Breast cancer (BC) is one of the most prevalent malignant tumors in women (1). Current treatments include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy (2). While chemotherapy remains the primary treatment for advanced BC, the high rates of drug resistance limit its effectiveness in many patients (3). The emergence of immunotherapy as a promising treatment modality has spurred increasing interest in the potential synergistic effects of combining chemotherapy with immunotherapy across various cancer types (4), with many studies linking this combination therapy to better outcomes in BC and other malignancies (5–8).

A variety of markers have been identified that can predict the efficacy of immunotherapy in breast cancer, such as programmed cell death-ligand 1 (PD-L1) expression level (9), tumor mutation burden (TMB) (10), microsatellite instability-high (MSI-H) (11), and defective mismatch repair (dMMR) (11). Additionally, gene mutations such as those in EGFR (12), VEGF (13), and genes related to immunotherapy outcomes (e.g., Tp53, Ras, WDR4, and STK11) (14–17), may serve as potential predictive markers for the effectiveness of BC immunotherapy. Nevertheless, these biomarkers are not easily accessible, as they require the acquisition of sufficient tumor tissue for immunohistochemistry and genetic testing, and cannot fully predict the efficacy of immunotherapy.

Inflammation and nutritional indicators have made significant progress in predicting cancer prognosis (18). Inflammation-nutrition indicators primarily include cellular components such as neutrophils, lymphocytes, and monocytes, along with proteins albumin (ALB) and C-reactive protein (CRP). Additionally, these indicators encompass derived ratios, such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte Ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammatory index (SII), and prognostic nutritional index (PNI). These markers collectively provide insights into the balance between inflammatory responses and nutritional status, offering valuable prognostic and diagnostic information in various clinical contexts. PNI is calculated by combining the ALB (g/dL) with five times the peripheral blood lymphocyte count (10^9/L), using the formula: PNI = ALB (g/dL) + 5 × (lymphocyte count in 10^9/L). This index is a widely used biomarker that reflects both nutritional status and immune competence, providing valuable prognostic information in various clinical settings, including oncology, surgery, and chronic disease management, such as gastric cancer (19), renal cancer (20), lung cancer (21), etc. In the above studies, interfering factors such as underlying diseases were excluded. This research was designed to investigate the potential of the PNI as a prognostic biomarker in advanced BC receiving immunotherapy. Compared with similar studies, this study integrates information on the clinical characteristics, immunohistochemistry status and composite inflammatory indicators to achieve combined modeling of multidimensional immunometabolism microenvironment and metastatic heterogeneity, providing a novel and clinically applicable tool for prognostic assessment in advanced BC patients treated with immunotherapy.

Our study proposes a novel predictive model incorporating the PNI, which demonstrates excellent predictive performance. The model was internally validated using multiple metrics, including C-index, ROC curves, calibration curves, and DCA. The nomogram further highlights the significant prognostic value of PNI in predicting clinical outcomes.

The PNI calculation relies on ALB and lymphocyte levels. ALB serves as a common measure for determining nutritional status. Many studies have shown that low ALB levels are an independent indicator of poor survival in various cancers (22). Liu et al (23). Conducted a retrospective analysis on 2,425 white female patients with non-metastatic invasive BC (stage I-III), revealing that low ALB levels were a prognostic factor for reduced survival, independent of cancer stage. Inflammation plays a critical role in all stages of tumorigenesis, including initiation, promotion, and metastatic progression (24). Inflammatory cells, like neutrophils, lymphocytes, monocytes and platelets, are integral to these processes. Lymphocytes, in particular, are crucial for immune defense, surveillance, and anti-tumor responses. Scores derived from lymphocytes have been proven to have prognostic value in BC, with studies showing that NLR (25), MLR (26), and PLR (27) are significantly associated with breast cancer prognosis. PNI is closely related to the development of tumors. Fu et al (28). showed that lymphocytes and fibroblasts infiltrate more densely in the tumor microenvironment of the high PNI group, suggesting a stronger anti-tumor immune response. Choi et al (29). further showed that the density of CD4⁺T cells in the tumor microenvironment of patients in the low PNI group, was significantly reduced, and CD4⁺T cells exert anti-tumor effects by activating CD8⁺T cells. Therefore, low PNI may lead to immunosuppression and promote tumor progression. Chen et al (30). analyzed 785 breast cancer patients and identified a PNI cutoff value of 51 based on the ROC analysis, finding that patients with a higher PNI, accompanied by longer disease-free survival (DFS) and OS, had significant outcomes (P<0.001). In addition, PNI is often used in combination with inflammatory markers (such as NLR, PLR, CRP) to form a more comprehensive prognostic model. Our approach involved using recognized statistical methods, such as Cox regression analysis and Kaplan–Meier survival analysis, to assess the connection between PNI levels and patient outcomes like PFS and OS. The findings revealed that PNI was a significant predictor (p < 0.05), highlighting its usefulness in categorizing patients according to prognosis. Based on Multivariate analyses, this study constructed a nomogram model including PNI. Clinicians can calculate patient risk scores based on the model and estimate long-term survival rates, so as to better identify advanced BC patients who can benefit from immunotherapy and guide clinical individualized treatment.

This research, however, is not without its limitations. Initially, the study was conducted retrospectively and only involved Chinese subjects, which fails to entirely eliminate the possibility of selection bias and limited the generalizability of findings across different ethnic groups. Multi-center prospective research is needed to further verify in the future. Second, the research was constrained by a relatively small participant pool and insufficient follow-up duration, necessitating further investigation with an expanded sample size and extended follow-up periods. Furthermore, this study included only the baseline data of laboratory indicators, did not dynamically monitor the changes during treatment, and only conducted internal verification and no external verification, so relevant analysis is required. Despite these limitations, the findings also provide valuable insights for the clinical application of immunotherapy.

In this research, PNI was determined to be an independent prognostic factor for patients with advanced BC receiving immunotherapy. Patients with higher PNI levels exhibited significantly longer PFS and OS compared to those with lower levels. Furthermore, a predictive model was constructed based on PNI, with the resulting nomogram exhibiting satisfactory predictive ability, suggesting its potential as a clinical tool for estimating PFS and OS in this patient population.