Karina Dalsgaard Sørensen

Germline polygenic score for prostate cancer aggressiveness

This paper develops a germline polygenic score (PRSagg) to predict prostate cancer aggressiveness and validates it across multiple datasets.

From a genomic risk model to clinical trial implementation in a learning health system: the ProGRESS Study

The paper develops a polygenic risk model for prostate cancer using VA data and validates it in external cohorts, showing its clinical utility in risk stratification.

Sensitive detection of copy number alterations in samples with low circulating tumor DNA fraction.

The paper introduces Jumble, an algorithm that improves the sensitivity of detecting copy number alterations in low ctDNA samples, achieving detection limits of 1-2% for amplifications and 4-8% for deletions.

Discovering fragment length signatures of circulating tumor DNA using Non-negative Matrix Factorization

This paper uses non-negative matrix factorization to analyze cfDNA fragment length distributions, showing it can infer tumor load, detect cancer, and identify open chromatin-associated signatures.

Prostate cancer risk stratification improved across multiple ancestries with new polygenic hazard score

The paper improves prostate cancer risk stratification across multiple ancestries using a new polygenic hazard score with additional SNPs, showing higher hazard ratios and positive predictive value for clinically significant prostate cancer.

Polygenic hazard score is associated with prostate cancer in multi-ethnic populations

This study evaluates a polygenic hazard score in multi-ethnic populations and finds it is associated with age at prostate cancer diagnosis, aggressive diagnosis, and death across European, Asian, and African ancestries.

Additional SNPs improve the performance of a polygenic hazard score for prostate cancer

This study improves a polygenic hazard score for prostate cancer by adding 120 SNPs, enhancing hazard ratios but not positive predictive value of PSA testing.

Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

This study integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.