Stem11 score: toward rapid clinical prognostication for acute myeloid leukemia
Tomoya Isobe1, Manja Meggendorfer2, Sebastian Wolf3
1Department of Hematology, Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom; Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA.
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Acute myeloid leukemia (AML) is an aggressive form of myeloid malignancy with high relapse and poor survival rates. Despite recent advances in genomics-based risk classification, accurate prediction of patient outcomes remains a challenge, posing the need for complementary molecular information to enable precise treatment stratification. In the current study, we assessed the prognostic value of the recently developed 11-gene Stem11 signature in a uniformly treated cohort of 107 de novo AML patients and showed that Stem11 classification stratifies overall survival and response to allogeneic hematopoietic cell transplantation across the European LeukemiaNet risk groups. We further developed a NanoString-based Stem11 scoring system and validate its high concordance with RNA-sequencing-based scoring and its retained prognostic power to identify the most refractory AML subgroup. With its rapid turnaround time and standardized built-in analysis pipeline, our NanoString-based Stem11 scoring panel represents a faster yet reliable alternative to RNA sequencing, providing preclinical proof of concept for Stem11-based clinical decision support. Acute myeloid leukemia (AML) is an aggressive hematological malignancy with a poor prognosis. Current genetic risk stratification remains insufficient for accurately predicting patient outcomes, and additional information that can refine and improve prospective risk prediction is urgently needed. Here, we developed a NanoString-based diagnostic assay measuring the prognostic Stem11 score. Our assay provided highly concordant Stem11 scores with RNA-sequencing-based quantification, thereby establishing a rapid and reliable transcriptional prognostication system for time-sensitive clinical decision support for AML.
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