Nature CommunicationsGenetic & Genomic MedicinePeer-reviewed journal article
Functional investigation of inherited noncoding genetic variation impacting the pharmacogenomics of childhood acute lymphoblastic leukemia treatment
Kashi Raj BhattaraiiD, Robert J. MobleyiD, Kelly R. BarnettiD, Daniel C. J. FergusoniD, Baranda S. HanseniD, Jonathan D. DiedrichiD, Brennan P. BergeroniD, Satoshi YoshimuraiD, Wenjian YangiD, Kristine R. CrewsiD, Christopher S. ManringiD, Elias Joseph JabbouriD, Elisabeth M. PaiettaiD, Mark Robert LitzowiD, Steven Mitchell KornblauiD, Wendy StockiD, Hiroto InabaiD, Sima JehaiD, Ching‐Hon PuiiD, Cheng ChengiD, Shondra M. Pruett‐MilleriD, Mary V. RellingiD, Jun J.J. YangiD, William Edward EvansiD, Daniel SaviciD
Abstract
Although acute lymphoblastic leukemia (ALL) is the most common childhood cancer, there is limited understanding of the contribution of inherited genetic variation on inter-individual differences in chemotherapy response. Defining genetic factors impacting therapy failure can help better predict response and identify drug resistance mechanisms. We therefore mapped inherited noncoding variants associated with chemotherapeutic drug resistance and/or treatment outcome to ALL cis-regulatory elements and investigated their gene regulatory potential and genomic connectivity using massively parallel reporter assays and promoter capture Hi-C, respectively. We identified 53 variants with reproducible allele-specific effects on transcription and high-confidence gene targets. Subsequent functional interrogation of the top variant (rs1247117) determined that it disrupted a PU.1 consensus motif and PU.1 binding affinity. Importantly, deletion of the genomic interval containing rs1247117 sensitized ALL cells to vincristine. Together, these data demonstrate that noncoding regulatory variation associated with diverse pharmacological traits harbor significant effects on allele-specific transcriptional activity and impact sensitivity to chemotherapeutic agents in ALL.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 10
- FWCI
- 4.14
- Field percentile
- top 6%
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