The Role of Next-Generation Sequencing in the Cytologic Diagnosis of Pancreatic Lesions.
In most cases, PDAC is initiated by oncogenic mutant KRAS, which has been shown to drive pancreatic neoplasia. (7,8) In the past 2 decades several studies have shown how the analysis of KRAS mutations of pancreatic lesions improves diagnostic accuracy and is particularly useful in those cases where EUS-FNA cytology is inconclusive. (9-14)
Next-generation sequencing (NGS) has been instrumental for the understanding of the pancreatic cancer genome. Whole-exome sequencing of 20 661 genes has shown that any given PDAC contains an average of 63 genomic alterations, most of which are point mutations. (15) High-throughput molecular analysis has demonstrated that in addition to KRAS mutation, inactivations of TP53, SMAD4, and CDKN2A/p16 are the pivotal molecular alterations that define the development and progression of PDAC. (16,17) This finding, and the variety of molecular alterations found in different types of pancreatic tumors (summarized in Table 1), such as CTNNB1 mutations in solid pseudopapillary neoplasm (SPN) or GNAS mutations in intraductal papillary mucinous neoplasm (IPMN), underscore the need for the analysis of multiple biomarkers. (18-21)
The introduction of NGS to molecular diagnostics has allowed performance of molecular analysis of multiple genes in limited samples, opening new avenues to the study of preoperative specimens. Preoperative cytologic smears (ie, material obtained during FNA or biliary brushings smeared on a slide) and fine-needle biopsies (ie, small samples obtained using a 22-gauge coring needle that provides cylindrical specimens for the examination of tumor architecture) are characterized by a small quantity of diagnostic material, and are often composed of heterogeneous cell populations. For this reason, it is crucial to use molecular tests that offer high analytical sensitivity to detect small proportions of mutated cells. Next-generation sequencing combines high analytical sensitivity with multiple gene analysis and thus represents a very attractive option.
The scope of this review is to provide an updated survey of the studies that have used NGS for the preoperative molecular workup of pancreatic lesions. To identify the pertinent references, a PubMed search using the terms next-generation sequencing, pancreas, pancreatic lesions, pancreatic tumors, and EUS-FNA was performed covering the years 2000-2017. Our review addresses studies that analyzed (1) solid pancreatic lesions, (2) pancreatic cyst fluid, or (3) other fluid specimens from patients with pancreatic tumors.
NGS OF EUS-FNA SAMPLES OF SOLID PANCREATIC LESIONS
The studies that addressed NGS analysis of EUS-FNA samples of solid lesions are summarized in Table 2. In 2014, De Biase et al (13) demonstrated that NGS is more sensitive and specific than conventional mutation analysis for the preoperative identification of pancreatic lesions with malignant potential. The authors observed that using NGS to analyze KRAS mutations in pancreatic FNA specimens allows clinical sensitivity to increase up to ~74%, a value far superior to that obtained by allele-specific real-time polymerase chain reaction (PCR) (52.8%) or by Sanger sequencing (42.1%), while maintaining clinical specificity at 100%. The analytical sensitivity was higher if the analysis was performed starting from cells scraped from the smear preparations used for routine cytologic diagnosis, as opposed to FNA material directly submitted by the endoscopist for molecular diagnosis. (13) Targeted KRAS parallel sequencing had higher clinical sensitivity when compared with commercially available KRAS mutation-specific diagnostic kits. (22)
Kameta et al (23) analyzed a panel of 50 most commonly mutated oncogenes (Ampliseq Cancer Hotspot Panel v2.0, designed to amplify 207 amplicons covering ~2790 mutations; Thermo Fisher Scientific, Waltham, Massachusetts) in a cohort of 38 patients with pancreatic disease, 27 of whom were diagnosed with PDAC. The sequencing was performed on DNA extracted from EUS-FNA material and showed that KRAS was the most frequently mutated gene (96%; 26 of 27 PDACs). TP53 mutations were detected in 44% of the samples (12 of 27) and both SMAD4 and CDKN2A in 11% (3 of 27). Intriguingly, 2 mutations detected by NGS were not detected by the real-time PCR assay used by the authors for comparison (presumably because the mutation-specific real-time PCR assay was not designed to detect them). The authors also analyzed samples from metastatic tumors of unknown origin to investigate if the panel might be of help to identify the primary lesion, but the results were limited by the lack of specificity of KRAS mutations, which were commonly present not only in PDAC but also in adenocarcinomas of lung, colon, and stomach. They concluded that their assay could be used for the preoperative diagnosis of pancreatic EUS-FNA, even if a 50-gene panel might not be sufficient to fully profile the molecular landscape of PDAC.
Young and colleagues (24) tested a large multigene panel (287 cancer-related genes, covering a total of 4561 exons and 47 introns of 19 genes frequently rearranged in cancer) on 23 pancreatic FNAs. The cohort consisted of 17 PDACs, 3 mucinous adenocarcinomas, 2 adenocarcinomas not otherwise specified, and 1 neuroendocrine tumor. The authors observed a mean of 3.8 mutations per tumor (range 1-9). The most common alterations were detected in KRAS (78%; 18 of 23 cases), TP53 (74%; 17 of 23), CDKN2AJB (35%; 8 of 23) and SMAD4 (17%, 4 of 23). Mutations of PTEN were found in 13% of samples. The comparison of molecular analysis performed on FNA and on matched surgical specimens showed a perfect concordance of the mutational profile between the two.
In a study of 29 EUS-FNA samples, Gleeson and colleagues (25) used a 160-gene panel to preoperatively determine the mutational landscape of 21 PDACs, 4 ampullary carcinomas, 1 Lynch syndrome-associated PDAC (not previously treated with chemotherapy) and 3 IPMNs, and to verify its concordance with that obtained from surgical resection specimens. They demonstrated that a moderate to large targeted NGS analysis of preoperative EUS-FNA samples provides an excellent surrogate for the analysis of surgically resected specimens to detect mutations associated with pancreatic cancer. The main genes altered were KRAS (93%; 27 of 29), TP53 (72%; 21 of 29), SMAD4 (31%; 9 of 29), and GNAS (10%; 3 of 29). A TP53 mutation was observed frequently in association with a KRAS alteration (95%; 20 of 21 TP53-mutated cases). Pathogenic SMAD4 alterations were detected in about 30% of patients with PDAC (7 of 21), but in none of the patients with ampullary carcinoma. They reported an excellent concordance between mutations found in EUS-FNA specimens and those detected in the paired surgical material: in 15 of 18 cases the concordance was 100%. In 3 samples, they observed some mutations in EUS-FNA specimens (in GRIN2A, GATA3, GNAS, and KDM6A genes) but not in the surgical specimens: all discordant mutations had a percentage of mutated alleles lower than 14% and were therefore consistent with subclonal molecular events. Genetic heterogeneity (including heterogeneity of KRAS mutations, an early initiating event) has been reported in pancreatic cancers. (26,27) Considering that preoperative samples contain only a small part of the lesion, this represents a limitation of all preoperative diagnostic procedures (including conventional cytologic examination) in pancreatic cancers as well as in cancers from other organs. The proportion of mutated alleles identified by NGS represents an important clue to define a mutation as subclonal. It can be normalized to estimate the proportion of neoplastic cells carrying the mutation (28) and should be taken into account for molecular diagnosis. In the work by Gleeson and colleagues, (25) as well as in our experience, molecular heterogeneity represented a relevant issue in only a minority of pancreatic tumors.
Sibinga Mulder and colleagues (29) also reported a good correlation between mutations detected in preoperative FNA material and those found in the matched surgical resection specimen. (29) Using a panel of 50 genes frequently mutated in cancer for the profiling of FNA material from the PDAC of a 54-year-old man, they detected mutation in KRAS, TP53, SMAD4, and CDKN2A starting from both preoperative cytologic and surgical resection specimens.
Next-generation sequencing analysis has also been successfully performed from pancreatobiliary brush cytology samples, as demonstrated by Dudley and colleagues (30) in a cohort of 81 patients who underwent endoscopic retrograde cholangiopancreatography. Also in this cohort of samples, KRAS mutations were the most frequent alteration (26%; 21 of 81 cases analyzed by NGS), and TP53 was the second most commonly mutated gene (17%; 14 of 81 cases). The authors (30) observed that NGS is as sensitive as the analysis of aneuploidy by fluorescence in situ hybridization to preoperatively identify pancreatobiliary duct malignancies. Intriguingly, in this paper KRAS mutations were detected also in 2.3% (1 of 43) of nonneoplastic control samples. (30) It should be noted that KRAS mutations have been reported in some cases of chronic pancreatitis, where the presence of KRAS mutation has been associated with evolution of the pancreatitis to PDAC. (31,32)
KRAS (74%; 14 of 19 cases), TP53 (47%; 9 of 19 cases), and SMAD4 (32%; 6 of 19 cases) were also the most frequently mutated genes in the study by Valero and colleagues (33) performed on a cohort of 19 FNAs from patients with unresectable nonmetastatic pancreatic tumors, using a panel of 409 cancer-related genes. The authors also found mutations in ARIDI (16%; 3 of 19 cases), GRM8 (10%; 2 of 19 cases), and TRIM33 (10%; 2 of 19 cases). They concluded that somatic variants identified in preoperative FNA samples using NGS may be used to guide the clinical management of patients with pancreatic cancer.
Differently from what is observed in PDAC, SPNs do not have KRAS mutations, but harbor mutations in CTNNB1, the gene of the wnt pathway encoding for [beta]-catenin. Kubota et al (34) investigated CTNNB1 using NGS in a cohort of SPNs, PDACs, and pancreatic neuroendocrine tumors (P-NETs) starting from EUS-FNA material. Mutations of CTNNB1 were detected in all SPNs, 9% (1 of 11) of pancreatic neuroendocrine tumors, and no PDAC specimens. Even if the percentage of mutated alleles detected by NGS was 20% or more (a percentage compatible with the analytical sensitivity of Sanger sequencing), they were able to identify CTNNB1 mutation in only 1 of the samples using Sanger sequencing.
NGS OF EUS-FNA SAMPLES OF PANCREATIC CYST FLUID
The studies that have addressed NGS analysis of EUS-FNA samples of cystic lesions are summarized in Table 3. Pancreatic cysts are a heterogeneous group of lesions that include injury and inflammation-related conditions (~30% of cases) as well as neoplasms (~60% of cases). The large majority of neoplastic cysts are of ductal lineage, more frequently mucinous, IPMN, and mucinous cystic neoplasm (MCN), but also of serous lineage (serous cystadenoma and rarely cystadenocarcinoma). (35)
Al-Haddad and colleagues (36) have shown that the analysis of KRAS and loss of heterozygosity helps in the differential diagnosis of cystic mucinous pancreatic lesions (IPMN and MCN) when preoperative cytology is nondiagnostic or carcinoembryonic antigen (CEA) cyst fluid levels are indeterminate. The biochemical determination of CEA is one of the most accurate tumor markers to diagnose mucinous pancreatic cysts and to distinguish them from nonmucinous cysts: in fact, high levels of CEA (>200 ng/ mL) strongly suggest mucinous neoplasia although reported cutoff CEA values vary considerably. (37,38)
Several studies have now demonstrated that high-throughput analysis of multiple genetic markers with NGS platforms adds useful information to that obtained after the evaluation of CEA and cytologic specimens.
Amato et al (39) analyzed 51 cancer-associated genes using NGS in 48 IPMNs. The marker more commonly altered was GNAS (79%; 38 of 48 cases). KRAS was mutated in 50% of IPMNs (24 of 48), and in 37.5% (18 of 48) the mutation coexisted with GNAS alterations. Less frequently, mutations were found in TP53 (10%; 5 of 48 cases), BRAF (6%; 3 of 48 cases), and CTNNB1 and IDH1 (4%; 2 of 48 cases for each gene). KRAS and GNAS mutations coexisted in 37.5% of IPMNs (18 of 48). The amount of DNA obtained from cyst fluid was adequate for NGS (ie, at least 20 ng of DNA) in 10 of 48 IPMNs. In these 10 samples, sequencing allowed detection of 10 of the 13 mutations found in the matched surgical specimens (6 of 7 GNAS mutations, 3 of 3 KRAS mutations, and 1 of 2 TP53 mutations). (39) The combination of GNAS and KRAS preoperative testing was also demonstrated to be highly specific and sensitive for IPMNs by Singhi et al. (40)
Jones et al (41) analyzed 92 cyst fluid samples using a panel of 39 cancer-related genes. KRAS was the gene most frequently mutated (47%; 43 of 92 cases), followed by GNAS (24%; 22 of 92 cases) and CDKN2A (7%; 6 of 92 cases). Overall, 43% (40 of 92) of the samples did not show any mutation in at least 1 of the 39 genes in the panel. In 71% (65 of 92) of the samples a KRAS or GNAS mutation was consistent with a diagnosis of IPMN by imaging, in spite of low CEA levels. In one case, an elevated level of CEA was discordant with the impression based on imaging, but the finding of one KRAS mutation supported the preoperative diagnosis of MCN. (41) The data of the study supported the association between high-risk cysts and accumulation of genetic alterations. KRAS analysis provided useful information for the malignancy risk in those cases that would be diagnosed as benign by imaging and in those with low CEA levels in the cyst fluid. The use of a panel of genes helped in detecting those mutations typically associated with mucinous lesions (KRAS, GNAS, CDKN2A) and those additional changes in genes (SMAD4, TP53) that are associated with a higher malignancy risk and/or with cysts featuring an infiltrating adenocarcinoma component. (41)
Rosenbaum and colleagues (the same group of Jones et al. (41)) (42) studied 113 pancreatic cystic lesions with a panel of 39 cancer-related genes. The authors found a total of 119 variants in 67 samples. Most of them were mutations in KRAS (53%; 60 of 113 cases) or GNAS (24%; 27 of 113 cases). Other mutations were found in the following genes: CDKN2A (9%; 10 of 113 cases), TP53 (4%; 5 of 113 cases), and SMAD4 (2%; 2 of 113 cases). Alterations in BRAF, NOTCH1, and PIK3CA were found in only 1 sample each (0.9%; 1 of 113). (42) Considering only samples that underwent surgical resection (38; final diagnoses: 8 nonmucinous cysts, 6 IPMNs, and 24 carcinomas), KRAS mutations were found in 75% (18 of 24) of the samples diagnosed as cancer, 16% (1 of 6) of IPMNs, and only in 12.5% (1 of 8) of nonmucinous cystic lesions. GNAS alterations were found more frequently in IPMNs (33.3%; 3 of 6) than in cancers (25.0%; 6 of 24) or nonmucinous cysts (0%; 0 of 8 cases). All the other alterations (TP53, SMAD4, and CDKN2A) were found only in cystic lesions with associated PDAC. Overall, the presence of KRAS mutations had a sensitivity and specificity for cystic mucinous lesions (IPMNs or carcinomas) of 80% and 88%, respectively. In contrast, GNAS mutations had a low sensitivity (27%) but very high specificity (100%) for IPMN. In the study, the combination of NGS with the analysis of CEA in the cyst fluid reached 90% sensitivity and 88% specificity for cystic mucinous lesions. (42)
In a retrospective large multicenter study, Springer and colleagues (43) showed that the screening of cyst fluid for a panel of genetic markers (gene mutations, loss of heterozygosity, and aneuploidy), in conjunction with the clinical features of the cyst, can be used to fairly accurately classify cystic pancreatic lesions and to identify those cases that require surgical resection. The authors analyzed 11 genes (BRAF, CDKN2A, CTNNB1, GNAS, KRAS, NRAS, PIK3CA, RNF43, SMAD4, TP53, and VHL) known to be mutated in cystic lesions in 130 cyst fluid samples. KRAS was the most frequently mutated gene in IPMNs (78%; 75 of 96 cases) and MCNs (50%; 6 of 12 cases), whereas it was not altered in serous cystadenomas and SPNs. Serous cystadenomas frequently had VHL mutations (42%; 5 of 12 cases), whereas all 10 SPNs harbored mutations in CTNNB1. Overall, the authors observed that when molecular and clinical markers were combined, the sensitivity and specificity in detecting serous cystadenoma increased to 100% and 98%, respectively. For IPMN, the combination of molecular and clinical markers led to higher sensitivity when compared with "composite molecular markers" alone (94% versus 76%), but resulted in a decrease in specificity (84% versus 97%). On the other hand, the use of both molecular and clinical markers decreased sensitivity (90% versus 100%) but increased specificity (97% versus 75%) for the preoperative diagnosis of MCN. For SPN, combining molecular and clinical markers decreased the performance of preoperative assessment: sensitivity decreased to 89% (from 100% using molecular markers alone) and specificity to 92% (100% using molecular markers alone). (43)
MicroRNAs are differentially expressed in pancreatic tumors. (44) Wang and colleagues (45) analyzed by NGS a panel of microRNAs in a cohort of cyst fluid samples from patients with mucinous cysts, IPMNs, and pancreatic cancers. The study showed that multiple microRNAs are differentially abundant in high-grade invasive versus low-grade or benign lesions. Five microRNAs (miR-125, miR-214, miR-26, miR-30, and miR-217) found to be differentially expressed between high-grade and low-grade IPMNs by Wang et al (45) were also detected as differentially expressed by Matthaei et al (46) in IPMNs analyzed using an array-based real-time PCR method.
NEXT-GENERATION ANALYSIS OF OTHER FLUID BIOLOGICAL SPECIMENS FROM PATIENTS WITH PANCREATIC TUMORS
Studies that have addressed the NGS analysis of fluid specimens other than cyst fluid are summarized in Table 4. Yu and colleagues (47) analyzed the pancreatic juice of patients with PDAC (34 cases), patients with IPMN (57 cases), and controls with normal pancreas or chronic pancreatitis (total of 24 cases). The analysis was performed using NGS for 9 genes (KRAS, GNAS, TP53, SMAD4, CDKN2A, RNF43, TGFBR2, BRAF, and PIK3CA) and digital PCR to evaluate the accuracy of NGS. The pancreatic juice of patients with PDAC had a higher mutational load when compared with that of patients with IPMN (P = .003) and that of control groups (P < .001). (47) The most frequently mutated gene in PDAC pancreatic juice was KRAS (91%; 31 of 34 samples). Surprisingly, KRAS was found to be mutated in 42% of controls (10 of 24 cases). (47) The second most common mutation in patients with PDAC was TP53 (58%; 20 of 34), which was also detected in patients with IPMN (26%; 15 of 57), but in none of the controls. In addition to KRAS, GNAS and RNF43 were also sometimes mutated in control specimens: 17% (4 of 24 cases) and 4% (1 of 24 cases), respectively. The authors concluded that the identification of mutated KRAS in pancreatic juice is not specific enough to distinguish PDAC from IPMN or nonneoplastic controls. The most specific marker was SMAD4, mutated in only 1 of 80 cases without PDAC (an IPMN of 6 cm with intermediate-grade dysplasia), followed by TP53. The KRAS, SMAD4, and TP53 mutations detected in samples collected before the operation were confirmed in the resection specimens. However, in a few cases, mutations could not be demonstrated in the preoperative pancreatic juice samples, and were only identified in the surgical resections. Thus, the preoperative molecular analysis of pancreatic juice appears to have high positive but low negative predictive value. (47) According to the authors, the concentration of TP53 and SMAD4 mutations in pancreatic juice may distinguish patients with PDCA from those with IPMNs and disease controls. (47)
Zill and colleagues (48) analyzed with NGS cell-free DNA from the peripheral blood of 26 patients with advanced pancreatic (n = 18) or biliary (n = 8) carcinomas. KRAS and TP53 were the most frequently mutated genes, but alterations were commonly found also in APC, SMAD4, FBXW7, and BRAF genes. In the study, mutations of KRAS, TP53, APC, FBXW7, and SMAD4 genes had an average sensitivity of 92.3%, specificity of 100%, and diagnostic accuracy of 97.7% when compared with the mutation analysis results from tumor biopsy samples, (48) thus demonstrating that NGS analysis of cell-free DNA allows detection of tumor-derived mutations in patients with advanced pancreatobiliary carcinoma.
CONCLUSIONS AND FUTURE PERSPECTIVE
In spite of the extensive knowledge about the molecular alterations of pancreatic tumors accumulated in the recent past, the mortality rate of PDAC patients remains very high. As stated by the current European Society for Medical Oncology guidelines, (49) to date there are not targetable molecules for personalized patient treatment. Of the several markers investigated, KRAS remains the one most commonly used for single-gene testing, although its use is greatly limited by the identification of mutated KRAS in about 10% of chronic pancreatitis and/or low-grade pancreatobiliary epithelial cell dysplasia (in some studies frequencies >10% have been reported). (50-53) Thus, the Papanicolaou Society of Cytopathology guidelines do not support KRAS testing of solid pancreatic masses and bile duct strictures as a useful single-gene ancillary test. The same guidelines report that "a number of gene mutations (KRAS, GNAS, VHL, RNF43 and CTNNB1) may be of aid in the identification of specific cystic neoplasms." (50) Given this context, NGS may be instrumental for the preoperative molecular diagnosis of pancreatic lesions, because it offers the opportunity of screening simultaneously a wide number of mutations while using small amounts of DNA. It is becoming clear that the use of wide gene panels increases clinical sensitivity and specificity, minimizing the risk of false-positive results. High analytical sensitivity and positive predictive value can prevent repeat biopsies, and thus improve preoperative diagnosis and preoperative patient risk stratification and management, while reducing costs. On the other hand, NGS requires expensive instrumentation and specialized expertise, and its application to the preoperative diagnosis needs a robust validation in large multicenter series of paired preoperative and surgical samples. Further studies, with careful evaluation of costs versus benefits, will be necessary before the full implementation of NGS in clinical practice.
This work was supported in part by Italian Government-Ministero Della Salute grant RF-2011-02350857 to G.T.
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Accepted for publication October 4, 2017.
From the Department of Pharmacy and Biotechnology (Dipartimento di Farmacia e Biotecnologie)--Molecular Diagnostic Unit, Azienda USL di Bologna, University of Bologna, Bologna, Italy (Dr de Biase and Ms Pession); the Department of Medicine (Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale)--Molecular Diagnostic Unit, Azienda USL di Bologna, University of Bologna School of Medicine, Bologna, Italy (Drs Visani and Tallini and Ms Acquaviva); the Unit of Anatomic Pathology, Azienda USL-Maggiore Hospital, Bologna, Italy (Dr Fornelli); and the Units of Surgery (Dr Masetti) and Gastroenterology and Digestive Endoscopy (Dr Fabbri), Azienda USL Bologna Bellaria-Maggiore Hospitals, Bologna, Italy.
The authors have no relevant financial interest in the products or companies described in this article.
Presented in part at the V Molecular Cytopathology: Focus on Next Generation Sequencing in Cytopathology meeting; October 18, 2016; Napoli, Italy.
Reprints: Giovanni Tallini, MD, Department of Medicine (Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale), University of Bologna School of Medicine, Anatomic Pathology Unit, Ospedale Bellaria, Via Altura 3, 40139 Bologna, Italy (email: firstname.lastname@example.org).
Table 1. Main Genetic Alterations in Pancreatic Tumors Pancreatic Tumor Gene Altered Frequency, % PDAC KRAS 95 TP53 35-40 CDKN2A/p16 95-100 SMAD4 45-60 IPMN KRAS 45-50 GNAS 35-50 MCN KRAS 20-50 GNAS 25-50 PanIN KRAS 70 TP53 20 GNAS 5 SPN CTNNB1 90-100 PAAC APC 50-60 CTNNB1 5-10 TP53 30-40 SCA VHL 40-50 (a) P-NET MEN1 10-30 (b) Abbreviations: IPMN, intraductal papillary mucinous neoplasm; MCN, mucinous cystic neoplasm; PAAC, pancreatic acinic adenocarcinoma; PanIN, pancreatic intraepithelial neoplasia; PDAC, pancreatic ductal adenocarcinoma; P-NET, pancreatic neuroendocrine tumor; SCA, serous cystadenoma; SPN, solid pseudopapillary neoplasm. (a) Somatic alterations (mutation or loss of heterozygosity) in sporadic tumors; germline mutations are present in patients with the von Hippel-Lindau syndrome. (b) Somatic mutations in sporadic tumors; germline mutations for MEN1, VHL, NF-1, or TSC1/2 are present in P-NET that develop in patients with inherited genetic neuroendocrine syndromes. The percentages quoted are estimated from the literature quoted in the paper. Table 2. Next-Generation Sequencing Analysis of Solid Pancreatic Tumors Starting From Fine-Needle Aspiration (FNA) Material Type of Tumor Source, y (No.) Genes Targeted Young et al, PDAC (18), P-NET Panel of 287 cancer- (24) 2013 (1), PC NOS (2) related genes and 19 genes frequently rearranged in cancer de Biase, PDAC (20), KRAS 2014 (13) inoperable pancreatic tumors (6), P-NET (5) Di Marco et PDAC (16) Whole-exome al, (16) 2015 sequencing Kubota et SPN (7), PDAC (16), CTNNB1 al, (34) 2015 P-NET (11) Dudley et al, Bile and main Custom panel (39 (30) 2016 pancreatic duct cancer-related samples: genes) neoplastic (31), nonneoplastic (43) Gleeson et al, PDAC (22), AA (4) Human (25) 2016 Comprehensive Cancer GeneRead DNAseq Targeted Panel V2 (panel of 160 cancer genes) Kameta et al, PDAC (27) AmpliSeq Cancer (23) 2016 Panel v2 (50 genes) Sibinga Mulder PDAC (1) AmpliSeq Cancer et al, Panel V2 (50 (29) 2017 genes) Valero et al, Unresectable PDAC Ion Ampliseq (33) 2016 (19) Comprehensive Cancer Panel (409 cancer-related genes) More Frequent Source, y Platform Material Alterations (%) Young et al, HiSeq 2000 FNA KRAS (76.2), TP53 (24) 2013 (Illumina) (a) (71.4), CDKN2A/2B (38.1), SMAD4 (19.0), PTEN (14.3) de Biase, 454 GS- EUS-guided PDAC and inoperable 2014 (13) Junior FNA pancreatic tumors: (Roche) (b) KRAS (93.5) P-NET: KRAS (0) Di Marco et HiScanSQ EUS-FNB KRAS (93.7), TP53 al, (16) 2015 (Illumina) (56.0), CDKN2A (50.0) Kubota et Ion PGM EUS-FNA SPN: CTNNB1 (100) al, (34) 2015 (Thermo P-NET: CTNNB1 (9) Fisher) (c) PDAC: CTNNB1 (0) Dudley et al, MiSeq Brushing Neoplastic: KRAS (30) 2016 (Illumina) (64.5), TP53 (45.2), SMAD4 (19.4), CDKN2A (12.9) Nonneo- plastic: KRAS (2.3) Gleeson et al, HiSeq 2000 EUS-FNA KRAS (93.1),d TP53 (25) 2016 (Illumina) (72.4), (d) SMAD4 (31.0), (d) GNAS (10.3) (d) Kameta et al, Ion PGM EUS-FNA KRAS (96.0), TP53 (23) 2016 (Thermo (44.0), CDKN2A Fisher) (15.0), SMAD4 (11.0) Sibinga Mulder Ion PGM EUS-FNA KRAS, TP53, CDKN2A et al, (Thermo (29) 2017 Fisher) Valero et al, Ion PGM FNA KRAS (73.7), TP53 (33) 2016 (Thermo (47.4), SMAD4 Fisher) (31.6), ARID1 (15.8) Abbreviations: AA, ampullary adenocarcinoma; EUS, endoscopic ultrasound-guided; FNB, fine-needle biopsy; PC NOS, pancreatic carcinomas not otherwise specified; PDAC, pancreatic ductal adenocarcinoma; P-NET, pancreatic neuroendocrine tumor; SPN, solid pseudopapillary neoplasm. (a) Illumina Inc, San Diego, California. (b) Roche Diagnostics, Mannheim, Germany. (c) Thermo Fisher Scientific, Waltham, Massachusetts. (d) It is not possible to discriminate the mutation prevalence of solid versus cystic lesions: 3 intraductal papillary mucinous neoplasms are included in the study (see Table 3). Table 3. Next-Generation Sequencing Analysis of Cystic Pancreatic Lesions Starting From Fine-Needle Aspiration (FNA) Material Type of Tumor Source, y (No.) Genes Targeted Young et al, MCN (2, both Panel of 287 cancer- (24) 2013 adenocarcinomas) related genes and 19 genes frequently rearranged in cancer Amato et al, IPMN (7) AmpliSeq Cancer (39) 2014 Panel v2 (50 genes) Wang et al, IPMN, MCN, PC Noncoding RNA (45) 2015 NOS (17 total) de Biase et al, Cyst NOS (26) KRAS (13) 2014 Springer et al, IPMN (96), MCN Panel of 11 custom (43) 2015 (12), SCA (12), cancer-related SPN (10) genes Gleeson et al, IPMN (3) Human (41) 2016 Comprehensive Cancer GeneRead DNAseq Targeted Panel V2 (160 cancer genes panel) Jones et al, Cyst NOS (92) Custom panel (39 (15) 2016 cancer-related genes) Rosenbaum et Cyst NOS (113) Custom panel (39 al, (42) 2017) cancer-related genes) More Frequent Source, y Platform Material Alterations (%) Young et al, HiSeq 2000 FNA KRAS (100.0), (24) 2013 (Illumina) TP53 (100.0) (a) Amato et al, Ion PGM (Thermo Cyst fluid GNAS, KRAS, (39) 2014 Fisher) (b) TP53c Wang et al, SoliD (Thermo Cyst fluid 15 miRNAs (45) 2015 Fisher) differently expressed between low- grade and high- grade lesions de Biase et al, 454 GS-Junior EUS-FNA KRAS: 38.5 (83.3 (13) 2014 (Roche) (d) in IPMN) Springer et al, MiSeq Cyst fluid IPMN: KRAS (43) 2015 (Illumina) (78.0), GNAS (58.0), RNF43 (38.0) MCN: KRAS (50.0) SCA: VHL (42.0) SPN: CTNNB1 (100.0), PIK3CA (20.0) Gleeson et al, HiSeq 2000 EUS-FNA NA (e) (41) 2016 (Illumina) Jones et al, Anchored EUS-FNA KRAS (47.0), (15) 2016 multiplex GNAS (24.0) PCR NGS platform Rosenbaum et Anchored Cyst fluid Cancer: KRAS al, (42) 2017) multiplex (75.0), GNAS PCR NGS (25.0), TP53 platform (16.7), CDKN2A (33.3) (f) IPMN: KRAS (100.0), GNAS (33.0) (f) Nonmucinous: KRAS (12.5) (f) Abbreviations: Cyst NOS, cystic lesions not otherwise specified; EUS, endoscopic ultrasound-guided; IPMN, intraductal papillary mucinous neoplasm; MCN, mucinous cystic neoplasm; PC NOS, pancreatic carcinomas not otherwise specified; SCA, serous cystadenoma; SPN, solid pseudopapillary neoplasm. (a) Illumina Inc, San Diego, California. (b) Thermo Fisher Scientific, Waltham, Massachusetts. (c) Data about prevalence of mutations in the 7 IPMN cyst fluids are not available. (d) Roche Diagnostics, Mannheim, Germany. (e) Data about prevalence of mutations in the IPMN cyst fluids are not available (see Table 2). (f) Calculated on the 24 cysts with a final diagnosis in the series; the studies by Jones et al (41) and Rosenbaum et al (42) are from the same group. Table 4. Next-Generation Sequencing (NGS) Analysis of Pancreatic Lesions Starting From Body Fluid Source, y Type of Tumor (No) Genes Targeted Zill et al, Advanced Panel of 54 custom (48) 2015 pancreatobiliary cancer-related carcinoma (26) genes Yu et al, PDAC (34) Panel of 9 custom (47) 2017 (b) cancer-related genes More Frequent Source, y Platform Material Alterations, % Zill et al, HiSeq 2500 Blood KRAS (53.8), (48) 2015 (Illumina) (a) (cfDNA) TP53 (38.5) Yu et al, NGS (Thermo Pancreatic KRAS (74.0), (47) 2017 (b) Fisher) (c) juice TP53 (59.0), GNAS (38.0), RNF43 (24.0), SMAD4 (15.0) Abbreviations: cfDNA, cell-free DNA; PDAC, pancreatic ductal adenocarcinoma. (a) Illumina Inc, San Diego, California. (b) Results obtained using both NGS and digital polymerase chain reaction. (c) Thermo Fisher Scientific, Waltham, Massachusetts.
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|Author:||de Biase, Dario; Visani, Michela; Acquaviva, Giorgia; Fornelli, Adele; Masetti, Michele; Fabbri, Car|
|Publication:||Archives of Pathology & Laboratory Medicine|
|Date:||Apr 1, 2018|
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