Tumor mutational burden status and clinical characteristics of invasive lobular carcinoma of the breast

Tumor mutational burden status and clinical characteristics of invasive lobular carcinoma of the breast | 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 Tumor mutational burden status and clinical characteristics of invasive lobular carcinoma of the breast Yuko Takano, Kazuyuki Mizuno, Madoka Iwase, Sachi Morita, Nao Torii, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5578316/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2025 Read the published version in Breast Cancer → Version 1 posted 5 You are reading this latest preprint version Abstract Background High tumor mutational burden (TMB-H) is an established biomarker for a favorable response to immune checkpoint inhibitors. However, tumor mutational burden (TMB) in invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) has not been sufficiently investigated. Methods We collected data of patients with ILC or IDC from the Center for Cancer Genomics and Advanced Therapeutics database between June 2019 and August 2023. Furthermore, we examined the clinicopathological factors and TMB status. Results Patients with ILC (n = 170) had a median TMB score of 4.00 mut/Mb (interquartile range, 2.00–7.14 mut/Mb), whereas those with IDC (n = 2,598) had a score of 3.90 mut/Mb (2.00–6.00 mut/Mb). TMB-H was more common in patients with ILC than in those with IDC (18.2% vs. 10.1%, P < 0.001), particularly in the ER+/HER2 − subtype. Multivariate analysis revealed that the pathological diagnosis of ILC ( P = 0.006), tissue samples collected from metastatic sites ( P < 0.001), and older age (50 years, P < 0.001) were independent factors for TMB-H. Conclusions Patients with ILC were more likely to have TMB-H than those with IDC. The findings of this study would be invaluable in selecting treatment strategies for patients with ILC. high tumor mutational burden invasive lobular carcinoma invasive ductal carcinoma Figures Figure 1 Figure 2 Figure 3 Introduction Invasive lobular carcinoma (ILC) is the second most common histological type of invasive breast cancer next to invasive ductal carcinoma (IDC); it accounts for 5–15% of all breast cancer cases [ 1 , 2 ]. ILC and IDC differ in terms of clinicopathological factors, features, and prognosis [ 1 , 2 ]. Patients with ILC and those with IDC are usually treated similarly, depending on the molecular classification: anthracycline- and taxane-based chemotherapy, hormone therapy for estrogen receptor (ER)- and/or progesterone receptor (PgR) receptor-positive subtypes, and anti-HER2 therapy for HER2 + subtypes. Poly-adenosine diphosphate ribose polymerase inhibitors have been approved for the treatment of patients with pathogenic germline variants in BRCA1/2 genes with the HER2 − subtype. In addition, immune checkpoint inhibitors (ICIs) have recently been introduced into the standard of care for antiprogramed cell death ligand 1-positive recurrent triple-negative breast cancer (TNBC) [ 3 ]. They have also been approved for the treatment of solid tumors with a tumor mutational burden (TMB) status of ≥ 10 mutations/megabases (mut/Mb, high TMB [TMB-H]), the latter being a tumor agnostic indicator [ 4 ]. A high TMB status is recognized as a positive biomarker for better clinical response to ICIs in the treatment of many cancers, including breast cancer [ 4 ]. With the widespread use of comprehensive genomic profiling (CGP) testing in clinical practice, the TMB status as a biomarker of the therapeutic efficacy of ICIs had gained considerable interest among clinicians. The percentage of patients with TMB-H has been reported to be 3–10% in all breast cancer cases [ 5 ]; however, the distribution of TMB and the percentage of patients with TMB-H has not been sufficiently investigated across the molecular and histological subtypes. Therefore, this study investigated the TMB status and clinical characteristics of patients with various molecular and histological subtypes of breast cancer, particularly ILC, using a large nationwide database of genomic information in Japan. Patients and Methods To explore the TMB status and clinical characteristics of patients with various molecular and histological subtypes of breast cancer, we evaluated the genomic profiles of patients with breast cancer who had been registered in the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database between June 2019 and August 2023. The C-CAT database is a large-scale nationwide database that aggregates the data of the CGP test (panel), which is covered by public health insurance in Japan. Nearly all the patients in the study were highly likely to be Japanese. The database was searched on March 12, 2024. Patients with IDC or ILC were extracted based on the detailed pathological diagnosis registered in the database. Only patients who received two tissue-based panels, the OncoGuide™ NCC Oncopanel System (NOP) and FoundationOne® CDx Cancer Genomic Profile (F1CDx), were included in the analysis. Those tested using other panels (FoundationOne® CDx Liquid, Guardant 360®CDx, GenMineTOP®) were excluded. The following data were obtained from the database: age, gender, histological subtype, test panel, date and sample collection site, treatments before and after sample collection, ER and PgR status, HER2 status, pathogenic variants in the germline BRCA1/2 genes, TMB score, and microsatellite instability status. ER and PgR were registered in the database only as positive or negative, unknown or untested. HER2 was defined as HER2 positive when Immunohistochemical staining (IHC) was 3 + or positive by HER2 in-situ Hybridization (ISH) and HER2 negative when HER2 IHC was 0, 1+, or 2 + and negative by ISH. TMB was defined as the total number of synonymous or nonsynonymous somatic mutations in the target regions of the tumor genome and expressed as mutations per 1 megabase (mut/Mb). Owing to the log-normal distribution of the TMB score, the TMB score variable was log-transformed. A TMB status of 10 mut/Mb or greater was defined as TMB-H. The differences in the patient clinicopathological factors between IDC and ILC were compared using the Mann–Whitney U test or the chi-squared test. The influences of cancer subtype, test panel, and sample collection site on the TMB score or the percentage of patients with TMB-H were compared using the Mann–Whitney U test. All statistical analyses were conducted using IBM SPSS Statistics version 29.0 (IBM Japan Ltd., Tokyo, Japan) and R version 4.3.1 [ 6 ]. P -values < 0.05 were considered statistically significant This study was conducted in accordance with the Ethical Guidelines for Medical and Biological Research Involving Human Subjects (Ministry of Health, Labour and Welfare, Japan) and the Declaration of Helsinki. Furthermore, it was approved by the Institutional Review Boards of Nagoya University Hospital (approval no.: 2022-0025) and C-CAT (C-CAT control number: CDU2022-030N). All participants provided written consent for the use of their genomic data before enrollment into the C-CAT database. Results Of the 4,084 patients with breast cancer registered in the C-CAT database, 3,380 patients had been tested for NOP or F1CDx. We extracted 2,598 and 170 patients with IDC and ILC, respectively, for analysis (Fig. 1 , Table 1 ). Compared with the IDC group, the ILC group had a higher proportion of patients who were older and had the luminal type (ER+/HER2−) (Table 1 ). Table 1 Patient characteristics IDC (n = 2,598) ILC (n = 170) P value Age -median (range) 54 (26–91) 60 (27–82) < 0.001 Sex -n (%) Female 2,582 (99.4) 168 (98.8) 0.698 Male 16 (0.6) 2 (1.2) Subtype -n (%) < 0.001 Luminal (ER+, HER2−) 1,264 (48.7) 118 (69.4) ER+, PgR+, HER2− 878 (33.8) 68 (40.0) ER+, PgR−, HER2− 376 (14.5) 49 (28.8) ER+, PgR unknown, HER2- 10 (0.4) 1 (0.6) Luminal-HER2 (ER+, HER2+) 193 (7.4) 4 (2.4) HER2 (ER−, HER2+) 142 (5.5) 2 (1.2) Triple Negative (ER−, HER2−) 856 (32.9) 39 (23.5) Unknown 143 (5.5) 7 (4.1) Panel -n (%) 0.255 OncoGuide TM NCC Oncopanel System 321 (12.4) 16 (9.4) FoundationOne® CDx 2,277 (87.6) 154 (90.6) Sample collection site -n (%) 0.812 primary breast 1,377 (53.0) 87 (51.2) metastatic site 1,219 (46.9) 81 (47.6) unknown 2 (0.1) 2 (1.2) germline BRCA1 PV -n (%) 0.324 positive 73* (2.8) 1 (0.6) negative 1,546 (59.5) 108 (63.5) VUS 13 (0.5) 0 (0.0) unknown 966 (37.2) 61 (35.9) germline BRCA2 PV -n (%) 0.206 positive 115* (4.4) 2 (1.2) negative 1,502 (57.8) 105 (61.8) VUS 16 (0.6) 0 (0.0) unknown 965 (37.1) 63 (37.1) MSI − status -n (%) 0.859 high 10 (0.4) 1 (0.6) non-high or stable 2,288 (88.1) 151 (88.8) unknown or cannot be determined 300 (11.5) 18 (10.6) * Two patients had both of BRCA1 and BRCA2 pathogenic variants. Abbreviation; IDC: Invasive ductal carcinoma, ILC: invasive lobular carcinoma, ER: estrogen receptor, PgR: progesterone receptor, HER2: human epidermal growth factor receptor type 2, PV: pathogenic variants, VUS: variant with unknown significance, MSI: microsatellite instability The median TMB scores for the IDC and ILC groups were 3.90 and 4.00 mut/Mb, respectively (Fig. 2 a, Table 2 ). When tested with NOP, the ILC group (8.95 mut/Mb) exhibited a higher score than the IDC group (Table 2 ). There were no significant differences in the distribution of TMB by subtype, test panel (NOP vs. F1CDx), or specimen collection site (primary or metastatic site; Fig. 2 b–d). Compared with age, the distribution of TMB was significantly higher for patients aged > 50 years than for those aged < 50 years. (median TMB score 3.78 vs. 4.00; p < 0.001; Fig. 2 e). In addition, while comparing IDC and ILC for each patient characteristics, TMB was higher in IDC for positive g BRCA 2 pathogenic variants (median TMB 6.00 vs. 2.26 mut/Mb, p = 0.042) and in ILC for negative pathogenic variants of g BRCA 2 (median TMB 3.78 vs 4.00 mut/Mb, p = 0.017; Table 2 ). Table 2 Distribution of TMB between IDC and ILC IDC (n = 2,598) ILC (n = 170) P value n TMB median, (IQR) n TMB median, (IQR) All 2,598 3.90 (2.00–6.00) 170 4.00 (2.00-7.14) 0.280 Age (year) ≤ 50 983 3.78 (2.00-5.40) 29 2.52 (1.26–6.5) 0.639 > 50 1615 4.00 (2.00-6.30) 141 4.00 (2.00-7.57) 0.284 Subtypes ER + HER2− 1,264 3.84 (1.60-6.00) 118 4.00 (2.00-7.57) 0.158 ER + PgR + HER2− 878 3.78 (1.26-6.00) 68 3.90 (1.26–6.30) 0.676 ER + PgR − HER2− 376 4.00 (2.30–6.30) 49 4.00 (2.52–10.50) 0.251 ER − HER2+ 142 4.00 (1.51–7.57) 2 3.00 (2.00-NA) 0.598 ER + HER2+ 193 4.00 (2.00-6.30) 4 5.50 (1.75–13.75) 0.607 ER − HER2− 856 3.90 (2.00–6.00) 39 4.00 (2.00-7.57) 0.350 Sample collection site primary site 1,337 3.78 (1.26–5.04) 87 3.78 (1.26-6.00) 0.681 metastatic site 1,219 4.00 (2.52-7.00) 81 4.00 (2.15–10.95) 0.316 Panels NOP 321 3.90 (2.30–6.20) 16 8.95 (4.68–25.18) < 0.001 F1CDx 2,277 3.78 (2.00–6.00) 154 3.78 (1.26–6.30) 0.948 g BRCA1 PV positive 73 4.00 (2.00-6.30) 1 1.00 NA 0.216 negative 1546 3.90 (2.00–6.00) 108 4.00 (2.00–8.00) 0.060 VUS 13 5.04 (2.75–6.3) 0 NA NA unknown 899 3.90 (1.60–6.30) 50 3.78 (2.00-6.30) 0.996 g BRCA2 PV positive 115 6.00 (3.78-9.00) 2 2.26 (2.00-NA) 0.042 negative 1502 3.78 (2.00–6.00) 105 4.00 (2.00-8.42) 0.017 VUS 16 5.00 (1.78–6.83) 0 NA NA unknown 898 3.78 (1.60–6.20) 63 3.44 (2.00-6.23) 0.809 TMB: tumor mutational burden, IDC: Invasive ductal carcinoma, ILC: Invasive lobular carcinoma, ER: Estrogen receptor, PgR: Progesterone receptor, HER2: Human epidermal growth factor receptor type 2, F1CDx: Foundation One® CDx, NOP: OncoguideTM NCC Oncopanel system, g BRCA1 : germline BRCA1 , g BRCA2 : germline BRCA2 , PV: pathogenic variants, VUS: variant of uncertain significance, NA: not applicable The numbers of TMB-H cases in the IDC and ILC groups were 263 (10.1%) and 31 (18.2%), respectively (Table 3 , Fig. 3 a), with the ILC group showing a significantly higher proportion. In particular, while comparing the numbers of ≥ 20 mut/Mb, 13 (7.6%) in ILC, and 64 (2.5%) in IDC, the proportion is particularly high in ILC (Fig. 3 a). While comparing the numbers of TMB-H cases, 164 (11.9%) in ER + HER2−, 67 (15.8%) in the ER + PgR − HER2−, and 20 (13.9%) in the ER − HER2 + were significantly higher than the 72 (8.0%) of TNBC (Fig. 3 b). There was no significant difference between the NOP and F1CDx test panels; however, 103 (7.0%) and 189 (14.5%) specimens from the primary site and metastatic sites were TMB-H, and 73 (7.2%) and 221 (12.6%) of those aged ≤ 50 and > 50 were TMB-H. The proportion of TMB-H was significantly higher in specimens taken from metastatic sites and in those aged ≥ 50 (Fig. 3 c and d).When the IDC and ILC groups were compared by subtype, 140 (11.2%) and 24 (20.3%) patients had ER+/HER2 − breast cancer, respectively, with the latter group showing a significantly higher number of cases; however, for the other subtypes, no statistically significant difference was observed (Table 3 ). When comparison was performed based on sample collection site between the IDC and ILC groups, there were 95 (6.9%) and 8 (9.2%) patients, respectively, in whom samples were collected from the primary breast, and 168 (13.8%) and 21 (25.9%) patients, respectively, showed a significantly higher percentage of TMB-H in the ILC samples collected from metastatic sites for biopsy than in the IDC samples. When comparing IDC and ILC by test panels, 33 (10.3%) and 8 (50.0%) patients received NOP, whereas 230 (10.1%) and 23 (14.9%) received F1CDx, respectively. When comparing IDC and ILC by age, 70 (7.1%) and 3 (10.3%) patients were aged below 50 years, whereas 193 (12.0%) and 28 (19.9%) patients were aged over 50 years, respectively. As a result, the proportion of patients with TMB-H who were tested using the NOP panel and who were aged over 50 years was significantly higher in the ILC than in the IDC group (Table 3 ). When comparing IDC and ILC by the g BRCA1/2 status, 143 (9.2%) patients and 23 (21.3%), respectively, had negative for g BRCA1 pathogenic variants and 134 (8.9%) and 23 (21.9%) patients, respectively, had negative for g BRCA2 pathogenic variants, with the ILC group showing a significantly higher proportion of TMB-H cases (i.e., TMB-H cases within different cancer groups, such as ILC and IDC). Table 3 Percentage of patients with TMB-H IDC + ILC IDC ILC P value Total TMB-H n (%) Total TMB-H n (%) Total TMB-H n (%) All 2,768 294 (10.6) 2,598 263 (10.1) 170 31 (18.2) < 0.001 Subtype ER+, HER2− 1,382 164 (11.9) 1,264 140 (11.1) 118 24 (20.3) 0.003 ER+, PgR+, HER2− 946 95 (10.0) 878 85 (9.7) 68 10 (14.7) 0.184 ER+, PgR−, HER2− 425 67 (15.7) 376 53 (14.1) 49 14 (28.6) 0.009 ER+, HER2+ 197 23 (11.7) 193 22 (11.4) 4 1 (25.0) 0.402 ER−, HER2+ 144 20 (13.9) 142 20 (14.1) 2 0 (0) 1.000 ER−, HER2− 895 72 (8.0) 856 66 (7.7) 39 6 (15.4) 0.085 Sample collection site primary site 1,424 103 (7.0) 1,377 95 (6.9) 87 8 (9.2) 0.435 metastatic site 1,300 189 (14.5) 1,219 168 (13.8) 81 21 (25.9) 0.008 Panel NOP 337 41 (12.2) 321 33 (10.3) 16 8 (50.0) 50 1,756 221 (12.6) 1615 193 (12.0) 141 28 (19.9) 0.007 g BRCA1 PV positive 74 5 (6.8) 73 5 (6.8) 1 0(0) 0.786 negative 1,654 166(10.0) 1,546 143 (9.2) 108 23(21.3) < 0.001 VUS 13 1(7.7) 13 1 (7.7) 0 0 (NA) NA unknown 949 112 (11.7) 966 104(11.6) 63 8(13.3) 0.683 g BRCA2 PV positive 117 15 (12.8) 115 15 (13.0) 2 0(0) 0.584 negative 1,607 157 (9.8) 1,502 134 (8.9) 105 23 (21.9) < 0.001 VUS 16 2 (12.5) 16 2 (12.5) 0 0 (NA) NA unknown 961 114 (11.9) 965 106 (11.8) 63 8 (13.8) 0.648 IDC: Invasive ductal carcinoma, ILC: invasive lobular carcinoma, ER: estrogen receptor, PgR: progesterone receptor, HER2: human epidermal growth factor receptor type 2, NOP: OncoGuideTM NCC Oncopanel System, F1CDx: FoundationOne® CDx, PV: pathogenic variants, VUS: variant of uncertain significance, NA: not applicable To evaluate the impact of previous treatment on the proportion of TMB-H cases, we compared the samples collected before and after drug therapy in terms of the proportion of TMB-H cases. We found that 20 (13.2%) and 274 (10.5%) cases in the samples collected before and after therapy ( P = 0.282), respectively, had TMB-H, indicating that the treatment exerted no effect. According to treatment, TMB-H was observed in 104 (8.9%) and 141 (11.4%) cases in the samples collected before and after endocrine therapy ( P = 0.046), 24 (11.1%) and 221 (10.1%) in the samples collected before and after chemotherapy ( P = 0.639), and 222 (10.1%) and 221 (10.1%) in the samples collected before and after anti-HER2 therapy ( P = 0.639), respectively. As regards the ER+/HER2 − subtype, there were 30 (13.8%) and 105 (10.5%) cases before and after endocrine therapy, respectively ( P = 0.180), whereas for the ER−/HER2 − subtype, there were 47 (6.7%) and 18 (18.9%) cases, respectively ( P < 0.001). Table 4 presents the adjusted odds ratios and 95% confidence intervals (CI) from the logistic regression analysis adjusted for the aforementioned factors in patients with IDC and ILC (Table 4 ). The adjusted odds ratios (95% CI) for age ≥ 50 years, ILC, and sample collection from metastatic sites were 1.691 (1.264–2.263), 1.813 (1.181–2.783), and 2.109 (1.623–2.741), respectively. Meanwhile, subtype, test panel, and g BRCA1/2 were not related to the proportion of TMB-H cases. Table 4 Multivariate analysis of percentage of patients with TMB-H variable category Adjusted OR 95%CI P value Age ≤ 50 (reference) > 50 1.691 1.264–2.263 < 0.001 Type IDC (reference) ILC 1.813 1.181–2.783 0.006 Subtype ER+, HER2− (reference) 0.211 ER−, HER2+ 1.289 0.759–2.189 0.348 ER+, HER2+ 0.973 0.595–1.591 0.913 ER−, HER2− 0.747 0.553–1.011 0.059 unknown 0.661 0.290–1.509 0.326 Sample collection site primary site (reference) metastatic site 2.109 1.623–2.741 < 0.001 Panel F1CDx (reference) NOP 1.140 0.791–1.641 0.482 g BRCA1 PV negative (reference) positive 0.949 0.369–2.436 0.913 VUS 0.625 0.061–6.411 0.692 unknown 3.587 0.708–18.175 0.123 g BRCA2 PV negative (reference) positive 1.166 0.633–2.150 0.622 VUS 2.291 0.416–12.616 0.341 unknown 1.027 1.015–1.039 0.125 TMB-H: tumor mutational burden high, IDC: Invasive ductal carcinoma, ILC: Invasive lobular carcinoma, ER: Estrogen receptor, HER2: human epidermal growth factor type2, NOP: OncoGuideTM NCC Oncopanel System, F1CDx: FoundationOne® CDx, g BRCA1 : germline BRCA1 , g BRCA2 : germline BRCA2 , PV: pathogenic variants, VUS: variant of uncertain significance Discussion In this study, the median TMB scores were 4.00 and 3.90 (mut/Mb) for the ILC and IDC groups, respectively, and these values did not differ between ILCs and IDCs when evaluated by the subtype, test panel, age, or sample collection site. Furthermore, the proportion of TMB-H cases was statistically significantly higher in the ILC than in the IDC group (18.2% vs. 10.1%, respectively). Moreover, the proportion of TMB-H cases in the ILC group was particularly high among those with the ER+/HER2 − subtype and in whom metastatic lesion was the sample collection site. In addition, the proportion of TMB-H cases was higher among those with the ER+/HER2 − and ER−/HER2 + subtypes than in the TNBC and to be higher in cases sampled from metastatic sites and in those aged 50 years or older. On the other hand, when comparing ILC and IDC, there was no difference in the distribution of TMB scores between ILC and IDC, expect for BRCA1/2 pathogenic variant -negative cases or those tested by NOP. In other words, except for BRCA 1/2 pathogenic variant -negative cases, there is no difference in the distribution of TMB between ILC and IDC, but there is a special population of TMB-H cases that is found more frequently in ILC than in IDC. This cannot be predicted by clinical factors alone, and it is necessary to predict based on factors such as gene alterations. A high TMB is associated with a high neoantigen load, making the tumor in high immunogenic conditions. Compared with immunogenic tumors, such as skin squamous cell carcinoma (45.2 mut/Mb), melanoma (14.4 mut/Mb), and non-small cell lung carcinoma (8.1 mut/Mb), the TMB scores in breast cancer were reportedly lower (3.6–3.8 mut/Mb) [ 5 , 7 ]. TNBC has been reported to have a higher TMB score than ER + or HER2 + cancers because of its high response to immunotherapy[ 8 , 9 ]. Reportedly, TMB score is also high in ER + HER2 − breast cancer [ 10 ]. Herein, the proportion of TMB-H cases was higher in those with the ER+/HER2 − subtype than in the TNBC cohort. This is thought to be due to genomic diversity in HR + HER2 − breast cancer. Similarly, it was higher in the ILC than in the IDC group, which is consistent with the result of a previous study that included breast cancer cohorts [ 9 ]. The proportion of TMB-H was not affected by the treatment, but the proportion of TMB-H cases was found to be high in the TNBC cohort after hormone therapy. The reason why the TNBC cohort was administered hormone therapy is unknown, but this may indicate that the result of hormone therapy for HR+/HER2 − breast cancer patients changing to TNBC and may be related to the intratumonal heterogeneity of breast cancer for TMB status. More PIK3CA mutations were observed in ILC than in IDC, and it has been reported that specific PIK3CA mutations in ILC and metastatic lesions [ 1 ] induced mutations in APOBEC genes and that the presence of APOBEC gene mutations is related to TMB-H [ 11 ]. In this study, we examined the relationship between information obtained from clinical practice and TMB, and since we did not obtain information on gene alterations, the association between gene alterations and TMB scores was not evaluated; however, the differences in the molecular characteristics between IDC and ILC led to the difference in the proportion of TMB-H. Meanwhile, when comparing by the sample collection site, the proportion of TMB-H was higher in the brain metastasis of lung cancer than in other metastatic sites [ 12 , 13 ]. Although differences in the proportion of TMB-H may vary depending on the site from which the specimen was taken and on the type of cancer, the TMB is often higher at metastatic sites than at the primary site, even within breast cancer [ 9 , 13 , 14 ]. In this study, although the number of cases of brain metastasis was not enough to make comparisons, the proportion of TMB-H in metastatic lesions was higher than in primary tumors, indicating that there may be differences depending on the sample collection site. In patients without the pathogenic variant of g BRCA1/2 , the TMB was higher in ILC than in IDC, whereas in those with the pathogenic variant, no difference was observed. However, in the multivariate analysis, the status of g BRCA1/2 did not affect the proportion of TMB-H. Breast cancer with the BRCA1/2 gene mutations is thought to have relatively high TMB [ 15 ]; however, the number of ILC cases with g BRCA1/2 pathogenic variants is not enough to allow for sufficient consideration. ILC has more germline CDH1 variants than IDC, still only around 0.54% [ 1 ]. Patients without pathogenic variants of g BRCA1/2 should also include those with other germline gene variants associated with hereditary breast cancer; however, further investigation on the association between such germline variants and TMB-H is warranted. The KEYNOTE-158 study confirmed the efficacy of pembrolizumab in the treatment of solid tumors with TMB-H, with an overall response rate of 29% [ 4 ]; however, it did not include patients with breast cancer. The Checkmate 848 trial was a phase II study that randomly assigned patients with tumor TMB-H and/or blood TMB-H solid tumors to the nivolumab (NIVO) + ipilimumab (IPI) therapy or NIVO monotherapy. The objective response rates for t-TMB-H were 38.6% (28.4–49.6) in the NIVO + IPI group and 29.8% (17.3–44.9) in the NIVO group. Of the 211 randomized patients in this study, 15 (7.1%) had breast cancer [ 16 , 17 ]. The results of the TAPUR study confirmed the efficacy of pembrolizumab in the treatment of breast cancer with TMB-H (TNBC, 46%; HR+/HER2−, 43%), with disease control and response rates of 37% and 21%, respectively [ 18 ]. These suggest that patients with breast cancer with TMB-H may also benefit from ICI, even in other than TNBC, especially in ILC patients. Although some ILCs were highly immunogenic, this high immunogenicity does not necessarily correspond to TMB-H [ 19 ]. The efficacy of ICI in patients with TMB-H may be limited in ILCs that are not immunologically “hot,” and further investigation is needed. This study has several limitations. First, compared with IDC, the number of ILC cases was extremely small, particularly ILC cases tested using the NOP or with g BRCA1/2 pathogenic variants. Furthermore, the background of the patients who were tested may have greatly differed depending on the subtype as the tests were conducted under Japanese insurance reimbursement. For example, the proportion of TNBC patients was higher, and the proportion of HER2 + type patients was lower than the general population. Second, patients who had completed or were expected to complete the standard treatment were considered eligible for the tests. The small proportion of HER2 + types compared with the real world could be attributed to the fact that few clinicians were expected to benefit from the panel testing due to the already existing oncogene. Third, in this study, we excluded blood TMB to first elucidate tumor TMB. However, to the best of our knowledge, this is the first study to investigate in detail TMB in breast cancer patients using a public database in Japan. In conclusion, this study demonstrated that the patients with ILC were more likely to have TMB-H than those with IDC. From the perspective of ICI therapy based on the TMB status, the findings would be invaluable in selecting treatment strategies for patients with ILC. Declarations Compliance with Ethical Standards Disclosure of Potential Conflicts of Interest YT received a research grant from Eli Lilly and speaker honorarium from Chugai, MSD, Eli Lilly Japan K.K., Pfizer, Dai-ichi Sankyo, and AstraZeneca. MI received a research grant from AstraZeneca and Pfizer and speaker honorarium from Chugai., MSD, Eli Lilly, Pfizer, Kyowa Kirin, Taiho, and Exact Sciences. TK received a speaker honorarium from Chugai, Daiichi Sankyo, Pfizer, Novartis Pharma, Eli Lilly, Eisai, Kyowa Kirin, and Celltrion. YA received a research grant from Chugai, Kyowa Kirin, Nippon Kayaku, Mochida Pharma, Taiho, Daiichi Sankyo and BeiGene and speaker honorarium from Chugai, MSD K.K, Eli Lilly, Kyowa Kirin, Nippon Kayaku, Novartis Pharma, Daiichi Sankyo, Taiho, Ono, Guardant Health, and AstraZeneca. KM, SM, and NT have no conflict of interest. Research Involving Human Participants and/or Animals All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed Consent Informed consent was obtained from all individuals who agreed to be registered in the C-CAT database. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Yuko Takano and Kazuyuki Mizuno. The first draft of the manuscript was written by Yuko Takano, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgments This study was not supported by any grants or foundations. We greatly appreciate the patients who cooperated in the use of secondary data from C-CAT. References Van Baelen K, Geukens T, Maetens M, Tjan-Heijnen V, Lord CJ, Linn S, et al. Current and future diagnostic and treatment strategies for patients with invasive lobular breast cancer. Ann Oncol 2022;33:769–85. Adachi Y, Asaga S, Kumamaru H, Kinugawa N, Sagara Y, Niikura N, et al. Analysis of prognosis in different subtypes of invasive lobular carcinoma using the Japanese National Cancer Database-Breast Cancer Registry. Breast Cancer Res Treat 2023;201:397–408. Emens LA, Adams S, Barrios CH, Diéras V, Iwata H, Loi S, et al. First-line atezolizumab plus nab-paclitaxel for unresectable, locally advanced, or metastatic triple-negative breast cancer: IMpassion130 final overall survival analysis. Ann Oncol 2021;32:983–93. Marabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol 2020;21:1353–65. Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 2017;9:34. Wickham H, Averick M, Bryan J, Chang W, McGowan LDA, François R, et al. Welcome to the Tidyverse. J Open Source Softw 2019;4:1686. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013;499:214–8. Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 2012;486:395–9. Barroso-Sousa R, Jain E, Cohen O, Kim D, Buendia-Buendia J, Winer E, et al. Prevalence and mutational determinants of high tumor mutation burden in breast cancer. Ann Oncol 2020;31:387–94. Bertucci F, Ng CKY, Patsouris A, Droin N, Piscuoglio S, et al. Genomic characterization of metastatic breast cancers. Nature 2019;569:560–4. Sammons S, Raskina K, Danziger N, Alder L, Schrock AB, Venstrom JM, et al. APOBEC mutational signatures in hormone receptor-positive human epidermal growth factor receptor 2-negative breast cancers are associated with poor outcomes on CDK4/6 inhibitors and endocrine therapy. JCO Precis Oncol 2022;6:e2200149. Stein MK, Pandey M, Xiu J, Tae H, Swensen J, Mittal S, et al. Tumor mutational burden is site specific in non-small-cell lung cancer and is highest in lung adenocarcinoma brain metastases. JCO Precis Oncol 2019;3:1–13. Papillon-Cavanagh S, Hopkins JF, Ramkissoon SH, Albacker LA, Walsh AM. Pan-cancer analysis of the effect of biopsy site on tumor mutational burden observations. Commun Med (Lond) 2021;1:56. Angus L, Smid M, Wilting SM, van Riet J, Van Hoeck A, Nguyen L, et al. The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat Genet 2019;51:1450–8. Kraya AA, Maxwell KN, Wubbenhorst B, Wenz BM, Pluta J, Rech AJ, et al. Genomic signatures predict the immunogenicity of BRCA-deficient breast cancer. Clin Cancer Res 2019;25:4363–74. He J, Kalinava N, Doshi P, Pavlick DC, Albacker LA, Ebot EM, et al. Evaluation of tissue- and plasma-derived tumor mutational burden (TMB) and genomic alterations of interest in CheckMate 848, a study of nivolumab combined with ipilimumab and nivolumab alone in patients with advanced or metastatic solid tumors with high TMB. J Immunother Cancer 2023;11:e007339. Schenker M, Burotto M, Richardet M, Ciuleanu TE, Gonçalves A, Steeghs N, et al. Randomized, open-label, phase 2 study of nivolumab plus ipilimumab or nivolumab monotherapy in patients with advanced or metastatic solid tumors of high tumor mutational burden. J Immunother Cancer 2024;12:e008872. Alva AS, Mangat PK, Garrett-Mayer E, Halabi S, Hansra D, Calfa CJ, et al. Pembrolizumab in patients with metastatic breast cancer with high tumor mutational burden: results from the targeted agent and profiling utilization registry (TAPUR) study. J Clin Oncol 2021;39:2443–51. Michaut M, Chin SF, Majewski I, Severson TM, Bismeijer T, de Koning L, et al. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer. Sci Rep. 2016;6:18517. Cite Share Download PDF Status: Published Journal Publication published 02 May, 2025 Read the published version in Breast Cancer → Version 1 posted Editorial decision: Accept 18 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 08 Apr, 2025 Editor assigned by journal 03 Apr, 2025 First submitted to journal 02 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5578316","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439939612,"identity":"21d5cfa3-07ea-4c9c-8720-181cf3afb58c","order_by":0,"name":"Yuko Takano","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-3753-7683","institution":"Nagoya University Hospital: Nagoya Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":true,"prefix":"","firstName":"Yuko","middleName":"","lastName":"Takano","suffix":""},{"id":439939613,"identity":"e192fdb6-c9a6-49b5-a09a-645189efe3d1","order_by":1,"name":"Kazuyuki Mizuno","email":"","orcid":"","institution":"Nagoya University Hospital: Nagoya Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Kazuyuki","middleName":"","lastName":"Mizuno","suffix":""},{"id":439939614,"identity":"19a0f57a-bc50-4383-a994-66c40bd1096d","order_by":2,"name":"Madoka Iwase","email":"","orcid":"","institution":"Nagoya University Hospital: Nagoya Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Madoka","middleName":"","lastName":"Iwase","suffix":""},{"id":439939615,"identity":"5071913c-f57f-4240-a928-1d07832eab6e","order_by":3,"name":"Sachi Morita","email":"","orcid":"","institution":"Nagoya University Hospital: Nagoya Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Sachi","middleName":"","lastName":"Morita","suffix":""},{"id":439939616,"identity":"cdf47037-6983-42c4-b1f3-613f65af185c","order_by":4,"name":"Nao Torii","email":"","orcid":"","institution":"Nagoya University Hospital: Nagoya Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Nao","middleName":"","lastName":"Torii","suffix":""},{"id":439939617,"identity":"1b633052-bf0b-4518-a0cf-894f249007bf","order_by":5,"name":"Toyone Kikumori","email":"","orcid":"","institution":"Nagoya University Hospital: Nagoya Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Toyone","middleName":"","lastName":"Kikumori","suffix":""},{"id":439939618,"identity":"196a2467-5fe1-4e5e-89ff-0e294b94a236","order_by":6,"name":"Yuichi Ando","email":"","orcid":"","institution":"Nagoya University Hospital: Nagoya Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Yuichi","middleName":"","lastName":"Ando","suffix":""}],"badges":[],"createdAt":"2024-12-04 09:09:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5578316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5578316/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12282-025-01706-6","type":"published","date":"2025-05-02T15:57:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80279299,"identity":"9d007872-6149-4281-bc5f-ce992e4b88b9","added_by":"auto","created_at":"2025-04-10 05:33:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComposition of patients of study criteria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5578316/v1/ca66c37bd694bfe145f29a5d.png"},{"id":80279294,"identity":"8ea23946-b054-4c9d-a431-064d5652a4c3","added_by":"auto","created_at":"2025-04-10 05:33:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution and median of TMB in each subgroup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe y-axis is displayed in logarithmic form. The difference between IDCs and ILCs (a), between subtypes (b), between test panels (c), sample collection sites (d), or between ages (\u0026lt;50 vs. \u0026gt;50 years) (e) are shown.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5578316/v1/3b2b4b737650743cca0f6a50.png"},{"id":80280536,"identity":"139f7082-66bf-4a6a-a85a-2447b38be8d1","added_by":"auto","created_at":"2025-04-10 05:41:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of patients with TMB-H\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe difference between IDCs and ILCs (a), between subtypes (b), between test panels (c), sample collection sites (d), or between ages (≤50 vs. \u0026gt;50 years) (e) are shown.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5578316/v1/48d93b3da066661605304c48.png"},{"id":81988066,"identity":"bab4075d-64a5-4408-9e87-c00581970fbd","added_by":"auto","created_at":"2025-05-05 16:07:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1381773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5578316/v1/3d4d8854-28be-4e4f-83c2-bcd8dc8918e2.pdf"}],"financialInterests":"","formattedTitle":"Tumor mutational burden status and clinical characteristics of invasive lobular carcinoma of the breast","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInvasive lobular carcinoma (ILC) is the second most common histological type of invasive breast cancer next to invasive ductal carcinoma (IDC); it accounts for 5\u0026ndash;15% of all breast cancer cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ILC and IDC differ in terms of clinicopathological factors, features, and prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Patients with ILC and those with IDC are usually treated similarly, depending on the molecular classification: anthracycline- and taxane-based chemotherapy, hormone therapy for estrogen receptor (ER)- and/or progesterone receptor (PgR) receptor-positive subtypes, and anti-HER2 therapy for HER2\u0026thinsp;+\u0026thinsp;subtypes. Poly-adenosine diphosphate ribose polymerase inhibitors have been approved for the treatment of patients with pathogenic germline variants in \u003cem\u003eBRCA1/2\u003c/em\u003e genes with the HER2\u0026thinsp;\u0026minus;\u0026thinsp;subtype. In addition, immune checkpoint inhibitors (ICIs) have recently been introduced into the standard of care for antiprogramed cell death ligand 1-positive recurrent triple-negative breast cancer (TNBC) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. They have also been approved for the treatment of solid tumors with a tumor mutational burden (TMB) status of \u0026ge;\u0026thinsp;10 mutations/megabases (mut/Mb, high TMB [TMB-H]), the latter being a tumor agnostic indicator [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA high TMB status is recognized as a positive biomarker for better clinical response to ICIs in the treatment of many cancers, including breast cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. With the widespread use of comprehensive genomic profiling (CGP) testing in clinical practice, the TMB status as a biomarker of the therapeutic efficacy of ICIs had gained considerable interest among clinicians. The percentage of patients with TMB-H has been reported to be 3\u0026ndash;10% in all breast cancer cases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; however, the distribution of TMB and the percentage of patients with TMB-H has not been sufficiently investigated across the molecular and histological subtypes. Therefore, this study investigated the TMB status and clinical characteristics of patients with various molecular and histological subtypes of breast cancer, particularly ILC, using a large nationwide database of genomic information in Japan.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cp\u003eTo explore the TMB status and clinical characteristics of patients with various molecular and histological subtypes of breast cancer, we evaluated the genomic profiles of patients with breast cancer who had been registered in the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database between June 2019 and August 2023. The C-CAT database is a large-scale nationwide database that aggregates the data of the CGP test (panel), which is covered by public health insurance in Japan. Nearly all the patients in the study were highly likely to be Japanese.\u003c/p\u003e \u003cp\u003eThe database was searched on March 12, 2024. Patients with IDC or ILC were extracted based on the detailed pathological diagnosis registered in the database. Only patients who received two tissue-based panels, the OncoGuide\u0026trade; NCC Oncopanel System (NOP) and FoundationOne\u0026reg; CDx Cancer Genomic Profile (F1CDx), were included in the analysis. Those tested using other panels (FoundationOne\u0026reg; CDx Liquid, Guardant 360\u0026reg;CDx, GenMineTOP\u0026reg;) were excluded. The following data were obtained from the database: age, gender, histological subtype, test panel, date and sample collection site, treatments before and after sample collection, ER and PgR status, HER2 status, pathogenic variants in the germline \u003cem\u003eBRCA1/2\u003c/em\u003e genes, TMB score, and microsatellite instability status. ER and PgR were registered in the database only as positive or negative, unknown or untested. HER2 was defined as HER2 positive when Immunohistochemical staining (IHC) was 3\u0026thinsp;+\u0026thinsp;or positive by HER2 in-situ Hybridization (ISH) and HER2 negative when HER2 IHC was 0, 1+, or 2\u0026thinsp;+\u0026thinsp;and negative by ISH. TMB was defined as the total number of synonymous or nonsynonymous somatic mutations in the target regions of the tumor genome and expressed as mutations per 1 megabase (mut/Mb). Owing to the log-normal distribution of the TMB score, the TMB score variable was log-transformed. A TMB status of 10 mut/Mb or greater was defined as TMB-H.\u003c/p\u003e \u003cp\u003eThe differences in the patient clinicopathological factors between IDC and ILC were compared using the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test or the chi-squared test. The influences of cancer subtype, test panel, and sample collection site on the TMB score or the percentage of patients with TMB-H were compared using the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test. All statistical analyses were conducted using IBM SPSS Statistics version 29.0 (IBM Japan Ltd., Tokyo, Japan) and R version 4.3.1 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the Ethical Guidelines for Medical and Biological Research Involving Human Subjects (Ministry of Health, Labour and Welfare, Japan) and the Declaration of Helsinki. Furthermore, it was approved by the Institutional Review Boards of Nagoya University Hospital (approval no.: 2022-0025) and C-CAT (C-CAT control number: CDU2022-030N). All participants provided written consent for the use of their genomic data before enrollment into the C-CAT database.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 4,084 patients with breast cancer registered in the C-CAT database, 3,380 patients had been tested for NOP or F1CDx. We extracted 2,598 and 170 patients with IDC and ILC, respectively, for analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with the IDC group, the ILC group had a higher proportion of patients who were older and had the luminal type (ER+/HER2\u0026minus;) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003ePatient characteristics\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIDC (n\u0026thinsp;=\u0026thinsp;2,598)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eILC (n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge -median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(26\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(27\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex -n (%)\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\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(99.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\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\u003e(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype -n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal (ER+, HER2\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER+, PgR+, HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER+, PgR\u0026minus;, HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER+, PgR unknown, HER2-\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\u003e(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal-HER2 (ER+, HER2+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 (ER\u0026minus;, HER2+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple Negative (ER\u0026minus;, HER2\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\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\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePanel -n (%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOncoGuide\u003csup\u003eTM\u003c/sup\u003e\u0026nbsp;NCC Oncopanel System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFoundationOne\u0026reg;\u0026nbsp;CDx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSample collection site -n (%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eprimary breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emetastatic site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eunknown\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\u003e(0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egermline \u003cem\u003eBRCA1\u003c/em\u003e PV -n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\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\u003e73*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\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\u003e1,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVUS\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\u003e(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\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\u003e966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egermline \u003cem\u003eBRCA2\u003c/em\u003e PV -n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\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\u003e115*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\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\u003e1,502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVUS\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\u003e(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\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\u003e965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSI\u0026thinsp;\u0026minus;\u0026thinsp;status -n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh\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\u003e(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-high or stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknown or cannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Two patients had both of \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e pathogenic variants.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviation; IDC: Invasive ductal carcinoma, ILC: invasive lobular carcinoma, ER: estrogen receptor, PgR: progesterone receptor, HER2: human epidermal growth factor receptor type 2, PV: pathogenic variants, VUS: variant with unknown significance, MSI: microsatellite instability\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe median TMB scores for the IDC and ILC groups were 3.90 and 4.00 mut/Mb, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When tested with NOP, the ILC group (8.95 mut/Mb) exhibited a higher score than the IDC group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were no significant differences in the distribution of TMB by subtype, test panel (NOP vs. F1CDx), or specimen collection site (primary or metastatic site; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u0026ndash;d). Compared with age, the distribution of TMB was significantly higher for patients aged\u0026thinsp;\u0026gt;\u0026thinsp;50 years than for those aged\u0026thinsp;\u0026lt;\u0026thinsp;50 years. (median TMB score 3.78 vs. 4.00; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). In addition, while comparing IDC and ILC for each patient characteristics, TMB was higher in IDC for positive g\u003cem\u003eBRCA\u003c/em\u003e2 pathogenic variants (median TMB 6.00 vs. 2.26 mut/Mb, p\u0026thinsp;=\u0026thinsp;0.042) and in ILC for negative pathogenic variants of g\u003cem\u003eBRCA\u003c/em\u003e2 (median TMB 3.78 vs 4.00 mut/Mb, p\u0026thinsp;=\u0026thinsp;0.017; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eDistribution of TMB between IDC and ILC\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eIDC (n\u0026thinsp;=\u0026thinsp;2,598)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eILC (n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTMB median, (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTMB median, (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\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\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00-5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.26\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00-6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-7.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtypes\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\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.60-6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-7.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;PgR\u0026thinsp;+\u0026thinsp;HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.26-6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.26\u0026ndash;6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;PgR\u0026thinsp;\u0026minus;\u0026thinsp;HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.30\u0026ndash;6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.52\u0026ndash;10.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026thinsp;\u0026minus;\u0026thinsp;HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.51\u0026ndash;7.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00-6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.75\u0026ndash;13.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026thinsp;\u0026minus;\u0026thinsp;HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-7.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample collection site\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\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprimary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.26\u0026ndash;5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.26-6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetastatic site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.52-7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.15\u0026ndash;10.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanels\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\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.30\u0026ndash;6.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.68\u0026ndash;25.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1CDx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.26\u0026ndash;6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003e PV\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\" colspan=\"1\" nameend=\"c9\" namest=\"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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00-6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00\u0026ndash;8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.75\u0026ndash;6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.60\u0026ndash;6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003e PV\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\" colspan=\"1\" nameend=\"c9\" namest=\"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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(3.78-9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-8.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.78\u0026ndash;6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(1.60\u0026ndash;6.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.00-6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eTMB: tumor mutational burden, IDC: Invasive ductal carcinoma, ILC: Invasive lobular carcinoma, ER: Estrogen receptor, PgR: Progesterone receptor, HER2: Human epidermal growth factor receptor type 2, F1CDx: Foundation One\u0026reg; CDx, NOP: OncoguideTM NCC Oncopanel system, g\u003cem\u003eBRCA1\u003c/em\u003e: germline \u003cem\u003eBRCA1\u003c/em\u003e, g\u003cem\u003eBRCA2\u003c/em\u003e: germline \u003cem\u003eBRCA2\u003c/em\u003e, PV: pathogenic variants, VUS: variant of uncertain significance, NA: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe numbers of TMB-H cases in the IDC and ILC groups were 263 (10.1%) and 31 (18.2%), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), with the ILC group showing a significantly higher proportion. In particular, while comparing the numbers of \u0026ge;\u0026thinsp;20 mut/Mb, 13 (7.6%) in ILC, and 64 (2.5%) in IDC, the proportion is particularly high in ILC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). While comparing the numbers of TMB-H cases, 164 (11.9%) in ER\u0026thinsp;+\u0026thinsp;HER2\u0026minus;, 67 (15.8%) in the ER\u0026thinsp;+\u0026thinsp;PgR\u0026thinsp;\u0026minus;\u0026thinsp;HER2\u0026minus;, and 20 (13.9%) in the ER\u0026thinsp;\u0026minus;\u0026thinsp;HER2\u0026thinsp;+\u0026thinsp;were significantly higher than the 72 (8.0%) of TNBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). There was no significant difference between the NOP and F1CDx test panels; however, 103 (7.0%) and 189 (14.5%) specimens from the primary site and metastatic sites were TMB-H, and 73 (7.2%) and 221 (12.6%) of those aged\u0026thinsp;\u0026le;\u0026thinsp;50 and \u0026gt;\u0026thinsp;50 were TMB-H. The proportion of TMB-H was significantly higher in specimens taken from metastatic sites and in those aged\u0026thinsp;\u0026ge;\u0026thinsp;50 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and d).When the IDC and ILC groups were compared by subtype, 140 (11.2%) and 24 (20.3%) patients had ER+/HER2\u0026thinsp;\u0026minus;\u0026thinsp;breast cancer, respectively, with the latter group showing a significantly higher number of cases; however, for the other subtypes, no statistically significant difference was observed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When comparison was performed based on sample collection site between the IDC and ILC groups, there were 95 (6.9%) and 8 (9.2%) patients, respectively, in whom samples were collected from the primary breast, and 168 (13.8%) and 21 (25.9%) patients, respectively, showed a significantly higher percentage of TMB-H in the ILC samples collected from metastatic sites for biopsy than in the IDC samples. When comparing IDC and ILC by test panels, 33 (10.3%) and 8 (50.0%) patients received NOP, whereas 230 (10.1%) and 23 (14.9%) received F1CDx, respectively. When comparing IDC and ILC by age, 70 (7.1%) and 3 (10.3%) patients were aged below 50 years, whereas 193 (12.0%) and 28 (19.9%) patients were aged over 50 years, respectively. As a result, the proportion of patients with TMB-H who were tested using the NOP panel and who were aged over 50 years was significantly higher in the ILC than in the IDC group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When comparing IDC and ILC by the g\u003cem\u003eBRCA1/2\u003c/em\u003e status, 143 (9.2%) patients and 23 (21.3%), respectively, had negative for g\u003cem\u003eBRCA1\u003c/em\u003e pathogenic variants and 134 (8.9%) and 23 (21.9%) patients, respectively, had negative for g\u003cem\u003eBRCA2\u003c/em\u003e pathogenic variants, with the ILC group showing a significantly higher proportion of TMB-H cases (i.e., TMB-H cases within different cancer groups, such as ILC and IDC).\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\u003ePercentage of patients with TMB-H\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIDC\u0026thinsp;+\u0026thinsp;ILC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIDC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eILC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTMB-H\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTMB-H\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMB-H\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e263 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER+, HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e140 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER+, PgR+, HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER+, PgR\u0026minus;, HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER+, HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026minus;, HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026minus;, HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample collection site\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprimary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetastatic site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e168 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1CDx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e253 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e230 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e193 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003e PV\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.786\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e143 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23(21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003e PV\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.584\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eIDC: Invasive ductal carcinoma, ILC: invasive lobular carcinoma, ER: estrogen receptor, PgR: progesterone receptor, HER2: human epidermal growth factor receptor type 2, NOP: OncoGuideTM NCC Oncopanel System, F1CDx: FoundationOne\u0026reg; CDx, PV: pathogenic variants, VUS: variant of uncertain significance, NA: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the impact of previous treatment on the proportion of TMB-H cases, we compared the samples collected before and after drug therapy in terms of the proportion of TMB-H cases. We found that 20 (13.2%) and 274 (10.5%) cases in the samples collected before and after therapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.282), respectively, had TMB-H, indicating that the treatment exerted no effect. According to treatment, TMB-H was observed in 104 (8.9%) and 141 (11.4%) cases in the samples collected before and after endocrine therapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046), 24 (11.1%) and 221 (10.1%) in the samples collected before and after chemotherapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.639), and 222 (10.1%) and 221 (10.1%) in the samples collected before and after anti-HER2 therapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.639), respectively. As regards the ER+/HER2\u0026thinsp;\u0026minus;\u0026thinsp;subtype, there were 30 (13.8%) and 105 (10.5%) cases before and after endocrine therapy, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.180), whereas for the ER\u0026minus;/HER2\u0026thinsp;\u0026minus;\u0026thinsp;subtype, there were 47 (6.7%) and 18 (18.9%) cases, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the adjusted odds ratios and 95% confidence intervals (CI) from the logistic regression analysis adjusted for the aforementioned factors in patients with IDC and ILC (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The adjusted odds ratios (95% CI) for age\u0026thinsp;\u0026ge;\u0026thinsp;50 years, ILC, and sample collection from metastatic sites were 1.691 (1.264\u0026ndash;2.263), 1.813 (1.181\u0026ndash;2.783), and 2.109 (1.623\u0026ndash;2.741), respectively. Meanwhile, subtype, test panel, and g\u003cem\u003eBRCA1/2\u003c/em\u003e were not related to the proportion of TMB-H cases.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of percentage of patients with TMB-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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50 (reference)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.264\u0026ndash;2.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIDC (reference)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eILC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.181\u0026ndash;2.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER+, HER2\u0026minus; (reference)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.211\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\u003eER\u0026minus;, HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.759\u0026ndash;2.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.348\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\u003eER+, HER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.595\u0026ndash;1.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.913\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\u003eER\u0026minus;, HER2\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.553\u0026ndash;1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059\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\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.290\u0026ndash;1.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample collection site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprimary site (reference)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emetastatic site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.623\u0026ndash;2.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1CDx (reference)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.791\u0026ndash;1.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003e PV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative (reference)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.369\u0026ndash;2.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.913\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\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u0026ndash;6.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.692\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\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.708\u0026ndash;18.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003e PV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative (reference)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.633\u0026ndash;2.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.622\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\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u0026ndash;12.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.341\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\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.015\u0026ndash;1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTMB-H: tumor mutational burden high, IDC: Invasive ductal carcinoma, ILC: Invasive lobular carcinoma, ER: Estrogen receptor, HER2: human epidermal growth factor type2, NOP: OncoGuideTM NCC Oncopanel System, F1CDx: FoundationOne\u0026reg; CDx, g\u003cem\u003eBRCA1\u003c/em\u003e: germline \u003cem\u003eBRCA1\u003c/em\u003e, g\u003cem\u003eBRCA2\u003c/em\u003e: germline \u003cem\u003eBRCA2\u003c/em\u003e, PV: pathogenic variants, VUS: variant of uncertain significance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the median TMB scores were 4.00 and 3.90 (mut/Mb) for the ILC and IDC groups, respectively, and these values did not differ between ILCs and IDCs when evaluated by the subtype, test panel, age, or sample collection site. Furthermore, the proportion of TMB-H cases was statistically significantly higher in the ILC than in the IDC group (18.2% vs. 10.1%, respectively). Moreover, the proportion of TMB-H cases in the ILC group was particularly high among those with the ER+/HER2\u0026thinsp;\u0026minus;\u0026thinsp;subtype and in whom metastatic lesion was the sample collection site. In addition, the proportion of TMB-H cases was higher among those with the ER+/HER2\u0026thinsp;\u0026minus;\u0026thinsp;and ER\u0026minus;/HER2\u0026thinsp;+\u0026thinsp;subtypes than in the TNBC and to be higher in cases sampled from metastatic sites and in those aged 50 years or older. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOn the other hand, when comparing ILC and IDC, there was no difference in the distribution of TMB scores between ILC and IDC, expect for\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eBRCA1/2\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epathogenic variant -negative cases or those tested by NOP. In other words, except for\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eBRCA\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e1/2\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epathogenic variant -negative cases, there is no difference in the distribution of TMB between ILC and IDC, but there is a special population of TMB-H cases that is found more frequently in ILC than in IDC. This cannot be predicted by clinical factors alone, and it is necessary to predict based on factors such as gene alterations.\u003c/span\u003e\u003c/p\u003e \u003cp\u003eA high TMB is associated with a high neoantigen load, making the tumor in high immunogenic conditions. Compared with immunogenic tumors, such as skin squamous cell carcinoma (45.2 mut/Mb), melanoma (14.4 mut/Mb), and non-small cell lung carcinoma (8.1 mut/Mb), the TMB scores in breast cancer were reportedly lower (3.6\u0026ndash;3.8 mut/Mb) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. TNBC has been reported to have a higher TMB score than ER\u0026thinsp;+\u0026thinsp;or HER2\u0026thinsp;+\u0026thinsp;cancers because of its high response to immunotherapy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Reportedly, TMB score is also high in ER\u0026thinsp;+\u0026thinsp;HER2\u0026thinsp;\u0026minus;\u0026thinsp;breast cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Herein, the proportion of TMB-H cases was higher in those with the ER+/HER2\u0026thinsp;\u0026minus;\u0026thinsp;subtype than in the TNBC cohort. This is thought to be due to genomic diversity in HR\u0026thinsp;+\u0026thinsp;HER2\u0026thinsp;\u0026minus;\u0026thinsp;breast cancer. Similarly, it was higher in the ILC than in the IDC group, which is consistent with the result of a previous study that included breast cancer cohorts [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The proportion of TMB-H was not affected by the treatment, but the proportion of TMB-H cases was found to be high in the TNBC cohort after hormone therapy. The reason why the TNBC cohort was administered hormone therapy is unknown, but this may indicate that the result of hormone therapy for HR+/HER2\u0026thinsp;\u0026minus;\u0026thinsp;breast cancer patients changing to TNBC and may be related to the intratumonal heterogeneity of breast cancer for TMB status.\u003c/p\u003e \u003cp\u003eMore \u003cem\u003ePIK3CA\u003c/em\u003e mutations were observed in ILC than in IDC, and it has been reported that specific \u003cem\u003ePIK3CA\u003c/em\u003e mutations in ILC and metastatic lesions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] induced mutations in APOBEC genes and that the presence of APOBEC gene mutations is related to TMB-H [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this study, we examined the relationship between information obtained from clinical practice and TMB, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand since we did not obtain information on gene alterations, the association between gene alterations and TMB scores was not evaluated;\u003c/span\u003e however, the differences in the molecular characteristics between IDC and ILC led to the difference in the proportion of TMB-H.\u003c/p\u003e \u003cp\u003eMeanwhile, when comparing by the sample collection site, the proportion of TMB-H was higher in the brain metastasis of lung cancer than in other metastatic sites [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Although differences in the proportion of TMB-H may vary depending on the site from which the specimen was taken and on the type of cancer, the TMB is often higher at metastatic sites than at the primary site, even within breast cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, although the number of cases of brain metastasis was not enough to make comparisons, the proportion of TMB-H in metastatic lesions was higher than in primary tumors, indicating that there may be differences depending on the sample collection site. In patients without the \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epathogenic\u003c/span\u003e variant of g\u003cem\u003eBRCA1/2\u003c/em\u003e, the TMB was higher in ILC than in IDC, whereas in those with the \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epathogenic\u003c/span\u003e variant, no difference was observed. However, in the multivariate analysis, the status of g\u003cem\u003eBRCA1/2\u003c/em\u003e did not affect the proportion of TMB-H. Breast cancer with the \u003cem\u003eBRCA1/2\u003c/em\u003e gene mutations is thought to have relatively high TMB [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; however, the number of ILC cases with g\u003cem\u003eBRCA1/2\u003c/em\u003e pathogenic variants is not enough to allow for sufficient consideration. ILC has more germline \u003cem\u003eCDH1\u003c/em\u003e variants than IDC, still only around 0.54% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Patients without pathogenic variants of g\u003cem\u003eBRCA1/2\u003c/em\u003e should also include those with other germline gene variants associated with hereditary breast cancer; however, further investigation on the association between such germline variants and TMB-H is warranted.\u003c/p\u003e \u003cp\u003eThe KEYNOTE-158 study confirmed the efficacy of pembrolizumab in the treatment of solid tumors with TMB-H, with an overall response rate of 29% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; however, it did not include patients with breast cancer. The Checkmate 848 trial was a phase II study that randomly assigned patients with tumor TMB-H and/or blood TMB-H solid tumors to the nivolumab (NIVO)\u0026thinsp;+\u0026thinsp;ipilimumab (IPI) therapy or NIVO monotherapy. The objective response rates for t-TMB-H were 38.6% (28.4\u0026ndash;49.6) in the NIVO\u0026thinsp;+\u0026thinsp;IPI group and 29.8% (17.3\u0026ndash;44.9) in the NIVO group. Of the 211 randomized patients in this study, 15 (7.1%) had breast cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The results of the TAPUR study confirmed the efficacy of pembrolizumab in the treatment of breast cancer with TMB-H (TNBC, 46%; HR+/HER2\u0026minus;, 43%), with disease control and response rates of 37% and 21%, respectively [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These suggest that patients with breast cancer with TMB-H may also benefit from ICI, even in other than TNBC, especially in ILC patients. Although some ILCs were highly immunogenic, this high immunogenicity does not necessarily correspond to TMB-H [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The efficacy of ICI in patients with TMB-H may be limited in ILCs that are not immunologically \u0026ldquo;hot,\u0026rdquo; and further investigation is needed.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, compared with IDC, the number of ILC cases was extremely small, particularly ILC cases tested using the NOP or with g\u003cem\u003eBRCA1/2\u003c/em\u003e pathogenic variants. Furthermore, the background of the patients who were tested may have greatly differed depending on the subtype as the tests were conducted under Japanese insurance reimbursement. For example, the proportion of TNBC patients was higher, and the proportion of HER2\u0026thinsp;+\u0026thinsp;type patients was lower than the general population. Second, patients who had completed or were expected to complete the standard treatment were considered eligible for the tests. The small proportion of HER2\u0026thinsp;+\u0026thinsp;types compared with the real world could be attributed to the fact that few clinicians were expected to benefit from the panel testing due to the already existing oncogene. Third, in this study, we excluded blood TMB to first elucidate tumor TMB. However, to the best of our knowledge, this is the first study to investigate in detail TMB in breast cancer patients using a public database in Japan.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrated that the patients with ILC were more likely to have TMB-H than those with IDC. From the perspective of ICI therapy based on the TMB status, the findings would be invaluable in selecting treatment strategies for patients with ILC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompliance with Ethical Standards\u003c/h2\u003e \u003cp\u003e\u003cstrong\u003eDisclosure of Potential Conflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYT received a research grant from Eli Lilly and speaker honorarium from Chugai, MSD, Eli Lilly Japan K.K., Pfizer, Dai-ichi Sankyo, and AstraZeneca. MI received a research grant from AstraZeneca and Pfizer and speaker honorarium from Chugai., MSD, Eli Lilly, Pfizer, Kyowa Kirin, Taiho, and Exact Sciences. TK received a speaker honorarium from Chugai, Daiichi Sankyo, Pfizer, Novartis Pharma, Eli Lilly, Eisai, Kyowa Kirin, and Celltrion. YA received a research grant from Chugai, Kyowa Kirin, Nippon Kayaku, Mochida Pharma, Taiho, Daiichi Sankyo and BeiGene and speaker honorarium from Chugai, MSD K.K, Eli Lilly, Kyowa Kirin, Nippon Kayaku, Novartis Pharma, Daiichi Sankyo, Taiho, Ono, Guardant Health, and AstraZeneca. KM, SM, and NT have no conflict of interest.\u003c/p\u003e \u003ch2\u003eResearch Involving Human Participants and/or Animals\u003c/h2\u003e \u003cp\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eInformed Consent\u003c/h2\u003e \u003cp\u003e Informed consent was obtained from all individuals who agreed to be registered in the C-CAT database.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Yuko Takano and Kazuyuki Mizuno. The first draft of the manuscript was written by Yuko Takano, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis study was not supported by any grants or foundations. We greatly appreciate the patients who cooperated in the use of secondary data from C-CAT.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVan Baelen K, Geukens T, Maetens M, Tjan-Heijnen V, Lord CJ, Linn S, et al. Current and future diagnostic and treatment strategies for patients with invasive lobular breast cancer. Ann Oncol 2022;33:769\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eAdachi Y, Asaga S, Kumamaru H, Kinugawa N, Sagara Y, Niikura N, et al. Analysis of prognosis in different subtypes of invasive lobular carcinoma using the Japanese National Cancer Database-Breast Cancer Registry. Breast Cancer Res Treat 2023;201:397\u0026ndash;408.\u003c/li\u003e\n\u003cli\u003eEmens LA, Adams S, Barrios CH, Di\u0026eacute;ras V, Iwata H, Loi S, et al. First-line atezolizumab plus nab-paclitaxel for unresectable, locally advanced, or metastatic triple-negative breast cancer: IMpassion130 final overall survival analysis. Ann Oncol 2021;32:983\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eMarabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol 2020;21:1353\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eChalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 2017;9:34.\u003c/li\u003e\n\u003cli\u003eWickham H, Averick M, Bryan J, Chang W, McGowan LDA, Fran\u0026ccedil;ois R, et al. Welcome to the Tidyverse. J Open Source Softw 2019;4:1686.\u003c/li\u003e\n\u003cli\u003eLawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013;499:214\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eShah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 2012;486:395\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eBarroso-Sousa R, Jain E, Cohen O, Kim D, Buendia-Buendia J, Winer E, et al. Prevalence and mutational determinants of high tumor mutation burden in breast cancer. Ann Oncol 2020;31:387\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eBertucci F, Ng CKY, Patsouris A, Droin N, Piscuoglio S, et al. Genomic characterization of metastatic breast cancers. Nature 2019;569:560\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eSammons S, Raskina K, Danziger N, Alder L, Schrock AB, Venstrom JM, et al. APOBEC mutational signatures in hormone receptor-positive human epidermal growth factor receptor 2-negative breast cancers are associated with poor outcomes on CDK4/6 inhibitors and endocrine therapy. JCO Precis Oncol 2022;6:e2200149.\u003c/li\u003e\n\u003cli\u003eStein MK, Pandey M, Xiu J, Tae H, Swensen J, Mittal S, et al. Tumor mutational burden is site specific in non-small-cell lung cancer and is highest in lung adenocarcinoma brain metastases. JCO Precis Oncol 2019;3:1\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003ePapillon-Cavanagh S, Hopkins JF, Ramkissoon SH, Albacker LA, Walsh AM. Pan-cancer analysis of the effect of biopsy site on tumor mutational burden observations. Commun Med (Lond) 2021;1:56.\u003c/li\u003e\n\u003cli\u003eAngus L, Smid M, Wilting SM, van Riet J, Van Hoeck A, Nguyen L, et al. The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat Genet 2019;51:1450\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eKraya AA, Maxwell KN, Wubbenhorst B, Wenz BM, Pluta J, Rech AJ, et al. Genomic signatures predict the immunogenicity of BRCA-deficient breast cancer. Clin Cancer Res 2019;25:4363\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eHe J, Kalinava N, Doshi P, Pavlick DC, Albacker LA, Ebot EM, et al. Evaluation of tissue- and plasma-derived tumor mutational burden (TMB) and genomic alterations of interest in CheckMate 848, a study of nivolumab combined with ipilimumab and nivolumab alone in patients with advanced or metastatic solid tumors with high TMB. J Immunother Cancer 2023;11:e007339.\u003c/li\u003e\n\u003cli\u003eSchenker M, Burotto M, Richardet M, Ciuleanu TE, Gon\u0026ccedil;alves A, Steeghs N, et al. Randomized, open-label, phase 2 study of nivolumab plus ipilimumab or nivolumab monotherapy in patients with advanced or metastatic solid tumors of high tumor mutational burden. J Immunother Cancer 2024;12:e008872.\u003c/li\u003e\n\u003cli\u003eAlva AS, Mangat PK, Garrett-Mayer E, Halabi S, Hansra D, Calfa CJ, et al. Pembrolizumab in patients with metastatic breast cancer with high tumor mutational burden: results from the targeted agent and profiling utilization registry (TAPUR) study. J Clin Oncol 2021;39:2443\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eMichaut M, Chin SF, Majewski I, Severson TM, Bismeijer T, de Koning L, et al. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer. Sci Rep. 2016;6:18517.\u003c/li\u003e\n\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","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brca","sideBox":"Learn more about [Breast Cancer](http://link.springer.com/journal/12282)","snPcode":"12282","submissionUrl":"https://www.editorialmanager.com/brca/default2.aspx","title":"Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"high tumor mutational burden, invasive lobular carcinoma, invasive ductal carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-5578316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5578316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHigh tumor mutational burden (TMB-H) is an established biomarker for a favorable response to immune checkpoint inhibitors. However, tumor mutational burden (TMB) in invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) has not been sufficiently investigated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe collected data of patients with ILC or IDC from the Center for Cancer Genomics and Advanced Therapeutics database between June 2019 and August 2023. Furthermore, we examined the clinicopathological factors and TMB status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients with ILC (n\u0026thinsp;=\u0026thinsp;170) had a median TMB score of 4.00 mut/Mb (interquartile range, 2.00\u0026ndash;7.14 mut/Mb), whereas those with IDC (n\u0026thinsp;=\u0026thinsp;2,598) had a score of 3.90 mut/Mb (2.00\u0026ndash;6.00 mut/Mb). TMB-H was more common in patients with ILC than in those with IDC (18.2% vs. 10.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), particularly in the ER+/HER2\u0026thinsp;\u0026minus;\u0026thinsp;subtype. Multivariate analysis revealed that the pathological diagnosis of ILC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), tissue samples collected from metastatic sites (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and older age (50 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent factors for TMB-H.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePatients with ILC were more likely to have TMB-H than those with IDC. The findings of this study would be invaluable in selecting treatment strategies for patients with ILC.\u003c/p\u003e","manuscriptTitle":"Tumor mutational burden status and clinical characteristics of invasive lobular carcinoma of the breast","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 05:33:54","doi":"10.21203/rs.3.rs-5578316/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-04-18T23:21:33+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-09T12:56:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-08T07:47:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T06:04:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer","date":"2025-04-02T05:46:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brca","sideBox":"Learn more about [Breast Cancer](http://link.springer.com/journal/12282)","snPcode":"12282","submissionUrl":"https://www.editorialmanager.com/brca/default2.aspx","title":"Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d61357c8-b8fd-4e7b-8a64-415a24e16106","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:05:08+00:00","versionOfRecord":{"articleIdentity":"rs-5578316","link":"https://doi.org/10.1007/s12282-025-01706-6","journal":{"identity":"breast-cancer","isVorOnly":false,"title":"Breast Cancer"},"publishedOn":"2025-05-02 15:57:23","publishedOnDateReadable":"May 2nd, 2025"},"versionCreatedAt":"2025-04-10 05:33:54","video":"","vorDoi":"10.1007/s12282-025-01706-6","vorDoiUrl":"https://doi.org/10.1007/s12282-025-01706-6","workflowStages":[]},"version":"v1","identity":"rs-5578316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5578316","identity":"rs-5578316","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}