Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity
AuthorsFranco, N. R.
Massi, M. C.
Paganoni, A. M.
Talbot, C. J.
De Meerleer, G.
de Ruysscher, D.
Van Limbergen, E.
Elliott, Rebecca M
Veldwijk, M. R.
Noris Chiorda, B.
Farcy-Jacquet, M. P.
Rosenstein, B. S.
Stock, R. G.
Aguado-Barrera, M. E.
Dunning, A. M.
Kerns, S. L.
West, Catharine M L
AffiliationMOX, Department of Mathematics, Politecnico di Milano, Italy.
MetadataShow full item record
AbstractAim: 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.
CitationFranco NR, Massi MC, Ieva F, Manzoni A, Paganoni AM, Zunino P, et al. Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity. Radiotherapy and Oncology. 2021 Jun;159:241–8.
JournalRadiotherapy and Oncology
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- Issue date: 2020
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- Authors: Cella L, D'Avino V, Liuzzi R, Conson M, Doria F, Faiella A, Loffredo F, Salvatore M, Pacelli R
- Issue date: 2013 Sep 23
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- Authors: Kerns SL, Dorling L, Fachal L, Bentzen S, Pharoah PD, Barnes DR, Gómez-Caamaño A, Carballo AM, Dearnaley DP, Peleteiro P, Gulliford SL, Hall E, Michailidou K, Carracedo Á, Sia M, Stock R, Stone NN, Sydes MR, Tyrer JP, Ahmed S, Parliament M, Ostrer H, Rosenstein BS, Vega A, Burnet NG, Dunning AM, Barnett GC, West CM, Radiogenomics Consortium.
- Issue date: 2016 Aug
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- Issue date: 2014 Jul
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- Authors: Ospina JD, Zhu J, Chira C, Bossi A, Delobel JB, Beckendorf V, Dubray B, Lagrange JL, Correa JC, Simon A, Acosta O, de Crevoisier R
- Issue date: 2014 Aug 1