Explainable Machine Learning and Epigenomic Profiling Decipher the Topological Determinants of Lentiviral Integration and Longitudinal Persistence in SCID-X1 Gene Therapy

Abstract

Lentiviral vectors (LVs) are clinically established for SCID-X1 gene therapy, yet the quantitative epigenomic and spatial determinants governing integration targeting and long-term clonal persistence across chromosomes remain incompletely characterized. Using clinical multi-omics datasets from SCID-X1 trials, we curated 274,959 unique clinical integration sites (VIS) across 276,839 clonal records from 10 patients (hg38). Balanced against length-weighted genomic controls (total N = 549,918), five epigenomic and topological features were modeled. We evaluated Logistic Regression, XGBoost, and a Deep Genomic ResNet using inner 5-fold chromosome-grouped cross-validation and evaluation on held-out test chromosomes (chr19-22, chrX). Statistical uncertainty was quantified via 1-Mb block-bootstrap (5,000 replicates) and 1-Mb block permutations (10,000 replicates). On held-out chromosomes, Deep ResNet achieved an ROC-AUC of 0.7857 [95% CI: 0.7672-0.8029] and PR-AUC of 0.8027 [95% CI: 0.7640-0.8337] over the 0.5755 prevalence baseline, outperforming linear baselines. 1-Mb block permutations confirmed significant proximity to scATAC-seq peaks (Cliff's {delta}=-0.3364, P_perm100 kb) from proto-oncogenes without proximal enrichment (OR = 1.05, P = 0.617). Furthermore, longitudinal tracking identified 79,587 persistent clones ([≥]2 time points), which exhibited a significant 31% depletion near proto-oncogenes (OR = 0.69, P = 0.014). Compact topological and epigenomic features robustly predict lentiviral integration without spatial data leakage. The long-term depletion of persistent clones near oncogenes confirms the insertional safety of SIN lentiviral gene therapy.