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Prediction model for drug response of acute myeloid leukemia patients
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- Trac, Quang Thinh (author)
- Karolinska Institutet
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- Pawitan, Yudi (author)
- Karolinska Institutet
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- Mou, Tian (author)
- Shenzhen Univ, Sch Biomed Engn, Shenzhen, Peoples R China.
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- Karolinska Institutet Shenzhen Univ, Sch Biomed Engn, Shenzhen, Peoples R China (creator_code:org_t)
- 2023-03-24
- 2023
- English.
- In: npj Precision Oncology. - : Springer Nature. - 2397-768X. ; 7
- Journal article (peer-reviewed)
Abstract
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- 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/.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Hematologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Hematology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
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