Dataset Information

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

ABSTRACT: Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.

SUBMITTER: Massi MC

PROVIDER: S-EPMC7593843 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

Massi Michela Carlotta MC Gasperoni Francesca F Ieva Francesca F Paganoni Anna Maria AM Zunino Paolo P Manzoni Andrea A Franco Nicola Rares NR Veldeman Liv L Ost Piet P Fonteyne Valérie V Talbot Christopher J CJ Rattay Tim T Webb Adam A Symonds Paul R PR Johnson Kerstie K Lambrecht Maarten M Haustermans Karin K De Meerleer Gert G de Ruysscher Dirk D Vanneste Ben B Van Limbergen Evert E Choudhury Ananya A Elliott Rebecca M RM Sperk Elena E Herskind Carsten C Veldwijk Marlon R MR Avuzzi Barbara B Giandini Tommaso T Valdagni Riccardo R Cicchetti Alessandro A Azria David D Jacquet Marie-Pierre Farcy MF Rosenstein Barry S BS Stock Richard G RG Collado Kayla K Vega Ana A Aguado-Barrera Miguel Elías ME Calvo Patricia P Dunning Alison M AM Fachal Laura L Kerns Sarah L SL Payne Debbie D Chang-Claude Jenny J Seibold Petra P West Catharine M L CML Rancati Tiziana T

Frontiers in oncology 20201015

<b>Background:</b> REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk f ...[more]

PMID: 33178576

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Project description:BackgroundA total of 10%-20% of patients develop long-term toxicity following radiotherapy for prostate cancer. Identification of common genetic variants associated with susceptibility to radiotoxicity might improve risk prediction and inform functional mechanistic studies.MethodsWe conducted an individual patient data meta-analysis of six genome-wide association studies (n = 3871) in men of European ancestry who underwent radiotherapy for prostate cancer. Radiotoxicities (increased urinary frequency, decreased urinary stream, hematuria, rectal bleeding) were graded prospectively. We used grouped relative risk models to test associations with approximately 6 million genotyped or imputed variants (time to first grade 2 or higher toxicity event). Variants with two-sided Pmeta less than 5 × 10-8 were considered statistically significant. Bayesian false discovery probability provided an additional measure of confidence. Statistically significant variants were evaluated in three Japanese cohorts (n = 962). All statistical tests were two-sided.ResultsMeta-analysis of the European ancestry cohorts identified three genomic signals: single nucleotide polymorphism rs17055178 with rectal bleeding (Pmeta = 6.2 × 10-10), rs10969913 with decreased urinary stream (Pmeta = 2.9 × 10-10), and rs11122573 with hematuria (Pmeta = 1.8 × 10-8). Fine-scale mapping of these three regions was used to identify another independent signal (rs147121532) associated with hematuria (Pconditional = 4.7 × 10-6). Credible causal variants at these four signals lie in gene-regulatory regions, some modulating expression of nearby genes. Previously identified variants showed consistent associations (rs17599026 with increased urinary frequency, rs7720298 with decreased urinary stream, rs1801516 with overall toxicity) in new cohorts. rs10969913 and rs17599026 had similar effects in the photon-treated Japanese cohorts.ConclusionsThis study increases the understanding of the architecture of common genetic variants affecting radiotoxicity, points to novel radio-pathogenic mechanisms, and develops risk models for testing in clinical studies. Further multinational radiogenomics studies in larger cohorts are worthwhile.

Project description:Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3-98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.

Project description:Radiation-induced normal-tissue toxicities are common, complex, and distressing side effects that affect 90% of patients receiving breast-cancer radiotherapy and 40% of patients post radiotherapy. In this study, the authors investigated the use of spectrophotometry and ultrasound to quantitatively measure radiation-induced skin discoloration and subcutaneous-tissue fibrosis. The study's purpose is to determine whether skin discoloration correlates with the development of fibrosis in breast-cancer radiotherapy.Eighteen breast-cancer patients were enrolled in our initial study. All patients were previously treated with a standard course of radiation, and the median follow-up time was 22 months. The treated and untreated breasts were scanned with a spectrophotometer and an ultrasound. Two spectrophotometer parameters-melanin and erythema indices-were used to quantitatively assess skin discoloration. Two ultrasound parameters-skin thickness and Pearson coefficient of the hypodermis-were used to quantitatively assess severity of fibrosis. These measurements were correlated with clinical assessments (RTOG late morbidity scores).Significant measurement differences between the treated and contralateral breasts were observed among all patients: 27.3% mean increase in skin thickness (p < 0.001), 34.1% mean decrease in Pearson coefficient (p < 0.001), 27.3% mean increase in melanin (p < 0.001), and 22.6% mean increase in erythema (p < 0.001). All parameters except skin thickness correlated with RTOG scores. A moderate correlation exists between melanin and erythema; however, spectrophotometer parameters do not correlate with ultrasound parameters.Spectrophotometry and quantitative ultrasound are objective tools that assess radiation-induced tissue injury. Spectrophotometer parameters did not correlate with those of quantitative ultrasound suggesting that skin discoloration cannot be used as a marker for subcutaneous fibrosis. These tools may prove useful for the reduction of radiation morbidities and improvement of patient quality of life.

Dataset Information

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

Publications

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

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