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Anders Jacobsen Skanderup

Publications and source records attributed to Anders Jacobsen Skanderup.

2 recordsLinked to original sources

Integrated multi-omic profiling enables recurrence risk stratification beyond pathological stage in resected EGFR-mutant lung adenocarcinoma.

BACKGROUND: Early-stage EGFR-mutant lung adenocarcinoma (LUAD) demonstrates heterogeneous outcomes after curative surgery, yet adjuvant treatment decisions are guided by pathological stage alone. Following the ADAURA trial, adjuvant osimertinib is the standard of care for resected stage IB-IIIA EGFR-mutant LUAD; however, real-world data demonstrate that up to 40% of patients remain disease-free at five years without adjuvant osimertinib, underscoring the need for improved risk stratification. PATIENTS AND METHODS: We performed integrated clinical, genomic and transcriptomic profiling of 400 patients with resected stage IA-IIIA EGFR-mutant LUAD. EGFR-mutant recurrence risk models integrating clinical, genomic and transcriptomic data were developed and validated across one internal and three external cohorts. RESULTS: Genomic instability, including TP53 co-mutations, copy number alterations and APOBEC-associated mutational signatures, increased with pathological stage. RBM10 co-mutations were enriched in tumours with L858R mutations and correlated with upregulation of WNT signalling and epithelial-mesenchymal transition. Transcriptomic features outperformed clinical or genomic variables alone in predicting recurrence risk, and a multi-omic model demonstrated superior and reproducible performance, achieving a median concordance index of 75.4% across four independent validation cohorts. The multi-omic model stratified recurrence risk within individual pathological stages, including stage I disease, and identified patients most likely to benefit from adjuvant EGFR TKI. CONCLUSIONS: These findings define the molecular heterogeneity of early-stage EGFR-mutant LUAD and support multi-omic risk stratification to inform adjuvant EGFR TKI decisions beyond pathological stage. Prospective validation in larger cohorts will be required to confirm these findings.

Journal Article

GCfix: a fast and accurate fragment length-specific method for correcting GC bias in cell-free DNA.

MOTIVATION: Cell-free DNA (cfDNA) analysis has wide-ranging clinical applications due to its noninvasive nature. However, cfDNA fragmentomics and copy number analysis can be complicated by GC bias. There is a lack of GC correction software based on rigorous cfDNA GC bias analysis. Furthermore, there is no standardized metric for comparing GC bias correction methods across large sample sets, nor a rigorous experiment setup to demonstrate their effectiveness on cfDNA data at various coverage levels. RESULTS: We present GCfix, a method for robust GC bias correction in cfDNA data across diverse coverages. Developed following an in-depth analysis of cfDNA GC bias at the region and fragment length levels, GCfix is both fast and accurate. It works on all reference genomes and generates correction factors, tagged BAM files, and corrected coverage tracks. We also introduce two orthogonal performance metrics for (i) comparing the fragment count density distribution of GC content between expected and corrected samples, and (ii) evaluating coverage profile improvement post-correction. GCfix outperforms existing cfDNA GC bias correction methods on these metrics. AVAILABILITY AND IMPLEMENTATION: GCfix software and code for reproducing the figures are publicly accessible on GitHub: https://github.com/Rafeed-bot/GCfix_Software.

Software