Summary
T cell acute lymphoblastic leukaemia (T-ALL) is a rare disease that affects around 10-13 individuals in one million and affects pediatric and adult patients. High-dose chemotherapy is an adequate therapeutic treatment for most patients, however 10-20% of patients relapse and will require a bone marrow transplantation as a unique therapeutic window. T-ALL is a very aggressive disease and the rapidness in the response is crucial, however, one problem is that we cannot predict which patients will relapse. Several studies have been done to understand the origin of relapse, and there is evidence for several strategies for leukemia evolution: a) the relapse clone already exists at the time of diagnosis and is selected through treatment or b) treatment provides new mutations that will generate the relapse clone. Predicting the evolution of leukemic clones after chemotherapy pressure and understanding the specific mechanisms contributing to chemotherapy-resistant cell populations' appearance is necessary to improve cancer treatment. In our proposal, we wish to find feasible and clinically applicable ways to detect the earlier-stage clones that become resistant to chemotherapy. We wish to apply multiple approaches to better understand the specific clones like single-cell sequencing of primary T-ALL samples at diagnosis and relapse, and most importantly, compare their evolution with or without chemotherapy treatment through 2D, 3D culture and the current pdx in vivo model of leukaemia. Application of the 3D cell culture technique to T-ALL clonal evolution may represent a major scientific discovery to be used for different purposes focusing on disease modelling and relapse clone study. Our research will be pursued to optimize a method mimicking T-ALL in vitro, characterize the cells (and factors) causing the relapse, and use this information to identify new therapeutic options.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101152137 |
| Start date: | 01-09-2025 |
| End date: | 31-08-2027 |
| Total budget - Public funding: | - 165 312,00 Euro |
Cordis data
Original description
T cell acute lymphoblastic leukaemia (T-ALL) is a rare disease that affects around 10-13 individuals in one million and affects pediatric and adult patients. High-dose chemotherapy is an adequate therapeutic treatment for most patients, however 10-20% of patients relapse and will require a bone marrow transplantation as a unique therapeutic window. T-ALL is a very aggressive disease and the rapidness in the response is crucial, however, one problem is that we cannot predict which patients will relapse. Several studies have been done to understand the origin of relapse, and there is evidence for several strategies for leukemia evolution: a) the relapse clone already exists at the time of diagnosis and is selected through treatment or b) treatment provides new mutations that will generate the relapse clone. Predicting the evolution of leukemic clones after chemotherapy pressure and understanding the specific mechanisms contributing to chemotherapy-resistant cell populations' appearance is necessary to improve cancer treatment. In our proposal, we wish to find feasible and clinically applicable ways to detect the earlier-stage clones that become resistant to chemotherapy. We wish to apply multiple approaches to better understand the specific clones like single-cell sequencing of primary T-ALL samples at diagnosis and relapse, and most importantly, compare their evolution with or without chemotherapy treatment through 2D, 3D culture and the current pdx in vivo model of leukaemia. Application of the 3D cell culture technique to T-ALL clonal evolution may represent a major scientific discovery to be used for different purposes focusing on disease modelling and relapse clone study. Our research will be pursued to optimize a method mimicking T-ALL in vitro, characterize the cells (and factors) causing the relapse, and use this information to identify new therapeutic options.Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
25-11-2024
Images
No images available.
Geographical location(s)
Search
