The REQUITE project: validating predictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve quality of life in cancer survivors

A study demonstrating users' preference for the adapted-REQUITE patient-reported outcome questionnaire over PRO-CTCAE® in patients with lung cancer

Introduction

The use of patient-reported outcomes (PROs) has been shown to enhance the accuracy of symptom collection and improve overall survival and quality of life. This is the first study comparing concordance and patient preference for two PRO tools: Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE®) and the adapted-REQUITE Lung Questionnaire.

Materials and Methods

Patients with lung cancer were recruited to the study while attending outpatient clinics at a tertiary cancer centre. Clinician-reported outcomes were generated through initial patient assessment with CTCAE v4.03. Participants then completed the PRO-CTCAE® and adapted-REQUITE questionnaires. Concordance between the 2 questionnaires was assessed by calculating Pearson correlation coefficient. PRO-CTCAE® and CTCAE concordance was demonstrated by calculating Pearson correlation coefficient from the linear predictors of an ordinal logistic regression. P-values were also calculated.

Results

Out of 74 patients approached, 65 provided written informed consent to participate in the study. 63 (96.9%) patients completed both PRO-CTCAE® and adapted-REQUITE questionnaires. Pearson correlation coefficient between PRO tools was 0.8-0.83 (p <.001). Correlation between CTCAE and PRO-CTCAE® ranged between 0.66-0.82 (p <.001). Adapted-REQUITE and CTCAE correlation was higher for all symptoms ranging between 0.79-0.91 (p <.001). Acceptable discrepancies within one grade were present in 96.8%-100% of symptom domains for REQUITE and in 92.1%-96.8% for all domains in the PRO-CTCAE®. 54% of the total participant cohort favored the adapted-REQUITE questionnaire due to reduced subjectivity in the questions and ease of use.

Conclusion

The adapted-REQUITE questionnaire has shown a superior correlation to clinician-reported outcomes and higher patient preference than the PRO-CTCAE®. The results of this study suggest the use of the REQUITE questionnaire for patients with lung cancer in routine clinical practice.

[Photobiomodulation in the prevention and the management of side effects of cancer treatments: Bases, results and perspectives]

BACKGROUND

Assess the current and potential indications of photobiomodulation (PBM) therapy and their level of evidence in the prevention or treatment of side effects related to oncology treatments (radiation therapy, and to a minimal extent favored and hematopoietic stem cell transplants). And report on the recommended modalities (parameters and doses) of PBM therapy.

MATERIALS AND METHODS

The Embase, Medline/PubMed, Cochrane, EBSCO, Scopus, and LILACS databases were systematically reviewed to include and analyze publications of clinical studies that evaluated PBM in the prevention or management side effects related to cancer treatments. The keywords used were "photobiomodulation"; "low level laser therapy"; "acute oral mucositis"; "acute dysphagia"; "acute radiation dermatitis"; "lymphedema"; "xerostomia"; "dysgeusia"; "hyposalivation"; "lockjaw"; "bone necrosis"; "osteoradionecrosis"; "radiation induced fibrosis"; "voice and speech alterations"; "palmar-plantar erythrodysesthesia"; "graft versus host disease"; "peripheral neuropathy"; "chemotherapy induced alopecia". Prospective studies were included, while retrospective cohorts and non-original articles were excluded from the analysis.

RESULTS

PBM in the red or infrared spectrum has been shown to be effective in randomized controlled trials in the prevention and management of certain complications related to radiotherapy, in particular acute mucositis, epitheliitis and upper limb lymphedema. The level of evidence associated with PBM was heterogeneous, but overall remained moderate. The main limitations were the diversity and the lack of precision of the treatment protocols which could compromise the efficiency and the reproducibility of the results of the PBM. For other effects related to chemo/radiation therapy (dysgeusia, osteonecrosis, peripheral neuropathy, alopecia, palmar-plantar erythrodysaesthesia) and haematopoietic stem cell transplantation (graft versus host disease), treatment with PBM suffers from a lack of studies or limited studies at the origin of a weakened level of proof. However, based on these results, it was possible to establish safe practice parameters and doses of PBM.

CONCLUSION

Published data suggest that PBM could therefore be considered as supportive care in its own right for patients treated with radiation, chemotherapy, immunotherapy, hormone therapy or targeted therapies, whether in clinical practice or clinical trials. therapies. However, until solid data have been published on its long-term safety, the use of PBM should be considered with caution and within the recommended parameters and doses, particularly when practiced in areas of known or possible tumours. In this case, the patient should be informed of the theoretical benefits and risks of PBM in order to obtain informed consent before treatment.

Contouring variation affects estimates of normal tissue complication probability for breast fibrosis after radiotherapy

BACKGROUND

Normal tissue complication probability (NTCP) models can be useful to estimate the risk of fibrosis after breast-conserving surgery (BCS) and radiotherapy (RT) to the breast. However, they are subject to uncertainties. We present the impact of contouring variation on the prediction of fibrosis.

MATERIALS AND METHODS

280 breast cancer patients treated BCS-RT were included. Nine Clinical Target Volume (CTV) contours were created for each patient: i) CTV_crop (reference), cropped 5 mm from the skin and ii) CTV_skin, uncropped and including the skin, iii) segmenting the 95% isodose (Iso95%) and iv) 3 different auto-contouring atlases generating uncropped and cropped contours (Atlas_skin/Atlas_crop). To illustrate the impact of contour variation on NTCP estimates, we applied two equations predicting fibrosis grade ≥ 2 at 5 years, based on Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models, respectively, to each contour. Differences were evaluated using repeated-measures ANOVA. For completeness, the association between observed fibrosis events and NTCP estimates was also evaluated using logistic regression.

RESULTS

There were minimal differences between contours when the same contouring approach was followed (cropped and uncropped). CTV_skin and Atlas_skin contours had lower NTCP estimates (-3.92%, IQR 4.00, p < 0.05) compared to CTV_crop. No significant difference was observed for Atlas_crop and Iso95% contours compared to CTV_crop. For the whole cohort, NTCP estimates varied between 5.3% and 49.5% (LKB) or 2.2% and 49.6% (RS) depending on the choice of contours. NTCP estimates for individual patients varied by up to a factor of 4. Estimates from "skin" contours showed higher agreement with observed events.

CONCLUSION

Contour variations can lead to significantly different NTCP estimates for breast fibrosis, highlighting the importance of standardising breast contours before developing and/or applying NTCP models.

Delta-radiomics-based models for toxicity prediction in radiotherapy: A systematic review and meta-analysis

INTRODUCTION

Delta-radiomics models are potentially able to improve the treatment assessment than single-time point features. The purpose of this study is to systematically synthesize the performance of delta-radiomics-based models for radiotherapy (RT)-induced toxicity.

METHODS

A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included.

RESULTS

Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively.

CONCLUSION

Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.

Radiogenomics in lung cancer: Where are we?

Huge technological and biomedical advances have improved the survival and quality of life of lung cancer patients treated with radiotherapy. However, during treatment planning, a probability that the patient will experience adverse effects is assumed. Radiotoxicity is a complex entity that is largely dose-dependent but also has important intrinsic factors. One of the most studied is the genetic variants that may be associated with susceptibility to the development of adverse effects of radiotherapy. This review aims to present the current status of radiogenomics in lung cancer, integrating results obtained in association studies of SNPs (single nucleotide polymorphisms) related to radiotherapy toxicities. We conclude that despite numerous publications in this field, methodologies and endpoints vary greatly, making comparisons between studies difficult. Analyzing SNPs from the candidate gene approach, together with the study in cohorts limited by the sample size, has complicated the possibility of having validated results. All this delays the incorporation of genetic biomarkers in predictive models for clinical application. Thus, from all analysed SNPs, only 12 have great potential as esophagitis genetic risk factors and deserve further exploration. This review highlights the efforts that have been made to date in the radiogenomic study of radiotoxicity in lung cancer.

High weekly integral dose and larger fraction size increase risk of fatigue and worsening of functional outcomes following radiotherapy for localized prostate cancer

Introduction

We hypothesized that increasing the pelvic integral dose (ID) and a higher dose per fraction correlate with worsening fatigue and functional outcomes in localized prostate cancer (PCa) patients treated with external beam radiotherapy (EBRT).

Methods

The study design was a retrospective analysis of two prospective observational cohorts, REQUITE (development, n=543) and DUE-01 (validation, n=228). Data were available for comorbidities, medication, androgen deprivation therapy, previous surgeries, smoking, age, and body mass index. The ID was calculated as the product of the mean body dose and body volume. The weekly ID accounted for differences in fractionation. The worsening (end of radiotherapy versus baseline) of European Organisation for Research and Treatment of Cancer EORTC) Quality of Life Questionnaire (QLQ)-C30 scores in physical/role/social functioning and fatigue symptom scales were evaluated, and two outcome measures were defined as worsening in ≥2 (WS2) or ≥3 (WS3) scales, respectively. The weekly ID and clinical risk factors were tested in multivariable logistic regression analysis.

Results

In REQUITE, WS2 was seen in 28% and WS3 in 16% of patients. The median weekly ID was 13.1 L·Gy/week [interquartile (IQ) range 10.2-19.3]. The weekly ID, diabetes, the use of intensity-modulated radiotherapy, and the dose per fraction were significantly associated with WS2 [AUC (area under the receiver operating characteristics curve) =0.59; 95% CI 0.55-0.63] and WS3 (AUC=0.60; 95% CI 0.55-0.64). The prevalence of WS2 (15.3%) and WS3 (6.1%) was lower in DUE-01, but the median weekly ID was higher (15.8 L·Gy/week; IQ range 13.2-19.3). The model for WS2 was validated with reduced discrimination (AUC=0.52 95% CI 0.47-0.61), The AUC for WS3 was 0.58.

Conclusion

Increasing the weekly ID and the dose per fraction lead to the worsening of fatigue and functional outcomes in patients with localized PCa treated with EBRT.

Photobiomodulation therapy in management of cancer therapy-induced side effects: WALT position paper 2022

Disclaimer

This article is based on recommendations from the 12th WALT Congress, Nice, October 3-6, 2018, and a follow-up review of the existing data and the clinical observations of an international multidisciplinary panel of clinicians and researchers with expertise in the area of supportive care in cancer and/or PBM clinical application and dosimetry. This article is informational in nature. As with all clinical materials, this paper should be used with a clear understanding that continued research and practice could result in new insights and recommendations. The review reflects the collective opinion and, as such, does not necessarily represent the opinion of any individual author. In no event shall the authors be liable for any decision made or action taken in reliance on the proposed protocols.

Objective

This position paper reviews the potential prophylactic and therapeutic effects of photobiomodulation (PBM) on side effects of cancer therapy, including chemotherapy (CT), radiation therapy (RT), and hematopoietic stem cell transplantation (HSCT).

Background

There is a considerable body of evidence supporting the efficacy of PBM for preventing oral mucositis (OM) in patients undergoing RT for head and neck cancer (HNC), CT, or HSCT. This could enhance patients’ quality of life, adherence to the prescribed cancer therapy, and treatment outcomes while reducing the cost of cancer care.

Methods

A literature review on PBM effectiveness and dosimetry considerations for managing certain complications of cancer therapy were conducted. A systematic review was conducted when numerous randomized controlled trials were available. Results were presented and discussed at an international consensus meeting at the World Association of photobiomoduLation Therapy (WALT) meeting in 2018 that included world expert oncologists, radiation oncologists, oral oncologists, and oral medicine professionals, physicists, engineers, and oncology researchers. The potential mechanism of action of PBM and evidence of PBM efficacy through reported outcomes for individual indications were assessed.

Results

There is a large body of evidence demonstrating the efficacy of PBM for preventing OM in certain cancer patient populations, as recently outlined by the Multinational Association for Supportive Care in Cancer/International Society of Oral Oncology (MASCC/ISOO). Building on these, the WALT group outlines evidence and prescribed PBM treatment parameters for prophylactic and therapeutic use in supportive care for radiodermatitis, dysphagia, xerostomia, dysgeusia, trismus, mucosal and bone necrosis, lymphedema, hand-foot syndrome, alopecia, oral and dermatologic chronic graft-versus-host disease, voice/speech alterations, peripheral neuropathy, and late fibrosis amongst cancer survivors.

Conclusions

There is robust evidence for using PBM to prevent and treat a broad range of complications in cancer care. Specific clinical practice guidelines or evidence-based expert consensus recommendations are provided. These recommendations are aimed at improving the clinical utilization of PBM therapy in supportive cancer care and promoting research in this field. It is anticipated these guidelines will be revised periodically.

Improving Patients’ Life Quality after Radiotherapy Treatment by Predicting Late Toxicities

Simple Summary

Over 50% of patients with cancer will receive radiotherapy treatment. Five to ten percent of patients who received radiotherapy will develop side effects. Identifying these patients before treatment start would allow for treatment modification to minimize these effects and improve the life quality of these patients. Our team developed a test, which allows predicting these secondary effects before starting the treatment. This will help in proposing personalized treatments to improve the outcome. This review presents how this test is performed, its results, as well as its modification in order to be used in hospitals.

Abstract

Personalized treatment and precision medicine have become the new standard of care in oncology and radiotherapy. Because treatment outcomes have considerably improved over the last few years, permanent side-effects are becoming an increasingly significant issue for cancer survivors. Five to ten percent of patients will develop severe late toxicity after radiotherapy. Identifying these patients before treatment start would allow for treatment adaptation to minimize definitive side effects that could impair their long-term quality of life. Over the last decades, several tests and biomarkers have been developed to identify these patients. However, out of these, only the Radiation-Induced Lymphocyte Apoptosis (RILA) assay has been prospectively validated in multi-center cohorts. This test, based on a simple blood draught, has been shown to be correlated with late radiation-induced toxicity in breast, prostate, cervical and head and neck cancer. It could therefore greatly improve decision making in precision radiation oncology. This literature review summarizes the development and bases of this assay, as well as its clinical results and compares its results to the other available assays.

Development and optimisation of a machine-learning prediction model for acute desquamation following breast radiotherapy in the multi-centre REQUITE cohort

Purpose

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study.

Methods and Materials

Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation.

Results

One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the “hero” model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort.

Conclusions

ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.

Clinical and Preclinical Outcomes of Combining Targeted Therapy With Radiotherapy

In the era of precision medicine, radiation medicine is currently focused on the precise delivery of highly conformal radiation treatments. However, the tremendous developments in targeted therapy are yet to fulfill their full promise and arguably have the potential to dramatically enhance the radiation therapeutic ratio. The increased ability to molecularly profile tumors both at diagnosis and at relapse and the co-incident progress in the field of radiogenomics could potentially pave the way for a more personalized approach to radiation treatment in contrast to the current ‘‘one size fits all’’ paradigm. Few clinical trials to date have shown an improved clinical outcome when combining targeted agents with radiation therapy, however, most have failed to show benefit, which is arguably due to limited preclinical data. Several key molecular pathways could theoretically enhance therapeutic effect of radiation when rationally targeted either by directly enhancing tumor cell kill or indirectly through the abscopal effect of radiation when combined with novel immunotherapies. The timing of combining molecular targeted therapy with radiation is also important to determine and could greatly affect the outcome depending on which pathway is being inhibited.

The Principal Role of Several Members of HLA and IRF Genes in Prevention of Oral Mucositis After Chemoradiotherapy

Introduction: One of the chemoradiotherapy (CRT) side effects is oral mucositis (OM). Since photobiomodulation therapy (PBMT) is a public method for the repair process, in the present study the mechanism of PBMT in the prevention of OM is investigated via the bioinformatics approach. Methods: Six validated and significant differentially expressed genes (DEGs) associated with the prevention of OM in head and neck cancer (HNC) patients who had experienced CRT were extracted from the literature. After adding 50 neighbors from STRING, the network was constructed and analyzed. The results of the action map and network analysis were compared and discussed. Results: HLA gene family were identified as central nodes of the analyzed network. Based on action map finding, activation is prominent action and IRF9 was the potent activator. The role of the IRF gene family was highlighted by action map analysis. Conclusion: Regulation of the immune system by HLA and IRF genes family is a crucial factor in the prevention of OM in the studied patients.

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

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.

Low dose ionizing radiation effects on the immune system

Ionizing radiation interacts with the immune system in many ways with a multiplicity that mirrors the complexity of the immune system itself: namely the need to maintain a delicate balance between different compartments, cells and soluble factors that work collectively to protect, maintain, and restore tissue function in the face of severe challenges including radiation damage. The cytotoxic effects of high dose radiation are less relevant after low dose exposure, where subtle quantitative and functional effects predominate that may go unnoticed until late after exposure or after a second challenge reveals or exacerbates the effects. For example, low doses may permanently alter immune fitness and therefore accelerate immune senescence and pave the way for a wide spectrum of possible pathophysiological events, including early-onset of age-related degenerative disorders and cancer. By contrast, the so called low dose radiation therapy displays beneficial, anti-inflammatory and pain relieving properties in chronic inflammatory and degenerative diseases. In this review, epidemiological, clinical and experimental data regarding the effects of low-dose radiation on the homeostasis and functional integrity of immune cells will be discussed, as will be the role of immune-mediated mechanisms in the systemic manifestation of localized exposures such as inflammatory reactions. The central conclusion is that ionizing radiation fundamentally and durably reshapes the immune system. Further, the importance of discovery of immunological pathways for modifying radiation resilience amongst other research directions in this field is implied.

Validation of Polymorphisms Associated with the Risk of Radiation-Induced Oesophagitis in an Independent Cohort of Non-Small-Cell Lung Cancer Patients

Simple Summary

Genetic variants identified in association with radiation therapy side effects in non-small-cell lung cancer patients require an independent validation. Therefore, the aim of our study was to replicate, in an independent cohort, the analyses of previously published studies associating single-nucleotide polymorphisms with radiation-induced oesophagitis. Following the original models, 2 of the 18 variants associated with radiation-induced oesophagitis in non-small-cell lung cancer patients were confirmed. Furthermore, we meta-analysed our cohort together with those of the reference studies. Twelve variants located in genes of inflammation and DNA double-strand break repair pathways remained associated with oesophagitis. These variants could be included in models for clinical prediction of radiation-induced oesophagitis to evaluate their performance.

Abstract

Several studies have identified single-nucleotide polymorphisms (SNPs) associated with adverse effects in non-small-cell lung cancer (NSCLC) patients treated with radiation therapy. Here, using an independent cohort, we aimed to validate the reported associations. We selected 23 SNPs in 17 genes previously associated with radiation-induced oesophagitis for validation in a cohort of 178 Spanish NSCLC patients. Of them, 18 SNPs were finally analysed, following the methods described in the original published studies. Two SNPs replicated their association with radiation-induced oesophagitis (rs7165790 located in the BLM gene: odds ratio (OR) = 0.16, 95% CI = 0.04–0.65, p-value = 0.010; rs4772468 at FGF14: OR = 4.36, 95% CI = 1.15–16.46, p-value = 0.029). The SNP rs2868371 at HSPB1 was also validated but displayed an opposite effect to the formerly described (OR = 3.72; 95% CI = 1.49–9.25; p-value = 0.004). Additionally, we tested a meta-analytic approach including our results and the previous datasets reported in the referenced publications. Twelve SNPs (including the two previously validated) retained their statistically significant association with radiation-induced oesophagitis. This study strengthens the role of inflammation and DNA double-strand break repair pathways in the risk prediction of developing radiation-induced oesophagitis in NSCLC patients. The validated variants are good candidates to be evaluated in risk prediction models for patient stratification based on their radiation susceptibility.

External Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort

Background: Acute skin toxicity is a common and usually transient side-effect of breast radiotherapy although, if sufficiently severe, it can affect breast cosmesis, aftercare costs and the patient's quality-of-life. The aim of this study was to develop predictive models for acute skin toxicity using published risk factors and externally validate the models in patients recruited into the prospective multi-center REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve QUalITy of lifE in cancer survivors) study.

Methods: Patient and treatment-related risk factors significantly associated with acute breast radiation toxicity on multivariate analysis were identified in the literature. These predictors were used to develop risk models for acute erythema and acute desquamation (skin loss) in three Radiogenomics Consortium cohorts of patients treated by breast-conserving surgery and whole breast external beam radiotherapy (n = 2,031). The models were externally validated in the REQUITE breast cancer cohort (n = 2,057).

Results: The final risk model for acute erythema included BMI, breast size, hypo-fractionation, boost, tamoxifen use and smoking status. This model was validated in REQUITE with moderate discrimination (AUC 0.65), calibration and agreement between predicted and observed toxicity (Brier score 0.17). The risk model for acute desquamation, excluding the predictor tamoxifen use, failed to validate in the REQUITE cohort.

Conclusions: While most published prediction research in the field has focused on model development, this study reports successful external validation of a predictive model using clinical risk factors for acute erythema following radiotherapy after breast-conserving surgery. This model retained discriminatory power but will benefit from further re-calibration. A similar model to predict acute desquamation failed to validate in the REQUITE cohort. Future improvements and more accurate predictions are expected through the addition of genetic markers and application of other modeling and machine learning techniques.

Pharmacogenomics meets precision cardio-oncology: is there synergistic potential?

An individual's inherited genetic makeup and acquired genomic variants may account for a significant portion of observable variability in therapy efficacy and toxicity. Pharmacogenomics (PGx) is the concept that treatments can be modified to account for these differences to increase chances of therapeutic efficacy while minimizing risk of adverse effects. This is particularly applicable to oncology in which treatment may be multimodal. Each tumor type has a unique genomic signature that lends to inclusion of targeted therapy but may be associated with cumulative toxicity, such as cardiotoxicity, and can impact quality of life. A greater understanding of therapeutic agents impacted by PGx and subsequent implementation has the potential to improve outcomes and reduce risk of drug-induced adverse effects.

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

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.

[Treatment of radiation-induced late effects: What's new?]

Mechanisms of late radio-induced lesions are the result of multiple and complex phenomena, with many entangled cellular and tissue factors. The biological continuum between acute and late radio-induced effects will be described, with firstly a break in homeostasis that leads to cellular redistributions. New insights into late toxicity will finally be addressed. Individual radiosensitivity is a primary factor for the development of late toxicity, and clinicians urgently need predictive tests to offer truly personalized radiation therapy. An update will be made on the various functional and genetic tests currently being validated. The management of radio-induced side effects remains a frequent issue for radiation oncologists, and an update will be made for certain specific clinical situations. Finally, an innovative management for patients with significant side effects after pelvic radiotherapy will be developed, involved mesenchymal stem cell transplantation, with the presentation of the "PRISME" protocol currently open to patients recruitment.

Radiogenomics Consortium Genome-Wide Association Study Meta-Analysis of Late Toxicity After Prostate Cancer Radiotherapy

BACKGROUND

A 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.

METHODS

We 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.

RESULTS

Meta-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.

CONCLUSIONS

This 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.

Establishing mechanisms affecting the individual response to ionizing radiation

Purpose: Humans are increasingly exposed to ionizing radiation (IR). Both low (<100 mGy) and high doses can cause stochastic effects, including cancer; whereas doses above 100 mGy are needed to promote tissue or cell damage. 10-15% of radiotherapy (RT) patients suffer adverse reactions, described as displaying radiosensitivity (RS). Sensitivity to IR's stochastic effects is termed radiosusceptibility (RSu). To optimize radiation protection we need to understand the range of individual variability and underlying mechanisms. We review the potential mechanisms contributing to RS/RSu focusing on RS following RT, the most tractable RS group.Conclusions: The IR-induced DNA damage response (DDR) has been well characterized. Patients with mutations in the DDR have been identified and display marked RS but they represent only a small percentage of the RT patients with adverse reactions. We review the impacting mechanisms and additional factors influencing RS/RSu. We discuss whether RS/RSu might be genetically determined. As a recommendation, we propose that a prospective study be established to assess RS following RT. The study should detail tumor site and encompass a well-defined grading system. Predictive assays should be independently validated. Detailed analysis of the inflammatory, stress and immune responses, mitochondrial function and life style factors should be included. Existing cohorts should also be optimally exploited.

The Implications of Genetic Testing on Radiation Therapy Decisions: A Guide for Radiation Oncologists

The advent of affordable and rapid next-generation DNA sequencing technology, along with the US Supreme Court ruling invalidating gene patents, has led to a deluge of germline and tumor genetic variant tests that are being rapidly incorporated into clinical cancer decision-making. A major concern for clinicians is whether the presence of germline mutations may increase the risk of radiation toxicity or secondary malignancies. Because scarce clinical data exist to inform decisions at this time, the American Society for Radiation Oncology convened a group of radiation science experts and clinicians to summarize potential issues, review relevant data, and provide guidance for adult patients and their care teams regarding the impact, if any, that genetic testing should have on radiation therapy recommendations. During the American Society for Radiation Oncology workshop, several main points emerged, which are discussed in this manuscript: (1) variants of uncertain significance should be considered nondeleterious until functional genomic data emerge to demonstrate otherwise; (2) possession of germline alterations in a single copy of a gene critical for radiation damage responses does not necessarily equate to increased risk of radiation-induced toxicity; (3) deleterious ataxia-telangiesctasia gene mutations may modestly increase second cancer risk after radiation therapy, and thus follow-up for these patients after indicated radiation therapy should include second cancer screening; (4) conveying to patients the difference between relative and absolute risk is critical to decision-making; and (5) more work is needed to assess the impact of tumor somatic alterations on the probability of response to radiation therapy and the potential for individualization of radiation doses. Data on radiosensitivity related to specific genetic mutations is also briefly discussed.

Clinical and epidemiological observations on individual radiation sensitivity and susceptibility

Purpose: To summarize existing knowledge and to understand individual response to radiation exposure, the MELODI Association together with CONCERT European Joint Programme has organized a workshop in March 2018 on radiation sensitivity and susceptibility.Methods: The workshop reviewed the current evidence on this matter, to inform the MELODI Strategic Research Agenda (SRA), to determine social and scientific needs and to come up with recommendations for suitable and feasible future research initiatives to be taken for the benefit of an improved medical diagnosis and treatment as well as for radiation protection.Results: The present paper gives an overview of the current evidence in this field, including potential effect modifiers such as age, gender, genetic profile, and health status of the exposed population, based on clinical and epidemiological observations.Conclusion: The authors conclude with the following recommendations for the way forward in radiation research: (a) there is need for large (prospective) cohort studies; (b) build upon existing radiation research cohorts; (c) use data from well-defined cohorts with good exposure assessment and biological material already collected; (d) focus on study quality with standardized data collection and reporting; (e) improve statistical analysis; (f) cooperation between radiobiology and epidemiology; and (g) take consequences of radiosensitivity and radiosusceptibility into account.

REQUITE: A prospective multicentre cohort study of patients undergoing radiotherapy for breast, lung or prostate cancer

PURPOSE

REQUITE aimed to establish a resource for multi-national validation of models and biomarkers that predict risk of late toxicity following radiotherapy. The purpose of this article is to provide summary descriptive data.

METHODS

An international, prospective cohort study recruited cancer patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5300 patients. Eligible patients had breast, prostate or lung cancer and planned potentially curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics were generated.

RESULTS

The study recruited 2069 breast (99% of target), 1808 prostate (86%) and 561 lung (51%) cancer patients. The centralised, accessible database includes: physician- (47,025 forms) and patient- (54,901) reported outcomes; 11,563 breast photos; 17,107 DICOMs and 12,684 DVHs. Imputed genotype data are available for 4223 patients with European ancestry (1948 breast, 1728 prostate, 547 lung). Radiation-induced lymphocyte apoptosis (RILA) assay data are available for 1319 patients. DNA (n = 4409) and PAXgene tubes (n = 3039) are stored in the centralised biobank. Example prevalences of 2-year (1-year for lung) grade ≥2 CTCAE toxicities are 13% atrophy (breast), 3% rectal bleeding (prostate) and 27% dyspnoea (lung).

CONCLUSION

The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating predictive models and biomarkers.

PATIENT SUMMARY

Up to half of cancer patients undergo radiation therapy and irradiation of surrounding healthy tissue is unavoidable. Damage to healthy tissue can affect short- and long-term quality-of-life. Not all patients are equally sensitive to radiation "damage" but it is not possible at the moment to identify those who are. REQUITE was established with the aim of trying to understand more about how we could predict radiation sensitivity. The purpose of this paper is to provide an overview and summary of the data and material available. In the REQUITE study 4400 breast, prostate and lung cancer patients filled out questionnaires and donated blood. A large amount of data was collected in the same way. With all these data and samples a database and biobank were created that showed it is possible to collect this kind of information in a standardised way across countries. In the future, our database and linked biobank will be a resource for research and validation of clinical predictors and models of radiation sensitivity. REQUITE will also enable a better understanding of how many people suffer with radiotherapy toxicity.

Potential screening assays for individual radiation sensitivity and susceptibility and their current validation state

Purpose: The workshop on 'Individual Radiosensitivity and Radiosusceptibility' organized by MELODI and CONCERT on Malta in 2018, evaluated the current state of assays to identify sensitive and susceptible subgroups. The authors provide an overview on potential screening assays detecting individuals showing moderate to severe early and late radiation reactions or are at increased risk to develop cancer upon radiation exposure.Conclusion: It is necessary to separate clearly between tissue reactions and stochastic effects such as cancer when comparing the existing literature to validate various test systems. Requirements for the assays are set up. The literature is reviewed for assays that are reliable and robust. Sensitivity and specificity of the assays are regarded and scrutinized for modifying factors. Accuracy of an assay system is required to be more than 90% to balance risks of adverse reactions against risk to fail to cure the cancer. No assay/biomarker is in routine use. Assays that have shown predictive potential for radiosensitivity include SNPs, the RILA assay, and the pATM assay. A tree of risk guideline for radiologists is provided to assist medical treatment decisions. Recommendations for effective research include the setup of common retrospective and prospective cohorts/biobanks to validate current and future tests.

Mechanisms for Radioprotection by Melatonin; Can it be Used as a Radiation Countermeasure?

BACKGROUND

Melatonin is a natural body product that has shown potent antioxidant property against various toxic agents. For more than two decades, the abilities of melatonin as a potent radioprotector against toxic effects of ionizing radiation (IR) have been proved. However, in the recent years, several studies have been conducted to illustrate how melatonin protects normal cells against IR. Studies proposed that melatonin is able to directly neutralize free radicals produced by IR, leading to the production of some low toxic products.

DISCUSSION

Moreover, melatonin affects several signaling pathways, such as inflammatory responses, antioxidant defense, DNA repair response enzymes, pro-oxidant enzymes etc. Animal studies have confirmed that melatonin is able to alleviate radiation-induced cell death via inhibiting pro-apoptosis and upregulation of anti-apoptosis genes. These properties are very interesting for clinical radiotherapy applications, as well as mitigation of radiation injury in a possible radiation disaster. An interesting property of melatonin is mitochondrial ROS targeting that has been proposed as a strategy for mitigating effects in radiosensitive organs, such as bone marrow, gastrointestinal system and lungs. However, there is a need to prove the mitigatory effects of melatonin in experimental studies.

CONCLUSION

In this review, we aim to clarify the molecular mechanisms of radioprotective effects of melatonin, as well as possible applications as a radiation countermeasure in accidental exposure or nuclear/radiological disasters.

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

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The RILA assay is the leading candidate biomarker for radiotherapy toxicity.

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We describe work to standardise its use across multiple centres.

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Patient factors including smoking and arthritis were found to affect RILA score.

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RILA predicts acute breast pain but not other acute end-points.

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This work establishes the basis for implementing the assay clinically.

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.

Radiogenomics in the Era of Advanced Radiotherapy

Most radiogenomics studies investigate how genetic variation can help to explain the differences in early and late radiotherapy toxicity between individuals. The field of radiogenomics in photon beam therapy has grown rapidly in recent years, carving out a unique translational discipline, which has progressed from candidate gene studies to larger scale genome-wide association studies, meta-analyses and now prospective validation studies. Genotyping is increasingly sophisticated and affordable, and whole-genome sequencing may soon become readily available as a diagnostic tool in the clinic. The ultimate aim of radiogenomics research is to tailor treatment to the individual with a test based on a combination of treatment, clinical and genetic factors. This personalisation would allow the greatest tumour control while minimising acute and long-term toxicity. Here we discuss the evolution of the field of radiogenomics with reference to the most recent developments and challenges.

Development and internal validation of a multinomial NTCP model for the severity of acute dyspnea after radiotherapy for lung cancer

BACKGROUND AND PURPOSE

Dyspnea evolution after radiotherapy for lung cancer is complex with potential symptom deterioration and improvement from baseline. We developed and internally validated a multinomial normal tissue complication probability (NTCP) model predicting dyspnea grade.

MATERIALS AND METHODS

Patient-reported dyspnea was collected pre-treatment and during 6 months follow-up for 182 stage I-IV lung cancer patients treated with radical (chemo)radiotherapy. Dyspnea changes (ΔDys) from the baseline grade (Dys₀) to the follow-up grade (Dys) were evaluated. A multinomial logistic regression model simultaneously predicting 3 grades of Dys (Dys ≥ 3, Dys = 2 and Dys ≤ 1 (reference level)) was optimized. Reference NTCP models predicting Dys ≥ 2 and Dys ≥ 3 risks irrespective of Dys₀ were generated for comparison. Models were shrunken and performance was assessed using optimism-corrected AUC (bootstrapping).

RESULTS

Rates of ΔDys ≥ 1 (deterioration) and ΔDys ≤ -1 (improvement) at 6 months were 31.9% and 12.6%. Dys ≥ 3, Dys = 2 and Dys ≤ 1 rates were 13.7%, 20.9% and 65.4%, respectively. The multinomial model (combining the risk factors Dys₀ and MLD and the protective factor chemotherapy treatment) predicted Dys ≥ 3, Dys = 2 and Dys ≤ 1 with AUC (95% CI) of 0.72 (0.65-0.75) 0.76 (0.72-0.79) and 0.78 (0.74-0.80), respectively. Reference Dys ≥ 2 and Dys ≥ 3 models showed worse AUC: 0.64 (0.59-0.67) and 0.66 (0.50-0.70), respectively.

CONCLUSIONS

Dyspnea grade could be predicted with high accuracy using a multinomial NTCP model, yielding personalized dyspnea symptom improvement and deterioration risks.

Ano-rectal wall dose-surface maps localize the dosimetric benefit of hydrogel rectum spacers in prostate cancer radiotherapy

BACKGROUND AND PURPOSE

To evaluate spatial differences in dose distributions of the ano-rectal wall (ARW) using dose-surface maps (DSMs) between prostate cancer patients receiving intensity-modulated radiation therapy with and without implantable rectum spacer (IMRT+IRS; IMRT-IRS, respectively), and to correlate this with late gastro-intestinal (GI) toxicities using validated spatial and non-spatial normal-tissue complication probability (NTCP) models.

MATERIALS AND METHODS

For 26 patients DSMs of the ARW were generated. From the DSMs various shape-based dose measures were calculated at different dose levels: lateral extent, longitudinal extent, and eccentricity. The contiguity of the ARW dose distribution was assessed by the contiguous-DSH (cDSH). Predicted complication rates between IMRT+IRS and IMRT-IRS plans were assessed using a spatial NTCP model and compared against a non-spatial NTCP model.

RESULTS

Dose surface maps are generated for prostate radiotherapy using an IRS. Lateral extent, longitudinal extent and cDSH were significantly lower in IMRT+IRS than for IMRT-IRS at high-dose levels. Largest significant differences were observed for cDSH at dose levels >50 Gy, followed by lateral extent at doses >57 Gy, and longitudinal extent in anterior and superior-inferior directions. Significant decreases (p = 0.01) in median rectal and anal NTCPs (respectively, Gr 2 late rectal bleeding and subjective sphincter control) were predicted when using an IRS.

CONCLUSIONS

Local-dose effects are predicted to be significantly reduced by an IRS. The spatial NTCP model predicts a significant decrease in Gr 2 late rectal bleeding and subjective sphincter control. Dose constraints can be improved for current clinical treatment planning.

Genetic Variants Predict Optimal Timing of Radiotherapy to Reduce Side-effects in Breast Cancer Patients

AIMS

Radiotherapy is an important treatment for many types of cancer, but a minority of patients suffer long-term side-effects of treatment. Multiple lines of evidence suggest a role for circadian rhythm in the development of radiotherapy late side-effects.

MATERIALS AND METHODS

We carried out a study to examine the effect of radiotherapy timing in two breast cancer patient cohorts. The retrospective LeND cohort comprised 535 patients scored for late effects using the Late Effects of Normal Tissue-Subjective Objective Management Analytical (LENT-SOMA) scale. Acute effects were assessed prospectively in 343 patients from the REQUITE study using the CTCAE v4 scales. Genotyping was carried out for candidate circadian rhythm variants.

RESULTS

In the LeND cohort, patients who had radiotherapy in the morning had a significantly increased incidence of late toxicity in univariate (P = 0.03) and multivariate analysis (P = 0.01). Acute effects in the REQUITE group were also significantly increased in univariate analysis after morning treatment (P = 0.03) but not on multivariate analysis. Increased late effects in the LeND group receiving morning radiotherapy were associated with carriage of the PER3 variable number tandem repeat 4/4 genotype (P = 6 × 10-3) and the NOCT rs131116075 AA genotype (P = 5 × 10-3).

CONCLUSION

Our results suggest that it may be possible to reduce toxicity associated with breast cancer radiotherapy by identifying gene variants that affect circadian rhythm and scheduling for appropriate morning or afternoon radiotherapy.

Role of Germline Genetics in Identifying Survivors at Risk for Adverse Effects of Cancer Treatment

The growing population of cancer survivors often faces adverse effects of treatment, which have a substantial impact on morbidity and mortality. Although certain adverse effects are thought to have a significant heritable component, much work remains to be done to understand the role of germline genetic factors in the development of treatment-related toxicities. In this article, we review current understanding of genetic susceptibility to a range of adverse outcomes among cancer survivors (e.g., fibrosis, urinary and rectal toxicities, ototoxicity, chemotherapy-induced peripheral neuropathy, subsequent malignancies). Most previous research has been narrowly focused, investigating variation in candidate genes and pathways such as drug metabolism, DNA damage and repair, and inflammation. Few of the findings from these earlier candidate gene studies have been replicated in independent populations. Advances in understanding of the genome, improvements in technology, and reduction in laboratory costs have led to recent genome-wide studies, which agnostically interrogate common and/or rare variants across the entire genome. Larger cohorts of patients with homogeneous treatment exposures and systematic ascertainment of well-defined outcomes as well as replication in independent study populations are essential aspects of the study design and are increasingly leading to the discovery of variants associated with each of the adverse outcomes considered in this review. In the long-term, validated germline genetic associations hold tremendous promise for more precisely identifying patients at highest risk for developing adverse treatment effects, with implications for frontline therapy decision-making, personalization of long-term follow-up guidelines, and potential identification of targets for prevention or treatment of the toxicity.

Developing Predictive or Prognostic Biomarkers for Charged Particle Radiotherapy

The response to radiotherapy can vary greatly among individuals, even though advances in technology allow for the highly localized placement of therapeutic doses of radiation to a tumor. This variability in patient response to radiation is biologically driven, but the individuality of tumor and healthy tissue biology are not used to create individual treatment plans. Biomarkers of radiosensitivity, whether intrinsic or from hypoxia, would move radiation oncology from precision medicine to precise, personalized medicine. Charged particle radiotherapy allows for even greater dose conformity, but the biological advantages of charged particle radiotherapy have not yet been cultivated. The development of biomarkers that would drive biologically based clinical trials, identify patients for whom charged particles are most appropriate, or aid in particle-selection strategies could be envisioned with appropriate biomarkers. Initially, biomarkers for low-linear energy transfer (LET) radiation responses should be tested against charged particles. Biomarkers of tumor radioresistance to low-LET radiations could be used to identify patients for whom the enhanced relative biological effectiveness (RBE) of charged particles would be more effective compared with low-LET radiations and those for whom specific DNA-repair inhibitors, in combination with charged particles, may also be appropriate. Furthermore, heavy charged particles can overcome the radioresistance of hypoxic tumors when used at the appropriate LET. Biomarkers for hypoxia could identify hypoxic tumors and, in combination with imaging, define hypoxic regions of a tumor for specific ion selection. Moreover, because of the enhanced RBE for charged particles, the risk for adverse healthy tissue effects may be greater, even though charged particles have greater tumor conformality. There are many validated healthy-tissue biomarkers available to test against charged particle exposures. Lastly, newer biological techniques, as well as newer bioinformatic and computational methods, are rapidly changing the landscape for biomarker identification, validation, and clinical trial design.

Radiation biology and oncology in the genomic era

Radiobiology research is building the foundation for applying genomics in precision radiation oncology. Advances in high-throughput approaches will underpin increased understanding of radiosensitivity and the development of future predictive assays for clinical application. There is an established contribution of genetics as a risk factor for radiotherapy side effects. An individual's radiosensitivity is an inherited polygenic trait with an architecture that includes rare mutations in a few genes that confer large effects and common variants in many genes with small effects. Current thinking is that some will be tissue specific, and future tests will be tailored to the normal tissues at risk. The relationship between normal and tumor cell radiosensitivity is poorly understood. Data are emerging suggesting interplay between germline genetic variation and epigenetic modification with growing evidence that changes in DNA methylation regulate the radiosensitivity of cancer cells and histone acetyltransferase inhibitors have radiosensitizing effects. Changes in histone methylation can also impair DNA damage response signaling and alter radiosensitivity. An important effort to advance radiobiology in the genomic era was establishment of the Radiogenomics Consortium to enable the creation of the large radiotherapy cohorts required to exploit advances in genomics. To address challenges in harmonizing data from multiple cohorts, the consortium established the REQUITE project to collect standardized data and genotyping for ~5,000 patients. The collection of detailed dosimetric data is important to produce validated multivariable models. Continued efforts will identify new genes that impact on radiosensitivity to generate new knowledge on toxicity pathogenesis and tests to incorporate into the clinical decision-making process.

Radiogenomic Predictors of Adverse Effects following Charged Particle Therapy

Radiogenomics is the study of genomic factors that are associated with response to radiation therapy. In recent years, progress has been made toward identifying genetic risk factors linked with late radiation-induced adverse effects. These advances have been underpinned by the establishment of an international Radiogenomics Consortium with collaborative studies that expand cohort sizes to increase statistical power and efforts to improve methodologic approaches for radiogenomic research. Published studies have predominantly reported the results of research involving patients treated with photons using external beam radiation therapy. These studies demonstrate our ability to pool international cohorts to identify common single nucleotide polymorphisms associated with risk for developing normal tissue toxicities. Progress has also been achieved toward the discovery of genetic variants associated with radiation therapy-related subsequent malignancies. With the increasing use of charged particle therapy (CPT), there is a need to establish cohorts for patients treated with these advanced technology forms of radiation therapy and to create biorepositories with linked clinical data. While some genetic variants are likely to impact toxicity and second malignancy risks for both photons and charged particles, it is plausible that others may be specific to the radiation modality due to differences in their biological effects, including the complexity of DNA damage produced. In recognition that the formation of patient cohorts treated with CPT for radiogenomic studies is a high priority, efforts are underway to establish collaborations involving institutions treating cancer patients with protons and/or carbon ions as well as consortia, including the Proton Collaborative Group, the Particle Therapy Cooperative Group, and the Pediatric Proton Consortium Registry. These important radiogenomic CPT initiatives need to be expanded internationally to build on experience gained from the Radiogenomics Consortium and epidemiologists investigating normal tissue toxicities and second cancer risk.

Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology

It is an exciting time for big data efforts in radiation oncology. The use of big data to help aid both outcomes and decision-making research is becoming a reality. However, there are true challenges that exist in the space of gathering and utilizing performance and outcomes data. Here, we summarize the current state of big data in radiation oncology with respect to outcomes and discuss some of the efforts and challenges in radiation oncology big data.

Data Analysis in Radiotherapy Treatments

Radiotherapy is one of the main cancer treatments available today, together with chemotherapy and surgery. Radiotherapy treatments have to be planned for each patient in an individualized manner. The knowledge acquired from one single treatment can be used to improve the treatment planning and outcome of several other patients. In the last years, attention has been drawn to the added value of using data analysis for radiotherapy treatment planning, prediction of treatment outcomes, survival analysis and quality assurance. In this article, existing literature is reviewed.

The use of volunteers to implement electronic patient reported outcomes in lung cancer outpatient clinics

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104 eligible lung cancer patients were approached, 86 (83%) consented to take part.

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At 1st attempt 69% of patients completed the ePRO questionnaire without assistance.

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Assistance was defined as verbal/physical help to complete the ePRO questionnaire.

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Most patients requiring help had a companion that could have provided assistance.

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More patients preferred electronic than paper questionnaires.

Background

Treatment related toxicity is common after chemotherapy and radiotherapy. Our group has developed and validated an electronic Patient Reported Outcome questionnaire (ePRO) to assess symptoms and toxicity in lung cancer patients receiving (chemo)radiotherapy treatment. We assessed the need for volunteer support in clinics to assist patients in completing ePROs.

Methods

Lung Cancer patients attending outpatient or radiotherapy clinics at The Christie NHS Foundation Trust, Manchester were consented and asked to complete a Patient Reported Outcomes questionnaire using an electronic device (a touchscreen). Trained volunteers were available if patients required help such as verbal or physical assistance. The primary objective was to determine the need for volunteers to assist lung cancer patients in completing ePROs.

Results

27/86 (31.4%) of patients who consented to this study required assistance to complete the ePRO. After questioning, we found that only 7/86 (8.1%) would have relied on volunteers for assistance as the majority of patients had a companion that could have provided help. 81/86 (94.2%) of patients were satisfied with the use of a touchscreen tablet to complete the ePRO.

Conclusion

Our results demonstrate that the introduction of ePROs in lung cancer outpatient clinics is feasible, even without the use of volunteers for the majority of patients. The implementation of ePROs would allow large volumes of high quality (chemo)radiotherapy toxicity data to be collected accurately and quickly. This is essential for the development of predictive models of outcome using population-based data, which could allow the personalisation of (chemo)radiotherapy treatment for lung cancer patients.

Precision Oncology and Genomically Guided Radiation Therapy: A Report From the American Society for Radiation Oncology/American Association of Physicists in Medicine/National Cancer Institute Precision Medicine Conference

PURPOSE

To summarize important talking points from a 2016 symposium focusing on real-world challenges to advancing precision medicine in radiation oncology, and to help radiation oncologists navigate the practical challenges of precision, radiation oncology.

METHODS AND MATERIALS

The American Society for Radiation Oncology, American Association of Physicists in Medicine, and National Cancer Institute cosponsored a meeting on precision medicine in radiation oncology. In June 2016 numerous scientists, clinicians, and physicists convened at the National Institutes of Health to discuss challenges and future directions toward personalized radiation therapy. Various breakout sessions were held to discuss particular components and approaches to the implementation of personalized radiation oncology. This article summarizes the genomically guided radiation therapy breakout session.

RESULTS

A summary of existing genomic data enabling personalized radiation therapy, ongoing clinical trials, current challenges, and future directions was collected. The group attempted to provide both a current overview of data that radiation oncologists could use to personalize therapy, along with data that are anticipated in the coming years. It seems apparent from the provided review that a considerable opportunity exists to truly bring genomically guided radiation therapy into clinical reality.

CONCLUSIONS

Genomically guided radiation therapy is a necessity that must be embraced in the coming years. Incorporating these data into treatment recommendations will provide radiation oncologists with a substantial opportunity to improve outcomes for numerous cancer patients. More research focused on this topic is needed to bring genomic signatures into routine standard of care.

Acute Epithelial Toxicity Is Prognostic for Improved Prostate Cancer Response to Radiation Therapy: A Retrospective, Multicenter, Cohort Study

PURPOSE

To test the hypothesis that increased acute toxicity, measured using subdomains reflective of epithelial cell damage, will be associated with reduced late biochemical failure, as a surrogate for tumor radiosensitivity.

METHODS AND MATERIALS

The study design was retrospective, with discovery and validation cohorts involving routinely collected data. Eligible patients had prostate cancer, underwent radiation therapy with curative intent, and had acute toxicity assessed prospectively. The discovery cohort was from a single institution. Genitourinary and gastrointestinal acute toxicity related to epithelial cell damage (hematuria, dysuria, proctitis, or mucus) were related to freedom from late biochemical failure (FFBF; nadir + 2). The validation cohort was from two separate institutions.

RESULTS

In all, 503 patients were included in the discovery cohort and 658 patients in the validation cohort. In the validation cohort, patients with acute radiation toxicity reflecting epithelial damage had a longer FFBF on both univariate (hazard ratio [HR] 0.37; P = .004) and multivariate (HR 0.45; P = .035) analysis. The impact of acute toxicity on late FFBF seemed to be greater in patients treated with androgen deprivation (HR 0.19) than in those without (HR 0.48).

CONCLUSION

Patients reporting acute radiation toxicity reflective of epithelial cell damage during definitive radiation therapy for prostate cancer have significantly longer FFBF, consistent with an underlying genetic link between normal tissue and tumor radiosensitivity.

The Patient Perspective on Radiogenomics Testing for Breast Radiation Toxicity

Aims

In the field of radiogenomics, several potential predictive genetic markers have been identified that are associated with individual susceptibility to radiation toxicity. Predictive models of radiation toxicity incorporating radiogenomics and other biomarkers are being developed as part of the ongoing multicentre REQUITE trial. The purpose of this study was to explore patient attitudes towards future predictive radiogenomics testing for breast radiation toxicity.

Patients and methods

Twenty-one semi-structured interviews were conducted with breast cancer patients taking part in the REQUITE study at one centre. We used inductive thematic analysis to generate common themes.

Results

We identified three emerging themes describing attitudes and feelings towards a predictive radiogenomics test for breast radiation toxicity: theme 1 – willingness to undergo a test (subthemes – information, trusted expert); theme 2 – implications of a test (subthemes – preparation and planning, anxiety without recourse); theme 3 – impact on treatment decision-making (subthemes – prioritising cancer cure, preserving breast integrity, patient preferences).

Conclusions

Results from the present study indicate that patients support and have confidence in the validity of a radiogenomics test for breast radiation toxicity, but they would prefer the result be provided to healthcare professionals. Except in cases of significant chronic symptoms and pain or significant end-organ damage, participants in this study rarely felt that advance knowledge of their personal risk of breast radiation toxicity would influence their treatment decision-making. These findings provide a number of insights that will allow us to anticipate how patients are likely to engage with predictive radiogenomics testing in the future.

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Patients’ views and opinions on predictive radiogenomics testing were investigated.

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Semi-structured interviews with breast cancer patients enrolled in the prospective REQUITE cohort.

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Participants would prefer the test result to be provided to healthcare professionals rather than directly to patients.

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Participants rarely felt that advance knowledge of their risk of radiation toxicity would influence their treatment decision.

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Participants were prepared to consider a treatment change, if predicted to have significant chronic side-effects or end-organ damage.

Radiogenomics: Identification of Genomic Predictors for Radiation Toxicity

The overall goal of radiogenomics is the identification of genomic markers that are predictive for the development of adverse effects resulting from cancer treatment with radiation. The principal rationale for a focus on toxicity in radiogenomics is that for many patients treated with radiation, especially individuals diagnosed with early-stage cancers, the survival rates are high, and therefore a substantial number of people will live for a significant period of time beyond treatment. However, many of these patients could suffer from debilitating complications resulting from radiotherapy. Work in radiogenomics has greatly benefited from creation of the Radiogenomics Consortium (RGC) that includes investigators at multiple institutions located in a variety of countries. The common goal of the RGC membership is to share biospecimens and data so as to achieve large-scale studies with increased statistical power to enable identification of relevant genomic markers. A major aim of research in radiogenomics is the development of a predictive instrument to enable identification of people who are at greatest risk for adverse effects resulting from cancer treatment using radiation. It is anticipated that creation of a predictive assay characterized by a high level of sensitivity and specificity will improve precision radiotherapy and assist patients and their physicians to select the optimal treatment for each individual.