Establishment and clinical application of a prognostic index for inflammatory status in triple-negative breast cancer
Hao Sun1, Jian Liang1, Shuanglong Xue1
1Department of Breast Surgery, Sixth Affiliated Hospital of Harbin Medical University, Harbin, 150023, China.
Objective:
This study aims to establish a new prognostic index using machine learning models to predict the clinical outcomes of triple-negative breast cancer (TNBC) patients receiving neoadjuvant therapy.
Methods:
In this study, we collected data from the electronic medical records system of Harbin Medical University Cancer Hospital to establish a training set of 501 breast cancer patients who received neoadjuvant therapy from January 2017 to December 2021. Additionally, we collected data from Harbin Medical University Affiliated Cancer Hospital, Harbin Medical University Affiliated Second Hospital, and Harbin Medical University Affiliated Sixth Hospital to establish a validation set of 1533 patients during the same period. All patients underwent blood tests, and the following inflammatory and immune indices were calculated for each patient: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammatory index (SII), systemic inflammatory response index (SIRI), and advanced lung cancer inflammation index (ALI). The observed outcomes included Disease-free survival (DFS) and overall survival (OS). Survival analysis was performed using Kaplan‒Meier survival curves, Cox survival analysis, propensity score matching analysis (PSM), and a nomogram to comprehensively investigate the impact of inflammatory status on patient survival.
Results:
The training set comprised 501 patients with a mean age of 48.63 (9.41) years, while the validation set comprised 1533 patients with a mean age of 49.01 (9.51) years. The formula for ANLR established through Lasso regression analysis on the training set is: ANLR index = NLR - 0.04 × ALB (g/L). In both the training and validation sets, ANLR was significantly associated with patient DFS and OS (all P < 0.05). Additionally, ANLR was found to be an independent prognostic factor in this study. PSM analysis further confirmed its significant correlation with patient DFS and OS (76 cases vs. 76 cases, χ2 = 2.179, P = 0.001 and χ2 = 2.063, P = 0.002). The nomogram containing ANLR also demonstrated high prognostic value. The C-index for the nomogram in the training set was 0.742 (0.619-0.886) for DFS and 0.758 (0.607-0.821) for OS, while in the validation set, the C-index was 0.733 (0.655-0.791) for DFS and 0.714 (0.634-0.800) for OS.
Conclusion:
ANLR was associated with the prognosis of TNBC patients receiving neoadjuvant therapy and could identify high-risk postoperative patients.
