P. R. Carroll

This paper uses machine learning to design a targeted sequencing panel for detecting tumor variants in cell-free DNA from prostate cancer patients, showing improved performance over existing methods.

This study uses single-cell RNA-sequencing to analyze prostate cancer tumor microenvironment, identifying heterogeneous epithelial cell states and tumor-associated club cells.

This study finds that higher plasma cfDNA concentrations and smaller fragment sizes are associated with more advanced prostate cancer.