(1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method based on a deep neural network integrated into a network-based stratification framework (D3NS) is proposed to stratify tumors according to somatic mutations. (2) Methods: This approach leverages the power of deep neural networks to detect hidden information in the data by combining the knowledge contained in a network of gene interactions, as typical of network-based stratification methods. D3NS was applied using real-world data from The Cancer Genome Atlas for bladder, ovarian, and kidney cancers. (3) Results: This technique allows for the identification of tumor subtypes characterized by different survival rates and significant associations with several clinical outcomes (tumor stage, grade or response to therapy). (4) Conclusion: D3NS can provide a base model in cancer research and could be considered as a useful tool for tumor stratification, offering potential support in clinical settings.
(1) 背景：肿瘤亚型的识别是精准医疗中实现准确诊断和个性化治疗的基础。癌症的发展通常由体细胞突变的积累驱动，这些突变可能导致组织功能和形态的改变。本研究提出一种基于深度神经网络的方法，并将其整合到基于网络的分类框架中（D3NS），以根据体细胞突变对肿瘤进行分类。(2) 方法：该方法结合基因相互作用网络中的知识，利用深度神经网络的能力检测数据中的隐藏信息，这是基于网络的分类方法的典型特征。D3NS 使用来自癌症基因组图谱的真实世界数据，应用于膀胱癌、卵巢癌和肾癌。(3) 结果：该技术能够识别具有不同生存率并与多种临床结果（肿瘤分期、分级或治疗反应）显著相关的肿瘤亚型。(4) 结论：D3NS 可为癌症研究提供基础模型，并可作为肿瘤分类的有用工具，为临床环境提供潜在支持。
肿瘤(癌症)患者之家