This study reveals dynamic changes in the tumor and immune microenvironment of residual disease in patients with HER2+ early breast cancer after neoadjuvant therapy, and finds that post-treatment immune gene expression signatures (such as IgG signaling) have stronger prognostic value, highlighting the critical importance of timing in biomarker assessment.
Literature Overview
This article, titled 'Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in patients with HER2+ early breast cancer (EBC)', published in Annals of Oncology: official journal of the European Society for Medical Oncology, reviews and summarizes tumor and immune gene expression dynamics in patients with HER2+ early breast cancer from four neoadjuvant therapy clinical trials (CALGB 40601, PAMELA, NeoALTTO, and NSABP B-41). It focuses on the biological characteristics of residual disease (RD) and their prognostic value for event-free survival (EFS). Through multicenter, large-scale gene expression profiling, the study systematically compares intrinsic subtype shifts, immune microenvironment remodeling, and the prognostic performance of multiple gene expression signatures between baseline and post-treatment samples, providing key molecular evidence for personalized adjuvant treatment strategies.Background Knowledge
HER2-positive (HER2+) early breast cancer is a highly heterogeneous subtype, accounting for approximately 15–20% of all breast cancers. Although anti-HER2 targeted therapies (such as trastuzumab) combined with chemotherapy have significantly improved the rate of pathological complete response (pCR), about 40–60% of patients still exhibit residual disease (RD), which is associated with a significantly higher risk of recurrence compared to pCR patients. Currently, T-DM1 has been established as the standard post-neoadjuvant adjuvant therapy for RD patients, but its toxicity is considerable, and not all RD patients require treatment escalation. Therefore, reliable biomarkers are urgently needed to differentiate high-risk from low-risk RD patients to enable precision treatment decisions. In recent years, tumor intrinsic subtypes (e.g., PAM50) and immune-related gene signatures (e.g., B-cell and T-cell signaling) have demonstrated prognostic and predictive value. However, most previous studies relied on pre-treatment samples, while the treatment process itself may reshape both the tumor and immune microenvironment. Existing evidence suggests that HER2 expression declines after anti-HER2 therapy, tumor heterogeneity increases, and a shift toward luminal phenotypes may occur. Simultaneously, the immune microenvironment may also undergo dynamic changes, but its relationship with long-term outcomes remains unclear. This study systematically evaluates the evolution of tumor biology before and after neoadjuvant therapy and its impact on EFS, filling the gap in post-treatment dynamic biomarker assessment and providing theoretical support for subsequent precision intervention strategies.
Research Methods and Experiments
The study included 452 patients with HER2+ early breast cancer from four neoadjuvant therapy trials (CALGB 40601, PAMELA, NeoALTTO, and NSABP B-41) who had residual disease (RD) after receiving trastuzumab-based therapy (with or without lapatinib, and with or without chemotherapy). Among them, 169 patients had paired pre- and post-treatment tumor samples. RNA sequencing was performed on all samples, followed by uniform data quality control, alignment, normalization, and batch correction. Tumor intrinsic subtypes were determined using the PAM50 algorithm, and 25 published gene expression signatures (including 12 immune-related and 13 tumor-related) were calculated. Wilcoxon tests were used to compare gene expression changes in paired samples, 5-year EFS rates were estimated using Kaplan-Meier methods, and univariate and multivariate Cox regression models were employed to assess the association between biomarkers and EFS, with model performance evaluated by c-index.Key Conclusions and Perspectives
Research Significance and Prospects
This study is the first to systematically reveal the biological remodeling process of residual disease in HER2+ early breast cancer following neoadjuvant anti-HER2 therapy. It not only confirms dynamic changes such as phenotypic shift toward Luminal/Normal-like subtypes and immune microenvironment activation, but more importantly, clarifies the differential prognostic weight of various biomarkers at different time points. This finding challenges previous strategies that rely solely on baseline features for risk stratification, suggesting that clinical practice should prioritize molecular assessment of post-treatment samples, especially the state of the immune microenvironment.
Future research could further explore the cellular sources of post-treatment IgG signaling (e.g., extent of plasma cell infiltration) and its relationship with antigen presentation and B-cell–T-cell interactions. Additionally, these results provide a foundation for developing clinical tools based on post-treatment immune features, potentially guiding the personalized use of T-DM1 or novel antibody-drug conjugates (e.g., T-DXd) and avoiding overtreatment in low-risk patients. Meanwhile, patients with high IgG expression may be better candidates for immunotherapies such as immune checkpoint inhibitors, which warrants validation in clinical trials.
Conclusion
This study, through integrated analysis of four neoadjuvant clinical trials, systematically maps the molecular evolution of residual disease in patients with HER2+ early breast cancer following anti-HER2 therapy. It reveals that tumors often shift toward Normal-like or Luminal A phenotypes post-treatment, accompanied by significant upregulation of B-cell, CD8+ T-cell, and IgG-related signals, indicating immune microenvironment activation. More importantly, multivariate models show that the IgG gene signature in post-treatment samples is the strongest independent predictor of event-free survival, outperforming baseline tumor features in prognostic power. This indicates that the timing of treatment critically influences the prognostic value of biomarkers—baseline features reflect initial tumor aggressiveness, while post-treatment immune features reflect the strength of host anti-tumor immune response. Therefore, clinical decisions should integrate molecular information from both pre- and post-treatment timepoints. In the future, assessing immune features in residual post-treatment tissue may precisely identify high-risk patients who truly require intensified adjuvant therapy, enabling personalized precision treatment and reducing unnecessary toxicities. This study provides important theoretical foundations and candidate biomarkers for the precision management of HER2+ breast cancer.
