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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.)

Introduction/background: This single-arm, phase 2, multi-center, study aims to assess the safety and efficacy of a regimen of induction chemo-immunotherapy followed by de-intensified, hypo-fractionated thoracic radiotherapy (RT) given concurrently with durvalumab and maintenance durvalumab in patients with unresectable, stage III NSCLC., Material and Methods: we will enroll 45 patients with unresectable stage III NSCLC, any PD-L1, deemed ineligible for concurrent CRT by a thoracic oncology multidisciplinary team, and candidate to sequential chemoradiation followed by durvalumab., Results: Primary endpoint is safety, defined by the incidence of grade 3 and 4 possibly related adverse events (PRAEs) within 6 months from the initiation of treatment. The secondary objectives are PFS and OS (median and 12 months). Ancillary endpoints are molecular response evaluated by cfDNA isolation baseline, after chemo-immuno RT and at progression, and radiomics analysis on CT scans at baseline and before maintenance., Conclusion: DEDALUS phase 2 trial explores the safety and efficacy of a novel sequence of chemo-radiation (with de-intensified RT) plus the anti-PD-L1 agent durvalumab in patients with stage III unresectable NSCLC who are candidates to sequential chemoradiation plus maintenance immunotherapy., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The phase III S0819 trial investigated addition of cetuximab to first-line chemotherapy (CT) in NSCLC. Subgroup analyses suggested an OS benefit among patients with EGFR copy number gain in squamous cell carcinomas (SCC), (HR = 0.58 [0.39-0.86], P = .0071). A more detailed model based on EGFR FISH, EGFR IHC and KRAS mutation status was evaluated to yield a more precise predictive paradigm of cetuximab-based therapy in advanced NSCLC., Methods: FISH was performed using the Colorado Scoring Criteria; H-Score was used to quantify EGFR IHC expression (cut-off ≥ 200). A Cox model was used to assess treatment effects for OS and PFS within biomarker and clinical subgroups. KRAS mutation was analyzed using Therascreen. The false discovery rate controlled for multiple comparisons. S0819 ClinicalTrials.gov Identifier: NCT00946712., Results: Of 1,313 eligible patients, assay results were obtained for FISH on 976 patients (41% positive), for IHC on 945 patients (31% positive), and KRAS mutation status on 627 patients (26% positive). In SCC patients, OS was significantly improved with addition of cetuximab when both EGFR FISH and EGFR IHC were positive (N = 58), (OS HR: 0.32 [95% CI 0.18-0.59]; P = .0002, q = 0.08), median 12.6 versus 4.6 months. The results were independent of KRAS mutation status. In Non-SCC, no predictive value of EGFR IHC, EGFR FISH status and/or KRAS status was seen., Conclusions: In NSCLC SCC, a combination index of EGFR FISH plus EGFR IHC results was associated with improved OS when cetuximab was added to CT, representing a potential predictive molecular paradigm for patients suitable for EGFR-antibody therapy., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Introduction: Non-small-cell lung cancer (NSCLC) is predominantly a disease of the elderly population. Over the past few years, immunotherapy with monoclonal antibodies named checkpoint inhibitors (ICIs) greatly improved the clinical management of a significant proportion of patients with metastatic NSCLC. However, pivotal trials excluded older patients, although, given the favorable clinical profile of ICIs, this treatment may be revealed to be a most valuable option also for these patients. To this aim, a multicenter retrospective analysis was performed on patients aged ≥ 75 years with NSCLC treated with anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) immunotherapy., Material and Methods: Inclusion criteria were: diagnosis of locally advanced or metastatic NSCLC (stage IIIB or IV, according to the American Joint Committee on Cancer (AJCC) classification system, version 8.0); age ≥ 75 years; treatment with anti-PD-1/PD-L1 monoclonal antibodies in first or subsequent lines of treatment; absence of epidermal growth factor receptor-activating mutations or anaplastic lymphoma kinase and ROS-1 rearrangements. The primary endpoints of the study were the efficacy, in terms of overall response rate, progression-free survival, and overall survival, and safety, by means of evaluations of the incidence of immune-related adverse events., Results: Eighty-six patients were considered for the final analysis; 71 (82.6%) were male. The mean age was 78.5 years (range, 75-86 years; SD, 3.12 years). Of the 86 patients, 69 (80.2%) of patients had a performance status of 0 or 1. The overall median progression-free survival was 5.6 months (range 1-36 months; SD, 7.5 months,) whereas the median overall survival was 10.1 months (range, 1.7-34.8 months; SD, 8 months). At the Cox regression analysis, the only parameter significantly associated with survival was the smoking status (P = .008). No difference in survival was found between patients younger and older than 80 years., Conclusions: In the present real-world retrospective cohort, efficacy and toxicity profiles of ICIs in older patients with advanced NSCLC were comparable with those observed in younger patients enrolled in clinical trials., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: To test whether a microRNA (miRNA) panel may serve as an alternative biomarker of fibroblast growth factor receptor (FGFR) tyrosine kinase inhibitor sensitivity in lung cancer., Methods: Histologically diverse lung cancer cell lines were submitted to assays for ponatinib and AZD4547 sensitivity. miRNAs, FGFR1 messenger RNA, gene copy number, and protein expression were detected by real-time quantitative PCR, fluorescence in-situ hybridization, and immunoblotting in 34 lung cancer cell lines., Results: Among 34 cell lines, 14 exhibited ponatinib sensitivity and 20 exhibited AZD4547 sensitivity (drug concentration causing 50% inhibition values < 100 nmol/L). A total of 39 of the 377-miRNA set were initially identified from the 4 paired ponatinib-sensitive or -insensitive cell lines to have at least an 8-fold differential expression and then were detected in all the 34 cell lines. A predictive panel of 3 miRNAs (let-7c, miRNA155, and miRNA218) was developed that had an area under the curve (AUC) of 0.886 with a sensitivity of 71.4% and specificity of 77.3% to predict response to ponatinib. The miRNA panel performed similar to FGFR1 protein expression (AUC = 0.864) and messenger RNA expression (AUC = 0.939), and better than FGFR1 amplification (AUC = 0.696). Furthermore, we validated this panel using data for sensitivity to AZD4547 in the cell line cohort with an AUC of 0.931 and a sensitivity of 73.3% and specificity of 76.2%, respectively., Conclusion: The developed miRNA panel (let-7c, miRNA155, and miRNA218) may be useful in predicting response to FGFR tyrosine kinase inhibitors, either ponatinib or AZD4547 in lung cancer cell lines, and warrants further validation in the clinical setting., (Copyright © 2018 Elsevier Inc. All rights reserved.)