Statistical data mining for symbol associations in genomic databases

A methodology is proposed to automatically detect significant symbol associations in genomic databases. A new statistical test is proposed to assess the significance of a group of symbols when found in several genesets of a given database. Applied to symbol pairs, the thresholded p-values of the test define a graph structure on the set of symbols. The cliques of that graph are significant symbol associations, linked to a set of genesets where they can be found. The method can be applied to any database, and is illustrated MSigDB C2 database. Many of the symbol associations detected in C2 or in non-specific selections did correspond to already known interactions. On more specific selections of C2, many previously unkown symbol associations have been detected. These associations unveal new candidates for gene or protein interactions, needing further investigation for biological evidence

Large scale statistical analysis of GEO datasets

The problem addressed here is that of simultaneous treatment of several gene expression datasets, possibly collected under different experimental conditions and/or platforms. Using robust statistics, a large scale statistical analysis has been conducted over $20$ datasets downloaded from the Gene Expression Omnibus repository. The differences between datasets are compared to the variability inside a given dataset. Evidence that meaningful biological information can be extracted by merging different sources is provided

Simultaneous growth of two cancer cell lines evidences variability in growth rates

Cancer cells co-cultured in vitro reveal unexpected differential growth rates that classical exponential growth models cannot account for. Two non-interacting cell lines were grown in the same culture, and counts of each species were recorded at periodic times. The relative growth of population ratios was found to depend on the initial proportion, in contradiction with the traditional exponential growth model. The proposed explanation is the variability of growth rates for clones inside the same cell line. This leads to a log-quadratic growth model that provides both a theoretical explanation to the phenomenon that was observed, and a better fit to our growth data

Simulation of Gene Regulatory Networks

This limited review is intended as an introduction to the fast growing subject of mathematical modelling of cell metabolism and its biochemical pathways, and more precisely on pathways linked to apoptosis of cancerous cells. Some basic mathematical models of chemical kinetics, with emphasis on stochastic models, are presented

The Pro-tumorigenic IL-33 Involved in Antitumor Immunity: A Yin and Yang Cytokine

Interleukin-33 (IL-33), considered as an alarmin released upon tissue stress or damage, is a member of the IL-1 family and binds the ST2 receptor. First described as a potent initiator of type 2 immune responses through the activation of T helper 2 (TH2) cells and mast cells, IL-33 is now also known as an effective stimulator of TH1 immune cells, natural killer (NK) cells, iNKT cells, and CD8 T lymphocytes. Moreover, IL-33 was shown to play an important role in several cancers due to its pro and anti-tumorigenic functions. Currently, IL-33 is a possible inducer and prognostic marker of cancer development with a direct effect on tumor cells promoting tumorigenesis, proliferation, survival, and metastasis. IL-33 also promotes tumor growth and metastasis by remodeling the tumor microenvironment (TME) and inducing angiogenesis. IL-33 favors tumor progression through the immune system by inducing M2 macrophage polarization and tumor infiltration, and upon activation of immunosuppressive cells such as myeloid-derived suppressor cells (MDSC) or regulatory T cells. The anti-tumor functions of IL-33 also depend on infiltrated immune cells displaying TH1 responses. This review therefore summarizes the dual role of this cytokine in cancer and suggests that new proposals for IL-33-based cancer immunotherapies should be considered with caution

nwCompare and AutoCompare Softwares for Proteomics and Transcriptomics Data Mining – Application to the Exploration of Gene Expression Profiles of Aggressive Lymphomas

chapitre 22International audienc

A Functional γδTCR/CD3 Complex Distinct from γδT Cells Is Expressed by Human Eosinophils

BACKGROUND:Eosinophils are effector cells during parasitic infections and allergic responses. However, their contribution to innate immunity has been only recently unravelled. METHODOLOGY/PRINCIPAL FINDINGS:Here we show that human eosinophils express CD3 and gammadelta T Cell Receptor (TCR) but not alphabeta TCR. Surface expression of gammadeltaTCR/CD3 is heterogeneous between eosinophil donors and inducible by mycobacterial ligands. Surface immunoprecipitation revealed expression of the full gammadeltaTCR/CD3 complex. Real-time PCR amplification for CD3, gamma and delta TCR constant regions transcripts showed a significantly lower expression in eosinophils than in gammadeltaT cells. Limited TCR rearrangements occur in eosinophils as shown by spectratyping analysis of CDR3 length profiles and in situ hybridization. Release by eosinophils of Reactive Oxygen Species, granule proteins, Eosinophil Peroxidase and Eosinophil-Derived Neurotoxin and cytokines (IFN-gamma and TNF-alpha) was observed following activation by gammadeltaTCR-specific agonists or by mycobacteria. These effects were inhibited by anti-gammadeltaTCR blocking antibodies and antagonists. Moreover, gammadeltaTCR/CD3 was involved in eosinophil cytotoxicity against tumor cells. CONCLUSIONS/SIGNIFICANCE:Our results provide evidence that human eosinophils express a functional gammadeltaTCR/CD3 with similar, but not identical, characteristics to gammadeltaTCR from gammadeltaT cells. We propose that this receptor contributes to eosinophil innate responses against mycobacteria and tumors and may represent an additional link between lymphoid and myeloid lineages

Dual Relief of T-lymphocyte Proliferation and Effector Function Underlies Response to PD-1 Blockade in Epithelial Malignancies

Although understanding of T-cell exhaustion is widely based on mouse models, its analysis in patients with cancer could provide clues indicating tumor sensitivity to immune checkpoint blockade (ICB). Data suggest a role for costimulatory pathways, particularly CD28, in exhausted T-cell responsiveness to PD-1/PD-L1 blockade. Here, we used single-cell transcriptomic, phenotypic, and functional approaches to dissect the relation between CD8+ T-cell exhaustion, CD28 costimulation, and tumor specificity in head and neck, cervical, and ovarian cancers. We found that memory tumor–specific CD8+ T cells, but not bystander cells, sequentially express immune checkpoints once they infiltrate tumors, leading, in situ, to a functionally exhausted population. Exhausted T cells were nonetheless endowed with effector and tumor residency potential but exhibited loss of the costimulatory receptor CD28 in comparison with their circulating memory counterparts. Accordingly, PD-1 inhibition improved proliferation of circulating tumor–specific CD8+ T cells and reversed functional exhaustion of specific T cells at tumor sites. In agreement with their tumor specificity, high infiltration of tumors by exhausted cells was predictive of response to therapy and survival in ICB-treated patients with head and neck cancer. Our results showed that PD-1 blockade–mediated proliferation/reinvigoration of circulating memory T cells and local reversion of exhaustion occur concurrently to control tumors