Molecular Biomarkers in Radiation Oncology
Molecular biomarkers in radiation oncology could enable the selection of patients for individualization of radiation therapy and concomitant treatment with optimizing agents and are an important tool in precision medicine. The biomarkers are directed toward either determining the tumor radiosensitivity or as markers for the normal tissue response to radiotherapy and are focused on the biological characteristics that affect response to radiation. Although a lot of focus has been directed on identifying suitable molecular biomarkers for radiation oncology, almost none has made it into clinical use.
This chapter will introduce the basic definitions of biomarkers and the differences between prognostic and predictive biomarkers. It will go through the biological factors that influence differences in response to radiation, such as intrinsic radiosensitivity, hypoxia, HPV status and number of cancer stem cells (CSCs), and candidate biomarkers for these causes of radioresistance.
The ability to predict normal tissue complication risk prior to radiotherapy has been a long sought goal in radiobiology. For more than 25 years, various attempts have tried to establish a predictive assay for normal tissue radiosensitivity, and these efforts will be summarized. Different strategies for identifying predictive markers for the normal tissue response to radiotherapy will be described, and the current status of radiogenomics is evaluated.
Validation of identified biomarkers is a crucial step in getting a biomarker implemented in the clinic. The different levels of validation, analytical validity, clinical validity, and clinical utility, will be explained, and examples of biomarkers that have gone through this validation process are included.
KeywordsMolecular biomarkers Prognostic biomarkers Predictive biomarkers Radiation response Radiosensitivity Hypoxia HPV Cancer stem cells Radioresistance Validation of biomarkers Normal tissue response
Biomarkers are biological features that provide information of the biological state of a given tissue or organism. Examples include levels of particular proteins in tissue or body fluids, nucleic acid-based biomarkers such as gene expression levels, gene mutations or polymorphisms, or peptides, lipid metabolites, and other small molecules. The US National Institutes of Health (NIH) Biomarkers and Surrogate Endpoint Working Group defines a biological marker, a biomarker, as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic intervention (Atkinson et al. 2001).
Molecular biomarkers in radiation oncology are focused on the biological characteristics that have been shown to influence differences in response to radiation for both the tumor and the surrounding normal tissue. These characteristics include intrinsic radiosensitivity, hypoxia, HPV status, number of cancer stem cells (CSCs), and repopulation between radiotherapy fractions (Baumann et al. 2016). In the last 20 years, remarkable progresses in the knowledge of the biological factors influencing radiation response and the causative molecular basis, such as the DNA damage response and repair mechanisms, signaling pathways, and tumor microenvironmental factors, have been made and have generated a large number of potential biomarkers. Biomarkers are an important tool in individualization of patient treatment and can potentially guide treatment and stratify patients who may benefit from a specific treatment (Bibault et al. 2013).
Compared to surgery, radiotherapy has the advantage of being potentially organ and function preserving. Nevertheless, it may lead to severe acute or late toxicity (Kerns et al. 2015). Radiation-induced normal tissue reactions represent a dose-limiting factor in radiotherapy. Typically, treatment regimens are designed to ensure that the risk of severe late effects does not exceed 5–10% (Scaife et al. 2015). This means that a small fraction of radiosensitive patients limits the dose that can be given to the entire patient population, though the majority of patients could potentially tolerate a higher dose. If the variability in normal tissue response could be taken into account in the treatment-planning phase, the therapeutic strategy could be individualized accordingly (Kerns et al. 2014). Patients being relatively sensitive to the effects of ionizing radiation could (when possible) be offered a treatment strategy that does not include radiotherapy, whereas the resistant patients could be dose escalated to some extent. This would potentially lead to a substantial improvement in the therapeutic index for radiotherapy (Kerns et al. 2015). Consequently, the ability to predict normal tissue complication risk prior to therapy has been a long sought goal in radiobiology (Russell and Begg 2002; Scaife et al. 2015).
Although a lot of focus has been directed on identifying suitable molecular biomarkers for radiation oncology, almost none has made it into clinical use. This has been due to either time-consuming methods, heavy technical demand, or an insufficient level of evidence or validation of the biomarker. This chapter will focus on a section of molecular biomarkers in radiation oncology, and the biological rationale, as well as some fundamental aspects of biomarkers in general.
Prognostic Versus Predictive Biomarkers
Biomarkers are classified as either prognostic or predictive based on their association with outcome and their ability to predict the effect of different classes of, or even individual, therapeutic agents (Ballman 2015, reviewed by Hayes 2015).
Prognostic biomarkers can be defined as biomarkers that can inform on the future behavior of an established cancer or the risk of developing treatment-induced morbidity. The term “prognostic factor” refers to a biomarker test that infers a low or high risk of a cancer- or treatment-related event, assuming the patient receives no more therapy than he/she has already received, if any. The most commonly used prognostic factors for cancer-related events are the TNM classification system. Examples of molecular tumor biomarkers include PSA level at the time of a prostate cancer diagnosis and estrogen receptor (ER) level in breast cancer.
In contrast, a biomarker is predictive if the treatment effect (experimental compared with control) is different for biomarker-positive patients compared with biomarker-negative patients. Importantly, there must be at least two comparison groups available (e.g., two different treatment arms in a randomized trial) to determine whether a biomarker is predictive. A classic example of a predictive molecular tumor biomarker is again the ER level in breast cancer, where only patients with ER-positive tumors will benefit from endocrine (antiestrogen) therapy. Thus, ER level is an example of a biomarker that is both prognostic and predictive. In patients treated without endocrine therapy, ER-positive tumors are associated with a poor outcome (prognostic), and ER positivity identifies patients that will benefit from endocrine therapy (predictive).
There can be quite some confusion about the distinction between prognostic and predictive biomarkers. When trying to predict outcome for a patient, mathematical modeling is performed looking at the relationship between explanatory variables (independent) and an outcome variable (dependent). The explanatory variables are often biomarkers, and these are most often prognostic factors. However, in statistical terms, the explanatory variables are usually referred to as predictive variables, which may generate some confusion. Avoiding the term “predictive factor,” for what is in fact a prognostic factor, can help circumvent some of the confusion. An often-used terminology is therefore to describe these factors as being associated with outcome, rather than predicting outcome.
Figure 1e–h illustrate data from a different randomized trial. In this case the marker is both prognostic, i.e., associated with outcome in patients treated with standard radiotherapy (Fig. 1e), and predictive (Fig. 1f). The observation that a predictive marker can show no difference in outcome among patients treated with the sensitizer further illustrates the need for a randomized trial. The ideal way to present data on predictive biomarkers is to divide patients on biomarker status and look at the effect of the experimental treatment in both groups. This is illustrated in Fig. 1g, h which include the exact same data as Fig. 1e, f but organized by biomarker status. When plotted this way, it can be seen that the sensitizer does not have an effect in biomarker-negative patients but improves outcome in biomarker-positive patients. An example of such a biomarker is a 15-gene signature for hypoxia, which, as described below, is prognostic in patients treated with standard radiotherapy and predictive for the benefit of the sensitizer nimorazole (Toustrup et al. 2011, 2012a).
When analyzing data on the predicative value of a biomarker, it is tempting to conclude that if there is no association with outcome in the biomarker-negative cohort, but a significant association in the biomarker-positive group (as in Fig. 1g, h), then the biomarker is predictive for the benefit of the experimental arm. According the REMARK guidelines (Altman et al. 2012), a significant effect in one group and a nonsignificant effect in the other are not sound evidence that the effect of the marker differs by subgroup. A test of interaction is required to rigorously assess whether effects are different in subgroups. In the examples of Fig. 1g, h, a test for interaction (also known as effect modification) between biomarker and treatment can be performed by testing the multiplicative term for significance in a Cox model (see Knol and Vander Weele (2012) for details on how to perform and report tests for interaction).
Tumor Biomarkers: Biomarkers of Resistance to Radiation Therapy
Measurement of DNA damage foci as a marker for radiosensitivity can be employed in an ex vivo assay, where tissue samples from patients are cultured prior to ex vivo treatment and the DDR is detected at certain time points after treatment (Siddiqui et al. 2015). The γH2AX foci levels have clinically been tested as markers for normal tissue reactions, but also the use of γH2AX as a biomarker for sensitivity of tumors to radiotherapy, e.g., in lung tumors, has been the subject of studies (Rothkamm et al. 2015).
Cancer Stem Cells (CSCs)
Cancer stem cells are the cells within a tumor that are capable of self-renewal and generate the heterogeneous lineages of cancer cells that comprise the tumor. The level of CSC in the tumor is an important factor to the needed radiation dose to achieve local tumor control, as surviving CSCs determine tumor recurrences. Thus for a higher CSC number, a higher radiation dose is necessary to cure the tumor. Furthermore, CSC-related radiobiological mechanisms, for example, repopulation during treatment and recovery from radiation-induced damage between the single fractions, have been shown to increase tumor resistance against radiotherapy.
For detection of CSC, flow sorting techniques have been used to isolate cell populations on the basis of cell surface markers that are differentially expressed in tumor cell subpopulations. For the determination of levels of stem cell markers, both immunohistological techniques and qPCR have been employed. The expression level of CSC markers has been suggested as a potential surrogate of CSC density.
There are a range of markers that have been explored, such as CD133 in colon, brain, and pancreatic tumors, as well as CD24 and CD44 in breast, prostate, colon, and pancreatic cancer. The specific markers vary considerably depending on tumor type or tissue of origin, and there is so far no universal marker for CSCs. A concern is that inconsistency in the data on the effect of high stem cell populations, both in preclinical and clinical data, points toward issues on the specificity of the currently identified cancer stem cell markers. It can also be difficult to distinguish the different factors contributing from each other, for example, CSCs have been shown to reside in hypoxic niches in the tumor, and the hypoxia may contribute to increased radioresistance of cancer stem cells (CSCs) (Baumann et al. 2008, 2009).
Tumor hypoxia is a well-known trait in most solid tumors, which is mainly due to an inadequate and heterogeneous vascular network (Vaupel et al. 1989). Tumor hypoxia causes resistance to radiation therapy, as well as more aggressive and invasive tumors, and has been associated with poor prognosis in different cancers.
A lot of effort has gone into identifying usable biomarkers for tumor hypoxia, as the clinical impacts of being able to characterize patients in terms of prognosis or select patients for hypoxia-modifying treatments are remarkable. One of the used approaches is to use hypoxia-upregulated genes as endogenous markers. Gene expression is highly affected by cellular oxygen levels, and the majority of the affected genes are orchestrated by hypoxia-inducible factor, HIF1. HIF1 is a transcription factor, which is the main regulator of the transcriptional response to hypoxia. Some of these genes, such as CAIX and GLUT1, have been employed as hypoxic markers, but with varying success. There has been a great focus on developing gene expression signatures, which is a group of genes, instead of single markers. More than 30 hypoxia gene expression signatures have been published, which have been developed in different ways and with different characteristics. Currently, there is no consensus as to the best way to derive hypoxia gene expression signatures. No single gene is represented in all signatures, but some genes are heavily represented in the gene signatures, such as BNIP3, F3, and LOX. The signatures are at different levels in respect to clinical usability and validation.
For all signatures, at the current level, there is a need for a continued validation, especially in the decisive step of advancing from retrospective to prospective classification of patients and their allocation to hypoxia-modifying therapies in the clinic.
Besides mRNA-based biomarkers, microRNAs (miRNAs) have also been shown to be inducible by hypoxia, with hsa-mir-210 having shown a prognostic significance in HNSCC treated with postoperative radiotherapy (Baumann et al. 2016; Harris et al. 2015; Toustrup et al. 2012b).
Intra-tumor heterogeneity is a well-known feature of most cancers, which, among others, originates from genomic instability, one of the hallmarks of cancer (Cyll et al. 2017). Genomic instability covers a variety of alterations to the genome, ranging from small to large structural and numerical alterations, and potentially leads to diverse populations of cells. In the case of tumor hypoxia, the tumor heterogeneity originates from the heterogeneous vascular network, leading to areas with low oxygen concentration, whereas other areas are well perfused and with an adequate oxygen concentration. Recognizing the biological heterogeneity of tumors, there is a potential for sampling error when a small biopsy is taken from a much larger tumor. This can potentially also affect biomarker analyses, as a single sample from one small tumor region might not be optimal for characterizing the tumors’ radiosensitivity, level of CSCs, or hypoxic status. However, sampling multiple regions from the same tumor is impractical for most cancer types, and an analysis within a relatively small sample might also be of predictive value. This should be taken into consideration in the validation procedure, which is discussed at a later point.
Circulating biomarkers include factors that can be measure in the blood, such as circulating tumor cells, RNA, DNA, or proteins. As blood samples are easy obtainable and can be attained in a serial manner, as before, during, and after treatment, circulating biomarkers are a very promising area. One interest in the use of circulating biomarkers has been on circulating biomarkers of hypoxia, where the proteins evaluated include osteopontin (OPN) and interleukin-8 (IL8). The abovementioned hypoxia biomarkers, the hypoxia gene expression signatures, are measured in tumor tissue, e.g., in the diagnostic biopsy, which is obtained at baseline, before treatment starts. Using circulating biomarkers could be an advantage in measuring hypoxia biomarkers at different time points during treatment to monitor for reoxygenation effects using sequential blood samples.
The use of circulating DNA and circulating tumor cells (CTCs) as biomarker has in popular terms been named as liquid biopsy. Circulating tumor DNA (ctDNA) is a DNA that is released by the tumor cells into the circulation. The mechanisms of release of nucleic acid into the blood are not yet explained. It is thought to result from necrosis, apoptosis, and release of DNA by phagocytes that have engulfed tumor cells. Of the overall circulating cell-free DNA (cfDNA) in the blood, the fraction that is derived from tumors is for some tumor types very low, as in colorectal cancer where ctDNA as a fraction of total cfDNA was found to range from 0.01% to 1.7% and from 0.02% to 3.2% in NSCLC, whereas it was up to 47% in multiple myeloma. The level of ctDNA is thought to correlate to some degree with tumor size and the rate of tumor cell death. The half-life of ctDNA in the circulation is approximately 0.5–2 h, so only tumor cells that died several hours before sample collection contribute to ctDNA levels. The use of ctDNA as biomarker includes both the pretreatment level of ctDNA and monitoring the kinetics of the levels of ctDNA, as acute changes in ctDNA concentrations during radiotherapy or immediately following treatment might also have a prognostic or predictive value. Another use is noninvasive tumor genotyping via the plasma, which can be an advantage in the lack of tumor biopsy material.
For ctDNA analysis, a technical challenge has been to find techniques that are sensitive and specific enough to detect the ctDNA. The methodology to discriminate tumor-derived DNA from normal cell-free DNA is using the unique genetic profile of the tumor. Used techniques for detection of specific mutations within ctDNA include PCR-based methods, which can preferentially amplify low levels of mutant alleles in the pool of wild-type DNA. Especially digital PCR (dPCR), which has a very high sensitivity and specificity, is often used for ctDNA analysis (Chaudhuri et al. 2015; Rostami and Bratman 2017).
Predictive Markers for the Normal Tissue Response to Radiotherapy
As mentioned in the introduction, the ability to predict normal tissue complication risk prior to radiotherapy has been a long sought goal in radiobiology (Russell and Begg 2002). For more than 25 years, various attempts have tried to establish a predictive assay for normal tissue radiosensitivity (3B). In the following, these efforts will be summarized.
In Vitro Assays
In the early 1990s, radiobiology was heavily influenced by the so-called target-cell hypothesis according to which normal tissue toxicity was assumed to be the result of depletion of certain target cells. Under this hypothesis, it was investigated if the risk of normal tissue toxicity could be predicted from the study of cell irradiated in vitro. Associations were sought between in vitro radiosensitivity of fibroblasts and risk of radiation-induced fibrosis. In addition, some studies looked at the in vitro radiosensitivity of lymphocytes or lymphoblasts (West et al. 2001). Generally, no significant associations were found for acute toxicity. A number of relatively small studies reported significant associations between the in vitro radiosensitivity of fibroblast and risk of late toxicity. Nevertheless, these associations could not be confirmed in subsequent larger studies (Russell and Begg 2002). Interest was also taken in the development of rapid assays measuring cellular or subcellular damage such as DNA lesions, chromosome aberrations, or apoptosis. A classic example is the so-called alkaline comet assay. The results of these studies have been inconsistent. A major challenge in the interpretation of these results has been the limited size of the studies and a substantial heterogeneity concerning the investigated assays (Russell and Begg 2002). Thus, it has not been possible to establish an in vitro assay suitable for routine clinical use.
Radiogenomics: The Search for Genetic Predictors of Radiosensitivity
In the late 1990s, increasing interest was taken in the hypothesis that normal tissue radiosensitivity is under genetic control and that normal tissue complication risk could be predicted from genetic analyses (Andreassen et al. 2016a). This concept received support from the observation that patients suffering from certain rare genetic syndromes like ataxia telangiectasia, Bloom’s syndrome, Fanconi’s anemia, and Nijmegen breakage syndrome experience devastating normal tissue reactions if treated with radiotherapy. However, these syndromes characterized by Mendelian inheritance are extremely rare and probably of little relevance when addressing the average cancer patients. It was hypothesized that heterozygous carriers of truncating mutations in genes such as ATM, BRCA1, and BRCA2 could make up a radiosensitive subpopulation. Nevertheless, a number of relatively small studies did not provide indications that such sequence alterations are overrepresented among patients with excessive normal tissue reactions nor that carriers of these mutations exhibit a higher risk of normal tissue complications (Andreassen et al. 2016a).
Normal tissue radiosensitivity is as a complex trait dependent on the combined influence of sequence alteration in several genes.
The mere observation that normal tissue radiosensitivity is characterized by a continuous spectrum rather than falling into distinct categories is per se indicative of a polygenic inheritance. The biological response to irradiation is presumably very complex and involves a multitude of different pathways and genes. Given the widespread existence of sequence alterations in the genome, numerous loci with potential impact on clinical radiosensitivity are likely to exist.
Single nucleotide polymorphisms may make up a proportion of the genetics underlying differences in clinical normal tissue radiosensitivity.
SNPs account for approximately 90% of the inter-individual sequence variation within human populations. SNPs in coding regions may alter protein function, whereas SNPs in regulatory regions may affect gene expression/protein synthesis rate. Thus, SNPs have the potential to affect various phenotypes including normal tissue radiosensitivity.
Some genetic alterations are expressed selectively through certain types of normal tissue reactions, whereas others exhibit a “global” impact on radiosensitivity.
Clinical studies addressing normal tissue radiosensitivity have indicated that the risks of different types of adverse reactions after radiotherapy are not strongly associated with each other. This observation indicates that some genetic alterations have to be expressed selectively through certain types of normal tissue reactions. However, patients suffering from one of the aforementioned “radiosensitive syndromes” appear to experience a general enhancement of clinical radiosensitivity apparently affecting both acute and late morbidity. Based on this, it seems likely that some genetic alterations affect clinical radiosensitivity in a generalized manner, whereas others exhibit a differential expression through different types of normal tissue reactions (Andreassen et al. 2016a).
Studies in Radiogenomics
Overview of compelling associations in radiogenomics. Criteria for selection of SNPs: Candidate gene study; p-value below 0.05 in a study with several independent cohorts with total size of at least 1,000 patients. GWAS: p-value below 5.5 × 10−8
Author, publication year
Study type, N
Cancer site, normal tissue endpoint(s)
Candidate gene, 2036
SNP near TNFα
Breast, overall late toxicity
Candidate gene, 2636
Breast, overall late toxicity
Candidate gene, 5456
Breast and prostate, various endpoints
OR ≈ 1.2–1.5
Prostate, erectile dysfunction
5.46 × 10−8
Prostate, rectal bleeding
5.40 × 10−8
Prostate, rectal incontinence
1.05 × 10−12
Prostate, overall late toxicity
Per allele OR ≈ 6
4.46 × 10−11
Prostate, variuous endpoints
4.16 × 10−8
3.21 × 10−8
6.27 × 10−10
Candidate Gene Studies
In the candidate gene approach, a limited number of sequence alterations are selected for investigation based on the hypothesis that they are likely to affect the phenotype of interest. As mentioned previously, a few rather small studies have addressed the impact of truncating ATM and BRCA mutations upon normal tissue radiosensitivity. Apart from this, the candidate gene studies have exclusively focused on SNPs. These studies typically investigated SNPs in genes involved in processes such as detection of DNA damage (e.g., ATM), DNA repair (e.g., XRCC1, XRCC3, and APEX), tissue remodeling (e.g., TGFB1 and TIMP), and scavenging of reactive oxygen species (e.g., SOD2 and GSTP1). More than 100 different genes were investigated as part of this research. With a median sample size around 150 patients, most of the studies were very small. Around two thirds of these studies have reported significant associations. Nevertheless, the findings were often inconsistent and independent replication rarely took place. In hindsight, it seems obvious that the vast majority of these studies have suffered from methodological shortcomings such as lack of statistical power and unattended multiple testing problems. Nevertheless, a few candidate gene studies have actually reported compelling SNP associations (Herskind et al. 2016; Scaife et al. 2015) (Table 1). These studies included more than a thousand patients and involved independent replication cohorts. A study published in 2012 investigated 43 SNPs in 35 genes related to TGF-β signaling pathways in 2,036 breast cancer patients (Talbot et al. 2012). It showed an increased risk of late toxicity (OR = 2.45, 95% CI 1.52–3.98) for patients being homozygous for the minor allele of the rs1800629 SNP. This SNP is located a few hundred bases upstream of the transcription start site of the TNFα gene and relatively close to the lymphotoxin-alpha gene (LTA). More recently, a candidate gene study investigated 305 SNPs in 59 genes related to oxidative stress and DNA repair pathways in a discovery set of 753 breast cancer patients (Seibold et al. 2015). Subsequently, the top ranking 10 SNPs were tested in several replication cohorts with a total of 1,883 patients. This study identified a SNP (rs2682585) in the DNA repair gene XRCC1 for which carriers of the minor allele had a reduced risk of late skin toxicity (multivariate OR 0.77, 95% CI 0.61–0.96, p = 0.02) and overall toxicity (regression coefficient − 0.08, 95% CI −0.15 to −0.02, p = 0.02). Finally, in 2016 an “individual patient data meta-analysis” with more than 30,000 recordings of normal tissue toxicity from 5,456 breast and prostate cancer patients showed that the possession of the minor allele at the ATM rs1801516 SNP increases the risk of radiation-induced toxicity with an odds ratio of approximately 1.5 for acute toxicity and 1.2 for late toxicity (Andreassen et al. 2016b).
The Genome-Wide Association Study
GWASs in Radiogenomics
As of May 2018, eight GWASs addressing normal tissue radiosensitivity have been published (Fig. 5). Below, findings reaching or approaching genome-wide significance will be summarized. The first GWAS in normal tissue radiobiology investigated the risk of erectile dysfunction among 79 African Americans treated with radiotherapy for prostate cancer (Kerns et al. 2010). It identified a SNP (rs2268363) in the gene of the follicle-stimulating hormone receptor (FSHR) with a relative risk of 7.03 and a p-value of 5.46 × 10−8. Another GWAS included 1,149 prostate cancer patients in a two-stage GWAS (Kerns et al. 2013). This study showed an association between two SNPs in “a gene desert” (rs7120482 and rs17630638) and risk of rectal bleeding after radiotherapy (OR 6.7 and 3.1 in first and second stage, respectively, combined p-value = 5.4 × 10−8).The largest GWAS conducted so far in normal tissue radiobiology was published in 2014 (Barnett et al. 2014). This two-stage GWAS included more than 3,500 breast and prostate cancer patients. The study looked for associations with regard to overall toxicity (STAT score) as well as a number of “individual endpoints” (e.g., telangiectasia, rectal bleeding, nocturia, and rectal incontinence). Even though the study had substantially more power to detect associations for overall toxicity, the strongest associations were shown for individual endpoints. For the rs2788612 SNP in the gene KCND3, a significant association with risk of rectal incontinence was found (RR 9.91, p = 1.05 × 10−12). KCND3 encodes a voltage-gated potassium channel involved in the electrochemical processes underlying smooth muscle contraction. A GWAS published in 2014 was conducted as a collaborative project involving three different patient cohorts from Spain, the UK, and North America (Fachal et al. 2014). It included 1,742 prostate cancer patients. The study identified a locus in the TANC1 gene that was associated with risk of overall late toxicity with a per allele odds ratios around 6 and a p-value of 4.46 × 10−11. TANC1 is involved in regeneration of damaged muscle fibers. In 2016, a meta-analysis of individual level data from four genome-wide association studies including 1,564 prostate cancer patients was published (Kerns et al. 2016). It identified two SNPs: rs17599026 on 5q31.2 that was associated with urinary frequency (OR 3.12, 95% CI 2.08–4.69, p-value 4.16 × 10−8) and rs7720298 on 5p15.2 that was associated with decreased urine stream (OR 2.71, 95% CI 1.90–3.86, p-value = 3.21 × 10−8).
As accounted for above, the GWASs conducted so far in normal tissue radiobiology have identified a number of SNPs reaching or approaching genome-wide significance. Interestingly, these SNPs are not related to genes involved in radiobiology in a narrow sense. Instead, they are related to other physiological phenomena with a plausible relationship to the investigated normal tissue complications. This experience underlines the exploratory nature of genome-wide approaches and indicates that GWASs are likely to broaden our understanding of the mechanisms underlying radiation-induced normal tissue complications (Andreassen et al. 2016a). Furthermore, several of the SNPs were associated with specific types of normal tissue toxicity rather than radiosensitivity in general. This supports the aforementioned hypothesis that some genetic variants are expressed selectively through certain types of normal tissue reactions (Andreassen et al. 2016a).
Lessons Learned in Other Scientific Fields
During the last decade, substantial efforts were made to unravel the genetics of various phenotypes that are assumed to have a complex polygenic background. As of March 2019, more than 3,800 GWASs addressing various biomedical phenotypes were published. These studies have provided more than 130,000 trait-associated SNPs (updated lists are available at http://www.ebi.ac.uk/gwas/). This research has shed important new light on the allelic architecture presumably underlying the most complex traits (Andreassen et al. 2016a). The insights gained can be summarized as follows:
The Candidate Gene Approach May Not Be of Much Use
Most of the SNPs identified by GWAs were not located in genes anticipated to be of major importance for the phenotype. Often, the SNPs were located in noncoding sequences. Generally, this experience questions the value of the candidate gene approach (Andreassen et al. 2016a).
Noncoding Sequence Is of Major Importance
Less than 3% of the human genome is made up by coding sequence (i.e., sequence that is expressed as protein). The vast majority of traits with a Mendelian pattern of inheritance are related to alterations in coding sequence. Nevertheless, around 88% of SNPs affecting various complex traits are located in noncoding sequence. It is to an increasing extent recognized that the noncoding sequence harbors important regulatory functions. Sequence variants in such regulatory elements have the potential to affect phenotype through altered gene expression (Andreassen et al. 2016a).
The Typical Impact of each SNP Is Relatively Small
It is a common experience in most GWASs that the majority of the identified trait-associated SNPs only exhibited a modest impact on phenotype. Usually, relative risks well below 1.2 were found. Many of the GWASs were well powered to detect associations with GRRs above 1.5, but such associations were rarely reported. This probably implies that the typical impact of SNPs is rather small. As shown in Table 1, some of the SNPs identified by GWASs addressing normal tissue toxicity had substantially larger effect sizes. This may indicate that normal tissue radiosensitivity has an allelic architecture that differs from that of other complex traits. However, it may also reflect that GWASs tend to overestimate the effect size due to the so-called winner’s curse phenomenon (Andreassen et al. 2016a).
The Genomic Challenge
Future Directions in Radiogenomics
The aforementioned challenges can be dealt with in different ways. In genome-wide approaches, large sample size is key to success. Therefore, collaborative research projects should be encouraged in order to obtain sufficiently large patient cohorts. As accounted for, SNP-based experiments may need to be complimented by sequencing in order to identify rare sequence alterations affecting radiosensitivity. In addition, a better understanding of the processes and pathways underlying normal tissue toxicity could potentially be used to narrow in the focus to a smaller number of sequence variants, thereby getting rid of some of the multiple testing burdens that relate to genome-wide approaches (Andreassen et al. 2016a).
The International Radiogenomics Consortium (RgC)
The International Radiogenomics Consortium (RgC) was established in 2009 in order to foster large-scale collaborative research projects (West et al. 2010). It has around 200 members from 110 institutions in 26 countries. The consortium has published a set of reporting guidelines in radiogenomics. In 2013, the RgC obtained an EU grant of 6,000,000 Euros for the so-called REQUITE project that will prospectively collect outcome data and biological material from 5,300 lung, prostate, and breast cancer patients in order to establish and validate predictive tests for normal tissue radiosensitivity (West et al. 2014). As part of this project, the predictive test based on lymphocyte apoptosis mentioned below will be validated. The consortium currently undertakes a number of large GWAS meta-analyses in radiogenomics.
Should Alternative Strategies Be Considered?
Despite recent technological advances, it obviously remains a massive undertaking to pursue a comprehensive understanding of the genetics assumed to underlie differences in normal tissue radiosensitivity. In particular, the large sample sizes needed in genome-wide approaches represent a major challenge in radiogenomics. This may favor research into predictive assays that are not based on genotype. As described above, several attempts have previously been made to establish predictive assays based on in vitro radiosensitivity of various cell types (Russell and Begg 2002). Such approaches are still being explored (Andreassen et al. 2016a).
For instance, it has been shown that an inverse association may exist between lymphocyte apoptosis and risk of normal tissue toxicity (Azria et al. 2008). Initiatives are in progress to validate this finding (West et al. 2014). Another study has reported a relatively strong association between the amount of the autophosphorylated ATM protein after in vitro irradiation of fibroblasts and risk of radiation-induced toxicity (Pereira et al. 2018). Predictive assays based on gene expression have proven successful in various settings and could potentially become useful in normal tissue radiobiology (Andreassen et al. 2016a). An example of such a test is a classifier based on the expression pattern of nine genes assessed in fibroblasts after irradiation in vitro. This classifier identified a subset of patients (around 20%) that exhibited substantial radioresistance (Alsner et al. 2007). The test has been independently validated (Andreassen et al. 2013). The clinical utility of this particular test is limited by the fact that it is based on fibroblasts and that it takes several weeks to culture. Nevertheless, the classifier represents an important proof of principle and indicates that predictive assays based on gene expression may represent a rewarding alternative to genotype. Compared to cell survival (or apoptosis), gene expression has the advantage that distinct expression profiles could potentially be identified for different types of normal tissue toxicity. Therefore, this type of classifier may have the ability to reflect the multidimensional nature of clinical normal tissue radiosensitivity (i.e., that the phenotype is made up by a number of sub-phenotypes that are not necessarily strongly correlated) (Andreassen et al. 2016a).
Validation of Biomarkers
When discussing validation of biomarkers, it is important to distinguish between biomarker and biomarker test (Hayes 2015). A biomarker test is used to identify or measure the changes reflected by the biomarker. There may be one or more tests that give an indication of the status of the biomarker. They may measure the same thing, or they may measure very different aspects. An example is the biomarkers for hypoxia which may be measured on multiple different levels as described above.
Before they can be applied in a clinical setting, biomarker test must be validated on three different levels, analytical validity, clinical validity, and clinical utility (Hayes 2015). Analytical validity means that the test for the biomarker is accurate and reliable in the type of specimen to which it will be applied. Examples of such technical validation studies are rare for molecular biomarkers in radiotherapy, but two examples have been performed on hypoxia gene signatures (Betts et al. 2013; Toustrup et al. 2016).
Clinical validity addresses whether the biomarker test divides a population into two or more distinct groups, based on biology or clinical outcomes, with statistical significance. Using the hypoxia gene signatures, this is exemplified in (Eustace et al. 2013; Toustrup et al. 2011, 2012a).
Finally, clinical utility requires that high levels of evidence demonstrate that the use of the biomarker test improves clinical outcomes or that clinical outcomes are identical with less cost or toxicity. Ideally, this level of evidence should come from prospective clinical trials in which the clinical utility for a specific use of the biomarker is the main objective. Such clinical trials are rare in oncology in general and even more so in radiotherapy. One of the hypoxia gene signatures, the previously mentioned 26-gene classifier by Eustace et al., is currently being tested retrospectively within the NIMRAD trial on the hypoxia radiosensitizer nimorazole (ClinicalTrials.gov Identifier: NCT01950689). Another hypoxia gene signature, the Toustrup 15-gene classifier, is currently being tested prospectively in DAHANCA 30, a non-inferiority trial of hypoxia-profile guided hypoxic modification with nimorazole (NCT02661152).
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