Endometrial cancer is the most common gynaecological malignancy in Western populations, with nearly 100,000 cases each year in Europe alone1. Its prognosis is generally favourable, however our inability to estimate recurrence risk after surgery means that many women receive toxic therapies they did not need2. Furthermore, the limited efficacy of these therapies means that a substantial fraction of women with localised disease suffer incurable relapse. Recent work by our group and others demonstrates that defining the interplay between the tumour (epi)genome and the immune response holds promise to address these deficiencies. For example, endometrial cancers with POLE exonuclease domain mutations display ultramutation, a potent cytotoxic T cell response and excellent prognosis3-7. However, these account for only 10% of cases, and similar insights into the remaining 90% are required to improve care for this disease. The work in this proposal aims to deliver these by mechanistic analysis of genetically defined preclinical models and large-scale validation in human tumour samples.
WP1. To generate preclinical models representative of endometrial cancer molecular subgroups to define the anti-tumour immune response though tumour initiation and progression.
WP2. To contribute to a comprehensive immunogenetic analysis of human endometrial cancer using the Genomics England cohort
WP2. To intersect the data from WP1 and WP2 to define the key mechanisms underpinning immunogenicity and immune escape in endometrial cancer for clinical investigation
WP1. This will complement preclinical models of PTEN and TP53 driven endometrial cancer established in our laboratory with novel models of DNA mismatch repair deficiency and POLE mutation – collectively corresponding to the four principal endometrial cancer molecular subgroups. Tumorigenesis will monitored by non-invasive imaging with analysis at defined timepoints (pre-cancer, invasive malignancy). Tumour genomic architecture will be determined by whole exome sequencing and RNAseq. Type, density, location and clonality of tumour-infiltrating immune cells will be delineated at single cell level by high-dimensional methods, including in-situ analysis (e.g. GE CellDive/iCyTOF, TCR sequencing). These datasets will be intersected to identify the predictors of immune response and escape (neoantigen prediction and validation, HLA and antigen presentation pathway mutations, etc.) and their variation by endometrial cancer subtype.
WP 2. Whole genome sequencing (WGS) data from Genomics England endometrial cancers (currently available for 700 of a planned 750 cases; lead Church), will be complemented by detailed immunophenotyping of formalin fixed, paraffin embedded tumour blocks (e.g. Polaris Vectra). Immune cell density and localisation will quantified by automated machine learning-based image analysis and correlated with the immunogenetic data (analysis underway).
WP3. Data from WP1 & 2 will be intersected to identify commonalities and exclusivities for further study. Causality of candidate factors shaping the antitumour immune response and tumour progression will be confirmed by functional analysis. Potential examples include genetic deletion of candidate immune escape mutation (e.g. B2M) or depletion of critical immune cell population (e.g. CD8+ cells).
Identification of genetic and other determinants of the antitumour immune response in endometrial cancer
Prioritisation of these factors for biomarker investigation and therapeutic targeting
Proficiency in the design and detailed phenotyping of preclinical models using multimodal approaches
Experience in leading large-scale analysis
Bioinformatic analysis of genomic and immunologic datasets
Project reference number: 1081
|David Church||Wellcome Trust Centre for Human Genetics||Oxford University, Henry Wellcome Building of Genomic Medicine||GBRemail@example.com|
|Rachael Bashford-Rogers||Wellcome Trust Centre for Human Genetics||Oxford University, Henry Wellcome Building of Genomic Medicine||GBRfirstname.lastname@example.org|
There are no publications listed for this DPhil project.