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Wen, X.;  Pu, H.;  Liu, Q.;  Guo, Z.;  Luo, D. Circulating Tumor DNA—A Novel Biomarker of Tumor Progression. Encyclopedia. Available online: https://encyclopedia.pub/entry/39834 (accessed on 26 June 2024).
Wen X,  Pu H,  Liu Q,  Guo Z,  Luo D. Circulating Tumor DNA—A Novel Biomarker of Tumor Progression. Encyclopedia. Available at: https://encyclopedia.pub/entry/39834. Accessed June 26, 2024.
Wen, Xiaosha, Huijie Pu, Quan Liu, Zifen Guo, Dixian Luo. "Circulating Tumor DNA—A Novel Biomarker of Tumor Progression" Encyclopedia, https://encyclopedia.pub/entry/39834 (accessed June 26, 2024).
Wen, X.,  Pu, H.,  Liu, Q.,  Guo, Z., & Luo, D. (2023, January 06). Circulating Tumor DNA—A Novel Biomarker of Tumor Progression. In Encyclopedia. https://encyclopedia.pub/entry/39834
Wen, Xiaosha, et al. "Circulating Tumor DNA—A Novel Biomarker of Tumor Progression." Encyclopedia. Web. 06 January, 2023.
Circulating Tumor DNA—A Novel Biomarker of Tumor Progression
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This entry is adapted from the peer-reviewed paper 10.3390/cancers14246025

Cancer is the second leading cause of death in the world and seriously affects the quality of life of patients. The diagnostic techniques for tumors mainly include tumor biomarker detection, instrumental examination, and tissue biopsy. Liquid technology represented by circulating tumor DNA (ctDNA) has gradually replaced traditional technology with its advantages of being non-invasive and accurate, its high specificity, and its high sensitivity. ctDNA is a small DNA fragment derived from tumor cells, which contains tumor-related genomic information, such as mutation, methylation, microsatellite instability, etc. It is an ideal biomarker for real-time monitoring of tumor development. 

cancer ctDNA prognosis medication guide

1. Introduction

Recently, liquid biopsies have been favored by many researchers for their non-invasive, timely, and comprehensive characteristics. They are an emerging detection technology for cancer and contain circulating tumor cells (CTCs); circulating tumor DNA (ctDNA); circulating cell-free RNA (cfRNA), including small RNAs and mRNAs; circulating extracellular vehicles, including exosomes, proteins, and metabolites; tumor-educated platelets, which are almost always obtained from peripheral blood and other easily obtainable biological fluids, such as feces, urine, saliva, ascites, cerebrospinal fluid, and pleural effusions [1][2][3]. CTCs are a kind of tumor cell present in peripheral blood that is regarded as a marker related to tumor recurrence and prognosis. Chelain et al. reported that CTCs were detectable in 399 of 1697 patients with breast cancer in the NCDB cohort (23.5%) and in 294 of 1681 patients in the SUCCESS cohort (19.4%). CTC-positive, early-stage patients who were treated with radiotherapy after breast-conserving surgery in the NCDB and SUCCESS cohorts showed longer local recurrence-free survival, disease-free survival, and overall survival. However, for patients with CTC-negative or CTC-positive breast cancer, the overall survival was not related to radiotherapy after mastectomy [4]. Moreover, the CTC count was found to be an accurate method to support prognostic information. In castration-resistant prostate cancer, the CTC dynamics from 5–50 during therapy revealed an improved overall survival and were evaluated as an intermediate endpoint of the clinical outcome [5]. The reason why CTCs originating from tumors play a considerable role in cancer metastasis is that CTCs circulate in lymphatic and blood vessels, spread, and implant in distant organs through epithelial–mesenchymal transition. Further, the metastatic efficiency of CTC clusters is 23–50 times higher than that of a single CTC [6][7]. Li and his partners [8] developed cancer membrane-coated digoxin (DIG) and doxorubicin (DOX) co-encapsulated PLGA nanoparticles (CPDDs) to significantly target and precisely disaggregate CTC clusters. Moreover, CPDDs could inhibit the process of epithelial–mesenchymal transition, thus accomplishing an efficient anti-metastasis clinical outcome. cfRNA is also a valuable cancer marker. Matthew et al. [9] screened out tumor-specific cfRNA biomarkers from the plasma of individuals with and without cancer, called dark channel biomarker (DCB) genes. There were DCBs specific for lung cancer, such as SLC34A2, GABRG1, ROS1, AGR2, GNAT3, SFTPA2, MUC5B, SFTA3, SMIM22, CXCL17, BPIFA1, and WFDC2, as well as for breast cancer, such as CSN1S1, FABP7, OPN1SW, SCGB2A2, LALBA, CASP14, KLK5, and WFDC2. Unexpectedly, there were a few differentially expressed DCB genes in certain cancer subtypes. The DCB gene FABP7 was downregulated in the cfRNA of patients with hormone receptor-positive breast cancer and upregulated in patients with triple-negative breast cancer. Moreover, the ability to detect DCB genes was related to the tumor fraction of the plasma, suggesting that cfRNA could be a detectable biomarker for a variety of cancers and cancer subtypes. However, the strategies for the accurate capture of real CTCs in a complex blood environment and the degradation of RNA are major obstacles in clinical applications and pose great challenges to the effectiveness and accuracy of detection results.
Importantly, ctDNA is a cornerstone in liquid biopsies. ctDNA is secreted into the bloodstream through apoptosis, necrosis, and the active release of tumor cells. Thus, it reflects related alterations of the tumor characteristics, such as genetic mutations, gene rearrangements, epigenetic changes, microsatellite instability (MSI), and loss of heterozygosity [10][11]. Moreover, ctDNA has a short half-life of about 2 h and consists of 70–200 base pairs. Thus, ctDNA has been able to reflect the progression of cancer in a timely manner. An overwhelming number of researchers have studied how ctDNA plays a valuable role in cancer diagnosis, treatment, monitoring, prognosis, and relapse evaluation. For instance, Gillian et al. [12] demonstrated a large number of somatic alterations in blood samples and same-patient tumor tissue samples from 104 patients with metastatic urothelial carcinoma and found that aggressive disease could be predicted by high ctDNA levels. Meanwhile, as for real-time genomic biomarker assessment, ctDNA was more beneficial than tumor tissue. In the I-SPY 2 TRIAL, where 84 patients with early breast cancer were treated with standard neoadjuvant chemotherapy alone or in combination with an AKT inhibitor, Magbanua et al. [13] reported that ctDNA was detected before, during, and after neoadjuvant chemotherapy treatment, and it was a significant prognostic factor for patient survival analysis after neoadjuvant chemotherapy treatment. In gastric cancer, cancer progression has been evaluated by genomic alternations in ctDNA earlier than with CT scanning, a standard evaluation method for the therapeutic response. Moreover, novel genomic changes in ctDNA have the potential to reflect resistance to treatment with Pyrotinib, a dual EGFR/HER2 tyrosine kinase inhibitor [14]. Altogether, ctDNA is an ideal biomarker for cancer, and it is worthwhile to analyze and summarize ctDNA-related processes.

2. Biological Characteristics of ctDNA

In 1948, a fraction-free DNA fragment, cfDNA, was found in human blood plasma [15]. ctDNA is a type of cfDNA associated with the development of a tumor. However, where the source of ctDNA is not specified concretely, ctDNA mainly originates from programmed apoptosis or necrosis of tumor cells through a series of source comparison results [16]. This concept has always been embedded in the minds of researchers, but Allenson et al. [17] found that circulating exosomes in blood samples from cancer patients also contain the same mutation sites as ctDNA, such as the KRAS gene G12A. Moreover, the proportion of gene mutations in circulating exosomes is higher than that in ctDNA in the blood, indicating that circulating exosomes containing multiple components may be another source of ctDNA, broadening the origin of ctDNA, and providing new ideas for accurate ctDNA analysis. A deeper understanding of the source of ctDNA can further clarify the changes at specific genetic levels in various types of cancer, which is conducive to subsequent targeted drug research and therapy. The ctDNA reveals tumor-associated DNA changes at the genetic level, including gene mutations, DNA methylation, MSI, gene rearrangements, and loss of heterozygosity. Microsatellite instability refers to the phenomenon that a microsatellite locus in a tumor has a new microsatellite allele due to the insertion or deletion of repeat units compared with normal tissues [18]. The occurrence of MSI is due to functional defects in the DNA mismatch repair in tumor tissues, and MSI with DNA mismatch repair deficiency is an important clinical tumor marker. Markus et al. [19] first detected MSI in cfDNA and ctDNA from patients with prostate cancer, and the results were consistent with the results of tissue whole-exome sequencing in MSI-positive patients with metastases. Moreover, a sharp decrease in ctDNA levels was observed in three patients with mismatch repair deficiency/high MSI metastatic colorectal cancer after the administration of nivolumab, a cytotoxic T lymphocyte-associated protein-4 (CTLA-4) inhibitor, and pembrolizumab, an FDA-approved first-line drug for dMMR/MSI-H metastatic colorectal cancer [20], indicating that MSI in ctDNA is still a clinical biomarker. In addition, Han et al. developed an MSI bioinformatics tool based on cfDNA sequencing data with a sensitivity of 100% and a detection limit of 0.05% ctDNA content [21]. The loss of heterozygosity means that, in a pair of alleles at a specific locus on a homologous chromosome, one side has a harmful mutation, whereas the other side is normal, resulting in a semi-homozygous or homozygous gene locus [22]. The loss of heterozygosity is the major form of mutation in the NF1 gene. A previous study showed that 11 out of 14 patients with invasive lobular or ductal breast carcinoma have NF1 gene loss of heterozygosity, and this alternation was associated with endocrine therapy resistance and activation of the RAS/RAF kinase, indicating that patients’ prognoses will be better after they are treated with an appropriate kinase inhibitor [23]. A gene rearrangement, a common form of a genetic level change, is the repair result of an intragenic or intergenic rearrangement of DNA after double-strand breaks, including a gene fusion. A CAD-ALK gene fusion was found in the second-generation sequencing spectra of ctDNA derived from the blood and urine of patients with metastatic colorectal cancer. The combined rearrangement of CAD gene’s exon 35 and ALK gene’s exon 20, as well as the dynamic changes in the CAD-ALK gene fusion, were consistent with clinical progression in patients [24].
Gene mutations are referred to as changes in the base pair composition or arrangement order in a gene structure, including point mutations, frameshift mutations, deletion mutations, and insertion mutations. DNA methylation is an epigenetic modification, which is catalyzed by DNA methyltransferase (DNMT). S-adenosylmethionine (SAM) serves as a methyl donor, and methyl is added selectively to the DNA of two CG nucleotides of cytosine. Mainly, 5-methyl cytosine (5-mC) is formed, which is common in the gene 5′-CG-3′ sequence. DNA methylation could lead to changes in the chromatin structure of the corresponding regions of the genome, causing DNA to lose the cutting site of the ribozyme or restriction endonuclease and the sensitive site of the DNA enzyme. This leads to the formation of chromatin that is highly helical and condensed into clusters, which loses its transcriptional activity [25].
ctDNA, which is derived from tumor cells, contains tumor-specific gene mutation sites and methylation sites, such as the EGFR gene in non-small cell lung cancer (NSCLC), a BRCA1/2 mutation and methylation in breast cancer, a KRAS gene mutation in colorectal cancer, and a BRAF gene mutation in thyroid cancer. In addition, in the process of ctDNA detection, a mutation or methylation can be identified and determined by sequencing results or corresponding sensing signals, including current changes, optical changes, and absorbance.

3. ctDNA and Early Diagnosis of Tumor

It is well-known that cancer is the consequence of multiple gene mutations, which seriously threaten the health of patients. Early detection and diagnosis are currently major factors for the success of tumor treatment and for effectively improving the quality of life of patients. At present, the early diagnosis of tumors is mainly dependent on gene detection, and ctDNA released by pathological tumors into the blood has unique and excellent advantages. The specificity and sensitivity of molecules, such as ctDNA, cfDNA, CTC, and multiple biomarkers, were compared in existing liquid biopsies for the early diagnosis of cancer [26]. Unexpectedly, the detection efficiency of cfDNA and ctDNA was better than that of other parameters. Luo et al. [27] constructed a diagnostic score (cd-score), which composed of nine methylation markers in the ctDNA of patients with colorectal cancer. The cd-score had excellent sensitivity and specificity for the diagnosis of colorectal cancer, which were superior to those of the conventional colorectal cancer diagnostic marker CEA (AUC: 0.96 vs. 0.67). Moreover, the cd-score was also of use in the response to colorectal cancer tumor staging, treatment methods, and the identification of minimal residual disease after treatment. In addition, in a prospective study involving 16,890 participants, the methylation marker cg10673833 was selected and identified as an early diagnostic marker for high-risk patients with colorectal cancer, with high specificity (86.8%) and sensitivity (89.7%). Leung et al. [28] detected the highest frequency of EGFR, KRAS, and TP53 mutations in the ctDNA of 166 patients with lung cancer. In terms of cancer diagnosis, compared with conventional clinicopathological results, the ctDNA diagnosis had a 98% positive predictive ability, 89% specificity, and 85% sensitivity, but the negative predictive value was 35%, which is a serious and urgent problem that should be solved. It has been reported that pulmonary nodules are related to the formation of lung cancer lesions. Liang, in collaboration with 23 medical centers in China, collected ctDNA from 10,560 patients for next-generation sequencing and observed that pulmonary nodules can be diagnosed according to the methylation of ctDNA in patients [29]. KRAS is the most frequently mutated gene in pancreatic cancer, and the most common mutation sites are c.35 > A, p.G12D and c.38G > A, p.G13D. It was found that the KRAS mutant allele fraction (MAF) in ctDNA plays an important role in the diagnosis of pancreatic cancer. The combination of KRAS MAF with the conventional biomarker CA19-9 can significantly improve detection sensitivity, reaching 82%. Moreover, in the same patient, the KRAS MAF in ctDNA was consistent with that in the lesion tissue [30]. In addition, ctDNA could be combined with ultrasonic elastography to enhance the assessment of changes in the stiffness of breast cancer lesions and to realize the early diagnosis and prognosis of breast cancer [31].
Moreover, Cohen et al. describe a blood test, named CancerSEEK for cancers of the ovary, liver, stomach, pancreas, esophagus, colorectum, lung, or breast. The test contains 61 mutation sites of 16 common mutant genes, such as PIK3CA, APC, EGFR, TP53, and KRAS, and 41 cancer-specific protein biomarkers, such as AFP, CA-125, sHER2/sEGFR2/sErbB2, etc. The mutation is mainly used to analyze whether there is cancer to improve the detection sensitivity, and the tumor-specific protein biomarkers can be used for cancer types, improving the specificity of detection (>99%). In 1005 patients with the above eight tumors, the median positive rate of this method was 70% and the false positive rate was low (7/812). However, as the test is only compared to normal healthy people, will the positive rate increase when the method is applied to sub-healthy people? How to solve this practical problem requires researchers to further decode [32]. Similarly, Lennon et al. named DETECT-A (Detecting cancers Earlier Through Elective mutation-based blood Collection and Testing), combining blood tests and full-body diagnostic positron emission tomography-computed tomography (PET-CT). Blood tests also detect tumor-specific mutations, followed by PET-CT for re-diagnosis, localization, and precise treatment to improve treatment. A total of 134 subjects were screened from 9911 volunteers by the use of blood tests. Among 134 positive patients, 64 of them showed tumor-related imaging information by PET-CT and then confirmed by biopsy technology and 26 patients had cancer and subsequently received corresponding clinical treatment. Although it is complex, DETECT-A is accurate and reduces over-diagnosis and over-treatment decision making that could be included in routine clinical care [33]. For early tumor screening, methylation can be an independent predictor. Chen and his colleagues developed a ctDNA methylation detection scheme called PanSeer by screening 477 tumor-specific, 10613 CpG sites from some well-known tumor-related genes and sequencing blood samples using genome-wide bisulfite sequencing (WGBS). The method was used to test healthy subjects in the Taizhou Longitudinal Study for four years, and 191 of the 605 asymptomatic individuals were diagnosed with stomach, esophageal, colorectal, lung, and liver cancers. PanSeer detection detects cancer by targeting a limited number of genomic regions with aberrant methylation common in different cancer types, but it is likely not to predict patients who will develop cancer in the future. PanSeer is most likely to identify asymptomatic patients who have cancerous growths but cannot be detected by current detection methods as early as possible [34]. In the largest clinical genomics program, methylation detection in cfDNA still showed excellent tumor early-screening performance, and more than 50 cancer types were successfully detected at different stages with a specificity of 99.3% [35]. Nasopharyngeal carcinoma (NPC) is a type of cancer with unique geographical characteristics. It is highly prevalent in the southeast and south of China, and the occurrence and development of cancer are closely related to the Epstein–Barr virus (EBV) infection. Almost every cancer cell contains the EBV genome. Therefore, it is generally accepted that circulating tumor EBV DNA (ctEBV DNA) can be used as a unique biomarker for NPC [36]. Lam et al. screened 20,174 asymptomatic individuals for NPC by target-capture sequencing. The results showed that compared with non-NPC subjects, the plasma ctEBV DNA content of NPC patients increased and the fragment length was significantly prolonged, suggesting that the detection of ctEBV DNA provides a favorable tool for NPC screening [37]. In addition, ctEBV DNA also plays an important clinical role in the prognosis of NPC [38], risk stratification [39], and recurrence monitoring [40].

4. ctDNA and Prognosis of Tumor

ctDNA is an excellent tumor prognostic marker. In FIRSTANA and PROSELICA, two prospective phase three clinical trials, ctDNA was sequenced by low-pass whole-genome sequencing. Univariate analysis and a stratified multivariate analysis showed that the ctDNA score was associated with the overall prognosis of prostate cancer [41]. A high level of ctDNA was detected by Amanda et al. [42] in 24 patients with positive brain metastases of breast cancer using ULP-WGS, but none of the patients with negative ctDNA were detected. Further, the diagnostic effect of ctDNA was far better than that of the current gold standard, namely, cerebrospinal fluid cytology and the conventional diagnostic method MRI. Moreover, as patients received intrathecal treatment, the decrease in ctDNA content was related to their prolonged survival. Continuous detection of ctDNA in the cerebrospinal fluid of patients could predict disease progression after intrathecal treatment. For patients with metastatic bladder cancer, the alteration of the FGFR3 gene in ctDNA was related to increased sensitivity to erdafitinib and prolonged progression-free survival of patients [12]. BRCA1, SLFN11, and USP44 methylation markers were screened by real-time quantitative methylation PCR in cancer tissues and peripheral blood of patients with high-grade serous ovarian cancer. The methylation level of SLFN44 in the ctDNA of patients with advanced cancer was significantly correlated with poor progression-free survival [43]. A meta-analysis [44] of eight studies involving 672 patients with ovarian cancer showed that ctDNA was associated with the tumor size and stage. In addition, a higher level of ctDNA was associated with poor prognosis. Thus, ctDNA can be regarded as a potential molecular prognostic indicator for patients with ovarian cancer. The molecular tumor burden index (mTBI) is a term related to tumor progression. Compared with patients with mTBI > 0.02% in the initial tumor assessment of breast cancer, patients with breast cancer with an mTBI < 0.02% have better progression-free survival and overall survival. The level of mTBI in ctDNA showed a significant downward trend before clinical observation or imaging detected that the tumor volume had decreased, suggesting that mTBI can be used as an effective prognostic marker for breast cancer, which helps to identify patients with good therapeutic effects and to further optimize their targeted therapy [45]. In addition to solid tumors, ctDNA still plays an important role in neuroendocrine neoplasms [46]. In neuroendocrine tumors, the existence of ctDNA was related to the grade and location of primary tumors. Compared with ctDNA-negative patients, ctDNA-positive patients showed shorter overall survival and a higher risk of death, contributing to the prediction of tumor progression. Can ctDNA be used for targeted drug selection and therapeutic effect detection in neuroendocrine neoplasms? This is a question worthy of further exploration with extremely meaningful, innovative direction. Targeting tumor fraction (TF) in plasma ctDNA as an indicator, univariate/multivariate analysis was performed in 1725 cancer patients (198 Metastatic Castration-Resistant Prostate Cancer, 223 metastatic colorectal cancer, 902 NSCLC, and 402 breast cancer). The results showed that the overall survival of patients was independently and consistently correlated with a TF > 10%, enabling the accurate grading of cancer treatment and reducing the possibility of over-treatment [47]. Mo et al. [48] selected 191 meaningful methylated haplotype markers from 11,878 CpG sites and constructed algorithms in advanced adenoma and CRC training sets. In the blind verification set, the AUC of advanced adenoma and CRC was 0.903 and 0.937, respectively. Compared with patients with low methylation levels, patients with high preoperative methylation levels have a worse prognosis.

5. ctDNA and Tumor Molecular Subtyping Profiles

Cancers are usually complex diseases involving multiple genes. Different stages have different gene expression profiles. The heritability, individual differences, and complexity of cancer’s molecular mechanisms vary. In addition, cancer can be divided into different subtypes according to its molecular characteristics. The most common is based on the human epidermal growth factor receptor (HER) oncogene. Based on the Ki-67 labeling index, which is used to detect cell proliferation and analyze the status of the estrogen (E) and progesterone receptor (PgR), breast cancer is divided into basal-like, luminal A, and luminal B subtypes [49]. Accurate treatment strategies for different cancer subtypes are urgently needed, including accurate diagnosis, prognostic stratification, tumor staging, recurrence monitoring, and drug development. These steps can improve the efficiency of treatment, save cancer patients’ lives, and improve their quality of life. Recently, it has been found that ctDNA can also play a role in the molecular typing of cancers. Shi et al. [50] extracted ctDNA from more than 5000 Chinese lung cancer patients and found that ctDNA and bTMB levels were significantly lower in non-small cell lung cancer patients than in small cell lung cancer patients (p < 0.001), regardless of the cancer being adenocarcinoma, squamous carcinoma, adenosquamous carcinoma, or large cell lung cancer. Recent studies have reported that the subtypes of small cell lung cancer contained atonal bHLH transcription factor 1 (ATOH1), pOU class 2 homeobox 3 (POU2F3), neurogenic differentiation factor 1 (NEUROD1), and achaete–scute complex homolog-like (ASCL1) [51][52]. Additionally, NEUROD1, ASCL1HE, and double negative subtypes were the dominant subtypes according to the analysis of 177 SCLC clinical samples [53]. In this study, 366 differential methylation regions (DMRs) were screened using the methylation sequencing profile of 59 cell lines from the study of Francesca et al. [54], and three major subtypes of SCLC were successfully distinguished in fifty-six SCLC clinical blood samples by efficient DMRs, i.e., 73%, 13%, and 14%, and verified by immunohistochemical results. For the majority of de novo metastatic castration-sensitive prostate cancer (mCSPC) patients, tumor tissue and cfDNA sequencing alone were not sufficient to provide somatic information about the patient. The combination of ctDNA and tissue revealed gene-level changes in mCSPC patients, such as extensive TP53 mutation and MSH2 truncating mutation. Therefore, it is a favorable method for evaluating tumor molecular subtypes, laying a solid foundation for the development of the next targeted treatment strategies for cancer patients [55]. Moreover, Gao [56] optimized a WGBS-based ctDNA methylation detection method and identified 15 methylated biomarkers (DMRs) that could significantly distinguish between early and advanced breast cancer patients and healthy volunteers (AUC: 0.996). In addition, 12 ctDNA DMRs were identified as potential biomarkers for discriminating clinical subtypes of breast cancer, which were validated in the training set of 38 breast cancer patients and the validation set of 123 patients. The 12 biomarkers effectively distinguished ER (+) and ER (−) breast cancer patients (AUC values were 0.984 and 0.780, sensitivity was 93% and 73%, and specificity was 93% and 87%, respectively). This method was also applicable to hepatocellular carcinoma and lung cancer and could effectively differentiate their molecular subtypes. In summary, ctDNA can also reveal the molecular markers associated with tumor typing and contribute to tumor typing.

6. ctDNA and Tumor Recurrence Monitoring

ctDNA plays an important role in the monitoring of tumor recurrence. For instance, ctDNA mutations were detected in nine patients with gastric cancer after surgery, of which six patients had cancer recurrence and died of complications caused by cancer metastases. On the contrary, 11 patients without a ctDNA mutation had no recurrence after surgery and had a good quality of life, suggesting that ctDNA has the potential to become a detection parameter for postoperative prognosis and recurrence in patients with gastric cancer [57]. Qiu et al. [58] constructed a combined model of longitudinal ctDNA analysis and time-to-recurrence. Compared with the traditional Cox method, the combined model had a better prediction potential for the recurrence of NSCLC. With the extension of follow-up time, the ctDNA level of patient P062 remained unchanged. The recurrence rate was low, and the condition was stable. On the contrary, the ctDNA level of patient P017 gradually increased. The model predicted that the recurrence risk was significantly increased, and the recurrence occurred shortly after the last follow-up, indicating that the model could realize the dynamic monitoring of the ctDNA level in patients with NSCLC and predict the recurrence risk. According to the prediction results, neoadjuvant chemotherapy was used in advance to improve the prognosis of patients. After a resection of hepatocellular carcinoma, the positive rate of ctDNA was significantly decreased. Patients with major pathological response (MPR) or complete pathological resection had a lower ctDNA content than patients with non-MPR. After neoadjuvant therapy, the positive rate of ctDNA in patients with MPR increased from 33.3% to 83.3%, and the ctDNA in patients with non-MPR also showed a rising trend. The correlation between this rising trend and cancer recurrence was accurately verified in several confirmed patients, which indicates that ctDNA might play a role in evaluating the clinical pathological response and tumor recurrence [59]. Although previous research has found that changes in blood ctDNA in patients with cancer are associated with disease progression, sequencing of the diseased tissues is still needed to align ctDNA mutations with the original mutation of the tumor and to ensure the effectiveness of the detection. However, for some patients, it is not easy to obtain tissue samples, and the mutation types in tissue samples obtained by surgeons may be quite different from those in ctDNA because of tumor heterogeneity. The integration of ctDNA genomics and epigenetics in patients with colorectal cancer, as demonstrated by Parikh et al. [60], could be used for the evaluation of recurrence and prognosis of patients using plasma-only samples and for the realization of longitudinal disease monitoring. Total neoadjuvant therapy is an effective treatment for patients with locally advanced rectal cancer. In the GEMCAD 1402 multicenter clinical trial, Joana et al. conducted regular follow-ups of 180 patients with rectal cancer before and after surgery. The results showed that the preoperative ctDNA level could predict the response of patients to total neoadjuvant therapy and the recurrence and survival of patients. In addition, nine patients with preoperative ctDNA had disease metastases, seven had single organ metastases, and two had multiple organ metastases. The sensitivity and specificity of preoperative ctDNA for liver metastases were high (75% and 90%, respectively). However, it was difficult to detect ctDNA before an operation in patients with lung and peritoneal recurrence, indicating that ctDNA is related to the recurrence site of patients. This conclusion needs to be further verified in larger clinical samples. In colorectal cancer, being ctDNA-positive indicated that the effect of tumor recurrence was obvious. A total of 125 patients received ctDNA detection before surgery, and 122 patients were ctDNA-positive. After surgery, 14 of the 16 patients with recurrence were ctDNA-positive. After receiving surgery for 30 days, the recurrence rate of ctDNA-positive patients was significantly higher than that of ctDNA-negative patients. Not only surgical resection showed this phenomenon, but also patients who received neoadjuvant chemotherapy. All seven ctDNA-positive patients relapsed after neoadjuvant chemotherapy [61]. A small amount of residual tumor cells in the body is called minimal residual disease (MRD). In Loupakis‘ paper, ctDNA-based MRD was significantly negatively correlated with disease-free survival in patients with metastatic colorectal cancer (p < 0.001), suggesting it could be a prognostic factor for metastatic colorectal cancer [62]. In addition, ctDNA combined with the biomarker CEA also has a good prognostic effect [63]. In addition, the role of ctDNA in MRD and predicting the recurrence of melanoma [64][65], lung cancer [66], localized colon cancer [67], pancreatic ductal adenocarcinoma [68], and other cancers have been gradually confirmed.

7. ctDNA and Clinical Medication Guidance

Drug therapy, whether chemotherapy or immunotherapy, is an individualized choice. After a period of drug treatment, some patients will develop drug resistance. Through ctDNA detection, patients can receive individualized medication and choose more favorable drugs for treatment. The therapeutic effect of adjuvant chemotherapy on patients with stage III colon cancer is obvious, but the clinical benefits of adjuvant chemotherapy for patients with stage II cancer with ineffective surgical treatment are not clear. A randomized trial, called the circulating tumor DNA Analysis Informing adjuvant Chemotherapy in Stage II Colon Cancer (DYNAMIC), was designed to evaluate whether a method guided by ctDNA could reduce the frequency of adjuvant treatment use without compromising the risk of recurrence [69]. The trial, including 302 patients for ctDNA-guided management and 153 patients for standard management, showed that ctDNA detection could reduce the use of adjuvant therapy for patients with stage II colon cancer, when compared to standard management, and did not imperil recurrence-free survival. Therefore, ctDNA testing can predict whether patients with stage II colon cancer can benefit from adjuvant chemotherapy, thereby reducing psychological and physiological injuries and unnecessary medical expenses for patients. In an exploratory analysis evaluating the overall survival of patients with HR+/HER2+ advanced breast cancer (ABC) treated with CDK4/6 inhibitors, PALOMA-3, patients without TP53/PIK3CA/ESR2 mutations in ctDNA showed better overall survival and progression-free survival when treated with palbociclib plus fulvestrant, indicating that a driver gene mutation in ctDNA can support the predictive value for clinical medical management [70]. Lu et al. [71] constructed a ctDNA sequencing-based tumor mutation (TMI) model combined with blood tumor mutation burden (bTMB) in ctDNA. The sensitive blood tumor mutation burden (sbTMB) and susceptibility score (UMS) were used to evaluate whether patients with NSCLC can benefit from the anti-angiogenic agent anlotinib, the chemotherapeutic docetaxel, or the immune checkpoint inhibitor atezolizumab. The model included many influencing factors, such as: (a) multi-level gene mutations, (b) clinical characteristics (including the pathological type, driver gene status, number of metastases, sex, and smoking history), and (c) clinical effect comparisons by chemical immunotherapy or immunotherapy between TMI and bTMB. In addition, TMI could effectively predict the response to docetaxel or atezolizumab in patients with NSCLC, which could be considered an ideal biomarker. Moreover, patients with low TMI who received atezolizumab treatment generally showed improved overall survival. According to the principle of precision therapy, accurate drug selection can reduce the wastage of medical resources and avoid the possibility of ineffective treatment for patients. TMI derived from ctDNA can achieve this goal, and this method cannot rigidly adhere to NSCLC. The construction of corresponding TMI models in various cancers can be universal. While an anti-EGFR monoclonal antibody was used to treat patients with RAS wild-type metastatic colorectal cancer, the occurrence of drug-resistant mutations, such as in the RAS, BRAF, and EGFR genes, after a period of treatment would greatly reduce the therapeutic effect in patients, and these mutations could be detected in patients’ peripheral blood. In this regard, an open-label, single-arm phase II clinical trial, named CHRONOS [72], was first proposed to detect resistant mutations in patients receiving the EGFR monoclonal antibody panitumumab, thereby guiding precise clinical medications to obviate toxic and invalid treatments. In the course of panitumumab treatment in 52 patients, ddPCR was used to monitor the ctDNA mutation panel composed of KRAS, BRAF, and EGFR extracellular domain (ECD). There was at least one drug-resistant mutation in 16 patients with poor treatment efficacy, suggesting that the emergence of drug-resistant mutations is related to panitumumab failure. Based on the “zero mutation ctDNA triage” principle proposed by the author, 36 patients with no ctDNA mutations were continuously treated with panitumumab in the trial. The detection of ctDNA could predict the treatment response of patients, and the treatment time provided by ctDNA was more personalized and accurate than that provided in advance. Briefly, the detection of a ctDNA mutation in the CHRONOS trial enables the maximization of the therapeutic effect of panitumumab treatment for patients with metastatic colorectal cancer, avoids adverse reactions with time, and provides more accurate treatment strategies for patients with ctDNA mutations.
In the phase II B-F1RST trial that evaluated whether bTMB could be a novel biomarker for locally advanced or metastatic stage III B–IVB NSCLC treated with atezolizumab, the objective response rate of intent-to-treat patients was altered as the threshold value of bTMB. Finally, the trial report demonstrated that bTMB showed a positive correlation with longer overall survival, indicating that bTMB could be another predictive biomarker for a patient’s immunotherapy [73].

References

  1. Heitzer, E.; Haque, I.S.; Roberts, C.E.S.; Speicher, M.R. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat. Rev. Genet. 2019, 20, 71–88.
  2. Siravegna, G.; Marsoni, S.; Siena, S.; Bardelli, A. Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 2017, 14, 531–548.
  3. Zhou, B.; Xu, J.-W.; Cheng, Y.-G.; Gao, J.-Y.; Hu, S.-Y.; Wang, L.; Zhan, H.-X. Early detection of pancreatic cancer: Where are we now and where are we going? Int. J. Cancer 2017, 141, 231–241.
  4. Goodman, C.R.; Seagle, B.L.; Friedl, T.W.P.; Rack, B.; Lato, K.; Fink, V.; Cristofanilli, M.; Donnelly, E.D.; Janni, W.; Shahabi, S.; et al. Association of Circulating Tumor Cell Status with Benefit of Radiotherapy and Survival in Early-Stage Breast Cancer. JAMA Oncol. 2018, 4, e180163.
  5. Olmos, D.; Arkenau, H.T.; Ang, J.E.; Ledaki, I.; Attard, G.; Carden, C.P.; Reid, A.H.; A’Hern, R.; Fong, P.C.; Oomen, N.B.; et al. Circulating tumour cell (CTC) counts as intermediate end points in castration-resistant prostate cancer (CRPC): A single-centre experience. Ann. Oncol. 2009, 20, 27–33.
  6. Cheung, K.J.; Ewald, A.J. A collective route to metastasis: Seeding by tumor cell clusters. Science 2016, 352, 167–169.
  7. Aceto, N.; Bardia, A.; Miyamoto, D.T.; Donaldson, M.C.; Wittner, B.S.; Spencer, J.A.; Yu, M.; Pely, A.; Engstrom, A.; Zhu, H.; et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 2014, 158, 1110–1122.
  8. Li, D.; Wang, Y.; Li, C.; Wang, Q.; Sun, B.; Zhang, H.; He, Z.; Sun, J. Cancer-specific calcium nanoregulator suppressing the generation and circulation of circulating tumor cell clusters for enhanced anti-metastasis combinational chemotherapy. Acta Pharm. Sin. B 2021, 11, 3262–3271.
  9. Larson, M.H.; Pan, W.; Kim, H.J.; Mauntz, R.E.; Stuart, S.M.; Pimentel, M.; Zhou, Y.; Knudsgaard, P.; Demas, V.; Aravanis, A.M.; et al. A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection. Nat. Commun. 2021, 12, 2357.
  10. Ma, M.; Zhu, H.; Zhang, C.; Sun, X.; Gao, X.; Chen, G. “Liquid biopsy”-ctDNA detection with great potential and challenges. Ann. Transl. Med. 2015, 3, 235.
  11. Chen, W.; Yan, H.; Li, X.; Ge, K.; Wu, J. Circulating tumor DNA detection and its application status in gastric cancer: A narrative review. Transl. Cancer Res. 2021, 10, 529–536.
  12. Vandekerkhove, G.; Lavoie, J.M.; Annala, M.; Murtha, A.J.; Sundahl, N.; Walz, S.; Sano, T.; Taavitsainen, S.; Ritch, E.; Fazli, L.; et al. Plasma ctDNA is a tumor tissue surrogate and enables clinical-genomic stratification of metastatic bladder cancer. Nat. Commun. 2021, 12, 184.
  13. Magbanua, M.J.M.; Swigart, L.B.; Wu, H.T.; Hirst, G.L.; Yau, C.; Wolf, D.M.; Tin, A.; Salari, R.; Shchegrova, S.; Pawar, H.; et al. Circulating tumor DNA in neoadjuvant-treated breast cancer reflects response and survival. Ann. Oncol. 2021, 32, 229–239.
  14. Zhang, C.; Chen, Z.; Chong, X.; Chen, Y.; Wang, Z.; Yu, R.; Sun, T.; Chen, X.; Shao, Y.; Zhang, X.; et al. Clinical implications of plasma ctDNA features and dynamics in gastric cancer treated with HER2-targeted therapies. Clin. Transl. Med. 2020, 10, e254.
  15. Mandel, P.M.P. Nuclear acids in human blood plasma. C. R. Seances La Soc. Biol. Ses. Fil. 1948, 3–4, 241–243.
  16. Jahr, S.; Hentze, H.; Englisch, S.; Hardt, D.; Fackelmayer, F.O.; Hesch, R.D.; Knippers, R. DNA fragments in the blood plasma of cancer patients: Quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 2001, 61, 1659–1665.
  17. Allenson, K.; Castillo, J.; San Lucas, F.A.; Scelo, G.; Kim, D.U.; Bernard, V.; Davis, G.; Kumar, T.; Katz, M.; Overman, M.J.; et al. High prevalence of mutant KRAS in circulating exosome-derived DNA from early-stage pancreatic cancer patients. Ann. Oncol. 2017, 28, 741–747.
  18. Hause, R.J.; Pritchard, C.C.; Shendure, J.; Salipante, S.J. Classification and characterization of microsatellite instability across 18 cancer types. Nat. Med. 2016, 22, 1342–1350.
  19. Mayrhofer, M.; De Laere, B.; Whitington, T.; Van Oyen, P.; Ghysel, C.; Ampe, J.; Ost, P.; Demey, W.; Hoekx, L.; Schrijvers, D.; et al. Cell-free DNA profiling of metastatic prostate cancer reveals microsatellite instability, structural rearrangements and clonal hematopoiesis. Genome Med. 2018, 10, 85.
  20. Kasi, P.M.; Budde, G.; Krainock, M.; Aushev, V.N.; Koyen Malashevich, A.; Malhotra, M.; Olshan, P.; Billings, P.R.; Aleshin, A. Circulating tumor DNA (ctDNA) serial analysis during progression on PD-1 blockade and later CTLA-4 rescue in patients with mismatch repair deficient metastatic colorectal cancer. J. Immunother. Cancer 2022, 10, e003312.
  21. Han, X.; Zhang, S.; Zhou, D.C.; Wang, D.; He, X.; Yuan, D.; Li, R.; He, J.; Duan, X.; Wendl, M.C.; et al. MSIsensor-ct: Microsatellite instability detection using cfDNA sequencing data. Brief. Bioinform. 2021, 22, bbaa402.
  22. Steele, C.D.; Abbasi, A.; Islam, S.M.A.; Bowes, A.L.; Khandekar, A.; Haase, K.; Hames-Fathi, S.; Ajayi, D.; Verfaillie, A.; Dhami, P.; et al. Signatures of copy number alterations in human cancer. Nature 2022, 606, 984–991.
  23. Sokol, E.S.; Feng, Y.X.; Jin, D.X.; Basudan, A.; Lee, A.V.; Atkinson, J.M.; Chen, J.; Stephens, P.J.; Frampton, G.M.; Gupta, P.B.; et al. Loss of function of NF1 is a mechanism of acquired resistance to endocrine therapy in lobular breast cancer. Ann. Oncol. 2019, 30, 115–123.
  24. Siravegna, G.; Sartore-Bianchi, A.; Mussolin, B.; Cassingena, A.; Amatu, A.; Novara, L.; Buscarino, M.; Corti, G.; Crisafulli, G.; Bartolini, A.; et al. Tracking a CAD-ALK gene rearrangement in urine and blood of a colorectal cancer patient treated with an ALK inhibitor. Ann. Oncol. 2017, 28, 1302–1308.
  25. Van Loo, K.M.J.; Carvill, G.L.; Becker, A.J.; Conboy, K.; Goldman, A.M.; Kobow, K.; Lopes-Cendes, I.; Reid, C.A.; van Vliet, E.A.; Henshall, D.C. Epigenetic genes and epilepsy—Emerging mechanisms and clinical applications. Nat. Rev. Neurol. 2022, 18, 530–543.
  26. Jia, S.; Xie, L.; Li, L.; Qian, Y.; Wang, J.; Wang, S.; Zhang, W.; Qian, B. Values of liquid biopsy in early detection of cancer: Results from meta-analysis. Expert Rev. Mol. Diagn. 2021, 21, 417–427.
  27. Luo, H.; Zhao, Q.; Wei, W.; Zheng, L.; Yi, S.; Li, G.; Wang, W.; Sheng, H.; Pu, H.; Mo, H.; et al. Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer. Sci. Transl. Med. 2020, 12, eaax7533.
  28. Leung, M.; Freidin, M.B.; Freydina, D.V.; Von Crease, C.; De Sousa, P.; Barbosa, M.T.; Nicholson, A.G.; Lim, E. Blood-based circulating tumor DNA mutations as a diagnostic and prognostic biomarker for lung cancer. Cancer 2020, 126, 1804–1809.
  29. Liang, W.; Liu, D.; Li, M.; Wang, W.; Qin, Z.; Zhang, J.; Zhang, Y.; Hu, Y.; Bao, H.; Xiang, Y.; et al. Evaluating the diagnostic accuracy of a ctDNA methylation classifier for incidental lung nodules: Protocol for a prospective, observational, and multicenter clinical trial of 10,560 cases. Transl. Lung Cancer Res. 2020, 9, 2016–2026.
  30. Wang, Z.Y.; Ding, X.Q.; Zhu, H.; Wang, R.X.; Pan, X.R.; Tong, J.H. KRAS Mutant Allele Fraction in Circulating Cell-Free DNA Correlates With Clinical Stage in Pancreatic Cancer Patients. Front. Oncol. 2019, 9, 1295.
  31. Hao, Y.; Yang, W.; Zheng, W.; Chen, X.; Wang, H.; Zhao, L.; Xu, J.; Guo, X. Tumor elastography and its association with cell-free tumor DNA in the plasma of breast tumor patients: A pilot study. Quant. Imaging Med. Surg. 2021, 11, 3518–3534.
  32. Cohen, J.D.; Li, L.; Wang, Y.; Thoburn, C.; Afsari, B.; Danilova, L.; Douville, C.; Javed, A.A.; Wong, F.; Mattox, A.; et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 2018, 359, 926–930.
  33. Lennon, A.M.; Buchanan, A.H.; Kinde, I.; Warren, A.; Honushefsky, A.; Cohain, A.T.; Ledbetter, D.H.; Sanfilippo, F.; Sheridan, K.; Rosica, D.; et al. Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention. Science 2020, 369, eabb9601.
  34. Chen, X.; Gole, J.; Gore, A.; He, Q.; Lu, M.; Min, J.; Yuan, Z.; Yang, X.; Jiang, Y.; Zhang, T.; et al. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat. Commun. 2020, 11, 3475.
  35. Liu, M.C.; Oxnard, G.R.; Klein, E.A.; Swanton, C.; Seiden, M.V.; Consortium, C. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 2020, 31, 745–759.
  36. Lam, W.K.J.; Chan, K.C.A.; Lo, Y.M.D. Plasma Epstein-Barr virus DNA as an archetypal circulating tumour DNA marker. J. Pathol. 2019, 247, 641–649.
  37. Lam, W.K.J.; Jiang, P.; Chan, K.C.A.; Cheng, S.H.; Zhang, H.; Peng, W.; Tse, O.Y.O.; Tong, Y.K.; Gai, W.; Zee, B.C.Y.; et al. Sequencing-based counting and size profiling of plasma Epstein-Barr virus DNA enhance population screening of nasopharyngeal carcinoma. Proc. Natl. Acad. Sci. USA 2018, 115, E5115–E5124.
  38. Lo, Y.M.; Chan, A.T.; Chan, L.Y.; Leung, S.F.; Lam, C.W.; Huang, D.P.; Johnson, P.J. Molecular prognostication of nasopharyngeal carcinoma by quantitative analysis of circulating Epstein-Barr virus DNA. Cancer Res. 2000, 60, 6878–6881.
  39. Lv, J.; Wu, C.; Li, J.; Chen, F.; He, S.; He, Q.; Zhou, G.; Ma, J.; Sun, Y.; Wei, D.; et al. Improving on-treatment risk stratification of cancer patients with refined response classification and integration of circulating tumor DNA kinetics. BMC Med. 2022, 20, 268.
  40. Leung, S.-F.; Lo, Y.M.D.; Chan, A.T.C.; To, K.-F.; To, E.; Chan, L.Y.S.; Zee, B.; Huang, D.P.; Johnson, P.J. Disparity of sensitivities in detection of radiation-naïve and postirradiation recurrent nasopharyngeal carcinoma of the undifferentiated type by quantitative analysis of circulating Epstein-Barr virus DNA1,2. Clin. Cancer Res. 2003, 9, 3431–3434.
  41. Sumanasuriya, S.; Seed, G.; Parr, H.; Christova, R.; Pope, L.; Bertan, C.; Bianchini, D.; Rescigno, P.; Figueiredo, I.; Goodall, J.; et al. Elucidating Prostate Cancer Behaviour During Treatment via Low-pass Whole-genome Sequencing of Circulating Tumour DNA. Eur. Urol. 2021, 80, 243–253.
  42. Fitzpatrick, A.; Iravani, M.; Mills, A.; Childs, L.; Alaguthurai, T.; Clifford, A.; Garcia-Murillas, I.; Van Laere, S.; Dirix, L.; Harries, M.; et al. Assessing CSF ctDNA to Improve Diagnostic Accuracy and Therapeutic Monitoring in Breast Cancer Leptomeningeal Metastasis. Clin. Cancer Res. 2022, 28, 1180–1191.
  43. Tserpeli, V.; Stergiopoulou, D.; Londra, D.; Giannopoulou, L.; Buderath, P.; Balgkouranidou, I.; Xenidis, N.; Grech, C.; Obermayr, E.; Zeillinger, R.; et al. Prognostic Significance of SLFN11 Methylation in Plasma Cell-Free DNA in Advanced High-Grade Serous Ovarian Cancer. Cancers 2021, 14, 4.
  44. Lu, Y.; Li, L. The Prognostic Value of Circulating Tumor DNA in Ovarian Cancer: A Meta-Analysis. Technol. Cancer Res. Treat. 2021, 20, 15330338211043784.
  45. Yi, Z.; Ma, F.; Rong, G.; Liu, B.; Guan, Y.; Li, J.; Sun, X.; Wang, W.; Guan, X.; Mo, H.; et al. The molecular tumor burden index as a response evaluation criterion in breast cancer. Signal. Transduct. Target. Ther. 2021, 6, 251.
  46. Boons, G.; Vandamme, T.; Marien, L.; Lybaert, W.; Roeyen, G.; Rondou, T.; Papadimitriou, K.; Janssens, K.; Op de Beeck, B.; Simoens, M.; et al. Longitudinal Copy-Number Alteration Analysis in Plasma Cell-Free DNA of Neuroendocrine Neoplasms is a Novel Specific Biomarker for Diagnosis, Prognosis, and Follow-up. Clin. Cancer Res. 2022, 28, 338–349.
  47. Reichert, Z.R.; Morgan, T.M.; Li, G.; Castellanos, E.; Snow, T.; Dall’Olio, F.G.; Madison, R.W.; Fine, A.D.; Oxnard, G.R.; Graf, R.P.; et al. Prognostic value of plasma circulating tumor DNA fraction across four common cancer types: A real-world outcomes study. Ann Oncol 2022.
  48. Mo, S.; Dai, W.; Wang, H.; Lan, X.; Ma, C.; Su, Z.; Xiang, W.; Han, L.; Luo, W.; Zhang, L.; et al. Early detection and prognosis prediction for colorectal cancer by circulating tumour DNA methylation haplotypes: A multicentre cohort study. EClinicalMedicine 2023, 55, 101717.
  49. Goldhirsch, A.; Wood, W.C.; Coates, A.S.; Gelber, R.D.; Thürlimann, B.; Senn, H.J. Strategies for subtypes-dealing with the diversity of breast cancer: Highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann. Oncol. 2011, 22, 1736–1747.
  50. Shi, J.; Wang, Z.; Zhang, J.; Xu, Y.; Xiao, X.; Quan, X.; Bai, Y.; Yang, X.; Ming, Z.; Guo, X.; et al. Genomic Landscape and Tumor Mutational Burden Determination of Circulating Tumor DNA in Over 5,000 Chinese Patients with Lung Cancer. Clin. Cancer Res. 2021, 27, 6184–6196.
  51. Gay, C.M.; Stewart, C.A.; Park, E.M.; Diao, L.; Groves, S.M.; Heeke, S.; Nabet, B.Y.; Fujimoto, J.; Solis, L.M.; Lu, W.; et al. Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities. Cancer Cell 2021, 39, 346–360.e7.
  52. Rudin, C.M.; Brambilla, E.; Faivre-Finn, C.; Sage, J. Small-cell lung cancer. Nat. Rev. Dis. Prim. 2021, 7, 3.
  53. Baine, M.K.; Hsieh, M.-S.; Lai, W.V.; Egger, J.V.; Jungbluth, A.A.; Daneshbod, Y.; Beras, A.; Spencer, R.; Lopardo, J.; Bodd, F.; et al. SCLC Subtypes Defined by ASCL1, NEUROD1, POU2F3, and YAP1: A Comprehensive Immunohistochemical and Histopathologic Characterization. J. Thorac. Oncol. 2020, 15, 1823–1835.
  54. Chemi, F.; Pearce, S.P.; Clipson, A.; Hill, S.M.; Conway, A.M.; Richardson, S.A.; Kamieniecka, K.; Caeser, R.; White, D.J.; Mohan, S.; et al. cfDNA methylome profiling for detection and subtyping of small cell lung cancers. Nat. Cancer 2022, 3, 1260–1270.
  55. Vandekerkhove, G.; Struss, W.J.; Annala, M.; Kallio, H.M.L.; Khalaf, D.; Warner, E.W.; Herberts, C.; Ritch, E.; Beja, K.; Loktionova, Y.; et al. Circulating Tumor DNA Abundance and Potential Utility in De Novo Metastatic Prostate Cancer. Eur. Urol. 2019, 75, 667–675.
  56. Gao, Y.; Zhao, H.; An, K.; Liu, Z.; Hai, L.; Li, R.; Zhou, Y.; Zhao, W.; Jia, Y.; Wu, N.; et al. Whole-genome bisulfite sequencing analysis of circulating tumour DNA for the detection and molecular classification of cancer. Clin. Transl. Med. 2022, 12, e1014.
  57. Leal, A.; van Grieken, N.C.T.; Palsgrove, D.N.; Phallen, J.; Medina, J.E.; Hruban, C.; Broeckaert, M.A.M.; Anagnostou, V.; Adleff, V.; Bruhm, D.C.; et al. White blood cell and cell-free DNA analyses for detection of residual disease in gastric cancer. Nat. Commun. 2020, 11, 525.
  58. Qiu, B.; Guo, W.; Zhang, F.; Lv, F.; Ji, Y.; Peng, Y.; Chen, X.; Bao, H.; Xu, Y.; Shao, Y.; et al. Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC. Nat. Commun. 2021, 12, 6770.
  59. Xia, Y.; Tang, W.; Qian, X.; Li, X.; Cheng, F.; Wang, K.; Zhang, F.; Zhang, C.; Li, D.; Song, J.; et al. Efficacy and safety of camrelizumab plus apatinib during the perioperative period in resectable hepatocellular carcinoma: A single-arm, open label, phase II clinical trial. J. Immunother. Cancer 2022, 10, e004656.
  60. Parikh, A.R.; Van Seventer, E.E.; Siravegna, G.; Hartwig, A.V.; Jaimovich, A.; He, Y.; Kanter, K.; Fish, M.G.; Fosbenner, K.D.; Miao, B.; et al. Minimal Residual Disease Detection using a Plasma-only Circulating Tumor DNA Assay in Patients with Colorectal Cancer. Clin. Cancer Res. 2021, 27, 5586–5594.
  61. Reinert, T.; Henriksen, T.V.; Christensen, E.; Sharma, S.; Salari, R.; Sethi, H.; Knudsen, M.; Nordentoft, I.; Wu, H.T.; Tin, A.S.; et al. Analysis of Plasma Cell-Free DNA by Ultradeep Sequencing in Patients with Stages I to III Colorectal Cancer. JAMA Oncol. 2019, 5, 1124–1131.
  62. Loupakis, F.; Sharma, S.; Derouazi, M.; Murgioni, S.; Biason, P.; Rizzato, M.D.; Rasola, C.; Renner, D.; Shchegrova, S.; Koyen Malashevich, A.; et al. Detection of Molecular Residual Disease Using Personalized Circulating Tumor DNA Assay in Patients With Colorectal Cancer Undergoing Resection of Metastases. JCO Precis. Oncol. 2021, 5, 1166–1177.
  63. Polivka, J.; Windrichova, J.; Pesta, M.; Houfkova, K.; Rezackova, H.; Macanova, T.; Vycital, O.; Kucera, R.; Slouka, D.; Topolcan, O. The Level of Preoperative Plasma KRAS Mutations and CEA Predict Survival of Patients Undergoing Surgery for Colorectal Cancer Liver Metastases. Cancers 2020, 12, 2434.
  64. Tan, L.; Sandhu, S.; Lee, R.J.; Li, J.; Callahan, J.; Ftouni, S.; Dhomen, N.; Middlehurst, P.; Wallace, A.; Raleigh, J.; et al. Prediction and monitoring of relapse in stage III melanoma using circulating tumor DNA. Ann. Oncol. 2019, 30, 804–814.
  65. Gouda, M.A.; Polivka, J.; Huang, H.J.; Treskova, I.; Pivovarcikova, K.; Fikrle, T.; Woznica, V.; Dustin, D.J.; Call, S.G.; Meric-Bernstam, F.; et al. Ultrasensitive detection of BRAF mutations in circulating tumor DNA of non-metastatic melanoma. ESMO Open 2022, 7, 100357.
  66. Gale, D.; Heider, K.; Ruiz-Valdepenas, A.; Hackinger, S.; Perry, M.; Marsico, G.; Rundell, V.; Wulff, J.; Sharma, G.; Knock, H.; et al. Residual ctDNA after treatment predicts early relapse in patients with early-stage non-small cell lung cancer. Ann. Oncol. 2022, 33, 500–510.
  67. Tarazona, N.; Gimeno-Valiente, F.; Gambardella, V.; Zuniga, S.; Rentero-Garrido, P.; Huerta, M.; Rosello, S.; Martinez-Ciarpaglini, C.; Carbonell-Asins, J.A.; Carrasco, F.; et al. Targeted next-generation sequencing of circulating-tumor DNA for tracking minimal residual disease in localized colon cancer. Ann. Oncol. 2019, 30, 1804–1812.
  68. Lee, J.S.; Han, Y.; Yun, W.G.; Kwon, W.; Kim, H.; Jeong, H.; Seo, M.S.; Park, Y.; Cho, S.I.; Kim, H.; et al. Parallel Analysis of Pre- and Postoperative Circulating Tumor DNA and Matched Tumor Tissues in Resectable Pancreatic Ductal Adenocarcinoma: A Prospective Cohort Study. Clin. Chem. 2022.
  69. Tie, J.; Cohen, J.D.; Lahouel, K.; Lo, S.N.; Wang, Y.; Kosmider, S.; Wong, R.; Shapiro, J.; Lee, M.; Harris, S.; et al. Circulating Tumor DNA Analysis Guiding Adjuvant Therapy in Stage II Colon Cancer. N. Engl. J. Med. 2022, 386, 2261–2272.
  70. Cristofanilli, M.; Rugo, H.S.; Im, S.A.; Slamon, D.J.; Harbeck, N.; Bondarenko, I.; Masuda, N.; Colleoni, M.; DeMichele, A.; Loi, S.; et al. Overall Survival with Palbociclib and Fulvestrant in Women with HR+/HER2− ABC: Updated Exploratory Analyses of PALOMA-3, a Double-Blind, Phase 3 Randomized Study. Clin. Cancer Res. 2022, 28, 3433–3442.
  71. Lu, J.; Wu, J.; Lou, Y.; Shi, Q.; Xu, J.; Zhang, L.; Nie, W.; Qian, J.; Wang, Y.; Zhang, Y.; et al. Blood-based tumour mutation index act as prognostic predictor for immunotherapy and chemotherapy in non-small cell lung cancer patients. Biomark. Res. 2022, 10, 55.
  72. Sartore-Bianchi, A.; Pietrantonio, F.; Lonardi, S.; Mussolin, B.; Rua, F.; Crisafulli, G.; Bartolini, A.; Fenocchio, E.; Amatu, A.; Manca, P.; et al. Circulating tumor DNA to guide rechallenge with panitumumab in metastatic colorectal cancer: The phase 2 CHRONOS trial. Nat. Med. 2022, 28, 1612–1618.
  73. Kim, E.S.; Velcheti, V.; Mekhail, T.; Yun, C.; Shagan, S.M.; Hu, S.; Chae, Y.K.; Leal, T.A.; Dowell, J.E.; Tsai, M.L.; et al. Blood-based tumor mutational burden as a biomarker for atezolizumab in non-small cell lung cancer: The phase 2 B-F1RST trial. Nat. Med. 2022, 28, 939–945.
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Xiaosha Wen
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Huijie Pu
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Quan Liu
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Zifen Guo
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Dixian Luo
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