Due to their altered genetic context, cancer cells can become dependent on specific genes for their survival. Such cancer-specific dependencies may represent promising therapeutic targets. However, knowledge on which molecular features of cancer cells induce specific dependencies is still limited and hampers the development of effective targeted therapies. Several large scale studies have systematically measured the dependency of hundreds of known cancer cell lines on thousands of genes using gene silencing. These data have enabled the learning of supervised models to predict dependencies of cancer cells on each gene based on molecular features of the cells. In particular, linear regression with regularization, such as Elastic Net, has been used to select molecular features associated with such dependencies. Since these approaches model dependencies for each gene independently, the selected features provide limited insight into common mechanisms underlying gene dependency. Moreover, they may fail to identify robust associations with gene dependency due to the small size of the available training data. In this work, we apply a multi-task learning approach (Macau) to learn the relationship between transcriptome and gene dependency in cancer cell lines for multiple genes simultaneously. To do so, Macau projects genes, cancer cell lines and their features into a shared latent space. We explore this latent space to go beyond linking individual transcriptomic features with dependencies, and further associate pathway changes with functionally related genes without enforcing prior knowledge on pathway structure. Although Macau and Elastic Net yield similar predictive performance, they find different kinds of associations. First, Macau favors features that are relevant for predicting dependency across multiple genes. Second, Macau captures inherent functional relationships between genes and leverages these to predict cancer gene dependencies. Additionally, Macau can recover similarities between cancer cell lines belonging to the same cancer type based on their dependencies only. In summary, modelling cancer dependencies simultaneously for multiple genes can reveal underlying mechanisms shared by functionally related genes, which would be missed when learning models independently per gene.