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A biomarker signature for predicting response to cancer immunotherapy

The CSIC has developed, together with other institutions, a method to personalise therapeutic strategies combined with immune checkpoint inhibitors (ICIs) in cancer patients. It is based on 10 biomarkers, and facilitates the optimisation of therapeutic strategies for various types of tumours.

Breast cancer cells. Credits: Cecil Fox/NIH/Wikimedia.Immune checkpoints (ICs) are a normal mechanism of the immune system to prevent the defence response from becoming so strong that it also reacts against the body's own cells and tissues (autoimmunity). A simple way of explaining this is that healthy cells have proteins that are recognised by other proteins which are present in the lymphocytes or T cells of the immune system: when they recognise each other, the two proteins bind and prevent the first cells from being attacked by the second ones. This explains why they are called "checkpoints".

Many cancer cells have mutations that are normally recognised by the immune system as foreign, so that T cells and other immune cells should attack and destroy such cancer cells. However, cancer cells can also deploy immune checkpoint proteins, allowing them to evade attack by the immune system.
In order to boost the immune system's ability to detect and attack cancer cells, immune checkpoint inhibitors (ICIs) have been developed. This type of cancer immunotherapy has become an important strategy for the treatment of various types of cancer.

Nevertheles, not all patients and cancers respond to ICIs. It is therefore crucial to identify previously which patients and types of cancer will most likely respond to ICIs, in order to predict the best pharmacological option in each case.

To this end, CSIC scientists, in collaboration with the Hospital Clinic de Barcelona, IDIBAPS, the University of Barcelona and ICREA, have identified a set of 10 markers (also known as ‘immunometabolic signature’), which makes possible to classify tumour samples into three different groups, according to the combined expression levels of these markers. This new classification should provide oncologists with guidance in deciding and chosing the most appropriate therapeutic approaches with ICI drugs.

This signature has been generated by an analysis of a large cohort of 4,200 samples of 11 different tumour types, which were unequivocally classified into one of three immunometabolic groups.

This method can be used for immunometabolic classification, for prediction of response to ICIs (alone or in combination with other drugs), prognosis and follow-up, or as a drug screening tool. In this sense, it allows the personalisation of therapeutic strategies in cancer patients.

Currently, the efficacy of ICI treatment ranges between 30-40% for sensitive tumours and is less than 5% for immunoresistant tumours. Current predictive methods have low sensitivity and low specificity for identifying patients who could benefit from ICI treatment.

The researchers note that this method can be easily implemented to help specialists to adjust treatments depending on the basis of the most probable tumour’s response to ICI. This should result in better management of cancer patients and, in particular, speed up therapeutic decision-making, which is a critical factor in disease progression.

The team is currently seeking partnerships with pharmaceutical or diagnostic companies interested in licensing the patent for commercial exploitation.


Xavier Gregori
Deputy Vice-presidency
for Knowledge Transfer - CSIC
Tel.: 93 887 60 04
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
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