Back

A Real-World Retrospective Study of Sintilimab in Combination with Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer

Gao, Z.; Liang, H.; Bai, X.; Dong, K.; Li, J.; Qiao, W.; Shan, B.; Chen, X.; Tang, J.

2026-04-07 oncology
10.64898/2026.04.02.26349910 medRxiv
Show abstract

Purpose This study aimed to evaluate the efficacy and safety of neoadjuvant chemotherapy (NAC) combined with the programmed death protein 1 (PD-1) inhibitor sintilimab versus NAC alone in patients with triple-negative breast cancer (TNBC). Materials and Methods In this retrospective cohort study, we collected clinical data from 61 patients with triple-negative breast cancer (TNBC) who received neoadjuvant therapy at The First Hospital of Lanzhou University between July 2024 and July 2025. These patients were divided into two groups: the neoadjuvant chemotherapy (NAC) plus sintilimab group (n=27) and the NAC-alone group (n=34). The primary endpoint was the pathological complete response (pCR) rate. Secondary endpoints included objective response rate (ORR), safety, and changes in tumor markers. Results The combination therapy group showed significantly higher ORR (85.2% vs. 58.8%) and pCR rates (59.3% vs. 32.4%) compared to the NAC alone group (both P<0.05). Post-treatment Ki-67 levels were also significantly lower in the combination group (P<0.05). The overall incidence of adverse events was comparable between groups (P>0.05), although leukopenia was more frequent with sintilimab (P<0.05). Conclusion In the neoadjuvant setting for TNBC, the addition of sintilimab to NAC significantly improves ORR and pCR rates, effectively reduces the tumor proliferation index Ki-67, and does not significantly increase the overall burden of adverse events. The combination regimen shows a manageable safety profile and demonstrates positive clinical value. Keywords Triple Negative Breast Cancer, Immunotherapy, Sintilimab, Combination neoadjuvant chemotherapy, Efficacy, Real-World data.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.