Skip to main content
PMC home page
BJS Open logoLink to BJS Open
. 2024 Jan 19;8(1):zrad146. doi: 10.1093/bjsopen/zrad146

Assessment of postoperative circulating tumour DNA to predict early recurrence in patients with stage I–III right-sided colon cancer: prospective observational study

Kristin B Lygre 1,2,3, Rakel B Forthun 4,5, Trude Høysæter 6,7, Sigrun M Hjelle 8, Geir E Eide 9,10, Bjørn T Gjertsen 11, Frank Pfeffer 12,13, Randi Hovland 14,15,16,✉
PMCID: PMC10799327  PMID: 38242575

Abstract

Background

Right-sided colon cancer (RCC) differs in mutation profile and risk of recurrence compared to distal colon cancer. Circulating tumour DNA (ctDNA) present after surgery can identify patients with residual disease after curative surgery and predict risk of early recurrence.

Methods

This is a prospective observational biomarker trial with exploration of ctDNA in 50 non-metastatic RCC patients for which oncological right-sided colectomy was performed. Blood samples were collected preoperatively, within 1 month post surgery, 3 months (not mandatory), 6 months and every 6 months thereafter. Plasma cell free DNA and/or tumour was investigated for cancer-related mutations by the next-generation sequencing (NGS) panel AVENIO surveillance specifically designed for ctDNA analysis. Detected mutations were quantified using digital droplet PCR (ddPCR) for follow-up. Recurrence-free survival was explored.

Results

50 patients were recruited. Somatic cancer-related mutations were detected in 47/50 patients. ddPCR validated results from NGS for 27/34 (plasma) and 72/72 samples (tumour). Preoperative ctDNA was detected in 31/47 of the stage I/III patients and the majority of ctDNA positive patients showed reduction of ctDNA after surgery (27/31). ctDNA-positive patients at first postoperative sample had high recurrence risk compared to patients without measurable ctDNA (adjusted hazard ratio: 172.91; 95% c.i.: 8.70 to 3437.24; P: 0.001).

Conclusion

ctDNA was detectable in most patients with non-metastatic RCC before surgery. Positive postoperative ctDNA was strongly associated with early recurrence. Detectable postoperative ctDNA is a prognostic factor with high (100%) positive predictive value for recurrence in this cohort of non-metastatic RCC.

Clinical Trial Registration

ClinicalTrials.gov ID: NCT03776591

Postoperative positive ctDNA is a strong predictor for early recurrence in patients with stage I–III right-sided colon cancer.

Introduction

Colorectal cancer remains one of the most common cancers worldwide. Incidence is 17.2/100 000 per year with annual mortality rates of 8.4/100 0001. In Norway, incidence of colorectal cancer is among the highest in the world2. Surgery is the main treatment for stages I–III. Despite improved surgical technique, pathological staging and advances in neo-adjuvant and adjuvant therapy, 30–50% of colorectal cancer patients will develop recurrence. About 25% of patients with recurrence were initially classified as stages I and II3.

TNM classification is part of the current risk stratification to guide recommendations regarding adjuvant chemotherapy (ACT). However, lymph node negative patients can still develop recurrence, suggesting that the TNM classification and histopathological adverse features alone are not adequate4. The histopathological basis of stratifying into ‘high risk’ and ‘low risk’ does not consider whether minimal residual disease (MRD) is present. Therefore, are there better methods for improving identification of patients at higher risk of recurrence? The evidence supports that circulating tumour DNA (ctDNA) can contribute to a more precise risk stratification by detecting MRD5–8. ctDNA is one of the most investigated and promising tumour-derived detached constituents detected in blood9–12. Levels of ctDNA correlate with tumour burden, and detection of postoperative ctDNA indicates presence of MRD5–8,13. Initial studies focused on metastatic disease and monitoring of treatment response14; however, there is no greater focus on non-metastatic disease and the role of liquid biopsy in predicting and detecting recurrence9–12.

Colorectal cancer is often presented as one disease process; however, prognosis improves the more distally the cancer is located. Right-sided colon cancer (RCC) stage III has lower survival rates and higher risk of recurrence compared to distal colorectal cancer15–17. Knowledge of the embryology, morphological differences, mutation profile and worse prognosis in RCC compared to distal cancers has focused research efforts on right-sided colon cancer18,19. The RCT ‘Open D3 Right Colectomy Compared to Laparoscopic CME for Right-Sided Colon Cancer (D3/CME)’ started recruiting in 2016 and has included liquid biopsy since 2017.

The aim of this study was to assess whether ctDNA provides additional information about prognosis beyond established risk stratification in a study population of non-metastatic RCCs.

Methods

Study information

This is a substudy of ‘Open D3 Right Colectomy Compared to Laparoscopic CME for Right-Sided Colon Cancer (D3/CME)’ (Clinicaltrials.gov identifier NCT03776591). The trial was conducted at Haukeland University Hospital (HUH) and Haraldsplass Deaconess Hospital (HDH), Bergen, Norway. Patients were randomized to receive either open D3 resection (HUH) or laparoscopic complete mesocolic excision (CME) with central vascular ligation (CVL) (HDH)20. The trial was approved by the regional committee of ethics (REK Sor-Ost, REK 2015/2396) and is in accordance with the Declaration of Helsinki21. This is a prospective observational biomarker trial in patients with stages I–III right-sided colon cancer and includes the first consecutive 50 patients analysed with liquid biopsies. Patients underwent surgical resection for colon cancer from September 2017 to July 2019 with clinical follow-up until 14 April 2023. Median follow-up was 4.4 years (1–5.6). Cost and practical issues limited the sample size.

Patients 18–85 years of age with non-metastatic adenocarcinoma in the right colon verified by biopsy, colonoscopy or CT were eligible for inclusion. Patients received treatment and follow-up according to national guidelines (Norwegian Directorate of Health)22 (Supplementary material).

Sample collection

Blood samples were collected between September 2017 and January 2021, prior to and after surgery (2–7 days or 1 month, 3 months (not mandatory), 6 months, and then successively every 6 months). Plasma was separated from K2-EDTA blood within 1 h of blood draw by centrifuging the blood for 820×g, 10 min, before a second centrifugation for the supernatant at 10 000×g, 10 min. Purified plasma was stored at −80°C in six aliquots until further processing performed within 2 years. Biopsies from the primary tumour were collected intraoperatively by the surgeon immediately after removal of the specimen and were snap frozen as four aliquots in liquid nitrogen. Samples were stored at −150°C until time of analysis. Analysis was performed on presurgical plasma samples (n = 29), tumour biopsies (n = 45), and/or postoperative plasma samples (n = 34). ctDNA and tumour analysis were performed retrospectively, blinded to patient outcome.

AVENIO ctDNA surveillance panel

Purified plasma, 3–5 ml (n = 62), was thawed prior to enrichment of cell-free DNA by the AVENIO ctDNA Analysis Kit (Roche) according to the protocol provided by the producers (Supplementary material). Sequencing libraries were prepared from 13–50 ng cell-free DNA using the AVENIO ctDNA Analysis Kit paired with the AVENIO ctDNA Surveillance Kit (Roche) as described by the manufacturers. The libraries for both ctDNA and tumour were sequenced on a NextSeq550 (Illumina) using the NextSeq 500/550 High Output v2 kit (300 cycles) (Illumina) and results were analysed using AVENIO ctDNA Analysis Software version 2.0.0 (Roche) as recommended by the suppliers, with hg38 as reference genome (Supplementary material). The detection threshold was 0.1% for single nucleotide variants.

AVENIO tumour tissue surveillance panel

A Cryostat microtome was used to make 30-µm slides of fresh frozen tissue (n = 45). Slides from tumour were stained routinely with haematoxylin/eosin and tumour content was verified by microscopy. DNA was purified from 10 to 25 mg tissue using QIAamp DNA Mini and Blood Mini kit (Qiagen) as recommended by the manufacturer (Supplementary material). Sequencing libraries were prepared from 20 to 24 ng DNA harvested from fresh frozen primary biopsies, as described above, using the AVENIO Tumour Tissue Analysis Kit paired with the AVENIO Tumour Surveillance Kit (both from Roche) as recommended by the manufacturer, with minor alterations (Supplementary material). Detection threshold was 5% for single nucleotide variants.

Digital droplet PCR

Cell-free DNA was harvested from 4–5 ml purified plasma (n = 311) using the QIAamp Circulating Nucleic Acid Kit (Qiagen) according to the protocol provided by the producer (Supplementary material). Digital droplet PCR (ddPCR) assays for mutations detected by the AVENIO Surveillance gene panel were purchased from Bio-Rad (Table S1) and ddPCR was performed as previously described23, with minor alterations. Briefly, all samples were run as triplicates, and results are presented as an average between replicates calculating number of mutant DNA copies per millilitre plasma and fractional abundance (FA) as mutant DNA copies/total DNA copies. Results were presented as percentage of FA. Samples with <12 000 droplets generated per parallel were excluded from further analysis. Based on validation of detection thresholds for each individual assay using positive controls, normal controls and non-template controls, samples generating a total of <3 mutation-positive droplets or having an FA < 0.1% were defined as having no detectable tumour DNA. ddPCR analysis was performed on three neoadjuvant, 46 preoperative and 262 postoperative samples (total: 311 samples, median: 7 samples per patient, range: 2–9). Twenty-five patients were assessed by two assays (53%) and 22 by one assay (47%).

Statistical analyses

Baseline and tumour characteristics were summarized using descriptive statistics. Primary clinical endpoint examined is recurrence-free survival (RFS). Unadjusted RFS was explored using Kaplan–Meier plots24 and Cox regression25 was used for unadjusted and adjusted analyses. Results were reported as unadjusted HR or adjusted HR (aHR) with 95% c.i.s and likelihood ratio P. All statistical analyses were performed using SPSS version 26.0.0.1 (Supplementary material).

Results

Patient and tumour characteristics are presented in Table 1. An overview of analysed tissue, sample time, method for analysis and results is presented in Table 2.

Table 1.

Patient and tumour characteristics for n = 50 patients with right-sided non-metastatic colon cancer included in D3/CME-study and operated at Haraldsplass Deaconess Hospital and Haukeland University Hospital, Bergen (Norway) between September 2017 and July 2019

Patient variables Statistic
Age (years), mean(s.d.), median (range) 69.1(10.1), 69 (39–84)
BMI (kg/m2), mean(s.d.), median (range) 25.6(4.3), 25.4 (17.8–38.7)
Sex
 Male 24
 Female 26
ASA class
 I 5 (10)
 II 40 (80)
 III 5 (10)
pTumour-stage
 T1 1 (2)
 T2 5 (10)
 T3 28 (56)
 T4a 15 (30)
 T4b 1 (2)
pNode-stage
 N0 27 (54)
 N1a 9 (18)
 N1b 5 (10)
 N1c 3 (6)
 N2a 4 (8)
 N2b 2 (4)
Tumour differentiation
 Poor 6 (12)
 Moderate 43 (86)
 Well 1 (2)
Tumour morphology
 Adenocarcinoma 49 (98)
 Signet ring cell carcinoma 1 (2)
Mucinous differentiation/component 5 (10)
Tumour deposit 4 (8)
Venous invasion 12 (24)
MSI status
 MSS 37 (74)
 MSI 11 (22)
 Unknown 2 (4)
Mutations
 APC 29 (58)
 BRAF 19 (38)
 KRAS 25 (50)
 NRAS 6 (12)
 TP53 29 (58)
 PIK3CA 12 (24)
Neo-adjuvant chemotherapy
 No 47 (94)
 Yes 3 (6)
Adjuvant chemotherapy
 No 30 (60)
 Yes 20 (40)
Recurrence during follow-up
 No 40 (80)
 Yes 10 (20)
Observation time (days), mean(s.d.), median (range) 1417(354), 1448 (351–1861)
Status (April 2023)
 Alive without recurrence 37 (74)
 Alive with recurrence 5 (10)
 Dead without recurrence 3 (6)
 Dead with recurrence 5 (10)
Open in a new tab

Values are n (%) unless otherwise stated. p, pathologic; T, tumour; N, node; MSI, microsatellite instable; MSS, microsatellite stable; APC, adenomatous polyposis coli gene; BRAF, proto-oncogene B-Raf; KRAS, Kirsten rat sarcoma; NRAS, neuroblastoma-RAS; TP53, tumour protein 53; PIK3CA, phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha.

Table 2.

Analysed tissue, sampling time and method for analysis for n = 50 patients with right-sided non-metastatic colon cancer included in D3/CME-study and operated at Haraldsplass Deaconess Hospital and Haukeland University Hospital, Bergen (Norway) between September 2017 and July 2019

Biomarker method Number of patients Tumour biopsy blood (liquid biopsy)
Intraoperative Prior to surgery Postoperative* Monitor†
AVENIO Total 45 29 29 5
Biomarker positive 42 22 2 1
ddPCR assay Total 47 46 47 46
Biomarker positive 47 23 5 8
AVENIO and/or ddPCR Total 50 49 49 46
Biomarker positive 49 31 5 8
Open in a new tab

AVENIO: AVENIO ctDNA targeted kit; ctDNA: circulating tumor DNA; ddPCR: digital droplet polymerase chain reaction. *Postoperative sample between 2 and 83 days after operation. Forty/50 patients were sampled within the first postoperative week. For the 10 patients with delayed sampling, four received chemotherapy and were sampled after initiation of treatment. Mean cycles before sampling: 1.75. Mean time from last treatment: 17.5 days. †Monitor includes all time points after operation excluding the first postoperative sampling, sampling at 3–38 months after operation.

Biomarker detection by next-generation sequencing

Cancer-specific mutations were found in 49/50 patients (98%). Of these, next-generation sequencing (NGS) of the tumour biopsy was possible for 45 patients. Mutation profiles were provided by NGS of plasma for the other four patients (Table 2).

The patient with no cancer-specific mutations detected underwent colonic resection after incomplete removal of a malignant polyp (T1sm2/3). There was no remaining tumour at the time of formal resection and the first sampling was after polypectomy. Forty-seven of 50 patients (94%) had one to two mutations eligible for monitoring with commercially available ddPCR assays.

The genes most currently mutated were APC (29 patients, 58%), TP53 (29 patients, 58%), KRAS (25 patients, 50%), BRAF (19 patients, 38%), PIK3CA (12 patients, 24%) and NRAS (6 patients, 12%) (Table S1).

ctDNA monitoring by ddPCR

ddPCR is more cost-effective for monitoring ctDNA than NGS, with high sensitivity and high negative predictive value given a known mutation profile26. To evaluate the utility of ddPCR for MRD monitoring in stage I–III patients, plasma and tumour samples were analysed by selected ddPCR markers based on mutations identified by NGS analysis in tumour and/or plasma (Table S2).

Tumour samples from patients with mutations eligible for monitoring by commercially available ddPCR assays (n = 47) were tested by 72 ddPCR analyses in total using 21 different assays. All mutations found by NGS in either plasma or tumour were confirmed present by ddPCR in the tumour (Table S2), showing mutational concordance between pretreatment samples and tissue. ddPCR was possible for 26 of the 29 preoperative plasma samples initially analysed by NGS. Samples were analysed by 34 ddPCR analyses in total using 13 different assays. NGS and ddPCR returned concurring results for 27/34 analyses (79% concordance). ctDNA was not detected by ddPCR in plasma samples found negative by NGS, despite tumour biopsies being mutation positive.

Monitoring ctDNA in pre- and postoperative cell-free plasma DNA

Median (range) preoperative plasma cell-free DNA concentration was 11.3 (4.3–63.5) ng/ml plasma (n = 47). Postoperative cell-free DNA concentration was 38.95 (12.1–145.6) ng/ml plasma for patients sampled 2–7 days post surgery (n = 38), and 7.9 (6.7–18.9) ng/ml plasma for patients sampled 1–2 months post surgery (n = 9).

Preoperatively, 31/47 (66%) of stage I–III patients were ctDNA-positive using NGS and/or ddPCR (Table 2). Monitoring by ddPCR during follow-up demonstrated that 42 patients were negative for ctDNA, whereas five patients were positive in the first postoperative sample. During surveillance, 38 patients remained negative, eight patients were positive for ctDNA or became positive (four not analysed). Ten patients had recurrent cancer during surveillance (Table 3). Five of 10 patients with recurrence were positive for ctDNA in their first postoperative sample, and an additional three patients became positive during monitoring (34, 22 and 9 months after surgery), two of whom had positive ctDNA prior to radiological confirmation, and one after. One patient that developed lung metastasis and one with metastasis to the liver and peritoneum/retroperitoneum remained negative in analysed postoperative samples27. Patients with recurrence and positive ctDNA in the first postoperative sample had a mean RFS of 0.5 years (180 days). Patients with recurrence and negative ctDNA in the first postoperative sample had a mean RFS of 2.3 years (831 days).

Table 3.

Characteristics of 10 patients (a–j) with recurrent cancer after oncologic resection of right-sided colon cancer. Patients were operated from September 2017 until July 2019 and with follow-up until 31 October 2022

Variable Patient a Patient b Patient c Patient d Patient e Patient f Patient g Patient h Patient i Patient j
Neo-ACT (cycles) No No No No No No No Yes (3) No No
T-stage T4a T4a T3 T3 T3 T3 T4a T3 T3 T4a
N-stage N0 N2b N1a N0 N2a N2a N1c N2a N1a N0
Differentiation Low Middle Middle Middle Low Middle Middle Middle Middle High
Morphology AC AC AC AC SRCC AC AC AC AC AC
Mucinous diff Yes No No No Yes No No No No No
Tumour deposit No No Yes No No No Yes No No No
Venous invasion No No Yes No Yes Yes Yes Yes No Yes
MSI-status MSS MSS MSS MSS MSS MSS MSS MSS MSS MSS
ddPCR monitoring marker BRAF BRAF/TP53 BRAF KRAS/PIK3CA* BRAF/TP53 APC/KRAS APC/NRAS KRAS/APC TP53 TP53
Source NGS T/plasma T/plasma T/plasma T T/plasma Plasma T T T T
ACT (cycles) No Yes (3) Yes (1) No No Yes (1) No Yes (4) Yes (12) Yes (12)
ctDNA postop† (days‡) Neg§ (1032) Pos (3) Pos (4) Neg (−) Neg§ (685) Neg§ (257) Pos (107) Pos (3) Pos (3) Neg (−)
RFS in days# 1647 310 41 761 235 448 105 259 186 1063
Site of recurrence Per Liver/per Liver Lung Retro Liver/lung Liver/retro Lung Liver Liver/per/retro
Status 14 April 2023 Alive Dead Dead Alive Dead Dead Dead Alive Alive Alive
Open in a new tab

ACT, adjuvant chemotherapy; T, tumour; N, node; AC, adenocarcinoma; SRCC, signet ring cell carcinoma; MSI, micro satellite instable; MSS, microsatellite stable; ddPCR, digital droplet PCR; BRAF, proto-oncogene B-Raf; TP53, tumour protein 53; KRAS, Kirsten rat sarcoma; PIK3CA, phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha; APC, adenomatous polyposis coli gene; NRAS, neuroblastoma-RAS; NGS, next-generation sequencing; ctDNA, circulating tumour DNA; Neg, negative; Pos, positive; RFS, recurrence-free survival; per, peritoneum; retro, retroperitoneal. *A monitorable PIK3CA hotspot mutation was detected. However, a KRAS mutation with higher VAF was selected for monitoring. †In the first postoperative sample. ‡Days from operation to positive ctDNA. §Turned positive during monitoring. ¶Last analysed sample was more than one year before detected recurrence. #Days from operation to radiologically verified recurrence.

Clinical validity of ctDNA analysis

No correlation was found between preoperative positive ctDNA and recurrence rate. Positive postoperative ctDNA was associated with recurrence (aHR: 172.91; 95% c.i.: 8.70 to 3437.24; P: 0.001). None of the traditional histopathological variables demonstrated association with recurrence (Cox regression). Morphology and venous invasion were the only negative histopathological prognostic factors in the unadjusted analysis. They were not significant in the adjusted model (Table 4) and are not included in traditional risk stratification. Kaplan–Meier plots for RFS and postoperative ctDNA status are presented in Fig. 1.

Table 4.

Results from Cox regression analyses of the risk of recurrence based on traditional histopathological characteristics and presence of pre- and postoperative ctDNA in 50 patients with right-sided non-metastatic colon cancer included in D3/CME-study and operated between September 2017 and July 2019

Variables Unadjusted models Adjusted model
n HR 95% c.i. P aHR 95% c.i. P
ctDNA positive
 Preoperative 46 1.28 0.34,4.76 0.714 n.i. n.i. n.i.
 Postoperative 47 91.37 10.22,817.26 <0.001 172.91 8.70,3437.24 0.001
T4 versus T1-3 50 1.45 0.41,5.16 0.564 0.66 0.12,3.68 0.637
N-positive* 50 3.85 0.99,15.01 0.052 0.95 0.12,7.25 0.959
T differentiation† 50 0.72 0.14,3.71 0.698 0.40 0.05,3.38 0.398
Morphology‡ 50 15.83 1.65,152.17 0.017 n.i. n.i. n.i.
Mucinous diff 50 2.00 0.42,9.65 0.386 6.26 0.70,55.89 0.100
Tumour deposit 50 3.62 0.71,18.35 0.121 4.62 0.47,45.86 0.192
Venous invasion 50 5.93 1.67,21.09 0.006 4.13 0.93,18.29 0.062
MSI/MSS§ 48 n.i. n.i. n.i. n.i n.i. n.i.
Open in a new tab

*N-stage: N-positive versus N-negative. †Middle/well versus poor. ‡Adenocarcinoma versus Signet ring cell carcinoma. Only one patient with Signet ring cell carcinoma: this patient had recurrence. §Not possible to analyse because all patients with recurrence were MSS. aHR, adjusted HR; ctDNA, circulating tumour DNA; T, tumour; N, node; MSI/MSS, microsatellite instable/stable; n.i., not included in the model.

Fig. 1.

Fig. 1

Open in a new tab

Kaplan–Meier plot for days from postoperative sampling (liquid biopsy) until postoperative recurrence in 50 patients operated for right-sided non-metastatic colon cancer in the D3/CME-study at Haraldsplass Deaconess Hospital or Haukeland University Hospital, Bergen (Norway) between September 2017 and July 2019 according to postoperative ctDNA status (P = 0.001)

Discussion

The aim of this substudy was to investigate whether ctDNA provided additional information about prognosis beyond established risk stratification, and to explore best timing and source of primary gene mapping. RCC was chosen based on adverse prognosis and its distinct biology compared to more distal colorectal cancers. The study confirmed that both tumour and plasma are good sources for primary gene mapping. ctDNA was detectable preoperatively in 66% of stage I–III RCC28. Furthermore, mutations detected by NGS could be confirmed and monitored by ddPCR. The major finding was that the presence of postoperative ctDNA is a strong predictor for early recurrence.

Mutated key oncogenes and tumour suppression genes were comparable with rates from The Cancer Genome Atlas Dataset. There was a higher proportion of TP53 (58% versus 34.8%) and BRAF (38% versus 24.2%) mutations than other materials with RCC (transverse excluded). The presence of APC (58% versus 63.6%), KRAS (50% versus 45.5%), PIK3CA (24% versus 27.3%) and NRAS (12% versus 7.6%) was comparable29.

ctDNA constitutes a small portion of total cell-free DNA, in some studies less than 1%30. The half-life of cell-free DNA is short (minutes to hours). High levels of cell-free DNA (median 38.95 ng/ml) early postoperatively (2–7 days), due to surgical trauma, dilute the ctDNA concentration and make it difficult to detect cancer specific mutations. The dilution effect is less relevant for patients with tumours that shed high levels of ctDNA, but for patients with low tumour burden and less ctDNA, the dilution effect can result in undetectable ctDNA in the early postoperative period. Negative postoperative ctDNA should be interpreted with caution as it may be related to detection threshold. One month postoperatively, levels of cell-free DNA dropped significantly (median 7.9 ng/ml). As ACT should be initiated within 6 weeks, 4 weeks after operation is a good time point for postoperative sampling31.

NGS and ddPCR are complementary methods for monitoring ctDNA. With a unique molecular identifier and digital error suppression, NGS is highly sensitive and gives a broad mapping of the genetic profile. However, it is labour-intensive and expensive32. ddPCR is sensitive, robust and cost-effective for detection of selected mutations, but holds potential for missing relevant mutations by selection of a suboptimal surveillance marker. To identify MRD with targeted analyses like RT-PCR/ddPCR/BEAMing, the selected mutation must be present in all cancer cells. It is known that intra-tumour mutational heterogeneity can be present33,34. Many ctDNA studies choose a tumour-agnostic approach with selection of surveillance mutations without knowledge of the actual mutation profile30. Using this strategy, a negative result by ddPCR is not synonymous with negative ctDNA or no MRD present. It only confirms that the selected mutation is not present. In this trial, we chose an approach with a broad-coverage NGS assay for initial mutation profiling for plasma, tumour or both. This approach increased the probability of detection of relevant mutations and allowed monitoring of eight patients (17% of the patient cohort) lacking classical codon 600 BRAF and codon 12/13/61 KRAS mutations. Without a priori knowledge of tumour genotype, they would not be included. Monitoring of ctDNA was performed by ddPCR, and criteria for selection of surveillance markers were that mutations were detected by NGS and confirmed by ddPCR (requires commercially available assays). Concordance between NGS and ddPCR was high with 100% confirmation of NGS with ddPCR for tumour and 79% for plasma. Selection bias was reduced with a broad NGS-based approach, and the high concordance and cost-effectiveness of ddPCR makes it possible to implement in routine diagnostics, especially for surveillance.

Initial genomic profiling can be conducted on plasma or tumour tissue. Plasma has been shown to be a good option for patients receiving neo-adjuvant treatment before surgery35, and is promising for cancer patients when tumour biopsies are not available. Theoretically, plasma will reflect intra-tumoural heterogeneity better than single tumour biopsies36–39. However, there was little discrepancy between variants selected for monitoring by NGS in tumour and plasma (one mutation detected in plasma and not tumour). There was little additional gain in capturing tumour heterogeneity by performing NGS on plasma rather than tumour. Both plasma and tumour could be reference material for detecting markers for monitoring, even in cases where only one tumour biopsy was analysed. In addition to intra-tumoural heterogeneity, there is a risk of altered mutation profile due to clonal selection during treatment and surveillance40,41. To increase the likelihood of capturing relevant changes, we followed two variants when possible. Due to the limited number of patients and sampling period in this trial, evaluation of the role of ctDNA as a diagnostic tool for early detection of recurrence was restricted and we cannot evaluate the predictive precision of ctDNA during surveillance. Analysis of the remaining study population, with complete surveillance of 5 years, may clarify this.

As surgery is a curative treatment for limited disease, theoretically, successful surgery would lead to undetectable postoperative ctDNA. Exploration of MRD after surgery is not included in traditional risk assessment, and tools for surveillance are limited to less-sensitive diagnostic tools such as carcinoembryonic antigen (CEA) measurement and CT imaging. CEA does not detect recurrence at an early stage35,42,43, whereas CT scanning has a threshold of 5–10 mm for detection of lesions and often yields unspecific findings44–46. In accordance with previous studies6,7,47,48, this trial confirms that ctDNA is a marker for MRD. Early postoperative ctDNA positivity was associated with risk of recurrence, whereas traditional risk stratification variables were non-significant. Improvements can be made to the current method for selection of patients for ACT in colon cancer. ctDNA holds potential to guide a tailored adjuvant treatment decision. The clinical breakthrough would be if ctDNA status could identify the stage III patients who will not benefit from ACT and select high-risk stage II patients who might benefit. The potential for downscaling of ACT or possibly skipping adjuvant treatment is currently being further explored in clinical intervention trials with ctDNA-guided management4,49–54. Only 5 of 10 patients with recurrence were positive for ctDNA in their first postoperative sample. Recurrence risk was low in the negative group (11% negative versus 100% positive). Additional information from ctDNA status compared to established risk stratification is difficult to interpret, especially for ctDNA-negative patients. Postoperative ctDNA status alone cannot yet guide treatment decisions, but can supplement traditional risk stratification. ctDNA is a reliable predictor for early recurrence by detecting MRD.

In summary, ctDNA was detectable prior to surgery for most patients with stages I–III RCC, but its presence was not predictive for negative outcome. Postoperative (2–83 days) positive ctDNA was a marker for MRD and a predictor for early recurrence. The clinical utility remains to be proven in clinical intervention trials.

Supplementary Material

zrad146_Supplementary_Data
zrad146_supplementary_data.docx (75.3KB, docx)

Acknowledgements

The authors acknowledge all patients participating. K.B.L, R.B.F contributed equally to this paper.

Contributor Information

Kristin B Lygre, Department of Gastrointestinal Surgery, Haraldsplass Deaconess Hospital, Bergen, Norway; Department of Gastrointestinal Surgery, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.

Rakel B Forthun, Department of Medicine, Haukeland University Hospital, Bergen, Norway; Section for Cancer Genomics, Haukeland University Hospital, Bergen, Norway.

Trude Høysæter, Section for Cancer Genomics, Haukeland University Hospital, Bergen, Norway; Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.

Sigrun M Hjelle, Department of Medicine, Haukeland University Hospital, Bergen, Norway.

Geir E Eide, Centre for Clinical Research, Haukeland University Hospital, Bergen, Norway; Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.

Bjørn T Gjertsen, Department of Medicine, Haukeland University Hospital, Bergen, Norway.

Frank Pfeffer, Department of Gastrointestinal Surgery, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.

Randi Hovland, Section for Cancer Genomics, Haukeland University Hospital, Bergen, Norway; Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway; Department of Biosciences, University of Bergen, Bergen, Norway.

Author contributions

Kristin Lygre (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Validation, Visualization, Writing—original draft), Rakel Brendsdal Forthun (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing—original draft), Trude Høysæter (Data curation, Methodology, Writing—review & editing), Sigrun Margrethe Hjelle (Conceptualization, Data curation, Methodology, Project administration, Writing—review & editing), Geir Eide (Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing—review & editing), Bjørn Tore Gjertsen (Conceptualization, Data curation, Funding acquisition, Methodology, Resources, Software, Supervision, Writing—review & editing), Frank Pfeffer (Conceptualization, Funding acquisition, Investigation, Resources, Supervision, Writing—review & editing) and Randi Hovland (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing—review & editing)

Funding

This study was supported by Norwegian Health West Strategic funding (grant 912009) and Haukeland University Hospital, Laboratory Medicine and Pathology (grant #20/01).

Disclosure

The authors declare no conflict of interest.

Supplementary material

Supplementary material is available at BJS Open online.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary materials. Raw data are available from the corresponding author upon reasonable request.

References

  • 1. Bray  F, Ferlay  J, Soerjomataram  I, Siegel  RL, Torre  LA, Jemal  A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin  2018;68:394–424 [DOI] [PubMed] [Google Scholar]
  • 2. Kreftregisteret . Cancer in Norway 2018 Cancer incidence, mortality, survival and prevalence in Norway 2018 (kreftregisteret.no).
  • 3. Young  PE, Womeldorph  CM, Johnson  EK, Maykel  JA, Brucher  B, Stojadinovic  A  et al.  Early detection of colorectal cancer recurrence in patients undergoing surgery with curative intent: current status and challenges. J Cancer  2014;5:262–271 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Tie  J, Cohen  JD, Lahouel  K, Lo  SN, Wang  Y, Kosmider  S  et al.  Circulating tumor DNA analysis guiding adjuvant therapy in stage II colon cancer. N Engl J Med  2022;386:2261–2272 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Bach  S, Sluiter  NR, Beagan  JJ, Mekke  JM, Ket  JCF, van Grieken  NCT  et al.  Circulating tumor DNA analysis: clinical implications for colorectal cancer patients. A systematic review. JNCI Cancer Spectr  2019;3:pkz042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Tie  J, Wang  Y, Tomasetti  C, Li  L, Springer  S, Kinde  I  et al.  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] [PMC free article] [PubMed] [Google Scholar]
  • 7. Tie  J, Cohen  JD, Wang  Y, Christie  M, Simons  K, Lee  M  et al.  Circulating tumor DNA analyses as markers of recurrence risk and benefit of adjuvant therapy for stage III colon cancer. JAMA Oncol  2019;5:1710–1717 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Diehn  M, Alizadeh  AA, Adams  H-P, Lee  JJ, Klassen  S, Palma  JF  et al.  Early prediction of clinical outcomes in resected stage II and III colorectal cancer (CRC) through deep sequencing of circulating tumor DNA (ctDNA). J Clin Oncol  2017;35:359128892431 [Google Scholar]
  • 9. Wan  JCM, Massie  C, Garcia-Corbacho  J, Mouliere  F, Brenton  JD, Caldas  C  et al.  Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer  2017;17:223–238 [DOI] [PubMed] [Google Scholar]
  • 10. Ignatiadis  M, Lee  M, Jeffrey  SS. Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin Cancer Res  2015;21:4786–4800 [DOI] [PubMed] [Google Scholar]
  • 11. Nordgård  O, Tjensvoll  K, Gilje  B, Søreide  K. Circulating tumour cells and DNA as liquid biopsies in gastrointestinal cancer. Br J Surg  2018;105:e110–e120 [DOI] [PubMed] [Google Scholar]
  • 12. Rasmussen  SL, Krarup  HB, Sunesen  KG, Pedersen  IS, Madsen  PH, Thorlacius-Ussing  O. Hypermethylated DNA as a biomarker for colorectal cancer: a systematic review. Colorectal Dis  2016;18:549–561 [DOI] [PubMed] [Google Scholar]
  • 13. Schøler  LV, Reinert  T, Ørntoft  M-BW, Kassentoft  CG, Árnadóttir  SS, Vang  S  et al.  Clinical implications of monitoring circulating tumor DNA in patients with colorectal cancer. Clin Cancer Res  2017;23:5437–5445 [DOI] [PubMed] [Google Scholar]
  • 14. de Figueiredo Barros  BD, Kupper  BEC, Aguiar Junior  S, de Mello  CAL, Begnami  MD, Chojniak  R  et al.  Mutation detection in tumor-derived cell free DNA anticipates progression in a patient with metastatic colorectal cancer. Front Oncol  2018;8:306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Wang  CB, Shahjehan  F, Merchea  A, Li  Z, Bekaii-Saab  TS, Grothey  A  et al.  Impact of tumor location and variables associated with overall survival in patients with colorectal cancer: a Mayo Clinic Colon and Rectal Cancer Registry study. Front Oncol  2019;9:76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Qin  Q, Yang  L, Sun  YK, Ying  JM, Song  Y, Zhang  W  et al.  Comparison of 627 patients with right- and left-sided colon cancer in China: differences in clinicopathology, recurrence, and survival. Chronic Dis Transl Med  2017;3:51–59 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Arnold  D, Lueza  B, Douillard  JY, Peeters  M, Lenz  HJ, Venook  A  et al.  Prognostic and predictive value of primary tumour side in patients with RAS wild-type metastatic colorectal cancer treated with chemotherapy and EGFR directed antibodies in six randomized trials. Ann Oncol  2017;28:1713–1729 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Song  Y, Wang  L, Ran  W, Li  G, Xiao  Y, Wang  X  et al.  Effect of tumor location on clinicopathological and molecular markers in colorectal cancer in eastern China patients: an analysis of 2,356 cases. Front Genet  2020;11:96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Mukund  K, Syulyukina  N, Ramamoorthy  S  Subramaniam  S. Right and left-sided colon cancers—specificity of molecular mechanisms in tumorigenesis and progression. BMC Cancer  2020;20:317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Lygre  KB, Eide  GE, Forsmo  HM, Dicko  A, Storli  KE, Pfeffer  F. Complications after open and laparoscopic right-sided colectomy with central lymphadenectomy for colon cancer: randomized controlled trial. BJS Open  2023;7:zrad074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. World Medical Association . World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA  2013;310:2191–2194 [DOI] [PubMed] [Google Scholar]
  • 22. Helsedirektoratet . Nasjonalt handlingsprogram med retningslinjer for diagnostikk, behandling og oppfølging av kreft i tykktarm og endetarm: 2017. http://www.helsebiblioteket.no/retningslinjer/kreft-i-tykktarm-og-endetarm/8-tykktarmskreft-u.metastaser/8.6-adjuvant-kjemoterapi.
  • 23. Forthun  RB, Hovland  R, Schuster  C, Puntervoll  H, Brodal  HP, Namløs  HM  et al.  ctDNA detected by ddPCR reveals changes in tumour load in metastatic malignant melanoma treated with bevacizumab. Sci Rep  2019;9:17471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Kaplan  EL, Meier  P. Nonparametric estimation from incomplete observations. J Am Stat Assoc  1958;53:457–481 [Google Scholar]
  • 25. Cox  DR. Regression models and life-tables. J R Stat Soc Ser B Methodol  1972;34:187–202 [Google Scholar]
  • 26. Tsao  SC-H, Weiss  J, Hudson  C, Christophi  C, Cebon  J, Behren  A  et al.  Monitoring response to therapy in melanoma by quantifying circulating tumour DNA with droplet digital PCR for BRAF and NRAS mutations. Sci Rep  2015;5:11198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Hamfjord  J, Guren  TK, Glimelius  B, Sorbye  H, Pfeiffer  P, Dajani  O  et al.  Clinicopathological factors associated with tumour-specific mutation detection in plasma of patients with RAS-mutated or BRAF-mutated metastatic colorectal cancer. Int J Cancer  2021;149:1385–1397 [DOI] [PubMed] [Google Scholar]
  • 28. Hofste  LSM, Geerlings  MJ, von Rhein  D, Rütten  H, Westenberg  AH, Weiss  MM  et al.  Circulating tumor DNA detection after neoadjuvant treatment and surgery predicts recurrence in patients with early-stage and locally advanced rectal cancer. Eur J Surg Oncol  2023;49:1283–1290 [DOI] [PubMed] [Google Scholar]
  • 29. Lee  MS, Menter  DG, Kopetz  S. Right versus left colon cancer biology: integrating the consensus molecular subtypes. J Natl Compr Canc Netw  2017;15:411–419 [DOI] [PubMed] [Google Scholar]
  • 30. Diehl  F, Li  M, Dressman  D, He  Y, Shen  D, Szabo  S  et al.  Detection and quantification of mutations in the plasma of patients with colorectal tumors. Proc Natl Acad Sci USA  2005;102:16368–16373 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Cohen  SA, Kasi  PM, Aushev  VN, Hanna  DL, Botta  GP, Sharif  S  et al.  Kinetics of postoperative circulating cell-free DNA and impact on minimal residual disease detection rates in patients with resected stage I–III colorectal cancer. J Clin Oncol  2023;41:5 [Google Scholar]
  • 32. Newman  AM, Lovejoy  AF, Klass  DM, Kurtz  DM, Chabon  JJ, Scherer  F  et al.  Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol  2016;34:547–555 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Morris  LGT, Riaz  N, Desrichard  A, Şenbabaoğlu  Y, Hakimi  AA, Makarov  V  et al.  Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget  2016;7:10051–10063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. McGranahan  N, Swanton  C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell  2017;168:613–628 [DOI] [PubMed] [Google Scholar]
  • 35. Parikh  AR, Van Seventer  EE, Siravegna  G, Hartwig  AV, Jaimovich  A, He  Y  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 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Navin  N, Krasnitz  A, Rodgers  L, Cook  K, Meth  J, Kendall  J  et al.  Inferring tumor progression from genomic heterogeneity. Genome Res  2010;20:68–80 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Gerlinger  M, Rowan  AJ, Horswell  S, Math  M, Larkin  J, Endesfelder  D  et al.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med  2012;366:883–892 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Swanton  C. Intratumor heterogeneity: evolution through space and time. Cancer Res  2012;72:4875–4882 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Rajput  A, Bocklage  T, Greenbaum  A, Lee  J-H, Ness  SA. Mutant-allele tumor heterogeneity scores correlate with risk of metastases in colon cancer. Clin Colorectal Cancer  2017;16:e165–e170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Diaz  LA  Jr, Williams  RT, Wu  J, Kinde  I, Hecht  JR, Berlin  J  et al.  The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature  2012;486:537–540 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Li  C, Xu  J, Wang  X, Zhang  C, Yu  Z, Liu  J  et al.  Whole exome and transcriptome sequencing reveal clonal evolution and exhibit immune-related features in metastatic colorectal tumors. Cell Death Discov  2021;7:222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Sørensen  CG, Karlsson  WK, Pommergaard  H-C, Burcharth  J, Rosenberg  J. The diagnostic accuracy of carcinoembryonic antigen to detect colorectal cancer recurrence—a systematic review. Int J Surg  2016;25:134–144 [DOI] [PubMed] [Google Scholar]
  • 43. Kotani  D, Oki  E, Nakamura  Y, Yukami  H, Mishima  S, Bando  H  et al.  Molecular residual disease and efficacy of adjuvant chemotherapy in patients with colorectal cancer. Nat Med  2023;29:127–134 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Ko  Y, Kim  J, Park  JK-H, Kim  H, Jai Young  C, Sung-Bum  K  et al.  Limited detection of small (≤10 mm) colorectal liver metastasis at preoperative CT in patients undergoing liver resection. PLoS One  2017;12: e0189797. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Regge  D, Campanella  D, Anselmetti  GC, Cirillo  S, Gallo  TM, Muratore  A  et al.  Diagnostic accuracy of portal-phase CT and MRI with mangafodipir trisodium in detecting liver metastases from colorectal carcinoma. Clin Radiol  2006;61:338–347 [DOI] [PubMed] [Google Scholar]
  • 46. Chung  C-C, Hsieh  C-C, Lee  H-C, Wu  M-H, Huang  M-H, Hsu  W-H  et al.  Accuracy of helical computed tomography in the detection of pulmonary colorectal metastases. J Thorac Cardiovasc Surg  2011;141:1207–1212 [DOI] [PubMed] [Google Scholar]
  • 47. Reinert  T, Henriksen  TV, Christensen  E, Sharma  S, Salari  R, Sethi  H  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 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Tie  J, Cohen  JD, Wang  Y, Lu  L, Christie  M, Simons  K  et al.  Serial circulating tumour DNA analysis during multimodality treatment of locally advanced rectal cancer: a prospective biomarker study. Gut  2019;68:663–671 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Taïeb  J, Benhaim  L, Laurent Puig  P, Le Malicot  K, Emile  JF, Geillon  F  et al.  Decision for adjuvant treatment in stage II colon cancer based on circulating tumor DNA: the CIRCULATE-PRODIGE 70 trial. Dig Liver Dis  2020;52:730–733 [DOI] [PubMed] [Google Scholar]
  • 50. Folprecht  G, Reinacher-Schick  A, Weitz  J, Lugnier  C, Kraeft  A-L, Wisser  S  et al.  The CIRCULATE trial: circulating tumor DNA based decision for adjuvant treatment in colon cancer stage II evaluation (AIO-KRK-0217). Clin Colorectal Cancer  2022;21:170–174 [DOI] [PubMed] [Google Scholar]
  • 51. Taniguchi  H, Nakamura  Y, Kotani  D, Yukami  H, Mishima  S, Sawada  K  et al.  CIRCULATE-Japan: circulating tumor DNA–guided adaptive platform trials to refine adjuvant therapy for colorectal cancer. Cancer Sci  2021;112:2915–2920 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Morris  VK, Yothers  G, Kopetz  S, Jacobs  SA, Lucas  PC, Iqbal  A  et al.  Phase II/III study of circulating tumor DNA as a predictive biomarker in adjuvant chemotherapy in patients with stage II colon cancer: nRG-GI005 (COBRA). J Clin Oncol  2021;39:TPS3622-TPS [Google Scholar]
  • 53. Schraa  SJ, van Rooijen  KL, van der Kruijssen  DEW, Rubio Alarcón  C, Phallen  J, Sausen  M  et al.  Circulating tumor DNA guided adjuvant chemotherapy in stage II colon cancer (MEDOCC-CrEATE): study protocol for a trial within a cohort study. BMC Cancer  2020;20:790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Anandappa  G, Starling  N, Peckitt  C, Bryant  A, Begum  R, Carter  P  et al.  TRACC: tracking mutations in cell-free DNA to predict relapse in early colorectal cancer—a randomized study of circulating tumour DNA (ctDNA) guided adjuvant chemotherapy versus standard of care chemotherapy after curative surgery in patients with high risk stage II or stage III colorectal cancer (CRC). J Clin Oncol  2020;38:TPS4120-TPS [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

zrad146_Supplementary_Data
zrad146_supplementary_data.docx (75.3KB, docx)

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary materials. Raw data are available from the corresponding author upon reasonable request.

References

  • 1. Bray  F, Ferlay  J, Soerjomataram  I, Siegel  RL, Torre  LA, Jemal  A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin  2018;68:394–424 [DOI] [PubMed] [Google Scholar]
  • 2. Kreftregisteret . Cancer in Norway 2018 Cancer incidence, mortality, survival and prevalence in Norway 2018 (kreftregisteret.no).
  • 3. Young  PE, Womeldorph  CM, Johnson  EK, Maykel  JA, Brucher  B, Stojadinovic  A  et al.  Early detection of colorectal cancer recurrence in patients undergoing surgery with curative intent: current status and challenges. J Cancer  2014;5:262–271 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Tie  J, Cohen  JD, Lahouel  K, Lo  SN, Wang  Y, Kosmider  S  et al.  Circulating tumor DNA analysis guiding adjuvant therapy in stage II colon cancer. N Engl J Med  2022;386:2261–2272 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Bach  S, Sluiter  NR, Beagan  JJ, Mekke  JM, Ket  JCF, van Grieken  NCT  et al.  Circulating tumor DNA analysis: clinical implications for colorectal cancer patients. A systematic review. JNCI Cancer Spectr  2019;3:pkz042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Tie  J, Wang  Y, Tomasetti  C, Li  L, Springer  S, Kinde  I  et al.  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] [PMC free article] [PubMed] [Google Scholar]
  • 7. Tie  J, Cohen  JD, Wang  Y, Christie  M, Simons  K, Lee  M  et al.  Circulating tumor DNA analyses as markers of recurrence risk and benefit of adjuvant therapy for stage III colon cancer. JAMA Oncol  2019;5:1710–1717 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Diehn  M, Alizadeh  AA, Adams  H-P, Lee  JJ, Klassen  S, Palma  JF  et al.  Early prediction of clinical outcomes in resected stage II and III colorectal cancer (CRC) through deep sequencing of circulating tumor DNA (ctDNA). J Clin Oncol  2017;35:359128892431 [Google Scholar]
  • 9. Wan  JCM, Massie  C, Garcia-Corbacho  J, Mouliere  F, Brenton  JD, Caldas  C  et al.  Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer  2017;17:223–238 [DOI] [PubMed] [Google Scholar]
  • 10. Ignatiadis  M, Lee  M, Jeffrey  SS. Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin Cancer Res  2015;21:4786–4800 [DOI] [PubMed] [Google Scholar]
  • 11. Nordgård  O, Tjensvoll  K, Gilje  B, Søreide  K. Circulating tumour cells and DNA as liquid biopsies in gastrointestinal cancer. Br J Surg  2018;105:e110–e120 [DOI] [PubMed] [Google Scholar]
  • 12. Rasmussen  SL, Krarup  HB, Sunesen  KG, Pedersen  IS, Madsen  PH, Thorlacius-Ussing  O. Hypermethylated DNA as a biomarker for colorectal cancer: a systematic review. Colorectal Dis  2016;18:549–561 [DOI] [PubMed] [Google Scholar]
  • 13. Schøler  LV, Reinert  T, Ørntoft  M-BW, Kassentoft  CG, Árnadóttir  SS, Vang  S  et al.  Clinical implications of monitoring circulating tumor DNA in patients with colorectal cancer. Clin Cancer Res  2017;23:5437–5445 [DOI] [PubMed] [Google Scholar]
  • 14. de Figueiredo Barros  BD, Kupper  BEC, Aguiar Junior  S, de Mello  CAL, Begnami  MD, Chojniak  R  et al.  Mutation detection in tumor-derived cell free DNA anticipates progression in a patient with metastatic colorectal cancer. Front Oncol  2018;8:306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Wang  CB, Shahjehan  F, Merchea  A, Li  Z, Bekaii-Saab  TS, Grothey  A  et al.  Impact of tumor location and variables associated with overall survival in patients with colorectal cancer: a Mayo Clinic Colon and Rectal Cancer Registry study. Front Oncol  2019;9:76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Qin  Q, Yang  L, Sun  YK, Ying  JM, Song  Y, Zhang  W  et al.  Comparison of 627 patients with right- and left-sided colon cancer in China: differences in clinicopathology, recurrence, and survival. Chronic Dis Transl Med  2017;3:51–59 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Arnold  D, Lueza  B, Douillard  JY, Peeters  M, Lenz  HJ, Venook  A  et al.  Prognostic and predictive value of primary tumour side in patients with RAS wild-type metastatic colorectal cancer treated with chemotherapy and EGFR directed antibodies in six randomized trials. Ann Oncol  2017;28:1713–1729 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Song  Y, Wang  L, Ran  W, Li  G, Xiao  Y, Wang  X  et al.  Effect of tumor location on clinicopathological and molecular markers in colorectal cancer in eastern China patients: an analysis of 2,356 cases. Front Genet  2020;11:96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Mukund  K, Syulyukina  N, Ramamoorthy  S  Subramaniam  S. Right and left-sided colon cancers—specificity of molecular mechanisms in tumorigenesis and progression. BMC Cancer  2020;20:317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Lygre  KB, Eide  GE, Forsmo  HM, Dicko  A, Storli  KE, Pfeffer  F. Complications after open and laparoscopic right-sided colectomy with central lymphadenectomy for colon cancer: randomized controlled trial. BJS Open  2023;7:zrad074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. World Medical Association . World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA  2013;310:2191–2194 [DOI] [PubMed] [Google Scholar]
  • 22. Helsedirektoratet . Nasjonalt handlingsprogram med retningslinjer for diagnostikk, behandling og oppfølging av kreft i tykktarm og endetarm: 2017. http://www.helsebiblioteket.no/retningslinjer/kreft-i-tykktarm-og-endetarm/8-tykktarmskreft-u.metastaser/8.6-adjuvant-kjemoterapi.
  • 23. Forthun  RB, Hovland  R, Schuster  C, Puntervoll  H, Brodal  HP, Namløs  HM  et al.  ctDNA detected by ddPCR reveals changes in tumour load in metastatic malignant melanoma treated with bevacizumab. Sci Rep  2019;9:17471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Kaplan  EL, Meier  P. Nonparametric estimation from incomplete observations. J Am Stat Assoc  1958;53:457–481 [Google Scholar]
  • 25. Cox  DR. Regression models and life-tables. J R Stat Soc Ser B Methodol  1972;34:187–202 [Google Scholar]
  • 26. Tsao  SC-H, Weiss  J, Hudson  C, Christophi  C, Cebon  J, Behren  A  et al.  Monitoring response to therapy in melanoma by quantifying circulating tumour DNA with droplet digital PCR for BRAF and NRAS mutations. Sci Rep  2015;5:11198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Hamfjord  J, Guren  TK, Glimelius  B, Sorbye  H, Pfeiffer  P, Dajani  O  et al.  Clinicopathological factors associated with tumour-specific mutation detection in plasma of patients with RAS-mutated or BRAF-mutated metastatic colorectal cancer. Int J Cancer  2021;149:1385–1397 [DOI] [PubMed] [Google Scholar]
  • 28. Hofste  LSM, Geerlings  MJ, von Rhein  D, Rütten  H, Westenberg  AH, Weiss  MM  et al.  Circulating tumor DNA detection after neoadjuvant treatment and surgery predicts recurrence in patients with early-stage and locally advanced rectal cancer. Eur J Surg Oncol  2023;49:1283–1290 [DOI] [PubMed] [Google Scholar]
  • 29. Lee  MS, Menter  DG, Kopetz  S. Right versus left colon cancer biology: integrating the consensus molecular subtypes. J Natl Compr Canc Netw  2017;15:411–419 [DOI] [PubMed] [Google Scholar]
  • 30. Diehl  F, Li  M, Dressman  D, He  Y, Shen  D, Szabo  S  et al.  Detection and quantification of mutations in the plasma of patients with colorectal tumors. Proc Natl Acad Sci USA  2005;102:16368–16373 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Cohen  SA, Kasi  PM, Aushev  VN, Hanna  DL, Botta  GP, Sharif  S  et al.  Kinetics of postoperative circulating cell-free DNA and impact on minimal residual disease detection rates in patients with resected stage I–III colorectal cancer. J Clin Oncol  2023;41:5 [Google Scholar]
  • 32. Newman  AM, Lovejoy  AF, Klass  DM, Kurtz  DM, Chabon  JJ, Scherer  F  et al.  Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol  2016;34:547–555 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Morris  LGT, Riaz  N, Desrichard  A, Şenbabaoğlu  Y, Hakimi  AA, Makarov  V  et al.  Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget  2016;7:10051–10063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. McGranahan  N, Swanton  C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell  2017;168:613–628 [DOI] [PubMed] [Google Scholar]
  • 35. Parikh  AR, Van Seventer  EE, Siravegna  G, Hartwig  AV, Jaimovich  A, He  Y  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 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Navin  N, Krasnitz  A, Rodgers  L, Cook  K, Meth  J, Kendall  J  et al.  Inferring tumor progression from genomic heterogeneity. Genome Res  2010;20:68–80 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Gerlinger  M, Rowan  AJ, Horswell  S, Math  M, Larkin  J, Endesfelder  D  et al.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med  2012;366:883–892 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Swanton  C. Intratumor heterogeneity: evolution through space and time. Cancer Res  2012;72:4875–4882 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Rajput  A, Bocklage  T, Greenbaum  A, Lee  J-H, Ness  SA. Mutant-allele tumor heterogeneity scores correlate with risk of metastases in colon cancer. Clin Colorectal Cancer  2017;16:e165–e170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Diaz  LA  Jr, Williams  RT, Wu  J, Kinde  I, Hecht  JR, Berlin  J  et al.  The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature  2012;486:537–540 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Li  C, Xu  J, Wang  X, Zhang  C, Yu  Z, Liu  J  et al.  Whole exome and transcriptome sequencing reveal clonal evolution and exhibit immune-related features in metastatic colorectal tumors. Cell Death Discov  2021;7:222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Sørensen  CG, Karlsson  WK, Pommergaard  H-C, Burcharth  J, Rosenberg  J. The diagnostic accuracy of carcinoembryonic antigen to detect colorectal cancer recurrence—a systematic review. Int J Surg  2016;25:134–144 [DOI] [PubMed] [Google Scholar]
  • 43. Kotani  D, Oki  E, Nakamura  Y, Yukami  H, Mishima  S, Bando  H  et al.  Molecular residual disease and efficacy of adjuvant chemotherapy in patients with colorectal cancer. Nat Med  2023;29:127–134 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Ko  Y, Kim  J, Park  JK-H, Kim  H, Jai Young  C, Sung-Bum  K  et al.  Limited detection of small (≤10 mm) colorectal liver metastasis at preoperative CT in patients undergoing liver resection. PLoS One  2017;12: e0189797. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Regge  D, Campanella  D, Anselmetti  GC, Cirillo  S, Gallo  TM, Muratore  A  et al.  Diagnostic accuracy of portal-phase CT and MRI with mangafodipir trisodium in detecting liver metastases from colorectal carcinoma. Clin Radiol  2006;61:338–347 [DOI] [PubMed] [Google Scholar]
  • 46. Chung  C-C, Hsieh  C-C, Lee  H-C, Wu  M-H, Huang  M-H, Hsu  W-H  et al.  Accuracy of helical computed tomography in the detection of pulmonary colorectal metastases. J Thorac Cardiovasc Surg  2011;141:1207–1212 [DOI] [PubMed] [Google Scholar]
  • 47. Reinert  T, Henriksen  TV, Christensen  E, Sharma  S, Salari  R, Sethi  H  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 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Tie  J, Cohen  JD, Wang  Y, Lu  L, Christie  M, Simons  K  et al.  Serial circulating tumour DNA analysis during multimodality treatment of locally advanced rectal cancer: a prospective biomarker study. Gut  2019;68:663–671 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Taïeb  J, Benhaim  L, Laurent Puig  P, Le Malicot  K, Emile  JF, Geillon  F  et al.  Decision for adjuvant treatment in stage II colon cancer based on circulating tumor DNA: the CIRCULATE-PRODIGE 70 trial. Dig Liver Dis  2020;52:730–733 [DOI] [PubMed] [Google Scholar]
  • 50. Folprecht  G, Reinacher-Schick  A, Weitz  J, Lugnier  C, Kraeft  A-L, Wisser  S  et al.  The CIRCULATE trial: circulating tumor DNA based decision for adjuvant treatment in colon cancer stage II evaluation (AIO-KRK-0217). Clin Colorectal Cancer  2022;21:170–174 [DOI] [PubMed] [Google Scholar]
  • 51. Taniguchi  H, Nakamura  Y, Kotani  D, Yukami  H, Mishima  S, Sawada  K  et al.  CIRCULATE-Japan: circulating tumor DNA–guided adaptive platform trials to refine adjuvant therapy for colorectal cancer. Cancer Sci  2021;112:2915–2920 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Morris  VK, Yothers  G, Kopetz  S, Jacobs  SA, Lucas  PC, Iqbal  A  et al.  Phase II/III study of circulating tumor DNA as a predictive biomarker in adjuvant chemotherapy in patients with stage II colon cancer: nRG-GI005 (COBRA). J Clin Oncol  2021;39:TPS3622-TPS [Google Scholar]
  • 53. Schraa  SJ, van Rooijen  KL, van der Kruijssen  DEW, Rubio Alarcón  C, Phallen  J, Sausen  M  et al.  Circulating tumor DNA guided adjuvant chemotherapy in stage II colon cancer (MEDOCC-CrEATE): study protocol for a trial within a cohort study. BMC Cancer  2020;20:790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Anandappa  G, Starling  N, Peckitt  C, Bryant  A, Begum  R, Carter  P  et al.  TRACC: tracking mutations in cell-free DNA to predict relapse in early colorectal cancer—a randomized study of circulating tumour DNA (ctDNA) guided adjuvant chemotherapy versus standard of care chemotherapy after curative surgery in patients with high risk stage II or stage III colorectal cancer (CRC). J Clin Oncol  2020;38:TPS4120-TPS [Google Scholar]

Articles from BJS Open are provided here courtesy of Oxford University Press

RESOURCES