Développement d'un algorithme permettant la prédiction des métastases à partir de mutations germinales et celles du clone fondateur chez des patients atteints du cancer

Authors: Milanese, Jean-Sébastien
Advisor: Droit, Arnaud
Abstract: With the constant progress in neext generation sequencing, the quantity of data available for investigation becomes massive. In parallel, cancer detection methods and treatments remain very specific and barely accurate. Moreover, the patients survival rate are directly linked with tumoral progression and therefore, to cancer detection methods. Despite continual technological advances in recent years, the global cancer mortality rate keeps rising. The creation of new detection methods accessible to all cancer types becomes a necessity. As of now, there is no model available that using sequencing data to predict cancer traits (ex: recurrence, resistance, etc.). The following sections demonstrate the creation of such model using somatic and germline mutations to predict recurrence and its applicability across all cancer types (and even across different diseases). By using gene signatures specific to each cancer types, we were able to obtain an accuracy of 90% (and more) for the cohort where the cancer was recurrent. To our knowledge, this is the first attempt to develop a model that can predict the patient’s prognosis using genome sequencing data. This will affect future studies and improve personalized medicine as well as cancer detection methods.
Document Type: Mémoire de maîtrise
Issue Date: 2018
Open Access Date: 24 April 2018
Permalink: http://hdl.handle.net/20.500.11794/28327
Grantor: Université Laval
Collection:Thèses et mémoires

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