Evaluation of a Gene Expression Score, regarding its Predictive Potential in the Response to Induction Chemotherapy, in Patients with Myeloid Neoplasms

Abstract

The diagnostic classification and prognostic risk stratification of Myelodysplastic Syndromes (MDS) and Acute Myeloid Leukemias (AML) are fundamental for the selection of more effective therapeutic approaches. The resistance of leukemic blasts to standard chemotherapy in the induction phase may result in an inadequate response or failure to achieve complete remission (CR), constituting one of the main barriers to cure. The ability to predict, at diagnosis, which patients are at greater risk of not achieving post-induction CR, or of relapse after chemotherapy given in the consolidation phase, allows for a more assertive choice of the therapeutic approach to be used; for example, opting for Hematopoietic Stem/Progenitor Cell (HSCT) transplantation, rather than chemotherapy, for consolidation. For most patients without determining cytogenetic aberrations, prognostic risk classification depends on sequencing a large number of genes to identify mutations; however, the high cost and complexity of this approach restricts its use. SMDs and AMLs are clonal neoplasms derived from CTPHs that acquire oncogenic aberrations, giving rise to leukemic SCs (CTLs) with an imbalance between self-renewal and differentiation. The worst prognosis cytogenetic and molecular characteristics lead to a greater accumulation of CTLs and blasts in the bone marrow (BM) and peripheral blood (SP), resulting in worse clinical outcomes, due to the role of CTLs in treatment resistance, relapses, and progression from SMD to AML. The expression of CTL-specific genes in MO or SP reflects the presence of CTLs, and prognostic scores composed of the balanced sum of the expression of the genes that most contribute to the prediction of a given outcome, allow risk stratification. Thus, we will use RT-qPCR to quantify the expression of 6 genes of the LSC6 score in RNA samples extracted from SP collected at diagnosis, and we will evaluate its predictive value in identifying patients resistant to induction chemotherapy (described in the literature). For this, we will use samples and clinical and laboratory data from patients, collected and processed at Biobanco Hemocentro-RP.