Prevalence and molecular features of TMB-high and MSI-high breast cancer assessed by gene panel testing

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Nonetheless, their prevalence is low in breast cancer, and evidence remains scarce compared with that in other cancer types. Therefore, the TMB-H and MSI-H prevalence in breast cancer was assessed, and the mechanisms underlying these conditions were examined here using gene panel testing (GPT). Methods GPT was performed in primary invasive breast cancer cases to calculate tumor mutational burden (TMB) and microsatellite instability (MSI) and to determine the prevalence of TMB-H and MSI-H breast cancer. Gene mutation patterns and mismatch repair (MMR) protein expression were confirmed in patients with TMB-H. Single-base substitution (SBS) signature analysis was performed on samples with ≥ 50 mutations to estimate the mechanisms underlying TMB-H. Results The median TMB was 2.58 mut/Mb. TMB-H was detected in 2.6% (n = 6) of all cases (n = 234). MSI-H cases were identified in 1.3% (n = 3) of cases; all were included among the TMB-H cases. Among the three TMB-H/MSI-H cases, MLH1 mutations were detected in all; however, none were detected in the TMB-H/microsatellite instability-low (MSI-L) cases. SBS signature analysis was performed for three patients. The APOBEC and dMMR signatures were predominant in the TMB-H/MSI-H and TMB-H/MSI-L cases, respectively. Conclusion GPT detects TMB-H and MSI-H while enabling assessment of their underlying mechanisms, potentially supporting more accurate treatment selection, including ICIs, and prognosis prediction. tumor mutational burden microsatellite instability gene panel testing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Tumor mutational burden-high (TMB-H) and microsatellite instability-high (MSI-H) have attracted attention as biomarkers for immune checkpoint inhibitors (ICIs). Tumor mutational burden (TMB) is defined as the total number of somatic mutations in the tumor genome. It is generally indicated as the number of single-nucleotide variants (SNVs) per megabase (Mb) of DNA and is denoted mutations per megabase (mut/Mb). Importantly, TMB-H tumors may produce a large number of tumor-specific antigens (neoantigens) that induce an immune response, which correlates with high T cell infiltration and response rates to ICIs [ 1 – 4 ]. TMB-H can be caused by DNA repair mechanism abnormalities, including mismatch repair (MMR), exposure to mutagens, and endogenous APOBEC enzyme activation [ 1 ]. A TMB-H cause is microsatellite instability (MSI), a phenotype caused by MMR deficiency (dMMR). Congenital dMMR is known as Lynch syndrome and is characterized by early onset tumorigenesis and MSI-H [ 5 ]. Conversely, in colorectal cancer, tumors with nonhereditary dMMR account for 12% of the cases [ 6 ]. Notably, these tumors are primarily caused by methylation of the MLH1 promoter, an MMR protein [ 7 ]. Another cause of TMB-H is endogenous APOBEC enzyme activation, which leads to C-to-T mutations in the genome and a characteristic mutation spectrum [ 8 , 9 ]. Mutations accumulating in the genome exhibit characteristic spectra, depending on their cause, which are referred to as mutational signatures. COSMIC has registered several single-base substitution (SBS) signatures, with SBS2 and SBS13 observed in APOBEC activation and SBS6, SBS21, and others observed in dMMR [ 10 – 12 ]. Nevertheless, signature analysis requires comparing large variants and is therefore typically performed using whole-genome sequencing (WGS) or whole-exome sequencing (WES) rather than gene panel testing (GPT). GPT is commonly used in clinical settings. Nonetheless, in breast cancer, the prevalence of TMB-H and MSI-H is low, and unusual analyses such as WGS or methylation analyses have only been conducted for research purposes [ 13 , 14 ]. Therefore, research on the characteristics and mechanisms of TMB-H breast cancer remains limited. Here, we evaluated the prevalence, genetic mutation patterns, and mechanisms of TMB-H/MSI-H in breast cancer by using GPT. Materials and methods Patients and tissue specimens Between April 2022 and October 2025, 234 patients diagnosed with breast cancer were recruited at Hokuto Hospital, Obihiro, Hokkaido, Japan, to ensure the collection of sufficient tissue samples during mastectomy, breast-conserving surgery, or biopsy. Women aged 18 years or older who had undergone GPT and whose breast cancer diagnosis was confirmed by histopathological evaluation were eligible for inclusion. Patients with non-invasive carcinoma or metastatic samples, including those obtained from lymph nodes, were excluded. The study population comprised all individuals who received breast cancer treatment at the hospital during the defined enrollment period. The hospital’s diverse urban population represents a wide range of socioeconomic backgrounds. The study followed the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of Hokuto Hospital (approval number 1006-R7). Clinical and pathological data collected for analysis included patient age, tumor size, histological grade, disease stage, tumor histology, lymph node involvement, and intrinsic molecular subtype. Intrinsic subtypes were determined using surrogate definitions based on immunohistochemical assessment of hormone receptor status and HER2 expression. Fluorescence in situ hybridization was performed to evaluate HER2 gene amplification in cases with an HER2-IHC score of 2+. Luminal tumors were further categorized as Luminal A when the Ki-67 labeling index was below 30%, whereas tumors with a Ki-67 index of 30% or greater were classified as Luminal B. GPT Genomic analysis was performed using a standard analytical workflow. Genomic DNA was isolated from formalin-fixed, paraffin-embedded tumor samples sectioned at 10 µm using the Maxwell 16 FFPE Plus LEV DNA Purification Kit (Promega, Madison, WI, USA). Targeted sequencing libraries were constructed using SureSelect XT Low Input Reagents in combination with a SureSelect PrePool Custom Tier2 panel encompassing 143 genes (Agilent, USA). Sequencing was performed on the MiSeq platform (Illumina, USA). Downstream analyses, including assessment of TMB, MSI, and sequence variants, were conducted using the Genome Jack bioinformatics pipeline (Mitsubishi Electric Software; https://genomejack.net/ ) based on FASTQ output files. TMB was calculated as the total number of somatic mutations per megabase of the interrogated genomic region using a targeted panel with a total covered size of 1.6 Mb. Samples were classified as TMB-high when the calculated value was ≥ 10 mutations per megabase. Microsatellite instability was evaluated by analyzing 11 microsatellite loci included in the GPT assay, with MSI-H defined as the presence of aberrant-length reads accounting for at least 12.5% of reads at the assessed markers. MMR protein immunohistochemistry (IHC) MMR-IHC was outsourced to a clinical laboratory (Morpho Technology, Japan) using the same tumor tissue blocks used for GPT. Protein expression was assessed using antibodies against MLH1 (ES05; Agilent), MSH2 (FE11; Agilent), MSH6 (EP49; Agilent), and PMS2 (EP51; Agilent) with a HISTOSTAINER (Nichirei Bioscience, Japan). Mutational signature analysis To improve somatic mutation extraction, additional matched-pair analysis using blood-derived DNA was performed with GPT in TMB-H cases. Mutational spectra were extracted for samples with at least 50 mutations, based on the distribution of the six substitution categories (C > A, C > G, C > T, T > A, T > C, and T > G) and the bases directly adjacent to the mutated base on both the 5’ and 3’ sides, using SigProfilerMatrixGenerator. Mutational signatures were extracted by comparing the mutational spectrum with known COSMIC signatures using the SigProfiler Assignment. The contribution rates of the four signatures (APOBEC, dMMR, POLE, and HRD) that caused hypermutation were extracted using non-negative matrix factorization. Results Clinicopathological characteristics and TMB/MSI status are presented in Table 1 and Fig. 1 . The age range was 29–89 years, with a median age of 60 years. Tumor size ranged from 0.2 cm to 13.2 cm, with a median of 1.8 cm. For histopathological grade, 157 cases were grade I or II, whereas 75 cases were grade III. Clinical stage was I, II, III, IV, and local recurrence in 95, 86, 29, 6, and 18 patients, respectively. Histological types comprised invasive breast cancer of the non-specific type (IBC-NST), lobular carcinoma mucinous carcinoma invasive micropapillary carcinoma and others in 197, 18, 11, 4, and 4 patients, respectively. Lymph node metastases were detected in 82 patients. Intrinsic subtypes were Luminal A (n = 120), Luminal B (n = 59), HER2-positive (n = 13), Luminal-HER2 (n = 16), and triple-negative (n = 26). The TMB score ranged from 0.37 to 50.40 mut/Mb, with a median of 2.58 mut/Mb (Fig. 1 ). Among the 234 patients, six and three were classified as TMB-H (2.6%) and MSI-H (1.3%), respectively. Table 1 Clinicopathological characteristics and TMB/MSI status༎ Number of patients N = 234 Age, years (median) 29–89 (60) 2 105 N/A 4 Histological grade I & II 157 III 75 N/A 2 Stage I 95 II 86 III 29 IV 6 Local recurrence 18 Histological type Invasive carcinoma of no special type 197 Invasive lobular carcinoma 18 Mucinous carcinoma 11 Invasive micropapillary 4 Others 4 Lymph node metastasis Absent 152 Present 82 Intrinsic subtype Luminal A 120 Luminal B 59 HER2-positive 13 Luminal-HER2 16 Triple negative 26 TMB status TMB-L (< 10 mut/Mb) 228 TMB-H (10 mut/Mb≤) 6 MSI status MSI-L 231 MSI-H 3 HER2, human epidermal growth factor receptor 2; MSI, microsatellite instability; MSI-H, high microsatellite instability; MSI-L, low microsatellite instability; TMB, tumor mutational burden; TMB-H, high tumor mutational burden; TMB-L, low tumor mutational burden The detailed clinicopathological and genomic characteristics of patients with TMB-H and MSI-H are summarized in Tables 2 and 3 , respectively. All MSI-H patients were included in the TMB-H group. The histological types of the TMB-H/MSI-L patients included invasive breast carcinoma of no special type (IBC-NST; n = 1), pleomorphic invasive lobular carcinoma (ILC; n = 1), and mucinous carcinoma (n = 1). The intrinsic subtypes were Luminal A (n = 2) and Luminal-HER2 (n = 1). Conversely, all patients with TMB-H/MSI-H had IBC-NST histology, and the intrinsic subtypes included Luminal B (n = 2) and triple-negative (n = 1). Lymph node metastasis was not detected in any of the six patients with TMB-H. Immunohistochemical staining revealed that all MSI-low (MSI-L) cases were positive for MMR proteins, including MLH1, MSH2, MSH6, and PMS2, whereas MLH1/PMS2 expression was absent in all MSI-H cases (Fig. 2 ); MSH2/MSH6 expression was present (data not shown). In all patients with MSI-H, SNVs of MLH1 were detected. Nevertheless, no MLH1 mutations were found in any patient with TMB-H/MSI-L (Table 3 ). Table 2 The detailed clinicopathological characteristics of patients with TMB-H/MSI-L and TMB-H/MSI-H. Case number TMB-H / MSI-L TMB-H / MSI-H #1051 #1115 #1132 #880 #974 #1289 Age 79 88 80 51 65 78 Tumor size (mm) 19 22 4 23 18 22 Histological grade 3 2 1 2 3 3 Stage IA IIA IA IIA Local recurrence IIA Histological type Pleomorphic-ILC IBC-NST Mucinous carcinoma IBC-NST IBC-NST IBC-NST Lymph node metastasis - - - - - - Intrinsic subtype Luminal-HER2 Luminal A Luminal A Triple negative Luminal B Luminal B Ki-67(%) 30% 5–10% < 5% 20–30% 40% 60% MSI status Low Low Low High High High TMB score (mut/Mb) 17.29 22.44 11.77 28.72 11.78 50.40 IA, stage IA; IBC-NST, invasive breast carcinoma of no special type; IIA, stage IIA; ILC, invasive lobular carcinoma; MSI, microsatellite instability; MSI-H, high microsatellite instability; MSI-L, low microsatellite instability; mut/Mb, mutations per megabase; TMB, tumor mutational burden; TMB-H, high tumor mutational burden Table 3 Detailed genomic characteristics of patients with TMB-H/MSI-L and TMB-H/MSI-H. TMB-H / MSI-L ID SNV Amplification Loss #1051 CDH1 ERBB2 ENO1 FGFR1 STAT3 #1115 PIK3CA ENO1 TP53 #1132 TMB-H / MSI-H #880 MLH1 AR BRCA1 PIK3CA CDK4 KRAS CDK12 CDKN2A NF1 ECT2L #974 MLH1 MYC TP53 TP53 EZH2 AXIN1 CDKN2A BRAF STK11 NF1 POLD1 POLE TSC2 RAD51C #1289 MLH1 ERBB2 ERBB3 DNMT3A TP53 ARID1A MAP3K1 SETD2 NOTCH3 CUL3 POLD1 MSI, microsatellite instability; MSI-H, high microsatellite instability; MSI-L, low microsatellite instability; SNV, single-nucleotide variant; TMB, tumor mutational burden; TMB-H, high tumor mutational burden For mutation signature analysis, additional matched-pair analysis was conducted in patients with TMB-H using blood-derived DNA. Among the six patients, three (one TMB-H/MSI-L and two TMB-H/MSI-H) had ≥ 50 mutations detected; thus, 96 SBS spectrum extractions were performed (Fig. 3 ). Signature analysis was subsequently performed using this mutation spectrum. In the TMB-H/MSI-L case, SBS2 and SBS13, which are characteristic of activation of the APOBEC family of cytidine deaminases, were predominant. In the two TMB-H/MSI-H cases, SBS6 and SBS21, which are typical of dMMR, were prevalent (Fig. 4 A). The contribution rates of four hypermutation-associated signatures (APOBEC, dMMR, POLE, and HRD) were extracted. The APOBEC signature (signatures 2 and 13) was dominant in the TMB-H/MSI-L case, whereas the dMMR signature (signatures 6, 15, 20, 21, and 44) was dominant in TMB-H/MSI-H. Conversely, the contribution rates of the HRD signature (signature 3) and POLE signature (signature 10) were low (Fig. 4 B). Discussion Here, we investigated the frequencies of TMB-H and MSI-H and estimated the underlying mechanisms using GPT in breast cancer. Notably, TMB-H and MSI-H are not prevalent in breast cancer [ 13 , 14 ]. Nonetheless, these factors have been recognized as predictors of ICI treatment efficacy and prognosis, and elucidating their frequency and mechanisms of action is clinically significant. In this study, the frequencies of TMB-H and MSI-H were 2.6% and 1.3%, respectively, and all patients with MSI-H were included in the TMB-H group. No clear trends were observed across histological or intrinsic subtypes of TMB-H. In TMB-H cases, the SNV of MLH1 was detected in all patients with MSI-H. The primary cause of dMMR in Lynch syndrome is thought to be mutations in the MMR gene cluster ( MLH1 , MSH2 , MSH6 , and PMS2) [ 5 ]. However, MLH1 mutations account for a low proportion of cases of nonhereditary MSI-H colorectal cancer, for which reduced MLH1 expression due to MLH1 promoter methylation is the primary cause and accounts for 80% of cases [ 7 ]. Other causes include mutations in genes such as POLE [ 15 ]. Although the number of cases was small, these findings suggest that MLH1 mutations may be the primary cause of MSI-H in breast cancer. IHC results also confirmed loss of MLH1 and PMS2 expression; PMS2 forms a stable heterodimer with MLH1 . Notably, MLH1 mutations were detected in the germline of MSI-H patient #1289 but not in the other two patients. Various factors, such as ultraviolet radiation and genetic defects, cause mutations and produce characteristic patterns called mutation signatures. Generally, SBS signatures registered in COSMIC are used [ 10 , 11 ]. Signature analysis requires comparing several mutations and is therefore typically performed using WGS or WES rather than GPT. There are a few reports using GPT, with low coverage [ 16 , 17 ]. The minimum number of required mutations has been reported to be at least 50–100 SNVs; nonetheless, owing to variations in detectability among SBS signatures, it is currently not possible to establish a universal cutoff value [ 18 , 19 ]. In this study, we performed signature analysis on three cases in which ≥ 50 SNVs were detected and attempted to calculate the contribution rate of the SBS signature. APOBEC-related signatures were predominant in one TMB-H/MSI-L case, whereas dMMR-related signatures were predominant in two TMB-H/MSI-H cases. The cosine similarity values ranged from 0.98 to 0.83 (data not shown). Therefore, signature analysis and GPT-based estimation of TMB-H mechanisms may be feasible in TMB-H cases. Signature analysis was also attempted in TMB-H cases with ≤ 50 SNVs, but the cosine similarity was too low (approximately 0.5) (data not shown). In this study, we used a gene panel covering the exons of 143 genes. Nevertheless, a more accurate and reliable analysis may be possible using higher-coverage data, such as FoundationOne CDx, which covers all exons of the 324 genes. Previous studies have reported APOBEC activity in 59.2% of TMB-H breast cancer cases and dMMR in 36.4% of cases, suggesting that APOBEC activity is the main cause of TMB-H in breast cancer [ 8 , 14 ]. Regarding ICI efficiency in TMB-H cases with endogenous APOBEC activity, ICI efficacy was lower than that of dMMR when the TMB was approximately 10 mut/Mb, whereas a TMB of ≥ 14 mut/Mb has been reported to yield efficacy similar to that of dMMR [ 20 ]. Currently, pembrolizumab is covered by health insurance in Japan, based on the KEYNOTE-158 trial, which uses a threshold of 10 mut/Mb across all organ types [ 21 ]. Nonetheless, this threshold may not be optimal in breast cancer [ 20 , 22 ]. Furthermore, in cases with APOBEC activity, CDK4/6 inhibitors and hormone therapies have been associated with poor prognosis [ 20 ]. Consequently, clarifying TMB-H mechanisms may facilitate optimal treatment selection and improve prognosis prediction in the future. Conclusion GPT detects TMB-H and MSI-H and, in some cases, enables estimation of underlying mechanisms through genetic mutation and signature analyses. In this study, APOBEC activity was estimated to underlie TMB-H/MSI-L cases, and dMMR caused by MLH1 mutations was estimated to underlie TMB-H/MSI-H cases. GPT-based estimation of TMB-H mechanisms may improve treatment selection accuracy, including ICIs, and strengthen prognostic prediction. Declarations Author contributions All authors contributed to the study conception and design. Material preparation and data collection and analysis were performed by Shogo Baba, Mami Koketsu, and Yasutaka Kato. The first draft of the manuscript was written by Shogo Baba, and all authors commented on the previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements We express our gratitude to the staff of the Department of Pathology and Genetics at Hokuto Hospital. In addition, we would like to thank Editage (www.editage.jp) for English language editing. Funding There are no relevant financial or non-financial interests to disclose. Compliance with Ethical Standards Conflict of interest The authors have no relevant financial or non-financial interest to disclose. Ethical approval The present study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Hokuto Hospital (No. 1006-R7). Informed consent Informed consent was obtained from all study participants. Consent for publication Written informed consent was obtained from all participants before study participation. No personally identifiable patient data are presented in this manuscript; therefore, consent for publication was not required. References Barroso-Sousa R, Pacífico JP, Sammons S, Tolaney SM. 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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-8964413","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604135420,"identity":"8b81c624-e3da-4405-b116-713d8f9a5f53","order_by":0,"name":"Shogo Baba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYFACHgZmBgabBCDNwMD4DyyUQIyWNJAWxgYGNuK1HEbRgh/IN/Ae/FxQcz6Pn+eM+YMPPAxy5g0Mzx7g02JwgC9Zesax28WSvT2GjTMkGIxlDjCkG+DVIv/GQJqH7XbihvM8hs08BgyJM4A+k8DvMB7j3zz/zkG1JBChheEAj5k0b9uBxA1ne4BaDhChBeiXNOuZfcmJM3uOFc6c2SBhLMFMwC/AEDt8u+CbXWI/T/KGDx8bbOQk2HvSHuB1GBoAOomZJ40UHWDAfoxkLaNgFIyCUTCsAQCo0EOH1S19wgAAAABJRU5ErkJggg==","orcid":"","institution":"Hokuto Hospital: Hokuto Byoin","correspondingAuthor":true,"prefix":"","firstName":"Shogo","middleName":"","lastName":"Baba","suffix":""},{"id":604135422,"identity":"f8ed70c1-6521-4c0e-8750-76115f10369e","order_by":1,"name":"Mami Koketsu","email":"","orcid":"","institution":"Hokuto Hospital: Hokuto Byoin","correspondingAuthor":false,"prefix":"","firstName":"Mami","middleName":"","lastName":"Koketsu","suffix":""},{"id":604135424,"identity":"f452739f-3d19-4602-a3e4-2f8256cd13ff","order_by":2,"name":"Hajime Kuroda","email":"","orcid":"","institution":"Tokyo Women's Medical University: Tokyo Joshi Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Hajime","middleName":"","lastName":"Kuroda","suffix":""},{"id":604135426,"identity":"ab710305-fc50-4745-ae8f-15552a21ab18","order_by":3,"name":"Megumi Suzuki","email":"","orcid":"","institution":"Megumi Breast Clinic","correspondingAuthor":false,"prefix":"","firstName":"Megumi","middleName":"","lastName":"Suzuki","suffix":""},{"id":604135428,"identity":"cd1ff9be-b4b0-488d-ba2b-0d2a768fc60d","order_by":4,"name":"Hiroyuki Kawami","email":"","orcid":"","institution":"Hokuto Hospital: Hokuto Byoin","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Kawami","suffix":""},{"id":604135429,"identity":"780cb068-f380-411f-869c-92e8ad121e35","order_by":5,"name":"Hiroshi Nishihara","email":"","orcid":"","institution":"Hokuto Hospital: Hokuto Byoin","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Nishihara","suffix":""},{"id":604135434,"identity":"ce77edd8-45dc-4199-bbbe-1b38ba9ca3dc","order_by":6,"name":"Yasutaka Kato","email":"","orcid":"","institution":"Hokuto Hospital: Hokuto Byoin","correspondingAuthor":false,"prefix":"","firstName":"Yasutaka","middleName":"","lastName":"Kato","suffix":""},{"id":604135435,"identity":"66682e1b-46e8-41cb-abea-ea7a71973f87","order_by":7,"name":"Oi Harada","email":"","orcid":"","institution":"Kameda Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Oi","middleName":"","lastName":"Harada","suffix":""}],"badges":[],"createdAt":"2026-02-25 07:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8964413/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8964413/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104594022,"identity":"9f1b7e98-be16-432d-8ea0-e391b1a0d38c","added_by":"auto","created_at":"2026-03-13 17:47:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1685707,"visible":true,"origin":"","legend":"\u003cp\u003eTumor mutational burden (TMB) status of 234 cases with breast cancer. The TMB score ranged from 0.37 to 50.40 mut/Mb, with a median of 2.58 mut/Mb.\u003c/p\u003e","description":"","filename":"Fig01.png","url":"https://assets-eu.researchsquare.com/files/rs-8964413/v1/c3684d7f23176e4c5efb9ec9.png"},{"id":104594021,"identity":"cd7e5b70-d2c2-4245-b254-a3b7a9053816","added_by":"auto","created_at":"2026-03-13 17:47:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical staining dMMR proteins in TMB-H cases. MLH1/PMS2 expression was present in all TMB-H/MSI-L cases but absent in all TMB-H/MSI-H cases (scale bar, 200 µm). MSH2/MSH6 expression was present in all cases (data not shown). MSI-H, high microsatellite instability; MSI-L, low microsatellite instability; mut/Mb, mutations per megabase; TMB, tumor mutational burden; TMB-H, high tumor mutational burden.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8964413/v1/afffa58ba2142d9f9c45d829.png"},{"id":104594024,"identity":"7c7c0225-3726-45ff-837e-42f799c832b0","added_by":"auto","created_at":"2026-03-13 17:47:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17436864,"visible":true,"origin":"","legend":"\u003cp\u003eNinety-six SBS spectrum extractions of TMB-H cases. To estimate the number of tumor mutations, matched-pair analysis was performed in TMB-H cases using blood-derived DNA. Among the six patients, three (one TMB-H/MSI-L and two TMB-H/MSI-H) had ≥50 detectable mutations.\u003c/p\u003e","description":"","filename":"Fig03.png","url":"https://assets-eu.researchsquare.com/files/rs-8964413/v1/fc60a00b23f38c266f17543e.png"},{"id":104594025,"identity":"b27ae2e9-d5f4-4e67-b1b4-da039c48f6c6","added_by":"auto","created_at":"2026-03-13 17:47:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20699004,"visible":true,"origin":"","legend":"\u003cp\u003eSignature analysis using this mutation spectrum in TMB-H cases. (A) In one case of TMB-H/MSI-L, signatures SBS2 and SBS13 were predominant, which are characteristic of APOBEC family activation. In two cases of TMB-H/MSI-H, signatures SBS6 and SBS21 were predominant, which are characteristic of dMMR. (B) Contribution rates of four hypermutation-associated signatures: APOBEC, dMMR, POLE, and HRD.APOBEC signature is dominant in TMB-H/MSI-L, whereas the dMMR signature dominates in TMB-H/MSI-H.\u003c/p\u003e","description":"","filename":"Fig04.png","url":"https://assets-eu.researchsquare.com/files/rs-8964413/v1/6a6bc4d502dac4f3c747d159.png"},{"id":108492304,"identity":"7baa30be-f15a-4b29-837d-79b83b09e7ce","added_by":"auto","created_at":"2026-05-05 09:57:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":40603747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8964413/v1/079fde66-4e16-4e88-ad52-0ee87d9cc7de.pdf"}],"financialInterests":"","formattedTitle":"Prevalence and molecular features of TMB-high and MSI-high breast cancer assessed by gene panel testing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTumor mutational burden-high (TMB-H) and microsatellite instability-high (MSI-H) have attracted attention as biomarkers for immune checkpoint inhibitors (ICIs). Tumor mutational burden (TMB) is defined as the total number of somatic mutations in the tumor genome. It is generally indicated as the number of single-nucleotide variants (SNVs) per megabase (Mb) of DNA and is denoted mutations per megabase (mut/Mb). Importantly, TMB-H tumors may produce a large number of tumor-specific antigens (neoantigens) that induce an immune response, which correlates with high T cell infiltration and response rates to ICIs [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTMB-H can be caused by DNA repair mechanism abnormalities, including mismatch repair (MMR), exposure to mutagens, and endogenous APOBEC enzyme activation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A TMB-H cause is microsatellite instability (MSI), a phenotype caused by MMR deficiency (dMMR). Congenital dMMR is known as Lynch syndrome and is characterized by early onset tumorigenesis and MSI-H [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Conversely, in colorectal cancer, tumors with nonhereditary dMMR account for 12% of the cases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, these tumors are primarily caused by methylation of the \u003cem\u003eMLH1\u003c/em\u003e promoter, an MMR protein [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Another cause of TMB-H is endogenous APOBEC enzyme activation, which leads to C-to-T mutations in the genome and a characteristic mutation spectrum [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMutations accumulating in the genome exhibit characteristic spectra, depending on their cause, which are referred to as mutational signatures. COSMIC has registered several single-base substitution (SBS) signatures, with SBS2 and SBS13 observed in APOBEC activation and SBS6, SBS21, and others observed in dMMR [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, signature analysis requires comparing large variants and is therefore typically performed using whole-genome sequencing (WGS) or whole-exome sequencing (WES) rather than gene panel testing (GPT).\u003c/p\u003e \u003cp\u003eGPT is commonly used in clinical settings. Nonetheless, in breast cancer, the prevalence of TMB-H and MSI-H is low, and unusual analyses such as WGS or methylation analyses have only been conducted for research purposes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, research on the characteristics and mechanisms of TMB-H breast cancer remains limited. Here, we evaluated the prevalence, genetic mutation patterns, and mechanisms of TMB-H/MSI-H in breast cancer by using GPT.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and tissue specimens\u003c/h2\u003e \u003cp\u003eBetween April 2022 and October 2025, 234 patients diagnosed with breast cancer were recruited at Hokuto Hospital, Obihiro, Hokkaido, Japan, to ensure the collection of sufficient tissue samples during mastectomy, breast-conserving surgery, or biopsy. Women aged 18 years or older who had undergone GPT and whose breast cancer diagnosis was confirmed by histopathological evaluation were eligible for inclusion. Patients with non-invasive carcinoma or metastatic samples, including those obtained from lymph nodes, were excluded. The study population comprised all individuals who received breast cancer treatment at the hospital during the defined enrollment period. The hospital\u0026rsquo;s diverse urban population represents a wide range of socioeconomic backgrounds. The study followed the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of Hokuto Hospital (approval number 1006-R7). Clinical and pathological data collected for analysis included patient age, tumor size, histological grade, disease stage, tumor histology, lymph node involvement, and intrinsic molecular subtype. Intrinsic subtypes were determined using surrogate definitions based on immunohistochemical assessment of hormone receptor status and HER2 expression. Fluorescence in situ hybridization was performed to evaluate \u003cem\u003eHER2\u003c/em\u003e gene amplification in cases with an HER2-IHC score of 2+. Luminal tumors were further categorized as Luminal A when the Ki-67 labeling index was below 30%, whereas tumors with a Ki-67 index of 30% or greater were classified as Luminal B.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGPT\u003c/h3\u003e\n\u003cp\u003eGenomic analysis was performed using a standard analytical workflow. Genomic DNA was isolated from formalin-fixed, paraffin-embedded tumor samples sectioned at 10 \u0026micro;m using the Maxwell 16 FFPE Plus LEV DNA Purification Kit (Promega, Madison, WI, USA). Targeted sequencing libraries were constructed using SureSelect XT Low Input Reagents in combination with a SureSelect PrePool Custom Tier2 panel encompassing 143 genes (Agilent, USA). Sequencing was performed on the MiSeq platform (Illumina, USA). Downstream analyses, including assessment of TMB, MSI, and sequence variants, were conducted using the Genome Jack bioinformatics pipeline (Mitsubishi Electric Software; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genomejack.net/\u003c/span\u003e\u003cspan address=\"https://genomejack.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on FASTQ output files. TMB was calculated as the total number of somatic mutations per megabase of the interrogated genomic region using a targeted panel with a total covered size of 1.6 Mb. Samples were classified as TMB-high when the calculated value was \u0026ge;\u0026thinsp;10 mutations per megabase. Microsatellite instability was evaluated by analyzing 11 microsatellite loci included in the GPT assay, with MSI-H defined as the presence of aberrant-length reads accounting for at least 12.5% of reads at the assessed markers.\u003c/p\u003e\n\u003ch3\u003eMMR protein immunohistochemistry (IHC)\u003c/h3\u003e\n\u003cp\u003eMMR-IHC was outsourced to a clinical laboratory (Morpho Technology, Japan) using the same tumor tissue blocks used for GPT. Protein expression was assessed using antibodies against MLH1 (ES05; Agilent), MSH2 (FE11; Agilent), MSH6 (EP49; Agilent), and PMS2 (EP51; Agilent) with a HISTOSTAINER (Nichirei Bioscience, Japan).\u003c/p\u003e\n\u003ch3\u003eMutational signature analysis\u003c/h3\u003e\n\u003cp\u003eTo improve somatic mutation extraction, additional matched-pair analysis using blood-derived DNA was performed with GPT in TMB-H cases. Mutational spectra were extracted for samples with at least 50 mutations, based on the distribution of the six substitution categories (C\u0026thinsp;\u0026gt;\u0026thinsp;A, C\u0026thinsp;\u0026gt;\u0026thinsp;G, C\u0026thinsp;\u0026gt;\u0026thinsp;T, T\u0026thinsp;\u0026gt;\u0026thinsp;A, T\u0026thinsp;\u0026gt;\u0026thinsp;C, and T\u0026thinsp;\u0026gt;\u0026thinsp;G) and the bases directly adjacent to the mutated base on both the 5\u0026rsquo; and 3\u0026rsquo; sides, using SigProfilerMatrixGenerator. Mutational signatures were extracted by comparing the mutational spectrum with known COSMIC signatures using the SigProfiler Assignment. The contribution rates of the four signatures (APOBEC, dMMR, POLE, and HRD) that caused hypermutation were extracted using non-negative matrix factorization.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClinicopathological characteristics and TMB/MSI status are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The age range was 29\u0026ndash;89 years, with a median age of 60 years. Tumor size ranged from 0.2 cm to 13.2 cm, with a median of 1.8 cm. For histopathological grade, 157 cases were grade I or II, whereas 75 cases were grade III. Clinical stage was I, II, III, IV, and local recurrence in 95, 86, 29, 6, and 18 patients, respectively. Histological types comprised invasive breast cancer of the non-specific type (IBC-NST), lobular carcinoma mucinous carcinoma invasive micropapillary carcinoma and others in 197, 18, 11, 4, and 4 patients, respectively. Lymph node metastases were detected in 82 patients. Intrinsic subtypes were Luminal A (n\u0026thinsp;=\u0026thinsp;120), Luminal B (n\u0026thinsp;=\u0026thinsp;59), HER2-positive (n\u0026thinsp;=\u0026thinsp;13), Luminal-HER2 (n\u0026thinsp;=\u0026thinsp;16), and triple-negative (n\u0026thinsp;=\u0026thinsp;26). The TMB score ranged from 0.37 to 50.40 mut/Mb, with a median of 2.58 mut/Mb (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 234 patients, six and three were classified as TMB-H (2.6%) and MSI-H (1.3%), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics and TMB/MSI status༎\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;234\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u0026ndash;89 (60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≧\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size, cm (median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u0026ndash;13.2 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≦\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI \u0026amp; II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive carcinoma of no special type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive lobular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive micropapillary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrinsic subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal-HER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMB status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMB-L (\u0026lt;\u0026thinsp;10 mut/Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMB-H (10 mut/Mb\u0026le;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSI status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSI-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSI-H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eHER2, human epidermal growth factor receptor 2; MSI, microsatellite instability; MSI-H, high microsatellite instability; MSI-L, low microsatellite instability; TMB, tumor mutational burden; TMB-H, high tumor mutational burden; TMB-L, low tumor mutational burden\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe detailed clinicopathological and genomic characteristics of patients with TMB-H and MSI-H are summarized in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively. All MSI-H patients were included in the TMB-H group. The histological types of the TMB-H/MSI-L patients included invasive breast carcinoma of no special type (IBC-NST; n\u0026thinsp;=\u0026thinsp;1), pleomorphic invasive lobular carcinoma (ILC; n\u0026thinsp;=\u0026thinsp;1), and mucinous carcinoma (n\u0026thinsp;=\u0026thinsp;1). The intrinsic subtypes were Luminal A (n\u0026thinsp;=\u0026thinsp;2) and Luminal-HER2 (n\u0026thinsp;=\u0026thinsp;1). Conversely, all patients with TMB-H/MSI-H had IBC-NST histology, and the intrinsic subtypes included Luminal B (n\u0026thinsp;=\u0026thinsp;2) and triple-negative (n\u0026thinsp;=\u0026thinsp;1). Lymph node metastasis was not detected in any of the six patients with TMB-H. Immunohistochemical staining revealed that all MSI-low (MSI-L) cases were positive for MMR proteins, including MLH1, MSH2, MSH6, and PMS2, whereas MLH1/PMS2 expression was absent in all MSI-H cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); MSH2/MSH6 expression was present (data not shown). In all patients with MSI-H, SNVs of \u003cem\u003eMLH1\u003c/em\u003e were detected. Nevertheless, no \u003cem\u003eMLH1\u003c/em\u003e mutations were found in any patient with TMB-H/MSI-L (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eThe detailed clinicopathological characteristics of patients with TMB-H/MSI-L and TMB-H/MSI-H.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCase number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTMB-H / MSI-L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eTMB-H / MSI-H\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#1051\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e#1115\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e#1132\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e#880\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e#974\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e#1289\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\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\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\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 \u003cp\u003e3\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLocal recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIIA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePleomorphic-ILC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIBC-NST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMucinous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIBC-NST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIBC-NST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIBC-NST\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrinsic subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLuminal-HER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLuminal A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLuminal A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTriple negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLuminal B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLuminal B\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 \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u0026ndash;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSI status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMB score (mut/Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eIA, stage IA; IBC-NST, invasive breast carcinoma of no special type; IIA, stage IIA; ILC, invasive lobular carcinoma; MSI, microsatellite instability; MSI-H, high microsatellite instability; MSI-L, low microsatellite instability; mut/Mb, mutations per megabase; TMB, tumor mutational burden; TMB-H, high tumor mutational burden\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed genomic characteristics of patients with TMB-H/MSI-L and TMB-H/MSI-H.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTMB-H / MSI-L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmplification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLoss\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#1051\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCDH1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eERBB2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eENO1\u003c/em\u003e\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\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFGFR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSTAT3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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#1115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eENO1\u003c/em\u003e\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\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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#1132\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMB-H / MSI-H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBRCA1\u003c/em\u003e\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\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCDK4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eKRAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eCDK12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eCDKN2A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eNF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eECT2L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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#974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMYC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\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\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEZH2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAXIN1\u003c/em\u003e\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\u003cem\u003eCDKN2A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBRAF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSTK11\u003c/em\u003e\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\u003cem\u003eNF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePOLD1\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePOLE\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTSC2\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRAD51C\u003c/em\u003e\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#1289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eERBB2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eERBB3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eARID1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eMAP3K1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eSETD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eNOTCH3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003eCUL3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003cem\u003ePOLD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMSI, microsatellite instability; MSI-H, high microsatellite instability; MSI-L, low microsatellite instability; SNV, single-nucleotide variant; TMB, tumor mutational burden; TMB-H, high tumor mutational burden\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor mutation signature analysis, additional matched-pair analysis was conducted in patients with TMB-H using blood-derived DNA. Among the six patients, three (one TMB-H/MSI-L and two TMB-H/MSI-H) had\u0026thinsp;\u0026ge;\u0026thinsp;50 mutations detected; thus, 96 SBS spectrum extractions were performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Signature analysis was subsequently performed using this mutation spectrum. In the TMB-H/MSI-L case, SBS2 and SBS13, which are characteristic of activation of the APOBEC family of cytidine deaminases, were predominant. In the two TMB-H/MSI-H cases, SBS6 and SBS21, which are typical of dMMR, were prevalent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The contribution rates of four hypermutation-associated signatures (APOBEC, dMMR, POLE, and HRD) were extracted. The APOBEC signature (signatures 2 and 13) was dominant in the TMB-H/MSI-L case, whereas the dMMR signature (signatures 6, 15, 20, 21, and 44) was dominant in TMB-H/MSI-H. Conversely, the contribution rates of the HRD signature (signature 3) and POLE signature (signature 10) were low (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we investigated the frequencies of TMB-H and MSI-H and estimated the underlying mechanisms using GPT in breast cancer. Notably, TMB-H and MSI-H are not prevalent in breast cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nonetheless, these factors have been recognized as predictors of ICI treatment efficacy and prognosis, and elucidating their frequency and mechanisms of action is clinically significant.\u003c/p\u003e \u003cp\u003eIn this study, the frequencies of TMB-H and MSI-H were 2.6% and 1.3%, respectively, and all patients with MSI-H were included in the TMB-H group. No clear trends were observed across histological or intrinsic subtypes of TMB-H. In TMB-H cases, the SNV of \u003cem\u003eMLH1\u003c/em\u003e was detected in all patients with MSI-H. The primary cause of dMMR in Lynch syndrome is thought to be mutations in the MMR gene cluster (\u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eMSH6\u003c/em\u003e, and PMS2) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, \u003cem\u003eMLH1\u003c/em\u003e mutations account for a low proportion of cases of nonhereditary MSI-H colorectal cancer, for which reduced \u003cem\u003eMLH1\u003c/em\u003e expression due to \u003cem\u003eMLH1\u003c/em\u003e promoter methylation is the primary cause and accounts for 80% of cases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other causes include mutations in genes such as \u003cem\u003ePOLE\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although the number of cases was small, these findings suggest that \u003cem\u003eMLH1\u003c/em\u003e mutations may be the primary cause of MSI-H in breast cancer. IHC results also confirmed loss of \u003cem\u003eMLH1\u003c/em\u003e and PMS2 expression; PMS2 forms a stable heterodimer with \u003cem\u003eMLH1\u003c/em\u003e. Notably, \u003cem\u003eMLH1\u003c/em\u003e mutations were detected in the germline of MSI-H patient #1289 but not in the other two patients.\u003c/p\u003e \u003cp\u003eVarious factors, such as ultraviolet radiation and genetic defects, cause mutations and produce characteristic patterns called mutation signatures. Generally, SBS signatures registered in COSMIC are used [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Signature analysis requires comparing several mutations and is therefore typically performed using WGS or WES rather than GPT. There are a few reports using GPT, with low coverage [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The minimum number of required mutations has been reported to be at least 50\u0026ndash;100 SNVs; nonetheless, owing to variations in detectability among SBS signatures, it is currently not possible to establish a universal cutoff value [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this study, we performed signature analysis on three cases in which\u0026thinsp;\u0026ge;\u0026thinsp;50 SNVs were detected and attempted to calculate the contribution rate of the SBS signature. APOBEC-related signatures were predominant in one TMB-H/MSI-L case, whereas dMMR-related signatures were predominant in two TMB-H/MSI-H cases. The cosine similarity values ranged from 0.98 to 0.83 (data not shown). Therefore, signature analysis and GPT-based estimation of TMB-H mechanisms may be feasible in TMB-H cases. Signature analysis was also attempted in TMB-H cases with \u0026le;\u0026thinsp;50 SNVs, but the cosine similarity was too low (approximately 0.5) (data not shown). In this study, we used a gene panel covering the exons of 143 genes. Nevertheless, a more accurate and reliable analysis may be possible using higher-coverage data, such as FoundationOne CDx, which covers all exons of the 324 genes.\u003c/p\u003e \u003cp\u003ePrevious studies have reported APOBEC activity in 59.2% of TMB-H breast cancer cases and dMMR in 36.4% of cases, suggesting that APOBEC activity is the main cause of TMB-H in breast cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Regarding ICI efficiency in TMB-H cases with endogenous APOBEC activity, ICI efficacy was lower than that of dMMR when the TMB was approximately 10 mut/Mb, whereas a TMB of \u0026ge;\u0026thinsp;14 mut/Mb has been reported to yield efficacy similar to that of dMMR [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Currently, pembrolizumab is covered by health insurance in Japan, based on the KEYNOTE-158 trial, which uses a threshold of 10 mut/Mb across all organ types [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nonetheless, this threshold may not be optimal in breast cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, in cases with APOBEC activity, CDK4/6 inhibitors and hormone therapies have been associated with poor prognosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consequently, clarifying TMB-H mechanisms may facilitate optimal treatment selection and improve prognosis prediction in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGPT detects TMB-H and MSI-H and, in some cases, enables estimation of underlying mechanisms through genetic mutation and signature analyses. In this study, APOBEC activity was estimated to underlie TMB-H/MSI-L cases, and dMMR caused by \u003cem\u003eMLH1\u003c/em\u003e mutations was estimated to underlie TMB-H/MSI-H cases. GPT-based estimation of TMB-H mechanisms may improve treatment selection accuracy, including ICIs, and strengthen prognostic prediction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection and analysis were performed by Shogo Baba, Mami Koketsu, and Yasutaka Kato. The first draft of the manuscript was written by Shogo Baba, and all authors commented on the previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the staff of the Department of Pathology and Genetics at Hokuto Hospital. In addition, we would like to thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Hokuto Hospital (No. 1006-R7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants before study participation. No personally identifiable patient data are presented in this manuscript; therefore, consent for publication was not required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarroso-Sousa R, Pac\u0026iacute;fico JP, Sammons S, Tolaney SM. Tumor mutational burden in breast cancer: Current evidence, challenges, and opportunities. Cancers (Basel) [Internet]. 2023;15. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3390/cancers15153997\u003c/span\u003e\u003cspan address=\"10.3390/cancers15153997\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDvir K, Giordano S, Leone JP. Immunotherapy in breast cancer. Int J Mol Sci. 2024;25:7517.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJardim DL, Goodman A, de Melo Gagliato D, Kurzrock R. 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Ann Oncol. 2021;32:661\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"tumor mutational burden, microsatellite instability, gene panel testing","lastPublishedDoi":"10.21203/rs.3.rs-8964413/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8964413/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTumor mutational burden-high (TMB-H) and microsatellite instability-high (MSI-H) are useful biomarkers for immune checkpoint inhibitors (ICIs) across multiple cancer types. Nonetheless, their prevalence is low in breast cancer, and evidence remains scarce compared with that in other cancer types. Therefore, the TMB-H and MSI-H prevalence in breast cancer was assessed, and the mechanisms underlying these conditions were examined here using gene panel testing (GPT).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGPT was performed in primary invasive breast cancer cases to calculate tumor mutational burden (TMB) and microsatellite instability (MSI) and to determine the prevalence of TMB-H and MSI-H breast cancer. Gene mutation patterns and mismatch repair (MMR) protein expression were confirmed in patients with TMB-H. Single-base substitution (SBS) signature analysis was performed on samples with \u0026ge;\u0026thinsp;50 mutations to estimate the mechanisms underlying TMB-H.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median TMB was 2.58 mut/Mb. TMB-H was detected in 2.6% (n\u0026thinsp;=\u0026thinsp;6) of all cases (n\u0026thinsp;=\u0026thinsp;234). MSI-H cases were identified in 1.3% (n\u0026thinsp;=\u0026thinsp;3) of cases; all were included among the TMB-H cases. Among the three TMB-H/MSI-H cases, \u003cem\u003eMLH1\u003c/em\u003e mutations were detected in all; however, none were detected in the TMB-H/microsatellite instability-low (MSI-L) cases. SBS signature analysis was performed for three patients. The APOBEC and dMMR signatures were predominant in the TMB-H/MSI-H and TMB-H/MSI-L cases, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGPT detects TMB-H and MSI-H while enabling assessment of their underlying mechanisms, potentially supporting more accurate treatment selection, including ICIs, and prognosis prediction.\u003c/p\u003e","manuscriptTitle":"Prevalence and molecular features of TMB-high and MSI-high breast cancer assessed by gene panel testing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 17:47:08","doi":"10.21203/rs.3.rs-8964413/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1bee3e10-b568-479c-b879-f5ddf7bfb214","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject","date":"2026-05-03T04:48:54+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T08:49:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 17:47:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8964413","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8964413","identity":"rs-8964413","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}