Acute lymphoblastic leukemia (ALL) is the most common hematological and overall malignancy in pediatrics, accounting for around 30% of all childhood cancers and around 80% of all childhood leukemias. It is one of the pediatric malignancies with the highest cure rate, exceeding 80%, when treated with standardized protocols like the European standard, the Berlin-Frankfurt-Munster (BFM) protocol.1, 2, 3 However, there is still more than 10% of patients with unfavorable outcome. The treatment of childhood ALL is based on risk stratification. Patients can be classified into groups according to the features that have been shown to affect prognosis and risk of treatment failure. In time, more elements are considered in order to modulate the treatment protocols and make them more efficient. Implementation of pharmacogenomics in the childhood ALL therapeutic strategy is the most promising approach to improve the outcome of childhood ALL.4
The four main components of ALL therapy are remission induction, consolidation, maintenance, and central nervous system-directed therapy. According to the BFM protocol, in the initial phase of the remission induction treatment of childhood ALL, glucocorticoid (GC) monotherapy is administered during the first 8 days. Its goal is to lower the number of lymphoblasts since GC have the ability to induce apoptosis in leukemic cells mediated through the glucocorticoid receptor (GR).5 The lymphoblast count on the day 8 is one of the stratification criteria important for therapy regime and survival.6 If the blast count in blood is below 1000/microL, the patient is declared as a GC sensitive patient or a prednisone good responder (PGR). If the peripheral blast count of a patient remains over 1000/microL, the patient is declared as GC resistant patient or a prednisone poor responder (PPR) and this is associated with a poor prognosis.
The mechanism of GC resistance in childhood ALL is still poorly understood, but genetic factors might play an important role.7, 8, 9 Therefore, it is of great importance for better treatment of childhood ALL to investigate, understand and overcome the problems related to pharmacogenomics profile of patients with a poor response to the initial GC treatment.
The glucocorticoid receptor gene (
Three variants in the
Three glutathione S-transferase (GST) genes (
The multidrug resistance 1 gene (
There have been a few reports which dealt with the topic of pharmacogenomics of GC resistance in adult leukemias, but they lacked conclusive evidence of a single contributing mechanism.26 The topic of pharmacogenomics of GC resistance in ALL, when it comes to the pediatric population, has not been sufficiently studied. In the reported results, only tendencies towards association with GC response for certain genotypes27 have been found, while most of the genetic variants, shown to be relevant for GC response, have never been studied in childhood ALL.
The aim of this study is to investigate the association between variants in
Peripheral blood samples (n = 122) have been collected from unselected patients with the diagnosis of childhood ALL from the University Children’s Hospital in Belgrade. The samples for genetic analyses were collected on the day of the diagnosis. Childhood ALL patients were diagnosed, stratified in risk groups and treated according to Berlin-Frankfurt-Munster protocols: BFM ALL IC-2002 and BFM ALL IC-2009. All patients received induction therapy with prednisone. This study was approved by the Ethics Committee of the University Children’s Hospital, University of Belgrade. The study was conducted according to the principles of Declaration of Helsinki.
Genomic DNA was extracted from peripheral blood samples of the participants’ using a QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) and stored at - 20 °C until analysis.
The detection of
The variant rs2032582 of
Variants rs1045642 and rs1128503 of
Variants rs33389, rs33388 and rs6198 of
Variants rs1695 and rs1138272 of
Hardy-Weinberg equilibrium conformance was examined using χ2 test. Haplotype phases and frequencies were estimated using Arlequin software.31 The associations between carrier status of specific allele or haplotype and the number of blasts at the day 8 have been analyzed in 2×2 contingency tables using the χ2 test or the Fisher’s exact test, when appropriate. Both dominant and recessive genetic model were applied when we considered single variant at the time, and stronger association with GC response was reported. Carriers of a specific haplotype were compared to all other patients with any other haplotype for each haplotype. Odds ratio with 95% confidence interval was used to assess the impact of clinical or genetic variable on GC drug response. The cut-off for statistical significance has been chosen at the value of p = 0.05, while the cutoff value for borderline significance has been chosen at the value of p = 0.07. To control for demographic and clinical difference between groups, multivariate analysis was performed using logistic regression. Correlation between continuous variables were estimated using Spearman’s correlation coefficient (rs). The SPSS software package (IBM SPSS Statistics v.21) was used for statistical analyses.
Out of 122 childhood ALL patients, there were 66 boys (54.1%) and the median age was 5.2 (inter-quartile range: 3.3–10.2) years. B-cell leukemia was represented with 108 (88.5%) cases and the rest of patients were diagnosed with T-cell leukemia. About 47% of patients had initially over 20,000 white blood cells (WBC) per microliter of blood, which is considered as unfavorable factor according to both BFM ALL IC-2002 and BFM ALL IC-2009 protocols (Table 1).
Clinical and demografic characteristics and their association with glucocorticoid (GC) response. The GC response is assesed by absolute number of blasts per mm3 of blood on day 8. Statistically significant associations (p < 0.05) were bolded Association with prednosine response on day 8 according to Berlin-Frankfurt-Munster (BFM) protocol: prednisone poor responder (PPR) group (≥1000 blasts) vs. prednisone good responder (PGR) group (<1000 blasts) Association with number of blasts on day 8 with cut-off value of 100: higher (≥ 100 blasts) vs lower (< 100 blasts) number of blasts Association with blast status on day 8: blast positive vs blast negative patients. Association with prednosine response on day 8 according to Berlin-Frankfurt-Munster (BFM) protocol: prednisone poor responder (PPR) group (≥1000 blasts) vs. prednisone good responder (PGR) group (<1000 blasts) Association with number of blasts on day 8 with cut-off value of 100: higher (≥ 100 blasts) vs lower (< 100 blasts) number of blasts Association with blast status on day 8: blast positive vs blast negative patients. Fisher exact test OR = Odds ratio between a group with higher number of blasts in comparison with a group with lower number of blasts. The group with lower number of blasts represents reference group. CI = Confidence intervalPatients characteristics Group Entire group ≥1000 blasts 100≤ blasts <1000 1≤ blasts <100 blast negative patients GC response (cutoff=1000 blasts) GC response (cutoff=100 blasts) GC response (cutoff=0 blasts) n (%) n (%) n (%) n (%) n (%) OR [95%CI], p OR [95%CI], p OR [95%CI], p Age ≥1 and <6 (non-risk) 65 (53.3) 4 (30.8) 17 (54.8) 21 (55.3) 23 (57.5) reference reference reference <1 or ≥6 (risk) 57 (46.7) 9 (69.2) 14 (45.2) 17 (44.7) 17 (42.5) 2.86 [0.83-9.85], 0.085 1.42 [0.68-2.98], 0.356 1.29 [0.60-2.76], 0.514 Gender male 66 (54.1) 10 (76.9) 13 (41.9) 20 (52.6) 23 (57.5) reference reference reference female 56 (45.9) 3 (23.1) 18 (58.1) 18 (47.4) 17 (42.5) 0.32 [0.083-1.26], 0.081 1.12 [0.54-2.35], 0.761 1.28 [0.57-2.63], 0.598 Initial WBC count∗ <20,000/microL 64 (53.3) 1 (8.3) 13 (43.3) 21 (55.3) 29 (72.5) reference reference reference ≥20,000/microL 56 (46.7) 11 (91.7) 17 (56.7) 17 (44.7) 11 (27.5) 15.40 [1.92-123.6], 3.57 [1.62-7.88], 3.39 [1.49-7.72], Immunophenotype B 108 (88.5) 9 (69.2) 27 (87.1) 35 (92.1) 37 (92.5) reference reference reference T 14 (11.5) 4 (30.8) 4 (12.9) 3 (7.9) 3 (7.5) 4.40 [1.15-16.90], 2.67 [0.86-8.27], 0.081 1.91 [0.50-7.28], 0.546
In our study, blast count per microliter of blood on day 8 was used as surrogate marker of GC response. There were thirteen patients (11%) with more than 1000 blasts/microL on day 8 of GC treatment in our cohort of patients. We have analyzed the correlation of clinical and demographic characteristics of patients with prednisone response. Namely, leukocyte count on diagnosis was positively correlated with absolute blast count on day 8 (rs = 0.44, p = 0.000001). In addition, patients suffering from T-cell leukemia were in greater risk to respond poorly to initiation GC treatment (≥ 1000 blasts/microL on day 8) (Fisher’s exact test, p = 0.043) than B-cell leukemia patients. Furthermore, age and gender of childhood ALL patients showed borderline association with prednisone response (Table 1).
Two homozygous deletions in
When we carried out analysis in which 1000 blasts/microL set the limit of PGR and PPR, we found some positive correlation of pharmacogenomic markers with GC response. Regarding
Genotype frequencies and association with glucocorticoid (GC) response. The GC response is assesed taking into account absolute number of blasts per mm3 of blood on day 8. For univariate analysis, chi square test was used, unless differently stated. Dominant model was used unless differently stated. Statistically significant associations (p < 0.05) were bolded Association with prednosine response on day 8 according to Berlin-Frankfurt-Munster (BFM) protocol: prednisone poor responder (PPR) group (≥1000 blasts) vs. prednisone good responder (PGR) group (< 1000 blasts) Association with prednosine response on day 8 according to Berlin-Frankfurt-Munster (BFM) protocol: prednisone poor responder (PPR) group (≥1000 blasts) vs. prednisone good responder (PGR) group (< 1000 blasts) Adjusted for age, gender and initial white blood cells (WBC) count using logistic regression Association with number of blasts on day 8 with cut-off value of 100: higher (≥ 100 blasts) vs lower (< 100 blasts) number of blasts Association with number of blasts on day 8 with cut-off value of 100: higher (≥ 100 blasts) vs lower (< 100 blasts) number of blasts Adjusted for age, gender and initial white blood cells (WBC) count using logistic regression Association with blast status on day 8: blast positive vs blast negative patients. Association with blast status on day 8: blast positive vs blast negative patients. Adjusted for age, gender and initial white blood cells (WBC) count using logistic regression Fisher exact test Fisher exact test Recessive model Fisher exact test Recessive model Recessive model Recessive model Recessive model Recessive model Fisher exact test Fisher exact test Fisher exact test Fisher exact test Fisher exact test OR = Odds ratio between a group with higher number of blasts in comparison with a group with lower number of blasts. The group with lower number of blasts represents reference group. CI = Confidence intervalGenotype ≥ 1000 blasts 100 ≤ blasts < 1000 1 ≤ blasts < 100 blast negative patients GC response (cutoff=1000 blasts) GC response (cutoff=1000 blasts) GC response (cutoff=100 blasts) GC response (cutoff=100 blasts) GC response (cutoff=0 blasts) GC response (cutoff=0 blasts) n (%) n (%) n (%) n (%) OR[95%CI] P value OR[95%CI] P value OR[95%CI] P value OR[95%CI] P value OR[95%CI] P value OR[95%CI] P value CC 10 (76.9) 21 (67.7) 33 (86.8) 32 (80.0) reference reference reference reference reference reference CT 3 (23.1) 9 (29.0) 4 (10.5) 6 (15.0) 1.12[0.29-4.41] 1 1.10[0.21-5.92] 0.910 2.1[0.87-5.05] 0.095 1.85[0.73-4.71] 0.195 1.12[0.44-2.86] 0.805 0.89[0.32-2.44] 0.826 TT 0 (0.0) 1 (3.2) 1 (2.6) 2 (5.0) AA 4 (30.8) 11 (35.5) 7 (18.4) 9 (22.5) reference reference reference reference reference reference AT 4 (30.8) 15 (48.4) 19 (50.0) 19 (47.5) 0.74[0.21-2.60] 0.737 0.71[0.16-3.13] 0.658 0.5[0.22-1.15] 0.098 0.53[0.28-1.26] 0.148 0.79[0.32-1.92] 0.606 0.83[0.32-2.11] 0.699 TT 5 (38.5) 5 (16.1) 12 (31.6) 12 (30.0) AA 7 (53.8) 21 (67.7) 28 (73.7) 30 (75.0) reference reference reference reference reference reference AG 4 (30.8) 10 (32.3) 9 (23.7) 10 (25.0) GG 2 (15.4) 0 (0.0) 1 (2.6) 0 (0.0) 19.64[1.65-234.32] 16.76[1.20-234.27] 1.66[0.75-3.68] 0.222 4.01[0.34-47.4] 0.27 1.04[0.99-1.08] 0.22 - 1 AA 7 (53.8) 15 (48.4) 16 (42.1) 16 (40.0) reference reference reference reference reference reference AG 5 (38.5) 13 (41.9) 18 (47.4) 19 (47.5) 0.65[0.21-2.06] 0.46 0.91[0.26-3.25] 0.885 0.70[0.33-1.46] 0.338 0.73[0.34-1.58] 0.423 0.77[0.36-1.66] 0.508 0.84[0.37-1.9] 0.682 GG 1 (7.7) 3 (9.7) 4 (10.5) 5 (12.5) CC 9 (69.2) 25 (80.6) 31 (81.6) 38 (95.0) reference reference reference reference reference reference CT 3 (23.1) 6 (19.4) 7 (18.4) 2 (5.0) 2.79[0.76-10.20] 0.119 3.17[0.76-13.28] 0.115 2.26[0.84-6.07] 0.122 2.23[0.81-6.15] 0.121 4.97[1.09-22.69] 4.44[0.9-21.08] 0.060 TT 1 (7.7) 0 (0.0) 0 (0.0) 0 (0.0) WT 6 (46.2) 13 (41.9) 18 (47.4) 19 (47.5) reference reference reference reference reference reference DEL 7 (53.8) 18 (58.1) 20 (52.6) 21 (52.5) 0.99[0.31-3.13] 0.99 0.96[0.25-3.70] 0.953 1.19[0.56-2.50] 0.707 1.03[0.47-2.27] 0.941 1.0[0.51-2.38] 0.805 0.96[0.43-2.18] 0.935 WT 8 (61.5) 23 (74.2) 31 (81.6) 35 (87.5) reference reference reference reference reference reference DEL 5 (38.5) 8 (25.8) 7 (18.4) 5 (12.5) 2.78[0.82-9.09] 0.138 3.70[0.95-14.08] 0.058 2.33[0.94-5.56] 0.063 2.39[1.03-6.25] 2.27[0.78-6.67] 0.127 2.06[0.67-6.29] 0.202 CC 5 (38.5) 10 (32.3) 13 (34.2) 13 (32.5) reference reference reference reference reference reference CT 6 (46.2) 16 (51.6) 21 (55.3) 17 (42.5) 0.79[0.24-2.59] 0.759 0.74[0.20-2.76] 0.651 0.97[0.44-2.11] 0.932 0.89[0.40-1.99] 0.773 0.92[0.41-2.07] 0.857 0.84[0.36-1.97] 0.691 TT 2 (15.4) 5 (16.1) 4 (10.5) 10 (25.0) GG 5 (31.3) 9 (24.3) 15 (30.6) 14 (27.5) reference reference reference reference reference reference GT 4 (25.0) 16 (43.2) 17 (34.7) 16 (31.4) 0.86[0.26-2.80] 0.769 1.08[0.29-4.05] 0.908 1.27[0.58-2.78] 0.693 1.27[0.56-2.88] 0.57 0.98[0.44-2.17] 0.968 0.9[0.39-2.1] 0.813 TT 3 (18.8) 5 (13.5) 6 (12.2) 9 (17.6) GA 1 (6.3) 1 (2.7) 0 (0.0) 1 (2.0) CC 3 (23.1) 6 (19.4) 11 (28.9) 11 (27.5) reference reference reference reference reference reference CT 7 (53.8) 16 (51.6) 19 (50.0) 18 (45.0) 1.15[0.30-4.49] 1 1.47[0.31-6.35] 0.657 1.53[0.63-3.70] 0.393 1.92[0.74-4.98] 0.18 1.17[0.5-2.77] 0.711 1.31[0.52-3.28] 0.559 TT 3 (23.1) 9 (29.0) 8 (21.1) 11 (27.5)
Haplotype carring status and association with glucocorticoid (GC) response. The GC response is assesed taking into account absolute number of blasts per mm3 of blood on day 8. For univariate analysis, chi square test was used, unless differently stated. Statistically significant associations (p < 0.05) were bolded Association with blast status on day 8: blast positive vs blast negative patients. Association with blast status on day 8: blast positive vs blast negative patients. Adjusted for age, gender and initial white blood cells (WBC) count using logistic regression Association with number of blasts on day 8 with cut-off value of 100: higher (≥ 100 blasts) vs lower (< 100 blasts) number of blasts Association with number of blasts on day 8 with cut-off value of 100: higher (≥ 100 blasts) vs lower (< 100 blasts) number of blasts Adjusted for age, gender and initial white blood cells (WBC) count using logistic regression Association with prednosine response on day 8 according to Berlin-Frankfurt-Munster (BFM) protocol: prednisone poor responder (PPR) group (≥ 1000 blasts) vs. prednisone good responder (PGR) group (< 1000 blasts) Association with prednosine response on day 8 according to Berlin-Frankfurt-Munster (BFM) protocol: prednisone poor responder (PPR) group (≥ 1000 blasts) vs. prednisone good responder (PGR) group (< 1000 blasts) Adjusted for age, gender and initial white blood cells (WBC) count using logistic regression Fisher exact test Fisher exact test Fisher exact test Fisher exact test Fisher exact test Fisher exact test Fisher exact test Fisher exact test OR = Odds ratio between a group with higher number of blasts in comparison with a group with lower number of blasts. The group with lower number of blasts represents reference group. CI = Confidence intervalHaplotype (estimated frequency) Carrier status∗ ≥ 1000 blasts 100 ≤ blasts < 1000 1 ≤ blasts < 100 blast negative patients GC response (cutoff=1000 blasts) GC response (cutoff=1000 blasts) GC response (cutoff=100 blasts) GC response (cutoff=100 blasts) GC response (cutoff=0 blasts) GC response (cutoff=0 blasts) n (%) n (%) n (%) n (%) OR [95%CI] p value OR [95%CI] p value OR [95%CI] p value OR [95%CI] p value OR [95%CI] p value OR [95%CI] p value CTA (51.2%) absent 4 (30.8) 11 (35.5) 7 (18.4) 9 (22.5) reference reference reference reference reference reference present 9 (69.2) 20 (64.5) 31 (81.6) 31 (77.5) 0.71[0.21-2.60] 0.737 0.72[0.16-3.13] 0.658 0.50[0.22-1.15] 0.098 0.53[0.22-1.26] 0.148 0.79[0.32-1.92] 0.606 0.83[0.32-2.11] 0.699 CAA (20.5%) absent 12 (92.3) 15 (48.4) 25 (65.8) 25 (62.5) reference reference reference reference reference reference present 1 (7.7) 16 (51.6) 13 (34.2) 15 (37.5) 0.12[0.015-0.98] 0.12[0.013-1.02] 0.052 1.12[0.52-2.41] 0.763 1.27[0.57-2.80] 0.561 0.96[0.44-2.1] 0.922 1.22[0.53-2.80] 0.643 CAG (16.0%) absent 7 (53.8) 20 (64.5) 28 (73.7) 29 (72.5) reference reference reference reference reference reference present 6 (46.2) 11 (35.5) 10 (26.3) 11 (27.5) 2.06[0.64-6.62] 0.222 1.82[0.49-6.74] 0.372 1.71[0.78-3.75] 0.18 1.60[0.70-3.65] 0.262 1.29[0.56-2.97] 0.543 1.28[0.53-3.1],0.576 TAA (12.3%) absent 10 (76.9) 21 (67.7) 33 (86.8) 32 (80.0) reference reference reference reference reference reference present 3 (23.1) 10 (32.3) 5 (13.2) 8 (20.0) 1.12[0.29-4.41] 1.000 1.10[0.21-5.92] 0.91 2.10[0.87-5.05] 0.095 1.85[0.73-4.71] 0.195 1.12[0.44-2.86] 0.805 0.89[0.32-2.44] 0.826 AC (66.4%) absent 1 (7.7) 3 (9.7) 4 (10.5) 5 (12.5) reference reference reference reference reference reference present 12 (92.3) 28 (90.3) 34 (89.5) 35 (87.5) 1.49[0.18-12.45] 1.000 0.99[0.11-9.21] 0.993 1.30[0.38-4.51] 0.768F 1.18[0.33-4.19] 0.797 1.32[0.4-4.33] 0.756 1.15[0.33-3.93] 0.825 GC (25.4%) absent 11 (84.6) 20 (64.5) 21 (55.3) 18 (45.0) reference reference reference reference reference reference present 2 (15.4) 11 (35.5) 17 (44.7) 22 (55.0) 0.22[0.045-1.01] 0.27[0.054-1.38] 0.117 0.42[0.19-9.20] 0.42[0.19-0.96] 0.47[0.22-1.02] 0.054 0.55[0.24-1.23] 0.149 GT (7.8%) absent 9 (69.2) 25 (80.6) 32 (84.2) 38 (95.0) reference reference reference reference reference reference present 4 (30.8) 6 (19.4) 6 (15.8) 2 (5.0) 3.02[0.82-11.12] 0.101 3.41[0.81-14.34] 0.094 2.57[0.93-7.11] 0.062 2.61[0.93-7.37] 0.069 4.6[1.00-21.12] 4.33[0.91-20.62] 0.065 CGC (45.9%) absent 4 (30.8) 10 (32.3) 8 (21.1) 12 (30.0) reference reference reference reference reference reference present 9 (69.2) 21 (67.7) 30 (78.9) 28 (70.0) 0.85[0.25-2.98] 0.754 0.74[0.19-2.81] 0.654 0.74[0.33-1.67] 0.465 0.73[0.32-1.69] 0.461 1.17[0.5-2.7] 0.714 1.43[0.58-3.53] 0.432 TTT (36.9%) absent 6 (46.2) 12 (38.7) 17 (44.7) 16 (40.0) reference reference reference reference reference reference present 7 (53.8) 19 (61.3) 21 (55.3) 24 (60.0) 0.82[0.26-2.60] 0.772 0.85[0.24-3.08] 0.805 1.06[0.50-2.24] 0.88 1.13[0.52-2.47] 0.76 0.89[0.41-1.93] 0.778 0.91[0.4-2.05] 0.821 CGT (8.6%) absent 10 (76.9) 24 (77.4) 30 (78.9) 38 (95.0) reference reference reference reference reference reference present 3 (23.1) 7 (22.6) 8 (21.1) 2 (5.0) 1.62[0.40-6.52] 0.446 2.29[0.50-10.59] 0.289 2.00[0.76-5.27] 0.156 2.34[0.86-6.33] 0.095 5.34[1.17-24.31] 7.56[1.6-35.82]
When variants in
Besides cut-off value of 1000 blasts/microL on day 8, used to delimit patients with good or poor GC response according to BFM protocol, other values of blast count on day 8 might be potentially used as a marker of GC response. In order to confirm importance of analyzed genetic variants to GC response, we carried out additional analyses in which cut-off value for prednisone response was 0 (blast negative) or 100 blasts in peripheral blood. In our group of childhood ALL patients, 40 (32.8%) were blast negative, while 38 (31.1%) patients had between 1 and 99 blasts/microL after 8 days of GC treatment. Initial WBC count was correlated with blast positive status and higher number of blasts (≥ 100 blasts/microL) (Table 1).
Regarding
Additional analysis regarding
Regarding
Regarding
Pharmacogenomics is dealing with the fact that the efficacy of the drug depends on the patient’s ability to absorb and metabolize the drug, which influences the effectiveness of the treatment. Furthermore, the toxicity of drug depends on the patient’s genome. Pharmacogenomics testing is already incorporated as a dosage-calibrating tool in the maintenance phase of childhood ALL treatment in order to minimize the occurrence of serious toxicities during 6-MP treatment.4, 32
Glucocorticoids are an essential component to induction remission phase of childhood ALL therapy. A poor response to the standard initial GC treatment and the persistence of blast count over 1000 per microliter on the day 8, puts a patient in a higher risk group with a poor prognosis. The following phases of treatment are dependent on risk-directed stratification of patients. However, many children experience severe toxicity associated with treatment with dangerous side effects, while some of them are not cured.33 So, it could be argued that these groupings are not yet comprehensive enough.34 As for induction remission phase of ALL treatment, it is essential to find as many potential markers of GC resistance as possible. By analyzing the associations between the pharmacogenetic variants and GC resistance or good response, this study was meant to contribute to individualization of GC treatment, so that the patients could be in future adequately treated according to their genetic background.
A few studies dealt with variants in
In this study we focused on variants in non-coding region of
Concerning the variant rs6198 in the
Our results have shown that carriers of minor
Carriers of
Carriers of the
Regarding
Despite the promising results, the limitations of the study need to be affirmed. The sample size is not big, since this is a single centric study enrolling patients suffering from a rare disease. Moreover, certain alleles of genetic variants we studied are not frequent, meaning that in some cases there are only a few carriers of certain genotypes. As a consequence, conclusions drawn analyzing such small groups of patients need to be taken with caution. Considering the shortcomings mentioned, it would be of great benefit to validate the results gained in this study on a larger sample preferably using prospective approach.
Association studies on the pharmacogenomic profile of patients and data on the toxicity of drugs are the most promising directions on the road to personalized medicine. The ultimate goal of the ongoing multicentric clinical trials is to optimize the use of known antileukemic drugs in the context of individual pharmacogenomic profile of each patient and molecular markers of the leukemic cells and modulate the treatment resulting in less toxicity and adverse reactions, and a higher survival rates.52 Personalized medicine approach of tailoring treatment to the individual characteristics of each patient has been a great success in several diseases. One thing that we have learnt from those successful examples is that a personalized childhood ALL approach implementation may be difficult. Our study pointed out the association between several variants in