Dataset on the identification of a prognostic radio-immune signature in surgically resected Non Small Cell Lung Cancer

• Main Authors: Giulia Mazzaschi, Gianluca Milanese, Paolo Pagano, Denise Madeddu, Letizia Gnetti, Francesca Trentini, Angela Falco, Caterina Frati, Bruno Lorusso, Costanza Lagrasta, Roberta Minari, Luca Ampollini, Mario Silva, Nicola Sverzellati, Federico Quaini, Giovanni Roti, Marcello Tiseo
• Format: Article
• Language: English
• Published: Elsevier 2020-08-01
• Series: Data in Brief
• Subjects: Lung cancer; CT imaging; Immune contexture; Radiomics; Prognostic signature
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352340920306752

The immune regulation of cancer growth and regression has been underscored by the recent success of immunotherapy. The possibility that immune microenvironmental factors may impact on clinical outcome and treatment response still requires intense investigations. Hereby, supporting data of the research article “Integrated CT Imaging and Tissue Immune Features Disclose a Radio-Immune Signature with High Prognostic Impact on Surgically Resected NSCLC” [1], are presented. With the ultimate aim to provide non-invasive prognostic scores, we report on our approach to correlate different Tumor Immune Microenvironment (TIME) profiles with CT imaging-derived qualitative (semantic, CT-SFs) and quantitative (radiomic, CT-RFs) features in a cohort of 60 surgically resected NSCLC. The renowned characterization of TIME, essentially based on the score evaluation of Programme Death Ligand-1 (PD-L1) and Tumor Infiltrating Lymphocytes (TILs), was implemented here by the assessment of effector and suppressor phenotypes including the analysis of Programme Death receptor 1 (PD-1). Thus, we defined two main TIME categories: hot inflamed (PD-L1high, CD8/CD3high and PD-1/CD8low) as opposed to cold inactive (PD-L1low, CD8/CD3lowand PD-1/CD8high). Importantly, as reported in the extended publication [1], these distinctive immune contextures identified different prognostic classes and were decoded by radiomics. To corroborate our radiomic approach, a comparative estimation of CT-RFs extracted from 60 NSCLC and 13 non neoplastic tissues was undertaken, documenting high discrimination ability. Moreover, we tested the potential association of qualitative radiologic features with clinico-pathological and TIME parameters. Taken together, our findings suggest that CT-SFs and CT-RFs may underlay specific patterns of lung cancer.