DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy.
DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy.
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DOI：
10.1038/s41746-021-00381-z
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发表时间：
2021-02-02
影响因子：
15.2
通讯作者：
Linghu B
中科院分区：
文献类型：
--
作者：
Fang C;Xu D;Su J;Dry JR;Linghu B
免疫肿瘤学（IO）疗法已经改变了非小细胞肺癌（NSCLC）的治疗前景。然而，患者对IO的反应是可变的，并受到健康，免疫和肿瘤因素的异质组合的影响。迫切需要发现影响缓解的不同NSCLC亚组。我们开发了一种深度患者图卷积网络，我们称之为“DeePaN”，以发现影响IO受益的数据模式中的NSCLC复杂性。DeePaN采用了来自1937名接受IO治疗的NSCLC患者的基于真实世界证据（RWE）的电子健康记录（EHR）和基因组学的高维数据。DeePaN证明了将患者分层为具有显著差异（P值为2.2 × 10−11）的亚组的有效性，IO治疗后的总中位生存期为20.35个月和9.42个月。从多种非基于图的无监督方法中未观察到IO结果的显著差异。此外，我们证明了DeePaN的患者分层有可能增加肿瘤突变负荷（TMB）的新兴IO生物标志物。DeePaN发现的亚组的特征表明有可能为IO治疗提供信息，包括分别在IO有益亚组和IO非有益亚组中富集突变的KRAS和高血液单核细胞计数。我们的工作已经证明了基于图形的AI是可行的，并且可以有效地整合高维基因组和EHR数据，以有意义地对癌症患者进行不同临床结果的分层，并有可能为精确的肿瘤学提供信息。
Immuno-oncology (IO) therapies have transformed the therapeutic landscape of non-small cell lung cancer (NSCLC). However, patient responses to IO are variable and influenced by a heterogeneous combination of health, immune, and tumor factors. There is a pressing need to discover the distinct NSCLC subgroups that influence response. We have developed a deep patient graph convolutional network, we call “DeePaN”, to discover NSCLC complexity across data modalities impacting IO benefit. DeePaN employs high-dimensional data derived from both real-world evidence (RWE)-based electronic health records (EHRs) and genomics across 1937 IO-treated NSCLC patients. DeePaN demonstrated effectiveness to stratify patients into subgroups with significantly different (P-value of 2.2 × 10−11) overall median survival of 20.35 months and 9.42 months post-IO therapy. Significant differences in IO outcome were not seen from multiple non-graph-based unsupervised methods. Furthermore, we demonstrate that patient stratification from DeePaN has the potential to augment the emerging IO biomarker of tumor mutation burden (TMB). Characterization of the subgroups discovered by DeePaN indicates potential to inform IO therapeutic insight, including the enrichment of mutated KRAS and high blood monocyte count in the IO beneficial and IO non-beneficial subgroups, respectively. Our work has proven the concept that graph-based AI is feasible and can effectively integrate high-dimensional genomic and EHR data to meaningfully stratify cancer patients on distinct clinical outcomes, with potential to inform precision oncology.
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