Cancer systems immunology is a highly interdisciplinary field that is advancing our ability to understand and predict the complex behavior that orchestrates the interplay between tumors and the immune system.

Data-driven systems biology models of signaling predict cellular response to untested perturbations and can nominate drug combinations to overcome drug resistance in cancer cells.

The theory of Buffered Qualitative Stability uses the importance of biological robustness to explain many features of gene regulatory networks in a wide range of organisms, and has implications for diverse biological phenomena including the ability of bacteria and cancer cells to ‘loosen’ their robustness and hence evade treatment.

A novel method predicts cancer and immune cell types from bulk tumor gene expression data with the ability to consider uncharacterized and possibly highly variable cell types, which is validated in human genome.

Results from the Reproducibility Project: Cancer Biology provide a picture of the rate of false positives in cancer preclinical research, but it is still not clear if research in this field is less reproducible than it should be.

Global transcriptome analysis reveals that the transcription factor SOX4 can promote breast tumor development via tumor angiogenesis by promoting paracrine signalling through endothelin-1.

A novel reporter system that employs cell surface antigen-nanobody pairing enables physical interaction-dependent labeling and functional modification between diverse cell types.

The transcriptomic profiles of the constituent monotherapies of synergistic drug pairs tend to be correlated and result in novel gene expression in the combinations.

Editors' Summary: This Replication Study did not reproduce those experiments in the original paper that it attempted to reproduce.

Investigation of regulatory network topology reveals latent design principles of multistable phenotypic landscape.