ROLE OF STAT1 AND CXCL10 IN THE TUMOUR IMMUNE MICROENVIRONMENT OF HIGH-GRADE SEROUS OVARIAN CANCER

High-grade serous ovarian cancer (HGSC) is the most prevalent epithelial ovarian cancer characterized by late detection, metastasis and resistance to chemotherapy. Previous studies on the tumour immune microenvironment in HGSC identified STAT1 and CXCL10 as the most differentially expressed genes between treatment naïve chemotherapy resistant and sensitive tumours. Interferon-induced STAT1 is a transcription factor, which induces many genes including tumour suppressor genes and those involved in recruitment of immune cells to the tumour immune microenvironment (TME), including CXCL10. CXCL10 is a chemokine that recruits tumour infiltrating lymphocytes (TILs) and exhibits angiostatic function. The current study was performed to determine the effects of differential STAT1 and CXCL10 expression on HGSC disease progression and TME. STAT1 expression and intratumoural CD8+ T cells were evaluated as prognostic and predictive biomarkers via immunohistochemistry on 734 HGSC tumours accrued from the Terry Fox Research Institute-Canadian Ovarian Experimental Unified Resource. The combined effect of STAT1 expression and CD8+ TIL density was confirmed as prognostic and predictive companion biomarkers in the second independent biomarker validation study. Significant positive correlation between STAT1 expression and intratumoral CD8+ TIL density was observed. The effects of enforced CXCL10 expression on HGSC tumour growth, vasculature and immune tumour microenvironment were studied in the ID8 mouse ovarian cancer cell engraftment in immunocompetent C57BL/6 mice. Significant decrease in tumour progression in mice injected with ID8 CXCL10 overexpressing cells compared to mice injected with ID8 vector control cells was observed. Multiplexed cytokine analysis of ascites showed differential expression of IL-6, VEGF and CXCL9 between the two groups. Endothelial cell marker staining showed differences in tumour vasculature between the two groups. Immune transcriptomic profiling identified distinct expression profiles in genes associated with cytokines, chemokines, interferons, T cell function and apoptosis between the two groups. These findings provide evidence that STAT1 is an independent biomarker and in combination with CD8+ TIL density could be applied as novel immune-based biomarkers in HGSC. These results provide the basis for future studies aimed at understanding mechanisms underlying differential tumour STAT1 and CXCL10 expression and its role in pre-existing tumour immunologic diversity, thus potentially contributing to biomarker guided immune modulatory therapies.

Unraveling the function of bacterial-type phosphoenolpyruvate carboxylase in vascular plants

Two distinct phosphoenolpyruvate carboxylase (PEPC) isozymes occur in vascular plants and green algae: plant-type PEPC (PTPC) and bacterial-type PEPC (BTPC). PTPC polypeptides typically form a tightly regulated cytosolic Class-1 PEPC homotetramer. BTPCs, however, appear to be less widely expressed and to exist only as catalytic and regulatory subunits that physically interact with co-expressed PTPC subunits to form hetero-octameric Class-2 PEPC complexes that are highly desensitized to Class-1 PEPC allosteric effectors. Yeast two-hybrid studies indicated that castor plant BTPC (RcPPC4) interacts with all three Arabidopsis thaliana PTPC isozymes, and that it forms stronger interactions with AtPPC2 and AtPPC3, suggesting that specific PTPCs are preferred for Class-2 PEPC formation. In contrast, Arabidopsis BTPC (AtPPC4) appeared to interact very weakly with AtPPC2 and AtPPC3, suggesting that BTPCs from different species may have different physical properties, hypothesized to be due to sequence dissimilarities within their ~10 kDa intrinsically disordered region. Recent RNA-seq and microarray data were analyzed to obtain a better understanding of BTPC expression patterns in different tissues of various monocot and dicot species. High levels of BTPC transcripts, polypeptides and Class-2 PEPC complexes were originally discovered in developing castor seeds, but the analysis revealed a broad range of diverse tissues where abundant BTPC transcripts are also expressed, such as the developing fruits of cucumber, grape, and tomato. Marked BTPC expression correlated well with the presence of ~116 kDa immunoreactive BTPC polypeptides, as well as Class-2 PEPC complexes in the immature fruit of cucumbers and tomatoes. It is therefore hypothesized that in vascular plants BTPC and thus Class-2 PEPC complexes maintain anaplerotic PEP flux in tissues with elevated malate levels that would potently inhibit ‘housekeeping’ Class-1 PEPCs. Elevated levels of malate can be used by biosynthetically active sink tissues such as immature tomatoes and cucumbers for rapid cell expansion, drought or salt stressed roots for osmoregulation, and developing seeds and pollen as a precursor for storage lipid and protein biosynthesis.

An R-package for the Estimation and Testing of Multiple Covariates and Biomarker Interactions for Survival Data Based on Local Partial Likelihood

When we study the variables that a ffect survival time, we usually estimate their eff ects by the Cox regression model. In biomedical research, e ffects of the covariates are often modi ed by a biomarker variable. This leads to covariates-biomarker interactions. Here biomarker is an objective measurement of the patient characteristics at baseline. Liu et al. (2015) has built up a local partial likelihood bootstrap model to estimate and test this interaction e ffect of covariates and biomarker, but the R code developed by Liu et al. (2015) can only handle one variable and one interaction term and can not t the model with adjustment to nuisance variables. In this project, we expand the model to allow adjustment to nuisance variables, expand the R code to take any chosen interaction terms, and we set up many parameters for users to customize their research. We also build up an R package called "lplb" to integrate the complex computations into a simple interface. We conduct numerical simulation to show that the new method has excellent fi nite sample properties under both the null and alternative hypothesis. We also applied the method to analyze data from a prostate cancer clinical trial with acid phosphatase (AP) biomarker.