Prediction of Tumor Progression During Neoadjuvant Chemotherapy and Survival Outcome in Patients With Triple-Negative Breast Cancer

Diagnostic Efficacy and Clinical Significance of Lymphocyte Subsets, Granzyme B and Perforin in the Peripheral Blood of Patients with Invasive Breast Cancer Following Neoadjuvant Chemotherapy

Purpose

Breast cancer, a predominant contributor to cancer-related mortality worldwide, is increasingly managed through the application of neoadjuvant chemotherapy (NAC). Analyzing the dynamic changes in peripheral blood lymphocyte subsets, granzyme B and perforin are crucial for investigating their roles in tumorigenesis, development and treatment; this study aimed to use these analyses to diagnose malignant breast tumor, assess the anti-tumor immunity and predict chemotherapy efficacy in breast cancer patients.

Patients and Methods

To address this objective, a total of 582 peripheral blood samples were collected from healthy controls (n=47), benign breast disease patients (n=401) and breast cancer patients (n=134). Lymphocyte subsets, along with granzyme B and perforin expression, were assessed using flow cytometry. Changes before and after NAC were also monitored.

Results

Breast cancer patients exhibited reduced proportions and absolute counts of CD3+ and CD8+ T cells, increased NK cell percentage and CD4+/CD8+ ratio, and higher levels of granzyme B and perforin in CD3+, CD8+ T cells and NK cells. Post-NAC, the percentages of CD3+, CD4+, CD8+ T cells and NK cells increased, along with a higher CD4+/CD8+ ratio, while B cell percentages decreased compared to pre-NAC. Furthermore, the effective group showed higher percentages of CD3+, CD8+ T cells and lower percentages of B cells than the ineffective group post-NAC. Incidentally, Granzyme B and perforin expression in CD3+ and CD8+ T cells was elevated following postoperative chemotherapy.

Conclusion

These findings indicated that peripheral blood lymphocyte subsets, along with granzyme B and perforin levels, could serve as potential biomarkers for differentiating benign from malignant breast tumors, assessing anti-tumor immunity and predicting chemotherapy efficacy.

Predicting progression in triple-negative breast cancer patients undergoing neoadjuvant chemotherapy: Insights from peritumoral radiomics

OBJECTIVE

To investigate whether radiomic features obtained from the intratumoral and peritumoral regions of pretreatment magnetic resonance imaging (MRI) can predict progression in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC) in comparison with the previously determined clinical score.

METHODS

This single-center retrospective study evaluated 224 women with TNBC who underwent NAC between 2010 and 2019. Women were randomly allocated to the training set (n = 169) for model development and the test set (n = 55) for model validation. The clinical score consisted of the histologic type, Ki-67 index, and degree of edema on T2-weighted imaging. Intratumoral and peritumoral radiomic features were extracted from T2-weighted images and the first- and last-phase images of dynamic contrast-enhanced MRI. The radiomics model was built using only radiomic features, whereas the combined model incorporated the clinical score along with radiomic features. The area under the receiver operating characteristic curve (AUC) was used to assess performance.

RESULTS

Progression occurred in 18 and five patients in the training and test sets, respectively. The radiomics model selected three radiomic features (two peritumoral and one intratumoral), while the combined model selected the clinical score and five radiomic features (four peritumoral and one intratumoral). Among the total radiomic features, Inverse Difference Normalized of the peritumoral region of the T2-weighted images, reflective of peritumoral heterogeneity, demonstrated the highest level of association with tumor progression. In the test set, the AUC values of the radiomics-only model, the combined model, and the clinical score were 0.592, 0.764, and 0.720, respectively. Compared to the clinical score, the radiomics-only model (0.720 vs. 0.592, p = 0.468) and the combined model (0.720 vs. 0.764, p = 0.553) did not show superior performance.

CONCLUSION

The radiomics features were not superior in predicting the progression of TNBC compared to the clinical score, although the peritumoral heterogeneity on T2-weighted images showed a potential.

A Comprehensive Analysis of Neoadjuvant Chemotherapy in Breast Cancer: Adverse Events, Clinical Response Rates, and Surgical and Pathological Outcomes-Bozyaka Experience

Objectives: To evaluate the neoadjuvant chemotherapy (NACTx) process in breast cancer (BC), its significant treatment-related adverse events (trAEs), tumor clinical response rates, and surgical and pathological outcomes, and to analyze factors influencing cavity shaving and axillary lymph node dissection (ALND) following sentinel lymph node biopsy (SLNB). Methods: A comprehensive retrospective study was conducted at a single center on patients who received NACTx for BC between 2015 and 2021. Results: Medical records of 242 patients were reviewed. Approximately one-fifth encountered grade ≥ 3 trAEs (21.5%), leading 3.3% to discontinue chemotherapy. Anthracycline cardiotoxicity (2.2%) caused one death (mortality rate = 0.4%). For clinical response and surgical and pathological outcomes, 229 patients were eligible. Clinical progression occurred in 3.9% of the patients (14% in triple-negative BC, p = 0.004). Breast-conserving surgery (BCS) was performed in 55% of the patients. There was no significant difference between the type of breast surgery (BCS vs. mastectomy) and molecular subtype, histology, tumor size, or tumor's pathological response degree. Cavity shaving was required in one-fifth of the patients who underwent BCS (n = 134) due to an invasive tumor at the surgical margin (SM). Tumor histology (invasive ductal vs. invasive lobular carcinoma; OR: 4.962, 95% CI 1.007-24.441, p = 0.049) and tumor SUVMax value (OR: 0.866, 95% CI 0.755-0.993, p = 0.039) had significant independent efficacy on SM positivity. Initially, 75% underwent SLNB, but nearly half of them needed ALND. ALND rates were significantly higher in the luminal A and LB-HER2(-) groups (87% vs. 69%) than in the HER2(+) and TN groups (43% to 50%) (p = 0.001). All luminal A patients and those with lobular histology required ALND after SLNB, but no patients in the HER2-enriched group required ALND. ER positivity and higher PR expression levels were associated with an increased need for ALND after SLNB, whereas HER2 positivity and higher SUVMax values of LN(s) were associated with a significantly reduced need for ALND. About 27% of the patients achieved overall pCR. No pCR was achieved in the LA group. Conclusions: The BC NACTx process requires close monitoring due to severe AEs and disease progression. NACTx decisions must be made on experienced multidisciplinary tumor boards, considering tumor characteristics and expected targets.

Ki-67 With MRI in Predicting the Complete Pathological Response Post-neoadjuvant Chemotherapy

Neoadjuvant chemotherapy (NAC) is increasingly used for high-risk breast cancer to achieve pathologic complete response (pCR), an indicator of event-free survival and favorable survival outcomes. Integrating MRI and Ki-67 biomarker analysis into predictive models offers a promising approach to optimize NAC response assessment and guide personalized treatment strategies. This study evaluates the validity of combined MRI and Ki-67 metrics for predicting pCR. We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, including studies on NAC-treated breast cancer patients assessed by MRI and Ki-67. The predictive models were evaluated based on key parameters, including MRI-based tumor size reduction and Ki-67 levels, with outcomes measured by area under the receiver operating characteristic curve (AUC) and calibration metrics. Findings across ten studies consistently show that high Ki-67 levels and significant tumor size reduction on MRI are predictive of pCR, achieving AUCs near 0.90. The analysis highlighted that models integrating MRI with Ki-67 metrics outperformed single-modality approaches, showing enhanced predictive accuracy and calibration. However, high heterogeneity (I² = 77%) was noted, suggesting variability in imaging and Ki-67 assessment protocols across studies. This study underscores the combined utility of MRI and Ki-67 for the non-invasive prediction of pCR, offering both structural and biological insights into tumor responsiveness. The results align with prior research, affirming the role of Radiomic-clinicopathological models in providing a more comprehensive assessment compared to individual markers. Further refinement of imaging and biomarker protocols could improve model reproducibility and applicability. Our findings highlight the robust predictive accuracy of MRI-Ki-67 integrated models for assessing pCR, marking a significant step toward personalized cancer care. Future studies should focus on refining these models with additional biomarkers and standardized protocols, facilitating their integration into routine clinical oncology to enhance treatment decision-making and patient outcomes.

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study

RATIONALE AND OBJECTIVES

The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC).

MATERIALS AND METHODS

In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness.

RESULTS

The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI.

CONCLUSION

The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.

Unveiling the Immune Microenvironment's Role in Breast Cancer: A Glimpse into Promising Frontiers

Breast cancer (BC), one of the most widespread and devastating diseases affecting women worldwide, presents a significant public health challenge. This review explores the emerging frontiers of research focused on deciphering the intricate interplay between BC cells and the immune microenvironment. Understanding the role of the immune system in BC is critical as it holds promise for novel therapeutic approaches and precision medicine strategies. This review delves into the current literature regarding the immune microenvironment's contribution to BC initiation, progression, and metastasis. It examines the complex mechanisms by which BC cells interact with various immune cell populations, including tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs). Furthermore, this review highlights the impact of immune-related factors, such as cytokines and immune checkpoint molecules. Additionally, this comprehensive analysis sheds light on the potential biomarkers associated with the immune response in BC, enabling early diagnosis and prognostic assessment. The therapeutic implications of targeting the immune microenvironment are also explored, encompassing immunotherapeutic strategies and combination therapies to enhance treatment efficacy. The significance of this review lies in its potential to pave the way for novel therapeutic interventions, providing clinicians and researchers with essential knowledge to design targeted and personalized treatment regimens for BC patients.