Matthew L. Freedman

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

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.

APEX infers cancer gene expression from plasma cfChIP-seq data, outperforming existing methods and enabling subtype classification and biomarker identification.

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.

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 study shows EED inactivation impairs SCLC tumorigenesis and drives LUAD-to-SCLC transformation, highlighting its role in maintaining SCLC neuroendocrine identity.

This study shows that ARv7 drives AR activity in castration-resistant prostate cancer through increased chromatin accessibility mediated by NFI factors.

The paper investigates how AR-bound CREs influence gene expression through epigenetic changes and chromatin looping, finding that contact frequency with promoters drives expression.

This study identifies MYBL2 as a master regulator of phenotypic plasticity in prostate cancer and shows CDK2 inhibition as a potential therapeutic strategy for MYBL2-driven tumors.

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 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 distinct structural variation patterns in prostate cancer across disease states, defining clinically actionable subgroups based on whole genome profiling.

This study shows MYC overexpression disrupts AR transcriptional pause release, promoting aggressive prostate cancer progression.

This study identifies three germline variants associated with immune-related adverse events in patients treated with immune checkpoint inhibitors.

The paper identifies non-coding RNAs that mediate gene expression regulation in breast and prostate tissue and overlap with genetic signals for cancer susceptibility.

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.

The paper identifies RCCD1 as a susceptibility gene for breast and ovarian cancer by fine-mapping a risk locus and showing its expression and interaction patterns are associated with cancer risk.

PLASMA improves QTL fine-mapping by integrating allele-specific QTL information, achieving better resolution and smaller credible sets than existing methods.

The paper identifies allele-specific quantitative trait loci (asQTLs) in RCC, showing their enrichment in GWAS risk loci and linking them to hypoxia pathway interactions.

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.

This study identifies 21 novel candidate susceptibility genes for high grade serous ovarian cancer using transcriptome-wide association analysis across multiple tissue types.

The paper uses transcriptomic and epigenomic profiling to show that high-grade serous ovarian cancers predominantly originate from fallopian tube secretory epithelial cells, with distinct transcription factors driving tumorigenesis based on cell-of-origin.