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      A 17-gene stemness score for rapid determination of risk in acute leukaemia.

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          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 diverse AML subtypes (n = 908) and contributed greatly to accurate prediction of initial therapy resistance. Patients with high LSC17 scores had poor outcomes with current treatments including allogeneic stem cell transplantation. The LSC17 score provides clinicians with a rapid and powerful tool to identify AML patients who do not benefit from standard therapy and who should be enrolled in trials evaluating novel upfront or post-remission strategies.

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          Most cited references21

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          Evolution of the cancer stem cell model.

          Genetic analyses have shaped much of our understanding of cancer. However, it is becoming increasingly clear that cancer cells display features of normal tissue organization, where cancer stem cells (CSCs) can drive tumor growth. Although often considered as mutually exclusive models to describe tumor heterogeneity, we propose that the genetic and CSC models of cancer can be harmonized by considering the role of genetic diversity and nongenetic influences in contributing to tumor heterogeneity. We offer an approach to integrating CSCs and cancer genetic data that will guide the field in interpreting past observations and designing future studies. Copyright © 2014 Elsevier Inc. All rights reserved.
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              We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of ℓ1 and ℓ2 penalties (elastic net). Our algorithm fits via cyclical coordinate descent, and employs warm starts to find a solution along a regularization path. We demonstrate the efficacy of our algorithm on real and simulated data sets, and find considerable speedup between our algorithm and competing methods.
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                Author and article information

                Journal
                Nature
                Nature
                Springer Nature
                1476-4687
                0028-0836
                Dec 15 2016
                : 540
                : 7633
                Affiliations
                [1 ] Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 1A1, Canada.
                [2 ] Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada.
                [3 ] Division of Medical Oncology and Hematology, Department of Medicine, University Health Network, Toronto, Ontario M5G 2M9, Canada.
                [4 ] Department of Medicine, University of Toronto, Toronto, Ontario M5G 1A1, Canada.
                [5 ] Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1A1, Canada.
                [6 ] Department of Internal Medicine III, University Hospital of Ulm, 89081 Ulm, Germany.
                [7 ] Department of Internal Medicine III, University of Munich, 81377 Munich, Germany.
                [8 ] German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
                [9 ] Institute of Biostatistics and Clinical Research, University of Münster, 48149 Münster, Germany.
                [10 ] Department of Medicine, Hematology and Oncology, University of Münster, 48149 Münster, Germany.
                [11 ] Department of Hematology, Oncology and Tumor Immunology, Charité University Medicine, Campus Virchow, 10117 Berlin, Germany.
                [12 ] Jean-Pierre AUBERT Research Center UMR-S 1172, Institute for Cancer Research Lille, 59045 Lille, France.
                [13 ] University Hospital of Lille, Center of Pathology, Laboratory of Hematology, 59037 Lille, France.
                [14 ] Saint-Louis Hospital, Department of Hematology, University of Paris Diderot, 75010 Paris, France.
                [15 ] Comprehensive Cancer Center Ulm, Institute of Experimental Cancer Research, University Hospital of Ulm, 89081 Ulm, Germany.
                [16 ] Department of Hematology, Erasmus University Medical Centre, 3015 CE Rotterdam, the Netherlands.
                [17 ] Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A1, Canada.
                Article
                nature20598
                10.1038/nature20598
                27926740
                8763e5ac-5776-4d62-bc86-15b0e376ff2a
                History

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