ArticleLeukemia research2019
Predicting response to BET inhibitors using computational modeling: A BEAT AML project study.
Article in Leukemia research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Article
- Article
- Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia.Cell reports. Medicine · 2024Article
- A Situational Analysis of the Impact of the COVID-19 Pandemic on Digital Health Research Initiatives in South Asia.Cureus · 2023Review
- Inhibition of BET Family Proteins Suppresses African Swine Fever Virus Infection.Microbiology spectrum · 2022Article
- Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML.Clinical proteomics · 2022Article
- Understanding Drug Sensitivity and Tackling Resistance in Cancer.Cancer research · 2022Article
- Epigenetic derepression converts PPARγ into a druggable target in triple-negative and endocrine-resistant breast cancers.Cell death discovery · 2021Article
- Monocytic differentiation and AHR signaling as Primary Nodes of BET Inhibitor Response in Acute Myeloid Leukemia.Blood cancer discovery · 2021Article
- Advances and unanswered questions in management of acute myeloid leukemia in older adults: A glimpse into the future.Journal of geriatric oncology · 2021Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Despite advances in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), overall survival rates remain low. The ability to predict treatment response based on individual cancer genomics using computational modeling will aid in the development of novel therapeutics and personalize care. Here, we used a combination of genomics, computational biology modeling (CBM), ex vivo chemosensitivity assay, and clinical data from 100 randomly selected patients in the Beat AML project to characterize AML sensitivity to a bromodomain (BRD) and extra-terminal (BET) inhibitor. Computational biology modeling was used to generate patient-specific protein network maps of activated and inactivated protein pathways translated from each genomic profile. Digital drug simulations of a BET inhibitor (JQ1) were conducted by quantitatively measuring drug effect using a composite AML disease inhibition score. 93% of predicted disease inhibition scores matched the associated ex vivo IC
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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.