A 17-gene stemness score for rapid determination of risk in acute leukaemia
Abstract
Refractoriness to induction chemotherapy and relapse after achievement of remission are the main obstacles to cure in acute myeloid leukaemia (AML). After standard induction chemotherapy, patients are assigned to different post-remission strategies on the basis of cytogenetic and molecular abnormalities that broadly define adverse, intermediate and favourable risk categories. However, some patients do not respond to induction therapy and another subset will eventually relapse despite the lack of adverse risk factors. There is an urgent need for better biomarkers to identify these high-risk patients before starting induction chemotherapy, to enable testing of alternative induction strategies in clinical trials. The high rate of relapse in AML has been attributed to the persistence of leukaemia stem cells (LSCs), which possess a number of stem cell properties, including quiescence, that are linked to therapy resistance. Here, to develop predictive and/or prognostic biomarkers related to stemness, we generated a list of genes that are differentially expressed between 138 LSC + and 89 LSC - cell fractions from 78 AML patients validated by xenotransplantation. To extract the core transcriptional components of stemness relevant to clinical outcomes, we performed sparse regression analysis of LSC gene expression against survival in a large training cohort, generating a 17-gene LSC score (LSC17). The LSC17 score was highly prognostic in five independent cohorts comprising patients of…
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The 17-gene LSC17 gene set was retrieved from ref. 31.
“The 17-gene LSC17 gene set was retrieved from ref. 31.”
From Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution (2023) · cites this paper as [31]
The p53LSC signature performed well as a predictor of survival, including in sAML patients, as compared to the previously published LSC17 score and p53-mutant score generated using all TP53-mutant HSPC rather than LSCs.
“The p53LSC signature performed well as a predictor of survival, including in sAML patients, as compared to the previously published LSC17 score31 and p53-mutant score generated using all TP53-mutant HSPC rather than LSCs (Extended Data Fig. 6f,g and Supplementary Table 4), providing a powerful tool to aid risk stratification in AML.”
From Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution (2023) · cites this paper as [31]
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- Authors
- Ng, S. W. K., Mitchell, A., Kennedy, J. A., Chen, W. C., McLeod, J., Ibrahimova, N., Arruda, A., Popescu, A., Gupta, V., Schimmer, A. D., Schuh, A. C., Yee, K. W., Bullinger, L., Herold, T., Görlich, D., Büchner, T., Hiddemann, W., Berdel, W. E., Wörmann, B., Cheok, M., Preudhomme, C., Dombret, H., Metzeler, K., Buske, C., Löwenberg, B., Valk, P. J. M., Zandstra, P. W., Minden, M. D., Dick, J. E., & Wang, J. C. Y.
- Affiliations
- University of Toronto (CA)
- University Health Network (CA)
- University Hospital Ulm (DE)
- German Cancer Research Center (DE)
- University of Münster (DE)
- Centre de Recherche Jean Pierre Aubert (FR)
- Centre Hospitalier Universitaire de Lille (FR)
- Hôpital Saint-Louis (FR)
- Universität Ulm (DE)
- Erasmus MC (NL)
- Corresponding author
- Wang, Jean C. Y.
- ORCID
- Ng, Stanley W. K.
- Mitchell, Amanda
- McLeod, Jessica
- Arruda, Andrea
- Gupta, Vikas
- Schimmer, Aaron D.
- Schuh, Andre C.
- Yee, Karen W.
- Bullinger, Lars
- Herold, Tobias
- Görlich, Dennis
- Büchner, Thomas
- Hiddemann, Wolfgang
- Berdel, Wolfgang E.
- Cheok, Meyling
- Preudhomme, Claude
- Dombret, Hervé
- Metzeler, Klaus
- Buske, Christian
- Löwenberg, Bob
- Valk, Peter J. M.
- Zandstra, Peter W.
- Minden, Mark D.
- Dick, John E.
- Wang, Jean C. Y.
- Venue
- Nature
- Published
- 2016-12-07
- Volume
- 540
- Issue
- 7633
- Pages
- 433-437
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2560012305
- Cited by
- 912
- References
- 4
- Citations by year
- 2026: 87 · 2025: 92 · 2024: 92 · 2023: 127 · 2022: 117 · 2021: 122 · 2020: 85 · 2019: 80 · 2018: 61 · 2017: 43 · 2016: 1
- FWCI
- 58.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- European Hematology Association (TRTH1)
- Topics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Health Sciences › Medicine › Hematology › Hematopoietic Stem Cell Transplantation
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Keywords
- acute myeloid leukemia, leukemia stem cells, prognostic factors, treatment resistance, relapse, induction chemotherapy response, risk stratification, allogeneic stem cell transplantation, xenotransplantation, sparse regression, survival prediction, minimal residual disease detection
- MeSH terms
- Algorithms, Animals, Female, Humans, Prognosis, Transplantation, Homologous, Neoplastic Stem Cells, Cohort Studies, Leukemia, Myeloid, Acute, Gene Expression Regulation, Leukemic, Survival Analysis, Treatment Outcome, Risk Assessment, Xenograft Model Antitumor Assays, Stem Cell Transplantation, Mice, Transcriptome
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