Deciphering and predicting the evolution of cancer cell populations

Informations projet

CANCEREVO

N° de convention de subvention: 820137

DOI

10.3030/820137

Projet clôturé

Date de signature de la CE 23 Janvier 2019

Date de début 1 Mars 2019

Date de fin 31 Mars 2025

Financé au titre de

• EXCELLENT SCIENCE - European Research Council (ERC)

Coût total

€ 2 000 000,00

Contribution de l’UE

€ 2 000 000,00

Coordonné par

QUEEN MARY UNIVERSITY OF LONDON
United Kingdom

Periodic Reporting for period 4 - CANCEREVO (Deciphering and predicting the evolution of cancer cell populations)

Période du rapport: 2023-09-01 au 2025-03-31

Résumé du contexte et des objectifs généraux du projet

Metastatic gastro-oesophageal cancers are incurable and are the third commonest cause of cancer death in the EU. Many of them become resistant to available treatments quickly but the cause of resistance remains largely unknown. These tumours demonstrate a high level of genetic instability which is thought to enable the evolution of drug resistance through Darwinian adaptation. We have developed new liquid biopsy technologies (called ‘whole exome circulating tumour DNA sequencing’ which only require a simple blood test) to identify how resistance evolves in detail and to develop methods that can predict resistance evolution.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

We established new liquid biopsy sequencing and computational methods to improve the sensitivity and specificity of these genetic analyses. We have also developed techniques to track evolution in blood samples and to make forecasts about the likely development of drug resistance even before patients have started a particular treatment. The approach is similar to making weather forecasts: we have shown that analysing basic genetic processes such as which mutations are most likely generated in an individual cancer and how they might undergo selection can be used to predict how long it will take for a tumour to develop resistance and which mechanisms of resistance is most likely to develop. We have completed genetic sequencing of tumours and blood samples from over 130 patients with metastatic gastro-oesophageal cancers who received treatment with chemotherapies, targeted drugs and immunotherapies. The analysis of this complete and large datatset is still ongoing but they have already provided unprecedented insights into the Darwinian evolution of these tumours over time and the complete results will be published in 2025. We furthermore demonstrated that biopsies are unreliable to identify mutations that can be targeted by cancer vaccines as a significant proportion of mutations detected in a small biopsy are not present in the clone that seeds metastases.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

Our sequencing technology achieves unprecedented detail and sensitivity for the analysis of genetic changes across all protein coding genes from simple blood samples. This is useful for the design of better cancer vaccines and the detection of targetable genetic drivers which only occur in the metastatic clone. Demonstrating that long-range evolution forecasts are possible in principle is a major breakthrough. We are now testing this in a larger cohort of gastro-oesophageal cancer patients who received different therapies, including chemotherapy, immunotherapy and targeted therapies. If we know which drugs are likely to stop working and when, it would help clinicians to select the treatment that works the longest and give them more time to plan for the next steps of the patient’s treatment. The new techniques that we are developing through this study will allow us to make predictions at the outset of a patient’s treatment and to stay a step ahead of the cancer.

Ultra-deep whole-exome ctDNA sequencing provides unprecedented insights into cancer evolution