Biology subjects

Gibbs, D. L.

Publications and source records attributed to Gibbs, D. L..

8 recordsLinked to original sources

bioRxiv2025

A Fibroblast State Choreographs an Epithelial YAP-dependent Regenerative Program Essential to (Pre)malignancy via ECM-mediated Mechanotransduction

Chronic lung injury generates metaplasia which occasionally, but ominously, progresses to squamous dysplasia and squamous lung cancer. To identify mechanisms through which disrupted tissue homeostasis contributes to malignant initiation and progression, we used in vivo and in vitro heterotypic recombinant models of human bronchial epithelial cells (hBECs) and fibroblasts. We demonstrate that injury-associated TGF-{beta} signaling creates a fibroblast state dependent upon HSP47 upregulation. These fibroblasts accumulated collagen, thus elevating tissue stiffness and activating mechanosignaling that sustained YAP-dependent embryonic-like, pro-malignant activities in adjacent hBECs. This Stress/Tension-Instructive Fibroblast (STIF) state, exhibited by stressed fibroblasts in premalignant and malignant lesions across multiple cancer types, was sufficient to reprogram disease-free hBECs to metaplasia and to drive hBECs with compromised tumor suppressor function to dysplasia, yet could be inhibited and reversed. STIFs suffice to activate epithelial phenotypes reminiscent of oncogene-mediated cell transformation and induce (pre)malignancy via increased force transmission, providing novel targets for prevention. Statement of significanceTissue injury creates a regenerative pro-tumorigenic Stress/Tension-Instructive Fibroblast (STIF) state which is sufficient to activate a YAP-dependent, pre-malignant program to induce or unmask pre-cancerous phenotypes in epithelial cells through mechanotransduction. Inhibition of STIF activity or mechanosignaling prevents metaplasia and progression to dysplasia. HighlightsO_LITissue injury creates a pro-tumorigenic Stress/Tension-Instructive Fibroblast (STIF) state in multiple organs that precedes and persists through cancer C_LIO_LISTIF signaling alone, working through fibroblasts and not epithelial cells, is sufficient to activate embryonic-like plasticity and induce epithelial pre-cancerous metaplastic lesions C_LIO_LISTIFs program (pre)malignant phenotypes in adjacent epithelial cells through mechanosignaling by activating YAP prior to tumor formation C_LIO_LIInhibiting STIFs or mechanosignaling prevents/reverts metaplasia and prevents progression to dysplasia C_LI

cancer biology↗

bioRxiv2024

Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement

The Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) has played a crucial role in enabling the broad sharing and effective utilization of HTAN data within the scienti[fi]c community. Data from the [fi]rst phase of HTAN are now available publicly. We describe the diverse datasets and modalities shared, multiple access routes to HTAN assay data and metadata, data standards, technical infrastructure and governance approaches, as well as our approach to sustained community engagement. HTAN data can be accessed via the HTAN Portal, explored in visualization tools--including CellxGene, Minerva, and cBioPortal--and analyzed in the cloud through the NCI Cancer Research Data Commons nodes. We have developed a streamlined infrastructure to ingest and disseminate data by leveraging the Synapse platform. Taken together, the HTAN DCCs approach demonstrates a successful model for coordinating, standardizing, and disseminating complex cancer research data via multiple resources in the cancer data ecosystem, offering valuable insights for similar consortia, and researchers looking to leverage HTAN data.

cancer biology↗

bioRxiv2023

Concerted epithelial and stromal changes during progression of Barrett's Esophagus to invasive adenocarcinoma exposed by multi-scale, multi-omics analysis

Esophageal adenocarcinoma arises from Barretts esophagus, a precancerous metaplastic replacement of squamous by columnar epithelium in response to chronic inflammation. Multi-omics profiling, integrating single-cell transcriptomics, extracellular matrix proteomics, tissue-mechanics and spatial proteomics of 64 samples from 12 patients paths of progression from squamous epithelium through metaplasia, dysplasia to adenocarcinoma, revealed shared and patient-specific progression characteristics. The classic metaplastic replacement of epithelial cells was paralleled by metaplastic changes in stromal cells, ECM and tissue stiffness. Strikingly, this change in tissue state at metaplasia was already accompanied by appearance of fibroblasts with characteristics of carcinoma-associated fibroblasts and of an NK cell-associated immunosuppressive microenvironment. Thus, Barretts esophagus progresses as a coordinated multi-component system, supporting treatment paradigms that go beyond targeting cancerous cells to incorporating stromal reprogramming. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/544265v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@64c375org.highwire.dtl.DTLVardef@15c6ef2org.highwire.dtl.DTLVardef@177dee0org.highwire.dtl.DTLVardef@c945e7_HPS_FORMAT_FIGEXP M_FIG Graphical AbstractTo obtain a comprehensive picture of the coordinated changes in epithelial, stromal and immune compartments during development of Barretts-associated esophageal adenocarcinoma, patient-matched samples corresponding to various phases of disease progression were collected from 12 patients, each of which had at a given time point lesions at multiple stages progression (matched-normal, metaplasia, dysplasia, and carcinoma). Matched "normal" gastric tissues were also collected. These sample were analyzed by single cell RNA-sequencing (scRNAseq) for single-cell resolution transcriptomics and Copy Number Variant (CNV), by proteomics for extracellular matrix (ECM) proteins, by Atomic Force Microscopy (AFM for tissue stiffness and by CODEX spatial proteomics imaging The integrative multi-omics analysis exposed drastic alterations in cell type composition and shifts in cell states in all three compartments. A large subpopulation of fibroblasts absent in the normal esophagus and characteristic of dysplasia and adenocarcinoma sample, that based on markers would indeed be considered cancer associated fibroblasts (CAF), appeared already in the metaplastic phase. This fibroblast subpopulation had transcriptomes virtually indistinguishable with fibroblasts of the cancer free gastric epithelium in these patients C_FIG

cancer biology↗

bioRxiv2022

Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq (scRNA-seq)

Gene set scoring (or enrichment) is a common dimension reduction task in bioinformatics that can be focused on differences between groups or at the single sample level. Gene sets can represent biological functions, molecular pathways, cell identities, and more. Gene set scores are context dependent values that are useful for interpreting biological changes following experiments or perturbations. Single sample scoring produces a set of scores, one for each member of a group, which can be analyzed with statistical models that can include additional clinically important factors such as gender or age. However, the sparsity and technical noise of single cell expression measures create difficulties for these methods, which were originally designed for bulk expression profiling (microarrays, RNAseq). This can be greatly remedied by first applying a smoothing transformation that shares gene measure information within transcriptomic neighborhoods. In this work, we use the nearest neighbor graph of cells for matrix smoothing to produce high quality gene set scores on a per-cell, per-group, level which is useful for visualization and statistical analysis. Availability and implementationThe gssnng software is available using the python package index (PyPI) and works with Scanpy AnnData objects. It can be installed using pip install gssnng. More information and demo notebooks: See https://github.com/IlyaLab/gssnng

bioinformatics↗

bioRxiv2021

Polyclonal lymphoid expansion drives paraneoplastic autoimmunity in neuroblastoma

Neuroblastoma is a lethal childhood solid tumor of developing peripheral nerves. Two percent of children with neuroblastoma develop Opsoclonus Myoclonus Ataxia Syndrome (OMAS), a paraneoplastic disease characterized by cerebellar and brainstem-directed autoimmunity, but typically with outstanding cancer-related outcomes. We compared tumor transcriptomes and tumor infiltrating T- and B-cell repertoires from 38 OMAS subjects with neuroblastoma to 26 non- OMAS associated neuroblastomas. We found greater B- and T-cell infiltration in OMAS- associated tumors compared to controls, but unexpectedly showed that both were polyclonal expansions. Tertiary lymphoid structures (TLS) were enriched in OMAS-associated tumors. We identified significant enrichment of the MHC Class II allele HLA-DOB*01:01 in OMAS patients. OMAS severity scores were associated with the expression of several candidate autoimmune genes. We propose a model in which polyclonal autoreactive B lymphocytes act as antigen presenting cells and drive TLS formation, thereby crucially supporting both sustained polyclonal T-cell-mediated anti-tumor immunity and paraneoplastic OMAS neuropathology.

cancer biology↗

bioRxiv2021

Patient-specific cell communication networks associate with disease progression in cancer

The maintenance and function of tissues in health and disease depends on cell-cell communication. This work shows how high-level features, representing cell-cell communication, can be defined and used to associate certain signaling axes with clinical outcomes. Using cell-sorted gene expression data, we generated a scaffold of cell-cell interactions and define a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for TCGA patient samples, each representing likely channels of intercellular communication in the tumor microenvironment. It was shown that particular edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

bioinformatics↗

bioRxiv2020

Robust Classification of Immune Subtypes in Cancer

As part of the immune landscape of cancer, six immune subtypes were defined which describe a categorization of tumor-immune states. A number of phenotypic variables were found to associate with immune subtypes, such as nonsilent mutation rates, regulation of immunomodulator genes, and cytokine network structures. An ensemble classifier based on XGBoost is introduced with the goal of classifying tumor samples into one of six immune subtypes. Robust performance was accomplished through feature engineering; quartile-levels, binary gene-pair features, and gene-set-pair features were computed for each sample independently. The classifier is robust to software pipeline and normalization scheme, making it applicable to any expression data format from raw count data to TPMs since the classification is essentially based on simple binary gene-gene level comparisons within a given sample. The classifier is available as an R package or part of the CRI iAtlas portal. Code / Tool availabilitySource Code https://github.com/Gibbsdavidl/ImmuneSubtypeClassifier Web App Tool https://www.cri-iatlas.org/

bioinformatics↗

bioRxiv2019

ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

Immune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

biophysics↗