No association between TGF-β1 polymorphisms and radiation-induced lung toxicity in a European cohort of lung cancer patients

A Likely Role for a Novel Cell Therapeutic Target of Transforming Growth Factor-β1 on Radiation Pneumonitis in Lung and Nasopharyngeal Cancer Patients

The association between the polymorphism of transforming growth factor (TGF)-β1 and risk of radiation pneumonitis has been extensively investigated; however, conclusive results were unavailable. Eligible studies were identified from the database of Medline, Web of Science, EMBASE, and CNKI (China Knowledge Resource Integrated Database) up to September 2019. The odds ratio (OR) and 95% confidence interval (95% CI) were used to assess the strength of the relationship. The results showed that there were associations between TGF 869 T/C (rs1982073) and risks of radiation pneumonitis. Subgroup analyses showed that TGF 869 T/C was associated with risk of radiation pneumonitis in Caucasians (OR [95% CI]: 0.45 [0.31 to 0.67] for C carriers vs. TT). In addition, subgroup analyses also suggested that the C allele was associated with decreased risks of radiation pneumonitis among hospital-based case–control studies (0.56 [0.39 to 0.82] for C carriers vs. TT). Meanwhile, C allele was also suggested to be associated with decreased risk of radiation pneumonitis among PCC (0.60 [0.38 to 0.96] for C carriers vs. TT). Especially, C allele was also found to be associated with decreased risk of radiation pneumonitis from the participants with lung cancer (0.57 [0.37 to 0.90] for C carriers vs. TT). Our meta-analysis shows that T allele in TGF 869 T/C is significantly associated with the increased risk of radiation pneumonitis, especially for Caucasians, and for the participants with lung cancer.

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.

The Possibility of Using Genotoxicity, Oxidative Stress and Inflammation Blood Biomarkers to Predict the Occurrence of Late Cutaneous Side Effects after Radiotherapy

Despite the progresses performed in the field of radiotherapy, toxicity to the healthy tissues remains a major limiting factor. The aim of this work was to highlight blood biomarkers whose variations could predict the occurrence of late cutaneous side effects. Two groups of nine patients treated for Merkel Cell Carcinoma (MCC) were established according to the grade of late skin toxicity after adjuvant irradiation for MCC: grade 0, 1 or 2 and grade 3 or 4 of RTOG (Radiation Therapy Oncology Group)/EORTC (European Organization for Research and Treatment of Cancer). To try to discriminate these 2 groups, biomarkers of interest were measured on the different blood compartments after ex vivo irradiation. In lymphocytes, cell cycle, apoptosis and genotoxicity were studied. Oxidative stress was evaluated by the determination of the erythrocyte antioxidant capacity (superoxide dismutase, catalase, glutathione peroxidase, reduced and oxidized glutathione) as well as degradation products (protein carbonylation, lipid peroxidation). Inflammation was assessed in the plasma by the measurement of 14 cytokines. The most radiosensitive patients presented a decrease in apoptosis, micronucleus frequency, antioxidant enzyme activities, glutathione and carbonyls; and an increase in TNF-a (Tumor Necrosis Factor a), IL-8 (Interleukin 8) and TGF-β1 (Transforming Growth Factor β1) levels. These findings have to be confirmed on a higher number of patients and before radiotherapy and could allow to predict the occurrence of late skin side effects after radiotherapy.

External Validation of Radiation-Induced Dyspnea Models on Esophageal Cancer Radiotherapy Patients

Purpose: Radiation-induced lung disease (RILD), defined as dyspnea in this study, is a risk for patients receiving high-dose thoracic irradiation. This study is a TRIPOD (Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis) Type 4 validation of previously-published dyspnea models via secondary analysis of esophageal cancer SCOPE1 trial data. We quantify the predictive performance of these two models for predicting the maximal dyspnea grade ≥ 2 within 6 months after the end of high-dose chemo-radiotherapy for primary esophageal cancer.

Materials and methods: We tested the performance of two previously published dyspnea risk models using baseline, treatment and follow-up data on 258 esophageal cancer patients in the UK enrolled into the SCOPE1 multi-center trial. The tested models were developed from lung cancer patients treated at MAASTRO Clinic (The Netherlands) from the period 2002 to 2011. The adverse event of interest was dyspnea ≥ Grade 2 (CTCAE v3) within 6 months after the end of radiotherapy. As some variables were missing randomly and cannot be imputed, 212 patients in the SCOPE1 were used for validation of model 1 and 255 patients were used for validation of model 2. The model parameter Forced Expiratory Volume in 1 s (FEV₁), as a predictor to both validated models, was imputed using the WHO performance status. External validation was performed using an automated, decentralized approach, without exchange of individual patient data.

Results: Out of 258 patients with esophageal cancer in SCOPE1 trial data, 38 patients (14.7%) developed radiation-induced dyspnea (≥ Grade 2) within 6 months after chemo-radiotherapy. The discrimination performance of the models in esophageal cancer patients treated with high-dose external beam radiotherapy was moderate, area under curve (AUC) of 0.68 (95% CI 0.55–0.76) and 0.70 (95% CI 0.58–0.77), respectively. The curves and AUCs derived by distributed learning were identical to the results from validation on a local host.

Conclusion: We have externally validated previously published dyspnea models using an esophageal cancer dataset. FEV₁ that is not routinely measured for esophageal cancer was imputed using WHO performance status. Prediction performance was not statistically different from previous training and validation sets. Risk estimates were dominated by WHO score in Model 1 and baseline dyspnea in Model 2. The distributed learning approach gave the same answer as local processing, and could be performed without accessing a validation site's individual patients-level data.

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.

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.

Blood-based biomarkers for precision medicine in lung cancer: precision radiation therapy

Both tumors and patients are complex and models that determine survival and toxicity of radiotherapy or any other treatment ideally must take into account this variability as well as its dynamic state. The genetic features of the tumor and the host, and increasingly also the epi-genetic and proteomic characteristics, are being unraveled. Multiple techniques, including histological examination, blood sampling, measurement of circulating tumor cells (CTCs), and functional and molecular imaging, can be used for this purpose. However, the effects of radiation on the tumor and on organs at risk (OARs) are also influenced by the applied dose and volume of irradiated tissues. Combining all these biological, clinical, imaging, and dosimetric parameters in a validated prognostic or predictive model poses a major challenge. Here we aimed to provide an objective review of the potential of blood markers to guide high precision radiation therapy. A combined biological-mathematical approach opens new doors beyond prognostication of patients, as it allows truly precise oncological treatment. Indeed, the core for individualized and precision medicine is not only selection of patients, but even more the optimization of the therapeutic window on an individual basis. A holistic model will allow for determination of an individual dose-response relationship for each organ at risk for each tumor in each individual patient for the complete oncological treatment package. This includes, but is not limited to, radiotherapy alone. Individualized dose-response curves will allow for consideration of different doses of radiation and combinations with other drugs to plan for both optimal toxicity and complete response. Insights into the interactions between a multitude of parameters will lead to the discovery of new pathways and networks that will fuel new biological research on target discovery.

Immunological Aspect of Radiation-Induced Pneumonitis, Current Treatment Strategies, and Future Prospects

Delivery of high doses of radiation to thoracic region, particularly with non-small cell lung cancer patients, becomes difficult due to subsequent complications arising in the lungs of the patient. Radiation-induced pneumonitis is an early event evident in most radiation exposed patients observed within 2-4 months of treatment and leading to fibrosis later. Several cytokines and inflammatory molecules interplay in the vicinity of the tissue developing radiation injury leading to pneumonitis and fibrosis. While certain cytokines may be exploited as biomarkers, they also appear to be a potent target of intervention at transcriptional level. Initiation and progression of pneumonitis and fibrosis thus are dynamic processes arising after few months to year after irradiation of the lung tissue. Currently, available treatment strategies are challenged by the major dose limiting complications that curtails success of the treatment as well as well being of the patient's future life. Several approaches have been in practice while many other are still being explored to overcome such complications. The current review gives a brief account of the immunological aspects, existing management practices, and suggests possible futuristic approaches.

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review 'omics' approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different 'omics' approaches may be more efficient in identifying critical pathways than pathway analysis based on single 'omics' data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterised by different mechanisms. Thus 'omics' and functional approaches may synergise if they are integrated into radiogenomics 'systems biology' to facilitate the goal of individualised radiotherapy.

TGF-β1 rs1982073 polymorphism contributes to radiation pneumonitis in lung cancer patients: a meta-analysis

Transforming growth factor beta 1(TGF-β1) polymorphism was associated with radiation pneumonitis (RP) susceptibility, but their results have been inconsistent. The PubMed and CNKI were searched for case-control studies published up to Januray 01, 2016 was Data were extracted and pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated. In this meta-analysis, we assessed eight publications involving 368 radiation pneumonitis cases and 855 controls of the association between TGF-β1 T869C (rs1982073) and G915C (rs1800471) polymorphism and RP susceptibility. Our analysis suggested that TGF-β1 T869C rs1982073 polymorphism was associated with lower RP risk for CT combined CC versus TT model (OR = 0.58, 95% CI = 0.43-0.77). However, for the G915C rs1800471 polymorphism, no association was found between the polymorphism and the susceptibility to RP in GC combined CC versus GG model (OR = 0.82, 95% CI = 0.50-1.35). These results from the meta-analysis suggest that T869C rs1982073 polymorphism of TGF-β1 may be associated with RP risk, and there may be no association between G915C polymorphism and RP risk.

Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine

BACKGROUND

Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided.

AIM

The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm.

DISCUSSION

Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.

Predictive SNPs for radiation-induced damage in lung cancer patients with radiotherapy: a potential strategy to individualize treatment

In the treatment of lung cancer, radiotherapy has become one of the most important therapies, despite its sometimes unpredictable side effects. As such, identifying lung cancer patients who are at high risk of developing severe radiation-induced damage (mainly radiation pneumonitis and radiation-induced esophageal toxicity) and applying effect intervention or monitoring techniques are important. Although human diversity to a certain amount is explained by clinical and dosimetric factors, the presence of specific genetic determinants also influences the occurrence of radiation-induced damage. Here we summarize the data on mechanisms of radiation pneumonitis and radiation-induced esophageal toxicity supporting the involvement of variances of genes in the evolution of radiation-induced damage. Furthermore, the available evidence from current clinical studies of genetic polymorphisms for the prediction of radiation pneumonitis and radiation-induced esophageal toxicity is discussed. Eventually, this may help to truly individualize radiotherapy, using a personal genetic profile of the most relevant genes for each lung cancer patient.

Pre-Radiation Therapy Fluorine 18 Fluorodeoxyglucose PET Helps Identify Patients with Esophageal Cancer at High Risk for Radiation Pneumonitis

PURPOSE

To examine the association between pre-radiation therapy (RT) fluorine 18 fluorodeoxyglucose (FDG) uptake and post-RT symptomatic radiation pneumonitis (RP).

MATERIALS AND METHODS

In accordance with the retrospective study protocol approved by the institutional review board, 228 esophageal cancer patients who underwent FDG PET/CT before chemotherapy and RT were examined. RP symptoms were evaluated by using the Common Terminology Criteria for Adverse Events, version 4.0, from the consensus of five clinicians. By using the cumulative distribution of standardized uptake values (SUVs) within the lungs, those values greater than 80%-95% of the total lung voxels were determined for each patient. The effect of pre-chemotherapy and RT FDG uptake, dose, and patient or treatment characteristics on RP toxicity was studied by using logistic regression.

RESULTS

The study subjects were treated with three-dimensional conformal RT (n = 36), intensity-modulated RT (n = 135), or proton therapy (n = 57). Logistic regression analysis demonstrated elevated FDG uptake at pre-chemotherapy and RT was related to expression of RP symptoms. Study subjects with elevated 95% percentile of the SUV (SUV95) were more likely to develop symptomatic RP (P < .000012); each 0.1 unit increase in SUV95 was associated with a 1.36-fold increase in the odds of symptomatic RP. Receiver operating characteristic (ROC) curve analysis resulted in area under the ROC curve of 0.676 (95% confidence interval: 0.58, 0.77), sensitivity of 60%, and specificity of 71% at the 1.17 SUV95 threshold. CT imaging and dosimetric parameters were found to be poor predictors of RP symptoms.

CONCLUSION

The SUV95, a biomarker of pretreatment pulmonary metabolic activity, was shown to be prognostic of symptomatic RP. Elevation in this pretreatment biomarker identifies patients at high risk for posttreatment symptomatic RP.

A polymorphism in the DNA repair domain of APEX1 is associated with the radiation-induced pneumonitis risk among lung cancer patients after radiotherapy

OBJECTIVE

To examine the association of tag single nucleotide polymorphisms (tagSNPs) (rs1130409, rs1760944, rs2307486 and rs3136817) in APEX1 with the risk of severe radiation-induced pneumonitis (RP) after radiotherapy among Han Chinese patients with lung cancer.

METHODS

A total of 168 patients with lung cancer who were receiving radiotherapy were prospectively recruited. RP was evaluated according to the Radiation Therapy Oncology Group. A case-control study was performed. The case group included patients with RP grade of ≥3, while the control group comprised patients with RP grades <3. Four tagSNPs of APEX1 were genotyped in 126 patients with complete follow-up by multi-SNaPshot® (Genesky Biotechnologies Inc., Shanghai, China) genotyping assays. RESULTS were assessed by a logistic regression model for RP risk and Mantal-Cox log-rank test for the cumulative RP probability by the genotypes.

RESULTS

rs1130409 was associated with severe RP. GT genotype of rs1130409 was significantly higher in patients with RP than in those of the control group [68.8% vs 41.8%; p = 0.025; resulting odds ratio (OR), 5.98]. Patients with lung cancer bearing the G allele had a 5.83-fold higher risk of RP than those with the wild TT genotype [OR = 5.83; 95% confidence interval (CI), 1.27-26.90; p = 0.024], and this was further confirmed by the binary regression adjusted by some confounding factors, including Karnofsky performance scale, concurrent chemotherapy-radiotherapy and lung volume receiving >30 Gy (OR = 6.96; 95% CI, 1.36-35.77; p = 0.02). rs1130409 was also associated with the time to occurrence of severe RP (p = 0.04). Three-dimensional model APEX1 protein showed that rs1130409 is located in the random coil structure corresponding to the DNA repair function region.

ADVANCES IN KNOWLEDGE

rs1130409 of APEX1 can be a predictor of RP grades ≥3 among patients with lung cancer.

The association between TGF-β1 polymorphisms and radiation pneumonia in lung cancer patients treated with definitive radiotherapy: a meta-analysis

Background

Previous studies investigating the association between TGF-β1 polymorphisms and Radiation Pneumonia (RP) risk have provided inconsistent results. The aim of our study was to assess the association between the TGF-β1 genes C509T, G915C and T869C polymorphisms and risk of RP in lung cancer patients treated with definitive radiotherapy.

Methods

Two investigators independently searched the Medline, Embase, CNKI, and Chinese Biomedicine Databases for studies published before September 2013. Summary odds ratios (ORs) and 95% confidence intervals (CIs) for TGF-β1 polymorphisms and RP were calculated in a fixed-effects model or a random-effects model when appropriate.

Results

Ultimately, each 7 studies were found to be eligible for meta-analyses of C509T, G915C and T869C, respectively. Our analysis suggested that the variant genotypes of T869C were associated with a significantly increased RP risk in dominant model (OR = 0.59, 95% CI = 0.45–0.79) and CT vs. TT model (OR = 0.47, 95% CI = 0.32–0.69). In the subgroup analyses by ethnicity/country, a significantly increased risk was observed among Caucasians. For C509T and G915C polymorphism, no obvious associations were found for all genetic models.

Conclusion

This meta-analysis suggests that T869C polymorphism of TGF-β1 may be associated with RP risk only in Caucasians, and there may be no association between C509T and G915C polymorphism and RP risk.

Polymorphisms in radio-responsive genes and its association with acute toxicity among head and neck cancer patients

Cellular and molecular approaches are being explored to find a biomarker which can predict the development of radiation induced acute toxicity prior to radiation therapy. SNPs in radiation responsive genes may be considered as an approach to develop tools for finding the inherited basis of clinical radiosensitivity. The current study attempts to screen single nucleotide polymorphisms/deletions in DNA damage response, DNA repair, profibrotic cytokine as well as antioxidant response genes and its predictive potential with the normal tissue adverse reactions from 183 head and neck cancer patients undergoing platinum based chemoradiotherapy or radiotherapy alone. We analysed 22 polymorphisms in 17 genes having functional relevance to radiation response. Radiation therapy induced oral mucositis and skin erythema was considered as end point for clinical radiosensitivity. Direct correlation of heterozygous and mutant alleles with acute reactions as well as haplotype correlation revealed NBN variants to be of predictive significance in analysing oral mucositis prior to radiotherapy. In addition, genetic linkage disequilibrium existed in XRCC1 polymorphisms for >grade 2 oral mucositis and skin reaction indicating the complex inheritance pattern. The current study indicates an association for polymorphism in NBN with normal tissue radiosensitivity and further warrants the replication of such studies in a large set of samples.

Quantification of radiation-induced lung damage with CT scans: the possible benefit for radiogenomics

BACKGROUND

Radiation-induced lung damage (RILD) is an important problem. Although physical parameters such as the mean lung dose are used in clinical practice, they are not suited for individualised radiotherapy. Objective, quantitative measurements of RILD on a continuous instead of on an ordinal, semi-quantitative, semi-subjective scale, are needed.

METHODS

Hounsfield unit (HU) changes before versus three months post-radiotherapy were correlated per voxel with the radiotherapy dose in 95 lung cancer patients. Deformable registration was used to register pre- and post-CT scans and the density increase was quantified for various dose bins. The dose-response curve for increased HU was quantified using the slope of a linear regression (HU/Gy). The end-point for the toxicity analysis was dyspnoea ≥ grade 2.

RESULTS

Radiation dose was linearly correlated with the change in HU (mean R(2) = 0.74 ± 0.28). No differences in HU/Gy between groups treated with stereotactic radiotherapy, conventional radiotherapy alone, sequential or concurrent chemo- radiotherapy were observed. In the whole patient group, 33/95 (34.7%) had dyspnoea ≥ G2. Of the 48 patients with a HU/Gy below the median, 16 (33.3%) developed dyspnoea ≥ G2, while in the 47 patients with a HU/Gy above the median, 17 (36.1%) had dyspnoea ≥ G2 (not significant). Individual patients showed a nearly 21-fold difference in radiosensitivity, with HU/Gy ranging from 0 to 10 HU/Gy.

CONCLUSIONS

HU changes identify objectively the whole range of individual radiosensitivity on a continuous, quantitative scale. CT density changes may allow more robust and accurate radiogenomics studies.

Time evolution of regional CT density changes in normal lung after IMRT for NSCLC

PURPOSE

This study investigates the clinical radiobiology of radiation induced lung disease in terms of regional computed tomography (CT) density changes following intensity modulated radiotherapy (IMRT) for non-small-cell lung cancer (NSCLC).

METHODS

A total of 387 follow-up CT scans in 131 NSCLC patients receiving IMRT to a prescribed dose of 60 or 66 Gy in 2 Gy fractions were analyzed. The dose-dependent temporal evolution of the density change was analyzed using a two-component model, a superposition of an early, transient component and a late, persistent component.

RESULTS

The CT density of healthy lung tissue was observed to increase significantly (p<0.0001) for all dose levels after IMRT. The time evolution and the size of the density signal depend on the local delivered dose. The transient component of the density signal was found to peak in the range of 3-4 months, while the density tends to stabilize at times >12 months.

CONCLUSIONS

The radiobiology of lung injury may be analyzed in terms of CT density change. The initial transient change in density is consistent with radiation pneumonitis, while the subsequent stabilization of the density is consistent with pulmonary fibrosis.

Cardiac comorbidity is an independent risk factor for radiation-induced lung toxicity in lung cancer patients

PURPOSE

To test the hypothesis that cardiac comorbidity before the start of radiotherapy (RT) is associated with an increased risk of radiation-induced lung toxicity (RILT) in lung cancer patients.

MATERIAL AND METHODS

A retrospective analysis was performed of a prospective cohort of 259 patients with locoregional lung cancer treated with definitive radio(chemo)therapy between 2007 and 2011 (ClinicalTrials.gov Identifiers: NCT00572325 and NCT00573040). We defined RILT as dyspnea CTCv.3.0 grade ≥2 within 6 months after RT, and cardiac comorbidity as a recorded treatment of a cardiac pathology at a cardiology department. Univariate and multivariate analyses, as well as external validation, were performed. The model-performance measure was the area under the receiver operating characteristic curve (AUC).

RESULTS

Prior to RT, 75/259 (28.9%) patients had cardiac comorbidity, 44% of whom (33/75) developed RILT. The odds ratio of developing RILT for patients with cardiac comorbidity was 2.58 (p<0.01). The cross-validated AUC of a model with cardiac comorbidity, tumor location, forced expiratory volume in 1s, sequential chemotherapy and pretreatment dyspnea score was 0.72 (p<0.001) on the training set, and 0.67 (p<0.001) on the validation set.

CONCLUSION

Cardiac comorbidity is an important risk factor for developing RILT after definite radio(chemo)therapy of lung cancer patients.

[Intrinsic radiosensitivity: predictive assays that will change daily practice]

The impact of curative radiotherapy depends mainly on the total dose delivered homogenously in the targeted volume. Nevertheless, the dose delivered to the surrounding healthy tissues may reduce the therapeutic ratio of many radiation treatments. In a same population treated in one center with the same technique, it appears that individual radiosensitivity clearly exists, namely in terms of late side effects that are in principle non-reversible. This review details the different radiobiological approaches that have been developed to better understand the mechanisms of radiation-induced late effects. We also present the possibilities of clinical use of predictive assays in the close future.

'Rapid Learning health care in oncology' - an approach towards decision support systems enabling customised radiotherapy'

PURPOSE

An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy.

MATERIAL AND RESULTS

Rapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes.

CONCLUSION

Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making.