scholarly journals The Role of Serum Free Light Chain and M-Protein Measurements in Determining Recurrent Disease in Multiple Myeloma After Stem Cell Transplantation

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
M Rabie Al-Turkmani
Keyword(s):  
Multiple Myeloma ◽  
Stem Cell ◽  
Stem Cell Transplantation ◽  
Cell Transplantation ◽  
Light Chain ◽  
Recurrent Disease ◽  
M Protein ◽  
Serum Free ◽  
Serum Free Light Chain

Use of a mathematical model of serum-free light chain decline to identify a kinetic predictive marker of survival in patients with newly diagnosed multiple myeloma treated with bortezomib plus dexamethasone and autologous stem cell transplantation.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e19515-e19515
Author(s):  
Heiblig Mael ◽  
Lachenal Florence ◽  
Olivier Colomban ◽  
Lionel Karlin ◽  
Benoit You
Keyword(s):  
Multiple Myeloma ◽  
Stem Cell ◽  
Stem Cell Transplantation ◽  
Kinetic Parameters ◽  
Production Rate ◽  
Light Chain ◽  
Newly Diagnosed ◽  
Predictive Values ◽  
Serum Free ◽  
Serum Free Light Chain

e19515 Background: According to 2011 International Myeloma Working Group recommandations, serum-free light chain (sFLC) assessment is indicated in non secretory Multiple Myeloma (MM). sFLC monitoring is now routinely used for diagnosis and early response treatment assessment but its actual prognosis values regarding survival is not clear. Based on mathematical modeling, this study aimed at evaluate the predictive value on progression free survival (PFS) of modeled sFLC kinetics during frontline treatment. Methods: Model-based analysis of sFLC kinetics from 28 newly diagnosed MM patients treated at a single institution with 4 cycles of bortezomib and dexamethasone plus autologous stem cell transplantation (ASCT). MONOLIX program was used to fit Box-Cox transformed [sFLC] concentration-time profiles to the equation : [sFLC(t) = [sFLC1*exp(-KDEC1*t)]+[sFLC2*exp(-KDEC2*t)]+[sFLC3*exp(KPROD*t)] ] where KDEC1 and KDEC2 represent the decline rates of sFLC while KPROD is the involved light chain production rate observed after the end of the decrease. The predictive values of the modeled kinetic parameters , regarding PFS were tested using logistic regression, and survival analysis. Results: Median PFS was 17.9 months . Three modeled kinetic parameters categorized by their medians had strong predictive values regarding PFS using univariate tests: sFLC decline rate KDEC1 (median PFS = 38.6 months if KDEC1 ≥ median vs 14 months if KDEC1 < median, P=0.01); light chain production rate KPROD (median PFS = 24.9 months if KPROD < median vs 7.8 months if KPROD ≥ median, p=0.01); and initial SFLC production slope sFLC3 (median PFS = 24.9 months if sFLC3 < median vs 14 months if sFLC3 ≥ median, p=0.03). The same kinetic model for M-protein decline was not significant (p=0.53). Using multivariate analysis, the only remaining significant predictive factor of PFS was: KDEC1 (HR, 0.11; 95% CI 0.01-0.88; P=0.04). Conclusions: The modeled kinetic parameter of sFLC decline KDEC1, may be easily used as an early predictor of survival after frontline ASCT in MM. Validation in larger studies is warranted.

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