Resumen de Exploring protein structure changes due to somatic mutations in cancer
Cancer is driven by the accumulation of mutations in the genome. The massive increase of sequencing and its public availability enable us to study a joint dataset of primary and metastatic tumours from 25,499 donors across different cancer types. We provide an overview of a pan-cancer landscape of somatic mutations. Focusing on breast, colorectal and uterus cancer we describe the landscape of mutational signatures in primary and metastatic tumours. We classify the cancer genomes according to their dominant mutational signatures and are able to identify profiles shared across cancer types and differences between primary and metastatic tumours. Using a subset of the dataset as a use case, we divide cancer genomes in biologically relevant clusters using 42 statistics based on either all or only the recurrent mutations. The study of recurrent mutations also reveals susceptible sequence motifs, including TT[C>A]TTT for the POLE cluster.
To go beyond the genomic landscape, we assess the amino acid changes resulting from the somatic mutations by computing eight protein features, like amino acid conservation or folding free energy change. Focused on PIK3CA, we elucidate differences in the proportion of mutations across the different protein domains in breast, colorectal and uterus cancer. We investigate potential underlying causes of the different mutations and relate mutational processes such as hypermutation activity of POLE or defective DNA damage repair in uterus cancer to mutations in the ABD domain. The survival rate is higher in uterus cancer patients with PIK3CA mutated compared to non-mutated. For breast cancer (the largest cohort) we look at the cellular composition of the tumour immune microenvironment (TIME) by deconvoluting the RNA-Seq samples using a single-cell reference. The cellular composition of the TIME is different in PIK3CA mutated tumours compared to non-mutated. The analysis of the TIME across tumours with different PIK3CA domains mutated shows that breast tumours with the linker ABD-RBD mutated have an exhausted profile in T cells.
In conclusion, the analysis of somatic mutations and corresponding protein changes combined with the evaluation of clinical data and the TIME across and within cancer types is useful to stratify cancer patients and identify groups eligible for a specific treatment strategy, such as immunotherapy.
