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CDA as a predictive marker for life-threatening toxicities in patients with AML treated with cytarabine
Raphaelle Fanciullino1, Laure Farnault2, Mélanie Donnette1
1SMARTc Unit, Pharmacokinetics Laboratory, Unité Mixte de Recherche (UMR) INSERM U1068 Centre de Recherche en Cancerologie de Marseille, Aix Marseille University, Marseille, France.
Abstract:
Cytarabine (Ara-C) is the backbone of acute myeloid leukemia (AML) chemotherapy. Little is known about possible risk factors predictive for the frequent (ie, up to 16%) life-threatening or lethal toxicities caused by Ara-C. Ara-C is detoxified in the liver by a single enzyme, cytidine deaminase (CDA), coded by a gene known to be highly polymorphic. In this proof-of-concept study, we particularly investigated the role of the CDA poor metabolizer (PM) phenotype in Ara-C toxicities. CDA phenotyping (measurement of CDA residual activity in serum) and genotyping (search for the CDA*2 allelic variant) were performed in 58 adult patients with AML treated with the standard 7+3 (Ara-C + anthracyclines) protocol. Statistically significantly lower CDA activity was observed in patients experiencing severe/lethal toxicities as compared with patients who did not (1.5 ± 0.7 U/mg vs 3.95 ± 3.1 U/mg; Student t test P < .001). Subsequent receiver operating characteristic analysis identified a threshold in CDA activity (ie, 2 U/mg) associated with PM syndrome and increased risk of developing severe toxicities. Five percent of patients experienced lethal toxicities, all displaying CDA PM status (1.3 ± 0.5 U/mg). In terms of efficacy, a trend toward higher response rates and longer progression-free survival and overall survival were observed in patients with low CDA activity. Taken together, the results of this study strongly suggest that CDA is a predictive marker of life-threatening toxicities in patients with AML receiving induction therapy with standard Ara-C.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.