Dataset Information

A Computational Approach Identifies Immunogenic Features of Prognosis in Human Cancers.

ABSTRACT: A large number of tumor intrinsic and extrinsic factors determine long-term survival in human cancers. In this study, we stratified 9120 tumors from 33 cancers with respect to their immune cell content and identified immunogenomic features associated with long-term survival. Our analysis demonstrates that tumors infiltrated by CD8+ T cells expressing higher levels of activation marker (PD1hi) along with TCR signaling genes and cytolytic T cell markers (IL2hi/TNF-?hi/IFN-?hi/GZMA-Bhi) extend survival, whereas survival benefit was absent for tumors infiltrated by anergic and hyperexhausted CD8+ T cells characterized by high expression of CTLA-4, TIM3, LAG3, and genes linked to PI3K signaling pathway. The computational approach of using robust and highly specific gene expression signatures to deconvolute the tumor microenvironment has important clinical applications, such as selecting patients who will benefit from checkpoint inhibitor treatment.

SUBMITTER: Manoharan M

PROVIDER: S-EPMC6308325 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Computational Approach Identifies Immunogenic Features of Prognosis in Human Cancers.

Manoharan Malini M Mandloi Nitin N Priyadarshini Sushri S Patil Ashwini A Gupta Rohit R Iyer Laxman L Gupta Ravi R Chaudhuri Amitabha A

Frontiers in immunology 20181221

A large number of tumor intrinsic and extrinsic factors determine long-term survival in human cancers. In this study, we stratified 9120 tumors from 33 cancers with respect to their immune cell content and identified immunogenomic features associated with long-term survival. Our analysis demonstrates that tumors infiltrated by CD8+ T cells expressing higher levels of activation marker (PD1hi) along with TCR signaling genes and cytolytic T cell markers (IL2hi/TNF- ...[more]

PMID: 30622534

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Project description:BACKGROUND: Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides. RESULTS: PEPOP uses the 3D coordinates of a protein both to predict clusters of surface accessible segments that might correspond to epitopes and to design peptides to be used to raise antibodies that target the cognate antigen at specific sites. To verify the ability of PEPOP to identify epitopes, 13 crystallographically defined epitopes were compared with PEPOP clusters: specificity ranged from 0.75 to 1.00, sensitivity from 0.33 to 1.00, and the positive predictive value from 0.19 to 0.89. Comparison of these results with those obtained with two other prediction algorithms showed comparable specificity and slightly better sensitivity and PPV. To prove the capacity of PEPOP to predict immunogenic peptides that induce protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was also demonstrated. CONCLUSION: PEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at http://diagtools.sysdiag.cnrs.fr/PEPOP/.

Project description:Background:Deubiquitinase OTU domain containing 4 (OTUD4) is initially identified as a K48-specific deubiquitinase and plays an important role in DNA damage repair signaling transduction. However, the expression level, prognostic role, biological function and mechanism of OTUD4 in multiple human cancers are unclear. Methods:GEPIA online (http://gepia.cancer-pku.cn/; The Cancer Genome Atlas (TCGA) database) was used to analyze the mRNA expression of OTUD4 in multiple human cancers. Kaplan-Meier plotter (KM plotter) database and TCGA database were used to evaluate the prognostic value of OTUD4 expression in multiple human cancers. MTT, Transwell and 3D culture assays were used to detect the role of OTUD4 in breast, liver and lung cancer cells. The correlation between OTUD4 and apoptosis signaling pathway and AKT signaling pathway was analyzed by Gene set enrichment analysis (GSEA). Results:OTUD4 mRNA expression is significantly downregulated in multiple human cancer tissues. Survival analysis establishes that the downregulation of OTUD4 predicts poor prognosis in many solid tumors, including breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), and ovarian serous cystadenocarcinoma (OV). Furthermore, overexpression of OTUD4 could inhibit tumor cell proliferation, migration and invasion of breast, liver and lung cancer cells through inhibiting the AKT signaling pathway. Conclusion:This study found that OTUD4 may be a potential predictive factor for several human cancers and a tumor suppressor for breast, liver and lung cancer. The overexpression of OTUD4 restrained proliferation, migration and invasion of human breast, liver and lung cancer cells through promoting cancer cells apoptosis and inhibiting AKT signaling pathway. Notably, our results indicated that OTUD4 could be a useful biomarker for the prognosis of human cancers and a potential molecular target for diagnosis and treatment of breast, liver and lung cancer.

Project description:BACKGROUND: A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior. RESULTS: We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource. CONCLUSIONS: The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

Dataset Information

A Computational Approach Identifies Immunogenic Features of Prognosis in Human Cancers.

Publications

A Computational Approach Identifies Immunogenic Features of Prognosis in Human Cancers.

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