PRMT5 is an actionable therapeutic target in CDK4/6 inhibitor-resistant ER+/RB-deficient breast cancer.

CDK4/6 inhibitors (CDK4/6i) have improved survival of patients with estrogen receptor-positive (ER+) breast cancer. However, patients treated with CDK4/6i eventually develop drug resistance and progress. RB1 loss-of-function alterations confer resistance to CDK4/6i, but the optimal therapy for these patients is unclear. Through a genome-wide CRISPR screen, we identify protein arginine methyltransferase 5 (PRMT5) as a molecular vulnerability in ER+/RB1-knockout breast cancer cells. Inhibition of PRMT5 blocks the G1-to-S transition in the cell cycle independent of RB, leading to growth arrest in RB1-knockout cells. Proteomics analysis uncovers fused in sarcoma (FUS) as a downstream effector of PRMT5. Inhibition of PRMT5 results in dissociation of FUS from RNA polymerase II, leading to hyperphosphorylation of serine 2 in RNA polymerase II, intron retention, and subsequent downregulation of proteins involved in DNA synthesis. Furthermore, treatment with the PRMT5 inhibitor pemrametostat and a selective ER degrader fulvestrant synergistically inhibits growth of ER+/RB-deficient cell-derived and patient-derived xenografts. These findings highlight dual ER and PRMT5 blockade as a potential therapeutic strategy to overcome resistance to CDK4/6i in ER+/RB-deficient breast cancer.

Protein arginine methyltransferase 5 (PRMT5) is an actionable therapeutic target in CDK4/6 inhibitor-resistant ER+/RB-deficient breast cancer.

CDK4/6 inhibitors (CDK4/6i) have improved survival of patients with estrogen receptor-positive (ER+) breast cancer. However, patients treated with CDK4/6i eventually develop drug resistance and progress. RB1 loss-of-function alterations confer acquired resistance to CDK4/6i, but the optimal therapy for these patients is unclear. Using a genome-wide CRISPR screen, we identified protein arginine methyltransferase 5 (PRMT5) as a molecular vulnerability in ER+/RB1-knockout (RBKO) breast cancer cells. PRMT5 inhibition blocked cell cycle G1-to-S transition independent of RB, thus arresting growth of RBKO cells. Proteomics analysis uncovered fused in sarcoma (FUS) as a downstream effector of PRMT5. Pharmacological inhibition of PRMT5 resulted in dissociation of FUS from RNA polymerase II (Pol II), Ser2 Pol II hyperphosphorylation, and intron retention in genes that promote DNA synthesis. Treatment with the PRMT5i inhibitor pemrametostat and fulvestrant synergistically inhibited growth of ER+/RB-deficient patient-derived xenografts, suggesting dual ER and PRMT5 blockade as a novel therapeutic strategy to treat ER+/RB-deficient breast cancer.

Optimization of small extracellular vesicle isolation from expressed prostatic secretions in urine for in-depth proteomic analysis.

The isolation and subsequent molecular analysis of extracellular vesicles (EVs) derived from patient samples is a widely used strategy to understand vesicle biology and to facilitate biomarker discovery. Expressed prostatic secretions in urine are a tumor proximal fluid that has received significant attention as a source of potential prostate cancer (PCa) biomarkers for use in liquid biopsy protocols. Standard EV isolation methods like differential ultracentrifugation (dUC) co-isolate protein contaminants that mask lower-abundance proteins in typical mass spectrometry (MS) protocols. Further complicating the analysis of expressed prostatic secretions, uromodulin, also known as Tamm-Horsfall protein (THP), is present at high concentrations in urine. THP can form polymers that entrap EVs during purification, reducing yield. Disruption of THP polymer networks with dithiothreitol (DTT) can release trapped EVs, but smaller THP fibres co-isolate with EVs during subsequent ultracentrifugation. To resolve these challenges, we describe here a dUC method that incorporates THP polymer reduction and alkaline washing to improve EV isolation and deplete both THP and other common protein contaminants. When applied to human expressed prostatic secretions in urine, we achieved relative enrichment of known prostate and prostate cancer-associated EV-resident proteins. Our approach provides a promising strategy for global proteomic analyses of urinary EVs.

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

Targeted Mass Spectrometry of a Clinically Relevant PSA Variant from Post-DRE Urines for Quantitation and Genotype Determination.

PURPOSE: The rs17632542 single nucleotide polymorphism (SNP) results in lower serum prostate specific antigen (PSA) levels which may further mitigate against its clinical utility as a prostate cancer biomarker. Post-digital rectal exam (post-DRE) urine is a minimally invasive fluid that is currently utilized in prostate cancer diagnosis. To detect and quantitate the variant protein in urine. EXPERIMENTAL DESIGN: Fifty-three post-DRE urines from rs17632542 genotyped individuals processed and analyzed by liquid chromatography/mass spectrometry (LC-MS) in a double-blinded randomized study. The ability to distinguish between homozygous wild-type, heterozygous, or homozygous variant is examined before unblinding. RESULTS: Stable-isotope labeled peptides are used in the detection and quantitation of three peptides of interest in each sample using parallel reaction monitoring (PRM). Using these data, groupings are predicted using hierarchical clustering in R. Accuracy of the predictions show 100% concordance across the 53 samples, including individuals homozygous and heterozygous for the SNP. CONCLUSIONS AND CLINICAL RELEVANCE: The study demonstrates that MS based peptide variant quantitation in urine could be useful in determining patient genotype expression. This assay provides a tool to evaluate the utility of PSA variant (rs17632542) in parallel with current and forthcoming urine biomarker panels.

Gut immunoglobulin alpha anti-glycan binding profiles as a research tool for local disease detection.

A promising approach capitalizing on the specific and highly sensitive characteristics of the body's own immune system is demonstrated in the context of revealing pancreatic ductal adenocarcinoma cancer (PDAC). IgA from a local biofluid called gastrointestinal lavage fluid (GLF) is used to investigate glycan reactivity to show the potential of this approach. IgA antibody responses, just as with IgG, result in amplification of a small signal which aids in detecting changes from a healthy state. IgA from GLF was screened against glycan arrays containing 609 glycan structures to investigate differential binding patterns associated with the disease. Samples included PDAC (n = 14) and non-PDAC (n = 6). Non-PDAC conditions included samples from healthy patients and the potentially confounding conditions of colon cancer and its precancerous lesion, colon adenoma. Results demonstrated characteristic reactivity in the PDAC sample group to a glycan structure. Also, IgA non-reactive motifs arose showing remarkable consistency within and between sample groups. While sample sizes are too small to identify putative biomarkers, these data show the use of IgA from GLF to be a promising avenue of research for local disease biomarker discovery.