Skip to main content
  • AACR Journals
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

AACR logo

  • Register
  • Log in
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Collections
      • COVID-19 & Cancer Resource Center
      • Focus on Radiation Oncology
      • Novel Combinations
      • Reviews
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

  • AACR Journals
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • My Cart

Search

  • Advanced search
Molecular Cancer Therapeutics
Molecular Cancer Therapeutics
  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Collections
      • COVID-19 & Cancer Resource Center
      • Focus on Radiation Oncology
      • Novel Combinations
      • Reviews
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

Companion Diagnostics, Pharmacogenomic, and Cancer Biomarkers

Clinical Application of Circulating Tumor DNA in the Genetic Analysis of Patients with Advanced GIST

Hao Xu, Liang Chen, Yang Shao, Dongqin Zhu, Xiaofei Zhi, Qiang Zhang, Fengyuan Li, Jianghao Xu, Xisheng Liu and Zekuan Xu
Hao Xu
1Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu Province, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Liang Chen
1Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu Province, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yang Shao
2Nanjing Geneseeq Biotechnology Inc., Nanjing, Jiangsu Province.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dongqin Zhu
2Nanjing Geneseeq Biotechnology Inc., Nanjing, Jiangsu Province.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiaofei Zhi
3Department of General Surgery, The Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Qiang Zhang
1Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu Province, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fengyuan Li
1Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu Province, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jianghao Xu
1Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu Province, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xisheng Liu
4Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zekuan Xu
1Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu Province, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: xuzekuan@njmu.edu.cn
DOI: 10.1158/1535-7163.MCT-17-0436 Published January 2018
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumor of digestive tract. In the past, tissue biopsy was the main method for the diagnosis of GISTs. Although, circulating tumor DNA (ctDNA) detection by next-generation sequencing (NGS) may be a feasible and replaceable method for diagnosis of GISTs. We retrospectively analyzed the data for ctDNA and tissue DNA detection from 32 advanced GIST patients. We found that NGS obviously increased the positive rate of ctDNA detection. ctDNA detection identified rare mutations that were not detected in tissue DNA detection. Tumor size and Ki-67 were significant influencing factors of the positive rate of ctDNA detection and concordance between ctDNA and tissue DNA detection. In all patients, the concordance rate between ctDNA and tissue DNA detection was 71.9%, with moderate concordance, but the concordance was strong for patients with tumor size > 10 cm or Ki-67 > 5%. Tumor size, mitotic figure, Ki-67, and ctDNA mutation type were the significant influencing factors of prognosis, but only tumor size and ctDNA mutation type, were the independent prognostic factors for advanced GIST patients. We confirmed that ctDNA detection by NGS is a feasible and promising method for the diagnosis and prognosis of advanced GIST patients. Mol Cancer Ther; 17(1); 290–6. ©2017 AACR.

Introduction

Gastrointestinal stromal tumors (GIST) originating from interstitial cells of Cajal (ICC) are the most common abdominal soft-tissue sarcomas (1). Advances in genetic diagnosis and the development of tyrosine kinase inhibitors (TKI) have yielded great improvements in the treatment of patients with GISTs. The introduction of imatinib has increased the median survival of advanced GIST patients from 10 to 20 months to 51 to 57 months (2). Mutations of KIT and platelet-derived growth factor receptor alpha (PDGFRA) are the main focuses of genetic analysis, which is a critical method for diagnosis and targeted therapy in patients with GISTs.

Circulating tumor DNA (ctDNA), small fragments of extracellular DNA released by apoptotic tumor cells, contains information on the specific somatic mutations in tumor cells (3). The detection of ctDNA in peripheral blood, referred to as “liquid biopsy,” is a promising method for the early diagnosis and prognostic evaluation of human malignancies (4). Compared with traditional tissue biopsy, liquid biopsy of ctDNA is noninvasive, simpler, and safer. The ctDNA level is associated with tumor burden, indicating a potential role of ctDNA as an informative, inherently specific, and highly sensitive biomarker in the diagnosis and prognosis of patients with various types of cancers (5–7). ctDNA detection has also been used to determine genotype in patients with GIST, which is beneficial for guiding targeted therapy (8).

The positive rates of methods such as BEAMing (beads, emulsions, amplification, and magnetics) and PCR for detecting ctDNA mutations are insufficient (8, 9). With the development of next-generation sequencing (NGS), a technology capable of simultaneously sequencing millions of DNA fragments without previous sequence knowledge, increases in the positive rate of ctDNA mutation detection have become feasible (10). NGS has been widely used in genetic analysis of patients with various tumors including GIST because of the advantages of higher reliability and lower costs (10, 11).

National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines recommend genetic analysis to guide targeted therapy in patients with GISTs (12). ctDNA detection represents a safer alternative to high-risk traditional tissue biopsy for the genetic analysis of GIST patients. A previous study demonstrated the reliability of ctDNA detection by NGS for diagnosing genotype in GIST patients (13), but the association between ctDNA and tissue DNA detection has not been examined. The aim of our study was to evaluate the feasibility of ctDNA detection by NGS, the concordance between ctDNA and tissue DNA detection, and independent prognostic factors in patients with advanced GISTs.

Patients and Methods

Patients

We retrospectively analyzed the data of genetic analysis of ctDNA and tissue DNA detection from 32 advanced GIST patients admitted in the First Affiliated Hospital of Nanjing Medical University from March 2015 to February 2017. Inclusion criteria are as below: patients with confirmed GISTs, good physical condition, and normal function in important organs. Exclusion criteria are as below: patients could not undergo percutaneous CT-guided puncture and biopsy because of any cause, simultaneously with other tumors, with dysfunction of important organs, being pregnant or in lactation period, simultaneously joined in other clinical trials. Our study was approved by The Institutional Ethical Board of the First Affiliated Hospital of NJMU (Ethical number: 2013-SR-142). All patients have been informed of sample collection and signed informed consent. Registration number of the study was ChiCTR-RNC-14004667. Risk levels of patients were estimated according to Armed Forces Institute of Pathology (AFIP) criteria (14).

Methods

Sample collection

Fresh tumor tissues (no less than 1 cm, 1–3 strips) obtained by 1-mm puncture needle or formalin-fixed paraffin-embedded (FFPE) blocks/sections were collected from each patient. Diagnosis of GISTs and tumor purity had been confirmed in pathology department of our hospital. Necrotic tissues of tumor samples should be less than 30%. Peripheral blood (no less than 8 mL) collected from each patient was stored in EDTA-coated tubes (BD Biosciences). Plasma was extracted using a precooling centrifugal machine (Eppendorf 5424, 1,800 × g, 4°C, for 10 minutes) within 2 hours since blood collection and stored at 4°C, then transferred to the testing laboratory within 48 hours. Tumor tissue samples and blood samples were collected at the same time. All detections were performed in Nanjing Geneseeq Biotechnology Incorporation according to the instructions reviewed and approved by the institutional ethical board of the hospital.

DNA extraction and quantification

Following FFPE blocks being deparaffinized with xylene for twice, DNA was extracted using a QIAamp DNA FFPE Tissue Kit (Qiagen) according to the manufacturer's instructions. Fresh tissue DNA and plasma DNA were extracted using a DNeasy Blood & Tissue kit (Qiagen) according to the manufacturer's recommendations. Extracted DNA was purified and qualified employing the Nanodrop2000 (Thermo Fisher Scientific) and then quantified by Qubit3.0 (Life Technology) with a dsDNA HS Assay Kit (Life Technologies) according to the manufacturer's protocols. A260/280 and A260/A230 values were recorded. DNA purity criteria are as follows, A260/280 value was 1.5–2.0 and A260/A230 value was 1.8–2.3.

Fragment distribution of plasma DNA was analyzed using Analyser 2100 Bio-analyzer (Agilent Technology) with an Agilent High Sensitivity DNA kit (Agilent Technology) according to the manufacturer's instructions. Small fragmented DNA was specifically selected from ctDNA samples adopting the Agencourt AMPure XP beads (Beckman Coulter) method according to manufacturer's protocols.

Sequencing library establishment

Genomic DNA was sheared into 350-bp fragments using M220 instrument (Covaris). Then end-repairing, A-tailing, and adaptor-adding were sequentially performed in genomic DNA fragments and small fragmented ctDNA adopting a qPCR instrument (Bio-Rad) according to the manufacturer's recommendations, followed by fragment selection in genomic DNA with Agencourt AMPure XP beads method. Sequencing libraries were established with a KAPA Hyper Prep kit (KAPA Biosystems) and amplified by PCR according to the manufacturer's protocols.

Target enrichment of gene fragments

xGen blocking oligos (Integrated DNA Technology, 1 nmol/μL in TE buffer) and human cot-1 DNA (Life Technologies) were used as blocking reagents. Capture reactions were performed using NimbleGen SeqCap EZ Hybridization and Wash Kit (Roche) and Dynabeads M-270 Streptavidin (Life Technologies) in accordance with the manufacturer's protocols. Illumina p5 (5′-AATGATACGGCGACCACCGA-3′) and p7 (5′-CAAGCAGAAGACGGCATACGAGAT-3′) primers were used in amplifying captured libraries in KAPA HiFi HotStart ReadyMix (KAPA Biosystems). Amplified libraries were purified with the Agencourt AMPure XP beads method and quantified by qPCR with a KAPA Library Quantification kit (KAPA Biosystems). Analyser 2100 Bio-analyzer (Agilent Technologies) was employed to determine the size of library fragments. Target-enriched libraries were sequenced with HiSeq4000 NGS platform (Illumina) according to the manufacturer's instructions. Sequencing depth was no less than 600 × mean coverage by non-PCR duplicate read pairs in tissue samples. While in ctDNA samples, sequencing depth of majority of samples was no less than 3,000 × mean coverage by non-PCR duplicate read pairs in spite of different sequencing depths achieved for the assessment.

Sequencing

Sequencing reagents were prepared with HiSeq 4000 PE Cluster Kit (Illumina) according to user guidelines. Paired-end sequencing was performed in sequencing process. The 416 panel (with sequencing length of 150 bp) produced by Nanjing Geneseeq Biotechnology Incorporation was used as DNA capture probe of tumor-related genes. HiSeq 4000 (Illumina) was employed as a sequencing platform in this study. Data collection software (Illumina) was used to control sequencing process and perform the analysis of real-time data.

Bioinformatics analysis

Original image data acquired from HiSeq 4000 sequencing platform were transferred by base calling analysis into raw sequence data, which contained sequence information and corresponding sequencing quality information. Quality control was performed using Trimmomatic (Illumina). Comprehensive information of tissue DNA and ctDNA mutations, including point mutation, fusion, amplification, deletion and insertion mutations, was determined by bioinformatics analysis, which further performed quality control of sequencing in mapping process.

Statistical analysis

χ2 test was used in univariate analysis of ctDNA-positive rate and concordance detection. Concordance analysis was performed by Kappa concordance test using SPSS 20 and SAS 9.3. Log-rank test was employed in univariate analysis of progression-free survival (PFS). Multivariate Cox regression was performed to estimate independent prognostic factors of advanced GIST patients. PFS was defined from the date of being selected for this study to the date of disease progression or death. P < 0.05 was considered statistically significant.

Results

Clinicopathologic characteristics of patients

The patients included 19 males (59.4%) and 13 females (40.6%). Ten patients (31.2%) were >70 years of age, and 22 (68.8%) were ≤70 years of age. The average age was 62.8 years (range, 42–83 years), and the median age was 64 years. The primary tumor site was the stomach in 18 patients (56.3%) and non-stomach in 14 patients (43.7%). The tumor size was >10 cm in 23 patients (71.9%) and ≤10 cm in 9 patients (28.1%). The number of mitotic figures per 50 high-power fields (HPF) was >5 in 12 patients (37.5%) and ≤5 in 20 patients (62.5%). The recurrence risk level was low for 5 patients (15.6%), intermediate for 6 patients (18.8%), and high for 21 patients (65.6%). At the beginning of the study, 5 patients (15.6%) had previously received imatinib, and 27 (84.4%) had received no treatment. IHC detected Ki-67 > 5% in 19 patients (59.4%) and ≤5% in 13 patients (40.6%). The cell morphology was spindle cells in 27 patients (84.4%), epithelioid cells in 1 patient (3.1%), and mixed in 4 patients (12.5%; Table 1).

View this table:
  • View inline
  • View popup
Table 1.

Univariate analysis of influence factors of positive rate of ctDNA detection

After the beginning of the study, most patients received imatinib continuously. The dosages were as follows: 25 patients (78.1%) received 400 mg daily, and 3 patients (9.4%) received 600 mg daily; 4 patients (12.5%) received other treatments (including 1 patient received 200 mg daily, 1 patient did not receive imatinib, and 2 patients received imatinib purchased from India; Supplementary Fig. S1A). At last follow-up, 8 patients had undergone surgery (including 5 underwent surgery after neoadjuvant treatment and 3 underwent direct surgery), whereas the remaining 24 patients had not undergone surgery (Supplementary Fig. S1B).

Advantages of ctDNA detection by NGS

Among the patients, ctDNA mutation detection was positive in 18. The positive rate of ctDNA mutation was 56.3% (18/32), obviously higher than the previously reported rates of ctDNA tests using other technologies, such as BEAMing (16.7%, 5/30) and PCR (39.5%, 15/38; refs. 8, 9). This result indicates that NGS can remarkably increase the positive rate of ctDNA mutation detection. (Fig. 1A).

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Positive rate and number of ctDNA mutation. A, Compared with other technologies such as BEAMing and PCR reported previously, NGS obviously increased the positive rate of ctDNA mutation detection. B, It almost could not detect any mutation when tumor size ≤ 10 cm, while one or more mutation types had been detected when tumor size > 10 cm. The number of ctDNA mutation is positively correlated with tumor size.

Furthermore, in one patient, KIT exon14 mutation was detected by the ctDNA test, whereas a negative result was obtained in tissue DNA detection. This result reveals that ctDNA detection by NGS can identify gene mutations (even rare mutations) in cases in which the results of tissue DNA detection are negative. ctDNA detection may thus indicate tumor abnormalities more comprehensively than tissue biopsy.

Correlation between the number of ctDNA mutations and tumor size

The ability of ctDNA analysis to detect mutations was limited when the tumor size ≤ 10 cm, whereas ctDNA detection typically revealed one or more mutations when the tumor size > 10 cm. The number of ctDNA mutations was obviously higher in patients with tumor size> 10 cm than in patients with tumor size ≤ 10 cm. These findings support a positive correlation between the number of ctDNA mutations and tumor size. (Fig. 1B)

Univariate analysis of influencing factors of the positive rate of ctDNA detection

The positive rate of ctDNA detection was significantly higher for tumor size > 10 cm than for tumor size ≤ 10 cm [73.9% (17/23) vs. 11.1% (1/9), P = 0.004]. In addition, the positive rate of ctDNA detection was significantly higher for Ki-67 > 5% than for Ki-67 ≤ 5% [78.9% (15/19) vs. 23.1% (3/13), P = 0.002; (Table 1; Supplementary Fig. S2)]. Therefore, tumor size and Ki-67 were important influencing factors for the positive rate of ctDNA detection in advanced GIST patients.

Univariate analysis of influencing factors of concordance between ctDNA and tissue DNA detection

The concordance rate between ctDNA and tissue DNA detection was significantly higher for tumor size >10 cm than for tumor size ≤10 cm [87.0% (20/23) vs. 33.3% (3/9), P = 0.006]. Furthermore, the concordance rate between ctDNA and tissue DNA detection was significantly higher for Ki-67 > 5% than for Ki-67 ≤ 5% [89.5% (17/19) vs. 46.2%(6/13), P = 0.015; (Supplementary Table S1; Fig. 2A and B)]. Tumor size and Ki-67 were therefore significant influencing factors of concordance between ctDNA and tissue DNA detection in patients with advanced GISTs.

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

Correlation between tumor size, Ki-67, and the concordance rate of ctDNA detection. The concordance rates between ctDNA and tissue DNA detections in patients with tumor size > 10 cm (87.0%) or Ki-67 > 5% (89.5%) were significantly higher than that in patients with tumor size ≤ 10 cm (33.3%) or Ki-67 ≤ 5% (46.2%), respectively.

Analysis of concordance between ctDNA and tissue DNA detection

The ctDNA test detected KIT exon 9 mutation in 2 patients, KIT exon 11 mutation in 14 patients, KIT exon 14 mutation in 1 patient, PDGFRA exon 18 mutation in 1 patient, and normal genotype in 14 patients. By comparison, the tissue DNA test detected KIT exon 9 mutation in 5 patients, KIT exon 11 mutation in 18 patients, PDGFRA exon 18 mutation in 2 patients, and normal genotype in 7 patients. The outcomes of ctDNA and tissue DNA detection were concordant for 71.9% (23/32) patients, including 2 patients with KIT exon 9 mutation, 14 patients with KIT exon 11 mutation, 1 patient with PDGFRA exon 18 mutation, and 6 patients with normal genotype. Evaluation of the concordance by the kappa concordance test yielded a weighted kappa value of 0.489 (P < 0.001), indicating moderate concordance (Table 2).

View this table:
  • View inline
  • View popup
Table 2.

Concordance analysis between ctDNA and tissue DNA detections in genetic analysis (Kappa concordance test, n = 32)

Among the 23 patients with tumor size > 10 cm, the outcomes between ctDNA and tissue DNA detection were concordant for 20 patients, including 2 patients with KIT exon 9 mutation, 14 patients with KIT exon 11 mutation, 1 patient with PDGFRA exon 18 mutation, and 3 patients with normal genotype. The concordance rate was 87.0% (20/23), and the kappa value was 0.754 (P < 0.001), indicating high concordance (Supplementary Table S2).

Among the 19 patients with Ki-67 > 5%, concordant outcomes between ctDNA and tissue DNA detection were obtained for 17, including 2 patients with KIT exon 9 mutation, 12 patients with KIT exon 11 mutation, 1 patient with PDGFRA exon 18 mutation, and 2 patients with normal genotype. The concordance rate was 89.5% (17/19), and the kappa value was 0.800 (P < 0.001), indicating strong concordance (Supplementary Table S3).

Univariate analysis of the influencing factors of prognosis

At last follow-up, 11 patients were progressed (including 2 patients died); progression did not occur in 21 patients (including 8 patients underwent surgery). The average PFS was 12.2 months. The log-rank test showed that tumor size (P = 0.023), mitotic figures (P = 0.025), Ki-67 (P = 0.039), and mutation type (P = 0.045) were significant influencing factors of prognosis in advanced GIST patients (Supplementary Table S4).

Figure 3 shows the PFS curves for the significant influencing factors. The prognosis of patients with tumor size ≤ 10 cm was significantly superior to that of patients with tumor size > 10 cm (mean PFS: 14.1 months vs. 10.6 months, Fig. 3A). The prognosis of patients with mitotic figures ≤ 5/50 HPF was significantly better than that of patients with mitotic figures > 5/50 HPF (mean PFS:12.9 months vs. 10.9 months, Fig. 3B). The prognosis of patients with Ki-67 ≤ 5% was significantly better than that of patients with Ki-67 > 5% (mean PFS: 14.3 months vs. 11.2 months, Fig. 3C). The prognosis of patients with KIT exon11 mutation of ctDNA was significantly superior to that of patients with other mutation types (mean PFS: 13.9 months vs. 10.8 months, Fig. 3D).

Figure 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 3.

PFS curves of significant prognostic factors. PFS was defined from the date of being selected for this study to the date of disease progression or death. At last follow-up, 11 patients had been with progression and 21 patients without progression. Average time of PFS was 12.2 months. Tumor size, mitotic figure, Ki-67, and ctDNA mutation type were indicated by log-rank test as the significant prognostic factors of patients. A, Tumor size, mean time of PFS: 14.1 months versus 10.6 months (P = 0.023). B, Mitotic figure, mean time of PFS: 12.9 months versus 10.9 months (P = 0.025). C, Ki-67, mean time of PFS: 14.3 months versus 11.2 months (P = 0.039). D, Type of ctDNA mutation, mean time of PFS: 13.9 months vs. 10.8 months (P = 0.045).

Multivariate Cox regression analysis of significant influencing factors of prognosis

The four significant influencing factors were imported into the Cox regression model. The results indicated that tumor size [95% confidence interval (CI) of HR, 1.299–602.921, P = 0.033)] and type of ctDNA mutation (95% CI of HR, 0.026–0.971, P = 0.046) were independent prognostic factors for advanced GIST patients (Table 3).

View this table:
  • View inline
  • View popup
Table 3.

Multivariate Cox regression analysis between significant influence factors and prognosis

Discussion

The incidence of GIST, the most common mesenchymal tumor of the digestive tract, has increased greatly in recent years with the development of biotechnological detection (1). Despite improvements in the prognosis of GIST patients due to the introduction of TKIs and improved surgical strategies, clinical efficiency and PFS remain primarily dependent on the types of KIT and PDGFRA mutations (2). Therefore, genetic analysis is necessary to guide targeted therapy in GIST patients.

ctDNA detection is a promising method for genetic analysis and has been used in the genetic diagnosis and prognosis evaluation of diverse cancer types, including GIST (15–17). ctDNA can be easily obtained from the bloodstream, theoretically overcoming limitations of tissue biopsy such as invasiveness, the inability to characterize molecular heterogeneity, and unsuitability of FFPE blocks for wider genome analysis (15). In this study, ctDNA and tissue DNA detection were performed to assess the reliability of ctDNA in the diagnosis of GISTs and the concordance between ctDNA and tissue DNA detection in advanced GIST patients.

NGS, a high-throughput sequencing technology, was employed to detect ctDNA. Similar to the positive ctDNA mutation rate (52%) reported by Kang and colleagues (13), our results revealed a higher positive rate of ctDNA mutation (56.3%) than those detected by technologies such as BEAMing and PCR (8, 9). Repeated positive results or increased ctDNA mutations have been observed in GIST patients with relapse or progressive disease, indicating that ctDNA can be used as a tumor-specific biomarker (9). Similarly, we demonstrated a positive correlation between the number of ctDNA mutations and tumor size; the number of ctDNA mutations was much higher in patients with tumor size >10 cm than in those with tumor size ≤ 10 cm.

In one patient, KIT exon 14 mutation was identified by ctDNA detection but not tissue DNA detection, indicating that ctDNA detection can reveal gene mutations or even rare mutations in the case of a negative result by tissue DNA detection. We hypothesize that cell subsets with different mutations exist in tumor tissues, and thus tissue biopsy may only partly reflect the cell types in the tumor. In contrast, ctDNA detection can identify alloplasmic cell subsets or rare mutations and thus more comprehensively reflects tumor abnormalities. ctDNA detection will therefore be more beneficial in guiding treatment strategies.

A few studies have examined influencing factors of ctDNA detection in GIST patients. Brychta and colleagues (18) reported a positive correlation between ctDNA detection and the total number of tumor cells in the primary tumor in the early stage of pancreatic cancer. Gao and colleagues (19) showed that the presence of ctDNA was associated with larger tumor size, TNM stage, and Helicobacter pylori infection in gastric cancer. Similarly, we demonstrated that the positive rate of ctDNA detection was positively correlated with tumor size and Ki-67 in advanced GIST patients. The positive rates of ctDNA detection were obviously higher in patients with tumor size > 10 cm or Ki-67 > 5%.

The concordance of gene mutations detected by ctDNA and tissue DNA tests has been analyzed in several studies. Yao and colleagues (20) showed that the overall concordance rate of gene mutations between tissue DNA and ctDNA detection was 78.21% in patients with advanced non–small cell lung cancer. Another study indicated that the concordance of genomic alterations between tissue DNA and ctDNA was 91.0%–94.2% in breast cancer (21). Consistent with these previous studies, we obtained an overall concordance rate between ctDNA and tissue DNA detection of 71.9%, with moderate concordance. In addition to influencing the positive rate of ctDNA detection, tumor size and Ki-67 were significant influencing factors of the concordance rate for mutations detected by ctDNA and tissue DNA. The concordance of genetic mutations between ctDNA and tissue DNA detection was significantly higher in patients with tumor size > 10 cm or Ki-67 > 5% than in all patients with GISTs. These results support the potential of ctDNA detection for tumor-related mutation profiling.

Several studies have examined prognostic factors for GIST patients. Tumor mitotic rate, Ki-67, tumor size, tumor site, and tumor rupture are considered independent prognostic factors for GIST recurrence (2). Resistant mutations identified by ctDNA have been potentially associated with poor prognosis in patients with GIST (8). Feng and colleagues (22) showed that mitotic index and tumor size are also independent prognostic factors in patients with mesenteric GISTs. Furthermore, positive expression of Ki-67 is associated with poor prognosis in GIST patients (23). Similarly, we identified tumor size, mitotic figures, Ki-67, and ctDNA mutation type as significant influencing factors of prognosis, but only tumor size and ctDNA mutation type were identified as independent prognostic factors for advanced GIST patients. The prognoses of patients with tumor size > 10 cm or KIT exon11 mutation detected by ctDNA were significantly superior to those of patients with tumor size ≤ 10 cm or other mutations. Mitotic figures, Ki-67, and tumor site were not independent prognostic factors for GIST patients in our study, possibly due to the small sample size. Our results await further validation by additional clinical data.

In conclusion, as an alternative to tissue biopsy, ctDNA detection by NGS is a feasible method for the genetic analysis of advanced GIST patients. The concordance between ctDNA and tissue DNA detection was sufficiently high in patients with tumor size > 10 cm or Ki-67 > 5%. Tumor size and ctDNA mutation type were independent prognostic factors for advanced GIST patients. These findings support the potential role of ctDNA as a tumor-specific biomarker in the diagnosis and prognosis of patients with advanced GIST. Furthermore, with improvements in biotechnological detection, the tumor size cutoff of 10 cm in GIST patients may be reduced because of the higher sensitivity of ctDNA detection.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Authors' Contributions

Conception and design: H. Xu, X. Zhi, Z. Xu

Development of methodology: H. Xu, L. Chen, Y. Shao, D. Zhu, Q. Zhang, J. Xu

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): H. Xu, L. Chen, D. Zhu

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): H. Xu, L. Chen, X. Zhi, Q. Zhang, J. Xu

Writing, review, and/or revision of the manuscript: H. Xu, L. Chen, Q. Zhang, F. Li

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): H. Xu, L. Chen, Y. Shao, Q. Zhang, J. Xu, X. Liu

Study supervision: Z. Xu

Acknowledgments

We thank Nanjing Geneseeq Biotechnology Incorporation for their kindly help in genetic analysis of advanced GIST patients.

This work was supported by grants from the National Natural Science Foundation of China (81572362, to Z.K. Xu), Jiangsu Key Medical Discipline (General Surgery)(ZDXKA2016005, to Z.K. Xu), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD, JX10231801, to Z.K. Xu), Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University (to Z.K. Xu) and Natural Science Foundation of Jiangsu Province (BK20141493, to H. Xu).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Footnotes

  • Note: Supplementary data for this article are available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/).

  • Received June 4, 2017.
  • Revision received September 15, 2017.
  • Accepted October 11, 2017.
  • ©2017 American Association for Cancer Research.

References

  1. 1.↵
    1. Valsangkar N,
    2. Sehdev A,
    3. Misra S,
    4. Zimmers TA,
    5. O'Neil BH,
    6. Koniaris LG
    . Current management of gastrointestinal stromal tumors: surgery, current biomarkers, mutations, and therapy. Surgery 2015;158:1149–64.
    OpenUrl
  2. 2.↵
    1. Joensuu H,
    2. Hohenberger P,
    3. Corless CL
    . Gastrointestinal stromal tumour. Lancet 2013;382:973–83.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Mouliere F,
    2. Thierry AR
    . The importance of examining the proportion of circulating DNA originating from tumor, microenvironment and normal cells in colorectal cancer patients. Expert Opin Biol Ther 2012;1:S209–15.
    OpenUrl
  4. 4.↵
    1. Bettegowda C,
    2. Sausen M,
    3. Leary RJ,
    4. Kinde I,
    5. Wang Y,
    6. Agrawal N,
    7. et al.
    Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Translat Med 2014;6:224ra24.
    OpenUrl
  5. 5.↵
    1. Dawson SJ,
    2. Tsui DW,
    3. Murtaza M,
    4. Biggs H,
    5. Rueda OM,
    6. Chin SF,
    7. et al.
    Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med 2013;368:1199–209.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. No JH,
    2. Kim K,
    3. Park KH,
    4. Kim YB
    . Cell-free DNA level as a prognostic biomarker for epithelial ovarian cancer. Anticancer Res 2012;32:3467–72.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    1. Mitchell S,
    2. Ho T,
    3. Brown G,
    4. Baker R,
    5. Thomas M,
    6. McEvoy A,
    7. et al.
    Evaluation of methylation biomarkers for detection of circulating tumor DNA and application to colorectal cancer. Genes 2016;7:125.
    OpenUrl
  8. 8.↵
    1. Yoo C,
    2. Ryu MH,
    3. Na YS,
    4. Ryoo BY,
    5. Park SR,
    6. Kang YK
    . Analysis of serum protein biomarkers, circulating tumor DNA, and dovitinib activity in patients with tyrosine kinase inhibitor-refractory gastrointestinal stromal tumors. Ann Oncol 2014;25:2272–7.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Maier J,
    2. Lange T,
    3. Kerle I,
    4. Specht K,
    5. Bruegel M,
    6. Wickenhauser C,
    7. et al.
    Detection of mutant free circulating tumor DNA in the plasma of patients with gastrointestinal stromal tumor harboring activating mutations of CKIT or PDGFRA. Clin Cancer Res 2013;19:4854–67.
    OpenUrlAbstract/FREE Full Text
  10. 10.↵
    1. Kamps R,
    2. Brandão R,
    3. Bosch B,
    4. Paulussen A,
    5. Xanthoulea S,
    6. Blok M,
    7. et al.
    Next-generation sequencing in oncology: genetic diagnosis, risk prediction and cancer classification. Int J Mol Sci 2017;18:308.
    OpenUrl
  11. 11.↵
    1. Saponara M,
    2. Urbini M,
    3. Astolf A,
    4. Indio V,
    5. Ercolani G,
    6. Del Gaudio M,
    7. et al.
    Molecular characterization of metastatic exon 11 mutant gastrointestinal stromal tumors (GIST) beyond KIT/PDGFRα genotype evaluated by next generation sequencing (NGS). Oncotarget 2015;6:42243–57.
    OpenUrl
  12. 12.↵
    1. von Mehren M,
    2. Randall RL,
    3. Benjamin RS,
    4. Boles S,
    5. Bui MM,
    6. Conrad EU III.,
    7. et al.
    Soft Tissue Sarcoma, Version 2.2016, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2016;14:758–86.
    OpenUrlAbstract/FREE Full Text
  13. 13.↵
    1. Kang G,
    2. Sohn BS,
    3. Pyo JS,
    4. Kim JY,
    5. Lee B,
    6. Kim KM
    . Detecting primary KIT mutations in presurgical plasma of patients with gastrointestinal stromal tumor. Mol Diagn Ther 2016;20:347–51.
    OpenUrl
  14. 14.↵
    1. Miettinen M,
    2. Lasota J
    . Gastrointestinal stromal tumors: Pathology and prognosis at different sites. Semin Diagn Pathol 2006;23:70–83.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Nannini M,
    2. Astolfi A,
    3. Urbini M,
    4. Biasco G,
    5. Pantaleo MA
    . Liquid biopsy in gastrointestinal stromal tumors: a novel approach. J Transl Med 2014;12:210.
    OpenUrl
  16. 16.↵
    1. Tabernero J,
    2. Lenz H-J,
    3. Siena S,
    4. Sobrero A,
    5. Falcone A,
    6. Ychou M,
    7. et al.
    Analysis of circulating DNA and protein biomarkers to predict the clinical activity of regorafenib and assess prognosis in patients with metastatic colorectal cancer: a retrospective, exploratory analysis of the CORRECT trial. Lancet Oncol 2015;16:937–48.
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Pasquale R,
    2. Fenizia F,
    3. Esposito Abate R,
    4. Sacco A,
    5. Esposito C,
    6. Forgione L,
    7. et al.
    Assessment of high-sensitive methods for the detection of EGFR mutations in circulating free tumor DNA from NSCLC patients. Pharmacogenomics 2015;16:1135–48.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Brychta N,
    2. Krahn T,
    3. von Ahsen O
    . Detection of KRAS mutations in circulating tumor DNA by digital PCR in early stages of pancreatic cancer. Clin Chem 2016;62:1482–91.
    OpenUrlAbstract/FREE Full Text
  19. 19.↵
    1. Gao Y,
    2. Zhang K,
    3. Xi H,
    4. Cai A,
    5. Wu X,
    6. Cui J,
    7. et al.
    Diagnostic and prognostic value of circulating tumor DNA in gastric cancer: a meta-analysis. Oncotarget 2017;8:6330–40.
    OpenUrl
  20. 20.↵
    1. Yao Y,
    2. Liu J,
    3. Li L,
    4. Yuan Y,
    5. Nan K,
    6. Wu X,
    7. et al.
    Detection of circulating tumor DNA in patients with advanced non-small cell lung cancer. Oncotarget 2017;8:2130–40.
    OpenUrl
  21. 21.↵
    1. Chae YK,
    2. Davis AA,
    3. Jain S,
    4. Santa-Maria C,
    5. Flaum L,
    6. Beaubier N,
    7. et al.
    Concordance of genomic alterations by next-generation sequencing (NGS) in tumor tissue versus circulating tumor DNA in breast cancer. Mol Cancer Ther 2017;16:1412–20.
    OpenUrlAbstract/FREE Full Text
  22. 22.↵
    1. Feng F,
    2. Feng B,
    3. Liu S,
    4. Liu Z,
    5. Xu G,
    6. Guo M,
    7. et al.
    Clinicopathological features and prognosis of mesenteric gastrointestinal stromal tumor: evaluation of a pooled case series. Oncotarget 2017;8:46514–22.
    OpenUrl
  23. 23.↵
    1. Lu C,
    2. Liu L,
    3. Wu X,
    4. Xu W
    . CD133 and Ki-67 expression is associated with gastrointestinal stromal tumor prognosis. Oncol Lett 2013;6:1289–94.
    OpenUrlPubMed
View Abstract
PreviousNext
Back to top
Molecular Cancer Therapeutics: 17 (1)
January 2018
Volume 17, Issue 1
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Editorial Board (PDF)

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Molecular Cancer Therapeutics article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Clinical Application of Circulating Tumor DNA in the Genetic Analysis of Patients with Advanced GIST
(Your Name) has forwarded a page to you from Molecular Cancer Therapeutics
(Your Name) thought you would be interested in this article in Molecular Cancer Therapeutics.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Clinical Application of Circulating Tumor DNA in the Genetic Analysis of Patients with Advanced GIST
Hao Xu, Liang Chen, Yang Shao, Dongqin Zhu, Xiaofei Zhi, Qiang Zhang, Fengyuan Li, Jianghao Xu, Xisheng Liu and Zekuan Xu
Mol Cancer Ther January 1 2018 (17) (1) 290-296; DOI: 10.1158/1535-7163.MCT-17-0436

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Clinical Application of Circulating Tumor DNA in the Genetic Analysis of Patients with Advanced GIST
Hao Xu, Liang Chen, Yang Shao, Dongqin Zhu, Xiaofei Zhi, Qiang Zhang, Fengyuan Li, Jianghao Xu, Xisheng Liu and Zekuan Xu
Mol Cancer Ther January 1 2018 (17) (1) 290-296; DOI: 10.1158/1535-7163.MCT-17-0436
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Patients and Methods
    • Methods
    • Results
    • Discussion
    • Disclosure of Potential Conflicts of Interest
    • Authors' Contributions
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • ctDNA in Gastrointestinal Tumors
  • CDK12 Expression as a Biomarker in Breast Cancer
Show more Companion Diagnostics, Pharmacogenomic, and Cancer Biomarkers
  • Home
  • Alerts
  • Feedback
  • Privacy Policy
Facebook  Twitter  LinkedIn  YouTube  RSS

Articles

  • Online First
  • Current Issue
  • Past Issues
  • Meeting Abstracts

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians

About MCT

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2021 by the American Association for Cancer Research.

Molecular Cancer Therapeutics
eISSN: 1538-8514
ISSN: 1535-7163

Advertisement