Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Abstract

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns in Breast Multiplexed Digital Pathology

“…Modern deep learning methods, including graph neural networks, constitute an appealing alternative for data-driven biomarkers for prognosis [21, 22]. Yet, their employment in real-world clinical settings remains constrained by the lack of rigorous external validation [35, 36] and limited explain-ability [25, 26, 28, 29, 32, 33].…”

“…On the other hand, there is a growing interest in harnessing graph-based learning techniques to analyze the association between the TME and disease without the need for explicit hypotheses in a data-driven manner [21, 22]. Numerous recent studies have reported promising results by leveraging graph neural networks (GNNs) to model the TME and predict the presence [23, 24], grade [25, 26], stage [27], subtype [28, 29], and prognosis [30–34] of diverse types of cancers.…”