Approved Research

Sex-stratified investigation of the correlation/anti-correlation between neurodegeneration and cancer using systems bioinformatics, multi-source data integration and AI.

Lay summary

Aging has been associated with both neurodegeneration and cancer. Alzheimer's disease (AD) and Parkinson's disease (PD) are the two most prevalent neurodegenerative disorders in the human population. The relationship between cancer and neurodegeneration is complex and not fully understood. Some epidemiological/observational studies have found an inverse comorbidity for patients with AD/PD for certain cancers while other studies have reported an increased risk of neurodegeneration on a cancer background and vice versa. Exploring molecular, clinical and other clinical factors is crucial to understand the correlations between neurodegeneration and cancer. In an orthogonal view, sex dimorphism has been observed across both cancers and neurodegenerative diseases. AD is more prevalent in women whereas PD is more prevalent in men. Cancer types that are not sex-specific also exhibit sex-dimorphism in terms of their prevalence and severity. This project aims to develop a computational framework that exploits some of the most powerful computational methodologies such as network analytics, multi-source data integration and machine learning/deep learning methods to identify the correlation between selected neurodegenerative diseases and several cancer types at various levels of data integration and under the prism of sex stratification. This effort will generate unique information regarding sex-specific related pathways and candidate repurposed drugs against neurodegeneration exploiting the knowledge from the existing wealth of cancer data, mechanisms and drugs, based on the neurodegeneration/cancer association routes that we are going to investigate.

The duration of the project will be up to 36 months.