Radiosensitivity, radiogenomics and RAPPER

Imaging genomics: data fusion in uncovering disease heritability

Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.

Radiomics in veterinary medicine: Overview, methods, and applications

Radiomics, or quantitative image analysis from radiographic image data, borrows the suffix from other emerging -omics fields of study, such as genomics, proteomics, and metabolomics. This report provides an overview of the general principles of how radiomic features are computed, describes major types of morphological, first order, and texture features, and the applications, challenges, and opportunities of radiomics as applied in veterinary medicine. Some advantages radiomics has over traditional semantic radiological features include standardized methodology in computing semantic features, the ability to compute features in multi-dimensional images, their newfound associations with genomic and pathological abnormalities, and the number of perceptible and imperceptible features available for regression or classification modeling. Some challenges in deploying radiomics in a clinical setting include sensitivity to image acquisition settings and image artifacts, pre- and post-image reconstruction and calculation settings, variability in feature estimates stemming from inter- and intra-observer contouring errors, and challenges with software and data harmonization and generalizability of findings given the challenges of small sample size and patient selection bias in veterinary medicine. Despite this, radiomics has enormous potential in patient-centric diagnostics, prognosis, and theragnostics. Fully leveraging the utility of radiomics in veterinary medicine will require inter-institutional collaborations, data harmonization, and data sharing strategies amongst institutions, transparent and robust model development, and multi-disciplinary efforts within and outside the veterinary medical imaging community.

Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis

Radiogenomics is a new field of medical science that integrates two omics, radiomics and genomics, and may bring a major paradigm shift in traditional personalized medicine strategies that require tumor tissue samples. In addition, the acquisition of the data does not require special imaging equipment or special imaging conditions, and it is possible to use image information from computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography in clinical practice, so the versatility and cost-effectiveness of radiogenomics are expected. So far, the field of radiogenomics has developed, especially in the fields of brain tumors and breast cancer, but recently, reports of radiogenomic research on gastroenterological cancer are increasing. This review provides an overview of radiogenomic research methods and summarizes the current radiogenomic research in gastroenterological cancer. In addition, the application of artificial intelligence is considered to be indispensable for the integrated analysis of enormous omics information in the future, and the future direction of this research, including the latest technologies, will be discussed.

Successful use of three-dimensional conformal radiotherapy as adjuvant treatment for alveolar soft part sarcoma. A case report

Introduction: Alveolar soft part sarcoma is a very rare and aggressive type of sarcoma. Although its histology and genetic characteristics have been identified, the benefits of adjuvant radiotherapy for its treatment are still being studied. Case presentation: In November 2007, a 21-year-old woman presented with a primary tumor in the right thigh, with histological and immunohistochemical confirmation of an alveolar soft part sarcoma, which was totally resected in December 2007. Also, the large size of the mass suggested an unfavorable evolution. Two years after the first surgery, two metastatic tumors were detected in the right lung, which were completely resected separately. Two years later, the patient had two independent relapse events, five months apart: a mass in the right tight, and a metastatic tumor in the adrenal gland, together with a relapse in the tight. All tumors were successfully resected. In June 2014, after the last local relapse, adjuvant radiotherapy was started because of the risk of thigh amputation. At the end of treatment, the patient’s general condition was good. Currently, at age 34, the patient is monitored through periodic evaluations, showing disease regression and stabilization. Conclusions: Currently, it is known that radiation not only produces cytotoxic effects on the target region but also induces an immune system-mediated systemic response with potential antimetastatic properties. Emerging radiobiological paradigms should be considered, particularly since they could explain some encouraging and unexpected results, such as those described in this case.

Genomics models in radiotherapy: From mechanistic to machine learning

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.

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

BACKGROUND

A total of 10%-20% of patients develop long-term toxicity following radiotherapy for prostate cancer. Identification of common genetic variants associated with susceptibility to radiotoxicity might improve risk prediction and inform functional mechanistic studies.

METHODS

We conducted an individual patient data meta-analysis of six genome-wide association studies (n = 3871) in men of European ancestry who underwent radiotherapy for prostate cancer. Radiotoxicities (increased urinary frequency, decreased urinary stream, hematuria, rectal bleeding) were graded prospectively. We used grouped relative risk models to test associations with approximately 6 million genotyped or imputed variants (time to first grade 2 or higher toxicity event). Variants with two-sided Pmeta less than 5 × 10-8 were considered statistically significant. Bayesian false discovery probability provided an additional measure of confidence. Statistically significant variants were evaluated in three Japanese cohorts (n = 962). All statistical tests were two-sided.

RESULTS

Meta-analysis of the European ancestry cohorts identified three genomic signals: single nucleotide polymorphism rs17055178 with rectal bleeding (Pmeta = 6.2 × 10-10), rs10969913 with decreased urinary stream (Pmeta = 2.9 × 10-10), and rs11122573 with hematuria (Pmeta = 1.8 × 10-8). Fine-scale mapping of these three regions was used to identify another independent signal (rs147121532) associated with hematuria (Pconditional = 4.7 × 10-6). Credible causal variants at these four signals lie in gene-regulatory regions, some modulating expression of nearby genes. Previously identified variants showed consistent associations (rs17599026 with increased urinary frequency, rs7720298 with decreased urinary stream, rs1801516 with overall toxicity) in new cohorts. rs10969913 and rs17599026 had similar effects in the photon-treated Japanese cohorts.

CONCLUSIONS

This study increases the understanding of the architecture of common genetic variants affecting radiotoxicity, points to novel radio-pathogenic mechanisms, and develops risk models for testing in clinical studies. Further multinational radiogenomics studies in larger cohorts are worthwhile.

Precision Radiotherapy and Radiation Risk Assessment: How Do We Overcome Radiogenomic Diversity?

Precision medicine is a rapidly developing area that aims to deliver targeted therapies based on individual patient characteristics. However, current radiation treatment is not yet personalized; consequently, there is a critical need for specific patient characteristics of both tumor and normal tissues to be fully incorporated into dose prescription. Furthermore, current risk assessment following environmental, occupational, or accidental exposures to radiation is based on population effects, and does not account for individual diversity underpinning radiosensitivity. The lack of personalized approaches in both radiotherapy and radiation risk assessment resulted in the current situation where a population-based model, effective dose, is being used. In this review article, to stimulate scientific discussion for precision medicine in both radiotherapy and radiation risk assessment, we propose a novel radiological concept and metric - the personalized dose and the personalized risk index - that incorporate individual physiological, lifestyle-related and genomic variations and radiosensitivity, outlining the potential clinical application for precision medicine. We also review on recent progress in both genomics and biobanking research, which is promising for providing novel insights into individual radiosensitivity, and for creating a novel conceptual framework of precision radiotherapy and radiation risk assessment.

Validation of a normal tissue complication probability model for late unfavourable aesthetic outcome after breast-conserving therapy

PURPOSE

To validate a normal tissue complication probability (NTCP) model for late unfavourable aesthetic outcome (AO) after breast-conserving therapy.

MATERIALS/METHODS

The BCCT.core software evaluated the AO using standardized photographs of patients treated at the University Hospitals Leuven between April 2015 and April 2016. Dose maps in 2 Gy equivalents were calculated assuming α/β = 3.6 Gy. The discriminating ability of the model was described by the AUC of the receiver operating characteristic curve. A 95% confidence interval (CI) of AUC was calculated using 10,000 bootstrap replications. Calibration was evaluated with the calibration plot and Nagelkerke R². Patients with unfavourable AO at baseline were excluded. Patient, tumour and treatment characteristics were compared between the development and the validation cohort. The prognostic value of the characteristics in the validation cohort was further evaluated in univariable and multivariable analysis.

RESULTS

Out of 175 included patients, 166 were evaluated two years after RT and 44 (26.51%) had unfavourable AO. AUC was 0.66 (95% CI 0.56; 0.76). Calibration was moderate with small overestimations at higher risk. When applying all of the univariable significant clinicopathological and dosimetrical variables from the validation cohort in a multivariable model, the presence of a seroma and V45 were selected as significant risk factors for unfavourable AO (Odds Ratio 4.40 (95% CI 1.96; 9.86) and 1.14 (95% CI 1.03; 1.27), p-value <.001 and .01, respectively).

CONCLUSIONS

The NTCP model for unfavourable AO shows a moderate discrimination and calibration in the present prospective validation cohort with a small overestimation in the high risk patients.

Single nucleotide polymorphisms in ATM, TNF-α and IL6 genes and risk of radiotoxicity in breast cancer patients

Although oncological therapies have improved in the last decades, breast cancer (BC) remains a serious health problem worldwide. Radiotherapy (RT) is one of the most frequently used treatments for cancer aimed at eliminating tumor cells. However, it can also alter the surrounding normal tissue, especially the skin, and patient reactions may vary as a result of extrinsic and intrinsic factors. We evaluated the association of gene polymorphisms ATM Asp1853Asn, IL-6 G-174C and TNF-α G-308A involved in central phenotype pathways and development of individual radiosensitivity in BC patients with an exacerbated response to RT. Although univariate analysis results did not show a significant association with this trait, the interaction analysis between polymorphisms showed an increased risk of patients presenting wild-type TNF-α G-308A genotype and mutant IL-6 G-174C genotype, and heterozygous TNF-α G-308A genotype and heterozygous IL-6G-174C genotype. On the other hand, our results showed that breast size and patient age influenced the determination of RT-associated effects. Considering that the trait is multifactorial, other significant elements for the determination of individual radiosensitivity should be considered, together with the establishment of specific polymorphic variants.

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.

Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer

Nearly 50% of cancer patients undergo radiotherapy. Late radiotherapy toxicity affects quality-of-life in long-term cancer survivors and risk of side-effects in a minority limits doses prescribed to the majority of patients. Development of a test predicting risk of toxicity could benefit many cancer patients. We aimed to meta-analyze individual level data from four genome-wide association studies from prostate cancer radiotherapy cohorts including 1564 men to identify genetic markers of toxicity. Prospectively assessed two-year toxicity endpoints (urinary frequency, decreased urine stream, rectal bleeding, overall toxicity) and single nucleotide polymorphism (SNP) associations were tested using multivariable regression, adjusting for clinical and patient-related risk factors. A fixed-effects meta-analysis identified two SNPs: rs17599026 on 5q31.2 with urinary frequency (odds ratio [OR] 3.12, 95% confidence interval [CI] 2.08-4.69, p-value 4.16×10(-8)) and rs7720298 on 5p15.2 with decreased urine stream (OR 2.71, 95% CI 1.90-3.86, p-value=3.21×10(-8)). These SNPs lie within genes that are expressed in tissues adversely affected by pelvic radiotherapy including bladder, kidney, rectum and small intestine. The results show that heterogeneous radiotherapy cohorts can be combined to identify new moderate-penetrance genetic variants associated with radiotherapy toxicity. The work provides a basis for larger collaborative efforts to identify enough variants for a future test involving polygenic risk profiling.

Patients with a High Polygenic Risk of Breast Cancer do not have An Increased Risk of Radiotherapy Toxicity

PURPOSE

It has been hypothesized that increased predisposition to breast cancer may correlate with radiosensitivity, and thus increased risk of toxicity following breast irradiation. This study investigated the relationship between common breast cancer risk variants and radiotherapy toxicity.

EXPERIMENTAL DESIGN

SNP genotypes were determined in female breast cancer patients from the RAPPER (Radiogenomics: Assessment of polymorphisms for predicting the effects of radiotherapy) study using the Illumina CytoSNP12 genome-wide array. A further 15,582,449 genotypes were imputed using the 1000 Genomes Project reference panel. Patient (n = 1,160) polygenic risk scores were generated by summing risk-allele dosages, both unweighted and weighted by published effect sizes for breast cancer risk. Regression models were used to test associations of individual variants and polygenic risk scores with acute and late toxicity phenotypes (telangiectasia, breast edema, photographically assessed shrinkage, induration, pigmentation, breast pain, breast sensitivity, and overall toxicity).

RESULTS

Genotypes of 90 confirmed breast cancer risk variants were accurately determined and polygenic risk scores were approximately normally distributed. Variant rs6964587 was associated with increased breast edema 5 years following radiotherapy (Beta, 0.22; 95% confidence interval, 0.09-0.34; P = 7 × 10(-4)). No other associations were found between individual variants or the unweighted (P > 0.17) or weighted (P > 0.13) polygenic risk score and radiotherapy toxicity. This study had >87% power to detect an association between the polygenic risk score (relative risk > 1.1) and toxicity.

CONCLUSIONS

Cancer patients with a high polygenic predisposition to breast cancer do not have an increased risk of radiotherapy toxicity up to 5 years following radiotherapy but individual variants may increase risk.

ERCC2 polymorphisms and radiation-induced adverse effects on normal tissue: systematic review with meta-analysis and trial sequential analysis

Background

The relationship between ERCC2 polymorphisms and the risk of radiotoxicity remains inconclusive. The aim of our study is to systematically evaluate the association between ERCC2 polymorphisms and the risk of radiotoxicity.

Methods

Publications were identified through a search of the PubMed and Web of Science databases up to August 15, 2015. The pooled odds ratios (ORs) with corresponding 95 % confidence intervals (CIs) were calculated to evaluate the association between ERCC2 polymorphisms and radiotoxicity. Trial sequential analysis (TSA) and power calculation were performed to evaluate the type 1 and type 2 errors.

Results

Eleven studies involving 2584 patients were ultimately included in this meta-analysis. Conventional meta-analysis identified a significant association between ERCC2 rs13181 polymorphism and radiotoxicity (OR = 0.71, 95 % CI: 0.55-0.93, P = 0.01), but this association failed to get the confirmation of TSA.

Conclusions

The minor allele of rs13181 polymorphism may confer a protect effect against radiotoxicity. To confirm this correlation at the level of OR = 0.71, an overall information size of approximate 2800 patients were needed.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-015-0558-6) contains supplementary material, which is available to authorized users.

Incorporating Genetic Biomarkers into Predictive Models of Normal Tissue Toxicity

There is considerable variation in the level of toxicity patients experience for a given dose of radiotherapy, which is associated with differences in underlying individual normal tissue radiosensitivity. A number of syndromes have a large effect on clinical radiosensitivity, but these are rare. Among non-syndromic patients, variation is less extreme, but equivalent to a ±20% variation in dose. Thus, if individual normal tissue radiosensitivity could be measured, it should be possible to optimise schedules for individual patients. Early investigations of in vitro cellular radiosensitivity supported a link with tissue response, but individual studies were equivocal. A lymphocyte apoptosis assay has potential, and is currently under prospective validation. The investigation of underlying genetic variation also has potential. Although early candidate gene studies were inconclusive, more recent genome-wide association studies are revealing definite associations between genotype and toxicity and highlighting the potential for future genetic testing. Genetic testing and individualised dose prescriptions could reduce toxicity in radiosensitive patients, and permit isotoxic dose escalation to increase local control in radioresistant individuals. The approach could improve outcomes for half the patients requiring radical radiotherapy. As a number of patient- and treatment-related factors also affect the risk of toxicity for a given dose, genetic testing data will need to be incorporated into models that combine patient, treatment and genetic data.

A genome wide association study (GWAS) providing evidence of an association between common genetic variants and late radiotherapy toxicity

BACKGROUND AND PURPOSE

This study was designed to identify common single nucleotide polymorphisms (SNPs) associated with toxicity 2years after radiotherapy.

MATERIALS AND METHODS

A genome wide association study was performed in 1850 patients from the RAPPER study: 1217 received adjuvant breast radiotherapy and 633 had radical prostate radiotherapy. Genotype associations with both overall and individual endpoints of toxicity were tested via univariable and multivariable regression. Replication of potentially associated SNPs was carried out in three independent patient cohorts who had radiotherapy for prostate (516 RADIOGEN and 862 Gene-PARE) or breast (355 LeND) cancer.

RESULTS

Quantile-quantile plots show more associations at the P<5×10(-7) level than expected by chance (164 vs. 9 for the prostate cases and 29 vs. 4 for breast cases), providing evidence that common genetic variants are associated with risk of toxicity. Strongest associations were for individual endpoints rather than an overall measure of toxicity in all patients. However, in general, significant associations were not validated at a nominal 0.05 level in the replication cohorts.

CONCLUSIONS

This largest GWAS to date provides evidence of true association between common genetic variants and toxicity. Associations with toxicity appeared to be tumour site-specific. Future GWAS require higher statistical power, in particular in the validation stage, to test clinically relevant effect sizes of SNP associations with individual endpoints, but the required sample sizes are achievable.

Radiogenomics: using genetics to identify cancer patients at risk for development of adverse effects following radiotherapy

Normal-tissue adverse effects following radiotherapy are common and significantly affect quality of life. These effects cannot be accounted for by dosimetric, treatment, or demographic factors alone, and evidence suggests that common genetic variants are associated with radiotherapy adverse effects. The field of radiogenomics has evolved to identify such genetic risk factors. Radiogenomics has two goals: (i) to develop an assay to predict which patients with cancer are most likely to develop radiation injuries resulting from radiotherapy, and (ii) to obtain information about the molecular pathways responsible for radiation-induced normal-tissue toxicities. This review summarizes the history of the field and current research.

SIGNIFICANCE

A single-nucleotide polymorphism–based predictive assay could be used, along with clinical and treatment factors, to estimate the risk that a patient with cancer will develop adverse effects from radiotherapy. Such an assay could be used to personalize therapy and improve quality of life for patients with cancer.

Investigation of genetic polymorphisms related to the outcome of radiotherapy for prostate cancer patients

The purpose of this study was to evaluate the association between ATM, TP53 and MDM2 polymorphisms in prostate cancer patients and morbidity after radiotherapy. The presence of ATM (rs1801516), TP53 (rs1042522, rs1800371, rs17878362, rs17883323, and rs35117667), and MDM2 (rs2279744) polymorphisms was assessed by direct sequencing of PCR fragments from 48 patients with histologically proven prostate adenocarcinoma and treated with external beam radiation. The side effects were classified according to the Radiation Therapy Oncology Group (RTOG) score. The results showed no association between clinical characteristics and the development of radiation toxicities (P > 0.05). The C>T transition in the position 16273 (intron 3) of TP53 (rs35117667) was significantly associated with the risk of acute skin toxicity (OR: 0.0072, 95% CI 0.0002–0.227, P = 0.003). The intronic TP53 polymorphism at position 16250 (rs17883323) was associated with chronic urinary toxicity (OR: 0.071, 95%CI 0.006–0.784, P = 0.032). No significant associations were found for the remaining polymorphisms (P > 0.05). The results show that clinical characteristics were not determinant on the developing of radiation sensitivity in prostate cancer patients, and intronic TP53 polymorphisms would be associated with increased acute and chronic radiation toxicities. These observations corroborate the importance of investigating the genetic profile to predict adverse side effects in patients undergoing radiotherapy.

Clinical risk factors for late intestinal toxicity after radiotherapy: a systematic review protocol

BACKGROUND

Late intestinal toxicity after radiotherapy (LITAR) not only limits the radiation dose, which subsequently leads to unfavorable clinical outcomes, but also significantly lowers the quality of life in an increasing number of cancer survivors. Therefore, identifying clinical risk factors for LITAR is important for establishing a predictive model in the clinical setting of decision-making for these patients. This review aims to systematically summarize and clarify the clinical factors that can be potentially associated with an increased risk of moderate/severe LITAR in patients with abdominal or pelvic malignancies.

METHODS/DESIGN

MEDLINE, EMBASE, Web of Science, Cochrane Central Register of Controlled Trials, Scopus, Google Scholar and Chinese BioMed will be systematically searched to identify appropriate studies. Citations of the retrieved studies and recent reviews will also be searched separately by case.The enrolled studies should at least have the following information: (1) a clear definition and information on the LITAR severity; (2) assess clinical factors for moderate/severe toxicity with adjusted risk estimates; (3) have a cohort, case-control, randomized controlled trial and controlled clinical trial study design.Two authors will independently review the abstract and full text of retrieved studies, extract data from eligible studies and assess the risk of bias. Disagreements will be discussed among reviewers until a consensus is reached. The effect of identified risk factors will be displayed in forest plots. If the information is sufficient, results will be synthesized by a meta-analysis with the random effects model to pool the estimate of risk posed by clinical factors. Subgroup and sensitivity analysis will be used to explore the sources of heterogeneity.

DISCUSSION

This review will summarize the evidence of clinical risk factors for moderate/severe LITAR. The results may help guide decision-making and minimize the side effects of therapeutic modalities in the clinical setting.

Cerebral radiation necrosis: a review of the pathobiology, diagnosis and management considerations

Radiation therapy forms one of the building blocks of the multi-disciplinary management of patients with brain tumors. Improved survival following radiation therapy may come with a cost, including the potential complication of radiation necrosis. Radiation necrosis impacts the quality of life in cancer survivors, and it is essential to detect and effectively treat this entity as early as possible. Significant progress in neuro-radiology and molecular pathology facilitate more straightforward diagnosis and characterization of cerebral radiation necrosis. Several therapeutic interventions, both medical and surgical, may halt the progression of radiation necrosis and diminish or abrogate its clinical manifestations, but there are still no definitive guidelines to follow explicitly that guide treatment of radiation necrosis. We discuss the pathobiology, clinical features, diagnosis, available treatment modalities, and outcomes in the management of patients with intracranial radiation necrosis that follows radiation used to treat brain tumors.

Features of cancer management in obese patients

There is worldwide increased in obesity prevalence and statistical almost half of United-States, including children, could be obese by 2050. Obesity in cancer patients is a major issue in oncology because weight gain and obesity account for approximately 20% of all cancer cases. Indeed, increased obesity is linked with higher risk of various types of cancer and a poorer survival. Although biological mechanisms underlying how obesity causes an increased risk of cancer are suggested, overweight as a putative direct cause of death is still debated. Numerous confounding factors may impact on survival, including comorbidities and imaging limitations. Moreover, difficulties to achieve the standard oncologic care with surgery, chemotherapy and/or radiation may also be concerned. Herein, we examined the specific features and potential adaptation of the cancer management in overweighed patients. Then, we reviewed how implicated molecular pathways may provide new strategies to decrease cancer risk and predict toxicities in an increasingly obese population.

Independent validation of genes and polymorphisms reported to be associated with radiation toxicity: a prospective analysis study

BACKGROUND

Several studies have reported associations between radiation toxicity and single nucleotide polymorphisms (SNPs) in candidate genes. Few associations have been tested in independent validation studies. This prospective study aimed to validate reported associations between genotype and radiation toxicity in a large independent dataset.

METHODS

92 (of 98 attempted) SNPs in 46 genes were successfully genotyped in 1613 patients: 976 received adjuvant breast radiotherapy in the Cambridge breast IMRT trial (ISRCTN21474421, n=942) or in a prospective study of breast toxicity at the Christie Hospital, Manchester, UK (n=34). A further 637 received radical prostate radiotherapy in the MRC RT01 multicentre trial (ISRCTN47772397, n=224) or in the Conventional or Hypofractionated High Dose Intensity Modulated Radiotherapy for Prostate Cancer (CHHiP) trial (ISRCTN97182923, n=413). Late toxicity was assessed 2 years after radiotherapy with a validated photographic technique (patients with breast cancer only), clinical assessment, and patient questionnaires. Association tests of genotype with overall radiation toxicity score and individual endpoints were undertaken in univariate and multivariable analyses. At a type I error rate adjusted for multiple testing, this study had 99% power to detect a SNP, with minor allele frequency of 0·35, associated with a per allele odds ratio of 2·2.

FINDINGS

None of the previously reported associations were confirmed by this study, after adjustment for multiple comparisons. The p value distribution of the SNPs tested against overall toxicity score was not different from that expected by chance.

INTERPRETATION

We did not replicate previously reported late toxicity associations, suggesting that we can essentially exclude the hypothesis that published SNPs individually exert a clinically relevant effect. Continued recruitment of patients into studies within the Radiogenomics Consortium is essential so that sufficiently powered studies can be done and methodological challenges addressed.

FUNDING

Cancer Research UK, The Royal College of Radiologists, Addenbrooke's Charitable Trust, Breast Cancer Campaign, Cambridge National Institute of Health Research (NIHR) Biomedical Research Centre, Experimental Cancer Medicine Centre, East Midlands Innovation, the National Cancer Institute, Joseph Mitchell Trust, Royal Marsden NHS Foundation Trust, Institute of Cancer Research NIHR Biomedical Research Centre for Cancer.

Prediction of normal tissue toxicity as part of the individualized treatment with radiotherapy in oncology patients

Normal tissue toxicity caused by radiotherapy conditions the success of the treatment and the quality of life of patients. Radiotherapy is combined with surgery in both the preoperative or postoperative setting for the treatment of most localized solid tumour types. Furthermore, radical radiotherapy is an alternative to surgery in several tumour locations. The possibility of predicting such radiation-induced toxicity would make possible a better treatment schedule for the individual patient. Radiation-induced toxicity is, at least in part, genetically determined. From decades, several predictive tests have been proposed to know the individual sensitivity of patients to the radiotherapy schedules. Among them, initial DNA damage, radiation-induced apoptosis, gene expression profiles, and gene polymorphisms have been proposed. We report here an overview of the main studies regarding to this field. Radiation-induced apoptosis in peripheral blood lymphocytes seem to be the most promising assay tested in prospective clinical trials, although they have to be validated in large clinical studies. Other promising assays, as those related with single nucleotide polymorphisms, need to be validated as well.

Genetics and genomics of radiotherapy toxicity: towards prediction

Radiotherapy is involved in many curative treatments of cancer; millions of survivors live with the consequences of treatment, and toxicity in a minority limits the radiation doses that can be safely prescribed to the majority. Radiogenomics is the whole genome application of radiogenetics, which studies the influence of genetic variation on radiation response. Work in the area focuses on uncovering the underlying genetic causes of individual variation in sensitivity to radiation, which is important for effective, safe treatment. In this review, we highlight recent advances in radiotherapy and discuss results from four genome-wide studies of radiotoxicity.

The Cambridge Breast Intensity-modulated Radiotherapy Trial: patient- and treatment-related factors that influence late toxicity

AIMS

The effect of patient- and treatment-related factors in the development of late normal tissue toxicity after radiotherapy is not yet fully established. The aim of this study was to elucidate the relative importance of such factors in the development of late toxicity after breast-conserving surgery and adjuvant breast radiotherapy.

MATERIALS AND METHODS

Patient- and treatment-related factors were analysed in 1014 patients who had received adjuvant radiotherapy to the breast in the Cambridge Breast Intensity-modulated Radiotherapy (IMRT) Trial. Late toxicity data were collected using photographic and clinical assessments and patient-reported questionnaires at 2 years after radiotherapy.

RESULTS

On multivariate analysis, a larger breast volume was statistically significantly associated with the development of breast shrinkage assessed by serial photographs (odds ratio per litre increase in breast volume = 1.98, 95% confidence interval 1.41, 2.78; P < 0.0005), telangiectasia (odds ratio = 3.94, 95% confidence interval 2.49, 6.24; P < 0.0005), breast oedema (odds ratio = 3.65, 95% confidence interval 2.54, 5.24; P < 0.0005) and pigmentation (odds ratio = 1.75, 95% confidence interval 1.21, 2.51; P = 0.003). Current smokers had an increased risk of developing pigmentation (odds ratio = 2.09, 95% confidence interval 1.23, 3.54; P = 0.006). Patients with a moderate or poor post-surgical cosmesis had a greatly increased risk of moderate or poor overall cosmesis (odds ratio = 38.19; 95% confidence interval 21.9, 66.7; P < 0.0005). Postoperative infection requiring antibiotics was associated with increased risk of telangiectasia (odds ratio = 3.39, 95% confidence interval 1.94, 5.91; P < 0.0005) and breast oversensitivity (odds ratio = 1.78, 95% confidence interval 1.27, 2.49; P = 0.001).

CONCLUSIONS

In this study, the greatest risk factors for the development of late toxicity 2 years after breast-conserving surgery and adjuvant radiotherapy were larger breast volume, baseline pre-radiotherapy surgical cosmesis, postoperative infection and possibly smoking. These factors seem to be more important than relatively small differences in dose inhomogeneity and the addition of boost radiotherapy at 2 years after the completion of radiotherapy. The modification of potentially preventable risk factors, such as postoperative infection and smoking, may limit the development of late toxicity after breast radiotherapy.

Molecular biology: the key to personalised treatment in radiation oncology?

We know considerably more about what makes cells and tissues resistant or sensitive to radiation than we did 20 years ago. Novel techniques in molecular biology have made a major contribution to our understanding at the level of signalling pathways. Before the "New Biology" era, radioresponsiveness was defined in terms of physiological parameters designated as the five Rs. These are: repair, repopulation, reassortment, reoxygenation and radiosensitivity. Of these, only the role of hypoxia proved to be a robust predictive and prognostic marker, but radiotherapy regimens were nonetheless modified in terms of dose per fraction, fraction size and overall time, in ways that persist in clinical practice today. The first molecular techniques were applied to radiobiology about two decades ago and soon revealed the existence of genes/proteins that respond to and influence the cellular outcome of irradiation. The subsequent development of screening techniques using microarray technology has since revealed that a very large number of genes fall into this category. We can now obtain an adequately robust molecular signature, predicting for a radioresponsive phenotype using gene expression and proteomic approaches. In parallel with these developments, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) can now detect specific biological molecules such as haemoglobin and glucose, so revealing a 3D map of tumour blood flow and metabolism. The key to personalised radiotherapy will be to extend this capability to the proteins of the molecular signature that determine radiosensitivity.

Radiation pharmacogenomics: a genome-wide association approach to identify radiation response biomarkers using human lymphoblastoid cell lines

Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels and 1.3 million genome-wide single nucleotide polymorphism (SNP) markers from both Affymetrix and Illumina platforms were assayed for all 277 human LCLs. MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium] assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression, and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. A total of 27 loci, each containing at least two SNPs within 50 kb with P-values less than 10(-4) were associated with radiation AUC. A total of 270 expression probe sets were associated with radiation AUC with P < 10(-3). The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes, which were also associated with radiation AUC (P < 10(-3)). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52, and DEPDC1B each significantly altered radiation sensitivity in at least two cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation.

No association between SNPs regulating TGF-β1 secretion and late radiotherapy toxicity to the breast: results from the RAPPER study

BACKGROUND AND PURPOSE

Several small studies have reported associations between TGFB1 single nucleotide polymorphisms (SNPs), considered to increase secretion of TGF-β1, and greater than 3-fold increases in incidence of fibrosis - an indicator of late toxicity after radiotherapy in breast cancer patients.

MATERIALS AND METHODS

Two SNPs in TGFB1, C-509T (rs1800469) and L10P (rs1800470), were genotyped in 778 breast cancer patients who had received radiotherapy to the breast. Late radiotherapy toxicity was assessed two years after radiotherapy using a validated photographic technique, clinical assessment and patient questionnaires.

RESULTS

On photographic assessment, 210 (27%) patients showed some degree of breast shrinkage, whilst 45 (6%) patients showed marked breast shrinkage. There was no significant association of genotype at either of the TGFB1 SNPs with any measure of late radiation toxicity.

CONCLUSION

This adequately powered trial failed to confirm previously reported increases in fibrosis with TGFB1 genotype - any increase greater than 1.36 can be excluded with 95% confidence. Similar frequent failures to replicate associations with candidate genes have been resolved using genome-wide association scans: this methodology detects common, low risk alleles but requires even larger patient numbers for adequate statistical power.

Normal tissue reactions to radiotherapy: towards tailoring treatment dose by genotype

A key challenge in radiotherapy is to maximize radiation doses to cancer cells while minimizing damage to surrounding healthy tissue. As severe toxicity in a minority of patients limits the doses that can be safely given to the majority, there is interest in developing a test to measure an individual's radiosensitivity before treatment. Variation in sensitivity to radiation is an inherited genetic trait and recent progress in genotyping raises the possibility of genome-wide studies to characterize genetic profiles that predict patient response to radiotherapy.

From cellular to high-throughput predictive assays in radiation oncology: challenges and opportunities

Substantial research efforts into predictive radiation oncology have so far produced very little in terms of clinically applicable assays. This may change with the development of novel high-throughput assays that are of potential interest in a radiation oncology setting. However, it seems that much current research is opportunistic, driven by the available technologies rather than addressing pertinent clinical or biological questions. This review looks at the experience gained from the attempts to develop cellular radiobiology assays. The research process and, in particular, the need for rigorous validation of any promising assay in an independent dataset are stressed. Some common design problems are discussed using examples from radiation oncology. The statistical challenges and some of the key concepts in analyzing dense datasets from high-throughput assays are briefly reviewed. Single nucleotide polymorphisms, immunohistochemical markers, and DNA microarray gene signatures are used as examples of assays that show promise in radiation oncology applications. Some recent studies suggest a differential treatment response between tumor stem cells and other tumor cells. If this is a general pattern, then future predictive assays may have to be performed on stems cells rather than on unselected tumor cells. Advances in radiogenomics or radioproteomics will come from large collaborative research networks, collecting high-quality dosimetric and clinical outcome data and combining state-of-the-art laboratory techniques with appropriate biostatical methods.

Genetic markers for prediction of normal tissue toxicity after radiotherapy

During the last decade, a number of studies have supported the hypothesis that there is an important genetic component to the observed interpatient variability in normal tissue toxicity after radiotherapy. This review summarizes the candidate gene association studies published so far on the risk of radiation-induced morbidity and highlights some recent successful whole-genome association studies showing feasibility in other research areas. Future genetic association studies are discussed in relation to methodological problems such as the characterization of clinical and biological phenotypes, genetic haplotypes, and handling of confounding factors. Finally, candidate gene studies elucidating the genetic component of radiation-induced morbidity and the functional consequences of single nucleotide polymorphisms by studying intermediate phenotypes will be discussed.

Early radiotherapy dose response and lack of hypersensitivity effect in normal brain tissue: a sequential dynamic susceptibility imaging study of cerebral perfusion

AIMS

To determine if magnetic resonance perfusion markers can be used as an analytical marker of subclinical normal brain injury after radiotherapy, by looking for a dose-effect relationship.

MATERIALS AND METHODS

Four patients undergoing conformal radiotherapy to 54Gy in 30 fractions for low-grade gliomas were imaged with conventional T(2)-weighted and fluid attenuated inversion recovery imaging as well as dynamic contrast susceptibility perfusion imaging. Forty regions of interest were determined from the periventricular white matter. All conventional sequences were examined for evidence of radiation-induced changes. Patients were imaged before radiotherapy, after one fraction, at the end of treatment and then at 1 and 3 months from the end of radiotherapy. For each region the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF) and mean transit time (MTT) expressed as a ratio of the baseline value, and radiotherapy dose were determined.

RESULTS

Of the 40 regions, seven occurred within the gross tumour volume and a further four occurred in regions later infiltrated by tumour, and were thus excluded. Regions within the 80% isodose showed a reduction in rCBV and rCBF over the 3 month period. There was no significant alteration in rCBV or rCBF in regions outside the 60% isodose (i.e. <32Gy). MTT did not alter in any region. There seemed to be a threshold effect at 132 days from the end of radiotherapy of 47% (standard error of the mean 11.5, about 25.4Gy) for rCBV and 59% (standard error of the mean 14.2, about 31.9Gy) for rCBF.

CONCLUSIONS

There was a dose-related reduction in rCBV and rCBF in normal brain after radiotherapy at higher dose levels. Although this study used a limited number of patients, it suggests that magnetic resonance perfusion imaging seems to act as a marker of subclinical response of normal brain and that there is an absence of an early hypersensitivity effect with small doses per fraction. Further studies are required with larger groups of patients to show that these results are statistically robust.

The genomics revolution and radiotherapy

The expansion of our knowledge through the Human Genome Project has been accompanied by the development of new high-throughput techniques, which provide extensive capabilities for the analysis of a large number of genes or the whole genome. These assays can be carried out in various clinical samples at the DNA (genome), RNA (transcriptome) or protein (proteome) level. There is a belief that this genomic revolution, i.e. sequencing of the human genome and developments in high-throughput technology, heralds a future of personalised medicine. For clinical oncology, this progress should increase the possibility of predicting individual patient responses to radiotherapy. This review highlights some of the work involving sparsely ionising radiation and the new technologies.