Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors: A Report From COG-ALTE03N1, BMTSS, and CCSS

Sharafeldin N, Zhou L, Singh P, Crossman DK, Wang X, Hageman L, Landier W, Blanco JG, Burridge PW, Sapkota Y, Yasui Y, Armstrong GT, Robison LL, Hudson MM, Oeffinger K, Chow EJ, Armenian SH, Weisdorf DJ, Bhatia S. Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors: A Report From COG-ALTE03N1, BMTSS, and CCSS. JACC CardioOncol. 2023 Dec;5(6):807-818. doi: 10.1016/j.jaccao.2023.06.007. eCollection 2023 Dec. PubMed PMID: 38205005; PubMed Central PMCID: PMC10774788.

Anthracyclines are highly effective in treating cancer, albeit with increased cardiomyopathy risk. Although risk is attributed to associations with single nucleotide polymorphisms (SNPs), multiple SNPs on a gene and their interactions remain unexamined. For discovery, 278 childhood cancer survivors (129 cases; 149 matched control subjects) from the COG (Children’s Oncology Group) study ALTE03N1 were included. Logic regression (machine learning) was used to identify gene-level SNP combinations for 7,212 genes and ordinal logistic regression to estimate gene-level associations with cardiomyopathy. Models were adjusted for primary cancer, age at cancer diagnosis, sex, race/ethnicity, cumulative anthracycline dose, chest radiation, cardiovascular risk factors, and 3 principal components. Statistical significance threshold of 6.93 × 10−6 accounted for multiple testing. Three independent cancer survivor populations (COG study, BMTSS [Blood or Marrow Transplant Survivor Study] and CCSS [Childhood Cancer Survivor Study]) were used to replicate gene-level associations and examine SNP-level associations from discovery genes using ordinal logistic, conditional logistic, and Cox regression models, respectively.