Soluble B7-H4 and its association with clinical characteristics and prognosis in patients with early breast cancer

Soluble B7-H4 and its association with clinical characteristics and prognosis in patients with early breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Soluble B7-H4 and its association with clinical characteristics and prognosis in patients with early breast cancer Pawel Mach, Oliver Hoffmann, Sabine Kasimir-Bauer, Rainer Kimmig, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-93991/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Immunotherapy is a promising area for treatment of breast cancer (BC) that has transformed patient care. Immune checkpoint inhibitors are only effective in a subset of patients, and the identification of biomarkers that predict response to therapy is crucial to increase the rates of responders. B7-H4 is a potentially novel target for cancer therapy. Methods: We examined the association of sB7-H4 with clinical characteristics and prognosis in patients with early BC. Using ELISA, we analyzed sB7-H4 serum concentrations in a total of 572 early BC patients before the onset of therapy, 109 patients (cohort 1) in the neo-adjuvant setting and 463 patients in the adjuvant setting (cohort 2). In cohort 1, measurements were also performed after neo-adjuvant therapy (NACT). Results: In cohort 1, sB7-H4 blood serum concentration was delectable in 27/109 (26%) patients before and in 50/109 (48%) patients after NACT. In cohort 2, the detection rate was only 4% (18/461 patients). In cohort 1, no significant differences between patients, even when stratifying for particular intrinsic subtypes, before and after NACT were observed. No significant chances in sB7-H4 blood serum concentration levels before and after NACT were associated with clinical parameters, prognosis and the risk of recurrence. The median blood serum concentration levels in cohort 2 were significantly higher than in cohort 1 after NACT (p=0.04) but not before NACT. Conclusions: sB7-H4 concentration levels in serum of non-metastatic BC patients are neither associated with prognosis nor with clinical characteristics in the adjuvant and neo-adjuvant setting. Cancer Biology sB7-H4 Breast cancer immune checkpoints Figures Figure 1 Figure 2 Figure 3 1. Background The development of cancer is associated with the growth of a suppressive tumor microenvironment including mechanisms of avoiding immune destruction by alternating the immune checkpoint pathway. Immune checkpoint blockades have shown dramatic effects in various tumor types including breast cancer (BC) [ 1 ]. However, this effect has especially been observed in the triple negative BC (TNBC) subgroup, but also in a small subgroup of Luminal B and HER2-positive BC patients [ 2 , 3 ]. One possible explanation is higher tumor mutational load of TNBC and metastatic BC versus HER2-positive or luminal tumors and early BC, although the expression of immune-checkpoints in all subtypes of BC was identified [ 4 , 5 ]. Therefore, a better understanding of the interactions between immune cells and BC is needed to develop new therapeutic options and identify subgroups of patients that could benefit from the targeted therapies. B7 family members have both, stimulatory and inhibitory effects on T cells and play a critical role in maintaining immune tolerance. They can help a tumor to escape from host surveillance [ 6 ]. B7-H4 is a co-stimulatory ligand that inhibits T-cell responses by interacting with as yet unidentified receptors [ 7 ]. B7-H4 is highly expressed in various types of neoplasms including ovarian, endometrial or BC and its expression correlates with the progression of the disease and a poor prognosis in many cases [ 8 – 10 ]. Moreover, B7-H4 is a potentially novel target for cancer therapy [ 11 ]. B7-H4 has a membrane-bound and soluble form (sB7-H4), which has also been detected in serum of patients with cancer with its expression closely related to progression and prognosis [ 12 ]. However, it remains unclear whether sB7-H4 is associated with diagnostics and outcomes in BC patients. Immune checkpoint inhibitors are only effective in a subset of patients, and the identification of biomarkers that predict response to therapy is crucial to increase the rates of responders. Moreover, soluble serum biomarkers are useful diagnostic tools because they can reflect the tumor status and predict patient prognosis. Here, we investigate the distribution of blood serum sB7-H4 in patients with early BC across different clinico-pathological characteristics and the prognostic value sB7-H4 for patients’ outcome. The aim of this study was to explore whether sB7-H4 is a reliable marker for BC prognosis. 2. Methods 2.1. Patient population and patient characteristics In total, 572 primary, non-metastatic BC patients, diagnosed between 2004 and 2009 at the University Hospital of Essen, Department of Gynecology, were analyzed.Cohort 1 consisted of 109 pts in the neo-adjuvant setting and cohort 2 of 463 pts in the adjuvant setting. In both groups, serum was obtained before surgery and in cohort 1, additionally before neo-adjuvant chemotherapy (NACT). The eligibility criteria were: histologically proven BC, no severe uncontrolled comorbidities or medical conditions and no further malignancies at the time of enrollment or in the patient history, completion of neoadjuvant or adjuvant treatment according to current national guidelines [ 13 ] including adjuvant chemotherapy (ACT) and NACT (anthracyclines, taxanes, cyclophosphamide, carboplatin, gemcitabine, 5-fluorouracil), anti-hormonal therapy in the case of hormone responsive tumors (tamoxifen or an aromatase inhibitor), Herceptin (after FDA approval in November 2006) in the case of HER2 positivity and radiotherapy. For each of the 572 patients, the tumor type, TNM-staging and grading were assessed in the Institute of Pathology, at the University Hospital Essen as part of the West German Comprehensive Cancer Center. In the neoadjuvant setting, pathological response to therapy was defined according to the grading system of Sinn and colleagues [Sinn = 0, no pathological response; Sinn = 1–3, pathological partial response (pPR) and Sinn = 4, pathological complete response (pCR) [ 14 ]. Patients positive for disseminated tumor cells in the bone marrow were recommended to complete a prescription of clodronate (2 × 520 mg/d) for at least two years. 2.2. Sampling of Serum and Measurement of sB7-H4 protein levels Nine ml of blood were collected from each patient using S-Monovettes (Sarstedt AG & Co, Nümbrecht, Germany), stored at 4 °C and processed within 4 hours to avoid blood cell lysis. Blood fractionation was carried out by centrifugation for 10 min at 2500 x g. Subsequently, 3–4 ml of the upper phase, constituting blood serum, were removed and stored at -80 °C. All samples were assayed in batch form for blood serum protein levels of sB7-H4. sB7-H4 serum levels were analyzed using the sandwich Enzyme-Linked Immunosorbent Assay Kit (Cusabio, Cologne, Germany) according to the manual instructions. For ELISA measurement, 100 µl undiluted serum samples and control samples were dispensed into wells coated with an antibody specific for B7-H4 and incubated for 2 hours at 37 °C. Subsequently, after removing any unbound substances, a biotin-conjugated antibody specific for B7-H4 was added to the wells for 1 hour at 37 °C. After washing three times, 100 µL of avidin conjugated Horseradish Peroxidase (HRP) was added for 1 h at 37 °C. Subsequently, after washing five times, 90 µL of substrate solution containing TMB (Tetramethylbenzidine) was added for 15–30 minutes at 37 °C, protected from light. Color development was stopped by the addition of 50 µl stop solution to each well and the degree of enzymatic turnover of the substrate was investigated by dual-wavelength absorbance measurement at 450 and 620 nm as a reference wavelength within 5 minutes using an ELISA reader (TECAN, Model Sunrise; Austria GmbH, Grodig, Austria) and the data analysis software Magellan™ (TECAN, Mannedorf, Switzerland). To quantify blood serum concentration levels of B7-H4, a non-linear regression model (4-parameter Marquardt) was used with a log/lin type of graph according to the manufacturer’s instructions. The observed absorbance was directly proportional to the concentration level of sB7-H4 in the samples, which was calculated from the calibration curve. The sB7-H4 serum levels were expressed in ng/mL according to the established standard curve (detection range: 7.8–500 ng/mL). The minimum detectable dose of sB7-H4 was typically less than 1.95 ng/mL. The lower limit of detection (LLD) was defined as the lowest protein concentration that could be differentiated from zero. Intra-assay variation was < 8%, while inter-assay variation was < 10%. 2.3. Statistical analysis The distribution of sB7-H4 in each study group was different from normal. Descriptive statistics were computed and reported as median with interquartile range (IQR) or frequency counts (%). The differences between two groups were defined by Mann-Whitney U test or paired Wilcoxon test. More than two groups were analyzed using Kruskal-Willis test. Differences in frequency counts were analyzed using the chi-squared test. ROC curve analysis was performed to obtain cut-off values representing the optimal separation of survival curve. The optimal cut-off value, i.e. the threshold that maximizes the sum of (sensitivity + specificity) was calculated according to Youden [ 15 ]. Kaplan-Meier analysis was performed to analyze progression free survival (PFS) and overall survival (OS) probabilities. The difference between survival curves was assessed by using the log rank test. Age-adjusted hazard ratios (HR) with corresponding 95%-confidence intervals (95%-CI) were calculated by using Cox proportional hazards regression. All analyses were performed using the MedCalc version 17.9.7 (MedCalc Software bvba, Ostend, Belgium) and the R statistical package version 3.4.0. Table 1 Clinical data of patients in the neoadjuvant cohort (cohort I) Total (%) Neoadjuvant cohorte (% of all applicable/known) p-value Total 109 Menopausal Status Pre- and perimenopausal Postmenopausal 64/109 (58.72) 45/109 (41.28) 0.06 Histology Ductal Lobular Others nk 73/102 (71.57) 16/102 (15.69) 13/102 (12.75) 7/109 (6.42) < 0.0001 Grading I II III nk 8/108 (7.41) 48/108 (44.44) 52/108 (48.15) 1/109 (0.92) < 0.0001 Tumor before NACT (cT) T1 T2 T3 T4 17/109 (15.60) 75/109 (68.81) 11/109 (10.09) 6/109 (5.50) < 0.0001 Nodal status before NACT (cN) Node negative Node positive N1 N2 N3 53/109 (48.62) 56/109 (51.38) 47/109 (43.12) 8/109 (7.23) 1/109 (0.92) < 0.0001 Pathological response Response Complete response Partial response No response nk 101/107 (94,39) 23/107 (21.50) 78/107 (72.90) 6/107(5.61) 2/109 (1.83) < 0.0001 Neoadjuvant therapy Chemotherapy Endocrine therapy 102/109 (93.58) 7/109 (6.42) < 0.0001 Immunhistochemical Subtype ER-, PR-, HER2- HER2+ (ER + and/ or PR+, HER2-) 18/109 (16.51) 13/109 (27.52) 61/109 (55.96) < 0.0001 Recurrence No Yes nk 72/103 (69.90) 31/103 (30.20) 6/109 (5.50) < 0.0001 Distant Recurrence No Yes nk 78/102 (76.47) 17/102 (16.67) 7/109 (6.42) < 0.0001 ER = estrogen receptor, PR = progesterone receptor, HER2 = human epidermal growth factor receptor-2, TNBC = triple-negative breast cancer, nk = not known; nd = not done Table 2 Clinical data of patients in the adjuvant cohort (cohort II) Total (% of all applicable/known) p-value Total 463 Menopausal Status Pre- and perimenopausal Postmenopausal 109/463 (23.54) 354 (76.46) < 0.0001 Histology Ductal Lobular Others nk 354/453 (78.16) 60/453 (13.25) 39/453 (8.61) 10/463 (2.16) < 0.0001 Grading I II III 90/463(19.44) 251/463 (54.21) 122/463 (26.35) < 0.0001 Tumor size at first diagnosis (pT) T1 T2 T3 T4 nk 300/460 (65.22) 140/460 (30.43) 14/460 (3.04) 6/460 (1.30) 3/463 (0.65) < 0.0001 Nodal Status at first diagnosis Node negative Node positive N1 N2 N3 nk 313/462 (67.75) 149/462 (32.25) 136/462 (29.44) 10/462 (2.16) 3/462 (0.65) 1 (0.22) < 0.0001 Chemotherapy No Yes (adjuvant) nk 168/388 (43.30) 220/388(56.70) 75/463(16.20) < 0.0001 Immunhistochemical Subtype ER-, PR-, HER2- HER2+ ER + and/ or PR+, HER2- nk 49/459 (10.68) 55/459 (11.73) 355/459 (77.34) 4/463(0.86) < 0.0001 Recurrence No Yes nk 360/384 (93,75) 24/384 (6.35) 79/463 (17.06) < 0.0001 Distant Recurrence No Yes nk 266/283 (94.00) 17/283 (16.01) 180/463 (38.88) < 0.0001 ER = estrogen receptor, PR = progesterone receptor, HER2 = human epidermal growth factor receptor-2, TNBC = triple-negative breast cancer, nk = not known; nd = not done 3. Results The clinical characteristics of all patients are shown in Tables 1 and 2 . The median age was 51 years (IQR 43,5–61) in cohort 1 and 61 years (IQR 51–68) in cohort 2. The predominant histological subtype was invasive ductal carcinoma in both groups (72% and 76%). In cohort 1, the majority of patients (58%) were pre- and perimenopausal whereas most of the patients in cohort 2 were postmenopausal (76%). In contrast to cohort 2 with most of the patients characterized as T1 (65%), no lymph node involvement (68%) and grade 2 (54%) tumors, 69% of the patients in cohort 1 showed a T2 tumor, 52% of the patients were node positive and had more aggressive tumors, characterized as grade 2 (44%) and grade 3 (48%). When stratifying according to BC subtypes, 17% were triple-negative and 28% Her2-positive in cohort 1. In cohort 2, the values were 11% and 12% while all other patients (77%) were characterized as hormone receptor-positive. In cohort 1, 102/109 patients (93.58%) received NACT and 7 patients (6.42%) a neo-adjuvant endocrine therapy.. Overall, response to therapy resulted in a ratio of 94% (22% pCR; 73% pPR) of responders and 6% of non-responders. In cohort 2, 220/463 (48%) of the patients received ACT, 168/463 (36%) did not get chemotherapy and in 75/463 patients (16%), no information was available. In cohort 1, sB7-H4 blood serum concentration was delectable in 27/109 (26%) patients before and in 50/109 (48%) patients after NACT. In cohort 2, sB7-H4 blood serum concentration was only delectable in 18/461 (4%) patients. Comparision of sB7-H4 in blood serum between groups The median blood serum concentration levels in cohort 1 were 10.49 ng/ml (IQR 7.78–17.27) before and 8.43 ng/ml (IQR 3.46–18.79) after NACT. For cohort 2, the value was 16.96 ng/ml (IQR 6.49–30.59) which was significantly higher than in cohort 1 after NACT (p = 0.04) but not before NACT. No significant differences between patients before and after NACT were observed (Fig. 1 ). 3.1. sB7-H4 serum levels among particular intrinsic subtypes of BC in cohort 1 Median sB7-H4 blood serum concentration levels in cohort 1 before NACT was 13.5 ng/ml (IQR 8.8–24.2) for the ER + PR + HER2- subtype, 9.13 ng/ml (IQR 6.84–13.26) for the HER2 + subtype and 8.92 ng/ml (IQR 6.84–14.87) for the TNBC subtype, respectively. After NACT, the values were 8.26 ng/ml (IQR 3.71–16.48), 8.37 ng/ml (IQR 3.34–18.97) and 9.71 ng/ml (IQR 2.01–26.29), respectively. However, differences before (p = 0.25) and after NACT(p = 0.96) were not statistically significant (Fig. 2 ). In cohort 2, the comparison among intrinsic subtypes was not performed due to low detection rates of sB7-H4 in blood serum. 3.2. Association of sB7-H4 and clinicopathological characteristics in cohort 1 No significant associations between clinical characteristics and chances in sB7-H4 blood serum concentration levels before and after NACT were obtained (Table 3 ). Table 3 Changes in sB7-H4 blood serum concentration levels in patient cohort 1. Parameter sB7-H4 (ng/ml) Before NACT; median (IQR) n P value sB7-H4 (ng/ml) After NACT; median (IQR) n P value Menopausal status Pre-menopausal 9.03 (7.32–13.32) 16 0.18 9.80 (3.34–16.43) 26 0.85 Post-menopausal 13.5 (9.37–23.70) 11 7.98 (3.53–20.55) 24 Histology Ductal 12.14 (7.98–16.72) 20 0.49 11.02 (3.64–19.16) 35 0.17 Lobular 8.80 (4.82–18.16) 3 13.69 (8.43–39.73) 4 Others 20.25 (10.49–30.01) 2 3,34 (1,7–10,29) 5 Tumor size cT1 8.93 (6.41–14.81) 7 0.42 10.99 (5.33–14.65) 6 0.28 cT2 10.39 (7.98–16.28) 16 6.15 (3.34–18.79) 38 <cT2 17.03 (10.78–25.64) 4 22.11 (8.59–30.97) 6 Nodal status cN0 12.95 (7.98–18.45) 12 0.8 5.33 (2.91–12.13) 19 0.8 cN1 10.39 (6.19–17.82) 14 12.27 (3.53–21.54) 27 <cN1 8.8 1 8.27 (4.57–28.48) 4 Tumor Grading G1 21.73 (13.31–31.63) 4 0.19 10.96 (3.45–37.57) 8 0.81 G2 9.61 (6.62–18.45) 12 11.02 (4.63–14.59) 19 G3 10.49 (7.78–13.32) 11 6.09 (3.37–19.16) 23 Distant metastasis No 9.81 (6.62–16.72) 20 0.92 8.42 (3.75–19.23) 30 0.37 Yes 8.92 (8.84-11,81) 3 5.81 (3.34–12.27) 14 Tumor subtyp ER + PR + Her2- 13.5 (8.81–24.21) 13 0.25 8.27 (3.71–16.45) 29 0.93 Her2+ 9.13 (6.84–13.27) 9 5.33 (3.26–17.07) 13 TNBC 8.93 (6.84–14.88) 5 10,99 (2,46 − 32,96) 8 Response to the therapy Complete remission 7.58 (6.12–15.23) 5 0.71 4.23 (3.10-12.35) 11 0.41 Partial remission 11.09 (8.58–16.72) 20 11.42 (3.50-19.16) 35 No remission 10.49 1 4.98 (4.42–5.53) 2 Recurrence No 9.13 (6.40-17.27) 19 0.45 8.42 (3.54–19.10) 28 0.72 Yes 10.49 (8.89–17.08) 5 6.09 (3.43–15.07) 17 3.3. The prognostic value of sB7-H4 in blood serum To obtain cut-off values representing the optimal separation of survival curve, Receiver Operating Curve (ROC) analysis was performed. For patients before NACT, the area under the curve (AUC) was 0.68 (95%-CI 0.47–0.84) with the optimal threshold of 8.8 ng/mL for OS and 0.61 (95%-CI 0.39–0.8) for DFS with the optimal threshold of 8.8 ng/mL. For patients after NACT, the AUC was 0.63 (95%-CI 0.48–0.76 with the optimal threshold of 3.6 ng/mL for OS) and 0.53 (95%-CI 0.38–0.68 with the optimal threshold of 16.3 ng/mL for DFS. The survival analysis showed no significant differences in OS and in DFS before and after NACT (Fig. 3). Age-adjusted Cox regression analysis indicated that sB7-H4 levels before and after NACT did not influence the prognosis and risk of recurrence (Table 4 ). Table 4 Results from Cox regression analysis of patients in cohort 1. sB7-H4 (ng/ml) OS DFS Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Cohort 1 before NACT 0.91 (0.73–1.12) 0.37 0.94 (0.85–1.04) 0.28 Cohort 1 after NACT 0.94 (0.84–1.05) 0.29 0.99 (0.96–1.03) 0.94 Cohort 2 1.00 (0.95–1.05) 0.98 1.02 (0.97–1.07) 0.29 4. Discussion B7-H4 plays a significant role in tumor escape from the immune surveillance [ 16 ]. While the mechanism is still not clear, sB7-H4 may also play a role in cancer development through negative regulation of T-cell immunity. So far, there is no evidence in the literature comparing sB7-H4 blood serum levels with clinical characteristics and outcomes of patients with early BC. Here, we assessed changes in sB7-H4 blood serum levels in paired pre-NACT and post-NACT as well as adjuvant serum samples and correlated these findings with clinico-pathological parameters and prognosis. Although we observed higher sB7-H4 levels after therapy, there were no significant associations between sB7-H4 and prognosis in the neo-adjuvant setting. In addition, no differences in sB7-H4 levels could be documented for the different BC subtypes. Expression of B7-H4 in tumor tissue has been linked to a worse prognosis in some types of cancer [ 17 ]. However, data in the context of BC are inconsistent. Huang et al. showed that the OS rate of patients with higher B7-H4 expression was significantly worse than in those with lower expression [ 18 ]. Similarly, TNBC patients with B7-H4 overexpression had significantly shorter survival and recurrence time than those with low B7-H4 expression [ 19 ]. These data suggest that B7-H4 might be a potential negative prognostic indicator. In contrast, other studies showed that B7-H4 expression was not associated with worse survival in BC [ 20 ], and expression of B7-H4 has even been linked to a favorable 5-year PFS [ 22 ]. There is no data available with regard to the prognostic features of sB7-H4 in BC patients. We found that sB7-H4 was not associated with prognosis, however, due to the limited data in some BC subgroups, we could not perform subgroup analyses for survival in the adjuvant setting. Nevertheless, we speculate that sB7-H4 may be a part of tumor-immune tolerance because B7-H4 is a negative regulator of immune response which might be important for BC patients. Treatment with antibodies targeting B7-H4 may result in a reduction of tumor progression and better patient outcomes. Indeed, an in vivo study using humanized animal model showed that a B7-H4/CD3-bispecific antibody might be a therapeutic agent against B7-H4-expressing tumors [ 11 ]. Therefore, it is essential to identify biomarkers that can predict the possible response to anti-B7-H4 treatment. The existence of sB7-H4 could be a predictive marker for immunotherapy targeting T lymphocytes as suggested by Ohki et al [ 22 ]. B7-H4 is frequently expressed on tumor cells including BC. Data on the expression among particular intrinsic subtypes of BC— defined by expression of ER, PR or HER2—is inconsistent [ 20 , 21 ]. In our study, sB7-H4 was independent of intrinsic BC subtypes. Since the source and function of sB7-H4 is not known, it is not clear whether serum sB7-H4 reflects the expression of B7-H4 in tumor tissue. Kamimura et al. demonstrated that sB7-H4 is secreted in inflammatory environments [ 23 ] and it was also reported to act as a decoy molecule that blocks suppressive functions of cell-associated B7-H4 leading to enhanced T-cell-mediated autoimmune responses [ 24 ]. Zhang et al. suggested that different origins of sB7-H4 define its distinct structure and function [ 25 ]. Nevertheless, there is a growing body of evidence indicating that sB7-H4 negatively regulates T-cells and plays a regulatory role in immune tolerance [ 26 , 27 ]. On the other hand, chemotherapy may have beneficial effects on anticancer immunity by enhancing mutational load or direct elimination of immunosuppressive cells [ 28 – 30 ]. Thus, understanding the effect of DNA-damaging agents on the immune system is critical to identify optimal strategies that combine checkpoint inhibitors and chemotherapy agents. In our study, the detection of serum sB7-H4 almost doubled after NACT versus before NACT. Some preclinical studies have suggested that immune checkpoint expression like Programmed Cell Death Ligand 1 (PD-L1) might be stimulated by chemotherapy; others observed a significant decrease in PD-L1 expression after NACT in BC patients [ 31 , 32 ]. The changes collectively induced by NACT in immune-checkpoint expression could provide a rationale for the use of immune checkpoint inhibitors in the neoadjuvant setting for BC patients. The primary limitation of this study is its retrospective character, the lack of corresponding B7-H4 tissue availability and its expression in the tumor microenvironment. However, our study performed on a representative group of BC patients suggests a lack of an association between sB7-H4 serum levels and prognosis. Therapeutic properties of anti-B7-H4 therapy in BC patients is still not known. Since B7-H4 remains a candidate for targeted inhibition in cancer immunotherapy, further prospective studies with blockade of B7-H4 in association with sB7-H4 levels should be proposed to investigate the applicability to anti-B7-H4 immune therapy in BC. 5. Conclusion sB7-H4 concentration levels in serum of non-metastatic BC patients are neither associated with prognosis nor with clinical characteristics in the adjuvant and neo-adjuvant setting. Abbreviations ACT adjuvant chemotherapy BC breast cancer ER estrogen-receptor HER human epitdermal growth factor receptor NACT neoadjuvant chemotherapy pCR pathological complete response pPR pathological partial response PR progesterone-receptor TNBC triple negative breast cancer Declarations Ethics approval and consent to participate All blood samples were obtained and collected after written informed consent from all subjects using protocols approved by the clinical Ethic committee of the University Hospital Essen (17-7495-BO). Consent for publication Not applicable Availability of data and materials The datasets for the current study are available from the corresponding author upon request. Competing interests The authors declare that they have no competing interests Funding The study was supported by the internal budget of the Department of Gynecology and Obstetrics of the University Hospital of Essen, Germany Authors' contributions PM gave substantial contributions to conception and design, acquisition, analysis and interpretation of data, and was a major contributor in writing the manuscript. AKB gave substantial contributions to conception, design, acquisition and interpretation of data, drafting and revising the article and final approval of the version to be published. SKB gave substantial contributions to conception and design, interpretation and revising the article and final approval of the version to be published. BS gave substantial contributions to analysis and interpretation of data, revising the article and final approval of the version to be published. OH and RK gave substantial contributions to acquisition of data, revising the article and final approval of the version to be published. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the laboratory staff for the support and continual help in maintaining and analyzing the samples and documentation of data. We are also grateful to all women who participated in this study. References Schmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH, Iwata H, et al. IMpassion130 Trial Investigators. Atezolizumab and Nab-paclitaxel in advanced triple-negative breast cancer. New Engl J Med. 2018;379(22):2108–21. Schmid P, Cortes J, Pusztai L, McArthur H, Kümmel S, Bergh J, et al. KEYNOTE-522 Investigators. Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med. 2020;382(9):810–21. Anurag M, Zhu M, Huang C, Vasaikar S, Wang J, Hoog J, et al. Immune Checkpoint Profiles in Luminal B Breast Cancer (Alliance). 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Geburtshilfe Frauenheilkd. 1994;54:552–8. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–5. Liu WH, Chen YY, Zhu SX, Li YN, Xu YP, Wu XJ, et al. B7-H4 expression in bladder urothelial carcinoma and immune escape mechanisms. Oncol Lett. 2014 Dec;8(6):2527–34. Fauci JM, Straughn JM Jr, Ferrone S, Buchsbaum DJ. A review of B7-H3 and B7-H4 immune molecules and their role in ovarian cancer. Gynecol Oncol. 2012;127(2):420–5. Huang H, Li C, Ren G. Clinical significance of the B7-H4 as a novel prognostic marker in breast cancer. Gene. 2017;623:24–8. Wang L, Yang C, Liu XB, Wang L, Kang FB. B7-H4 overexpression contributes to poor prognosis and drug-resistance in triple-negative breast cancer. Cancer Cell Int. 2018;18:100. Altan M, Kidwell KM, Pelekanou V, Carvajal-Hausdorf DE, Schalper KA, Toki MI, et al. Association of increased B7 protein expression by infiltrating immune cells with progression of gastric carcinogenesis. Medicine. 2019;98(8):e14663. Lee DW, Ryu HS, Jin MS, Lee KH, Suh KJ, Youk J, et al. Immune recurrence score using 7 immunoregulatory protein expressions can predict recurrence in stage I-III breast cancer patients. Br J Cancer. 2019;121(3):230–6. Ohki S, Shibata M, Gonda K, Machida T, Shimura T, Nakamura I, et al. Circulating myeloid-derived suppressor cells are increased and correlate to immune suppression, inflammation and hypoproteinemia in patients with cancer. Oncol Rep. 2012;28(2):453 ± 8. Kamimura Y, Kobori H, Piao J, Hashiguchi M, Matsumoto K, Hirose S, et al. Possible involvement of soluble B7-H4 in T cell-mediated inflammatory immune responses. Biochem Biophys Res Commun. 2009;389(2):349–53. Azuma T, Zhu G, Xu H, Rietz AC, Drake CG, Matteson EL, et al. Potential role of decoy B7-H4 in the pathogenesis of rheumatoid arthritis: a mouse model informed by clinical data. PLoS Med. 2009;6(10):e1000166. Zhang L, Wu H, Lu D, Li G, Sun C, Song H, et al. The costimulatory molecule B7-H4 promote tumor progression and cell proliferation through translocating into nucleus. Oncogene. 2013;32(46):5347–58. Leandersson P, Kalapotharakos G, Henic E, Borgfeldt H, Petzold M, Høyer-Hansen G, et al. A Biomarker Panel Increases the Diagnostic Performance for Epithelial Ovarian Cancer Type I and II in Young Women. Anticancer Res. 2016;36(3):957–65. Thompson RH, Zang X, Lohse CM, Leibovich BC, Slovin SF, Reuter VE, et al. Serum-soluble B7x is elevated in renal cell carcinoma patients and is associated with advanced stage. Cancer Res. 2008;68(15):6054–8. Galluzzi L, Buque A, Kepp O, Zitvogel L, Kroemer G. Immunological effects of conventional chemotherapy and targeted anticancer agents. Cancer Cell. 2015;28:690–714. Gotwals P, Cameron S, Cipolletta D, Cremasco V, Crystal A, Hewes B, et al. Prospects for combining targeted and conventionalcancer therapy with immunotherapy. Nat Rev Cancer. 2017;17:286–301. Brown JS, Sundar R, Lopez J. Combining DNA damaging therapeutics with immunotherapy: More haste, less speed. Br J Cancer. 2018;118:312–24. Zhang P, Su DM, Liang M, Fu J. Chemopreventive agents induce programmed death-1-ligand 1 (PD-L1) surface expression in breast cancer cells and promote PD-L1-mediated T cell apoptosis. Mol Immunol. 2008;45(5):1470–6. Pelekanou V, Carvajal-Hausdorf DE, Altan M, Wasserman B, Carvajal-Hausdorf C, Wimberly H, et al. Effect of neoadjuvant chemotherapy on tumor-infiltrating lymphocytes and PD-L1 expression in breast cancer and its clinical significance. Breast Cancer Res. 2017;19(1):91. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-93991","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":3774802,"identity":"b035eaaa-bd67-43d4-9252-783a3fa4ed38","order_by":0,"name":"Pawel Mach","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYLCCByBCgvkAYwNMhIeQlgSwFrYEkrXwGBCnxZz9jOGDBIZt8vyze75JzmCwsZdv7z3A8KYCtxbLnhxjgwSG24Yz7pzdJrmBIS1xw5lzCYxzzuDWYnAgd5sEUAvjBoncbZIP/x1OMJDIMWDmbcOj5fzb7T+AWuw3SOQ8k3zA8N9efv4boJZ/eLTcyN0G9P7tRKAWNqDDgAF9gweopQG3FssZ7z9LJBjcTp5xI83YcgZDMtAvOQYH5xzDrcWcPy3xw4eK27b9M5If3uxhsAOGGDAM39TgcRgSiQAHcGvAVDwKRsEoGAWjABMAACJIU+miQx4sAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7301-6746","institution":"University Hospital Essen: Universitatsklinikum Essen","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Pawel","middleName":"","lastName":"Mach","suffix":""},{"id":3774803,"identity":"2c6438b0-f37a-48f0-bded-702c6954bce1","order_by":1,"name":"Oliver Hoffmann","email":"","orcid":"","institution":"University Hospital Essen: Universitatsklinikum Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Hoffmann","suffix":""},{"id":3774804,"identity":"9f684ddc-ab8e-432a-bde3-db8d6af64a59","order_by":2,"name":"Sabine Kasimir-Bauer","email":"","orcid":"","institution":"University Hospital Essen: Universitatsklinikum Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sabine","middleName":"","lastName":"Kasimir-Bauer","suffix":""},{"id":3774805,"identity":"b64e51d1-0f21-4aa7-bbf3-222afaa8774f","order_by":3,"name":"Rainer Kimmig","email":"","orcid":"","institution":"University Hospital Essen: Universitatsklinikum Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rainer","middleName":"","lastName":"Kimmig","suffix":""},{"id":3774806,"identity":"cfb4ef30-3086-4125-9121-c75a8d929045","order_by":4,"name":"Boerge Schmidt","email":"","orcid":"","institution":"University Hospital Essen: Universitatsklinikum Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Boerge","middleName":"","lastName":"Schmidt","suffix":""},{"id":3774807,"identity":"5951dba9-ffd6-4b49-b953-91a7ad91c34b","order_by":5,"name":"Julia Schapeler","email":"","orcid":"","institution":"University Hospital Essen: Universitatsklinikum Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Schapeler","suffix":""},{"id":3774808,"identity":"426c47de-380b-4fef-8597-82c6eb3fb911","order_by":6,"name":"Ann-Kathrin Bittner","email":"","orcid":"","institution":"University Hospital Essen: Universitatsklinikum Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ann-Kathrin","middleName":"","lastName":"Bittner","suffix":""}],"badges":[],"createdAt":"2020-10-16 21:25:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-93991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-93991/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":3157553,"identity":"9f69ae51-62f3-4a24-811c-4356f36529f1","added_by":"auto","created_at":"2020-10-23 15:06:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27573,"visible":true,"origin":"","legend":"Comparision of sB7-H4 levels in blood serum between cohort 1 (n=109) before (10.49 ng/ml; IQR 7.78-17.27) and after NACT (8.43 ng/ml; IQR 3.46-18.79) versus cohort 2 (16.96 ng/ml; IQR 6.49-30.59, n=463).","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-93991/v1/fea6cd4b3ebec596d948a913.jpg"},{"id":3157554,"identity":"1ac8f7ab-dab4-42dc-a224-87962fff3d3a","added_by":"auto","created_at":"2020-10-23 15:06:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48465,"visible":true,"origin":"","legend":"Box plots for sB7-H4 blood serum concentration levels among particular intrinsic subtypes of BC in cohort 1 (N=109).","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-93991/v1/4aba8667371a4ed280b966e8.jpg"},{"id":3157555,"identity":"57877c46-6461-46e2-8b71-81fe48b19f43","added_by":"auto","created_at":"2020-10-23 15:06:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155312,"visible":true,"origin":"","legend":"Overall survival and progression-free survival of patients from cohort 1 stratified by sB7-H4 detection in blood serum. ","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-93991/v1/2a0fd01fa4ba4b8e229b9d03.png"},{"id":13606133,"identity":"95b1a0a0-97e4-4cf9-a0f6-770a62ff169a","added_by":"auto","created_at":"2021-09-17 06:06:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":749595,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-93991/v1/f9ab1956-9fb1-4003-9749-65986c86cc49.pdf"}],"financialInterests":"","formattedTitle":"Soluble B7-H4 and its association with clinical characteristics and prognosis in patients with early breast cancer","fulltext":[{"header":"1. Background","content":" \u003cp\u003eThe development of cancer is associated with the growth of a suppressive tumor microenvironment including mechanisms of avoiding immune destruction by alternating the immune checkpoint pathway. Immune checkpoint blockades have shown dramatic effects in various tumor types including breast cancer (BC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, this effect has especially been observed in the triple negative BC (TNBC) subgroup, but also in a small subgroup of Luminal B and HER2-positive BC patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. One possible explanation is higher tumor mutational load of TNBC and metastatic BC versus HER2-positive or luminal tumors and early BC, although the expression of immune-checkpoints in all subtypes of BC was identified [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, a better understanding of the interactions between immune cells and BC is needed to develop new therapeutic options and identify subgroups of patients that could benefit from the targeted therapies.\u003c/p\u003e \u003cp\u003eB7 family members have both, stimulatory and inhibitory effects on T cells and play a critical role in maintaining immune tolerance. They can help a tumor to escape from host surveillance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. B7-H4 is a co-stimulatory ligand that inhibits T-cell responses by interacting with as yet unidentified receptors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. B7-H4 is highly expressed in various types of neoplasms including ovarian, endometrial or BC and its expression correlates with the progression of the disease and a poor prognosis in many cases [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, B7-H4 is a potentially novel target for cancer therapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. B7-H4 has a membrane-bound and soluble form (sB7-H4), which has also been detected in serum of patients with cancer with its expression closely related to progression and prognosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, it remains unclear whether sB7-H4 is associated with diagnostics and outcomes in BC patients.\u003c/p\u003e \u003cp\u003eImmune checkpoint inhibitors are only effective in a subset of patients, and the identification of biomarkers that predict response to therapy is crucial to increase the rates of responders. Moreover, soluble serum biomarkers are useful diagnostic tools because they can reflect the tumor status and predict patient prognosis. Here, we investigate the distribution of blood serum sB7-H4 in patients with early BC across different clinico-pathological characteristics and the prognostic value sB7-H4 for patients\u0026rsquo; outcome. The aim of this study was to explore whether sB7-H4 is a reliable marker for BC prognosis.\u003c/p\u003e "},{"header":"2. Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patient population and patient characteristics\u003c/h2\u003e \u003cp\u003eIn total, 572 primary, non-metastatic BC patients, diagnosed between 2004 and 2009\u0026nbsp;at the University Hospital of Essen, Department of Gynecology, were analyzed.Cohort 1 consisted of 109 pts in the neo-adjuvant setting and cohort 2 of 463 pts in the adjuvant setting. In both groups, serum was obtained before surgery and in cohort 1, additionally before neo-adjuvant chemotherapy (NACT).\u003c/p\u003e \u003cp\u003eThe eligibility criteria were: histologically proven BC, no severe uncontrolled comorbidities or medical conditions and no further malignancies at the time of enrollment or in the patient history, completion of neoadjuvant or adjuvant treatment according to current national guidelines [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] including adjuvant chemotherapy (ACT) and NACT (anthracyclines, taxanes, cyclophosphamide, carboplatin, gemcitabine, 5-fluorouracil), anti-hormonal therapy in the case of hormone responsive tumors (tamoxifen or an aromatase inhibitor), Herceptin (after FDA approval in November 2006) in the case of HER2 positivity and radiotherapy. For each of the 572 patients, the tumor type, TNM-staging and grading were assessed in the Institute of Pathology, at the University Hospital Essen as part of the West German Comprehensive Cancer Center. In the neoadjuvant setting, pathological response to therapy was defined according to the grading system of Sinn and colleagues [Sinn\u0026thinsp;=\u0026thinsp;0, no pathological response; Sinn\u0026thinsp;=\u0026thinsp;1\u0026ndash;3, pathological partial response (pPR) and Sinn\u0026thinsp;=\u0026thinsp;4, pathological complete response (pCR) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients positive for disseminated tumor cells in the bone marrow were recommended to complete a prescription of clodronate (2\u0026thinsp;\u0026times;\u0026thinsp;520\u0026nbsp;mg/d) for at least two years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling of Serum and Measurement of sB7-H4 protein levels\u003c/h2\u003e \u003cp\u003eNine ml of blood were collected from each patient using S-Monovettes (Sarstedt AG \u0026amp; Co, N\u0026uuml;mbrecht, Germany), stored at 4\u0026nbsp;\u0026deg;C and processed within 4 hours to avoid blood cell lysis. Blood fractionation was carried out by centrifugation for 10\u0026nbsp;min at 2500 x g. Subsequently, 3\u0026ndash;4\u0026nbsp;ml of the upper phase, constituting blood serum, were removed and stored at -80\u0026nbsp;\u0026deg;C. All samples were assayed in batch form for blood serum protein levels of sB7-H4. sB7-H4 serum levels were analyzed using the sandwich Enzyme-Linked Immunosorbent Assay Kit (Cusabio, Cologne, Germany) according to the manual instructions. For ELISA measurement, 100\u0026nbsp;\u0026micro;l undiluted serum samples and control samples were dispensed into wells coated with an antibody specific for B7-H4 and incubated for 2 hours at 37\u0026nbsp;\u0026deg;C. Subsequently, after removing any unbound substances, a biotin-conjugated antibody specific for B7-H4 was added to the wells for 1 hour at 37\u0026nbsp;\u0026deg;C. After washing three times, 100 \u0026micro;L of avidin conjugated Horseradish Peroxidase (HRP) was added for 1\u0026nbsp;h at 37\u0026nbsp;\u0026deg;C. Subsequently, after washing five times, 90 \u0026micro;L of substrate solution containing TMB (Tetramethylbenzidine) was added for 15\u0026ndash;30 minutes at 37\u0026nbsp;\u0026deg;C, protected from light. Color development was stopped by the addition of 50\u0026nbsp;\u0026micro;l stop solution to each well and the degree of enzymatic turnover of the substrate was investigated by dual-wavelength absorbance measurement at 450 and 620\u0026nbsp;nm as a reference wavelength within 5 minutes using an ELISA reader (TECAN, Model Sunrise; Austria GmbH, Grodig, Austria) and the data analysis software Magellan\u0026trade; (TECAN, Mannedorf, Switzerland). To quantify blood serum concentration levels of B7-H4, a non-linear regression model (4-parameter Marquardt) was used with a log/lin type of graph according to the manufacturer\u0026rsquo;s instructions. The observed absorbance was directly proportional to the concentration level of sB7-H4 in the samples, which was calculated from the calibration curve. The sB7-H4 serum levels were expressed in ng/mL according to the established standard curve (detection range: 7.8\u0026ndash;500\u0026nbsp;ng/mL). The minimum detectable dose of sB7-H4 was typically less than 1.95\u0026nbsp;ng/mL. The lower limit of detection (LLD) was defined as the lowest protein concentration that could be differentiated from zero. Intra-assay variation was \u0026lt;\u0026thinsp;8%, while inter-assay variation was \u0026lt;\u0026thinsp;10%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe distribution of sB7-H4 in each study group was different from normal. Descriptive statistics were computed and reported as median with interquartile range (IQR) or frequency counts (%). The differences between two groups were defined by Mann-Whitney U test or paired Wilcoxon test. More than two groups were analyzed using Kruskal-Willis test. Differences in frequency counts were analyzed using the chi-squared test. ROC curve analysis was performed to obtain cut-off values representing the optimal separation of survival curve. The optimal cut-off value, i.e. the threshold that maximizes the sum of (sensitivity\u0026thinsp;+\u0026thinsp;specificity) was calculated according to Youden [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Kaplan-Meier analysis was performed to analyze progression free survival (PFS) and overall survival (OS) probabilities. The difference between survival curves was assessed by using the log rank test. Age-adjusted hazard ratios (HR) with corresponding 95%-confidence intervals (95%-CI) were calculated by using Cox proportional hazards regression. All analyses were performed using the MedCalc version 17.9.7 (MedCalc Software bvba, Ostend, Belgium) and the R statistical package version 3.4.0.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical data of patients in the neoadjuvant cohort (cohort I)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (%)\u003c/p\u003e \u003cp\u003eNeoadjuvant cohorte\u003c/p\u003e \u003cp\u003e(% of all applicable/known)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMenopausal Status\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePre- and perimenopausal\u003c/p\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64/109 (58.72)\u003c/p\u003e \u003cp\u003e45/109 (41.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003cp\u003eLobular\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73/102 (71.57)\u003c/p\u003e \u003cp\u003e16/102 (15.69)\u003c/p\u003e \u003cp\u003e13/102 (12.75)\u003c/p\u003e \u003cp\u003e7/109 (6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrading\u003c/b\u003e\u003c/p\u003e \u003cp\u003eI\u003c/p\u003e \u003cp\u003eII\u003c/p\u003e \u003cp\u003eIII\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/108 (7.41)\u003c/p\u003e \u003cp\u003e48/108 (44.44)\u003c/p\u003e \u003cp\u003e52/108 (48.15)\u003c/p\u003e \u003cp\u003e1/109 (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor before NACT (cT)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eT1\u003c/p\u003e \u003cp\u003eT2\u003c/p\u003e \u003cp\u003eT3\u003c/p\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17/109 (15.60)\u003c/p\u003e \u003cp\u003e75/109 (68.81)\u003c/p\u003e \u003cp\u003e11/109 (10.09)\u003c/p\u003e \u003cp\u003e6/109 (5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNodal status before NACT (cN)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNode negative\u003c/p\u003e \u003cp\u003eNode positive\u003c/p\u003e \u003cp\u003eN1\u003c/p\u003e \u003cp\u003eN2\u003c/p\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53/109 (48.62)\u003c/p\u003e \u003cp\u003e56/109 (51.38)\u003c/p\u003e \u003cp\u003e47/109 (43.12)\u003c/p\u003e \u003cp\u003e8/109 (7.23)\u003c/p\u003e \u003cp\u003e1/109 (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological response\u003c/b\u003e\u003c/p\u003e \u003cp\u003eResponse\u003c/p\u003e \u003cp\u003eComplete response\u003c/p\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003cp\u003eNo response\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101/107 (94,39)\u003c/p\u003e \u003cp\u003e23/107 (21.50)\u003c/p\u003e \u003cp\u003e78/107 (72.90)\u003c/p\u003e \u003cp\u003e6/107(5.61)\u003c/p\u003e \u003cp\u003e2/109 (1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeoadjuvant therapy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003cp\u003eEndocrine therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102/109 (93.58)\u003c/p\u003e \u003cp\u003e7/109 (6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImmunhistochemical Subtype\u003c/b\u003e\u003c/p\u003e \u003cp\u003eER-, PR-, HER2-\u003c/p\u003e \u003cp\u003eHER2+\u003c/p\u003e \u003cp\u003e(ER\u0026thinsp;+\u0026thinsp;and/ or PR+, HER2-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18/109 (16.51)\u003c/p\u003e \u003cp\u003e13/109 (27.52)\u003c/p\u003e \u003cp\u003e61/109 (55.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecurrence\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72/103 (69.90)\u003c/p\u003e \u003cp\u003e31/103 (30.20)\u003c/p\u003e \u003cp\u003e6/109 (5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistant Recurrence\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78/102 (76.47)\u003c/p\u003e \u003cp\u003e17/102 (16.67)\u003c/p\u003e \u003cp\u003e7/109 (6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eER\u0026thinsp;=\u0026thinsp;estrogen receptor, PR\u0026thinsp;=\u0026thinsp;progesterone receptor, HER2\u0026thinsp;=\u0026thinsp;human epidermal growth factor receptor-2, TNBC\u0026thinsp;=\u0026thinsp;triple-negative breast cancer, nk\u0026thinsp;=\u0026thinsp;not known; nd\u0026thinsp;=\u0026thinsp;not done\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\u003eClinical data of patients in the adjuvant cohort (cohort II)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(% of all applicable/known)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMenopausal Status\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePre- and perimenopausal\u003c/p\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109/463 (23.54)\u003c/p\u003e \u003cp\u003e354 (76.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003cp\u003eLobular\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e354/453 (78.16)\u003c/p\u003e \u003cp\u003e60/453 (13.25)\u003c/p\u003e \u003cp\u003e39/453 (8.61)\u003c/p\u003e \u003cp\u003e10/463 (2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrading\u003c/b\u003e\u003c/p\u003e \u003cp\u003eI\u003c/p\u003e \u003cp\u003eII\u003c/p\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90/463(19.44)\u003c/p\u003e \u003cp\u003e251/463 (54.21)\u003c/p\u003e \u003cp\u003e122/463 (26.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size at first diagnosis (pT)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eT1\u003c/p\u003e \u003cp\u003eT2\u003c/p\u003e \u003cp\u003eT3\u003c/p\u003e \u003cp\u003eT4\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300/460 (65.22)\u003c/p\u003e \u003cp\u003e140/460 (30.43)\u003c/p\u003e \u003cp\u003e14/460 (3.04)\u003c/p\u003e \u003cp\u003e6/460 (1.30)\u003c/p\u003e \u003cp\u003e3/463 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNodal Status at first diagnosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNode negative\u003c/p\u003e \u003cp\u003eNode positive\u003c/p\u003e \u003cp\u003eN1\u003c/p\u003e \u003cp\u003eN2\u003c/p\u003e \u003cp\u003eN3\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313/462 (67.75)\u003c/p\u003e \u003cp\u003e149/462 (32.25)\u003c/p\u003e \u003cp\u003e136/462 (29.44)\u003c/p\u003e \u003cp\u003e10/462 (2.16)\u003c/p\u003e \u003cp\u003e3/462 (0.65)\u003c/p\u003e \u003cp\u003e1 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes (adjuvant)\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168/388 (43.30)\u003c/p\u003e \u003cp\u003e220/388(56.70)\u003c/p\u003e \u003cp\u003e75/463(16.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImmunhistochemical Subtype\u003c/b\u003e\u003c/p\u003e \u003cp\u003eER-, PR-, HER2-\u003c/p\u003e \u003cp\u003eHER2+\u003c/p\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;and/ or PR+, HER2-\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49/459 (10.68)\u003c/p\u003e \u003cp\u003e55/459 (11.73)\u003c/p\u003e \u003cp\u003e355/459 (77.34)\u003c/p\u003e \u003cp\u003e4/463(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecurrence\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360/384 (93,75)\u003c/p\u003e \u003cp\u003e24/384 (6.35)\u003c/p\u003e \u003cp\u003e79/463 (17.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistant Recurrence\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003enk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266/283 (94.00)\u003c/p\u003e \u003cp\u003e17/283 (16.01)\u003c/p\u003e \u003cp\u003e180/463 (38.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eER\u0026thinsp;=\u0026thinsp;estrogen receptor, PR\u0026thinsp;=\u0026thinsp;progesterone receptor, HER2\u0026thinsp;=\u0026thinsp;human epidermal growth factor receptor-2, TNBC\u0026thinsp;=\u0026thinsp;triple-negative breast cancer, nk\u0026thinsp;=\u0026thinsp;not known; nd\u0026thinsp;=\u0026thinsp;not done\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":" \u003cp\u003eThe clinical characteristics of all patients are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The median age was 51\u0026nbsp;years (IQR 43,5\u0026ndash;61) in cohort 1 and 61\u0026nbsp;years (IQR 51\u0026ndash;68) in cohort 2. The predominant histological subtype was invasive ductal carcinoma in both groups (72% and 76%). In cohort 1, the majority of patients (58%) were pre- and perimenopausal whereas most of the patients in cohort 2 were postmenopausal (76%). In contrast to cohort 2 with most of the patients characterized as T1 (65%), no lymph node involvement (68%) and grade 2 (54%) tumors, 69% of the patients in cohort 1 showed a T2 tumor, 52% of the patients were node positive and had more aggressive tumors, characterized as grade 2 (44%) and grade 3 (48%). When stratifying according to BC subtypes, 17% were triple-negative and 28% Her2-positive in cohort 1. In cohort 2, the values were 11% and 12% while all other patients (77%) were characterized as hormone receptor-positive.\u003c/p\u003e \u003cp\u003eIn cohort 1, 102/109 patients (93.58%) received NACT and 7 patients (6.42%) a neo-adjuvant endocrine therapy.. Overall, response to therapy resulted in a ratio of 94% (22% pCR; 73% pPR) of responders and 6% of non-responders. In cohort 2, 220/463 (48%) of the patients received ACT, 168/463 (36%) did not get chemotherapy and in 75/463 patients (16%), no information was available.\u003c/p\u003e \u003cp\u003eIn cohort 1, sB7-H4 blood serum concentration was delectable in 27/109 (26%) patients before and in 50/109 (48%) patients after NACT. In cohort 2, sB7-H4 blood serum concentration was only delectable in 18/461 (4%) patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparision of sB7-H4 in blood serum between groups\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe median blood serum concentration levels in cohort 1 were 10.49\u0026nbsp;ng/ml (IQR 7.78\u0026ndash;17.27) before and 8.43\u0026nbsp;ng/ml (IQR 3.46\u0026ndash;18.79) after NACT. For cohort 2, the value was 16.96\u0026nbsp;ng/ml (IQR 6.49\u0026ndash;30.59) which was significantly higher than in cohort 1 after NACT (p\u0026thinsp;=\u0026thinsp;0.04) but not before NACT. No significant differences between patients before and after NACT were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. sB7-H4 serum levels among particular intrinsic subtypes of BC in cohort 1\u003c/h2\u003e \u003cp\u003eMedian sB7-H4 blood serum concentration levels in cohort 1 before NACT was 13.5\u0026nbsp;ng/ml (IQR 8.8\u0026ndash;24.2) for the ER\u0026thinsp;+\u0026thinsp;PR\u0026thinsp;+\u0026thinsp;HER2- subtype, 9.13\u0026nbsp;ng/ml (IQR 6.84\u0026ndash;13.26) for the HER2\u0026thinsp;+\u0026thinsp;subtype and 8.92\u0026nbsp;ng/ml (IQR 6.84\u0026ndash;14.87) for the TNBC subtype, respectively. After NACT, the values were 8.26\u0026nbsp;ng/ml (IQR 3.71\u0026ndash;16.48), 8.37\u0026nbsp;ng/ml (IQR 3.34\u0026ndash;18.97) and 9.71\u0026nbsp;ng/ml (IQR 2.01\u0026ndash;26.29), respectively. However, differences before (p\u0026thinsp;=\u0026thinsp;0.25) and after NACT(p\u0026thinsp;=\u0026thinsp;0.96) were not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn cohort 2, the comparison among intrinsic subtypes was not performed due to low detection rates of sB7-H4 in blood serum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Association of sB7-H4 and clinicopathological characteristics in cohort 1\u003c/h2\u003e \u003cp\u003eNo significant associations between clinical characteristics and chances in sB7-H4 blood serum concentration levels before and after NACT were obtained (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in sB7-H4 blood serum concentration levels in patient cohort 1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esB7-H4 (ng/ml) Before NACT; median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003esB7-H4 (ng/ml) After\u003c/p\u003e \u003cp\u003eNACT; median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-menopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.03 (7.32\u0026ndash;13.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.80 (3.34\u0026ndash;16.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-menopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5 (9.37\u0026ndash;23.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.98 (3.53\u0026ndash;20.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.14 (7.98\u0026ndash;16.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.02 (3.64\u0026ndash;19.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.80 (4.82\u0026ndash;18.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.69 (8.43\u0026ndash;39.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.25 (10.49\u0026ndash;30.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,34 (1,7\u0026ndash;10,29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.93 (6.41\u0026ndash;14.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.99 (5.33\u0026ndash;14.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.39 (7.98\u0026ndash;16.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.15 (3.34\u0026ndash;18.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;cT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.03 (10.78\u0026ndash;25.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.11 (8.59\u0026ndash;30.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNodal status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.95 (7.98\u0026ndash;18.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.33 (2.91\u0026ndash;12.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.39 (6.19\u0026ndash;17.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.27 (3.53\u0026ndash;21.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;cN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.27 (4.57\u0026ndash;28.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTumor Grading\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.73 (13.31\u0026ndash;31.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.96 (3.45\u0026ndash;37.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.61 (6.62\u0026ndash;18.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.02 (4.63\u0026ndash;14.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.49 (7.78\u0026ndash;13.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.09 (3.37\u0026ndash;19.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDistant metastasis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.81 (6.62\u0026ndash;16.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.42 (3.75\u0026ndash;19.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.92 (8.84-11,81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.81 (3.34\u0026ndash;12.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTumor subtyp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;PR\u0026thinsp;+\u0026thinsp;Her2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5 (8.81\u0026ndash;24.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.27 (3.71\u0026ndash;16.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHer2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.13 (6.84\u0026ndash;13.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.33 (3.26\u0026ndash;17.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.93 (6.84\u0026ndash;14.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,99 (2,46\u0026thinsp;\u0026minus;\u0026thinsp;32,96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eResponse to the therapy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete remission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.58 (6.12\u0026ndash;15.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.23 (3.10-12.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial remission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.09 (8.58\u0026ndash;16.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.42 (3.50-19.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\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\u003eNo remission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.98 (4.42\u0026ndash;5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\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=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.13 (6.40-17.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.42 (3.54\u0026ndash;19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.49 (8.89\u0026ndash;17.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.09 (3.43\u0026ndash;15.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The prognostic value of sB7-H4 in blood serum\u003c/h2\u003e \u003cp\u003eTo obtain cut-off values representing the optimal separation of survival curve, Receiver Operating Curve (ROC) analysis was performed. For patients before NACT, the area under the curve (AUC) was 0.68 (95%-CI 0.47\u0026ndash;0.84) with the optimal threshold of 8.8\u0026nbsp;ng/mL for OS and 0.61 (95%-CI 0.39\u0026ndash;0.8) for DFS with the optimal threshold of 8.8\u0026nbsp;ng/mL. For patients after NACT, the AUC was 0.63 (95%-CI 0.48\u0026ndash;0.76 with the optimal threshold of 3.6\u0026nbsp;ng/mL for OS) and 0.53 (95%-CI 0.38\u0026ndash;0.68 with the optimal threshold of 16.3\u0026nbsp;ng/mL for DFS. The survival analysis showed no significant differences in OS and in DFS before and after NACT (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eAge-adjusted Cox regression analysis indicated that sB7-H4 levels before and after NACT did not influence the prognosis and risk of recurrence (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults from Cox regression analysis of patients in cohort 1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003esB7-H4 (ng/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDFS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort 1 before NACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.73\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.85\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort 1 after NACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.84\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.95\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.97\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"4. Discussion","content":" \u003cp\u003eB7-H4 plays a significant role in tumor escape from the immune surveillance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While the mechanism is still not clear, sB7-H4 may also play a role in cancer development through negative regulation of T-cell immunity. So far, there is no evidence in the literature comparing sB7-H4 blood serum levels with clinical characteristics and outcomes of patients with early BC. Here, we assessed changes in sB7-H4 blood serum levels in paired pre-NACT and post-NACT as well as adjuvant serum samples and correlated these findings with clinico-pathological parameters and prognosis. Although we observed higher sB7-H4 levels after therapy, there were no significant associations between sB7-H4 and prognosis in the neo-adjuvant setting. In addition, no differences in sB7-H4 levels could be documented for the different BC subtypes.\u003c/p\u003e \u003cp\u003eExpression of B7-H4 in tumor tissue has been linked to a worse prognosis in some types of cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, data in the context of BC are inconsistent. Huang et al. showed that the OS rate of patients with higher B7-H4 expression was significantly worse than in those with lower expression [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, TNBC patients with B7-H4 overexpression had significantly shorter survival and recurrence time than those with low B7-H4 expression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These data suggest that B7-H4 might be a potential negative prognostic indicator.\u003c/p\u003e \u003cp\u003eIn contrast, other studies showed that B7-H4 expression was not associated with worse survival in BC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and expression of B7-H4 has even been linked to a favorable 5-year PFS [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. There is no data available with regard to the prognostic features of sB7-H4 in BC patients. We found that sB7-H4 was not associated with prognosis, however, due to the limited data in some BC subgroups, we could not perform subgroup analyses for survival in the adjuvant setting. Nevertheless, we speculate that sB7-H4 may be a part of tumor-immune tolerance because B7-H4 is a negative regulator of immune response which might be important for BC patients. Treatment with antibodies targeting B7-H4 may result in a reduction of tumor progression and better patient outcomes. Indeed, an \u003cem\u003ein vivo\u003c/em\u003e study using humanized animal model showed that a B7-H4/CD3-bispecific antibody might be a therapeutic agent against B7-H4-expressing tumors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, it is essential to identify biomarkers that can predict the possible response to anti-B7-H4 treatment. The existence of sB7-H4 could be a predictive marker for immunotherapy targeting T lymphocytes as suggested by Ohki et al [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eB7-H4 is frequently expressed on tumor cells including BC. Data on the expression among particular intrinsic subtypes of BC\u0026mdash; defined by expression of ER, PR or HER2\u0026mdash;is inconsistent [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, sB7-H4 was independent of intrinsic BC subtypes. Since the source and function of sB7-H4 is not known, it is not clear whether serum sB7-H4 reflects the expression of B7-H4 in tumor tissue. Kamimura et al. demonstrated that sB7-H4 is secreted in inflammatory environments [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and it was also reported to act as a decoy molecule that blocks suppressive functions of cell-associated B7-H4 leading to enhanced T-cell-mediated autoimmune responses [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Zhang et al. suggested that different origins of sB7-H4 define its distinct structure and function [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Nevertheless, there is a growing body of evidence indicating that sB7-H4 negatively regulates T-cells and plays a regulatory role in immune tolerance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, chemotherapy may have beneficial effects on anticancer immunity by enhancing mutational load or direct elimination of immunosuppressive cells [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Thus, understanding the effect of DNA-damaging agents on the immune system is critical to identify optimal strategies that combine checkpoint inhibitors and chemotherapy agents. In our study, the detection of serum sB7-H4 almost doubled after NACT versus before NACT. Some preclinical studies have suggested that immune checkpoint expression like Programmed Cell Death Ligand 1 (PD-L1) might be stimulated by chemotherapy; others observed a significant decrease in PD-L1 expression after NACT in BC patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The changes collectively induced by NACT in immune-checkpoint expression could provide a rationale for the use of immune checkpoint inhibitors in the neoadjuvant setting for BC patients.\u003c/p\u003e \u003cp\u003eThe primary limitation of this study is its retrospective character, the lack of corresponding B7-H4 tissue availability and its expression in the tumor microenvironment. However, our study performed on a representative group of BC patients suggests a lack of an association between sB7-H4 serum levels and prognosis. Therapeutic properties of anti-B7-H4 therapy in BC patients is still not known. Since B7-H4 remains a candidate for targeted inhibition in cancer immunotherapy, further prospective studies with blockade of B7-H4 in association with sB7-H4 levels should be proposed to investigate the applicability to anti-B7-H4 immune therapy in BC.\u003c/p\u003e "},{"header":"5. Conclusion","content":" \u003cp\u003esB7-H4 concentration levels in serum of non-metastatic BC patients are neither associated with prognosis nor with clinical characteristics in the adjuvant and neo-adjuvant setting.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eACT\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; adjuvant chemotherapy\u003c/p\u003e\n\u003cp\u003eBC \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; breast cancer\u003c/p\u003e\n\u003cp\u003eER\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; estrogen-receptor\u003c/p\u003e\n\u003cp\u003eHER\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; human epitdermal growth factor receptor\u003c/p\u003e\n\u003cp\u003eNACT \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; neoadjuvant chemotherapy\u003c/p\u003e\n\u003cp\u003epCR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; pathological complete response\u003c/p\u003e\n\u003cp\u003epPR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; pathological partial response\u003c/p\u003e\n\u003cp\u003ePR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; progesterone-receptor\u003c/p\u003e\n\u003cp\u003eTNBC\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; triple negative breast cancer\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll blood samples were obtained and collected after written informed consent from all subjects using protocols approved by the clinical Ethic committee of the University Hospital Essen (17-7495-BO).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets for the current study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the internal budget of the Department of Gynecology and Obstetrics of the University Hospital of Essen, Germany\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM gave substantial contributions to conception and design, acquisition, analysis and interpretation of data, and was a major contributor in writing the manuscript. AKB gave substantial contributions to conception, design, acquisition and interpretation of data, drafting and revising the article and final approval of the version to be published. SKB gave substantial contributions to conception and design, interpretation and revising the article and final approval of the version to be published. BS gave substantial contributions to analysis and interpretation of data, revising the article and final approval of the version to be published. OH and RK gave substantial contributions to acquisition of data, revising the article and final approval of the version to be published. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the laboratory staff for the support and continual help in maintaining and analyzing the samples and documentation of data. We are also grateful to all women who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH, Iwata H, et al. IMpassion130 Trial Investigators. Atezolizumab and Nab-paclitaxel in advanced triple-negative breast cancer. New Engl J Med. 2018;379(22):2108\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmid P, Cortes J, Pusztai L, McArthur H, K\u0026uuml;mmel S, Bergh J, et al. KEYNOTE-522 Investigators. Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med. 2020;382(9):810\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnurag M, Zhu M, Huang C, Vasaikar S, Wang J, Hoog J, et al. Immune Checkpoint Profiles in Luminal B Breast Cancer (Alliance). 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Breast Cancer Res. 2017;19(1):91.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sB7-H4, Breast cancer, immune checkpoints","lastPublishedDoi":"10.21203/rs.3.rs-93991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-93991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Immunotherapy is a promising area for treatment of breast cancer (BC) that has transformed patient care. Immune checkpoint inhibitors are only effective in a subset of patients, and the identification of biomarkers that predict response to therapy is crucial to increase the rates of responders. B7-H4 is a potentially novel target for cancer therapy.\u003c/p\u003e\u003cp\u003eMethods: We examined the association of sB7-H4 with clinical characteristics and prognosis in patients with early BC. Using ELISA, we analyzed sB7-H4 serum concentrations in a total of 572 early BC patients before the onset of therapy, 109 patients (cohort 1) in the neo-adjuvant setting and 463 patients in the adjuvant setting (cohort 2). In cohort 1, measurements were also performed after neo-adjuvant therapy (NACT).\u003c/p\u003e\u003cp\u003eResults: In cohort 1, sB7-H4 blood serum concentration was delectable in 27/109 (26%) patients before and in 50/109 (48%) patients after NACT. In cohort 2, the detection rate was only 4% (18/461 patients). In cohort 1, no significant differences between patients, even when stratifying for particular intrinsic subtypes, before and after NACT were observed. No significant chances in sB7-H4 blood serum concentration levels before and after NACT were associated with clinical parameters, prognosis and the risk of recurrence. The median blood serum concentration levels in cohort 2 were significantly higher than in cohort 1 after NACT (p=0.04) but not before NACT. \u003c/p\u003e\u003cp\u003eConclusions: sB7-H4 concentration levels in serum of non-metastatic BC patients are neither associated with prognosis nor with clinical characteristics in the adjuvant and neo-adjuvant setting.\u003c/p\u003e","manuscriptTitle":"Soluble B7-H4 and its association with clinical characteristics and prognosis in patients with early breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-10-23 15:06:32","doi":"10.21203/rs.3.rs-93991/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20e5de3e-6a13-40fd-ad4f-799e5467cd9e","owner":[],"postedDate":"October 23rd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":863036,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2020-10-23T15:06:34+00:00","versionOfRecord":[],"versionCreatedAt":"2020-10-23 15:06:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-93991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-93991","identity":"rs-93991","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}