1. Introduction
In 2020, breast cancer (BC) has already exceeded lung cancer as the leading cause of worldwide cancer incidence, accounting for 11.7% of all cancer cases [1], and the incidence and mortality of BC have also been increasing in China over the past several years [2]. The hormone receptor-positive and human epidermal growth factor receptor 2-Negative (HR+/HER2−) subtype, accounting for approximately half of BC cases in China [3], is the most common molecular subtype [4]. In the next three years, its incidence rate will increase by 2% yearly [5,6,7]. Although tailored anti-estrogen drugs for estrogen receptor (ER)-positive BC have been applied in adjuvant therapy, 30–50% of BC patients will experience relapse due to primary or acquired resistance to drugs [8]. A recent study with 1848 patients reported that disease progression occurs in over 60% of patients with advanced HR+/HER2− BC [9].
For locally advanced and inoperable BC, neoadjuvant chemotherapy (NAC) is the current standardized treatment [10]; achieving a complete pathological response (pCR) leads to a favorable prognosis [11]. Specifically, patients with HR+/HER2− BC are less likely to achieve pCR than those with triple-negative BC (TNBC) or HER2+ BC [12,13,14]. A pooled analysis of 12 randomized trials with approximately 2000 HR+/HER2− BC patients showed that pCR was significantly associated with event-free survival (EFS). However, this trend was primarily observed in poorly differentiated tumors [11]. Another analysis of 8244 patients with HR+/HER− BC showed that the pCR rate was 8.1% and that pCR was significantly linked to 5-year overall survival (OS) [15]. However, the predictors for pCR were also inconsistent across the above studies. Moreover, some studies have noted that residual disease after NAC does not necessarily result in a worse prognosis for the HR+/HER2− BC subtype [11,16]. In general, it is necessary to find reliable prognostic factors to identify HR+/HER2− BC patients after NAC with recurrence risk and provide individualized adjuvant treatment. Although several researchers have reached conflicting conclusions about prognostic factors for HR+/HER2− BC [17,18], a comprehensive summary and evaluation of these poor prognostic factors are still lacking.
This study aimed to evaluate the clinicopathological characteristics, pCR, and prognosis of HR+/HER2− BC patients in the NAC setting.
2. Materials and Methods
2.1. Patients
Patients with HR+/HER2− BC treated with NAC who were hospitalized at the Tianjin Medical University Cancer Institute and Hospital from January 2011 to January 2022 were included in this retrospective study. Only HR+ and HER2− BC female patients without distant metastasis at initial diagnosis were included. The main selection criteria included (1) pathologically diagnosed invasive ductal breast carcinoma; (2) HR-positive by immunohistochemistry (IHC) and HER2-negative by IHC or fluorescence in situ hybridization (FISH); (3) received NAC; and (4) underwent surgery followed NAC. The main exclusion criteria included (1) no surgery was performed followed NAC and efficacy of NAC could not be assessed based on pathology; and (2) follow-up data were incomplete.
Clinical information was collected, including age, menopausal status, family history of BC, clinical TNM stage, cancer recurrence details, and survival status. Pathological information was also collected, including IHC results (ER, PR, HER2, Ki67, p53), pathological TNM stage, and response to NAC. According to St. Gallen guidelines 2013 [19], 14% was adopted as the cutoff value of Ki67 for classification. In reference to ASCO guidelines [20], 1% was set as the cutoff index between “negative” and “positive” for PR, and HER2 status was strictly graded as follows: 0, 1+, 2+, and 3+. HER2(−) status was defined as 0, 1+, and 2+ with negative FISH results. To ensure accuracy, all pathology specimens of the patients were reviewed by two experienced pathologists. The deadline for follow-up was September 2022. Disease-free survival (DFS) was defined as the date of surgery to the first recurrence event (local-regional recurrence or distant metastasis), and OS was calculated from the date of surgery to cancer-related death or the last follow-up.
2.2. Statistical Analysis
The chi-square test was performed to identify discrepancies in variable distributions. The log-rank test was performed to investigate the association between univariate factors and survival time, and Kaplan–Meier curves were utilized to visualize the results of the survival analysis. The Cox proportional hazards regression model was used to identify significant independent risk factors related to DFS and OS for overall patients; multivariable binary logistic regression analysis was used to identify prognostic factors predicting pCR and factors assciated with HER2 status. The SPSS 25.0 software (IBM., Armonk, NY, USA) was employed for all statistical analyses. In all studies, a p value < 0.05 was defined as statistically significant.
3. Results
3.1. Patient and Treatment Characteristics
In total, 3070 consecutive patients with HR+/HER2− BC after NAC were included in this study. Patients with unknown HER2 status (n = 48) and those who missed follow-up (n = 55) were excluded (Figure 1). The baseline characteristics of the patients are shown in Table 1. The 3070 patients were all female, with a median age of 50 years (range: 22–85 years). The proportions of premenopausal and postmenopausal women were 59.7% and 40.3%, respectively. Most tumors were ypT2 (44.1%) and ypN1-3 (69.9%). A total of 87.3% of patients had ER scores over 50%, and 74.2% of BCs were PR positivity. The rates of extranodal extension (ENE) and lymphovascular invasion were 34.1% and 26.3%, respectively. All patients received a minimum of two cycles of the NAC regimen, including taxane and anthracycline. After surgery, all patients received adjuvant endocrine therapy and regimens, including aromatase inhibitors (AIs) and selective estrogen receptor modulators (SERMs). Ovarian function suppression (OFS) was routinely adopted for premenopausal women. Radiotherapy was required in cases of cT3-T4 or nodal involvement before NAC or ypN+ tumors; 69.1% of the patients in this cohort received radiotherapy. A total of 577 patients (18.8%) experienced a recurrence or metastasis, and 361 patients (11.7%) died due to BC.
3.2. Follow-Up and Survival
Following a median follow-up period of 40 months (range from 2 to 141 months), the 5-year DFS and OS rates were 82.6% and 90.4%, respectively. As displayed in Table 1, univariate analysis showed that ypT, ypN, PR, Ki67 level, radiotherapy, endocrine therapy, and ENE were significant prognostic factors for DFS. Multivariate Cox regression analysis (shown in Table 2) identified PR (0.730, 95% CI: 0.606–0.881, p = 0.001), radiotherapy (0.537, 95% CI: 0.433–0.665, p = 0.000), Ki67 level (1.399, 95% CI: 1.181–1.656, p = 0.000), and ENE (1.298, 95% CI: 1.072–1.570, p = 0.007) as independent prognostic factors for DFS. ypT, ypN, ER score, PR, HER2 status, Ki67 level, pCR, radiotherapy, and ENE were significant prognostic factors for OS in univariate analysis (Table 1), and radiotherapy (0.592, 95% CI: 0.452–0.774, p = 0.000), PR (0.486, 95% CI: 0.380–0.621, p = 0.000), Ki67 levels (1.635, 95% CI: 1.310–2.042, p = 0.000), and HER2 status (1.385, 95% CI: 1.051–1.826, p = 0.040) were independent prognostic factors for OS in multivariate analysis (Table 2). Notably, PR and Ki67 levels were independent prognostic factors for DFS and OS in this cohort; HER2 status was an independent significant prognostic factor only for OS but not for DFS.
3.3. Pathological Response to NAC
The pCR rate of the cohort was 14.5%. As shown in Table 3, we subdivided patients into two subgroups according to their pathological response to NAC. The univariate analysis showed that these clinicopathologic factors, including age, menopausal status, cT, cN, ER score, PR, HER2, and Ki67 level, were associated with pCR. Next, a binary logistic regression analysis was conducted to evaluate the associated factors, which showed that cT (0.723, 95%, CI: 0.607–0.862, p = 0.000), ER score (0.713, 95% CI: 0.636–0.798, p = 0.000), PR (0.644, 95% CI: 0.515–0.806, p = 0.000), and Ki67 levels (0.490, 95% CI: 0.394–0.608, p = 0.000) were significant independent factors of pCR. Although the distribution of HER2-low was statistically significant between the two subgroups (77.0% vs. 71.7%, p = 0.016), HER2-low had no predictive value for pCR (0.800, 95% CI: 0.635–1.009, p = 0.059). pCR was significantly associated with OS between the two subgroups (p = 0.006) (Figure 2b) but with no significant difference in DFS (p = 0.340) (Figure 2a).
3.4. Disease Outcomes in PR Negativity vs. PR Positivity Subgroups
3.4.1. Comparison of Clinicopathological Characteristics by PR Status
As shown in Table 4, compared with the PR positivity subgroup, more patients in the PR negativity subgroup showed ypT0 (22.4% vs. 13.6%, p = 0.000), ypN0 (35.7% vs. 28.1%, p = 0.000) and higher pCR (21.1% vs. 12.2%, p = 0.000). However, fewer PR negativity patients showed Ki67 > 14% (47.0% vs. 55.8%, p = 0.000), ENE positivity (30.0% vs. 35.6%, p = 0.004), ER scores > 50% (72.4% vs. 92.4%, p = 0.000), and P53 positivity (49.7% vs. 61.6%, p = 0.000). No statistically significant difference was found in age, family history, menopausal status, radiotherapy, endocrine therapy, or lymphovascular invasion between these two groups.
3.4.2. Comparison of DFS and OS by PR Status
The DFS rates of the PR negativity and PR positivity subgroups were 80.5% and 81.3%, respectively, and the OS rates were 86.6% and 88.8%, respectively. Patients in the PR negativity subgroup experienced more recurrence than those in the PR positivity subgroup (p = 0.007, Figure 3a). Patients in the PR negativity subgroup survived for a significantly shorter period of time than those in the PR positivity subgroup (p < 0.001, Figure 3b).
3.5. Disease Outcomes in HER2-Zero vs. HER2-Low Tumors
3.5.1. Comparison of Clinicopathological Characteristics by HER2 Status
Table 5 lists the baseline clinicopathological features according to HER2 status. Of the 3070 patients recruited, 2340 (76.2%) had HER2-low tumors. Compared with HER2-zero patients, more HER2-low patients showed PR positivity (75.5% vs. 70.1%, p = 0.004), ENE positivity (35.3% vs. 30.3%, p = 0.012), lymphovascular invasion (27.9% vs. 21.1%, p = 0.000), and P53 positivity (60.5% vs. 52.2%, p = 0.000), and fewer patients achieved pCR (13.7% vs. 17.3%, p = 0.016). More patients with HER2-low tumors were classified as ER ≥50% (88.3% vs. 84.1%, p = 0.001) and high ypT (T1-4: 85.2% vs. 80.7%, p = 0.001) compared with the HER2-zero subgroup. No statistically significant difference was found in age, family history, menopausal status, stage N, or Ki67 status between these two groups. Binary logistic regression (Table 6) was conducted to analyze the relationship between HER2 status and statistically significant factors identified by univariate analysis, showing that the pCR status, ypT, and PR were not significantly linked to HER2 status. The ER score (1.327, 95% CI: 1.197–1.472, p = 0.000), P53 (1.381, 95% CI: 1.164–1.637, p = 0.000), ENE (1.233, 95% CI: 1.020–1.489, p = 0.030), and lymphovascular invasion (1.431, 95% CI: 1.168–1.752, p = 0.001) were statistically significant factors associated with HER2 status.
3.5.2. Comparison of DFS and OS by HER2 Status
We also analyzed DFS and OS by painting Kaplan–Meier curves to determine the effect of HER2 status on survival. The DFS rates of the HER2-low and HER2-zero subgroups were 80.7% and 82.9%, respectively. However, no significant difference in DFS was found between these two subgroups (p = 0.61; Figure 4a). The OS of the HER2-low and HER2-zero subgroups was 87.3% and 91.4%, respectively, and patients in the HER2-low subgroup survived for a significantly shorter period of time than those in the HER2-zero subgroup (p = 0.037, Figure 4b).
4. Discussion
Although the CTNeoBC pooled analysis has showed HR+/HER2− BC patients with a high grade who attained pCR could improve survival [21], the results of further subgroup analysis of HR+/HER2− BC patients were not yet reported. To the best of our knowledge, this retrospective study is the first and largest cohort of HR+/HER2− BC patients after NAC to evaluate factors affecting the prognosis and identify predictors of the pCR in China.
The pCR rate of this cohort was only 14.5%, which was similar to the range of 7.5% to 16.2% in a previous study [11]. ER-positive BC is thought to be less susceptible to chemotherapy than other subtypes [11], which may indicate that additional neoadjuvant endocrine therapy (NET) will be beneficial. A retrospective study reported that HR+/HER2− tumors treated with NET (n = 127) or NAC (n = 338) reached similar ratios of nodal pCR (11% vs. 18%, respectively), which means that NET may have an effect equivalent to that of NCT [22]. Moreover, our study showed that pCR was significantly associated with OS (p = 0.006) but not DFS, which agrees with a previous study [15]. Reasonable neoadjuvant therapy, including neoadjuvant endocrine therapy, can improve the pCR rate and prolong survival to some extent.
Additionally, we found that PR, Ki-67 level (14% in this study), ENE, and postmastectomy radiotherapy were significant independent factors for DFS and that PR, Ki-67 level, HER2, and postmastectomy radiotherapy were related to OS.
Several studies have proven the correlation between PR levels and the Oncotype DX recurrence score (RS), showing that combining the PR and mitotic rate can serve as a substitute index for Oncotype RS [23,24,25]. According to our results, although PR negativity tumors displayed higher pCR, more Ki67 levels <14%, and lower rates of ENE, they still had a poorer prognosis than PR positivity tumors. A previous study indicated that reduced PR levels might explain the different prognoses and specific ER modulator resistances in patients with ER+/PR– BC [26]. As the final product of ER metabolism, the PR level is usually thought to depend on ER activity. Thus, a decrease in PR expression reflects a hypofunctional status of ER and resistance to endocrine drugs [27,28]. Bardou et al. found that PR status is an independent factor in predicting the benefit of adjuvant anti-estrogen therapy [29]. In our study, PR status was associated with ER scores, and PR negativity tumors showed lower ER scores. PR negativity BC showed lower DFS and OS, indicating that PR status may be related to the effectiveness of endocrine therapy. However, a review of the ATAC and BIG 1–98 trials found that PR levels did not affect the survival benefits of adjuvant endocrine therapy [30,31]. More clinical studies are needed to determine the relationship between PR status, endocrine therapy resistance, and prognosis in HR+/HER2− BC patients.
Since Cheang et al. reported that Ki67 effectively distinguishes Luminal A from Luminal B subtypes [32,33], the critical value of Ki67 in BC has been intensively explored through clinical research but is still under debate. Many studies have demonstrated that a high Ki67 index is associated with poor prognosis [10,34]. Furthermore, some studies have noted that the Ki67 index of residual tumors is predictive of long-term outcomes for patients after NAC [33,34]. In this study, we used 20% as the cutoff value for Ki67, but no signs related to prognosis were found (data not shown). Therefore, we adopted the proposal of the 2013 St. Gallen consensus statement, setting 14% as the cutoff value for dichotomous classification of Ki67 [11] and identifying its significance for prognosis. This may indicate that for HR+ and HER2− patients after NAC, the low cutoff value of 14% might reflect the true efficacy of NAC to some extent. Nevertheless, few studies have examined the Ki67 index as a continuous variable to explore its clinical significance, and additional research is needed.
In this study, postmastectomy radiotherapy was a significant independent factor for DFS and OS. Patients with a significant residual tumor burden, such as lymph node metastasis or ypN2/3 status, derive a survival advantage from postmastectomy radiotherapy [35]. Additionally, JC Ma et al. [36] proposed a new prognostic nomogram based on 1118 BC patients after NAC, revealing that locoregional recurrence control is improved after radiotherapy in the HR+, HER2− BC subtype, which was supported by our study’s findings.
The introduction of innovative anti-HER2 antibody–drug conjugates, such as trastuzumab deruxtecan, has boosted interest in HER2-low BC in recent years. Our study showed that patients in the HER2-low subgroup had a lower OS rate than those in the HER2-zero subgroup (87.3% vs. 91.4%, p = 0.037). However, the impact of HER2-low status on prognosis is still under debate. Chen M et al. [35] found no statistically significant difference in survival outcomes between HER2-low and HER2-zero among HR+ patients. Other studies have compared HER2-zero/HER2 1+ with HER2 2+/ISH-negative patients and noted that the latter had a higher risk of worse outcomes [37,38]. Further analysis showed that disparities were more likely in the HR-positive subgroup than in the HR-negative subgroup [39].
We also found that more cases of HER2-low tumors were classified as ER ≥50% (88.3% vs. 84.1%, p = 0.001) than cases of HER2-zero tumors, which agrees with the findings of earlier studies [40,41]. In the present study, pCR rates differed significantly between the HER2-low and HER2-zero subgroups (17.3% vs. 13.7%, p = 0.016). In a cohort encompassing 63.4% of patients with HR+/HER2− BC, Moura Leite et al. [42] found that the difference between the pCR rates of HER2-low tumors and HER2-zero tumors was not statistically significant at 13% vs. 9.5%, respectively, with no predictive value for pCR after standard NAC. Diverging from these data, Denkert et al. reported lower pCR rates in the HR+ HER2-low group than in the HR+ HER2-zero group (17.5% vs. 23.6%, p = 0.024) when studying 2310 patients in neoadjuvant clinical trials [43]). The reason for our different conclusions may be that not all patients recruited in the studies above received NAC, and that not all studies stratified patients with specific HR+ subtypes for analysis; all of the subjects in our research had HR+ and HER2− BC and underwent NAC. CDK4/6 inhibitors were not used in our cohort for adjuvant endocrine therapy or NET. More studies are needed to determine whether the prognosis is related to HER2 status for HR+/HER2− patients receiving CDK4/6 inhibitors.
We identified ENE as a significant independent factor for DFS. Fisher et al. [44] showed that ENE results in a worse prognosis, which agrees with our findings. Recently, according to the new CAP guidelines, the size/extent of ENE must be reported in pathology papers because it seems to reflect axillary nodal status or poor prognosis. Although ENE, which is significantly associated with DFS, is not utilized to guide treatment decisions, accompanying treatment and enhanced follow-up for patients with ENE may be beneficial. In our study, pN revealed the status of axillary lymph nodes, but it showed no prognostic value, which may be explained by the low proportion of pN0 at 30%.
It should be noted that our study has several limitations. Firstly, over the course of 10 years, it is impossible to totally prevent the potential bias of various chemotherapy regimens adopted and the timing of surgeries. Secondly, pathological assessment methods for Ki-67 and HER2 status have been updating in the last decade, potentially leading to diagnosis and selection errors, but two experienced pathologists were required to review slices to avoid these deviations in our study. Additionally, this study was conducted exclusively at our institution, which may result in inevitable patient selection bias. Further multicenter research is needed to verify our findings.
5. Conclusions
We reported the clinicopathological characteristics, pCR, and prognosis for HR+/HER2− BC after NAC. PR may be used as an independent predictor for pCR and prognosis of ER+/HER2− BC in the NAC setting. HER2-low tumors are related to poorer OS than HER2-zero tumors, and combined targeted therapy for HER2-low patients should be implemented.
Conceptualization, methodology, J.Z.; software, S.Z.; validation, Y.L. (Yan Liu), J.Z., Y.L. (Yingxue Liu), X.L. and S.Z.; formal analysis, S.Z.; investigation, Y.L. (Yingxue Liu), X.L. and S.Z.; resources, Y.L. (Yingxue Liu), X.L. and S.Z.; data curation, Y.L. (Yan Liu), X.L. and S.Z.; writing—original draft preparation, Y.L. (Yingxue Liu), X.L. and S.Z.; writing—review and editing, Y.L. (Yan Liu), J.Z.; visualization, S.Z.; supervision, J.Z.; project administration, J.Z.; funding acquisition, Y.L. (Yan Liu), J.Z., Y.L. (Yingxue Liu), X.L. and S.Z. All authors have read and agreed to the published version of the manuscript.
The study conformed to the principles of the Declaration of Helsinki and was authorized by the Medical Ethics Committee of Tianjin Medical University Cancer Institute and Hospital and Tianjin Medical University Cancer Institute and Hospital Laboratory Animal Care and Use Committee (Approval code: bc2023009, date of approval: 10 January 2023).
Patient consent was waived due to the retrospective nature of this study.
The data can be shared up on request.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 2. Kaplan–Meier curves for disease-free survival (DFS) and overall survival (OS) by pCR. (a) DFS for non-pCR vs. pCR (b) OS for non-pCR vs. pCR.
Enlarge this image.Figure 3. Kaplan–Meier curves for DFS and OS by PR status. (a) DFS for PR negativity vs. PR positivity (b) OS for PR negativity vs. PR positivity.
Enlarge this image.Figure 4. Kaplan–Meier curves for DFS and OS by HER2 status. (a) DFS for HER2-zero vs. HER-low (b) OS for HER2-zero vs. HER-low.
Baseline patient characteristics and log rank test of DFS and OS.
| Variable | Total (n = 3070) | DFS (n = 577) | Events | p | OS (n = 361) | Events | p |
|---|---|---|---|---|---|---|---|
| Events-Free | Events-Free | ||||||
| Age | 0.193 | 0.492 | |||||
| <50 years | 1688 (55.0) | 1386 (55.6) | 302 (52.3) | 1497 (55.3) | 191 (52.9) | ||
| ≥50 years | 1382 (45.0) | 1107 (44.4) | 275 (47.7) | 1212 (44.7) | 170 (47.1) | ||
| Menopausal status | 0.058 | 0.201 | |||||
| Pre/Peri- | 1833 (59.7) | 1509 (60.5) | 324 (56.2) | 1629 (60.1) | 204 (56.5) | ||
| Post- | 1237 (40.3) | 984 (39.5) | 253 (43.8) | 1080 (39.9) | 157 (43.5) | ||
| Family history | 0.120 | 0.140 | |||||
| No | 2443 (79.6) | 1987 (79.7) | 456 (79.0) | 2158 (79.7) | 285 (78.9) | ||
| Breast | 221 (7.2) | 167 (6.7) | 54 (9.4) | 182 (6.7) | 39 (10.8) | ||
| Others | 406 (13.2) | 339 (13.6) | 67 (11.6) | 369 (13.6) | 37 (10.2) | ||
| ypT | 0.000 * | 0.001 * | |||||
| ypT0 | 487 (15.9) | 410 (16.4) | 77 (13.4) | 454 (16.8) | 33 (9.1) | ||
| ypT1 | 765 (24.9) | 652 (26.2) | 113 (19.6) | 691 (25.5) | 74 (20.5) | ||
| ypT2 | 1353 (44.1) | 1085 (43.5) | 268 (46.4) | 1176 (43.4) | 177 (49.0) | ||
| ypT3-4 | 465 (15.1) | 346 (13.9) | 119 (20.6) | 388 (14.3) | 77 (21.3) | ||
| ypN | 0.000 * | 0.000 * | |||||
| ypN0 | 923 (30.1) | 786 (31.5) | 137 (23.7) | 850 (31.4) | 73 (20.2) | ||
| ypN1 | 782 (25.4) | 665 (26.7) | 117 (20.3) | 713 (26.3) | 69 (19.1) | ||
| ypN2-3 | 1365 (44.5) | 1042 (41.8) | 323 (56.0) | 1146 (42.3) | 219 (60.7) | ||
| ER | 0.230 | 0.070 * | |||||
| 0–9% | 225 (7.3) | 175 (7.0) | 50 (8.7) | 187 (6.9) | 38 (10.5) | ||
| 10–49% | 165 (5.4) | 123 (4.9) | 42 (7.2) | 134 (5.0) | 31 (8.6) | ||
| 50–89% | 1110 (36.2) | 880 (35.3) | 230 (39.9) | 954 (35.2) | 156 (43.2) | ||
| 90–100% | 1570 (51.1) | 1315 (52.8) | 255 (44.2) | 1434 (52.9) | 136 (37.7) | ||
| PR | 0.007 * | 0.000 * | |||||
| Negativity | 791 (25.8) | 637 (25.6) | 154 (26.7) | 685 (25.3) | 106 (29.4) | ||
| Positivity | 2279 (74.2) | 1856(74.4) | 423 (73.3) | 2024 (74.7) | 255 (70.6) | ||
| HER2 | 0.611 | 0.037 * | |||||
| Zero | 730 (23.8) | 605 (24.3) | 125 (21.7) | 667 (24.6) | 63 (17.5) | ||
| Low | 2340 (76.2) | 1888 (75.7) | 452 (78.3) | 2042 (75.4) | 298 (82.5) | ||
| Ki67 | 0.000 * | 0.000 * | |||||
| ≤14% | 1427 (46.5) | 1204 (48.3) | 223 (38.6) | 1302 (48.1) | 125 (34.6) | ||
| >14% | 1643 (53.5) | 1289 (51.7) | 354 (61.4) | 1407 (51.9) | 236 (65.4) | ||
| P53 | 0.572 | 0.322 | |||||
| Negative | 1273 (41.5) | 1042 (41.8) | 231 (40.0) | 1138 (42.0) | 135 (37.4) | ||
| Positive | 1797 (58.5) | 1451 (58.2) | 346 (60.0) | 1571 (58.0) | 226 (62.6) | ||
| pCR (ypT0/is, ypN0) | 0.344 | 0.006 * | |||||
| No | 2624 (85.5) | 2119 (85.0) | 505 (87.5) | 2293 (84.6) | 331 (91.7) | ||
| Yes | 446 (14.5) | 374 (15.0) | 72 (12.5) | 416 (15.4) | 30 (8.3) | ||
| Surgery | 0.440 | 0.389 | |||||
| Mastectomy | 2694 (87.7) | 2303 (85.5) | 391 (86.0) | 2378 (87.8) | 316 (87.5) | ||
| Breast-conserving surgery | 376 (12.3) | 190 (14.5) | 186 (14.0) | 331 (12.2) | 45 (12.5) | ||
| Radiotherapy | 0.000 * | 0.000 * | |||||
| No | 948 (30.9) | 840 (33.7) | 108 (18.7) | 877 (32.4) | 71 (19.7) | ||
| Yes | 2122 (69.1) | 1653 (66.3) | 469 (81.3) | 1832 (67.6) | 290 (80.3) | ||
| Endocrine therapy | 0.000 * | 0.138 | |||||
| AI ± OFS | 2372 (77.3) | 1901 (76.2) | 471 (81.6) | 2090 (77.2) | 282 (78.1) | ||
| SERM ± OFS | 698 (22.7) | 592 (23.8) | 106 (18.4) | 619 (22.8) | 79 (21.9) | ||
| Extranodal extension | 0.000 * | 0.000 * | |||||
| Yes | 1048 (34.1) | 783 (31.4) | 265 (45.9) | 876 (32.3) | 172 (47.6) | ||
| No | 2022 (65.9) | 1710 (68.6) | 312 (54.1) | 1833 (67.7) | 189 (52.4) | ||
| Lymphovascular Invasion | 0.146 | 0.999 | |||||
| Yes | 807 (26.3) | 639 (25.6) | 168 (29.1) | 712 (26.3) | 95 (26.3) | ||
| No | 2263 (73.7) | 1854 (74.4) | 409 (70.9) | 1997 (73.7) | 266 (73.7) |
* indicates statistically significant results. ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; pCR, pathologic complete response; DFS, disease-free survival; OS, overall survival.
Multivariate COX regression analysis of overall DFS and OS.
| Multivariate Analysis | |||
|---|---|---|---|
| Variables | HR | 95%CI | p |
| DFS | |||
| ypT | - | - | 0.195 |
| ypT1 | 0.873 | 0.626–1.218 | 0.421 |
| ypT2 | 1.004 | 0.732–1.377 | 0.980 |
| ypT3-4 | 1.172 | 0.824–1.666 | 0.379 |
| ypN | - | - | 0.189 |
| ypN1 | 0.885 | 0.660–1.186 | 0.420 |
| ypN2-3 | 1.098 | 0.828–1.456 | 0.518 |
| Radiotherapy | 0.537 | 0.433–0.665 | 0.000 * |
| Endocrine therapy | 0.851 | 0.682–1.062 | 0.151 |
| PR | 0.730 | 0.606–0.881 | 0.001 * |
| Ki67 | 1.399 | 1.181–1.656 | 0.000 * |
| Extranodal extension | 1.298 | 1.072–1.570 | 0.007 * |
| OS | |||
| ypT | - | - | 0.644 |
| ypT1 | 1.197 | 0.424–3.375 | 0.734 |
| ypT2 | 1.312 | 0.471–3.655 | 0.604 |
| ypT3-4 | 1.461 | 0.518–4.118 | 0.474 |
| ypN | - | - | 0.007 |
| ypN1 | 0.908 | 0.615–1.342 | 0.629 |
| ypN2-3 | 1.42 | 0.979–2.059 | 0.065 |
| Radiotherapy | 0.592 | 0.452–0.774 | 0.000 * |
| ER | - | - | 0.021 |
| 10–49% | 1.349 | 0.858–2.119 | 0.194 |
| 50–89% | 1.414 | 0.975–2.051 | 0.068 |
| 90–100% | 1.407 | 1.120–1.768 | 0.003 |
| PR | 0.486 | 0.380–0.0621 | 0.000 * |
| Ki67 | 1.635 | 1.310–2.042 | 0.000 * |
| Extranodal extension | 0.256 | 0.904–1.459 | 1.149 |
| HER2 | 1.385 | 1.051–1.826 | 0.040 * |
| pCR status | 0.978 | 0.326–2.939 | 0.969 |
* Indicates statistically significant results.
Patient characteristics stratified by pCR status (non pCR vs. pCR).
| Variable | NonpCR |
pCR |
Univariate Analysis | Binary Logistic Regression Analysis | ||
|---|---|---|---|---|---|---|
| χ2 | p Value * | Odds Ratio (95%CI) | p Value * | |||
| Age | 17.619 | 0.000 * | 0.758 (0.563–1.013) | 0.061 | ||
| <50 years | 1402 (53.4) | 286 (64.1) | ||||
| ≥50 years | 1222 (46.6) | 160 (35.9) | ||||
| Menopausal status | 29.237 | 0.000 * | 1.023 (0.762–1.375) | 0.877 | ||
| Pre/Peri- | 1535 (58.5) | 298 (66.8) | ||||
| Post- | 1089 (41.5) | 148 (33.2) | ||||
| cT | 29.315 | 0.000 * | 0.723 (0.607–0.862) | 0.000 * | ||
| cT1 | 199 (7.6) | 48 (10.8) | ||||
| cT2 | 1549 (59.0) | 280 (62.7) | ||||
| cT3-4 | 876 (33.4) | 118 (26.5) | ||||
| cN | 18.782 | 0.005 * | 1.123 (0.975–1.293) | 0.107 | ||
| cN0 | 979 (37.3) | 162 (36.3) | ||||
| cN1 | 1109 (42.3) | 185 (41.5) | ||||
| cN2-3 | 536 (20.4) | 99 (22.2) | ||||
| ER | 39.833 | 0.000 * | 0.713 (0.636–0.798) | 0.000 * | ||
| 0–9% | 167 (6.4) | 58 (13.0) | ||||
| 10–49% | 129 (4.9) | 36 (8.1) | ||||
| 50–89% | 937 (35.7) | 173 (38.8) | ||||
| 90–100% | 1391 (53.0) | 179 (40.1) | ||||
| PR | 37.208 | 0.000 * | 0.644 (0.515–0.806) | 0.000 * | ||
| Negativity | 624 (23.8) | 167 (37.4) | ||||
| Positivity | 2000 (76.2) | 279 (62.6) | ||||
| HER2 | 5.759 | 0.016 * | 0.800 (0.635–1.009) | 0.059 | ||
| Zero | 604 (23.0) | 126 (28.3) | ||||
| Low | 2020 (77.0) | 320 (71.7) | ||||
| Ki67 | 46.900 | 0.000 * | 0.490 (0.394–0.608) | 0.000 * | ||
| ≤14% | 1153 (43.9) | 274 (61.4) | ||||
| >14% | 1471 (56.1) | 172 (38.6) | ||||
| P53 | 3.526 | 0.060 | - | - | ||
| Negative | 1070 (40.8) | 203 (45.5) | ||||
| Positive | 1554 (59.2) | 243 (54.5) | ||||
* indicates statistically significant results.
Baseline patient characteristics stratified by PR status (PR negativity vs. PR positivity).
| Variable | PR− (n = 791) | PR+ (n = 2279) | χ2 | p Value * |
|---|---|---|---|---|
| Age | 0.345 | 0.557 | ||
| <50 years | 442 (55.9) | 1246 (54.7) | ||
| ≥50 years | 349 (44.1) | 1033 (45.3) | ||
| Menopausal status | 0.422 | 0.516 | ||
| Pre/Peri- | 480 (60.7) | 1353 (59.4) | ||
| Post- | 311 (39.3) | 926 (40.6) | ||
| Family history | 1.689 | 0.430 | ||
| No | 617 (78.0) | 1826 (80.1) | ||
| Breast | 60 (7.6) | 161 (7.1) | ||
| Others | 114 (14.4) | 292 (12.8) | ||
| ypT | 34.178 | 0.000 * | ||
| ypT0 | 177 (22.4) | 310 (13.6) | ||
| ypT1 | 185 (23.4) | 580 (25.4) | ||
| ypT2 | 323 (40.8) | 1030 (45.2) | ||
| ypT3-4 | 106 (13.4) | 359 (15.8) | ||
| ypN | 19.073 | 0.000 * | ||
| ypN0 | 282 (35.7) | 641 (28.1) | ||
| ypN1 | 203 (25.7) | 579 (25.4) | ||
| ypN2-3 | 306 (38.6) | 1059 (46.5) | ||
| ER | 235.469 | 0.000 * | ||
| 0–9% | 146 (18.5) | 79 (3.5) | ||
| 10–49% | 72 (9.1) | 93 (4.1) | ||
| 50–89% | 248 (31.3) | 862 (37.8) | ||
| 90–100% | 325 (41.1) | 1245 (54.6) | ||
| HER-2 | 8.407 | 0.004 * | ||
| Zero | 218 (27.6) | 512 (22.5) | ||
| Low | 573 (72.4) | 1767 (77.5) | ||
| Ki67 | 18.035 | 0.000 * | ||
| ≤14% | 419 (53.0) | 1008 (44.2) | ||
| >14% | 372 (47.0) | 1271 (55.8) | ||
| P53 | 34.386 | 0.000 * | ||
| Negative | 398 (50.3) | 875 (38.4) | ||
| Positive | 393 (49.7) | 1404 (61.6) | ||
| pCR (ypYT0/N0) | 37.208 | 0.000 * | ||
| No | 624 (78.9) | 2000 (87.8) | ||
| Yes | 167 (21.1) | 279 (12.2) | ||
| Radiotherapy | 0.420 | 0.517 | ||
| No | 237 (30.0) | 711 (31.2) | ||
| Yes | 554 (70.0) | 1568 (68.8) | ||
| Endocrine therapy | 0.013 | 0.909 | ||
| AI ± OFS | 610 (77.1) | 1762 (77.3) | ||
| SERM ± OFS | 181 (22.9) | 517 (22.7) | ||
| Extranodal extension | 8.260 | 0.004 * | ||
| No | 554 (70.0) | 1468 (64.4) | ||
| Yes | 237 (30.0) | 811 (35.6) | ||
| Lymphovascular Invasion | 0.324 | 0.569 | ||
| No | 577 (72.9) | 1686 (74.0) | ||
| Yes | 214 (27.1) | 593 (26.0) |
* Indicates statistically significant results.
Baseline patient characteristics stratified by HER2 status (HER2-zero vs. HER2-low).
| Variable | Total (n = 3070) | HER2-0 |
HER2-Low |
χ2 | p Value * |
|---|---|---|---|---|---|
| Age | 0.001 | 0.504 | |||
| <50 years | 1688 (55.0) | 401 (54.9) | 1287 (55.0) | ||
| ≥50 years | 1382 (45.0) | 329 (45.1) | 1053 (45.0) | ||
| Menopausal status | 0.352 | 0.291 | |||
| Pre/Peri- | 1833 (59.7) | 429 (58.8) | 1404 (60.0) | ||
| Post- | 1237 (40.3) | 301 (41.2) | 936 (40.0) | ||
| Family history | 3.787 | 0.151 | |||
| No | 2443 (79.6) | 568 (77.8) | 1875 (80.1) | ||
| Breast | 221 (7.2) | 50 (6.9) | 171 (7.3) | ||
| Others | 406 (13.2) | 112 (15.3) | 294 (12.6) | ||
| ypT | 9.759 | 0.021 * | |||
| ypT0 | 487 (15.9) | 141 (19.3) | 346 (14.8) | ||
| ypT1 | 765 (24.9) | 174 (23.8) | 591 (25.2) | ||
| ypT2 | 1353 (44.1) | 300 (41.1) | 1053 (45.0) | ||
| ypT3-4 | 465 (15.1) | 115 (15.8) | 350 (15.0) | ||
| ypN | 1.417 | 0.492 | |||
| ypN0 | 923 (30.1) | 229 (31.4) | 694 (29.6) | ||
| ypN1 | 782 (25.4) | 190 (26.0) | 592 (25.3) | ||
| ypN2-3 | 1365 (44.5) | 311 (42.6) | 1054 (45.1) | ||
| ER | 17.301 | 0.001 * | |||
| 0–9% | 225 (7.3) | 65 (8.9) | 160 (6.8) | ||
| 10–49% | 165 (5.4) | 51 (7.0) | 114 (4.9) | ||
| 50–89% | 1110 (36.2) | 223 (30.5) | 887 (37.9) | ||
| 90–100% | 1570 (51.1) | 391 (53.6) | 1179 (50.4) | ||
| PR | 8.407 | 0.004 * | |||
| Negativity | 791 (25.8) | 218 (29.9) | 573 (24.5) | ||
| Positivity | 2279 (74.2) | 512 (70.1) | 1767 (75.5) | ||
| Ki67 | 0.824 | 0.364 | |||
| ≤14% | 1427 (46.5) | 350 (47.9) | 1077 (46.0) | ||
| >14% | 1643 (53.5) | 380 (52.1) | 1263 (54.0) | ||
| P53 | 15.873 | 0.000 * | |||
| Negative | 1273 (41.5) | 349 (47.8) | 924 (39.5) | ||
| Positive | 1797 (58.5) | 381 (52.2) | 1416 (60.5) | ||
| pCR (ypYT0/is, ypN0) | 5.759 | 0.016 * | |||
| No | 2624 (85.5) | 604 (82.7) | 2020 (86.3) | ||
| Yes | 446 (14.5) | 126 (17.3) | 320 (13.7) | ||
| Radiotherapy | 0.464 | 0.496 | |||
| No | 948 (30.9) | 218 (29.9) | 730 (31.2) | ||
| Yes | 2122 (69.1) | 512 (70.1) | 1610 (68.8) | ||
| Endocrine therapy | 2.013 | 0.156 | |||
| AI ± OFS | 2372 (77.3) | 550 (75.3) | 1822 (77.9) | ||
| SERM ± OFS | 698 (22.7) | 180 (24.7) | 518 (22.1) | ||
| Extranodal extension | 6.356 | 0.012 * | |||
| No | 2022 (65.9) | 509 (69.7) | 1513 (64.7) | ||
| Yes | 1048 (34.1) | 221 (30.3) | 827 (35.3) | ||
| Lymphovascular Invasion | 13.318 | 0.000 * | |||
| No | 2263 (73.7) | 576 (78.9) | 1687 (72.1) | ||
| Yes | 807 (26.3) | 154 (21.1) | 653 (27.9) |
* Indicates statistically significant results.
Binary logistic regression analysis of HER2 status.
| Variable | OR | 95%CI | p |
|---|---|---|---|
| ypT | 0.975 | 0.856–1.111 | 0.706 |
| ER | 1.327 | 1.197–1.472 | 0.000 * |
| PR | 1.131 | 0.933–1.371 | 0.210 |
| P53 | 1.381 | 1.164–1.637 | 0.000 * |
| Extranodal extension | 1.233 | 1.020–1.489 | 0.030 * |
| pCR status | 1.389 | 0.766–2.517 | 0.279 |
| Lymphovascular Invasion | 1.431 | 1.168–1.752 | 0.001 * |
* Indicates statistically significant results.
References
1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin.; 2021; 71, pp. 209-249. [DOI: https://dx.doi.org/10.3322/caac.21660] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33538338]
2. Mohammad, N.S.; Nazli, R.; Zafar, H.; Fatima, S. Effects of lipid based Multiple Micronutrients Supplement on the birth outcome of underweight pre-eclamptic women: A randomized clinical trial. Pak. J. Med. Sci.; 2022; 38, pp. 219-226. [DOI: https://dx.doi.org/10.12669/pjms.38.1.4396]
3. CancerMPact® Database, Kantar. Available online: www.cancermpact.com (accessed on 3 December 2022).
4. Zochowski, R.J.; Sypula, S.; Golembiewska, B.; Szelemetko, J.; Michalak, J.M. Thrombi of the left ventricle, infarction location and method of treatment. Wiad. Lek.; 1990; 43, pp. 61-64. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/2114704]
5. Copur, M.S. Systemic Therapy for Estrogen Receptor-Positive, HER2-Negative Breast Cancer. N. Engl. J. Med.; 2021; 384, 1176. [DOI: https://dx.doi.org/10.1056/NEJMc2101282] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33761225]
6. Xue, C.; Wang, X.; Peng, R.; Shi, Y.; Qin, T.; Liu, D.; Teng, X.; Wang, S.; Zhang, L.; Yuan, Z. Distribution, clinicopathologic features and survival of breast cancer subtypes in Southern China. Cancer Sci.; 2012; 103, pp. 1679-1687. [DOI: https://dx.doi.org/10.1111/j.1349-7006.2012.02339.x]
7. Zuo, T.; Zeng, H.; Li, H.; Liu, S.; Yang, L.; Xia, C.; Zheng, R.; Ma, F.; Liu, L.; Wang, N. et al. The influence of stage at diagnosis and molecular subtype on breast cancer patient survival: A hospital-based multicmulti-center study. Chin. J. Cancer; 2017; 36, 84. [DOI: https://dx.doi.org/10.1186/s40880-017-0250-3]
8. Szostakowska, M.; Trebinska-Stryjewska, A.; Grzybowska, E.A.; Fabisiewicz, A. Resistance to endocrine therapy in breast cancer: Molecular mechanisms and future goals. Breast Cancer Res. Treat.; 2019; 173, pp. 489-497. [DOI: https://dx.doi.org/10.1007/s10549-018-5023-4]
9. Murali, B.; Durbin, L.; Vijaykumar, S.; Yang, L.; Li, S.; Zhao, L.; Hawthorne, S.; Kanas, G.; Davis, C.; Clark, O. Treatment of HR+/HER2− breast cancer in urban mainland China: Results from the CancerMPact Survey 2019. Breast Cancer Res. Treat.; 2022; 195, pp. 441-451. [DOI: https://dx.doi.org/10.1007/s10549-022-06709-x]
10. Van de Wiel, M.; Dockx, Y.; Van den Wyngaert, T.; Stroobants, S.; Tjalma, W.A.A.; Huizing, M.T. Neoadjuvant systemic therapy in breast cancer: Challenges and uncertainties. Eur. J. Obstet. Gynecol. Reprod. Biol.; 2017; 210, pp. 144-156. [DOI: https://dx.doi.org/10.1016/j.ejogrb.2016.12.014]
11. Cortazar, P.; Zhang, L.; Untch, M.; Mehta, K.; Costantino, J.P.; Wolmark, N.; Bonnefoi, H.; Cameron, D.; Gianni, L.; Valagussa, P. et al. Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis. Lancet; 2014; 384, pp. 164-172. [DOI: https://dx.doi.org/10.1016/S0140-6736(13)62422-8]
12. Parker, J.S.; Mullins, M.; Cheang, M.C.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol.; 2009; 27, pp. 1160-1167. [DOI: https://dx.doi.org/10.1200/JCO.2008.18.1370]
13. Coates, A.S.; Colleoni, M.; Goldhirsch, A. Is adjuvant chemotherapy useful for women with luminal a breast cancer?. J. Clin. Oncol.; 2012; 30, pp. 1260-1263. [DOI: https://dx.doi.org/10.1200/JCO.2011.37.7879]
14. Schott, A.F.; Hayes, D.F. Defining the benefits of neoadjuvant chemotherapy for breast cancer. J. Clin. Oncol.; 2012; 30, pp. 1747-1749. [DOI: https://dx.doi.org/10.1200/JCO.2011.41.3161]
15. Fayanju, O.M.; Ren, Y.; Thomas, S.M.; Greenup, R.A.; Plichta, J.K.; Rosenberger, L.H.; Tamirisa, N.; Force, J.; Boughey, J.C.; Hyslop, T. et al. The Clinical Significance of Breast-only and Node-only Pathologic Complete Response (pCR) After Neoadjuvant Chemotherapy (NACT): A Review of 20,000 Breast Cancer Patients in the National Cancer Data Base (NCDB). Ann. Surg; 2018; 268, pp. 591-601. [DOI: https://dx.doi.org/10.1097/SLA.0000000000002953]
16. Von Minckwitz, G.; Untch, M.; Blohmer, J.U.; Costa, S.D.; Eidtmann, H.; Fasching, P.A.; Gerber, B.; Eiermann, W.; Hilfrich, J.; Huober, J. et al. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J. Clin. Oncol.; 2012; 30, pp. 1796-1804. [DOI: https://dx.doi.org/10.1200/JCO.2011.38.8595]
17. Davie, A.; Cuyun Carter, G.; Rider, A.; Bailey, A.; Lewis, K.; Price, G.; Ostojic, H.; Ringeisen, F.; Pivot, X. Real-world clinical profile, treatment patterns and patient-reported outcomes in a subset of HR+/HER2− advanced breast cancer patients with poor prognostic factors: Data from an international study. ESMO Open; 2021; 6, 100226. [DOI: https://dx.doi.org/10.1016/j.esmoop.2021.100226]
18. Hattori, M.; Novick, D.; Takaura, K.; Tanizawa, Y.; Kawaguchi, T.; Haro, J.M.; Monistrol-Mula, A.; Onishi, A.; Iwata, H. A systematic literature review of prognostic factors in patients with HR+/HER2− advanced breast cancer in Japan. Jpn J. Clin. Oncol.; 2021; 51, pp. 1498-1508. [DOI: https://dx.doi.org/10.1093/jjco/hyab131]
19. Goldhirsch, A.; Winer, E.P.; Coates, A.S.; Gelber, R.D.; Piccart-Gebhart, M.; Thurlimann, B.; Senn, H.J.; Panel, M. Personalizing the treatment of women with early breast cancer: Highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann. Oncol; 2013; 24, pp. 2206-2223. [DOI: https://dx.doi.org/10.1093/annonc/mdt303]
20. Wolff, A.C.; Hammond, M.E.H.; Allison, K.H.; Harvey, B.E.; Mangu, P.B.; Bartlett, J.M.S.; Bilous, M.; Ellis, I.O.; Fitzgibbons, P.; Hanna, W. et al. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update. J. Clin. Oncol.; 2018; 36, pp. 2105-2122. [DOI: https://dx.doi.org/10.1200/JCO.2018.77.8738]
21. Gianni, L.; Baselga, J.; Eiermann, W.; Guillem Porta, V.; Semiglazov, V.; Lluch, A.; Zambetti, M.; Sabadell, D.; Raab, G.; Llombart Cussac, A. et al. Feasibility and tolerability of sequential doxorubicin/paclitaxel followed by cyclophosphamide, methotrexate, and fluorouracil and its effects on tumor response as preoperative therapy. Clin. Cancer Res.; 2005; 11, pp. 8715-8721. [DOI: https://dx.doi.org/10.1158/1078-0432.CCR-05-0539]
22. Montagna, G.; Sevilimedu, V.; Fornier, M.; Jhaveri, K.; Morrow, M.; Pilewskie, M.L. How Effective is Neoadjuvant Endocrine Therapy (NET) in Downstaging the Axilla and Achieving Breast-Conserving Surgery?. Ann. Surg. Oncol.; 2020; 27, pp. 4702-4710. [DOI: https://dx.doi.org/10.1245/s10434-020-08888-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32839900]
23. Thibodeau, S.; Voutsadakis, I.A. Prediction of Oncotype Dx recurrence score using clinical parameters: A comparison of available tools and a simple predictor based on grade and progesterone receptor. Hematol. Oncol. Stem. Cell Ther.; 2019; 12, pp. 89-96. [DOI: https://dx.doi.org/10.1016/j.hemonc.2019.02.001] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30796885]
24. Tang, P.; Wang, J.; Hicks, D.G.; Wang, X.; Schiffhauer, L.; McMahon, L.; Yang, Q.; Shayne, M.; Huston, A.; Skinner, K.A. et al. A lower Allred score for progesterone receptor is strongly associated with a higher recurrence score of 21-gene assay in breast cancer. Cancer Investig.; 2010; 28, pp. 978-982. [DOI: https://dx.doi.org/10.3109/07357907.2010.496754] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20690804]
25. Clark, B.Z.; Dabbs, D.J.; Cooper, K.L.; Bhargava, R. Impact of progesterone receptor semiquantitative immunohistochemical result on Oncotype DX recurrence score: A quality assurance study of 1074 cases. Appl. Immunohistochem. Mol. Morphol.; 2013; 21, pp. 287-291. [DOI: https://dx.doi.org/10.1097/PAI.0b013e31826f80c9]
26. Cui, X.; Schiff, R.; Arpino, G.; Osborne, C.K.; Lee, A.V. Biology of progesterone receptor loss in breast cancer and its implications for endocrine therapy. J. Clin. Oncol.; 2005; 23, pp. 7721-7735. [DOI: https://dx.doi.org/10.1200/JCO.2005.09.004]
27. Horwitz, K.B.; McGuire, W.L. Estrogen control of progesterone receptor in human breast cancer. Correlation with nuclear processing of estrogen receptor. J. Biol. Chem.; 1978; 253, pp. 2223-2228. [DOI: https://dx.doi.org/10.1016/S0021-9258(17)38062-6]
28. Horwitz, K.B.; McGuire, W.L. Predicting response to endocrine therapy in human breast cancer: A hypothesis. Science; 1975; 189, pp. 726-727. [DOI: https://dx.doi.org/10.1126/science.168640]
29. Bardou, V.J.; Arpino, G.; Elledge, R.M.; Osborne, C.K.; Clark, G.M. Progesterone receptor status significantly improves outcome prediction over estrogen receptor status alone for adjuvant endocrine therapy in two large breast cancer databases. J. Clin. Oncol.; 2003; 21, pp. 1973-1979. [DOI: https://dx.doi.org/10.1200/JCO.2003.09.099]
30. Viale, G.; Regan, M.M.; Dell’Orto, P.; Mastropasqua, M.G.; Maiorano, E.; Rasmussen, B.B.; MacGrogan, G.; Forbes, J.F.; Paridaens, R.J.; Colleoni, M. et al. Which patients benefit most from adjuvant aromatase inhibitors? Results using a composite measure of prognostic risk in the BIG 1-98 randomized trial. Ann. Oncol.; 2011; 22, pp. 2201-2207. [DOI: https://dx.doi.org/10.1093/annonc/mdq738]
31. Viale, G.; Regan, M.M.; Maiorano, E.; Mastropasqua, M.G.; Golouh, R.; Perin, T.; Brown, R.W.; Kovacs, A.; Pillay, K.; Ohlschlegel, C. et al. Chemoendocrine compared with endocrine adjuvant therapies for node-negative breast cancer: Predictive value of centrally reviewed expression of estrogen and progesterone receptors—International Breast Cancer Study Group. J. Clin. Oncol.; 2008; 26, pp. 1404-1410. [DOI: https://dx.doi.org/10.1200/JCO.2007.10.6393]
32. Cheang, M.C.; Chia, S.K.; Voduc, D.; Gao, D.; Leung, S.; Snider, J.; Watson, M.; Davies, S.; Bernard, P.S.; Parker, J.S. et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J. Natl. Cancer Inst.; 2009; 101, pp. 736-750. [DOI: https://dx.doi.org/10.1093/jnci/djp082]
33. Jacquemier, J.; Ginestier, C.; Rougemont, J.; Bardou, V.J.; Charafe-Jauffret, E.; Geneix, J.; Adelaide, J.; Koki, A.; Houvenaeghel, G.; Hassoun, J. et al. Protein expression profiling identifies subclasses of breast cancer and predicts prognosis. Cancer Res.; 2005; 65, pp. 767-779. [DOI: https://dx.doi.org/10.1158/0008-5472.767.65.3]
34. Nishimura, R.; Osako, T.; Nishiyama, Y.; Tashima, R.; Nakano, M.; Fujisue, M.; Toyozumi, Y.; Arima, N. Prognostic significance of Ki-67 index value at the primary breast tumor in recurrent breast cancer. Mol. Clin. Oncol.; 2014; 2, pp. 1062-1068. [DOI: https://dx.doi.org/10.3892/mco.2014.400]
35. Chen, M.; Chen, W.; Liu, D.; Chen, W.; Shen, K.; Wu, J.; Zhu, L. Prognostic values of clinical and molecular features in HER2 low-breast cancer with hormonal receptor overexpression: Features of HER2-low breast cancer. Breast Cancer; 2022; 29, pp. 844-853. [DOI: https://dx.doi.org/10.1007/s12282-022-01364-y]
36. Ma, J.C.; Zhong, X.R.; Luo, T.; Xiang, Z.Z.; Li, J.Y.; Luo, C.; Yan, X.; He, P.; Tian, T.L.; Liu, F. et al. The Effect of Postmastectomy Radiotherapy on Breast Cancer Patients After Neoadjuvant Chemotherapy by Molecular Subtype. Ann. Surg. Oncol.; 2021; 28, pp. 5084-5095. [DOI: https://dx.doi.org/10.1245/s10434-020-09523-1]
37. Eggemann, H.; Ignatov, T.; Burger, E.; Kantelhardt, E.J.; Fettke, F.; Thomssen, C.; Costa, S.D.; Ignatov, A. Moderate HER2 expression as a prognostic factor in hormone receptor positive breast cancer. Endocr. Relat. Cancer; 2015; 22, pp. 725-733. [DOI: https://dx.doi.org/10.1530/ERC-15-0335]
38. Rossi, V.; Sarotto, I.; Maggiorotto, F.; Berchialla, P.; Kubatzki, F.; Tomasi, N.; Redana, S.; Martinello, R.; Valabrega, G.; Aglietta, M. et al. Moderate immunohistochemical expression of HER-2 (2+) without HER-2 gene amplification is a negative prognostic factor in early breast cancer. Oncologist; 2012; 17, pp. 1418-1425. [DOI: https://dx.doi.org/10.1634/theoncologist.2012-0194]
39. Li, Y.; Abudureheiyimu, N.; Mo, H.; Guan, X.; Lin, S.; Wang, Z.; Chen, Y.; Chen, S.; Li, Q.; Cai, R. et al. In Real Life, Low-Level HER2 Expression May Be Associated with Better Outcome in HER2-Negative Breast Cancer: A Study of the National Cancer Center, China. Front. Oncol.; 2021; 11, 774577. [DOI: https://dx.doi.org/10.3389/fonc.2021.774577]
40. Tarantino, P.; Jin, Q.; Tayob, N.; Jeselsohn, R.M.; Schnitt, S.J.; Vincuilla, J.; Parker, T.; Tyekucheva, S.; Li, T.; Lin, N.U. et al. Prognostic and Biologic Significance of ERBB2-Low Expression in Early-Stage Breast Cancer. JAMA Oncol.; 2022; 8, pp. 1177-1183. [DOI: https://dx.doi.org/10.1001/jamaoncol.2022.2286]
41. Zhang, H.; Katerji, H.; Turner, B.M.; Audeh, W.; Hicks, D.G. HER2-low breast cancers: Incidence, HER2 staining patterns, clinicopathologic features, MammaPrint and BluePrint genomic profiles. Mod. Pathol.; 2022; 35, pp. 1075-1082. [DOI: https://dx.doi.org/10.1038/s41379-022-01019-5]
42. De Moura Leite, L.; Cesca, M.G.; Tavares, M.C.; Santana, D.M.; Saldanha, E.F.; Guimaraes, P.T.; Sa, D.D.S.; Simoes, M.F.E.; Viana, R.L.; Rocha, F.G. et al. HER2-low status and response to neoadjuvant chemotherapy in HER2 negative early breast cancer. Breast Cancer Res. Treat.; 2021; 190, pp. 155-163. [DOI: https://dx.doi.org/10.1007/s10549-021-06365-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34409551]
43. Denkert, C.; Seither, F.; Schneeweiss, A.; Link, T.; Blohmer, J.U.; Just, M.; Wimberger, P.; Forberger, A.; Tesch, H.; Jackisch, C. et al. Clinical and molecular characteristics of HER2-low-positive breast cancer: Pooled analysis of individual patient data from four prospective, neoadjuvant clinical trials. Lancet Oncol.; 2021; 22, pp. 1151-1161. [DOI: https://dx.doi.org/10.1016/S1470-2045(21)00301-6] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34252375]
44. Fisher, E.R.; Gregorio, R.M.; Redmond, C.; Kim, W.S.; Fisher, B. Pathologic findings from the national surgical adjuvant breast project. (Protocol No. 4). III. The significance of extranodal extension of axillary metastases. Am. J. Clin. Pathol.; 1976; 65, pp. 439-444. [DOI: https://dx.doi.org/10.1093/ajcp/65.4.439] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/1266807]
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