Cancer and germline exomes, and cancer RNA-seq consisiting of FASTQ paired-end reads from melanoma, lung and colon cancer samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001005513 | EGA |
REPOSITORIES: EGA
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2009-02-08 | E-GEOD-14662 | biostudies-arrayexpress
Project description:This model of the immune system response to antigen presentation is based on the publication:
Eduardo D.Sontag (2017) 'A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination', Cell Systems, 4(2)
DOI: 10.1016/j.cels.2016.12.003
Comment:
This model is based on the "toy model" as described by Equations 1A-1C from the manuscript and the system represented by Equation 2, which exhibits a type of incoherent feedforward loop (IFFL).
Abstract:
The immune system must discriminate between agents of disease and an organism’s healthy cells. While the identification of an antigen as self/non-self is critically important, the dynamic features of antigen presentation may also determine the immune system’s response. Here, we use a simple mathematical model of immune activation to explore the idea of antigen discrimination through dynamics. We propose that antigen presentation is coupled to two nodes, one regulatory and one effecting the immune response, through an incoherent feedforward loop and repressive feedback. This circuit would allow the immune system to effectively estimate the increase of antigens with respect to time, a key determinant of immune reactivity in vivo. Our model makes the prediction that tumors growing at specific rates evade the immune system despite the continuous presence of antigens indicating disease, a phenomenon closely related to clinically observed “two-zone tolerance.” Finally, we discuss a plausible biological instantiation of our circuit using combinations of regulatory and effector T cells.
2024-09-02 | BIOMD0000001030 | BioModels
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Project description:Colorectal cancer is the second leading cause of cancer death worldwide, and the incidence of this disease is expected to increase as global socioeconomic changes occur. Immune checkpoint inhibition therapy is effective in treating a minority of colorectal cancer tumors; however, microsatellite stable tumors do not respond well to this treatment. Emerging cancer immunotherapeutic strategies aim to activate a cytotoxic T cell response against tumor-specific antigens, presented exclusively at the cell surface of cancer cells. These antigens are rare and are most effectively identified with a mass spectrometry-based approach, which allows the direct sampling and sequencing of these peptides. While the few tumor-specific antigens identified to date derived from coding regions of the genome, recent findings indicate that a large proportion of tumor-specific antigens originate from allegedly noncoding regions. Here, we employed a novel proteogenomic approach to identify tumor antigens in a collection of colorectal cancer-derived cell lines and biopsy samples consisting of matched tumor and normal adjacent tissue. The generation of personalized cancer databases paired with mass spectrometry analyses permitted the identification of more than 30 000 unique MHC I-associated peptides. We identified 19 putative tumor-specific antigens in both microsatellite stable and unstable tumors, over two-thirds of which were derived from non-coding regions. Many of these peptides were derived from source genes known to be involved in colorectal cancer progression, suggesting that antigens from these genes could have therapeutic potential in a wide range of tumors. These findings could benefit the development of T cell-based vaccines, in which T cells are primed against these antigens to target and eradicate tumors. Such a vaccine could be used in tandem with existing immune checkpoint inhibition therapies, to bridge the gap in treatment efficacy across subtypes of colorectal cancer with varying prognoses.
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