Genomic Evidence for Imiquimod-Mediated Clearance of Mutated Skin in Xeroderma Pigmentosum

Published: 18 August 2026| Version 1 | DOI: 10.17632/zxjhxbcb8k.1
Contributor:
Lilian Rocha

Description

In this study, we performed whole-exome sequencing of paired pre- and post-treatment biopsies from XP patients and demonstrated a consistent and substantial reduction in detectable somatic mutational burden following imiquimod therapy. We further characterized mutational signatures, showing persistence of oxidative damage–associated patterns in XP-V patients. Importantly, no tumors developed in treated areas during 24 months of follow-up.

Files

Steps to reproduce

Whole-exome libraries were prepared using the Nextera Rapid Capture Exome Kit (Illumina) and sequenced on a NovaSeq 6000 platform (2×175 bp). Reads were aligned to the human reference genome (GRCh38) using BWA-MEM (v0.7.17), and PCR duplicates were removed with Picard (v2.9.2) [13]. The average sequencing coverage was 30× across 94.9% of target regions (Supplementary Table 1S). One sample (IMQ23MDA, patient P5 pre-treatment) showed lower coverage (67.9% at 30×). Somatic single-nucleotide variants (SNVs) were identified using GATK Mutect2 [14], with skin samples (pre- and post-treatment) designated as “tumor” and matched saliva as “normal,” generating paired analyses for each patient. Mutational signatures were characterized using COSMIC single base substitution (SBS) signatures (v3.4)[15] and analyzed with the MutationalPatterns package [16]. Sequence context analysis was performed using Probability Logo (pLogo) [17], with the human exome as background. Statistical significance was assessed using non-parametric permutation testing (10,000 iterations; p<0.05). Given the limited DNA input in selected samples requiring whole genome amplification and the moderate sequencing depth (~30×), detection of low-frequency variants may be reduced, and amplification-related biases cannot be fully excluded. Statistical significance was assessed using non-parametric permutation tests (10,000 iterations, p<0.05).

Categories

Genetics, Oncology

Licence