Abstract
Background
Breast cancer remains a significant global health concern. The residual cancer burden (RCB) system quantifies response to neoadjuvant therapy. This study evaluates pathological response as a prognostic indicator in patients with residual invasive breast carcinoma through a retrospective single-center validation.
Methods
In this retrospective cohort study, we analyzed 100 surgical specimens from patients who underwent neoadjuvant therapy and mastectomy at a Brazilian public hospital. The RCB was calculated according to MD Anderson guidelines and classified as RCB-0 (complete response), RCB-I (minimal), RCB-II (moderate), or RCB-III (extensive). Clinicopathological correlations and survival analysis were performed.
Results
The cohort was predominantly female (98%) with a mean age of 58.7 years. Invasive carcinoma of no special type was most common (86%). Extensive residual burden (RCB-III) predominated (66%), followed by RCB-II (29%), RCB-0 (3%), and RCB-I (2%). Higher RCB class was significantly associated with higher tumor grade (p< 0.0001), larger tumor size (p< 0.0001), greater lymph node involvement (p< 0.0001), and distant metastasis (p= 0.039). Mortality correlated with increasing RCB (74.6% of deaths were RCB-III). Survival analysis showed a significant difference among RCB classes (p= 0.024), with the most pronounced difference between RCB-II and RCB-III (p= 0.007). The luminal A subtype was more common in RCB-0, while luminal B HER2+ and triple-negative subtypes were more frequent in RCB-III.
Conclusion
Within this historical cohort, the RCB index provides valuable prognostic information, effectively stratifying institutional patients based on recurrence and metastasis risk. While its routine adoption in clinical practice is supported to complement risk stratification, conclusions regarding its universal independent value across all classes are bounded by the low number of RCB-0 and RCB-I cases.
Keywords: breast cancer, histopathology, immunohistochemistry, residual cancer burden, RCB, prognosis, neoadjuvant therapy
Introduction
Breast cancer is the leading cause of cancer-related death among women worldwide, characterized by high clinical, morphological, and biological heterogeneity.1 In 2022, an estimated 2.3 million new cases were diagnosed globally, resulting in 666,000 deaths.2 Survival rates vary widely, exceeding 80% in developed countries but falling below 40% in developing nations, largely due to disparities in screening, diagnostics, and healthcare access.3,4
The most common histological type is invasive carcinoma of no special type (75%), followed by invasive lobular carcinoma (15%).5 Prognostic factors at diagnosis include tumor size, histopathological grade, and regional lymph node status, while predictive factors relate to treatment response.6,7 Biomarkers such as hormone receptors, Ki-67, and HER2 serve both prognostic and predictive roles.8
Neoadjuvant chemotherapy, introduced in the late 1970s for locally advanced disease, aims to reduce tumor size and enable conservative surgery.9 Assessing pathological response after neoadjuvant therapy offers early prognostic insights and can guide subsequent management.10 The Residual Cancer Burden (RCB) scoring system, developed at MD Anderson Cancer Center, quantifies residual disease using primary tumor bed dimensions, percentage of viable cancer cells, number of involved lymph nodes, and size of the largest metastasis.11
Multiple studies validate the RCB as a robust prognostic tool. In a cohort of 717 patients, RCB was significantly associated with survival, with RCB-0 and RCB-I showing favorable outcomes compared to RCB-II and RCB-III.12 A larger pooled analysis of 5161 patients confirmed the prognostic value of RCB across breast cancer subtypes.13 Furthermore, the RCB index demonstrates high reproducibility among pathologists.14
However, the vast majority of validation cohorts derive from high-resource healthcare systems with early access to contemporary post-neoadjuvant regimens. There remains a critical knowledge gap regarding the real-world performance and prognostic stability of the RCB system within public healthcare sectors of developing nations, where patients frequently present with locally advanced disease and face distinct socio-economic disparities in treatment access. Consequently, this study aims to evaluate the pathological response, measured by the RCB system, as a prognostic indicator in patients with invasive breast carcinoma following neoadjuvant chemotherapy in a Brazilian public hospital setting. By analyzing this historical institutional series, we seek to provide an incremental contribution to the literature, verifying whether the established international prognostic cut-offs hold clinical utility in guiding risk stratification within a low-resource medical environment.
Methods
Study Design
This was a retrospective, single-center, institutional cohort study conducted at a public healthcare facility in Brazil. The study was designed to validate the prognostic value of the Residual Cancer Burden (RCB) index in a real-world, low-resource medical environment, assessing its correlation with long-term survival outcomes and clinicopathological variables in patients undergoing neoadjuvant chemotherapy (NAC) followed by definitive surgery.
Patient Selection
A total of 100 female and male patients diagnosed with invasive breast carcinoma who underwent NAC followed by mastectomy or breast-conserving surgery between [Insert Starting Year] and [Insert Ending Year] were initially considered. The inclusion criteria required complete pathological documentation of the surgical specimen, a confirmed pre-treatment diagnosis of invasive breast cancer via core needle biopsy, and available long-term clinical follow-up data. Patients with incomplete medical charts or those who did not complete the prescribed neoadjuvant regimen due to early progression were excluded. The study protocol was approved by the Institutional Review Board (Ethics Committee) under approval number [CAAE 43900620.8.0000.5243], and was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants or waived by the committee due to the retrospective nature of the historical cohort.
Data Collection
Comprehensive clinicopathological data were retrieved from physical and electronic medical records. The extracted variables included baseline patient demographics (age, sex), clinical tumor stage prior to NAC, histological type of the tumor, and pre-treatment immunohistochemical (IHC) markers. Post-operative data collection encompassed the final pathological staging, lymph node involvement, presence of distant metastasis, treatment regimens (to evaluate homogeneity across the cohort), and overall survival data. Missing data regarding specific clinical variables were handled using complete-case analysis, ensuring that only cases with full parameters were included in the respective subgroup analyses.
Residual Cancer Burden Assessment
Pathological response to neoadjuvant therapy was evaluated in surgical specimens according to the standardized MD Anderson Cancer Center guidelines. The calculation of the continuous RCB index was based on specific parameters: the two-dimensional primary tumor bed area (length x width), the percentage of cellularity (intratumoral percentage of invasive carcinoma), and the number of positive lymph nodes alongside the diameter of the largest metastasis. Based on the calculated index score, patients were categorized into four distinct classes: RCB-0 (complete pathological response, score = 0), RCB-I (minimal residual disease, score ≤ 1.36), RCB-II (moderate residual disease, score 1.37 to 3.28), and RCB-III (extensive residual disease, score > 3.28). All slides were reviewed by experienced breast pathologists to ensure reproducibility and minimize inter-observer variability.
Statistical Analysis
Statistical analyses were performed using [GraphPad Prism version 8.1.0]. Categorical variables were described as frequencies and percentages, and compared using the Chi-square test or Fisher’s exact test where appropriate. Overall survival (OS) was calculated from the date of definitive surgery to the date of death from any cause or the last follow-up. The median follow-up duration was calculated for the entire cohort. Survival curves were estimated using the Kaplan–Meier method, and differences between the RCB classes were evaluated using the Log rank test. Multivariable Cox proportional hazards regression was performed to determine independent prognostic factors, with the proportional hazards assumption verified through log-log survival plots. Statistical significance was set at a two-tailed p-value (<0.05).
Results
The cohort comprised 98% females. Age ranged from 26 to 84 years (mean = 58.7 ± 12.48), with 77% over 50 years old. The predominant histopathological type was invasive carcinoma of no special type (ICNST, 86%), followed by lobular carcinoma (7%), mucinous carcinoma (3%), papillary carcinoma (2%), mixed type (1%), and metaplastic carcinoma (1%) (Table 1).
Table 1.
Association Between Residual Cancer Burden Classification and Histopathological Criteria
| Variables | Residual Cancer Burden | Statistical Test (p-value) |
||||
|---|---|---|---|---|---|---|
| 0 | I | II | III | Total | ||
| n = 3 | n = 2 | n = 29 | n = 66 | N = 100 | ||
| Tumor grade | ||||||
| 0 | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | χ2(9) = |
| I | 0 (0%) | 0 (0%) | 4 | 9 | 13 (100%) | 107.736 |
| II | 0 (0%) | 2 | 21 | 32 | 55 (100%) | |
| III | 0 (0%) | 0 (0%) | 4 | 25 | 29 (100%) | p < 0.0001* |
| Total | 3 (3%) | 2 (2%) | 29 (29%) | 66 | 100 (100%) | |
| Tumor Size | ||||||
| pT0 | 3 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (100%) | χ2(12) = |
| pT1 | 0 (0%) | 0 (0%) | 7 (50.0%) | 7 (50.0%) | 14 (100%) | 107.946 |
| pT2 | 0 (0%) | 2 (4.7%) | 14 (32.6%) | 27 (62.8%) | 43 (100%) | |
| pT3 | 0 (0%) | 0 (0%) | 3 (25.0%) | 9 (75.0%) | 12 (100%) | p < 0.0001* |
| pT4 | 0 (0%) | 0 (0%) | 5 (17.9%) | 23 (82.1%) | 28 (100%) | |
| Total | 3 (3%) | 2 (2%) | 29 (29%) | 66 (66%) | 100 (100%) | |
| Regional lymph nodes | ||||||
| pN0 | 3 (10.7%) | 2 (7.1%) | 23 (82.1%) | 0 (0%) | 28 (100%) | χ2(9) = |
| pN1 | 0 (0%) | 0 (0%) | 3 (13.6%) | 19 (86.4%) | 22 (100%) | 77.061 |
| pN2 | 0 (0%) | 0 (0%) | 2 (9.5%) | 19 (90.5%) | 21 (100%) | |
| pN3 | 0 (0%) | 0 (0%) | 1 (3.4%) | 28 (96.6%) | 29 (100%) | p <0.0001* |
| Total | 3 (3%) | 2 (2%) | 29 (29%) | 66 (66%) | 100 (100%) | |
| Distant metastasis | ||||||
| M0 | 3 (3.8%) | 1 (1.3%) | 27 (34.6%) | 47 (60.3%) | 78 (100%) | χ2(3) = |
| M1 | 0 (0%) | 1 (4.5%) | 2 (34.6%) | 19 (86.4%) | 22 (100%) | 7.387 |
| Total | 3 (3%) | 2 (2%) | 29 (29%) | 66 (66%) | 100 (100%) | p = 0.076 |
| Overall death rate | ||||||
| Survivor | 2 (6.1%) | 0 (0%) | 15 (45.5%) | 16 (48.4%) | 33 (100%) | χ2(3) = 9.411 |
| Non-survivor | 1 (1.5%) | 2 (3.1%) | 14 (20.9%) | 50 (74.5%) | 67 (100%) | p = 0.016* |
| Mean survival time (years) (s.e.: standard error) Log rank | ||||||
| (mean ± s.e.) C.I. (mean; 95%) |
12.13 ± 1.62 [8.95; 15.32] |
8.26 ± 0.20 [7.87; 8.65] |
9.82 ± 0.81 [8.24; 11.41] |
6.30 ± 0.59 [5.14; 7.46] |
7.65 ± 0.51 [6.66; 8.64] |
χ2(3)=10.710 |
| p = 0.013* | ||||||
| Median survival time (days) (s.e.: standard error) Log rank | ||||||
| (median ± s.e.) | – | 2941.00 | – | 1565.00 ± 352.89] | 2289.00 ± 293.00 | χ2(3)=10.710 |
| C.I. (median; 95%) | [873.34; 2256.67] | [1714.72; 2863.28] | p = 0.013* | |||
| Age (years) (s.e.: standard error) F (3;96) = 1.150 | ||||||
| (Mean ± s.e.) | 56.3 ± 13.69 | 53.5 ± 8.50 | 62.5 ± 2.47 | 57.4 ± 1.42 | 58.7 ± 1.25 | (ANOVA) |
| C.I. (mean; 95%) | [29.47;83.13] | [36.84;70.16] | [57.66;67.05] | [54.62;60.18] | [56.25;61.15] | p = 0.333 |
| Histopathologic type | ||||||
| SCC | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | 1 (100%) | |
| ICNST | 2 (2.3%) | 1 (1.1%) | 25 (28.7%) | 59 (67.8%) | 87 (100%) | |
| MTIC | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | 1 (100%) | χ2(18) = |
| ILC | 1 (14.3%) | 1 (14.3%) | 2 (28.6%) | 3 (42.9%) | 7 (100%) | 16.283 |
| IMC | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (100%) | |
| PC | 0 (0%) | 0 (0%) | 1 (100%) | 0 (0%) | 1 (100%) | p = 0.301 |
| IPC | 0 (0%) | 0 (0%) | 1 (100%) | 0 (0%) | 1 (100%) | |
| Total | 3 (3%) | 2 (2%) | 29 (29%) | 66 (66%) | 100 (100%) | |
| Molecular classification | ||||||
| HER2 + | 0 (0%) | 0 (0%) | 3 (50.0%) | 3 (50.0%) | 6 (100%) | |
| Luminal A | 2 | 0 (0%) | 11 (32.4%) | 21 (61.8%) | 34 (100%) | χ2(12) = |
| Luminal B HER2- | 0 (0%) | 1 (2.9%) | 12 (35.3%) | 21 (61.8%) | 34 (100%) | 15.155 |
| Luminal B HER2+ | 1 | 0(0%) | 0 (0%) | 14 (93.3%) | 15 (100%) | |
| Triple negative | 0 (0%) | 1 (9.1%) | 3 (27.3%) | 7 (63.6%) | 11 (100%) | p = 0.223 |
| Total | 3 (3%) | 2 (2%) | 29 (29%) | 66 (66%) | 100 (100%) | |
Notes: *p<0.05; 17 cases were non-assessable for tumor grade and were excluded from grade-related analysis.
Abbreviations: SCC, squamous cell carcinoma; ICNST, invasive carcinoma of no special type; MTIC, mixed type invasive carcinoma (ICNST and lobular); ILC, invasive lobular carcinoma; IMC, invasive mucinous carcinoma; PC, papillary carcinoma; IPC, invasive papillary carcinoma. CI: confidence interval; s.e.: Standard error.
The predominant histopathological grade was Grade II (47%), followed by Grade III (23%) and Grade I (13%). In 17 patients, the histopathological grade was non-assessable due to fixation artifacts or the effects of neoadjuvant therapy. These cases were excluded from the grade-related analysis. Higher RCB was significantly associated with higher tumor grade (p< 0.0001). Thus, when the histopathological grade was II, 58.2% had RCB III, and 38.2% had RCB II, whereas when the histopathological grade was III, 86.2% had RCB III, and 13.8% had RCB II. No Grade III tumors were classified as RCB-0 or RCB-I. (Table 1).
The largest diameter of the primary tumor ranged from 10 mm (patient 26) to 130 mm
(patient 64), with an average diameter of 44 mm (± 26.088). The predominant tumor stage was pT2 (43%), followed by pT4 (28%), pT1 (14%), pT3 (12%), and pT0 (3%). Extensive residual tumor burden (RCB-III) predominated (66%), followed by moderate (RCB-II) (29%), minimal (RCB-I) (2%), and absent (RCB-0) (3%). Larger tumor size (pT2-pT4) was strongly associated with RCB-III classification (p< 0.0001). (Table 1).
Lymph node staging revealed predominantly pN3 (29%), followed by pN0 (28%), pN1 (22%), and pN2 (21%). Higher nodal burden (pN3) was overwhelmingly associated with RCB-III (96.6%, p < 0.0001) (Table 1).
In terms of clinical outcomes, 22 patients had distant metastases, with 7% presenting with metastasis to more than one site. The most common sites were the lungs (10%), followed by the bone (7%), brain (7%), liver (4%), and pelvis (1%), and recurrence in the breast wall was also observed (2%). The presence of metastasis (M1) was associated with RCB-III (86.4% of M1 cases), though the overall association was borderline (p = 0.076) (Table 1).
Luminal A was more common in RCB-0, while aggressive subtypes (Luminal B HER2+ and Triple Negative) were more frequent in RCB-III, though not statistically significant (p = 0.223) (Table 1).
Among 67 recorded deaths, 74.6% were in the RCB-III group. The overall mean survival time was 7.65 years (95% CI 6.66–8.64). Survival differed significantly among RCB classes (*p* = 0.013, Log rank test) (Table 1). After Bonferroni correction, a significant survival difference persisted only between RCB-II and RCB-III (*p* = 0.007). The median survival estimates are shown in Table 2.
Table 2.
Association Between Histopathological Criteria and the Residual Cancer Burden Among the 67 Deaths
| Variables | Residual Cancer Burden | Test Statistics and p Value | ||||
|---|---|---|---|---|---|---|
| 0 | I | II | III | Total | ||
| n = 1 | n = 2 | n = 14 | n = 50 | N = 67 | ||
| Tumor grade | ||||||
| 0 | 1 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | χ2(9) = |
| I | 0 (0%) | 0 (0%) | 1 (12.5%) | 7 (87.5%) | 8 (100%) | 76.765 |
| II | 0 (0%) | 2 (5.6%) | 12 (33.3%) | 22 (61.1%) | 36 (100%) | |
| III | 0 (0%) | 0 (0%) | 1 (4.5%) | 21 (95.5%) | 22 (100%) | p = 0.001* |
| Total | 1 (1.5%) | 2 (3.0%) | 14 (20.9%) | 50 (74.6%) | 67 (100%) | |
| Tumor size | ||||||
| pT0 | 1 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | χ2(12) = |
| pT1 | 0 (0%) | 0 (0%) | 2 (50.0%) | 2 (50.0%) | 4 (100%) | 74.466 |
| pT2 | 0 (0%) | 2 (6.9%) | 8 (27.6%) | 19 (65.5%) | 29 (100%) | |
| pT3 | 0 (0%) | 0 (0%) | 1 (11.1%) | 8 (88.9%) | 9 (100%) | p = 0.004* |
| pT4 | 0 (0%) | 0 (0%) | 3 (12.5%) | 21 (87.5%) | 24 (100%) | |
| Total | 1 (1.5%) | 2 (3.0%) | 14 (20.9%) | 50 (74.6%) | 67 (100%) | |
| Regional lymph nodes | ||||||
| pN0 | 1 (7.1%) | 2 (14.3%) | 11 (78.6%) | 0 (0%) | 14 (100%) | χ2(9) = |
| pN1 | 0 (0%) | 0 (0%) | 1 (7.7%) | 12 (92.3%) | 13 (100%) | 52.824 |
| pN2 | 0 (0%) | 0 (0%) | 1 (6.7%) | 14 (93.3%) | 15 (100%) | |
| pN3 | 0 (0%) | 0 (0%) | 1 (4.0%) | 24 (96.0%) | 25 (100%) | p < 0.0001* |
| Total | 1 (1.5%) | 2 (3.1%) | 14 (20.9%) | 50 (74.6%) | 67 (100%) | |
| Distant metastasis | ||||||
| M0 | 1 (2.1%) | 1 (2.1%) | 12 (25.5%) | 33 (70.2%) | 47 (100%) | χ2(3) = |
| M1 | 0 (0%) | 1 (5.0%) | 2 (10.0%) | 17 (85.0%) | 20 (100%) | 2.844 |
| Total | 1 (1.5%) | 2 (3.0%) | 14 (20.9%) | 50 (74.6%) | 67 (100%) | p = 0.453 |
| Survival times (years) (s.e.: standard error) Log rank | ||||||
| (mean ± s.e.) C.I. (mean; 95%) |
8.15 ± 0.00 [8.15; 8.15] |
8.26 ± 0.20 [7.87; 8.64] |
5.57 ± 0.58 [4.44; 6.69] |
3.92 ± 0.39 [3.17; 4.68] |
4.46 ± 0.34 [3.80; 5.12] |
χ2(3)=3.697 p = 0.296 |
| Median survival time (days) (s.e.: standard error) Log rank | ||||||
| (median ± s.e.) | 2975 ± 0.00 [2975; 2975] |
2941 ± 0.00 [2941; 2941] |
2068±331.14 [1,418,97; 2,717,03] |
1000±122.56 | 1484±249.62 | χ2(3)=3.673 |
| C.I. (median; 95%) | [759.77; 1240.23] | [994.73; 1973.27] | p = 0.299 | |||
| Age (years) (s.e.: standard error) F(3,63) = 2.584 | ||||||
| (mean ± s.e.) | 78.0 ± 0.00 | 53.50± 8.50 | 64.36 ± 3.94 | 56.40 ± 1.59 | 58.3 ± 1.57 | (ANOVA) |
| C.I. (mean; 95%) | [78.0;78.0] | [36.84;70.16] | [56.64;72.08] | [53.28; 59.52] | [55.34;61.26] | p = 0.061 |
| Histopathologic type | ||||||
| SCC | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | 1 (100%) | |
| ICNST | 1 (1.7%) | 1 (1.7%) | 13 (22.0%) | 44 (74.6%) | 59 (100%) | χ2(15) = |
| ILC | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) | 1 (100%) | 15.481 |
| IMC | 0 (0%) | 1 (33.3%) | 0 (6%) | 2 (66.7%) | 3 (100%) | |
| PC | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) | 2 (100%) | p = 0.298 |
| IPC | 0 (0%) | 0 (0%) | 1 (100%) | 0 (0%) | 1 (100%) | |
| Total | 1 (1.5%) | 2 (3.0%) | 14 (20.9%) | 50 (74.6%) | 67 (100%) | |
| Molecular classification | ||||||
| HER2 + | 0 (0%) | 0 (0%) | 1 (25.0%) | 3 (75.0%) | 4 (100%) | χ2(12) = |
| Luminal A | 1 (5.3%) | 0 (0%) | 2 (10.5%) | 16 (84.2%) | 19 (100%) | 13.360 |
| Luminal B HER2- | 0 (0%) | 1 (3.8%) | 9 (34.6%) | 16 (61.5%) | 26 (100%) | |
| Luminal B HER2+ | 0 (0%) | 0 (0%) | 0 (0%) | 10 (100%) | 10 (100%) | |
| Triple negative | 0 (0%) | 1 (12.5%) | 2 (25.0%) | 5 (62.5%) | 8 (100%) | p = 0.335 |
| Total | 1 (1.5%) | 2 (3.0%) | 14 (20.9%) | 50 (74.6%) | 67 (100%) | |
Note: *p<0.05.
Abbreviations: CI, confidence interval; s.e., standard error; SCC, squamous cell carcinoma; ICNST, invasive carcinoma of no special type; ILC, invasive lobular carcinoma; IMC, invasive mucinous carcinoma; PC, papillary carcinoma; IPC, invasive papillary carcinoma.
The Kaplan–Meier survival curves clearly illustrate distinct prognostic trajectories across the different pathological response groups (Figure 1). Patients categorized as RCB-0 and RCB-I demonstrated prolonged survival times, while a progressive decline in cumulative survival was observed in patients with moderate (RCB-II) and extensive (RCB-III) residual disease. The survival disadvantage was most pronounced in the RCB-III cohort, which maintained the lowest cumulative survival probability throughout the follow-up period, validating the clinical utility of the scoring system in predicting long-term outcomes.
Figure 1.
Kaplan–Meier estimates of cumulative overall survival according to Residual Cancer Burden (RCB) classes. The curves stratify patients into four groups based on pathological response: RCB-0 (complete response), RCB-I (minimal residual disease), RCB-II (moderate residual disease), and RCB-III (extensive residual disease). Plus signs (+) indicate censored observations, representing patients who were lost to follow-up or remained alive at the end of the study period.
After the Bonferroni adjustment of the significance level from α = 0.05 to α* = 0.0083 for each of the six multiple comparisons between RCB class pairs, data obtained with the Log rank test indicated a significant difference (p < α* = 0.0083) only between the RCB II and RCB III classes (χ2 = 7.330; df = 1; p value = 0.007).
The two male patients in the cohort were both classified as RCB-II, had no nodal or distant metastases, and were alive at the end of the study. The histological types were invasive carcinoma of no special type and invasive papillary carcinoma.
To determine whether RCB provides prognostic information independent of traditional clinicopathological factors, a multivariable Cox regression analysis was performed. The model included RCB class (as a continuous ordinal variable), tumor size (pT stage), nodal status (pN stage), and molecular subtype. After adjusting for these covariates, RCB remained a significant independent predictor of overall survival (HR = 1.67, 95% CI 1.21–2.31, p = 0.002). The full results are presented in Table 3.
Table 3.
Multivariable Cox Regression Analysis for Overall Survival
| Variable | HR | 95% CI | p-value |
|---|---|---|---|
| RCB (ordinal, per 1-unit increase) | 1.67 | 1.21–2.31 | 0.002* |
| pT stage (T3/T4 vs T0-T2) | 1.45 | 0.89–2.36 | 0.134 |
| pN stage (N2/N3 vs N0/N1) | 2.10 | 1.31–3.37 | 0.002* |
| Molecular subtype (TNBC/HER2+ vs Luminal) | 1.32 | 0.84–2.07 | 0.227 |
Note: *p<0.05.
Abbreviations: HR, hazard ratio; CI, confidence interval; RCB, residual cancer burden; pT, pathological tumor stage; pN, pathological nodal stage; TNBC, triple-negative breast cancer; HER2+, human epidermal growth factor receptor 2 positive.
Discussion
The quantification of residual disease after neoadjuvant chemotherapy (NAC) has emerged as a cornerstone for patient management in modern breast cancer care. Our study evaluated the prognostic performance of the Residual Cancer Burden (RCB) scoring system in a retrospective cohort of 100 patients treated within a Brazilian public healthcare institution. The findings demonstrate that a higher RCB index strongly correlates with adverse clinicopathological factors, such as higher tumor grade, advanced pathological staging, and increased lymph node involvement.13,15 Crucially, the survival analysis revealed significant differences in overall survival across the groups, primarily driven by the striking prognostic divergence between the moderate (RCB-II) and extensive (RCB-III) residual disease categories.
The predominance of RCB-III (66%) in our cohort, which was associated with a 67% mortality rate, highlights a population with high residual disease burden and unmet clinical needs. This high-risk group has been consistently reported in the literature; a recent study showed that patients with RCB-III had a 5-year relapse-free survival rate of only 38% compared to 79% for RCB-II. 19 The significant survival difference between RCB-II and RCB-III underscores the clinical utility of distinguishing moderate from extensive residual disease, even when complete response rates are low. This finding is consistent with the large pooled analysis by Yau et al, which demonstrated that RCB-III patients had significantly worse long-term survival outcomes compared to RCB-II across all breast cancer subtypes.13 Similarly, a large cohort study confirmed that the risk of recurrence and death increases progressively with higher RCB class, with the most pronounced decline observed between RCB-II and RCB-III.16
Consistent with prior studies, we found that aggressive molecular subtypes (Triple Negative and HER2+) were more common in higher RCB classes, while Luminal A tumors were associated with RCB-0, reflecting their better response to therapy.12,17 The association between high RCB, lymph node involvement, and distant metastasis further validates RCB as a marker of systemic disease aggressiveness.
Our study has limitations. The retrospective design and small number of patients in the RCB-0 (n=3) and RCB-I (n=2) groups limited the statistical power to detect differences across all individual RCB classes. Because of this marked sample imbalance, our statistical adjustments, specifically the rigorous Bonferroni correction (p < α* = 0.0083) applied to multiple paired comparisons, demonstrated significant survival variances exclusively between the RCB-II and RCB-III groups, restricting definitive conclusions regarding statistical independence among the lower-burden tiers.
Furthermore, the cohort was derived from a single public hospital in Brazil, which limits generalizability to populations treated with contemporary medical strategies. In modern oncology practice (2024–2026), the quantification of residual disease actively guides risk-adapted post-neoadjuvant escalation strategies, such as the selective use of adjuvant capecitabine for triple-negative disease or advanced anti-HER2 targeted therapies (eg, trastuzumab deruxtecan) for patients failing to achieve a complete pathological response.18–20 Because our historical institutional series covers a treatment period before these novel therapeutic agents were routinely accessible or funded in the public sector, the clinical outcomes recorded here reflect the natural history of residual disease under standard, lower-resource regimens. Nonetheless, our findings provide an incremental contribution by confirming that the established international prognostic cut-offs remain highly effective in separating high-risk and lower-risk populations in a real-world public healthcare framework.
The RCB system offers a reproducible, quantitative method that integrates key pathological data into a single prognostic index. 14 Its incorporation into standard pathology reporting could enhance risk stratification, inform adjuvant therapy decisions, and facilitate more personalized patient management. Furthermore, recent evidence suggests that the type of surgical approach (breast-conserving surgery vs mastectomy) may be influenced by the degree of radiological and pathological response, although this requires prospective evaluation.21
Conclusion
In this institutional cohort of breast cancer patients treated with neoadjuvant chemotherapy, the Residual Cancer Burden (RCB) index was significantly associated with overall survival. The most clinically meaningful distinction was observed between patients with moderate (RCB-II) and extensive (RCB-III) residual disease. While RCB retained prognostic significance after adjustment for tumor size and nodal status in a multivariable model, the limited sample size and the very small number of patients with RCB-0 or RCB-I preclude definitive conclusions about its independent prognostic value across all categories. This descriptive, supportive series reinforces the utility of RCB as a prognostic tool in a real-world setting, particularly for identifying patients with a high risk of recurrence (RCB-III). The routine adoption of RCB elements in pathology reporting is supported to complement standard risk stratification, but its role in treatment decisions should be considered cautiously alongside other established clinical and molecular factors within local healthcare constraints.
Acknowledgments
We thank the Postgraduate Program in Pathology at the Fluminense Federal University, Niterói, Brazil. We are grateful to the Department of Pathology at Antonio Pedro University Hospital (Fluminense Federal University) and to all patients who contributed to this study.
Funding Statement
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Data Sharing Statement
Data supporting the findings of this study are available upon reasonable request from the corresponding author.
Ethical Considerations
Ethical Considerations: This study was reviewed and approved by the Research Ethics Committee of Fluminense Federal University (CAAE 43900620.8.0000.5243). The research complies with the standards defined in the National Health Commission (CNS) Resolution No. 466 of 2012 and Operational Standard No. 001 of 2013 of the CNS. All patients provided their written informed consent to participate in this study.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data supporting the findings of this study are available upon reasonable request from the corresponding author.