Biology subjects

Skanderup, A. J.

Publications and source records attributed to Skanderup, A. J..

5 recordsLinked to original sources

bioRxiv2026

Systematic benchmarking of multi-modal approaches for tumor-naive ctDNA detection and quantification

Longitudinal monitoring of circulating tumor DNA (ctDNA) has emerged as a promising framework for characterizing treatment response dynamics in cancer. Scalable tumor-naive approaches for quantifying ctDNA often involve whole-genome sequencing (WGS) or DNA methylation profiling, but their comparative performance and capacity for complementary integration remain poorly understood. Here we systematically benchmarked tumor-naive WGS- and methylation-based ctDNA quantification methods using plasma from 150 patients with colorectal, lung and breast cancer. Using paired high-depth WGS and EM-seq data, we generated 40,000 in silico samples and evaluated detection accuracy, limits of detection (LoD) and quantification (LoQ) across cancer types and sequencing depths (0.1x-30x). We further assessed single- and multimodal method combinations, identifying conditions under which integrated approaches enhance analytical performance for detection and quantification relative to single modalities. This benchmark delineates key performance trade-offs and provides a practical framework to support method development and guide future research applications in ctDNA-based biomarker studies.

bioinformatics↗

bioRxiv2025

Comprehensive benchmarking of methods for mutation calling in circulating tumor DNA

Detection of somatic mutations in cell-free DNA (cfDNA) is challenging due to low variant allele frequencies and pronounced DNA degradation. Here, we present a novel approach and resource for benchmarking of somatic variant calling algorithms in cfDNA samples from cancer patients. Using longitudinally collected cfDNA samples from colorectal and breast cancer patients, we identify patient-matched samples with high and ultra-low circulating tumor DNA (ctDNA) levels. These sample pairs, preserving patient-specific germline and somatic haematopoiesis variant backgrounds, were used to generate dilution series capturing characteristics of bona-fide cfDNA samples. To benchmark the accuracy and limit of detection of 9 somatic variant calling algorithms, we used deep Whole Genome Sequencing (WGS, 150x) and ultra-deep Whole Exome Sequencing (WES, 2,000x) to construct a reference set of [~]37,000 Single Nucleotide Variants and [~]58,000 Insertions/Deletions. We tested methods under variable ctDNA levels and depth of sequencing, generating guidelines for method choice depending on use case. Using a machine learning approach, we further evaluated the potential of fine-tuning individual variant callers, revealing features that may improve accuracy in cfDNA samples. Overall, we present a new resource for benchmarking of somatic variant calling methods in cfDNA, providing insights on method choice to realize the potential of liquid biopsies in precision oncology.

bioinformatics↗

bioRxiv2025

Convergent genomic and molecular features predict risk of metachronous metastasis in clear cell renal cell carcinoma

The molecular features determining the risk of metachronous metastases in clear cell renal cell carcinoma (ccRCC) are poorly defined. Using a systematic tumor transcriptome deconvolution approach, we investigated the genomic and transcriptomic profiles of 192 ccRCC primary tumors with extended clinical follow-up to identify cancer and stromal cell molecular features associated with metastatic risk. At the genomic level, we identified a significantly higher frequency of copy number loss at 1p31-36 in primary tumors that later progressed with metastases. Tumor transcriptome deconvolution identified significant down-regulation of epithelial cell polarity, including PATJ (1p31), and fatty acid metabolism, including CYP4A11 (1p33), in cancer cells of tumors that developed metastatic progression. We developed and benchmarked a compact 5-feature predictive model (5G) that demonstrated improved accuracy over existing ccRCC gene signatures in the prediction of metachronous metastasis risk. Overall, our study highlights convergent genomic and transcriptomic alterations in chromosome 1p, driving dysregulation of epithelial cell polarity and fatty acid metabolism, as putative novel risk factors of metachronous metastasis in ccRCC.

cancer biology↗

bioRxiv2020

A pan-cancer metabolic atlas of the tumor microenvironment

Tumors are heterogeneous cellular environments with entwined metabolic dependencies. Here, we used a tumor transcriptome deconvolution approach to profile the metabolic states of cancer and non-cancer (stromal) cells in bulk tumors of 20 solid tumor types. We identified metabolic genes and processes recurrently altered in cancer cells across tumor types, including pan-cancer upregulation of deoxythymidine triphosphate (dTTP) production. In contrast, the tryptophan catabolism rate limiting enzymes, IDO1 and TDO2, were highly overexpressed in stroma, suggesting that kynurenine-mediated suppression of antitumor immunity is predominantly constrained by the stroma. Oxidative phosphorylation was unexpectedly the most upregulated metabolic process in cancer cells compared to both stromal cells and a large atlas of cancer cell lines, suggesting that the Warburg effect may be less pronounced in cancer cells in vivo. Overall, our analysis highlights fundamental differences in metabolic states of cancer and stromal cells inside tumors and establishes a pan-cancer resource to interrogate tumor metabolism.

cancer biology↗

bioRxiv2018

SMuRF: Portable and accurate ensemble-based somatic variant calling

ABSTARCTO_ST_ABSSummaryC_ST_ABSSMuRF is an ensemble method for prediction of somatic point mutations (SNVs) and small insertions/deletions (indels) in cancer genomes. The method integrates predictions and auxiliary features from different somatic mutation callers using a Random Forest machine learning approach. SMuRF is trained on community-curated tumor whole genome sequencing data, is robust across cancer types, and achieves improved accuracy for both SNV and indel predictions of genome and exome-level data. The software is user-friendly and portable by design, operating as an add-on to the community-developed bcbio-nextgen somatic variant calling pipeline.\n\nContactskanderupamj@gis.a-star.edu.sg

bioinformatics↗