Development of a prediction model for ctDNA detection (Cir-Predict) in breast cancer

Development of a prediction model for ctDNA detection (Cir-Predict) in breast cancer | 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 Development of a prediction model for ctDNA detection (Cir-Predict) in breast cancer Chiaki Nakauchi, Nanae Masunaga, Naofumi Kagara, Chiya Oshiro, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4627880/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Mar, 2025 Read the published version in Breast Cancer Research and Treatment → Version 1 posted 7 You are reading this latest preprint version Abstract Purpose The detection of circulating tumor DNA (ctDNA) is a valuable method to predict the risk of recurrence and to detect real-time gene changes. The amount of ctDNA is affected by many factors. Moreover, the detection rate of ctDNA varies from report to report. Methods The present study evaluated differentially expressed genes using a DNA microarray assay for gene expression in tumors with and without detected ctDNA and constructed a prediction model for the detectability of ctDNA in breast tumor tissues. The model, named Cir-Predict, consisted of 73 probe sets (56 genes) and was constructed in a training set of breast cancer patients ( n = 35) and validated in a validation set ( n = 13). Results The accuracy, sensitivity and specificity in training and validation sets were over 95%, and Cir-Predict was significantly associated with ctDNA detection independently of the other conventional clinicopathological parameters in all cohorts. Pathway analysis revealed that nine pathways including tight junction and cell cycle tended to be related to ctDNA detectability. Conclusion Cir-Predict not only provides information useful for breast cancer treatment, but also helps the understanding of the mechanism by which ctDNA is detected. Breast cancer ctDNA Liquid biopsy DNA microarray Figures Figure 1 1. Introduction Breast cancer is a complex and heterogeneous disease. Tissue biopsy and immunohistochemistry are the gold standard techniques to guide the selection of breast cancer therapy; however, these techniques do not assess the molecular heterogeneity of tumors and are unable to capture mutations that change over time. In contrast, liquid biopsy allows the detection of specific tumor biomarkers in a real-time manner, and specimens can be obtained using a minimally invasive method 1) . Liquid biopsy involves the analysis of circulating tumor cells, circulating tumor DNA (ctDNA), circulating tumor RNA, messenger RNA, and microRNA, which are released from the primary tumor and/or metastatic deposits into blood, urine, salvia, and other biological samples 2) . Among these liquid biopsy techniques, ctDNA has been the subject of much attention. ctDNA is detected for a wide range of purposes, such as monitoring real-time genomic or epigenomic alterations, identifying genes that are targets of treatment, determining early treatment efficacy, monitoring minimal residual disease, and evaluating real-time treatment resistance 3) . Previous studies showed that ctDNA may be associated with shorter disease-free survival in both early-stage and advanced metastatic breast cancer 4)5) . Whether ctDNA can be detected in a sample is useful information. Cell-free DNA (cfDNA) consists of fragments of DNA circulating freely in the peripheral blood, most of which are released from blood cells, erythrocyte progenitors, and vascular endothelial cells 2) . In cancer patients, cfDNA contains a small amount of tumor-derived DNA fragments, which is called ctDNA. The frequency of ctDNA in cfDNA is generally very small (< 1.0%) 6) . Because of fragmentation and a short half-life (1–2 h) 7) , ctDNA is difficult to detect. However, recent advances in molecular-based technologies, including high sensitivity digital PCR (dPCR) or next-generation sequencing technology, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, have enabled the detection of ctDNA 3)8)9) . PIK3CA mutations in cancer occur predominantly in exons 9 and 20, including H1047R, E545K, and E542K, which are called ‘‘hot-spot’’ mutations. These three mutations account for 70–80% of PIK3CA mutations in cancer. We previously reported that PIK3CA mutations in serum DNA detected using dPCR for PIK3CA mutations (H1047R, E545K, and E542K) are predictive of recurrence in primary breast cancer. The sensitivity of the dPCR assay for the mutant alleles in cell lines with PIK3CA mutations was 0.01%. The detection rate of PIK3CA mutation in serum DNA was 22.7% (25/110) in primary breast cancer patients with PIK3CA mutant tumors 4) . Beaver et al. evaluated PIK3CA mutations in the plasma of breast cancer patients and the sensitivity was 93.3% and specificity was 100% for detecting early-stage breast cancer 10) . The frequency with which ctDNA is detected in early breast cancer varies among reports, but ctDNA is not detected in all early breast cancer cases. This is partly because the level of ctDNA in patients is very low and its half-life is short; moreover, the amount of ctDNA is affected by multiple factors, such as tumor location, size, metastasis, vascular infiltration, tumor status, and stage. Recently, cfDNA-based comprehensive genomic profiling (CGP) assays have been reported as a complement to tissue-based testing to ensure potentially life-extending therapies are administered to patients. The FoundationOne®Liquid CDx assay is a pan-cancer cfDNA-based CGP assay that was approved by the FDA 11) . The rates of concordance between cfDNA-based CGP assays and tumor tissue genomic assays are not 100%. This is partly because the blood and tumor samples are collected at different times and because the ctDNA is not included in the cfDNA used for the assay. Thus, when genomic alteration information cannot be obtained, a tool that predicts the detection of ctDNA can help determine whether cfDNA is tested and not ctDNA by CGP assays or if the genomic alteration is not present in ctDNA. DNA microarray assays for the evaluation of gene expression in breast cancer tissues have been used to develop multiple gene classifiers for the prediction of recurrence 12)13)14) and response to chemotherapy 15)16) . To date, there is no prediction model for ctDNA detection. In the current study, we evaluated differences in gene expression in breast cancer tumors with and without detectable ctDNA. This analysis will provide insights into whether the gene expression of the tumor itself has a role in whether ctDNA can be detected. 2. Materials and methods 2.1 Patients Forty-eight primary breast cancer patients (stage I–III) who had received no neoadjuvant systemic therapy and had undergone mastectomy or breast-conserving surgery followed by radiation therapy at Osaka University Hospital between June 2000 and November 2009 were retrospectively included in this study. Informed written consent was obtained from all patients before surgery. The study protocols were approved by the Ethics Committee of Osaka University Graduate School of Medicine (approval number: 11337, date of approval: May 7, 2012). Digital PCR was performed in serum samples from 313 preoperative breast cancer patients with PIK3CA mutant tumors. Cases in which the same PIK3CA mutation in the tumor was found in the serum were classified as ctDNA positive; cases in which the serum did not show the same PIK3CA mutation were classified as ctDNA negative. Cases with both ctDNA analysis data and tumor microarray expression data were used to establish the training and validation sets. The training set, which was used for ranking probes related to ctDNA detection, consisted of 25 patients with invasive breast cancer whose expression was analyzed using microarray at Osaka University Hospital in 2011 (OUH_1) and 10 patients whose expression was analyzed using microarray at Osaka University Hospital in 2012 (OUH_2). The validation set consisted of 13 patients whose expression was analyzed using microarray at Osaka University Hospital in 2015 (OUH_3). These three sets (OUH-1, -2, -3) were considered different cohorts because of the difference in the experimental reagents of the microarray process. The characteristics of patients in the training sets and the validation set are shown in Table 1 . Table 1 Clinicopathological characteristics of patients in the training sets (OUH-1 and OUH-2) and the validation set (OUH-3). Training set (OUH-1 and OUH-2) Validation set (OUH-3) All patients ctDNA P-value All patients ctDNA P-value Negative Positive Negative Positive (n = 35) (n = 25) (n = 10) (n = 13) (n = 12) (n = 1) Age (years) ≤50 23 9 3 0.7355 1 1 0 0.7638 >50 12 16 7 12 11 1 Tumor diameter ≤ 20mm 17 14 3 0.1644 8 7 1 0.4106 > 20mm 18 11 7 5 5 0 Histological grade 1 + 2 29 23 6 0.0658 13 12 1 N.A. 3 5 2 3 0 0 0 ER Positive 29 21 8 0.7767 13 12 1 N.A. Negative 6 4 2 0 0 0 PR Positive 24 17 7 0.9612 13 12 1 N.A. Negative 10 7 3 0 0 0 HER2 Positive 6 4 2 0.7767 0 0 0 N.A. Negative 29 21 8 13 12 1 Ki-67 ≥ 20% 16 10 6 0.3828 0 0 0 N.A. < 20% 17 13 4 0 0 0 Unknown 0 0 0 13 12 1 Lymphatic invasion 0 13 9 4 0.9778 0 0 0 N.A. 1 16 11 5 0 0 0 Unknown 0 0 0 13 12 1 Node metastasis Negative 11 8 3 0.9083 13 12 1 N.A. Positive 24 17 7 0 0 0 Cir-Predict Negative 24 24 0 < 0.0001* 13 12 0 0.0003* Positive 11 1 10 1 0 1 ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor; N.A., not available 2.2 RNA extraction and DNA microarray analysis Tumor tissues were obtained at the time of surgery, immediately snap frozen in liquid nitrogen, and kept at − 80°C until RNA extraction. The Qiagen RNeasy® mini kit (QIAGEN Science, Germantown, MD, USA) was used to extract RNA from tumor tissues. RNA (200 ng for OUH_1 and 1 µg for OUH_2) was subjected to DNA microarray assay (U133 Plus 2.0 Array; Affymetrix, Santa Clara, CA, USA) following the manufacturer’s instructions. Gene Profiling Reagents (Affymetrix) and One Cycle Target Labeling were used for OUH_1. One Cycle Target Labeling and Control Reagents (Affymetrix) were used for OUH_2 and OUH_3. 2.3 DNA extraction and real-time PCR DNA was extracted from frozen tumor tissues with the DNeasy Blood & Tissue Kit (QIAGEN, Germantown, MD, USA) following the manufacturer’s instructions. TaqMan-based real-time PCR analysis was conducted to detect the three ‘‘hot-spot’’ PIK3CA mutations (H1047R, E545K, and E542K) using a LightCycler 480 Real-Time PCR System (Roche Applied Science, Mannheim, Germany). 2.4 DNA extraction and digital PCR DNA was extracted from 500 µl of serum using the QIAamp Circulating Nucleic Acid Kit (QIAGEN, Hilden, Germany) following the manufacturer’s instructions. The DNA was eluted into 50 µl of AVE buffer and stored at − 20°C. dPCR was performed to detect the three PIK3CA mutations using a QuantStudio™ 3D digital PCR system (Life Technologies, Carlsbad, CA, USA). For the dPCR, 9 µl of template DNA was mixed with 1 µl of 20× TaqMan Assay primer/probe mix and 10 µl of 2× QuantStudio™ 3D Digital PCR Master Mix (Life Technologies) following the manufacturer’s instructions. Fifteen microliter aliquots of the PCR solutions were then loaded into QuantStudio™ 3D Digital PCR 20 K chips, and the PCR reaction was performed. The thermal cycler protocol was as follows: 10 min at 96°C, 39 cycles at 60°C for 2 min, 98°C for 30 s, and 60°C for 1 min. All samples were analyzed in a single assay for each mutation. The data were analyzed with the QuantStudio™ 3D AnalysisSuite™ v1.1.3 (Life Technologies) for mutation search and quantification of the DNA copies in the serum. The mutant allele fraction (MAF, %) was defined as the proportion of mutant DNA copies relative to the sum of mutant and wild-type DNA copies obtained by dPCR. The samples were defined as positive for mutations (ctDNA positive) when one or more mutant alleles were detected per assay and negative (ctDNA negative) when no mutant alleles were detected. 2.5 Immunohistochemical (IHC) assay ER and PR were expressions defined as positive when 10% or more of the tumor cells were stained by immunohistochemistry (ER: clone 6F11; PR clone 16; Ventana Japan K.K. and SRL Inc. Tokyo, Japan). Human epidermal growth factor receptor 2 (HER2) was examined by immunohistochemistry (anti-human c-erb-2 polyclonal antibody; Nichirei Biosciences, Tokyo, Japan) or by fluorescent in situ hybridization (FISH) using the PathVysion Her2 DNA probe kits (SRL Inc., Tokyo, Japan). For the FISH scoring, the ratio of the HER2 gene signals to the chromosome 17 signals was calculated for each of the specimens. A tumor that exhibited a + 3 immunohistostaining score or a FISH ratio ≥ 2.0 was considered HER2-positive. The histological grade was determined by the Scarff-Bloom-Richardson grading system. Ki-67 was defined as positive when 20% or more of the tumor cells were stained by immunohistochemistry (anti-Ki67 antibody clone 30 − 9; Roche Applied Science, Mannheim, Germany) as previously described 17) . 2.6 Microarray data processing The gene expression data sets were obtained by GeneChip™ Human Genome U133 Plus 2.0 Array DNA microarray (Affymetrix). Data were normalized with the Robust Multi-array Average (RMA) procedure 18) . 2.7 Statistical analysis Statistical software R version 3.4.2 ( http://www.R-project.org/ ) , Bioconductor packages ( http://www.bioconductor.org/ ) , and JMP software were used for statistical analyses. Pathway analysis was analyzed using National Institute of Health DAVID Bioinformatics ( https://david.ncifcrf.gov/home.jsp ). The associations between the various clinicopathological parameters and cfDNA detection were evaluated with Chi square or Fisher’s exact tests. Univariate and multivariate analyses of the various parameters for the predictions of recurrence and death were conducted with the Cox proportional hazards model. 3. Results 3.1 Construction of the Cir-Predict model We investigated differentially expressed genes between samples with and without ctDNA in the 35 training patient samples (OUH_1 and OUH_2). Of the 54675 probes, 370 probes were significantly different ( P < 0.010). The metafor Package (A Meta-Analysis Package for R) was used statistically integrate the two training data (OUH_1 and OUH_2) and to rank the differentially expressed genes. Supervised analysis using Diagonal Linear Discriminant Analysis was used for the construction of the prediction model using these probes. The sequential forward filtering method, which assesses the leave-one-out cross-validation, was used to optimize the prediction model. The Diagonal Linear Discriminant Analysis model comprising 73 probes (56 genes; supplementary Table S1 ) exhibited the highest accuracy in the training set (OUH_1 and OUH_2) and was therefore adopted as the prediction model of ctDNA and named Cir-Predict. Cir-Predict was then applied to the validation set (OUH_3) after normalization with the RMA procedure. Among the 73 probes, 34 probes were up-regulated and 39 genes were down-regulated in the ctDNA-positive group. Among the 56 genes, 3 genes, PTP4A2 (up-regulated), NBEA (up-regulated), and MED12 (down-regulated), appeared more than twice. The top three ranked genes are OR10A5 (down-regulated), EIF3A (up-regulated), and STX12 (up-regulated). The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of Cir-Predict in the training sets (OUH_1 and OUH_2) were 97.1%, 100%, 96%, 90.9%, and 100%, respectively (Table 2 a, 2 b). The diagnostic accuracy, sensitivity, specificity, PPV, and NPV in the validation set were 100%, 100%, 100%, 100%, and 100%, respectively (Table 2 a, 2 b). Table 2 a. The contingency table of Cir-Predict. Training set (n = 35) Validation set (n = 13) Cir-Predict Cir-Predict (-) (+) (-) (+) Actual ctDNA Detection (-) 24 0 12 0 (+) 1 10 0 1 Table 2 b. The diagnostic accuracy, sensitivity, specificity, PPV, and NPV of Cir-Predict. Training set (n = 35) Validation set (n = 13) Accuracy (%) 97.1 100 Sensitivity (%) 100 100 Specificity (%) 96 100 PPV (%) 90.9 100 NPV (%) 100 100 3.2 Univariate and multivariate analyses of Cir-Predict in the prediction of ctDNA detection We next examined the association of various clinicopathological parameters and Cir-Predict with ctDNA detection in the training sets and validation set (Table 3 ). Table 3 Univariate and multivariate analyses of parameters associated with ctDNA detectability in the training sets and the validation set. Training set Validation set Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis Odds ratio 95% CI P-value P-value Odds ratio 95% CI P-value P-value Age (years) 1.31 0.27–6.37 0.74 1.00 N.A. N.A. 0.76 1.00 > 50 vs. ≤50 Tumor diameter(mm) 2.97 0.62–14.22 0.16 1.00 N.A. N.A. 0.41 1.00 > 20 vs. ≤20 Histological grade 5.75 0.78–42.58 0.07 1.00 N.A. N.A. N.A. N.A. 3 vs. 1 + 2 ER 0.76 0.12–5.01 0.78 1.00 N.A. N.A. N.A. N.A. (+) vs. (-) PR 0.96 0.19–4.82 0.96 1.00 N.A. N.A. N.A. N.A. (+) vs. (-) HER2 1.31 0.20–8.62 0.78 1.00 N.A. N.A. N.A. N.A. (+) vs. (-) Ki-67 1.95 0.43–8.83 0.38 1.00 N.A. N.A. N.A. N.A. (+) vs. (-) Lymphatic invasion 1.02 0.21–4.98 0.98 1.00 N.A. N.A. N.A. N.A. Ly1 vs. Ly0 Node metastasis 1.10 0.22–5.40 0.91 1.00 N.A. N.A. N.A. N.A. (+) vs. (-) Cir-Predict N.A. N.A. < 0.0001* < 0.0001* N.A. N.A. 0.0003* 0.0141* (+) vs. (-) CI, confidence interval; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor; N.A., not available Univariate analysis showed that Cir-Predict was most significantly associated with ctDNA detection in the training set ( P < 0.001) and validation set ( P = 0.0003). Multivariate analysis showed that Cir-Predict was most significantly associated with ctDNA detection in the training set and validation set independently of the other parameters ( P < 0.001, P = 0.0141, respectively). 3.3 Visualization of Cir-Predict gene expression by gene clustering analysis (heat map) Clustered heat maps were built for visualization and interpretation of genome-scale molecular profiling data. Among the 73 probes in the Cir-Predict, 34 probes had increased expression, and 39 genes had decreased expression in the ctDNA-positive group. The gene expression tendency was the same in the validation set and the training sets (Fig. 1 ). 3.4 Pathway analysis Kyoto Encyclopedia of Genes and Genomes ( http://www.genome.jp//kegg/pathway.htm l ) 19) pathway enrichment analysis was performed of the 370 probes used as the source of genes for the construction of Cir-Predict. Nine pathways were significantly associated with ctDNA ( P < 0.05; Table 4 ). The tight junction pathway was the most strongly associated ( P = 3.85E-3) followed by the ubiquitin mediated proteolysis pathway ( P = 5.27E-03). Previous studies indicated that the cll cycle and junction function are related to ctDNA. In our analysis, the cell cycle pathway was the seventh most strongly associated with ctDNA ( P = 2.99E-2) and the adherens junction pathway was the ninth most strongly associated ( P = 4.97E-2). Among the 370 probes, 9 genes ( JUN, NEDD4L, RAP1A, RAC1, ARHGEF2, ACTR3, HSPA4, PARD3, STK11 ) were involved in the tight junction pathway. Table 4 KEGG pathway analysis of pathways significantly associated with ctDNA detectability. Pathway Count % P-value Pop Hits Fold Enr. Bonf. Benj. FDR 1 hsa04530:Tight junction 9 3.02 3.85E-03 170 3.52 0.57 0.4 0.4 2 hsa04120:Ubiquitin mediated proteolysis 8 2.68 5.27E-03 142 3.75 0.69 0.4 0.4 3 hsa03013:Nucleocytoplasmic transport 7 2.35 5.44E-03 108 4.31 0.7 0.4 0.4 4 hsa05210:Colorectal cancer 6 2.01 9.10E-03 86 4.64 0.87 0.47 0.47 5 hsa05161:Hepatitis B 8 2.68 1.06E-02 162 3.28 0.9 0.47 0.47 6 hsa04972:Pancreatic secretion 6 2.01 1.80E-02 102 3.91 0.98 0.66 0.66 7 hsa04110:Cell cycle 7 2.35 2.99E-02 157 2.96 1.00 0.94 0.94 8 hsa03040:Spliceosome 8 2.68 4.26E-02 216 2.46 1.00 1.00 1.00 9 hsa04520:Adherens junction 5 1.68 4.97E-02 93 3.58 1.00 1.00 1.00 Fold Enr., fold enrichment; Bonf., Bonerroni; Benj., Benjamini; FDR, false discovery rate 3.5 Overlapping genes with conventional prognosis prediction models Gene expression assays provide prognostic and therapy-predictive information that complements biomarker information. The 21-gene assay (Oncotype Dx) is preferred by the NCCN Breast Cancer Panel for the prognosis and prediction of chemotherapy benefit 20)21) . Previous studies reported that the presence of ctDNA is related to poor prognosis in primary breast cancer patients 4)5) . We examined whether the genes in Cir-Predict overlapped with the 21 genes in the assay, and we identified only one overlapping gene, Bcl2 (207005_s_at). 4. Discussion In this present study, we constructed the Cir-Predict model, which can classify breast cancers into the ctDNA-positive group and ctDNA-negative group with statistical significance, and demonstrated its success in the training sets and the validation set. The amount of ctDNA is affected by many factors. Our results showed that Cir-Predict, comprising differentially expressed genes, was significantly related to ctDNA detectability; this indicates that tumor gene expression plays a major role in ctDNA detectability. The Cir-Predict can predict the detection of ctDNA, which helps us to know the cause whether ctDNA not included in the cfDNA or not. The information helps in the understanding of the results of cfDNA-based CGP assays, especially when there is a discrepancy between the result of cfDNA-based CGP assays and the result of tumor tissue-based CGP assays. Pathway analysis of the probe set source for the construction of the Cir-Predict indicated that breakdown of junctions and cell cycle were associated with differentially expressed genes in ctDNA-positive samples. Tight junctions are intercellular adhesion complexes in epithelia and endothelia that control paracellular permeability, which support the maintenance of cell polarity by restricting intermixing of apical and basolateral transmembrane components 22) . Cell-cell adherens junctions are the most common type of intercellular adhesions and are important for maintaining tissue architecture and cell polarity 23) . In the adhesion between epithelial cells, three structures, tight junction, adherens junction, and desomosome, play the main role. These structures enable selective permeability of substances between cells and develop epithelial cell polarity between the cell membrane in contact with the extracellular space and the cell membrane in contact with the internal environment 24)25) . The breakdown of tight junction and adherens junction pathways may mean breakdown of epithelial cell structure, function, and polarity. Of the 56 genes of the Cir-Predict, 3 genes, PTP4A2 (up-regulated), NBEA (up-regulated), and MED12 (down-regulated), appeared more than twice. PTP4A2 (also known as PRL2) has been examined in a variety of human carcinomas, although its role in breast cancer remains inconclusive. In multiple cellular and in vivo models, overexpression of PRL-2 was advantageous under stringent growth conditions, such as growth in soft agar 26) . Moreover, both our in vivo models showed that PRL-2 expression contributes to breast cancer cell growth. The top three ranked genes in the Cir-Predict are OR10A5 (down-regulated), EIF3A (up-regulated), and STX12 (up-regulated). Recent reports indicated that EIF3A functions in various cancers. EIF3a is the largest subunit of EIF3, which is a key player in all steps of translation initiation. EIF3a is suggested to be correlated with cancer occurrence, metastasis, prognosis, and therapeutic response 27) . There was only one overlapping gene between the Cir-Predict and Oncotype Dx: the Bcl2 gene (up-regulated). Bcl2 is involved in the regulation of apoptosis. Antisense drugs (antisense oligonucleotides) that inactivate Bcl-2 mRNA to prevent the production Bcl2 protein have been developed for breast cancer treatments 28) . There are many genes whose functions have not yet been clarified among the genes in Cir-Predict. Our results indicate that Cir-Predict may be able to predict the detectability of ctDNA independently of other factors. However, each gene needs to be studied to understand the mechanism of ctDNA secretion into blood. Elucidating the functions of these genes may help clarify the mechanism by which ctDNA appears in the blood. Abbreviations circulating tumor DNA (ctDNA), digital PCR (dPCR), cfDNA-based comprehensive genomic profiling (CGP) Declarations Acknowledgments We gratefully acknowledge the work of past and present members of our laboratory. We thank Gabrielle White Wolf, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. Funding No funding was received for conducting this study. Author Contributions The corresponding author is responsible for ensuring that the descriptions are accurate and agreed by all authors. 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Cancer Res 70(21):8959–8967. 10.1158/0008-5472.CAN-10-2041 Yin JY, Zhang JT, Zhang W, Zhou HH, Liu ZQ (2018) eIF3a: A new anticancer drug target in the eIF family. Cancer Lett 412:81–87. 10.1016/j.canlet.2017.09.055 Dias N, Stein CA (2002) Potential roles of antisense oligonucleotides in cancer therapy. The example of Bcl-2 antisense oligonucleotides. Eur J Pharm Biopharm 54(3):263–269. 10.1016/s0939-6411(02)00060-7 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Published Journal Publication published 07 Mar, 2025 Read the published version in Breast Cancer Research and Treatment → Version 1 posted Editorial decision: Revision requested 27 Jan, 2025 Reviews received at journal 21 Jan, 2025 Reviewers agreed at journal 05 Aug, 2024 Reviewers invited by journal 11 Jul, 2024 Editor assigned by journal 26 Jun, 2024 Submission checks completed at journal 26 Jun, 2024 First submitted to journal 24 Jun, 2024 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. 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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-4627880","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327899799,"identity":"d688e4a9-f354-40fe-9b70-e3d1d21cdef3","order_by":0,"name":"Chiaki Nakauchi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACAwhlw8PAwNgM5B6A8CUIaklII13LYRDJDMQHCDvMXPrwwY8/f5yXkW9vbjb4UXAnj4G99wGD5Q7cWiz70pKleRJu8xicOdic2GPwrJiB57gBg+QZPA47w2MgzQDSIpHYfIDH4HBig0QaA4NkGz4t/J9//kg4xyM//2HzwT/EaeFhk+BJOMDDcIOxOZkoWyx72MysedKAis8kNhvLGBwuZuM5xnAAn1/MeZgf3/xhY2cv3378seSbP4fz+NnbGB9L4gkxDJDABiQOSzaQogVEMH4kRcsoGAWjYBQMdwAArbVOuoq62SEAAAAASUVORK5CYII=","orcid":"","institution":"ISEIKAI International General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chiaki","middleName":"","lastName":"Nakauchi","suffix":""},{"id":327899800,"identity":"83983279-ba4d-43b3-9391-efaee8e82dc7","order_by":1,"name":"Nanae Masunaga","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Nanae","middleName":"","lastName":"Masunaga","suffix":""},{"id":327899801,"identity":"fe5fb702-cb50-4ad5-802d-61ae07a4548f","order_by":2,"name":"Naofumi Kagara","email":"","orcid":"","institution":"Osaka City General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Naofumi","middleName":"","lastName":"Kagara","suffix":""},{"id":327899802,"identity":"1b5fdb05-2aff-4618-81b4-868e519c5d2e","order_by":3,"name":"Chiya Oshiro","email":"","orcid":"","institution":"Kaizuka City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chiya","middleName":"","lastName":"Oshiro","suffix":""},{"id":327899803,"identity":"faf3a64e-372a-4764-ac06-81b572cf6288","order_by":4,"name":"Masafumi Shimoda","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Masafumi","middleName":"","lastName":"Shimoda","suffix":""},{"id":327899804,"identity":"fcfc2241-674a-46dc-b705-291655fffa1f","order_by":5,"name":"Kenzo Shimazu","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Kenzo","middleName":"","lastName":"Shimazu","suffix":""}],"badges":[],"createdAt":"2024-06-24 06:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4627880/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4627880/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10549-025-07647-0","type":"published","date":"2025-03-07T15:58:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60710972,"identity":"541f57c7-7e4f-4bfd-8fa1-97d9e1035004","added_by":"auto","created_at":"2024-07-19 20:16:17","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":531019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes in breast cancer samples with and without detected ctDNA. \u003c/strong\u003eThe heat map for the training set (OUH-1, \u003cem\u003en\u003c/em\u003e=25), the heat mapfor the training set (OUH-2, \u003cem\u003en\u003c/em\u003e=10), and\u003cstrong\u003e \u003c/strong\u003ethe heat map for the validation set (OUH-3, \u003cem\u003en\u003c/em\u003e=13)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4627880/v1/ab111fd107e61c5a89e73af3.jpeg"},{"id":78191449,"identity":"a4fb0ed2-c244-4a46-a5b1-800843edc347","added_by":"auto","created_at":"2025-03-10 20:01:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1689837,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4627880/v1/49b06170-ca97-4796-aada-abf7aa5f3178.pdf"},{"id":60710970,"identity":"48c5e958-775e-4746-89a3-cd4f5ee61716","added_by":"auto","created_at":"2024-07-19 20:16:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29080,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4627880/v1/ea4c44533af8898e761c2aeb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a prediction model for ctDNA detection (Cir-Predict) in breast cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer is a complex and heterogeneous disease. Tissue biopsy and immunohistochemistry are the gold standard techniques to guide the selection of breast cancer therapy; however, these techniques do not assess the molecular heterogeneity of tumors and are unable to capture mutations that change over time. In contrast, liquid biopsy allows the detection of specific tumor biomarkers in a real-time manner, and specimens can be obtained using a minimally invasive method\u003csup\u003e1)\u003c/sup\u003e. Liquid biopsy involves the analysis of circulating tumor cells, circulating tumor DNA (ctDNA), circulating tumor RNA, messenger RNA, and microRNA, which are released from the primary tumor and/or metastatic deposits into blood, urine, salvia, and other biological samples\u003csup\u003e2)\u003c/sup\u003e. Among these liquid biopsy techniques, ctDNA has been the subject of much attention. ctDNA is detected for a wide range of purposes, such as monitoring real-time genomic or epigenomic alterations, identifying genes that are targets of treatment, determining early treatment efficacy, monitoring minimal residual disease, and evaluating real-time treatment resistance\u003csup\u003e3)\u003c/sup\u003e. Previous studies showed that ctDNA may be associated with shorter disease-free survival in both early-stage and advanced metastatic breast cancer\u003csup\u003e4)5)\u003c/sup\u003e. Whether ctDNA can be detected in a sample is useful information.\u003c/p\u003e \u003cp\u003eCell-free DNA (cfDNA) consists of fragments of DNA circulating freely in the peripheral blood, most of which are released from blood cells, erythrocyte progenitors, and vascular endothelial cells\u003csup\u003e2)\u003c/sup\u003e. In cancer patients, cfDNA contains a small amount of tumor-derived DNA fragments, which is called ctDNA. The frequency of ctDNA in cfDNA is generally very small (\u0026lt;\u0026thinsp;1.0%)\u003csup\u003e6)\u003c/sup\u003e. Because of fragmentation and a short half-life (1\u0026ndash;2 h)\u003csup\u003e7)\u003c/sup\u003e, ctDNA is difficult to detect. However, recent advances in molecular-based technologies, including high sensitivity digital PCR (dPCR) or next-generation sequencing technology, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, have enabled the detection of ctDNA\u003csup\u003e3)8)9)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePIK3CA mutations in cancer occur predominantly in exons 9 and 20, including H1047R, E545K, and E542K, which are called \u0026lsquo;\u0026lsquo;hot-spot\u0026rsquo;\u0026rsquo; mutations. These three mutations account for 70\u0026ndash;80% of PIK3CA mutations in cancer. We previously reported that PIK3CA mutations in serum DNA detected using dPCR for PIK3CA mutations (H1047R, E545K, and E542K) are predictive of recurrence in primary breast cancer. The sensitivity of the dPCR assay for the mutant alleles in cell lines with PIK3CA mutations was 0.01%. The detection rate of PIK3CA mutation in serum DNA was 22.7% (25/110) in primary breast cancer patients with PIK3CA mutant tumors\u003csup\u003e4)\u003c/sup\u003e. Beaver et al. evaluated PIK3CA mutations in the plasma of breast cancer patients and the sensitivity was 93.3% and specificity was 100% for detecting early-stage breast cancer\u003csup\u003e10)\u003c/sup\u003e. The frequency with which ctDNA is detected in early breast cancer varies among reports, but ctDNA is not detected in all early breast cancer cases. This is partly because the level of ctDNA in patients is very low and its half-life is short; moreover, the amount of ctDNA is affected by multiple factors, such as tumor location, size, metastasis, vascular infiltration, tumor status, and stage.\u003c/p\u003e \u003cp\u003eRecently, cfDNA-based comprehensive genomic profiling (CGP) assays have been reported as a complement to tissue-based testing to ensure potentially life-extending therapies are administered to patients. The FoundationOne\u0026reg;Liquid CDx assay is a pan-cancer cfDNA-based CGP assay that was approved by the FDA\u003csup\u003e11)\u003c/sup\u003e. The rates of concordance between cfDNA-based CGP assays and tumor tissue genomic assays are not 100%. This is partly because the blood and tumor samples are collected at different times and because the ctDNA is not included in the cfDNA used for the assay. Thus, when genomic alteration information cannot be obtained, a tool that predicts the detection of ctDNA can help determine whether cfDNA is tested and not ctDNA by CGP assays or if the genomic alteration is not present in ctDNA. DNA microarray assays for the evaluation of gene expression in breast cancer tissues have been used to develop multiple gene classifiers for the prediction of recurrence\u003csup\u003e12)13)14)\u003c/sup\u003e and response to chemotherapy\u003csup\u003e15)16)\u003c/sup\u003e. To date, there is no prediction model for ctDNA detection.\u003c/p\u003e \u003cp\u003eIn the current study, we evaluated differences in gene expression in breast cancer tumors with and without detectable ctDNA. This analysis will provide insights into whether the gene expression of the tumor itself has a role in whether ctDNA can be detected.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003e Forty-eight primary breast cancer patients (stage I\u0026ndash;III) who had received no neoadjuvant systemic therapy and had undergone mastectomy or breast-conserving surgery followed by radiation therapy at Osaka University Hospital between June 2000 and November 2009 were retrospectively included in this study. Informed written consent was obtained from all patients before surgery. The study protocols were approved by the Ethics Committee of Osaka University Graduate School of Medicine (approval number: 11337, date of approval: May 7, 2012).\u003c/p\u003e \u003cp\u003eDigital PCR was performed in serum samples from 313 preoperative breast cancer patients with PIK3CA mutant tumors. Cases in which the same PIK3CA mutation in the tumor was found in the serum were classified as ctDNA positive; cases in which the serum did not show the same PIK3CA mutation were classified as ctDNA negative. Cases with both ctDNA analysis data and tumor microarray expression data were used to establish the training and validation sets.\u003c/p\u003e \u003cp\u003eThe training set, which was used for ranking probes related to ctDNA detection, consisted of 25 patients with invasive breast cancer whose expression was analyzed using microarray at Osaka University Hospital in 2011 (OUH_1) and 10 patients whose expression was analyzed using microarray at Osaka University Hospital in 2012 (OUH_2). The validation set consisted of 13 patients whose expression was analyzed using microarray at Osaka University Hospital in 2015 (OUH_3). These three sets (OUH-1, -2, -3) were considered different cohorts because of the difference in the experimental reagents of the microarray process.\u003c/p\u003e \u003cp\u003eThe characteristics of patients in the training sets and the validation set are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics of patients in the training sets (OUH-1 and OUH-2) and the validation set (OUH-3).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTraining set (OUH-1 and OUH-2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eValidation set (OUH-3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ectDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ectDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP-value\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=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePositive\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(n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.7355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.7638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor diameter\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.4106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.7767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\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\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.9612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\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\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.7767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.3828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphatic invasion\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.9778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode metastasis\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\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\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.9083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCir-Predict\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\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 \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor; N.A., not available\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 RNA extraction and DNA microarray analysis\u003c/h2\u003e \u003cp\u003eTumor tissues were obtained at the time of surgery, immediately snap frozen in liquid nitrogen, and kept at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction. The Qiagen RNeasy\u0026reg; mini kit (QIAGEN Science, Germantown, MD, USA) was used to extract RNA from tumor tissues. RNA (200 ng for OUH_1 and 1 \u0026micro;g for OUH_2) was subjected to DNA microarray assay (U133 Plus 2.0 Array; Affymetrix, Santa Clara, CA, USA) following the manufacturer\u0026rsquo;s instructions. Gene Profiling Reagents (Affymetrix) and One Cycle Target Labeling were used for OUH_1. One Cycle Target Labeling and Control Reagents (Affymetrix) were used for OUH_2 and OUH_3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 DNA extraction and real-time PCR\u003c/h2\u003e \u003cp\u003eDNA was extracted from frozen tumor tissues with the DNeasy Blood \u0026amp; Tissue Kit (QIAGEN, Germantown, MD, USA) following the manufacturer\u0026rsquo;s instructions. TaqMan-based real-time PCR analysis was conducted to detect the three \u0026lsquo;\u0026lsquo;hot-spot\u0026rsquo;\u0026rsquo; PIK3CA mutations (H1047R, E545K, and E542K) using a LightCycler 480 Real-Time PCR System (Roche Applied Science, Mannheim, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 DNA extraction and digital PCR\u003c/h2\u003e \u003cp\u003eDNA was extracted from 500 \u0026micro;l of serum using the QIAamp Circulating Nucleic Acid Kit (QIAGEN, Hilden, Germany) following the manufacturer\u0026rsquo;s instructions. The DNA was eluted into 50 \u0026micro;l of AVE buffer and stored at \u0026minus;\u0026thinsp;20\u0026deg;C. dPCR was performed to detect the three PIK3CA mutations using a QuantStudio\u0026trade; 3D digital PCR system (Life Technologies, Carlsbad, CA, USA). For the dPCR, 9 \u0026micro;l of template DNA was mixed with 1 \u0026micro;l of 20\u0026times; TaqMan Assay primer/probe mix and 10 \u0026micro;l of 2\u0026times; QuantStudio\u0026trade; 3D Digital PCR Master Mix (Life Technologies) following the manufacturer\u0026rsquo;s instructions. Fifteen microliter aliquots of the PCR solutions were then loaded into QuantStudio\u0026trade; 3D Digital PCR 20 K chips, and the PCR reaction was performed. The thermal cycler protocol was as follows: 10 min at 96\u0026deg;C, 39 cycles at 60\u0026deg;C for 2 min, 98\u0026deg;C for 30 s, and 60\u0026deg;C for 1 min. All samples were analyzed in a single assay for each mutation. The data were analyzed with the QuantStudio\u0026trade; 3D AnalysisSuite\u0026trade; v1.1.3 (Life Technologies) for mutation search and quantification of the DNA copies in the serum. The mutant allele fraction (MAF, %) was defined as the proportion of mutant DNA copies relative to the sum of mutant and wild-type DNA copies obtained by dPCR. The samples were defined as positive for mutations (ctDNA positive) when one or more mutant alleles were detected per assay and negative (ctDNA negative) when no mutant alleles were detected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Immunohistochemical (IHC) assay\u003c/h2\u003e \u003cp\u003eER and PR were expressions defined as positive when 10% or more of the tumor cells were stained by immunohistochemistry (ER: clone 6F11; PR clone 16; Ventana Japan K.K. and SRL Inc. Tokyo, Japan). Human epidermal growth factor receptor 2 (HER2) was examined by immunohistochemistry (anti-human c-erb-2 polyclonal antibody; Nichirei Biosciences, Tokyo, Japan) or by fluorescent in situ hybridization (FISH) using the PathVysion Her2 DNA probe kits (SRL Inc., Tokyo, Japan). For the FISH scoring, the ratio of the HER2 gene signals to the chromosome 17 signals was calculated for each of the specimens. A tumor that exhibited a\u0026thinsp;+\u0026thinsp;3 immunohistostaining score or a FISH ratio\u0026thinsp;\u0026ge;\u0026thinsp;2.0 was considered HER2-positive. The histological grade was determined by the Scarff-Bloom-Richardson grading system. Ki-67 was defined as positive when 20% or more of the tumor cells were stained by immunohistochemistry (anti-Ki67 antibody clone 30\u0026thinsp;\u0026minus;\u0026thinsp;9; Roche Applied Science, Mannheim, Germany) as previously described\u003csup\u003e17)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Microarray data processing\u003c/h2\u003e \u003cp\u003eThe gene expression data sets were obtained by GeneChip\u0026trade; Human Genome U133 Plus 2.0 Array DNA microarray (Affymetrix). Data were normalized with the Robust Multi-array Average (RMA) procedure\u003csup\u003e18)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical software R version 3.4.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org/\u003c/span\u003e\u003cspan address=\"http://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, Bioconductor packages (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/\u003c/span\u003e\u003cspan address=\"http://www.bioconductor.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and JMP software were used for statistical analyses. Pathway analysis was analyzed using National Institute of Health DAVID Bioinformatics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/home.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/home.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The associations between the various clinicopathological parameters and cfDNA detection were evaluated with Chi square or Fisher\u0026rsquo;s exact tests. Univariate and multivariate analyses of the various parameters for the predictions of recurrence and death were conducted with the Cox proportional hazards model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Construction of the Cir-Predict model\u003c/h2\u003e \u003cp\u003eWe investigated differentially expressed genes between samples with and without ctDNA in the 35 training patient samples (OUH_1 and OUH_2). Of the 54675 probes, 370 probes were significantly different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010). The metafor Package (A Meta-Analysis Package for R) was used statistically integrate the two training data (OUH_1 and OUH_2) and to rank the differentially expressed genes. Supervised analysis using Diagonal Linear Discriminant Analysis was used for the construction of the prediction model using these probes. The sequential forward filtering method, which assesses the leave-one-out cross-validation, was used to optimize the prediction model. The Diagonal Linear Discriminant Analysis model comprising 73 probes (56 genes; supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) exhibited the highest accuracy in the training set (OUH_1 and OUH_2) and was therefore adopted as the prediction model of ctDNA and named Cir-Predict. Cir-Predict was then applied to the validation set (OUH_3) after normalization with the RMA procedure.\u003c/p\u003e \u003cp\u003eAmong the 73 probes, 34 probes were up-regulated and 39 genes were down-regulated in the ctDNA-positive group. Among the 56 genes, 3 genes, \u003cem\u003ePTP4A2\u003c/em\u003e (up-regulated), \u003cem\u003eNBEA\u003c/em\u003e (up-regulated), and \u003cem\u003eMED12\u003c/em\u003e (down-regulated), appeared more than twice. The top three ranked genes are \u003cem\u003eOR10A5\u003c/em\u003e (down-regulated), \u003cem\u003eEIF3A\u003c/em\u003e (up-regulated), and \u003cem\u003eSTX12\u003c/em\u003e (up-regulated).\u003c/p\u003e \u003cp\u003eThe diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of Cir-Predict in the training sets (OUH_1 and OUH_2) were 97.1%, 100%, 96%, 90.9%, and 100%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The diagnostic accuracy, sensitivity, specificity, PPV, and NPV in the validation set were 100%, 100%, 100%, 100%, and 100%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ea.\u003c/b\u003e The contingency table of Cir-Predict.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTraining set (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eValidation set (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCir-Predict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCir-Predict\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\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eActual ctDNA Detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eb.\u003c/b\u003e The diagnostic accuracy, sensitivity, specificity, PPV, and NPV of Cir-Predict.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining set (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate and multivariate analyses of Cir-Predict in the prediction of ctDNA detection\u003c/h2\u003e \u003cp\u003eWe next examined the association of various clinicopathological parameters and Cir-Predict with ctDNA detection in the training sets and validation set (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses of parameters associated with ctDNA detectability in the training sets and the validation set.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eValidation set\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMultivariate analysis\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\u003eOdds ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026ndash;6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50 vs. \u0026le;50\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor diameter(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026ndash;14.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20 vs. \u0026le;20\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;42.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 vs. 1\u0026thinsp;+\u0026thinsp;2\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u0026ndash;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+) vs. (-)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u0026ndash;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+) vs. (-)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u0026ndash;8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+) vs. (-)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026ndash;8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+) vs. (-)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphatic invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026ndash;4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLy1 vs. Ly0\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u0026ndash;5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+) vs. (-)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCir-Predict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN.A.\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\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0141*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+) vs. (-)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI, confidence interval; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor; N.A., not available\u003c/p\u003e \u003cp\u003eUnivariate analysis showed that Cir-Predict was most significantly associated with ctDNA detection in the training set (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and validation set (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003). Multivariate analysis showed that Cir-Predict was most significantly associated with ctDNA detection in the training set and validation set independently of the other parameters (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0141, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Visualization of Cir-Predict gene expression by gene clustering analysis (heat map)\u003c/h2\u003e \u003cp\u003eClustered heat maps were built for visualization and interpretation of genome-scale molecular profiling data. Among the 73 probes in the Cir-Predict, 34 probes had increased expression, and 39 genes had decreased expression in the ctDNA-positive group. The gene expression tendency was the same in the validation set and the training sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Pathway analysis\u003c/h2\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp//kegg/pathway.htm\u003c/span\u003e\u003cspan address=\"http://www.genome.jp//kegg/pathway.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003el\u003c/span\u003e)\u003csup\u003e19)\u003c/sup\u003e pathway enrichment analysis was performed of the 370 probes used as the source of genes for the construction of Cir-Predict. Nine pathways were significantly associated with ctDNA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The tight junction pathway was the most strongly associated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.85E-3) followed by the ubiquitin mediated proteolysis pathway (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.27E-03). Previous studies indicated that the cll cycle and junction function are related to ctDNA. In our analysis, the cell cycle pathway was the seventh most strongly associated with ctDNA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.99E-2) and the adherens junction pathway was the ninth most strongly associated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.97E-2). Among the 370 probes, 9 genes (\u003cem\u003eJUN, NEDD4L, RAP1A, RAC1, ARHGEF2, ACTR3, HSPA4, PARD3, STK11\u003c/em\u003e) were involved in the tight junction pathway.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG pathway analysis of pathways significantly associated with ctDNA detectability.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePop Hits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFold Enr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBonf.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBenj.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04530:Tight junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.85E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04120:Ubiquitin mediated proteolysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.27E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa03013:Nucleocytoplasmic transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.44E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05210:Colorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05161:Hepatitis B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04972:Pancreatic secretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04110:Cell cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.99E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa03040:Spliceosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.26E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04520:Adherens junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.97E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFold Enr., fold enrichment; Bonf., Bonerroni; Benj., Benjamini; FDR, false discovery rate\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Overlapping genes with conventional prognosis prediction models\u003c/h2\u003e \u003cp\u003eGene expression assays provide prognostic and therapy-predictive information that complements biomarker information. The 21-gene assay (Oncotype Dx) is preferred by the NCCN Breast Cancer Panel for the prognosis and prediction of chemotherapy benefit\u003csup\u003e20)21)\u003c/sup\u003e. Previous studies reported that the presence of ctDNA is related to poor prognosis in primary breast cancer patients\u003csup\u003e4)5)\u003c/sup\u003e. We examined whether the genes in Cir-Predict overlapped with the 21 genes in the assay, and we identified only one overlapping gene, Bcl2 (207005_s_at).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this present study, we constructed the Cir-Predict model, which can classify breast cancers into the ctDNA-positive group and ctDNA-negative group with statistical significance, and demonstrated its success in the training sets and the validation set. The amount of ctDNA is affected by many factors. Our results showed that Cir-Predict, comprising differentially expressed genes, was significantly related to ctDNA detectability; this indicates that tumor gene expression plays a major role in ctDNA detectability.\u003c/p\u003e \u003cp\u003eThe Cir-Predict can predict the detection of ctDNA, which helps us to know the cause whether ctDNA not included in the cfDNA or not. The information helps in the understanding of the results of cfDNA-based CGP assays, especially when there is a discrepancy between the result of cfDNA-based CGP assays and the result of tumor tissue-based CGP assays.\u003c/p\u003e \u003cp\u003ePathway analysis of the probe set source for the construction of the Cir-Predict indicated that breakdown of junctions and cell cycle were associated with differentially expressed genes in ctDNA-positive samples. Tight junctions are intercellular adhesion complexes in epithelia and endothelia that control paracellular permeability, which support the maintenance of cell polarity by restricting intermixing of apical and basolateral transmembrane components\u003csup\u003e22)\u003c/sup\u003e. Cell-cell adherens junctions are the most common type of intercellular adhesions and are important for maintaining tissue architecture and cell polarity\u003csup\u003e23)\u003c/sup\u003e. In the adhesion between epithelial cells, three structures, tight junction, adherens junction, and desomosome, play the main role. These structures enable selective permeability of substances between cells and develop epithelial cell polarity between the cell membrane in contact with the extracellular space and the cell membrane in contact with the internal environment\u003csup\u003e24)25)\u003c/sup\u003e. The breakdown of tight junction and adherens junction pathways may mean breakdown of epithelial cell structure, function, and polarity.\u003c/p\u003e \u003cp\u003eOf the 56 genes of the Cir-Predict, 3 genes, \u003cem\u003ePTP4A2\u003c/em\u003e (up-regulated), \u003cem\u003eNBEA\u003c/em\u003e (up-regulated), and \u003cem\u003eMED12\u003c/em\u003e (down-regulated), appeared more than twice. PTP4A2 (also known as PRL2) has been examined in a variety of human carcinomas, although its role in breast cancer remains inconclusive. In multiple cellular and in vivo models, overexpression of PRL-2 was advantageous under stringent growth conditions, such as growth in soft agar\u003csup\u003e26)\u003c/sup\u003e. Moreover, both our in vivo models showed that PRL-2 expression contributes to breast cancer cell growth.\u003c/p\u003e \u003cp\u003eThe top three ranked genes in the Cir-Predict are \u003cem\u003eOR10A5\u003c/em\u003e (down-regulated), \u003cem\u003eEIF3A\u003c/em\u003e (up-regulated), and \u003cem\u003eSTX12\u003c/em\u003e (up-regulated). Recent reports indicated that EIF3A functions in various cancers. EIF3a is the largest subunit of EIF3, which is a key player in all steps of translation initiation. EIF3a is suggested to be correlated with cancer occurrence, metastasis, prognosis, and therapeutic response\u003csup\u003e27)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere was only one overlapping gene between the Cir-Predict and Oncotype Dx: the Bcl2 gene (up-regulated). Bcl2 is involved in the regulation of apoptosis. Antisense drugs (antisense oligonucleotides) that inactivate Bcl-2 mRNA to prevent the production Bcl2 protein have been developed for breast cancer treatments\u003csup\u003e28)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are many genes whose functions have not yet been clarified among the genes in Cir-Predict. Our results indicate that Cir-Predict may be able to predict the detectability of ctDNA independently of other factors. However, each gene needs to be studied to understand the mechanism of ctDNA secretion into blood. Elucidating the functions of these genes may help clarify the mechanism by which ctDNA appears in the blood.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ecirculating tumor DNA (ctDNA), digital PCR (dPCR), cfDNA-based comprehensive genomic profiling (CGP)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the work of past and present members of our laboratory. We thank Gabrielle White Wolf, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author is responsible for ensuring that the descriptions are accurate and agreed by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C et al (2017) Liquid biopsies come of age: towards implementation of circulating tumour DNA. 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Eur J Pharm Biopharm 54(3):263\u0026ndash;269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0939-6411(02)00060-7\u003c/span\u003e\u003cspan address=\"10.1016/s0939-6411(02)00060-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":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":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Breast cancer, ctDNA, Liquid biopsy, DNA microarray","lastPublishedDoi":"10.21203/rs.3.rs-4627880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4627880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe detection of circulating tumor DNA (ctDNA) is a valuable method to predict the risk of recurrence and to detect real-time gene changes. The amount of ctDNA is affected by many factors. Moreover, the detection rate of ctDNA varies from report to report.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe present study evaluated differentially expressed genes using a DNA microarray assay for gene expression in tumors with and without detected ctDNA and constructed a prediction model for the detectability of ctDNA in breast tumor tissues. The model, named Cir-Predict, consisted of 73 probe sets (56 genes) and was constructed in a training set of breast cancer patients (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35) and validated in a validation set (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe accuracy, sensitivity and specificity in training and validation sets were over 95%, and Cir-Predict was significantly associated with ctDNA detection independently of the other conventional clinicopathological parameters in all cohorts. Pathway analysis revealed that nine pathways including tight junction and cell cycle tended to be related to ctDNA detectability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCir-Predict not only provides information useful for breast cancer treatment, but also helps the understanding of the mechanism by which ctDNA is detected.\u003c/p\u003e","manuscriptTitle":"Development of a prediction model for ctDNA detection (Cir-Predict) in breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 20:16:11","doi":"10.21203/rs.3.rs-4627880/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-27T15:42:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-21T22:49:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226559454324032654447851197379829363944","date":"2024-08-05T21:05:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-11T14:20:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-26T10:48:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-26T10:47:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research and Treatment","date":"2024-06-24T06:18:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f1f0578f-b43c-497c-b491-b6a3eec98263","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T20:01:06+00:00","versionOfRecord":{"articleIdentity":"rs-4627880","link":"https://doi.org/10.1007/s10549-025-07647-0","journal":{"identity":"breast-cancer-research-and-treatment","isVorOnly":false,"title":"Breast Cancer Research and Treatment"},"publishedOn":"2025-03-07 15:58:19","publishedOnDateReadable":"March 7th, 2025"},"versionCreatedAt":"2024-07-19 20:16:11","video":"","vorDoi":"10.1007/s10549-025-07647-0","vorDoiUrl":"https://doi.org/10.1007/s10549-025-07647-0","workflowStages":[]},"version":"v1","identity":"rs-4627880","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4627880","identity":"rs-4627880","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}