Somatic mutations with low variant allele frequencies offer a highly sensitive lens for detecting cancer relapse driven by diverse causes, including clonal evolution and therapy resistance. Advances in next-generation sequencing have enabled robust subclonal variant identification that typically fall below conventional detection limits, supporting a comprehensive understanding of individual molecular profiles that can lead to relapse. These low-level alterations frequently emerge before clinical or radiological relapse and can inform response-adaptive treatment decisions. This review integrates the current biological and technical insights into low-frequency mutations and evaluates their emerging roles in tumor relapse management and precision oncology.
cancer relapselow-VAFnext-generation sequencingsomatic mutationultra-deep sequencingThe author(s) declared that financial support was received for this work and/or its publication. This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), RS-2025-23323982. This study was also supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea (RS-2023-00219399).section-at-acceptanceCancer GeneticsIntroduction
Tumor relapse poses a persistent challenge to cancer management and often contributes to poor long-term outcomes (1, 2). Despite advances in tumor treatment, methods for predicting and preventing relapse remain relatively unexplored. Cancer relapse is a complex genomic phenomenon driven by various factors, such as residual disease and malignant mutation-containing clones (3, 4).
Despite diverse treatment options for eliminating tumors, a small number of cancer cells remain unremoved and relapse is induced (5). Minimal residual disease (MRD) refers to these small clones, and is widely used in cancer management. Achieving MRD negativity is crucial for the overall cancer treatment course (6, 7).
Circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are critical indicators of cancer relapse (8). CTCs are viable cancer cells released into the bloodstream and may contribute to the recurrence of cancer. In contrast, ctDNA comprises tumor-derived DNA fragments released from cancer cells and serves primarily as a biomarker reflecting tumor burden (9, 10).
Somatic mutations, defined as post-zygotic mutations in individual cells at any point in life, are among the most common causes of cancer (11, 12). Somatic mutations can exist in normal cells in a benign state, but can also accumulate with natural aging and develop into cancer (12–14).
The variant allele frequency (VAF) concept has been introduced as a tool for understanding somatic mutations in cancer using next-generation sequencing (NGS) (15, 16). The VAF is the proportion of sequencing reads supporting a given variant relative to the total number of reads covering the allele.
In oncology, a high VAF generally indicates a dominant clone or large tumor burden, whereas low VAF mutations reflect subclone populations, MRD, or early relapse signals (17). NGS enables highly sensitive and accurate measurement of VAF across a wide genomic range. VAF quantification using NGS provides critical insights into cancer diagnosis and treatment (15).
Furthermore, low VAF mutations, often defined as variants with VAFs of < 5% or 10%, have a unique position in cancer genomics. Historically, these alterations have often been regarded as sequencing artifacts or clinically irrelevant noise, primarily because of sensitivity-related limitations of the technology (18, 19).
However, developing precise bioinformatics pipelines has revolutionized mutation analysis and revealed that low VAF mutations can represent small cancer cell populations that are often undetected by traditional assays (20). This has transformed low-VAF mutations into critical components of cancer relapse management. Therefore, frameworks that distinguish between clinically relevant mutations and technical noise are required.
This review aims to consolidate the current knowledge on the role of low-frequency mutations in cancer relapse diagnosis and treatment. By examining studies that associate VAF with cancer relapse, we explore the clinical significance of molecular monitoring systems and low-VAF mutations in managing tumor relapse (Figure 1).
Clinical utility of detecting low-VAF mutations in post-treatment MRD via liquid biopsy. Posttreatment MRD is a potential source of metastasis mediated by CTCs and ctDNA. Liquid biopsy enables the isolation and analysis of these biomarkers to detect low VAF mutations using advanced sequencing technologies. This genomic profile provides insights into diagnosis and therapeutic decision-making.
Infographic illustrating the process of detecting tumor relapse via minimal residual disease (MRD), beginning with liquid biopsy sampling of circulating tumor DNA (ctDNA), followed by duplex and ultra deep sequencing to identify low variant allele frequency (VAF) mutations, and concluding with diagnosis using bioinformatics to inform clinical decisions.Relapse statistics of 11 major cancer types
Cancer relapse is a considerable challenge in oncology. Despite enhanced treatment methods, including surgery, chemotherapy, radiotherapy, and immunotherapy, relapses remain unresolved. Patients experiencing recurrence face limited treatment options and adverse effects of therapies, resulting in a diminished quality of life and prognosis (21, 22).
Breast cancer
Breast cancer in women has become the second most diagnosed cancer worldwide, representing the fourth leading cause of cancer-related mortality in 2022. Breast cancer accounts for 11.6% of the total cancer incidence globally, with 2.3 million new cases and 665,684 deaths as of 2022 (23). A large meta-analysis reported that after stopping 5-year adjuvant endocrine therapy, ER-positive early breast cancer continued to recur during years 5–20, ranging from 10% to 41% across different TN groups (24). These late events would correspond with the expansion of small persistent subclones, emphasizing sensitive monitoring for low VAF detection.
Colorectal cancer
In the U.S., 107,320 cases of colon cancer and 46,950 cases of rectal cancer are estimated to occur by 2025 (25). Approximately 903,000 deaths from colorectal cancer occurred worldwide in 2022, ranking third in incidence and second in mortality (23). Colorectal cancer relapse threatens patient survival. A cohort of 2475 colon cancer patients demonstrated that right-sided colon cancer (RCC) (7.35%, CI 6.55–8.25) has a worse mortality rate than left-sided colon cancer (LCC) (5.32%, CI 4.57–6.20) (26). Furthermore, perforated colorectal cancer (PCC) is associated with inferior relapse outcomes compared with non-perforated colorectal cancer (27–29). High relapse risk is mainly attributed to leakage of tumor-related content into the peritoneum (27).
Pancreatic cancer
Pancreatic cancer has the highest mortality rate among all types of cancer. Regardless of tumor stage, the five-year survival rate in the United States is estimated to reach 13% by 2025 (25). An estimated 467,000 deaths occurred from 510,000 incidents worldwide in 2022 (23). The prognosis of pancreatic cancer is the poorest among all types of solid cancers, and selecting patients suitable for surgery is challenging (30, 31). Patients who can undergo curative surgery account for only 15%–20% of all patients with pancreatic cancer (32, 33). Relapse remains common even after surgery or aggressive therapy (32, 33).
Liver cancer
In 2020, liver cancer was the sixth most diagnosed cancer and the third leading cause of cancer-related mortality worldwide in 2020 (25). In 2022, 865,200 new cases and 757,900 deaths are estimated worldwide (23). A meta-analysis of 125 hepatocellular carcinoma studies demonstrated a pooled relapse rate of 17% and a mortality rate after recurrence of 9% (34). In addition, a cohort of hepatoblastoma patients revealed that combined relapse (simultaneous local and distant recurrence) was associated with particularly unfavorable outcomes compared to sole metastatic relapse (35).
Non-small cell lung cancer
In a population study in the U.S., non-small cell lung cancer (NSCLC) accounted for approximately 84% of all lung cancer subtypes between 2010 and 2017, and its prognosis was poor (36). In a study of 775 patients with NSCLC who underwent curative surgery, 133 experienced relapse. The two-year OS of patients who relapsed was 37%, and 83% of patients who underwent limited surgery relapsed within a year (37).
Prostate cancer
Prostate cancer is one of the most frequently diagnosed cancers among men, with over 1,446,680 cases and 396,700 deaths worldwide by 2022 (23). In the United States, prostate cancer-related mortality has steadily declined over the past decades (25). However, prostate cancer relapse is a common phenomenon. According to two population studies, 30% of patients who underwent radical prostatectomy relapsed within 10 years (38, 39).
Blood cancer
The global incidence and mortality of blood cancer have steadily increased over the last 30 years (40). In 2022, approximately 553,000 cases of non-Hodgkin’s lymphoma, 486,700 cases of leukemia, and 187,700 cases of multiple myeloma occurred globally (23). The incidence rates are predicted to increase annually by 1.7% for multiple myeloma, 0.79% for leukemia, and 1.65% for non-Hodgkin’s lymphoma from 2020 to 2030 (41). Relapse occurs frequently after transplantation, leading to a poor prognosis. In a cohort of 1080 patients, 351 relapsed during a four-year follow-up period, with a 19% three-year OS rate after relapse (42).
Stomach cancer
Stomach cancer accounts for 968,350 new cases in 2022 globally, with the fifth highest total cancer incidence and mortality (23). Two studies demonstrated that 42% and 46.5% of patients with stomach cancer who underwent curative surgery relapsed (43, 44). Postoperative relapse occurring within two years of surgery seriously affects patient survival (44).
Kidney cancer
The global incidence of kidney cancer is expected to increase from 160,000 cases in 1990 to 390,000 in 2021 (45). Although the overall mortality rate is relatively lower than that of other cancer types, renal pelvic tumors can be fatal (25, 46). In a study of 143 kidney cancer patients who underwent renal transplantation, 13 patients relapsed, and only 3 of them survived at the end of the follow-up (47). Additionally, a study on renal cell carcinoma demonstrated that 30% of patients experienced relapse after curative treatment (48).
Uterine cancer
Uterine cancer recurrence results in inferior outcomes compared to non-recurrent cases, especially when the time to relapse after surgery is short. A study of 35 endometrial cancer relapse patients demonstrated that the three-year survival rate was 64.9% in patients with one relapse site and 39.2% in those with multiple relapse sites (49, 50). Multi-site relapse can arise through multiple subclones or monoclonal seeding followed by diversification. Additionally, a study of 1503 patients with endometrial cancer presented the histological type, grade, and secondary radical surgery as meaningful factors for post-relapse survival (51).
Brain cancer
According to a 30-year epidemiological study, the global burden of central nervous system cancer has steadily increased from 2,831,075 new cases in 1992 to 3,420,786 in 2021. The number of deaths from brain cancer increased by 80.62% (52). Relapse of cancer related to the brain can be damaging. Central nervous system involvement in mature T- and NK-neoplasms results in a significantly high mortality rates (53). Similarly, glioblastoma, the most frequent form of brain cancer, has a poor prognosis, with a 15-month median OS upon relapse (54).
Relapse rates vary between cancer types, primarily because of differences in tumor biology or specific mutations. Different cancers and subtypes can have distinct genetic characteristics (55, 56). Several tumor subtypes tend to have high mutation burdens, malignant mutations, and relapses (57).
Advances of NGS and oncology applicationNGS overview
NGS development represents a pivotal step in the history of genomics (58). Early sequencing methods, such as Sanger sequencing, provided the foundation for interpreting DNA but had limitations, such as low throughput and time-consuming protocols (59). With completion of the Human Genome Project, the need for fast and scalable sequencing has become evident (60, 61). This demand has driven the emergence of NGS, which has enabled parallel sequencing of massive amounts of data and improved the efficiency of genome analysis (62).
NGS sequences millions of short DNA fragments in parallel, followed by computational alignment to reconstruct the genome (63, 64). This fundamental shift to parallel processing has helped address broader purposes such as whole genome sequencing (WGS), whole exome sequencing (WES), and targeted sequencing (65, 66). These widespread applications have benefitted clinical medicine, where NGS is increasingly used to diagnose diseases, guide treatment, and monitor disease progression (67, 68).
NGS methodology in cancer diagnosis and surveillance
Enhanced NGS technologies are advantageous in oncology, where tumor heterogeneity and mosaicism pose challenges for diagnosis and treatment (62, 69). By capturing mutations across a wide range, NGS has reinforced our understanding of the genetic profiles of patients (67, 70).
For relapse monitoring, NGS assays often adopt either tumor-informed tracking of patient-specific variants or panel-based tests using fixed panels targeting known recurrent alterations (66). While fixed panels often prioritize previously identified mutations, tumor-informed monitoring can find patient-specific variants found at diagnosis, including non-hotspot and private variants, enabling individualized surveillance.
Accumulating evidence indicates that NGS is superior to other molecular assays and nonmolecular methodologies for diagnosis (71). NGS detects cancer mutations at lower frequencies that may be missed using traditional methods, including pathological tests (66, 72). Likewise, a study of 1266 lung cancer cases compared the accuracy of NGS and clinicopathological methods and demonstrated that the latter had lower accuracy than NGS, with one-third of the errors. However, NGS can accurately predict prognosis, contributing to an improved OS (72).
Furthermore, several studies comparing the accuracy of NGS and PCR have presented NGS as a better method for detecting cancer mutations (73, 74). In a study focusing on hotspot mutations in tumors, NGS-based tests detected eight mutations that were not detected using PCR (73). Another study analyzed the ability of NGS to detect EGFR, KRAS, and BRAF mutations and showed that NGS detected seven mutations that PCR could not detect (74).
Ultra-deep sequencing to detect low-frequency mutations
As NGS platforms and methodologies have advanced, the identification of mutations with low VAFs has become reliable (75, 76). For example, the introduction of blocker displacement amplification, which uses qPCR and Sanger sequencing to filter and confirm mutations, achieved a 0.1% limit of detection (LoD) (77). Duplex sequencing labels both strands of a DNA molecule with distinct molecular indices, sequences them independently, and accepts a variant if an identical change is observed in both complementary strands, to suppress artifactual errors (78).
Error-corrected NGS uses consensus sequences selected by multiple rounds of sequencing to minimize errors and unique molecular identifiers to label mutations (79). Hotspot cancer mutations can also be detected using targeted ultra-deep sequencing. A study focusing on hotspot mutations achieved 97.1% sensitivity and 97.9% specificity for detecting BRAF with a LoD of 0.025 (80). Accelerated developments in molecular biology have paved the way for precision medicine in clinical settings, including monitoring and personalized treatment.
Clinical implications of low-frequency mutations in cancer relapseBiological sources of low-frequency mutation
Technical progress has uncovered the burden of low-VAF mutations in tumors, highlighting their potential for cancer relapse prevention and treatment (81, 82). A previous study sequenced 300,000 tumor samples and revealed that 29% of patients retained more than one mutation in 10% of cases, and 16% had mutations in less than 5% of cases (83). Detectability of low-VAF variants is primarily dependent on sample quality and assay sensitivity, with varying clinical severity based on patient context.
Low-VAF mutations can be detected in diverse sources (84, 85). ctDNA is widely used for solid cancers because blood is easily accessible and less invasive than tissue biopsies (86–88). CtDNA can be detected across a broad VAF spectrum that depends on disease burden, tumor fraction, and assay sensitivity, ranging from ultra-low levels to higher, dominant status (89). Therefore, sampling frequency and duration are not standardized across cancer types, highlighting the necessity for tailored strategies.
In addition, CTCs are being increasingly explored as key biomarkers of cancer relapse (90). Single-cell DNA analysis can be used to detect mutations in CTCs and to predict cancer relapse (91). A previous study analyzed CTCs from metastatic breast cancer patients and detected PIK3CA mutations at a frequency of 1% (92).
Mutations in blood cancers can be detected in the bone marrow, peripheral blood, or lymphocyte tissues (93, 94). One study demonstrated that targeted NGS of cell-free DNA (cfDNA) detected several mutations that were not found solely in liquid tissue, usually extracted from the bone marrow (95).
Furthermore, extracellular vesicle DNA (EV-DNA) can be used for mutation profiling (96). An extracellular vesicle (EV) is a particle enclosed in a membrane containing DNA, RNA, and proteins. As EVs play a significant role in the pathological mechanisms of cancer and are stable due to their larger size, they have the potential to be used as a cancer biomarker (96–98). A study comparing the frequencies of tissue DNA and EV-DNA demonstrated that mutations detected by EV-DNA (<5%) had lower VAFs than those detected by tissue DNA (10%–25%). However, the relationship between tissue DNA and EV-DNA can depend on diverse elements, requiring cautious interpretation (99). At present, there is no consensus on which biomarker should be prioritized over others. EV-DNA may provide complementary information in specific cases, but it is not yet established as a universal method for cancer screening.
Application of low-VAF mutations in the diagnosis of cancer relapseEarly diagnosis and prevention of cancer relapse
NGS diagnoses cancer relapse earlier than traditional imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). Advanced molecular approaches, including duplex sequencing and error-corrected NGS, can be used to detect mutations in ctDNA, CTCs, and EV DNA.
In a study that developed a method applying targeted sequencing to phased variants of ctDNA, the lowest VAF was 0.000094% in patients with stage I NSCLC. These mutations are highly predictive of cancer relapse, enabling diagnosis 5–10 months earlier than radiology (100). Another study examining the relationship between MRD and acute myeloid leukemia relapse using error-corrected sequencing demonstrated that MRD positivity predicted recurrence with high sensitivity. Targeted ctDNA sequencing of 29 genes was performed using bone marrow or peripheral blood samples. MRD was detected in 35% of patients using duplex sequencing, and the relapse risk was 8.8 times higher than that in patients who are MRD-negative (101).
Furthermore, longitudinal monitoring can help diagnose early relapses. A previous study analyzed the ctDNA of 130 patients with colorectal cancer throughout treatment and surveillance. Deep sequencing with > 10,000 coverage has been applied, supporting relapse prediction up to 16.5 months earlier than traditional methods (102).
CTCs and EV-DNA play crucial roles in recurrence surveillance and diagnosis (103, 104). One study that applied single-cell sequencing to bulk CTC samples from patients with small-cell lung cancer demonstrated that low-VAF mutations in CTCs corresponded with mutations in the original tumor. For instance, the VAF of a CLCA2 mutation is approximately 4% in bulk CTCs and 7% in primary tumor tissue (105). Another study conducted EV-DNA analysis in early-stage NSCLC patients and captured EGFR mutations in 38 EV-DNA samples, with VAFs ranging from 0.1% to 1.3%, suggesting that EV-DNA is an indicator of cancer relapse (106).
Detection of <italic>de novo</italic> cancer mutations during recurrence
Cancer biomarkers can detect de novo mutations with the potential for relapse, which often occur during therapy or monitoring. Discovering de novo mutations requires high sensitivity as they typically emerge at low frequencies.
A study analyzing cfDNA from NSCLC patients using targeted sequencing revealed de novo oncogenic mutations with high sensitivity and specificity. Driver and resistance mutations were detected in several patients. Most variants, including hotspot mutations such as EGFR and KRAS, were detected at VAFs below 10% (107).
A case report of a 66-year-old breast cancer patient with metastasis to the bone and liver involved de novo mutation analysis of the primary tumor site, the liver metastatic site, and four plasma samples. A fraction of mutations with VAFs below 5% underwent clonal evolution under selective pressure, expanding to dominant fractions ranging from 26-68%. Accordingly, some low-VAF mutations can later be found as dominant clones (108).
Additionally, serial DNA monitoring of CTCs in patients with CRC detected new missense mutations. A patient who received irinotecan and cetuximab treatment developed a de novo SMRCB1 mutation with a CTC frequency of 7.22%. Similarly, another patient developed a PIK3CA mutation at a 7.05% frequency (109).
Cancer relapse risk stratification with high sensitivity
Stratification of cancer relapse risk can help identify high-risk patients who can benefit from intensive monitoring and treatment. Similarly, low-risk patients, who do not require as much screening as high-risk patients, can avoid overtreatment. The accurate measurement of cancer variants can guide tailored treatment adjustments.
A study involving 137 patients with large B-cell lymphoma conducted risk stratification of PET-negative patients using ctDNA. Although classified as a low-risk group based on PET scans, the ctDNA-positive group exhibits a 30-fold increased relapse risk and worse progression-free survival (PFS) (110).
A study conducted WES to monitor MRD in seven high-risk epithelial ovarian cancer patients, five of whom were MRD-positive at baseline, with a median VAF of 2.79%. Two patients were MRD positive after surgery, with VAFs of 0.04% and 0.09%, respectively. Two patients were reclassified in the high-risk group and received additional chemotherapy to prevent relapse (111).
By comparing the effectiveness of risk stratification using clinicopathological markers and ctDNA, risk assessment using clinicopathological methods tended to be less sensitive. However, ctDNA enables sensitive measurements, thus reducing overtreatment by adaptive therapies based on individual genetic profiles (112).
Furthermore, EV-DNA demonstrated higher sensitivity than ctDNA in predicting colorectal cancer recurrence. Measurement of KRAS mutations using EV-DNA showed better prediction than ctDNA and tissue biopsy. Moreover, applying NGS to EV DNA is more effective than simply measuring DNA concentration (113).
Application of low-VAF mutations in relapsed cancer treatment
Low-VAF mutations can indicate therapeutic resistance and assist in early detection before progression. Precise monitoring of subtle mutational changes can provide a basis for personalized treatment. When low-VAF mutations persist or increase, they may signal residual disease, guiding treatment adjustments such as drug switching or extended treatment duration (114). Moreover, low-VAF mutations after primary treatment can inform decisions regarding adjuvant therapy and long-term surveillance.
In a study of NSCLC patients treated with an immune checkpoint blockade, serial ctDNA analysis supported the adjustment of treatment intensity. The median VAF of cancer mutations was 1.87%, ranging from 0.09%–34.7%. Patients without molecular clearance had a substantially shorter PFS than those in the ctDNA-negative group (115).
Sequencing plasma samples from patients with NSCLC receiving PD-1 inhibitors revealed that subtle changes in ctDNA signaled the emergence of immune-escaping clones. Increases in low-frequency variants related to immune escape precede disease progression by months, emphasizing the importance of regular screening (116).
In urothelial bladder cancer, ctDNA profiling enables real-time treatment adjustments by reflecting therapeutic responses. In this cohort, decreased ctDNA levels during chemotherapy predicted pathological downstaging. Among ctDNA-positive patients before or during therapy, ctDNA clearance was associated with complete remission, whereas persistent ctDNA levels implied a higher risk of recurrence (117).
Moreover, a study addressing the efficacy of drug switching monitored patients with advanced breast cancer receiving first-line aromatase inhibitors and palbociclib. Patients with emergent ESR1 mutations were randomized to either continue the current therapy or switch to fulvestrant. The molecular-guided drug switch was shown to improve PFS (median 11.9 vs 5.7 months) (118).
A study that used EV-DNA to detect therapy resistance in patients with NSCLC after first-line EGFR-TKI treatment demonstrated that most patients acquired resistance within 1–2 years, and T790M was the key mutation guiding the switch to osimertinib. Combining cfDNA with EV-DNA improves the sensitivity of detecting therapy-resistant variants (119).
A study on early stage breast cancer demonstrated that deep ctDNA sequencing can guide adjuvant therapy decisions. Using patient-specific multiplex sequencing, baseline ctDNA was detected in 32 patients, with a median VAF of 0.11%. After therapy, patients diagnosed with pathological complete remission showed near clearance, with VAFs < 0.003% (120).
The development of sensitive sequencing to detect low-VAF mutations has enabled precise clinical decision-making across various stages of cancer. Quantifying low-VAF alterations allows for earlier identification of recurrence compared to radiological or pathological methods. This finding also supports proactive interventions and treatments. NGS can guide response-adaptive decisions during cancer treatment and maintenance phases. The integration of sensitive mutation tracking into routine relapse management will lead to a personalized and sensitive approach to the detection and treatment of cancer.
Conclusion
Enhanced capability to detect low-VAF mutations represents a milestone in oncology, moving toward precise and personalized cancer care. Cancer is intrinsically prone to relapse due to its mutational diversity. The advent of ultra-deep sequencing has enabled the identification of low-VAF mutations that were previously missed, opening a new window to understand cancer biology and improving patient prognosis.
The clinical applications of low-VAF mutations span the entire process of cancer relapse management. Sensitive sequencing provides a basis for therapeutic intervention when tumor burden is minimal, often predicting relapse months before imaging and radiology can. In treatment, low-VAF mutations can inform treatment directions, including drug switches and therapy intensity adjustments. Importantly, clinical actions triggered by low-VAF mutation profiling are context-dependent. Some alterations would primarily support risk stratification and intensified surveillance, whereas others may suggest treatment adaptation.
However, several challenges should be addressed before low-VAF mutations are fully utilized in routine practice. Tumor heterogeneity from continuous mutagenesis and clonal evolution hinders the detection of low-frequency mutations (121). This spatial and temporal variation implies that mutations from one site or time point may not reflect the complete disease landscape (122).
Additionally, standardized laboratory protocols are insufficient, causing inconsistent results across laboratories using distinct bioinformatic pipelines. Establishing guidelines would improve inter-laboratory compatibility (123). Standardization in clinical decision-making can also help define when to prioritize one biomarker over another based on specific patient conditions, specimen types, and mutation assessment methods.
The value of low-VAF mutation lies in its potential to transform cancer relapse management into truly personalized care by guiding precise decision-making. These advances will improve cancer survival and prognosis by tailoring strategies based on molecular profiles.
Author contributions
EK: Data curation, Visualization, Writing – original draft. GSR: Supervision, Writing – original draft. SGK: Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
ReferencesMarzagalliMFontanaFRaimondiMLimontaP.
Cancer stem cells—Key players in tumor relapse. Cancers. (2021) 13:376. doi: 10.3390/cancers13030376, PMID: 33498502TufailMJiangC-HLiN.
Tumor dormancy and relapse: understanding the molecular mechanisms of cancer recurrence. Military Med Res. (2025) 12:7. doi: 10.1186/s40779-025-00595-2, PMID: 39934876ChakravarthiBVSKNepalSVaramballyS.
Genomic and epigenomic alterations in cancer. Am J Pathol. (2016) 186:1724–35. doi: 10.1016/j.ajpath.2016.02.023, PMID: 27338107ShlushLIHershkovitzD.
Clonal evolution models of tumor heterogeneity. Am Soc Clin Oncol Educ Book. (2015) 35:e662–5. doi: 10.14694/EdBook_AM.2015.35.e662, PMID: 25993239GreavesMMaleyCC.
Clonal evolution in cancer. Nature. (2012) 481:306–13. doi: 10.1038/nature10762, PMID: 22258609MunshiNCAvet-LoiseauHAndersonKCNeriPPaivaBSamurM.
A large meta-analysis establishes the role of MRD negativity in long-term survival outcomes in patients with multiple myeloma. Blood Adv. (2020) 4:5988–99. doi: 10.1182/bloodadvances.2020002827, PMID: 33284948LiYSolis-RuizJYangFLongNTongCHLacbawanFL.
NGS-defined measurable residual disease (MRD) after initial chemotherapy as a prognostic biomarker for acute myeloid leukemia. Blood Cancer J. (2023) 13:59. doi: 10.1038/s41408-023-00833-7, PMID: 37088803ChelakkotCYangHShinYK.
Relevance of circulating tumor cells as predictive markers for cancer incidence and relapse. Pharmaceuticals. (2022) 15:75. doi: 10.3390/ph15010075, PMID: 35056131UlrichBPradinesAMazièresJGuibertN.
Detection of tumor recurrence via circulating tumor DNA profiling in patients with localized lung cancer: clinical considerations and challenges. Cancers. (2021) 13:3759. doi: 10.3390/cancers13153759, PMID: 34359659MentisA-FAGrivasPDDardiotisERomasNAPapavassiliouAG.
Circulating tumor cells as Trojan Horse for understanding, preventing, and treating cancer: a critical appraisal. Cell Mol Life Sci. (2020) 77:3671–90. doi: 10.1007/s00018-020-03529-4, PMID: 32333084FreedDStevensEPevsnerJ.
Somatic mosaicism in the human genome. Genes. (2014) 5:1064–94. doi: 10.3390/genes5041064, PMID: 25513881LodatoMAVijgJ.
Editorial: Somatic mutations, genome mosaicism and aging. Front Aging. (2023) 3:1115408. doi: 10.3389/fragi.2022.1115408, PMID: 36698749RisquesRAKennedySR.
Aging and the rise of somatic cancer-associated mutations in normal tissues. PloS Genet. (2018) 14:e1007108. doi: 10.1371/journal.pgen.1007108, PMID: 29300727VijgJDongX.
Pathogenic mechanisms of somatic mutation and genome mosaicism in aging. Cell. (2020) 182:12–23. doi: 10.1016/j.cell.2020.06.024, PMID: 32649873Boscolo BieloLTrapaniDRepettoMCriminiEValenzaCBelliC.
Variant allele frequency: a decision-making tool in precision oncology? Trends Cancer. (2023) 9:1058–68. doi: 10.1016/j.trecan.2023.08.011, PMID: 37704501KampsRBrandãoRBoschBPaulussenAXanthouleaSBlokM.
Next-generation sequencing in oncology: genetic diagnosis, risk prediction and cancer classification. IJMS. (2017) 18:308. doi: 10.3390/ijms18020308, PMID: 28146134GalantNNicośMKuźnar-KamińskaBKrawczykP.
Variant allele frequency analysis of circulating tumor DNA as a promising tool in assessing the effectiveness of treatment in non-small cell lung carcinoma patients. Cancers. (2024) 16:782. doi: 10.3390/cancers16040782, PMID: 38398173CostelloMPughTJFennellTJStewartCLichtensteinLMeldrimJC.
Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Res. (2013) 41:e67–7. doi: 10.1093/nar/gks1443, PMID: 23303777XuCNezami RanjbarMRWuZDiCarloJWangY.
Detecting very low allele fraction variants using targeted DNA sequencing and a novel molecular barcode-aware variant caller. BMC Genomics. (2017) 18:5. doi: 10.1186/s12864-016-3425-4, PMID: 28049435ShinH-TChoiY-LYunJWKimNKDKimS-YJeonHJ.
Prevalence and detection of low-allele-fraction variants in clinical cancer samples. Nat Commun. (2017) 8:1377. doi: 10.1038/s41467-017-01470-y, PMID: 29123093MalikYGBenthJŠChecktaeHamreHMFærdenAEIgnjatovicDSchultzJK.
Chemotherapy reduces long-term quality of life in recurrence-free colon cancer survivors (LATE study)—a nationwide inverse probability of treatment-weighted registry-based cohort study and survey. Colorectal Dis. (2024) 26:22–33. doi: 10.1111/codi.16807, PMID: 38036898OlivaDAnderssonB-ÅNilssonMLewinNLewinF.
Risk for relapse and death after adjuvant chemotherapy associated with SNPs in patients with breast cancer - A retrospective study. Cancer Treat Res Commun. (2022) 30:100505. doi: 10.1016/j.ctarc.2021.100505, PMID: 35065426BrayFLaversanneMSungHFerlayJSiegelRLSoerjomataramI.
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clin. (2024) 74:229–63. doi: 10.3322/caac.21834, PMID: 38572751PanHGrayRBraybrookeJDaviesCTaylorCMcGaleP.
20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years. N Engl J Med. (2017) 377:1836–46. doi: 10.1056/NEJMoa1701830, PMID: 29117498Cancer facts & Figures 2025. Am Cancer Soc. (2025). Available online at: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/2025-cancer-facts-figures.html (Accessed February 8, 2026).
Asghari-JafarabadiMWilkinsSPlazzerJPYapRMcMurrickPJ.
Prognostic factors and survival disparities in right-sided versus left-sided colon cancer. Sci Rep. (2024) 14:12306. doi: 10.1038/s41598-024-63143-3, PMID: 38811769TakagiMKimSSuzukiMTakayamaTAsanoH.
Factors associated with postoperative recurrence in perforated colorectal cancer: unraveling the high recurrence rate of perforated colorectal cancer. World J Surg Onc. (2025) 23:139. doi: 10.1186/s12957-025-03783-5, PMID: 40217242YangKMJeongM-JYoonKHJungYTKwakJY.
Oncologic outcome of colon cancer with perforation and obstruction. BMC Gastroenterol. (2022) 22:247. doi: 10.1186/s12876-022-02319-5, PMID: 35570293ZamarayBVan VelzenRASnaebjornssonPConstenECJTanisPJVan WestreenenHL.
Outcomes of patients with perforated colon cancer: A systematic review. Eur J Surg Oncol. (2023) 49:1–8. doi: 10.1016/j.ejso.2022.08.008, PMID: 35995649SausenMPhallenJAdleffVJonesSLearyRJBarrettMT.
Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nat Commun. (2015) 6:7686. doi: 10.1038/ncomms8686, PMID: 26154128TummersWSGroenJVSibinga MulderBGFarina-SarasquetaAMorreauJPutterH.
Impact of resection margin status on recurrence and survival in pancreatic cancer surgery. Br J Surg. (2019) 106:1055–65. doi: 10.1002/bjs.11115, PMID: 30883699AnsariDGustafssonAAnderssonR.
Update on the management of pancreatic cancer: Surgery is not enough. WJG. (2015) 21:3157–65. doi: 10.3748/wjg.v21.i11.3157, PMID: 25805920ZengSPöttlerMLanBGrützmannRPilarskyCYangH.
Chemoresistance in pancreatic cancer. IJMS. (2019) 20:4504. doi: 10.3390/ijms20184504, PMID: 31514451BzeiziKIAbdullahMVidyasagarKAlqahthaniSABroeringD.
Hepatocellular carcinoma recurrence and mortality rate post liver transplantation: meta-analysis and systematic review of real-world evidence. Cancers. (2022) 14:5114. doi: 10.3390/cancers14205114, PMID: 36291898MaxwellRHäberleBKapplerRVon SchweinitzDRassnerMVon FroweinJ.
Hepatoblastoma relapse—Findings from the german HB99 trial and the german liver tumor registry. Cancers. (2024) 16:696. doi: 10.3390/cancers16040696, PMID: 38398087GantiAKKleinABCotarlaISealBChouE.
Update of incidence, prevalence, survival, and initial treatment in patients with non–small cell lung cancer in the US. JAMA Oncol. (2021) 7:1824. doi: 10.1001/jamaoncol.2021.4932, PMID: 34673888BabaTUramotoHTakenakaMChikaishiYOkaSShigematsuY.
Prognostic factors before and after recurrence of resected non-small cell lung cancer. Respir Invest. (2012) 50:151–6. doi: 10.1016/j.resinv.2012.08.007, PMID: 23199979BrawleyOW.
Prostate cancer epidemiology in the United States. World J Urol. (2012) 30:195–200. doi: 10.1007/s00345-012-0824-2, PMID: 22476558ScilipotiPGarmoHGedeborgRRobinsonDStattinPWesterbergM.
Incidence and prognostic implications of prostate-specific antigen persistence and relapse after radical prostatectomy: population-based study. JNCI: J Natl Cancer Institute. (2025) 117:1142–50. doi: 10.1093/jnci/djaf012, PMID: 39821045ZhangNWuJWangQLiangYLiXChenG.
Global burden of hematologic Malignancies and evolution patterns over the past 30 years. Blood Cancer J. (2023) 13:82. doi: 10.1038/s41408-023-00853-3, PMID: 37193689AnZ-YFuHHeYZhuXHuangQ-SWuJ.
Projected global trends in hematological Malignancies: incidence, mortality, and disability-adjusted life years from 2020 to 2030. Blood. (2023) 142:3810–0. doi: 10.1182/blood-2023-187856ThanarajasingamGKimHTCutlerCHoVTKorethJAlyeaEP.
Outcome and prognostic factors for patients who relapse after allogeneic hematopoietic stem cell transplantation. Biol Blood Marrow Transplant. (2013) 19:1713–8. doi: 10.1016/j.bbmt.2013.09.011, PMID: 24076323KangW-MMengQ-BYuJ-CMaZ-QLiZ-T.
Factors associated with early recurrence after curative surgery for gastric cancer. WJG. (2015) 21:5934–40. doi: 10.3748/wjg.v21.i19.5934, PMID: 26019458D’AngelicaMGonenMBrennanMFTurnbullADBainsMKarpehMS.
Patterns of initial recurrence in completely resected gastric adenocarcinoma. Ann Surg. (2004) 240:808–16. doi: 10.1097/01.sla.0000143245.28656.15, PMID: 15492562ZhaoHWeiQ.
Global and regional burden of kidney cancer: Analysis and future predictions based on GBD data from 1990 to 2021. JCO. (2025) 43:521–1. doi: 10.1200/JCO.2025.43.5_suppl.521CirilloLInnocentiSBecherucciF.
Global epidemiology of kidney cancer. Nephrol Dialysis Transplant. (2024) 39:920–8. doi: 10.1093/ndt/gfae036, PMID: 38341277CognardNAnglicheauDGataultPGirerdSEssigMHurault De LignyB.
Recurrence of renal cell cancer after renal transplantation in a multicenter french cohort. Transplantation. (2018) 102:860–7. doi: 10.1097/TP.0000000000002009, PMID: 29215458KlatteTRossiSHStewartGD.
Prognostic factors and prognostic models for renal cell carcinoma: a literature review. World J Urol. (2018) 36:1943–52. doi: 10.1007/s00345-018-2309-4, PMID: 29713755OdagiriTWatariHHosakaMMitamuraTKonnoYKatoT.
Multivariate survival analysis of the patients with recurrent endometrial cancer. J Gynecol Oncol. (2011) 22:3. doi: 10.3802/jgo.2011.22.1.3, PMID: 21607089DouYSongKFuYShenYZhangCYaoS.
Risk factors and prognosis of early recurrence in stage I–II endometrial cancer: A large-scale, multi-center, and retrospective study. Front Med. (2022) 9:808037. doi: 10.3389/fmed.2022.808037, PMID: 35492356LeggeFRestainoSLeoneLCaroneVRonsiniCDi FioreGLM.
Clinical outcome of recurrent endometrial cancer: analysis of post-relapse survival by pattern of recurrence and secondary treatment. Int J Gynecological Cancer. (2020) 30:193–200. doi: 10.1136/ijgc-2019-000822, PMID: 31792085LiYZhaoK.
The epidemiological trends and prediction of brain and central nervous system cancer incidence and mortality, 1992-2021: Age-period-cohort analysis. Neuroepidemiology. (2026) 60:131–43. doi: 10.1159/000544932, PMID: 40031893BhansaliRSBartaSK.
Central nervous system progression/relapse in mature T- and NK-cell lymphomas. Cancers. (2023) 15:925. doi: 10.3390/cancers15030925, PMID: 36765882BirzuCFrenchPCacceseMCerrettiGIdbaihAZagonelV.
Recurrent glioblastoma: from molecular landscape to new treatment perspectives. Cancers. (2020) 13:47. doi: 10.3390/cancers13010047, PMID: 33375286IgnatovAEggemannHBurgerEIgnatovT.
Patterns of breast cancer relapse in accordance to biological subtype. J Cancer Res Clin Oncol. (2018) 144:1347–55. doi: 10.1007/s00432-018-2644-2, PMID: 29675790McAleeseJTaylorAWallsGMHannaGG.
Differential relapse patterns for non-small cell lung cancer subtypes adenocarcinoma and squamous cell carcinoma: implications for radiation oncology. Clin Oncol. (2019) 31:711–9. doi: 10.1016/j.clon.2019.07.008, PMID: 31351746FumagalliCRanghieroAGandiniSCorsoFTaorminaSDe CamilliE.
Inter-tumor genomic heterogeneity of breast cancers: comprehensive genomic profile of primary early breast cancers and relapses. Breast Cancer Research : BCR. (2020) 22:107. doi: 10.1186/s13058-020-01345-z, PMID: 33059724KulskiJK.
Next-generation sequencing — An overview of the history, tools, and “Omic” Applications. Next Generation Sequencing - Advances Appl Challenges. (2016). doi: 10.5772/61964KircherMKelsoJ.
High-throughput DNA sequencing – concepts and limitations. BioEssays. (2010) 32:524–36. doi: 10.1002/bies.200900181, PMID: 20486139GianiAMGalloGRGianFranceschiLFormentiG.
Long walk to genomics: History and current approaches to genome sequencing and assembly. Comput Struct Biotechnol J. (2020) 18:9–19. doi: 10.1016/j.csbj.2019.11.002, PMID: 31890139HoodLRowenL.
The human genome project: big science transforms biology and medicine. Genome Med. (2013) 5:79. doi: 10.1186/gm483, PMID: 24040834SatamHJoshiKMangroliaUWaghooSZaidiGRawoolS.
Next-generation sequencing technology: current trends and advancements. Biology. (2023) 12:997. doi: 10.3390/biology12070997, PMID: 37508427TrapnellCSalzbergSL.
How to map billions of short reads onto genomes. Nat Biotechnol. (2009) 27:455–7. doi: 10.1038/nbt0509-455, PMID: 19430453MillerJRKorenSSuttonG.
Assembly algorithms for next-generation sequencing data. Genomics. (2010) 95:315–27. doi: 10.1016/j.ygeno.2010.03.001, PMID: 20211242VinkšelMWritzlKMaverAPeterlinB.
Improving diagnostics of rare genetic diseases with NGS approaches. J Community Genet. (2021) 12:247–56. doi: 10.1007/s12687-020-00500-5, PMID: 33452619SerratìSDe SummaSPilatoBPetriellaDLacalamitaRTommasiS.
Next-generation sequencing: advances and applications in cancer diagnosis. OTT. (2016) 9:7355–65. doi: 10.2147/OTT.S99807, PMID: 27980425GibbsSNPenevaDCuyun CarterGPalomaresMRThakkarSHallDW.
Comprehensive review on the clinical impact of next-generation sequencing tests for the management of advanced cancer. JCO Precis Oncol. (2023) 7:e2200715. doi: 10.1200/PO.22.00715, PMID: 37285561MarshallCRChowdhurySTaftRJLeboMSBuchanJGHarrisonSM.
Best practices for the analytical validation of clinical whole-genome sequencing intended for the diagnosis of germline disease. NPJ Genomic Med. (2020) 5:47. doi: 10.1038/s41525-020-00154-9, PMID: 33110627ZhuLJiangMWangHSunHZhuJZhaoW.
A narrative review of tumor heterogeneity and challenges to tumor drug therapy. Ann Transl Med. (2021) 9:1351. doi: 10.21037/atm-21-1948, PMID: 34532488GhoreyshiNHeidariRFarhadiAChamanaraMFarahaniNVahidiM.
Next-generation sequencing in cancer diagnosis and treatment: clinical applications and future directions. Discover Oncol. (2025) 16:578. doi: 10.1007/s12672-025-01816-9, PMID: 40253661SabourLSabourMGhorbianS.
Clinical applications of next-generation sequencing in cancer diagnosis. Pathol Oncol Res. (2017) 23:225–34. doi: 10.1007/s12253-016-0124-z, PMID: 27722982WangZYuanXNieYWangJJiangGChenK.
Next-generation sequencing vs. Clinical-pathological assessment in diagnosis of multiple lung cancers: A systematic review and meta-analysis. Thorac Cancer. (2025) 16:e70039. doi: 10.1111/1759-7714.70039, PMID: 40118796SuW-CTsaiY-CTsaiH-LChangT-KYinT-CHuangC-W.
Comparison of next-generation sequencing and polymerase chain reaction for personalized treatment-related genomic status in patients with metastatic colorectal cancer. CIMB. (2022) 44:1552–63. doi: 10.3390/cimb44040106, PMID: 35723364TuononenKMäki-NevalaSSarhadiVKWirtanenARöntyMSalmenkiviK.
Comparison of targeted next-generation sequencing (NGS) and real-time PCR in the detection of EGFR, KRAS, and BRAF mutations on formalin-fixed, paraffin-embedded tumor material of non-small cell lung carcinoma—Superiority of NGS. Genes Chromosomes Cancer. (2013) 52:503–11. doi: 10.1002/gcc.22047, PMID: 23362162DaiPWuLRChenSXWangMXChengLYZhangJX.
Calibration-free NGS quantitation of mutations below 0.01% VAF. Nat Commun. (2021) 12:6123. doi: 10.1038/s41467-021-26308-6, PMID: 34675197YangZZhouYChenYTengJAnHChengS.
Analytical and clinical validation of a personalized, tumor-informed circulating tumor DNA assay for minimal residual disease detection. JCO. (2024) 42:e14545–5. doi: 10.1200/JCO.2024.42.16_suppl.e14545YanYHChenSXChengLYRodriguezAYTangRCabreraK.
Confirming putative variants at ≤ 5% allele frequency using allele enrichment and Sanger sequencing. Sci Rep. (2021) 11:11640. doi: 10.1038/s41598-021-91142-1, PMID: 34079006KennedySRSchmittMWFoxEJKohrnBFSalkJJAhnEH.
Detecting ultralow-frequency mutations by Duplex Sequencing. Nat Protoc. (2014) 9:2586–606. doi: 10.1038/nprot.2014.170, PMID: 25299156CrowgeyELMahajanNWongWHGopalakrishnapillaiABarweSPKolbEA.
Error-corrected sequencing strategies enable comprehensive detection of leukemic mutations relevant for diagnosis and minimal residual disease monitoring. BMC Med Genomics. (2020) 13:32. doi: 10.1186/s12920-020-0671-8, PMID: 32131829LeeJ-YJeonSJunHRSungCOJangSJChoiC-M.
Revolutionizing non–small cell lung cancer diagnosis: ultra-high-sensitive ctDNA analysis for detecting hotspot mutations with long-term stored plasma. Cancer Res Treat. (2024) 56:484–501. doi: 10.4143/crt.2023.712, PMID: 37871897JacobsMTMohindraNAShantzerLChenILPhullHMitchellW.
Use of low-frequency driver mutations detected by cell-free circulating tumor DNA to guide targeted therapy in non–small-cell lung cancer: A multicenter case series. JCO Precis Oncol. (2018) 2:1–10. doi: 10.1200/PO.17.00318, PMID: 35135131LennonNJAdalsteinssonVAGabrielSB.
Technological considerations for genome-guided diagnosis and management of cancer. Genome Med. (2016) 8:112. doi: 10.1186/s13073-016-0370-4, PMID: 27784341SisoudiyaSDTukachinskyHKeller-EvansRBSchrockABHuangRSPGjoerupO.
Tissue-based genomic profiling of 300,000 tumors highlights the detection of variants with low allele fraction. NPJ Precis Onc. (2025) 9:190. doi: 10.1038/s41698-025-00991-w, PMID: 40527927HarismendyOSchwabRBBaoLOlsonJRozenzhakSKotsopoulosSK.
Detection of low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing. Genome Biol. (2011) 12:R124. doi: 10.1186/gb-2011-12-12-r124, PMID: 22185227KimJKimDLimJSMaengJHSonHKangH-C.
The use of technical replication for detection of low-level somatic mutations in next-generation sequencing. Nat Commun. (2019) 10:1047. doi: 10.1038/s41467-019-09026-y, PMID: 30837471ChenMZhaoH.
Next-generation sequencing in liquid biopsy: cancer screening and early detection. Hum Genomics. (2019) 13:34. doi: 10.1186/s40246-019-0220-8, PMID: 31370908GaleDLawsonARJHowarthKMadiMDurhamBSmalleyS.
Development of a highly sensitive liquid biopsy platform to detect clinically-relevant cancer mutations at low allele fractions in cell-free DNA. PloS One. (2018) 13:e0194630. doi: 10.1371/journal.pone.0194630, PMID: 29547634Tébar-MartínezRMartín-AranaJGimeno-ValienteFTarazonaNRentero-GarridoPCervantesA.
Strategies for improving detection of circulating tumor DNA using next generation sequencing. Cancer Treat Rev. (2023) 119:102595. doi: 10.1016/j.ctrv.2023.102595, PMID: 37390697AbboshCBirkbakNJSwantonC.
Early stage NSCLC — challenges to implementing ctDNA-based screening and MRD detection. Nat Rev Clin Oncol. (2018) 15:577–86. doi: 10.1038/s41571-018-0058-3, PMID: 29968853AuwalAHossainMMPronoyTUHRashelKMNurujjamanMLamAKY.
Clinical significance of genomic sequencing of circulating tumour cells (CTCs) in cancer. J Liquid Biopsy. (2024) 3:100135. doi: 10.1016/j.jlb.2023.100135, PMID: 40026568XuJLiaoKYangXWuCWuW.
Using single-cell sequencing technology to detect circulating tumor cells in solid tumors. Mol Cancer. (2021) 20:104. doi: 10.1186/s12943-021-01392-w, PMID: 34412644SchneckHBlasslCMeier-StiegenFNevesRPJanniWFehmT.
Analysing the mutational status of PIK3CA in circulating tumor cells from metastatic breast cancer patients. Mol Oncol. (2013) 7:976–86. doi: 10.1016/j.molonc.2013.07.007, PMID: 23895914Delsing MalmbergERehammarAPereiraMBAbrahamssonJSamuelssonTStåhlmanS.
Accurate and sensitive analysis of minimal residual disease in acute myeloid leukemia using deep sequencing of single nucleotide variations. J Mol Diagnostics. (2019) 21:149–62. doi: 10.1016/j.jmoldx.2018.08.004, PMID: 30273780KimTMoonJHAhnJ-SKimY-KLeeS-SAhnS-Y.
Next-generation sequencing–based posttransplant monitoring of acute myeloid leukemia identifies patients at high risk of relapse. Blood. (2018) 132:1604–13. doi: 10.1182/blood-2018-04-848028, PMID: 30108064ShortNJPatelKPAlbitarMFranquizMLuthraRKanagal-ShamannaR.
Targeted next-generation sequencing of circulating cell-free DNA vs bone marrow in patients with acute myeloid leukemia. Blood Adv. (2020) 4:1670–7. doi: 10.1182/bloodadvances.2019001156, PMID: 32324887KalluriRMcAndrewsKM.
The role of extracellular vesicles in cancer. Cell. (2023) 186:1610–26. doi: 10.1016/j.cell.2023.03.010, PMID: 37059067Casanova-SalasIAguilarDCordoba-TerrerosSAgundezLBrandarizJHerranzN.
Circulating tumor extracellular vesicles to monitor metastatic prostate cancer genomics and transcriptomic evolution. Cancer Cell. (2024) 42:1301–1312.e7. doi: 10.1016/j.ccell.2024.06.003, PMID: 38981440GhanamJLicháKChettyVKPourOAReinhardtDTamášováB.
Unravelling the significance of extracellular vesicle-associated DNA in cancer biology and its potential clinical applications. J Extracellular Vesicles. (2025) 14:e70047. doi: 10.1002/jev2.70047, PMID: 40091452LeeSEParkHYHurJYKimHJKimIAKimWS.
Genomic profiling of extracellular vesicle-derived DNA from bronchoalveolar lavage fluid of patients with lung adenocarcinoma. Transl Lung Cancer Res. (2021) 10:104–16. doi: 10.21037/tlcr-20-888, PMID: 33569297KurtzDMSooJCo Ting KehLAligSChabonJJSworderBJ.
Enhanced detection of minimal residual disease by targeted sequencing of phased variants in circulating tumor DNA. Nat Biotechnol. (2021) 39:1537–47. doi: 10.1038/s41587-021-00981-w, PMID: 34294911DillonLWHigginsJNasifHOthusMBeppuLSmithTH.
Quantification of measurable residual disease using duplex sequencing in adults with acute myeloid leukemia. Haematologica. (2023) 109:401–10. doi: 10.3324/haematol.2023.283520, PMID: 37534515ReinertTHenriksenTVChristensenESharmaSSalariRSethiH.
Analysis of plasma cell-free DNA by ultradeep sequencing in patients with stages I to III colorectal cancer. JAMA Oncol. (2019) 5:1124–31. doi: 10.1001/jamaoncol.2019.0528, PMID: 31070691BergantimRPeixoto Da SilvaSPolóniaBBarbosaMAGAlbergariaALimaJ.
Detection of measurable residual disease biomarkers in extracellular vesicles from liquid biopsies of multiple myeloma patients—A proof of concept. IJMS. (2022) 23:13686. doi: 10.3390/ijms232213686, PMID: 36430163PantelKAlix-PanabièresC.
Liquid biopsy and minimal residual disease — latest advances and implications for cure. Nat Rev Clin Oncol. (2019) 16:409–24. doi: 10.1038/s41571-019-0187-3, PMID: 30796368SuZWangZNiXDuanJGaoYZhuoM.
Inferring the evolution and progression of small-cell lung cancer by single-cell sequencing of circulating tumor cells. Clin Cancer Res. (2019) 25:5049–60. doi: 10.1158/1078-0432.CCR-18-3571, PMID: 31113842WanYLiuBLeiHZhangBWangYHuangH.
Nanoscale extracellular vesicle-derived DNA is superior to circulating cell-free DNA for mutation detection in early-stage non-small-cell lung cancer. Ann Oncol. (2018) 29:2379–83. doi: 10.1093/annonc/mdy458, PMID: 30339193LiBTJankuFJungBHouCMadwaniKAldenR.
Ultra-deep next-generation sequencing of plasma cell-free DNA in patients with advanced lung cancers: results from the Actionable Genome Consortium. Ann Oncol. (2019) 30:597–603. doi: 10.1093/annonc/mdz046, PMID: 30891595De Mattos-ArrudaLWeigeltBCortesJWonHHNgCKYNuciforoP.
Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle. Ann Oncol. (2014) 25:1729–35. doi: 10.1093/annonc/mdu239, PMID: 25009010OnidaniKShojiHKakizakiTYoshimotoSOkayaSMiuraN.
Monitoring of cancer patients via next-generation sequencing of patient-derived circulating tumor cells and tumor DNA. Cancer Sci. (2019) 110:2590–9. doi: 10.1111/cas.14092, PMID: 31169336RoschewskiMKurtzDMWestinJRLynchRCGopalAKAligSK.
Remission assessment by circulating tumor DNA in large B-cell lymphoma. J Clin Oncol. (2025) 43:JCO–25-01534. doi: 10.1200/JCO-25-01534, PMID: 40802906YingXZhengXWangYLiJLiXLinG.
Circulating tumor DNA-based molecular residual disease detection for the monitoring of high-grade serous ovarian cancer. JCO. (2023) 41:e17600–0. doi: 10.1200/JCO.2023.41.16_suppl.e17600TieJWangYTomasettiCLiLSpringerSKindeI.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci Transl Med. (2016) 8:346ra92. doi: 10.1126/scitranslmed.aaf6219, PMID: 27384348KuriyamaSYamadaTMiyasakaTUeharaKOhtaRMatsudaA.
Detection of KRAS mutations using extracellular vesicle DNA in colorectal cancer patients. Cancer Sci. (2025) 116:1661–70. doi: 10.1111/cas.70059, PMID: 40129057ZhangJZhongWWuY.
Molecular residual disease: A new clue for individualized approach in non-small cell lung cancer. Med Adv. (2023) 1:79–88. doi: 10.1002/med4.11AnagnostouVFordePMWhiteJRNiknafsNHrubanCNaidooJ.
Dynamics of tumor and immune responses during immune checkpoint blockade in non–small cell lung cancer. Cancer Res. (2019) 79:1214–25. doi: 10.1158/0008-5472.CAN-18-1127, PMID: 30541742LiLWangYShiWZhuMLiuZLuoN.
Serial ultra-deep sequencing of circulating tumor DNA reveals the clonal evolution in non-small cell lung cancer patients treated with anti-PD1 immunotherapy. Cancer Med. (2019) 8:7669–78. doi: 10.1002/cam4.2632, PMID: 31692284ChristensenEBirkenkamp-DemtröderKSethiHShchegrovaSSalariRNordentoftI.
Early detection of metastatic relapse and monitoring of therapeutic efficacy by ultra-deep sequencing of plasma cell-free DNA in patients with urothelial bladder carcinoma. J Clin Oncol. (2019) 37:1547–57. doi: 10.1200/JCO.18.02052, PMID: 31059311BidardF-CHardy-BessardA-CDalencFBachelotTPiergaJ-Yde la Motte RougeT.
Switch to fulvestrant and palbociclib versus no switch in advanced breast cancer with rising ESR1 mutation during aromatase inhibitor and palbociclib therapy (PADA-1): a randomised, open-label, multicentre, phase 3 trial. Lancet Oncol. (2022) 23:1367–77. doi: 10.1016/S1470-2045(22)00555-1, PMID: 36183733KimYShinSLeeK-A.
Exosome-based detection of EGFR T790M in plasma and pleural fluid of prospectively enrolled non-small cell lung cancer patients after first-line tyrosine kinase inhibitor therapy. Cancer Cell Int. (2021) 21:50. doi: 10.1186/s12935-021-01761-x, PMID: 33435996McDonaldBRContente-CuomoTSammutS-JOdenheimer-BergmanAErnstBPerdigonesN.
Personalized circulating tumor DNA analysis to detect residual disease after neoadjuvant therapy in breast cancer. Sci Transl Med. (2019) 11:eaax7392. doi: 10.1126/scitranslmed.aax7392, PMID: 31391323Ramón y CajalSSeséMCapdevilaCAasenTDe Mattos-ArrudaLDiaz-CanoSJ.
Clinical implications of intratumor heterogeneity: challenges and opportunities. J Mol Med. (2020) 98:161–77. doi: 10.1007/s00109-020-01874-2, PMID: 31970428RodgersCBBroitNJohanssonPAPritchardAL.
Attack of the subclones: accurate detection of mutational heterogeneity in bulk DNA from tumors. J Invest Dermatol. (2020) 140:1501–3. doi: 10.1016/j.jid.2020.02.017, PMID: 32709275PetrackovaAVasinekMSedlarikovaLDyskovaTSchneiderovaPNovosadT.
Standardization of sequencing coverage depth in NGS: recommendation for detection of clonal and subclonal mutations in cancer diagnostics. Front Oncol. (2019) 9:851. doi: 10.3389/fonc.2019.00851, PMID: 31552176
Edited by: Hua Tan, National Human Genome Research Institute (NIH), United States
Reviewed by: Liying Wu, University of Southern California, United States