Differential impact of lipid profile according to neutrophil-to-lymphocyte ratio status in patients with advanced cancer treated with immunotherapy.

Aim: To investigate the different impact of each component of lipid profile in advanced cancer patients treated with immune checkpoints inhibitors (ICIs) according to neutrophil-to-lymphocyte ratio (NLR) value. Methods: We retrospectively collected total cholesterol (TC), triglycerides (TGs), low-density lipoproteins (LDL), high-density lipoproteins (HDL). Results: 407 patients were enrolled. In NLR <4 subgroup, TGs <150 mg/dl led to longer PFS (p = 0.01) and OS (p = 0.02) compared with TGs ≥150 mg/dl; LDL <100 mg/dl led to longer PFS (p = 0.004) and OS (p = 0.007) compared with LDL ≥100 mg/dl. In NLR ≥4 subgroup, TC >200 mg/dl led to longer PFS (p = 0.008) and OS (p = 0.004) compared with TC <200 mg/dl. Conclusion: We showed a distinct prognostic impact of lipid profile according to NLR.

[Box: see text].

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research.

INTRODUCTION: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). METHODS AND OBJECTIVES: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. CONCLUSION: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project.

Neoadjuvant therapy in hormone Receptor-Positive/HER2-Negative breast cancer.

Neoadjuvant therapy is commonly used in patients with locally advanced or inoperable breast cancer (BC). Neoadjuvant chemotherapy (NACT) represents an established treatment modality able to downstage tumours, facilitate breast-conserving surgery, yet also achieve considerable pathologic complete response (pCR) rates in HER2-positive and triple-negative BC. For patients with HR+/HER2- BC, the choice between NACT and neoadjuvant endocrine therapy (NET) is still based on clinical and pathological features and not guided by biomarkers of defined clinical utility, differently from the adjuvant setting where gene-expression signatures have been widely adopted to drive decision-making. In this review, we summarize the evidence supporting the choice of NACT vs NET in HR+/HER2- BC, discussing the issues surrounding clinical trial design and proper selection of patients for every treatment. It is time to question the binary paradigm of responder vs non-responders as well as the "one size fits all" approach in luminal BC, supporting the utilization of continuous endpoints and the adoption of tissue and plasma-based biomarkers at multiple timepoints. This will eventually unleash the full potential of neoadjuvant therapy which is to modulate patient treatment based on treatment sensitivity and surgical outcomes. We also reviewed the current landscape of neoadjuvant studies for HR+/HER2- BC, focusing on antibody-drug conjugates (ADCs) and immunotherapy combinations. Finally, we proposed a roadmap for future neoadjuvant approaches in HR+/HER2- BC, which should be based on a staggered biomarker-driven treatment selection aiming at impacting long-term relevant endpoints.