Searching for genetic determinants of normal tissue radiosensitivity – Are we on the right track?

Susceptibility to radiation adverse effects in veterans with Gulf War illness and healthy civilians

We evaluated whether veterans with Gulf War illness (VGWI) report greater ionizing radiation adverse effects (RadAEs) than controls; whether radiation-sensitivity is tied to reported chemical-sensitivity; and whether environmental exposures are apparent risk factors for reported RadAEs (rRadAEs). 81 participants (41 VGWI, 40 controls) rated exposure to, and rRadAEs from, four radiation types. The relations of RadAE-propensity (defined as the ratio of rRadAEs to summed radiation exposures) to Gulf War illness (GWI) presence and severity, and to reported chemical-sensitivity were assessed. Ordinal logistic regression evaluated exposure prediction of RadAE-propensity in the full sample, in VGWI, and stratified by age and chemical-sensitivity. RadAE-propensity was increased in VGWI (vs. controls) and related to GWI severity (p < 0.01) and chemical-sensitivity (p < 0.01). Past carbon monoxide (CO) exposure emerged as a strong, robust predictor of RadAE-propensity on univariable and multivariable analyses (p < 0.001 on multivariable assessment, without and with adjustment for VGWI case status), retaining significance in age-stratified and chemical-sensitivity-stratified replication analyses. Thus, RadAE-propensity, a newly-described GWI-feature, relates to chemical-sensitivity, and is predicted by CO exposure-both features reported for nonionizing radiation sensitivity, consistent with shared mitochondrial/oxidative toxicity across radiation frequencies. Greater RadAE vulnerability fits an emerging picture of heightened drug/chemical susceptibility in VGWI.

Biological Pathways Associated With the Development of Pulmonary Toxicities in Mesothelioma Patients Treated With Radical Hemithoracic Radiation Therapy: A Preliminary Study

Radical hemithoracic radiotherapy (RHR), after lung-sparing surgery, has recently become a concrete therapeutic option for malignant pleural mesothelioma (MPM), an asbestos-related, highly aggressive tumor with increasing incidence and poor prognosis. Although the toxicity associated to this treatment has been reduced, it is still not negligible and must be considered when treating patients. Genetic factors appear to play a role determining radiotherapy toxicity. The aim of this study is the identification of biological pathways, retrieved through whole exome sequencing (WES), possibly associated to the development of lung adverse effects in MPM patients treated with RHR. The study included individuals with MPM, treated with lung-sparing surgery and chemotherapy, followed by RHR with curative intent, and followed up prospectively for development of pulmonary toxicity. Due to the strong impact of grade 3 pulmonary toxicities on the quality of life, compared with less serious adverse events, for genetic analyses, patients were divided into a none or tolerable pulmonary toxicity (NoSTox) group (grade ≤2) and a severe pulmonary toxicity (STox) group (grade = 3). Variant enrichment analysis allowed us to identify different pathway signatures characterizing NoSTox and Stox patients, allowing to formulate hypotheses on the protection from side effects derived from radiotherapy as well as factors predisposing to a worst response to the treatment. Our findings, being aware of the small number of patients analyzed, could be considered a starting point for the definition of a panel of pathways, possibly helpful in the management of MPM patients.

Breast cancer radiotherapy: What physicians need to know in the era of the precision medicine

Breast cancer is the most common cancer in women worldwide and encompasses a broad spectrum of diseases in one with significant epidemiological, clinical, and biological heterogeneity, which determines a different natural history and prognostic profile. Although classical tumour staging (TNM) still provides valuable information, the current reality is that the clinicians must consider other biological and molecular factors that directly influence treatment decision-making. The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. This knowledge has brought with it a major impact regarding surgical and systemic management and has been practice-changing, but it has also created significant uncertainties regarding how best integrate the radiotherapy treatment into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualised treatment approaches. In this context, "precision medicine" seeks to provide an answer to these open questions and uncertainties. The aim of the present review is to clarify the meaning of this term and to critically evaluate its role and impact on contemporary breast cancer radiotherapy.

Insights into the theranostic value of precision medicine on advanced radiotherapy to breast cancer

Breast cancer is the most common cancer in women worldwide. "Breast cancer" encompasses a broad spectrum of diseases (i.e., subtypes) with significant epidemiological, clinical, and biological heterogeneity. Each of these subtypes has a different natural history and prognostic profile. Although tumour staging (TNM classification) still provides valuable information in the overall management of breast cancer, the current reality is that clinicians must consider other biological and molecular factors that directly influence treatment decision-making, including extent of surgery, indication for chemotherapy, hormonal therapy, and even radiotherapy (and treatment volumes). The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. While these changes have been extremely positive in terms of surgical and systemic management, they have also created significant uncertainties concerning integration of local and locoregional radiotherapy into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances. Beyond the evident technological advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualized treatment approaches. In this context, "precision medicine" seeks to provide an answer to these open questions and uncertainties. Although precision medicine has been much discussed in the last five years or so, the concept remains somewhat ambiguous, and it often appear to be used as a "catch-all" term. The present review aims to clarify the meaning of this term and, more importantly, to critically evaluate the role and impact of precision medicine on breast cancer radiotherapy. Finally, we will discuss the current and future of precision medicine in radiotherapy.

Prediction of the Acute or Late Radiation Toxicity Effects in Radiotherapy Patients Using Ex Vivo Induced Biodosimetric Markers: A Review

A search for effective methods for the assessment of patients' individual response to radiation is one of the important tasks of clinical radiobiology. This review summarizes available data on the use of ex vivo cytogenetic markers, typically used for biodosimetry, for the prediction of individual clinical radiosensitivity (normal tissue toxicity, NTT) in cells of cancer patients undergoing therapeutic irradiation. In approximately 50% of the relevant reports, selected for the analysis in peer-reviewed international journals, the average ex vivo induced yield of these biodosimetric markers was higher in patients with severe reactions than in patients with a lower grade of NTT. Also, a significant correlation was sometimes found between the biodosimetric marker yield and the severity of acute or late NTT reactions at an individual level, but this observation was not unequivocally proven. A similar controversy of published results was found regarding the attempts to apply G2- and γH2AX foci assays for NTT prediction. A correlation between ex vivo cytogenetic biomarker yields and NTT occurred most frequently when chromosome aberrations (not micronuclei) were measured in lymphocytes (not fibroblasts) irradiated to relatively high doses (4-6 Gy, not 2 Gy) in patients with various grades of late (not early) radiotherapy (RT) morbidity. The limitations of existing approaches are discussed, and recommendations on the improvement of the ex vivo cytogenetic testing for NTT prediction are provided. However, the efficiency of these methods still needs to be validated in properly organized clinical trials involving large and verified patient cohorts.

A review of radiation genomics: integrating patient radiation response with genomics for personalised and targeted radiation therapy

Background

The success of radiation therapy for cancer patients is dependent on the ability to deliver a total tumouricidal radiation dose capable of eradicating all cancer cells within the clinical target volume, however, the radiation dose tolerance of the surrounding healthy tissues becomes the main dose-limiting factor. The normal tissue adverse effects following radiotherapy are common and significantly impact the quality of life of patients. The likelihood of developing these adverse effects following radiotherapy cannot be predicted based only on the radiation treatment parameters. However, there is evidence to suggest that some common genetic variants are associated with radiotherapy response and the risk of developing adverse effects. Radiation genomics is a field that has evolved in recent years investigating the association between patient genomic data and the response to radiation therapy. This field aims to identify genetic markers that are linked to individual radiosensitivity with the potential to predict the risk of developing adverse effects due to radiotherapy using patient genomic information. It also aims to determine the relative radioresponse of patients using their genetic information for the potential prediction of patient radiation treatment response.

Methods and materials

This paper reports on a review of recent studies in the field of radiation genomics investigating the association between genomic data and patients response to radiation therapy, including the investigation of the role of genetic variants on an individual’s predisposition to enhanced radiotherapy radiosensitivity or radioresponse.

Conclusion

The potential for early prediction of treatment response and patient outcome is critical in cancer patients to make decisions regarding continuation, escalation, discontinuation, and/or change in treatment options to maximise patient survival while minimising adverse effects and maintaining patients’ quality of life.

Predictive value of single nucleotide polymorphisms in XRCC1 for radiation-induced normal tissue toxicity

Purpose

X-Ray Repair Cross Complementing 1 (XRCC1) functioning in the base excision repair pathway plays an important role in the repair of DNA single-strand breaks caused by ionizing radiation. The relationship between XRCC1 polymorphisms and the risk of radiation-induced side effects on normal tissues remains controversial. Therefore, we performed a comprehensive meta-analysis to elucidate these associations.

Materials and methods

A systematic literature search was carried out in PubMed, Medline (Ovid), Embase, Web of Science, Cochrane database, and the references of relevant studies. The pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the strength of the association.

Results

A total of 40 studies including 6,682 patients were eventually identified in this meta-analysis. Pooled results suggested that rs25487 Arg399Gln polymorphism significantly increased the risk of acute radiation-induced side effects (OR=1.29, 95% CI: 1.10–1.52, P=0.002), especially acute mucositis (OR=1.91, 95% CI: 1.17–3.11, P=0.01) and acute gastrointestinal and genitourinary toxicity (OR=1.49, 95% CI: 1.04–2.11, P=0.03). Furthermore, patients who received head and neck irradiation with rs25487 Arg399Gln polymorphism were more likely to experience radiotherapy (RT)-induced side effects (OR=1.46, 95% CI: 1.12–1.90, P=0.005). However, no statistically significant correlations were identified between rs25487 polymorphism and any late side effects and other irradiation areas. Likewise, no significant associations were detected between rs25489, rs1799782, or rs3213245 polymorphism and RT-induced toxicity.

Conclusion

Our meta-analysis demonstrated that XRCC1 rs25487 Arg399Gln polymorphism had a significant predictive value and might predict a risk of severely acute RT-induced adverse effects, especially in acute mucositis and acute gastrointestinal and genitourinary toxicity, or in patients with head and neck irradiation. However, large-scale and well-designed studies are required to further evaluate the predictive value of XRCC1 variations on radiation-induced side effects in order to identify radiosensitive patients and predict radiotoxicity.

Machine Learning and Radiogenomics: Lessons Learned and Future Directions

Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

Systemic effects of ionizing radiation at the proteome and metabolome levels in the blood of cancer patients treated with radiotherapy: the influence of inflammation and radiation toxicity

PURPOSE

Blood is the most common replacement tissue used to study systemic responses of organisms to different types of pathological conditions and environmental insults. Local irradiation during cancer radiotherapy induces whole body responses that can be observed at the blood proteome and metabolome levels. Hence, comparative blood proteomics and metabolomics are emerging approaches used in the discovery of radiation biomarkers. These techniques enable the simultaneous measurement of hundreds of molecules and the identification of sets of components that can discriminate different physiological states of the human body. Radiation-induced changes are affected by the dose and volume of irradiated tissues; hence, the molecular composition of blood is a hypothetical source of biomarkers for dose assessment and the prediction and monitoring of systemic responses to radiation. This review aims to provide a comprehensive overview on the available evidence regarding molecular responses to ionizing radiation detected at the level of the human blood proteome and metabolome. It focuses on patients exposed to radiation during cancer radiotherapy and emphasizes effects related to radiation-induced toxicity and inflammation.

CONCLUSIONS

Systemic responses to radiation detected at the blood proteome and metabolome levels are primarily related to the intensity of radiation-induced toxicity, including inflammatory responses. Thus, several inflammation-associated molecules can be used to monitor or even predict radiation-induced toxicity. However, these abundant molecular features have a rather limited applicability as universal biomarkers for dose assessment, reflecting the individual predisposition of the immune system and tissue-specific mechanisms involved in radiation-induced damage.

Genetic Variants in MTHFR Gene Predict ≥ 2 Radiation Pneumonitis in Esophageal Squamous Cell Carcinoma Patients Treated with Thoracic Radiotherapy

Reactive oxygen species (ROS), formed as an indirect production of radiotherapy (RT), could cause DNA damage of normal tissues. Meanwhile, our body possesses the ability to restore the damage by DNA repair pathways. The imbalance between the two systems could finally result in radiation injury. Therefore, in this prospective cohort study, we explored the association of genetic variants in ROS metabolism and DNA repair pathway-related genes with radiation pneumonitis (RP). A total of 265 locally advanced esophageal squamous cell carcinoma (ESCC) patients receiving RT in Chinese Han population were enrolled. Five functional single nucleotide polymorphisms (SNPs) (rs1695 in GSTP1; rs4880 in SOD2; rs3957356 in GSTA1; and rs1801131, rs1801133 in MTHFR) were genotyped using the MassArray system, and rs1801131 was found to be a predictor of ≥ 2 RP. Our results showed that, compared with TT genotype, patients with GG/GT genotypes of rs1801131 had a notably lower risk of developing ≥ 2 RP (HR = 0.339, 95% CI = 0.137–0.839, P = 0.019). Further independent studies are required to confirm this findings.

Individual patient data meta-analysis shows a significant association between the ATM rs1801516 SNP and toxicity after radiotherapy in 5456 breast and prostate cancer patients

PURPOSE

Several small studies have indicated that the ATM rs1801516 SNP is associated with risk of normal tissue toxicity after radiotherapy. However, the findings have not been consistent. In order to test this SNP in a well-powered study, an individual patient data meta-analysis was carried out by the International Radiogenomics Consortium.

MATERIALS AND METHODS

The analysis included 5456 patients from 17 different cohorts. 2759 patients were given radiotherapy for breast cancer and 2697 for prostate cancer. Eight toxicity scores (overall toxicity, acute toxicity, late toxicity, acute skin toxicity, acute rectal toxicity, telangiectasia, fibrosis and late rectal toxicity) were analyzed. Adjustments were made for treatment and patient related factors with potential impact on the risk of toxicity.

RESULTS

For all endpoints except late rectal toxicity, a significantly increased risk of toxicity was found for carriers of the minor (Asn) allele with odds ratios of approximately 1.5 for acute toxicity and 1.2 for late toxicity. The results were consistent with a co-dominant pattern of inheritance.

CONCLUSION

This study convincingly showed a significant association between the ATM rs1801516 Asn allele and increased risk of radiation-induced normal tissue toxicity.

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.

Genetic Variants in CD44 and MAT1A Confer Susceptibility to Acute Skin Reaction in Breast Cancer Patients Undergoing Radiation Therapy

PURPOSE

Heterogeneity in radiation therapy (RT)-induced normal tissue toxicity is observed in 10% of cancer patients, limiting the therapeutic outcomes. In addition to treatment-related factors, normal tissue adverse reactions also manifest from genetic alterations in distinct pathways majorly involving DNA damage-repair genes, inflammatory cytokine genes, cell cycle regulation, and antioxidant response. Therefore, the common sequence variants in these radioresponsive genes might modify the severity of normal tissue toxicity, and the identification of the same could have clinical relevance as a predictive biomarker.

METHODS AND MATERIALS

The present study was conducted in a cohort of patients with breast cancer to evaluate the possible associations between genetic variants in radioresponsive genes described previously and the risk of developing RT-induced acute skin adverse reactions. We tested 22 genetic variants reported in 18 genes (ie, NFE2L2, OGG1, NEIL3, RAD17, PTTG1, REV3L, ALAD, CD44, RAD9A, TGFβR3, MAD2L2, MAP3K7, MAT1A, RPS6KB2, ZNF830, SH3GL1, BAX, and XRCC1) using TaqMan assay-based real-time polymerase chain reaction. At the end of RT, the severity of skin damage was scored, and the subjects were dichotomized as nonoverresponders (Radiation Therapy Oncology Group grade <2) and overresponders (Radiation Therapy Oncology Group grade ≥2) for analysis.

RESULTS

Of the 22 single nucleotide polymorphisms studied, the rs8193 polymorphism lying in the micro-RNA binding site of 3'-UTR of CD44 was significantly (P=.0270) associated with RT-induced adverse skin reactions. Generalized multifactor dimensionality reduction analysis showed significant (P=.0107) gene-gene interactions between MAT1A and CD44. Furthermore, an increase in the total number of risk alleles was associated with increasing occurrence of overresponses (P=.0302).

CONCLUSIONS

The genetic polymorphisms in radioresponsive genes act as genetic modifiers of acute normal tissue toxicity outcomes after RT by acting individually (rs8193), by gene-gene interactions (MAT1A and CD44), and/or by the additive effects of risk alleles.

Pitfalls in Prediction Modeling for Normal Tissue Toxicity in Radiation Therapy: An Illustration With the Individual Radiation Sensitivity and Mammary Carcinoma Risk Factor Investigation Cohorts

PURPOSE

To identify the main causes underlying the failure of prediction models for radiation therapy toxicity to replicate.

METHODS AND MATERIALS

Data were used from two German cohorts, Individual Radiation Sensitivity (ISE) (n=418) and Mammary Carcinoma Risk Factor Investigation (MARIE) (n=409), of breast cancer patients with similar characteristics and radiation therapy treatments. The toxicity endpoint chosen was telangiectasia. The LASSO (least absolute shrinkage and selection operator) logistic regression method was used to build a predictive model for a dichotomized endpoint (Radiation Therapy Oncology Group/European Organization for the Research and Treatment of Cancer score 0, 1, or ≥2). Internal areas under the receiver operating characteristic curve (inAUCs) were calculated by a naïve approach whereby the training data (ISE) were also used for calculating the AUC. Cross-validation was also applied to calculate the AUC within the same cohort, a second type of inAUC. Internal AUCs from cross-validation were calculated within ISE and MARIE separately. Models trained on one dataset (ISE) were applied to a test dataset (MARIE) and AUCs calculated (exAUCs).

RESULTS

Internal AUCs from the naïve approach were generally larger than inAUCs from cross-validation owing to overfitting the training data. Internal AUCs from cross-validation were also generally larger than the exAUCs, reflecting heterogeneity in the predictors between cohorts. The best models with largest inAUCs from cross-validation within both cohorts had a number of common predictors: hypertension, normalized total boost, and presence of estrogen receptors. Surprisingly, the effect (coefficient in the prediction model) of hypertension on telangiectasia incidence was positive in ISE and negative in MARIE. Other predictors were also not common between the 2 cohorts, illustrating that overcoming overfitting does not solve the problem of replication failure of prediction models completely.

CONCLUSIONS

Overfitting and cohort heterogeneity are the 2 main causes of replication failure of prediction models across cohorts. Cross-validation and similar techniques (eg, bootstrapping) cope with overfitting, but the development of validated predictive models for radiation therapy toxicity requires strategies that deal with cohort heterogeneity.

[Radiomics: Definition and clinical development]

The ultimate goal in radiation oncology is to offer a personalized treatment to all patients indicated for radiotherapy. Radiomics is a tool that reinforces a deep analysis of tumors at the molecular aspect taking into account intrinsic susceptibility in a long-term follow-up. Radiomics allow qualitative and quantitative performance analyses with high throughput extraction of numeric radiologic data to obtain predictive or prognostic information from patients treated for cancer. A second approach is to define biological or constitutional that could change the practice. This technique included normal tissue individual susceptibility but also potential response of tumors under ionizing radiation treatment. These "omics" are biological and technical techniques leading to simultaneous novel identification and exploration a set of genes, lipids, proteins.

Ataxia telangiectasia-mutated gene polymorphisms and acute normal tissue injuries in cancer patients after radiation therapy: a systematic review and meta-analysis

PURPOSE

Studies of the association between ataxia telangiectasia-mutated (ATM) gene polymorphisms and acute radiation injuries are often small in sample size, and the results are inconsistent. We conducted the first meta-analysis to provide a systematic review of published findings.

METHODS AND MATERIALS

Publications were identified by searching PubMed up to April 25, 2014. Primary meta-analysis was performed for all acute radiation injuries, and subgroup meta-analyses were based on clinical endpoint. The influence of sample size and radiation injury incidence on genetic effects was estimated in sensitivity analyses. Power calculations were also conducted.

RESULTS

The meta-analysis was conducted on the ATM polymorphism rs1801516, including 5 studies with 1588 participants. For all studies, the cut-off for differentiating cases from controls was grade 2 acute radiation injuries. The primary meta-analysis showed a significant association with overall acute radiation injuries (allelic model: odds ratio = 1.33, 95% confidence interval: 1.04-1.71). Subgroup analyses detected an association between the rs1801516 polymorphism and a significant increase in urinary and lower gastrointestinal injuries and an increase in skin injury that was not statistically significant. There was no between-study heterogeneity in any meta-analyses. In the sensitivity analyses, small studies did not show larger effects than large studies. In addition, studies with high incidence of acute radiation injuries showed larger effects than studies with low incidence. Power calculations revealed that the statistical power of the primary meta-analysis was borderline, whereas there was adequate power for the subgroup analysis of studies with high incidence of acute radiation injuries.

CONCLUSIONS

Our meta-analysis showed a consistency of the results from the overall and subgroup analyses. We also showed that the genetic effect of the rs1801516 polymorphism on acute radiation injuries was dependent on the incidence of the injury. These support the evidence of an association between the rs1801516 polymorphism and acute radiation injuries, encouraging further research of this topic.

Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer

BACKGROUND AND PURPOSE

We explore analytical and data-driven approaches to investigate the integration of genetic variations (single nucleotide polymorphisms [SNPs] and copy number variations [CNVs]) with dosimetric and clinical variables in modeling radiation-induced rectal bleeding (RB) and erectile dysfunction (ED) in prostate cancer patients.

MATERIALS AND METHODS

Sixty-two patients who underwent curative hypofractionated radiotherapy (66 Gy in 22 fractions) between 2002 and 2010 were retrospectively genotyped for CNV and SNP rs5489 in the xrcc1 DNA repair gene. Fifty-four patients had full dosimetric profiles. Two parallel modeling approaches were compared to assess the risk of severe RB (Grade⩾3) and ED (Grade⩾1); Maximum likelihood estimated generalized Lyman-Kutcher-Burman (LKB) and logistic regression. Statistical resampling based on cross-validation was used to evaluate model predictive power and generalizability to unseen data.

RESULTS

Integration of biological variables xrcc1 CNV and SNP improved the fit of the RB and ED analytical and data-driven models. Cross-validation of the generalized LKB models yielded increases in classification performance of 27.4% for RB and 14.6% for ED when xrcc1 CNV and SNP were included, respectively. Biological variables added to logistic regression modeling improved classification performance over standard dosimetric models by 33.5% for RB and 21.2% for ED models.

CONCLUSION

As a proof-of-concept, we demonstrated that the combination of genetic and dosimetric variables can provide significant improvement in NTCP prediction using analytical and data-driven approaches. The improvement in prediction performance was more pronounced in the data driven approaches. Moreover, we have shown that CNVs, in addition to SNPs, may be useful structural genetic variants in predicting radiation toxicities.

A three-stage genome-wide association study identifies a susceptibility locus for late radiotherapy toxicity at 2q24.1

There is increasing evidence supporting the role of genetic variants in the development of radiation-induced toxicity. However, previous candidate gene association studies failed to elucidate the common genetic variation underlying this phenotype, which could emerge years after the completion of treatment. We performed a genome-wide association study on a Spanish cohort of 741 individuals with prostate cancer treated with external beam radiotherapy (EBRT). The replication cohorts consisted of 633 cases from the UK and 368 cases from North America. One locus comprising TANC1 (lowest unadjusted P value for overall late toxicity=6.85×10(-9), odds ratio (OR)=6.61, 95% confidence interval (CI)=2.23-19.63) was replicated in the second stage (lowest unadjusted P value for overall late toxicity=2.08×10(-4), OR=6.17, 95% CI=2.25-16.95; Pcombined=4.16×10(-10)). The inclusion of the third cohort gave unadjusted Pcombined=4.64×10(-11). These results, together with the role of TANC1 in regenerating damaged muscle, suggest that the TANC1 locus influences the development of late radiation-induced damage.

Identification of a radiation sensitivity gene expression profile in primary fibroblasts derived from patients who developed radiotherapy-induced fibrosis

BACKGROUND AND PURPOSE

During radiotherapy, normal tissue is unavoidably exposed to radiation which results in severe normal tissue reactions in a small fraction of patients. Because those who are sensitive cannot be determined prior to radiotherapy, the doses are limited to all patients to avoid an unacceptable number of severe adverse normal tissue responses. This limitation restricts the optimal treatment for individuals who are more tolerant to radiation. Genetic variation is a likely source for the normal tissue radiosensitivity variation observed between individuals. Therefore, understanding the radiation response at the genomic level may provide knowledge to develop individualized treatment and improve radiotherapy outcomes.

MATERIAL AND METHODS

Exon arrays were utilized to compare the basal expression profile between cell lines derived from six cancer patients with and without severe fibrosis. These data were supported by qRT-PCR and RNA-Seq techniques.

RESULTS

A set of genes (FBN2, FST, GPRC5B, NOTCH3, PLCB1, DPT, DDIT4L and SGCG) were identified as potential predictors for radiation-induced fibrosis. Many of these genes are associated with TGFβ or retinoic acid both having known links to fibrosis.

CONCLUSION

A combinatorial gene expression approach provides a promising strategy to predict fibrosis in cancer patients prior to radiotherapy.

Genetic variants in inducible nitric oxide synthase gene are associated with the risk of radiation-induced lung injury in lung cancer patients receiving definitive thoracic radiation

BACKGROUND AND PURPOSE

Nitric oxide (NO), mainly synthesized by inducible nitric oxide synthase (NOS2) in pathological conditions, plays an important role in cytotoxicity, inflammation and fibrosis. Elevations in exhaled NO after thoracic radiation have been reported to predict radiation-induced lung injury (RILI). This study examined whether genetic variations in NOS2 gene is associated with the risk of RILI.

MATERIAL AND METHODS

A cohort of 301 patients between 2009 and 2011 were genotyped for 21 single nucleotide polymorphisms (SNPs) in the NOS2 gene by the Sequenom MassArray system. Kaplan-Meier cumulative probability was used to assess RILI risk and Cox proportional hazards analyses were performed to evaluate the effect of NOS2 genotypes on RILI.

RESULTS

Multivariate analysis found that three SNPs (rs2297518, rs1137933 and rs16949) in NOS2 were significantly associated with risk of RILI⩾2 (P value=0.001, 0.000092, 0.001, respectively) after adjusting for other covariates. Their associations were independent of radiation dose and mean lung dose. Further haplotype analysis indicated that the ATC haplotype of three SNPs is associated with reducing the risk of developing RILI.

CONCLUSION

Our results demonstrate that genetic variants of NOS2 may serve as a reliable predictor of RILI in lung cancer patients treated with thoracic radiation.

Follistatin is induced by ionizing radiation and potentially predictive of radiosensitivity in radiation-induced fibrosis patient derived fibroblasts

Follistatin is a potent regulator of the inflammatory response and binds to and inhibits activin A action. Activin A is a member of the TGFβ protein superfamily which has regulatory roles in the inflammatory response and in the fibrotic process. Fibrosis can occur following cell injury and cell death induced by agents such as ionizing radiation (IR). IR is used to treat cancer and marked fibrotic response is a normal tissue (non-tumour) consequence in a fraction of patients under the current dose regimes. The discovery and development of a therapeutic to abate fibrosis in these radiosensitive patients would be a major advance for cancer radiotherapy. Likewise, prediction of which patients are susceptible to fibrosis would enable individualization of treatment and provide an opportunity for pre-emptive fibrosis control and better tumour treatment outcomes. The levels of activin A and follistatin were measured in fibroblasts derived from patients who developed severe radiation-induced fibrosis following radiotherapy and compared to fibroblasts from patients who did not. Both follistatin and activin A gene expression levels were increased following IR and the follistatin gene expression level was lower in the fibroblasts from fibrosis patients compared to controls at both basal levels and after IR. The major follistatin transcript variants were found to have a similar response to IR and both were reduced in fibrosis patients. Levels of follistatin and activin A secreted in the fibroblast culture medium also increased in response to IR and the relative follistatin protein levels were significantly lower in the samples derived from fibrosis patients. The decrease in the follistatin levels can lead to an increased bioactivity of activin A and hence may provide a useful measurement to identify patients at risk of a severe fibrotic response to IR. Additionally, follistatin, by its ability to neutralise the actions of activin A may be of value as an anti-fibrotic for radiation induced fibrosis.

No association between TGFB1 polymorphisms and late radiotherapy toxicity: a meta-analysis

Background

Transforming growth factor-beta 1 (TGF-β1) protein may be multifunctional and related to the development of fibrosis, induction of apoptosis, extracellular signaling and inhibition of proliferation in response to radiation-induced DNA damage. Several studies have investigated associations between single nucleotide polymorphisms (SNPs) in the TGFB1 gene and risk of late radiation-induced injury of normal tissue, but the conclusions remain controversial.

Methods

We searched three electronic databases (i.e., MEDLINE, EMBASE and EBSCO) for eligible publications and performed a meta-analysis assessing the association of three commonly studied SNPs in TGFB1 (i.e., rs1800469, rs1800470 and rs1800471) with risk of late radiation-induced injury of normal tissue.

Results

We finally included 28 case-only studies from 16 publications on aforementioned SNPs in TGFB1. However, we did not find statistical evidence of any significant association with overall risk of late radiotherapy toxicity in the pooled analysis or in further stratified analysis by cancer type, endpoint, ethnicity and sample size.

Conclusions

This meta-analysis did not find statistical evidence for an association between SNPs in TGFB1 and risk of late radiation-induced injury of normal tissue, but this finding needs further confirmation by a single large study.

Will SNPs be useful predictors of normal tissue radiosensitivity in the future?

The ability to predict individual risk of radiation-induced normal tissue complications is a long sought goal in radiobiology. The last decade saw increasing interest in identifying associations between single nucleotide polymorphisms (SNPs) and normal tissue complication risk. Nevertheless, it remains controversial whether SNPs will be useful predictors of normal tissue radiosensitivity. This paper provides a summary of a scientific debate held at the 31st ESTRO conference in which four scientists argued in favor or against the motion that SNPs will be useful predictors of normal tissue radiosensitivity in the future.

Individual patient data meta-analysis shows no association between the SNP rs1800469 in TGFB and late radiotherapy toxicity

BACKGROUND AND PURPOSE

Reported associations between risk of radiation-induced normal tissue injury and single nucleotide polymorphisms (SNPs) in TGFB1, encoding the pro-fibrotic cytokine transforming growth factor-beta 1 (TGF-β1), remain controversial. To overcome publication bias, the international Radiogenomics Consortium collected and analysed individual patient level data from both published and unpublished studies.

MATERIALS AND METHODS

TGFB1 SNP rs1800469 c.-1347T>C (previously known as C-509T) genotype, treatment-related data, and clinically-assessed fibrosis (measured at least 2years after therapy) were available in 2782 participants from 11 cohorts. All received adjuvant breast radiotherapy. Associations between late fibrosis or overall toxicity, reported by STAT (Standardised Total Average Toxicity) score, and rs1800469 genotype were assessed.

RESULTS

No statistically significant associations between either fibrosis or overall toxicity and rs1800469 genotype were observed with univariate or multivariate regression analysis. The multivariate odds ratio (OR), obtained from meta-analysis, for an increase in late fibrosis grade with each additional rare allele of rs1800469 was 0.98 (95% Confidence Interval (CI) 0.85-1.11). This CI is sufficiently narrow to rule out any clinically relevant effect on toxicity risk in carriers vs. non-carriers with a high probability.

CONCLUSION

This meta-analysis has not confirmed previous reports of association between fibrosis or overall toxicity and rs1800469 genotype in breast cancer patients. It has demonstrated successful collaboration within the Radiogenomics Consortium.

Correlation of in vitro lymphocyte radiosensitivity and gene expression with late normal tissue reactions following curative radiotherapy for breast cancer

BACKGROUND AND PURPOSE

Identification of mechanisms of late normal tissue responses to curative radiotherapy that discriminate individuals with marked or mild responses would aid response prediction. This study aimed to identify differences in gene expression, apoptosis, residual DNA double strand breaks and chromosomal damage after in vitro irradiation of lymphocytes in a series of patients with marked (31 cases) or mild (28 controls) late adverse reaction to adjuvant breast radiotherapy.

MATERIALS AND METHODS

Gene expression arrays, residual γH2AX, apoptosis, G2 chromosomal radiosensitivity and G0 micronucleus assay were used to compare case and control lymphocyte radiation responses.

RESULTS

Five hundred and thirty genes were up-regulated and 819 down-regulated by ionising radiation. Irradiated samples were identified with an overall cross-validated error rate of 3.4%. Prediction analyses to classify cases and controls using unirradiated (0Gy), irradiated (4Gy) or radiation response (4-0Gy) expression profiles correctly identified samples with, respectively, 25%, 22% or 18.5% error rates. Significant inter-sample variation was observed for all cellular endpoints but cases and controls could not be distinguished.

CONCLUSIONS

Variation in lymphocyte radiosensitivity does not necessarily correlate with normal tissue response to radiotherapy. Gene expression analysis can predict of radiation exposure and may in the future help prediction of normal tissue radiosensitivity.

Association of a XRCC3 polymorphism and rectum mean dose with the risk of acute radio-induced gastrointestinal toxicity in prostate cancer patients

BACKGROUND AND PURPOSE

We have performed a case-control study among prostate cancer patients treated with three-dimensional conformational radiotherapy (3D-CRT) in order to investigate the association between single nucleotide polymorphisms (SNPs), treatment and patient features with gastrointestinal and genitourinary acute toxicity.

MATERIAL AND METHODS

A total of 698 patients were screened for 14 SNPs located in the ATM, ERCC2, LIG4, MLH1 and XRCC3 genes. Gastrointestinal and genitourinary toxicities were recorded prospectively using the Common Terminology Criteria for Adverse Events v3.0.

RESULTS

The XRCC3 SNP rs1799794 (G/G OR=5.65; 95% CI: 1.95-16.38; G/A OR=2.75; 95% CI: 1.25-6.05; uncorrected p-value=2.8×10(-03); corrected p-value=0.03; FDR q-value=0.06) as well as the mean dose received by the rectum (OR=1.06; 95% CI: 1.02-1.1; uncorrected p-value=2.49×10(-03); corrected p-value=0.03; FDR q-value=0.06) were significantly associated with gastrointestinal toxicity after correction for multiple testing. Those patients who undergone previous prostatectomy were less prone to develop genitourinary toxicity (OR=0.38; 95% CI: 0.18-0.71; uncorrected p-value=4.95×10(-03); corrected p-value=0.03; FDR q-value=0.08). Our study excludes the possibility of a >2-fold risk increase in genitourinary acute toxicity being due to rs1801516 ATM SNP, the rs1805386 and rs1805388 LIG4 markers, as well as all the SNPs evaluated in the ERCC2, MLH1 and XRCC3 genes.

CONCLUSIONS

The XRCC3 rs1799794 SNP and the mean dose received by the rectum are associated with the development of gastrointestinal toxicity after 3D-CRT.

Association between single nucleotide polymorphisms of the transforming growth factor β1 gene and the risk of severe radiation esophagitis in patients with lung cancer

PURPOSE

We investigated the association between single nucleotide polymorphisms (SNPs) in the transforming growth factor β1 (TGFβ1) gene and the risk of radiation-induced esophageal toxicity (RE) in patients with non-small-cell lung cancer (NSCLC).

METHODS AND MATERIALS

Ninety-seven NSCLC patients with available genomic DNA samples and mostly treated with intensity modulated radio(chemo)therapy from 2003 to 2006 were used as a test dataset and 101 NSCLC patients treated with 3-dimensional conformal radio(chemo)therapy from 1998 to 2002 were used as a validation set. We genotyped three SNPs of the TGFβ1 gene (rs1800469:C-509T, rs1800471:G915C, and rs1982073:T869C) by the polymerase chain reaction restriction fragment length polymorphism method.

RESULTS

In the test dataset, the CT/TT genotypes of TGFβ1 rs1800469:C-509T were associated with a statistically significant higher risk of RE grade⩾3 in univariate (P=0.026) and multivariate analysis (P=0.045) when compared with the CC genotype. These results were again observed in both univariate (P=0.045) and multivariate (P=0.023) analysis in the validation dataset.

CONCLUSION

We found and validated that the TGFβ1 rs1800469:C-509T genotype is associated with severe RE. This response marker may be used for guiding therapy intensity in an individual patient, which would further the goal of individualized therapy.

Mouse genetic approaches applied to the normal tissue radiation response

The varying responses of inbred mouse models to radiation exposure present a unique opportunity to dissect the genetic basis of radiation sensitivity and tissue injury. Such studies are complementary to human association studies as they permit both the analysis of clinical features of disease, and of specific variants associated with its presentation, in a controlled environment. Herein I review how animal models are studied to identify specific genetic variants influencing predisposition to radiation-induced traits. Among these radiation-induced responses are documented strain differences in repair of DNA damage and in extent of tissue injury (in the lung, skin, and intestine) which form the base for genetic investigations. For example, radiation-induced DNA damage is consistently greater in tissues from BALB/cJ mice, than the levels in C57BL/6J mice, suggesting there may be an inherent DNA damage level per strain. Regarding tissue injury, strain specific inflammatory and fibrotic phenotypes have been documented for principally, C57BL/6 C3H and A/J mice but a correlation among responses such that knowledge of the radiation injury in one tissue informs of the response in another is not evident. Strategies to identify genetic differences contributing to a trait based on inbred strain differences, which include linkage analysis and the evaluation of recombinant congenic (RC) strains, are presented, with a focus on the lung response to irradiation which is the only radiation-induced tissue injury mapped to date. Such approaches are needed to reveal genetic differences in susceptibility to radiation injury, and also to provide a context for the effects of specific genetic variation uncovered in anticipated clinical association studies. In summary, mouse models can be studied to uncover heritable variation predisposing to specific radiation responses, and such variations may point to pathways of importance to phenotype development in the clinic.

Irradiation with protons for the individualized treatment of patients with locally advanced rectal cancer: a planning study with clinical implications

BACKGROUND AND PURPOSE

Ongoing clinical trials aim to improve local control and overall survival rates by intensification of therapy regimen for patients with locally advanced rectal cancer. It is well known that whenever treatment is intensified, risk of therapy-related toxicity rises. An irradiation with protons could possibly present an approach to solve this dilemma by lowering the exposure to the organs-at-risk (OAR) without compromising tumor response.

MATERIAL AND METHODS

Twenty five consecutive patients were treated from 04/2009 to 5/2010. For all patients, four different treatment plans including protons, RapidArc, IMRT and 3D-conformal-technique were retrospectively calculated and analyzed according to dosimetric aspects.

RESULTS

Detailed DVH-analyses revealed that protons clearly reduced the dose to the OAR and entire normal tissue when compared to other techniques. Furthermore, the conformity index was significantly better and target volumes were covered consistent with the ICRU guidelines.

CONCLUSIONS

Planning results suggest that treatment with protons can improve the therapeutic tolerance for the irradiation of rectal cancer, particularly for patients scheduled for an irradiation with an intensified chemotherapy regimen and identified to be at high risk for acute therapy-related toxicity. However, clinical experiences and long-term observation are needed to assess tumor response and related toxicity rates.