Blood Test Predicts Immunotherapy Efficacy in Triple-Negative Breast Cancer

Current biomarkers such as PD-L1 expression or tumor mutational burden often fail to reliably predict success due to the complexity of immune responses. Invasive tumor biopsies are also impractical for frequent monitoring. To address this gap, researchers have now developed a blood-based tool that predicts treatment response with high accuracy.

In the study, researchers from Fudan University Shanghai Cancer Center (Shanghai, China), along with collaborators, analyzed plasma samples from 195 TNBC patients undergoing immunotherapy, using high-sensitivity assays to track 92 immune-related proteins before, during, and after treatment. They identified three key proteins—ARG1, NOS3, and CD28—and combined them with others to create the PIPscore, a predictive model with 85.8% accuracy.

The study, published in Cancer Biology & Medicine, revealed dramatic shifts in plasma protein levels after immunotherapy. Responders showed rises in immune-activating proteins like CXCL9 and IFN-γ, while patients with pathologic complete response (pCR) had higher ARG1 and CD28 but lower NOS3. The PIPscore stratified patients into high- and low-response groups with strong precision and predicted 12-month progression-free survival with 96% accuracy.

To validate these findings, the researchers integrated single-cell RNA sequencing to link plasma proteomic changes to the tumor microenvironment. Elevated NOS3 was associated with reduced CD8+ T-cell presence, suggesting immunosuppressive effects, while ARG1’s role in arginine metabolism may enhance T-cell activity. ELISA tests confirmed the reliability of the proteomic platform, further strengthening the clinical value of the PIPscore.

The PIPscore’s prognostic power offers oncologists a non-invasive method to guide treatment selection. By identifying ideal candidates for immunotherapy, the model could minimize unnecessary side effects and reduce treatment costs. Its ability to monitor patients in real time also supports dynamic treatment adjustments. Beyond TNBC, this approach may extend to other cancers where immunotherapy response remains highly variable.

“This study transforms how we approach TNBC immunotherapy. By translating complex plasma proteomics into a practical score, we've bridged the gap between research and clinical utility. The PIPscore not only predicts response but also opens doors to targeting metabolic pathways like arginine deprivation to overcome resistance. These findings underscore that systemic immunity, not just the tumor microenvironment, dictates treatment success,” said Dr. Yizhou Jiang, co-corresponding author of the study.