Bironzo Paolo, Primo Luca, Novello Silvia, Righi Luisella, Candeloro Silvana, Manganaro Lorenzo, Bussolino Federico, Pirri Fabrizio, Scagliotti Giorgio V
University of Torino, Department of Oncology, Medical Oncology Division at San Luigi Hospital, Orbassano, Turin, Italy.
University of Torino, Department of Oncology, Candiolo, Turin, Italy; Candiolo Cancer Institute-IRCCS-FPO, Laboratory of Vascular Oncology, Candiolo, Turin, Italy.
Clin Lung Cancer. 2022 Sep;23(6):e347-e352. doi: 10.1016/j.cllc.2022.05.007. Epub 2022 May 11.
Lung cancers account for over 90% of thoracic malignancies and the rapid development of specific cytotoxic drugs and molecular therapies requires a detailed identification of the different histologies, gene drivers or immune microenvironment biomarkers. Nevertheless, the heterogeneous clonal evolution, the emergency of drug-induced resistance and the limited occurrence of genetic alterations claim the need of a deep integration of the tumor's and the patient's biological features. The aim of the present study is to generate a tecnological platform for precision medicine in order to set predictive personalized algorithms for patient diagnosis and therapy. All resectable patients having histologically confirmed stage IB-IIIA non-small cell lung cancer will be enrolled for tissue sampling. A large biobank of lung cancer samples and the corresponding healthy tissues and biological components (ie, blood, stools, etc.) with complete clinical, pathological and molecular information will be collected. The platform will include: a) digital patient data collection; b) whole NGS molecular analyses (exome, transcriptome, methylome) for tumor characterization; c) exploitation and collection of organoids from tissue patients; d) Surface Amplified Raman Spectroscopy; e) microfluidic-based technological drug screening; f) preclinical in vivo models based on patient-derived xenografts; g) generation of specific predictive algorithms taking into account all collected multiparameters. The project will lay the basis of a knowledge hub and qualified technology aimed not only at answering the medical and scientific community's questions, but also meant to be useful to individual patients by predicting the response to adjuvant and second-line drugs in case of relapse of the disease.
肺癌占胸部恶性肿瘤的90%以上,特异性细胞毒性药物和分子疗法的快速发展需要详细鉴定不同的组织学类型、基因驱动因素或免疫微环境生物标志物。然而,肿瘤的异质性克隆进化、药物诱导抗性的出现以及基因改变的有限发生率表明,需要深入整合肿瘤和患者的生物学特征。本研究的目的是生成一个精准医学技术平台,以便为患者诊断和治疗制定预测性个性化算法。所有组织学确诊为IB-IIIA期非小细胞肺癌的可切除患者将被纳入组织采样。将收集一个包含肺癌样本以及相应健康组织和生物成分(如血液、粪便等)的大型生物样本库,并提供完整的临床、病理和分子信息。该平台将包括:a)数字患者数据收集;b)用于肿瘤特征分析的全外显子组测序(NGS)分子分析(外显子组、转录组、甲基化组);c)从组织患者中培养和收集类器官;d)表面增强拉曼光谱;e)基于微流控技术的药物筛选;f)基于患者来源异种移植的临床前体内模型;g)生成考虑所有收集到的多参数的特定预测算法。该项目将奠定一个知识中心和合格技术的基础,不仅旨在回答医学和科学界的问题,而且通过预测疾病复发时对辅助药物和二线药物的反应,对个体患者也有用。