Genomics and precision medicine

We're taking part in a 5-year research partnership to develop a new machine learning model that combines:

• multimodal data
• genetic and molecular traits
• tumour location
• images
• biomarker blood tests

We'll look for:

• dynamic biomarkers, which are molecules found in blood
• other body fluids or tissues that are a sign of disease

They can predict resistance during treatment or a later relapse.

Professor Tony Ng, head of the comprehensive cancer centre at King's College London, said that half of cancer patients who were diagnosed with advanced but operable cancers came back 1 to 2 years after treatment when it was discovered that the cancer had spread to other parts of the body.

To identify those at high risk, the team will create a 'digital biological twin' of the patient, to test multiple drugs, and multiple doses, at multiple time points.

We are linking up the patient with the twin and can immediately feed back information to the clinical trial or clinical management algorithms. The biological twin will not only tell us this person has a high risk, but also what we can do about it.

Tony added that the twin can be used to develop a 'multimodal monitoring tool'. This is because different parameters besides genomics can be looked at within the twin, such as whether the immune system is suppressed through contact with cancer cells. The team hope to create specialist equipment over the 5 years.