Different peripheral blood parameters have emerged as prognostic biomarkers in breast cancer (BC), but their predictive role in Human Epidermal growth factor Receptor 2 positive (HER2+) advanced BC (aBC) patients receiving dual anti-HER2 blockade remains unclear. We evaluated the impact of the Pan-Immune-Inflammatory Value (PIV), defined as the product of peripheral blood neutrophil, platelet, and monocyte counts divided by lymphocyte counts, on the prognosis of HER2+ aBC patients treated with first line trastuzumab-pertuzumab-based biochemotherapy. We also evaluated the association between the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), and the monocyte to lymphocyte ratio (MLR) and clinical outcomes. Cox regression models were used to estimate the impact of these variables, as well as of other clinically relevant covariates, on patient survival. We included 57 HER2+ aBC patients treated with taxane-trastuzumab-pertuzumab in our Institution. High baseline MLR, PLR, and PIV were similarly predictive of worse PFS at univariate analysis, but only high PIV was associated with a trend toward worse PFS at multivariable analysis. Regarding OS, both high PIV and MLR were associated with significantly worse patient survival at univariate analysis, but only the PIV was statistically significantly associated with worse overall survival at multivariable analysis (HR 7.96; 95% CI: 2.18–29.09). Our study reveals the PIV as a new and potent predictor of OS in HER2+ aBC patients treated with first line trastuzumab-pertuzumab-containing biochemotherapy. Prospective studies are needed to validate this new prognostic parameter in HER2+ aBC.

Hormone receptor-positive breast cancer (HR+ BC) accounts for approximately 75% of new BC diagnoses. Despite the undisputable progresses obtained in the treatment of HR+ BC in recent years, primary or acquired resistance to endocrine therapies still represents a clinically relevant issue, and is largely responsible for disease recurrence after curative surgery, as well as for disease progression in the metastatic setting. Among the mechanisms causing primary or acquired resistance to endocrine therapies is the loss of estrogen/progesterone receptor expression, which could make BC cells independent of estrogen stimulation and, consequently, resistant to estrogen deprivation or the pharmacological inhibition of estrogen receptors. This review aims at discussing the molecular mechanisms and the clinical implications of HR loss as a result of the therapies used in the neoadjuvant setting or for the treatment of advanced disease in HR+ BC patients.

PD-1 pathway blockade has been shown to promote proatherogenic T-cell responses and destabilization of atherosclerotic plaques. Moreover, preclinical evidence suggests a potential synergy of antiplatelet drugs with immune checkpoint inhibitors (ICIs). We conducted an analysis within a prospective observational protocol (APOLLO study) to investigate the rates, predictors, and prognostic significance of thromboembolic events (TE) and thromboprophylaxis in patients with advanced NSCLC treated with ICIs. Among 217 patients treated between April 2014 and September 2018, 13.8% developed TE events. Current smoking status (HR 3.61 (95% CI 1.52–8.60), p = 0.004) and high (>50%) PD-L1 (HR 2.55 (95% CI 1.05–6.19), p = 0.038) resulted in being independent TE predictors. An increased risk of death following a diagnosis of TE (HR 2.93; 95% CI 1.59–5.42; p = 0.0006) was observed. Patients receiving antiplatelet treatment experienced longer progression-free survival (PFS) (6.4 vs. 3.4 months, HR 0.67 (95% CI 0.48–0.92), p = 0.015) and a trend toward better OS (11.2 vs. 9.6 months, HR 0.78 (95% CI 0.55–1.09), p = 0.14), which were not confirmed in a multivariate model. No impact of anticoagulant treatment on patients’ outcomes was observed. NSCLC patients treated with ICIs bear a consistent risk for thrombotic complications, with a detrimental effect on survival. The impact of antiplatelet drugs on ICIs efficacy deserves further investigation in prospective trials.

Background: Advanced triple-negative breast cancer (aTNBC) has a poor prognosis; thus, there is a need to identify novel biomarkers to guide future research and improve clinical outcomes. Objectives: We tested the prognostic ability of an emerging, complete blood count (CBC)-based inflammatory biomarker, the pan-immune-inflammation value (PIV), in patients with aTNBC treated with first-line, platinum-based chemotherapy. Design: This was a retrospective, monocentric, observational study. Methods: We included consecutive aTNBC patients treated with platinum-based, first-line chemotherapy at our Institution, and for whom baseline (C1) CBC data were available. We collected CBC data early on-treatment, when available. PIV was calculated as: (neutrophil count × platelet count × monocyte count)/lymphocyte count. Patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer (aBC) were included in a control, non-TNBC cohort. Results: A total of 78 aTNBC patients were included. When evaluated as a continuous variable, PIV-C1 was associated with worse overall survival (OS; p < 0.001) and progression-free survival (PFS; p < 0.001). On the other hand, when PIV-C1 was assessed on the basis of its quantile distribution, patients with ‘high PIV-C1’ experienced worse OS [adjusted hazard ratio (HR): 4.46, 95% confidence interval (CI): 2.22–8.99; adjusted p < 0.001] and PFS (adjusted HR: 2.03, 95% CI: 1.08–3.80; adjusted p = 0.027) when compared to patients with ‘low PIV-C1’. Higher PIV-C1 was also associated with primary resistance to chemotherapy. Similarly, a higher PIV calculated from CBC at C2D1 (PIV-C2) was associated with worse survival outcomes. We also created a PIV-based score combining information about both PIV-C1 and PIV-C2 and allowing the stratification of patients at low, intermediate, and high risk of death. No association was observed between PIV-C1 and clinical outcomes of HR+/HER2− aBC patients. Conclusion: PIV has a promising prognostic discrimination ability in aTNBC patients treated with first-line, platinum-based chemotherapy. Both baseline and early on-treatment PIV are associated with clinical outcomes and may be exploited for creating PIV-based risk classifiers if further validated.

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.