Tissue factor-positive monocytes in children with sickle cell disease: correlation with biomarkers of haemolysis

Tissue Factor (TF) initiates thrombin generation, and whole blood TF (WBTF) is elevated in sickle cell disease (SCD). We sought to identify the presence of TF-positive monocytes in SCD and their relationship with the other coagulation markers including WBTF, microparticle-associated TF, thrombin-antithrombin (TAT) complexes and D-dimer. Whether major SCD-related pathobiological processes, including haemolysis, inflammation and endothelial activation, contribute to the coagulation abnormalities was also studied. The cohort comprised children with SCD (18 HbSS, 12 HbSC, mean age 3.6 years). We demonstrated elevated levels of TF-positive monocytes in HbSS, which correlated with WBTF, TAT and D-Dimer (p=0.02 to p=0.0003). While TF-positive monocytes, WBTF, TAT and D-dimer correlated with several biomarkers of haemolysis, inflammation and endothelial activation in univariate analyses, in multiple regression models the haemolytic markers (reticulocytes and lactate dehydrogenase) contributed exclusively to the association with all four coagulant markers evaluated. The demonstration that haemolysis is the predominant operative pathology in the associated perturbations of coagulation in HbSS at a young age provides additional evidence for the early use of therapeutic agents, such as hydroxycarbamide to reduce the haemolytic component of this disease

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Genetic Architecture of Group A Streptococcal Necrotizing Soft Tissue Infections in the Mouse

<div><p>Host genetic variations play an important role in several pathogenic diseases, and we have previously provided strong evidences that these genetic variations contribute significantly to differences in susceptibility and clinical outcomes of invasive Group A <i>Streptococcus</i> (GAS) infections, including sepsis and necrotizing soft tissue infections (NSTIs). Our initial studies with conventional mouse strains revealed that host genetic variations and sex differences play an important role in orchestrating the severity, susceptibility and outcomes of NSTIs. To understand the complex genetic architecture of NSTIs, we utilized an unbiased, forward systems genetics approach in an advanced recombinant inbred (ARI) panel of mouse strains (BXD). Through this approach, we uncovered interactions between host genetics, and other non-genetic cofactors including sex, age and body weight in determining susceptibility to NSTIs. We mapped three NSTIs-associated phenotypic traits (i.e., survival, percent weight change, and lesion size) to underlying host genetic variations by using the WebQTL tool, and identified four NSTIs-associated quantitative genetic loci (QTL) for survival on mouse chromosome (Chr) 2, for weight change on Chr 7, and for lesion size on Chr 6 and 18 respectively. These QTL harbor several polymorphic genes. Identification of multiple QTL highlighted the complexity of the host-pathogen interactions involved in NSTI pathogenesis. We then analyzed and rank-ordered host candidate genes in these QTL by using the QTLminer tool and then developed a list of 375 candidate genes on the basis of annotation data and biological relevance to NSTIs. Further differential expression analyses revealed 125 genes to be significantly differentially regulated in susceptible strains compared to their uninfected controls. Several of these genes are involved in innate immunity, inflammatory response, cell growth, development and proliferation, and apoptosis. Additional network analyses using ingenuity pathway analysis (IPA) of these 125 genes revealed interleukin-1 beta network as key network involved in modulating the differential susceptibility to GAS NSTIs.</p></div

QTL mapping and haplotype analysis for survival against GAS NSTIs.

<p>(A) Genome-wide interval mapping of survival data (expressed as cRSI across BXD and parental strains) reveal a significant QTL on mouse Chr 2 (brown arrow). Red and gray horizontal lines indicate significant and suggestive LRS thresholds, respectively. (B) Haplotype analysis of the QTL region between 24.5 and 35 Mb on mouse Chr 2 is shown. BXD strains were rank-ordered on the basis of their cRSI values from susceptible to more resistant. Red and green bars (within each loci/position) indicate D and B alleles, respectively, whereas blue bars indicate heterozygous alleles. BXD cohorts harboring either D (red arrows) or B (green arrows) haplotypes within the QTL region and their parental strains selected for <i>in silico</i> validation analyses are indicated.</p

GLM analysis of lesion size data.

<p>GLM analysis of lesion size data.</p

Differences in lesion size among BXD mice with GAS NSTIs.

<p>Lesion sizes in 33 BXD (black bars), B6 (green bar), D2 (red bar) and their F1 (blue bar) strains are expressed as mean values of corrected maximum lesion area. Data are rank-ordered with positive values indicating increased lesion sizes and negative values indicating reduced lesion size. Error bars indicate SEM. <i>P</i> values were calculated by GLM analysis using OLS ANOVA.</p

Differences in bacterial loads and dissemination between BXD mice harboring either B or D haplotypes within their survival QTL regions.

<p>Shown are the bacterial loads in (A) skin, (B) blood, and (C) spleen collected during the seven-day infection timeline, from surviving and dead mice of BXD strains harboring either a B (left) or D (right) haplotypes within their survival QTL regions. Similar data from their parental strains (B6 –green and D2 –red) are also shown. BXD mice are rank-ordered, within their groups, based on their bacterial counts. Each mouse is represented by a symbol with the horizontal bar representing the mean, and the horizontal dotted line indicating the inoculum given. Bacteremia counts for dead mice (cross symbols) for which blood collections were missed were assigned an arbitrary value of 10¹⁰ CFU/mL (a value near the maximum bacteremia count). Data presented are log-transformed bacterial loads (<i>n</i> ≥ 4 for each strain). <i>P</i> values were calculated by one-way ANOVA. (D) The PCA ggbiplot displays the first two principal components (PC1 and PC2) of three non-independent bacterial measurements. BXD strains harboring D and B haplotypes are indicated by red and green, respectively. The BXD strains are grouped together on the basis of their haplotypes.</p

Sex-based differences in survival of D2 and BXD mice with GAS NSTIs.

<p>Survival curves for the male and female mice of D2 and other BXD strains, subcutaneously infected with an equal dose of GAS bacteria, are shown. Data presented are percent survival (<i>n</i> ≥ 3 for each sex). <i>P</i> values were calculated by log-rank (Mantel-Cox) tests. *<i>P</i> < 0.05, **<i>P</i> < 0.01, ***<i>P</i> < 0.001, ****<i>P <</i> 0.0001.</p