Abstract
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.
Highlights
Immuno-oncology (IO) therapies including checkpoint inhibitors have transformed the therapeutic landscape of nonsmall cell lung cancer (NSCLC)[1,2,3]
Our work demonstrates the feasibility and effectiveness of employing a graph-based artificial intelligence (AI) approach to integrate RWE-based high-dimensional electronic health records (EHRs) and genomics to stratify NSCLC patients by IO benefit
We explored the feasibility and effectiveness of a graph AI-based unsupervised framework, “deep patient graph” (DeePaN), to stratify IO-treated NSCLC patients from integrating rich genomics and EHR-derived clinical data
Summary
Immuno-oncology (IO) therapies including checkpoint inhibitors have transformed the therapeutic landscape of nonsmall cell lung cancer (NSCLC)[1,2,3]. Distinct molecular subtypes[13,14,15,16,17,18] derived from rich genomic resources, including high tumor mutational burden (TMB) and high PDL1 protein expression, have been associated with beneficial responses to checkpoint inhibitor therapies in NSCLC1,19–21. The integration of both genomic and EHR evidence is expected to reveal a fuller description of tumor and patient characteristics impacting drug response. We sought to explore the feasibility and effectiveness of applying GCN for patient subtype discovery through integrative usage of EHR and genomic data
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