Evolution of tumor suppressor genes can involve a trade-off because the acquisition of certain anti-cancer characteristics diminishes the ability to regenerate damaged tissue.

Tumor-growth-factor-beta signaling helps cancer cells to evolve and become resistant to drugs by down-regulating accurate DNA repair.

Phenotypic diversity and cell state transition (i.e., acquisition of a CD44+/CD24- cell state or exposure to TGF-beta) can spur intra-tumor genetic heterogeneity and contribute to acquired resistance.

A new, high-throughput in vivo MHC-I peptide minigene library platform shows that the naive immune system cannot eliminate cells presenting immunogenic antigens found at low frequencies within a growing tumor.

The multi-stage model of carcinogenesis requires the incorporation of aging-dependent somatic selection and life history-dependent evolution of species-specific tumor suppressor mechanisms in order to generalize carcinogenesis across tissues and species.

A study that models the evolution of drug resistance in tumors reveals that drugs are more effective when given in combination than sequentially, and that cure is much more likely when the drugs target different pathways.

Why does cancer develop in situations where the immune system is perfectly capable of eliminating it?

Each stage of the scientific life cycle stands to benefit from the introduction of data science techniques.

Recent advances in technology now make it possible to carry out biomedical research on animals living in the wild, or captive animals living in naturalistic conditions.

Multi-modal MRI techniques have identified biomarkers that could help to predict whether someone will develop epilepsy.