多组学数据库的开发用于头颈部癌症的个体化预后预测：大数据决策欧盟项目。
Development of a multiomics database for personalized prognostic forecasting in head and neck cancer: The Big Data to Decide EU Project.
机构信息
Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.
Integrated Biology Platform, Department of Applied Research and Technology Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.
出版信息
Head Neck. 2021 Feb;43(2):601-612. doi: 10.1002/hed.26515. Epub 2020 Oct 27.
BACKGROUND
Despite advances in treatments, 30% to 50% of stage III-IV head and neck squamous cell carcinoma (HNSCC) patients relapse within 2 years after treatment. The Big Data to Decide (BD2Decide) project aimed to build a database for prognostic prediction modeling.
METHODS
Stage III-IV HNSCC patients with locoregionally advanced HNSCC treated with curative intent (1537) were included. Whole transcriptomics and radiomics analyses were performed using pretreatment tumor samples and computed tomography/magnetic resonance imaging scans, respectively.
RESULTS
The entire cohort was composed of 71% male (1097)and 29% female (440): oral cavity (429, 28%), oropharynx (624, 41%), larynx (314, 20%), and hypopharynx (170, 11%); median follow-up 50.5 months. Transcriptomics and imaging data were available for 1284 (83%) and 1239 (80%) cases, respectively; 1047 (68%) patients shared both.
CONCLUSIONS
This annotated database represents the HNSCC largest available repository and will enable to develop/validate a decision support system integrating multiscale data to explore through classical and machine learning models their prognostic role.
背景
尽管治疗方法有所进步，但仍有 30%至 50%的 III 期至 IV 期头颈部鳞状细胞癌（HNSCC）患者在治疗后 2 年内复发。Big Data to Decide（BD2Decide）项目旨在建立一个用于预后预测建模的数据库。
方法
本研究纳入了 1537 例接受根治性治疗的局部晚期 HNSCC 患者。使用预处理肿瘤样本和计算机断层扫描/磁共振成像扫描分别进行全转录组学和放射组学分析。
结果
整个队列由 71%的男性（1097 例）和 29%的女性（440 例）组成：口腔（429 例，28%）、口咽（624 例，41%）、喉（314 例，20%）和下咽（170 例，11%）；中位随访时间为 50.5 个月。转录组学和影像学数据分别可用于 1284 例（83%）和 1239 例（80%）患者；1047 例（68%）患者两者兼有。
结论
该注释数据库代表了目前最大的 HNSCC 可用存储库，将能够开发/验证一个决策支持系统，该系统将整合多尺度数据，通过经典和机器学习模型探索其预后作用。
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