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Gabor, L.

Publications and source records attributed to Gabor, L..

2 recordsLinked to original sources
bioRxiv2023

Tumor-targeted delivery of Tetanus toxoid by Listeria improves immunotherapy against ovarian cancer in mice

Ovarian cancer is known for its poor neoantigen expression and strong immunosuppression. Here, we utilized an attenuated non-pathogenic bacterium Listeria monocytogenes to deliver a highly immunogenic Tetanus Toxoid protein (Listeria-TT), as a neoantigen surrogate, into tumor cells through infection in a metastatic mouse ovarian cancer model (Id8p53-/-Luc). Gemcitabine (GEM) was added to reduce immune suppression. Listeria-TT+GEM treatments resulted in tumors expressing TT and reactivation of pre-existing CD4 and CD8 memory T cells to TT (generated early in life). These T cells were then attracted to the TT-expressing tumors now producing perforin and granzyme B. This correlated with a strong reduction in tumor burden, and significant improvement of the survival time compared to all control groups. Checkpoint inhibitors have little effect on ovarian cancer partly because of low neoantigen expression. Here we demonstrated that Listeria-TT+GEM+anti-PD1 was significantly more effective (efficacy and survival) than anti-PD1 or Listeria-TT+GEM alone. Of clinical interest, high doses of anti-PD1 (PD1H) (when added to Listeria-TT+GEM) were less effective than the low doses (PD1L). IHC and ELISPOT demonstrated that high doses of anti-PD1 inhibited T cell function in the TME. Using RNAseq, Differentially Expressed Genes (DEG) analysis and Genes Set Enrichment Analysis (GSEA) showed that gene expression levels and biological pathways were predominantly upregulated in the PD1H compared to the PD1L group, in correlation with low immune infiltration in tumors, more immune suppression, and more aggressive ovarian cancer. In summary, this study suggests that our approach may benefit ovarian cancer patients. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/561944v3_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@197942eorg.highwire.dtl.DTLVardef@8180d4org.highwire.dtl.DTLVardef@3113e6org.highwire.dtl.DTLVardef@116851_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Human concept: Childhood vaccinations with the highly immunogenic tetanus toxoid (TT) generate TT-specific memory T cells, which circulate in the blood for life. After appearance of ovarian cancer (late in life), the patients will receive one high dose with Listeria-TT to deliver TT into tumor cells, followed by multiple low doses of Listeria-TT over a period of 2 weeks to restimulate the pre-existing memory T cells to TT. MDSC are involved in the delivery of Listeria-TT to the TME. Reactivated memory T cells will in turn destroy the tumor cells expressing TT. Multiple low doses of GEM will be added after TT has been delivered at the tumor site, which reduce immune suppression by eliminating MDSC and TAM (not shown here). Since individuals have seen TT earlier in life (during childhood vaccinations) and since TT is highly immunogenic (attracting T cells to the TME) but not expressed in normal cells, TT functions here as a vaccine recall antigen and as a neoantigen surrogate, respectively. C_FIG

cancer biology↗
bioRxiv2023

Assessing the impact of binary land cover variables on species distribution models: A North Americanstudy on water birds

AimSpecies distribution models (SDMs) are an important tool for predicting species occurrences in geographic space and for understanding the drivers of these occurrences. An effect of environmental variable selection on SDM outcomes has been noted, but how the treatment of variables influences models, including model performance and predicted range area, remains largely unclear. For example, although landcover variables included in SDMs in the form of proportions, or relative cover, recent findings suggest that for species associated with uncommon habitats the simple presence or absence of a landcover feature is most informative. Here we investigate the generality of this hypothesis and determine which representation of environmental features produces the best-performing models and how this affects range area estimates. Finally, we document how outcomes are modulated by spatial grain size, which is known to influence model performance and estimated range area. LocationNorth America MethodsWe fit species distribution models (via Random Forest) for 57 water bird species using proportional and binary estimates of water cover in a grid cell using occurrence data from the eBird citizen science initiative. We evaluated four different thresholds of feature prevalence (land cover representations) within the cell (1%, 10%, 20% or 50%) and fit models across both breeding and non-breeding seasons and multiple grain sizes (1, 5, 10, and 50 km cell lengths). ResultsModel performance was not significantly affected by the type of land cover representation. However, when the models were fitted using binary variables, the model-assessed importance of water bodies significantly decreased, especially at coarse grain sizes. In this binary variable-case, models relied more on other land cover variables, and over-or under-predicted the species range by 5-30%. In some cases, differences up to 70% in predicted species ranges were observed. Main conclusionsMethods for summarizing landcover features are often an afterthought in species distribution modelling. Inaccurate range areas resulting from treatment of landcover features as binary or proportional could lead to the prioritization of conservation efforts in areas where the species do not occur or cause the importance of crucial habitats to be missed. Importantly, our results suggest that at finer grain sizes, binary variables might be more useful for accurately measuring species distributions. For studies using relatively coarse grain sizes, we recommend fitting models with proportional land cover variables.

ecology↗