Map out specific gene signatures to predict for disease progression in HUMANS and utilising this as the basis to formulating different immunomodulation strategies
Objectives: Our approach here is to find prognostic genes for human disease progression and chart them towards pathways. Publically available data will be utilized to form a training set of algorithms for which the novelty would be to personalize prediction towards the specific patient sample. A technique known as t-SNE will be used to visualise high-dimensional datasets. The immune gene expression of mice will be compared with humans so whole blood analysis to identify gene profiles will be made on cancer (ovarian, breast and colorectal) patients at time intervals of 1h before surgery and 1h, 24h, 72h and 3 weeks after surgery. This will be equivalent to the time points in mice but we will use the bioinformatics to perform differential analysis to identify those genes that are of more relevance in humans. The gene expression data from our study will be correlated to similar profiles identified from databases such as The Cancer Genome Atlas (TCGA). The differences found will be mapped to known annotations and pathways and relevant immune gene profiles can be made into point-of-care assays. The analysis will be performed using control whole blood samples from non-cancer patients before and after surgery.

Our objectives are as follows:
2.1: Utilisation of preclinical data from WP1 and determine only those immune regulatory pathways that predict for disease progression in humans using t-SNE to visualise and then narrow down the high-dimensional datasets,
2.2: Design immune gene profiling assay panels that can predict for outcome and which can be used to monitor therapeutic efficacy.

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