Prediction of response to neoadjuvant chemotherapy in breast cancer: A nomogram model based on androgen receptors and clinicopathological features

Prediction of response to neoadjuvant chemotherapy in breast cancer: A nomogram model based on androgen receptors and clinicopathological features | 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 Prediction of response to neoadjuvant chemotherapy in breast cancer: A nomogram model based on androgen receptors and clinicopathological features Wenjie Ma#, Weiwei Ma#, Xiaojing Li#, Jianli Ma#, Yuan Fang, Wenhui Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9251048/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 It is significant for breast cancer patients to precisely predict the efficacy of neoadjuvant chemotherapy (NAC) . Current studies have shown that the pathological complete response (pCR) rate after NAC is lower in patients with positive estrogen receptors (ER) and progesterone receptors (PR) . Although androgen receptors (AR) and glucocorticoid receptors (GR) , which are also steroid hormone receptors, play a role in the development of breast cancer, their effect on pCR is unclear. In this study, we explored the correlation between clinicopathological features including AR and GR and pCR rate in BC patients after NAC. Results showed that tumor size, ER status, tumor infiltrating lymphocytes (TILs) and AR status were independent predictive factors of pCR. Based on these indicators, a predictive model and a nomogram were constructed: the probability of reaching pCR was P (y = achieve pCR|x) = 1/[1 + e^ (-(the score of New))] and New = 1.0530 + score of tumor size + score of ER + score of TILs + score of AR. According to Hosmer-Lemeshow fit test and ROC curve, it showed satisfactory agreement between the predicted and observed probabilities (P-value 0.09466 > 0.05, area under the curve, 0.906; 95% confidence interval, 0.863–0.950). The predictive model will help to identify sensitive patients for NAC and provide instructional opinions for treatment selection. breast cancer neoadjuvant chemotherapy pathologic complete response androgen receptor glucocorticoid receptors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Breast cancer (BC) is the most common cause of cancer-related death in women. Neoadjuvant chemotherapy (NAC) refers to systemic chemotherapy before local treatment such as surgery or radiotherapy. It has been increasingly accepted as a standard therapeutic strategy for patients with locally advanced BC, as it can reduce tumor burden prior to surgery, improve the success rate of operation [ 1 ] and provide personalized chemotherapy-sensitive information to instruct subsequent treatment for BC patients [ 2 ] . However, during NAC, there are still a small group of patients who have disease progression and even lose the operation opportunity [ 3 ] . Current studies have shown that compared to those who retained residual cancer after NAC, patients who achieved pathologic complete response (pCR) had significantly longer disease-free survival (DFS) and overall survival (OS). Regrettably, there are only a small portion of BC patients achieving pCR, which means those BC patients who have not achieved pCR may develop chemotherapy resistance or disease progression during NAC [ 4 ] . With an increasing number of BC patients treated with NAC, it is significant to identify the predictors of pCR in individual cases and establish a reliable predictive model. Nowadays, the number of studies on predictors of pCR has been on the rise. A previous study has demonstrated that pCR rate in patients with low-grade or hormone-receptor positive tumors was low, while patients with more aggressive subtypes such as triple negative had higher pCR rate [ 5 ] . In addition, some studies reached the conclusion that high proliferation, absence of hormone receptor expression, poor differentiation, and HER-2 over expression were associated with higher pCR rate [ 6 ] . Currently, although there have been some related studies on predicting pCR, it has not yet been clear which accurate predictors have an influence on pCR. There also have been few well-designed models predicting pCR. Therefore, further research for a predictive model combined with the clinicopathological features for NAC efficacy is helpful and necessary. Nearly 70% of BC is estrogen receptor (ER) and/or progesterone receptor (PR) positive BC. Hormone receptors play an important role in the occurrence and development of BC. It has been widely accepted that compared with ER negative BC, ER positive BC patients who received NAC had a lower pCR rate [ 7 ] . Current studies have found that, as the same steroid hormone receptors with ER and PR, androgen receptor (AR) and glucocorticoid receptor (GR) also have important effect on BC. AR can regulate the proliferation, metastasis and invasion of BC through different signal transduction pathways such as HER-2, Wnt, ERα and MAPK pathways [ 8 , 9 ] . GR can increase the heterogeneity and metastasis of BC [ 10 ] . However, the effect of AR and GR on pCR rate remains unknown. As AR and GR can affect the development of BC through different signaling pathways, it is necessary to explore the correlation between AR and GR and pCR rate in BC patients who received NAC. In this retrospective study, we analyzed the relationship between pCR attainment and clinicopathological characteristics including AR and GR of BC patients after NAC and explored the independent predictors of pCR. Based on these predictors, we constructed a predictive model which can help us select patients who benefit more from NAC and provide an approach to select precise treatment in clinical application. PATIENTS AND METHODS 2.1 Data acquisition and preprocessing for bioinformatics analysis The study's workflow is depicted in Fig. 1 . Initially, Gene expression datasets (GSE25066) were sourced from the Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/geo/ ), which included 488 BC patients receiving NAC containing taxanes-anthracyclines. Patients in the datasets were grouped on the basis of achieving pCR or not. The datasets were obtained from the GPL96 sequencing platform and the origin of the species was Homo Sapiens. R limma was used for differential expression analysis. Fold change (FC) (absolute value) > 1.5 and P 1.5 and P < 0.05 were differentially expressed genes with up-regulated expression, while genes with FC < 1.5 and P < 0.05 were differentially expressed genes with down-regulated expression. 2.2 Patients samples for clinical cohort analysis We retrospectively collected data from 162 BC patients who received NAC in Harbin Medical University Cancer Hospital from January 2015 to December 2016. The criteria of patients included in the analysis were as follows: 1) patients were diagnosed with unilateral BC for the first time by pathologists; 2) patients underwent complete examinations and clinical staging to exclude distant metastases; 3) patients were females; 4) patients had no history of other malignant tumors; 5) patients had no underlying diseases that might affect NAC and surgery; 6) patients underwent immunohistochemical detection of ER, PR, HER-2, Ki-67 and P53; 7) patients had complete medical records. After applying these criteria, a total of 162 BC patients were included in this study. We subsequently collected clinical information and recorded whether the patient achieved pCR. According to the previous studies, the most common pCR definition was used in this study: absence of malignant tumor cells in breast and axillary lymph nodes after NAC (ypT0 ypN0). 2.3 Evaluation of clinicopathological features and immunohistochemistry (IHC) We extracted the status of molecular classification, ER, PR, Ki-67, HER-2, P53 and TILs from the pathology reports of the patients. Four molecular classifications respectively were luminal A, luminal B, HER-2 positive and triple negative. ER/PR ≥ 1% was defined as positive, otherwise defined as negative; Ki-67 (%) > 20% was defined as high expression; TILs were based on the proportion of mononuclear inflammatory cells in the tumor stromal area around the nest of infiltrative tumor cells. Proportion > 10% was classified as high levels of TILs and proportion ≤ 10% was classified as low levels of TILs. Nuclear expression of AR was scored in accordance with previously published Allred criterion [ 11 ] . Briefly, percentage of AR expressed cells was visually estimated. Allred score was calculated by taking into consideration the proportion (P) scored as 0–5 and the staining intensity (I) scored as 1–3. Proportion and intensity were then summed up to generate a score from 1 to 8. Score ≥ 3 was defined as positive and score < 3 was defined as negative [ 12 ] . GR immunoreactivity was scored using differential nuclear staining intensity scores (0, null; 1+, low or weak; 2+, moderate; 3+, high or strong). A total percentage score (% of tumor cells staining ≥ 1 + intensity; the sum of the percentage of cells at 1+, 2+, and 3 + intensities) was used to evaluate tumor expression of GR. The H-score was calculated with the percentage of nuclear-stained tumor cells multiplied by the relative immunointensity, and the formula was as follows: H-score = [(% at 0) × 0] + [(% at 1+) ×1] + [(% at 2+) × 2] + [(% at 3+) × 3] [ 13 ] . X-tile software [ 14 ] was used to classify the tumors with a GR H-score of less than or equal to 10 as negative, and those with an H-score of more than 10 as positive. 2.4 Statistical Analysis We described data for patients in the pCR and non-pCR groups according to the clinicopathological and immunohistochemical features. R-Studio (R3.6.3) was used for data analysis. Univariate analysis and multivariate logistic regression analysis were used to examine the significant predictors for achieving pCR after NAC. Hosmer-Lemeshow fit test was performed to test the sensitivity of the model and the receiver operating characteristic (ROC) curve was drawn to obtain the area under curve (AUC) value. P -value < 0.05 was considered as statistically significant in all analyses. RESULTS 3.1 Bioinformatics analysis of the correlation between steroid hormone receptor and pCR after NAC To explore the correlation between steroid hormone receptor expression in breast cancer patients and pCR after NAC, we obtained a gene expression profile dataset GSE25066 from a group of BC patients receiving NAC with Taxanes and Anthracyclines in the NCBI GEO database. GSE25066 datasets included 99 samples who achieved pCR (pCR group) and 389 samples who did not achieve pCR (RD group). Analysis results were shown as volcano plots (Fig. 2 A), among which NR3C4 and ESR1 were significantly lower expressed in the pCR group. Then we separately calculated the significant correlation between the genes encoding steroid hormone receptors and pCR rates (NR3C1 is the encoding gene of GR, NR3C4 is the encoding gene of AR, ESR1 and ESR2 are the encoding genes of ER, and PGR is the encoding gene of PR). Results showed that NR3C4 and ESR1 were still significant factors affecting pCR rates (Fig. 2 B). We then analyzed the expression of the five genes in the pCR group and RD group, concluding that NR3C4 and ESR1 were significantly lower expressed in pCR group (Fig. 2 C-G). Further analysis of the effects of the five genes on DFS of the patients showed that in addition to NR3C1, the expression levels of NR3C4, PGR, ESR1 and ESR2 significantly affected the prognosis of patients (Fig. 3 A-E). 3.2 Patient Characteristics Then we constructed a clinical cohort of 162 BC patients receiving NAC, 39 of whom (24.1%) achieved pCR. Table 1 summarized clinicopathological features of patients, such as age, tumor size, histologic grade, ect. The expression of AR and GR were analyzed by immunohistochemical (Fig. 4 ), showing that the AR status of 113 patients was positive (69.8%) and GR status of 91 patients was positive (56.2%)(Table 1 ). Table 1 Clinicopathological characteristics of 162 BC patients Clinicopathological characteristics frequency percentage (%) Age ≤ 50 years 97 59.9 > 50 years 65 40.1 BMI ≤ 25 kg/cm² 106 65.4 > 25 kg/cm² 56 34.6 Menopausal status premenopausal 84 51.8 postmenopausal 78 48.2 Tumor size < 2 cm 31 19.1 ≥ 2 cm, < 5 cm 105 64.8 ≥ 5 cm 26 16.1 Histologic type IDC 121 74.7 ILC 16 9.9 others 25 15.4 Histologic grade ≤ grade 2 94 58.0 grade 3 68 42.0 Lymphovascular invasion negative 137 84.6 positive 25 15.4 Lymph node metastasis negative 39 24.1 positive 123 75.9 ER status negative 51 31.5 positive 111 68.5 PR status negative 79 48.8 positive 83 51.2 HER-2 status negative 99 61.1 positive 63 38.9 Ki-67 ≤ 20% 66 40.7 > 20% 96 59.3 P53 status wild-type 105 64.8 mutation 57 35.2 Molecular classification luminal A 46 28.4 luminal B 58 35.8 HER-2 positive 32 19.7 triple negative 26 16.1 TILs low 101 62.4 high 61 37.6 Neoadjuvant chemotherapy regimen TEC/TAC 92 56.8 TE/TA, TC/EC/AC 39 24.1 others 31 19.1 Neoadjuvant chemotherapy cycles 4 cycles 61 37.7 6 cycles 53 32.7 other cycles 48 29.6 AR status negative 49 30.2 positive 113 69.8 GR status negative 71 43.8 positive 91 56.2 pCR status No 123 75.9 Yes 39 24.1 BC, breast cancer; BMI, body mass index; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; TILs, tumor infiltrating lymphocytes; AR, androgen receptor; GR, glucocorticoid receptor; T, taxane; A, doxorubicin; E, epirubicin; C, cyclophosphamide; IDC, invasive duct carcinoma; ILC, invasive lobular carcinoma; pCR, pathologic complete response 3.3 Univariate analysis of pCR Patients were divided into pCR group and non-pCR group. By comparing different groups, evidence has been provided that tumor size, histologic grade, ER status, Ki-67, molecular classification, TILs and AR status were correlative with pCR (p < 0.05). The pCR rates were 41.0%, 53.9% and 5.1% for patients with tumor size 20% had a higher pCR rate (76.9%) than patients of Ki-67 ≤ 20% (23.1%) (p = 0.0169). The pCR rate was 53.9% in patients of ER-, which was higher than patients of ER+ (46.1%) (p = 0.0011). And the rate was 51.3% in patients of AR-, which was higher than patients of AR+ (48.7%) (p = 0.0021). The difference in pCR rates between patients with different molecular classifications was statistically significant (p = 0.0021): luminal A (10.3%); luminal B (43.6%); HER-2 overexpression (25.6%); triple negative type (20.5%). The pCR rate of high levels of TILs (71.8%) was higher than that of low levels of TILs (28.2%) (p < 0.0001) (Table 2 ). Table 2 The univariate logistic regression analysis of pCR in patients receiving NAC Clinicopathological characteristics pCR (No.=39) Non-pCR (No.=123) P -value No. % No. % Age ≤ 50 years 21 53.8% 76 61.8% 0.4875 > 50 years 18 46.2% 47 38.2% BMI ≤ 25 kg/cm² 22 56.4% 84 68.3% 0.2435 > 25 kg/cm² 17 43.6% 39 31.7% Menopausal status premenopausal 19 48.7% 65 52.8% 0.7905 postmenopausal 20 51.3% 58 47.2% Tumor size < 2 cm 16 41.0% 15 12.2% 0.0001* ≥ 2 cm, < 5 cm 21 53.9% 84 68.3% ≥ 5 cm 2 5.1% 24 19.5% Histologic type IDC 27 69.2% 94 76.4% 0.0886 ILC 2 5.1% 14 11.4% others 10 25.7% 15 12.2% Histologic grade ≤ grade 2 16 41.0% 78 63.4% 0.0225* grade 3 23 59.0% 45 36.6% Lymphovascular invasion negative 34 87.2% 103 83.7% 0.7920 positive 5 12.8% 20 16.3% Lymph node metastasis negative 5 12.8% 34 27.6% 0.0946 positive 34 87.2% 89 72.4% ER status negative 21 53.9% 30 24.4% 0.0011* positive 18 46.1% 93 75.6% PR status negative 24 61.5% 55 44.7% 0.0994 positive 15 38.5% 68 55.3% HER-2 status negative 19 48.7% 80 65.0% 0.1024 positive 20 51.3% 43 35.0% Ki-67 ≤ 20% 9 23.1% 57 46.3% 0.0169* > 20% 30 76.9% 66 53.7% P53 status wild-type 28 71.8% 77 62.6% 0.3925 mutation 11 28.2% 46 37.4% Molecular classification luminalA 4 10.3% 42 34.2% 0.0391* luminalB 17 43.6% 41 33.3% HER-2 positive 10 25.6% 22 17.9% triple negative 8 20.5% 18 14.6% TILs low 11 28.2% 90 73.2% < 0.0001* high 28 71.8% 33 26.8% Neoadjuvant chemotherapy regimen TEC/TAC 20 51.3% 72 58.5% 0.4888 TE/TA, TC/EC/AC 9 23.1% 30 24.4% others 10 25.6% 21 17.1% Neoadjuvant chemotherapy cycles 4 cycles 13 33.3% 48 39.0% 0.1038 6 cycles 18 46.2% 35 28.5% other cycles 8 20.5% 40 32.5% AR status negative 20 51.3% 29 23.6% 0.0021* positive 19 48.7% 94 76.4% GR status negative 18 46.2% 53 43.1% 0.8801 positive 21 53.8% 70 56.9% pCR, pathological complete response; NAC, neoadjuvant chemotherapy; BMI, body mass index; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; TILs, tumor infiltrating lymphocytes; AR, androgen receptor; GR, glucocorticoid receptor; T, taxane; A, doxorubicin; E, epirubicin; C, cyclophosphamide; IDC, invasive duct carcinoma; ILD, invasive lobular carcinoma; * P < 0.05 significant. 3.4 Multivariate logistic regression analysis of pCR According to the results of univariate analysis, seven statistically significant variables (tumor size, histologic grade, ER status, Ki-67 status, molecular classification, TILs and AR status) were taken into the multivariate logistic regression analysis. The results demonstrated that tumor size, ER status, TILs and AR status were independent predictive factors with statistically significant and OR (95%CI) values were 0.0984 (0.0258–0.3166), P = 0.0002; 0.0263 (0.0027–0.1558), P = 0.0003; 0.1797 (0.0615–0.4836), P = 0.0010; 18.5994 (6.3046–67.8021), P < 0.0001; 0.2882 (0.1040–0.7625), P = 0.0135 respectively (Table 3 ). The results of corresponding forest plot are shown in Fig. 5 . Table 3 The multivariate logistic regression analysis of pCR in patients receiving NAC variable classification estimate value SE statistic P OR (95%CI) constant term 1.053 0.6124 1.719 0.0856 Tumor size < 2cm Ref 2 ~ 5cm -2.3187 0.631 -3.675 0.0002 0.0984 (0.0258–0.3166) ≥ 5cm -3.6396 1.0116 -3.598 0.0003 0.0263 (0.0027–0.1558) ER status negative Ref positive -1.7166 0.5213 -3.293 0.0010 0.1797 (0.0615–0.4836) TILs low Ref high 2.9231 0.5982 4.887 < 0.0001 18.5994(6.3046–67.8021) AR status negative Ref positive -1.2442 0.5039 -2.469 0.0135 0.2882 (0.1040–0.7625) pCR, pathologic complete response; NAC, neoadjuvant chemotherapy; SE,standard error; OR, odds ratio; CI, confidence interval; 2–5 cm, means including 2 cm but not including 5 cm; ER, estrogen receptor; TILs, tumor infiltrating lymphocytes; AR, androgen receptor 3.5 Logistic regression prediction probability model of pCR The predictors of pCR in BC patients after NAC were screened by univariate analysis ( P < 0.05), and then the predictive regression equation of pCR for NAC was established by multivariate logistic regression analysis: New = 1.0530 + score of tumor size + score of ER + score of TILs + score of AR (If the tumor size was 5cm, the score was − 3.6396; the score of ER-negative was 0, the score of ER-positive was − 1.7166; the score of TILs was 0 if there was low levels of lymphocytic infiltration and was 2.9231 if there were high levels of lymphocytic infiltration; the score of AR-negative was 0; the score of AR-positive was − 1.2442.). A probabilistic model for predicting the achievement of pCR was P (y=achieve pCR|x) = 1/[1 + e^(-(the score of New))] and 1-P represented the probability that pCR was not achieved. Then we evaluated the accuracy of the pCR prediction model. The result of Hosmer-Lemeshow fit test of the prediction model was χ 2 = 13.537, P = 0.09466 > 0.05, indicating that there was no statistical difference between the expected probability and the observed probability of the prediction model. The fitting degree was good. Then we plotted the ROC curve for evaluation. The sensitivity of the prediction model was 1.000 and the specificity was 0.724. The ROC curve was drawn as Fig. 6 : AUC value was 0.906, and the 95% confidence interval of AUC value was [0.863,0.950]. 3.6 Construction of a nomogram for predicting pCR A nomogram could be drawn for clinicians to apply this prediction model in clinical practice. The tumor size, ER status, TILs and AR status of the patient were calculated on the basis of the corresponding conditions in the figure above, and then the four scores were added to obtain a total score. According to the P -value corresponding to the total score, the probability of obtaining pCR after NAC for a BC patient was predicted (Fig. 7 ). DISCUSSION As a remarkable potential therapy to decrease extent of the tumor and provide a prognostic value, NAC has been increasingly used in the management of both locally advanced and earlier operable stage BC. Current studies have indicated that pCR, as a predictor for long-term survival, was closely related with the characteristics of tumor [ 1 ] . However, previous studies were not comprehensive on the predictive factors of pCR in BC patients after NAC. In this study, using univariate and multivariate logistic analyses, we have provided evidence that ER, AR, TILs and tumor size were all independent predictors. And then we constructed a predictive model which could be used to calculate the probability of postoperative pCR in BC patients after NAC. As we all know, ER, PR, AR and GR belong to the steroid receptor superfamily. They share a similar structure that includes hormone binding, nuclear translocation, DNA binding and transactivation domains [ 15 ] . In recent years, it has been shown that these different receptors can interact with each other in the promoters of the target genes, showing a higher level of complexity in the regulation of biological responses [ 16 ] . Current studies found that AR could affect the proliferation of BC cells by regulating multiple pathways [ 8 , 9 ] . Obradović MMS et al. also confirmed that abnormally activated GR could promote the heterogeneity and distant metastasis of BC [ 10 ] . However, no final conclusions have been made about the roles of AR and GR in predicting NAC efficacy in BC. In this study, we incorporated ER, PR, AR and GR status of BC into a prediction analysis. Evidence suggested that AR and ER negative patients had a higher pCR rate after NAC, whereas the expression of PR and GR was not correlated with pCR rate. In a retrospective analysis of 1731 patients treated with various neoadjuvant regimens, pCR rate was 24% in patients with ER-negative tumors and 8% in patients with ER-positive tumors [ 17 ] . Furthermore, MacGrogan et al. reported that negative ER was highly correlated with chemosensitivity, while negative PR was not [ 18 ] . Müller Vet al. found that high AR mRNA levels were associated with lower pCR rates in a study of 418 BC patients [ 19 ] . Our findings were consistent with studies above. Currently, there are few studies on GR as a predictor of NAC efficacy in BC patients. Results in this study showed that there was no statistically significant interaction between GR expression and pCR attainment in BC patients after NAC. The effect of GR on achieving pCR may be further explored with more researches. TILs, the foremost mononuclear immune cells infiltrating the tumor microenvironment (TME), are increasingly recognized as an important prognostic and predictive marker of immune response, especially with respect to chemotherapy response in some BC subtypes [ 20 ] . As a crucial component of TME which could reflect the host antitumor immune response [ 21 ] , the value of TILs for prediction of pCR in BC patients after NAC has received increasing attention. Hwang HW et al. have addressed that 44.9% of pre-NAC lymphocyte-predominant breast cancer (LPBC) patients had pCR, as did 6.3% of non-LPBC patients. Notably, high TILs levels were associated with a 25.9% increase in pCR rate compared with low TILs levels [ 7 ] . Our study is consistent with the results of prior studies. TILs may affect the pCR rate of BC patients after NAC by TME or immune response interacting with tumor cells. Tumor size has historically been used to stage BC and guide treatment. Livingston-Rosanoff et al. analyzed data from a national database of 38,864 women who received NAC, revealing that tumor size was independently associated with pCR following NAC [ 22 ] . Goorts et al. reported that pCR rate was substantially higher in patients with T1-2 disease compared to T3-4 [ 23 ] . Our research has reached similar conclusions to these studies: the pCR rate of T > 5cm was the lowest, only 5.13%, and the pCR rate of 2cm ≤ T < 5cm was the highest, reaching 53.85%. Currently, a couple of prediction models have been reported to predict the probability of pCR after NAC. Some of them were mainly based on clinicopathological features or chemotherapy regimens [ 15 ] , some predictive models were based on BC-related imaging features [ 24 ] , while few studies focused on the immune inflammatory indicators and steroid receptors [ 25 ] . Therefore, our study is unique in that we are the first to simultaneously explore the predictive role of the four steroid hormone receptors (ER, PR, GR, AR) as well as TILs on pCR. For patients whose P -value is small, timely surgery should be performed when the expected efficacy and tumor stage reduction are achieved. CONCLUSIONS In conclusion, this study suggested that independent predictors of NAC in BC patients achieving pCR included tumor size, ER status, TILs and AR status, and then we established a predictive model. This provides the possibility to improve the pCR rate and the precision of treatment options in BC patients. However, this model needs to be validated by a larger sample size of clinical research, and its practicality and accuracy need to be verified in clinical practice. Declarations Funding This study was supported by the provincial natural science foundation project of Heilongjiang Province, China (grant No. H2018044) (WZ) Competing Interests The authors declare that they have no competing interests. Author Contributions Wenhui Zhao: Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition. Wenjie Ma: Methodology, Project administration, Funding acquisition. Weiwei Ma: Formal analysis, Visualization, Writing- Original draft. Xiaojing Li: Software, Validation, Methodology, Data curation. Jianli Ma: Validation, Data curation. Yuan Fang: Validation, Data curation. Data Availability The data that support the findings of this study are available from the corresponding author ( [email protected] ) upon reasonable request. Ethics Approval and Informed consent The studies involving human participants were reviewed and approved by Ethics committee of Harbin Medical University Cancer Hospital. The patients/participants provided their written informed consent to participate in this study. Acknowledgements We are grateful to all participants and all coauthors in the study. References Li X, Wang M, Wang M, Yu X, Guo J, Sun T, et al. Predictive and Prognostic Roles of Pathological Indicators for Patients with BC on Neoadjuvant Chemotherapy. J BC. 2019;22(4):497–521. Asaoka M, Gandhi S, Ishikawa T, Takabe K. Neoadjuvant Chemotherapy for BC: Past, Present, and Future. BC (Auckl). 2020;14:1178223420980377. Asano Y, Kashiwagi S, Onoda N, Kurata K, Morisaki T, Noda S, et al. Clinical verification of sensitivity to preoperative chemotherapy in cases of androgen receptor-expressing positive breast cancer. Br J Cancer. 2016;114(1):14–20. Montemurro F, Nuzzolese I, Ponzone R. Neoadjuvant or adjuvant chemotherapy in early breast cancer? Expert Opin Pharmacother. 2020;21(9):1071–82. Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, et al. Pathological complete response and long-term clinical benefit in BC: the CTNeoBC pooled analysis. Lancet. 2014;384(9938):164–72. Wolf DM, Yau C, Wulfkuhle J, Brown-Swigart L, Gallagher RI, Lee PRE, et al. Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies. Cancer Cell. 2022;40(6):609–e6236. Hwang HW, Jung H, Hyeon J, Park YH, Ahn JS, Im YH, et al. A nomogram to predict pathologic complete response (pCR) and the value of tumor-infiltrating lymphocytes (TILs) for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Breast Cancer Res Treat. 2019;173(2):255–66. Kono M, Fujii T, Lim B, Karuturi MS, Tripathy D, Ueno NT. Androgen Receptor Function and Androgen Receptor-Targeted Therapies in Breast Cancer: A Review. JAMA Oncol. 2017;3(9):1266–73. Hickey TE, Selth LA, Chia KM, Laven-Law G, Milioli HH, Roden D, et al. The androgen receptor is a tumor suppressor in estrogen receptor-positive breast cancer. Nat Med. 2021;27(2):310–20. Obradović MMS, Hamelin B, Manevski N, Couto JP, Sethi A, Coissieux MM, et al. Glucocorticoids promote breast cancer metastasis. Nature. 2019;567(7749):540–4. Allred DC, Harvey JM, Berardo M, Clark GM. Prognostic and predictive factors in BC by immunohistochemical analysis. Mod Pathol. 1998;11(2):155–68. Riaz N, Idress R, Habib S, Azam I, Lalani EM. Expression of Androgen Receptor and Cancer Stem Cell Markers (CD44+/CD24- and ALDH1+): Prognostic Implications in Invasive BC. Transl Oncol. 2018;11(4):920–9. Block TS, Murphy TI, Munster PN, Nguyen DP, Lynch FJ. Glucocorticoid receptor expression in 20 solid tumor types using immunohistochemistry assay. Cancer Manag Res. 2017;9:65–72. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10(21):7252–9. Pufall MA. Glucocorticoids and Cancer. Adv Exp Med Biol. 2015;872:315–33. Lamb CA, Vanzulli SI, Lanari C. Hormone receptors in BC: more than estrogen receptors. Med (B Aires). 2019;79(Spec 6/1):540–5. Guarneri V, Broglio K, Kau SW, Cristofanilli M, Buzdar AU, Valero V, et al. Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. J Clin Oncol. 2006;24(7):1037–44. MacGrogan G, Mauriac L, Durand M, Bonichon F, Trojani M, de Mascarel I, et al. Primary chemotherapy in breast invasive carcinoma: predictive value of the immunohistochemical detection of hormonal receptors, p53, c-erbB-2, MiB1, pS2 and GST pi. Br J Cancer. 1996;74(9):1458–65. Witzel I, Loibl S, Wirtz R, Fasching PA, Denkert C, Weber K, et al. Androgen receptor expression and response to chemotherapy in breast cancer patients treated in the neoadjuvant TECHNO and PREPARE trial. Br J Cancer. 2019;121(12):1009–15. de Melo Gagliato D, Cortes J, Curigliano G, Loi S, Denkert C, Perez-Garcia J, et al. Tumor-infiltrating lymphocytes in Breast Cancer and implications for clinical practice. Biochim Biophys Acta Rev Cancer. 2017;1868(2):527–37. Zhang D, He W, Wu C, Tan Y, He Y, Xu B, et al. Scoring System for Tumor-Infiltrating Lymphocytes and Its Prognostic Value for Gastric Cancer. Front Immunol. 2019;10:71. Livingston-Rosanoff D, Schumacher J, Vande Walle K, Stankowski-Drengler T, Greenberg CC, Neuman H, et al. Does Tumor Size Predict Response to Neoadjuvant Chemotherapy in the Modern Era of Biologically Driven Treatment? A Nationwide Study of US BC Patients. Clin BC. 2019;19(6):e741–7. Goorts B, van Nijnatten TJ, de Munck L, Moossdorff M, Heuts EM, de Boer M, et al. Clinical tumor stage is the most important predictor of pathological complete response rate after neoadjuvant chemotherapy in BC patients. BC Res Treat. 2017;163(1):83–91. Hwang HW, Jung H, Hyeon J, Park YH, Ahn JS, Im YH, Nam SJ, Kim SW, Lee JE, Yu JH, Lee SK, Choi M, Cho SY, Cho EY. A nomogram to predict pathologic complete response (pCR) and the value of tumor-infiltrating lymphocytes (TILs) for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Breast Cancer Res Treat. 2019;173(2):255–66. 2018 Oct 15. Shi Z, Huang X, Cheng Z, Xu Z, Lin H, Liu C, Chen X, Liu C, Liang C, Lu C, Cui Y, Han C, Qu J, Shen J, Liu Z. MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology. 2023;308(1):e222830. Erratum in: Radiology. 2023;308(1):e239021. Footnotes NAC: neoadjuvant chemotherapy pCR: pathological complete response ER: estrogen receptors PR: progesterone receptors AR: androgen receptors GR: glucocorticoid receptors TILs: tumor infiltrating lymphocytes Additional Declarations No competing interests reported. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9251048","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629883320,"identity":"4f0d9f3e-9857-464c-b29a-ae2900966eca","order_by":0,"name":"Wenjie Ma#","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital, Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Ma#","suffix":""},{"id":629883323,"identity":"f8c14242-0869-4be1-9a08-be4008f553ed","order_by":1,"name":"Weiwei Ma#","email":"","orcid":"","institution":"Xiaogan Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Ma#","suffix":""},{"id":629883325,"identity":"534705c1-dfe4-4099-9e24-fd9b5413e7f2","order_by":2,"name":"Xiaojing Li#","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital, Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Li#","suffix":""},{"id":629883327,"identity":"eacf6ffd-1459-43ce-99ab-f406c485edda","order_by":3,"name":"Jianli Ma#","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital, Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianli","middleName":"","lastName":"Ma#","suffix":""},{"id":629883329,"identity":"0b0c62e8-ad4b-48d3-999f-acf381612ae5","order_by":4,"name":"Yuan Fang","email":"","orcid":"","institution":"Liuzhou People's Hospital affiliated to Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Fang","suffix":""},{"id":629883330,"identity":"2c52e8fe-4194-4d84-a27c-7f65d2fdb2e3","order_by":5,"name":"Wenhui Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACNmbm4z8+NjAkgHk8xGjhZ2dLkJxJkhbJfh4DaV6StBgcZkswtt1xL093RgLjg7dtDPLmhLUwH0jOPVNcbHYjgdlwbhuD4c4GImw5nNuWkLjtRgKbNG8bQ4LBAYJaeAybLSFa2H8TpUWymceYmRFqCzNRWviZ2dIYe9sSis3OPGyWnHNOwnADIS1s/IePMfxsS8gzO5588MObMht5grYgAcYGICFBvPpRMApGwSgYBbgBADWmP88/biCDAAAAAElFTkSuQmCC","orcid":"","institution":"Harbin Medical University Cancer Hospital, Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-03-28 08:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9251048/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9251048/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108181938,"identity":"1e087a4e-830a-41e1-8fad-384576fea987","added_by":"auto","created_at":"2026-04-30 08:59:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9251048/v1/bcb5eb95cc16ab4d4b1e335b.png"},{"id":108071288,"identity":"d66d3274-2479-4215-bfc6-6da1448f447a","added_by":"auto","created_at":"2026-04-29 06:07:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1019842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation between steroid hormone receptor expressions and pCR after NAC.\u003c/strong\u003e A, Volcano map of DEGs between pCR and RD groups. B, The forest plot and p-values of the steroid hormone receptor encoding genes in pCR and RD groups. C, The expressions of NR3C4 in pCR and RD groups. D, The expressions of ESR1 in pCR and RD groups. E, The expressions of ESR2 in pCR and RD groups. F, The expressions of PGR in pCR and RD groups. G, The expressions of NR3C1 in pCR and RD groups.(DEGs:Differential Expression Analysis; pCR group: pathological complete response group; RD group:samples who did not achieve pCR.)\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9251048/v1/c5229953658ddabc6a0d0d95.png"},{"id":108181339,"identity":"455f66a1-4f1f-4ab8-8e5f-b0f00d279cf3","added_by":"auto","created_at":"2026-04-30 08:58:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":631016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of the prognosis of BC patients who received NAC with steroid hormone receptor expressions in clinical specimens. \u003c/strong\u003eA, Kaplan-Meier DFS curves of BC patients with high expression of NR3C4 and low expression of NR3C4 who received NAC. B, Kaplan-Meier DFS curves of BC patients with high expression of ESR1 and low expression of ESR1 who received NAC. C, Kaplan-Meier DFS curves of BC patients with high expression of ESR1 and low expression of ESR2 who received NAC. D, Kaplan-Meier DFS curves of BC patients with high expression of NR3C1 and low expression of NR3C1 who received NAC. E, Kaplan-Meier DFS curves of BC patients with high expression of PGR and low expression of PGR who received NAC.(BC: Breast cancer; DFS: disease-free survival; NAC: neoadjuvant chemotherapy.)\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9251048/v1/210b91254832744a6fb7ac63.png"},{"id":108071290,"identity":"ac3f5a4b-3713-44f0-8446-6b4adcf27a3d","added_by":"auto","created_at":"2026-04-29 06:07:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":670378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImages of AR and GR immunohistochemical staining of BC tissues. \u003c/strong\u003e(A) Positive and (B) negative AR expression in BC tissues; (C) Positive and (D) negative GR expression in BC tissues.(AR: androgen receptors; GR: glucocorticoid receptors; BC: Breast cancer.)\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9251048/v1/89b0870a85d89276ffe563c0.png"},{"id":108071291,"identity":"341ce9e5-27d8-4043-9079-c679349f456b","added_by":"auto","created_at":"2026-04-29 06:07:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":115927,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of multivariate logistic regression analysis results.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9251048/v1/96fc7596ed98ba1d4ee1b58c.png"},{"id":108071293,"identity":"87d9b69f-f957-490b-bd49-5793b9e556f5","added_by":"auto","created_at":"2026-04-29 06:07:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curve of the prediction model.\u003c/strong\u003e(ROC: receiver operating characteristic)\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9251048/v1/d0509bce527a8895ef99191c.png"},{"id":108071294,"identity":"7cc5e16d-bcde-4525-a1f1-f6376f4da74d","added_by":"auto","created_at":"2026-04-29 06:07:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":45922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram to predict the probability of pathologic complete response in BC patients.\u003c/strong\u003e(BC: Breast cancer)\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9251048/v1/3cb0921b524e8c85efc6b611.png"},{"id":108332050,"identity":"37fee219-0b5c-47c3-94e4-c90445f79c39","added_by":"auto","created_at":"2026-05-02 18:24:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2426399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9251048/v1/59edea1e-45be-4d55-82db-48f9be8f121e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of response to neoadjuvant chemotherapy in breast cancer: A nomogram model based on androgen receptors and clinicopathological features","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBreast cancer (BC) is the most common cause of cancer-related death in women. Neoadjuvant chemotherapy (NAC) refers to systemic chemotherapy before local treatment such as surgery or radiotherapy. It has been increasingly accepted as a standard therapeutic strategy for patients with locally advanced BC, as it can reduce tumor burden prior to surgery, improve the success rate of operation \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e and provide personalized chemotherapy-sensitive information to instruct subsequent treatment for BC patients \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, during NAC, there are still a small group of patients who have disease progression and even lose the operation opportunity \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Current studies have shown that compared to those who retained residual cancer after NAC, patients who achieved pathologic complete response (pCR) had significantly longer disease-free survival (DFS) and overall survival (OS). Regrettably, there are only a small portion of BC patients achieving pCR, which means those BC patients who have not achieved pCR may develop chemotherapy resistance or disease progression during NAC \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. With an increasing number of BC patients treated with NAC, it is significant to identify the predictors of pCR in individual cases and establish a reliable predictive model.\u003c/p\u003e \u003cp\u003eNowadays, the number of studies on predictors of pCR has been on the rise. A previous study has demonstrated that pCR rate in patients with low-grade or hormone-receptor positive tumors was low, while patients with more aggressive subtypes such as triple negative had higher pCR rate \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In addition, some studies reached the conclusion that high proliferation, absence of hormone receptor expression, poor differentiation, and HER-2 over expression were associated with higher pCR rate \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Currently, although there have been some related studies on predicting pCR, it has not yet been clear which accurate predictors have an influence on pCR. There also have been few well-designed models predicting pCR. Therefore, further research for a predictive model combined with the clinicopathological features for NAC efficacy is helpful and necessary.\u003c/p\u003e \u003cp\u003eNearly 70% of BC is estrogen receptor (ER) and/or progesterone receptor (PR) positive BC. Hormone receptors play an important role in the occurrence and development of BC. It has been widely accepted that compared with ER negative BC, ER positive BC patients who received NAC had a lower pCR rate \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Current studies have found that, as the same steroid hormone receptors with ER and PR, androgen receptor (AR) and glucocorticoid receptor (GR) also have important effect on BC. AR can regulate the proliferation, metastasis and invasion of BC through different signal transduction pathways such as HER-2, Wnt, ERα and MAPK pathways \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. GR can increase the heterogeneity and metastasis of BC \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, the effect of AR and GR on pCR rate remains unknown. As AR and GR can affect the development of BC through different signaling pathways, it is necessary to explore the correlation between AR and GR and pCR rate in BC patients who received NAC.\u003c/p\u003e \u003cp\u003eIn this retrospective study, we analyzed the relationship between pCR attainment and clinicopathological characteristics including AR and GR of BC patients after NAC and explored the independent predictors of pCR. Based on these predictors, we constructed a predictive model which can help us select patients who benefit more from NAC and provide an approach to select precise treatment in clinical application.\u003c/p\u003e"},{"header":"PATIENTS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data acquisition and preprocessing for bioinformatics analysis\u003c/h2\u003e \u003cp\u003eThe study's workflow is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Initially, Gene expression datasets (GSE25066) were sourced from the Gene Expression Omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which included 488 BC patients receiving NAC containing taxanes-anthracyclines. Patients in the datasets were grouped on the basis of achieving pCR or not. The datasets were obtained from the GPL96 sequencing platform and the origin of the species was Homo Sapiens. R limma was used for differential expression analysis. Fold change (FC) (absolute value)\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were set as the threshold for differential genes. Genes with FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were differentially expressed genes with up-regulated expression, while genes with FC\u0026thinsp;\u0026lt;\u0026thinsp;1.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were differentially expressed genes with down-regulated expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Patients samples for clinical cohort analysis\u003c/h2\u003e \u003cp\u003eWe retrospectively collected data from 162 BC patients who received NAC in Harbin Medical University Cancer Hospital from January 2015 to December 2016. The criteria of patients included in the analysis were as follows: 1) patients were diagnosed with unilateral BC for the first time by pathologists; 2) patients underwent complete examinations and clinical staging to exclude distant metastases; 3) patients were females; 4) patients had no history of other malignant tumors; 5) patients had no underlying diseases that might affect NAC and surgery; 6) patients underwent immunohistochemical detection of ER, PR, HER-2, Ki-67 and P53; 7) patients had complete medical records. After applying these criteria, a total of 162 BC patients were included in this study.\u003c/p\u003e \u003cp\u003eWe subsequently collected clinical information and recorded whether the patient achieved pCR. According to the previous studies, the most common pCR definition was used in this study: absence of malignant tumor cells in breast and axillary lymph nodes after NAC (ypT0 ypN0).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Evaluation of clinicopathological features and immunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eWe extracted the status of molecular classification, ER, PR, Ki-67, HER-2, P53 and TILs from the pathology reports of the patients. Four molecular classifications respectively were luminal A, luminal B, HER-2 positive and triple negative. ER/PR\u0026thinsp;\u0026ge;\u0026thinsp;1% was defined as positive, otherwise defined as negative; Ki-67 (%)\u0026thinsp;\u0026gt;\u0026thinsp;20% was defined as high expression; TILs were based on the proportion of mononuclear inflammatory cells in the tumor stromal area around the nest of infiltrative tumor cells. Proportion\u0026thinsp;\u0026gt;\u0026thinsp;10% was classified as high levels of TILs and proportion\u0026thinsp;\u0026le;\u0026thinsp;10% was classified as low levels of TILs.\u003c/p\u003e \u003cp\u003eNuclear expression of AR was scored in accordance with previously published Allred criterion \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Briefly, percentage of AR expressed cells was visually estimated. Allred score was calculated by taking into consideration the proportion (P) scored as 0\u0026ndash;5 and the staining intensity (I) scored as 1\u0026ndash;3. Proportion and intensity were then summed up to generate a score from 1 to 8. Score\u0026thinsp;\u0026ge;\u0026thinsp;3 was defined as positive and score\u0026thinsp;\u0026lt;\u0026thinsp;3 was defined as negative \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGR immunoreactivity was scored using differential nuclear staining intensity scores (0, null; 1+, low or weak; 2+, moderate; 3+, high or strong). A total percentage score (% of tumor cells staining\u0026thinsp;\u0026ge;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;intensity; the sum of the percentage of cells at 1+, 2+, and 3\u0026thinsp;+\u0026thinsp;intensities) was used to evaluate tumor expression of GR. The H-score was calculated with the percentage of nuclear-stained tumor cells multiplied by the relative immunointensity, and the formula was as follows: H-score = [(% at 0) \u0026times; 0] + [(% at 1+) \u0026times;1] + [(% at 2+) \u0026times; 2] + [(% at 3+) \u0026times; 3] \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. X-tile software \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e was used to classify the tumors with a GR H-score of less than or equal to 10 as negative, and those with an H-score of more than 10 as positive.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eWe described data for patients in the pCR and non-pCR groups according to the clinicopathological and immunohistochemical features. R-Studio (R3.6.3) was used for data analysis. Univariate analysis and multivariate logistic regression analysis were used to examine the significant predictors for achieving pCR after NAC. Hosmer-Lemeshow fit test was performed to test the sensitivity of the model and the receiver operating characteristic (ROC) curve was drawn to obtain the area under curve (AUC) value. \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant in all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Bioinformatics analysis of the correlation between steroid hormone receptor and pCR after NAC\u003c/h2\u003e \u003cp\u003eTo explore the correlation between steroid hormone receptor expression in breast cancer patients and pCR after NAC, we obtained a gene expression profile dataset GSE25066 from a group of BC patients receiving NAC with Taxanes and Anthracyclines in the NCBI GEO database. GSE25066 datasets included 99 samples who achieved pCR (pCR group) and 389 samples who did not achieve pCR (RD group). Analysis results were shown as volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), among which NR3C4 and ESR1 were significantly lower expressed in the pCR group. Then we separately calculated the significant correlation between the genes encoding steroid hormone receptors and pCR rates (NR3C1 is the encoding gene of GR, NR3C4 is the encoding gene of AR, ESR1 and ESR2 are the encoding genes of ER, and PGR is the encoding gene of PR). Results showed that NR3C4 and ESR1 were still significant factors affecting pCR rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). We then analyzed the expression of the five genes in the pCR group and RD group, concluding that NR3C4 and ESR1 were significantly lower expressed in pCR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-G). Further analysis of the effects of the five genes on DFS of the patients showed that in addition to NR3C1, the expression levels of NR3C4, PGR, ESR1 and ESR2 significantly affected the prognosis of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Patient Characteristics\u003c/h2\u003e \u003cp\u003eThen we constructed a clinical cohort of 162 BC patients receiving NAC, 39 of whom (24.1%) achieved pCR. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarized clinicopathological features of patients, such as age, tumor size, histologic grade, ect. The expression of AR and GR were analyzed by immunohistochemical (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), showing that the AR status of 113 patients was positive (69.8%) and GR status of 91 patients was positive (56.2%)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics of 162 BC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinicopathological characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003efrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;25 kg/cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25 kg/cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 cm, \u0026lt; 5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eILC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le; grade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egrade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphovascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER-2 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP53 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eluminal A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eluminal B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHER-2 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etriple negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoadjuvant chemotherapy regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTEC/TAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTE/TA, TC/EC/AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoadjuvant chemotherapy cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eother cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epCR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.1\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\u003eBC, breast cancer; BMI, body mass index; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; TILs, tumor infiltrating lymphocytes; AR, androgen receptor; GR, glucocorticoid receptor; T, taxane; A, doxorubicin; E, epirubicin; C, cyclophosphamide; IDC, invasive duct carcinoma; ILC, invasive lobular carcinoma; pCR, pathologic complete response\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Univariate analysis of pCR\u003c/h2\u003e \u003cp\u003ePatients were divided into pCR group and non-pCR group. By comparing different groups, evidence has been provided that tumor size, histologic grade, ER status, Ki-67, molecular classification, TILs and AR status were correlative with pCR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The pCR rates were 41.0%, 53.9% and 5.1% for patients with tumor size \u0026lt;2cm, 2-5cm and \u0026ge;5cm respectively (p\u0026thinsp;=\u0026thinsp;0.0001). The pCR rates in patients of histologic grade 3 (59.0%) were higher than those of grade\u0026thinsp;\u0026le;\u0026thinsp;2 (41.0%) (p\u0026thinsp;=\u0026thinsp;0.0225). Patients of Ki-67\u0026thinsp;\u0026gt;\u0026thinsp;20% had a higher pCR rate (76.9%) than patients of Ki-67\u0026thinsp;\u0026le;\u0026thinsp;20% (23.1%) (p\u0026thinsp;=\u0026thinsp;0.0169). The pCR rate was 53.9% in patients of ER-, which was higher than patients of ER+ (46.1%) (p\u0026thinsp;=\u0026thinsp;0.0011). And the rate was 51.3% in patients of AR-, which was higher than patients of AR+ (48.7%) (p\u0026thinsp;=\u0026thinsp;0.0021). The difference in pCR rates between patients with different molecular classifications was statistically significant (p\u0026thinsp;=\u0026thinsp;0.0021): luminal A (10.3%); luminal B (43.6%); HER-2 overexpression (25.6%); triple negative type (20.5%). The pCR rate of high levels of TILs (71.8%) was higher than that of low levels of TILs (28.2%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe univariate logistic regression analysis of pCR in patients receiving NAC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinicopathological characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003epCR (No.=39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNon-pCR (No.=123)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.4875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;25 kg/cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.2435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25 kg/cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.7905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e0.0001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 cm, \u0026lt; 5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.4%\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; grade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.0225*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphovascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.7920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.0011*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER-2 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.0169*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP53 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.3925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eluminalA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.0391*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eluminalB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER-2 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etriple negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoadjuvant chemotherapy regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEC/TAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.4888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTE/TA, TC/EC/AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.4%\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoadjuvant chemotherapy cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.1038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.0021*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.8801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.9%\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\u003epCR, pathological complete response; NAC, neoadjuvant chemotherapy; BMI, body mass index; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; TILs, tumor infiltrating lymphocytes; AR, androgen receptor; GR, glucocorticoid receptor; T, taxane; A, doxorubicin; E, epirubicin; C, cyclophosphamide; IDC, invasive duct carcinoma; ILD, invasive lobular carcinoma; \u003cb\u003e*\u003c/b\u003e \u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multivariate logistic regression analysis of pCR\u003c/h2\u003e \u003cp\u003eAccording to the results of univariate analysis, seven statistically significant variables (tumor size, histologic grade, ER status, Ki-67 status, molecular classification, TILs and AR status) were taken into the multivariate logistic regression analysis. The results demonstrated that tumor size, ER status, TILs and AR status were independent predictive factors with statistically significant and OR (95%CI) values were 0.0984 (0.0258\u0026ndash;0.3166), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002; 0.0263 (0.0027\u0026ndash;0.1558), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003; 0.1797 (0.0615\u0026ndash;0.4836), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0010; 18.5994 (6.3046\u0026ndash;67.8021), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; 0.2882 (0.1040\u0026ndash;0.7625), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0135 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results of corresponding forest plot are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\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\u003eThe multivariate logistic regression analysis of pCR in patients receiving NAC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eclassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eestimate value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003estatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0856\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\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;~\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.3187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0984 (0.0258\u0026ndash;0.3166)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.6396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0263 (0.0027\u0026ndash;0.1558)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.7166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1797 (0.0615\u0026ndash;0.4836)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.5994(6.3046\u0026ndash;67.8021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.2442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2882 (0.1040\u0026ndash;0.7625)\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\u003epCR, pathologic complete response; NAC, neoadjuvant chemotherapy; SE,standard error; OR, odds ratio; CI, confidence interval; 2\u0026ndash;5 cm, means including 2 cm but not including 5 cm; ER, estrogen receptor; TILs, tumor infiltrating lymphocytes; AR, androgen receptor\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Logistic regression prediction probability model of pCR\u003c/h2\u003e \u003cp\u003eThe predictors of pCR in BC patients after NAC were screened by univariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and then the predictive regression equation of pCR for NAC was established by multivariate logistic regression analysis: New\u0026thinsp;=\u0026thinsp;1.0530\u0026thinsp;+\u0026thinsp;score of tumor size\u0026thinsp;+\u0026thinsp;score of ER\u0026thinsp;+\u0026thinsp;score of TILs\u0026thinsp;+\u0026thinsp;score of AR (If the tumor size was \u0026lt;\u0026thinsp;2 cm, the score of tumor size was 0; if the tumor size was 2\u0026ndash;5 cm (excluding 5cm), the score was \u0026minus;\u0026thinsp;2.3187; if the tumor size was \u0026gt;5cm, the score was \u0026minus;\u0026thinsp;3.6396; the score of ER-negative was 0, the score of ER-positive was \u0026minus;\u0026thinsp;1.7166; the score of TILs was 0 if there was low levels of lymphocytic infiltration and was 2.9231 if there were high levels of lymphocytic infiltration; the score of AR-negative was 0; the score of AR-positive was \u0026minus;\u0026thinsp;1.2442.). A probabilistic model for predicting the achievement of pCR was \u003cem\u003eP\u003c/em\u003e (y=achieve pCR|x)\u0026thinsp;=\u0026thinsp;1/[1\u0026thinsp;+\u0026thinsp;e^(-(the score of New))] and \u003cem\u003e1-P\u003c/em\u003e represented the probability that pCR was not achieved.\u003c/p\u003e \u003cp\u003eThen we evaluated the accuracy of the pCR prediction model. The result of Hosmer-Lemeshow fit test of the prediction model was χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.537, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09466\u0026thinsp;\u0026gt;\u0026thinsp;0.05, indicating that there was no statistical difference between the expected probability and the observed probability of the prediction model. The fitting degree was good. Then we plotted the ROC curve for evaluation. The sensitivity of the prediction model was 1.000 and the specificity was 0.724. The ROC curve was drawn as Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e: AUC value was 0.906, and the 95% confidence interval of AUC value was [0.863,0.950].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Construction of a nomogram for predicting pCR\u003c/h2\u003e \u003cp\u003eA nomogram could be drawn for clinicians to apply this prediction model in clinical practice. The tumor size, ER status, TILs and AR status of the patient were calculated on the basis of the corresponding conditions in the figure above, and then the four scores were added to obtain a total score. According to the \u003cem\u003eP\u003c/em\u003e-value corresponding to the total score, the probability of obtaining pCR after NAC for a BC patient was predicted (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAs a remarkable potential therapy to decrease extent of the tumor and provide a prognostic value, NAC has been increasingly used in the management of both locally advanced and earlier operable stage BC. Current studies have indicated that pCR, as a predictor for long-term survival, was closely related with the characteristics of tumor \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, previous studies were not comprehensive on the predictive factors of pCR in BC patients after NAC. In this study, using univariate and multivariate logistic analyses, we have provided evidence that ER, AR, TILs and tumor size were all independent predictors. And then we constructed a predictive model which could be used to calculate the probability of postoperative pCR in BC patients after NAC.\u003c/p\u003e \u003cp\u003eAs we all know, ER, PR, AR and GR belong to the steroid receptor superfamily. They share a similar structure that includes hormone binding, nuclear translocation, DNA binding and transactivation domains \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In recent years, it has been shown that these different receptors can interact with each other in the promoters of the target genes, showing a higher level of complexity in the regulation of biological responses \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Current studies found that AR could affect the proliferation of BC cells by regulating multiple pathways \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Obradović MMS et al. also confirmed that abnormally activated GR could promote the heterogeneity and distant metastasis of BC \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, no final conclusions have been made about the roles of AR and GR in predicting NAC efficacy in BC.\u003c/p\u003e \u003cp\u003eIn this study, we incorporated ER, PR, AR and GR status of BC into a prediction analysis. Evidence suggested that AR and ER negative patients had a higher pCR rate after NAC, whereas the expression of PR and GR was not correlated with pCR rate. In a retrospective analysis of 1731 patients treated with various neoadjuvant regimens, pCR rate was 24% in patients with ER-negative tumors and 8% in patients with ER-positive tumors \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Furthermore, MacGrogan et al. reported that negative ER was highly correlated with chemosensitivity, while negative PR was not \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. M\u0026uuml;ller Vet al. found that high AR mRNA levels were associated with lower pCR rates in a study of 418 BC patients \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Our findings were consistent with studies above. Currently, there are few studies on GR as a predictor of NAC efficacy in BC patients. Results in this study showed that there was no statistically significant interaction between GR expression and pCR attainment in BC patients after NAC. The effect of GR on achieving pCR may be further explored with more researches.\u003c/p\u003e \u003cp\u003eTILs, the foremost mononuclear immune cells infiltrating the tumor microenvironment (TME), are increasingly recognized as an important prognostic and predictive marker of immune response, especially with respect to chemotherapy response in some BC subtypes \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. As a crucial component of TME which could reflect the host antitumor immune response \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, the value of TILs for prediction of pCR in BC patients after NAC has received increasing attention. Hwang HW et al. have addressed that 44.9% of pre-NAC lymphocyte-predominant breast cancer (LPBC) patients had pCR, as did 6.3% of non-LPBC patients. Notably, high TILs levels were associated with a 25.9% increase in pCR rate compared with low TILs levels \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Our study is consistent with the results of prior studies. TILs may affect the pCR rate of BC patients after NAC by TME or immune response interacting with tumor cells.\u003c/p\u003e \u003cp\u003eTumor size has historically been used to stage BC and guide treatment. Livingston-Rosanoff et al. analyzed data from a national database of 38,864 women who received NAC, revealing that tumor size was independently associated with pCR following NAC \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Goorts et al. reported that pCR rate was substantially higher in patients with T1-2 disease compared to T3-4 \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Our research has reached similar conclusions to these studies: the pCR rate of T\u0026thinsp;\u0026gt;\u0026thinsp;5cm was the lowest, only 5.13%, and the pCR rate of 2cm\u0026thinsp;\u0026le;\u0026thinsp;T \u0026lt; 5cm was the highest, reaching 53.85%.\u003c/p\u003e \u003cp\u003eCurrently, a couple of prediction models have been reported to predict the probability of pCR after NAC. Some of them were mainly based on clinicopathological features or chemotherapy regimens \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, some predictive models were based on BC-related imaging features \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, while few studies focused on the immune inflammatory indicators and steroid receptors \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Therefore, our study is unique in that we are the first to simultaneously explore the predictive role of the four steroid hormone receptors (ER, PR, GR, AR) as well as TILs on pCR. For patients whose \u003cem\u003eP\u003c/em\u003e-value is small, timely surgery should be performed when the expected efficacy and tumor stage reduction are achieved.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, this study suggested that independent predictors of NAC in BC patients achieving pCR included tumor size, ER status, TILs and AR status, and then we established a predictive model. This provides the possibility to improve the pCR rate and the precision of treatment options in BC patients. However, this model needs to be validated by a larger sample size of clinical research, and its practicality and accuracy need to be verified in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by\u0026nbsp;the provincial natural science foundation project of Heilongjiang Province, China (grant No. H2018044) (WZ)\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\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWenhui Zhao: Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Funding acquisition. Wenjie Ma: Methodology, Project administration, Funding acquisition. Weiwei Ma: Formal analysis, Visualization, Writing- Original draft. Xiaojing Li: Software, Validation, Methodology, Data curation.\u0026nbsp;Jianli Ma: Validation, Data curation.\u0026nbsp;Yuan Fang: Validation, Data curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author ([email protected]) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Ethics committee of Harbin Medical University Cancer Hospital. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all participants and all coauthors in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi X, Wang M, Wang M, Yu X, Guo J, Sun T, et al. Predictive and Prognostic Roles of Pathological Indicators for Patients with BC on Neoadjuvant Chemotherapy. J BC. 2019;22(4):497\u0026ndash;521.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsaoka M, Gandhi S, Ishikawa T, Takabe K. Neoadjuvant Chemotherapy for BC: Past, Present, and Future. BC (Auckl). 2020;14:1178223420980377.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsano Y, Kashiwagi S, Onoda N, Kurata K, Morisaki T, Noda S, et al. Clinical verification of sensitivity to preoperative chemotherapy in cases of androgen receptor-expressing positive breast cancer. Br J Cancer. 2016;114(1):14\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontemurro F, Nuzzolese I, Ponzone R. Neoadjuvant or adjuvant chemotherapy in early breast cancer? Expert Opin Pharmacother. 2020;21(9):1071\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, et al. Pathological complete response and long-term clinical benefit in BC: the CTNeoBC pooled analysis. Lancet. 2014;384(9938):164\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf DM, Yau C, Wulfkuhle J, Brown-Swigart L, Gallagher RI, Lee PRE, et al. Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies. Cancer Cell. 2022;40(6):609\u0026ndash;e6236.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang HW, Jung H, Hyeon J, Park YH, Ahn JS, Im YH, et al. A nomogram to predict pathologic complete response (pCR) and the value of tumor-infiltrating lymphocytes (TILs) for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Breast Cancer Res Treat. 2019;173(2):255\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKono M, Fujii T, Lim B, Karuturi MS, Tripathy D, Ueno NT. Androgen Receptor Function and Androgen Receptor-Targeted Therapies in Breast Cancer: A Review. JAMA Oncol. 2017;3(9):1266\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHickey TE, Selth LA, Chia KM, Laven-Law G, Milioli HH, Roden D, et al. The androgen receptor is a tumor suppressor in estrogen receptor-positive breast cancer. Nat Med. 2021;27(2):310\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObradović MMS, Hamelin B, Manevski N, Couto JP, Sethi A, Coissieux MM, et al. Glucocorticoids promote breast cancer metastasis. Nature. 2019;567(7749):540\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllred DC, Harvey JM, Berardo M, Clark GM. Prognostic and predictive factors in BC by immunohistochemical analysis. Mod Pathol. 1998;11(2):155\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiaz N, Idress R, Habib S, Azam I, Lalani EM. Expression of Androgen Receptor and Cancer Stem Cell Markers (CD44+/CD24- and ALDH1+): Prognostic Implications in Invasive BC. Transl Oncol. 2018;11(4):920\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlock TS, Murphy TI, Munster PN, Nguyen DP, Lynch FJ. Glucocorticoid receptor expression in 20 solid tumor types using immunohistochemistry assay. Cancer Manag Res. 2017;9:65\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10(21):7252\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePufall MA. Glucocorticoids and Cancer. Adv Exp Med Biol. 2015;872:315\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamb CA, Vanzulli SI, Lanari C. Hormone receptors in BC: more than estrogen receptors. Med (B Aires). 2019;79(Spec 6/1):540\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuarneri V, Broglio K, Kau SW, Cristofanilli M, Buzdar AU, Valero V, et al. Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. J Clin Oncol. 2006;24(7):1037\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacGrogan G, Mauriac L, Durand M, Bonichon F, Trojani M, de Mascarel I, et al. Primary chemotherapy in breast invasive carcinoma: predictive value of the immunohistochemical detection of hormonal receptors, p53, c-erbB-2, MiB1, pS2 and GST pi. Br J Cancer. 1996;74(9):1458\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitzel I, Loibl S, Wirtz R, Fasching PA, Denkert C, Weber K, et al. Androgen receptor expression and response to chemotherapy in breast cancer patients treated in the neoadjuvant TECHNO and PREPARE trial. Br J Cancer. 2019;121(12):1009\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Melo Gagliato D, Cortes J, Curigliano G, Loi S, Denkert C, Perez-Garcia J, et al. Tumor-infiltrating lymphocytes in Breast Cancer and implications for clinical practice. Biochim Biophys Acta Rev Cancer. 2017;1868(2):527\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang D, He W, Wu C, Tan Y, He Y, Xu B, et al. Scoring System for Tumor-Infiltrating Lymphocytes and Its Prognostic Value for Gastric Cancer. Front Immunol. 2019;10:71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivingston-Rosanoff D, Schumacher J, Vande Walle K, Stankowski-Drengler T, Greenberg CC, Neuman H, et al. Does Tumor Size Predict Response to Neoadjuvant Chemotherapy in the Modern Era of Biologically Driven Treatment? A Nationwide Study of US BC Patients. Clin BC. 2019;19(6):e741\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoorts B, van Nijnatten TJ, de Munck L, Moossdorff M, Heuts EM, de Boer M, et al. Clinical tumor stage is the most important predictor of pathological complete response rate after neoadjuvant chemotherapy in BC patients. BC Res Treat. 2017;163(1):83\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang HW, Jung H, Hyeon J, Park YH, Ahn JS, Im YH, Nam SJ, Kim SW, Lee JE, Yu JH, Lee SK, Choi M, Cho SY, Cho EY. A nomogram to predict pathologic complete response (pCR) and the value of tumor-infiltrating lymphocytes (TILs) for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Breast Cancer Res Treat. 2019;173(2):255\u0026ndash;66. 2018 Oct 15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Z, Huang X, Cheng Z, Xu Z, Lin H, Liu C, Chen X, Liu C, Liang C, Lu C, Cui Y, Han C, Qu J, Shen J, Liu Z. MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology. 2023;308(1):e222830. Erratum in: Radiology. 2023;308(1):e239021.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e NAC: neoadjuvant chemotherapy\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e pCR: pathological complete response\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e ER: estrogen receptors\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e PR: progesterone receptors\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e AR: androgen receptors\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e GR: glucocorticoid receptors\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e TILs: tumor infiltrating lymphocytes\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":"breast cancer, neoadjuvant chemotherapy, pathologic complete response, androgen receptor, glucocorticoid receptors","lastPublishedDoi":"10.21203/rs.3.rs-9251048/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9251048/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIt is significant for breast cancer patients to precisely predict the efficacy of neoadjuvant chemotherapy (NAC)\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e. Current studies have shown that the pathological complete response (pCR) \u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003erate after NAC is lower in patients with positive estrogen receptors (ER)\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e and progesterone receptors (PR)\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e. Although androgen receptors (AR)\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e and glucocorticoid receptors (GR)\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e, which are also steroid hormone receptors, play a role in the development of breast cancer, their effect on pCR is unclear. In this study, we explored the correlation between clinicopathological features including AR and GR and pCR rate in BC patients after NAC. Results showed that tumor size, ER status, tumor infiltrating lymphocytes (TILs)\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e and AR status were independent predictive factors of pCR. Based on these indicators, a predictive model and a nomogram were constructed: the probability of reaching pCR was P (y\u0026thinsp;=\u0026thinsp;achieve pCR|x)\u0026thinsp;=\u0026thinsp;1/[1\u0026thinsp;+\u0026thinsp;e^ (-(the score of New))] and New\u0026thinsp;=\u0026thinsp;1.0530\u0026thinsp;+\u0026thinsp;score of tumor size\u0026thinsp;+\u0026thinsp;score of ER\u0026thinsp;+\u0026thinsp;score of TILs\u0026thinsp;+\u0026thinsp;score of AR. According to Hosmer-Lemeshow fit test and ROC curve, it showed satisfactory agreement between the predicted and observed probabilities (P-value 0.09466\u0026thinsp;\u0026gt;\u0026thinsp;0.05, area under the curve, 0.906; 95% confidence interval, 0.863\u0026ndash;0.950). The predictive model will help to identify sensitive patients for NAC and provide instructional opinions for treatment selection.\u003c/p\u003e","manuscriptTitle":"Prediction of response to neoadjuvant chemotherapy in breast cancer: A nomogram model based on androgen receptors and clinicopathological features","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 06:07:35","doi":"10.21203/rs.3.rs-9251048/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":"21d7e0e1-8f0c-42a7-8500-47ac26c812e8","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-02T18:21:53+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-02T18:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 06:07:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9251048","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9251048","identity":"rs-9251048","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}