Biomedical subjects

Elana J Fertig

Publications and source records attributed to Elana J Fertig.

5 recordsLinked to original sources

PubMed2026

GSTT1 promotes stemness and FGFR inhibitor sensitivity in pancreatic cancer through regulation of CD133 (PROM1).

Pancreatic ductal adenocarcinoma (PDA) is among the deadliest malignancies, driven by metastatic progression and profound cellular heterogeneity. We previously identified glutathione S-transferase theta 1 (GSTT1) as a regulator of a slow-cycling, highly metastatic tumor cell population, suggesting that GSTT1High cells may possess stem-like properties. Here, we define the functional and molecular features of this subpopulation in metastatic PDA. Using a mCherry-tagged Gstt1 reporter system in metastatic murine PDAC cells, we enriched for Gstt1High cells and observed increased tumor sphere formation, accompanied by upregulation of stemness-associated genes including PROM1 (CD133) and activation of Wnt and FGF signaling pathways. In human PDA models, CD133HighGSTT1High cells exhibited enhanced tumor sphere initiation and expansion compared to other populations, defining a maximal stem-like state. Notably, sensitivity to FGFR inhibitors was observed only under tumor sphere conditions, highlighting a context-dependent therapeutic vulnerability. Mechanistically, FGFR3 expression correlated with GSTT1 and CD133 levels, and FGF signaling was required to sustain this state. GSTT1 knockdown reduced CD133 protein levels, impaired tumor sphere formation, and altered sensitivity to FGFR inhibition. These findings were largely recapitulated in patient-derived PDA organoids, where GSTT1 and PROM1 co-expression predicted increased tumor sphere formation and enhanced response to the multi-kinase inhibitor Nintedanib. Together, these results identify a GSTT1HighCD133High stem-like subpopulation in metastatic PDA and identify an FGFR-dependent signaling axis that sustains this state, representing a potential therapeutic vulnerability.

AC133 Antigen↗

PubMed2026

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal.

MOTIVATION: Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring. We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. RESULTS: This new functionality includes the compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems. AVAILABILITY: dominoSignal is available through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/dominoSignal.html.

Cell Communication↗

PubMed2025

A cell-state axis underlying colonization in carcinomas with implications for metastasis risk prediction and interception.

Metastasis to the liver drives mortality in pancreatic ductal adenocarcinoma (PDAC), yet mechanisms of colonization remain unclear. Using genomic barcoding, we developed a clonal competition model under immune surveillance, isolating murine PDAC subclones with high or low liver-colonization potential. Combined transcriptome and chromatin-accessibility analyses revealed a distinct "metastatic-potential axis," separate from the normal-to-PDAC and classical-basal axes. We established "MetScore" as a biomarker of this axis. MetScore distinguishes metastases from primary PDAC tumors in patients, predicts outcomes beyond classical-basal classifications, and generalizes across carcinoma subtypes, suggesting conserved colonization mechanisms. High-MetScore PDAC cells preferentially occupy immune cell-enriched niches, suggesting they remodel the metastatic microenvironment. Functional screening identified c-Fos as a positive mediator of colonization and a candidate anti-metastatic target. Collectively, we identify a cell-state axis underpinning PDAC liver colonization, introduce MetScore as a broadly applicable biomarker, and nominate actionable targets for peri-operative therapeutic intervention.

Animals↗

PubMed2025

Neoadjuvant Immunotherapy Promotes the Formation of Mature Tertiary Lymphoid Structures in a Remodeled Pancreatic Tumor Microenvironment.

Pancreatic ductal adenocarcinoma (PDAC) is a rapidly progressing cancer that responds poorly to immunotherapies. Intratumoral tertiary lymphoid structures (TLS) have been associated with rare long-term PDAC survivors, but the role of TLS in PDAC and their spatial relationships within the context of the broader tumor microenvironment remain unknown. In this study, we report the generation of a spatial multiomic atlas of PDAC tumors and tumor-adjacent lymph nodes from patients treated with combination neoadjuvant immunotherapies. Using machine learning-enabled hematoxylin and eosin image classification models, imaging mass cytometry, and unsupervised gene expression matrix factorization methods for spatial transcriptomics, we characterized cellular states within and adjacent to TLS spanning distinct spatial niches and pathologic responses. Unsupervised learning identified TLS-specific spatial gene expression signatures that are significantly associated with improved survival in patients with PDAC. We identified spatial features of pathologic immune responses, including intratumoral TLS-associated B-cell maturation colocalizing with IgG dissemination and extracellular matrix remodeling. Our findings offer insights into the cellular and molecular landscape of TLS in PDACs during immunotherapy treatment.

Humans↗

PubMed2025

BIWT: a bioinformatics walkthrough for embedding spatial multiomics in agent-based models for virtual cells.

SUMMARY: Whereas transcriptomic and spatial profiling offer static snapshots of tissue structure, mechanistic models use biological rules to predict how tissues evolve. We present the BioInformatics WalkThrough (BIWT) software to directly initialize spatial agent-based models from single-cell and spatial molecular data. We demonstrate how initialization strategies affect tumor-immune dynamics and spatial clustering, positioning BIWT as a software suite to generate data-driven virtual cells representing both experimental and clinical contexts. AVAILABILITY AND IMPLEMENTATION: The BIWT software is available at https://github.com/PhysiCell-Tools/PhysiCell-Studio. The sample dataset for running the BIWT is available at https://zenodo.org/records/16365625. The code and instructions for reproducing the use case example is available at https://github.com/drbergman/BIWT-Paper.

Software↗