Predicting Radiotherapy Responses and
`Treatment Outcomes Through Analysis of
`Circulating Tumor DNA
`Aadel A. Chaudhuri, MD, PhD,* Michael S. Binkley, BA,*
`Evan C. Osmundson, MD, PhD,* Ash A. Alizadeh, MD, PhD,†,‡,§,¶ and
`Maximilian Diehn, MD, PhD*,§,¶
`
`Tumors continually shed DNA into the blood where it can be detected as circulating tumor
`DNA (ctDNA). Although this phenomenon has been recognized for decades, techniques that
`are sensitive and specific enough to robustly detect ctDNA have only become available
`recently. Quantification of ctDNA represents a new approach for cancer detection and disease
`burden quantification that has the potential to revolutionize response assessment and
`personalized treatment in radiation oncology. Analysis of ctDNA has many potential
`applications, including detection of minimal residual disease following radiotherapy, non-
`invasive tumor genotyping, and early detection of tumor recurrence. Ultimately, ctDNA-based
`assays could lead to personalization of therapy based on identification of somatic alterations
`present in tumors and changes in ctDNA concentrations before and after treatment. In this
`review, we discuss methods of ctDNA detection and clinical applications of ctDNA-based
`biomarkers in radiation oncology, with a focus on recently developed techniques that use next-
`generation sequencing for ctDNA quantification.
`Semin Radiat Oncol 25:305-312 C 2015 Elsevier Inc. All rights reserved.
`
`*Department of Radiation Oncology, Stanford University, Stanford, CA.
`†Division of Oncology, Department of Medicine, Stanford University,
`Stanford, CA.
`‡Division of Hematology, Department of Medicine, Stanford University,
`Stanford, CA.
`§Institute for Stem Cell Biology and Regenerative Medicine, Stanford
`University, Stanford, CA.
`¶Stanford Cancer Institute, Stanford University.
`M.D. and A.A. are inventors on patent applications related to the CAPP-Seq
`technology and are cofounders and consultants for CAPP Medical. No
`writing assistance was used in the production of this manuscript.
`This work was supported by the US Department of Defense (M.D.), the US
`National Institutes of Health Director's New Innovator Award Program
`(M.D., 1-DP2-CA186569), the National Cancer Institute (M.D. and A.A.A.,
`1-R01-CA188298), the Ludwig Institute for Cancer Research (M.D. and
`A.A.A.), the CRK Faculty Scholar Fund (M.D.), and the Radiological
`Society of North America (E.O.)
`Address reprint requests to Ash A. Alizadeh MD, PhD, Division of Oncology,
`Department of Medicine, Stanford Cancer Institute, 875 Blake Wilbur Dr,
`Stanford, CA 94305. E-mail: arasha@stanford.edu
`Address reprint requests to Maximilian Diehn, MD, PhD, Department of
`Radiation Oncology, Stanford Cancer Institute, 875 Blake Wilbur Dr,
`Stanford, CA 94305. E-mail: diehn@stanford.edu
`
`http://dx.doi.org/10.1016/j.semradonc.2015.05.001
`1053-4296/& 2015 Elsevier Inc. All rights reserved.
`
`Introduction
`The field of radiation oncology is currently going through a
`
`revolution, as new technologies allow treatment of
`patients with more precision than ever before. Although much
`progress has been made in improving delivery of radiation
`therapy (RT), response assessment after treatment continues to
`rely primarily on imaging for most types of cancers. Although,
`imaging-based response assessment has significant clinical
`value, this approach cannot detect minimal residual disease
`(MRD), has a lag between treatment and when tumor
`responses can be detected, is expensive, and often exposes
`patients to additional ionizing radiation. For these reasons,
`there continues to be an unmet need for developing blood-
`based biomarkers for cancers that are treated with RT.
`Although such biomarkers exist for a few cancer types (eg,
`prostate-specific antigen for prostate cancer), they do not for
`most cancers. The ideal biomarker would be readily general-
`izable, such that it could be applied to all cancer types.
`Although most work on cancer biomarkers has focused
`on detection of proteins, recently, circulating tumor DNA
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`(ctDNA) has emerged as a plasma-based biomarker with the
`potential to redefine prediction and monitor the response to
`anticancer therapies. The cell-free component of whole blood
`has long been recognized to contain fragmented DNA mole-
`cules1 that are collectively referred to as cell-free DNA (cfDNA).
`These molecules are derived from dying cells, and in healthy
`individuals, they mainly originate from hematopoietic cells,2-4
`although diverse tissues contribute to the total cfDNA pool.
`Concentrations of cfDNA in plasma of healthy individuals
`range from 1-10 ng/ml. Patients with advanced malignancies
`can display elevated levels of cfDNA, which led several early
`studies to suggest that measurement of total cfDNA concen-
`tration may be a useful biomarker for detection and monitoring
`of cancers.5-9 However, further work revealed that a variety of
`nonmalignant physiological states and stresses can lead to
`dramatic elevations of cfDNA concentrations, including exer-
`cise, trauma, infection, and inflammation.10-12 Furthermore, as
`detailed later, the fraction of cfDNA that is derived from tumors
`in patients with cancer is often extremely low. Thus, the
`measurement of total cfDNA concentration does not have
`sufficient specificity or sensitivity to serve as a reliable cancer
`biomarker.
`
`Circulating Tumor DNA
`Malignant tumors continually shed DNA into the circula-
`tion.13-18 The mechanisms of release of nucleic acid into the
`blood are not well understood but are thought to result from
`necrosis, apoptosis, and release of DNA by phagocytes that
`have engulfed tumor cells (Fig. 1).17,19-21 Importantly, most of
`the cfDNA found in plasma of patients with cancer usually
`originates from nonmalignant cells. This is often the case even
`for patients with metastatic disease. For example, in patients
`with metastatic non–small cell lung cancer (NSCLC), pretreat-
`ment ctDNA percentages can be less than 1% at diagnosis, and
`
`Figure 1 Sources of circulating tumor DNA. Tumor DNA is released
`into blood predominantly by tumor cell death mechanisms including
`necrosis and apoptosis. Phagocytes that have engulfed tumor cells
`may also actively release tumor DNA into the extracellular space.
`
`decline if active treatment is initiated.16,18,22 Similarly, when
`Dawson et al14 quantified ctDNA in patients with metastatic
`breast cancer, the median mutant allele fraction was  4%.
`This problem is magnified in patients with earlier stages of
`cancer and lower disease burdens, as well as in settings where
`no gross disease remains after therapy and microscopic
`residual disease must be detected. Thus, only highly sensitive
`detection techniques are likely to be of clinical use.13,23 Here
`we describe, compare and contrast methods to detect ctDNA
`(Table).
`
`Allele-Specific PCR–Based
`Methods for ctDNA Detection
`Initial efforts to quantitate ctDNA focused on detection using
`polymerase chain reaction (PCR)–based methods such as
`quantitative PCR or PCR approaches that can preferentially
`amplify low levels of mutant alleles in the presence of large
`amounts of wild-type DNA. Examples of the latter include
`coamplification at
`lower denaturation temperature-PCR
`(COLD-PCR), peptide nucleic acids, locked nucleic acids,
`and amplification-refractory mutation systems. These
`approaches have analytic sensitivities in the range of 0.1%-
`1%.24 Studies using such approaches documented the pres-
`ence of somatic mutations in genes such as KRAS or APC in the
`plasma of patients with a variety of cancer types.25-27 Similarly,
`it was shown that PCR-based methods could detect micro-
`satellite alterations in cfDNA.28 Advantages of PCR-based
`approaches include low cost and relative ease of use. However,
`the key limitation of these methods is that they are usually not
`sufficiently sensitive to detect ctDNA in patients with early-
`stage disease or patients with advanced disease who are
`responding to therapy. Consequently, a number of early
`studies concluded that ctDNA was unlikely to be a clinically
`useful biomarker, which for a period decreased interest in the
`field.21,29,30
`
`Digital PCR-Based ctDNA
`Detection Methods
`In the last several years, there has been a renewed interest in
`developing ctDNA as a clinical biomarker because of the
`addition of 2 major classes of techniques for ctDNA detection.
`The first of these classes was digital PCR (dPCR), which allows
`more precise quantitation of minor mutant allele fractions than
`the aforementioned PCR-based techniques. The 2 most
`prominent examples of dPCR-based methods are beads,
`emulsion, amplification, and magnetics (BEAMing27) and
`droplet dPCR.27,31 Both the methods involve separation of
`template molecules into thousands of tiny droplets that each
`represents isolated reaction chambers, which contain at most 1
`template molecule. Paired with allele-specific PCR assays, this
`allows extremely precise enumeration of the number of total
`template molecules that carry a mutation of interest and can
`achieve detection limits as low as 0.01%. Using these methods,
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`Table Comparison of Methods for Detecting Circulating Tumor DNA
`Allele-Specific
`Digital PCR
`NGS
`PCR
`Amplicon
`Based
`Deep
`sequencing of
`PCR amplicons
`
`Method
`
`Detection limit
`(as % ctDNA)
`
`Preferentially
`amplifies rare
`mutant DNA
`molecules
`0.1%-1%
`
`Counts mutant
`molecules via
`partitioning of
`DNA molecules
`0.01%
`
`WGS
`
`WES
`
`CAPP-Seq
`
`Deep
`sequencing of
`entire genome
`
`Deep
`sequencing of
`exome
`
`Targeted hybrid
`capture
`
`0.01%-2.0% 1%
`
`5%
`
`0.01%
`
`Advantages
`
`Ease of use
`
`High sensitivity High sensitivity
`(some
`methods)
`
`Entire genome is
`interrogated
`
`Entire exome is
`interrogated
`
`Lowest cost
`
`Less expensive
`than other NGS
`methods
`
`Broadly
`applicable
`without
`personalization
`
`Broadly
`applicable
`without
`personalization
`
`High sensitivity
`Detects SNVs,
`indels,
`rearrangement,
`and SCNAs
`Broadly
`applicable
`without
`personalization
`
`Limitations
`
`Lower sensitivity Can only test
`small number of
`genomic
`positions in a
`sample
`
`Can only test
`small number of
`genomic
`positions in a
`sample
`
`Less
`comprehensive
`than other NGS
`methods
`Inability to detect
`SCNAs
`Inability to detect
`rearrangements
`without assay
`personalization
`Abbreviations: indels, insertions or deletions; NGS, next-generation sequencing; PCR, polymerase chain reaction; SCNA, somatic copy number
`alteration; SNV, single nucleotide variation; WES, whole-exome sequencing; WGS, whole-genome sequencing.
`
`Less
`comprehensive
`than WGS or
`WES
`
`Expensive
`
`Expensive
`
`Low sensitivity
`
`Low sensitivity
`
`Mostly limited to
`SCNA
`detection
`
`ctDNA has been detected in a wide range of patients with
`cancer.22,32-34
`The dPCR techniques are currently the gold-standard
`methods for detection of specific mutations within ctDNA,
`given their high sensitivity, specificity, and relatively low cost.
`However, an important limitation of these approaches is that
`only a single or a small number of mutations can be
`interrogated at a time. Owing to the low concentration of
`ctDNA found in many patients, often an entire 10-20 mL of
`blood draw needs to be exhausted for evaluation of only a
`handful of mutations. Additionally, to use dPCR to monitor
`treatment responses, at least one mutation must first be
`identified via analysis of tumor tissue, thus usually requiring
`an initial invasive procedure.
`
`Next-Generation Sequencing–
`Based ctDNA Detection Assays
`Owing to the practical limitation of the number of genomic
`positions that can be interrogated by dPCR, a number of
`groups recently focused efforts on developing next-generation
`sequencing (NGS)–based approaches for ctDNA detection.
`NGS allows for massively parallel sequencing of hundreds of
`millions of DNA fragments from a single sample and thus
`opens the possibility of genotyping many potential mutations
`
`at a time. The NGS-based approaches can be categorized based
`on the number of genomic regions they interrogate as either
`being “focused” or “broad.”
`The focused NGS-based approaches have mainly used PCR
`amplicon-based strategies. Specifically, PCR is used to amplify
`a handful of regions of the genome, and the resulting amplified
`products are subjected to NGS. For example, Forshew et al15
`developed Tagged-Amplicon Sequencing (TAm-Seq) and used
`it to detect mutations in plasma samples from patients with
`advanced ovarian cancer, showing that this technique can
`achieve a detection limit of  2%. In parallel, Narayan et al
`developed an amplicon-based method that incorporates an
`error-suppression approach. Focusing on hotspots in EGFR,
`BRAF, and KRAS,
`they showed that
`they could detect
`mutations in these genes in the plasma of some patients with
`lung cancer.35 Finally, Kinde et al36 developed an amplicon-
`based approach called the Safe-Sequencing System (Safe-
`SeqS), which uses a barcoding approach to suppress errors
`and thus increase sensitivity while maintaining high specificity.
`This method was recently successfully applied to detect ctDNA
`in a large variety of tumor types.13 Key advantages of the
`amplicon-based approaches are that they are relatively inex-
`pensive and sensitive, especially if error-suppression strategies
`are used. Limitations of these approaches include interrogation
`of relatively few genomic loci owing to difficulties related to
`multiplexing of PCR assays, the inability to detect somatic copy
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`number alterations, and the inability to detect breakpoints
`or rearrangements without patient-specific optimization of
`amplicons.
`The “broad” NGS-based ctDNA detection methods include
`whole-exome sequencing (WES) and whole-genome sequenc-
`ing (WGS). WES involves sequencing of all exons within the
`genome using approaches routinely used for tumor genotyp-
`ing. Murtaza et al published the first application of this
`approach and conducted serial sampling of plasma from
`6 patients with metastatic breast cancer, ovarian cancer, and
`NSCLC who were undergoing systemic therapy. Multiple time
`points were analyzed, and samples were preselected to contain
`high percentages of ctDNA (between 5% and 55%). Results
`revealed that many of the mutations detected in the plasma
`matched those in biopsies of metastatic deposits. Additionally,
`they found that new mutations arose as patients developed
`resistance to therapy.37 The major limitations of WES for
`ctDNA analysis are its high cost and relatively low sensitivity,
`making it applicable primarily for research purposes in patients
`with high burden of metastatic disease.
`Finally, several groups have performed WGS directly on
`cfDNA from patients with cancer. Leary et al used WGS and
`identified copy number alterations and chromosomal rear-
`rangements in 9 of 10 patients with colorectal and breast
`cancers, with ctDNA fractions ranging between 1.4% and
`48% of total cfDNA. In a modeling exercise, maximum
`sensitivity of this approach was predicted to be 490% at a
`specificity 499% when ctDNA levels were Z0.75%.38 Other
`groups have similarly performed WGS on plasma samples
`from patients with metastatic prostate cancer or hepatocellular
`carcinoma, indicating that this approach is widely applica-
`ble.39,40 Strengths of the WGS approach include that it does
`not restrict the sequencing space. Limitations include high
`cost owing to the amount of sequencing required, and
`dependence primarily on copy number variation analysis,
`with inadequate sensitivity to detect specific single nucleotide
`variations, insertions, deletions, or rearrangements.38,39
`
`Development of Cancer
`Personalized Profiling by Deep
`Sequencing
`To take advantage of the key strengths of both the “focused”
`and “broad” ctDNA detection approaches described earlier, we
`recently developed a novel method called Cancer Personalized
`Profiling by Deep Sequencing (CAPP-Seq). This approach allows
`ultraspecific and ultrasensitive quantification of tumor-derived
`ctDNA (detection limit  0.01%),
`is economical, and is
`generalizable to nearly all tumor types (Fig. 2).16 The method
`allows detection of specific single nucleotide variations,
`insertions or deletions, somatic copy number alterations, and
`gene rearrangements in a single assay. CAPP-Seq uses a
`multiphase bioinformatics approach using existing genomic
`data to design a variably sized “selector” consisting of tagged
`nucleic acids that allow enrichment for recurrently mutated
`regions in the cancer of interest via hybrid capture.16,23 The
`same selector is applied to all patients with a given tumor type,
`
`allowing identification of multiple mutations per tumor with-
`out the need for personalization of assays. To use CAPP-Seq for
`detecting or monitoring ctDNA, a sequencing library is
`prepared from cfDNA, the selector is applied to enrich for
`genomic regions of interest, and the resulting enriched library
`is subjected to NGS.
`We initially developed CAPP-Seq for NSCLC and showed
`that in our initial cohort, it detected ctDNA in all the patients
`with stage II-IV disease and in 50% of the patients with stage I
`disease, with 96% specificity.16 Strengths of CAPP-Seq include
`that it can simultaneously cover thousands of distinct genomic
`regions, can detect all major classes of somatic alterations, does
`not require prior knowledge of a tumor's mutations owing to
`its breadth of coverage, does not require patient-specific
`optimization,
`is economical, and has very high analytic
`sensitivity. Limitations include that it is somewhat more time
`intensive than amplicon-based approaches are and that, similar
`to WES or the focused NGS-based methods, it can only detect
`alterations in genomic regions that are covered.
`
`Applications of ctDNA Analysis in
`Radiation Oncology
`The development of novel methods for ctDNA detection offers
`the opportunity to investigate the potential clinical use of
`incorporating ctDNA analysis into treatment strategies for
`patients receiving RT. A major advantage of ctDNA when
`compared with other cancer biomarkers is that it is easily
`generalizable. For example, protein-based biomarkers depend
`on the identification of specific proteins that are overexpressed
`by a cancer of
`interest and subsequent optimization of
`detection strategies that are usually antibody based. For this
`reason, a protein-based biomarker for one cancer type usually
`does not readily extend to other cancer types. On the contrary,
`
`Population-level Bioinformatics
`
`Patient-level Analysis
`
`5cc
`Blood
`
`Cell-Free DNA
`(cfDNA)
`
`Recurrent Mutations
` T G A T C T G A C G T
` T G A T A G G A C G T
` T G A T A T G A C G G
` T G A T C T G A C G G
`
`Custom
`Oligos
`CAPP-Seq
`Selector Library
`
`Hybrid
`Capture
`
`Mutation
`Discovery
`
`Figure 2 Design and implementation of CAPP-Seq. Bioinformatic
`analysis of cancer WES or WGS data is used to select recurrently
`mutated genomic regions for inclusion in a CAPP-Seq selector
`consisting of biotinylated oligonucleotides. The same CAPP-Seq
`selector can then be applied in nearly all patients with a given cancer
`type. The selector is used for hybrid capture followed by next-
`generation sequencing of circulating tumor DNA. (Color version of
`figure is available online.)
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`as all cancers contain somatic alterations, analysis of ctDNA
`could potentially be applied to all tumor types. It is possible
`that successful integration of ctDNA analysis into treatment
`paradigms for one cancer type could allow for rapid extension
`of the approach to many other cancer types.
`
`evidence of increasing ctDNA levels or when patients develop
`new symptoms. The latter approach is analogous to the use of
`prostate-specific antigen to follow up patients treated with
`definitive RT for prostate cancer and thus has clinical precedent.
`
`Treatment Response Assessment
`A potential application of ctDNA detection in radiation
`oncology is for treatment response assessment. Currently,
`imaging represents the mainstay of response assessment after
`RT. However, surveillance imaging is not without risks; the
`tests typically performed, computed tomography (CT) and
`positron emission tomography (PET), expose patients to
`additional
`ionizing radiation which, although significantly
`lower than therapeutic RT doses, often expose a larger
`fraction of
`the body, and for patients with frequent
`surveillance or long follow-up, can summate to a clinically
`meaningful risk of carcinogenesis.41-43 Indeed, for an increas-
`ing number of diseases, the risks of surveillance imaging are felt
`to potentially outweigh the benefits.44 Additionally, post-RT
`imaging studies are often difficult to interpret because of
`normal tissue changes such as scarring induced by treatment.
`This may complicate the interpretation of posttreatment scans
`and lead to significant subjectivity in determining treatment
`response or disease status.
`As opposed to imaging studies, analysis of ctDNA
`readily produces quantitative results that can be tracked over
`time. For example, studies in patients with advanced mela-
`noma, ovarian, breast, lung, and colon cancers have shown
`precise tumor burden quantification with ctDNA, with ele-
`vations in ctDNA concentration strongly correlating with
`disease progression and ctDNA decline correlating with
`successful treatment.14-16,32,45,46 Thus, ctDNA analysis appears
`to offer an attractive adjunct or potentially an alternative to
`standard follow-up imaging in patients with advanced disease.
`Additionally, early results suggest that ctDNA analysis likely
`is more sensitive than conventional imaging studies are for
`detection of disease progression. For example, Dawson et al14
`showed that in patients with metastatic breast cancer with
`increasing ctDNA levels during surveillance, progression was
`identified on an average of 5 months before imaging. Similarly,
`several recent studies focusing on EGFR mutant advanced
`NSCLC have shown that in many cases, progression can be
`detected via elevations in ctDNA levels months before changes
`on imaging studies.18,47 Thus, ctDNA quantification offers
`several potential benefits over standard imaging for cancer
`surveillance.
`Few studies to date have explored the application of ctDNA
`analysis for surveillance after RT. We recently provided several
`examples of using CAPP-Seq to distinguish between normal
`tissue changes and residual disease in patients with NSCLC
`treated with fractionated RT or stereotactic ablative RT.16 These
`results suggest that analysis of ctDNA may have clinical utility in
`aiding the interpretation of post-RT imaging studies. Alter-
`natively, if it is shown that ctDNA analysis has sufficient
`sensitivity, one could envision using it as the primary surveil-
`lance assay and performing imaging only when there is
`
`Detection of MRD
`The concept of MRD is well established in hematologic
`malignancies and refers to microscopic deposits of malignant
`cells present after therapy in patients who do not have any
`symptoms or imaging findings confirming their presence.
`MRD is responsible for tumor relapse and can only be detected
`with modern and extremely sensitive molecular methods.48
`Although similar methods currently do not exist for most solid
`tumors, quantification of ctDNA could potentially allow for the
`detection of MRD after treatment with RT, because it is possible
`that patients with residual ctDNA after definitive treatment
`would be enriched for patients who will ultimately develop
`recurrence. An example that suggests this may be feasible
`comes from a recent study of ctDNA in patients with stage II
`colorectal cancer treated with surgery. Tie et al49 reported that
`in their cohort, 5 of 6 patients with detectable postoperative
`ctDNA developed recurrence, whereas only 5 of 72 patients
`with no residual ctDNA had recurrence. These data suggest
`that residual ctDNA after curative intent treatment may provide
`a means to identify the subset of patients who are at the highest
`risk for recurrence.
`The most significant body of literature indicating that a
`similar approach may be fruitful in patients treated with RT
`comes from the analysis of circulating Epstein-Barr virus (EBV)
`DNA in patients with nasopharyngeal carcinoma treated with
`chemoRT. Development of nasopharyngeal carcinoma is
`strongly associated with EBV, and circulating EBV DNA levels
`are detectable in many patients before RT. Successful treatment
`results in loss of circulating EBV DNA, whereas detection of
`EBV DNA in the circulation after therapy portends significantly
`worse overall and relapse-free survival.50-53 Other studies have
`similarly quantitated circulating human papillomavirus DNA
`in patients with head and neck cancer.54,55 Analysis of ctDNA
`from other cancers is analogous to the analysis of circulating
`EBV and HPV DNA from head and neck cancers, except that
`the analyte comes from the cancer genome instead of a cancer-
`associated virus. For example, our initial description of CAPP-
`Seq included an example of a patient with stage III NSCLC
`who was definitively treated with chemoRT.16 Posttreatment
`imaging revealed an apparent complete response, but ctDNA
`concentrations did not change, suggesting progression of
`micrometastatic disease that compensated for the volume of
`tumor eliminated by RT. This patient developed metastases in
`multiple organs several months later, clinically confirming the
`ctDNA result. Thus, ctDNA analysis could potentially facilitate
`the identification of patients with MRD after RT. In this
`scenario, this would facilitate the development of clinical trials
`that test escalating therapy for patients with residual disease
`(eg, with adjuvant chemotherapy, targeted therapy, or immu-
`notherapy) and de-escalation of therapy for patients without
`residual disease (eg, by eliminating adjuvant chemotherapy in
`clinical situations where it is currently given to all patients).
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`Analysis of ctDNA Kinetics for RT Response
`Prediction
`Acute changes in ctDNA concentrations during RT or imme-
`diately following treatment might also have a prognostic or
`predictive value. As ctDNA release is a result of cell death, cell
`killing by RT could potentially be monitored via changes in
`ctDNA concentration. Given that the half-life of ctDNA in the
`circulation is approximately 0.5-2 hours,32 only tumor cells
`that died several hours before sample collection contribute to
`ctDNA levels, and early changes in ctDNA levels after initiation
`of RT might be useful for predicting the ultimate response to
`treatment. Indeed, Lo et al56 showed that for nasopharyngeal
`carcinoma treated definitively with RT, plasma EBV DNA
`levels elevated during the first week of treatment (suggesting an
`increase in rate of cell death) and subsequently declined
`(suggesting decreasing tumor burden). In some patients, the
`initial elevation occurred immediately following treatment
`initiation, whereas in others, the elevation occurred later in
`the week. It is therefore possible that ctDNA level changes early
`during a course of RT may predict treatment outcome and that
`early analysis of ctDNA kinetics during treatment could allow
`clinicians to modify RT and add adjuvant systemic therapy,
`thus facilitating delivery of truly personalized RT regimens.
`
`Noninvasive Tumor Genotyping
`The application of ctDNA analysis that is likely to achieve
`routine clinical use first is tumor genotyping via the plasma, an
`approach referred to as noninvasive tumor genotyping. This
`application is particularly attractive in situations where tumor
`biopsy is medically risky or all biopsy tissue was exhausted in
`establishing a diagnosis, which are 2 scenarios frequently
`encountered in patients who are candidates for RT. Addition-
`ally, as blood draws are easily repeated, noninvasive tumor
`genotyping via ctDNA analysis allows monitoring of genomic
`evolution of tumors over the course of therapy.
`Numerous studies have provided evidence that this appli-
`cation is feasible. For example, Murtaza et al37 performed serial
`ctDNA analysis for patients with advanced cancers using WES
`and showed emergence of mutant alleles at the time of
`treatment failure, including a truncating RB1 mutation in a
`patient who developed acquired resistance to cisplatin. Anal-
`ysis of ctDNA has also been used to identify specific somatic
`resistance mutations, primarily in EGFR and KRAS, following
`anti-EGFR therapy in patients with lung cancer and colorectal
`cancer.13,22,33,34 For example, Diaz et al33 showed that KRAS
`mutations arise in patients with previous wild-type colorectal
`cancer 5-6 months following treatment initiation with anti-
`EGFR monoclonal antibodies. Similarly, Misale et al34 showed
`that in such patients, resistance mutations could be detected in
`the plasma as early as 10 months before radiographic disease
`progression. Thus, ctDNA analysis provides a noninvasive
`approach for monitoring evolution of the tumor genome and
`predicting resistance to systemic therapy. Detection of resist-
`ance mutations before clinical progression provides an oppor-
`tunity for exploring if early intervention, for example, by adding
`another agent that targets the resistance mutations or using
`
`an agent with a different resistance profile, could improve
`outcomes.
`Noninvasive genotyping could also be useful for identifying
`currently unexplored resistance mechanisms. This is partic-
`ularly true for RT, where resistance has been linked to
`mutations in prosurvival, tumor suppressor, reactive oxygen
`species, cell cycle checkpoint, and telomerase pathways.57-59
`However, there is a paucity of literature comparing mutation
`profiles in tumors before RT and after recurrence, in large part
`owing to the difficulty of obtaining tumor tissue at both time
`points. Thus, use of ctDNA analysis in patients whose tumors
`recur after definitive RT could enable the identification of
`novel resistance mechanisms and ultimately lead to strategies
`for improving RT outcomes.
`
`Conclusions and Future
`Directions
`Analysis of ctDNA is a noninvasive approach to detect,
`genotype, and quantify tumor burden that has the potential
`to be incorporated into the practice of radiation oncology.
`Clinical applications include prediction of radiation treatment
`response, posttreatment disease surveillance, and resistance
`mutation detection. We envision that in the future, ctDNA
`assays may be used to personalize RT treatment based on
`ctDNA levels, kinetics, and tumor genetics. In the future,
`ctDNA analysis may also complement or in some cases
`supplement traditional imaging–based and pathology-based
`staging systems for risk stratification and selection of therapy.
`Given the large number of potential applications,
`it
`is
`anticipated that there will be a significant number of studies
`published on ctDNA in the upcoming years exploring the use
`of ctDNA in a variety of different clinical contexts. Identifica-
`tion of the optimal technical approach for ctDNA assessment in
`different clinical situations is critical. Finally, as with any
`proposed cancer biomarker, well-designed and prospective
`clinical trials need to be performed to fully explore the clinical
`applications of ctDNA in patients treated with RT.
`
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