Biology subjects

Jacobsen Skanderup, A.

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

9 recordsLinked to original sources

bioRxiv2025

A Computational Framework for Analysis of cfDNA Fragmentation Profiles

Circulating cell-free DNA (cfDNA) has emerged as a promising non-invasive medium for studying tumor molecular profiles. Non-random fragmentation patterns in plasma cfDNA, particularly around nucleosome-depleted regions (NDRs) near transcription start sites (TSS), have been shown to reflect epigenetic regulation and gene expression. In this study, coverage profiles of the NDR were utilized to derive an NDR score, which was subsequently used as a proxy for inferring gene expression. To reduce transcript-to-transcript variability and enhance the clarity of these expression-associated signals, we implement a method for GC-bias correction of cfDNA samples. A computational framework (NDRDiff) was then developed to enable comparative analyses of NDR score profiles across different sample groups. The GC-bias correction preserved the overall trend of the NDR signal while improving the separation of gene expression levels, as demonstrated by comparisons of healthy donor cfDNA samples with matched blood RNA-seq data. Validation on a simulated dataset showed that NDRDiff achieved an area under the precision-recall curve (AUPRC) of 0.916, outperforming a standard t-test (AUPRC of 0.777). When applied to a comparison of healthy donor cfDNA and metastatic colorectal cancer (mCRC) cfDNA, NDRDiff identified 531 differential NDR score (DNS) genes that facilitated clear separation between the two groups. These DNS genes were found to correlate with tumor fraction estimates (down-regulated DNS genes: Pearson R = 0.89, p < 0.05; up-regulated DNS genes: Pearson R = -0.88, p < 0.05) and included CLDN4, BIN2, and IRAG2, which exhibit strong associations with colorectal cancer or blood cell expression signatures. Gene set enrichment analysis further revealed enrichment of colon and other gastrointestinal tissue signatures. Collectively, these findings underscore the potential of NDR-based cfDNA analysis as a minimally invasive tool for monitoring tumor-related molecular features in cancer.

cancer biology↗

bioRxiv2024

GCfix: A Fast and Accurate Fragment Length-SpecificMethod for Correcting GC Bias in Cell-Free DNA

MotivationCell-free DNA (cfDNA) analysis has wide-ranging clinical applications due to its non-invasive 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. ResultsWe 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 (1) comparing the fragment count density distribution of GC content between expected and corrected samples, and (2) evaluating coverage profile improvement post-correction. GCfix outperforms existing cfDNA GC bias correction methods on these metrics. AvailabilityGCfix software and code for reproducing the figures are publicly accessible on GitHub: https://github.com/Rafeed-bot/GCfix_Software.

genomics↗

bioRxiv2023

A transcriptome-wide meta-analysis reveals lack of cancer-cell intrinsic determinants of response to immune checkpoint blockade

Immune-checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some cancer patients. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic features of ICB response conserved across tumor types. Overall, our results challenge the prevailing dogma that cancer cells present tissue-agnostic molecular markers that modulate immune activity and ICB treatment response. These results have implications for the development of improved ICB treatments and diagnostics.

cancer biology↗

bioRxiv2023

Quantification of circulating tumor DNA using deep learning

Quantification of circulating tumor DNA (ctDNA) levels in blood enables non-invasive surveillance of cancer progression. Fragle is an ultra-fast deep learning-based method for ctDNA quantification directly from cell-free DNA fragment length profiles. We developed Fragle using low-pass whole genome sequence (lpWGS) data from multiple cancer types and healthy control cohorts, demonstrating high accuracy, and improved lower limit of detection in independent cohorts as compared to existing tumor-naive methods. Uniquely, Fragle is also compatible with targeted sequencing data, exhibiting high accuracy across both research and commercial targeted gene panels. We used this method to study longitudinal plasma samples from colorectal cancer patients, identifying strong concordance of ctDNA dynamics and treatment response. Furthermore, prediction of minimal residual disease in resected lung cancer patients demonstrated significant risk stratification beyond a tumor-naive gene panel. Overall, Fragle is a versatile, fast, and accurate method for ctDNA quantification with potential for broad clinical utility.

bioinformatics↗

bioRxiv2022

Accurate pan-cancer tumor purity estimation from gene expression data

Tumors are complex masses composed of malignant and non-malignant cells. Variation in tumor purity (proportion of cancer cells in a sample) can both confound integrative analysis and enable studies of tumor heterogeneity. Here we developed PUREE, which uses a weakly supervised learning approach to infer tumor purity from a tumor gene expression profile. PUREE was trained on gene expression data and genomic consensus purity estimates from 7864 solid tumor samples. PUREE predicted purity with high accuracy across distinct solid tumor types and generalized to tumor samples from unseen tumor types and cohorts. Gene features of PUREE were further validated using single-cell RNA-seq data from distinct tumor types. In a comprehensive benchmark, PUREE outperformed existing transcriptome-based purity estimation approaches. Overall, PUREE is a highly accurate and versatile method for estimating tumor purity and interrogating tumor heterogeneity from bulk tumor gene expression data.

bioinformatics↗

bioRxiv2021

Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study

Tumor purity is the proportion of cancer cells in the tumor tissue. An accurate tumor purity estimation is crucial for accurate pathologic evaluation and for sample selection to minimize normal cell contamination in high throughput genomic analysis. We developed a novel deep multiple instance learning model predicting tumor purity from H&E stained digital histopathology slides. Our model successfully predicted tumor purity from slides of fresh-frozen sections in eight different TCGA cohorts and formalin-fixed paraffin-embedded sections in a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values, which were inferred from genomic data and accepted as the golden standard. Besides, we obtained spatially resolved tumor purity maps and showed that tumor purity varies spatially within a sample. Our analyses on tumor purity maps also suggested that pathologists might have chosen high tumor content regions inside the slides during tumor purity estimation in the TCGA cohorts, which resulted in higher values than genomic tumor purity values. In short, our model can be utilized for high throughput sample selection for genomic analysis, which will help reduce pathologists workload and decrease inter-observer variability. Moreover, spatial tumor purity maps can help better understand the tumor microenvironment as a key determinant in tumor formation and therapeutic response.

cancer biology↗

bioRxiv2021

Integrative Epigenomic and High-Throughput Functional Enhancer Profiling Reveals Determinants of Enhancer Heterogeneity in Gastric Cancer

BackgroundEnhancers are distal cis-regulatory elements required for cell-specific gene expression and cell fate determination. In cancer, enhancer variation has been proposed as a major cause of inter-patient heterogeneity - however, most predicted enhancer regions remain to be functionally tested. ResultsAnalyzing 128 epigenomic histone modification profiles of primary GC samples, normal gastric tissues, and GC cell lines, we report a comprehensive catalog of 75,730 recurrent predicted enhancers, the majority of which are tumor-associated in vivo (>50,000) and associated with lower somatic mutation rates inferred by whole-genome sequencing. Applying Capture-based Self-Transcribing Active Regulatory Region sequencing (CapSTARR-seq) to the enhancer catalog, we observed significant correlations between CapSTARR-seq functional activity and H3K27ac/H3K4me1 levels. Super-enhancer regions exhibited increased CapSTARR-seq signals compared to regular enhancers even when decoupled from native chromatin contexture. We show that combining histone modification and CapSTARR-seq functional enhancer data improves the prediction of enhancer-promoter interactions and pinpointing of germline single nucleotide polymorphisms (SNPs), somatic copy number alterations (SCNAs), and trans-acting TFs involved in GC expression. Specifically, we identified cancer-relevant genes (e.g. ING1, ARL4C) whose expression between patients is influenced by enhancer differences in genomic copy number and germline SNPs, and HNF4 as a master trans-acting factor associated with GC enhancer heterogeneity. ConclusionsOur study indicates that combining histone modification and functional assay data may provide a more accurate metric to assess enhancer activity than either platform individually, and provides insights into the relative contribution of genetic (cis) and regulatory (trans) mechanisms to GC enhancer functional heterogeneity.

cancer biology↗

bioRxiv2019

Data-driven inference of crosstalk in the tumor microenvironment

Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is key to tumorigenesis yet challenging to decipher from tumor transcriptomes. Here, we report an unbiased, data-driven approach to deconvolute bulk tumor transcriptomes and predict crosstalk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. Our approach recovers known transcriptional hallmarks of cancer and stromal cells and is concordant with single-cell and immunohistochemistry data, underlining its robustness. Pan-cancer analysis reveals previously unrecognized features of cancer-stromal crosstalk. We find that autocrine cancer cell cross-talk varied between tissues but often converged on known cancer signaling pathways. In contrast, many stromal cross-talk interactions were highly conserved across tumor types. Interestingly, the immune checkpoint ligand PD-L1 was overexpressed in stromal rather than cancer cells across all tumor types. Moreover, we predicted and experimentally validated aberrant ligand and receptor expression in cancer cells of basal and luminal breast cancer, respectively. Collectively, our findings validate a data-driven method for tumor transcriptome deconvolution and establishes a new resource for hypothesis generation and downstream functional interrogation of the TME in tumorigenesis and disease progression.

cancer biology↗

bioRxiv2019

MutSpot: detection of non-coding mutation hotspots in cancer genomes

SummaryRecurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes.\n\nAvailability and implementationMutSpot is implemented as an R package and is available at https://github.com/skandlab/MutSpot/\n\nSupplementary informationSupplementary data are available at https://github.com/skandlab/MutSpot/

genomics↗