Recent evidence shows that inducing ferroptosis may improve efficacy of tumor therapy. However, ferroptosis-related genes have been little studied in patients with breast cancer especially in the neoadjuvant setting. ACSL4 and GPX4 have been well established as the positive and negative regulator of ferroptosis, respectively. This study aimed to explore the predictive value of ACSL4 and GPX4 for patients with breast cancer administered neoadjuvant chemotherapy. This study included patients treated with paclitaxel-cisplatin-based neoadjuvant chemotherapy. Immunohistochemistry staining of ACSL4 and GPX4 was carried out on the core needle biopsy specimens. Logistic regression was performed to explore the predictive biomarkers of pathological complete response (pCR). Survival analyses were examined by log-rank test and Cox proportional hazard regression. A total of 199 patients were included for the analyses. Both ACSL4 expression and ACSL4/GPX4 combination status could serve as independent predictive factors for pCR. The interaction for pCR was observed between ACSL4 and clinical tumor stage. Besides, ACSL4 expression, GPX4 expression, and their combination status were independent prognostic factors for disease-free survival. Analyses of the Kaplan-Meier Plotter database suggested that higher ACSL4 expression is related to better overall survival, and higher GPX4 expression is related to better distant metastasis-free survival. Pathway analyses revealed that ACSL4 and GPX4 might function in crucial pathways including apoptosis, autophagy, cell adhesion, lipid metabolism, etc. This study revealed the critical value of ACSL4 and GPX4 serving as novel predictive and prognostic biomarkers for patients with breast cancer receiving neoadjuvant chemotherapy. It might be a novel strategy to induce ferroptosis to promote chemosensitivity. Future studies are required to elucidate the potential mechanisms. This work was supported by Shanghai Natural Science Foundation [grant number 19ZR1431100], Clinical Research Plan of Shanghai Hospital Development Center [grant numbers SHDC2020CR3003A, 16CR3065B, and 12016231], Shanghai “Rising Stars of Medical Talent” Youth Development Program for Youth Medical Talents - Specialist Program [grant number 2018-15], Shanghai “Rising Stars of Medical Talent” Youth Development Program for Outstanding Youth Medical Talents [grant number 2018-16], Shanghai Collaborative Innovation Center for Translational Medicine [grant number TM201908], Multidisciplinary Cross Research Foundation of Shanghai Jiao Tong University [grant numbers YG2017QN49, ZH2018QNA42, and YG2019QNA28], Nurturing Fund of Renji Hospital [grant numbers PYMDT-002, PY2018-IIC-01, PY2018-III-15, and PYIII20-09], Science and Technology Commission of Shanghai Municipality [grant numbers 20DZ2201600 and 15JC1402700], and Shanghai Municipal Key Clinical Specialty.

Purpose: Despite accumulating evidence on dual blockade of HER2 for locally advanced HER2-positive breast cancer, no robust evidence supports the addition of pyrotinib to trastuzumab in the neoadjuvant setting. The NeoATP trial aimed to evaluate the efficacy and safety of pyrotinib with neoadjuvant trastuzumab and chemotherapy. Patients and Methods: The phase II NeoATP trial included female patients with histologically confirmed stage IIA to IIIC and HER2-positive primary invasive breast cancer. Eligible patients received pyrotinib and trastuzumab with weekly paclitaxel–cisplatin neoadjuvant chemotherapy for four cycles. The primary endpoint was pathologic complete response (pCR; ypT0 ypN0) rate. Key secondary endpoints included locoregional pCR (ypT0/is ypN0) rate, biomarker analysis, and safety. Results: Among 53 enrolled patients (median age, 47 years; 73.58% stage III), 52 completed the study treatment and surgery. Overall, 37 patients (69.81%) achieved pCR. For women with hormone receptor–negative and –positive tumors, the pCR rates were 85.71% and 59.38% (P = 0.041), while the corresponding rates were 69.23% and 70.00%, respectively, for those with and without PIK3CA mutation (P = 0.958). The most frequently reported Grade 3 to 4 adverse events were diarrhea (45.28%), leukopenia (39.62%), and neutropenia (32.08%). No deaths occurred, and no left ventricular ejection fraction 10 points drop from baseline to before surgery was reported. Conclusions: The addition of pyrotinib to trastuzumab plus chemotherapy is an efficacious and safe regimen for patients with HER2-positive locally advanced breast cancer in the neoadjuvant setting. The randomized controlled clinical trial is warranted to validate our results.

We aimed to explore the value of contrast-enhanced ultrasound (CEUS) in early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) in locally advanced breast cancer (LABC) patients treated with neoadjuvant chemotherapy (NAC). LABC patients who underwent CEUS before and during NAC from March 2014 to October 2018 were included and assessed. Logistic regression analysis and the Cox proportional hazards model were used to identify independent variables associated with pCR and RFS. Among 122 women, 44 underwent pCR. Molecular subtype, peak intensity (PEAK) and change in diameter were independent predictors of pCR after one cycle of NAC (area under the receiver operating characteristic curve [AUC], 0.81; 95% CI: 0.73, 0.88); Molecular subtype, PEAK and change in time to peak (TTP) were independently associated with pCR after two cycles of NAC (AUC, 0.85; 95% CI: 0.77, 0.91). A higher clinical T (hazard ratio [HR] = 4.75; 95% CI: 1.75, 12.87; p = 0.002) and N stages (HR = 3.39; 95% CI: 1.25, 9.19; p = 0.02) and a longer TTP (HR = 1.06; 95% CI: 1.01, 1.11; p = 0.02) at pre-NAC CEUS were independently associated with poorer RFS. CEUS can be used as a technique to predict pCR and RFS early in LABC patients treated with NAC.

Background and objective: Breast-conserving surgery (BCS) plays a crucial role in breast cancer treatment, with a primary focus on ensuring cancer-free surgical margins, particularly for patients undergoing neoadjuvant treatment. After neoadjuvant treatment, tumor regression can complicate the differentiation between breast cancer tumor and adjacent tissues. Raman spectroscopy, as a rapid and non-invasive optical technique, offers the advantage of providing detailed biochemical information and molecular signatures of internal molecular components in tissue samples. Despite its potential, there is currently no research on using label-free Raman spectroscopy to distinguish between breast cancer tumors and adjacent tissues after neoadjuvant treatment. This study intends to distinguish between tumor and adjacent tissues after neoadjuvant treatment in breast cancer through label-free Raman spectroscopy. Methods: In this study, the intraoperative frozen samples of breast cancer tumor and adjacent tissue were collected from patients who underwent neoadjuvant treatment during surgery. The samples were examined using Raman confocal microscopy, and Raman spectra were collected by LabSpec6 software. Spectra were preprocessed by Savitz-Golay filter, adaptive iterative reweighted penalized least squares and MinMax normalization method. The differences in Raman spectra between breast cancer tumor and adjacent tissues after neoadjuvant treatment were analyzed by Wilcoxon rank-sum test, with a Bonferroni correction for multiple comparisons. Based on the support vector machine (SVM) method in machine learning, a predictive model for classification was established in the total group and subgroups of different hormone receptor (HR) status, human epidermal growth factor receptor 2 (HER2) status and Ki-67 expression level. The independent test set was used to evaluate the performance of the model, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity and accuracy of different models were obtained. Result: This study comprised 4260 Raman spectra of breast cancer tumor and adjacent frozen tissue samples from 142 breast cancer patients treated with neoadjuvant treatment. The Raman peaks associated with nucleotides and their metabolites in the Raman spectra of breast cancer tumor tissues were higher in intensities than those of adjacent tissues after neoadjuvant therapy (676 cm⁻¹: Bonferroni adjusted P < 0.0001; 724 cm⁻¹: P < 0.0001; 754 cm⁻¹: P < 0.0001), and the Raman peaks from amide III bands were more intense (1271 cm⁻¹: P < 0.01). Multivariate curve resolution-alternating least squares (MCR-ALS) decomposition of Raman spectra revealed reduced lipid content and increased collagen and nucleic acid content in breast cancer tumor tissues compared to adjacent tissues following neoadjuvant therapy. The predictive model based on the Raman spectral signature of breast cancer tumor and adjacent tissues after neoadjuvant treatment achieved an AUC of 0.98, with accuracy, sensitivity, and specificity values of 0.89, 0.97, and 0.83, respectively. The AUC of subgroup analysis according to different status of molecular pathological biomarkers was stably around 99%. Conclusion: This study demonstrated that label-free Raman spectroscopy can differentiate tumor and adjacent tissues of breast cancer patients treated with neoadjuvant therapy thorough getting the panoramic perspective of the biochemical compounds for the first time. Our study provided a novel technique for determining the margin status in BCS in breast cancer following neoadjuvant treatment rapidly and precisely.