TP53 is the most commonly mutated gene in human cancer. When mutated, TP53 gene is target of different types of alterations such as breakage of chromosomes or changes in the DNA sequence. TP53 alterations are important in the prediction of prognosis and therapies including bone marrow transplantation.
Our proposal has the goal to improve diagnostic precision of patients with leukemia. We designed a new way to accurately define the many alterations affecting TP53 gene using computer science to build new statistical methods useful to make better prediction of response to treatments.
We will study cells from patients with leukemia using a new technique which permits to identify defects in TP53 gene in a single cell. This approach will improve the time of detecting leukemia and will help doctors to make decision about treatments.