肺癌是发病率和死亡率最高的恶性肿瘤，靶向治疗因确切疗效在临床广泛应用。但由于肿瘤异质性、高肿瘤突变负荷和靶向药物耐药的出现，无靶向药物、靶向药物联合治疗及联合免疫治疗等方案优化成为精准治疗新难题。有学者尝试用生物信息学的方法，用疾病-基因-化学关联性寻找个体化治疗方法；而基于临床、病理、基因和18F-FDG PET/CT对肺癌患者预后和疗效评估及个体化治疗方案的筛选尚未见报道。团队前期自主研发数据扰动云系统，提出“疾病表观生物治疗”新方法，完成了98807个基因组学数据库和Web of Science数据库113年题录信息的标准化，在疑难病例筛选药物的疗效上取得极大进展。本课题将运用信息扰动云技术处理多维信息，通过“疾病-影像-基因-药物”的相似性分析法进行比对，筛选个体化治疗药物，尤其是“老药新用”，经过人源性肺癌体外细胞和动物模型活体实验反向验证，摸索个体化治疗筛选和优化的方法。

Lung cancer has the highest clinical morbidity and mortality worldwide. With the ability to prolong some patients' survival time, targeted therapy has been widely used in clinical. However, due to the existence of tumor heterogeneity, increase of gene mutation burden and emergence of targeted drug resistance, the new problem is what kind of regium we should take: targeted combined chemical therapy, sequential targeted therapy or immunotherapy? Fast computing and state of the art software has facilitated the quantification of tumor heterogeneity non-invasively using PET/CT or other modalities. 18F-FDG PET imaging and radiomics analysis are gradually getting more attention as their potential on prognosis evaluation, even though the diagnosis of disease mainly relies on pathological and gene detection. Basing on the multi-omcis studies, we have developed the cloud computing system and completed the standardization of the 113807 genomic database and the web of science database of past 113 years, by which, the concept of "Disease epigenetics treatment" was launched. In this study, the multi-omics information integrated Cloud computing technology, PET images, pathological result and genetic mutation status were applied to screening out the potential targeted therapy drugs, especially "renewing of old medicine". By using the animal models of human-source lung cancer, the efficacy of conventional and newly selected targeted drugs was tested, and the models of individualized therapy was also verified.