA, USA), including end repair and A-tailing, adapter ligation, post-ligation cleanup, library amplification, and post-amplification cleanup. The DNA polymerase displayed strong 3\u0026prime;\u0026rarr;5\u0026prime; exonuclease activity and a low error rate. Agencourt AMPure XP beads (Beckman Coulter, Fullerton, CA, USA) were employed for \u0026ldquo;with-bead\u0026rdquo; enzymatic and cleanup steps. Aliquots of 20\u0026ndash;30 ng of plasma DNA were used directly for library construction. For gDNA, 500\u0026ndash;1000ng DNA was sheared with a Covaris S2 instrument (Boston, MA, USA) set for 200-bp fragments and then used for library construction.\u003c/p\u003e \u003cp\u003eGroups of four libraries were incorporated in a single capture pool. Hybridization and target capture were performed with custom probes from the SeqCap EZ Choice Library (Roche). The captured DNA products were amplified and purified with NimbleGen SeqCap Kits (Roche). After quality control of the DNA concentration and fragment distribution, we applied targeted NGS using 2 \u0026times; 75bp or 2 \u0026times; 150bp paired-end reads on the MiSeq Sequencing system (Illumina, San Diego, CA, USA) in accordance with the manufacturer\u0026rsquo;s recommendations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData processing and statistical analysis\u003c/h2\u003e \u003cp\u003eSoftware fastQC (version 0.11.9) was used for sequencing data quality control. Low-quality data, including N bases\u0026ge; 50%, proportion of bases with a Phred quality score\u0026le; 15 above 80%, or read length\u0026le; 30bp, were excluded. The terminal adaptor sequences were removed with cutadapt (version 3.0). The reads were then mapped to the hg19 reference human genome using Burrows-Wheeler Aligner (BWA, version 0.7.17). Genome Analysis Toolkit (GATK, version 4.1.6), Picard (version 2.22.3), and SAMtools (version 1.10) were used to call small insertions/deletions (indels) and mutations according to the COSMIC and dbSNP databases. Annovar (2020Apr01) was used for annotation with multiple databases.\u003c/p\u003e \u003cp\u003eVariations were filtered out if they had low depth\u0026lt; 2000\u0026times;in cfDNA or\u0026lt; 1000\u0026times;in gDNA, they were supported by less than five high-quality sequencing reads for cfDNA and two high-quality reads for gDNA, or if they were synonymous variants, including SNVs and indels. Variants detected in the plasma DNA that were wild type in the gDNA were considered ctDNA mutations and included for statistical analysis.\u003c/p\u003e \u003cp\u003eGO and KEGG enrichment analyses of the mutated genes were performed using topGO_2.40.0 (clusterProfiler_3.16.1). The associations between ctDNA mutations and clinical characteristics were analyzed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA). The endpoint of clinical observation for the survival analysis was the last follow-up (Oct.31, 2019) or death. A progression-free survival (PFS) analysis was estimated by the Kaplan\u0026ndash;Meier method using survival_3.2-7(survminer_0.4.9). Survival curves with a hazard ratio (HR) and 95% confidence interval (CI) were plotted with ggplot2_3.3.3 (circlize_0.4.12). The tests were two-sided, and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was taken to indicate statistical significance.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eMBC: Metastatic breast cancer; ctDNA: Circulating tumor DNA; cfDNA: Cell-free DNA; gDNA: Genomic DNA; NGS: Next-generation sequencing; PR: Partial remission; SD: Stable disease; PD: Progressive disease; GO: Gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; SNVs: single-nucleotide variants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis observational study was approved by the ethical committee of Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.\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\u003eThe data of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by The Key Project of Medicine Discipline of Guangzhou (No.2021-2023-12) and Basic Research Project of Key Laboratory of Guangzhou (21BRP004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSM, YX and LM designed the study. CL enrolled the patients and performed follow-up. SM and LH performed the NGS and collected all the data. LF and LW analysed and interpreted the data. SM drafted the manuscript. DH revised the manuscript. YX and LM oversaw the work. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the patients who participated in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, China. \u003csup\u003e2\u003c/sup\u003e GMU-GIBH Joint School of Life Science, Guangzhou Medical University, Guangzhou, 511436, China. \u003csup\u003e3\u003c/sup\u003e Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China. \u003csup\u003e4\u003c/sup\u003e Department of Primary Public Health, Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China. \u003csup\u003e5\u003c/sup\u003e Institute of Public Health, Guangzhou Medical University \u0026amp; Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China.\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-GB\"\u003eThe English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-GB\"\u003ehttp://www.textcheck.com/certificate/fZ5yXi\u003c/span\u003e\u003c/pre\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFahad Ullah M. Breast Cancer: Current Perspectives on the Disease Status. Adv Exp Med Biol 2019; 1152: 51-64.\u003c/li\u003e\n\u003cli\u003ePeart O. Metastatic Breast Cancer. Radiol Technol 2017; 88: 519M-539M.\u003c/li\u003e\n\u003cli\u003eWelch HG, Gorski DH, Albertsen PC. Trends in Metastatic Breast and Prostate Cancer--Lessons in Cancer Dynamics. N Engl J Med 2015; 373: 1685-1687.\u003c/li\u003e\n\u003cli\u003eLiang Y, Zhang H, Song X, Yang Q. Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets. Semin Cancer Biol 2020; 60: 14-27.\u003c/li\u003e\n\u003cli\u003eTang W, Guo X, Niu L et al. Identification of key molecular targets that correlate with breast cancer through bioinformatic methods. J Gene Med 2020; 22: e3141.\u003c/li\u003e\n\u003cli\u003eWinters S, Martin C, Murphy D, Shokar NK. Breast Cancer Epidemiology, Prevention, and Screening. Prog Mol Biol Transl Sci 2017; 151: 1-32.\u003c/li\u003e\n\u003cli\u003eWang R, Li X, Zhang H et al. Cell-free circulating tumor DNA analysis for breast cancer and its clinical utilization as a biomarker. Oncotarget 2017; 8: 75742-75755.\u003c/li\u003e\n\u003cli\u003eCheng F, Su L, Qian C. Circulating tumor DNA: a promising biomarker in the liquid biopsy of cancer. Oncotarget 2016; 7: 48832-48841.\u003c/li\u003e\n\u003cli\u003eCampos-Carrillo A, Weitzel JN, Sahoo P et al. Circulating tumor DNA as an early cancer detection tool. Pharmacol Ther 2020; 207: 107458.\u003c/li\u003e\n\u003cli\u003eOliveira KCS, Ramos IB, Silva JMC et al. Current Perspectives on Circulating Tumor DNA, Precision Medicine, and Personalized Clinical Management of Cancer. Mol Cancer Res 2020; 18: 517-528.\u003c/li\u003e\n\u003cli\u003eRohanizadegan M. Analysis of circulating tumor DNA in breast cancer as a diagnostic and prognostic biomarker. Cancer Genet 2018; 228-229: 159-168.\u003c/li\u003e\n\u003cli\u003eArnedos M, Vicier C, Loi S et al. Precision medicine for metastatic breast cancer--limitations and solutions. Nat Rev Clin Oncol 2015; 12: 693-704.\u003c/li\u003e\n\u003cli\u003eBuono G, Gerratana L, Bulfoni M et al. Circulating tumor DNA analysis in breast cancer: Is it ready for prime-time? Cancer Treat Rev 2019; 73: 73-83.\u003c/li\u003e\n\u003cli\u003eBudny A, Staroslawska E, Budny B et al. [Epidemiology and diagnosis of breast cancer]. Pol Merkur Lekarski 2019; 46: 195-204.\u003c/li\u003e\n\u003cli\u003eReiland-Smith J. Diagnosis and surgical treatment of breast cancer. S D Med 2010; Spec No: 31-37.\u003c/li\u003e\n\u003cli\u003eGarcia-Murillas I, Schiavon G, Weigelt B et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med 2015; 7: 302ra133.\u003c/li\u003e\n\u003cli\u003ePessoa LS, Heringer M, Ferrer VP. ctDNA as a cancer biomarker: A broad overview. Crit Rev Oncol Hematol 2020; 155: 103109.\u003c/li\u003e\n\u003cli\u003eAlimirzaie S, Bagherzadeh M, Akbari MR. Liquid biopsy in breast cancer: A comprehensive review. Clin Genet 2019; 95: 643-660.\u003c/li\u003e\n\u003cli\u003eZhang X, Ju S, Wang X, Cong H. Advances in liquid biopsy using circulating tumor cells and circulating cell-free tumor DNA for detection and monitoring of breast cancer. Clin Exp Med 2019; 19: 271-279.\u003c/li\u003e\n\u003cli\u003eButler TM, Spellman PT, Gray J. Circulating-tumor DNA as an early detection and diagnostic tool. Curr Opin Genet Dev 2017; 42: 14-21.\u003c/li\u003e\n\u003cli\u003eZhang X, Zhao W, Wei W et al. Parallel Analyses of Somatic Mutations in Plasma Circulating Tumor DNA (ctDNA) and Matched Tumor Tissues in Early-Stage Breast Cancer. Clin Cancer Res 2019; 25: 6546-6553.\u003c/li\u003e\n\u003cli\u003eRibeiro IP, de Melo JB, Carreira IM. Head and neck cancer: searching for genomic and epigenetic biomarkers in body fluids - the state of art. Mol Cytogenet 2019; 12: 33.\u003c/li\u003e\n\u003cli\u003eEigeliene N, Saarenheimo J, Jekunen A. Potential of Liquid Biopsies for Breast Cancer Screening, Diagnosis, and Response to Treatment. Oncology 2019; 96: 115-124.\u003c/li\u003e\n\u003cli\u003eNicolini A, Ferrari P, Duffy MJ. Prognostic and predictive biomarkers in breast cancer: Past, present and future. Semin Cancer Biol 2018; 52: 56-73.\u003c/li\u003e\n\u003cli\u003eClatot F. Review ctDNA and Breast Cancer. Recent Results Cancer Res 2020; 215: 231-252.\u003c/li\u003e\n\u003cli\u003eTzanikou E, Lianidou E. The potential of ctDNA analysis in breast cancer. Crit Rev Clin Lab Sci 2020; 57: 54-72.\u003c/li\u003e\n\u003cli\u003eZhou Y, Xu Y, Gong Y et al. Clinical factors associated with circulating tumor DNA (ctDNA) in primary breast cancer. Mol Oncol 2019; 13: 1033-1046.\u003c/li\u003e\n\u003cli\u003eGorgannezhad L, Umer M, Islam MN et al. Circulating tumor DNA and liquid biopsy: opportunities, challenges, and recent advances in detection technologies. Lab Chip 2018; 18: 1174-1196.\u003c/li\u003e\n\u003cli\u003eMa F, Guan Y, Yi Z et al. Assessing tumor heterogeneity using ctDNA to predict and monitor therapeutic response in metastatic breast cancer. Int J Cancer 2020; 146: 1359-1368.\u003c/li\u003e\n\u003cli\u003eRomero D. Breast cancer: Tracking ctDNA to evaluate relapse risk. Nat Rev Clin Oncol 2015; 12: 624.\u003c/li\u003e\n\u003cli\u003eZhang Y, Yao Y, Xu Y et al. Pan-cancer circulating tumor DNA detection in over 10,000 Chinese patients. Nat Commun 2021; 12: 11.\u003c/li\u003e\n\u003cli\u003eFernandez-Garcia D, Hills A, Page K et al. Plasma cell-free DNA (cfDNA) as a predictive and prognostic marker in patients with metastatic breast cancer. Breast Cancer Res 2019; 21: 149.\u003c/li\u003e\n\u003cli\u003eCullinane C, Fleming C, O'Leary DP et al. Association of Circulating Tumor DNA With Disease-Free Survival in Breast Cancer: A Systematic Review and Meta-analysis. JAMA Netw Open 2020; 3: e2026921.\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"circulating tumor DNA, mutation, breast cancer, targeted next-generation sequencing","lastPublishedDoi":"10.21203/rs.3.rs-494826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-494826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eLiquid biopsy through the detection of circulating tumor DNA (ctDNA) has potential advantages in cancer monitoring and prediction. However, most previous studies in this area were performed with a few hotspot genes, single time point detection, or insufficient sequencing depth.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eIn this study, we performed targeted next-generation sequencing (NGS) with a customized panel in metastatic breast cancer (MBC) patients. Fifty-four plasma samples were taken before chemotherapy and after the third course of treatment for detection and analysis. Paired lymphocytes were also included to eliminate clonal hematopoiesis (CH)-related alternatives.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 1182 nonsynonymous mutations on 419 genes were identified. More ctDNA mutations were detected in patients with tumors\u0026gt; 3cm (\u003cem\u003eP\u003c/em\u003e = 0.035) and HER2(−) patients (\u003cem\u003eP\u003c/em\u003e = 0.029). For a single gene, the distribution of ctDNA mutations was also correlated with clinical characteristics. Multivariate regression analysis revealed that HER2 status was significantly associated with mutation burden (\u003cem\u003eOR\u003c/em\u003e 0.02, 95% CI 0–0.62, \u003cem\u003eP\u003c/em\u003e = 0.025). The profiles of ctDNA mutations exhibited marked discrepancies between two time points, and baseline ctDNA was more sensitive and specific than that after chemotherapy. Finally, elevated ctDNA mutation level was positively correlated with poor survival (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eMutations in ctDNA could serve as a potential biomarker for the evaluation and prediction, and guide the clinical management of MBC patients with chemotherapy.\u003c/p\u003e","manuscriptTitle":"Targeted Next-Generation Sequencing of Circulating Tumor DNA Mutations among Metastatic Breast Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-19 19:23:52","doi":"10.21203/rs.3.rs-494826/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":"68dc6361-b33f-4544-9164-f0b3aece7502","owner":[],"postedDate":"May 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4434101,"name":"Molecular Biology"},{"id":4434102,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2024-07-14T16:04:27+00:00","versionOfRecord":{"articleIdentity":"rs-494826","link":"https://doi.org/10.3390/curroncol28040214","journal":{"identity":"current-oncology","isVorOnly":true,"title":"Current Oncology"},"publishedOn":"2021-06-24 16:04:27","publishedOnDateReadable":"June 24th, 2021"},"versionCreatedAt":"2021-05-19 19:23:52","video":"","vorDoi":"10.3390/curroncol28040214","vorDoiUrl":"https://doi.org/10.3390/curroncol28040214","workflowStages":[]},"version":"v1","identity":"rs-494826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-494826","identity":"rs-494826","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}