Descrizione del progetto
Una tecnologia di sequenziamento innovativa per l’analisi dell’evoluzione del cancro
È risaputo che i tumori subiscono un’evoluzione, dando origine a diversi cloni genetici durante il proprio ciclo di vita. Il progetto CANCEREVO, finanziato dall’UE, si dedica alla comprensione delle dinamiche di tali mutamenti e delle loro ramificazioni a livello clinico. A questo proposito, alcuni scienziati studieranno il DNA tumorale circolante (ctDNA) proveniente da carcinomi gastro-esofagei metastatici utilizzando una tecnologia innovativa per il sequenziamento profondo dell’esoma. Questa tecnologia fornisce la sensibilità necessaria per il rilevamento di subcloni tumorali rari e il tracciamento dell’evoluzione dell’intera popolazione delle cellule tumorali. Tuttavia, l’aspetto più importante è che questa tecnologia può essere impiegata sia in ambito clinico che per la previsione dei meccanismi di resistenza ai farmaci per la chemioterapia e l’immunoterapia.
Obiettivo
The fundamental evolutionary nature of cancer is well recognized but an understanding of the dynamic evolutionary changes occurring throughout a tumour’s lifetime and their clinical implications is in its infancy. Current approaches to reveal cancer evolution by sequencing of multiple biopsies remain of limited use in the clinic due to sample access problems in multi-metastatic disease. Circulating tumour DNA (ctDNA) is thought to comprehensively sample subclones across metastatic sites. However, available technologies either have high sensitivity but are restricted to the analysis of small gene panels or they allow sequencing of large target regions such as exomes but with too limited sensitivity to detect rare subclones. We developed a novel error corrected sequencing technology that will be applied to perform deep exome sequencing on longitudinal ctDNA samples from highly heterogeneous metastatic gastro-oesophageal carcinomas. This will track the evolution of the entire cancer population over the lifetime of these tumours, from metastatic disease over drug therapy to end-stage disease and enable ground breaking insights into cancer population evolution rules and mechanisms. Specifically, we will: 1. Define the genomic landscape and drivers of metastatic and end stage disease. 2. Understand the rules of cancer evolutionary dynamics of entire cancer cell populations. 3. Predict cancer evolution and define the limits of predictability. 4. Rapidly identify drug resistance mechanisms to chemo- and immunotherapy based on signals of Darwinian selection such as parallel and convergent evolution. Our sequencing technology and analysis framework will also transform the way cancer evolution metrics can be accessed and interpreted in the clinic which will have major impacts, ranging from better biomarkers to predict cancer evolution to the identification of drug targets that drive disease progression and therapy resistance.
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Meccanismo di finanziamento
ERC-COG - Consolidator GrantIstituzione ospitante
E1 4NS London
Regno Unito