Neoadjuvant chemotherapy (NACT) is the standard treatment for locally advanced, high-risk breast cancer. Pathological complete response (pCR) improves survival. Peripheral blood-derived indices reflecting systemic inflammation and nutritional status have long been used as predictive and prognostic markers in solid malignancies. This retrospective study investigates whether eight commonly used indices in patients receiving NACT affect pCR and survival. This study includes 624 locally advanced breast cancer patients who received NACT. The biomarker indices were calculated from peripheral blood samples taken two weeks before starting chemotherapy. The indices’ optimal cut-off values were determined using ROC Curve analysis. During a median follow-up period of 42 months, recurrence was detected in 146 patients, and 75 patients died. pCR was observed in 166 patients (26.6%). In univariate analysis, NLR, PLR, SII, PNI, HALP, and HRR were statistically significantly associated (p = 0.00; p = 0.03; p = 0.03; p = 0.02; p = 0.00; p = 0.02 respectively), but in multivariate analysis, only NLR was significantly predictive for pCR(p = 0.04). In multivariate analysis, the HGB/RDW score significantly predicted DFS(p = 0.04). The PNI score was identified as a marker predicting survival for both OS and PFS (p = 0.01, p = 0.01, respectively). In conclusion, peripheral blood-derived indices have prognostic and predictive values on pCR and survival. However, further studies are needed to validate our findings.

Background and Objectives: This investigation aimed to determine the impacts of concurrent proton pump inhibitors (PPIs) on progression-free survival (PFS) in patients with hormone receptor-positive and HER2-negative metastatic breast cancer managed with palbociclib or ribociclib as either the initial or subsequent line of therapy option. Materials and Methods: In this retrospective study, patients were classified as “concurrent PPIs” if PPIs were given for at least two-thirds of the palbociclib or ribociclib therapy period, and “no concurrent PPIs” if no PPIs were given during the period of palbociclib or ribociclib therapy. Each patient was also classified as endocrine-sensitive or endocrine-resistant according to the duration of previous endocrine responses. “Concurrent PPIs” and “no concurrent PPIs” groups were compared with each other in terms of PFS. This comparison was performed for both ribociclib and palbociclib groups. Results: The research included 220 patients in total. The PFS of 57 patients on palbociclib using concomitant PPIs was 14.4 months. Among 63 patients using palbociclib without concomitant PPIs, the PFS was 15.8 months. No statistically significant difference was found with PPI use (p = 0.82). Among 29 patients using ribociclib concurrently with PPIs, the PFS was 22.4 months. Among 71 patients using ribociclib without PPIs, the PFS was 20.2 months. No statistically significant difference was found with PPI use (p = 0.40). Conclusion: The results of our investigation showed that concomitant use of the most commonly used PPIs in the study (lansoprazole, pantoprazole, and esomeprazole) with palbociclib or ribociclib did not have any detrimental effects on PFS. Where appropriate, PPIs can be used concurrently with palbociclib and ribociclib. However, the effect of PPIs on cycling-dependent kinase 4/6 inhibitors deserves further investigation.

Background/Objectives: Triple-negative breast cancer (TNBC) is a heterogeneous entity lacking ER, PR, and HER2, with aggressive biology and high recurrence risk. Neoadjuvant chemotherapy (NACT) is the standard of care, and a pathological complete response (pCR) is a surrogate marker for survival. Within TNBC, apocrine differentiation (TNAC) is a distinct subtype, often androgen receptor (AR)-positive, with lower chemosensitivity but a favorable prognosis. Comparative studies of TNAC versus classical TNBC remain limited. This study aimed to define clinical and biological differences between TNAC and non-apocrine TNBC (NA-TNBC), representing the largest TNAC cohort to date. Methods: This retrospective study included 129 non-metastatic TNBC patients treated with NACT and surgery (2010–2020). Patients were classified as TNAC or NA-TNBC. Demographic, clinicopathological, and immunohistochemical data (including Ki-67 and AR) were collected. Tumor-infiltrating lymphocytes (TILs), delta Ki-67, pathological complete response (pCR), and survival outcomes were evaluated. Results: Of 129 TNBC patients, 45 (34.9%) were TNAC. AR positivity occurred in 64.4% of TNACs. TNAC patients were predominantly postmenopausal. pCR rates were significantly lower in TNAC (6.6% vs. 30.9%, p = 0.002). TNACs exhibited lower baseline Ki-67, delta Ki-67, and TIL positivity (13.3% vs. 30%). Despite this, 5-year overall survival was higher in TNAC (86% vs. 78%). Delta Ki-67 > 20% strongly predicted pCR across the cohort (p < 0.001). Carboplatin was rarely used in TNAC (8.3%), but was associated with a higher pCR rate (50% vs. 2.4%, p = 0.018). Conclusions: TNAC represents a biologically distinct TNBC subtype, characterized by low pCR but favorable survival. Recognition of its unique features may guide treatment de-escalation and exploration of AR-targeted therapies. Prospective studies focusing on TNAC are warranted.

Background: Systemic inflammation is an essential factor in the formation of the tumor microenvironment and has an impact on patient response to immune checkpoint inhibitors. Although there is a growing interest in biomarkers of inflammation, there is a gap in understanding their predictive value for response to nivolumab in clinical practice. The objective of this research was to design and assess a multi-algorithmic machine learning (ML) model based on regular systemic inflammation measurements to forecast the response of treatment to nivolumab. Methods: An analysis of a retrospective real-world cohort of 177 nivolumab-treated patients was performed. Baseline inflammatory biomarkers, such as neutrophils, lymphocytes, platelets, CRP, LDH, albumin, and derived indices (NLR, PLR, SII), were derived. After preprocessing, 5 ML models (Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and Neural Network) were trained and tested on a 70/30 stratified split. Accuracy, AUC, precision, recall, F1-score, and Brier score were used to evaluate predictive performance. The interpretability of the model was analyzed based on feature-importance ranking and SHAP. Results: Gradient Boosting performed best in terms of discriminative (AUC = 0.816), whereas Support Vector Machine performed best on overall predictive profile (accuracy = 0.833; F1 = 0.909; recall = 1.00; and Brier Score = 0.134) performance. CRP and LDH became the most common predictors of all models, and then neutrophils and platelets. SHAP analysis has verified that high CRP and LDH were strong predictors that forced the prediction to non-response, whereas higher lymphocyte levels were weak predictors that increased the response probability prediction. Conclusions: Machine learning models based on common inflammatory systemic markers give useful predictive information about nivolumab response. Their discriminative ability is moderate, but the high performance of SVM and Gradient Boosting pays attention to the opportunities of inflammation-based ML tools in making personalized decisions regarding immunotherapy. A combination of clinical, radiomic, and molecular biomarkers in the future can increase predictive capabilities and clinical use.

Background: Time to prostate-specific antigen (PSA) nadir (TTN) has been proposed as an early indicator of treatment responsiveness in metastatic hormone-sensitive prostate cancer (mHSPC). However, its prognostic relevance in patients treated with next-generation androgen receptor pathway inhibitors (ARPIs), such as abiraterone or enzalutamide, remains incompletely defined. Methods: This retrospective cohort study included 147 patients with mHSPC treated with abiraterone or enzalutamide between 2019 and 2024. TTN, PSA kinetics, radiologic progression-free survival (rPFS), and PSA progression-free survival (PSA-PFS) were analyzed using Kaplan–Meier methods and multivariable Cox regression. TTN was evaluated both as a continuous variable and dichotomized at the cohort median (≤9 vs. >9 months). Results: TTN distributions were comparable between treatment groups (median 9.0 vs. 6.0 months, p = 0.197). Patients with a shorter TTN (≤9 months) experienced significantly longer median rPFS compared with those with longer TTN (>9 months) (10.7 vs. 7.95 months; p = 0.036). No significant association was observed between TTN and PSA-PFS (9.3 vs. 10.75 months; p = 0.34). In multivariable analysis, enzalutamide was independently associated with a reduced risk of radiologic progression compared with abiraterone (HR 0.622; 95% CI 0.441–0.877), whereas TTN was not an independent predictor. Conclusions: A shorter TTN was associated with improved radiologic outcomes, suggesting that rapid PSA suppression may reflect more favorable disease biology in patients receiving ARPI therapy. Although TTN showed limited value in predicting biochemical progression, it may serve as a simple and accessible biomarker for early risk stratification and tailoring follow-up intensity in mHSPC. Validation in larger, multicenter cohorts is warranted.

Background and Objectives: HER2-positive breast cancer accounts for approximately 20–30% of all breast cancer cases and is associated with aggressive tumor behavior. Trastuzumab emtansine (T-DM1), an antibody-drug conjugate targeting HER2, is a standard second-line therapy for patients with metastatic disease. However, the impact of HER2 immunohistochemistry (IHC) expression levels on T-DM1 efficacy remains unclear. Materials and Methods: This retrospective study examined 87 patients with HER2-positive metastatic breast cancer who received T-DM1 following trastuzumab-based therapy. Patients were divided into IHC 2+ and IHC 3+ groups. Progression-free survival (PFS) and overall survival (OS) were evaluated via Kaplan–Meier analysis, and group comparisons were conducted using the log-rank test. Results: The median progression-free survival (PFS) for the entire cohort was 7.3 months (95% CI: 5.277–9.323), with a numerically longer PFS in the IHC 3+ group (8.4 months, 95% CI: 5.915–10.952) compared to the IHC 2+ group (6.3 months, 95% CI: 4.178–8.422). However, this difference was insignificant (HR: 0.91, 95% CI: 0.61–1.35; p = 0.778). Similarly, the median overall survival (OS) was 23.3 months (95% CI: 18.039–28.495), with the IHC 3+ group exhibiting a slightly longer OS (24.5 months, 95% CI: 18.600–30.400) compared to the IHC 2+ group (23.2 months, 95% CI: 12.387–34.147). Again, this difference did not reach statistical significance (HR: 0.93, 95% CI: 0.63–1.42; p = 0.369). Conclusions: Although the association between HER2 IHC 3+ expression and longer PFS and OS is promising, the lack of statistical significance suggests that IHC-based HER2 stratification alone may not be sufficient to predict the response to T-DM1. The potential of conducting prospective studies with larger cohorts and comprehensive molecular profiling to refine predictive biomarkers for optimizing therapeutic outcomes in HER2-positive metastatic breast cancer is a beacon of hope and should be pursued with optimism.