Phase II randomized trial comparing metronomic anthracycline-containing chemotherapy versus standard schedule in untreated HER2 negative advanced breast cancer: activity and quality of life results of the GOIM 21003 trial.

BACKGROUND: Optimizing chemotherapy to achieve disease and symptoms control is a noteworthy purpose in advanced breast cancer (ABC). We reported the activity and quality of life of a phase II study, comparing metronomic regimen with standard schedule as first line chemotherapy for ABC. METHODS: Patients with HER2 negative ABC were randomized to non-pegylated liposomal doxorubicin (NPLD, 60 mg/m2 every 3 weeks) and cyclophosphamide (CTX, 600 mg/m2 every 3 weeks) (Arm A) or NPLD (20 mg/m2 day, on day 1, 8 and 15 every 4 weeks) and metronomic daily oral CTX 50 mg (ARM B). Primary end-points were overall response rate (ORR) and quality of life, secondary progression-free survival (PFS), overall survival (OS) and toxicity. RESULTS: From August 2012 to December 2017, 121 patients were enrolled, 105 evaluable. Median follow-up was 21.3 months. Most patients had hormone receptor positive. ORR was 43 % in arm A and 50 % in arm B. Median PFS was 8.9 months in arm A and 6,4 months in arm B. There was no difference in OS. Total score was not clinically different between the two arms. Grade 4 neutropenia was observed in 12 patients and 16 patients respectively; alopecia G2 in 41 % (77 %) vs 14 (27 %) in arm A and in arm B respectively. One cardiac toxicity was observed (arm A). CONCLUSIONS: First line metronomic chemotherapy for HER2 negative ABC had similar clinical activity and quite better tolerability than standard schedule and could be considered a further treatment option when chemotherapy is indicated.

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.