The predictive model of chemotherapy response in multiple myeloma patients
N. Kostukova1, Z. Rossokha2, S. Kiryachenko2, S. Vudyborets1, N. Gorovenko1
1P.L. Shupik National Medical Academy of Post-Graduate Education, Kyiv, Ukraine
2Reference-Centre for Molecular Diagnostic, Ministry of Public Health of Ukraine, Kyiv, Ukraine
Introduction: Multiple myeloma (MM) is plasma cell neoplasm with different sensitivity to drug treatment. The drug resistance worsens the individual prognosis for MM patients. Aim: To determine the predictive markers of refractory forms in MM patients. Methods: 130 patients with first time diagnosed ММ from different regions of Ukraine were examined during the period of 2007–2011. 26 (20%) of 130 patients underwent treatment according to the scheme МР, 55 (42.31%) — according to the scheme М2, 49 (37.69%) — according to the scheme VAD. Patients were divided into two groups: the 1st group — 52 patients, who had no response to treatment, the 2nd group — 78 patients with response to treatment. Patients of both groups were scrutinized closely with a help of 68 clinical-laboratory indexes and molecular-genetic tests, and GSTT1, GSTM1 genes deletion polymorphism, GSTP1 gene А313G polymorphism, MDR1 gene C3435T polymorphism were characterized. Binary logistic regression with consequent including of predictors (program SPSS 16.0) was implemented to create a predictive model. Results: The patients of the 1st group had significantly lower frequency of GSTM1 gene deletion polymorphism as compared to patients of the 2nd group (32.20% and 62.82%, respectively; χ2 = 11.33, OR = 0.29 (0.14–0.60), p < 0.001). TheGSTM1 gene allele polymorphism correlated with elevated risk of development of refractory forms in MM patients (OR = 3.48 (1.66–7.29)). There was no difference in the frequency of others genes polymorphism. We have found that the higher prognostic value had GSTM1 gene polymorphism (69.90%), while calcium and α2-globulin level in blood serum before treatment had lower prognostic value (62.30%). The better prognostic value was obtained using statistical model which includes GSTM1 gene polymorphism, α2-globulin and calcium levels in blood serum (73.60%). Conclusion: Implementation of predictive model which considers GSTM1 polymorphism, α2-globulin and calcium levels in blood serum from the beginning of treatment increases the accuracy of prognosis of response to treatment for each individual patient.
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