ArticleScientific reports2016
Combined Population Dynamics and Entropy Modelling Supports Patient Stratification in Chronic Myeloid Leukemia.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Leukemia in pregnancy: Diagnosis and therapeutic approach (Review).Molecular and clinical oncology · 2024Review
- Computational modeling reveals key factors driving treatment-free remission in chronic myeloid leukemia patients.NPJ systems biology and applications · 2024Article
- Comparative Gene Expression Analysis Reveals Similarities and Differences of Chronic Myeloid Leukemia Phases.Cancers · 2022Article
- Multivariate Entropy Characterizes the Gene Expression and Protein-Protein Networks in Four Types of Cancer.Entropy (Basel, Switzerland) · 2018Article
- Wide-transcriptome analysis and cellularity of bone marrow CD34+/lin- cells of patients with chronic-phase chronic myeloid leukemia at diagnosis vs. 12 months of first-line nilotinib treatment.Cancer biomarkers : section A of Disease markers · 2017Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34(+) similarity as a disease progression marker to patient-derived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients' disease history within chronic phase (CP) and significantly separates "early" from "late" CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis.
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Registered trials
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