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We developed a model for improving the prediction of survival outcome using postoperative Ki-67 value in combination with residual cancer burden (RCB) in patients with breast cancer (BC) who underwent neoadjuvant chemotherapy (NAC). We analyzed the data from BC patients who underwent NAC between 2010 and 2019 at Samsung Medical Center and developed our residual proliferative cancer burden (RPCB) model using semi-quantitative Ki-67 value and RCB class. The Cox proportional hazard model was used to develop our RPCB model according to disease free survival (DFS) and overall survival (OS). In total, 1,959 patients were included in this analysis. Of 1,959 patients, 905 patients were excluded due to RCB class 0, and 32 were due to a lack of Ki-67 data. Finally, an RPCB model was developed using data from 1,022 patients. The RPCB score was calculated for DFS and OS outcomes, respectively (RPCB-DFS and RPCB-OS). For further survival analysis, we divided the population into 3 classes according to the RPCB score. In the prediction of DFS, C-indices were 0.751 vs 0.670 and time-dependent areas under the receiver operating characteristic curves (AUCs) at 3-year were 0.740 vs 0.669 for RPCB-DFS and RCB models, respectively. In the prediction of OS, C-indices were 0.819 vs 0.720 and time-dependent AUCs at 3-year were 0.875 vs 0.747 for RPCB-OS and RCB models, respectively. The RPCB model developed using RCB class and semi-quantitative Ki-67 had superior predictive value for DFS and OS compared with that of RCB class. This prediction model could provide the basis to decide risk-stratified treatment plan for BC patients who had residual disease after NAC. [ABSTRACT FROM AUTHOR]

We developed a model for improving the prediction of survival outcome using postoperative Ki-67 value in combination with residual cancer burden (RCB) in patients with breast cancer (BC) who underwent neoadjuvant chemotherapy (NAC). We analyzed the data from BC patients who underwent NAC between 2010 and 2019 at Samsung Medical Center and developed our residual proliferative cancer burden (RPCB) model using semi-quantitative Ki-67 value and RCB class. The Cox proportional hazard model was used to develop our RPCB model according to disease free survival (DFS) and overall survival (OS). In total, 1,959 patients were included in this analysis. Of 1,959 patients, 905 patients were excluded due to RCB class 0, and 32 were due to a lack of Ki-67 data. Finally, an RPCB model was developed using data from 1,022 patients. The RPCB score was calculated for DFS and OS outcomes, respectively (RPCB-DFS and RPCB-OS). For further survival analysis, we divided the population into 3 classes according to the RPCB score. In the prediction of DFS, C-indices were 0.751 vs 0.670 and time-dependent areas under the receiver operating characteristic curves (AUCs) at 3-year were 0.740 vs 0.669 for RPCB-DFS and RCB models, respectively. In the prediction of OS, C-indices were 0.819 vs 0.720 and time-dependent AUCs at 3-year were 0.875 vs 0.747 for RPCB-OS and RCB models, respectively. The RPCB model developed using RCB class and semi-quantitative Ki-67 had superior predictive value for DFS and OS compared with that of RCB class. This prediction model could provide the basis to decide risk-stratified treatment plan for BC patients who had residual disease after NAC.

Background: The integration of residual cancer burden (RCB) and post‐treatment Ki67 as residual proliferative cancer burden (RPCB) has been proposed as a stronger predictor of long‐term outcome in unselected patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NACT), as compared with RCB. However, no specific analysis in hormone‐receptor‐positive (HR+) human epidermal growth receptor 2‐negative (HER2−) BC is available so far. Materials and Methods: A cohort of 130 patients with HR+/HER2− BC who underwent NACT between 2000 and 2014 was included. Archival surgical specimens were evaluated for RCB. RPCB was calculated by combining RCB and Ki67 as previously described. Patients were categorized in four RCB and RPCB categories (pathological complete response and tertiles). Disease‐free survival (DFS) and overall survival (OS) estimates were determined by Kaplan‐Meier analysis and compared using the log‐rank test. Overall change of χ2 and c‐indexes were used to compare the performance of the prognostic models. Results: RPCB was calculated for 85 patients. After a median follow up of 8.5 years, RCB was associated with OS (p =.048) but not with DFS (p =.152); RPCB was instead significantly associated with both DFS and OS (p =.034 and p <.001, respectively). In terms of OS, RPCB provided a significant amount of prognostic information beyond RCB (∆χ2 5.73, p <.001). In addition, c‐index for OS prediction was significantly higher for RPCB as compared with RCB (0.79 vs. 0.61, p =.03). Conclusion: This is the first study evaluating RPCB in patients with HR+/HER2− BC treated with NACT. In this independent cohort, RPCB was a strong predictor of DFS and OS. The better performance of RPCB versus RCB was in part due to the ability of RPCB to discriminate a subgroup of patients with a particularly worse prognosis after NACT, who may be candidates for clinical trials evaluating novel adjuvant strategies. Implications for Practice: The present work validated residual proliferative cancer burden (RPCB) as a strong predictor of long‐term outcome in patients with hormone receptor‐positive human epidermal growth receptor 2‐negative (HR+/HER2−) breast cancer (BC) treated with neoadjuvant chemotherapy. In addition, results from the present study suggest RPCB as a promising tool to identify patients with HR+/HER2− BC who might potentially benefit from the inclusion in clinical trials evaluating novel or escalated postneoadjuvant treatment strategies because it allowed to discriminate a subgroup of patients with particularly poor prognosis despite having received subsequent endocrine therapy in the adjuvant setting. This article reports on the prognostic value of the Residual Proliferative Cancer Burden index in a cohort of patients with HR‐positive HER2‐negative breast cancer undergoing neoadjuvant chemotherapy. [ABSTRACT FROM AUTHOR]