Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement

Integrating local and distant radiation-induced lung injury: Development and validation of a predictive model for ventilation loss

BACKGROUND

Investigations on radiation-induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation-induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post-radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma. These observations emphasize the critical need for predictive dose-response models that effectively incorporate both local and distant RILI effects.

PURPOSE

We develop and validate a predictive model for ventilation loss after lung RT. This model, referred to as P+A, integrates local (parenchyma [P]) and distant (central and peripheral airways [A]) radiation-induced damage, modeling partial (narrowing) and complete (collapse) obstruction of airways.

METHODS

In an IRB-approved prospective study, pre-RT breath-hold CTs (BHCTs) and pre- and one-year post-RT 4DCTs were acquired from lung cancer patients treated with definitive RT. Up to 13 generations of airways were automatically segmented on the BHCTs using a research virtual bronchoscopy software. Ventilation maps derived from the 4DCT scans were utilized to quantify pre- and post-RT ventilation, serving, respectively, as input data and reference standard (RS) in model validation. To predict ventilation loss solely due to parenchymal damage (referred to as P model), we used a normal tissue complication probability (NTCP) model. Our model used this NTCP-based estimate and predicted additional loss due radiation-induced partial or complete occlusion of individual airways, applying fluid dynamics principles and a refined version of our previously developed airway radiosensitivity model. Predictions of post-RT ventilation were estimated in the sublobar volumes (SLVs) connected to the terminal airways. To validate the model, we conducted a k-fold cross-validation. Model parameters were optimized as the values that provided the lowest root mean square error (RMSE) between predicted post-RT ventilation and the RS for all SLVs in the training data. The performance of the P+A and the P models was evaluated by comparing their respective post-RT ventilation values with the RS predictions. Additional evaluation using various receiver operating characteristic (ROC) metrics was also performed.

RESULTS

We extracted a dataset of 560 SLVs from four enrolled patients. Our results demonstrated that the P+A model consistently outperformed the P model, exhibiting RMSEs that were nearly half as low across all patients (13 ± 3 percentile for the P+A model vs. 24 ± 3 percentile for the P model on average). Notably, the P+A model aligned closely with the RS in ventilation loss distributions per lobe, particularly in regions exposed to doses ≥13.5 Gy. The ROC analysis further supported the superior performance of the P+A model compared to the P model in sensitivity (0.98 vs. 0.07), accuracy (0.87 vs. 0.25), and balanced predictions.

CONCLUSIONS

These early findings indicate that airway damage is a crucial factor in RILI that should be included in dose-response modeling to enhance predictions of post-RT lung function.

Moving the Needle Forward in Genomically-Guided Precision Radiation Treatment

Radiation treatment (RT) is a mainstay treatment for many types of cancer. Recommendations for RT and the radiation plan are individualized to each patient, taking into consideration the patient's tumor pathology, staging, anatomy, and other clinical characteristics. Information on germline mutations and somatic tumor mutations is at present rarely used to guide specific clinical decisions in RT. Many genes, such as ATM, and BRCA1/2, have been identified in the laboratory to confer radiation sensitivity. However, our understanding of the clinical significance of mutations in these genes remains limited and, as individual mutations in such genes can be rare, their impact on tumor response and toxicity remains unclear. Current guidelines, including those from the National Comprehensive Cancer Network (NCCN), provide limited guidance on how genetic results should be integrated into RT recommendations. With an increasing understanding of the molecular underpinning of radiation response, genomically-guided RT can inform decisions surrounding RT dose, volume, concurrent therapies, and even omission to further improve oncologic outcomes and reduce risks of toxicities. Here, we review existing evidence from laboratory, pre-clinical, and clinical studies with regard to how genetic alterations may affect radiosensitivity. We also summarize recent data from clinical trials and explore potential future directions to utilize genetic data to support clinical decision-making in developing a pathway toward personalized RT.

Normal Tissue Toxicity Prediction: Clinical Translation on the Horizon

Improvements in radiotherapy delivery have enabled higher therapeutic doses and improved efficacy, contributing to the growing number of long-term cancer survivors. These survivors are at risk of developing late toxicity from radiotherapy, and the inability to predict who is most susceptible results in substantial impact on quality of life and limits further curative dose escalation. A predictive assay or algorithm for normal tissue radiosensitivity would allow more personalized treatment planning, reducing the burden of late toxicity, and improving the therapeutic index. Progress over the last 10 years has shown that the etiology of late clinical radiotoxicity is multifactorial and informs development of predictive models that combine information on treatment (eg, dose, adjuvant treatment), demographic and health behaviors (eg, smoking, age), co-morbidities (eg, diabetes, collagen vascular disease), and biology (eg, genetics, ex vivo functional assays). AI has emerged as a useful tool and is facilitating extraction of signal from large datasets and development of high-level multivariable models. Some models are progressing to evaluation in clinical trials, and we anticipate adoption of these into the clinical workflow in the coming years. Information on predicted risk of toxicity could prompt modification of radiotherapy delivery (eg, use of protons, altered dose and/or fractionation, reduced volume) or, in rare instances of very high predicted risk, avoidance of radiotherapy. Risk information can also be used to assist treatment decision-making for cancers where efficacy of radiotherapy is equivalent to other treatments (eg, low-risk prostate cancer) and can be used to guide follow-up screening in instances where radiotherapy is still the best choice to maximize tumor control probability. Here, we review promising predictive assays for clinical radiotoxicity and highlight studies that are progressing to develop an evidence base for clinical utility.

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.

Cell Senescence-Related Pathways Are Enriched in Breast Cancer Patients With Late Toxicity After Radiotherapy and Low Radiation-Induced Lymphocyte Apoptosis

Background

Radiation-induced late effects are a common cause of morbidity among cancer survivors. The biomarker with the best evidence as a predictive test of late reactions is the radiation-induced lymphocyte apoptosis (RILA) assay. We aimed to investigate the molecular basis underlying the distinctive RILA levels by using gene expression analysis in patients with and without late effects and in whom we had also first identified differences in RILA levels.

Patients and Methods

Peripheral blood mononuclear cells of 10 patients with late severe skin complications and 10 patients without symptoms, selected from those receiving radiotherapy from 1993 to 2007, were mock-irradiated or irradiated with 8 Gy. The 48-h response was analyzed in parallel by RILA assay and gene expression profiling with Affymetrix microarrays. Irradiated and non-irradiated gene expression profiles were compared between both groups. Gene set enrichment analysis was performed to identify differentially expressed biological processes.

Results

Although differentially expressed mRNAs did not reach a significant adjusted p-value between patients suffering and not suffering clinical toxicity, the enriched pathways indicated significant differences between the two groups, either in irradiated or non-irradiated cells. In basal conditions, the main differentially expressed pathways between the toxicity and non-toxicity groups were the transport of small molecules, interferon signaling, and transcription. After 8 Gy, the differences lay in pathways highly related to cell senescence like cell cycle/NF-κB, G-protein-coupled receptors, and interferon signaling.

Conclusion

Patients at risk of developing late toxicity have a distinctive pathway signature driven by deregulation of immune and cell cycle pathways related to senescence, which in turn may underlie their low RILA phenotype.

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.

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.

Regional lung avoidance by CT numbers to reduce radiation-induced lung damage risk in non-small-cell lung cancer: a simulation study

Background: Selective avoidance aims at sparing functional lung regions. Here, we preferentially direct radiation to irreversibly nonfunctional lung areas based on planning CT imaging to reduce functional lung damage.Materials and methods: For 12 stage I-IV NSCLC patients, 5 lung substructures were segmented on the planning CT, combining voxels <-900HU, -900HU to -801HU, -800HU to -701HU, -700HU to -601HU and ≥-600HU (Level 1 to 5). Two VMAT plans were optimized: a reference plan blinded from substructures and a selective avoidance plan (AV) imposing gradually stricter constraints on Level 1-5, based on previously validated associations between lung subvolume baseline density and density increase (ΔHU) after treatment. Characteristics of treatment plans were evaluated, including subvolumes, dose, and predicted ΔHU (with reported 95% CI reflecting prediction model uncertainty).Results: Segmented substructures were on average 477 cc, 1157 cc, 484 cc, 69 cc, and 123 cc (Level 1-5). AV plans could spare Level 3-5, e.g., mean dose decrease of 3.5 Gy (range 0.6 Gy; 6.0 Gy) for Level 5, p<.001. This significantly reduced the average lung mass with predicted ΔHU>20HU by 12.5 g (95% CI: 5.4-16.9) and 27.1 g (95% CI: 10.2-32.9) for a median and upper 10th percentile patient susceptibility for damage simulation, respectively.Conclusions: Lung damage avoidance based on CT density is feasible and easy to implement. A biomarker providing a reliable selection of patients with high susceptibility for lung damage will be crucial to show the clinical relevance of this avoidance planning strategy.

Radiation-Induced Lung Injury (RILI)

Radiation pneumonitis (RP) and radiation fibrosis (RF) are two dose-limiting toxicities of radiotherapy (RT), especially for lung, and esophageal cancer. It occurs in 5-20% of patients and limits the maximum dose that can be delivered, reducing tumor control probability (TCP) and may lead to dyspnea, lung fibrosis, and impaired quality of life. Both physical and biological factors determine the normal tissue complication probability (NTCP) by Radiotherapy. A better understanding of the pathophysiological sequence of radiation-induced lung injury (RILI) and the intrinsic, environmental and treatment-related factors may aid in the prevention, and better management of radiation-induced lung damage. In this review, we summarize our current understanding of the pathological and molecular consequences of lung exposure to ionizing radiation, and pharmaceutical interventions that may be beneficial in the prevention or curtailment of RILI, and therefore enable a more durable therapeutic tumor response.

[Individual modification of the dose, volume and fractionation of breast radiotherapy]

Randomized trials demonstrated similar overall survival between mastectomy and breast-conservative surgery followed by adjuvant radiation therapy. Breast-conservative surgery, with adjuvant radiation therapy, with or without neoadjuvant systemic therapy has become the standard of care for women with early or locally advanced breast cancer. Nevertheless, certain cardiac, lung or cutaneous toxicities may alter the long-term body image and the quality of life of a limited number of patients who consider having had "overtreatment" or treatment outside the best knowledge of science. In case of low-risk breast cancer, several trials have evaluated the carcinologic outcome in absence of radiation therapy after breast-conservative surgery. Local recurrences increased in case of breast-conservative surgery alone but without impact on overall survival. Multiple debates have emerged in order to select the most appropriate evaluation criteria. Finally, a large consensus has considered that reducing local recurrences is important but with modern technologies and after identifying patients of individual radiosensitivity. Indeed, in case of a low absolute risk of local recurrence, radiation therapy techniques have been developed to allow a focal treatment especially for patients with high risk of developing late effects. This kind of compromise takes into account the reduction risk of local recurrences but also the probability of developing radiation-induced cutaneous sequelae. In the same way, for patients considered at high risk of recurrence, the huge volumes need specific techniques to better cover the targets while protecting the surrounding critic organs such as heart and lung. Intensity-modulated radiation therapy and the local high boost may help to decrease local recurrences of these more extended and aggressive diseases while considering the individual radiosensitivity that paves the way of long-term sequelae. In this article, we detail a personalized approach of breast radiation therapy considering the absolute risk of local recurrences and the probability of radiation-induced toxicity appearance.

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.

Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era

The ability to stratify patients using a set of biomarkers, which predict that toxicity risk would allow for radiotherapy (RT) modulation and serve as a valuable tool for precision medicine and personalized RT. For patients presenting with tumors with a low risk of recurrence, modifying RT schedules to avoid toxicity would be clinically advantageous. Indeed, for the patient at low risk of developing radiation-associated toxicity, use of a hypofractionated protocol could be proposed leading to treatment time reduction and a cost-utility advantage. Conversely, for patients predicted to be at high risk for toxicity, either a more conformal form or a new technique of RT, or a multidisciplinary approach employing surgery could be included in the trial design to avoid or mitigate RT when the potential toxicity risk may be higher than the risk of disease recurrence. In addition, for patients at high risk of recurrence and low risk of toxicity, dose escalation, such as a greater boost dose, or irradiation field extensions could be considered to improve local control without severe toxicities, providing enhanced clinical benefit. In cases of high risk of toxicity, tumor control should be prioritized. In this review, toxicity biomarkers with sufficient evidence for clinical testing are presented. In addition, clinical trial designs and predictive models are described for different clinical situations.