Prediction of toxicity after prostate cancer RT: the value of a SNP-interaction polygenic risk score
Rancati, T. Massi, M. Franco, N. Avuzzi, B. Azria, D. Choudhury, Ananya Cicchetti, A. Dirk, D. R. Dunning, A. Elliott, Rebecca M
Rancati, T.
Massi, M.
Franco, N.
Avuzzi, B.
Azria, D.
Choudhury, Ananya
Cicchetti, A.
Dirk, D. R.
Dunning, A.
Elliott, Rebecca M
Citations
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Abstract
accounting for SNP-SNP interactions (PRSi) was developed; the PRSi shows which SNPs and alleles are included, whether they increase or decrease the risk of toxicity and their combined effect sizes. The added value of incorporating PRSi was evaluated (effects size of PRSi through Odds Ratios, AUC, p-value for increase in AUC). Internal validation was performed using bootstrapping (10000 resamples). Results pts had conventional fractionation (60-81 Gy), 25% received hypofractionation. 70% pts had VMAT, 12% static field IMRT, 18% 3DCRT. 30% had post-prostatectomy RT, 32% pelvic RT and 72% adjuvant/neo-adjuvant Conclusion
The present analysis showed for the first time the benefit of adding PRSi in toxicity risk prediction models. As
the PRSi included only 13 validated SNPs, the performance of PRSi and future toxicity risk prediction models
should be improved by adding additional SNPs from new discovery studies. Some common (with a frequency
≥10%) SNP-SNP interactions were found, including up to 13 alleles from 13 SNPs. hormone therapy. Toxicity rates were 11.7% (grade≥1 rectal bleeding), 4.2% (grade≥2 urinary frequency), 5.5% (grade≥1 haematuria) and 17.8% (grade≥2 nocturia) and 17.0% (grade≥1 decreased urinary stream). Details on models including PRSi are given in Fig 1, ROC curves in Fig 2b. Only 13/43 SNPs validated but some common SNP-SNP interactions were found (i.e. SNPs with a frequency ≥10% in the population of pts with toxicity) and included in PRSi. PRSi included combinations of 8-15 different SNP-allele sets. Adding PRSi improved AUC for all endpoints, ranging from an increase of 5% (p=0.014) for rectal bleeding to an increase of 19% for urinary frequency (p<0.0001).
Authors
Rancati, T.
Massi, M.
Franco, N.
Avuzzi, B.
Azria, D.
Choudhury, Ananya
Cicchetti, A.
Dirk, D. R.
Dunning, A.
Elliott, Rebecca M
Ieva, F.
Kerns, S.
Lambrecht, M.
Manzoni, A.
Paganoni, A.
Rosenstein, B.
Seibold, P.
Sperk, E.
Talbot, C.
Vega, A.
Veldeman, L.
Zunino, P.
Webb, A.
Chang-Claude, J.
West, Catharine M L
Massi, M.
Franco, N.
Avuzzi, B.
Azria, D.
Choudhury, Ananya
Cicchetti, A.
Dirk, D. R.
Dunning, A.
Elliott, Rebecca M
Ieva, F.
Kerns, S.
Lambrecht, M.
Manzoni, A.
Paganoni, A.
Rosenstein, B.
Seibold, P.
Sperk, E.
Talbot, C.
Vega, A.
Veldeman, L.
Zunino, P.
Webb, A.
Chang-Claude, J.
West, Catharine M L
Description
Date
2021
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Rancati T, Massi M, Franco N, Avuzzi B, Azria D, Choudhury A, et al. Prediction of toxicity after prostate cancer RT: the value of a SNP-interaction polygenic risk score. Radiotherapy and Oncology. 2021;161:S526-S8.