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PhD student on genetic and environmental prediction of lung cancer

Improve lung cancer screening by developing and adding genetic and environmental risk factors.
  • Research & Education
  • 36 hours
  • Pulmonary Diseases

  • Closing date 08-12-2021
  • 47.25.21.TT

Job description

Lung cancer is the leading cause of cancer-related death worldwide, for which the five-year survival rates have yet to surpass 20%. Tobacco smoking remains the main risk factor for lung cancer. Although there is a decreasing prevalence of smokers in most countries, tobacco control is not the only measure for decreasing lung cancer mortality. In 2011, the National Lung Screening Trial (NLST) was the first multicenter randomized controlled trial (RCT) to demonstrate that three rounds of annual screening of a high-risk population using low-dose chest computed tomography (CT) lead to 20% fewer lung cancer deaths after seven years of follow-up, compared to annual screening with chest radiography. Over 53,000 participants were included in this landmark study. The Dutch-Belgian NELSON trial – the second largest RCT with 15,789 participants – recently published their results and showed a 24% mortality reduction in a high-risk population of men compared to no screening (De Koning et al. NEJM 2020).

Problem description
If a population were invited for screening based on age and long-term smoking behaviour (like in NELSON), many individuals would not benefit. Many screenees have a low risk of lung cancer despite their smoking history (in NELSON <4% developed lung cancer), while some screenees have insufficient life expectancy to benefit from screening. Secondly, screen-detected lung nodules lead to extra investigations in 25-30% of screenees while most are benign. Thus, there is a critical need for stricter selection of screenees who will benefit from screening, and for improved nodule stratification.

Research direction
We offer a fully-funded PhD position that focuses on improving the efficiency of lung cancer screening by adding genetic and environmental risk factors to the selection of screenees. This position is a PhD position within a larger consortium project: NELSON-POP. In this consortium, the unique expertise and data from the different NELSON sites and associated research groups are combined to leverage various unexplored data sources, in order to identify the factors most predictive of lung cancer. Using multi-source data, the consortium aims to maximize lung cancer screening efficiency, by developing prediction models to 1) optimize screenee selection, and 2) limit unnecessary nodule work-up.

This PhD position focuses on the development and inclusion of genetic and environmental risk factor models for lung cancer screening. Hundreds of genetic variants have been shown to influence risk of lung cancer, and can be used to evaluate a person’s genetic susceptibility by so-called polygenic risk scores. A person’s exposome captures exposures to environmental factors, such as air pollution, which further alters risk of developing lung cancer. Measuring these risks before starting lung cancer screening can help identify the individuals which will benefit most from screening, and will aid in optimizing the screening procedures. Therefore, the aim of this PhD project is to accurately determine the probability of lung cancer using genetic and environmental risk models, in order to reduce unnecessary screening and optimize the screening procedures in early detection and mortality prevention of lung cancer. You will develop a polygenic risk model for lung cancer in line with previous such efforts on breast cancer and asthma performed at Erasmus MC. You will also develop exposome models using existing data on air pollution collected by the consortium. Finally, you will validate the accuracy of the models on the NELSON cohort and investigate how these can be used to develop novel optimized screening guidelines

Work environment

You will work in close collaboration with a multi-disciplinary team of PhD candidates, consisting of geneticists, epidemiologists and basic researchers. Your daily supervision will be performed by dr. Ralph Stadhouders (Pulmonary Medicine Department) and dr. Jeroen van Rooij (Internal Medicine Department). Your PhD supervision team will also involve experts from the other consortium members, to contribute to the synergy necessary to reach the goals of the consortium. The research should result in a Ph.D. thesis.

Qualifications and skills

  • You should be a creative and enthusiastic researcher with a MSc in a relevant field, such as Epidemiology, Medicine, Biomedical Sciences or similar.
  • Excellent proficiency in English (both spoken and written) is required.
  • You must be able to work independently and communicate clearly.
  • Experiences in statistical analyses and/or R scripting is recommended.
  • Interest in decision-modeling and/or working with large datasets is preferred.

Before you apply please check our conditions for employment.

Terms of employment

  • You will receive a temporary position for 4 years.
  • The gross monthly salary is € 2.495 ,- in the 1st year and increases to € 3.196,- in the 4th year (scale OIO).
  • Excellent fringe benefits, such as a 13th month that is already paid out in November and an individual travel expense package.
  • Pension insurance with ABP. We take care of approximately 2/3 of the monthly contribution.
  • Special benefits, such as an in-company physiotherapist and bicycle repairer. And there is also a gym where you can work on your fitness after work.

Information and application

For more information about this position, please contact Ralph Stadhouders, Assistant Professor, phone number: +31(0)10 704 36 97 or e-mail:

No agencies please.

Erasmus MC

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Erasmus MC is an international leading academic hospital in Rotterdam. We are recognized as a world-class scientific research organization aiming to improve our understanding of diseases and disorders and helps to predict, treat and prevent them. We have access to the latest equipment and techniques. A working environment that gets the best out of people.

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