Biology subjects

Wetterskog, D.

Publications and source records attributed to Wetterskog, D..

3 recordsLinked to original sources

bioRxiv2022

Treatment-mediated selection of lethal prostate cancer clones defined by copy number architectures

Despite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient relationships of metastases that evade treatment, we performed genomewide copy number profiling and bespoke approaches targeting the androgen receptor (AR) on 142 metastatic regions from 10 organs harvested post-mortem from nine men who died from prostate cancer. We identified diverse and patient-unique alterations clustering around the AR in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of AR-neutral clones. Using the genomic boundaries of pan-autosome copy number change, we confirmed a common clone of origin across metastases and diagnostic biopsies; and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations. Autosome-defined clusters were characterized by cluster-specific AR gene architectures that in two index cases were topologically more congruent than by chance (p-values 0.03, 3.07x10-8). Integration with anatomical site suggested patterns of spread and points of genomic divergence. Copy number boundaries identified treatment-selected clones with putatively distinct lethal trajectories. Statement of significanceLethal prostate cancer evolves from a single clone of origin and upon a treatment-mediated selection, progresses to lethal disease via a limited number of related clones harboring patient-unique androgen receptor gene architectures.

cancer biology↗

bioRxiv2021

Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer

Prostate cancer is profoundly heterogeneous and patients would benefit from methods that stratify clinically indolent from more aggressive forms of the disease. We employed single-cell assay for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identified pre-existing and treatment-persistent cell subpopulations that possess transcriptional stem-like features and regenerative potential when subjected to treatment. We found distinct chromatin landscapes associated with enzalutamide treatment and resistance that are linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent stem-like cells were able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal hitherto unrecognized molecular predictors of treatment response. This suggests that high analytical resolution of pre-clinical models may powerfully inform clinical decision-making.

cancer biology↗

bioRxiv2018

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BackgroundTargeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).\n\nMethodsTo address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.\n\nResultsOur tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.\n\nConclusionsAmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.

genomics↗