Biology subjects

Fields, R. C.

Publications and source records attributed to Fields, R. C..

8 recordsLinked to original sources

bioRxiv2025

Targeting Tumor-intrinsic TAK1 triggers anti-tumor immunity and sensitizes pancreatic cancer to checkpoint blockade

Background and AimsTargeting the Transforming Growth Factor-{beta} (TGF-{beta}) pathway to reverse the immunologically "cold" tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) remains clinically unsuccessful, warranting novel therapeutic strategies. MethodsWe developed a novel tumor-CD8 T cell co-culture to interrogate the TGF-{beta} signaling pathways that promotes T cell-mediated cytotoxicity. We performed multiplex immunohistochemistry (mIHC) on human PDAC samples to correlate cell-type specific TGF-{beta} pathway activation and CD8 T cell abundance. We employed specific pathway inhibitor and newly generated genetically-engineered mouse models (GEMMs) and confirmed our findings using single-cell RNA sequencing, flow cytometry and mIHC. We performed proteomics and various in vitro assays to establish the molecular mechanisms. ResultsWe identify TGF-{beta}-activated kinase 1 (TAK1 or MAP3K7) as an aberrantly activated kinase in human and mouse PDAC tissues that is associated with T cell dysfunction. Pharmacological inhibition of TAK1 with Takinib, or genetic deletion of MAP3K7 in autochthonous p48-Cre;TP53flox/flox;LSL-KRASG12DGEMM, enhances intratumoral CD4+ and CD8+ effector T cell infiltration and renders immune checkpoint blockade (ICB) effective. Mechanistically, TAK1 inhibition induces DNA damage and cytoplasmic DNA leakage, which activates the cyclic GMP-AMP synthase-Stimulator of Interferon Genes (cGAS-STING) DNA sensing pathway, triggering inflammatory responses that promote adaptive immune cell infiltration. At the molecular level, TAK1 phosphorylates Ephrin Receptor A2 (EphA2) at Serine 897, which in turn phosphorylates RAD51 at Tyrosine 315, a key DNA repair protein involved in homologous recombination. ConclusionsWe uncover TAK1 as a critical mediator in maintaining genomic integrity and highlights its potential as a therapeutic target to induce an inflamed TME that sensitizes PDAC to ICB.

cancer biology↗

bioRxiv2024

BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica, an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica

bioinformatics↗

bioRxiv2024

Mapping single-cell developmental potential in health and disease with interpretable deep learning

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.

genomics↗

bioRxiv2022

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

bioinformatics↗

bioRxiv2022

Pancreas resident macrophage-induced fibrosis has divergent roles in pancreas inflammatory injury and PDAC

Tissue-resident macrophages (TRMs) are long-lived cells that maintain locally and can be phenotypically distinct from monocyte-derived macrophages (MDMs). However, whether TRMs and MDMs have functional distinction under differing pathologies is not understood. Here, we show a significant portion of macrophages that accumulated during pancreatitis and pancreatic cancer were expanded from TRMs. We further established that pancreas TRMs have a distinct extracellular matrix remodeling phenotype that was critical for maintaining tissue homeostasis during inflammation. Loss of TRMs led to exacerbation of severe pancreatitis and animal death, due to impaired acinar cell survival and recovery. In pancreatitis, TRMs elicited protective effects by triggering the accumulation and activation of fibroblasts, which was necessary for initiating fibrosis as a wound healing response. The same TRM-driven fibrosis, however, drove pancreas cancer pathogenesis and progression. Together, these findings indicate that TRMs play divergent roles in the pathogenesis of pancreatitis and cancer through regulation of stromagenesis.

immunology↗

bioRxiv2021

SC2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimers Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

bioinformatics↗

bioRxiv2021

Macrophage proliferation machinery leads to PDAC progression, but susceptibility to innate immunotherapy.

Tumor-associated macrophages (TAMs) are involved in many aspects of cancer progression and correlate with poor clinical outcomes in many cancer types, including pancreatic ductal adenocarcinomas (PDACs). Previous studies have shown that TAMs can populate PDAC tumors not only by monocyte recruitment but also by local proliferation. However, the impact local proliferation might have on macrophage phenotype and cancer progression is unknown. Here, we utilized genetically engineered cancer models, single-cell RNA-sequencing data, and in vitro systems to show that proliferation of TAMs was driven by colony stimulating factor-1 (CSF1) produced by cancer-associated fibroblasts. CSF1 induced high levels of p21 in macrophages, which regulated both TAM proliferation and phenotype. TAMs in human and mouse PDACs with high levels of p21 had more inflammatory and immunosuppressive phenotypes. The p21 expression in TAMs was induced by both stromal interaction and/or chemotherapy treatment. Finally, by modeling p21 expression levels in TAMs, we found that p21-driven macrophage immunosuppression in vivo drove tumor progression. Serendipitously, the same p21-driven pathways that drive tumor progression, also drive response to CD40 agonist. These data suggest that stromal or therapy-induced regulation of cell cycle machinery can regulate both macrophage-mediated immune suppression and susceptibility to innate immunotherapy. SummaryTAMs are indicative of poor clinical outcomes and in PDAC their number is sustained in part by local proliferation. This study shows that stromal desmoplasia drives local proliferation of TAMs, and induces their immunosuppressive ability through altering cell cycle machinery, including p21 expression. Serendipitously, these changes in p21 in TAMs also potentially render tumors more sensitive to CD40 agonist therapy.

cancer biology↗

bioRxiv2020

Tumor-on-a-chip platform to interrogate the role of macrophages in tumor progression

Tumor-infiltrating leukocytes, in particular macrophages, play an important role in tumor behavior and clinical outcome. The spectrum of macrophage subtypes ranges from antitumor "M1"-type to protumor "M2"-type macrophages. Tumor-associated macrophages (TAMs) typically display phenotypic features of both M1 and M2, and the population distribution is thought to be dynamic and evolve as the tumor progresses. However, our understanding of how TAMs impact the tumor microenvironment remains limited by the lack of appropriate 3D in vitro models that can capture cell to cell dynamics at high spatial and temporal resolution. Using our recently developed micro-physiological "tumor-on-a-chip" (TOC) device, we present here our findings on the impact of defined macrophage subsets on tumor behavior. The TOC device design contains three adjacent and connected chambers in which both the upper and lower chambers are loaded with tumor cells while the central chamber contains a dynamic, perfused, living microvascular network. Introduction of human pancreatic or colorectal cancer cells together with M1-polorized macrophages significantly inhibited tumor growth and tumor-induced angiogenesis. Protein analysis and antibody-based neutralization studies confirmed that these effects were mediated through production of chemokines CXCL9, CXCL10, and CXCL11. By contrast, M2-macrophages mediated increased tumor cell migration into the vascularized chamber and did not inhibit tumor growth or angiogenesis. In fact, single-cell RNA-sequencing showed that M2 macrophages further segregated endothelial cells into two distinct subsets, corresponding to static cells in vessels versus active cells involved in angiogenesis. The impact of M2 macrophages was mediated mostly by production of MMP7 and ANGPT2. In summary, our data demonstrate the utility of the TOC device to mechanistically probe biological questions in a 3D in vitro microenvironment. Insight BoxMacrophages in the tumor microenvironment are key determinants of tumor behavior and clinical outcome. The macrophage subset composition and its functional impact change as tumors progress or during treatment, but adequate models to study this are lacking. We developed a tumor-on-a-chip model of perfused 3D tumor growth to probe the impact of defined macrophage subsets. Our data is consistent with previously described macrophage activity in the tumor microenvironment, and provides potential new molecular targets. Herein, we demonstrate feasibility of probing immuno-oncology questions in a 3D in vitro microenvironment and at a spatiotemporal resolution.

cancer biology↗