Abstract During the last decade, nearly 60 studies have addressed possible associations between various genetic sequence alterations and risk of adverse reactions after radiotherapy. We report here ...an overview of these studies with information on the genetic variants, tumour type, number of patients included, the endpoint studied, the mechanism(s) by which the candidate genes are involved in the pathogenesis of normal tissue toxicity, and odds ratios (ORs) for candidate variants. Though many positive results have been reported, inconsistent findings and non-replication of previous results have frequently occurred. This can presumably be attributed to certain methodological shortcomings including lack of statistical power to detect small effect sizes. Based on theoretical considerations and experiences from other scientific fields, we discuss how future studies should be designed in order to successfully unravel the genetics of normal tissue radiosensitivity. We propose a model of the allelic architecture that may underlie differences in normal tissue radiosensitivity. Genome wide association studies have proven a powerful tool to identify novel loci that affect various phenotypes. Nonetheless, genome wide association studies are extremely demanding in terms of sample size. Furthermore, certain limitations still relate to this kind of studies, emphasizing the need for international consortia such as the ESTRO GENEPI.
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.
Single nucleotide polymorphisms (SNPs) in genes related to the biological response to radiation injury may affect clinical normal tissue radiosensitivity. This study investigates whether seven ...selected SNPs in five candidate genes influence risk of subcutaneous fibrosis and telangiectasia after radiotherapy.
The 41 patients included in this study were given post-mastectomy radiotherapy in 1978–1982 and subsequently evaluated in detail with regard to several different normal tissue reactions. SNPs in
TGFB1 (codons 10, 25 and position −509),
SOD2 (codon 16),
XRCC3 (codon 241),
XRCC1 (codon 399) and
APEX (codon 148) were analyzed by PCR and single nucleotide primer extension. Dose–response curves were established for subcutaneous fibrosis and telangiectasia in patients with different genotypes. Differences in radiosensitivity were quantified in terms of ED
50 values and enhancement ratios.
For
TGFB1, the Pro/Pro genotype in codon 10 and the T/T genotype in position −509 correlated positively with risk of subcutaneous fibrosis. The
SOD 2 codon 16 Val/Ala genotype was associated with increased risk of subcutaneous fibrosis when compared to the Val/Val genotype. The Thr/Thr genotype in
XRCC3 codon 241 correlated with increased risk of subcutaneous fibrosis as well as telangiectasia. The Arg/Arg genotype in
XRCC1 codon 399 was associated with increased risk of radiation-induced subcutaneous fibrosis. For these polymorphisms, enhancement ratios between 1.09 and 1.25 were found. Combined analysis of multiple SNPs demonstrated that the risk of subcutaneous fibrosis correlated with the number of risk alleles in such a manner that patients with few risk alleles exhibited a remarkable degree of radioresistance.
The present study established significant correlations between five SNPs and risk of radiation-induced normal tissue reactions. These findings support the assumption that clinical normal tissue radiosensitivity should be regarded as a phenomenon dependent on the combined effect of variation in several genes and indicate that models based on multiple genetic markers may have the potential to predict normal tissue responses after radiotherapy.
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•Acute radiation-induced toxicities (RITS) are heritable phenotypes and ∼29% of their variances are explained by common genetic variants.•Three potential novel mechanisms for RITs are ...exoribonuclease activity, inositol phosphate-mediated signaling, and drug catabolic process.•Using genetic markers is effective to understand the mechanism of acute RITs, and to predict acute RITs by employing polygenic risk scores.•Genetically enriched prediction model for RITs followed by radiation-dose adjustment contributes to attaining personalized radiotherapy.
We aimed to the genetic components and susceptibility variants associated with acute radiation-induced toxicities (RITs) in patients with head and neck cancer (HNC).
We performed the largest meta-GWAS of seven European cohorts (n = 4,042). Patients were scored weekly during radiotherapy for acute RITs including dysphagia, mucositis, and xerostomia. We analyzed the effect of variants on the average burden (measured as area under curve, AUC) per each RIT, and standardized total average acute toxicity (STATacute) score using a multivariate linear regression. We tested suggestive variants (p < 1.0x10-5) in discovery set (three cohorts; n = 2,640) in a replication set (four cohorts; n = 1,402). We meta-analysed all cohorts to calculate RITs specific SNP-based heritability, and effect of polygenic risk scores (PRSs), and genetic correlations among RITS.
From 393 suggestive SNPs identified in discovery set; 37 were nominally significant (preplication < 0.05) in replication set, but none reached genome-wide significance (pcombined < 5 × 10-8). In-silico functional analyses identified “3′-5'-exoribonuclease activity” (FDR = 1.6e-10) for dysphagia, “inositol phosphate-mediated signalling” for mucositis (FDR = 2.20e-09), and “drug catabolic process” for STATacute (FDR = 3.57e-12) as the most enriched pathways by the RIT specific suggestive genes. The SNP-based heritability (±standard error) was 29 ± 0.08 % for dysphagia, 9 ± 0.12 % (mucositis) and 27 ± 0.09 % (STATacute). Positive genetic correlation was rg = 0.65 (p = 0.048) between dysphagia and STATacute. PRSs explained limited variation of dysphagia (3 %), mucositis (2.5 %), and STATacute (0.4 %).
In HNC patients, acute RITs are modestly heritable, sharing 10 % genetic susceptibility, when PRS explains < 3 % of their variance. We identified numerus suggestive SNPs, which remain to be replicated in larger studies.
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.
The use of chemical radioprotectors represents an obvious strategy to improve the therapeutic index in radiotherapy. Amofostine (WR-2721) has recently been approved for use in head and neck cancer to ...protect against radiation-induced xerostomia. Currently, the question has arisen whether amifostine could be used for radioprotection in broader terms. Amifostine may have the potential to enable intensified treatment by ameliorating mucosal reactions that are often a limiting factor in accelerated fractionation or concomitant chemoradiation. However, it has as yet not been clarified whether sufficient amifostine to reduce mucositis can be administered before each radiation fraction without causing unacceptable toxicity. Also, the optimal dosage and schedule of amifostine in chemoradiation combinations have not yet been established. The major concern related to radioprotectiors is the potential hazard of collateral tumor protection. A number of clinical studies have concluded that amifostine does not reduce antitumor efficacy. However, not even the largest study conducted, with over 300 patients, has sufficient statistical power to detect a clinically significant reduction in tumor control rate. To put this issue ultimately to a rest, a clinical trial with a sufficient accrual to definitely rule out a tumor protective effect of amifostine needs to be conducted. Substances reducing radiation-induced toxicity by modulating the biological response to radiation injury may represent an alternative concept in radioprotection. However, such agents are still at a developmental stage. Copyright 2003, Elsevier Science (USA). All rights reserved.
Abstract Several relatively small studies have established predictive models for normal tissue radiosensitivity based on multiple SNPs. Even though these models yielded statistically significant ...results, the models were often inconsistent with each other. This can presumably be attributed to certain methodological problems related to the way these models were established and tested. In order to explore this potential problem, we conducted 10 simulated SNP experiments based on randomly assigned ‘SNP genotypes’ applied to a set of real clinical data. In 8 out of 10 times, a significant result was found for the model. This clearly demonstrates that the process of fitting the model to the dataset is indeed per se capable of producing nominally significant results. Thus, great caution should be taken when a multiple SNP model is established and tested within the same patient cohort.
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