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Germline and somatic variants in the p53 pathway cooperate to influence cancer risk, progression and response to therapy.

There are two types of genetic variation that affect cancer. So-called somatic variation results from changes (mutations) in a person’s DNA that are acquired during their lifetime in individual parts of the body. These mutations only occur in some cells in the body and are often the result of damage with age or by carcinogens such as sunlight, smoking and some infections. By contrast, germline variation is inherited and so occurs in every cell in the body since birth. An example of germline variation is inheriting a mutation in the BRCA1 gene, which is associated with increased risk of breast cancer in families with these mutations.

Many research studies have investigated the separate effects of somatic and germline variation on cancer risk, progression and response to therapy. However, these studies generate an incomplete picture. For example, designing bespoke therapies to target cancer cells containing specific somatic mutations has had variable success, perhaps in part due to differing underlying germline variation between individuals. To make further progress, we need to learn more about whether germline and somatic variants interact to affect cancer. This is the question that Dr Ping Zhang asked as a post-doc in Dr Gareth Bond’s lab when it was at Ludwig Oxford.

In this paper published recently in the journal Cancer Research, the Ludwig Oxford researchers worked with colleagues at several other institutions to investigate the interplay between germline and somatic variants affecting the activity of the p53 tumour suppressor protein. p53 is a key protector against the development of cancers and somatic mutations in the gene coding for p53 are found in over half of all human cancers. Perturbation of p53 activity also influences cancer progression and drug response.

In this study, the team discovered evidence that germline cancer-risk p53 pathway mutations cooperate with somatic p53 gene mutations to alter cancer risk, progression and response to therapy, and can be used to identify novel, more effective therapies. With this increased understanding, this work has the potential to guide further discovery of future anti-cancer drug targets and novel combination therapies for enhancing precision medicine.