Developing more sensitive genomic approaches to detect radioresponse in precision radiation oncology: From tissue DNA analysis to circulating tumor DNA

Journal Pre-proof Developing more sensitive genomic approaches to detect radioresponse in precision radiation oncology: From tissue DNA analysis to circulating tumor DNA Kewen He, Shaotong Zhang, Liang L. Shao, Jiani C. Yin, Xue Wu, Yang W. Shao, Shuanghu Yuan, Jinming Yu PII:

S0304-3835(19)30610-X

DOI:

https://doi.org/10.1016/j.canlet.2019.12.004

Reference:

CAN 114600

To appear in:

Cancer Letters

Received Date: 4 October 2019 Revised Date:

2 December 2019

Accepted Date: 2 December 2019

Please cite this article as: K. He, S. Zhang, L.L. Shao, J.C. Yin, X. Wu, Y.W. Shao, S. Yuan, J. Yu, Developing more sensitive genomic approaches to detect radioresponse in precision radiation oncology: From tissue DNA analysis to circulating tumor DNA, Cancer Letters, https://doi.org/10.1016/ j.canlet.2019.12.004. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Developing more sensitive genomic approaches to detect radioresponse in precision radiation oncology: From tissue DNA analysis to circulating tumor DNA Kewen Hea,b, Shaotong Zhangc, Liang L. Shaod, Jiani C. Yine, Xue Wud, Yang W. Shaoe,f Shuanghu Yuanb*, Jinming Yub* a

Department of Radiology, Shandong Cancer Hospital affiliated to Shandong

University, Jinan, Shandong, 250117, People’s Republic of China b

Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First

Medical University and Shandong Academy of Medical Sciences，Jinan, Shandong, 250117, People’s Republic of China c

Department of Cardiology, Jinan Central Hospital Affiliated to Shandong University,

Jinan, Shandong, 250013, People’s Republic of China d

Geneseeq Technology Inc., Toronto, Ontario, M5G 1L7, Canada

e

Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, 210032, People’s Republic of

China f

School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 210029,

People’s Republic of China * Corresponding authors: Shuanghu Yuan, Postal address: Department of Radiology, Shandong Cancer Hospital affiliated to Shandong University, Jinan, Shandong, 250117, People’s Republic of China. Email address: [email protected]; Phone: +86-13853106916 Jinming Yu, Postal address: Department of Radiology, Shandong Cancer Hospital affiliated to Shandong University, Jinan, Shandong, 250117, People’s Republic of China. Email address: [email protected] Phone: +86-13806406293

1

ABSTRACT Despite the common application and considerable efforts to achieve precision radiotherapy (RT) in several types of cancer, RT has not yet entered the era of precision medicine; the ability to predict radiosensitivity and treatment responses in tumors and normal tissues is lacking. Therefore, development of genome-based methods for individual prognosis in radiation oncology is urgently required. Traditional DNA sequencing requires tissue samples collected during invasive operations; therefore, repeated tests are nearly impossible. Intra- and inter-tumoral heterogeneity may undermine the predictive power of a single assay from tumor samples. In contrast, analysis of circulating tumor DNA (ctDNA) allows for non-invasive and near real-time sampling of tumors. By investigating the genetic composition of tumors and monitoring dynamic changes during treatment, ctDNA analysis may potentially be clinically valuable in prediction of treatment responses prior to RT, surveillance of responses during RT, and evaluation of residual disease following RT. As a biomarker for RT response, ctDNA profiling may guide personalized treatments. In this review, we will discuss approaches of tissue DNA sequencing and ctDNA detection and summarize their clinical applications in both traditional RT and in combination with immunotherapy.

Keywords: Radiotherapy; DNA sequencing; Liquid biopsy; Precision radiation medicine; Immunotherapy

2

1. Introduction

Radiotherapy (RT) is one of the most effective cancer treatments, and is currently used in approximately half of all cancer patients [1]. However, RT alone does not provide biological precision. Currently, patients with the same type of cancer and stage of disease are usually subjected to similar treatments. Yet, many patients suffer from local recurrences and there are significant differences between individuals in the development of adverse events following RT. Thus, measurable biomarkers are urgently required to reliably stratify cancer patients based on the likelihood to respond or develop adverse events following RT, and thereby preventing over- or undertreatment [2]. Almost all cancers harbor somatic genetic alterations that may vary widely between patients and occur at negligible frequencies in normal cells, and thus would serve as exquisitely specific biomarkers [3]. Genotyping tumor tissues to find functional somatic alterations has become a routine practice in clinical oncology. For personalized RT treatments, DNA sequencing technologies also have the potential to provide predictive tools for identifying radiosensitive patients [4]. As an emerging area of research, radiogenomics aims to study the effects of genetic variations on radiation response. However, obtaining tumor tissue samples is challenging, especially in advanced patients, and is inherently limited due to selection bias that causes failure to capture intra-tumoral heterogeneity. In contrast, analysis of circulating tumor DNA (ctDNA) in blood samples represents an ideal approach to circumvent these problems. ctDNA is released into the blood via necrosis and apoptosis of tumor cells, and active secretion by cancer cells [5, 6]. In fact, quantification of pretreatment plasma ctDNA levels has shown potential for patient monitoring and prognosis [7-10]. This review summarizes and discusses current methods and clinical implications of tissue DNA sequencing and ctDNA analysis in RT. In addition, the review covers significant advances and limitations of these approaches in clinical applications, as 3

well as potential directions for genomic analysis of radioresponse in precision radiation oncology.

2. Evaluation of radioresponse via genomic assays of tumor tissues Treatment of patients who are unlikely to respond to RT may reduce quality of life, add unnecessary medical costs, and delay initiation of other potentially effective treatments [11]. Factors such as age, concurrent chemotherapy, or recent surgery can influence the incidence of toxicity, but genetic factors are considered to be of uppermost importance [4]. Genotyping of the tumor tissue to identify somatic genetic alterations has become a routine practice, especially for guided targeted therapies [12]. Similarly, genetic alterations seem to be important in predicting patients’ response to RT. Most studies to date have investigated single nucleotide polymorphisms (SNPs) because of their high prevalence in a population. With the advancement of next generation sequencing (NGS) and genome-wide assays, genomic studies have been extraordinarily facilitated [13]. Several observations support the hypothesis that clinical normal tissue radiosensitivity is influenced by genetics. In the future, genotypic analysis of patients prior to RT may be necessary to identify non-responders or patients who may experience severe side effects. 1.1 Methods of tissue DNA sequencing Over the past few years, many technological advances have been made in the area of DNA sequencing. First-generation sequencing technologies include Sanger sequencing [14], pyrosequencing, and ligase-mediated sequencing [15, 16], which generally involve polymerase chain reaction (PCR) and real time quantitative PCR (qPCR) to detect low-level allele-specific somatic DNA mutations. However, the sensitivity and specificity of first-generation DNA testing is relatively low [17, 18]. For germ-line mutations, first-generation sequencing can only detect mutations when the fraction of mutant alleles is greater than 20% [19, 20]. Consequently, this method could only be used to investigate in parallel a small number of mutations in certain genes and fails to detect large segments of genetic or intergenic regions [21]. In 4

contrast, second-generation DNA sequencing, also known as NGS, allows for massive parallel high-throughput sequencing, while also reducing cost [22, 23]. Currently, three platforms are involved in massive parallel DNA sequencing: Roche/454 FLX [24], Illumina/Solexa Genome Analyzer, and Applied Biosystems SOLiDTM System. There has also been enormous progress in DNA amplification technologies, including the development of digital PCR (micro-reaction chamber or well plate), large-scale integrated microfluidic chip and droplet digital PCR (emulsion PCR), which are used in NGS [25]. In particular, digital PCR provides reliable and quantitative measurements of the proportion of variant sequences within a DNA sample, which allows for detection of mutations at relatively low frequencies [26, 27]. Third-generation DNA technologies include Helicos HeliscopeTM, Pacific Biosciences SMRT [15] and Oxford Nanopore Technologies [28]. A break-through achieved by Helicos HeliscopeTM and Pacific Biosciences SMRT is that amplification of DNA fragments is not required [29, 30]. Hence, both Helicos and Pacific Biosystems instruments are considered as “single-molecule” sequencers. Although detection of DNA isolated from formalin-fixed paraffin-embedded (FFPE) samples is challenging due to fragmentation and DNA-protein crosslinks, high throughput NGS overcomes these challenges and is able to test DNA in FFPE biopsies and surgical samples with only 5 ng of DNA [31]. As NGS enables highly sensitive and simultaneous detection of multiple mutations, it is now being applied in multiple clinical settings [32]. In routine clinical care of cancer patients, NGS plays an important role in diagnosis [33], identification of therapeutic targets and resistance mechanisms [12], as well as disease monitoring [34]. It also allows for the identification of predictive and prognostic biomarkers for targeted therapies, chemo-radiotherapies, as well as immune checkpoint inhibitor-based therapies [13, 35, 36]. However, tissue-based sequencing has its own limitations. Specifically, a single biopsy is not sufficient to capture the mutational landscape of the disease given the high intra- and inter-tumoral heterogeneity of cancers. Furthermore, obtaining adequate tissue samples is often challenging, particularly for patients with advanced disease. 1.2 Clinical applications of tissue DNA sequencing in radiation oncology 5

Prediction of radiation-related toxicity RT inevitably exposes surrounding normal tissues to radiation and may result in early (during the initial weeks of treatment) or late (three months to years after completion of RT) adverse events. Much interest exists in better understanding the factors that cause some patients to develop toxicity following RT. In addition to dosimetry, treatment, clinical, and demographic and genetic factors are important in tissue response to RT [37]. Heritability analysis suggested that 58-78% of variance may be attributed to the genetic composition of the patient [38]. Understanding the specific genetic risk factors of normal tissue response to RT has the potential to enhance our ability to predict adverse outcomes during the treatment planning phase. To date, most studies exploring the impact of genomic markers have focused on the association between SNPs and radiation toxicity [11]. Currently, there is a shift from investigating selective candidate genes in germline DNA from RT patients towards a broader, genome-wide approach - leading to genome-wide association studies (GWASs) - in part due to the fact that no single genetic variant has been definitively linked to adverse responses to RT [11]. Candidate genes usually include genes involved in DNA damage repair (e.g., ATM, BRCA1, BRCA2 and TP53), antioxidant enzymes (e.g., superoxide dismutase 1 (SOD1), glutathione S-transferases and catalases, and cytokines (e.g., TGFB1 and TNF), because nucleotide alterations in these genes in germline DNA from RT patients have been associated with RT-related adverse events [39-41]. Many studies have suggested that potential SNPs in these genes are associated with RT toxicities [42-44]. Cesaretti et al [42] have reported that adjusting the rectal dose after examining certain ATM sequence alterations can improve patient stratification to prevent rectal bleeding, due to a significant correlation between the presence of ATM variants and development of radiation-induced proctitis. A more recent publication from Tucker et al [43] suggested that a new model integrating SNP information from candidate genes (TGFB, VEGF, TNFA, XRCC1, and APEX1) into a dosimetry-based Lyman NTCP model could improve predictive power. Kerns et al. [44] reported that, compared with a combination of clinical factors (age, stage, radiation dose, hormone use, diabetes, 6

smoking), four SNPs identified in a GWAS improved prediction for erectile dysfunction. While multiple approaches have been performed to identify genetic variants that may explain differences in individual responses to RT, no markers with convincing clinical applicability have been identified yet [11]. Prediction of radiosensitivity Tumors can induce unique molecular responses to DNA damage, leading to increased radiosensitivity. Recurrent single-gene mutations in tumors may serve as indicators of radioresistance or radiosensitivity and have the potential to instruct RT treatment accordingly. Biomarkers such as NRF2, KEAP1, and KRAS mutations reportedly confer radioresistance [45, 46]. The status of KEAP1/NRF2 mutations has shown predictive value in the RT outcome of NSCLC patients [46]. The KEAP1-NRF2 pathway protects cells from oxidative and toxic stress, contributing to the development and progression of lung cancer [47-49]. A recent study by Jeong et al [46] explored the role of the KEAP1-NRF2 pathway in radiation resistance. They found that the loss of KEAP1 promoted tumor aggressiveness, metastasis, and resistance to oxidative stress, as well as RT. Consistently, KEAP1/NRF2 mutations led to an increased trend of local recurrence in patients with localized NSCLC following RT. However, whether additional somatic and/or epigenetic alterations may synergize with KEAP1/NRF2 mutations is still unknown. Therefore, further research in human patients with more complicated genetics is required to fully characterize the role of KEAP1/NRF2 in RT. As KEAP1/NRF2 mutations are found in various types of cancer [50-54], it is likely that testing for KEAP1/NRF2 mutation status may improve RT and outcomes of many cancer patients. As a key component of the mitogen-activated kinase pathway, KRAS can be activated by many cell surface receptors, including epidermal growth factor receptor (EGFR). Activating mutations in KRAS lead to dysregulated downstream processes and uncontrolled cell growth [55]. A number of studies that investigated the association between KRAS mutations and radiation responses have reported inconsistent results. Duldulao et al have shown that patients with rectal cancer and 7

KRAS mutations are less likely to achieve complete pathological remission after neoadjuvant chemoradiation, compared with patients with wild-type KRAS [45]. On the other hand, Gaedcke et al screened 95 rectal cancer patients who underwent concurrent chemoradiation for mutations in codons 12, 13, and 61 of KRAS, and found no difference in the response rate between KRAS mutants and wild-type tumors [56]. These inconsistent findings may reflect differences in the tumor regression grading system. In Gaedcke’s biopsies, the sample sizes were smaller and were measured by a five-point grading system rather than a complete regression defined as the complete absence of viable tumor cells at the primary site. 1.3 Challenges and future directions for tissue DNA sequencing in radiation oncology Although there is increasingly better understanding of genomic heterogeneity and precision radiation medicine (in which the dosage of radiation is modified based on the molecular characteristics of the tumor and normal tissue), DNA-based radiation oncology has not still been effectively translated into clinical practice, due to the lack of reproducible biomarkers. Generally, biomarkers should be validated for their predictive power in preclinical and clinical studies with objective and consistent end point measurements. Given that many published datasets in this field lack validation and biostatistical corrections for multiple comparisons, the Radiogenomics Consortium (RGC) has attempted to create uniform guidelines for reporting such data [57]. In addition to SNPs and somatic mutations of the tumor tissue, the level of non-coding RNA, metabolite, and protein expression and their post-translational modifications may also affect the intrinsic response of cells to radiation. Hegi et al. showed a promising linkage between tumor-specific epigenetic variations and response

to

RT.

The

O6-methylguanine-DNA

median

survival

methyltransferase

of

patients

(MGMT)

with

glioblastoma

methylated following

RT-temozolomide was 28 weeks longer than that of patients without MGMT methylation receiving the same treatments [58]. Recently, a genome-based model for adjusting radiotherapy dose (GARD) based on the expression level of 10 genes was 8

published, allowing for the individualization of RT dose according to tumor radiosensitivity [59]. The higher the value of GARD, the better the efficacy of RT. However, tissue sample transcriptome sequencing (RNA-seq) requires strict sample handling and distinct transport conditions (requires cutting of fresh tissue samples into small pieces <0.5 cm and immersion in 5 volumes RNA, overnight incubation at 4°C and then storage in -20°C or -80°C, dry ice transport) [60]. Our group recently identified a set of novel potential biomarkers based on DNA-seq, which could potentially predict the likelihood of developing severe toxicity or resistance to radiotherapy in individual patients with unresectable NSCLC treated with radiation therapy (data not published). Our results suggest that pre-treatment testing for a combination of genetic variants may confer possible toxicity or resistance to RT and is clinically valuable. In the near future, personalized RT driven by concomitant DNA testing of radiotoxicity and radiation response should become the standard of care, as it is currently the case for targeted therapies in medical oncology.

2. Genomic assays for radioresponse using ctDNA

Imaging-based approaches, such as CT, PET, and MRI, are generally used to assess the efficacy of RT [61, 62]. Despite the significant clinical value of these methods, sensitivity and specificity is usually suboptimal for accurate determination of treatment response and unsatisfactory for detection of minimal residual disease (MRD) [63]. For example, it is difficult to distinguish between inflammatory pulmonary infiltrates and fibrosis caused by lung RT from residual or recurrent cancers using medical imaging. There also exists a lag between the timing of medical imaging and tumor response, especially for patients exposed to ionizing radiation. ctDNA is cancer-specific, and thus, can be equally or even more sensitive than traditional imaging techniques in assessing the therapeutic response of multiple cancer types [64]. The development and application of highly sensitive genomic assays, in particular PCR and NGS-based techniques, generates broader clinical applications of ctDNA analysis [65, 66]. 9

2.1. Methods of ctDNA analysis Unlike tissue DNA sequencing, ctDNA represents merely a fraction of cell-free DNA (cfDNA) in the blood, and its levels change dynamically according to tumor size, cancer stage, treatment status, etc. The standard DNA analysis approaches (e.g. Sanger sequencing and ligase-mediated sequencing) are inadequate for the detection of ctDNA, especially for early-stage cancers [67]. From single mutation detection to genome-wide analysis, ctDNA can be detected with PCR-based and NGS-based methods. Based on the extent of genome coverage, the latter can also be further categorized into two subgroups: targeted deep sequencing and genome-wide sequencing. ctDNA can be distinguished from non-tumor DNA based on tumor-specific mutations, aberrant DNA methylation and copy number alteration (CNA). We will discuss the applicability of ctDNA by reviewing from the perspective of these genetic circumstances (Table 1). Tumor-specific mutations Over the past two decades, a large number of tumor-associated mutations have been identified in ctDNAs in different types of cancers, including colorectal, breast, ovarian, pancreatic and lung cancer [68, 69]. PCR-based detection and targeted deep sequencing are two main types of analytical methods for cancer mutation detection. Allele-specific PCR (AS-PCR) was first used for the identification of KRAS mutations in pancreatic carcinoma in 1994 [70]; the Cobas EGFR Mutation Test v2 is based on this technique. However, due to limited analytical sensitivity [67], AS-PCR is currently

substituted

by

more

sensitive

approaches,

including

digital

amplification-refractory mutation system PCR (ARMS-PCR) [71], digital PCR (dPCR) [72, 73], droplet digital PCR (ddPCR, a variation of dPCR) [74-76], and Beads, Emulsion, Amplification, Magnetics (BEAMing) [77]. Despite the high sensitivity of dPCR (ranging from 0.001% to 0.01% mutant allele fraction) [78] and the merits of absolute quantification [79], only a limited number of mutation hotspots can be analyzed at the same time. Other approaches such as PNAClamp PCR [80], CastPCR [81], and Intplex [82] have been developed to further reduce non-specific amplification. Targeted deep sequencing is an NGS-based technique that involves 10

multiplexed amplicon sequencing (the sequencing of specific loci ranging from individual exons to the whole exome) and hybrid capture sequencing. Compared to dPCR-based approaches, targeted deep sequencing has the advantage of interrogating more loci, albeit at a higher cost, being more time-consuming and with slightly lower sensitivity [83]. In order to circumvent false-positive detection of ctDNA, the incorporation of error-suppression techniques, such as molecular barcodes with unique identifier (UID) combinations in Safe-SeqS [84] and iDES-enhanced CAPP-seq [85] into NGS libraries can significantly improve technical performance. In addition, methods to concentrate mutant DNA have been developed, which include Enhanced Tam-Seq [86], Ion AmpliSeq [87], SiMSen-Seq [88], and the Nuclease-assisted Minor-allele Enrichment with Probe-Overlap (NaME-PrO) [89]. Targeted error correction sequencing (TEC-Seq), which is capable of detecting the majority of early-stage tumors without large amounts of tumor DNA or prior knowledge of cancer mutations, has been developed. Using TEC-seq, 62% of 138 patients with stage I or II breast, colorectal, lung, or ovarian cancers had detectable ctDNAs [65]. Other error suppression methods, such as batch-specific error distributions may further improve ctDNA detection sensitivity [90]. Targeted gene panels to whole exome sequencing (WES) can be achieved by hybrid capture-based technologies. It has been demonstrated that WES can be used for the comparison of CNA profiles between ctDNA and matched tumor biopsies and is associated with a high degree of consistency between different biopsy types [91]. However, given that the number of loci analyzed is inversely proportional to the sensitivity of hybrid capture-based methods [92], cancer-specific assays such as CAPP-Seq [93] and digital sequencing [94] are used to enhance sensitivity. DNA methylation Ideally, ctDNA analysis should be able to detect multiple types of cancer and provide information on tumor location for further clinical investigation [95]. The molecular aberrations and their functional roles in different types of cancer inform the extent to which effective therapies can be applied to other tumors with similar genomic profiles. Compared to mutational analysis, one potential advantage of 11

ctDNA methylation analysis is the identification of the tissue-of-origin in cancers with unknown origin [96]. Another advantage is the possible translation of aberrant methylation of specific promoter regions that occurs early in tumorigenesis (with more dysregulated events shared across all subclones) into higher sensitivity of tumor detection. The first analysis of plasma/serum DNA methylation was performed in breast cancer [97]. Subsequent studies have demonstrated the value of DNA methylation as biomarker in early diagnosis, screening and prognosis of cancer, and in real-time follow-up of the dynamics of tumor response to treatment [98, 99]. ctDNA genome-wide.

methylation

methodologies

Gene-specific

analyses

also

range

include

from

gene-specific

pyrosequencing

to

[100],

methylation-specific PCR (MAP) [101], quantitative MSP (qMSP) [102], digital MethyLight (dMethyLight) [103] and droplet digital MethyLight (ddMethyLight) [104]. Genome-wide methylation detection techniques, including array-based hybridization and WGBS [105] for detection of low frequency ctDNAs, have improved clinical utility. Studies have investigated changes in DNA methylation patterns by isolating cfDNA from the plasma/serum, followed by bisulfite conversion, and sequencing to map the original tissue or to directly detect the presence of tumors [96,106]. Sun et al estimated the proportion of cfDNAs contributed by different tissues and reported that the likelihood of tumor existence in a particular tissue could be indicated by the abnormally high proportion of cfDNA from that tissue [106]. More recently, Kang et al proposed a novel approach using CancerLocator, which is a probabilistic tool that uses genome-wide DNA methylation data to infer the proportion and tissue source of ctDNA in blood samples, even in cases of low coverage of DNA methylation sequences [95]. Methylation analysis of multiple genes rather than a single gene improves clinical utility. For instance, methylation of a six-gene panel consisting of CYCD2, HIC1, PAX5, RASSF1A, RB1 and SRBC reportedly detects colorectal cancer with a sensitivity and specificity of 84% and 68%, respectively [107]. In another case, methylation of APC, BIN1, BRCA1, CST6, GSTP1, P16, P21 and TIMP3 helped distinguish breast cancer patients from the control group, with a sensitivity and specificity over 90% [108]. In a prospective study (TBCRC 12

005), Visvanathan et al used a novel quantitative multiplex assay, cMethDNA, using a panel of cfDNA methylation markers to examine their value in predicting treatment responses in metastatic breast cancer. Their findings suggest that a high post-treatment cumulative methylation index (CMI) using the six-gene panel is associated with poor therapeutic response and survival [109]. Copy number alterations CNAs indicate recurrent losses, gains and high levels of amplifications of genes. CNAs could be the most significant influence on the genome relative to any other somatic alterations. In contrast to somatic point mutations, CNAs may in some cases be less susceptible to technical artefacts, including errors introduced during PCR followed by sequencing, and ctDNA quantitation [110]. Identification of CNAs from ctDNA is typically genome-wide, including a non-targeted method that does not require prior knowledge of features of the primary tumor genome. Whole genome sequencing (WGS) was used to detect chromosomal changes in patients with hepatocellular carcinoma and a patient with breast and ovarian cancer [111]. WGS with a shallow sequencing depth of about 0.1× is sufficient for robust and reliable analysis of CNA from a single cell. Heitzer et al developed a low coverage WGS method, Plasma-seq, with shallow sequencing depth [112], to analyze blood samples from patients with prostate, colon and breast cancer [113,114]. Its low cost and rapid turnaround time make it cost-effective in clinical practice. Similar approaches have been implemented in patients with metastatic castrate-resistant prostate cancer [115] and neuroblastoma [116]. Two novel methods, personalized analysis of rearranged ends (PARE) [110] and digital karyotyping [117] are able to detect chromosomal rearrangements in the plasma of cancer patients. Analysis of patients’ ctDNA can provide both quantitative and qualitative information on their disease status. Quantitative information refers to tumor burden reflected by ctDNA concentration, expressed as the number of mutant fragments per mL or as the mutant allele fraction in a single sample. It has been shown to correlate with tumor stage, tumor size, treatment response, MRD and prognosis [118-120]. There are two potential processes for quantitative analysis. The first approach is to 13

target specific mutations in the tumor tissue and quantify their levels in ctDNA [121]. The second requires scanning of DNA regions extracted from the plasma/serum for mutations of interest, although this is conducted blindly due to inaccessible tumor tissue [84,121]. Qualitative information includes analysis of mutations, amplifications, deletions and translocations in ctDNA at selected loci or throughout the genome. Qualitative data have been used to continuously monitor clonal evolution of tumors and detect new resistance mutations [123-125]. Furthermore, qualitative analysis of ctDNA can also reveal tumor-specific epigenetic variations, such as methylation [126]. Recent advances in ctDNA detection provide new opportunities to incorporate ctDNA analysis into therapeutic paradigms for patients receiving RT.

2.2 Clinical applications of ctDNA analysis in RT Prediction of radioresponse and prognosis before RT. Although RT is primarily local, patients are nonetheless at risk of being exposed to toxicities in the treatment field and surrounding tissues. Normal tissue toxicity in a subset of patients limits the dose that can be used in standard RT protocols and impairs RT efficacy. RT efficacy is affected by tumors’ four main biological factors: the number of cancer stem cells, the hypoxic fraction, repopulation and reoxygenation capacity, and intrinsic radiation resistance of the tumor cells [127]. Indeed, intrinsic differences in radiosensitivity within the patient population is a major causal factor for toxicity of normal tissues [128]. Improved predictive technologies can gauge the risk of normal tissues developing severe adverse effects, thus allowing for more personalized treatments and significantly reducing the incidence of post-RT complications. ctDNA can be detected in the blood of many cancer patients, with ctDNA levels reflecting tumor load [129]. Thus, the abundance of ctDNAs for genes involved in radiotherapeutic sensitivities and/or toxicity genes may provide a direct indication of the magnitude of RT effects. An early study investigated the prognostic utility of pre-treatment plasma/serum EBV DNA load by quantitative PCR in patients with nasopharyngeal carcinoma [47]. Patients with higher median plasma EBV DNA 14

concentration within the first year after RT had an increased risk of recurrence and metastasis, independent of clinical disease stage. Analogous to the analysis of circulating viral-derived tumor DNA, somatic mutations in ctDNA could also be quantified and used to predict cancer recurrence. As mentioned above, Jeong et al reported that KEAP1/NRF2 mutations lead to increased rates of local failure in patients with localized NSCLC after treatment with RT [46] and such mutations can be identified noninvasively in the ctDNA. Their findings suggest that KEAP1/NRF2 mutation status identified in ctDNA can serve as a potential predictive marker for clinical decision-making in the treatment of NSCLC patients. Gene methylation patterns in ctDNA are also associated with prognosis. Numerous studies have demonstrated that gene methylation patterns in the tumor tissue—whether individual genes [130,131] or gene panels [132-134]— can be indicative of tumor aggressiveness and the likelihood of patient survival [135]. As tumors shed DNA into the blood, the methylation status of a tumor can be non-invasively analyzed through ctDNA. Methylation facilitates tumor progression by silencing genes that directly regulate cell growth and metastatic potential. It can also reflect tumor subtype, which is in turn associated with prognosis. Given that many types of cancer contain a larger number of hypermethylated regions than mutated sequences, analysis of ctDNA methylation could potentially allow for more precise tumor surveillance [136]. Multiple methylated genes in ctDNA, including TIMP3 [137], XAF1 [138], ABPA2 [139], SOX17 [140]and RARb2 [141], correlate with prognosis in various cancers. In addition, whether features of the tumor microenvironment can be discerned from ctDNA analysis, is also worthy of study. Tumor hypoxia is known to correlate with adverse responses to RT and is a key contributor to radiation resistance. It has been shown to cause global DNA hypermethylation through reduction of TET enzymatic activity [127, 142]. Although it is tempting to speculate that tumor hypoxia could be reflected in the ctDNA methylome, it is not always clear whether increased detection of particular circulating methylated genes in patients with poor outcomes reflects the impact of gene methylation on tumor biology, or rather it is simply increased in ctDNA due to high 15

tumor burden. These findings provide the basis for trials that examine whether patients with specific somatic mutations, amplification, or translocation of driver genes in oncogenic pathways can clinically benefit from treatment with RT or with combinations of radiation and targeted agents. Currently, new RT trials focus on identifying tumor mutations, amplification, or translocation of genes that drive radiation therapy sensitivity or resistance (NCT02888743, NCT01096368). Taken together, analyses of pre-treatment ctDNA levels appear to provide insight into patient prognosis, and in the future, may assist radiation oncologists with treatment decisions and provide quantitative information regarding patient treatment response. Surveillance of treatment response during RT The eradication of cancer, or control of cancer growth may be adversely affected by de novo or acquired resistance during treatment [143, 144]. However, there is lack of data regarding mutational profiles in tumors before RT and after recurrence, which is in part due to the difficulty to obtain tumor tissue at both time points. Cancer monitoring by measuring tumor DNA dynamics in the blood is a new and developing area, representing a promising strategy for disease surveillance in solid tumors treated with RT [144]. In fact, ctDNA detection has already shown initial promise compared to conventional protein biomarkers and imaging modalities for monitoring disease progression and treatment responses [146,147]. Acute changes in ctDNA concentrations during or immediately after treatment may be of prognostic and/or predictive value. As ctDNA release is a result of cell death, cell death induced by RT could potentially be monitored via changes in ctDNA concentration. ctDNA levels have been shown to decrease after surgical excision of lung tumors; however, this reduction has not been confirmed after RT [148]. Lo et al showed that for nasopharyngeal carcinoma treated definitively with RT, plasma EBV DNA levels rose during the first week of treatment and subsequently declined [149], which is correlated with the increased cell death following RT, and decrease of tumor burden after successful treatment [150]. Studies in patients with advanced melanoma, lymphoma, ovarian, breast, lung and colon cancer have shown that increases in ctDNA concentration strongly correlate with disease progression, whereas a decline in 16

ctDNA correlates with successful treatment [146, 147, 151, 152]. Additionally, the optimal schedule of monitoring treatment response by ctDNA analysis may depend on the timing and frequency of sample collection. For example, in EBV-associated nasopharyngeal cancer, mid-RT ctDNA analysis was more predictive than analysis of post-treatment samples [153]. Tumors can acquire molecular alterations over time, and detection of resistance mutations prior to clinical progression provides an opportunity to explore early interventions that could possibly improve patient outcomes. Blood-based genotyping via ctDNA provides a molecular snapshot that detects, for example, 1) resistance markers, such as EGFR T790M, in the absence of tissue sample, in lung cancer patients treated with EGFR-targeted TKIs [154, 155]; 2) increase in variant allele fractions of genes such as PIK3CA, MED1 or EGFR in patients with breast cancer treated with various therapies [156, 157]; and 3) KRAS mutations in patients with colorectal cancer treated with anti-EGFR therapy [91, 158]. Non-invasive genotyping using ctDNA analysis could also be useful in identifying currently unexplored resistance mechanisms, particularly for patients treated with RT, in which radioresistance has been linked to mutations in genes involved in supporting survival, tumor suppression, reactive oxygen species, cell cycle checkpoints, and telomerase pathways [159]. In addition, the efficacy and toxicity of several radiosensitizing drugs used clinically are likely to vary based on the genetic characteristics of individual tumors [160]. Individual gene sets and their correlations with radiation response can identify and validate targets for potential radiotherapeutic sensitization. Thus, the use of ctDNA analysis in patients whose tumors recur after definitive RT could facilitate the identification of genotype-selective radiation sensitizers and ultimately lead to strategies to improve RT outcomes. In the near future, analysis of ctDNA may be informative in several areas, including, but not limited to, the identification of somatic mutations beyond known driver genes, evaluation of tumor burden and the presence of occult disease progression. In summary, as ctDNA analysis has the potential to provide an attractive adjunct or an alternative to standard follow-up imaging in patients with advanced disease, it may provide early markers of disease resistance to 17

allow prompt cessation of ineffective regimens and spare patients of unnecessary RT-associated toxicities and potentially suggest alternative treatments [125]. Additional clinical studies are required to evaluate whether the proposed clinical uses of ctDNA may be of value to existing clinical workflows. The levels of ctDNA could also be an additional biomarker to detect RT-associated toxicities. Zwirner et al. reported that elevated levels of cfDNA were associated with infection but not with inflammation (moderate RT-associated toxicity) during radiochemotherapy in patients with head and neck cancer (HNSCC) [161]. Therefore, ctDNA levels would be valuable to improve differentiation between toxicity and severe infections in RT and could

represent

an

informative

complementary

biomarker

for

therapeutic

decision-making. Early detection of minimal residual disease after RT Tumor DNA can be released not only from primary tumors, but also from micrometastasis or overt metastases in patients with cancer. For example, Newman et al described one patient with stage IIB NSCLC receiving RT, in which ctDNA analysis was concordant with clinical remission of disease [93]. ctDNA was undetectable after RT, and subsequent resection revealed no viable tumor cells. Identification of tumor-specific changes in ctDNA has enabled detection of very early emergence of MRD and prediction of recurrence with lead times of several months in patients with colon, breast and lung cancer [120, 162], thus opening a new window of opportunity to treat patients while tumor burden and heterogeneity are at the lowest level. The decision to administer adjuvant therapy based on MRD detection rather than nodal status or clinical risk factors, such as primary tumor size, helps patients to avoid unnecessary adverse effects. Although ctDNA is unlikely to replace diagnostic imaging in the near future, it can be used as a complementary tool when imaging results are inconclusive. For instance, pulmonary tissue inflammation and fibrosis induced by RT can be difficult to distinguish from residual or recurrent disease based on imaging [163]. Thus, ctDNA has also the potential to identify post-RT inflammatory lung changes from residual or recurrent cancer. Newman et al used a quantifying ctDNA method called CAPP-seq to capture genomic regions with known 18

and suspected driver mutations in NSCLC [93]. This method, which unveiled a strong correlation between tumor volume and identified somatic mutations, may have the ability to detect occult microscopic progression of disease, thereby supplementing ambiguous radiographic findings of residual disease after RT. In addition, tracking both driver and passenger mutations improved sensitivity of MRD detection. Chaudhuri et al showed that lung cancer MRD detection rate combined with single-mutation tracking was 58%, which is significantly lower than the 94% detection rate when using all variants [162]. As ctDNA analysis becomes more precise, detecting MRD will be more clinically important and feasible in solid tumors and useful for radiation oncologists to better personalize the radiation dose and fractionation for each patient. 2.3 Challenges and future directions for ctDNA analysis in RT We highlight the potential and impact of ctDNA analysis on personalized cancer management. Nevertheless, there are enormous challenges in implementing this method. The first obstacle is that low levels of ctDNA are detected despite the high rates of genetic mutations in many cancer tissues. As described in previous studies, ctDNA often represents a small fraction (<1.0%) of total cfDNA, even for patients with metastatic disease. The clinical application of ctDNA was primarily limited due to substantial challenges in detecting cancer-specific DNA variants from the heterogeneous background of cfDNA released by nonmalignant cells. ctDNA released by malignant cells in cancer patients is diluted out, especially in patients submitted to tissue-damaging therapies, such as surgery, chemotherapy or RT. However, the DNA fragment length and the tumor’s unique genetic profile may provide some information to discriminate whether DNA derived from tumors of from healthy tissues [68]. Despite the recent development of highly sensitive technologies, current approaches lack sufficient sensitivity at reasonable costs. Therefore, the development of a reliable and cost-effective test for early identification of very low amounts of ctDNA in the blood is urgently required. Another challenge for ctDNA detection includes the incidence of cancer-associated driver mutations occurring with increasing age, even in individuals who have never developed cancer during their lifetime. Moreover, as 19

cfDNA is removed mainly by the liver and kidneys, dysfunction of these organs may affect cfDNA clearance, which may be a confounding factor modulating the concentration of ctDNA in some patients with cancer [164]. In addition, single or small panels of mutations may not have sufficient predictive power for cancer. For example, mutations in TP53 have been detected in more than 10% of non-cancer controls. Simultaneous measurement of multiple cancer-specific markers in ctDNA should improve technical performance and may enable the lower limit of detection to become <0.1% [93]. Furthermore, despite that several clinical studies have attempted to evaluate the utility of ctDNA detection, the majority of them are less informative and retrospective. Future investigation should include collection of prospective samples that are rationally designed to fully elucidate the ctDNA profiles in response to RT. Such information would help to predict patient response to therapy, determine specific biomarkers associated with radiosensitivity, and comprehensively assess the clinical applicability of ctDNA. Additionally, the ctDNA detection process is not currently standardized. This hinders the ability of ctDNA analysis to facilitate clinical decisions as it is difficult to directly compare quantities between different tumor types. Despite these challenges, ctDNA analysis remains a promising tool for the prediction of tumor radiosensitivity in clinical trials, monitoring treatment efficacy, and detecting early recurrence or occult progression of disease.

3. Genomic assays and radioimmune oncology

It is rare for RT to generate radiation that meets the threshold for systemic anti-tumor immunity. RT and immunotherapy may act synergistically, as immunotherapy can enhance responses initiated by RT to achieve systemic antitumor responses through “abscopal effects” [165]. Despite the remarkable success of clinical applications of radioimmunotherapy reported in the past decade, the efficacy and effectiveness of these therapies vary significantly across individual patients and between different tumor types. Inherent heterogeneity of tumors may dictate 20

variability in the optimal dose fractionation to induce immune activation dependent on the individual tumor characteristics [166]. Therefore, it is critical to select the optimal radiation dose and fractionation for a given patient with a set of predictive biomarkers for radioimmunotherapy. Multiple factors, ranging from tumor-infiltrating immune cells to systemic factors, have been associated with immune-related responses [167]. Previous studies have suggested that tumor PD-L1 expression correlates with positive clinical responses to treatment [168]. Nevertheless, it should be noted that responses have also been observed in PD-L1 negative patients and the majority of PD-L1 positive patients do not demonstrate objective responses to anti-PD-L1 therapies [169, 170]. Recently, it has been reported that the genetic landscape of tumors has an important role in tumor responses to immunotherapy [171]. For example, tumor mutational burden (TMB) is associated with durable clinical benefit and progression free survival (PFS) in patients treated with immunotherapy, as TMB reflects the presence of immunogenic neoantigens, which trigger activation of the immune system [167]. In addition, DNA polymerases and mismatch repair (MMR) proteins are critical for the maintenance of replicative integrity. Mutations in DNA polymerase genes (POLE/POLD1) or deficient MMR with microsatellite instability (MSI) are often associated with high TMB and durable outcome following immunotherapy [172]. Multiple studies have also revealed that somatic mutations, such as those in EGFR, influence patient responses to immunotherapy [173, 174]. In the subgroup analysis of CheckMate057 and KEYNOTE-010, EGFR wild-type patients showed greater survival benefit from immunotherapy compared with those having gain-of-function mutations of EGFR [173, 174]. Given the complexity of molecular determinants of immunotherapy responses, there is an unmet medical need for diagnostic approaches that overcome the challenge of insufficient tumor tissue in patients with advanced diseases. ctDNA analysis presents the perfect opportunity to resolve these issues in the era of radioimmune oncology. Indeed, Gandara et al developed a novel, validated assay to measure TMB using cfDNA in non-small-cell lung cancer patients treated with atezolizumab. Their 21

data showed that measuring blood-based TMB is feasible and that using a cut-off point ≥16 reproducibly identifies patients with increased PFS benefit from atezolizumab [175]. Whether other features of the tumor microenvironment can be discerned from ctDNA analysis awaits further investigation. In a more recent study, Lee et al demonstrated that ctDNA profiles can accurately differentiate between pseudoprogression and true progression of disease in patients with melanoma treated with anti-PD-1 antibodies [176]. The mutational landscape has an important impact on responses to immunotherapy, especially with the advent of NGS, which enables large-scale genomic analysis of patient tumors. However, there are limitations for using the mutational landscape to stratify patients. First, the enormous cost of exome sequencing renders personalized genomics-based immunotherapy difficult to achieve. Therefore, developing smaller but better immune gene-targeted panels may serve as a more affordable method of mutational analysis. Second, a better understanding of the molecular determinants beyond TMB is required, as TMB does not definitely determine potential responders.

4. Conclusion

Genetic analyses using conventional tissue biopsies and ctDNA profiling from liquid biopsies are important for guiding treatment decisions, patient stratification and evaluation of patient outcomes. Although both approaches have important limitations, our thorough understanding of the mutational landscape and key predictive and/or prognostic biomarkers in various cancer types provides reasonable optimism for uncovering genetic alterations that affect RT responses. With continued advances in genomic assays and techniques and ongoing clinical investigations, ctDNA analysis is a potential choice at various stages of cancer management. Together with imaging-based

approaches

and

tissue

biopsy,

ctDNA

analysis

provides

complementary and more comprehensive characterization of an individual’s tumor. In the future, radiation oncologists may follow ctDNA profiles with or without tissue 22

DNA analysis throughout the course of treatment and make data-driven, personalized decisions for cancer treatment.

CRediT authorship contribution statement

Kewen He: Conceptualization, Visualization, Writing - original draft. Shaotong Zhang: Writing - review & editing. Liang L. Shao: Writing - review & editing. Jiani C. Yin: Writing - review & editing. Xue Wu: Writing - review & editing. Yang W. Shao: Writing - review & editing. Shuanghu Yuan: Funding acquisition, Writing - review & editing. Jinming Yu: Funding acquisition, Writing - review & editing.

Declaration of competing interest

The authors declare that they have no conflict of interest.

Acknowledgment

This study was supported by Grant (2018YFC1313201) from the Key Research and Development Program of China to JM Yu. We would like to thank Editage (www.editage.com) for English language editing.

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36

Table 1. ctDNA Analytical Approaches and Detect Methods

Methods

Characterization

Comments

AS-PCR [70]

optimize the specificity of ctDNA

Unlike conventional PCR AS-qPCR is

amplification by utilizing primers with

quantitative

1. Tumor-Specific Mutation

a. PCR-based analysis

sense/antisense sequences containing the

mutant allele of interest

ARMS-PCR [71]

uses a primer system that takes advantage of

Demonstrated to have a LLOD of 0.01%

Taq DNA polymerase’s ability to distinguish

complementary and mismatched nucleotides

at the 3’ end of primers, preferentially

amplifying mutant-allele ctDNA unquenching

the Scorpion fluorophore

dPCR [72,73]

providing a means for absolute quantification

Screening and early detection of cancer 48-73%

of ctDNA abundance

ddPCR [74-76]

BEAMing [77]

processing thousands of partitioned reactions

detecting ctDNA mutant-alleles ≤0.1% in

in parallel

abundance

magnetic beads bind to amplified PCR

absolute quantification of ctDNA with a LLOD

fragments within individual partitions and are

as low as 0.001%

then analyzed by flow cytometry

PNAClamp PCR [80]

incorporate the addition of a peptide nucleic

lowering the LLOD to 0.1%; detecting 1-2

CastPCR [81]

acid or minor groove binders specific to the

mutant copies of ctDNA within 40,000

Intplex [82]

wild-type allele, further limiting non-specific

(0.0025 – 0.005%) wild-type copies of

amplification

cfDNA

employs a similar principle to PNAClamp

LLOD of 0.001% ctDNA relative to background

PCR melting curve analysis, while also

incorporating a set of primers that amplify a

reference sequence common in both mutant

ctDNA and wild-type cfDNA allowing for

simultaneous amplification of both fragments

b. Targeted deep sequencing

i. Amplicon sequencing

Safe-SeqS [84]

employ molecular barcodes with up to 108

allowing SNV detection at a LLOD of <0.1%

possible UID combinations

iDES-enhanced

employ molecular barcodes with up to 256

allowing SNV detection at a LLOD of <0.1%

CAPP-Seq [85]

possible UID combinations

Enhanced Tam-Seq

tagging each template molecule with a

InVisionTM, detecting mutant alleles down to a

[86]

“molecular barcode”, a unique sequence,

MAF of 0.02% with high reproducibility

during the library preparation phase

Ion AmpliSeq [87]

SiMSen-Seq [88]

tagging each template molecule with a

assess mutations in a large panel of genes in lung

“molecular barcode”, a unique sequence,

cancer patients

during the library preparation phase

be exploited for the in vitro or in silico

concentration of ctDNA, thus further increasing

NaME-Pro [89]

based on the selective digestion of wild-type

the sensitivity of the following tests

alleles through the use of a double stranded

enables mutation detection at 0.01 - 0.00003%

DNA-specific nuclease guided on the target

MAF

sequence by selective oligonucleotide probes

that pair with wild-type alleles of the genes of

interest.

WES [91]

Sequencing a number of target sequences of

limit of detection is around 5% MAF

the whole exome Identified mutations in >95% of tumors with ii. Hybrid capture

CAPP-Seq [93]

utilizes previous population-based tumor 96% specificity for mutant allele fractions down

sequencing

mutation profiling data to maximize the

to approximately 0.02%; Achieved a LLOD of 0.004% while profiling

number of cancer-derived mutations per hundreds of genomic regions patient while minimizing the total capture

panel size and thus sequencing cost Near-perfect analytic specificity (> 99.9999%) Digital sequencing

high-quality sequencing of circulating tumor enables complete coverage of many genes

[94]

DNA simultaneously across a comprehensive

without the false positives typically seen with traditional sequencing assays at mutant allele

panel of over 50 cancer-related genes frequencies or fractions below 5%.

2. DNA Methylation

Pyrosequencing [100]

provides real-time quantitative data on the

require specific equipment; the used clinical

methylation status of multiple CpGs within

specimens are the length of the sequence read,

an amplicon.

which decreases the quantitative accuracy of the

methylation status of CpG sites distant from the

3’ end of the primer.

MAP [101]

ubiquitous technique that allows for

generally associated with high false-priming

measuring the relative proportion of a

events when high numbers of PCR cycles are

particular template that harbors methylated

used; require a gel electrophoresis

CpGs.

dMethyLight [103]

a compartmentalized MethyLight that allows

provides a better detection than MethyLight; the

for the detection and counting of a single

sensitivity and the reproducibility of

methylated allele

dMethyLight are governed by the number of

wells, making it time-consuming and reagent

intensive.

ddMethyLight [104]

based on the same compartmentalization

has a very high sensitivity (0.001%) and has been

principle as dMethyLight, but at a larger

shown to be 25-fold more sensitive than

scale-up to 20,000 droplets are generated in

MethyLight

an emulsion

Array-based

based on microarray-hybridization, which is

ideal for a first pass methylation profiling.

hybridization [95]

subsequent to digestion of DNA with

However, the large amount of input DNA

MSREs, affinity purification or bisulfite

required (500 ng–1µg) precludes the widespread

conversion.

assessment of DNA methylation in liquid

biopsies.

WGBS [105]

theoretically possible to determine the

provide a higher genomic coverage compared to

methylation status of the 28 million CpGs

microarrays and a single nucleotide resolution;

contained in the human genome; can be

common enrichment techniques require high

performed with ~30 ng of DNA, and in some

amount of high-quality DNA and are therefore

cases as little as 125 pg

not easily applicable to ctDNA; online software

tools have been designed to specifically analyze

bisulfite sequencing data.

3. Copy Number Alterations

Plasma-Seq [112]

an approach based on WGS with a shallow

detecting ctDNA by NGS at minimal

sequencing depth, to detect somatic copy

coverage; LLOD of 0.1%, limited by

number alterations genome-wide

sequencing errors similar to conventional NGS approaches; allows generation of results in <48 h; the costs are low.

WGS [111]

sequencing and enumerating genomic DNA

time-consuming and the costs were

tags; high coverage; no require any prior

prohibitive for routine clinical

knowledge about characteristics of the

implementation

primary tumor genome

AS: Allele-specific; NaME-PrO: Nuclease-assisted Minor- allele Enrichment with Probe-Overlap ; TEC-Seq:

Targeted error correction sequencing; LLOD: the lower limit of detection; WGS: Whole-genome sequencing;

sWGS: Shallow whole genome sequencing; WES: Whole-exome sequencing; MSP: Methylation-specific PCR;

qMSP: quantitative Methylation-specific PCR; dMethyLight: digital MethyLight; ddMethyLight: droplet digital

MethyLight; WGBS: Whole-genome bisulfite sequencing

Highlights Traditional DNA sequencing requires tissue samples collected during invasive operations; therefore, repeated tests are nearly impossible. Intra- and inter-tumoral heterogeneity may undermine the predictive power of a single assay from tumor samples. The analysis of circulating tumor DNA (ctDNA) allows for non-invasive and near real-time sampling of tumors, reflecting the genetic composition of tumors and monitoring dynamic changes during treatment. ctDNA analysis may potentially be clinically valuable in prediction of treatment responses prior to RT, surveillance of response during RT, and evaluation of residual diseases following RT. As a biomarker for RT response, ctDNA could inform personalized treatments. With continued advances in genomic assays and techniques and ongoing clinical investigations, ctDNA has demonstrated its potential in becoming the modality of choice at various stages of cancer management.

Conflicts of Interest Statement The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.