A 17-gene stemness score for rapid determination of risk in acute leukaemia
By Stanley W. K. Ng, Amanda Mitchell, J. Kennedy, W. Chen, Jessica McLeod, N. Ibrahimova, A. Arruda, A. Popescu, Vikas Gupta, A. Schimmer and 20 more. Nature, 2016
817 citations recorded by the source corpus.
Subfields: Cancer Research, Genetics, Hematology
View Paper: https://www.semanticscholar.org/paper/1f416a883253eb58c45ec36d9f4573fe66c40bf5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cancer Research
- A. Haque
- Peter W. Laird
- Antonio Colaprico
- Sarah A. Teichmann
- T. Lönnberg
- Patrycja Czerwińska
- Bozena Kaminska
- Jessica A. Engel
In Genetics
- M. Wiznerowicz
- Patrycja Czerwińska
- Peter W. Laird
- Sarah A. Teichmann
- Christoph Bock
- M. Smulders
- G. Klerk
Citations in the corpus
- Chemotherapy Resistant Human Acute Myeloid Leukemia Cells are Not Enriched for Leukemic Stem Cells but Require Oxidative Metabolism (2017, methodology): “First, we performed a gene set enrichment analysis (GSEA) with two functionally identified stem cell signatures (13, 26).”
- Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation (2018, background): “We examined breast invasive carcinoma (BRCA), acute myeloid leukemia (AML), and gliomas to study if the mRNAsi/mDNAsi predict stemness in poorly differentiated tumors.”
- A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, methodology): “Taking just one clinically relevant example—RNA-seq was recently performed on haematopoietic stem cells to stratify acute myeloid leukaemia patients into cohorts requiring differing treatment regimens [4].”