Rahbari Group

Cancer predisposition and Ageing

Our group aims to provide a detailed description of the genetic changes in normal and precancerous tissues, and to understand how mutations acquired during ageing may contribute to the germline predisposition to cancer for future offspring.

Cancer predisposition and Ageing

During the natural process of ageing, somatic mutations accumulate in our genome and can cause cancer and cancer predisposition. However, little is known about mutagenesis or factors that influence rates of accumulation of mutations in normal tissue and their impact.

Our group aims to provide a detailed description of the genetic changes in normal and precancerous tissues, and to understand how mutations acquired during ageing may contribute to the germline predisposition to cancer for future offspring.

Mutational landscape in human somatic and germline cells

We explore the effects that natural ageing processes have on how cells function over time, in different tissues.

To achieve this, we survey and analyse the genomes of cancer-free cells from the same tissue to understand:

• Landscapes of mutations – the range and clustering of genetic changes that occur within cells from the same tissue over time.
• Clonal dynamics – the speed of growth, and physical spread, of cells with shared genetic changes.

In this way we seek to understand why specific genetic alterations enable some cells to grow and reproduce more successfully within a tissue, while overs do not.

Identifying pathogenic mutations in reproductive tissues that cause cancer predisposition in the offspring

Little is known about what causes mutations within  within the hierarchy of germline cells and factors, besides the age effect, that influence rate of mutation. We have several projects to identify factors that may cause accumulation of pathogenic mutations in human germline cells. We also study how normal cells in individuals with cancer predisposition syndromes respond to ageing.

Experimental and computational methods

We develop computational and experimental methods such as spatially informed multi-omics methods to study clonal dynamics and non-genetic factors that may assist cancer transformation.