Browse
Submit Data
Databases
API
Help

Dataset Information

13 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Context Matters: Response Heterogeneity to Collagen-Targeting Approaches in Desmoplastic Cancers.

ABSTRACT: The deposition of collagen-rich desmoplastic tissue is a well-documented feature of the solid tumor microenvironment (TME). However, efforts to target the desmoplastic extracellular matrix (ECM) en masse, or collagen molecules more specifically, have been met with mixed and sometimes paradoxical results. In this review, we posit that these discrepancies are due-at least in part-to the incredible diversity of the collagen superfamily. Specifically, whereas studies of "collagen-targeting" approaches frequently refer to "collagen" as a single molecule or relatively homogeneous molecular family, 28 individual collagens have been identified in mammalian tissues, each with a unique structure, supramolecular assembly pattern, tissue distribution, and/or function. Moreover, some collagen species have been shown to exert both pro- and anti-neoplastic effects in the desmoplastic TME, even within the same cancer type. Therefore, herein, we describe the diversity of the collagen family in normal tissues and highlight the context-specific roles of individual collagen molecules in desmoplastic tumors. We further discuss how this heterogeneity relates to the variable efficacy of "collagen-targeting" strategies in this setting and provide guidance for future directions in the field.

SUBMITTER: Fuller AM

PROVIDER: S-EPMC9264969 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models.

Project description:Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.

| S-EPMC8229374 | biostudies-literature

Cancer stemness, intratumoral heterogeneity, and immune response across cancers.

Project description:Regulatory programs that control the function of stem cells are active in cancer and confer properties that promote progression and therapy resistance. However, the impact of a stem cell-like tumor phenotype ("stemness") on the immunological properties of cancer has not been systematically explored. Using gene-expression-based metrics, we evaluated the association of stemness with immune cell infiltration and genomic, transcriptomic, and clinical parameters across 21 solid cancers. We found pervasive negative associations between cancer stemness and anticancer immunity. This occurred despite high stemness cancers exhibiting increased mutation load, cancer-testis antigen expression, and intratumoral heterogeneity. Stemness was also strongly associated with cell-intrinsic suppression of endogenous retroviruses and type I IFN signaling, and increased expression of multiple therapeutically accessible immunosuppressive pathways. Thus, stemness is not only a fundamental process in cancer progression but may provide a mechanistic link between antigenicity, intratumoral heterogeneity, and immune suppression across cancers.

| S-EPMC6500180 | biostudies-literature

Oxygen reactivity in flavoenzymes: context matters.

Project description:Many flavoenzymes--oxidases and monooxygenases--react faster with oxygen than free flavins do. There are many ideas on how enzymes cause this. Recent work has focused on the importance of a positive charge near N5 of the reduced flavin. Fructosamine oxidase has a lysine near N5 of its flavin. We measured a rate constant of 1.6 × 10(5) M(-1) s(-1) for its reaction with oxygen. The Lys276Met mutant reacted with a rate constant of 291 M(-1) s(-1), suggesting an important role for this lysine in oxygen activation. The dihydroorotate dehydrogenases from E. coli and L. lactis also have a lysine near N5 of the flavin. They react with O(2) with rate constants of 6.2 × 10(4) and 3.0 × 10(3) M(-1) s(-1), respectively. The Lys66Met and Lys43Met mutant enzymes react with rate constants that are nearly the same as those for the wild-type enzymes, demonstrating that simply placing a positive charge near N5 of the flavin does not guarantee increased oxygen reactivity. Our results show that the lysine near N5 does not exert an effect without an appropriate context; evolution did not find only one mechanism for activating the reaction of flavins with O(2).

| S-EPMC3203534 | biostudies-literature

Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies.

Project description:The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) and Monte Carlo control have been used to develop machine-readable context-dependent solutions for complex problems with many possible realizations ranging from video-games to the game of Go. RL could be a valuable tool to generate context-dependent policies for outbreak response, though translating the resulting policies into simple rules that can be read and interpreted by human decision-makers remains a challenge. Here we illustrate the application of RL to the development of context-dependent outbreak response policies to minimize outbreaks of foot-and-mouth disease. We show that control based on the resulting context-dependent policies, which adapt interventions to the specific outbreak, result in smaller outbreaks than static policies. We further illustrate two approaches for translating the complex machine-readable policies into simple heuristics that can be evaluated by human decision-makers. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

| S-EPMC6558555 | biostudies-literature

To disclose, or not to disclose? Context matters.

Project description:Progress in understanding childhood disease using next-generation sequencing (NGS) portends vast improvements in the nature and quality of patient care. However, ethical questions surrounding the disclosure of incidental findings (IFs) persist, as NGS and other novel genomic technologies become the preferred tool for clinical genetic testing. Thus, the need for comprehensive management plans and multidisciplinary discussion on the return of IFs in pediatric research has never been more immediate. The aim of this study is to explore the views of investigators concerning the return of IFs in the pediatric oncology research context. Our findings reveal at least four contextual themes underlying the ethics of when, and how, IFs could be disclosed to participants and their families: clinical significance of the result, respect for individual, scope of professional responsibilities, and implications for the healthcare/research system. Moreover, the study proposes two action items toward anticipatory governance of IF in genetic research with children. The need to recognize the multiplicity of contextual factors in determining IF disclosure practices, particularly as NGS increasingly becomes a centerpiece in genetic research broadly, is heightened when children are involved. Sober thought should be given to the possibility of discovering IF, and to proactive discussions about disclosure considering the realities of young participants, their families, and the investigators who recruit them.

| S-EPMC4062531 | biostudies-other

The Versatility of Collagen in Pharmacology: Targeting Collagen, Targeting with Collagen

Project description: Not available

| S-EPMC11203716 | biostudies-literature

Overcoming Heterogeneity of Antigen Expression for Effective CAR T Cell Targeting of Cancers.

Project description:Chimeric antigen receptor (CAR) gene-modified T cells (CAR T cells) can eradicate B cell malignancies via recognition of surface-expressed B lineage antigens. Antigen escape remains a major mechanism of relapse and is a key barrier for expanding the use of CAR T cells towards solid cancers with their more diverse surface antigen repertoires. In this review we discuss strategies by which cancers become amenable to effective CAR T cell therapy despite heterogeneous phenotypes. Pharmaceutical approaches have been reported that selectively upregulate individual target antigens on the cancer cell surface to sensitize antigen-negative subclones for recognition by CARs. In addition, advanced T cell engineering strategies now enable CAR T cells to interact with more than a single antigen simultaneously. Still, the choice of adequate targets reliably and selectively expressed on the cell surface of tumor cells but not normal cells, ideally by driving tumor growth, is limited, and even dual or triple antigen targeting is unlikely to cure most solid tumors. Innovative receptor designs and combination strategies now aim to recruit bystander cells and alternative cytolytic mechanisms that broaden the activity of CAR-engineered T cells beyond CAR antigen-dependent tumor cell recognition.

| S-EPMC7281243 | biostudies-literature

Targeting Tumor-Associated Fibroblasts for Therapeutic Delivery in Desmoplastic Tumors.

Project description:The off-target distribution of anticancer nanoparticles to fibroblasts creates a barrier to the effective treatment of desmoplastic tumors. However, we hypothesized that this nanoparticle detriment might be exploited to target the expression of secreted cytotoxic proteins from tumor-associated fibroblasts (TAF) as an anticancer strategy. In addressing this hypothesis, plasmids encoding the secretable TNF-related factor sTRAIL were loaded into lipid-coated protamine DNA complexes and administered by infusion in a murine xenograft model of human desmoplastic bladder carcinoma. Three doses were sufficient to generate approximately 70% of TAFs as sTRAIL-producing cells. sTRAIL triggered apoptosis in tumor cell nests adjacent to TAFs. Furthermore, it reverted residual fibroblasts to a quiescent state due to insufficient activation, further compromising tumor growth and remodeling the microenvironment to favor second-wave nanotherapy. We confirmed the efficacy of this strategy in an orthotopic xenograft model of human pancreatic cancer, where the desmoplastic stroma is well known to be a major barrier to the delivery of therapeutic nanoparticles. Collectively, our results offer a proof of concept for the use of nanoparticles to modify TAFs as an effective strategy to treat desmoplastic cancers. Cancer Res; 77(3); 719-31. ©2016 AACR.

| S-EPMC5290135 | biostudies-literature

Community context matters for bacteria-phage ecology and evolution.

Project description:Bacteria-phage symbioses are ubiquitous in nature and serve as valuable biological models. Historically, the ecology and evolution of bacteria-phage systems have been studied in either very simple or very complex communities. Although both approaches provide insight, their shortcomings limit our understanding of bacteria and phages in multispecies contexts. To address this gap, here we synthesize the emerging body of bacteria-phage experiments in medium-complexity communities, specifically those that manipulate bacterial community presence. Generally, community presence suppresses both focal bacterial (phage host) and phage densities, while sometimes altering bacteria-phage ecological interactions in diverse ways. Simultaneously, community presence can have an array of evolutionary effects. Sometimes community presence has no effect on the coevolutionary dynamics of bacteria and their associated phages, whereas other times the presence of additional bacterial species constrains bacteria-phage coevolution. At the same time, community context can alter mechanisms of adaptation and interact with the pleiotropic consequences of (co)evolution. Ultimately, these experiments show that community context can have important ecological and evolutionary effects on bacteria-phage systems, but many questions still remain unanswered and ripe for additional investigation.

| S-EPMC8528888 | biostudies-literature

Predicting responses to chemotherapy in the context that matters - the patient.

Project description:Guided by the belief that the most important setting for understanding tumor response to drugs is the human patient, we developed a technology called CIVO. CIVO enables analysis of up to 8 therapies simultaneously in a patient's tumor, without inducing systemic toxicity and while maintaining the integrity of the native tumor microenvironment.

| S-EPMC4845209 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data