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Epitranscriptomics & Cancer Adaptation : A.David

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Our research work focuses on the contribution of post-transcriptional mechanisms on cancer cell adaptation, in particular RNA epigenetic & translational control.

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Item Type Journal Article
Title Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity
Creator Franco et al.
Author Nicola Rares Franco
Author Michela Carlotta Massi
Author Francesca Ieva
Author Andrea Manzoni
Author Anna Maria Paganoni
Author Paolo Zunino
Author Liv Veldeman
Author Piet Ost
Author Valérie Fonteyne
Author Christopher J. Talbot
Author Tim Rattay
Author Adam Webb
Author Kerstie Johnson
Author Maarten Lambrecht
Author Karin Haustermans
Author Gert De Meerleer
Author Ben Vanneste
Author Evert Van Limbergen
Author Ananya Choudhury
Author Rebecca M. Elliott
Author Elena Sperk
Author Marlon R. Veldwijk
Author Carsten Herskind
Author Barbara Avuzzi
Author Barbara Noris Chiorda
Author Riccardo Valdagni
Author David Azria
Author Marie-Pierre Farcy-Jacquet
Author Muriel Brengues
Author Barry S. Rosenstein
Author Richard G. Stock
Author Ana Vega
Author Miguel E. Aguado-Barrera
Author Paloma Sosa-Fajardo
Author Alison M. Dunning
Author Laura Fachal
Author Sarah L. Kerns
Author Debbie Payne
Author Jenny Chang-Claude
Author Petra Seibold
Author Catharine M. L. West
Author Tiziana Rancati
Abstract AIM: To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). MATERIALS AND METHODS: Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2?years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in ?10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC). RESULTS: Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints. CONCLUSIONS: Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models.
Publication Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
Volume 159
Pages 241-248
Date 2021-06
Journal Abbr Radiother Oncol
Language eng
DOI 10.1016/j.radonc.2021.03.024
ISSN 1879-0887
Library Catalog PubMed
Extra PMID: 33838170 PMCID: PMC8754257
Tags Area Under Curve, Epistasis, Genetic risk factors, Humans, Late toxicity, Male, original, Polymorphism, Single Nucleotide, Prostate cancer, Prostatic Neoplasms, Radiation Injuries, Radiotherapy, Risk Factors, SNPs
Date Added 2023/11/23 - 12:48:35
Date Modified 2024/12/15 - 12:07:47
Notes and Attachments PubMed entry (Attachment)
Texte intégral (Attachment)


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