Net Good IndexSubmit a correction

Benefit Ledger · provisional · Biology

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

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

12 Sep 2026Tier 1 UsefulMethodology 0.1

Current score

+0.00

1 base · Useful (tier 1 of 5, 1 pts)
× 0.1000 attribution · Minor documented assistance
× 0.1000 evidence · Firsthand or social claim
× 0.2000 realization · Proposed
× 0.5000 durability
Event-level product before credit split: 0.00

Auto-published from news ingest as a provisional placeholder. Score is conservative until a named release is identified and the record is rescored.

What happened

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.

Model attribution

Unspecified AI system
Version unspecified
+0.00

Unspecified

Unspecified system mentioned or implied by a news item. Remap to a named release when identified.

News ingest does not infer a named model from the publisher alone. Attribution stays unspecified until a release is identified.

Attribution 0.1000 · Credit share 100% · Unknown

Claims

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

    outcome · supported

Sources

Revision history

  • 13 Sep 2026 · 0.00 0.00

    Auto-published from news ingest without human review.