Exploration of genetic, environmental, and sex-specific risk factors to reduce colorectal cancer incidence and inequalities

Background

With significant disparities in incidence, progression, and outcomes, influenced by genetic, environmental, and sex-specific factors, colorectal cancer remains one of the leading causes of cancer related mortality. The overarching goal of this project is to investigate mechanisms underlying this inequality and translational impact, by integrating clinical data and exploratory model research data to identify actionable biomarkers and potential intervention strategies. 

This project will leverage large patient-derived datasets and data from our established mouse models of CRC risk genes identified through GWAS, TWAS and sequencing studies. These models recapitulate key disease phenotypes, including sexually dimorphic tumour formation.  The patient data (clinical meta, with access to tissue markers and spatial information) and model systems can be used to investigate the interplay between genetic predisposition, environmental/dietary/microbiome/immune factors, and sex in CRC initiation, progression and survival, providing a number of interesting strands that can be developed by the successful student.

The CRC risk eQTLS being explored are: 

• SHROOM2 (Xp22): role in cytoskeletal organization and intestinal epithelial integrity.
• FUT2 (19q13): role in mucosal glycosylation, immune regulation and microbiome composition.
• POU2AF2 (11q23): role in abundance of tuft cells in colonic epithelium and affects tumorigenesis and survival in a well-studied CRC mouse model (ApcMin/+).

This project directly addresses cancer inequalities by integrating diverse risk factors such as genetics, sex-specific traits, and environmental influences, with a mechanistic and translational framework. With the use of cutting-edge in vivo, ex vivo, data generation and computational approaches utilised in this project, this research aims to generate actionable insights for CRC risk stratification and targeted therapy strategies. 

Aims

(A) Understanding sex specific mechanisms in CRC disparities:

• Assess X-chromosome inactivation patterns and the contribution to sex-biased CRC incidence.
• Characterize sex-specific differences in tumour microenvironment (primary tumour and adjacent normal colonic epithelium), using markers and spatial transcriptomics.

(B) Dietary/environmental/microbiome/immune influences on CRC risk:

• Examine how dietary components, microbiome diversity, and immune interactions collectively shape CRC risk, identifying key modulators.
• Determine the impact of genetic perturbations on tumour morphology, immune infiltration, and epithelial integrity, exploring dietary and microbial.

 (C) Translating exploratory research into clinical insights:

• Utilize multiple colorectal cancer datasets, including INCISE adenoma cohort; UKBB; colorectal cancer GRI TMA cohort, colorectal cancer budding information, as well as emerging cohorts (Glasgow and Edinburgh) which include primary tumour, adjacent normal tissue, metastasis data, and therapy response information. This will allow for comprehensive analysis of CRC risk eQTL expression across different clinical contexts.
• Integrate CRC risk eQTL expression with survival outcomes, tumour morphology, and immune phenotypes to refine CRC risk models and subtyping.
• Profile eQTL expression across adenoma, Stages I-IV, metastasis, and normal colorectal epithelium to track molecular changes through the development of CRC.
• Develop AI-based image analysis pipelines to integrate morphological and molecular data, leveraging available datasets to validate predictive models and improve precision in CRC diagnosis.

Training outcomes

The successful candidate will receive specialized, comprehensive training in imaging/statistical and bioinformatic analysis (VRS/SMF/KP/PB). This will encompass intestinal phenotyping, protein analysis, morphological profiling via cell painting, and genomic data analysis (imaging, proteomics, transcriptomics), utilizing R, bioinformatics and AI methodologies (VRS/SMF/KP/PB). This multidisciplinary training approach will equip the candidate with the skills and expertise needed for CRC genetics research, facilitated by exposure to diverse perspectives and methodologies within the team. There are numerous broader training opportunities including regular presentation platforms and professional development through the Programme and University. Available courses cover many areas of training support such as good research practices, data management, and scientific communication. Specific modules include designing effective presentation slides conference poster design, how to be an effective researcher, practical project management; researchers and the media, research ethics and integrity, and thesis writing. 

References

1. Dunlop et al. 2012. Common variation near CDKN1A, POLD3 and SHROOM2 influences colorectal cancer risk. Nature Genetics 44: 770-6
2. Harris BT et al. 2022. Transcriptional dynamics of colorectal cancer risk associated variation at 11q23.1 are correlated with tuft cell abundance and marker expression in silico. Scientific Rep: 12:13609.
3. Alexander PG et al. 2022. The relationship between the Glasgow Microenvironment Score and markers of epithelial-mesenchymal transition in TNM II-III colorectal cancer. Hum Pathol.:127:1-11.doi: 10.1016/j.humpath.2022.05.012.
4. Fernandez-Rozadilla C et al. 2023. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries. Nat Genetics 55:89-99.
5. Rajasekaran V et al. 2025. Genetic variation at 11q23.1 confers colorectal cancer risk by dysregulation of colonic tuft cell transcriptional activator POU2AF2. Gut. gutjnl-2024-332121. doi.org/10.1136/gutjnl-2024-332121