Genetic and environmental factors involved in cancer risk and prognosis
Approved Research ID: 66591
Approval date: September 29th 2021
Although in the past decades several genetic factors (what we inherit from our parents and ancestors) have been discovered that affect risk of developing various cancers, or of surviving after a diagnosis of cancer, many more remain to be found. Environmental factors (including life-style habits, nutrition etc.) are well known to affect cancer risk as well. Additionally, the interactions between genetic, environmental and clinical factors are not fully clear.
Moreover, autoimmune diseases (such as rheumatoid arthritis or inflammatory bowel diseases) are common in the population and it has been proposed that having an autoimmune disease may influence the risk of developing cancer too, yet this relation has not been fully understood.
A better comprehension of the factors that affect the probability of getting a cancer, or of surviving after a cancer diagnosis, is paramount for novel and more effective approaches to cancer prevention, diagnosis and treatment.
The main goal of our study is to deeply investigate how genetic factors and environmental factors predict the risk of developing cancer, or to the survival of cancer patients.
It is well established that the best strategy to study something as complex as cancer susceptibility is to use a large-scale population-based study such as UK biobank. We will use UK Biobank data for the following steps:
* We will analyze all genetic variants in the human genome, as well as a large number of environmental variables, and will calculate the probability that each of them gives of developing the most frequent cancers, or of surviving after a cancer diagnosis.
* We will also combine multiple genetic and environmental variables into "risk scores" and estimate how well scores predict cancer risk or survival of cancer patients.
* We will study genetic, environmental variables and scores in relation to risk of developing the most frequent autoimmune diseases.
* We will investigate what genetic and/or environmental risk factors the most frequent cancers and the most frequent autoimmune diseases have in common. For example, we will calculate how much a score for risk of Crohn's disease predicts risk of colorectal cancer, etc.
* We will evaluate how risk scores, by themselves of in combination with biochemical markers (for example levels of various proteins measured in blood), can be used to improve early detection of cancers.
Our methods will be based on established approaches for analysis of genetic and environmental data, as well as novel approaches based on machine learning tools (e.g. neural networks).