ArticleNature genetics2017
Precision oncology for acute myeloid leukemia using a knowledge bank approach.
Article in Nature genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 157 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.
An Integrated European Platform to Conduct Translational Studies in Myelodysplastic Syndromes Based on the EuroBloodNet Infrastructure
Personalized Medicine Program on Myelodysplastic Syndromes: Characterization of the Patient's Genome for Clinical Decision Making and Systematic Collection of Real World Data to Improve Quality of Health Care
Genomic and Personalized Medicine for All (GENOMED4ALL): Application of Artificial Intelligence to Improve Disease Classification and Prognosis in Myelodysplastic Syndrome.
Who cites it
157 citing papers in PubMed.
- Autonomous artificial intelligence prescribing a drug to prevent severe acute graft-versus-host disease in HLA-haploidentical transplants.Nature communications · 2025Trial
- Measurable residual disease by flow cytometry in acute myeloid leukemia is prognostic, independent of genomic profiling.Leukemia research · 2022Trial
- Machine learning identifies the independent role of dysplasia in the prediction of response to chemotherapy in AML.Leukemia · 2022Trial
- The added value of multi-state modelling in a randomized controlled trial: The HOVON 102 study re-analyzed.Cancer medicine · 2022Trial
- A precision medicine classification for treatment of acute myeloid leukemia in older patients.Journal of hematology & oncology · 2021Trial
- A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.Blood advances · 2019Trial
- Epidemiological, genetic, and clinical characterization by age of newly diagnosed acute myeloid leukemia based on an academic population-based registry study (AMLSG BiO).Annals of hematology · 2017Trial
- Novel targeted therapy and cellular immunotherapy enabling allogeneic hematopoietic stem cell transplantation for relapsed/refractory acute myeloid leukemia.Chinese medical journal · 2026Review
- Article
- Personalized multivariable clinical prognostic model for patients with acute myeloid leukemia receiving intensive chemotherapy.International journal of hematology · 2026Article
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Article
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- Integrating Genomic and Clinical Data in AML: Real-World Application of the Sanger Multistage Model.Genes · 2026Article
- Differential prognostic impact of myelodysplasia-related gene mutations in a European cohort of 4978 intensively treated AML patients.Leukemia · 2026Article
- Predicting Remission in Schizophrenia Using Machine Learning-Assessing the Impact of Sample Size and Predictor Overinclusion.Acta psychiatrica Scandinavica · 2025Article
- Unveiling CrypticInternational journal of molecular sciences · 2025Article
- Prognostic impact of co-mutations in adults withBlood science (Baltimore, Md.) · 2025Article
- Resolving Leukemia Heterogeneity and Lineage Aberrations with HematoMap.Genomics, proteomics & bioinformatics · 2025Article
- Age-stratified machine learning identifies divergent prognostic significance of molecular alterations in AML.HemaSphere · 2025Article
97 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Underpinning the vision of precision medicine is the concept that causative mutations in a patient's cancer drive its biology and, by extension, its clinical features and treatment response. However, considerable between-patient heterogeneity in driver mutations complicates evidence-based personalization of cancer care. Here, by reanalyzing data from 1,540 patients with acute myeloid leukemia (AML), we explore how large knowledge banks of matched genomic-clinical data can support clinical decision-making. Inclusive, multistage statistical models accurately predicted likelihoods of remission, relapse and mortality, which were validated using data from independent patients in The Cancer Genome Atlas. Comparison of long-term survival probabilities under different treatments enables therapeutic decision support, which is available in exploratory form online. Personally tailored management decisions could reduce the number of hematopoietic cell transplants in patients with AML by 20-25% while maintaining overall survival rates. Power calculations show that databases require information from thousands of patients for accurate decision support. Knowledge banks facilitate personally tailored therapeutic decisions but require sustainable updating, inclusive cohorts and large sample sizes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.