Transcriptomic analysis of nasal swab for the early detection of lung Cancer

 CDD · Thèse  · 36 mois    Bac+5 / Master   Insttitut Curie / Mines Paris · Paris (France)

 Date de prise de poste : 3 octobre 2022

Mots-Clés

Transcriptomic Lung Cancer Early Detection

Description

Context:
Despite substantial decrease in the prevalence of cigarette smoking worldwide during the last 30 years,
lung cancer remains a major cause of death in current and former smokers. Lung cancer remains
particularly deadly because it is often diagnosed at late stages, when clinical interventions are less
effective, because current diagnostic tools are invasive (e.g. bronchoscopy) or cost-intensive (e.g. low-
dose CT screening). Hence, there is an immediate need for non-invasive, cost-effective tests for risk
stratification of current and ex smokers. Based on the observation that cigarette smoke creates a field of
injury throughout the airway epithelium, causing similar damages in upper and lower airway tissues,
several studies have been using transcriptomic measurement from nasal swabs to analyse personal
smoke injury response and assess personalised lung cancer risk [1]. However, those proof-of-concept
studies have been conducted on small cohorts and suffer from reproducibility issues [2]. They also display
limited power, especially in ex-smokers. Finally, those classifiers solely assess the risk of patients at
sampling time. Studying how risk prediction transfers into the patient's future risk would be extremely
valuable to design testing strategies in a screening setting. The overarching aim of this project is to
improve early detection classifiers’ reproducibility, classification power, and to study the predictive
potential of the classifiers. Those 3 questions will constitute the 3 research objectives of our project. To
do that, the hired student will study a novel cohort containing an extra 150 patients that has recently
been assembled. In addition, he/she will develop statistical methods and bio informatics tools to
improve the reproducibilty of the classification method.

[1] AEGIS Study Team. Shared Gene Expression Alterations in Nasal and Bronchial Epithelium for Lung Cancer Detection. J Natl Cancer Inst. 2017;109. doi:10.1093/jnci/djw327

[2] Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk, Maria Stella de Biase, Florian Massip, et al; medRXiv, doi: https://doi.org/10.1101/2021.11.24.21266740


Funding:
The PhD is fully funded for a period of 3 years, as part of the funding of the Carnot Institut. Starting
date should be between September and December 2022.

Preferred Experience/Educational Background:
• Master degree second year (MS2 or equivalent Engineering degree) in Bioinformatics
or applied statistics.
• Experience in programming (R or Python)
• Personal qualities: autonomy, good communication skills (English).


Host Laboratory & supervision
The PhD will be supervised by Florian Massip at the Center for Computational Biology (CBIO) in
Paris. The CBIO is co hosted by Mines Paris (a top french ingeneer school) and the Institut Curie, and
is part of the U900 unit. This project is part of an international collaboration with Roland Schwarz
(Center for Integrative Oncology, Cologne) as well as Bruce Ponder (Cambridge University) and
Robert Rintoul (Papworth Royal Hospital, Cambridge).

Candidature

Procédure : Interested candidates should send a CV and a cover letter to Florian Massip (florian.massip@gmail.com)

Date limite : 1 décembre 2022

Contacts

Florian Massip

 flNOSPAMorian.massip@mines-paristech.f

Offre publiée le 18 juillet 2022, affichage jusqu'au 1 décembre 2022