Expression of genes potentially involved in loss of response in patients with chronic myeloid leukemia

BENEGAS, PAULA; ZIEGLER, BETIANA; DIEMINGER, VICTORIA; BENGIÓ, RAQUEL; ZAPATA, PEDRO; LARRIPA, IRENE; FERRI, CRISTIAN

GENE

ELSEVIER SCIENCE BV

Año: 2024 vol. 896

0378-1119

Chronic Myeloid Leukemia (CML) is a hematological malignancy characterized by the presence of the BCR::ABL1 fusion gene, which leads to uncontrolled cell growth and survival. Tyrosine kinase inhibitors (TKIs) have revolutionized the treatment of CML, but a significant proportion of patients develop resistance or lose response to these drugs. Understanding the molecular mechanisms underlying treatment response and resistance is crucial for improving patient outcomes. This study aimed to analyze the expression patterns of genes involved in treatment response and resistance in CML patients receiving TKI therapy. The expression levels of MET, FOXO3, p15, p16, HCK, and FYN genes were examined in CML patients and compared to healthy donors. Gene expression levels were compared between optimal responders (OR) and resistant patients (R) vs. healthy donors (HD). The MET and FOXO3 OR group showed significant differences compared with the HD, (p < 0.0001) and (p = 0.0003), respectively. p15 expression showed significant differences between OR and HD groups (p = 0.0078), while no significant differences were found in p16 expression between the HD groups. FYN showed a statistically significant difference between R vs. HD (p = 0.0157). The results of HCK expression analysis revealed significant differences between OR and HD (p = 0.0041) and between R and HD (p = 0.0026). When we analyzed OR patients with undetectable BCR::ABL1 transcripts, a greater expression of HCK was observed in the R group. These findings suggest that monitoring the expression levels of MET and FOXO3 genes could be valuable in predicting treatment response and relapse in CML patients. Our study provides important insights into the potential use of gene expression analysis as a tool for predicting treatment response and guiding treatment decisions in CML patients. This knowledge may ultimately contribute to the development of personalized treatment strategies to improve patient outcomes in CML.