Sylvan C. Baca

This paper identifies histone modification-associated nucleosome positions in cfDNA that correlate with tumor fraction and cancer state across multiple cancers.

The paper introduces decemedip, a Bayesian hierarchical model for cell type deconvolution of MeDIP-seq data, showing its accuracy and utility in identifying methylation signatures.

This study investigates how transcription factor subtypes in small cell lung cancer influence response and resistance to DLL3-targeted therapy, revealing subtype-specific mechanisms of resistance.

This paper detects and monitors translocation renal cell carcinoma (tRCC) using plasma cell-free epigenomic profiling, achieving high accuracy in distinguishing tRCC from other cancers and healthy controls.

SNAP improves classification performance for cell-free chromatin data while maintaining data retention through optimized quality filters and modular design.

The paper identifies cell-free chromatin QTLs (cfcQTLs) from cancer patients' blood to uncover regulatory effects of genetic variants across diverse tissues and diseases.

The paper infers enhancer activity from cfDNA fragmentation patterns and proposes a chromatin fiber model to explain these signatures.

This paper identifies the EZH2-TTP-mTORC1 axis as a driver of phenotypic plasticity and therapeutic vulnerability in prostate cancer, showing that combined inhibition of EZH2 and PI3K/mTORC1 improves anti-tumor activity.

This paper identifies a multi-layered mechanism by which Aire forms transcriptional condensates on enhancers through coordinated interactions between its CARD, PHD1, and CTT domains.

The paper identifies epigenomic signatures in sarcomatoid renal cell carcinoma (sRCC) that predict response to immune checkpoint inhibitors and are detectable in plasma, enabling non-invasive diagnosis.

This paper develops OncoNPC, a machine learning classifier for CUP patients using tumor sequencing data, showing improved survival outcomes with treatment aligned to predicted cancer types.

This study develops snpSTARRseq, a high-throughput method to test non-coding variants from GWAS, showing 37% of prostate cancer risk loci alter enhancer activity.

The paper introduces CWAS, a method linking genetic variation to chromatin states, identifying novel prostate cancer risk loci and regulatory mechanisms not detectable by eQTLs.

This study identifies PRMT1 as a critical regulator of AR signaling in prostate cancer through genome-scale CRISPR screening and shows its potential as a therapeutic target.

The paper uses HiChIP to link prostate cancer GWAS risk variants to target genes, finding new associations not detected by eQTL methods and validating them with CRISPRi.