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Title

A comprehensive multi-omic evaluation of neuroendocrine tumours and neuroendocrine carcinomas

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Lay summary

Context and Motivation

Neuroendocrine tumours (NETs) are a rare and complex type of cancer that can develop in various organs. They are difficult to diagnose and treat because their cell of origin is unknown, and their behaviour does not always match their appearance under a microscope. This inconsistency makes it challenging to predict how the disease will progress and respond to treatment. Additionally, there are limited treatment options available, and patients often respond differently to the same treatments. These factors make NETs a particularly challenging group of cancers.

Patients' Perspectives

Patients with NETs face uncertainty due to the unpredictable nature of their disease. The lack of clear diagnostic markers and effective treatments can lead to anxiety and a sense of helplessness. Understanding the biological underpinnings of NETs could provide more accurate diagnoses, better treatment options, and ultimately improve the quality of life for these patients.

Proposed Activities

Our research aims to thoroughly investigate the biological landscape of NETs by examining various aspects of the disease at different levels:

Genetic and Epigenetic Analysis: Studying the DNA and gene expression patterns to understand the genetic factors that contribute to NETs.

Transcriptomic and Proteomic Analysis: Investigating how genes are turned into proteins and how these proteins behave in NET cells.

Metabolomic and Microbiome Analysis: Exploring the chemical processes within NET cells and the role of gut bacteria in the disease.

We are conducting a study where patients with NETs will undergo these analyses at multiple points during their diagnosis and treatment. This will help us build a comprehensive picture of the disease.

Prospective Aims

Holistic Understanding and Biomarker Identification: By integrating various types of data, we aim to gain a complete understanding of NET biology. This could help identify new biomarkers (indicators of disease) and potential targets for treatment.

Validation with External Data: We plan to use data from Genomics England and the NHS Genomic Medicine Service to validate our findings. This will help us understand how NETs evolve and why some treatments fail.

Functional Characterisation of Variants: We will use laboratory models to study specific genetic changes and their roles in NET development and progression.

Immune Landscape Characterisation: We aim to understand how the immune system interacts with NET cells and identify genetic variants related to immune responses.

Predictive Mutational Signatures: We will investigate whether certain genetic patterns can predict complications of NETs and if these patterns are also present in people with similar complications but without NETs.

Comparing Well-Differentiated and Poorly Differentiated NETs: We will look for common genetic signatures in aggressive well-differentiated NETs and poorly differentiated neuroendocrine carcinomas.

Predictive modelling for prognosis and treatment: Using the data collected, we aim to develop a bioinformatic tool that can predict patient outcomes, response to treatments, and identify new treatment targets. This could lead to more personalised and effective treatments for NET patients. This will not involve machine learning or training machine learning models on Genomics England data. 

Conclusion

This research seeks to unravel the complexities of neuroendocrine tumours by leveraging advanced multi-omic analyses and bioinformatics. By understanding the disease at a deeper level, we hope to provide better diagnostic tools, treatment options, and improve the overall care for patients with NETs.

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