Abstract
BackgroundAcute myeloid leukemia (AML) remains a devastating disease with a 5-year survival rate of less than 30%. AML treatment has undergone significant changes in recent years, incorporating novel targeted therapies along with improvements in allogeneic bone marrow transplantation techniques. However, the standard of care remains cytarabine and anthracyclines, and the primary hindrance towards curative treatment is the frequent emergence of intrinsic and acquired anticancer drug resistance. In this respect, patients presenting with chemoresistant AML face dismal prognosis even with most advanced therapies. Herein, we aimed to explore the potential implementation of the characterization of chemoresistance mechanisms in individual AML patients towards efficacious personalized medicine.MethodsTowards the identification of tailored treatments for individual patients, we herein present the cases of relapsed AML patients, and compare them to patients displaying durable remissions following the same chemotherapeutic induction treatment. We quantified the expression levels of specific genes mediating drug transport and metabolism, nucleotide biosynthesis, and apoptosis, in order to decipher the molecular mechanisms underlying intrinsic and/or acquired chemoresistance modalities in relapsed patients. This was achieved by real-time PCR using patient cDNA, and could be readily implemented in the clinical setting.ResultsThis analysis revealed pre-existing differences in gene expression levels between the relapsed patients and patients with lasting remissions, as well as drug-induced alterations at different relapse stages compared to diagnosis. Each of the relapsed patients displayed unique chemoresistance mechanisms following similar treatment protocols, which could have been missed in a large study aimed at identifying common drug resistance determinants.ConclusionsOur findings emphasize the need for standardized evaluation of key drug transport and metabolism genes as an integral component of routine AML management, thereby allowing for the selection of treatments of choice for individual patients. This approach could facilitate the design of efficacious personalized treatment regimens, thereby reducing relapse rates of therapy refractory disease.
Highlights
Acute myeloid leukemia (AML) remains a devastating disease with a 5-year survival rate of less than 30%
We found a major upregulation of both the lysosomal acidification pump V-type proton ATPase subunit H (ATP6v1H, 11-fold, Fig. 4d) and the acidic lysosomal protease cathepsin D (CTSD, 25-fold, Fig. 4d) [111], which indicated a stable expansion of the lysosomal compartment
The genes studied in the current paper are crucial for treatment outcome and should be routinely evaluated and taken into consideration when selecting the treatment of choice for individual AML patients
Summary
Acute myeloid leukemia (AML) remains a devastating disease with a 5-year survival rate of less than 30%. The standard of care remains cytarabine and anthracyclines, and the primary hindrance towards curative treatment is the frequent emergence of intrinsic and acquired anticancer drug resistance In this respect, patients presenting with chemoresistant AML face dismal prognosis even with most advanced therapies. While no new drugs were approved for AML treatment in almost 50 years, the FDA granted approval to eight novel agents for various AML indications in the last three years [3] Some of these agents target specific mutations that present only in a limited subset of AML patients, and traditional intensive protocols remain the therapy of choice for most AML patients [4]. These include, for example, decreased cellular accumulation due to impaired drug uptake and/or enhanced drug efflux predominantly via ATP-binding cassette (ABC) efflux transporters [9,10,11,12,13,14,15,16,17], loss of metabolic activation of a prodrug [11, 17,18,19,20,21,22,23], enhanced drug degradation [17, 24], qualitative and quantitative alterations in the target enzymes [25,26,27,28], as well as drug sequestration [29]
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