Biology subjects

Shriver, C. D.

Publications and source records attributed to Shriver, C. D..

7 recordsLinked to original sources

bioRxiv2026

Combining RNA-seq and proteomics boosts sensitivity to detect differentially expressed genes by rpMeta

Differential gene expression analysis is a powerful technique to characterize biological systems. Differentially expressed genes (DEGs) are commonly identified by analysis of RNA sequencing datasets or mass spectrometry proteomics datasets. Increasingly, multi-omic studies include both RNA sequencing and proteomics from the same biospecimens. However, the common multi-omic DEG analysis practice involves separate DEG analysis by RNA and by protein with limited comparison by fold change correlations or Venn Diagrams. This disjoint practice may not utilize all of the available signal from both datatypes as transcripts and proteins have correlated expression levels. To integrate RNA and proteomics, we developed a new method, RNA protein meta-analysis called rpMeta. rpMeta takes RNA and protein expression data and calculates DEGs by RNA, by protein, and by meta-analysis models, following user-specified options. Spike-in, downsampling and cross-cohort benchmarks revealed that the meta-analysis model had greater sensitivity than RNA and protein models. In a real-world use case, the meta-analysis model discovered new genes and biological processes in lung cancer subtypes that were not detectable by the RNA or protein models. To streamline interpretation, rpMeta produces publication-ready data visualizations of the integrated DEGs. rpMeta is an open-source R package, available at https://github.com/zhangx8/rpMeta.

bioinformatics↗

bioRxiv2026

Spatial epigenomics reveals ancestry-associated regulatory states linked to aggressive prostate cancer biology

Prostate cancer (PCa) exhibits substantial biological heterogeneity, including population associated differences in tumor aggressiveness and clinical outcomes. Although socioeconomic and environmental factors contribute to these disparities, intrinsic tumor regulatory programs may also influence disease behavior. Here, we investigated whether differences in chromatin regulation contribute to distinct tumor states in Black and White men with localized PCa. We performed spatial chromatin accessibility profiling (ATAC-seq) of prostatectomy specimens from Black and White patients receiving equal healthcare access at the Center for Prostate Disease Research (USUHS) and Walter Reed National Military Medical Center. Tumors showed distinct chromatin accessibility landscapes between the two populations, with tumors from Black patients exhibiting reduced accessibility of motifs associated with the chromatin architectural regulator CTCF and enrichment of motifs linked to epithelial-mesenchymal transition and inflammatory programs. Reduced CTCF expression was independently validated by immunohistochemistry in 99 prostatectomy specimens. These findings identify population-associated differences in chromatin regulation in localized PCa and suggest that altered CTCF-associated regulation, inflammatory signaling, and epithelial plasticity may contribute to the biological heterogeneity underlying ancestry-dependent PCa disparities.

cancer biology↗

bioRxiv2025

Integrated proteogenomics uncovers ancestry-specific and shared molecular drivers in localized prostate cancer

Our integrative proteogenomics (genome, proteome and phosphoproteome) of localized prostate cancer (PCa) in an equal-access Military Health System patient cohort (57 Black and 55 White) revealed significant ancestry-associated differences. Somatic and germline regulatory differences converged on androgen, metabolic, PI3K/AKT/mTOR, and DNA damage response (DDR) pathways, with ancestry-specific immune- and stromal-associated signals. Black patients displayed greater genomic variability, enhanced androgen response, fatty-acid metabolism, and epithelial-mesenchymal transition, while White patients showed prevalent DDRG alterations, activated oncogenic signaling (MYC, E2F, mTORC1), and cell cycle regulation. Phosphoproteomics highlighted distinct kinase activities and candidate druggable dependencies. Multiomics integration revealed three exploratory tumor subtypes whose distinct biological programs were reproducibly validated. Ancestry-associated eQTLs supported inherited regulation of the proteome independent of CNAs. Ancestry-specific CNA and protein panels improved progression risk prediction beyond PSA and pathology models. These findings provide a framework for ancestry-informed prognostic models and generate testable hypotheses for precision therapies to reduce outcome disparities.

cancer biology↗

bioRxiv2025

Primary tumor microbiomes predict distant metastasis of colorectal cancer

Metastasis causes most cancer-related deaths in colorectal carcinoma (CRC), and microbiome markers may have prognostic value. We hypothesized that primary tumor microbiomes predict distant metastases. We analyzed 5-year metastasis-free survival (MFS) in a retrospective cohort of 900 ORIEN CRC tumor microbiomes (RNAseq). ORIEN findings were validated on an independent cohort using 16S rDNA sequencing and pathobiont-specific qPCR. Microbiome alpha diversity was higher in primary tumors than metastases and positively correlated with metastasis risk. Microbiome beta diversity distinguished primary vs. metastasis and predicted 5-year MFS. High primary tumor abundance of B. fragilis and low F. nucleatum were associated with short MFS. Enterobacteriaceae, including E. coli, were enriched in metastases. qPCR identified increased enterotoxigenic B. fragilis and pks+ E. coli detection in CRC metastasizers. Microbial co-occurrence analysis identified a 3-species clique that predicts metastasis (OR 1.9 [1.4-2.6]). Results suggest that primary tumor microbiomes and specific pathobionts are precision markers for metastasis risk.

cancer biology↗

bioRxiv2024

A modified dual preparatory method for improved isolation of nucleic acids from laser microdissected fresh-frozen human cancer tissue specimens

A central theme in cancer research is to increase our understanding of the cancer tissue microenvironment (TME), which is comprised of a complex and spatially heterogeneous ecosystem of malignant and non-malignant cells, both of which actively contribute to an intervening extracellular matrix. Laser microdissection (LMD) enables histology selective harvest of cellular subpopulations from the tissue microenvironment for their independent molecular investigation, such as by high-throughput DNA and RNA sequencing. Although enabling, LMD often requires a labor-intensive investment to harvest enough cells to achieve the necessary DNA and/or RNA input requirements for conventional next generation sequencing workflows. To increase efficiencies, we sought to use a commonplace dual preparatory (DP) procedure to isolate DNA and RNA from the same LMD harvested tissue samples. While the yield of DNA from the DP protocol was satisfactory, the RNA yield from the LMD harvested tissue samples was significantly poorer compared to a dedicated RNA preparation procedure. We identified that this low yield of RNA was due to incomplete partitioning of RNA in this widely used DP protocol. Here we describe a modified DP protocol that effectively partitions nucleic acids and results in significantly improved RNA yields from LMD harvested cells.

biochemistry↗

bioRxiv2020

Peptide Ancestry Informative Markers in Uterine Neoplasms from Women of European, African and Asian Ancestry

Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with molecular disease pathogenesis. Nonsynonymous single nucleotide polymorphisms (SNPs) encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. In silico analysis shows that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R2=0.9905). Multiplexed proteomic analysis of >100 human endometrial cancer cell lines and uterine leiomyoma tissues resulted in the quantitation of 62 pAIMs that correlate with self-described race and genotype-confirmed patient ancestry. Candidates include a D451E substitution in GC vitamin D-binding protein previously associated with altered vitamin D levels in African and European populations. These efforts describe a generalized set of markers for proteoancestry assessment that will further support studies investigating the impact of ancestry on the human proteome and how this relates to the pathogenesis of uterine neoplasms.

cancer biology↗

bioRxiv2019

Extensive Intratumor Proteogenomic Heterogeneity Revealed by Multiregion Sampling in a High-Grade Serous Ovarian Tumor Specimen

Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten primary high-grade serous ovarian tumors and analyzed using proteomics (mass spectrometry and reverse phase protein microarray) and RNA-sequencing analyses. Comparative analyses of transcript and protein abundances revealed independent clustering of enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering between purified collections, driven by overall tumor purity. Comparison of historic prognostic molecular subtypes for HGSOC revealed protein and transcript expression from tumor epithelium correlated most strongly with the differentiated molecular subtype, whereas stromal proteins and transcripts most strongly correlated with mesenchymal subtype. Protein and transcript abundance in tumor epithelium and stromal collections from neighboring sections exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial protein and transcript expression heterogeneity within the tumor microenvironment that directly bears on prognostic signatures and underscore the need to enrich cellular subpopulations for expression profiling.

cancer biology↗