ArticleNPJ precision oncology2023
Prediction model for drug response of acute myeloid leukemia patients.
Article in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 14 citations in OpenAlex.
- AI-enabled multi-omics pharmacogenomic modeling guides resistance-aware multitarget optimization of venetoclax therapy in acute myeloid leukemia.NPJ precision oncology · 2026Article
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- A Nomogram Prediction Model and Scoring System for Resistance in Acute Myeloid Leukemia Patients Treated with Venetoclax Combined with Hypomethylating Agents.Current oncology (Toronto, Ont.) · 2026Article
- The microbial metabolome: remodeling the therapeutic landscape in hematologic malignancies.NPJ biofilms and microbiomes · 2026Review
- SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Machine learning prediction for AML based on 3D genome selected circRNA.NPJ systems biology and applications · 2026Article
- Immune monitoring for relapse of acute myeloid leukemia after allogeneic stem cell transplantation in clinical laboratories.Frontiers in immunology · 2026Review
- Article
- Predicting the Regulatory Dynamics of AML Disease Progression from Longitudinal Multi-Modal Clinical Data.Journal of medical systems · 2025Article
- Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review).Biomedical reports · 2025Review
- Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.Clinical and experimental medicine · 2025Review
- A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction.Frontiers in genetics · 2025Article
- A Combinatorial Functional Precision Medicine Platform for Rapid Therapeutic Response Prediction in AML.Cancer medicine · 2024Article
Corrections and comments
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Authors and funding
18 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite some encouraging successes, predicting the therapy response of acute myeloid leukemia (AML) patients remains highly challenging due to tumor heterogeneity. Here we aim to develop and validate MDREAM, a robust ensemble-based prediction model for drug response in AML based on an integration of omics data, including mutations and gene expression, and large-scale drug testing. Briefly, MDREAM is first trained in the BeatAML cohort (n = 278), and then validated in the BeatAML (n = 183) and two external cohorts, including a Swedish AML cohort (n = 45) and a relapsed/refractory acute leukemia cohort (n = 12). The final prediction is based on 122 ensemble models, each corresponding to a drug. A confidence score metric is used to convey the uncertainty of predictions; among predictions with a confidence score >0.75, the validated proportion of good responders is 77%. The Spearman correlations between the predicted and the observed drug response are 0.68 (95% CI: [0.64, 0.68]) in the BeatAML validation set, -0.49 (95% CI: [-0.53, -0.44]) in the Swedish cohort and 0.59 (95% CI: [0.51, 0.67]) in the relapsed/refractory cohort. A web-based implementation of MDREAM is publicly available at https://www.meb.ki.se/shiny/truvu/MDREAM/ .
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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.