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Laurent-Puig, P. Circulating Tumor DNA in Gastrointestinal Cancers. Encyclopedia. Available online: https://encyclopedia.pub/entry/16966 (accessed on 22 July 2024).
Laurent-Puig P. Circulating Tumor DNA in Gastrointestinal Cancers. Encyclopedia. Available at: https://encyclopedia.pub/entry/16966. Accessed July 22, 2024.
Laurent-Puig, Pierre. "Circulating Tumor DNA in Gastrointestinal Cancers" Encyclopedia, https://encyclopedia.pub/entry/16966 (accessed July 22, 2024).
Laurent-Puig, P. (2021, December 10). Circulating Tumor DNA in Gastrointestinal Cancers. In Encyclopedia. https://encyclopedia.pub/entry/16966
Laurent-Puig, Pierre. "Circulating Tumor DNA in Gastrointestinal Cancers." Encyclopedia. Web. 10 December, 2021.
Circulating Tumor DNA in Gastrointestinal Cancers
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This entry is adapted from the peer-reviewed paper 10.3390/cancers13194743

Gastrointestinal (GI) cancers appear as major health burdens worldwide with high incidences and mortality rates. For these cancers, stage at diagnosis remains the most important prognostic factor for clinical outcome. However, the emergence of simple and reproducible biomarkers is needed for the management of these diseases along their evolution.

circulating tumor DNA gastrointestinal cancers personalized medicine biomarker

1. Introduction

Gastrointestinal (GI) cancers appear as major health burdens worldwide with high incidences and mortality rates. For these cancers, stage at diagnosis remains the most important prognostic factor for clinical outcome. However, the emergence of simple and reproducible biomarkers is needed for the management of these diseases along their evolution. Circulating cell-free DNA (cfDNA) can be detected in plasma, urine, and other bodily fluids for everyone, and is increased in inflammatory diseases, infections and cancers [1][2]. For patients with cancer, a fraction of this cfDNA, called circulating tumor DNA (ctDNA), contains tumor-specific molecular alterations [3][4]. Detection of ctDNA is challenging: First, for the majority of patients, quantities remain very low. Moreover, ctDNA is diluted within total cfDNA and its identification can be difficult. New approaches aretherefore in development to overcome this sensitivity challenge. Depending on cancers subtypes, specific molecular alterations can attest for the presence of ctDNA, which is a promising non-invasive biomarker in the era of personalized medicine. In this review, we tried to resume the molecular aspects of ctDNA and in what extent this biomarker can help clinicians in the detection, screening, diagnosis, prognosis, monitoring and personalization of treatment in patients with gastrointestinal cancers.

2. Early Cancer Detection through Circulating Tumor DNA and Molecular Profile Determination

The GI cancer diagnosis is currently based on a histological assessment and therefore requires tissue sample collected by surgical resection, endoscopic ultrasound, or biopsy of primitive tumor or accessible metastasis. Several studies assessed the interest of ctDNA as screening tool for early tumor stage. However, further studies are still required to prove the clinical utility of ctDNA in early diagnosis as stipulated by American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP) in a recent report [5]. Some findings demonstrated that asymptomatic cancers could be detected years before conventional diagnosis through non-invasive blood tests. In a recent longitudinal study, analysis of ctDNA methylation was performed on plasma samples from 605 asymptomatic individuals. Among them, 191 later developed stomach, esophageal, colorectal, lung or liver cancer within four years of blood draw. This method was able to detect cancer in 95% of asymptomatic individuals who were later diagnosed [6]. However, future longitudinal studies are required to confirm these results. The main risk of this early screening would be over-diagnosis through false-positive results or through the detection of circulating genomic variants from cells that have taken the first step toward transformation but were never meant to become clinically important [5].
Tissue biopsy is usually only performed at diagnosis and can sometimes be hard to obtain. For these reasons, several studies have also evaluated in different GI cancers whether plasma molecular alterations can be detected with ctDNA and are correlated with tissue biopsies.

2.1. Colorectal Cancer

Tumor tissue is routinely used to search for KRAS or NRAS gene mutations that occur in around 55% of metastatic CRC (mCRC) and predict a lack of response to the EGFR-targeted monoclonal antibodies, such as cetuximab and panitumumab [7][8]. In the same context, BRAF mutation is another alteration known as a poor prognostic factor that can be targeted by a doublet-therapy combining an anti-BRAF kinase inhibitor (encorafenib) and anti-EGFR monoclonal antibody (cetuximab) [9][10][11].
In the context of mCRC, the quantitative PCR (Intplex qPCR) on ctDNA was described by Thierry et al. as a valuable detection method with a high rate of specificity and sensitivity, especially for BRAF V600E and KRAS mutations, in a prospective study on 106 patients with mCRC [12]. The digital droplet PCR (ddPCR) has also been validated by other group for detection of KRAS mutations in mCRC [13]. More recently, in a large prospective multicenter study. Another method consists in using the NGS-BEPER-method (22 genes), and two specific methylated biomarkers (WIF1 and NPY) as a second-step test for NGS-negative specimens. Bachet et al. used this technique to evaluate the concordance of RAS mutations between plasma and tissue among 406 chemotherapy-naive patients with mCRC with detectable ctDNA (n = 329/412). By comparing the results of RAS status in ctDNA and in matched tumor tissus, they founded an accuracy of 83% with NGS alone versus 93% with NGS plus methylated biomarkers [14]. Supplementary studies also suggested a good concordance rate between mutations observed in tumor biopsy and those identified on ctDNA [12][13][14][15][16][17][18].

2.2. Pancreatic Cancer

The KRAS gene mutations occur in more than 90% of pancreatic cancer (PC), and appears therefore as the best candidate to assess the presence of ctDNA in this tumor [19][20][21][22]. However, the ctDNA detection rate in metastatic PC varies widely from 40% to 80% and could therefore explain some discordance between tumor and plasma mutation assessment [23][24][25][26]. It could explain the results of the recent meta-analysis of Luchini et al. including 14 studies involving 369 patients, that reported a concordance rate of only 32% between ctDNA and tissue based on large NGS multi-gene mutation panels [27]. The overall pooled sensitivity and specificity of the mutational analysis on liquid biopsy compared to tumor tissue were 70% and 86% respectively. However, when focusing on studies analyzing KRAS mutations only, the sensitivity slightly decreased but the specificity increased and were 65% and 91%, respectively [27].
Indeed, apart from KRAS mutations for PC screening, adding NGS-based panel for other mutations such as SMAD4, CDKN2A, ROS1, BRAF and TP53 could lead to higher levels of ctDNA detection [28][29][30]. More recently, methylation of promoter of ADAMST1 and BNC1 genes were also described as potential tool to assess the presence of ctDNA in PC [31].
However, the use of highly sensitive detection methods of ctDNA might lead to false diagnosis of PC. Indeed, KRAS mutations can be detected in plasma in some non-cancerous diseases such as chronic pancreatitis. In a pilot study from Rashid et al., 21.8% of patients with chronic pancreatitis were tested positive for KRAS mutations in plasma [32]. Among these 64 patients, none developed a PC, with a mean follow-up duration (by clinic and by positron emission tomography or endoscopic ultrasound) of 2.5 years [32].
Quantitative ctDNA assessment, or combining biomarkers and methylation detection may improve the specificity of ctDNA detection and therefore help to discriminate benign from malignant pancreatic diseases, even at early tumor stages [33][34][35][36].

2.3. Esophageal and Gastric Cancer

In gastric cancer (GC), despite a low-frequency of genomic alterations [37][38], routine tissue-based NGS showed that at least 37% of patients harbor somatic mutations (TP53, KRAS) or gene amplification, such as HER2, MET, EGFR, and FGFR2 [39][40][41][42]. Some retrospective studies evaluated the feasibility of ctDNA detection by NGS among GC patients. In a recent study including 55 patients with GC tested by NGS, Kato et al. showed that 31 had concordant mutations between tumor tissue and ctDNA with levels ranged from 61.3% (for TP53 mutation) to 87.1% (for KRAS mutation) [43]. In their meta-analysis, Gao et al., reported that ctDNA detection might be a specific, but still a low sensitive test in GC patients [44]. More recently, the analysis of a large cohort of 1630 patients with GC revealed that ctDNA-NGS genomic landscape was similar but not identical to tissue-NGS [45]. This could reflect the molecular heterogeneity, with some targetable molecular alterations identified at higher frequency via ctDNA-NGS compared with previous matched primary tissue-NGS samples [45].
Despite increasing use of genomic alterations to detect ctDNA in GC, the most investigated technique to prove the presence of ctDNA is detection of hypermethylation of gene promoters which might result in an inappropriate silencing of tumor suppressor genes [44][46]. The promoter methylation of APC and RASSF1A in cfDNA was described as frequent epigenetic events in patients with early operable GC [47]. Aberrant methylation of other genes such as PCDH10, SOX17, TIMP3, MINT2 and WAF1 also showed promising results in GC [46].
In esophageal squamous cell carcinoma (ESCC), preliminary studies suggested the feasibility of ctDNA detection [48]. Luo et al. used exome or targeted sequencing to detect somatic mutations in 11 patients with ESCC and compared ctDNA from pre- and post-surgery plasma [48]. They compared plasma somatic mutations that were also identified in matched tumors and founded that mutant allelic franction (MAF) decreased after surgery [48].

2.4. Hepatocellular Carcinoma

The analysis of the mutational landscape of hepatocellular carcinoma (HCC) over 3000 samples in the Catalog of Somatic Mutation in Cancer showed that the most frequent tumor mutations were TP53 (27%), TERT (25%) and CTNNB1 (18%) [49][50][51]. Using targeted methods to detect these three genes mutations in plasma, ctDNA presence was proven from 20% to 55% of patients with HCC across different studies [50][51][52][53][54]. In one prospective study including 27 patients with proven ctDNA, only 22% of them (6/27) also had matched mutants in tumor tissues, underlying the heterogeneity of HCC [50]. Therefore, single specific molecular alterations do not seem to be sensitive or specific enough to be used as a diagnostic tool in HCC. Moreover, some molecular alterations could be unspecific for HCC, such as TERT mutations that were present in plasma for 9% of patients with cirrhosis and without evidence of HCC on imaging [52].
When using NGS techniques with panel of frequently altered genes in HCC, ctDNA detection rate reached 63% in a prospective cohort of 30 patients, with two thirds of patients with stage A according to the Barcelona Clinic Liver Cancer score (BCLC A). In this study, the concordance rate between plasma and tissue biopsy was 81% [55].
Despite the utility of gene point mutations, DNA methylation seems to be more broadly informative in HCC. In a recent study, a combination of five aberrant methylation biomarkers was able to distinguish HCC samples from control cirrhotic and not cirrhotic tissue samples, with a specificity of 95% [56].
Some single aberrant methylation genes have shown high concordance rates between plasma and tissue in HCC [57][58][59]. Among patients with hypermethylation of CDKN2A, which is described in up to 73% of HCC patients, Wong et al., reported a concordance rate of 81% between plasma and tissue biopsy with a specificity of 100% among control patients [57]. Hypermethylation of RASSF1A promoter could also to be a candidate and was found in up to 90% of HCC tissues [60][61][62][63][64]. However, it seems to be also detected in patients with non-malignant liver tumor, such as liver cirrhosis, chronic hepatitis B or in healthy controls, with a lower rate (13%, 4%, and 4%, respectively) [64]. Other single hypermethylated candidates, such as SEPT9, VIM, FBLN1, TFPI2, TGR5, MT1M, MT1G, APC, SPINT2, SFRP1, GSTP1, or hypomethylated candidates such as LINE-1 showed promising results for HCC screening [61][65][66][67][68][69][70].
More recently, whole methylome analysis allowed discovering novel methylated DNA markers in HCC. Creation of a new panel with 6 methylated biomarkers (HOXA1, EMX1, AK055957, ECE1, PFKP and CLEC11A) was able to detect 75% of BLCL 0 and 93% of BCLC B HCC patients meeting Milan criteria and was superior to AFP [56].

2.5. Other GI Cancers

Molecular landscape of cholangiocarcinoma (CC) has been widely studied in the past few years trying to detect therapeutic targets [71][72][73]. The cholangiocarcinoma (CC) is usually separated between intrahepatic CC (IHCC) and extra hepatic CC (EHCC). Some mutations such as KRAS, BRAF or TP53 are more frequent in EHCC but remain rare, whereas others, such as FGFR1-3 fusions and IDH1/2 mutations are preferentially detected in IHCC and occur in around 15–20% of tumors [71][72][73]. A recent study including 24 CC patients has reported a concordance rate of 74% between mutations in tumor tissue and ctDNA. When stratifying on tumor localization, concordance rate was 92% for IHCC, but only 55% for EHCC [74].

3. Circulating Tumor DNA to Monitor Treatment Response and Detect Acquired Resistance

The non-invasive nature of ctDNA allows for repeated testing and molecular assessment of tumor during treatment. This dynamic assessment is a clear advantage over traditional tissue biopsy. In the advanced tumor stage, baseline ctDNA could be more helpful to capture the molecular spatial and temporal heterogeneity of the disease which is a particularly important biological issue, at diagnosis or later because of clonal evolution and selection [15]. Differences in molecular characteristics have been described between primary tumor and metastases, especially in metachronous lesions [16].
Moreover, the monitoring of ctDNA may also anticipate the evaluation of treatment efficacy by detecting emergent actionable molecular alterations implicated in therapeutic resistance to ongoing treatment.

3.1. Colorectal Cancer

In mCRC, longitudinal quantification of ctDNA appears to be correlated with tumor evolution in several studies [75][76]. By sequencing a panel of 15 genes with frequent somatic variant in CRC tissue sample at diagnosis of 53 patients with mCRC, Tie et al. evaluated ctDNA as disease monitoring. They reported that a level of reduction in ctDNA concentration during first cycle of chemotherapy was significantly associated with the objective radiologic response rate at 8–10 weeks and with a trend for a better PFS. [75]. Similarly, Garlan et al. showed that early changes of the ctDNA concentration could predict the efficacy of first- or second-line chemotherapy in a prospective cohort of 82 mCRC. They used ctDNA monitoring between the first and second or/and third cycle of chemotherapy to define a composite marker that allowed to separate patients in two groups of “bad” or “good” ctDNA responder. This marker was based on the “normalization” of the ctDNA concentration (thresholds of 0.1 ng/mL) and the slope of ctDNA concentration decrease. The group of better ctDNA responders demonstrated a significantly better tumor response rate, PFS and OS [76]. The changes of ctDNA concentration during treatment therefore appear as a relevant early tool to assess treatment efficacy and this biomarker should be evaluated in larger prospective series.
Furthermore, ctDNA can also be used to track clonal evolution. It has been suggested that CRC presumably contains resistant mutant clones before treatment that emerge under therapeutic pressure [77]. The acquisition of resistance can be accompanied by the emergence of RAS pathway mutations that could allow to anticipate radiologic progression [78][79]. Several studies have already described emergence of mutations detected by ctDNA under anti-EGFR treatment up to 5–10 months before imaging diagnostic [78][79][80]. By monitoring ctDNA, Siravegna et al. also showed in a subset of patients, that the proportion of ctDNA, based on the detection of KRAS mutations, dynamically varied depending on the presence or the absence of anti-EGFR treatment. These possible dynamic clonal evolutions induced by therapeutic pressure justified to re-challenge anti-EGFR based treatment after a withdrawal period in mCRC. [80][81]. Some retrospective analyses of the phase 2 CRICKET and E-Rechallenge studies suggested that ctDNA could guide this re-challenge therapy because only patients without RAS or BRAF circulating mutations detected plasma at the time of re-challenge might achieve clinical benefit from the retreatment with anti-EGFRs [82][81][83].
In squamous cell carcinomas of the anal canal (SCCA), Human papillomavirus (HPV) is found in 90% [84]. Therefore HPV DNA appears as the best candidate to assess the presence of ctDNA in SCCA and can be detected in plasma by ddPCR with sensitivity up to 93% in HPV positive-cancers [85]. In a recent study enrolling 8 SCCA patients, ddPCR demonstrated 100% of specificity for the detection of HVP ctDNA [86].

3.2. Pancreatic Cancer

In advanced PC, some regimens such as FOLFIRINOX (5-fluorouracil, leucovorin, oxaliplatin, and irinotecan) and gemcitabine plus nab-paclitaxel are effective but are not devoided of toxicities [87][88][89][90]. The monitoring of KRAS mutation through ctDNA has been performed in several studies and suggested that its detection could predict radiological progression, but some results were however discordant [91][92][93]. The clearance of KRAS ctDNA during treatment predicted better PFS than remaining positive ctDNA [93], and increasing levels of KRAS ctDNA were also associated with worse PFS and OS [94][95]. Finally, the decline slope of ctDNA concentration based on mutation of KRAS was associated with OS in another study [96]. Apart from KRAS mutations, evolution of other mutations in plasma, such as TP53, SMAD4, CDKN2A, KRAS, APC, ATM, FBXW7 and others could also be used to reliably reflect response to therapy [29][30].
Unlike other GI cancers, there is currently no targetable molecular alteration for all patients with advanced PC in clinical routine. However, some new treatment could be promising in PC, such as PARP inhibitors in case of germline BRCA1/2 mutations [97]. Moreover, like in other tumors, checkpoints inhibitors seem to be efficient in advanced PC with microsatellite instability [98][99][100]. Molecular alterations could be detected in ctDNA in PC [92] and therefore maybe screen patients for targeted therapies in the future. In this context, Bachet et al. recently confirmed from the data of a randomized phase II trial that the ctDNA could be a predictive biomarker of l-asparaginase encapsulated in erythrocytes (eryaspase) efficacy in advanced PC [101].

3.3. Esophageal and Gastric Cancer

In patients with advanced gastroesophageal adenocarcinoma, the addition of trastuzumab to chemotherapy was associated with improvement of clinical outcomes for tumors with a high level of HER2 expression (IHC3+ or IHC2+ and FISH+) [102]. Some studies have already described the potential for ctDNA to detect HER2 amplification by ddPCR with high concordance with classic immunohistochemistry and fluorescent in situ hybridization on tissue samples [103][104]. However, in the recent cohort of Maron et al. seven patients with advanced disease were tested for HER2 amplification in both primary and metastatic tumor, and in ctDNA. Among them, only 2 patients (28%) were concordant for HER2 amplification detection in the three samples, underlying possible missed detection of HER2 amplification by NGS and then the risk of missed opportunities to use anti-HER2 therapies [45]. Despite its lack of sensitivity, ctDNA could however be used in combination with tissue NGS to define a group of extremely sensitive HER2 amplified patients when treated with trastuzumab [45].
Moreover, some authors already suggested that ctDNA could also be used to monitor response to therapy in GC. In a recent study, tumor responses to lapatinib plus capecitabine were closely related with changes of the level of amplification of HER2 detected in plasma through serial ctDNA sequencing [105]. In the study of Maron et al. dynamic measurements of ctDNA before and during treatment showed that a decrease superior to 50% in MAF was correlated with better OS [45]. The detection of therapeutic resistance to treatment in advanced GC could also be improved by ctDNA. In the cohort of Maron et al. some anti-HER2 therapy acquired resistance mechanisms were detected using ctDNA [45].

3.4. Hepatocellular Carcinoma

In advanced HCC, ctDNA could be used to monitor tumor burden under therapy. A diagnostic prediction model with 10 selected methylation markers through ctDNA was recently developed by Xu et al. and correlated with tumor burden, treatment response, and disease stage [106].
In a study using whole exome sequencing to evaluate ctDNA among HCC patients who underwent surgery, in patients with positive ctDNA after surgery, the levels of serum ctDNA increased with disease progression and responded to the additional treatments [107].
The somatic MAF of ctDNA could also reflect clinical dynamics as demonstrated in one patient with advanced HCC undergoing trans-arterial chemoembolization in whom increasing level of 8 somatic mutations in plasma was detected before imaging diagnosis and the increase of standard biomarker AFP [108].

3.5. Other GI Cancers

In CC, until past years, chemotherapy was the only validated treatment for advanced disease [73][109]. Recently, some targeted therapies emerged in the therapeutic arsenal. Ivosidenib, a first-in-class oral IDH1 inhibitor, has demonstrated an improvement of PFS over placebo in advanced CC with IDH1 mutations in the phase III ClarIDHy study [110]. In another phase II study (NCT-02150967), BGJ398, an orally bioavailable, selective pan-FGFR kinase inhibitor demonstrated clinical activity against chemotherapy-refractory CC with FGFR2 fusions [111]. Lastly, the phase II study FIGHT-202 also supported the efficiency of pemigatinib, an oral inhibitor of FGFR1, 2, and 3 in previously treated patients with cholangiocarcinoma with FGFR2 fusions or rearrangements [112]. Therefore, the interest in monitoring ctDNA in CC is increasing. Goyal et al. already monitored 9 patients with FGFR2 fusions and detected de novo point mutations that conferred resistance to BGJ298 in all patients (n = 3) who underwent progression [113]. Ettrich et al. recently demonstrated that 63% of treatment naïve patients with advanced CC had changes in their mutational profile during chemotherapy. They evaluated and identified a set of 76 potential progression driver genes among a large-scale panel sequencing of 710 cancer-related genes [74]. These data suggest that ctDNA could be used to track disease progression.
In GIST, one main application of ctDNA seems to be monitoring response to therapy and tracking therapeutic resistance to tyrosine kinase inhibitors (TKI) [114][115][116][117][118]. Indeed, despite the revolution in GIST management through the contribution of first line TKI such as imatinib targeting KIT or PDGFRA molecular drivers, the majority of GIST will progress with the acquisition of secondary KIT or PDGFRA mutations. In this context, second and third line TKI have been used in some refractory GIST patients [119][120][121][122]. Maier et al. first described a dynamic change in MAF in plasma of advanced GIST under treatment. A decrease or a disappearance of ctDNA occurred in patients responding to TKIs [123]. In other studies, the usefulness of ctDNA for the identification of TKI resistance mutations and their prognostic utility was demonstrated [117][118]. In a phase II study patients with secondary KIT mutations had significantly worse OS than those with no detectable secondary mutations [117]. ctDNA can also be used to detect resistance mutations in other gene than KIT, as demonstrated in a prospective study that collected 30 plasma samples from 22 patients with metastatic GIST [124]. Monitoring ctDNA using NGS patients with GIST under TKI treatment detected primary but also secondary mutations emerging in patients who had a progressive disease whereas only primary mutations were detected in patients with stable disease. These resistance mutations in ctDNA could represent early biomarkers for treatment response [116][125].

References

  1. Chan, A.K.C.; Chiu, R.W.K.; Lo, Y.M.D. Clinical Sciences Reviews Committee of the Association of Clinical Biochemists Cell-Free Nucleic Acids in Plasma, Serum and Urine: A New Tool in Molecular Diagnosis. Ann. Clin. Biochem. 2003, 40, 122–130.
  2. Schwarzenbach, H.; Hoon, D.S.B.; Pantel, K. Cell-Free Nucleic Acids as Biomarkers in Cancer Patients. Nat. Rev. Cancer 2011, 11, 426–437.
  3. Leon, S.A.; Shapiro, B.; Sklaroff, D.M.; Yaros, M.J. Free DNA in the Serum of Cancer Patients and the Effect of Therapy. Cancer Res. 1977, 37, 646–650.
  4. Haber, D.A.; Velculescu, V.E. Blood-Based Analyses of Cancer: Circulating Tumor Cells and Circulating Tumor DNA. Cancer Discov. 2014, 4, 650–661.
  5. Merker, J.D.; Oxnard, G.R.; Compton, C.; Diehn, M.; Hurley, P.; Lazar, A.J.; Lindeman, N.; Lockwood, C.M.; Rai, A.J.; Schilsky, R.L.; et al. Circulating Tumor DNA Analysis in Patients with Cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. J. Clin. Oncol. 2018, 36, 1631–1641.
  6. Chen, X.; Gole, J.; Gore, A.; He, Q.; Lu, M.; Min, J.; Yuan, Z.; Yang, X.; Jiang, Y.; Zhang, T.; et al. Non-Invasive Early Detection of Cancer Four Years before Conventional Diagnosis Using a Blood Test. Nat. Commun. 2020, 11, 3475.
  7. Pécuchet, N.; Rozenholc, Y.; Zonta, E.; Pietrasz, D.; Didelot, A.; Combe, P.; Gibault, L.; Bachet, J.-B.; Taly, V.; Fabre, E.; et al. Analysis of Base-Position Error Rate of Next-Generation Sequencing to Detect Tumor Mutations in Circulating DNA. Clin. Chem. 2016, 62, 1492–1503.
  8. Garrigou, S.; Perkins, G.; Garlan, F.; Normand, C.; Didelot, A.; Le Corre, D.; Peyvandi, S.; Mulot, C.; Niarra, R.; Aucouturier, P.; et al. A Study of Hypermethylated Circulating Tumor DNA as a Universal Colorectal Cancer Biomarker. Clin. Chem. 2016, 62, 1129–1139.
  9. Van Cutsem, E.; Köhne, C.-H.; Hitre, E.; Zaluski, J.; Chang Chien, C.-R.; Makhson, A.; D’Haens, G.; Pintér, T.; Lim, R.; Bodoky, G.; et al. Cetuximab and Chemotherapy as Initial Treatment for Metastatic Colorectal Cancer. N. Engl. J. Med. 2009, 360, 1408–1417.
  10. Douillard, J.-Y.; Oliner, K.S.; Siena, S.; Tabernero, J.; Burkes, R.; Barugel, M.; Humblet, Y.; Bodoky, G.; Cunningham, D.; Jassem, J.; et al. Panitumumab-FOLFOX4 Treatment and RAS Mutations in Colorectal Cancer. N. Engl. J. Med. 2013, 369, 1023–1034.
  11. Kopetz, S.; Grothey, A.; Yaeger, R.; Van Cutsem, E.; Desai, J.; Yoshino, T.; Wasan, H.; Ciardiello, F.; Loupakis, F.; Hong, Y.S.; et al. Encorafenib, Binimetinib, and Cetuximab in BRAF V600E-Mutated Colorectal Cancer. N. Engl. J. Med. 2019, 381, 1632–1643.
  12. Thierry, A.R.; Mouliere, F.; El Messaoudi, S.; Mollevi, C.; Lopez-Crapez, E.; Rolet, F.; Gillet, B.; Gongora, C.; Dechelotte, P.; Robert, B.; et al. Clinical Validation of the Detection of KRAS and BRAF Mutations from Circulating Tumor DNA. Nat. Med. 2014, 20, 430–435.
  13. Taly, V.; Pekin, D.; Benhaim, L.; Kotsopoulos, S.K.; Le Corre, D.; Li, X.; Atochin, I.; Link, D.R.; Griffiths, A.D.; Pallier, K.; et al. Multiplex Picodroplet Digital PCR to Detect KRAS Mutations in Circulating DNA from the Plasma of Colorectal Cancer Patients. Clin. Chem. 2013, 59, 1722–1731.
  14. Bachet, J.B.; Bouché, O.; Taieb, J.; Dubreuil, O.; Garcia, M.L.; Meurisse, A.; Normand, C.; Gornet, J.M.; Artru, P.; Louafi, S.; et al. RAS Mutation Analysis in Circulating Tumor DNA from Patients with Metastatic Colorectal Cancer: The AGEO RASANC Prospective Multicenter Study. Ann. Oncol. 2018, 29, 1211–1219.
  15. Gerlinger, M.; Rowan, A.J.; Horswell, S.; Math, M.; Larkin, J.; Endesfelder, D.; Gronroos, E.; Martinez, P.; Matthews, N.; Stewart, A.; et al. Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing. N. Engl. J. Med. 2012, 366, 883–892.
  16. Kleppe, M.; Levine, R.L. Tumor Heterogeneity Confounds and Illuminates: Assessing the Implications. Nat. Med. 2014, 20, 342–344.
  17. Spindler, K.-L.G.; Pallisgaard, N.; Vogelius, I.; Jakobsen, A. Quantitative Cell-Free DNA, KRAS, and BRAF Mutations in Plasma from Patients with Metastatic Colorectal Cancer during Treatment with Cetuximab and Irinotecan. Clin. Cancer Res. 2012, 18, 1177–1185.
  18. Spindler, K.-L.G.; Pallisgaard, N.; Appelt, A.L.; Andersen, R.F.; Schou, J.V.; Nielsen, D.; Pfeiffer, P.; Yilmaz, M.; Johansen, J.S.; Hoegdall, E.V.; et al. Clinical Utility of KRAS Status in Circulating Plasma DNA Compared to Archival Tumour Tissue from Patients with Metastatic Colorectal Cancer Treated with Anti-Epidermal Growth Factor Receptor Therapy. Eur. J. Cancer 2015, 51, 2678–2685.
  19. Almoguera, C.; Shibata, D.; Forrester, K.; Martin, J.; Arnheim, N.; Perucho, M. Most Human Carcinomas of the Exocrine Pancreas Contain Mutant C-K-Ras Genes. Cell 1988, 53, 549–554.
  20. Hruban, R.H.; van Mansfeld, A.D.; Offerhaus, G.J.; van Weering, D.H.; Allison, D.C.; Goodman, S.N.; Kensler, T.W.; Bose, K.K.; Cameron, J.L.; Bos, J.L. K-Ras Oncogene Activation in Adenocarcinoma of the Human Pancreas. A Study of 82 Carcinomas Using a Combination of Mutant-Enriched Polymerase Chain Reaction Analysis and Allele-Specific Oligonucleotide Hybridization. Am. J. Pathol. 1993, 143, 545–554.
  21. Algül, H.; Treiber, M.; Lesina, M.; Schmid, R.M. Mechanisms of Disease: Chronic Inflammation and Cancer in the Pancreas—A Potential Role for Pancreatic Stellate Cells? Nat. Clin. Pract. Gastroenterol Hepatol 2007, 4, 454–462.
  22. Bailey, P.; Chang, D.K.; Nones, K.; Johns, A.L.; Patch, A.-M.; Gingras, M.-C.; Miller, D.K.; Christ, A.N.; Bruxner, T.J.C.; Quinn, M.C.; et al. Genomic Analyses Identify Molecular Subtypes of Pancreatic Cancer. Nature 2016, 531, 47–52.
  23. Kim, M.K.; Woo, S.M.; Park, B.; Yoon, K.-A.; Kim, Y.-H.; Joo, J.; Lee, W.J.; Han, S.-S.; Park, S.-J.; Kong, S.-Y. Prognostic Implications of Multiplex Detection of KRAS Mutations in Cell-Free DNA from Patients with Pancreatic Ductal Adenocarcinoma. Clin. Chem. 2018, 64, 726–734.
  24. Chen, L.; Zhang, Y.; Cheng, Y.; Zhang, D.; Zhu, S.; Ma, X. Prognostic Value of Circulating Cell-Free DNA in Patients with Pancreatic Cancer: A Systemic Review and Meta-Analysis. Gene 2018, 679, 328–334.
  25. Singh, N.; Gupta, S.; Pandey, R.M.; Chauhan, S.S.; Saraya, A. High Levels of Cell-Free Circulating Nucleic Acids in Pancreatic Cancer Are Associated with Vascular Encasement, Metastasis and Poor Survival. Cancer Invest. 2015, 33, 78–85.
  26. Pietrasz, D.; Pécuchet, N.; Garlan, F.; Didelot, A.; Dubreuil, O.; Doat, S.; Imbert-Bismut, F.; Karoui, M.; Vaillant, J.-C.; Taly, V.; et al. Plasma Circulating Tumor DNA in Pancreatic Cancer Patients Is a Prognostic Marker. Clin. Cancer Res. 2017, 23, 116–123.
  27. Luchini, C.; Veronese, N.; Nottegar, A.; Cappelletti, V.; Daidone, M.G.; Smith, L.; Parris, C.; Brosens, L.A.A.; Caruso, M.G.; Cheng, L.; et al. Liquid Biopsy as Surrogate for Tissue for Molecular Profiling in Pancreatic Cancer: A Meta-Analysis Towards Precision Medicine. Cancers 2019, 11, 1152.
  28. Adamo, P.; Cowley, C.M.; Neal, C.P.; Mistry, V.; Page, K.; Dennison, A.R.; Isherwood, J.; Hastings, R.; Luo, J.; Moore, D.A.; et al. Profiling Tumour Heterogeneity through Circulating Tumour DNA in Patients with Pancreatic Cancer. Oncotarget 2017, 8, 87221–87233.
  29. Berger, A.W.; Schwerdel, D.; Ettrich, T.J.; Hann, A.; Schmidt, S.A.; Kleger, A.; Marienfeld, R.; Seufferlein, T. Targeted Deep Sequencing of Circulating Tumor DNA in Metastatic Pancreatic Cancer. Oncotarget 2018, 9, 2076–2085.
  30. Park, G.; Park, J.K.; Son, D.-S.; Shin, S.-H.; Kim, Y.J.; Jeon, H.-J.; Lee, J.; Park, W.-Y.; Lee, K.H.; Park, D. Utility of Targeted Deep Sequencing for Detecting Circulating Tumor DNA in Pancreatic Cancer Patients. Sci. Rep. 2018, 8, 11631.
  31. Eissa, M.A.L.; Lerner, L.; Abdelfatah, E.; Shankar, N.; Canner, J.K.; Hasan, N.M.; Yaghoobi, V.; Huang, B.; Kerner, Z.; Takaesu, F.; et al. Promoter Methylation of ADAMTS1 and BNC1 as Potential Biomarkers for Early Detection of Pancreatic Cancer in Blood. Clin. Epigenetics 2019, 11, 59.
  32. Rashid, S.; Singh, N.; Gupta, S.; Rashid, S.; Nalika, N.; Sachdev, V.; Bal, C.S.; Datta Gupta, S.; Chauhan, S.S.; Saraya, A. Progression of Chronic Pancreatitis to Pancreatic Cancer: Is There a Role of Gene Mutations as a Screening Tool? Pancreas 2018, 47, 227–232.
  33. Berger, A.W.; Schwerdel, D.; Costa, I.G.; Hackert, T.; Strobel, O.; Lam, S.; Barth, T.F.; Schröppel, B.; Meining, A.; Büchler, M.W.; et al. Detection of Hot-Spot Mutations in Circulating Cell-Free DNA From Patients with Intraductal Papillary Mucinous Neoplasms of the Pancreas. Gastroenterology 2016, 151, 267–270.
  34. Sefrioui, D.; Blanchard, F.; Toure, E.; Basile, P.; Beaussire, L.; Dolfus, C.; Perdrix, A.; Paresy, M.; Antonietti, M.; Iwanicki-Caron, I.; et al. Diagnostic Value of CA19.9, Circulating Tumour DNA and Circulating Tumour Cells in Patients with Solid Pancreatic Tumours. Br. J. Cancer 2017, 117, 1017–1025.
  35. Berger, A.W.; Schwerdel, D.; Reinacher-Schick, A.; Uhl, W.; Algül, H.; Friess, H.; Janssen, K.-P.; König, A.; Ghadimi, M.; Gallmeier, E.; et al. A Blood-Based Multi Marker Assay Supports the Differential Diagnosis of Early-Stage Pancreatic Cancer. Theranostics 2019, 9, 1280–1287.
  36. Wang, Z.-Y.; Ding, X.-Q.; Zhu, H.; Wang, R.-X.; Pan, X.-R.; Tong, J.-H. KRAS Mutant Allele Fraction in Circulating Cell-Free DNA Correlates with Clinical Stage in Pancreatic Cancer Patients. Front Oncol. 2019, 9, 1295.
  37. Pectasides, E.; Stachler, M.D.; Derks, S.; Liu, Y.; Maron, S.; Islam, M.; Alpert, L.; Kwak, H.; Kindler, H.; Polite, B.; et al. Genomic Heterogeneity as a Barrier to Precision Medicine in Gastroesophageal Adenocarcinoma. Cancer Discov. 2018, 8, 37–48.
  38. Frankell, A.M.; Jammula, S.; Li, X.; Contino, G.; Killcoyne, S.; Abbas, S.; Perner, J.; Bower, L.; Devonshire, G.; Ococks, E.; et al. The Landscape of Selection in 551 Esophageal Adenocarcinomas Defines Genomic Biomarkers for the Clinic. Nat. Genet. 2019, 51, 506–516.
  39. Zang, Z.J.; Ong, C.K.; Cutcutache, I.; Yu, W.; Zhang, S.L.; Huang, D.; Ler, L.D.; Dykema, K.; Gan, A.; Tao, J.; et al. Genetic and Structural Variation in the Gastric Cancer Kinome Revealed through Targeted Deep Sequencing. Cancer Res. 2011, 71, 29–39.
  40. Deng, N.; Goh, L.K.; Wang, H.; Das, K.; Tao, J.; Tan, I.B.; Zhang, S.; Lee, M.; Wu, J.; Lim, K.H.; et al. A Comprehensive Survey of Genomic Alterations in Gastric Cancer Reveals Systematic Patterns of Molecular Exclusivity and Co-Occurrence among Distinct Therapeutic Targets. Gut. 2012, 61, 673–684.
  41. Stachler, M.D.; Taylor-Weiner, A.; Peng, S.; McKenna, A.; Agoston, A.T.; Odze, R.D.; Davison, J.M.; Nason, K.S.; Loda, M.; Leshchiner, I.; et al. Paired Exome Analysis of Barrett’s Esophagus and Adenocarcinoma. Nat. Genet. 2015, 47, 1047–1055.
  42. Wong, G.S.; Zhou, J.; Liu, J.B.; Wu, Z.; Xu, X.; Li, T.; Xu, D.; Schumacher, S.E.; Puschhof, J.; McFarland, J.; et al. Targeting Wild-Type KRAS-Amplified Gastroesophageal Cancer through Combined MEK and SHP2 Inhibition. Nat. Med. 2018, 24, 968–977.
  43. Kato, S.; Okamura, R.; Baumgartner, J.M.; Patel, H.; Leichman, L.; Kelly, K.; Sicklick, J.K.; Fanta, P.T.; Lippman, S.M.; Kurzrock, R. Analysis of Circulating Tumor DNA and Clinical Correlates in Patients with Esophageal, Gastroesophageal Junction, and Gastric Adenocarcinoma. Clin. Cancer Res. 2018, 24, 6248–6256.
  44. Gao, Y.; Zhang, K.; Xi, H.; Cai, A.; Wu, X.; Cui, J.; Li, J.; Qiao, Z.; Wei, B.; Chen, L. Diagnostic and Prognostic Value of Circulating Tumor DNA in Gastric Cancer: A Meta-Analysis. Oncotarget 2017, 8, 6330–6340.
  45. Maron, S.B.; Chase, L.M.; Lomnicki, S.; Kochanny, S.; Moore, K.L.; Joshi, S.S.; Landron, S.; Johnson, J.; Kiedrowski, L.A.; Nagy, R.J.; et al. Circulating Tumor DNA Sequencing Analysis of Gastroesophageal Adenocarcinoma. Clin. Cancer Res. 2019, 25, 7098–7112.
  46. Saluja, H.; Karapetis, C.S.; Pedersen, S.K.; Young, G.P.; Symonds, E.L. The Use of Circulating Tumor DNA for Prognosis of Gastrointestinal Cancers. Front. Oncol. 2018, 8, 275.
  47. Balgkouranidou, I.; Matthaios, D.; Karayiannakis, A.; Bolanaki, H.; Michailidis, P.; Xenidis, N.; Amarantidis, K.; Chelis, L.; Trypsianis, G.; Chatzaki, E.; et al. Prognostic Role of APC and RASSF1A Promoter Methylation Status in Cell Free Circulating DNA of Operable Gastric Cancer Patients. Mutat. Res. 2015, 778, 46–51.
  48. Luo, H.; Li, H.; Hu, Z.; Wu, H.; Liu, C.; Li, Y.; Zhang, X.; Lin, P.; Hou, Q.; Ding, G.; et al. Noninvasive Diagnosis and Monitoring of Mutations by Deep Sequencing of Circulating Tumor DNA in Esophageal Squamous Cell Carcinoma. Biochem. Biophys. Res. Commun. 2016, 471, 596–602.
  49. Tate, J.G.; Bamford, S.; Jubb, H.C.; Sondka, Z.; Beare, D.M.; Bindal, N.; Boutselakis, H.; Cole, C.G.; Creatore, C.; Dawson, E.; et al. COSMIC: The Catalogue of Somatic Mutations In Cancer. Nucleic Acids Res. 2019, 47, D941–D947.
  50. Huang, A.; Zhang, X.; Zhou, S.-L.; Cao, Y.; Huang, X.-W.; Fan, J.; Yang, X.-R.; Zhou, J. Detecting Circulating Tumor DNA in Hepatocellular Carcinoma Patients Using Droplet Digital PCR Is Feasible and Reflects Intratumoral Heterogeneity. J. Cancer 2016, 7, 1907–1914.
  51. Liao, W.; Yang, H.; Xu, H.; Wang, Y.; Ge, P.; Ren, J.; Xu, W.; Lu, X.; Sang, X.; Zhong, S.; et al. Noninvasive Detection of Tumor-Associated Mutations from Circulating Cell-Free DNA in Hepatocellular Carcinoma Patients by Targeted Deep Sequencing. Oncotarget 2016, 7, 40481–40490.
  52. Jiao, J.; Watt, G.P.; Stevenson, H.L.; Calderone, T.L.; Fisher-Hoch, S.P.; Ye, Y.; Wu, X.; Vierling, J.M.; Beretta, L. Telomerase Reverse Transcriptase Mutations in Plasma DNA in Patients with Hepatocellular Carcinoma or Cirrhosis: Prevalence and Risk Factors. Hepatol Commun. 2018, 2, 718–731.
  53. Marchio, A.; Amougou Atsama, M.; Béré, A.; Komas, N.-P.; Noah Noah, D.; Atangana, P.J.A.; Camengo-Police, S.-M.; Njouom, R.; Bekondi, C.; Pineau, P. Droplet Digital PCR Detects High Rate of TP53 R249S Mutants in Cell-Free DNA of Middle African Patients with Hepatocellular Carcinoma. Clin. Exp. Med. 2018, 18, 421–431.
  54. Jiao, J.; Niu, W.; Wang, Y.; Baggerly, K.; Ye, Y.; Wu, X.; Davenport, D.; Almeda, J.L.; Betancourt-Garcia, M.M.; Forse, R.A.; et al. Prevalence of Aflatoxin-Associated TP53R249S Mutation in Hepatocellular Carcinoma in Hispanics in South Texas. Cancer Prev. Res. 2018, 11, 103–112.
  55. Ng, C.K.Y.; Di Costanzo, G.G.; Tosti, N.; Paradiso, V.; Coto-Llerena, M.; Roscigno, G.; Perrina, V.; Quintavalle, C.; Boldanova, T.; Wieland, S.; et al. Genetic Profiling Using Plasma-Derived Cell-Free DNA in Therapy-Naïve Hepatocellular Carcinoma Patients: A Pilot Study. Ann. Oncol. 2018, 29, 1286–1291.
  56. Kisiel, J.B.; Dukek, B.A.; Kanipakam, V.S.R.; Ghoz, H.M.; Yab, T.C.; Berger, C.K.; Taylor, W.R.; Foote, P.H.; Giama, N.H.; Onyirioha, K.; et al. Hepatocellular Carcinoma Detection by Plasma Methylated DNA: Discovery, Phase I Pilot, and Phase II Clinical Validation. Hepatology 2019, 69, 1180–1192.
  57. Wong, I.H.; Lo, Y.M.; Zhang, J.; Liew, C.T.; Ng, M.H.; Wong, N.; Lai, P.B.; Lau, W.Y.; Hjelm, N.M.; Johnson, P.J. Detection of Aberrant P16 Methylation in the Plasma and Serum of Liver Cancer Patients. Cancer Res. 1999, 59, 71–73.
  58. Wong, I.H.; Lo, Y.M.; Yeo, W.; Lau, W.Y.; Johnson, P.J. Frequent P15 Promoter Methylation in Tumor and Peripheral Blood from Hepatocellular Carcinoma Patients. Clin. Cancer Res. 2000, 6, 3516–3521.
  59. Wang, J.; Qin, Y.; Li, B.; Sun, Z.; Yang, B. Detection of Aberrant Promoter Methylation of GSTP1 in the Tumor and Serum of Chinese Human Primary Hepatocellular Carcinoma Patients. Clin. Biochem. 2006, 39, 344–348.
  60. Zhang, Y.-J.; Wu, H.-C.; Shen, J.; Ahsan, H.; Tsai, W.Y.; Yang, H.-I.; Wang, L.-Y.; Chen, S.-Y.; Chen, C.-J.; Santella, R.M. Predicting Hepatocellular Carcinoma by Detection of Aberrant Promoter Methylation in Serum DNA. Clin Cancer Res. 2007, 13, 2378–2384.
  61. Zhang, C.; Li, J.; Huang, T.; Duan, S.; Dai, D.; Jiang, D.; Sui, X.; Li, D.; Chen, Y.; Ding, F.; et al. Meta-Analysis of DNA Methylation Biomarkers in Hepatocellular Carcinoma. Oncotarget 2016, 7, 81255–81267.
  62. Chan, K.C.A.; Lai, P.B.S.; Mok, T.S.K.; Chan, H.L.Y.; Ding, C.; Yeung, S.W.; Lo, Y.M.D. Quantitative Analysis of Circulating Methylated DNA as a Biomarker for Hepatocellular Carcinoma. Clin. Chem. 2008, 54, 1528–1536.
  63. Yeo, W.; Wong, N.; Wong, W.-L.; Lai, P.B.S.; Zhong, S.; Johnson, P.J. High Frequency of Promoter Hypermethylation of RASSF1A in Tumor and Plasma of Patients with Hepatocellular Carcinoma. Liver Int. 2005, 25, 266–272.
  64. Dong, X.; Hou, Q.; Chen, Y.; Wang, X. Diagnostic Value of the Methylation of Multiple Gene Promoters in Serum in Hepatitis B Virus-Related Hepatocellular Carcinoma. Dis. Markers 2017, 2017, 977.
  65. Holmila, R.; Sklias, A.; Muller, D.C.; Degli Esposti, D.; Guilloreau, P.; Mckay, J.; Sangrajrang, S.; Srivatanakul, P.; Hainaut, P.; Merle, P.; et al. Targeted Deep Sequencing of Plasma Circulating Cell-Free DNA Reveals Vimentin and Fibulin 1 as Potential Epigenetic Biomarkers for Hepatocellular Carcinoma. PLoS ONE 2017, 12, e0174265.
  66. Oussalah, A.; Rischer, S.; Bensenane, M.; Conroy, G.; Filhine-Tresarrieu, P.; Debard, R.; Forest-Tramoy, D.; Josse, T.; Reinicke, D.; Garcia, M.; et al. Plasma MSEPT9: A Novel Circulating Cell-Free DNA-Based Epigenetic Biomarker to Diagnose Hepatocellular Carcinoma. EBioMedicine 2018, 30, 138–147.
  67. Hlady, R.A.; Zhao, X.; Pan, X.; Yang, J.D.; Ahmed, F.; Antwi, S.O.; Giama, N.H.; Patel, T.; Roberts, L.R.; Liu, C.; et al. Genome-Wide Discovery and Validation of Diagnostic DNA Methylation-Based Biomarkers for Hepatocellular Cancer Detection in Circulating Cell Free DNA. Theranostics 2019, 9, 7239–7250.
  68. Sun, F.-K.; Fan, Y.-C.; Zhao, J.; Zhang, F.; Gao, S.; Zhao, Z.-H.; Sun, Q.; Wang, K. Detection of TFPI2 Methylation in the Serum of Hepatocellular Carcinoma Patients. Dig. Dis. Sci. 2013, 58, 1010–1015.
  69. Han, L.-Y.; Fan, Y.-C.; Mu, N.-N.; Gao, S.; Li, F.; Ji, X.-F.; Dou, C.-Y.; Wang, K. Aberrant DNA Methylation of G-Protein-Coupled Bile Acid Receptor Gpbar1 (TGR5) Is a Potential Biomarker for Hepatitis B Virus Associated Hepatocellular Carcinoma. Int. J. Med. Sci. 2014, 11, 164–171.
  70. Ji, X.-F.; Fan, Y.-C.; Gao, S.; Yang, Y.; Zhang, J.-J.; Wang, K. MT1M and MT1G Promoter Methylation as Biomarkers for Hepatocellular Carcinoma. World J. Gastroenterol 2014, 20, 4723–4729.
  71. Zou, S.; Li, J.; Zhou, H.; Frech, C.; Jiang, X.; Chu, J.S.C.; Zhao, X.; Li, Y.; Li, Q.; Wang, H.; et al. Mutational Landscape of Intrahepatic Cholangiocarcinoma. Nat. Commun. 2014, 5, 5696.
  72. Farshidfar, F.; Zheng, S.; Gingras, M.-C.; Newton, Y.; Shih, J.; Robertson, A.G.; Hinoue, T.; Hoadley, K.A.; Gibb, E.A.; Roszik, J.; et al. Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles. Cell. Rep. 2017, 18, 2780–2794.
  73. Valle, J.W.; Lamarca, A.; Goyal, L.; Barriuso, J.; Zhu, A.X. New Horizons for Precision Medicine in Biliary Tract Cancers. Cancer Discov. 2017, 7, 943–962.
  74. Ettrich, T.J.; Schwerdel, D.; Dolnik, A.; Beuter, F.; Blätte, T.J.; Schmidt, S.A.; Stanescu-Siegmund, N.; Steinacker, J.; Marienfeld, R.; Kleger, A.; et al. Genotyping of Circulating Tumor DNA in Cholangiocarcinoma Reveals Diagnostic and Prognostic Information. Sci. Rep. 2019, 9, 13261.
  75. Tie, J.; Kinde, I.; Wang, Y.; Wong, H.L.; Roebert, J.; Christie, M.; Tacey, M.; Wong, R.; Singh, M.; Karapetis, C.S.; et al. Circulating Tumor DNA as an Early Marker of Therapeutic Response in Patients with Metastatic Colorectal Cancer. Ann. Oncol. 2015, 26, 1715–1722.
  76. Garlan, F.; Laurent-Puig, P.; Sefrioui, D.; Siauve, N.; Didelot, A.; Sarafan-Vasseur, N.; Michel, P.; Perkins, G.; Mulot, C.; Blons, H.; et al. Early Evaluation of Circulating Tumor DNA as Marker of Therapeutic Efficacy in Metastatic Colorectal Cancer Patients (PLACOL Study). Clin. Cancer Res. 2017, 23, 5416–5425.
  77. Santini, D.; Fratto, M.E.; Spoto, C.; Russo, A.; Galluzzo, S.; Zoccoli, A.; Vincenzi, B.; Tonini, G. Cetuximab in Small Bowel Adenocarcinoma: A New Friend? Br. J. Cancer 2010, 103, 1305.
  78. Misale, S.; Yaeger, R.; Hobor, S.; Scala, E.; Janakiraman, M.; Liska, D.; Valtorta, E.; Schiavo, R.; Buscarino, M.; Siravegna, G.; et al. Emergence of KRAS Mutations and Acquired Resistance to Anti-EGFR Therapy in Colorectal Cancer. Nature 2012, 486, 532–536.
  79. Diaz, L.A.; Williams, R.T.; Wu, J.; Kinde, I.; Hecht, J.R.; Berlin, J.; Allen, B.; Bozic, I.; Reiter, J.G.; Nowak, M.A.; et al. The Molecular Evolution of Acquired Resistance to Targeted EGFR Blockade in Colorectal Cancers. Nature 2012, 486, 537–540.
  80. Siravegna, G.; Mussolin, B.; Buscarino, M.; Corti, G.; Cassingena, A.; Crisafulli, G.; Ponzetti, A.; Cremolini, C.; Amatu, A.; Lauricella, C.; et al. Clonal Evolution and Resistance to EGFR Blockade in the Blood of Colorectal Cancer Patients. Nat. Med. 2015, 21, 795–801.
  81. Cremolini, C.; Rossini, D.; Dell’Aquila, E.; Lonardi, S.; Conca, E.; Del Re, M.; Busico, A.; Pietrantonio, F.; Danesi, R.; Aprile, G.; et al. Rechallenge for Patients with RAS and BRAF Wild-Type Metastatic Colorectal Cancer With Acquired Resistance to First-Line Cetuximab and Irinotecan. JAMA Oncol 2019, 5, 343–350.
  82. Parseghian, C.M.; Loree, J.M.; Morris, V.K.; Liu, X.; Clifton, K.K.; Napolitano, S.; Henry, J.T.; Pereira, A.A.; Vilar, E.; Johnson, B.; et al. Anti-EGFR-Resistant Clones Decay Exponentially after Progression: Implications for Anti-EGFR Re-Challenge. Ann. Oncol. 2019, 30, 243–249.
  83. Nakamura, M. MO3-12-5—Phase II Study of Cetuximab Rechallenge in Patients with RAS Wild-Type Metastatic Colorectal Cancer: E-Rechallenge Trial. Ann. Oncol. 2019, 30, 343–350.
  84. Vincent-Salomon, A.; de la Rochefordière, A.; Salmon, R.; Validire, P.; Zafrani, B.; Sastre-Garau, X. Frequent Association of Human Papillomavirus 16 and 18 DNA with Anal Squamous Cell and Basaloid Carcinoma. Mod. Pathol. 1996, 9, 614–620.
  85. Jeannot, E.; Becette, V.; Campitelli, M.; Calméjane, M.-A.; Lappartient, E.; Ruff, E.; Saada, S.; Holmes, A.; Bellet, D.; Sastre-Garau, X. Circulating Human Papillomavirus DNA Detected Using Droplet Digital PCR in the Serum of Patients Diagnosed with Early Stage Human Papillomavirus-Associated Invasive Carcinoma. J. Pathol. Clin. Res. 2016, 2, 201–209.
  86. Damerla, R.R.; Lee, N.Y.; You, D.; Soni, R.; Shah, R.; Reyngold, M.; Katabi, N.; Wu, V.; McBride, S.M.; Tsai, C.J.; et al. Detection of Early Human Papillomavirus-Associated Cancers by Liquid Biopsy. JCO Precis. Oncol. 2019, 3.
  87. Conroy, T.; Desseigne, F.; Ychou, M.; Bouché, O.; Guimbaud, R.; Bécouarn, Y.; Adenis, A.; Raoul, J.-L.; Gourgou-Bourgade, S.; de la Fouchardière, C.; et al. FOLFIRINOX versus Gemcitabine for Metastatic Pancreatic Cancer. N. Engl. J. Med. 2011, 364, 1817–1825.
  88. Von Hoff, D.D.; Ervin, T.; Arena, F.P.; Chiorean, E.G.; Infante, J.; Moore, M.; Seay, T.; Tjulandin, S.A.; Ma, W.W.; Saleh, M.N.; et al. Increased Survival in Pancreatic Cancer with Nab-Paclitaxel plus Gemcitabine. N. Engl. J. Med. 2013, 369, 1691–1703.
  89. Conroy, T.; Hammel, P.; Hebbar, M.; Ben Abdelghani, M.; Wei, A.C.; Raoul, J.-L.; Choné, L.; Francois, E.; Artru, P.; Biagi, J.J.; et al. FOLFIRINOX or Gemcitabine as Adjuvant Therapy for Pancreatic Cancer. N. Engl. J. Med. 2018, 379, 2395–2406.
  90. Castillo, C.F.F.-D. A Changing Landscape in Pancreatic Cancer. Ann. Surg. 2018, 268, 9–10.
  91. Watanabe, F.; Suzuki, K.; Tamaki, S.; Abe, I.; Endo, Y.; Takayama, Y.; Ishikawa, H.; Kakizawa, N.; Saito, M.; Futsuhara, K.; et al. Longitudinal Monitoring of KRAS-Mutated Circulating Tumor DNA Enables the Prediction of Prognosis and Therapeutic Responses in Patients with Pancreatic Cancer. PLoS ONE 2019, 14, e0227366.
  92. Cheng, H.; Liu, C.; Jiang, J.; Luo, G.; Lu, Y.; Jin, K.; Guo, M.; Zhang, Z.; Xu, J.; Liu, L.; et al. Analysis of CtDNA to Predict Prognosis and Monitor Treatment Responses in Metastatic Pancreatic Cancer Patients. Int. J. Cancer 2017, 140, 2344–2350.
  93. Bernard, V.; Kim, D.U.; San Lucas, F.A.; Castillo, J.; Allenson, K.; Mulu, F.C.; Stephens, B.M.; Huang, J.; Semaan, A.; Guerrero, P.A.; et al. Circulating Nucleic Acids Are Associated With Outcomes of Patients With Pancreatic Cancer. Gastroenterol 2019, 156, 108–118.e4.
  94. Del Re, M.; Vivaldi, C.; Rofi, E.; Vasile, E.; Miccoli, M.; Caparello, C.; d’Arienzo, P.D.; Fornaro, L.; Falcone, A.; Danesi, R. Early Changes in Plasma DNA Levels of Mutant KRAS as a Sensitive Marker of Response to Chemotherapy in Pancreatic Cancer. Sci. Rep. 2017, 7, 7931.
  95. Kruger, S.; Heinemann, V.; Ross, C.; Diehl, F.; Nagel, D.; Ormanns, S.; Liebmann, S.; Prinz-Bravin, I.; Westphalen, C.B.; Haas, M.; et al. Repeated MutKRAS CtDNA Measurements Represent a Novel and Promising Tool for Early Response Prediction and Therapy Monitoring in Advanced Pancreatic Cancer. Ann. Oncol. 2018, 29, 2348–2355.
  96. Perets, R.; Greenberg, O.; Shentzer, T.; Semenisty, V.; Epelbaum, R.; Bick, T.; Sarji, S.; Ben-Izhak, O.; Sabo, E.; Hershkovitz, D. Mutant KRAS Circulating Tumor DNA Is an Accurate Tool for Pancreatic Cancer Monitoring. Oncologist 2018, 23, 566–572.
  97. Golan, T.; Hammel, P.; Reni, M.; Van Cutsem, E.; Macarulla, T.; Hall, M.J.; Park, J.-O.; Hochhauser, D.; Arnold, D.; Oh, D.-Y.; et al. Maintenance Olaparib for Germline BRCA-Mutated Metastatic Pancreatic Cancer. N. Engl. J. Med. 2019, 381, 317–327.
  98. Le, D.T.; Uram, J.N.; Wang, H.; Bartlett, B.R.; Kemberling, H.; Eyring, A.D.; Skora, A.D.; Luber, B.S.; Azad, N.S.; Laheru, D.; et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N. Engl. J. Med. 2015, 372, 2509–2520.
  99. Marabelle, A.; Le, D.T.; Ascierto, P.A.; Di Giacomo, A.M.; De Jesus-Acosta, A.; Delord, J.-P.; Geva, R.; Gottfried, M.; Penel, N.; Hansen, A.R.; et al. Efficacy of Pembrolizumab in Patients with Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study. J. Clin. Oncol. 2020, 38, 1–10.
  100. Hu, Z.I.; Shia, J.; Stadler, Z.K.; Varghese, A.M.; Capanu, M.; Salo-Mullen, E.; Lowery, M.A.; Diaz, L.A.; Mandelker, D.; Yu, K.H.; et al. Evaluating Mismatch Repair Deficiency in Pancreatic Adenocarcinoma: Challenges and Recommendations. Clin. Cancer Res. 2018, 24, 1326–1336.
  101. Bachet, J.-B.; Blons, H.; Hammel, P.; Hariry, I.E.; Portales, F.; Mineur, L.; Metges, J.-P.; Mulot, C.; Bourreau, C.; Cain, J.; et al. Circulating Tumor DNA Is Prognostic and Potentially Predictive of Eryaspase Efficacy in Second-Line in Patients with Advanced Pancreatic Adenocarcinoma. Clin. Cancer Res. 2020, 26, 5208–5216.
  102. Bang, Y.-J.; Van Cutsem, E.; Feyereislova, A.; Chung, H.C.; Shen, L.; Sawaki, A.; Lordick, F.; Ohtsu, A.; Omuro, Y.; Satoh, T.; et al. Trastuzumab in Combination with Chemotherapy versus Chemotherapy Alone for Treatment of HER2-Positive Advanced Gastric or Gastro-Oesophageal Junction Cancer (ToGA): A Phase 3, Open-Label, Randomised Controlled Trial. Lancet 2010, 376, 687–697.
  103. Shoda, K.; Masuda, K.; Ichikawa, D.; Arita, T.; Miyakami, Y.; Watanabe, M.; Konishi, H.; Imoto, I.; Otsuji, E. HER2 Amplification Detected in the Circulating DNA of Patients with Gastric Cancer: A Retrospective Pilot Study. Gastric Cancer 2015, 18, 698–710.
  104. Shoda, K.; Ichikawa, D.; Fujita, Y.; Masuda, K.; Hiramoto, H.; Hamada, J.; Arita, T.; Konishi, H.; Komatsu, S.; Shiozaki, A.; et al. Monitoring the HER2 Copy Number Status in Circulating Tumor DNA by Droplet Digital PCR in Patients with Gastric Cancer. Gastric Cancer 2017, 20, 126–135.
  105. Kim, S.T.; Banks, K.C.; Pectasides, E.; Kim, S.Y.; Kim, K.; Lanman, R.B.; Talasaz, A.; An, J.; Choi, M.G.; Lee, J.H.; et al. Impact of Genomic Alterations on Lapatinib Treatment Outcome and Cell-Free Genomic Landscape during HER2 Therapy in HER2+ Gastric Cancer Patients. Ann. Oncol. 2018, 29, 1037–1048.
  106. Xu, R.-H.; Wei, W.; Krawczyk, M.; Wang, W.; Luo, H.; Flagg, K.; Yi, S.; Shi, W.; Quan, Q.; Li, K.; et al. Circulating Tumour DNA Methylation Markers for Diagnosis and Prognosis of Hepatocellular Carcinoma. Nat. Mater. 2017, 16, 1155–1161.
  107. Ono, A.; Fujimoto, A.; Yamamoto, Y.; Akamatsu, S.; Hiraga, N.; Imamura, M.; Kawaoka, T.; Tsuge, M.; Abe, H.; Hayes, C.N.; et al. Circulating Tumor DNA Analysis for Liver Cancers and Its Usefulness as a Liquid Biopsy. Cell Mol. Gastroenterol Hepatol 2015, 1, 516–534.
  108. Cai, Z.-X.; Chen, G.; Zeng, Y.-Y.; Dong, X.-Q.; Lin, M.-J.; Huang, X.-H.; Zhang, D.; Liu, X.-L.; Liu, J.-F. Circulating Tumor DNA Profiling Reveals Clonal Evolution and Real-Time Disease Progression in Advanced Hepatocellular Carcinoma. Int. J. Cancer 2017, 141, 977–985.
  109. Lamarca, A.; Palmer, D.H.; Wasan, H.S.; Ross, P.J.; Ma, Y.T.; Arora, A.; Falk, S.; Gillmore, R.; Wadsley, J.; Patel, K.; et al. ABC-06 | A Randomised Phase III, Multi-Centre, Open-Label Study of Active Symptom Control (ASC) Alone or ASC with Oxaliplatin / 5-FU Chemotherapy (ASC+mFOLFOX) for Patients (Pts) with Locally Advanced / Metastatic Biliary Tract Cancers (ABC) Previously-Treated with Cisplatin/Gemcitabine (CisGem) Chemotherapy. JCO 2019, 37, 4003.
  110. Abou-Alfa, G.K.; Macarulla, T.; Javle, M.M.; Kelley, R.K.; Lubner, S.J.; Adeva, J.; Cleary, J.M.; Catenacci, D.V.; Borad, M.J.; Bridgewater, J.; et al. Ivosidenib in IDH1-Mutant, Chemotherapy-Refractory Cholangiocarcinoma (ClarIDHy): A Multicentre, Randomised, Double-Blind, Placebo-Controlled, Phase 3 Study. Lancet Oncol 2020, 21, 796–807.
  111. Javle, M.; Lowery, M.; Shroff, R.T.; Weiss, K.H.; Springfeld, C.; Borad, M.J.; Ramanathan, R.K.; Goyal, L.; Sadeghi, S.; Macarulla, T.; et al. Phase II Study of BGJ398 in Patients With FGFR-Altered Advanced Cholangiocarcinoma. J. Clin. Oncol. 2018, 36, 276–282.
  112. Abou-Alfa, G.K.; Sahai, V.; Hollebecque, A.; Vaccaro, G.; Melisi, D.; Al-Rajabi, R.; Paulson, A.S.; Borad, M.J.; Gallinson, D.; Murphy, A.G.; et al. Pemigatinib for Previously Treated, Locally Advanced or Metastatic Cholangiocarcinoma: A Multicentre, Open-Label, Phase 2 Study. Lancet Oncol 2020, 21, 671–684.
  113. Goyal, L.; Saha, S.K.; Liu, L.Y.; Siravegna, G.; Leshchiner, I.; Ahronian, L.G.; Lennerz, J.K.; Vu, P.; Deshpande, V.; Kambadakone, A.; et al. Polyclonal Secondary FGFR2 Mutations Drive Acquired Resistance to FGFR Inhibition in Patients with FGFR2 Fusion-Positive Cholangiocarcinoma. Cancer Discov. 2017, 7, 252–263.
  114. Arshad, J.; Roberts, A.; Ahmed, J.; Cotta, J.; Pico, B.A.; Kwon, D.; Trent, J.C. Utility of Circulating Tumor DNA in the Management of Patients With GI Stromal Tumor: Analysis of 243 Patients. JCO Precis. Oncol. 2020.
  115. Kang, G.; Sohn, B.S.; Pyo, J.-S.; Kim, J.Y.; Lee, B.; Kim, K.-M. Detecting Primary KIT Mutations in Presurgical Plasma of Patients with Gastrointestinal Stromal Tumor. Mol. Diagn. Ther. 2016, 20, 347–351.
  116. Namløs, H.M.; Boye, K.; Mishkin, S.J.; Barøy, T.; Lorenz, S.; Bjerkehagen, B.; Stratford, E.W.; Munthe, E.; Kudlow, B.A.; Myklebost, O.; et al. Noninvasive Detection of CtDNA Reveals Intratumor Heterogeneity and Is Associated with Tumor Burden in Gastrointestinal Stromal Tumor. Mol. Cancer Ther. 2018, 17, 2473–2480.
  117. Yoo, C.; Ryu, M.-H.; Na, Y.S.; Ryoo, B.-Y.; Park, S.R.; Kang, Y.-K. 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, 2272–2277.
  118. Wada, N.; Kurokawa, Y.; Takahashi, T.; Hamakawa, T.; Hirota, S.; Naka, T.; Miyazaki, Y.; Makino, T.; Yamasaki, M.; Nakajima, K.; et al. Detecting Secondary C-KIT Mutations in the Peripheral Blood of Patients with Imatinib-Resistant Gastrointestinal Stromal Tumor. Oncology 2016, 90, 112–117.
  119. Demetri, G.D.; van Oosterom, A.T.; Garrett, C.R.; Blackstein, M.E.; Shah, M.H.; Verweij, J.; McArthur, G.; Judson, I.R.; Heinrich, M.C.; Morgan, J.A.; et al. Efficacy and Safety of Sunitinib in Patients with Advanced Gastrointestinal Stromal Tumour after Failure of Imatinib: A Randomised Controlled Trial. Lancet 2006, 368, 1329–1338.
  120. Ravegnini, G.; Nannini, M.; Zenesini, C.; Simeon, V.; Sammarini, G.; Urbini, M.; Gatto, L.; Saponara, M.; Biasco, G.; Pantaleo, M.A.; et al. An Exploratory Association of Polymorphisms in Angiogenesis-Related Genes with Susceptibility, Clinical Response and Toxicity in Gastrointestinal Stromal Tumors Receiving Sunitinib after Imatinib Failure. Angiogenesis 2017, 20, 139–148.
  121. George, S.; Wang, Q.; Heinrich, M.C.; Corless, C.L.; Zhu, M.; Butrynski, J.E.; Morgan, J.A.; Wagner, A.J.; Choy, E.; Tap, W.D.; et al. Efficacy and Safety of Regorafenib in Patients with Metastatic and/or Unresectable GI Stromal Tumor after Failure of Imatinib and Sunitinib: A Multicenter Phase II Trial. J. Clin. Oncol. 2012, 30, 2401–2407.
  122. Ravegnini, G.; Nannini, M.; Sammarini, G.; Astolfi, A.; Biasco, G.; Pantaleo, M.A.; Hrelia, P.; Angelini, S. Personalized Medicine in Gastrointestinal Stromal Tumor (GIST): Clinical Implications of the Somatic and Germline DNA Analysis. Int. J. Mol. Sci. 2015, 16, 15592–15608.
  123. Maier, J.; Lange, T.; Kerle, I.; Specht, K.; Bruegel, M.; Wickenhauser, C.; Jost, P.; Niederwieser, D.; Peschel, C.; Duyster, J.; 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, 4854–4867.
  124. Bauer, S.; Herold, T.; Mühlenberg, T.; Reis, A.-C.; Falkenhorst, J.; Backs, M.; Ketzer, J.; Breitenbuecher, F.; Schuler, M.H.; Grunewald, S. Plasma Sequencing to Detect a Multitude of Secondary KIT Resistance Mutations in Metastatic Gastrointestinal Stromal Tumors (GIST). JCO 2015, 33, 10518.
  125. Kang, G.; Bae, B.N.; Sohn, B.S.; Pyo, J.-S.; Kang, G.H.; Kim, K.-M. Detection of KIT and PDGFRA Mutations in the Plasma of Patients with Gastrointestinal Stromal Tumor. Target Oncol. 2015, 10, 597–601.
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Subjects: Oncology
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Pierre Laurent-Puig
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Entry Collection: Gastrointestinal Disease
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Update Date: 10 Dec 2021
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