Minimal residual disease in solid tumors: an overview

Abstract

Minimal residual disease (MRD) is termed as the small numbers of remnant tumor cells in a subset of patients with tumors. Liquid biopsy is increasingly used for the detection of MRD, illustrating the potential of MRD detection to provide more accurate management for cancer patients. As new techniques and algorithms have enhanced the performance of MRD detection, the approach is becoming more widely and routinely used to predict the prognosis and monitor the relapse of cancer patients. In fact, MRD detection has been shown to achieve better performance than imaging methods. On this basis, rigorous investigation of MRD detection as an integral method for guiding clinical treatment has made important advances. This review summarizes the development of MRD biomarkers, techniques, and strategies for the detection of cancer, and emphasizes the application of MRD detection in solid tumors, particularly for the guidance of clinical treatment.

Keywords

MRD / solid tumor / CTC / ctDNA

1 Introduction

Minimal residual disease (MRD), also known as molecular residual disease and measurable residual disease, refers to the unknown amount of tumor remaining in cancer patients after treatment. The concept of MRD and MRD detection was derived from hematological malignancies, which can be directly assessed over time in a simple blood draw, and leukemia patients with MRD are prone to cancer recurrence and exhibit shorter overall survival (OS) [1,2]. The use of MRD has therefore become standard in the management of multiple hematological malignancies [3,4], and it has been incorporated into the clinical guidelines of agencies, such as the European Leukemia Network (ELN) [5] and the National Comprehensive Cancer Network (NCCN) [6,7]. In addition, lots of registrational trials submitted to the US Food and Drug Administration (FDA) applied MRD in drug development in hematologic malignancies [8], reflecting the importance and widespread use of MRD in hematological malignancies.

The primary biomarker used for MRD detection in hematologic malignancies is circulating tumor cells (CTCs) [9], while very few CTCs are released into the circulatory system by early solid tumors. Besides, solid tumors are more heterogeneous and have fewer biomarkers that are considered a hallmark in majority of cases of a tumor type, such as B cell receptor/T cell receptor (BCR/TCR) in leukemia [10]. These characteristics make the detection of MRD in solid tumors more difficult, which has resulted in a delayed interest in the research and development of MRD detection for solid tumors. Fortunately, molecular biology revealed the circulating tumor DNA (ctDNA) as a promising biomarker to detect MRD in both hematological malignancy and solid tumor [11–13]. Advances in ctDNA detection assays, particularly droplet digital PCR (ddPCR) [14] and genomic sequencing [15], have reached levels of sensitivity sufficient for MRD detection of solid tumors [16], making the research and application of MRD in solid tumors feasible and routine. Imaging detection, such as computed tomography (CT) and magnetic resonance imaging (MRI), is currently the most common method to evaluate solid tumors [17–19], but factors, such as the sensitivity of imaging methods and tumor pseudoprogression during immunotherapy, limit the accuracy of imaging detection [20,21], which uncouples patient outcome with the pathological results [22]. With increased sensitivity and the ability to obtain genetic information, molecular MRD detection has undergone broader application in clinical practices [23]. Recent years, MRD detection has been recommended and valued by many authoritative organizations, such as National Cancer Institute (NCI) [24] and FDA [25], encouraging the development of MRD in clinical applications. This review briefly covers four aspects of the progress in the field of solid tumor MRD detection, including MRD biomarkers, detection techniques, strategies, and applications.

2 MRD biomarkers

Tumors may shrink enough or disappear after definitive treatments [26,27], leading to difficulty in detecting residual tumor by imaging. More sensitive markers and/or methods for tumor residual detection are needed to better manage patients. Based on the biological and physiological characteristics of tumors, the tumor tissues release many cells or molecules into the circulatory system during physiological processes such as metabolism and proliferation [28,29] (Fig.1). Solid tumors most often release circulating tumor cells (CTCs) and ctDNA into body fluids, so they have become the most common biomarkers for MRD detection. In addition, extracellular vesicles/exosomes [30,31] and highly expressed mRNAs [32] and proteins [33] in cancer are also potential biomarkers for MRD detection (Fig.1). However, these potential biomarkers require further analysis in more clinical studies before broader application. In this review, we will mainly discuss the use of CTC and ctDNA in MRD.

CTC refers to the free tumor cells shed from tumor tissues in peripheral blood or other body fluids [29]. CTCs are “seeds” of metastasis and usually are introduced into the circulation by shear stress, immune attack, and anoikis from primary or/and metastatic tumors [34,35]. CTCs were initially observed by Thomas Ashworth in 1869 [36] and the effective technique for CTCs isolation was developed nearly a hundred years later [37]. Then a number of studies have revealed the clinical applications of CTCs in tumors [38–41]. In 2004, Cristofanilli and colleagues demonstrated that the number of CTCs before treatment or at the first follow-up was an independent predictor of progression-free survival (PFS) and OS in patients with metastatic breast cancer [38]. Subsequent studies further confirmed that the number of CTCs correlates highly with disease progression in metastatic breast cancer patients who have received chemotherapy or endocrine therapy, suggesting that CTCs are a potential marker for monitoring treatment efficacy [39]. Analysis of CTCs in patients with early breast cancer revealed that mesenchymal CTCs were significantly associated with a higher risk of death [40]. CTCs are also helpful in predicting drug resistance and making treatment decisions since they carry tumor biological information, such as surface antigens and gene variants [41,42]. Nevertheless, many aspects of CTC detection require further improvement. For example, CTC detection is not used as often in early-stage solid tumors relative to advanced tumors, due to the low number of CTCs shed into peripheral blood during early stage cancers [43]. In addition, CTCs also face the challenges of the expense of separation and cellular heterogeneity. Therefore, the application of CTCs in MRD detection of solid tumors must overcome plaguing issues, such as sensitivity, specificity, cost, and validation in large-scale clinical cohorts.

Through secretion and cell death, DNA fragments are also continuously released into plasma or body fluids [11,44]. A large number of free DNA fragments circulate in plasma or body fluids, and these DNA fragments are called cell free DNA (cfDNA). ctDNA is a subset of the cfDNA and refers specifically to the DNA fragments derived from tumor cells [45]. ctDNA is currently the dominant marker used for MRD detection in solid tumors since it is continuously released by all stage of tumors and the high correlation with tumor dynamics [46–48]. ctDNA can be detected based on various genomic features, including cancer specific mutations [49], methylation [50], copy number variations (CNV) [51], or other characteristics [52,53] of ctDNA. Cancer specific mutations detection is the most commonly used genomic feature to detect ctDNA. Although ctDNA contains multiple tumor mutations, the frequency of these mutations is usually low due to the low ctDNA content relative to the overall cfDNA in blood or body fluids [54]. A tradeoff thus exists for the sensitivity and breadth of detection in the approach. That is either screening narrow genomic region containing tumor specific mutations with deep sequencing and highly sensitive methods, such as ddPCR [55] and targeted panel sequencing [15,49,56], or reaching the detection requirements for low tumor frequency samples through expanding the number of cancer mutations screened by whole exons sequencing (WES) [57] or whole genome sequencing (WGS) [58]. The second genomic feature is DNA methylation, which also undergoes large-scale changes during tumorigenesis of various tumors [59]. Along with its cell-specific characteristics [60], DNA methylation becomes an exceptional biomarker in that it can be used both to detect cancer and to determine the tissue of origin [61,62]. Detection of MRD based on methylation changes has been demonstrated in breast cancer [63], prostate cancer [64], colorectal cancer [65–67], and other types of solid tumors [68–70]. CNV is another genomic characteristic that distinguishs cancer cells from normal cells, has been applied to detect ctDNA in colorectal cancer (CRC) [71], medulloblastoma [72], liver cancer [33], and other solid tumors [73]. By the way, the sequence and physical characteristics of cfDNA, such as the end motif and fragment size, have been gradually incorporated into MRD detection [74], which may further improve the accuracy of MRD detection.

A multi-feature strategy may improve the detection sensitivity of MRD. Parikh et al. combined somatic mutation and DNA methylation to increase the sensitivity of longitudinal MRD detection after surgery from 72.7% (ctDNA alone) and 63.6% (methylation alone) to 91% (combination) in CRC patients undergoing radical treatment [65]. A study by Liu et al. showed that CNV combined with somatic mutation also improved the detection of MRD in local advanced rectal cancer (LARC) patients after neoadjuvant therapy [71]. The combination of CTC and ctDNA has been shown to improve the sensitivity of MRD detection. In 2020, Radovich et al. combined ctDNA with CTC which resulted in increased sensitivity of MRD detection in triple-negative breast cancer (TNBC) patients after neoadjuvant chemotherapy (ctDNA alone, 79%; CTC alone, 62%; and combination, 90%) [75]. Coincidentally, a recent study showed that the combination of CTC and ctDNA improved the sensitivity of MRD detection in liver cancer patients (combined vs. ctDNA or CTC: 85.7% vs. 75% or 70.4%) [76]. In summary, a multi-feature combination could be an effective method to improve the detection sensitivity of MRD, and it is also one of the current focuses in MRD research.

3 Detection techniques

3.1 Techniques for detection of CTCs

CTCs are released from primary and metastatic tumor sites into the blood. Previous studies have suggested that CTCs could serve as a real-time biomarker to predict cancer progression and patient survival. Techniques have been developed for CTC detection, enrichment and counting and are mainly based on size, immunoaffinity and density, or a combination of these methods, as well as positive and negative enrichment during separation [77]. Immunoaffinity-based CTC techniques use specific antigens expressed on the surface of CTCs but not on other non-neoplastic cells. Positive enrichment uses specific antibodies to capture the CTCs, while negative enrichment targets the CD45 antigen to capture the normal cells [77]. CellSearch and CellSpotter systems have been commonly used in CTC enumeration, and the technical details of CTC enumeration usually include accuracy, precision, linearity, and reproducibility [38,78]. After isolation, DNA, RNA, or protein data from CTCs can be profiled, and fluorescence in situ hybridization can be used to analyze gene structural variations in CTCs [79]. RNA in situ hybridization analysis also has been used for analysis of the mRNA expression changes in CTCs [80]. The data obtained from CTCs have been used for monitoring MRD in various tumors [39,41,43].

3.2 Techniques for ctDNA detection

ctDNA is one of the most commonly used biomarkers to detect MRD, monitor for recurrence and select patients for appropriate treatment modalities. The main analytes have been ctDNA from blood and non-blood sources (cerebrospinal fluid, pleural fluid, peritoneal fluid, and urine) [55,81–83]. ctDNA is mainly detected with ddPCR and next generation sequencing (NGS) (Tab.1, Fig.2).

3.3 Droplet digital PCR

Initially, ddPCR was used to detect somatic mutations with improved sensitivity [84]. KRAS mutation in CRC and HER2 amplification in breast and gastric cancer were accurately detected with ddPCR [84,85]. BEAMing is a representative approach for MRD surveillance based on ddPCR, which combines both emulsion PCR and flow cytometry of magnetic beads [14,86,87]. BEAMing has increased sensitivity relative to conventional ddPCR for mutation detection at levels as low as 0.01% and has a broad application across tumor types [14,88]. All in all, ddPCR is suitable for the detection of a limited number of mutations with a rapid turnaround time in different body fluids and tumors [89–91]. As hotspot driver mutations are not available in most tumors, MRD detection requires a technology that can be more generally applied.

3.4 NGS

NGS was investigated to screen for multiple mutations in any individual case. However, initially, rare mutations could not be generally detected with massively parallel sequencing due to the high error rate varying from ~1% to ~0.05% [92,93]. Years later, several techniques have been developed to distinguish tumor mutations from experimentally-induced errors, which are based on NGS, including PCR amplicon-based targeted NGS, hybridization capture-based targeted NGS, whole exome sequencing (WES), WGS, and whole genome bisulfite sequencing (WGBS) (Tab.1).

PCR amplicon-based targeted NGS includes two typical methods. Safe-SeqS, an approach that substantially increases the sensitivity of massive parallel sequencing, uses a unique identifier (UID) for each DNA template molecule and amplifies each uniquely tagged template molecule to create UID families [15]. This technique first introduced the idea of redundant sequencing and UID-based noise filtering. These approaches significantly minimize the error rates associated with the sequencing process and identify relatively rare mutations accurately [15,94,95]. In addition, tagged-amplicon deep sequencing (Tam-Seq) allows amplification and deep sequencing of genomic regions spanning thousands of bases and identifies both abundant and rare mutations in cfDNA at allele frequencies as low as 2%, with sensitivity and specificity of > 97% [49].

The field has also capitalized on hybridization capture-based targeted NGS. Cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA, is the first hybridization-based enrichment for ctDNA analysis and enables parallel profiling of hundreds of genes [96]. In addition to the large target regions, the technology enables duplex sequencing analysis which matches the readings from two complimentary strands of an original cfDNA duplex, which further suppresses false positive readings. CAPP-Seq achieved high sensitivity and specificity by covering multiple types of somatic mutations in > 95% of non-small cell lung cancer (NSCLC) [96]. Furthermore, CAPP-Seq has been used to monitor disease progression for esophageal cancer, prostate cancer, and melanoma [97–99]. Moreover, a combination of urine tumor DNA (utDNA) analysis and CAPP-Seq, termed urine cancer personalized profiling by deep sequencing (uCAPP-Seq), was developed to detect mutations in DNA isolated from urine and monitor MRD in bladder cancer [100,101].

An approach with improved sensitivity, phase variant enrichment and detection sequencing (PhasED-seq), has been used to diagnose MRD. This method uses multiple somatic mutations in individual DNA fragments and outperforms other methods [102]. Personalized panels were built based on identifying the specific variants of the primary tumor and precisely monitoring these variants. Another new technique, minor-allele-enriched sequencing through recognition oligonucleotides known as MAESTRO, combines massively parallel mutation enrichment with duplex sequencing [103]. This method tracks up to 10 000 distinct, low-frequency (< 0.1% VAF) mutations with up to 100-fold fewer reads per locus so that it could be used to monitor MRD in low input cfDNA samples. Thus, MAESTRO enriches and detects thousands of mutations with high-accuracy sequencing as a hybrid capture and sequencing methodology [103,104].

WGS identifies alterations across the entire tumor genome. While it provides the most comprehensive analysis of mutations in any tumor, WGS is limited by the cost, time, and quality [105]. Moreover, with low-coverage WGS, tumor-associated chromosomal CNVs in cfDNA derived from plasma, urine, and cerebrospinal fluid have been investigated as an MRD marker and show a promising predictive value [72,106–108]. WGS, however, has inferior detection limits, compared with the custom panel design techniques.

Methylation assays, including bisulfite conversion-based and bisulfite conversion-free methods, have also been performed on ctDNA to monitor MRD. Bisulfite conversion is based on bisulfite salts which deaminate unmethylated but not methylated cytosine residues to uracil. The representative technique is WGBS, which can be performed with ~30 ng of DNA, or as even as low as 125 pg of DNA, to provide informative methylation profiling [109,110]. Targeted bisulfite sequencing relative to WGS achieves higher sensitivity [111]. Bisulfite conversion-free methods are based on antibody enrichment and restriction enzymes. Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) is based on antibody enrichment with less DNA input, ~1 ng to ~10 ng [112]. Moreover, 5hmC-Seal, a newly developed method based on 5-hydroxymethylctyosine sequencing, allows rapid and sensitive sequencing of cfDNA [113,114]. This method uses DNA isolated from ~1000 cells which suggests it could be used on limited clinical samples [113].

4 Strategies

4.1 Tumor-informed vs. tumor-naïve ctDNA MRD analysis

Two strategies are used for ctDNA MRD analysis: tumor-informed and tumor-naïve. Tumor-informed ctDNA MRD is the profiling of the cfDNA with the mutations identified in the matched solid tumor tissue (Fig.3). This approach is accomplished through personalized targeted sequencing of cfDNA following WES of tumor tissue samples [115], or universal panel-based targeted sequencing or ddPCR of tumor and cfDNA [88,97]. This approach requires sufficient tumor tissue, which is not always available. In addition, heterogeneity exists in tumors, so that a tumor-informed approach might miss some mutations due to tissue sample bias. Furthermore, tumor-informed ctDNA MRD analysis was unable to track novel clonal variants that emerged during the follow-up [116,117]. Tumor-naïve approaches have also been reported in several studies. A plasma-only strategy, NGS applied to a targeted 198-kb panel, was used to measure changes in ctDNA levels to assess the response to chemotherapy in advanced NSCLC [118]. Integrated digital error suppression (iDES) has 92% sensitivity and > 99.99% specificity at the variant level in detecting EGFR mutations in NSCLC patients without the requirement of pre-treatment tumor sequencing [119]. Moreover, Parikh et al. reported that a plasma-only ctDNA assay integrating genomic and epigenomic alterations exhibited favorable sensitivity and specificity, comparable with tumor-informed approaches [65]. The challenge in the tumor naïve strategy is to determine whether an alteration is tumor specific, or derived from other sources, such as clonal hematopoiesis of indeterminate potential (CHIP) or other normal tissues [120]. The detection of non-tumor-specific mutations can lead to false positive calls of MRD. Stringent filtering criteria to remove such false positive mutations also has the possibility of filtering out real tumor mutations which may lead to false negative calls of MRD. Tumor-naïve detection of ctDNA by multimodal profiling may facilitate the application of MRD [121].

4.2 Panel size and number of mutations tracked

One blood draw provides a limited yield of cfDNA, ranging from 0 to 135.67 ng/mL [122]. The experimental technology has the effect of further decreasing the amount of cfDNA available for profiling. For example, if a cfDNA fragment do not cover the full length of the amplicon, it cannot be amplified and sequenced despite harboring the target mutation. cfDNA could furthermore be lost during various steps of the procedure, such as ligation to adapters or washing during purifications. Finally, 3000–10 000 copies of cfDNA are the lower limit for achieving the sensitivity to detect a mutation. For example, when profiling a 0.01% mutation in 5000 copies of cfDNA, the existence of a mutant cfDNA molecule in the 5000 profiled cfDNA molecules would be random. If one mutant cfDNA was included, the frequency would be 0.02%. However, if no mutant cfDNA molecule was included in the blood draw, the call of the mutation would be false negative. Even though some technologies detect mutations as rare as 0.0025% when the DNA yield is sufficient [119], such approaches cannot resolve the sampling issue, and the performance may be dramatically decreased when profiling samples in low yield. Optimization of the experimental design and procedure may render more original cfDNA molecules to be detectable. However, such improvement in the sensitivity is still limited by the yield of cfDNA and the nature of currently available molecular biology reagents. In this case, single mutation profiling assays, such as digital PCR targeting a hotspot mutation, are used less often for MRD profiling. A widely used solution to overcome the sampling issue is to track multiple mutations in parallel. When 16 mutations with 0.01% frequency are profiled in a cfDNA sample of 5000 copies, we would expect to detect ~8 mutations, and the chance of detecting no mutations would be low. In this case, the sensitivity to detect ctDNA would be significantly improved, to 0.01%–0.02% or lower [123,124].

A key parameter in the algorithm tracking multiple mutations is the number of mutations that can be profiled in the cfDNA. In some studies, whole exome sequencing was performed on the primary tumor, and 16 or more mutations were selected to profile in the matched cfDNA samples [115,125–127]. This solution provides an accurate evaluation of MRD, but the process is complicated and time-consuming, and requires customized primer design. Other studies profiled a panel of genes in the cfDNA sample with or without a matched tumor sample [97,128,129]. This approach can be completed in less time with a universal procedure. However, tumor tissues from a substantial proportion of patients might harbor only a limited number of mutations in the panel so that fewer mutations can be tracked. Studies indicate that at least four mutations must be profiled to achieve accurate detection of MRD [96]. Due to the heterogeneity of tumors, increasing the panel size does not lead to a proportional increase in the mutation number. A multi-omics panel could improve the sensitivity by profiling methylation changes or other biomarkers to compensate the insufficiency of the number of mutations available for tracking [65].

4.3 Landmark vs. longitudinal ctDNA MRD analysis

Numerous studies have mainly focused on the ctDNA status at landmark time points for the detection of MRD. Landmark time is defined as the time point of the completion of treatment, in particular following surgery, radiotherapy, and chemotherapy (Fig.3). Specifically, the collection of the plasma samples occurs within 1 month, 3 months or between 2 weeks and 4 months after surgery in studies [130–133]. Landmark ctDNA analysis helps to evaluate the efficacy of the treatment and provides valuable information for the selection of adjuvant chemotherapy for patients. Recently, several studies showed enhanced performance of serial ctDNA detection during disease surveillance for dynamically predicting relapse in patients. In lung cancer, longitudinal ctDNA analysis during postsurgical disease surveillance helped clarify equivocal radiological diagnosis, and dynamic changes in ctDNA from serial plasma samples correlated with radiological recurrence [130]. Moreover, the combination of landmark and longitudinal data to construct an integrating model to predict the risk of recurrence has promising application value.

5 The applications of MRD

MRD detection has been extensively applied in clinical settings, and it has evolved from monitoring recurrence in patients undergoing radical-intended therapy to guiding the treatment of cancer patients at all stages of their disease. Nowadays, MRD detection covers all stages of solid tumor treatment (Fig.4). We therefore summarize relevant articles on the application of MRD in solid tumors from three significant aspects, prognosis prediction, recurrence monitoring, and treatment guidance, with some representative studies listed in Tab.2.

5.1 Prognosis prediction

Prognosis prediction is currently the most investigated application of MRD. Patients who receive curative-intent radical treatment may receive various prognoses due to differences in the amount of residual tumor cells undetectable by technologies such as CT and MRI. Accurate evaluation of the load of the MRD would provide precise prediction of prognosis. Multiple studies have demonstrated that MRD positive cases have worse prognosis than MRD negative cases in various solid tumors.

5.1.1 Colorectal cancer

CRC is one of the first solid tumors to carry out MRD research. As early as 2008, Frank Diehl and colleagues found that ctDNA in patients with stage II–IV CRC was significantly reduced after treatment, and the detection of ctDNA after surgery had a strong predictive value for recurrence; none of the 4 patients with undetectable ctDNA after surgery relapsed, while 15 of the 16 patients with detectable ctDNA after surgery relapsed [46]. In the following ten years, study of the prognosis prediction of MRD in CRC rapidly increased. In 2016 and 2019, Tie et al. revealed that ctDNA detection after surgery or adjuvant chemotherapy predicted recurrence in CRC stage II and III patients, respectively [134,135]. They also illuminated the value of postoperative ctDNA in predicting recurrence in patients with locally advanced rectal cancer, finding that ctDNA detection after preoperative neoadjuvant chemotherapy was already a sign of the risk of recurrence [136]. Similarly, TRACC, DYNAMIC, and other studies have shown that ctDNA detection after neoadjuvant therapy, surgery or adjuvant chemoradiotherapy is valuable for prognosis prediction for CRC patients [137–140].

5.1.2 Breast cancer

The detection of MRD has been shown to be a valuable marker in predicting the prognosis of breast cancer patients after various therapies. The recurrence rate of patients with undetectable MRD has been shown to be significantly reduced compared with MRD-positive patients after neoadjuvant therapy [141–143]. The BRE12-158 clinical trial enrolled 196 patients with triple-negative breast cancer who received neoadjuvant therapy, and showed that ctDNA-positive and CTC count after neoadjuvant therapy were both signals of worse prognosis. ctDNA-positive patients had a 2.67-fold higher risk of recurrence (P = 0.009) and a 4.16-fold higher risk of death than ctDNA-negative patients (P = 0.01) [75]. Another study in 2020 conducted research on patients with early breast cancer. A total of 44 patients who received neoadjuvant therapy were enrolled and the result revealed that ctDNA clearance during neoadjuvant therapy was predictor of better prognosis, the recurrence risk of ctDNA-cleared patients was only about 5% of that of non-cleared patients [144]. Besides, the presence of MRD after surgery or adjuvant chemoradiotherapy is also a predictor of poor prognosis in breast cancer patients [75,125,145,146]. For example, in the study of 49 breast cancer patients by Coombes et al., it was found that detection of ctDNA after surgery effectively predict the recurrence of patients (HR = 11.8, P < 0.001), and continuous monitoring of ctDNA for a period of time after surgery improved the prediction accuracy (HR = 35.8, P < 0.001) [125].

5.1.3 Lung cancer

Xia et al. carried out a study and enrolled 330 patients with stage I–III NSCLC. Their results showed that positive ctDNA before surgery was associated with a worse recurrence-free survival (RFS) (HR = 4.2, P < 0.001), and positive ctDNA at 3 days and 1 month after surgery could predict the risk of recurrence more accurately (HR = 11.1, P < 0.001) [133]. Another study showed that ctDNA-positive postoperative (HR = 3.95, P < 0.001) or post-ACT (HR = 3.22, P = 0.009) predicted a higher risk of recurrence, and further revealed that patients with positive ctDNA in postoperative longitudinal monitoring had a further increased risk of recurrence (HR = 8.55, P < 0.001) [130]. Similarly, Wang et al. revealed that positive ctDNA detection at as early as 7 days postoperation identified high-risk patients with recurrence (HR = 3.90, P < 0.001) and longitudinal ctDNA monitoring of at least two postoperative time points indicated a significantly higher risk (HR = 7.59, P < 0.001) [147].

5.1.4 Pancreatic cancer

In the study by Lee et al., 81 patients with early pancreatic cancer underwent surgical resection, and tumor-informed ctDNA detection was performed in 42 patients [148]. Results showed that preoperative ctDNA was an indicator of worse RFS (10.3 months vs. not reached, P = 0.002) and OS (13.6 months vs. not reached, P = 0.015). Postoperative ctDNA monitoring improved prediction accuracy. All postoperative ctDNA-positive patients (n = 13) relapsed, and these patients had significantly worse PFS (P < 0.001) and OS (P = 0.003). Another study included 135 patients with pancreatic cancer, including patients with resectable tumors (n = 31), patients with locally advanced disease (n = 36), and patients with metastatic disease (n = 68), and quantitatively monitored ctDNA in patients. The results showed that ctDNA concentration increased with tumor stage (P = 0.05), and patients with higher ctDNA MAF were associated with poorer OS, 18.9, 7.8, and 4.9 months, respectively (P < 0.001) [149]. This study is consistent with the study of Patel et al. in 2019, they found that among patients with advanced pancreatic cancer, those with high ctDNA had significantly worse OS (11.7 vs. 6.3 months, P = 0.001) [150]. In addition, some other studies also demonstrated the application of MRD detection in prognosis prediction in pancreatic cancer patients [151–154].

5.1.5 Other tumors

MRD detection has also been applied to other solid tumor types to predict the prognosis of patients following treatment. For example, MRD-positive muscle-invasive urothelial carcinoma (MIUC) patients after neoadjuvant immunotherapy exhibited worse RFS than MRD-negative patients [132,155], and postoperative MRD detection was proved to be effective in predicting RFS and OS for patients with liver [33,156,157], gastric [127,158,159], esophageal [97,160] or other kind of cancers [100,155,161–163].

The dynamics of ctDNA can also be used to predict prognosis in solid tumors. The TRACEx trial found that the volume of lung tumors was correlated with the average VAF of ctDNA, and the dynamics of mutations and abundance of ctDNA were consistent with tumor evolution [47]. These results suggested that the dynamics of mutation types and abundance of ctDNA may be used to reflect tumor growth and progression. Bratman and colleagues recruited 106 patients with advanced cancers, including squamous cell head and neck cancer, TNBC, severe serous ovarian cancer, and malignant melanoma. The investigators treated the patients with pembrolizumab and monitored the dynamics of ctDNA. The results showed that the clearance or reduction of ctDNA was an indicator of benefit from pembrolizumab, as patients with decreased or clearance of ctDNA exhibited a higher rate of clinical benefit and better OS and PFS compared with patients with elevated ctDNA levels [115]. A recent study showed that patients with stage III CRC who underwent surgery with adjuvant chemotherapy could be grouped according to their MRD status after treatment, and the MRD-positive patients could be further divided into different risk groups based on the rate of increase in ctDNA. The 3-year OS for both patients with a gradual increase in ctDNA and MRD-negative patients was 100%, but for the patients with a rapid increase in ctDNA was only 37.5% [164]. Another study in advanced urothelial carcinoma analyzed the aggregate variant allele frequency (aVAF) of serial ctDNA samples. Patients with progression disease (PD) after treatment had a higher median aVAF value than patients without PD after treatment (12.31 vs. 2.10), and the dynamics of aVAF combined with PD status better stratified patients for OS, with the best OS associated with the group showing a decrease in aVAF and no PD [165]. In summary, both the dynamics and clearance of ctDNA reflect the evolving nature of the tumor, and can be used to predict the prognosis of patients with solid tumors.

5.2 Recurrence monitoring

Prognosis prediction stratifies patients for recurrence risk, but lacks the ability to predict the exact time of recurrence, which requires continuous monitoring of patients. Imaging methods detect recurrent tumors only when tumor tissue becomes visible, while MRD profiling detects tumor cell residues earlier, thus striving for more timely treatment opportunities for patients.

5.2.1 Colorectal cancer

Serial MRD detection after treatment was carried out to monitor recurrence in CRC and to evaluate the time frame of MRD detection relative to detection with imaging. Tie et al. monitored recurrence by ctDNA detection at intervals of 3 months and CT scans at intervals of 6 months for up to 2 years in patients with stage II CRC. The results showed that 85% of relapsed patients were either MRD positive earlier or at the same time as detection of recurrence with imaging, with a median lead time of 5 months [134]. Wang and Øgaard conducted two other surveillant studies in stage I–III CRC patients and CRC patients with liver metastases, respectively. Their results showed that MRD detection revealed recurrence about 3 months before CT detection in these two cohorts [166,167]. In other surveillance studies for CRC, MRD detection preceded imaging detection of recurrence by 8.7 months to 11.5 months [140,164,168,169], further demonstrating the excellent performance of MRD detection in recurrent surveillance of CRC.

5.2.2 Breast cancer

In early stage breast cancer, detection of MRD occurred as early as 7.9–12.4 months before detection of recurrence with imaging [125,146,170–172]. One of these studies monitored recurrence in 144 patients. Samples were taken every 3 months for the first year after treatment and every 6 months thereafter in the study. The study detected recurrence at least 8.9 months earlier with MRD detection than imaging [146]. MRD detection has also been extensively studied for monitoring recurrence in lung cancer patients.

5.2.3 Lung cancer

In 2017, the TRACEx study performed MRD detection for 24 NSCLC patients after surgery and thereafter. The results demonstrated that MRD detection revealed recurrence earlier than monitoring with imaging, and the median lead time was 70 days with a maximum of 346 days [47]. In another study, Chaudhuri et al. conducted MRD detection to monitor recurrence in 40 patients with local-stage lung cancer. The median time to detect recurrence reached 5.2 months in advance of detection with imaging [128]. A recent study expanded the number of patients enrolled to 128 and collected samples from patients every 3 months after surgery. In this study, MRD screening detected recurrence 6.8 months earlier than imaging [147].

5.2.4 Pancreatic cancer

Pancreatic cancer is a highly malignant tumor with rapid progression, and recurrence monitoring has a high value in the clinical treatment of pancreatic cancer. Multiple studies have shown that MRD detection can detect recurrence earlier than imaging methods in patients with pancreatic cancer [149,152,173]. A study enrolled 51 patients with pancreatic cancer, and screened 20 patients for evaluable ctDNA analysis. ctDNA was detected in 10 of the 20 patients after surgery, and the postoperative ctDNA-positive patients were more likely to relapse than those with no detectable ctDNA (P = 0.0199). The average time for disease progression detected by ctDNA was 3.1 months after surgery, while it was 9.6 months by CT, indicating that ctDNA predicted disease progression earlier than traditional methods by an average of 6.5 months [173]. The study by Groot et al. confirmed this point. They collected 59 patients with resectable pancreatic cancer and also found that patients with postoperative ctDNA-positive was worse for RFS (5 vs. 15 months, P < 0.001) and OS (17 months vs. not reached, P = 0.011.), and ctDNA detected relapse at a mean lead time of about 3 months prior to imaging method [154].

5.2.5 Other tumors

MRD detection has also been shown to be valuable for the recurrence monitoring of other solid tumors, such as liver cancer [33,76], esophageal cancer [158], gastric cancer [97,159], and MIBC [124]. In these studies, MRD detection revealed recurrence earlier than imaging in most patients, with a median advanced time of 3 months to greater than half a year. These results further emphasize the increased sensitivity of MRD detection for cancer residual detection than imaging.

The optimal time for MRD detection to reveal recurrence before imaging varies between cancers and studies, and may be related to the heterogeneity across cancers and the different sensitivities of technologies. In addition, the interval sampling time may also affect the time of detection. Reducing the sampling interval time may help to detect recurrence earlier, but will increase the economic burden and reduce patient compliance. How to balance these two factors requires further exploration. A possible future answer to this question may be effective risk stratification of post-treatment patients.

5.3 Treatment guidance

MRD detection has tremendous potential in treatment outcome prediction and treatment guidance, which will help not only high-risk patients to achieve a better prognosis, but also low-risk patients to avoid overtreatment. However, before MRD detection is incorporated into clinical management of cancer patients, sufficient evidence must prove two issues: first, de-escalation of treatment of MRD-negative patients after treatment will not impair the prognosis of patients, and second, escalating treatment of MRD-positive patients after treatment will help to achieve better prognosis. Only when these two issues are confirmed, can MRD be used in general clinical practice.

5.3.1 Postoperation

For non-pCR (non-pathological complete response) or metastatic patients, radical intended surgery is strongly recommended. As mentioned above, postoperative MRD detection is useful in predicting the prognosis of patients with diverse solid tumor types. However, the clinical value of such predictions remains unclear until MRD detection is shown to lead to more effective clinical treatment decisions. A key issue is to demonstrate that the de-escalation of treatment in MRD-negative patients after treatment does not affect prognosis. A recent breakthrough regarding this issue has occurred in the DYNAMIC-II study designed to test management of patients with standard treatment against ctDNA-guided treatment. DYNAMIC-II is a multicenter prospective randomized controlled clinical study for patients with stage II CRC. 455 patients who received radical surgery were randomly divided into two groups. One group included 153 patients receiving standard management. The other group included 302 patients who received ctDNA-guided management based on the following strategies: treatment decision was determined based on the results of MRD detection at the 4th and 7th weeks postoperatively, and MRD-negative patients did not receive adjuvant treatment. The results showed that the 2-year RFS and 3-year RFS of the two groups had no significant differences, confirming the safety of reducing adjuvant chemotherapy in MRD-negative patients after radical intended resection [138]. This study reduced the number of patients who received adjuvant therapy by half, which is of significant value for reducing treatment toxicity and improving the quality of life for patients. In addition, the CIRCULATE-Japan-VEGA clinical cohort (UMIN000039205) carried out on stage II–III CRC patients also aimed to demonstrate the consistency of prognosis between the observation group and the adjuvant CAPOX treatment group in patients being MRD-negative at 1 month postoperatively.

Another key issue is to demonstrate the benefit of escalating treatment in MRD-positive patients. In unresectable locally advanced NSCLC, investigators demonstrated that while MRD-negative patients derived no additional benefit from immunotherapy, treatment of MRD-positive patients with chemoradiotherapy followed by consolidation immunotherapy resulted in better PFS than treatment of MRD-positive patients with chemoradiotherapy alone. This result confirmed the value of MRD detection in the decision to include immunotherapy in the treatment strategy for NSCLC patients [174]. Similarly, in pancreatic cancer, the aforementioned study by Lee et al. not only demonstrated the role of postoperative ctDNA detection in predicting prognosis, but also showed the potential of ctDNA in chemoradiotherapy guidance. In this study, 13 patients with postoperative ctDNA-positive were treated differently. The results showed that chemotherapy had a tendency to improve RFS: the median RFS of chemotherapy group and chemotherapy group were 10.1 months and 5.1 months (HR = 0.36, P = 0.15), respectively [148]. The comparison did not reach significance, which may be due to the small number of patients. IMvigor010 is a global phase III clinical trial designed to evaluate the PD-1 inhibitor atezolizumab in the adjuvant treatment of muscle-invasive urothelial carcinoma. The initial analysis showed that atezolizumab failed to significantly improve disease-free survival (DFS) or OS of patients [175]. However, when MRD status was used to stratify patients, atezolizumab was shown to significantly improve the DFS and OS of MRD-positive patients but had no effect on MRD-negative patients. This study demonstrated that MRD detection can identify patients who may benefit from adjuvant immunotherapy, while eliminating those who will not, and thus may serve as a critical guide in effective clinical decision-making [176]. Additional clinical studies investigating MRD detection guided interventional cancer treatment are underway, such as DYNAMIC-III, IMPROVE-IT2, COBRA, and CIRCULATE, which will help to establish MRD detection as a critical parameter in guiding personalized treatment choices which may improve patient prognosis.

5.3.2 Post-neoadjuvant

Neoadjuvant therapy, including radiotherapy, chemotherapy, and immunotherapy, is a treatment modality performed before surgery, to help shrink tumors to improve the surgical outcome. Some patients achieved pCR after neoadjuvant therapy, which means they have been cured, and the “Watch and Wait” treatment strategy can be adopted to avoid further trauma due to surgery [177]. A study by Wang and colleagues in 2021 showed that MRD detection achieved more accurate pCR prediction results than MRI in LARC patients receiving neoadjuvant chemoradiotherapy, and the combination of MRD and MRI achieved better predictive results [178]. A year later, Liu et al. also demonstrated in LARC patients that post-NAT MRD detection effectively reflected treatment outcome, and the MRD-negative patients exhibited better PFS and OS than MRD-positive patients [71]. In other solid tumors, such as breast cancer [75,141,144], hepatocellular carcinoma [76], and esophageal cancer [97], MRD detection after neoadjuvant therapy also effectively predicts the outcomes of neoadjuvant therapy as well as patient prognosis. These studies illuminate the potential of MRD detection to guide patients’ choice of surgery after neoadjuvant therapy, and help to avoid unnecessary surgeries for pCR patients.

6 Challenges in MRD detection

ctDNA analysis has emerged as a powerful tool for MRD detection. However, the sensitivity and accuracy of ctDNA detection is influenced by several factors, both biological and technical [16]. Biologically, the amount of ctDNA shed from tumors is limited. Serial plasma samples collected after curative-intent treatment contain relatively low levels of ctDNA (below 0.1%), which is a major limitation in the use of this biomarker for solid tumors. In addition, CHIP is an acquisition of somatic mutations in blood cell types that drive clonal expansion without cytopenias or dysplastic hematopoiesis [179]. The blood cells in more than 2% of individuals have these mutations which are associated with premalignant status [180]. Usually, cfDNA is confounded by CHIP, which is the dominant biological source of false-positive mutations, especially in elderly patients [180]. Tumor-informed ctDNA MRD analysis helps to identify the mutations derived from primary tumors to avoid the confounding factors of CHIP. On the other hand, technical error is not completely avoided during sample processing and bioinformatic analysis. Hence, more sensitive and specific methods are needed for MRD detection. New NGS techniques are emerging to optimize ctDNA MRD analysis, such as PhasED-seq and MAESTRO [102,103]. In addition, the efficacy and cost impact the application of these techniques for MRD detection. BEAMing, a technique based on ddPCR, is rapid and cost-effective. Other NGS-based methods tend to be more costly and have a long turnaround time of two weeks or more.

Another concern is what is the optimal time point for MRD detection in guiding clinical decisions, particularly for the strategy of landmark ctDNA analysis. Surgery itself triggers the release of cfDNA, while radiotherapy or chemotherapy may increase the concentration of ctDNA. In addition, the degradation of ctDNA has a half-life of less than two hours [46,181]. Hence, the concentration of ctDNA shortly after treatment may be confounded, and not suitable for MRD detection. Indeed, one study focused on the perioperative dynamic changes in ctDNA in lung cancer patients, and found that MRD at 1 day after surgery was not a prognostic factor, while MRD at 3 or 30 days was [182]. As mentioned above, 1 month, 3 months or between 2 weeks and 4 months after surgery have been used as landmark time points for the collection of the plasma samples in some studies. Studies have not yet yielded a unified consensus for the optimal sampling time for MRD detection after treatment, thus warranting further investigation.

The criteria for MRD-positivity depends on the biomarker and methods used in studies. MRD-positivity based on CTC has a clear standard in early breast cancer. The AJCC 8th breast cancer staging system, which was officially implemented in 2018, indicates that early breast cancer patients with CTC ≥ 1/7.5 mL is associated with poor prognosis [183]. The standards for MRD-positivity assessed with ctDNA are based on the methods or techniques used in the studies. There are two common methods used to define ctDNA based MRD-positivity; one is to define MRD-positivity based on the number of mutations, and the other is to define MRD-positivity with a model trained on previous data. The cutoff may be varied among studies which defined MRD-positivity by SNV number since different techniques are applied. For example, the cutoff of MRD-positivity used in the Signatera assay is SNV ≥ 2 [123,124], while the threshold of SNV number ≥ 1 is also widely used in other SNV-based MRD studies [134,135,140]. The second method used to define MRD-positivity is based on models trained on data obtained from previous studies, which is suitable for integrating multiple ctDNA features to achieve better prediction. A typical example is the Guardant Reveal model which developed MRD-positivity through an internal model trained on previously obtained mutation and methylation data [65]. The second approach requires a larger number of patients for training to produce a reliable model. Although different MRD-positivity criteria make it difficult to compare different studies head-to-head, they expand the application scenarios for MRD detection. In this case, different criteria can provide accurate results for patients as long as they are fully validated in clinical studies.

Although the clinical application of MRD detection is gaining momentum, standards have not yet been developed. MRD detection includes a series of steps, such as blood collection, storage, transportation, detection, and reporting. The procedures and time for each step vary between laboratories or companies, and sometimes the variation is significant even within the same laboratory or company. These differences may affect the accuracy and reproducibility of detection results. For example, differences in storage and transportation conditions may cause variation in DNA fragment degradation, and subsequently irreproducibility. The standards for commercial MRD detection may vary in some aspects among various methodologies which may require, for example, different samples and detection times, but ultimately should be in agreement on the true MRD-positivity rate and lower limit of detection since these parameters directly affect the accuracy of MRD detection. MRD standards are not only a requirement for the industry, but also a guarantee for ensuring the rights of patients and promoting the development of the industry. Formulations of the standards for the use of MRD in clinical settings requires the joint efforts of the government, hospitals, and industry.

7 Prospects

The key clinical significance of MRD detection lies in the guidance of patient treatment. Although studies have confirmed the broad prospects of MRD detection, MRD detection still requires validation through rigorous prospective clinical trials before clinical application. As summarized above, two major issues must be resolved before MRD detection can be used to guide clinical management of patients: (1) de-escalation of treatment in MRD-negative patients does not affect prognosis; and (2) escalation of treatment improves prognosis in MRD-positive patients. Breakthroughs have recently been made in some cancer types that support these two overriding issues. However, due to the vast heterogeneity in solid tumors, the existing conclusions cannot be extended to other cancers or other stages of the same cancer. Some ongoing clinical trials, such as DYNAMIC-III, IMPROVE-IT2, COBRA, and CIRCULATE, will provide more evidence to potentially clarify these two issues, but the number of clinical trials investigating these issues remain insufficient compared to the diversity and complexity of solid tumors. More clinical trials and studies are therefore needed to address the benefit of MRD detection in treatment of various cancer types, cancer stages, and even ethnic backgrounds. Only after validating the parameter in more rigorous clinical trials can MRD detection be incorporated into the decision-making process for treatment of cancer patients.

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