bioRxivCancer BiologyPreprint
MYC overexpression drives poor prognosis and differential sensitivity to treatments according to TP53 status in chronic lymphocytic leukemia
Alba Garrote-de-BarrosiD, Juan Pérez‐FernándeziD, Andrés Arroyo-Barea, Irene Bragado-GarcíaiD, Roberto García-VicenteiD, Raquel Ancos-PintadoiD, María Velasco‐EsteveziD, María LinaresiD, Joaquín Martínez‐LópeziD, María Hernández‐SáncheziD
Abstract
Chronic lymphocytic leukemia (CLL) is a lymphoid neoplasm with very heterogeneous clinical and biological behavior. Among molecular variables, TP53 alterations are well-established adverse prognostic markers; however, MYC activation, which has been linked to disease progression, has not been completely defined in terms of clinical and biological impact, particularly in relation to TP53 status. Here, we investigated the effects of MYC overexpression according to TP53 status using clinical and transcriptomic data from CLL patients and novel cellular models. CLL patients with TP53WT and MYC overexpression exhibited significantly shorter time to first treatment and overall survival, indicating an aggressive disease course comparable to that of patients with TP53 alterations. Consistently, MYC overexpression in in vitro TP53WTmodels was associated with increased proliferation, enrichment of AKT/mTOR signaling and upregulation of genes involved in leukemogenesis and tumor progression such as FOXO6. Moreover, MYC overexpression was associated with increased sensitivity to venetoclax in TP53WT cells. By contrast, the concurrence of MYC overexpression and TP53 dysfunction conferred resistance to conventional CLL therapies such as BCL2 or BTK inhibitors. Of note, we identified a glycolysis inhibitor, in monotherapy or combined with BKT inhibitors, as a potential therapeutic strategy for CLL patients harboring MYC overexpression and TP53 alterations.
- organism: human
- assay: bulk RNA-seq
- research focus: cancer & tumor microenvironment
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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