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Multi-centre technical evaluation of the radiation-induced lymphocyte apoptosis assay as a predictive test for radiotherapy toxicity.

ABSTRACT: Predicting which patients will develop adverse reactions to radiotherapy is important for personalised treatment. Prediction will require an algorithm or nomogram combining clinical and biological data. The radiation-induced lymphocyte apoptosis (RILA) assay is the leading candidate as a biological predictor of radiotherapy toxicity. In this study we tested the potential of the assay for standardisation and use in multiple testing laboratories. The assay was standardised and reproducibility determined using samples from healthy volunteers assayed concurrently in three laboratories in Leicester (UK), Mannheim (Germany) and Montpellier (France). RILA assays were performed on samples taken prior to radiotherapy from 1319 cancer patients enrolled in the REQUITE project at multiple centres. The patients were being treated for breast (n?=?753), prostate (n?=?506) or lung (n?=?60) cancer. Inter-laboratory comparisons identified several factors affecting results: storage time, incubation periods and type of foetal calf serum. Following standardisation, there was no significant difference in results between the centres. Significant differences were seen in RILA scores between cancer types (prostate?>?breast?>?lung), by smoking status (non-smokers?>?smokers) and co-morbidity with rheumatoid arthritis (arthritics?>?non-arthritics). An analysis of acute radiotherapy toxicity showed as expected that RILA assay does not predict most end-points, but unexpectedly did predict acute breast pain. This result may elucidate the mechanism by which the RILA assay predicts late radiotherapy toxicity. The work shows clinical trials involving multiple laboratory measurement of the RILA assay are feasible and the need to account for tumour type and other variables when applying to predictive models.

SUBMITTER: Talbot CJ

PROVIDER: S-EPMC6610684 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Multi-centre technical evaluation of the radiation-induced lymphocyte apoptosis assay as a predictive test for radiotherapy toxicity.

Talbot Christopher J CJ Veldwijk Marlon R MR Azria David D Batini Chiara C Bierbaum Miriam M Brengues Muriel M Chang-Claude Jenny J Johnson Kerstie K Keller Anke A Smith Sheila S Sperk Elena E Symonds R Paul RP Wenz Frederik F West Catharine M L CML Herskind Carsten C Bourgier Celine C

Clinical and translational radiation oncology 20190610

Predicting which patients will develop adverse reactions to radiotherapy is important for personalised treatment. Prediction will require an algorithm or nomogram combining clinical and biological data. The radiation-induced lymphocyte apoptosis (RILA) assay is the leading candidate as a biological predictor of radiotherapy toxicity. In this study we tested the potential of the assay for standardisation and use in multiple testing laboratories. The assay was standardised and reproducibility dete ...[more]

PMID: 31341970

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Project description:BackgroundA predictive assay for late radiation toxicity would allow more personalized treatment planning, reducing the burden of toxicity for the more sensitive minority, and improving the therapeutic index for the majority. In a previous study in prostate cancer patients, the γ-H2AX foci decay ratio (γ-FDR) was the strongest predictor of late radiation toxicity. The current study aimed to validate this finding in a more varied group of patients with pelvic cancer. Additionally, the potential correlation between the γ-FDR and patient-reported outcomes was investigated.MethodsProstate and gynecological cancer patients with ≥ 24 months of follow-up were included in the current analysis. Toxicity was evaluated by physician (CTCAE version 4) and patient (EORTC questionnaires). γ-FDRs were determined in ex vivo irradiated lymphocytes. Correlation between γ-FDR and toxicity was assessed using both linear and logistic regression analyses. The highest toxicity grade recorded during follow-up was used. The association between global quality of life and γ-FDR was tested by comparing the change in quality of life over time in patients with γ-FDR < or ≥ 3.41, a previously established threshold.ResultsEighty-eight patients were included. Physician-assessed and patient-reported cumulative grade ≥ 2 toxicity was 25% and 29%, respectively; which is much lower than in the previous cohort (i.e., 51% CTCAE grade ≥ 2). Patients with toxicity exhibited less favorable dose-volume parameters. In men, these parameters showed significant improvement compared to the previous cohort. The proportion of patients with a low γ-FDR increased with severity of toxicity, but this trend was not statistically significant. In addition, a γ-FDR < 3.41 was not correlated with the development of moderate to severe toxicity. Post-treatment decline in global quality of life was minimal, and similar for patients with γ-FDR < or ≥ 3.41.ConclusionsIn the present study, the γ-H2AX foci decay ratio could not be validated as a predictor of late radiation toxicity in patients with pelvic cancer. Improved radiotherapy techniques with smaller irradiated bladder and bowel volumes have probably resulted in less toxicities. Future studies on genetic markers of toxicity should be powered on these lower incidences. We further recommend taking persistency, next to severity, into consideration.

Project description:Monocentric cohorts suggested that radiation-induced CD8 T-lymphocyte apoptosis (RILA) can predict late toxicity after curative intent radiotherapy (RT). We assessed the role of RILA as a predictor of breast fibrosis (bf +) after adjuvant breast RT in a prospective multicenter trial.A total of 502 breast-cancer patients (pts) treated by conservative surgery and adjuvant RT were recruited at ten centers. RILA was assessed before RT by flow cytometry. Impact of RILA on bf + (primary endpoint) or relapse was assessed using a competing risk method. Receiver-operator characteristic (ROC) curve analyses were also performed in intention to treat. This study is registered with ClinicalTrials.gov, number NCT00893035 and final analyses are presented here.Four hundred and fifty-six pts (90.8%) were included in the final analysis. One hundred and eight pts (23.7%) received whole breast and node irradiation. A boost dose of 10-16 Gy was delivered in 449 pts (98.5%). Adjuvant hormonotherapy was administered to 349 pts (76.5%). With a median follow-up of 38.6 months, grade ? 2 bf + was observed in 64 pts (14%). A decreased incidence of grade ? 2 bf + was observed for increasing values of RILA (p = 0.012). No grade 3 bf + was observed for patients with RILA ? 12%. The area under the ROC curve was 0.62. For cut-off values of RILA ? 20% and < 12%, sensitivity and specificity were 80% and 34%, 56% and 67%, respectively. Negative predictive value for grade ? 2 bf + was equal to 91% for RILA ? 20% and positive predictive value was equal to 22% for RILA < 12% where the overall prevalence of grade ? 2 bf + was estimated at 14%. A significant decrease in the risk of grade ? 2 bf + was found if patients had no adjuvant hormonotherapy (sHR = 0.31, p = 0.007) and presented a RILA ? 12% (sHR = 0.45, p = 0.002).RILA significantly predicts the risk of breast fibrosis. This study validates the use of RILA as a rapid screening test before RT delivery and will change definitely our daily clinical practice in radiation oncology.The French National Cancer Institute (INCa) through the "Program Hospitalier de Recherche Clinique (PHRC)".

Project description:BackgroundThe machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP.MethodsA total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models.ResultsThe deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009).ConclusionBased on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.

Project description:BackgroundApproximately half of all patients with tumors need radiotherapy. Long-term survivors may suffer from late sequelae of the treatment. The existing radiotherapeutic techniques are being refined so that radiation can be applied more precisely, with the goal of limiting the radiation exposure of normal tissue and reducing late sequelae.MethodsThis review is based on the findings of a selective search in PubMed for publications on late sequelae of conventional percutaneous radiotherapy, January 2000 to May 2020. Late sequelae affecting the central nervous system, lungs, and heart and the development of second tumors are presented, and radiobiological mechanisms and the relevant technical and conceptual considerations are discussed.ResultsThe current standard of treatment involves the use of linear accelerators, intensity-modulated radiotherapy (IMRT), image-guided and respiratory-gated radiotherapy, and the integration of positron emission tomography combined with computed tomography (PET-CT) in radiation treatment planning. Cardiotoxicity has been reduced with regard to the risk of coronary heart disease after radiotherapy for Hodgkin's lymphoma (hazard ratio [HR] 0.44 [0.23; 0.85]). It was also found that the rate of radiation- induced pneumonitis dropped from 7.9% with conformal treatment to 3.5% with IMRT in a phase III lung cancer trial. It is hoped that neurocognitive functional impairment will be reduced by hippocampal avoidance in modern treatment planning: an initial phase III trial yielded a hazard ratio of 0.74 [0.58; 0.94]. It is estimated that 8% of second solid tumors in adults are induced by radiotherapy (3 additional tumors per 1000 patients at 10 years).ConclusionSpecial challenges for research in this field arise from the long latency of radiation sequelae and the need for largescale, well-documented patient collectives in order to discern dose-effect relationships, and take account of cofactors, when the overall number of events is small. It is hoped that further technical and conceptual advances will be made in the areas of adaptive radiotherapy, proton and heavy-ion therapy, and personalized therapy.

Project description:PurposeTo stratify patients and aid clinical decision making, we developed machine learning models to predict treatment failure and radiation-induced toxicities after radiotherapy (RT) in patients with hepatocellular carcinoma across institutions.Materials and methodsThe models were developed using linear and nonlinear algorithms, predicting survival, nonlocal failure, radiation-induced liver disease, and lymphopenia from baseline patient and treatment parameters. The models were trained on 207 patients from Massachusetts General Hospital. Performance was quantified using Harrell's c-index, area under the curve (AUC), and accuracy in high-risk populations. Models' structures were optimized in a nested cross-validation approach to prevent overfitting. A study analysis plan was registered before external validation using 143 patients from MD Anderson Cancer Center. Clinical utility was assessed using net-benefit analysis.ResultsThe survival model stratified high-risk versus low-risk patients well in the external validation cohort (c-index = 0.75), better than existing risk scores. Predictions of 1-year survival and nonlocal failure were excellent (external AUC = 0.74 and 0.80, respectively), especially in the high-risk group (accuracy > 90%). Cause-of-death analysis showed differential modes of treatment failure in these cohorts and indicated that these models could be used to stratify RT patients for liver-sparing treatment regimen or combination approaches with systemic agents. Predictions of liver disease and lymphopenia were good but less robust (external AUC = 0.68 and 0.7, respectively), suggesting the need for more comprehensive consideration of dosimetry and better predictive biomarkers. The liver disease model showed excellent accuracy in the high-risk group (92%) and revealed possible interactions of platelet count with initial liver function.ConclusionMachine learning approaches can provide reliable outcome predictions in patients with hepatocellular carcinoma after RT in diverse cohorts across institutions. The excellent performance, particularly in high-risk patients, suggests novel strategies for patient stratification and treatment selection.

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Multi-centre technical evaluation of the radiation-induced lymphocyte apoptosis assay as a predictive test for radiotherapy toxicity.

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Multi-centre technical evaluation of the radiation-induced lymphocyte apoptosis assay as a predictive test for radiotherapy toxicity.

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