**1.3 Genetic markers and radiotoxicity**

Research on factors that influence the development of adverse reactions to RT has investigated the contribution of genetic factors to these reactions [25]. This concept emerged from the identification of syndromes that make individuals more sensitive to ionizing radiation, such as the ataxia-telangiectasia syndrome resulting from mutations in genes that respond to DNA damage and repair [26, 27]. Thus, biomarkers may help in treatment planning.

Thus, radiogenomics has emerged as an area of study that aims to identify biomarkers that can predict adverse reactions in cancer patients undergoing RT or to identify individuals who are more susceptible to developing a severe degree of these reactions [3, 10, 28].

Biomarkers are molecules/biomolecules that can be measured in biopsy samples, body fluids, and feces to indicate the state of normal metabolic processes, diseases, and responses to a particular treatment [3, 29].

In 2009, the Radiogenomics Consortium (Manchester, United Kingdom) was established and supported by the National Cancer Institute (NCI) [30]. In 2019, 133 institutions from 33 countries participated in the Consortium [31]. The objective of the Radiogenomics Consortium was to establish collaborations between countries so that studies on the association between biomarkers and adverse reactions to RT could be carried out in large cohorts [10, 32] in order to identify molecular pathways that participate in the development of adverse reactions to RT and variants in the genome that are capable of predicting the development and severity of these reactions [10, 30, 31, 33].

The primary biomarkers studied by the Radiogenomics Consortium are singlenucleotide polymorphisms (SNPs) [31, 34]. SNPs are considered suitable genetic markers in studies on their association with phenotypic characteristics, as they are frequent in populations and are easily genotyped [35]. Furthermore, samples for single-nucleotide polymorphism (SNP) screening can be obtained from any normal tissue, considering that polymorphisms are present in all normal cells, including blood cells [33].

### *1.3.1 Single-nucleotide polymorphism*

The DNA sequences of any two individuals in the world are approximately 99.9% similar to each other [36, 37]. Variations in only 0.1% of the genome make individuals phenotypically different from each other [3, 37–39]. Among these 0.1% variations, approximately 99% are due to SNPs [40].

Mutations and SNPs are genetic variants present at specific positions in the DNA sequence. SNPs are considerably common among individuals and have a probability of 1% or more of being identified in an individual, whereas "gene mutation" refers to variations in the DNA that are present in less than 1% of the population [36, 37]. Although these definitions are well established, the nomenclature remains confusing [36]. Condit et al. [41] suggest the use of the terms "genetic variant" or "genetic alteration" to replace the definitions of mutations and polymorphisms that can be complemented with the terms "pathogenic" or "benign" [36, 42]. However, the establishment of a generalist nomenclature has still been discussed.

SNPs are genetic variants that occur with the replacement of a single nucleotide in a genome sequence [27]. The variation that results in SNP can occur in non-coding regions such as intergenic and intron regions, which will not promote phenotypic changes, and in the exon coding region, which may or may not modify the gene function and consequently the phenotype (**Figure 2**) [35, 37, 44]. Although exchange of a nucleotide at a specific position can be performed by any other nucleotide (C, G, A, or T), SNPs are generally biallelic [35, 45].

To understand mechanism by which SNPs occur in DNA and their impact on the phenotype, let us look at the following example:

On chromosome 19, the locus that encodes *TGFβ* is most commonly found in exon 1, at a guanine nucleotide (G) at position 869. On the complementary strand of DNA, G pairs with a cytosine (C) encoding the amino acid Proline (Pro) at codon 10 *Radiogenomics: A Personalized Strategy for Predicting Radiation-Induced Dermatitis DOI: http://dx.doi.org/10.5772/intechopen.108745*

**Figure 2.**

*Schematic representation of the non-coding (intron) and coding (exon) region of a gene. Generated with reference to the schematic representation by Alberts et al. [43].*

(**Figure 3A**). Considering that it is most frequently found in the population, C, in this example, is called the wild allele. However, in some individuals, an exchange of G for adenine (A) at this position has been observed (**Figure 3B**). This exchange also leads to a change in the complementary strand of DNA, that is, the exchange of C for thymine (T), thus encoding the amino acid leucine (Leu) (**Figure 3C**). In this example, the T allele is called a variant allele because it is less frequent in the population. Considering that this allelic variation (G > A) is present in more than 1% of the population, it is called an SNP. This *TGFβ* SNP is referred to as Pro10Leu or encoded as rs1800470.

The human genome is diploid; that is, we inherited 23 chromosomes from the father and 23 from the mother, which are organized into pairs by similarity to each other. This organization into pairs of similar chromosomes is called homologous chromosomes, which have very similar nucleotide sequences. Therefore, SNPs can occur on one chromosome or on a homologous pair of chromosomes, and hence, they can be classified as homozygous for the wild allele, homozygous for the variant allele, or heterozygous (**Figure 4**).

### *1.3.2 Techniques for studying single-nucleotide polymorphisms*

The candidate gene approach has been used to assess the association between SNPs and adverse reactions to RT. For this, genes that are already known to participate in the molecular mechanism underlying the development of adverse reactions are selected [39, 46]. Seibold et al. [47] performed a study of candidate genes involved in oxidative stress to verify their ability to predict late toxicity in 753 breast cancer patients who underwent RT. The study showed that breast cancer patients carrying the rare allele for the SNP rs2682585 in *XRCC1* had a low occurrence of late cutaneous toxicities (OR: 0.77; 95% CI: 0.61–0.96; *p* = 0, 02) [47]. The association of this SNP with late skin toxicity in breast cancer patients undergoing RT has been validated by

### **Figure 3.**

*Schematic representation of the rs1800470 SNP in TGFβ. A) the nucleotide sequence that makes up TGFβ will be transcribed into RNA and one of the strands will be translated into a protein that has proline (pro) at codon 10; B) rs code of the SNP in TGFβ (rs1800470) and the respective exchange of base (G > a) and protein (pro>Leu); C) SNP occurs at position 869, of exons 1, of TGFβ (G > a) and originates a complementary strand with a thymine at this position. Thymine will be transcription into uracil which will give rise by translation to a protein with leucine (Leu) at codon 10.*

#### **Figure 4.**

*Classification according to the occurrence of SNP in homologous chromosomes.*

members of the Radiogenomics Consortium [28]. An important challenge in developing such research is that researchers must have basic knowledge about molecular biology and the effects of ionizing radiation on DNA [27].

*Radiogenomics: A Personalized Strategy for Predicting Radiation-Induced Dermatitis DOI: http://dx.doi.org/10.5772/intechopen.108745*

Other techniques that investigate susceptibility genes, including genome-wide linkage studies (GWLS) and genome-wide association studies (GWAS), are used to conduct a broader investigation of all genes rather than an investigation of those genes already known to participate in molecular pathways involved in disease development [37]. These techniques are based on full-genome scanning and are extremely useful for investigating polymorphisms that may be associated with adverse reactions to RT [39, 46]. However, they are rarely used in studies on the association between polymorphisms and ARD. The Radiogenomics Consortium aims to obtain resources to enable the evaluations in large cohorts using the GWAS technique [30, 32].
