Oferta TFM | ID 34

A significant proportion of patients with T-cell acute lymphoblastic leukemia do not respond to treatment or experience a relapse. A key challenge is to identify, at the time of diagnosis, those who are at higher risk of a poor prognosis. We have recently proposed a biomarker based on a transcriptional signature that may facilitate the early detection of cases prone to poor treatment response (characterized by high minimal residual disease, MRD) and that can be implemented at the time of diagnosis (doi: 10.1002/1878-0261.70234). Furthermore, we have proposed another biomarker consisting of a transcriptional signature that could be useful for identifying patients likely to develop an adverse clinical course despite having a favorable response to induction chemotherapy (characterized by low MRD) (doi: 10.1111/bjh.70118).

We hypothesize that these two biomarkers can not only predict prognosis but also identify specific therapeutic vulnerabilities. Using clinical and transcriptomics data from a cohort of n=1,335 pediatric T-ALL patients (Gabriella Miller Kids First), we are currently performing in silico drug prioritization. We expect that this analysis will provide a list of drugs with maximal predicted sensitivity in patients classified according to each biomarker.

• The objective of this Master's project is to evaluate selected therapies in cellular models. The project will involve:
• -Generation of cellular models of T-ALL representative of patients who are biomarker-positive or -negative, through genetic manipulation (electroporation, transfection, transduction). Validations will be performed by RT-qPCR and WB.
• - Drug sensitivity assays to the selected treatments. Viability, proliferation and cell cycle assays will be performed using flow cytometry.
• - Molecular characterization: in those cellular models that demonstrate a differential response to the drugs, we will perform whole-transcriptome sequencing (RNA-seq; treated vs. untreated). The RNA sequencing results will subsequently be analyzed to determine gene ontology and pathway enrichment.

The expected outcome is to identify therapeutic vulnerabilities for patients classified as biomarker-positive at diagnosis, to validate their potential in cellular models, and to unveil the molecular mechanisms underlying treatment sensitivity. In later stages of the project, beyond the TFM, we plan to perform functional precision medicine in prospective patient samples and patient-derived xenograft (PDX) mouse models.

The project will provide training in cell culture, genetic manipulation, functional assays, molecular characterization and bioinformatics analysis, while contributing to a broader project that aims to stratify high-risk pediatric T-ALL patients at diagnosis and offer them personalized therapeutic strategies.