Liquid biopsy with peripheral blood mononuclear cells: Deciphering systemic immunity to predict PARPi-immunotherapy efficacy in early HER2-negative breast cancer.

X Xiaying Kuang (Breast Surgery Department, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China) N Nan Shao R Runyi Ye (Breast Surgery Department, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China) Y Yunjian Zhang L Liang Yu (State Key Laboratory of Catalysis) Y Yawei Shi (Key Laboratory of Science and Technology on High‐Tech Polymer Materials Institute of Chemistry Chinese Academy of Sciences Beijing China) F Fei Yao L Limeng Chen (Department of Medicine, Amoy Diagnostics Co., Ltd., Xiamen, China, Xiamen, China) C Changbin Zhu Y Ying Lin (Induced Proximity Platform, Amgen Research)

Abstract

e14531 Background: Neoadjuvant immunotherapy shows promising efficacy in early HER2-negative breast cancer, predictive biomarkers for treatment response remain limited. Peripheral blood mononuclear cells (PBMCs) provide a noninvasive approach to assess systemic immune responses during neoadjuvant therapy (NAT). We investigated whether PBMC-based immune profiling could predict pathological complete response (pCR) in patients with HER2-negative early breast cancer treated with the combination regimen of Nab-Paclitaxel, Camrelizumab (PD-1 inhibitor) and Fuzuloparib (PARP inhibitor). Methods: PBMC samples (n = 48) were collected from 16 patients with stage II–III HER2-negative breast cancer harboring BRCA1/2 or PLAB2 germline pathogenic/likely pathogenic variants, who received the neoadjuvant combination therapy of Nab-Paclitaxel, Camrelizumab and Fuzuloparib. The samples were obtained at baseline (C1D1), during neoadjuvant treatment (C2D1, C4D1), and preoperatively. RNA sequencing was performed on PBMC-derived RNA. Gene sets from the GO and KEGG databases were used for enrichment analyses. Additionally, CIBERSORT, Xcell, MCPcounter and TIMER in-house established algorithm were applied to estimate immune cell populations from PBMC-derived gene expression profiles. Results: Sixteen patients completed NAT and underwent surgery, 12 with the luminal subtype and 4 with triple-negative breast cancer (TNBC). The overall pCR rate was 56.25% (9/16)with pCR rates of 50% (6/12) in the luminal tumors and 75% (3/4) in TNBC. At baseline, higher levels of CD4⁺ T cells (p = 0.021), CD4⁺ memory T cells (p = 0.009) and CD4⁺ Tem cells (p = 0.009) correlated with improved treatment response. Baseline PBMCs from patients achieving pCR showed enrichment of gene signatures related to positive thymic T cell selection (p.adjust = 0.086) and αβ-TCR complex (p.adjust = 0.028). During NAT, macrophage counts increased significantly in the pCR group at C2D1 (p = 0.029) and preoperatively (p = 0.011) vs baseline. Additionally, myeloid DCs (p = 0.033) and neutrophils (p = 0.013) were progressively elevated from C1D1 to preoperative time points during NAT. Immune activation-related features (leukocyte activation in inflammatory response, C2D1 vs C1D1, p.adjust = 0.028; macrophage activation, C2D1 vs C1D1, p.adjust = 0.0008) were also enriched in the pCR group, while energy metabolism-related features (ATP synthesis coupled electron transport, p.adjust = 0.021) were enriched in the non-pCR group. Conclusions: PBMC-based transcriptional profiling at baseline and during NAT is associated with response to neoadjuvant PARP inhibitor–immunotherapy and may provide a noninvasive approach to predict pCR in early HER2-negative breast cancer.

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (10)

X

Xiaying Kuang

Breast Surgery Department, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

N

Nan Shao

R

Runyi Ye

Breast Surgery Department, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

Y

Yunjian Zhang

L

Liang Yu

State Key Laboratory of Catalysis

Y

Yawei Shi

Key Laboratory of Science and Technology on High‐Tech Polymer Materials Institute of Chemistry Chinese Academy of Sciences Beijing China

F

Fei Yao

L

Limeng Chen

Department of Medicine, Amoy Diagnostics Co., Ltd., Xiamen, China, Xiamen, China

C

Changbin Zhu

Y

Ying Lin

Induced Proximity Platform, Amgen Research