Breast cancer is the most common cancer in the UK. Around 55,200 people are diagnosed with breast cancer every year in the UK. That is around 150 people a day.
15 out of 100 (15%) of all newly diagnosed cancers in the UK are breast cancer.
It is estimated that one in ten women may suffer from it at some point in their lives. Although the great majority of breast tumours are cured, even today almost 20% of patients do not survive.
One of the major problems of these tumours is that they are not a single disease, but that there is a great heterogeneity among patients. Even within breast tumours of the same type, they can be more or less aggressive, respond better or worse to therapies, or relapse more or less frequently.
That is why it is essential to find new methods to better understand the differences between patients, to classify tumours more precisely according to their weaknesses, and to seek increasingly accurate and personalised treatments.
The Breast Cancer Research Unit, led by Dr. Miguel Quintela, specialises in unravelling the characteristics of each type of breast cancer at the molecular level. Its aim is to make more precise diagnoses of patients, to search for more targeted and personalised therapies, and to avoid and combat possible resistance to treatments.
The work of the Breast Cancer Research Unit focuses on several lines of work, which address some of the major problems in the treatment of breast cancer. One of its strongest points is the team’s ability to generate and lead new clinical trials based on the results obtained in the laboratory. Some of the most important are:
Phosphoproteomics in triple-negative breast cancer:
Proteins are responsible for performing all day-to-day cell functions. Their functions include controlling cell division, moving elements from one cell site to another, accumulating or using resources, etc. Depending on the needs, many of these proteins may be activated or inactivated by a process called phosphorylation, equivalent to flicking a switch. Phosphorylation of one or other proteins can therefore define the behaviour of cells and is controlled by special proteins called kinases.
Triple-negative breast tumours are among the tumours that currently have the worst prognosis. Not only are they aggressive and relapse frequently, but they do not yet have specific treatment. However, Dr. Quintela’s group has identified in these tumours several of these kinases/switches whose increased activity is associated with an increased risk of relapse. Different patients have different switches, which could be targeted pharmacologically and more effectively treat patients for whom today there is still no targeted or fully effective treatment.
One of the focuses of research in recent years is precision medicine. This type of medicine is based primarily on studying the molecular changes that occur in each patient’s tumour and looking for weaknesses that allow it to be treated effectively.
However, precision medicine is not yet able to find suitable therapies or predict relapses with complete accuracy or how patients will respond to different treatments.
That’s why Dr. Quintela’s team is working on a step beyond precision medicine: High-definition medicine. This approach consists of obtaining a large amount of information about the patient, with a lot of data obtained in real time:
- The clinical data of each patient, family and socio-economic environment, other diseases, etc.
- Molecular data of the patient’s tumour, including genetic data, analytical data, kinase activity (molecular switches, see above), immune system, cell metabolism, etc.
- Data on environmental habits, lifestyles and risks.
- Real-time monitoring: Some data such as physical activity, heart rate, blood pressure, or oxygen saturation will be monitored using portable electronic devices (wearables)
This vast amount of diverse data will be integrated and analysed using Big Data, in order to establish predictive models and ultimately be able to target treatments and patient follow-up in a personalised way at an unprecedented level.