  Cancers are mainly caused by somatic genomic alterations (SGAs) that perturb
cellular signaling systems and eventually activate oncogenic processes.
Therefore, understanding the functional impact of SGAs is a fundamental task in
cancer biology and precision oncology. Here, we present a deep neural network
model with encoder-decoder architecture, referred to as genomic impact
transformer (GIT), to infer the functional impact of SGAs on cellular signaling
systems through modeling the statistical relationships between SGA events and
differentially expressed genes (DEGs) in tumors. The model utilizes a
multi-head self-attention mechanism to identify SGAs that likely cause DEGs, or
in other words, differentiating potential driver SGAs from passenger ones in a
tumor. GIT model learns a vector (gene embedding) as an abstract representation
of functional impact for each SGA-affected gene. Given SGAs of a tumor, the
model can instantiate the states of the hidden layer, providing an abstract
representation (tumor embedding) reflecting characteristics of perturbed
molecular/cellular processes in the tumor, which in turn can be used to predict
multiple phenotypes. We apply the GIT model to 4,468 tumors profiled by The
Cancer Genome Atlas (TCGA) project. The attention mechanism enables the model
to better capture the statistical relationship between SGAs and DEGs than
conventional methods, and distinguishes cancer drivers from passengers. The
learned gene embeddings capture the functional similarity of SGAs perturbing
common pathways. The tumor embeddings are shown to be useful for tumor status
representation, and phenotype prediction including patient survival time and
drug response of cancer cell lines.
