Cell division patterns in acute myeloid leukemia stem-like cells determine clinical course: a model to predict patient survival
Menée sur 31 patients atteints d'une leucémie myéloïde aiguë récidivante, cette étude évalue l'intérêt d'un modèle numérique, estimant les propriétés de cellules leucémiques analogues aux cellules souches, pour prédire la survie des patients
Acute myeloid leukemia is a heterogeneous disease in which a variety of distinct genetic alterations occur. Recent studies to identify the leukemia stem-like cells (LSCs) have also indicated heterogeneity of these cells. Based on mathematical modeling and computer simulations we have provided evidence that proliferation and self-renewal rates of the LSC population have greater impact on the course of disease than proliferation and self-renewal rates of leukemia blast populations, i.e. leukemia progenitor cells. The modeling approach has enabled us to estimate the LSC properties of 31 individuals with relapsed AML and to link them to patient survival. Based on the estimated LSC properties the patients can be divided into two prognostic groups which differ significantly with respect to overall survival after first relapse. The results suggest that high LSC self-renewal and proliferation rates are indicators of poor prognosis. Nevertheless, high LSC self-renewal rate may partially compensate for slow LSC proliferation and vice versa. Thus, model-based interpretation of clinical data allows estimation of prognostic factors that cannot be measured directly. This may have clinical implications for designing treatment strategies.
Cancer Research , article en libre accès, 2015