Biology subjects

Annunziata, C. M.

Publications and source records attributed to Annunziata, C. M..

4 recordsLinked to original sources

bioRxiv2026

IL1β/IL1R1/IRAK4 Drives Inflammatory Ovarian Cancer Seeding at the inflamed sites and Is Reversed by an IRAK4 inhibitor UR241-2

Inflammation-driven tumor implantation, such as port-site metastasis (PSM) following laparoscopic gynecologic surgery and peritoneal seeding during post-surgical recurrence, represents an aggressive clinical problem that remains poorly understood and lacks targeted therapies. To address this, we developed a non-surgical Mesothelium-Inflammation/Injury-Metastasis (MIM) model and investigated the role of the IL-1{beta}/IL1R1/MYD88/IRAK1/4 axis and NLRP3 in epithelial ovarian cancer (EOC) seeding at inflamed or injured sites. This model created by a needle injury recapitulates inflammation-driven peritoneal seeding and mimics PSM and inflammation associated dissemination in peritoneum during recurrence. Seeding was dependent on Il1r1 but not Nlrp3, despite its role in regulating IL-1{beta} production, as Il1ra-/- and Nlrp3-/- mice phenocopied wild-type C57BL/6 mice. Given the limited antitumor efficacy of IL-1{beta}-targeting agents such as Anakinra and Canakinumab, we focused on IRAK4 as a therapeutic target. IRAK4 knockdown significantly prolonged survival, reduced tumor cell adhesion, downregulated E-cadherin and Wnt4, and induced S-phase/mitotic arrest. This led to the development of UR241-2, a small-molecule IRAK4 inhibitor, which was validated through molecular simulations, hotspot analysis, nanoBRET, global kinome profiling, and NF-{kappa}{beta} reporter assays. UR241-2 inhibited NF-{kappa}{beta} nuclear translocation and blocked IL-1{beta}-induced IRAK4 phosphorylation. UR241-2 exhibited favorable drug-like properties, including absence of CYP or hERG inhibition, and acceptable CaCo-2 permeability, plasma protein binding, microsomal stability, and pharmacokinetics. In vivo, UR241-2 reduced SKOV3 xenograft growth, suppressed mesothelial seeding, and increased MHC-II macrophages and activated neutrophils in syngeneic high-grade epithelial ovarian HGS3 tumors. RNA-seq revealed enrichment of neutrophil activation signatures and suppression of extracellular matrix (ECM) gene programs. Together, these findings establish a role for the IL-1{beta}/IL1R1/IRAK4 axis in inflammation-driven PSM and peritoneal seeding and ECM regulation in EOC, and demonstrate that IRAK4 inhibition activates antitumor immune responses, providing a therapeutic strategy to block metastatic seeding and improve tumor control.

cancer biology↗

bioRxiv2022

UGDH promotes tumor-initiating cells and a fibroinflammatory tumor microenvironment in ovarian cancer.

Epithelial ovarian cancer (EOC) is a global health burden, with the poorest five-year survival rate of the gynecological malignancies due to diagnosis at advanced stage and high recurrence rate. Recurrence in EOC is driven by the survival of chemoresistant, stem-like tumor-initiating cells (TICs) that are supported by a complex extracellular matrix (ECM) and immunosuppressive microenvironment. To target TICs to prevent recurrence, we identified genes critical for TIC viability from a whole genome siRNA screen. A top hit was the cancer-associated, proteoglycan subunit synthesis enzyme UDP-glucose dehydrogenase (UGDH). Immunohistochemistry was used to delineate UGDH expression in histological and molecular subtypes of EOC. High UGDH expression was observed in the majority of high-grade serous ovarian cancers with variable expression in clear cell, mucinous and endometrioid histotypes. A distinctive prognostic difference was revealed when serous cancers were stratified by molecular subtype, where high UGDH was associated with poor prognosis in the C1/Mesenchymal subtype and low UGDH was associated with poor prognosis in the C4/Differentiated subtype. Ovarian cancer cell lines were subtyped according to the molecular subtypes, and we examined the effect of modulating UGDH expression in cell lines representing the C1/Mesenchymal subtype and C4/Differentiated subtypes. Knockdown of UGDH in the C1/Mesenchymal subtype reduced spheroid viability, sphere-formation and the CD133+/ALDH high TIC population. Conversely, overexpression of UGDH in the differentiated subtype enhanced spheroid formation but reduced the TIC population. Inflammatory cytokine expression was altered by UGDH expression. In co-culture models, altering UGDH expression in spheroids affected the gene expression of mesothelial cells causing changes to matrix remodeling proteins. The effect of UGDH knockdown or overexpression in the C1/Mesenchymal and C4/Differentiated subtypes, respectively, was tested on mouse intrabursal xenografts and showed dynamic changes to the tumor stroma. Knockdown of UGDH reduced tumor burden in C1/Mesenchymal xenografts compared to controls. These data show that modulation of UGDH expression in tumors influences cells in the microenvironment and reveals distinct roles for UGDH in the mesenchymal and differentiated molecular subtypes of EOC. UGDH is a potential therapeutic target in TICs, for the treatment of metastatic and recurrent EOC, particularly in patients with the mesenchymal molecular subtype.

cancer biology↗

bioRxiv2019

Accelerating iterative deconvolution and multiview fusion by orders of magnitude

We describe theoretical and practical advances in algorithm and software design, resulting in ten to several thousand-fold faster deconvolution and multiview fusion than previous methods. First, we adapt methods from medical imaging, showing that an unmatched back projector accelerates Richardson-Lucy deconvolution by at least 10-fold, in most cases requiring only a single iteration. Second, we show that improvements in 3D image-based registration with GPU processing result in speedups of 10-100-fold over CPU processing. Third, we show that deep learning can provide further accelerations, particularly for deconvolution with a spatially varying point spread function. We illustrate the power of our methods from the subcellular to millimeter spatial scale, on diverse samples including single cells, nematode and zebrafish embryos, and cleared mouse tissue. Finally, we show that our methods facilitate the use of new microscopes that improve spatial resolution, including dual-view cleared tissue light-sheet microscopy and reflective lattice light-sheet microscopy.

bioengineering↗

bioRxiv2019

A Methylation Density Binary Classifier for Predicting and Optimizing the Performance of Methylation Biomarkers in Clinical Samples

BackgroundVariation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass), that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification. ResultsWe developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-mL) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers. ConclusionsOur results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitate the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically-diverse ovarian carcinomas, indicating the broad utility of ZNF154 for use as a biomarker of ovarian cancer.

bioinformatics↗