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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., Competing Interests: Disclosures Arsela Prelaj certifies that all conflicts of interest reported can be considered outside the present paper: consulting or advisory role for BMS, AstraZeneca; had travel, accommodations, or other expenses paid or reimbursed by Roche, Italfarmaco; principal investigator of Spectrum Pharmaceuticals. Alessandra Laura Giulia Pedrocchi holds shares of Agade srl. Giuseppe Lo Russo has received fees for acting as a consultant from Roche, Novartis, BMS, MSD, AstraZeneca, Takeda, Amgen, Sanofi, Italfarmaco, Pfizer; has received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Roche, Novartis, BMS, MSD, AstraZeneca, Takeda, Amgen, Sanofi, has received support for attending meetings and/or travel from Roche, BMS, MSD; has participated on data safety monitoring board or advisory board for Roche, Novartis, BMS, MSD, AstraZeneca, Sanofi, has acted as principal investigator in sponsored clinical trials for Roche, Novartis, BMS, MSD, AstraZeneca, GSK, Amgen, Sanofi. Rossana Berardi has received fees for acting as a consultant, for lectures and/or for participating to advisory board from BI, EISAI, GSK, Italfarmaco, Otsuka, Lilly, MSD; has received funding to Institution from AZ, BMS, Pfizer, Novartis, Roche; AMGEN. Giulia Galli declares the following conflicts of interest: Italpharma (advisory board); Roche (travel accommodation); Astra Zeneca, BMS, MSD (honoraria for lectures). Federica Bertolini has received consultant fees from MSD, Astra-Zeneca, Lilly, Eisai, Sanofi and speakers fee from BMS, MSD, Astra Zeneca. Filippo de Braud reports a patent for PCT/IB2020/055956 pending and a patent for IT201900009954 pending; and Roche, EMD Serono, NMS Nerviano Medical Science, Sanofi, MSD, Novartis, Incyte, BMS, Menarini Healthcare Research & Pharmacoepidemiology, Merck Group, Pfizer, Servier, AMGEN, Incyte. No disclosures were reported by the other authors., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The patient selection for optimal adjuvant therapy in gastrointestinal stromal tumors (GISTs) is provided by nomogram based on tumor size, mitotic index, tumor location, and tumor rupture. Although mutational status is not currently used to risk assessment, tumor genotype showed a prognostic influence on natural history and tumor relapse. Innovative measures, such as KIT/PDGFRA-mutant-specific variant allele frequency (VAF) levels detection from next-generation sequencing (NGS), may act as a surrogate of tumor burden and correlate with prognosis and overall survival of patients with GIST, helping the choice for adjuvant treatment., Patients and Methods: This was a multicenter, hospital-based, retrospective/prospective cohort study to investigate the prognostic role of KIT or PDGFRA-VAF of GIST in patients with radically resected localized disease. In the current manuscript, we present the results from the retrospective phase of the study., Results: Two-hundred (200) patients with GIST between 2015 and 2022 afferent to 6 Italian Oncologic Centers in the EURACAN Network were included in the study. The receiver operating characteristic (ROC) curves analysis was used to classify "low" vs. "high" VAF values, further normalized on neoplastic cellularity (nVAF). When RFS between the low and high nVAF groups were compared, patients with GIST with KIT/PDGFRA nVAF > 50% showed less favorable RFS than patients in the group of nVAF ≤ 50% (2-year RFS, 72.6% vs. 93%, respectively; P = .003). The multivariable Cox regression model confirmed these results. In the homogeneous sub-population of intermediate-risk, patients with KIT-mutated GIST, the presence of nVAF >50% was statistically associated with higher disease recurrence., Conclusion: In our study, we demonstrated that higher nVAF levels were independent predictors of GIST prognosis and survival in localized GIST patients with tumors harboring KIT or PDGFRA mutations. In the cohort of intermediate-risk patients, nVAF could be helpful to improve prognostication and the use of adjuvant imatinib., (© The Author(s) 2023. Published by Oxford University Press.)

Introduction: Axonal loss is an important feature of Multiple Sclerosis (MS), being strongly related to irreversible disability accumulation. Nonetheless, the exact mechanisms underlying axonal loss remain unclear. Cerebrospinal fluid (CSF) levels of Tau and Beta-amyloid (Abeta) currently represent diagnostic biomarkers in other neurodegenerative diseases. In MS, studies on CSF Tau and Abeta provided preliminary informations on disease prognosis, but results have not yet been replicated., Methods: We investigated whether CSF Tau and Abeta levels could predict early disability accumulation in MS patients. 100 patients underwent CSF analysis during their diagnostic work-up. Demographic, clinical, radiological features and CSF were collected at baseline. MS severity score (MSSS) and age-related MSSS (ARMSS) were calculated at last follow-up. We performed Mann-Whitney test, Spearman's coefficient, and multiple regression analysis for significant predictors of disability based on CSF Abeta and Tau levels, gender, age at diagnosis and MRI characteristics at baseline., Results: Baseline CSF Tau levels moderately correlated with MSSS (r=0.372 p=0.0001) and weakly with ARMSS (r=0.237 p=0.0176) after a mean two years follow-up. Predictors of early disability evaluated with MSSS and ARMSS were CSF Tau (Beta:0.258 p=0.009 and Beta:0.252 p=0.01) and spinal cord involvement (Beta:0.196 p=0.029 and Beta:0.240 p=0.008); as well as age at MS diagnosis (Beta:0.286 p=0.001) for MSSS, and high brain lesion load (Beta:0.207 p=0.02) for ARMSS., Conclusion: CSF Tau levels at diagnosis possibly has a predictive value along with MRI features and age at diagnosis. We hypothesize that Tau levels may express chronic axonal damage, possibly contributing to early MS disability., (Copyright © 2021 Elsevier B.V. All rights reserved.)