Biology subjects

Terry, K. L.

Publications and source records attributed to Terry, K. L..

4 recordsLinked to original sources

bioRxiv2026

Functional Data Analysis of Spatial Clustering Identifies Prognostic T Cell Patterns in Ovarian Cancer

Spatial proteomic imaging technologies enable the simultaneous assessment of immune cell abundance and spatial organization within the tumor microenvironment. Spatial clustering is commonly summarized using measures such as Ripleys K or nearestOneighbor G-functions at a fixed radius. However, these approaches depend on scale selection and may obscure biologically relevant patterns occurring across spatial ranges. We propose a functional data analysis (FDA) framework to model spatial clustering trajectories derived across a continuum of radii. Functional principal component analysis (FPCA) was used to summarize dominant modes of spatial variation, and resulting scores were incorporated into Cox proportional hazards models as both main effects and interaction with immune cell abundance. The approach was applied to multiplex immunofluorescence data from five ovarian cancer studies, comprising 773 highOgrade ovarian serous tumors. Analyses focused on CD3+ and CD8+ T cell populations within the tumor compartment of the tissue, adjusting for age at diagnosis and cancer stage, with study-specific estimates combined using random-effects meta-analysis. Higher abundance of both T cells and CD8+ T cells was consistently associated with improved overall survival. Beyond abundance, spatial features captured by the leading functional principal component were independently associated with survival, particularly for CD8+ T cells. Interaction models further showed that the prognostic effect of immune infiltration depended on spatial clustering, with tumors characterized by high abundance and low spatial clustering exhibiting the most favorable outcomes. These findings indicate that spatial organization provides complementary prognostic information beyond abundance alone and suggests that more diffuse immune infiltration may reflect more effective anti-tumor activity in ovarian cancer. Overall, FDA offers a flexible and interpretable framework for modeling spatial clustering across scales and identifying prognostic spatial features not captured by fixed-radius or distance analyses.

cancer biology↗

bioRxiv2023

Targeting NGF but not VEGF or BDNF signaling reduces endometriosis-associated pain in mice

IntroductionEndometriosis is a chronic inflammatory disease that affects [~]10% of women. A significant fraction of patients experience limited or no efficacy with current therapies. Tissue adjacent to endometriosis lesions often exhibits increased neurite and vascular density, suggesting that disease pathology involves neurotrophic activity and angiogenesis. ObjectivesWe aim to evaluate the potential for key tyrosine-kinase-receptor-coupled neurotrophic molecules to contribute to endometriosis-associated pain in mice. MethodsThe levels of VEGFR1 regulators (VEGFA, VEGFB, PLGF, and sVEGFR1) were quantified by ELISA in peritoneal fluid from endometriosis patients undergoing surgery and used to calculate VEGFR1 occupancy. We used genetic depletion, neutralizing antibody, and pharmacological approaches to specifically block ligand (NGF or BDNF) or neurotrophic receptor (VEGFR1, TRKs) in a murine model of endometriosis-associated pain. Endometriosis-associated pain was determined using the von Frey filaments method, quantification of spontaneous abdominal pain-related behavior, and thermal discomfort. Diseases parameters were evaluated by lesion size and prevalence. ResultsWe found that entrectinib (pan-Trk inhibitor) or anti-NGF treatments reduced evoked pain, spontaneous pain, and thermal discomfort. In contrast, even though receptor occupancy revealing that VEGFR1 agonist levels are sufficient to support pain, blocking VEGFR1 signaling via antibody or tamoxifen-induced knockout did not reduce pain or lesion size in mice. Targeting BDNF-TrkB with an anti-BDNF antibody also proved ineffective. ConclusionsThis suggests NGF-TrkA signaling, but not BDNF-TrkB or VEGF-VEGFR1, mediates endometriosis-associated pain. Moreover, entrectinib blocks endometriosis-associated pain and reduces lesion sizes. Our results also indicated that entrectinib-like molecules are promising candidates for endometriosis treatment. Credit author statementConceptualization: T.H. Zaninelli, V. Fattori, and M.S. Rogers; investigation and data curation: T.H. Zaninelli, V. Fattori, O.K. Heintz, K.R. Wright; A.C. Andrello, W.A. Verri Jr, M.S. Rogers; funding acquisition: M.S. Rogers,, S.A. Missmer, R.M. Anchan; methodology: T.H. Zaninelli, V. Fattori, and M.S. Rogers; human sample collection: S.A. Missmer, A.F. Vitonis, K.L. Terry, R.M. Anchan; animal breading and VEGFR1 ablation: D. Sim and H. Bukhari; resources: A.C. Andrello; D. Bree, T. Zheng, J. Wagner, W.A. Verri Jr, and M.S. Rogers; project administration: T.H. Zaninelli; supervision: V. Fattori, W.A. Verri Jr, and M.S. Rogers; visualization: T.H. Zaninelli, V. Fattori, W.A. Verri Jr, and M.S. Rogers; writing-original draft: T.H. Zaninelli; writing - editing and reviewing: all authors. All authors have read and approved the final version of the manuscript.

pharmacology and toxicology↗

bioRxiv2022

Global Endometrial DNA Multi-omics Analysis Reveals Insights into mQTL Regulation and Associated Endometriosis Disease Risk

Endometriosis is a leading cause of pain and infertility affecting millions of women globally. Identifying biologic and genetic effects on DNA methylation (DNAm) in endometrium increases understanding of mechanisms that influence gene regulation predisposing to endometriosis and offers an opportunity for novel therapeutic target discovery. Herein, we characterize variation in endometrial DNAm and its association with menstrual cycle phase, endometriosis, and genetic variants through analysis of genome-wide genotype data and methylation at 759,345 DNAm sites in endometrial samples from 984 deeply-phenotyped participants. We identify significant differences in DNAm profiles between menstrual cycle phases and at four DNAm sites between stage III/IV endometriosis and controls. We estimate that 15.4% of the variation in endometriosis is captured by DNAm, and identify DNAm networks associated with endometriosis. DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTL including some tissue-specific effects. We find significant differences in DNAm profiles between endometriosis sub- phenotypes and a significant association between genetic regulation of methylation in endometrium and disease risk, providing functional evidence for genomic targets contributing to endometriosis risk and pathogenesis.

systems biology↗

bioRxiv2018

Genome-wide association analysis identifies 27 novel loci associated with uterine leiomyomata revealing common genetic origins with endometriosis

Uterine leiomyomata (UL), also known as uterine fibroids, are the most common neoplasms of the reproductive tract and the primary cause for hysterectomy, leading to considerable impact on womens lives as well as high economic burden1,2. Genetic epidemiologic studies indicate that heritable risk factors contribute to UL pathogenesis3. Previous genome-wide association studies (GWAS) identified five loci associated with UL at genome-wide significance (P < 5 x 10-8)4-6. We conducted GWAS meta-analysis in 20,406 cases and 223,918 female controls of white European ancestry, identifying 24 genome-wide significant independent loci; 17 replicated in an unrelated cohort of 15,068 additional cases and 43,587 female controls. Aggregation of discovery and replication studies (35,474 cases and 267,505 female controls) revealed six additional significant loci. Interestingly, four of the 17 loci identified and replicated in these analyses have also been associated with risk for endometriosis - another common gynecologic disorder. These findings increase our understanding of the biological mechanisms underlying UL development, and suggest overlapping genetic origins with endometriosis.

genomics↗