Identification of non-small cell lung cancer subgroups with distinct immuno-therapy outcomes from integrating genomics and electronic health records on a graph convolutional network
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 1,937 IO treated NSCLC patients. DeePaN demonstrated effectiveness to stratify patients into subgroups with significantly different (p-value of 2.2 x 10-11) overall 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. To the best of our knowledge, our work for the first time 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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