Graph convolutional networks improve the prediction of cancer driver genes.

Despite the vast increase of high-throughput molecular data, the prediction of important disease genes and the underlying molecular mechanisms of multi-factorial diseases remains a challenging task. In this work we use a powerful deep learning classifier, based on Graph Convolutional Networks (GCNs) to tackle the task of cancer gene prediction across different cancer types. Compared to previous cancer gene prediction methods, our GCN-based model is able to combine several heterogeneous omics data types with a graph representation of the data into a single predictive model and learn abstract features from both data types. The graph formalizes relations between genes which work together in regulatory cellular pathways. GCNs outperform other state-of-the-art methods, such as network propagation algorithms and graph attention networks in the prediction of cancer genes. Furthermore, they demonstrate that including the interaction network topology greatly helps to characterize novel cancer genes, as well as entire disease modules. In this work, we go one step forward and enable the interpretation of our deep learning model to answer the following question: what is the molecular cause underlying the prediction of a disease genes and are there differences across samples?

Model for MLL translocations in therapy-related leukemia involving topoisomerase IIβ-mediated DNA strand breaks and gene proximity

Topoisomerase poisons such as the epipodophyllotoxin etoposide are widely used effective cytotoxic anticancer agents. However, they are associated with the development of therapy-related acute myeloid leukemias (t-AMLs), which display characteristic balanced chromosome translocations, most often involving the mixed lineage leukemia (MLL) locus at 11q23. MLL translocation breakpoints in t-AMLs cluster in a DNase I hypersensitive region, which possesses cryptic promoter activity, implicating transcription as well as topoisomerase II activity in the translocation mechanism. We find that 2–3% of MLL alleles undergoing transcription do so in close proximity to one of its recurrent translocation partner genes, AF9 or AF4, consistent with their sharing transcription factories. We show that most etoposide-induced chromosome breaks in the MLL locus and the overall genotoxicity of etoposide are dependent on topoisomerase IIβ, but that topoisomerase IIα and -β occupancy and etoposide-induced DNA cleavage data suggest factors other than local topoisomerase II concentration determine specific clustering of MLL translocation breakpoints in t-AML. We propose a model where DNA double-strand breaks (DSBs) introduced by topoisomerase IIβ into pairs of genes undergoing transcription within a common transcription factory become stabilized by antitopoisomerase II drugs such as etoposide, providing the opportunity for illegitimate end joining and translocation