W61. GENETIC LIABILITY AND RISK PREDICTION FOR PSYCHIATRIC SYMPTOMS IN SURVIVORS OF CHILDHOOD CANCER: A REPORT FROM THE CHILDHOOD CANCER SURVIVOR STUDY

Sarah Benstock; Mingjuan Wang; Kendrick Li; Deokumar Srivastava; Ellen van der Plas; Kevin Krull; Cindy Im; Wendy Leisenring; Rebecca Howell; Gregory T. Armstrong; Vikki Nolan; Monica Gramatges; Yadav Sapkota; Tara Brinkman

doi:10.1016/j.euroneuro.2026.113179

Background Long-term survivors of childhood cancer are at increased risk of late effects, with up to 40% experiencing clinically significant mental health symptoms. Though disease- and treatment-related risk factors have been identified, substantial variability in risks of survivors with similar treatment exposures suggests a key role for genetic susceptibility. Previous studies of genetic risk of psychiatric outcomes in childhood cancer survivors have largely focused on candidate pathways or exome-based analyses, with limited use of whole-genome approaches. To address this gap, we utilized genome-wide data to examine the genetic risk for depression, anxiety, and global psychological distress in adult survivors of childhood cancer. Methods Participants included 7,898 adult survivors of childhood cancer from the Childhood Cancer Survivor Study (ancestry: European: 91%; African: 4%; Admixed/Asian: 5%) with WGS or imputed array data. The mean (standard deviation) age at primary cancer diagnosis was 10 (5.6) years, the mean age was 27.7 (6.0) years at baseline assessment, with relatively even female/male distribution (52.1% female). Linear regressions examined the associations between 7,446,479 common genetic variants (minor allele frequency, MAF > 1%) and anxiety, depression, and global psychological distress assessed by the Brief Symptom Inventory-18. Analyses used the baseline, continuous T-scores for each outcome. Multi-ancestry analyses were adjusted for sex, age at diagnosis, age at time of symptom assessment, cancer treatment exposures, and global genetic ancestry proportions estimated by ADMIXTURE. Cancer treatment exposure covariates were determined separately for each outcome using stepwise regression and validated using 10-fold cross-validated elastic net multivariable regression without the genetic variants. Results Preliminary discovery analyses did not identify genome-wide significant loci for any of our phenotypes. However, multiple loci approached suggestive significance (1 × 10-6) for depression and anxiety. For depression, the top three genomic loci are on chromosomes 5 (lead SNP: rs72772813, P = 2.4 × 10-7), 7 (lead SNP: rs78221183, P = 5.5 × 10-7), and 18 (lead SNP: rs77406723, P = 7.6 × 10-7). For anxiety, the top three genomic loci are on chromosomes 21 (lead SNP: rs77809963, P = 4.8 × 10-7), 4 (lead SNP: rs11731117, P = 4.7 × 10-7), and 6 (lead SNP: rs56167259, P = 8.2 × 10-7). A preliminary search determined the implicated loci on chromosome 5 for depression falls within the TBCA gene, which builds integral components of microtubules. The locus on chromosome 18 falls within the EPB41L3 gene, which develops structural components of the cytoskeleton and is associated with meningioma. Additionally, for anxiety, the locus on chromosome 4 falls in LINC00504, which is associated with the progression of different cancer cells. The locus on chromosome 6 falls in the EYS gene, which encodes a protein involved in photoreceptor maintenance and structural integrity. Discussion These preliminary findings are promising, given the limited sample size compared to studies in the general population. All lead SNPs are novel associations for anxiety and depression in childhood cancer survivors and warrant more investigation. Future analyses will include replication in independent survivors from the St. Jude Lifetime Cohort Study, functional annotation and characterization, and the development of clinical risk prediction models incorporating the genetic findings.

W61. GENETIC LIABILITY AND RISK PREDICTION FOR PSYCHIATRIC SYMPTOMS IN SURVIVORS OF CHILDHOOD CANCER: A REPORT FROM THE CHILDHOOD CANCER SURVIVOR STUDY

View Online

Abstract

Details

Metrics