Grupo de Genómica Computacional del Cáncer

Inicio | Investigación e innovación | Programas Científicos | Programa de Biología Estructural | Grupo de Genómica Computacional del Cáncer

Becarios Pre-Doctorales

  • Pelayo Gónzalez de Lena

Técnicos de Laboratorio

  • Luis García

According to the conventional point of view of the 1980s, cancer drivers were classified into tumour suppressor genes (TSGs) and oncogenes (OGs) based on their functional roles in cancer. Mutations in TSGs were considered recessive while mutations in OGs were expected to be dominant gain-of-function mutations. However, importantly, the success of large-scale cancer genomics has led to debate around the dogma in cancer genetics that all cancer genes should behave according to the same model, even in different contexts (e.g., cancer types). Furthermore, by analysing large-scale cancer genomics data, many exceptions have been observed, including haploinsufficiency in TSGs or amplification-linked mutation in OGs and even in dual-functional genes.

It is clear that activity levels of genomic alterations in cancer genes are disparate across cancer types, and optimal models for tumour progression may also vary depending on contexts or cancer types. The pioneering cancer genomics studies referenced above have triggered many interesting questions about how cancer genes change their models of tumour progression depending on cancer types or contexts.

By analysing large-scale cancer genomics data, we aim to further pursue novel questions about cancer-type and context-specific tumour progression to understand tumour heterogeneity.