Faculty Collaboration Database

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity. Radiother Oncol 2021 Jun;159:241-248

Date

04/11/2021

Pubmed ID

33838170

Pubmed Central ID

PMC8754257

DOI

10.1016/j.radonc.2021.03.024

Scopus ID

2-s2.0-85104332700 (requires institutional sign-in at Scopus site)   9 Citations

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.

Author List

Franco NR, Massi MC, Ieva F, Manzoni A, Paganoni AM, Zunino P, Veldeman L, Ost P, Fonteyne V, Talbot CJ, Rattay T, Webb A, Johnson K, Lambrecht M, Haustermans K, De Meerleer G, de Ruysscher D, Vanneste B, Van Limbergen E, Choudhury A, Elliott RM, Sperk E, Veldwijk MR, Herskind C, Avuzzi B, Noris Chiorda B, Valdagni R, Azria D, Farcy-Jacquet MP, Brengues M, Rosenstein BS, Stock RG, Vega A, Aguado-Barrera ME, Sosa-Fajardo P, Dunning AM, Fachal L, Kerns SL, Payne D, Chang-Claude J, Seibold P, West CML, Rancati T, REQUITE Consortium Collaborators

Author

Sarah L. Kerns PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Area Under Curve
Humans
Male
Polymorphism, Single Nucleotide
Prostatic Neoplasms
Radiation Injuries
Risk Factors