BMC Medical GenomicsGenetic & Genomic MedicinePeer-reviewed journal article
Exome-wide association study reveals 7 functional variants associated with ex-vivo drug response in acute myeloid leukaemia patients
Anil Kumar GiriiD, Jake LiniD, Konstantinos KyriakidisiD, Garima TripathiiD, Henrikki Almusa
Abstract
Acute myeloid leukemia (AML) is a rare aggressive blood cancer without any long-term cure. Further, due to the extreme molecular heterogeneity of the disease, drug treatment response varies from patient to patient. The variability of drug response can cause unnecessary treatment in more than half of the patients with no or partial therapy responses leading to severe side effects, economic as well as time loss. Understanding the genetic risk factors underlying the drug response in AML can help with improved prediction of treatment responses and identification of biomarkers in addition to mechanistic insights to monitor treatment response. Here, we report the results of the largest exome-wide association study (EWAS) of ex-vivo drug response performed to date with 175 AML cases and 47 drugs. We used information from 55423 exonic SNPs to perform the analysis. We identified exome-wide significant (p<9.02 x10-7) associations for rs113985677 in CCIN with tamoxifen response, rs115400838 in TRMT5 with idelalisib response, rs11878277 in HDGFL2 with entinostat, and rs2229092 in LTA associated with vorinostat response. Further, using multivariate genome-wide association analysis, we identified the association of rs11556165 in ATRAID, and rs11236938 in TSKU with the combined response of all 47 drugs and 29 nonchemotherapy drugs at the genome-wide significance level (p<5x10-8). Additionally, a significant association of rs35704242 in NIBAN1 was associated with the combined response for nonchemotherapy medicines (p=2.51x10-8), and BI.2536, gefitinib, and belinostat were identified as the central traits. Our study represents the largest EWAS study to date on ex-vivo drug response in AML and reports 7 new associated loci that help to understand the anticancer drug response in AML patients.
- organism: human
- assay: whole-exome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 3
- FWCI
- 0.73
- Field percentile
- top 31%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Clinical Forecasting using Ex Vivo Drug Sensitivity Profiling of Acute Myeloid Leukemia
Aram Nikolai Andersen, Andrea M. Brodersen, … Jorrit M. Enserink·bioRxiv·Oct 11, 2022
- Utilizing proteomics and phosphoproteomics to predict ex vivo drug sensitivity across genetically diverse AML patients
Sara J.C. Gosline, Cristina E. Tognon, … Karin Rodland·bioRxiv·Jun 4, 2021
- Machine Learning for Predicting Therapeutic Outcomes in Acute Myeloid Leukemia Patients
Nestoras Karathanasis, Panayiota L. Papasavva, … George M. Spyrou·medRxiv·Feb 29, 2024
- Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia
G. Qin, J. Dai, … P. S. Becker·bioRxiv·May 24, 2023
- Prioritizing Genes and Rare Protein-Coding Variants in Acute Myeloid Leukemia via Whole Genome Sequencing Data
Stephen Vieno, Madhurbain Singh, … Tan-Hoang Nguyen·medRxiv·Aug 22, 2026
- Functional investigation of inherited noncoding genetic variation impacting the pharmacogenomics of childhood acute lymphoblastic leukemia treatment
Kashi Raj Bhattarai, Robert J. Mobley, … Daniel Savic·Nature CommunicationsPeer-reviewed·May 1, 2024