GSE76008
GSE GEOA 17-Gene Stemness Score for Rapid Identification of High-Risk AML Patients [Illumina]
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Summary
In AML, most patients are initiated on standard chemotherapy and afterwards assigned to a post-remission strategy based on genetically-defined risk categories. However, outcomes remain heterogeneous, indicating the need for novel biomarker tests that can rapidly and accurately identify high-risk patients, allowing better stratification of both induction and post-remission therapy. As patient outcomes are linked to leukemia stem cell (LSC) properties that confer therapy resistance and drive relapse, LSC-based biomarkers may be highly informative. We tested 227 CD34/CD38 cell fractions from 78 AML patients for LSC activity in xenotransplantation assays. Comparison of microarray-based gene expression (GE) profiles between 138 LSC+ and 89 LSC? fractions identified 104 differentially-expressed LSC-specific genes. To obtain prognostic signatures, we performed statistical regression analysis of LSC GE against patient outcome using a training cohort of 495 AML patients treated with curative intent. A score calculated as the weighted sum of expression of 17 LSC signature genes (LSC17) was strongly associated with survival in 4 independent datasets (716 AML cases) spanning all risk categories in multi-variate analysis; an optimized 3-gene sub-score (LSC3) was prognostic in favorable risk subsets. These scores were robust across GE technology platforms, including the clinically serviceable NanoString system (LSC17: HR=2.73, P<0.0001; LSC3: HR=6.3, P<0.02). The LSC17 and LSC3 scores provide rapid and accurate identification of high-risk patients for whom conventional chemotherapy is non-curative. These scores will enable evaluation in clinical trials of whether such patients may benefit from novel and/or more intensified therapies during induction or in the post-remission setting.
Overall Design
Microarray GE profiling was performed on funcationally validated 138 LSC+ and 89 LSCâ cell fractions sorted from 83 primary AML patient samples based on the expression of CD34 and CD38
Analysis (1 step)
View Data Processing- The data were normalized with variance stabilization and quantile normalization using the lumi (v2.16.0) package in R (v3.1.0).