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.

Objective: Immunotherapies are commonly employed for the treatment of non-small-cell lung cancer (NSCLC). However, predictive biomarkers still need to be improved to predict responses to these agents. The lymphocyte–albumin (LA) laboratory index has not been evaluated before in this patient group. The aim of this study was to analyze the relation between the LA index and the survival rate of metastatic NSCLC patients who had immunotherapy after at least one round of chemotherapy. Methods: The research included 227 patients diagnosed with metastatic NSCLC, who were administered nivolumab after at least one round of chemotherapy. The LA index was calculated by multiplying lymphocyte count and albumin concentration. The optimal threshold values for the index were established by the examination of the ROC curve for both overall survival (OS) and progression-free survival (PFS). Oncological data were obtained retrospectively from patient files, and survival analyses were performed. Results: The median follow-up was 7.9 months. Progression was observed in 129 (56.9%) patients. A total of 97 (42.7%) patients died during the follow-up. The cutoff values of the LA index to predict OS and PFS were determined as 52.87 and 57.67, respectively. The low-LA group had significantly lowered OS and PFS compared to the high-LA group. LA was found to be an independent prognostic factor for PFS (hazard ratio 4.47; 95% confidence interval, 2.73–7.34; p < 0.001) and OS (hazard ratio 6.24; 95% confidence interval, 3.46–11.25; p < 0.001) in the multivariate regression analysis. Conclusions: In this study, we observed that the LA index independently predicts OS and PFS in immunotherapy-treated metastatic NSCLC patients. Its ease of application, low cost, and noninvasive nature make it a potential guide for clinicians in predicting treatment responses and survival.

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/Objectives Malignant melanoma is an aggressive skin cancer with significant metastatic potential. Immune checkpoint inhibitors (ICIs), particularly those targeting the PD-1 pathway, have revolutionized treatment, improving survival rates. A PD-1 inhibitor, Nivolumab, has demonstrated durable responses in advanced melanoma patients. However, response variability necessitates predictive biomarkers for patient stratification. Methods The Gustave Roussy Immune Score (GRIm Score) is a prognostic tool integrating lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), and albumin levels to predict ICI efficacy. This retrospective study evaluated the association between the GRIm Score and response to nivolumab monotherapy in 40 patients with stage IV malignant melanoma treated between 2020 and 2024. Patients were classified into low-risk (score 0–1) and high-risk (score 2–3) groups. Results Results showed that patients with a low GRIm Score had significantly longer median progression-free survival (21.4 vs. 6.3 months, p = 0.003) and overall survival (26.5 vs. 7.2 months, p < 0.001). Multivariate analysis confirmed the GRIm Score as an independent prognostic factor (HR: 1.593, 95% CI: 1.156–2.197, p = 0.004), surpassing the predictive power of its components. Conclusions This study is the first to validate the GRIm Score in malignant melanoma, suggesting it is a valuable biomarker for patient selection in immunotherapy trials. The findings highlight its potential in refining treatment decisions, though further validation in larger, multicenter cohorts is required.

Background and Objectives: Colorectal cancer (CRC) poses a major global health challenge, with high incidence rates and ongoing treatment debates. Adjuvant chemotherapy benefits for high-risk subgroups, particularly stage II disease, remain controversial. This study seeks to clarify this issue by specifically examining the impact of adjuvant chemotherapy on disease-free survival (DFS) and overall survival (OS) in patients diagnosed with T4 colon cancer. Materials and Methods: This retrospective study analyzed patients undergoing radical surgery for T4 colon cancer between 2002 and 2023. Results: Our study of 184 pT4 pN0 colon cancer patients revealed that 79.3% received adjuvant chemotherapy. Multivariate analysis demonstrated significant DFS improvement: a 60% reduction in risk for those who received adjuvant therapy (0.40 95% CI: 0.25–0.62, p < 0.001). Lymphovascular invasion (LVI) and adjuvant treatment were also significantly associated with OS. Adjuvant treatment reduced mortality by 60% (HR: 0.40, 95% CI: 0.23–0.68, p = 0.001). Patients with LVI had a 1.9-fold increase in mortality (HR: 1.94, 95% CI: 1.17–3.20, p = 0.011). These findings underscore the potential value of adjuvant chemotherapy and highlight the importance of treatment completion in managing T4 colon cancer. Conclusions: Our study identifies LVI and adjuvant chemotherapy as key prognostic factors in T4 colon cancer patients. These results support the consideration of adjuvant chemotherapy in this patient population.