ABSTRACT
The survival benefit of neoadjuvant chemotherapy (Neo‐CT) among male breast carcinoma (MaBC) remains unclear, and the specific population that can benefit from Neo‐CT is also not yet defined. We utilized the data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database and employed Kaplan–Meier analysis; Cox proportional hazards model and binary variable Logistic regression were performed to evaluate the effect of Neo‐CT on the therapeutic response and prognosis of MaBC patients. Among the 4171 MaBC subjects included, 194 (4.7%) patients received Neo‐CT only (Neo‐CT only group), 246 (5.9%) cases received adjuvant chemotherapy and Neo‐CT (CT + Neo‐CT group); the remaining 3731 (89.5%) cases received postoperative chemotherapy only (CT‐only group). Totally, 20 (12.12%) participants achieved pathological complete response (pCR). And patients who achieved a pCR (pCR subgroup) had improved 5‐year and 10‐year overall survival (OS) (100.0% vs. 79.70% vs. 73.81%, 100.0% vs. 59.12% vs. 28.82%, respectively) and breast cancer‐specific survival (BCSS) (86.30% vs. 91.49%, 69.04% vs. 83.32%, respectively) in comparison with those in the Non‐pCR subgroup. Further binary Logistic regression analysis showed that the achievement of pCR might be related to stage I‐II (p = 0.007) and hormone receptor (HR) negative (p = 0.010) disease, and the pCR rate of stage I‐II and HR− MaBC patients was 66.67%.
Keywords: male breast cancer, molecular subtyping, Neoadjuvant chemotherapy, pCR, TNM stage
1. Introduction
Neoadjuvant chemotherapy (Neo‐CT), an important strategy of multi‐disciplinary therapy for female breast carcinoma (FBC) introduced in the 1970s, was administrated to reduce tumor size and render otherwise inoperable disease operable, make breast‐conserving therapy feasible, provide important prognostic information through monitoring of the Neo‐CT response of the local tumor to recommended chemotherapy regimens, and help the decision‐making in the postoperative drug selection [1, 2, 3, 4, 5]. Neo‐CT for FBC had as a result become a preferred and standard option for locally advanced FBC recommended by NCCN (National Comprehensive Cancer Network) [6] and ASCO Guidelines. However, FBC, but not male breast carcinoma (MaBC), has historically had worse outcomes with Neo‐CT, largely unknown were the magnitudes of survival benefit and impact of Neo‐CT among MaBC owing to the scarcity of clinical resources and the paucity of prospective randomized trials in men [7, 8, 9, 10].
As a rare primary breast malignancy, MaBC accounts for 0.2%–1.5% of all male tumors and 0.5%–1.0% of all breast cancers [7, 8, 9] and has shown an increasing incidence in most countries in recent years [11]. Meanwhile, nearly 13.2%–62.2% men existed with large, locally advanced or metastatic breast cancer [9, 12] and should be indicated as the population recommended with Neo‐CT in NCCN [6] and ASCO Guidelines. But so far, the inclusion of Neo‐CT in MaBC was classified as having a low level of importance in ASCO Guideline in MaBC [10]. Previous studies had indicated that MaBC has smaller lesions [8] and lower chemotherapy sensitivity in MaBC than that in the FBC for each subtype of breast cancer [7]. Regrettably, the benefit of Neo‐CT for MaBC remained ambiguous. For this, we had rekindled the interest in identifying the survival benefit and impact of Neo‐CT in MaBC.
To further explore the impact of Neo‐CT on the survival of MaBC patients and to identify the groups that can benefit from Neo‐CT, a large cohort of men with MaBC from 2010 to 2021 from the population‐based database Surveillance, Epidemiology, and End Results (SEER) cancer registry program was conducted. Statistical methods such as Cox proportional hazards model and binary variable logistic regression were performed to further estimate the survival benefit of Neo‐CT among male patients with MaBC.
2. Methods
2.1. Study Population and Design
The participants in this study were sourced from the SEER Research Plus Data, 17 registries, Nov 2021 Sub (2000–2021). SEER*Stat Software version 8.4.3 (https://seer.cancer.gov/seerstat/) (Information Management Service Inc. Calverton, MD, USA, User ID: 16863‐ Nov2021) was employed to generate the case listing. We selected male patients in the SEER database who received a diagnosis of breast cancer between 2000 and 2021 and screened the cases using the following exclusion criteria: (1) younger than 18 years of age; (2) unclear chemotherapy status; (3) the breast was not the primary site of the tumor; (4) without surgical treatment; (5) the M stage was M1 or Unknown; (6) the follow‐up time was 0 month. The screening process is shown in Figure 1.
FIGURE 1.
Eligibility, inclusion, and exclusion of study population.
Among the 9837 men with breast cancer, a total of 4171 patients were included in the current study. The following data were collected: demographic information, such as year of diagnosis, age at diagnosis, marital status, household income, and region; clinicopathological variables, such as tumor stage, estrogen receptors (ER) and progesterone receptors (PR), human epidermal growth factor receptor 2 (HER2), molecular subtype, types of pathology, and histological grade; treatment options, such as radiotherapy, chemotherapy, and surgery information.
2.2. Grouping of the Population
The effect of neoadjuvant chemotherapy on the prognosis of MaBC patients was the primary question explored in this study. Therefore, we categorized the enrolled patients into three groups according to the standardized treatment sequence: adjuvant chemotherapy alone (CT only) group, neoadjuvant chemotherapy alone (Neo‐CT only) group, and neoadjuvant chemotherapy plus sequential postoperative adjuvant chemotherapy (CT + Neo‐CT).
On the basis of the item of “year” and “age” offered by the SEER database, the year of diagnosis was divided into 2007–2014 (the year pathological complete response (pCR) after neoadjuvant treatment being used as a surrogate endpoint for long‐term clinical outcomes [13]), 2015–2017 (the year of the intensive therapy with adjuvant capecitabine after preoperative chemotherapy being confirmed safe and effective [14]), and 2018 to 2021 subgroups. And the population was classified into younger than 40 years old (≤ 39 years), 40 to 64 years old, and 65 years or older (≥ 65 years) segments according to the age group previously reported [15, 16, 17].
The economic level of population was categorized into low‐income (≤ $49,999) group, middle‐income ($50,000–$74,999) group, and high‐income (≥ $75,000) group, on the basis of the content of “median household income inflation adjusted to 2022” and the income and poverty in the United States (2022, Release Tables: Median Household Income by State, Annual | FRED | St. Louis Fed (stlouisfed.org)). The geographic region of the males was grouped in the large metropolitan (city counties in metropolitan areas ge 1 million population), middle‐sized metropolitan (counties in metropolitan areas of 250,000 to 1 million population), tiny metropolitan (counties in metropolitan areas of lt 250 thousand population), and nonmetropolitan groups based on the items of “rural‐urban continuum code”.
2.3. Statistics Analysis
The demographic variables, clinicopathological characteristics, and information on treatment of patients were encapsulated by No. (%) for categorical variables. Pearson's χ2 test was performed to demonstrate the basic and clinical characteristics of MaBC patients.
The overall survival (OS) and breast cancer‐specific survival (BCSS) were defined as the time from the date of diagnosis until death caused by any reasons and breast cancer respectively. Kaplan–Meier (K‐M) analysis was used to assess OS and BCSS, while statistical differences among groups were determined by the Log‐rank test. Cox proportional hazards model was employed to analyze OS and BCSS under the premise of considering confounders.
Binary variable logistic regression was performed to assess the association of selected covariates with achievement of pCR. Patients were followed up until December 2021, with a median follow‐up of 55 months (range 0–179 months). IBM SPSS Statistics 23 and GraphPad Prism 8.0 were used to conduct analyzes in this study. A two‐sided p value of < 0.05 was selected as the statistical significance.
3. Results
3.1. Demographic and Clinical Characteristics
Among the 4171 MaBC subjects included in the current study (Table 1), 3731 (89.5%) of them received adjuvant chemotherapy only (CT only group), 194 (4.7%) of them received neoadjuvant chemotherapy only (Neo‐CT only group), and 246 (5.9%) of them received both adjuvant chemotherapy and Neo‐CT (CT + Neo‐CT group). The majority of MaBC patients were diagnosed in 2007–2014 (47.1%, 1965/4171), over 65 years old (56.9%, 2372/4171), in a stable marriage (69.3%, 2889/4171) and in better economic conditions (58.0%, 2421/4171). Compared with candidates in the CT only group, patients diagnosed between 2018 and 2021, with higher TNM stage and negative ER and PR status were more likely to choose Neo‐CT, but HER2− patients were less likely to choose Neo‐CT. In addition, the proportion of patients receiving radiotherapy was higher in the two Neo‐CT groups.
TABLE 1.
The clinical and pathological characteristics of patients.
| Characteristic | Total | CT only | Neo‐CT only | CT + Neo‐CT | p |
|---|---|---|---|---|---|
| N (%) | n (%) | n (%) | n (%) | ||
| 4171 (100) | 3731 (89.5) | 194 (4.7) | 246 (5.9) | ||
| Year | < 0.001 | ||||
| 2007–2014 | 1965 (47.1) | 1821 (48.8) | 82 (42.3) | 62 (25.2) | |
| 2015–2017 | 876 (21.0) | 777 (20.8) | 44 (22.7) | 55 (22.4) | |
| 2018–2021 | 1330 (31.9) | 1133 (30.4) | 68 (35.1) | 129 (52.4) | |
| Age | |||||
| ≤ 39 years | 80 (1.9) | 60 (1.6) | 7 (3.6) | 13 (5.3) | < 0.001 |
| 40–64 years | 1719 (41.2) | 1509 (40.4) | 84 (43.3) | 126 (51.2) | |
| ≥ 65 years | 2372 (56.9) | 2162 (57.9) | 103 (53.1) | 107 (43.5) | |
| Marital Status | 0.314 | ||||
| Married | 2889 (69.3) | 2602 (69.7) | 123 (63.4) | 164 (66.7) | |
| Unmarried | 1111 (26.6) | 978 (26.2) | 63 (32.5) | 70 (28.5) | |
| Unknown | 171 (4.1) | 151 (4.0) | 8 (4.1) | 12 (4.9) | |
| Median household income | 0.242 | ||||
| Low‐income | 276 (6.6) | 243 (6.5) | 13 (6.7) | 20 (8.1) | |
| Middle‐income | 1474 (35.3) | 1314 (35.2) | 81 (41.8) | 79 (32.1) | |
| High‐income | 2421 (58.0) | 2174 (58.3) | 100 (51.5) | 147 (59.8) | |
| Area | 0.092 | ||||
| Large metropolitan | 2492 (59.7) | 2237 (60.0) | 105 (54.1) | 150 (61.0) | |
| Middle‐sized metropolitan | 911 (21.8) | 811 (21.7) | 57 (29.4) | 43 (17.5) | |
| Tiny metropolitan | 293 (7.0) | 261 (7.0) | 14 (7.2) | 18 (7.3) | |
| Non‐metropolitan/Unknown | 475 (11.4) | 422 (11.3) | 18 (9.3) | 35 (14.2) | |
| T stage | < 0.001 | ||||
| T1 | 1894 (45.4) | 1788 (47.9) | 50 (25.8) | 56 (22.8) | |
| T2 | 1829 (43.9) | 1634 (43.8) | 80 (41.2) | 115 (46.7) | |
| T3 | 119 (2.9) | 74 (2.0) | 21 (10.8) | 24 (9.8) | |
| T4 | 276 (6.6) | 201 (5.4) | 34 (17.5) | 41 (16.7) | |
| Unknown | 53 (1.3) | 34 (0.9) | 9 (4.6) | 10 (4.1) | |
| N stage | < 0.001 | ||||
| N0 | 2221 (53.2) | 2073 (55.6) | 72 (37.1) | 76 (30.9) | |
| N1 | 1385 (33.2) | 1196 (32.1) | 77 (39.7) | 112 (45.5) | |
| N2 | 365 (8.8) | 303 (8.1) | 27 (13.9) | 35 (14.2) | |
| N3 | 192 (4.6) | 153 (4.1) | 16 (8.2) | 23 (9.3) | |
| Unknown | 8 (0.2) | 6 (0.2) | 2 (1.0) | 0 (0.0) | |
| TNM stage | < 0.001 | ||||
| I | 1900 (45.6) | 1767 (47.4) | 50 (25.8) | 83 (33.7) | |
| II | 1539 (36.9) | 1382 (37.0) | 78 (40.2) | 79 (32.1) | |
| III | 720 (17.3) | 574 (15.4) | 63 (32.5) | 83 (33.7) | |
| Unknown (not IV) | 12 (0.3) | 8 (0.2) | 3 (1.5) | 1 (0.4) | |
| ER | < 0.001 | ||||
| Positive | 4037 (96.8) | 3635 (97.4) | 170 (87.6) | 232 (94.3) | |
| Negative | 77 (1.8) | 50 (1.3) | 15 (7.7) | 12 (4.9) | |
| Borderline/Unknown | 57 (1.4) | 46 (1.2) | 9 (4.6) | 2 (0.8) | |
| PR | < 0.001 | ||||
| Positive | 3756 (90.1) | 3386 (90.8) | 159 (82.0) | 211 (85.8) | |
| Negative | 342 (8.2) | 283 (7.6) | 27 (13.9) | 32 (13.0) | |
| Borderline/Unknown | 73 (1.8) | 62 (1.7) | 8 (4.1) | 3 (1.2) | |
| HER2 | < 0.001 | ||||
| Positive | 404 (9.7) | 295 (7.9) | 34 (17.5) | 75 (30.5) | |
| Negative | 3003 (72.0) | 2728 (73.1) | 113 (58.2) | 162 (65.9) | |
| Borderline/Unknown | 764 (18.3) | 708 (19.0) | 47 (24.2) | 9 (3.7) | |
| Molecular Subtype | < 0.001 | ||||
| HR+/HER2− | 2962 (71.0) | 2700 (72.4) | 106 (54.6) | 156 (63.4) | |
| HR+/HER2+ | 386 (9.3) | 285 (7.6) | 31 (16.0) | 70 (28.5) | |
| HR−/HER2+ | 18 (0.4) | 10 (0.3) | 3 (1.5) | 5 (2.0) | |
| HR−/HER2− | 39 (0.9) | 26 (0.7) | 7 (3.6) | 6 (2.4) | |
| Unknown | 766 (18.4) | 710 (19.0) | 47 (24.2) | 9 (3.7) | |
| Types of pathology | 0.591 | ||||
| Infiltrating Duct Carcinoma | 3597 (86.2) | 3212 (86.1) | 172 (88.7) | 213 (86.6) | |
| Others | 574 (13.8) | 519 (13.9) | 22 (11.3) | 33 (13.4) | |
| Grade | 0.065 | ||||
| I | 479 (11.5) | 441 (11.8) | 12 (6.2) | 26 (10.6) | |
| II | 2200 (52.7) | 1978 (53.0) | 101 (52.1) | 121 (49.2) | |
| III | 1290 (30.9) | 1137 (30.5) | 66 (34.0) | 87 (35.4) | |
| Unknown | 202 (4.8) | 175 (4.7) | 15 (7.7) | 12 (4.9) | |
| Surgery Methods | 0.142 | ||||
| Breast‐Conserving Therapy | 489 (11.7) | 431 (11.6) | 25 (12.9) | 33 (13.4) | |
| Mastectomy | 3598 (86.3) | 3230 (86.6) | 165 (85.1) | 203 (82.5) | |
| Breast‐Reconstruction Surgery | 84 (2.0) | 70 (1.9) | 4 (2.1) | 10 (4.1) | |
| Radiation Therapy | < 0.001 | ||||
| Yes | 1504 (36.1) | 1265 (33.9) | 87 (44.8) | 152 (61.8) | |
| No/Unknown | 2667 (63.9) | 2466 (66.1) | 107 (55.2) | 94 (38.2) | |
| Response | 0.921 | ||||
| pCR | 20 (12.1) | Not available | 8 (11.8) | 12 (12.4) | |
| Non‐pCR | 145 (87.9) | 60 (88.2) | 85 (87.6) |
Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; HR, hormone receptor; pCR, pathological complete response; PR, progesterone receptor. Italic values in table1‐5 indicate p‐values < 0.05 (statistically significant).
3.2. Effect of Neoadjuvant Chemotherapy on Survival
To investigate whether neoadjuvant chemotherapy conferred a survival benefit in MaBC patients, we compared OS and BCSS among the CT only, Neo‐CT only, and CT + Neo‐CT group using K‐M curve. As shown in Figure 2a,b, the 5‐year and 10‐year OS of patients in the CT only group were 79.70% and 59.12%, which were slightly higher than those in the Neo‐CT only group (5‐year OS: 77.46%, 10‐year OS: 54.28%, p = 0.138) and CT + Neo‐CT group (5‐year OS: 73.73%, 10‐year OS: 36.16%, p = 0.056). The 5‐year and 10‐year BCSS in the CT only group were 91.49% and 83.32%, which were significantly higher than those in the Neo‐CT only (5‐year BCSS: 86.30%, 10‐year BCSS: 69.04%, p = 0.003) and CT + Neo‐CT group (5‐year BCSS: 87.18%, 10‐year BCSS: 67.56%, p = 0.045).
FIGURE 2.
Kaplan–Meier analysis of OS (a) and BCSS (b) associated with Neo‐CT.
The Cox proportional hazards model was used to verify the reliability of the results of K‐M analysis, and factors with significant differences in univariate analysis were included in multivariate analysis. The results showed that Neo‐CT was not associated with a significant survival benefit compared with the CT‐only group, while factors such as year of diagnosis, age, marital status, TNM stage, PR status, type of pathological, and histological grade were variably associated with OS or BCSS to varying degrees (Tables 2 and 3). These results seem to indicate that Neo‐CT appeared to correlate with less favorable long‐term survival outcomes among MaBC patients.
TABLE 2.
Univariate and multivariate Cox proportional hazards model associated with OS.
| Characteristics | Univariate Cox | Multiple Cox | ||
|---|---|---|---|---|
| HRs (95% CI) | p | HRs (95% CI) | p | |
| Year | 0.016 | 0.081 | ||
| 2010–2014 | Reference | Reference | ||
| 2012–2017 | 1.098 (0.928–1.277) | 0.296 | 1.022 (0.870–1.201) | 0.789 |
| 2018–2021 | 0.713 (0.543–0.936) | 0.015 | 0.741 (0.563–0.977) | 0.033 |
| Age | < 0.001 | < 0.001 | ||
| ≤ 39 years | Reference | Reference | ||
| 40–64 years | 0.936 (0.526–1.667) | 0.823 | 1.042 (0.584–1.862) | 0.888 |
| ≥ 65 years | 2.127 (1.203–3.762) | 0.009 | 2.508 (1.411–4.459) | 0.002 |
| Marital status | < 0.001 | < 0.001 | ||
| Married | Reference | Reference | ||
| Unmarried | 1.438 (1.267–1.633) | < 0.001 | 1.559 (1.370–1.774) | < 0.001 |
| Unknown | 0.742 (0.529–1.042) | 0.085 | 0.774 (0.550–1.089) | 0.141 |
| Median household income | 0.088 | 0.593 | ||
| Low income | Reference | Reference | ||
| Middle‐income | 0.853 (0.683–1.065) | 0.161 | 0.887 (0.695–1.134) | 0.339 |
| High‐income | 0.792 (0.637–0.985) | 0.036 | 0.873 (0.672–1.136) | 0.313 |
| Area | 0.021 | 0.200 | ||
| Large metropolitan | Reference | Reference | ||
| Middle‐sized metropolitan | 1.229 (1.067–1.416) | 0.004 | 1.154 (1.001–1.331) | 0.048 |
| Tiny metropolitan | 1.209 (0.968–1.509) | 0.094 | 1.162 (0.916–1.474) | 0.217 |
| Non‐metropolitan/Unknown | 1.125 (0.937–1.351) | 0.207 | 1.057 (0.851–1.314) | 0.615 |
| T stage | < 0.001 | < 0.001 | ||
| T1 | Reference | Reference | ||
| T2 | 1.647 (1.450–1.872) | < 0.001 | 1.430 (1.252–1.632) | < 0.001 |
| T3 | 2.243 (1.659–3.033) | < 0.001 | 2.049 (1.493–2.810) | < 0.001 |
| T4 | 2.499 (2.025–3.086) | < 0.001 | 1.963 (1.566–2.461) | < 0.001 |
| Unknown | 1.027 (0.591–1.783) | 0.926 | 0.996 (0.552–1.799) | 0.990 |
| N stage | < 0.001 | 0.007 | ||
| N0 | Reference | Reference | ||
| N1 | 1.150 (1.007–1.312) | 0.039 | 1.056 (0.922–1.210) | 0.429 |
| N2 | 1.640 (1.370–1.964) | < 0.001 | 1.265 (1.046–1.528) | 0.015 |
| N3 | 1.950 (1.541–2.468) | < 0.001 | 1.482 (1.162–1.889) | 0.002 |
| Unknown | 1.012 (0.325–3.148) | 0.984 | 1.557 (0.457–5.309) | 0.479 |
| TNM stage | < 0.001 | |||
| I | Reference | |||
| II | 1.505 (1.305–1.737) | < 0.001 | ||
| III | 2.251 (1.920–2.638) | < 0.001 | ||
| Unknown (not IV) | 1.793 (0.741–4.342) | 0.195 | ||
| ER | 0.447 | |||
| Positive | Reference | |||
| Negative | 0.789 (0.495–1.257) | 0.318 | ||
| Borderline/Unknown | 0.831 (0.528–1.308) | 0.423 | ||
| PR | 0.086 | 0.020 | ||
| Positive | Reference | Reference | ||
| Negative | 1.216 (0.999–1.481) | 0.051 | 1.331 (1.089–1.626) | 0.005 |
| Borderline/Unknown | 0.824 (0.554–1.225) | 0.339 | 0.967 (0.644–1.453) | 0.872 |
| HER2 | 0.250 | |||
| Positive | Reference | |||
| Negative | 0.919 (0.744–1.136) | 0.435 | ||
| Borderline/Unknown | 0.839 (0.667–1.056) | 0.134 | ||
| Molecular subtype | 0.108 | |||
| HR+/HER2− | Reference | |||
| HR+/HER2+ | 1.144 (0.925–1.416) | 0.214 | ||
| HR−/HER2+ | 0.177 (0.025–1.256) | 0.083 | ||
| HR−/HER2− | 0.779 (0.349–1.741) | 0.543 | ||
| Unknown | 0.906 (0.789–1.040) | 0.162 | ||
| Types of pathology | 0.011 | 0.025 | ||
| Infiltrating duct carcinoma | Reference | Reference | ||
| Others | 0.803 (0.676–0.955) | 0.013 | 0.816 (0.684–0.975) | 0.025 |
| Grade | < 0.001 | < 0.001 | ||
| I | Reference | Reference | ||
| II | 1.188 (0.951–1.484) | 0.129 | 1.059 (0.847–1.325) | 0.615 |
| III | 1.724 (1.376–2.160) | < 0.001 | 1.411 (1.121–1.774) | 0.003 |
| Unknown | 1.282 (0.931–1.765) | 0.128 | 1.174 (0.848–1.626) | 0.333 |
| Surgery methods | < 0.001 | 0.058 | ||
| Breast‐conserving therapy | Reference | Reference | ||
| Mastectomy | 1.571 (1.251–1.972) | < 0.001 | 1.294 (1.027–1.632) | 0.029 |
| Breast‐reconstruction surgery | 0.965 (0.526–1.771) | 0.909 | 0.953 (0.517–1.756) | 0.877 |
| Radiation therapy | 0.959 | |||
| Yes | Reference | |||
| No | 1.003 (0.888–1.134) | 0.960 | ||
| Neoadjuvant therapy | 0.061 | 0.578 | ||
| CT only | Reference | Reference | ||
| Neo‐CT only | 1.227 (0.937–1.606) | 0.008 | 0.987 (0.746–1.306) | 0.927 |
| CT + Neo‐CT | 1.310 (0.993–1.728) | 0.056 | 1.164 (0.872–1.553) | 0.304 |
Note: The rest are the same as Table 1. The italicized numbers in the tables represent p‐values < 0.05, indicating statistically significant differences.
Abbreviation: HRs, hazard ratios.
TABLE 3.
Univariate and multivariate Cox proportional hazards model associated with BCSS.
| Characteristics | Univariate Cox | Multiple Cox | ||
|---|---|---|---|---|
| HRs (95% CI) | p | HRs (95% CI) | p | |
| Year | < 0.001 | 0.001 | ||
| 2010–2014 | Reference | Reference | ||
| 2012–2017 | 0.739 (0.552–0.989) | 0.042 | 0.675 (0.503–0.907) | 0.009 |
| 2018–2021 | 0.342 (0.195–0.599) | < 0.001 | 0.396 (0.225–0.699) | 0.001 |
| Age | 0.008 | < 0.001 | ||
| ≤ 39 years | Reference | Reference | ||
| 40–64 years | 1.059 (0.434–2.583) | 0.900 | 1.389 (0.564–3.419) | 0.475 |
| ≥ 65 years | 1.478 (0.608–3.592) | 0.388 | 2.218 (0.902–5.455) | 0.083 |
| Marital status | < 0.001 | < 0.001 | ||
| Married | Reference | Reference | ||
| Unmarried | 1.889 (1.514–2.356) | < 0.001 | 1.785 (1.421–2.243) | < 0.001 |
| Unknown | 0.955 (0.545–1.673) | 0.873 | 1.069 (0.605–1.888) | 0.818 |
| Median household income | 0.370 | |||
| Low income | Reference | |||
| Middle‐income | 0.846 (0.564–1.269) | 0.419 | ||
| High‐income | 0.768 (0.516–1.144) | 0.195 | ||
| Area | 0.769 | |||
| Large metropolitan | Reference | |||
| Middle‐sized metropolitan | 1.133 (0.873–1.469) | 0.348 | ||
| Tiny metropolitan | 0.991 (0.638–1.538) | 0.966 | ||
| Non‐metropolitan/Unknown | 1.115 (0.802–1.550) | 0.517 | ||
| T stage | < 0.001 | < 0.001 | ||
| T1 | Reference | Reference | ||
| T2 | 3.270 (2.517–4.248) | < 0.001 | 2.258 (1.722–2.962) | < 0.001 |
| T3 | 3.931 (2.225–6.945) | < 0.001 | 2.658 (1.461–4.836) | 0.001 |
| T4 | 5.868 (4.072–8.455) | < 0.001 | 3.384 (2.270–5.045) | < 0.001 |
| Unknown | 2.393 (1.043–5.491) | 0.039 | 1.853 (0.758–4.532) | 0.176 |
| N stage | < 0.001 | < 0.001 | ||
| N0 | Reference | Reference | ||
| N1 | 1.855 (1.428–2.409) | < 0.001 | 1.548 (1.177–2.034) | 0.002 |
| N2 | 3.716 (2.727–5.063) | < 0.001 | 2.555 (1.803–3.622) | < 0.001 |
| N3 | 6.321 (4.551–8.780) | < 0.001 | 4.074 (2.857–5.810) | < 0.001 |
| Unknown | 1.691 (0.236–12.121) | 0.601 | 1.140 (0.139–9.357) | 0.903 |
| TNM stage | < 0.001 | |||
| I | Reference | |||
| II | 3.129 (2.235–4.380) | < 0.001 | ||
| III | 7.693 (5.496–10.769) | < 0.001 | ||
| Unknown (not IV) | 2.809 (0.387–20.394) | 0.307 | ||
| ER | 0.476 | |||
| Positive | Reference | |||
| Negative | 0.697 (0.288–1.686) | 0.424 | ||
| Borderline/Unknown | 1.336 (0.712–2.507) | 0.367 | ||
| PR | 0.001 | 0.002 | ||
| Positive | Reference | Reference | ||
| Negative | 1.769 (1.296–2.415) | < 0.001 | 1.776 (1.288–2.448) | < 0.001 |
| Borderline/Unknown | 1.204 (0.659–2.199) | 0.546 | 1.344 (0.720–2.509) | 0.354 |
| HER2 | 0.223 | |||
| Positive | Reference | |||
| Negative | 0.770 (0.538–1.103) | 0.154 | ||
| Borderline/Unknown | 0.903 (0.611–1.336) | 0.611 | ||
| Molecular subtype | 0.385 | |||
| HR+/HER2− | Reference | |||
| HR+/HER2+ | 1.390 (0.970–1.991) | 0.073 | ||
| HR−/HER2+ | 0.000 (−) | 0.924 | ||
| HR−/HER2− | 0.766 (0.190–3.085) | 0.708 | ||
| Unknown | 1.162 (0.907–1.490) | 0.235 | ||
| Types of pathology | 0.014 | 0.020 | ||
| Infiltrating duct carcinoma | Reference | Reference | ||
| Others | 0.657 (0.471–0.917) | 0.014 | 0.666 (0.473–0.938) | 0.020 |
| Grade | < 0.001 | < 0.001 | ||
| I | Reference | Reference | ||
| II | 1.741 (1.019–2.976) | 0.042 | 1.442 (0.843–2.469) | 0.182 |
| III | 4.363 (2.577–7.387) | < 0.001 | 2.758 (1.622–4.689) | < 0.001 |
| Unknown | 2.992 (1.577–5.677) | 0.001 | 2.258 (1.177–4.332) | 0.014 |
| Surgery methods | 0.002 | 0.201 | ||
| Breast‐conserving therapy | Reference | Reference | ||
| Mastectomy | 2.428 (1.491–3.954) | < 0.001 | 1.577 (0.958–2.599) | 0.074 |
| Breast‐reconstruction surgery | 2.135 (0.842–5.417) | 0.110 | 1.588 (0.619–4.073) | 0.336 |
| Radiation therapy | 0.002 | 0.109 | ||
| Yes | Reference | Reference | ||
| No | 0.711 (0.575–0.880) | 0.002 | 1.216 (0.957–1.545) | 0.109 |
| Neoadjuvant therapy | 0.002 | 0.371 | ||
| CT only | Reference | Reference | ||
| Neo‐CT only | 1.854 (1.233–2.789) | 0.003 | 1.357 (0.886–2.079) | 0.160 |
| CT + Neo‐CT | 1.589 (1.009–2.503) | 0.046 | 1.016 (0.628–1.643) | 0.949 |
Note: The italicized numbers in the tables represent p‐values < 0.05, indicating statistically significant differences.
We further performed stratified survival analyzes by TNM stage and molecular subtype to explore whether the observed associations between treatment modality and survival were confounded by baseline disease characteristics (Figures S1 and S2). No significant differences in OS or BCSS were observed among the three treatment groups (CT only, Neo‐CT only, CT + Neo‐CT) within each TNM stage. When stratified by molecular subtypes, in the HR+/HER2− subgroup, patients who only received CT had better OS (p = 0.032) and BCSS (p = 0.001). No significant differences were found in the HER2+ or TNBC subgroups.
3.3. Effect of Pathological Response on Survival
It is well known that the degree of pathological response is an important indicator to evaluate the efficacy of Neo‐CT [18]. Among the 165 patients for whom the degree of pathological response was available, 20 (12.12%) achieved complete response (pCR subgroup) and 145 (87.88%) just achieved partial response or no response (Non‐pCR subgroup).
As shown in Figure 3a,b, the 5‐year and 10‐year OS of the pCR subgroup were both 100.00%, which were significantly higher than that of the Non‐pCR subgroup (5‐year OS: 73.81%, 10‐year OS: 28.82%, p = 0.025). The 5‐year and 10‐year BCSS of the pCR subgroup were 100% and 100%, respectively, which were also much higher than those of the Non‐pCR subgroup (5‐year OS: 84.94%, 10‐year OS: 68.80%, p = 0.196), and this was consistent with the trend of OS, although the Log‐rank test was not significant because of limited cases. Therefore, there is an urgent need to identify patients who are more likely to achieve pCR and to implement a targeted treatment regimen.
FIGURE 3.
Kaplan–Meier analysis of OS (a) and BCSS (b) associated with pCR.
3.4. The Related Factors of pCR After Neo‐CT in MaBC Patients
Binary Logistics regression analysis was performed to further explore the correlation between various clinicopathological factors and pCR in MaBC patients. Univariate analysis (Table 4) showed that TNM stage, ER stage, PR stage, and molecular subtype were independent factors for achieving a pCR in MaBC patients. According to the univariate analysis, the similar classifications were combined, and binary Logistics regression analysis was performed again. The results showed that patients with stage III‐IV (OR = 0.057, 95% CI: 0.007–0.459, p = 0.007) tumor were less likely to achieve pCR compared with patients with stage I‐II tumor. Patients with hormone receptor (HR) negative tumor (OR = 8.291, 95% CI: 1.655–41.546, p = 0.010) were more likely to achieve pCR compared with HR+ patients (Table 5).
TABLE 4.
Binary variable logistic regression analysis of clinicopathological features and pCR.
| Characteristic | B | SE | OR (95% CI) | p |
|---|---|---|---|---|
| Year | ||||
| 2010–2014 | Reference | 0.245 | ||
| 2012–2017 | 1.050 | 0.868 | 2.857 (0.521–15.662) | 0.227 |
| 2018–2021 | 1.312 | 0.785 | 3.714 (0.797–17.301) | 0.095 |
| Age | ||||
| ≤ 39 years | Reference | 0.692 | ||
| 40–64 years | −0.388 | 0.846 | 0.678 (0.129–3.560) | 0.646 |
| ≥ 65 years | −0.693 | 0.877 | 0.500 (0.090–2.791) | 0.430 |
| Marital status | ||||
| Married | Reference | 0.642 | ||
| Unmarried | −0.065 | 0.531 | 0.938 (0.331–2.654) | 0.903 |
| Unknown | 0.762 | 0.859 | 2.143 (0.398–11.532) | 0.375 |
| Median household income | ||||
| Low‐income | Reference | 0.679 | ||
| Middle‐income | 0.487 | 1.138 | 1.628 (0.175–15.141) | 0.668 |
| High‐income | 0.801 | 1.074 | 2.227 (0.271–18.292) | 0.456 |
| T stage | ||||
| T1 | Reference | 0.125 | ||
| T2 | −0.410 | 0.576 | 0.664 (0.215–2.051) | 0.477 |
| T3 | −0.847 | 0.867 | 0.429 (0.078–2.343) | 0.328 |
| T4 | −2.022 | 1.110 | 0.132 (0.015–1.167) | 0.069 |
| Unknown | 1.504 | 1.097 | 4.500 (0.524–38.645) | 0.170 |
| TNM stage | ||||
| I | Reference | 0.031 | ||
| II | 0.195 | 0.515 | 1.215 (0.443–3.336) | 0.705 |
| III | −2.601 | 1.080 | 0.074 (0.009–0.617) | 0.016 |
| ER | ||||
| Positive | Reference | 0.019 | ||
| Negative | 1.595 | 0.680 | 4.929 (1.300–18.692) | 0.019 |
| PR | ||||
| Positive | Reference | 0.016 | ||
| Negative | 1.273 | 0.529 | 3.571 (1.265–10.080) | 0.016 |
| HER2 | ||||
| Positive | Reference | 0.577 | ||
| Negative | −0.519 | 0.500 | 0.595 (0.223–1.586) | 0.300 |
| Borderline/Unknown | −0.134 | 1.147 | 0.875 (0.092–8.285) | 0.907 |
| Molecular subtype | ||||
| HR+/HER2− | Reference | 0.119 | ||
| HR+/HER2+ | 0.403 | 0.559 | 1.496 (0.500–4.479) | 0.472 |
| HR−/HER2+ | 3.018 | 1.274 | 20.444 (1.685–248.108) | 0.018 |
| HR−/HER2− | 1.408 | 0.907 | 4.089 (0.692–24.172) | 0.120 |
| Unknown | 0.533 | 1.135 | 1.704 (0.184–15.764) | 0.639 |
| Types of pathology | ||||
| Infiltrating duct carcinoma | Reference | 0.614 | ||
| Others | −0.539 | 1.069 | 0.583 (0.072–4.744) | 0.614 |
Note: The italicized numbers in the tables represent p‐values < 0.05, indicating statistically significant differences.
TABLE 5.
Univariate and multivariate binary variable Logistic regression associated with pCR.
| Characteristic | Univariate analysis | Multivariate analysis | ||||||
|---|---|---|---|---|---|---|---|---|
| B | SE | OR (95% CI) | p | B | SE | OR (95% CI) | p | |
| TNM stage | ||||||||
| I–II | Reference | 0.009 | Reference | 0.007 | ||||
| III–IV | −2.709 | 1.040 | 0.067 (0.009–0.511) | 0.009 | −2.869 | 1.066 | 0.057 (0.007–0.459) | 0.007 |
| Molecular subtype | ||||||||
| HR+ | Reference | 0.040 | Reference | 0.036 | ||||
| HR− | 1.777 | 0.701 | 5.911 (1.497–23.336) | 0.011 | 2.115 | 0.822 | 8.291 (1.655–41.546) | 0.010 |
| Unknown | 0.391 | 1.114 | 1.478 (0.167–13.116) | 0.726 | 0.491 | 1.171 | 1.633 (0.165–16.201) | 0.675 |
Note: The italicized numbers in the tables represent p‐values < 0.05, indicating statistically significant differences.
Finally, we exploratively calculated the pCR rates among MaBC patients stratified by different TNM stages and molecular subtypes. Finally, the pCR rates of MaBC patients with different TNM stages and molecular subtypes were calculated. As shown in Figure 4, the overall pCR rate was 12.12% (20/165), and the pCR rate of stage I‐II and HR− patients was as high as 66.67% (4/6), the pCR rate of stage I‐II, HR+ and stage III‐IV, HR− patients was 14.89% (14/94), and stage III‐IV, HR+ patients had the lowest pCR rate of 1.72% (1/58). The above results suggest that male patients with stage I‐II, HR− breast cancer are well positioned to benefit from neoadjuvant therapy, resulting in improved survival.
FIGURE 4.
pCR rates of patients.
4. Discussion
In the current study, among the 4171 cases with MaBC, the rate of Neo‐CT was 10.6%, consistent with the 5% reported in a retrospective study from the United States [7] and the 15.9% to 25.3% reported in another multi‐center cross‐sectional study from China [19]. To our knowledge, this is the first and largest population‐based study to assess comprehensively the survival benefit and impact of preoperative chemotherapy in MaBC.
As a widely recognized and important component of multimodal treatment, systemic chemotherapy, which aims to remove the residual tumor cells that remained at the surgical site or subclinical lesions elsewhere in the body, improved disease‐free survival and overall survival of many FBC patients [20]. However, due to the limited data related to systemic chemotherapy, definitive evidence of its improvement in survival cannot be drawn from small‐size studies at present [21], and no evidence‐based recommendations on biomarker testing were recommended for the adjuvant therapy for MaBC patients by ASCO Guideline [22, 23]. For these reasons, only 10.6% cases with MaBC in our study had received Neo‐CT, although at least more than 20% patients (patients with stage II or above tumor) and breast and axillary preservation requirements or locally advanced inoperable patients (NCCN Clinical Practice Guidelines in Oncology‐Breast Cancer (Version 2, 2018) [EB/OL]. [2019‐01‐14]. http://www.nccn.org) met the indication for Neo‐CT according to the treatment standard for FBC patients.
OS and BCSS were both regarded as objective, reliable, precise, and bias‐free measurements for patients with breast carcinoma [9, 24]. In this study, we found that MaBC patients who received postoperative chemotherapy alone had better OS and BCSS than those who received Neo‐CT, whereas no difference has been recorded in survivorship of the candidates who received Neo‐CT, with or without chemotherapy. This finding is consistent with previous reports of no survival difference with neoadjuvant versus adjuvant chemotherapy [25]. Two major factors may account for this phenomenon. Firstly, one of the major contributors to OS and BCSS was the intrinsic biological characteristics in MaBC. In the current study, subjects included had as high as 96.8% ER‐positive disease, which was higher than that 80% in the FBC [26, 27] and corresponded with the existing scientific literature [28]. Therefore, neoadjuvant chemotherapy may have delayed the initiation of hormonal therapy and consequently attenuated its survival benefit in this population. Secondly, given the lack of high‐level evidence specific to the male population, major clinical guidelines such as NCCN, ESMO, and CSCO recommend that the indications for neoadjuvant chemotherapy in male breast cancer are largely consistent with those in female breast cancer. These include: (1) Locally advanced, primarily unresectable breast cancer; (2) Breast cancer requiring tumor downstaging to improve resectability or the rate of breast‐conserving surgery; (3) Aggressive and chemotherapy‐sensitive subtypes such as triple‐negative and HER2‐positive breast cancer; (4) Operable patients with high‐risk clinicopathological features. Patients meeting these criteria inherently carry a poorer prognosis. Although neoadjuvant chemotherapy may improve surgical outcomes and prognosis to a certain extent, it is not expected to elevate the prognosis of these patients to a level equivalent or superior to that of early‐stage or low‐risk breast cancer. These results indirectly suggested the unpredictable clinical benefit couldn't be validated in neo‐CT for the MaBC.
Adjuvant therapy (AT) following Neo‐CT could reduce the high risk of recurrence for the presence of residual disease after Neo‐CT in triple‐negative and HER2‐positive FBC, as shown in two important clinical trials, CREATE‐X (UMIN000000843) and KATHERINE (NCT01772472) [14, 29]. In our study, in the CT + Neo‐CT group, three‐year OS of 86.44% was slightly lower than that of 94.0% [14] in the CREATE‐X trial and higher than that of 66.0% in the ECOG‐ACRIN EA1131 trial [30]. The differences abovementioned could be attributed to the discrepancy in the clinicopathological characteristics in the corresponding trials. Further, it was important that, in our study, the performance of AT following Neo‐CT couldn't improve the OS or BCCS outcomes in patients with MaBC. One important reason that caused the divarication was the extraordinarily high percentage of ER‐positive disease in MaBC, which could benefit more from endocrine therapy rather than adjuvant CT.
Mounting evidence suggested that improved pCR rates imply improved survival and can be used as a surrogate endpoint to predict long‐term clinical benefit in aggressive tumor (non‐luminal) subtypes [13, 31, 32, 33]. Non‐luminal (HER2 positive, triple negative) subtypes and node‐positive breast cancer, which were associated with an increased risk of recurrence [34], were recognized as the preferred population for Neo‐CT recommended by the ASCO Guideline [35]. In the current study, 12.12% of MaBC patients demonstrated a pCR, which was slightly higher than that of luminal A subtype FBC (5.3%–7.5%) and close to that of luminal B/HER2‐negative subtype FBC (13.1%–15%) [31, 36]. Furthermore, we also found that patients who achieved pCR did have obvious advantages in long‐term survival.
Numerous evidence demonstrated that pCR, as an evaluation index response to Neo‐CT, had a diminished association with long‐term outcomes in luminal subtype (ER‐positive) tumors [31, 32, 33] and therefore could not predict the clinical benefit in luminal A like subtype diseases, which was defined as ER‐ and/or PR‐positive, HER2‐negative and grade 1 or 2 tumors in the corresponding research [31]. Accordingly, the role of Neo‐CT in patients with operable MaBC had been extensively underestimated, and no standard Neo‐CT regimen or detailed indications were recommended by the ASCO Guideline [35]. However, in this study, we not only found a close relationship between pCR and long‐term survival, but also found a correlation between hormone‐receptor status, TNM stage and pCR by further analysis. Notably, exploratory analysis showed that the pCR rate of HR−, stage I‐II MaBC patients reached 66.67%, which was higher than that of other breast cancer types reported in this study and relevant published literature [31, 36]. However, due to the limited sample size, this finding remains observational and is insufficient for definitive comparison with the average level of the overall cohort or published data of other breast cancer subtypes. This conclusion is similar to that of neoadjuvant chemotherapy in FBC published in the Lancet [33]. The difference is that our study additionally found an effect of tumor stage on achieving pCR, whereas evidence of an effect of HER2 was still lacking [33]. The lack of significant benefit from neoadjuvant chemotherapy in HER2‐positive male breast cancer is likely multifactorial. First, the hormonal profile in males differs from that in females, which may lead to differences in the efficacy of anti‐HER2 therapy. However, due to the rarity of male breast cancer, prospective clinical trials specifically investigating neoadjuvant chemotherapy in this population are lacking. Second, the SEER database does not record the use of targeted therapies; therefore, we could not determine whether HER2‐positive patients received standard anti‐HER2 treatment in combination with chemotherapy. Finally, only 9.7% of patients in this study were HER2‐positive subtype, and the relatively small sample size may have resulted in insufficient statistical power. In conclusion, this finding will provide a strong basis for the selection of neoadjuvant therapy for MaBC patients in clinical practice.
However, there were still some possible limitations in this study. On the one hand, this study has a major issue with immortal time bias. Patients receiving neoadjuvant chemotherapy must survive until treatment and surgery, whereas those treated with adjuvant chemotherapy have no such requirement. Since the SEER database does not provide specific information on the timing of treatment and follow‐up, formal correction for immortal time bias cannot be performed. Therefore, the observed survival differences should be interpreted cautiously as observational associations rather than definite causal conclusions. On the other hand, the SEER database lacks detailed information on key confounding variables that could affect survival outcomes, including the use, timing, duration, and adherence of endocrine therapy, anti‐HER2 therapy regimens, specific chemotherapy protocols, patient performance status, and comorbidities. Given that over 90% of our study cohort consists of HR+ patients, the absence of data on endocrine therapy represents a particularly significant limitation, as variations in endocrine treatment may substantially influence long‐term survival and could introduce unmeasured confounding into our analysis. As such, our findings should be interpreted cautiously, and prospective studies with comprehensive clinical and treatment data are needed to validate these results. In addition, the accuracy of recording neoadjuvant and adjuvant chemotherapy administration, as well as pCR status, is relatively limited in the SEER database, which may lead to uncertainty and potential bias in the analysis. Selection bias will inevitably occur due to the limitations of retrospective studies, and more powerful evidence is still needed from randomized perspective studies.
5. Conclusions
MaBC patients who achieved pCR have improved survival. And neoadjuvant chemotherapy might be the preferred therapeutic strategy for MaBC patients with stage I‐II, HR− disease. Randomized controlled clinical trials with long follow‐up time are still needed to provide a high level of evidence on the advantages of Neo‐CT among MaBC patients.
Author Contributions
Yu Wang: data curation. Peng Ni: data curation. Jianjun He: writing – review and editing. Zejian Yang: writing – original draft, formal analysis. Ruixin Wang: data curation. Bo Wang: validation. Guanqun Ge: funding acquisition. Yu Ren: supervision. Fan Liu: data curation. Can Zhou: project administration, funding acquisition, writing – review and editing.
Funding
This work was supported by Clinical research project of the First Affiliated Hospital of Xi'an Jiaotong University (Grant XJTU1AF2021LSL‐029) and Innovation Capability Support Program of Shaanxi (Grant 2022PT‐24).
Ethics Statement
The National Cancer Institute deemed this cohort study to be exempt from institutional review board approval because publicly available deidentified data were used.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Figure S1: cam471987‐sup‐0001‐FigureS1.tif.
Figure S2: cam471987‐sup‐0002‐FigureS2.tif.
Contributor Information
Jianjun He, Email: chinahjj@163.com.
Can Zhou, Email: zhoucanz2005@xjtufh.edu.cn.
Data Availability Statement
The data that support the findings of this study are openly available in the SEER database (SEER Research Plus Data, 17 registries, Nov 2021 Sub (2000–2021)).
References
- 1. Vaidya J. S., Massarut S., Vaidya H. J., et al., “Rethinking Neoadjuvant Chemotherapy for Breast Cancer,” BMJ 360 (2018): j5913, 10.1136/bmj.j5913. [DOI] [PubMed] [Google Scholar]
- 2. Early Breast Cancer Trialists' Collaborative Group (EBCTCG) , “Long‐Term Outcomes for Neoadjuvant Versus Adjuvant Chemotherapy in Early Breast Cancer: Meta‐Analysis of Individual Patient Data From Ten Randomised Trials,” Lancet Oncology 19 (2018): 27–39, 10.1016/s1470-2045(17)30777-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Yau C., Osdoit M., van der Noordaa M., et al., “Residual Cancer Burden After Neoadjuvant Chemotherapy and Long‐Term Survival Outcomes in Breast Cancer: A Multicentre Pooled Analysis of 5161 Patients,” Lancet Oncology 23 (2022): 149–160, 10.1016/s1470-2045(21)00589-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Wu Y., Zhang K., Xia X., et al., “The Neuroimmune Axis in Breast Cancer: From Mechanistic Insights to Clinical Applications,” BMC Medicine (2026), 10.1186/s12916-026-04870-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Sun Q., Varela‐Mattatall G., Vivekanantha P., et al., “Magnetic Resonance Imaging of Mammalian Cells Individually Expressing Membrane‐Associated Magnetosome Proteins I, L, B, and E,” Molecular Imaging 23 (2024): 1–14, 10.1177/15353508241289765. [DOI] [Google Scholar]
- 6. Gradishar W. J., Moran M. S., Abraham J., et al., “NCCN Guidelines Insights: Breast Cancer, Version 4.2023,” Journal of the National Comprehensive Cancer Network 21 (2023): 594–608, 10.6004/jnccn.2023.0031. [DOI] [PubMed] [Google Scholar]
- 7. Leone J. P., Hassett M. J., Leone J., et al., “Efficacy of Neoadjuvant Chemotherapy in Male Breast Cancer Compared With Female Breast Cancer,” Cancer 128 (2022): 3796–3803, 10.1002/cncr.34448. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Gao W., An A., Yang L., and Wang Q., “Neoadjuvant Chemotherapy for Male Breast Cancer: A Case Report,” Asian Journal of Surgery 47 (2024): 3730–3731, 10.1016/j.asjsur.2024.04.100. [DOI] [PubMed] [Google Scholar]
- 9. Li K., Wang B., Yang Z., et al., “Nomogram Predicts the Role of Contralateral Prophylactic Mastectomy in Male Patients With Unilateral Breast Cancer Based on SEER Database: A Competing Risk Analysis,” Frontiers in Oncology 11 (2021): 587797, 10.3389/fonc.2021.587797. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Hassett M. J., Somerfield M. R., Baker E. R., et al., “Management of Male Breast Cancer: ASCO Guideline,” Journal of Clinical Oncology 38 (2020): 1849–1863, 10.1200/jco.19.03120. [DOI] [PubMed] [Google Scholar]
- 11. Chen Z., Xu L., Shi W., et al., “Trends of Female and Male Breast Cancer Incidence at the Global, Regional, and National Levels, 1990–2017,” Breast Cancer Research and Treatment 180 (2020): 481–490, 10.1007/s10549-020-05561-1. [DOI] [PubMed] [Google Scholar]
- 12. Ben Dhiab T., Bouzid T., Gamoudi A., et al., “Male Breast Cancer: About 123 Cases Collected at the Institute Salah‐Azaiz of Tunis From 1979 to 1999,” Bulletin du Cancer 92 (2005): 281–285. [PubMed] [Google Scholar]
- 13. Kordecka A., Walkiewicz‐Żarek E., Łapa J., Sadowska E., and Kordecki M., “Selection of Endpoints in Clinical Trials: Trends in European Marketing Authorization Practice in Oncological Indications,” Value in Health 22 (2019): 884–890, 10.1016/j.jval.2019.03.007. [DOI] [PubMed] [Google Scholar]
- 14. Masuda N., Lee S. J., Ohtani S., et al., “Adjuvant Capecitabine for Breast Cancer After Preoperative Chemotherapy,” New England Journal of Medicine 376 (2017): 2147–2159, 10.1056/NEJMoa1612645. [DOI] [PubMed] [Google Scholar]
- 15. Jiang D., Zhang L., Liu W., et al., “Trends in Cancer Mortality in China From 2004 to 2018: A Nationwide Longitudinal Study,” Cancer Commun (Lond) 41 (2021): 1024–1036, 10.1002/cac2.12195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Seldon C., Shrivastava G., Al‐Awady A., et al., “Variation in Management of Extremity Soft‐Tissue Sarcoma in Younger vs Older Adults,” JAMA Network Open 4 (2021): e2120951, 10.1001/jamanetworkopen.2021.20951. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Brantley K. D., Rosenberg S. M., Collins L. C., et al., “Second Primary Breast Cancer in Young Breast Cancer Survivors,” JAMA Oncology 10 (2024): 718–725, 10.1001/jamaoncol.2024.0286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Wang H. and Mao X., “Evaluation of the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer,” Drug Design, Development and Therapy 14 (2020): 2423–2433, 10.2147/dddt.S253961. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Li J., Zhou J., Wang H., et al., “Trends in Disparities and Transitions of Treatment in Patients With Early Breast Cancer in China and the US, 2011 to 2021,” JAMA Network Open 6 (2023): e2321388, 10.1001/jamanetworkopen.2023.21388. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Wefel J. S., Lenzi R., Theriault R., Buzdar A. U., Cruickshank S., and Meyers C. A., “‘Chemobrain’ in Breast Carcinoma? A Prologue,” Cancer 101 (2004): 466–475, 10.1002/cncr.20393. [DOI] [PubMed] [Google Scholar]
- 21. Yildirim E. and Berberoğlu U., “Male Breast Cancer: A 22‐Year Experience,” European Journal of Surgical Oncology 24 (1998): 548–552, 10.1016/s0748-7983(98)93608-3. [DOI] [PubMed] [Google Scholar]
- 22. Andre F., Ismaila N., Allison K. H., et al., “Biomarkers for Adjuvant Endocrine and Chemotherapy in Early‐Stage Breast Cancer: ASCO Guideline Update,” Journal of Clinical Oncology 40 (2022): 1816–1837, 10.1200/jco.22.00069. [DOI] [PubMed] [Google Scholar]
- 23. Zhang L., Zhao L., Lin X., et al., “Comparison of Tumor Non‐Specific and PD‐L1 Specific Imaging by Near‐Infrared Fluorescence/Cerenkov Luminescence Dual‐Modality In‐Situ Imaging,” Molecular Imaging 23 (2024): 15353508241261473, 10.1177/15353508241261473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Ma Y., Yang Z., Gao Y., et al., “Research on the Role of Combined Chemotherapy and Radiotherapy in Patients With N+ Non‐Metastatic Metaplastic Breast Carcinoma: A Competing Risk Analysis Model Based on the SEER Database, 2000 to 2015,” Frontiers in Oncology 10 (2020): 583488, 10.3389/fonc.2020.583488. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Mauri D., Pavlidis N., and Ioannidis J. P., “Neoadjuvant Versus Adjuvant Systemic Treatment in Breast Cancer: A Meta‐Analysis,” Journal of the National Cancer Institute 97 (2005): 188–194, 10.1093/jnci/dji021. [DOI] [PubMed] [Google Scholar]
- 26. Zhou C., Zhang B. N., Fan J. H., et al., “A Nation‐Wide Multicenter 10‐Year (1999–2008) Retrospective Clinical Study of Endocrine Therapy for Chinese Females With Breast Cancer,” PLoS One 9 (2014): e100159, 10.1371/journal.pone.0100159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Hanker A. B., Sudhan D. R., and Arteaga C. L., “Overcoming Endocrine Resistance in Breast Cancer,” Cancer Cell 37 (2020): 496–513, 10.1016/j.ccell.2020.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Anderson W. F., Jatoi I., Tse J., and Rosenberg P. S., “Male Breast Cancer: A Population‐Based Comparison With Female Breast Cancer,” Journal of Clinical Oncology 28 (2010): 232–239, 10.1200/jco.2009.23.8162. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Mamounas E. P., Untch M., Mano M. S., et al., “Adjuvant T‐DM1 Versus Trastuzumab in Patients With Residual Invasive Disease After Neoadjuvant Therapy for HER2‐Positive Breast Cancer: Subgroup Analyses From KATHERINE,” Annals of Oncology 32 (2021): 1005–1014, 10.1016/j.annonc.2021.04.011. [DOI] [PubMed] [Google Scholar]
- 30. Mayer I. A., Zhao F., Arteaga C. L., et al., “Randomized Phase III Postoperative Trial of Platinum‐Based Chemotherapy Versus Capecitabine in Patients With Residual Triple‐Negative Breast Cancer Following Neoadjuvant Chemotherapy: ECOG‐ACRIN EA1131,” Journal of Clinical Oncology 39 (2021): 2539–2551, 10.1200/jco.21.00976. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. von Minckwitz G., Untch M., Blohmer J. U., et al., “Definition and Impact of Pathologic Complete Response on Prognosis After Neoadjuvant Chemotherapy in Various Intrinsic Breast Cancer Subtypes,” Journal of Clinical Oncology 30 (2012): 1796–1804, 10.1200/jco.2011.38.8595. [DOI] [PubMed] [Google Scholar]
- 32. Bonnefoi H., Litière S., Piccart M., et al., “Pathological Complete Response After Neoadjuvant Chemotherapy Is an Independent Predictive Factor Irrespective of Simplified Breast Cancer Intrinsic Subtypes: A Landmark and Two‐Step Approach Analyses From the EORTC 10994/BIG 1–00 Phase III Trial,” Annals of Oncology 25 (2014): 1128–1136, 10.1093/annonc/mdu118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Cortazar P., Zhang L., Untch M., et al., “Pathological Complete Response and Long‐Term Clinical Benefit in Breast Cancer: The CTNeoBC Pooled Analysis,” Lancet 384 (2014): 164–172, 10.1016/s0140-6736(13)62422-8. [DOI] [PubMed] [Google Scholar]
- 34. Kalinsky K., Barlow W. E., Gralow J. R., et al., “21‐Gene Assay to Inform Chemotherapy Benefit in Node‐Positive Breast Cancer,” New England Journal of Medicine 385 (2021): 2336–2347, 10.1056/NEJMoa2108873. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Korde L. A., Somerfield M. R., Carey L. A., et al., “Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline,” Journal of Clinical Oncology 39 (2021): 1485–1505, 10.1200/jco.20.03399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Garufi G., Carbognin L., Sperduti I., et al., “Development of a Nomogram for Predicting Pathological Complete Response in Luminal Breast Cancer Patients Following Neoadjuvant Chemotherapy,” Therapeutic Advances in Medical Oncology 15 (2023): 17588359221138657, 10.1177/17588359221138657. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1: cam471987‐sup‐0001‐FigureS1.tif.
Figure S2: cam471987‐sup‐0002‐FigureS2.tif.
Data Availability Statement
The data that support the findings of this study are openly available in the SEER database (SEER Research Plus Data, 17 registries, Nov 2021 Sub (2000–2021)).