Development, verification, and comparison of a risk stratification model integrating residual cancer burden to predict individual prognosis in early-stage breast cancer treated with neoadjuvant therapy

Abstract

Background A favorable model for predicting disease-free survival (DFS) and stratifying prognostic risk in breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) is lacking. The aim of the current study was to formulate an excellent model specially for predicting prognosis in these patients. Patients and methods Between January 2012 and December 2015, 749 early-stage BC patients who received NAC in Xijing hospital were included. Patients were randomly assigned to a training cohort ( n = 563) and an independent cohort ( n = 186). A prognostic model was created and subsequently validated. Predictive performance and discrimination were further measured and compared with other models. Results Clinical American Joint Committee on Cancer stage, grade, estrogen receptor expression, human epidermal growth factor receptor 2 (HER2) status and treatment, Ki-67 expression, lymphovascular invasion, and residual cancer burden were identified as independent prognostic variables for BC treated with NAC. The C-index of the model consistently outperformed other available models as well as single independent factors with 0.78, 0.80, 0.75, 0.82, and 0.77 in the training cohort, independent cohort, luminal BC, HER2-positive BC, and triple-negative BC, respectively. With the optimal cut-off values (280 and 360) selected by X-tile, patients were categorized as low-risk (total points ≤280), moderate-risk (280 < total points ≤ 360), and high-risk (total points >360) groups presenting significantly different 5-year DFS of 89.9%, 56.9%, and 27.7%, respectively. Conclusions In patients with BC, the first model including residual cancer burden index was demonstrated to predict the survival of individuals with favorable performance and discrimination. Furthermore, the risk stratification generated by it could determine the risk level of recurrence in whole early-stage BC cohort and subtype-specific cohorts, help tailor personalized intensive treatment, and select comparable study cohort in clinical trials.

Pathological response of breast cancer of patients treated by neoadjuvant chemotherapy and correlation with survival. A perspective of real-world pathology

“…Classical parameters that showed independent prognostic values were nuclear grading, histologic grading, mitotic index, lymphovascular invasion, pathologic AJCC/ypTNM stage, ypN, and molecular subtypes. These results are consistent with other studies [17][18][19].…”

Establishment and verification of a nomogram to predict tumor-specific mortality risk in triple-negative breast cancer: a competing risk model based on the SEER cohort study

“…However, most of the studies have applied conventional survival analysis methods, without considering the effects of competing events in the occurrence of events of interest, that is, deaths from nonbreast cancer reasons were not considered. Tumor-specific mortality in patients is likely to be overstated (24)(25)(26)(27). In order to identify and predict CSM risk for TNBC more accurately, we used a competing risk model to investigate 28,430 TNBC cases in the SEER database.…”

Epithelial-Mesenchymal Transition in Tumor Cells and Activated Intra-Tumor Immunity Respectively Are Correlated With Prognosis and Chemo-Sensitivity in Breast Cancer Patients With a Non-pathological Response to Neoadjuvant Chemotherapy

“…Furthermore, a number of biomarkers and their combination with standard clinico-pathological factors, such as nomograms, have been explored to improve the outcome prediction of breast cancer patients with non-pCR to NAC. These biomarkers include the tumor cell proliferation marker Ki67 LI, the cancer stem cell marker ALDH, the EMT markers ZEB1 and vimentin, the intra-tumor immune microenvironment marker TILs, the immune check point inhibitor PD-L1, and the invasive potential marker lympho-vascular invasion [5][6][7][8][9][10][11][13][14][15][16][17][18][19][20][21]. Most of these biomarkers were investigated in pre-NAC and/or post-NAC samples.…”