Damage-associated molecular patterns by dying cancer cells
Ontology highlight
ABSTRACT:
INSTRUMENT(S): TripleTOF 5600
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Chan, Keith Syson
PROVIDER: MSV000086407 | MassIVE | Wed Nov 04 09:09:00 GMT 2020
REPOSITORIES: MassIVE
Similar Datasets
Project description:The molecular hallmark of immunogenic cell death (ICD) features the release of damage-associated molecular patterns (DAMPs) by dying cancer cells. Herein, measuring DAMPs from gemcitabine-induced dying cancer cells, via mass spectrometry (MS) proteomic profiling.
2020-10-29 | MSV000086386 | MassIVE
Project description:In plants, recognition of immunogenic molecular patterns, such as bacterial flagellin (flg22 epitope), leads to an enhanced state of immunity, designated pattern-triggered immunity (PTI). Following cognate ligand perception, pattern recognition receptors initiate sequential phosphorylation events to activate defense responses against invading pathogens. To gain further insight into PTI signaling, we conducted phosphoproteome analyses in Arabidopsis seedlings with immunogenic molecular patterns.
2024-09-18 | PXD015730 | JPOST Repository
Project description:We evaluated the TGFβ blocking antibody NIS793 in combination with either gemcitabine/n(ab)-paclitaxel or FOLFIRINOX chemotherapy in orthotopic pancreatic cancer. Blockade of TGFβ with chemotherapy reduced tumor burden in poorly immunogenic pancreatic cancer, without affecting the metastatic rate of cancer cells. TGFβ blockade decreased total aSMA+ fibroblasts but had minimal effect on fibroblast heterogeneity.
2023-04-23 | GSE224688 | GEO
Project description:S4, a sulfonamide drug, has been confirmed to induce apoptosis and autophagy in cancer cells. Immunogenic cell death is a special cell death type which is closely related to apoptosis and autophagy. We performed RNA-seq to determine the impact of S4 on global gene expression profile in LN229 cells. Our results show that S4 induces immunogenic cell death via the response to endoplasmic reticulum stress.
2022-06-13 | GSE205538 | GEO
Project description:Zhu2015 - Combined gemcitabine and birinapant
in pancreatic cancer cells - basic PD model
Mathematical model to illustrate the
effectiveness of combination chemotherapy involving gemcitabine and
birinapant against pancreatic cancer.
This model is described in the article:
Mechanism-based mathematical
modeling of combined gemcitabine and birinapant in pancreatic
cancer cells.
Zhu X, Straubinger RM, Jusko
WJ.
J Pharmacokinet Pharmacodyn 2015 Oct;
42(5): 477-496
Abstract:
Combination chemotherapy is standard treatment for
pancreatic cancer. However, current drugs lack efficacy for
most patients, and selection and evaluation of new combination
regimens is empirical and time-consuming. The efficacy of
gemcitabine, a standard-of-care agent, combined with
birinapant, a pro-apoptotic antagonist of Inhibitor of
Apoptosis Proteins (IAPs), was investigated in pancreatic
cancer cells. PANC-1 cells were treated with vehicle,
gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and
combinations of the two drugs. Temporal changes in cell
numbers, cell cycle distribution, and apoptosis were measured.
A basic pharmacodynamic (PD) model based on cell numbers, and a
mechanism-based PD model integrating all measurements, were
developed. The basic PD model indicated that synergistic
effects occurred in both cell proliferation and death
processes. The mechanism-based model captured key features of
drug action: temporary cell cycle arrest in S phase induced by
gemcitabine alone, apoptosis induced by birinapant alone, and
prolonged cell cycle arrest and enhanced apoptosis induced by
the combination. A drug interaction term Ψ was employed in
the models to signify interactions of the combination when data
were limited. When more experimental information was utilized,
Ψ values approaching 1 indicated that specific mechanisms
of interactions were captured better. PD modeling identified
the potential benefit of combining gemcitabine and birinapant,
and characterized the key interaction pathways. An optimal
treatment schedule of pretreatment with gemcitabine for 24-48 h
was suggested based on model predictions and was verified
experimentally. This approach provides a generalizable modeling
platform for exploring combinations of cytostatic and cytotoxic
agents in cancer cell culture studies.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000668.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000668 | BioModels
Project description:Zhu2015 - combined gemcitabine and birinapant
in pancreatic cancer cells - mechanistic PD model
Mechanistic mathematical model to
illustrate the effectiveness of combination chemotherapy involving
gemcitabine and birinapant against pancreatic cancer.
This model is described in the article:
Mechanism-based mathematical
modeling of combined gemcitabine and birinapant in pancreatic
cancer cells.
Zhu X, Straubinger RM, Jusko
WJ.
J Pharmacokinet Pharmacodyn 2015 Oct;
42(5): 477-496
Abstract:
Combination chemotherapy is standard treatment for
pancreatic cancer. However, current drugs lack efficacy for
most patients, and selection and evaluation of new combination
regimens is empirical and time-consuming. The efficacy of
gemcitabine, a standard-of-care agent, combined with
birinapant, a pro-apoptotic antagonist of Inhibitor of
Apoptosis Proteins (IAPs), was investigated in pancreatic
cancer cells. PANC-1 cells were treated with vehicle,
gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and
combinations of the two drugs. Temporal changes in cell
numbers, cell cycle distribution, and apoptosis were measured.
A basic pharmacodynamic (PD) model based on cell numbers, and a
mechanism-based PD model integrating all measurements, were
developed. The basic PD model indicated that synergistic
effects occurred in both cell proliferation and death
processes. The mechanism-based model captured key features of
drug action: temporary cell cycle arrest in S phase induced by
gemcitabine alone, apoptosis induced by birinapant alone, and
prolonged cell cycle arrest and enhanced apoptosis induced by
the combination. A drug interaction term Ψ was employed in
the models to signify interactions of the combination when data
were limited. When more experimental information was utilized,
Ψ values approaching 1 indicated that specific mechanisms
of interactions were captured better. PD modeling identified
the potential benefit of combining gemcitabine and birinapant,
and characterized the key interaction pathways. An optimal
treatment schedule of pretreatment with gemcitabine for 24-48 h
was suggested based on model predictions and was verified
experimentally. This approach provides a generalizable modeling
platform for exploring combinations of cytostatic and cytotoxic
agents in cancer cell culture studies.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000669.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000669 | BioModels
Project description:Pancreatic ductal adenocarcinoma (PDAC) is the most lethal cancer with limited therapeutic options, and gemcitabine insensitivity remains a major challenge. N6-methyladenosine (m6A) is a prevalent nucleotide modification in mRNA that has been linked to various biological processes in human diseases, yet its role in drug sensitivity of cancer remains largely unknown. Herein, by detecting the global m6A profile in a panel of gemcitabine-sensitive and gemcitabine-insensitive PDAC cells, we identified the key role of hyper-m6A modification of the master G0/G1 regulator Fizzy and Cell Division Cycle 20 Related 1 (FZR1) in regulating gemcitabine sensitivity. Targeting m6A modification of FZR1 improves the gemcitabine treatment response of gemcitabine-insensitive PDAC cells in vitro and in vivo. Mechanistically, Gem Nuclear Organelle Associated Protein 5 (GEMIN5) was identified as a novel m6A mediator that specifically binds to m6A-modified FZR1 and recruits the eIF3 translation initiation complex to accelerate FZR1 translation. Upregulation of FZR1 maintains the G0/G1 quiescent state and impairs gemcitabine sensitivity of PDAC cells. Further clinical analysis showed that both high levels of FZR1 m6A modification and FZR1 protein indicated a poor response to gemcitabine. These findings unveil the critical function for m6A modification in regulating gemcitabine sensitivity in PDAC and identify the m6A-FZR1 axis as a potential target to enhance gemcitabine response.
2023-06-08 | GSE206969 | GEO
Project description:Inducing immunogenic clearance enhances the phagocytosis of dying cancer cells and activates antigen-presenting cells, facilitating the immune system against cancer. This approach can potentially generate robust systemic antitumor immune responses and increase tumor responsiveness to anti-programmed cell death protein 1 (αPD-1) therapy. However, the precise mechanisms underlying this antitumor response induced by immunogenic clearance remain unclear. We used single cell RNA sequencing (scRNA-seq) to understand the underlying mechanism of the therapeutic strategy of immunogenic clearance.
2024-11-29 | GSE282150 | GEO
Project description:Pre-clinical studies reported the immunogenic or immunomodulatory effects of traditional cancer therapies. However, the publicly available well-curated and harmonized breast cancer datasets, such as TCGA, METABRIC, and MetaGxBreast, lack careful curation of treatment regimens. Hence, limited exploration of the impact of therapies on the prognostic/predictive value of breast cancer biomarkers. Herein, we describe a pooled and treatment-curated gene-expression dataset to investigate the impact of treatments on the prognostic/predictive value of biomarkers. We searched the gene expression omnibus database to identify potential human breast cancer gene-expression datasets with anthracycline/taxane treatment. Published datasets with the detailed treatment regimen, clinical endpoint, clinical-pathological, and gene-expression data were extracted and harmonized. The dataset described herein would help researchers explore the interaction between gene-expression biomarkers and immunogenic/immunomodulatory treatments in breast cancer.
2024-03-09 | GSE205568 | GEO
Project description:Immunogenic cell death inducer cEmsy boosted cancer immunotherapy in lung adenocarcinoma
| PRJNA1106833 | ENA