Integration of genomics, clinical characteristics and baseline biological profiles to predict the risk of liver injury induced by high-dose methotrexate.

BACKGROUND

High-dose methotrexate (HD-MTX) is commonly employed in the treatment of malignant tumors in children and young adults due to its distinctive therapeutic efficacy. Nonetheless, the systemic exposure to MTX often results in liver injury (drug induced liver injury, DILI), thereby imposing limitations on the sustained administration of HD-MTX. Additionally, individual variations including genetic underpinnings attributable to disparities in therapeutic effects and clinical toxicity remain to be elucidated.

METHODS

A total of 374 patients receiving initial HD-MTX treatment were selected for this study, which aimed to establish a predictive model using binary logistic regression and a visual nomogram for DILI risk assessment. Demographic and clinical characteristics were collected at baseline and post-HD-MTX to explore their correlations with the occurrence of DILI. Additionally, genotyping of 25 single nucleotide polymorphisms from drug transporters and enzymes in the folic acid cycle was performed.

RESULT

G allele mutation in rs1128503, *1b/*1b and *1b/*15 haplotypic mutation in , female gender, and MTX dosage were identified as independent factors for moderate/severe DILI. Patients with GA or AA genotype in ABCB1 rs1128503 showed significant higher 24h MTX concentration than GG, and those with *1b/*1b haplotype group in SLCO1B1 exhibited lower dose adjusted concentration (C/D) than *1a/*1a group. Besides, patient administrated with HD-MTX were more prevalent to have higher C/D levels when using intravenous plus triple intrathecal injection route than those who were using intravenous injection alone. The composite predictive model (ROC curve: AUC = 0.805), comprising above four factors and 24h MTX concentration, exhibited high accuracy.

CONCLUSION

Female gender, recessive mutation in ABCB1 rs1128503, and a range of MTX concentration may be risk factors for increased susceptibility to DILI. Conversely, the *1b/*1b and *1b/*15 mutations in may have a protective effect against DILI. The proposed predictive model facilitates early individual risk assessment, enabling the implementation of proactive prevention strategies.