Systematic Reviews Open Access
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Nov 15, 2021; 13(11): 1799-1812
Published online Nov 15, 2021. doi: 10.4251/wjgo.v13.i11.1799
Cell-free DNA liquid biopsy for early detection of gastrointestinal cancers: A systematic review
Isabelle Uhe, Monika Elisabeth Hagen, Frédéric Ris, Jeremy Meyer, Christian Toso, Jonathan Douissard, Abdominal Surgery Division, Geneva University Hospitals, Geneva 1211, Switzerland
ORCID number: Isabelle Uhe (0000-0003-1224-2296); Monika Elisabeth Hagen (0000-0003-0158-1559); Frédéric Ris (0000-0001-7421-6101); Jeremy Meyer (0000-0003-3381-9146); Christian Toso (0000-0003-1652-4522); Jonathan Douissard (0000-0002-3931-3157).
Author contributions: Uhe I and Douissard J designed the review, performed studies selection, data analysis, and wrote the manuscript. All authors performed critical revision of the manuscript and approved its final version.
Conflict-of-interest statement: Dr. Uhe and Dr. Meyer have nothing to disclose. Dr. Hagen reports grants from Intuitive Surgical Inc., grants, personal fees and non-financial support from Johnson&Johnson Inc., personal fees and non-financial support from Verb Surgical Inc., personal fees from Verily, non-financial support from Quantgene Inc., personal fees from I2X, outside the submitted work. Pr. Ris reports personal fees and non-financial support from Stryker Inc., grants from Quantgene Inc., outside the submitted work. Pr. Toso reports grants, personal fees, and non-financial support from Johnson&Johnson Inc., outside the submitted work. Dr. Douissard reports grants and non-financial support from Intuitive Surgical Inc., personal fees from Verb Surgical Inc., grants, personal fees, and non-financial support from Johnson&Johnson Inc., outside the submitted work.
PRISMA 2009 Checklist statement: This systematic review of the literature was performed following the PRISMA 2009 guidelines.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jonathan Douissard, MD, Surgeon, Abdominal Surgery Division, Geneva University Hospitals, Rue Gabrielle Perret Gentil 4, Geneva 1211, Switzerland. jonathan.douissard@hcuge.ch
Received: March 1, 2021
Peer-review started: March 1, 2021
First decision: June 23, 2021
Revised: July 6, 2021
Accepted: September 7, 2021
Article in press: September 7, 2021
Published online: November 15, 2021

Abstract
BACKGROUND

Gastrointestinal tumors are among the most common cancer types, and early detection is paramount to improve their management. Cell-free DNA (cfDNA) liquid biopsy raises significant hopes for non-invasive early detection.

AIM

To describe current applications of this technology for gastrointestinal cancer detection and screening.

METHODS

A systematic review of the literature was performed across the PubMed database. Articles reporting the use of cfDNA liquid biopsy in the screening or diagnosis of gastrointestinal cancers were included in the analysis.

RESULTS

A total of 263 articles were screened for eligibility, of which 13 articles were included. Studies investigated colorectal cancer (5 studies), pancreatic cancer (2 studies), hepatocellular carcinoma (3 studies), and multi-cancer detection (3 studies), including gastric, oesophageal, or bile duct cancer, representing a total of 4824 patients. Test sensitivities ranged from 71% to 100%, and specificities ranged from 67.4% to 100%. Pre-cancerous lesions detection was less performant with a sensitivity of 16.9% and a 100% specificity in one study. Another study using a large biobank demonstrated a 94.9% sensitivity in detecting cancer up to 4 years before clinical symptoms, with a 61% accuracy in tissue-of-origin identification.

CONCLUSION

cfDNA liquid biopsy seems capable of detecting gastrointestinal cancers at an early stage of development in a non-invasive and repeatable manner and screening simultaneously for multiple cancer types in a single blood sample. Further trials in clinically relevant settings are required to determine the exact place of this technology in gastrointestinal cancer screening and diagnosis strategies.

Key Words: Cell-free DNA, Tumor DNA, Liquid biopsy, Next-generation sequencing, Cancer genomics, Pancreatic cancer, Colorectal cancer, Hepatocellular carcinoma, Multi-cancer detection, Cancer screening, Public health, Precision oncology

Core Tip: Liquid biopsy cell-free DNA represents a promising non-invasive method for detecting various gastrointestinal cancers at an early stage of development. The current literature suggests a high-performance profile for this technology and the potential to improve the global course of gastrointestinal cancers currently diagnosed at an advanced stage, such as pancreatic cancer. Prospective validation studies in relevant clinical settings are required to determine the applicability and added value of these new diagnostic and screening tests in global cancer care.



INTRODUCTION

Tumors developing from the gastrointestinal tract are among the most common cancer types, colorectal and stomach cancer, counting for 19.5% and 11.1% respectively worldwide in 2020[1]. Risk factors notably include smoking, obesity, poor diet, genetic factors, and infections with hepatitis B virus or Helicobacter pylori bacteria[2]. Early detection and diagnosis represent a crucial component to allow effective treatment and improve survival. Nowadays, different screening strategies have been developed, such as colonoscopy for colorectal cancer or blood testing for alpha-fetoprotein (AFP) or magnetic resonance imaging in high-risk patients for liver cancer, but other types of tumors often lack screening strategies and non-invasive testing. For instance, so far, no efficient screening methods are available for pancreatic cancer; most patients experience their first symptoms at advanced and metastatic stages, explaining the 5-year survival rate of only 5% to 10%[3].

These past few years, researchers have focused their attention on a new promising diagnostic method, liquid biopsy, which uses biomarkers such as circulating cell tumor, RNA fragments, or cell-free DNA (cfDNA). Unlike tissue samples obtained by invasive methods like needle biopsies or endoscopies, biomarkers can be detected in body fluids, mostly blood[4], and address limitations of tissues biopsies not only in diagnosis and screening, but also in diagnosis and screening the treatment response and follow-up[5-7]. Among liquid biopsy options, cfDNA raises the most significant hopes in early cancer detection. Historically, it was first reported in 1948 by Mandel et al among healthy patients. In 1977, Leon et al described elevated levels of cfDNA in the serum of cancerous patients for the first time[4,8,9]. CfDNA is continuously released in the bloodstream through different mechanisms such as apoptosis, necrosis, and active secretion by the tumor cell. When originating from a cancer cell, cfDNA is called circulating tumor DNA (ctDNA)[4]. Concentration levels seem to correlate with the cancer stage and size; advanced-stage cancer patients show a higher concentration of cfDNA[8,9]. While cfDNA quantification in the bloodstream might indicate the presence or absence of cancer, sequencing and analyzing the mutation patterns of this cfDNA goes one step further: mutational profiling might give the researchers clues on the tumor’s tissue of origin, providing information to target further specific investigations[9]. Recent progress in genomic technology also provides highly sensitive detection of low-prevalence mutations, even in high signal-to-noise configurations, thus theoretically enabling very early cancer diagnosis. The ability to run repeatable, non-invasive, multi-cancer early detection tests would bring significant advantages in the global care of frequently hardly reachable cancer locations, such as gastrointestinal cancers.

The present systematic review of the literature aims to describe the current state of developing cfDNA liquid biopsies as a means of early gastrointestinal cancer detection and screening.

MATERIALS AND METHODS

A systematic review of the literature was performed following the PRISMA guidelines[10]. All articles written in English from January 2010 to January 2021 were searched on January 19th, 2021, through the PubMed database using the following research algorithm: (liquid biopsy OR cfdna) AND (multiple OR gastrointestinal OR colon OR colorectal OR gastric OR oesophag* OR liver OR hepatocellular OR pancreatic) AND (cancer OR tumor OR tumour) AND (screening OR diagnos* OR detect*) AND early AND (blood OR venous OR plasma) NOT review.

After a first selection based on titles for screening, eligible articles were selected based on abstract analysis. Then, full-text analysis of the eligible articles searched for criteria of the finally included articles. Two investigators (I Uhe, J Douissard) independently assessed the articles for eligibility and inclusion. Discordances in study inclusions were solved by re-evaluation between the two reviewers.

All relevant articles reporting human studies investigating cfDNA liquid biopsy as a screening method or diagnosis method for newly discovered untreated primary gastrointestinal cancers were included. Studies investigating multiple cancer screening, including gastrointestinal but not limited to them, were also included. Excluded articles were studies investigating cfDNA as a follow-up method after cancer treatment, minimal residual disease detection, studies investigating cfDNA as a prognosis method only, reviews, meta-analyses, theoretical papers, and biological studies not reporting clinical outcomes. Studies reporting cancer patients who were already treated, surgically or medically, have also been excluded. To improve the present review’s clinical relevance, only the total number of participants in the papers’ validations cohorts were considered. If available, test performances were reported in terms of sensitivity (Se), specificity (Sp), positive and negative predictive values, or area under the curve (AUC).

Literature search and studies characteristics

A total of 263 articles were identified through the PubMed search. Two articles were not written in English, 11 were not original publications, and 119 did not involve cfDNA. Thirty-five articles did not mention gastrointestinal cancer, and 44 did not investigate cfDNA as a screening or diagnosis method, leaving 52 articles. After full-text reading, thirteen studies were ultimately included for analysis, representing a total of 4824 patients (Table 1, Figure 1). The largest study included blood samples from 1194 participants[11], while the smallest study included samples of 130 participants[12]. Six studies took place in China[11,13-17], three in the United States[9,18,19], and four in Europe[12,20-22]. Five were multicentric[9,11,16,18,19], four monocentric[13,14,17,22] and four studies did not mention the information. Five studies focused on colorectal cancer (CRC)[9,12,17,20,22], three on various cancer types[14,19,21] of which two included gastric cancers[14,19], three on hepatocellular carcinoma (HCC)[11,15,16] and two on pancreatic ductal adenocarcinoma (PDAC)[13,18]. All studies compared cancer and non-cancer individuals. Five of them also included in their analysis a group of patients with pre-cancerous lesions, such as colorectal adenoma or hyperplasia, liver cirrhosis, or chronic hepatitis B virus infection[11,12,15,16,22] (Table 2).

Figure 1
Figure 1 PRISMA flow diagram summarizing the search strategy.
Table 1 Characteristics of included studies.
Ref. YearCountryMono/multicentricType of cancer Total number of patients in validation cohortType of groups analyzed
Li et al[13]2020ChinaMonocentricPDAC208Cancer vs healthy
Chen et al[14]2020ChinaMonocentricGastric, esophagus, colorectal, lung or liver418Cancer diagnosed vs healthy; Pre-diagnosed patients vs healthy
Guler et al[18]2020United StatesMulticentricPDAC228Cancer vs healthy
Junca et al[12]2020FranceNAColorectal130Cancer vs healthy vs advanced-adenoma vs non-advanced adenoma and/or hyperplastic polyp(s)
Tao et al[15]2020ChinaNAHCC175HBV-related HCC vs cancer-free HBV patients
Cristiano et al[19]2019United StatesMulticentricBreast, colorectal, lung, ovarian, pancreatic, gastric, bile duct 423Cancer vs healthy
Li et al[17]2019ChinaMonocentricColorectal140Cancer vs healthy
Qu et al[16]2019ChinaMulticentric HCC331HBsAg1 positive without cancer based on screening with serum AFP and ultrasonography
Cai et al[11]2019ChinaMulticentricHCC1194Cancer vs healthy vs 392 LC/HB vs BLL
Wan et al[9]2019United StatesMulticentricColorectal817Cancer vs healthy
Jensen et al[20]2019DenmarkNAColorectal234Cancer vs healthy
Nunes et al[21]2018PortugalNABreast, colorectal, lung356Cancer vs healthy
Perrone et al[22]2014ItalyMonocentricColorectal170Cancer vs healthy vs premalignant lesion (adenoma/hyperplasia)
Table 2 Number of patients in each group.
Ref. Total patients in validation cohort Nbr patient cancer groupNbr patient healthy groupNbr patient additional group 1Nbr patient in aditionnal group 2
Li et al[13]208101107--
Chen et al[14]418113207198 pre-diagnosed patients-
Guler et al[18]22823205--
Junca et al[12]130204039 advance adenoma31 non-advance adenoma
Tao et al[15]1758986--
Cristiano et al[19]423208215--
Li et al[17]1407466--
Qu et al[16]331--HBsAg (+)-
Cai et al[11]1194809256129 LC/CHB-
Wan et al[9]817546271--
Jensen et al[20]23414391--
Nunes et al[21]356253103--
Perrone et al[22]170346373 adenoma/hyperplasia-
Risk of bias of included studies

The risk of bias of included studies was determined using the ROBINS-I tool (2016)[23]. Except for one study with an overall low risk of bias[16], all included studies were at moderate risk (Table 3).

Table 3 Risk of bias of included studies, determined using the ROBINS-I tool (2016).
Ref.

Entry
Judgement
Support for judgement
Li et al[13]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyNo informationNo information about the start of follow up and intervention for the participants
CBias in classification of interventionsNo informationNo information about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Chen et al[14]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyLow risk Information provided about the start of follow up and intervention for the participants
CBias in classification of interventionsLow risk Information provided about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Guler et al[18]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyNo informationNo information about the start of follow up and intervention for the participants
CBias in classification of interventionsNo informationNo information about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Junca et al[12]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyNo informationNo information about the start of follow up and intervention for the participants
CBias in classification of interventionsNo informationNo information about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analysesconsistent with a priori plan
Tao et al[15]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyLow risk Information provided about the start of follow up and intervention for the participants in the supplementary materials
CBias in classification of interventionsLow risk Information provided about the start of follow up and intervention for the participants in the supplementary materials
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Cristiano et al[19]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyNo informationNo information about the start of follow up and intervention for the participants
CBias in classification of interventionsNo informationNo information about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Li et al[17]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyNo informationNo information about the start of follow up and intervention for the participants
CBias in classification of interventionsNo informationNo information about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Qu et al[16]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyLow risk Information provided about the start of follow up and intervention for the participants
CBias in classification of interventionsLow risk Information provided about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultLow riskPre-registered protocol available (NCC201709011)
Cai et al[11]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyLow risk Information provided about the start of follow up and intervention for the participants
CBias in classification of interventionsLow risk Information provided about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Wan et al[9]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyNo informationNo information about the start of follow up and intervention for the participants
CBias in classification of interventionsNo informationNo information about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Jensen et al[20]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyLow risk Information provided about the start of follow up and intervention for the participants
CBias in classification of interventionsLow risk Information provided about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Nunes et al[21]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyNo informationNo information about the start of follow up and intervention for the participants
CBias in classification of interventionsNo informationNo information about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Perrone et al[22]ABias due to confoundingLow risk No confounding factors
BBias in selection of participants into the studyLow risk Information provided about the start of follow up and intervention for the participants
CBias in classification of interventionsLow risk Information provided about the start of follow up and intervention for the participants
DBias due to deviationsfrom intended interventionsLow risk No deviations from the planned interventions
EBias due to missing dataLow riskAll data were reported
FBias in measurement of outcomesLow risk Comparable methods of outcome assessment in the groups, intervention received in each group unlikely to influence the outcome measure, any error in measuring the outcome is unrelated to intervention
GBias in selection of the reported resultModerate riskNo pre-registered protocol available; outcome measurements and analyses consistent with a priori plan
Extraction and sequencing methods

All studies collected cfDNA from plasma samples. Kits used for cfDNA extraction from plasma samples can be found in Table 4. The QIAamp circulating nucleic acid kit was the most employed, a spin column-based kit (n = 7/13). A large majority of studies used next-generation sequencing (NGS) (n = 9/13), two used real-time polymerase chain reaction (RT-PCR), one digital droplet PCR, and one multiplex methylation-specific PCR. Various mutational patterns and genomic profiling strategies were investigated (Table 4). Most studies focused on methylation variations (n = 7/13), while others investigated specific mutation locations such as KRAS and BRAF or more complex mutational patterns.

Table 4 Details of extraction and sequencing methods used in each of the included studies.
Ref. Source of cfDNAFocus in cfDNAExtraction method (used kit)Sequencing methodSequencing method details
Li et al[13]PlasmaMethylated markers QIAamp Circulating Nucleic Acid Kit (Qiagen, 55114)NGSIllumina HiSeq 2000 platform
Chen et al[14]PlasmaCancer-specific methylation signaturesQIAamp Circulating Nucleic Acid kit (Qiagen, 55114)NGSAPA Library Quantification Kit for Illumina (KK4844) and sequenced on an Illumina NextSeq 500
Guler et al[18]Plasma5hmC modificationsQIAamp Circulating Nucleic Acid Kit (QIAGEN, Germantown, MD)NGSNextSeq550 instrument with version 2 reagent chemistry (Illumina, San Diego, CA).
Junca et al[12]PlasmaKRAS and BRAF mutational statusQIAamp Circulating Nucleic Acid kit (Qiagen, Hilden, Germany)RT-PCRQ24 PyroMark system (Qiagen, Hilden, Germany)
Tao et al[15]PlasmaSomatic copy number aberration QIAamp CirculatingNucleic Acid Kit (Qiagen)NGSNext generation sequencing (Illumina)
Cristiano et al[19]PlasmaFragmentation size Qiagen Circulating Nucleic Acids Kit (Qiagen GmbH) NGSNEBNext DNA Library Prep Kit for Illumina
Li et al[17]PlasmaAberrant DNA hypermethylation of CpGislandsDNeasy Blood & TissueKit (Qiagen)NGSMethylated CpG tandem ampli-fication and sequencing
Qu et al[16]PlasmaSpecific mutations ARCHITECT i2000SR Chemical luminescence immunity analyzerNGSNext generation sequencing
Cai et al[11]Plasma5hmC modificationsNANGS5hmC-Seal
Wan et al[9]PlasmacfDNA mutations patternsMagMAX cfDNA Isolation KitNGSIllumina NovaSeq 6000 Sequencing System
Jensen et al[20]PlasmaTumour-specific DNA methylationGentra Puregene Tissue Kit (Qiagen)DD-PCRBisulfite sequencing and methylation-specific droplet digital PCR
Nunes et al[21]PlasmaAberrant DNA methylationQIAamp MinElute ccfDNA (Qiagen, Hilden, Germany)qMSPqMSP
Perrone et al[22]PlasmaKRAS mutated cfDNAQiamp DNA Blood Extraction Kit (Qiagen)RT-PCRRT-PCR
Tests performance

Overall test performances for each cancer subgroup are described in Table 5.

Table 5 Sensibility and sensitivity of included studies.
Ref. Group of validation cohortsSensitivitySpecificityPositive predictive valueNegative predictive valueAUC
PDCALi et al[13]Cancer vs healthy93.295.2NANA0.943
Chen et al[14]Cancer vs healthyNANANANA0.921
HCCGuler et al[18]HBV-related HCC vs cancer-free HBV group 11897.4NANA0.92
HBV-related HCC vs cancer-free HBV group 22995.6NANA0.81
Junca et al[12]HCC vs cancer-free HBV1009417100NA
Tao et al[15]HCC vs healthy82.776.4NANA0.884
HCC vs high risk (HBV and cirrhosis)82.767.4NANA0.846
Various cancer typesCristiano et al[19]Pre-diagnosis vs healthy84.996.1NANANA
Post-diagnosis vs healthy87.596.1
Li et al[17]All cancer vs healthy8095NANA0.94
7398
Gastric cancer vs healthy8195
8198
Colorectal cancer vs healthy8195
7098
Bile duct cancer vs healthy8895
8198
Pancreatic cancer vs healthy7195
6598
Qu et al[16]All cancer vs healthy 74.273.587.152.1NA
Colorectal cancer vs healthy78.469.948.390
ColorectalCai et al[11]Cancer/adenoma vs healthy16.910010059.2NA
Wan et al[9]Cancer vs healthy7490NANA0.887
Jensen et al[20]Cancer vs healthy 8585NANa0.92
Nunes et al[21]Cancer vs healthy8599NANANA
Perrone et al[22]Cancer vs healthyNANANANA0.709
Adenomas vs healthyNANANANA0.535
RESULTS
CRC

Clinically relevant sensitivities and specificities to detect colorectal adenocarcinoma were achieved in three studies[9,20,21], Li et al[17] and Jensen et al[20] focusing on tumor-specific methylations. In contrast, Wan et al[9] investigated complex cfDNA mutational patterns using a machine-learning-based model. Sensitivities ranged from 74% to 85%, while specificities ranged from 85% to 99%. In a fourth study, Perrone et al[22] reported an AUC of 0.709 when discriminating CRC from healthy patients. However, for premalignant lesions, the performance was lower, with an AUC of 0.535[22]. Similarly, investigating adenomas and adenocarcinomas through cfDNA KRAS and BRAF mutations, Junca et al[12] found a mean sensitivity of 16.9% for a 100% specificity reflecting a still lower sensitivity in premalignant lesions detection but allowing a high level of precision.

Pancreatic cancer

Examining methylation patterns in cfDNA, Li et al[13] described eight methylation markers in patients suffering from PDAC; SIX3, TRIM73, MAPT, FAM150A, EPB41L3, MIR663, LOC100130148, and LOC100128977. These markers identified PDAC patients efficiently, with a sensitivity of 93.2% and a specificity of 95.2% (AUC = 0.943). By investigating 5-hydroxymethylcytosine (5hmC) changes in circulating cfDNA, Guler et al[18] achieved similar performance with an AUC of 0.921.

Hepatocellular carcinoma

Cai et al[11] found promising results using a mutational pattern of 32 gene markers to discriminate HCC patients from healthy individuals, with a sensitivity and specificity of 82.7% and 76.4%, respectively. Furthermore, when comparing HCC patients with cancer-free high-risk patients (chronic hepatitis B or liver cirrhosis), the model performed similarly with an 82.7% sensitivity and 67.4% specificity[11].

Comparing HCC patients with cancer-free asymptomatic HBV patients based on cfDNA mutational pattern of specific locations, Qu et al[16] achieved a sensitivity and specificity of 100% and 94%, respectively. Further, using somatic copy number aberration in cfDNA as an alternative to methylation or specific mutations analysis, Tao et al[15] investigated the possibility of discriminating HBV-related HCC from cancer-free chronic HBV patients. Their predictive model performed appropriately, showing a high level of precision in two validation cohorts, with an AUC of 0.92 and 0.81.

Multi-cancer detection

Nunes et al[21] investigated the possibility to diagnose lung, breast, and colorectal cancer patients simultaneously from healthy individuals by detecting aberrant methylations on specific locations. They achieved an overall specificity of 73.5% and a sensitivity of 74.2%. For colorectal cancer, specificity was 69.9%, and sensitivity was 78.4%[21].

With a comparable strategy targeting five cancers (gastric, oesophageal, lung, liver, and colorectal), Chen et al[14] demonstrated the potential ability of cfDNA liquid biopsy to achieve multicancer detection several years before the actual diagnosis. Based on blood samples from a large biobank, they analyzed samples from 3 groups. The post-diagnosis group included patients with a newly discovered and untreated malignancy at the time of sampling. The pre-diagnosis group included patients with no known malignancy at the sampling time but who developed cancer within four years after sampling (pre-diagnosis). Finally, the control group included healthy individuals who were still free of malignant disease four years after sampling. Their model achieved an overall detection specificity of 96% when comparing healthy individuals to pre-diagnosis and post-diagnosis groups. Overall sensitivity was 87.5% for the post-diagnosis group, ranging from 75% in colorectal cancer to 96% in lung cancer. It reached 94.9% in the pre-diagnosis group, ranging from 91% in oesophageal cancer to 100% in liver cancer[14].

In contrast to these two studies focused on cfDNA methylations, Cristiano et al[19] explored a multi-cancer detection model analyzing cfDNA fragmentation patterns, including gastric, bile duct, colorectal and pancreatic cancers. Their model reached an overall detection sensitivity of 80% for a specificity of 95%, or a sensitivity of 73% for a specificity of 98%, and a global AUC of 0.94. Furthermore, enhanced by a machine-learning algorithm, they were able to identify the tissue of origin of cancer samples with a 61% accuracy[19]. Detailed performances per cancer type of this model can be found in Table 3.

DISCUSSION

Liquid biopsy appears as a promising non-invasive method for the initial screening and diagnosis of various gastrointestinal cancers. High levels of sensitivity and specificity described in the included studies seem within acceptable ranges for eventual clinical use. In the case of HCC, cfDNA tests demonstrated better detection performances when compared to the standard surveillance of high-risk patients combining AFP dosage and ultra-sound monitoring. It also appears to be a viable solution regarding the challenge of pancreatic cancer screening; due to the paucity of symptoms in the early phases and the absence of acceptable screening strategies even for high-risk groups, this type of cancer remains frequently detected at metastatic or locally advanced and unresectable stages. Conversely, colorectal cancer is one of the few cancers with a standardized and efficient large-scale screening strategy based on the colonoscopy and the fecal occult blood test. Still, there is room for improved and more cost-effective strategies. Of note, cfDNA liquid biopsy’s ability to detect several cancer types simultaneously appears as a potential paradigm shift in global cancer care, and studies investigating such application achieved a high level of performance. Further, as demonstrated by Chen et al[13], this technology bears the potential to predict cancer several years before the onset of clinical symptoms and identify or direct investigations towards specific tissues of origin.

The central role of early cancer detection in improving oncologic and public-health outcomes is well established. However, it is a challenge for liquid biopsy since smaller and earlier-stage tumors tend to release lower levels of ctDNA[24]. The signal-to-noise ratio of ctDNA is thus meager compared to non-cancer-derived cfDNA, with a detection percentage ranging from 0 to 11.7%[25,26]. The extraction method plays a critical role in improving detection performance. Different procedures have been developed, the more widespread being column-based, polymer-based, phenol-chloroform, or magnet-based[9,27]. These methods are efficient and allow to reach a high DNA concentration but remain expensive and time-consuming[9,27]. In this context, some authors proposed plasma processing methods without the need for DNA extraction. Breitbach et al[28] notably used quantitative RT-PCR to measure cfDNA concentration in plasma. Not only did the method showed great feasibility with higher levels of cfDNA found among cancer patients, but it also proved to be more time effective and more efficient than the eluate of the QIAamp DNA Blood Mini Kit, for example, with levels of cfDNA in unpurified plasma 2.79 fold higher[28].

Regarding the sequencing method, some authors focused their attention on specific mutations while others analyzed the whole genome searching for non-specific mutational patterns, most of them using NGS methods. Different factors can explain the apparent predominance of NGS over other PCR methods such as RT-PCR in the published studies. Although more technically demanding and expensive, NGS is a hypothesis-free approach that carries a higher discovery power of new mutational patterns, in addition to a higher sensitivity to rare variants[29,30]. Further, its superior multiplex capabilities tend to improve the workflow when studying a large number of locations and samples. These high throughput and detection sensitivity capabilities might be valuable in a screening configuration for early cancer detection, which deals with lower levels of mutation than advanced stage cancers and aims at testing a high volume of patients.

As the field is at an early stage of clinical exploration, there is still a high variability in trial designs and reporting methods, thus undermining the global quality of tests’ performance analysis. Of note, biocomputational trials based on biobank samples often report higher levels of sensitivity and specificity but are less likely to translate into clinically relevant performances as prospective trials would. Applicability to real-life clinical applications is thus the most awaited step to achieve for the scientific validation of this technology, and upcoming clinical trials will need to address many questions, such as the appropriate balance between sensitivity and specificity in a screening purpose, the timing of screening tests, patient selection, socio-economic parameters and dealing with the uncertainty around tissues of origin in positive tests.

CONCLUSION

Liquid biopsy cfDNA represents an efficient, non-invasive, and promising method for detecting various gastrointestinal cancers at an early stage of development. These tools could improve the global prognosis of cancers currently diagnosed at an advanced stage due to the lack of effective screening and diagnostic methods, such as pancreatic cancer. Allowing early detection of several types of cancers and reducing the burden of multiple screening tests, cfDNA liquid biopsies could change the course of gastrointestinal cancers care for a significant number of patients and induce a paradigm shift in cancer-related public health policies, provided that they can demonstrate their clinical relevance in future studies.

ARTICLE HIGHLIGHTS
Research background

Liquid biopsy cell-free DNA (cfDNA) represents a promising non-invasive method for detecting various gastrointestinal cancers at an early stage of development.

Research motivation

Various and recent literature is available on this topic, with exponentially growing interest.

Research objectives

To review the current state of development of cfDNA liquid biopsy in the field of gastrointestinal cancer early detection.

Research methods

A systematic review of the literature according to the PRISMA guidelines.

Research results

The current literature suggests a high-performance profile for this technology and the potential to improve the global course of gastrointestinal cancers currently diagnosed at an advanced stage, such as pancreatic cancer.

Research conclusions

cfDNA liquid biopsy showed high potential in the diagnosis of early gastrointestinal cancers and simultaneous screening of multiple cancer types.

Research perspectives

Further trials in clinically relevant settings are required to determine the exact place of this technology in future diagnosis strategies.

Footnotes

Provenance and peer review: Invited article; Externally peer reviewed.

Specialty type: Oncology

Country/Territory of origin: Switzerland

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): 0

Grade C (Good): C

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Luglio G S-Editor: Zhang H L-Editor: A P-Editor: Li X

References
1.   Cancer today. [cited 1 February 2021]. Available from: http://gco.iarc.fr/today/home.  [PubMed]  [DOI]  [Cited in This Article: ]
2.  Dizdar Ö, Kılıçkap S.   Global Epidemiology of Gastrointestinal Cancers. In: Yalcin S, Philip PA. Textbook of Gastrointestinal Oncology. Switzerland: Springer International Publishing, 2019: 1-12.  [PubMed]  [DOI]  [Cited in This Article: ]
3.   Pancreatic Cancer Prognosis. [cited 1 February 2021]. Available from: https://www.hopkinsmedicine.org/health/conditions-and-diseases/pancreatic-cancer/pancreatic-cancer-prognosis.  [PubMed]  [DOI]  [Cited in This Article: ]
4.  Alberti LR, Garcia DP, Coelho DL, De Lima DC, Petroianu A. How to improve colon cancer screening rates. World J Gastrointest Oncol. 2015;7:484-491.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 14]  [Cited by in F6Publishing: 12]  [Article Influence: 1.3]  [Reference Citation Analysis (0)]
5.  Banys-Paluchowski M, Krawczyk N, Fehm T. Liquid Biopsy in Breast Cancer. Geburtshilfe Frauenheilkd. 2020;80:1093-1104.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 13]  [Cited by in F6Publishing: 7]  [Article Influence: 1.8]  [Reference Citation Analysis (0)]
6.  De Rubis G, Rajeev Krishnan S, Bebawy M. Liquid Biopsies in Cancer Diagnosis, Monitoring, and Prognosis. Trends Pharmacol Sci. 2019;40:172-186.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 252]  [Cited by in F6Publishing: 325]  [Article Influence: 65.0]  [Reference Citation Analysis (0)]
7.  He HJ, Stein EV, Konigshofer Y, Forbes T, Tomson FL, Garlick R, Yamada E, Godfrey T, Abe T, Tamura K, Borges M, Goggins M, Elmore S, Gulley ML, Larson JL, Ringel L, Haynes BC, Karlovich C, Williams PM, Garnett A, Ståhlberg A, Filges S, Sorbara L, Young MR, Srivastava S, Cole KD. Multilaboratory Assessment of a New Reference Material for Quality Assurance of Cell-Free Tumor DNA Measurements. J Mol Diagn. 2019;21:658-676.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 9]  [Cited by in F6Publishing: 11]  [Article Influence: 2.2]  [Reference Citation Analysis (0)]
8.  Huang Z, Gu B. Circulating tumor DNA: a resuscitative gold mine? Ann Transl Med. 2015;3:253.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 3]  [Reference Citation Analysis (0)]
9.  Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer. 2017;17:223-238.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1634]  [Cited by in F6Publishing: 1468]  [Article Influence: 209.7]  [Reference Citation Analysis (0)]
10.   PRISMA. [cited 28 February 2021]. Available from: http://prisma-statement.org/PRISMAStatement/.  [PubMed]  [DOI]  [Cited in This Article: ]
11.  Cai J, Chen L, Zhang Z, Zhang X, Lu X, Liu W, Shi G, Ge Y, Gao P, Yang Y, Ke A, Xiao L, Dong R, Zhu Y, Yang X, Wang J, Zhu T, Yang D, Huang X, Sui C, Qiu S, Shen F, Sun H, Zhou W, Zhou J, Nie J, Zeng C, Stroup EK, Chiu BC, Lau WY, He C, Wang H, Zhang W, Fan J. Genome-wide mapping of 5-hydroxymethylcytosines in circulating cell-free DNA as a non-invasive approach for early detection of hepatocellular carcinoma. Gut. 2019;68:2195-2205.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 163]  [Cited by in F6Publishing: 156]  [Article Influence: 31.2]  [Reference Citation Analysis (0)]
12.  Junca A, Tachon G, Evrard C, Villalva C, Frouin E, Karayan-Tapon L, Tougeron D. Detection of Colorectal Cancer and Advanced Adenoma by Liquid Biopsy (Decalib Study): The ddPCR Challenge. Cancers (Basel). 2020;12.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 6]  [Cited by in F6Publishing: 13]  [Article Influence: 3.3]  [Reference Citation Analysis (0)]
13.  Li S, Wang L, Zhao Q, Wang Z, Lu S, Kang Y, Jin G, Tian J. Genome-Wide Analysis of Cell-Free DNA Methylation Profiling for the Early Diagnosis of Pancreatic Cancer. Front Genet. 2020;11:596078.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 13]  [Article Influence: 3.3]  [Reference Citation Analysis (0)]
14.  Chen X, Gole J, Gore A, He Q, Lu M, Min J, Yuan Z, Yang X, Jiang Y, Zhang T, Suo C, Li X, Cheng L, Zhang Z, Niu H, Li Z, Xie Z, Shi H, Zhang X, Fan M, Wang X, Yang Y, Dang J, McConnell C, Zhang J, Wang J, Yu S, Ye W, Gao Y, Zhang K, Liu R, Jin L. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat Commun. 2020;11:3475.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 153]  [Cited by in F6Publishing: 273]  [Article Influence: 68.3]  [Reference Citation Analysis (0)]
15.  Tao K, Bian Z, Zhang Q, Guo X, Yin C, Wang Y, Zhou K, Wan S, Shi M, Bao D, Yang C, Xing J. Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma. EBioMedicine. 2020;56:102811.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 21]  [Cited by in F6Publishing: 32]  [Article Influence: 8.0]  [Reference Citation Analysis (0)]
16.  Qu C, Wang Y, Wang P, Chen K, Wang M, Zeng H, Lu J, Song Q, Diplas BH, Tan D, Fan C, Guo Q, Zhu Z, Yin H, Jiang L, Chen X, Zhao H, He H, Li G, Bi X, Zhao X, Chen T, Tang H, Lv C, Wang D, Chen W, Zhou J, Cai J, Wang X, Wang S, Yan H, Zeng YX, Cavenee WK, Jiao Y. Detection of early-stage hepatocellular carcinoma in asymptomatic HBsAg-seropositive individuals by liquid biopsy. Proc Natl Acad Sci U S A. 2019;116:6308-6312.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 78]  [Cited by in F6Publishing: 114]  [Article Influence: 22.8]  [Reference Citation Analysis (0)]
17.  Li J, Zhou X, Liu X, Ren J, Wang J, Wang W, Zheng Y, Shi X, Sun T, Li Z, Kang A, Tang F, Wen L, Fu W. Detection of Colorectal Cancer in Circulating Cell-Free DNA by Methylated CpG Tandem Amplification and Sequencing. Clin Chem. 2019;65:916-926.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 17]  [Cited by in F6Publishing: 17]  [Article Influence: 3.4]  [Reference Citation Analysis (0)]
18.  Guler GD, Ning Y, Ku CJ, Phillips T, McCarthy E, Ellison CK, Bergamaschi A, Collin F, Lloyd P, Scott A, Antoine M, Wang W, Chau K, Ashworth A, Quake SR, Levy S. Detection of early stage pancreatic cancer using 5-hydroxymethylcytosine signatures in circulating cell free DNA. Nat Commun. 2020;11:5270.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 89]  [Cited by in F6Publishing: 76]  [Article Influence: 19.0]  [Reference Citation Analysis (0)]
19.  Cristiano S, Leal A, Phallen J, Fiksel J, Adleff V, Bruhm DC, Jensen SØ, Medina JE, Hruban C, White JR, Palsgrove DN, Niknafs N, Anagnostou V, Forde P, Naidoo J, Marrone K, Brahmer J, Woodward BD, Husain H, van Rooijen KL, Ørntoft MW, Madsen AH, van de Velde CJH, Verheij M, Cats A, Punt CJA, Vink GR, van Grieken NCT, Koopman M, Fijneman RJA, Johansen JS, Nielsen HJ, Meijer GA, Andersen CL, Scharpf RB, Velculescu VE. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature. 2019;570:385-389.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 423]  [Cited by in F6Publishing: 605]  [Article Influence: 121.0]  [Reference Citation Analysis (0)]
20.  Jensen SØ, Øgaard N, Ørntoft MW, Rasmussen MH, Bramsen JB, Kristensen H, Mouritzen P, Madsen MR, Madsen AH, Sunesen KG, Iversen LH, Laurberg S, Christensen IJ, Nielsen HJ, Andersen CL. Novel DNA methylation biomarkers show high sensitivity and specificity for blood-based detection of colorectal cancer-a clinical biomarker discovery and validation study. Clin Epigenetics. 2019;11:158.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 50]  [Cited by in F6Publishing: 73]  [Article Influence: 14.6]  [Reference Citation Analysis (0)]
21.  Nunes SP, Moreira-Barbosa C, Salta S, Palma de Sousa S, Pousa I, Oliveira J, Soares M, Rego L, Dias T, Rodrigues J, Antunes L, Henrique R, Jerónimo C. Cell-Free DNA Methylation of Selected Genes Allows for Early Detection of the Major Cancers in Women. Cancers (Basel). 2018;10.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 29]  [Cited by in F6Publishing: 40]  [Article Influence: 6.7]  [Reference Citation Analysis (0)]
22.  Perrone F, Lampis A, Bertan C, Verderio P, Ciniselli CM, Pizzamiglio S, Frattini M, Nucifora M, Molinari F, Gallino G, Gariboldi M, Meroni E, Leo E, Pierotti MA, Pilotti S. Circulating free DNA in a screening program for early colorectal cancer detection. Tumori. 2014;100:115-121.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 22]  [Reference Citation Analysis (0)]
23.   ROBINS-I tool. [cited 27 June 2021]. Available from: https://methods.cochrane.org/methods-cochrane/robins-i-tool.  [PubMed]  [DOI]  [Cited in This Article: ]
24.  Fiala C, Diamandis EP. Utility of circulating tumor DNA in cancer diagnostics with emphasis on early detection. BMC Med. 2018;16:166.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 138]  [Cited by in F6Publishing: 151]  [Article Influence: 25.2]  [Reference Citation Analysis (0)]
25.  Newman AM, Bratman SV, To J, Wynne JF, Eclov NC, Modlin LA, Liu CL, Neal JW, Wakelee HA, Merritt RE, Shrager JB, Loo BW Jr, Alizadeh AA, Diehn M. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20:548-554.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1341]  [Cited by in F6Publishing: 1478]  [Article Influence: 147.8]  [Reference Citation Analysis (0)]
26.  Lu JL, Liang ZY. Circulating free DNA in the era of precision oncology: Pre- and post-analytical concerns. Chronic Dis Transl Med. 2016;2:223-230.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 20]  [Article Influence: 2.5]  [Reference Citation Analysis (0)]
27.  Jin CE, Koo B, Lee TY, Han K, Lim SB, Park IJ, Shin Y. Simple and Low-Cost Sampling of Cell-Free Nucleic Acids from Blood Plasma for Rapid and Sensitive Detection of Circulating Tumor DNA. Adv Sci (Weinh). 2018;5:1800614.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 47]  [Cited by in F6Publishing: 38]  [Article Influence: 6.3]  [Reference Citation Analysis (0)]
28.  Breitbach S, Tug S, Helmig S, Zahn D, Kubiak T, Michal M, Gori T, Ehlert T, Beiter T, Simon P. Direct quantification of cell-free, circulating DNA from unpurified plasma. PLoS One. 2014;9:e87838.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 95]  [Cited by in F6Publishing: 101]  [Article Influence: 10.1]  [Reference Citation Analysis (0)]
29.  Parilla M, Ritterhouse LL. Beyond the Variants: Mutational Patterns in Next-Generation Sequencing Data for Cancer Precision Medicine. Front Cell Dev Biol. 2020;8:370.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 2]  [Article Influence: 0.5]  [Reference Citation Analysis (0)]
30.  Federici G, Soddu S. Variants of uncertain significance in the era of high-throughput genome sequencing: a lesson from breast and ovary cancers. J Exp Clin Cancer Res. 2020;39:46.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 56]  [Cited by in F6Publishing: 90]  [Article Influence: 22.5]  [Reference Citation Analysis (0)]