Cell division patterns in acute myeloid leukemia stem-like cells determine clinical course: a model to predict patient survival
Acute myeloid leukemia (AML) is a heterogeneous disease in which a variety of distinct genetic alterations might occur. Recent attempts to identify the leukemia stem-like cells (LSC) have also indicated heterogeneity of these cells. On the basis of mathematical modeling and computer simulations, we...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
January 22, 2015
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| In: |
Cancer research
Year: 2015, Volume: 75, Issue: 6, Pages: 940-949 |
| ISSN: | 1538-7445 |
| DOI: | 10.1158/0008-5472.CAN-14-2508 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1158/0008-5472.CAN-14-2508 Verlag, Volltext: http://cancerres.aacrjournals.org/content/75/6/940 
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| Author Notes: | Thomas Stiehl, Natalia Baran, Anthony D. Ho, Anna Marciniak-Czochra |
| Summary: | Acute myeloid leukemia (AML) is a heterogeneous disease in which a variety of distinct genetic alterations might occur. Recent attempts to identify the leukemia stem-like cells (LSC) have also indicated heterogeneity of these cells. On the basis of 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, that is, 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. On the basis of the estimated LSC properties, the patients can be divided into two prognostic groups that 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 Res; 75(6); 940-9. ©2015 AACR. |
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| Item Description: | Gesehen am 01.08.2017 |
| Physical Description: | Online Resource |
| ISSN: | 1538-7445 |
| DOI: | 10.1158/0008-5472.CAN-14-2508 |