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`Detecting Liquid Remnants of Solid Tumors:
`Circulating Tumor DNA Minimal Residual
`Disease
`
`Everett J. Moding1,2, Barzin Y. Nabet1,2,3, Ash A. Alizadeh1,3,4, and Maximilian Diehn1,2,3
`
`ABSTRACT
`
`Growing evidence demonstrates that circulating tumor DNA (ctDNA) minimal resid-
`ual disease (MRD) following treatment for solid tumors predicts relapse. These
`results suggest that ctDNA MRD could identify candidates for adjuvant therapy and measure response
`to such treatment. Importantly, factors such as assay type, amount of ctDNA release, and technical and
`biological background can affect ctDNA MRD results. Furthermore, the clinical utility of ctDNA MRD
`for treatment personalization remains to be fully established. Here, we review the evidence support-
`ing the value of ctDNA MRD in solid cancers and highlight key considerations in the application of this
`potentially transformative biomarker.
`Significance: ctDNA analysis enables detection of MRD and predicts relapse after definitive treatment
`for solid cancers, thereby promising to revolutionize personalization of adjuvant and consolidation
`therapies.
`
`INTRODUCTION
`Nearly two thirds of patients with solid tumors present
`with locoregional disease and are amenable to curative thera-
`pies (1). Surgery, radiotherapy, systemic therapy, or a combi-
`nation of these approaches can achieve disease remission in
`the majority of these cases when using conventional measures
`of response, such as functional body imaging. However, in
`a significant subset of patients, small numbers of remnant
`tumor cells, termed minimal residual disease (MRD), can
`persist at levels below the detection threshold of imaging or
`physical exam and ultimately lead to disease relapse (2, 3).
`Systemic therapy delivered after surgery (i.e., adjuvant ther-
`apy) or radiotherapy (i.e., consolidation therapy) has been shown
`to improve long-term survival in multiple cancer types (4–7),
`
`1Stanford Cancer Institute, Stanford University School of Medicine,
`Stanford, California. 2Department of Radiation Oncology, Stanford
`University School of Medicine, Stanford, California. 3Institute for Stem
`Cell Biology and Regenerative Medicine, Stanford University School of
`Medicine, Stanford, California. 4Division of Oncology, Department of
`Medicine, Stanford University, Stanford, California.
`Current address for B.Y. Nabet: Department of Oncology Biomarker Devel-
`opment, Genentech, South San Francisco, California.
`Corresponding Authors: Maximilian Diehn, Department of Radiation
`Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA
`94305-5847. Phone: 650-721-1550; E-mail: diehn@stanford.edu; and
`Ash A. Alizadeh, Division of Oncology, Department of Medicine, Stanford
`University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847. Phone:
`650-725-0120; E-mail: arasha@stanford.edu
`Cancer Discov 2021;11:2968–86
`doi: 10.1158/2159-8290.CD-21-0634
`©2021 American Association for Cancer Research
`
`providing strong clinical evidence that eradication of MRD can
`improve rates of cure. However, for most solid cancers where
`adjuvant/consolidation therapy is currently part of the standard
`of care, the magnitude of benefit from adjuvant therapies is
`modest. This is likely in part because a significant subset (and
`sometimes the majority) of patients who receive adjuvant thera-
`pies are already cured by the preceding local therapy (4, 6, 8).
`For several hematologic malignancies, detection of MRD
`via flow cytometry for tumor cells or quantitative molecular
`techniques for patient-specific aberrations has long been
`established as a poor prognostic factor following induction
`therapy. Accordingly, modification of therapy based on the
`presence of MRD has become a standard of care for acute
`lymphoblastic leukemia, acute promyelocytic leukemia, and
`chronic myelogenous leukemia (9, 10). Given such actionabil-
`ity of MRD in blood neoplasms, biomarkers that can simi-
`larly identify which patients with solid tumors harbor MRD
`could also have significant utility in personalizing adjuvant/
`consolidation therapy. However, until recently, approaches to
`detect MRD in solid cancers have lacked the sensitivity and
`specificity required for clinical application (11).
`Recent work focused on circulating tumor DNA (ctDNA)
`has produced promising results, suggesting that this analyte
`could serve as a generalizable biomarker for MRD in solid
`cancers. Tumors release DNA into the blood that can be iso-
`lated from plasma collected via routine blood draws (12–14).
`Despite generally representing a small fraction of cell-free DNA
`(cfDNA) in blood plasma, ctDNA can be detected via polymer-
`ase chain reaction (PCR) or next-generation sequencing (NGS)
`assays targeting tumor-specific mutations, structural variants,
`copy-number alterations, and epigenetic features (15, 16).
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`Multiple commercial platforms have been developed based on
`these approaches, including Natera Inc.’s Signatera (34–36),
`ArcherDX’s personalized cancer monitoring assay (37), and
`Inivata’s RaDaR assay (38). Although beyond the scope of
`this review, numerous other PCR amplicon-based NGS meth-
`ods have been used for ctDNA analysis in the past and have
`been reviewed in detail elsewhere (39–41).
`
`Hybridization Capture–Based NGS
`DNA enrichment using hybrid capture with biotinylated
`oligonucleotides allows sequencing of larger targeted panels
`with better uniformity of coverage (42, 43). Capture-based
`NGS approaches such as CAncer Personalized Profiling by
`deep sequencing (CAPP-Seq) preserve cfDNA fragment size
`information and can also incorporate UMIs to minimize
`technical background (18). Capture panels can be used as “off-
`the-shelf” tools designed to target frequently mutated genes
`in one or more cancers of interest (e.g., AVENIO assay from
`Roche Diagnostics; ref. 44). Alternatively, personalized capture
`panels can be designed for each patient to enrich for patient-
`specific mutations identified from tumor sequencing (refs. 45,
`46; Fig. 1A). Recently, we developed a novel capture-based
`ctDNA MRD assay called Phased variant Enrichment and
`Detection Sequencing (PhasED-Seq), which leverages multi-
`ple somatic mutations within individual DNA fragments to
`decrease both technical and biological error rates (see below)
`and improves ctDNA MRD detection limits down to 1 part
`per million, 30–100-fold lower than previous approaches (47).
`
`Whole-Genome Sequencing
`In theory, application of whole-genome sequencing (WGS)
`to posttreatment plasma could allow detection of an even
`greater number of mutations than the aforementioned NGS
`approaches and not require custom panel design. However, due
`to assay background error rates, there are diminishing returns of
`increasing the number of tracked mutations past a certain point
`(see below). Therefore, recent work suggests that when combined
`with customized bioinformatics, direct WGS of plasma can
`potentially achieve similar but not superior limits of MRD detec-
`tion as the other NGS approaches (48). Additionally, the amount
`of sequencing required for the much larger targeted genomic
`space makes the per sample costs of WGS significantly higher.
`
`Emerging Techniques
`Although not a focus of this review, other analysis
`approaches based on DNA methylation or other epigenetic
`features reflecting chromatin state of tumor cells (termed
`“fragmentomics”) have been used to detect ctDNA (49).
`Although these emerging techniques have not been exten-
`sively evaluated in the context of MRD, epigenetic features
`may be complementary to somatic mutation tracking and
`may enable tumor genotype–naïve MRD detection. For exam-
`ple, the Guardant Reveal assay integrates somatic muta-
`tion and “epigenomic” (i.e., methylation and fragmentomic)
`approaches to detect ctDNA without prior sequencing of
`tumor DNA (50, 51). A recent study using this assay for
`detecting MRD in colorectal cancer demonstrated that incor-
`porating epigenomic analysis improved sensitivity compared
`with tumor-naïve somatic alterations alone (52). However,
`methylation-based approaches have been reported to have
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`Recent advances in molecular and computational biology
`have significantly improved the limit of detection (LOD) for
`ctDNA using somatic alterations (17, 18). These improve-
`ments have raised the possibility that ctDNA analysis could
`be used to identify patients harboring MRD following cura-
`tive therapy of solid cancers and to guide the administration
`of adjuvant or consolidation therapies (19). Here, we review
`the current evidence that detection of ctDNA following defin-
`itive therapy is prognostic in solid tumors and discuss the
`promise and limitations of ctDNA MRD testing.
`
`APPROACHES FOR ctDNA MRD ANALYSIS
`Digital PCR
`Several approaches have been utilized to identify known or
`common tumor mutations in plasma cfDNA samples. Digi-
`tal PCR (dPCR) improves on conventional allele-specific PCR
`amplification by partitioning a DNA sample into a large num-
`ber of smaller reactions to provide an absolute quantifica-
`tion that improves sensitivity (20). Digital PCR primers and
`probes can be designed to achieve very high specificity, and
`droplet-based approaches such as BEAMing (beads, emulsion,
`amplification, and magnetics) have maximized the number of
`individual DNA molecules that can be analyzed from a single
`sample (21). As a result, the detection limit of such digital PCR
`assays is limited in practice by the amount of cfDNA that can
`be isolated from a blood draw (22). With DNA inputs routinely
`achievable from patient plasma samples (∼30 ng), dPCR has
`been shown to have a detection limit of approximately 0.1%
`(23–25). However, due to differences in DNA input, sample
`quality, and analysis approaches, reported LODs vary substan-
`tially between studies. dPCR is very effective for tracking a small
`number of mutations identified from sequencing of tumor tis-
`sue or hotspot mutations with a high prevalence in the cancer
`of interest, such as KRAS mutations in pancreatic cancer (26).
`Due to complexities of multiplexing a large number of dPCR
`assays, the extent of multiplexing varies widely between studies
`and dPCR generally has inferior clinical sensitivity for MRD
`than highly parallel NGS methods monitoring multiple muta-
`tions (27, 28). Accordingly, dPCR is not a commonly preferred
`approach for solid tumor MRD detection in most contexts (29).
`
`PCR Amplicon–Based NGS
`NGS, also known as massively parallel sequencing, has
`been incorporated into several different ctDNA analysis
`approaches, enabling interrogation of millions to billions of
`DNA molecules from a biological sample. One approach to
`achieve sufficient sensitivity to detect rare ctDNA molecules
`uses gene-specific PCR amplicons to amplify one or more
`genomic regions expected to harbor tumor-derived muta-
`tions prior to NGS. Several approaches, including Safe-SeqS,
`introduce unique molecular identifier (UMI) sequences dur-
`ing preparation of DNA libraries for sequencing to reduce
`technical errors (17). Personalized multiplex PCR can be
`utilized to monitor multiple patient-specific mutations
`identified from sequencing of tumor tissue (30), and NGS
`sequencing of these lesions in cfDNA can achieve remarkable
`sensitivity levels (31). A related set of approaches uses a
`combination of ligation- and gene-specific PCR primers to
`partially preserve cfDNA fragment end information (32, 33).
`
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`REVIEW
`
`A
`
`Off-the-shelf panel
`Historical patients
`Individual patient
`
`Personalized panel
`Individual patient
`
`D
`
`Diagnosis
`
`Tumor genotype−naïve
`
`Tumor genotype−informed
`Variant identification in
`high allele fraction sample
`
`and/or
`
`KRASG12C
`GCCACCAGCT
`GCCACAAGCT
`
`WT:
`MUT:
`
`Positions analyzed
`↓
`GCCACCAGCT
`GCCACCAGCT
`GCCACAAGCT
`GCCACCAGCT
`GCCACCAGCT
`Monitor for patient-
`specific variants
`
`Tumor genotype–informed
`1.0
`
`6 mutations
`5,000× depth
`
`LOD95:
`0.01%
`
`0.8
`
`0.6
`
`0.4
`
`0.2
`
`No baseline tumor
`or
`ctDNA testing
`
`Curative-intent treatment
`
`MRD testing
`
`Cell-free DNA
`
`+
`
`Leukocyte DNA
`
`Positions analyzed
`
`GCCACCAGCT
`GCCACCAGCT
`GCCACAAGCT
`GCCACCAGCT
`GCCACCAGCT
`Multiple hypothesis testing
`limits sensitivity
`
`E
`
`Tumor genotype-naive
`
`Mutation
`
`123456
`
`LOD95:
`0.2%
`
`Cell-free DNA
`
`Patient mutations
`CAGTTGCG
`GGTTCACT
`
`Cell-free DNA
`
`Recurrent mutations
`GGTTCAGT
`CAATTGCG
`CAATAGCG
`GGTTCAGT
`CAATAGCG
`GGTTCAGT
`CAATAGCG
`GGTTCAGT
`GGTTCAGT
`CAATTGCG
`CAATTGCG
`GGTTCAGT
`
`Same panel
`for all patients
`
`Target
`enrichment
`
`Personalized
`panel
`
`Target
`enrichment
`
`Background
`error rate: 0.001%
`LOD95:
`0.001%
`
`LOD95:
`0.1%
`
`LOD95:
`0.01%
`
`Undetectable
`VAFs because
`below error rate
`
`B
`
`1.0
`
`0.8
`
`0.6
`
`0.4
`
`0.2
`
`
`
`M utationscfD N A InputDepth
`
`+
`+ +
`+ + +
`
`+
`+ +
`+ + +
`
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`F
`
`1
`0.1
`0.01
`0.001
`Allele fraction (%)
`Result: MRD not detected
`Assay
`type
`Plasma
`genotyping
`
`cfDNA
`methylation
`
`SNV
`ctDNA MRD
`
`Phased variant
`ctDNA MRD
`
`Tumor
`genotype
`
`Naïve
`
`Naïve
`
`Informed
`
`Informed
`
`Panel
`size
`~30 Mb
`
`355 kb
`
`~1.6 Mb
`
`2.1 Mb
`
`Sequencing
`depth
`95×
`4,075×
`380×
`4,400×
`
`Reference
`
`75
`
`46
`
`76
`
`77
`
`1
`
`0.01
`0.1
`Approximate limit of detection (%)
`
`0.0001
`0.001
`
`0.1
`0.01
`0.001
`0.0001
`Allele fraction (%)
`
`<40
`
`50−59
`40−49
`Age (years)
`
`>59
`
`0.0
`
`1
`
`100
`
`80
`
`60
`
`40
`
`20
`
`0
`
`Probability of detection
`
`Clonal hematopoiesis (%)
`
`C
`
`Probability of detection
`
`MRD plasma sample
`6 mutations in tumor
`ctDNA = 0.01%
`
`
`+
`+ +
`+ + +
`
`0.0
`0.1
`
`0.0001
`
`0.01
`0.001
`Allele fraction (%)
`Result: MRD detected
`Clinically/commercially available
`example(s) [reference]
`FoundationOne Liquid CD× [*], Guardant 360 CD× [^],
`MSK-ACCESS [105], TruSight Oncology 500 [106]
`
`Adela [54], GRAIL [53]
`
`ArcherD× [37], C2i Genomics [48], Inivata [38],
`Natera Signatera [31], Roche AVENIO [44]
`
`Foresight Diagnostics [47]
`
`Figure 1.  Technical approaches for ctDNA MRD detection and factors affecting assay sensitivity and specificity. A, Schematic comparing assays
`using off-the-shelf versus personalized sequencing panels. Off-the-shelf panels are designed to cover recurrently mutated genes in the cancer type(s)
`of interest. The same panel is applied to tumor tissue and plasma of every patient, and personalization is achieved by bioinformatically considering only
`the positions mutated in the matched tumor. Personalized panels are designed to cover patient-specific mutations identified through sequencing of their
`tumor DNA. In this approach, personalization is achieved by every patient having a unique panel. B, Factors affecting the probability of ctDNA detection.
`Increasing the number of mutations tracked, the sequencing depth at mutant positions, or the cfDNA input can increase the probability of detection.
`Technical background from sources such as polymerase errors and oxidative damage sets the lower LOD due to an inability to distinguish artifacts from
`true tumor variants. LOD with 95% probability = LOD95. C, Prevalence of at least one clonal hematopoiesis variant detected in plasma as a function of
`the sequencing panel size and sequencing depth in published studies (46, 75–77). Error bars represent binomial 95% confidence intervals. D, Schematic
`of tumor genotype–informed versus tumor genotype–naïve ctDNA analysis. In genotype-naïve analysis, the MRD sample is interrogated at all sequenced
`genomic positions, leading to reduced sensitivity due to multiple hypothesis testing. In tumor genotype–informed analysis, variants are identified from
`tumor tissue or pretreatment plasma samples with high tumor allele fraction. Only patient-specific variants are monitored in the MRD sample. In both
`cases, genotyping DNA from leukocytes in the peripheral blood can improve specificity by identifying variants stemming from clonal hematopoiesis.
`E, Comparison of the LODs of tumor genotype–naïve and tumor genotype–informed ctDNA analysis at a median sequencing depth of 5,000× for a patient
`with six tumor mutations and a ctDNA allele fraction of 0.01%. Due to multiple hypothesis testing with tumor genotype–naïve analysis, the LOD95 is
`0.2%, and no mutations are detected above background despite mutations with allele fractions of 0.02% (1/5,000 molecules) and 0.04% (2/5,000
`molecules) being present in the sample. In contrast, in the same sample, tumor genotype–informed analysis at 5,000× depth is associated with an LOD95
`of 0.01% (approximated by a binomial distribution), and therefore ctDNA MRD has a 95% chance of being detected. F, Summary of assay types, tumor
`genotyping requirements, and approximate LODs (i.e., analytic sensitivity) for commercially/clinically available ctDNA analysis tests. Because no data
`comparing these methods on the same samples are available, the approximate LOD at which 95% of samples would be expected to be called positive for
`each group of assays is estimated from published studies and/or conference abstracts and rounded to the nearest log (31, 37, 38, 44, 47, 48, 53, 54, 105,
`106). *, http://www.accessdata.fda.gov/cdrh_docs/pdf19/P190032B.pdf; ∧, https://www.accessdata.fda.gov/cdrh_docs/pdf20/P200010B.pdf
`
`LODs of approximately 0.1% (53, 54), and rigorous LOD
`analyses for fragmentomic-based approaches in the context
`of ctDNA MRD have not been published to date. Therefore,
`although more studies are clearly needed, it seems unlikely
`that these approaches will be able to match the LODs of the
`most sensitive tumor genotype–informed, somatic mutation–
`based ctDNA MRD approaches.
`
`TECHNICAL ASPECTS OF ctDNA MRD
`DETECTION USING NGS
`Physical Limits of ctDNA Analysis
`The LOD for an assay is the lowest quantity of an analyte
`that can be reliably detected (55). Unfortunately, most studies
`in the ctDNA field have not rigorously defined or established
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`for anemia limit the amount of blood that can be collected for
`ctDNA analysis (69). Although greater cfDNA input improves
`sensitivity across different methods, increasing input cannot
`increase the LOD beyond an assay’s background error rate.
`
`Technical and Biological Sources of Error
`Even if ctDNA molecules are efficiently recovered by an
`NGS MRD assay, their subsequent detection relies on the
`successful identification and quantitation by the associated
`bioinformatics pipeline. Within these pipelines, a key chal-
`lenge is resolving the desired ctDNA biological signal from
`the noise or background errors arising from various techni-
`cal or biological sources. In this context, there are two major
`components that together determine the overall background
`error rate of ctDNA MRD assays: (i) technical errors leading
`to artifactual mutations occurring ex vivo during the various
`molecular biology steps and (ii) bona fide somatic variants
`stemming from nontumor tissues that harbor mutations.
`Technical errors can be introduced during sample process-
`ing from sources such as unrepaired DNA polymerase errors
`arising during PCR (17) and oxidative DNA damage (70, 71).
`Because true tumor variants cannot be resolved below the
`error noise floor, these errors serve to limit the lowest pos-
`sible ctDNA concentration that can be detected. To address
`this issue, multiple strategies have been developed to decrease
`technical errors. These strategies include the use “barcoding”
`techniques that use UMIs, in silico elimination of stereotypi-
`cal background artifacts (i.e., “polishing”), and inclusion of
`free radical scavengers during library preparation to decrease
`oxidative damage (17, 18, 31, 46, 68). Collectively, these error-
`suppression strategies can dramatically reduce the technical
`errors arising ex vivo during cfDNA profiling.
`Biological background secondary to somatic mutations
`found in cfDNA but not originating from tumor cells rep-
`resents a second important source of potential false-posi-
`tive mutations. Through a process called age-related clonal
`hematopoiesis (CH), hematopoietic stem cells can acquire
`mutations that can be found in both circulating peripheral
`blood cells and cfDNA (72). When considering peripheral
`blood leukocytes, patients with clonal mutations above a
`variant allele fraction of 2% in genes that are canonically
`associated with hematologic malignancies, but who do not
`meet the criteria for diagnosis of leukemia, are considered to
`have clonal hematopoiesis of indeterminate potential (CHIP;
`ref. 73). Because the majority of cfDNA arises from hemat-
`opoietic sources (74), CH represents a major contributor to
`biological background in cfDNA profiling exercises, includ-
`ing detection of ctDNA MRD. Importantly, the prevalence of
`CH variants increases with patient age, broader panels, and
`increasing sequencing depth (Fig. 1C) and has been found to
`be as high as 100% in patients 60 years or older (46, 75–77).
`CH represents the dominant biological source of false-
`positive mutations when identifying somatic mutations in
`cfDNA. Importantly, sequencing peripheral blood leukocytes
`can help to identify cfDNA mutations due to CH. In support
`of this notion, a recent ctDNA study of localized non–small
`cell lung cancer (NSCLC) found that up to 15% of TP53
`mutations in the cfDNA were attributable to CH and found
`in matched leukocytes but not in matched tumor (78). In a
`separate study, deeper sequencing of matched leukocytes in
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`the LOD. To conform to clinical laboratory testing guide-
`lines, the LOD should ideally be defined as the ctDNA
`concentration at which 95% of clinical samples will be called
`positive (56). For mutation-based NGS ctDNA MRD assays,
`the LOD depends on both biological and technical factors.
`In practice, reliable LODs have not been published for most
`ctDNA analysis approaches, and multiple definitions have
`been used, making it challenging to compare across studies.
`In order to be meaningful, LODs should be determined in
`settings that match clinical samples as closely as possible,
`including in the amount of cfDNA input (57).
`Regardless of the detection limit, for noninvasive detection
`to be feasible using circulating nucleic acids in blood plasma,
`tumor DNA must first be released into the blood and collected
`in a blood draw. Although ctDNA is generally thought to be
`released from necrotic or apoptotic tumor cells (58–60), the
`exact mechanisms of ctDNA release into the bloodstream and
`the relative contributions of different cell death mechanisms,
`phagocytosis, exocytosis, active secretion, and other cellular
`processes have not been well characterized in human cancers
`(61). Across detection platforms, ctDNA levels generally cor-
`relate with tumor burden on imaging (28, 31, 46, 62, 63).
`Accordingly, the number of residual cells after therapy likely
`also correlates with ctDNA MRD levels. Prior studies have also
`demonstrated that ctDNA release varies within and between
`tumor types and histologies, with a recurring observation that
`squamous cell carcinomas tend to shed higher ctDNA levels
`than adenocarcinomas (31, 46, 64, 65). Furthermore, levels of
`normal cfDNA increase with tissue injury from inflammation
`and ischemia related to nonmalignant conditions, surgery, or
`even vigorous exercise (66), which can lower the allele fraction
`of ctDNA molecules below the LOD of an assay. Indeed, post-
`surgical inflammatory changes have been shown to induce a
`significant increase in cfDNA levels postoperatively for up to
`three to four weeks (67), suggesting that ctDNA MRD should
`not be measured immediately following surgery. Collectively
`and irrespective of the assay used, these biological factors can
`substantially affect the sensitivity of ctDNA MRD techniques
`for accurately detecting residual disease and predicting relapse.
`Assuming that ctDNA molecules are present in a blood
`sample, detection of these molecules relies on their efficient
`profiling. Each of the ctDNA MRD assay approaches includes
`multiple molecular biology steps that have imperfect mol-
`ecule recovery and at any of which rare mutant ctDNA
`molecules could potentially be lost. For instance, when inter-
`rogated by NGS profiling, mutant ctDNA molecules need to
`be incorporated into the sequencing library, and this library
`needs to be sequenced deeply enough to observe these rare
`molecules in the final result. Due to losses of molecules dur-
`ing library preparation and sequencing, NGS methods gener-
`ally have cfDNA molecule recovery efficiencies ≤50% (44, 68).
`Lastly, the probability of detecting ctDNA MRD is a func-
`tion of the ctDNA concentration, the number of mutations
`tracked, and the number of unique cfDNA molecules inter-
`rogated (Fig. 1B; ref. 18). For NGS approaches, tracking mul-
`tiple mutations, using more cfDNA input, and sequencing
`to a greater depth can improve the likelihood of identifying
`mutant ctDNA molecules in a given blood sample. cfDNA
`input is ultimately limited by the amount of plasma that can
`be collected and analyzed. In patients with cancer, concerns
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`patients with NSCLC found that 40.6% of TP53 mutations
`present in cfDNA were also detected in matched leukocytes
`(46). TP53 mutations in cfDNA that were also found in leu-
`kocytes displayed less evidence of the smoking mutational
`signature than TP53 mutations also found in matched tumor
`tissue, supporting their disparate biological sources.
`Although sequencing peripheral blood leukocytes is help-
`ful for distinguishing tumor-derived mutations from CH,
`approximately 10% of mutations found in cfDNA of noncan-
`cer controls are not found in matched leukocytes (46). This
`observation suggests that some CH mutations can be missed
`due to low prevalence of CH subclones in the peripheral
`blood, perhaps reflecting CH in noncirculating hematopoi-
`etic precursors. Additionally, it is possible that mutations
`from nonmalignant, nonhematopoietic cells can be found in
`cfDNA. In support of this, recent studies have demonstrated
`that somatic mutations can be present in diverse nonmalig-
`nant cell types, including epithelial (79, 80), endothelial (81),
`stromal (82), and others (83). As detailed below, incorporat-
`ing sequencing of tumor tissue to identify somatic mutations
`that are then monitored in plasma samples can guard against
`these sources of biological background.
`
`Tumor Genotype–Informed versus Tumor
`Genotype–Naïve ctDNA Analysis
`Due to the complications posed by technical and biological
`background mutations described above, most ctDNA MRD
`studies have performed “tumor genotype–informed” analyses
`to monitor known tumor variants in posttreatment plasma
`(Fig. 1D). This approach includes the genotyping of tumor
`tissue to identify mutations that are then tracked in plasma,
`which lowers the risk of false positives due to technical and
`biological background sources of error. Additionally, such a
`tumor genotype–informed approach limits the number of
`genomic positions interrogated in cfDNA, therefore decreas-
`ing multiple hypothesis testing. Accordingly, the tumor geno-
`type–informed approach can be less demanding for blood
`sample volumes, because fewer unique mutant cfDNA mol-
`ecules are required for detecting ctDNA as compared with a
`genotype-naïve approach (Fig. 1E).
`Several commercially available ctDNA platforms support
`tumor genotype–informed MRD detection, including assays
`for solid tumors from Natera, Roche Diagnostics, Invitae
`(ArcherDx), Inivata, and Foresight Diagnostics, among oth-
`ers. Notably, these platforms are technically distinct from
`liquid biopsy panels such as Guardant360 (https://www.
`accessdata.fda.gov/cdrh_docs/pdf20/P200010A.pdf) and
`FoundationOne Liquid CDx (http://www.accessdata.fda.gov/
`cdrh_docs/pdf19/P190032A.pdf) that have been developed
`for noninvasive genotyping in the setting of advanced disease
`(84). Although such noninvasive genotyping assays are very
`useful for identifying actionable tumor mutations in patients
`with metastatic disease (85), a recent analysis by the SEQC2
`Working Group led by the FDA found that detection of
`variants was less reliable below an allele fraction of 0.5% (86).
`Therefore, currently available assays designed for the primary
`purpose of noninvasive genotyping in advanced disease are
`not optimal for ctDNA MRD detection. This is mainly due
`to the low ctDNA allele fractions typically observed following
`definitive treatment of localized solid cancers in the absence
`
`of radiographic disease burden as well as the confounding
`effects of CH. In contrast, tumor genotype–informed MRD
`detection approaches can attain LODs of ≤0.01%, which
`makes them preferrable for detection of minute amounts of
`MRD (refs. 18, 37, 47, 87; Fig. 1F).
`
`EVIDENCE SUPPORTING THE PROGNOSTIC
`VALUE OF ctDNA MRD IN SOLID CANCERS
`Landmark versus Surveillance ctDNA
`MRD Analysis
`In ctDNA MRD studies focused on solid tumors, two main
`types of analysis have generally been reported: (i) MRD land-
`mark analysis and (ii) surveillance analysis (Fig. 2A). Although
`these two types of analysis are related, their distinct features
`are relevant for the clinical application of ctDNA MRD. MRD
`landmark analysis determines the ctDNA status of patients
`at a single, prespecified time point, which is typically shortly
`after completion of standard-of-care treatment (e.g., surgery
`and radiotherapy). In contrast, ctDNA surveillance analysis
`involves evaluating longitudinal blood draws at multiple time
`points during follow-up, with ctDNA status determined by
`whether any blood draw (irrespective of time point) is positive.
`From a clinical perspective, determining MRD status at
`an early posttreatment landmark time is attractive because it
`would facilitate immediate decision-making about adjuvant
`or consolidation therapies, while minimizing the costs from
`testing serial blood samples. However, one could also envision
`performing repeat MRD surveillance over time and initiating
`adjuvant or consolidation therapy at the first positive blood
`draw. This approach has the potential advantage of decreas-
`ing false negatives. For either the landmark or surveillance
`approaches to offer the potential of meaningful improvement
`over current standard of care, MRD must be detectable with
`a significant lead time over conventional imaging. Given the
`differences in these two approaches, the type of analysis must
`be considered when comparing across studies and techniques.
`
`Clinical Sensitivity and Specificity of
`Current Approaches
`More than 20 studies to date have demonstrated the prog-
`nostic value of ctDNA MRD detection in multiple solid can-
`cers with a variety of technical approaches (Table 1). We
`attempted to summarize the performance of current ctDNA
`analysis approaches from some of these seminal studies, which
`we selected from the literature based on inclusion of at least
`10 patients with ctDNA analysis performed after completion
`of curative-intent therapy for nonmetastatic solid tumors (Fig.
`2B). Studies and/or patients were excluded if adjuvant therapy
`was given after ctDNA analysis, but this information was not
`available for every study. It is important to note that different
`methodologies for ctDNA MRD analysis have not been tested
`side-by-side on the same set of samples, and these analyses
`would be challenging due to the large amounts of plasma
`that would be required. Additionally, the same method may
`perform differently across laboratories or in samples with dif-
`ferent quality. Our goal in this review is to synthesize the pub-
`lished data rather than to rigorously compare diverse methods.
`Despite variable study designs, different tumor types, and
`inconsistent study endpoints, detection of ctDNA MRD at
`
`2972 | CANCER DISCOVERY DECEMBER 2021
`
`AACRJournals.org
`
`Moding et al.
`
`Personalis EX2023
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