FIS-LMA

PRAIA Projects FIS-LMA

Summary

The FIS project, funded by ISCIII and coordinated between H12O and LAFE, involves ITI in analyzing biological and clinical data on acute myeloid leukemia (AML) to identify variables that could be used as biomarkers. Subsequently, a predictive model for individual therapy response will be developed using machine learning techniques, and the model will be evaluated against different patient cohorts.

Begin date: 2022-01-03
End date: 2022-12-31

Description

Acute Myeloid Leukemia (AML) is the most common type of leukemia in adults; in Spain, 3,000 cases are diagnosed annually, of which 60% result in death within the first 3 years. Refractoriness to induction treatment and relapse after achieving complete remission (CR) are the main causes of death in AML. Thanks to previous grants (coordinated projects PI13/02387-PI13/01640 and PI16/01530-PI16/0665), we have observed that certain mutational profiles are associated with primary resistance and prevent allogeneic transplantation.

In previous studies, we detected that the Ras-Raf-MEK-ERK1/2 pathway is overactivated in AML patients resistant to tyrosine kinase inhibitors (TKIs) (PI16/01530) and that the combination of TKIs with MEK inhibitors, such as midostaurin with trametinib, improves survival in in vivo models. We have also identified a resistance detection score (PI16/01530) based on an ex vivo pharmacological multidrug resistance test and mutational study at diagnosis, which predicts survival better than the 2017 ELN classification. Additionally, we optimized and published (PI16/01530) a new method of minimal residual disease (MRD) quantification using NGS, which predicts relapse better than standard methods (CMF, qPCR).

The proposed objectives of this project are:

  1. Characterize the mechanisms of resistance to treatment and develop new therapeutic strategies.
  2. Identify biomarkers of response to tyrosine kinase inhibitors and standard chemotherapy.
  3. Monitor MRD through NGS.
  4. Develop a machine learning model for detecting resistant cases and selecting optimal treatment, as an approach to precision medicine in AML.