Abstract PO-065: Artificial intelligence to improve selection for NSCLC patients treated with immunotherapy

• Published in: Clinical cancer research 2021-03, Vol.27 (5_Supplement), p.PO-065-PO-065
• Main Authors: Prelaj, Arsela, Boeri, Mattia, Robuschi, Alessandro, Proto, Claudia, Russo, Giuseppe Lo, Ferrara, Roberto, Galli, Giulia, De Toma, Alessandro, Brambilla, Marta, Occhipinti, Mario, Manglaviti, Sara, Labianca, Alice, Beninato, Teresa, Bini, Marta, Mensah, Mavis, Ganzinelli, Monica, Zilembo, Nicoletta, de Braud, Filippo, Sozzi, Gabriella, Restelli, Marcello, Pedrocchi, Alessandra, Garassino, Marina Chiara, Trovo, Francesco
• Format: Article
• Language: English
• ISSN: 1078-0432; 1557-3265
• DOI: 10.1158/1557-3265.ADI21-PO-065

Introduction In advanced Non-Small Cell Lung Cancer (aNSCLC), Programmed Death Ligand 1 (PD-L1) remains the only used biomarker to candidate patients (pts) to immunotherapy (IO) even if its predictive accuracy is not satisfactory. Indeed, given the complex dynamics underlying the cross-talk between the tumor and its microenvironment, it is unlikely that a single biomarker could be able to profile prediction with high precision. Artificial Intelligence (AI) and machine learning (ML) are techniques able to analyze and interpret big data, which cope with this complexity. The present study aims at using AI tools to improve response and efficacy prediction in aNSCLC pts treated with IO. Methods A classification task to determine if a pt is likely to benefit from IO was formulated using complete clinical data, PD-L1, histology, molecular data, and the blood microRNA signature classifier (MSC), which include 24 different microRNAs. Pts were divided into responders (R), who obtained a partial response or stable disease as best response, and non-R, who experienced progressive disease. A forward feature selection technique based on the Akaike Information Criterion was used to extract a specific subset of the pts data, being the most informative ones for the task. To develop the final predictive model, different ML methods have been tested: K-nearest neighbors, Logistic Regression, Kernel Support Vector Machines, Feedforward Neural Network, and Random Forest. Results Of 164 enrolled pts, 73 (44.5%) were R and 91 (55.5%) non-R. At data cut-off (Nov 2020), median Overall Survival (mOS) was 10.1 (95%IC 7.0 - 13.2) months (m). mOS for R pts was 38.5 m (95%IC 23.9 - 53.1) vs 3.8 m (95%IC 2.8 - 4.7) of non-R, p