Genome-Wide Association Study Identifies ROBO2 as a Novel Susceptibility Gene for Anthracycline-Related Cardiomyopathy in Childhood Cancer Survivors

Abstract

PURPOSE Interindividual variability in the dose-dependent association between anthracyclines and cardiomyopathy suggests a modifying role of genetic susceptibility. Few previous studies have examined gene-anthracycline interactions. We addressed this gap using the Childhood Cancer Survivor Study (discovery) and the Children's Oncology Group (COG) study COG-ALTE03N1 (replication). METHODS A genome-wide association study (Illumina HumanOmni5Exome Array) in 1,866 anthracycline-exposed Childhood Cancer Survivor Study participants (126 with heart failure) was used to identify single-nucleotide polymorphisms (SNPs) with either main or gene-environment interaction effect on anthracycline-related cardiomyopathy that surpassed a prespecified genome-wide threshold for statistical significance. We attempted replication in a matched case-control set of anthracycline-exposed childhood cancer survivors with (n = 105) and without (n = 160) cardiomyopathy from COG-ALTE03N1. RESULTS Two SNPs (rs17736312 [ ROBO2]) and rs113230990 (near a CCCTC-binding factor insulator [< 750 base pair]) passed the significance cutoff for gene-anthracycline dose interaction in discovery. SNP rs17736312 was successfully replicated. Compared with the GG/AG genotypes on rs17736312 and anthracyclines ≤ 250 mg/m2, the AA genotype and anthracyclines > 250 mg/m2 conferred a 2.2-fold (95% CI, 1.2 to 4.0) higher risk of heart failure in discovery and an 8.2-fold (95% CI, 2.0 to 34.4) higher risk in replication. ROBO2 encodes transmembrane Robo receptors that bind Slit ligands (SLIT). Slit-Robo signaling pathway promotes cardiac fibrosis by interfering with the transforming growth factor-β1/small mothers against decapentaplegic (Smad) pathway, resulting in disordered remodeling of the extracellular matrix and potentiating heart failure. We found significant gene-level associations with heart failure: main effect ( TGF-β1, P = .007); gene*anthracycline interaction ( ROBO2*anthracycline, P = .0003); and gene*gene*anthracycline interaction ( SLIT2* TGF-β1*anthracycline, P = .009). CONCLUSION These findings suggest that high-dose anthracyclines combined with genetic variants involved in the profibrotic Slit-Robo signaling pathway promote cardiac fibrosis via the transforming growth factor-β1/Smad pathway, providing credence to the biologic plausibility of the association between SNP rs17736312 ( ROBO2) and anthracycline-related cardiomyopathy.

Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors

“…In a genome-wide association study using the CCSS and COG cohorts for discovery and replication, respectively, we previously identified variants on the ROBO2 gene to be associated with high-dose anthracycline-related cardiomyopathy. 57 ROBO2 is involved in the Slit-Robo signaling pathway that promotes cardiac fibrosis. In the present study, using the COG data set for discovery, we used a gene-level approach and identified the P2RX7 gene, where the inflammatory pathway promotes cardiomyopathy.…”

Comprehensive investigating of mismatch repair genes (MMR) polymorphisms in participants with chronic hepatitis B virus infection

“…GWAS has identified thousands of SNPs related to diseases, and most of them are located in non-coding regions ( Suppiah et al, 2009 ; Wang et al, 2022 ). Compared to the transcription function of gene coding region, non-coding region has more regulatory functions ( Beermann et al, 2016 ).…”

Priorities in Cardio-Oncology Basic and Translational Science

“…Existing population-based cohorts generally lack data on cancer therapeutics, dosing, and the chronological sequence of various cancer treatments, although recent studies in childhood cancer survivors have uncovered novel and intriguing information in this space. 55 , 56 Moving forward, further development of multicenter or population-based cohorts focused on cardio-oncology variables and endpoints would enable the integration of clinical information with different -omics such as genomics, transcriptomics, epigenomics, proteomics, and metabolomics. Multiomics datasets, when combined with statistical approaches such as Mendelian randomization and colocalization, could lead to the discovery of new genetic biomarkers that contribute to CTR-CVT these genetic variants can provide information on the underlying mechanisms and new targets for drug development.…”