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Endoscopic Ultrasound-Guided Tissue Acquisition of Pancreaticobiliary Cancer Aiming for a Comprehensive Genome Profile

Department of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital, Tokyo 104-0045, Japan
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(7), 1275;
Received: 14 December 2022 / Revised: 24 March 2023 / Accepted: 27 March 2023 / Published: 28 March 2023


In recent years, cancer genomic medicine centered on comprehensive genome profile (CGP) analysis has become widely used in the field of pancreatic cancer. Endoscopic ultrasound-guided tissue acquisition (EUS-TA) has played an important role in pancreatic cancer, and recently, more EUS-TA tissue samples are considered for CGP analysis. Differences exist between the Oncoguide NCC Oncopanel System and Foundation One CDx Cancer Genome Profile, which are CGP tests approved by insurance programs in Japan, including the analysis criteria, optimal needle selection for meeting these criteria, and puncture target. It is important to understand not only the specimen collection factors, but also the specimen processing factors that can increase the success rate of CGP testing. Furthermore, cancer genome medicine is expected to enter an era of increasing turbulence in the future, and endoscopists need to respond flexibly to these changes. Herein, we review the current status of cancer genome medicine in pancreatic and biliary tract cancers and cancer gene panel testing using EUS-TA.

1. Introduction

Genetic mutations, which are the main cause of cancer, vary among individual patients, even those with the same cancer type, considerably affecting the response to anticancer drugs and prognosis. With the recent expansion and improvement in next generation sequencing (NGS), it has become possible to comprehensively analyze genetic alterations in many cancer-related genes, simultaneously, from tens to hundreds of ng of DNA extracted from a small portion of a tumor specimen, and to identify the many genetic abnormalities that accumulate in cancer. Furthermore, precision medicine for cancer treatment, centered on cancer gene panel testing, began to flourish around 2015. In Japan, comprehensive genome profile (CGP) testing has been covered by insurance programs since 2019, and in the years since, CGP testing has widely penetrated clinical practice.
Pancreatic and biliary tract cancers have an extremely poor prognosis, and are an area where genomic medicine based on the CGP test could be provided. On the other hand, pancreatic and biliary tract cancers are located in organs that are difficult to collect tissue sampling from.
Recently, endoscopic ultrasound (EUS)-guided tissue actuation (EUS-TA) has been commonly used for tissue collection in pancreatic and biliary tract cancers. EUS-TA has recently become important not only in pathological diagnosis, but also in genomic medicine, which enables personalized medicine by performing multiple gene analyses simultaneously. Comprehensive genomic profiling is currently applied in the clinical setting and there is an increasing need for gene analysis using EUS-TA samples.
Thus, in this review, we report the current status of cancer genome medicine in pancreatic and biliary tract cancers and cancer gene panel testing using EUS-TA.

2. Comprehensive Genome Profiling of Pancreatic Cancers

The detection of genetic abnormalities and/or the tumor mutation burden on panel tests may lead to opportunities in precision medicine for treatments covered by insurance programs, as well as further clinical trials. Unfortunately, the percentage of patients able to receive treatment for abnormalities found as a result of panel testing is not large, at <15% [1], and the percentage in pancreatic ductal adenocarcinoma (PDAC) is likely even lower. However, as pancreaticobiliary cancer has a very poor prognosis, many patients request CGP testing to attempt even the slightest possibility of receiving a new treatment. In the breakdown of CGP testing in Japan, pancreatic and biliary tract cancers were found to be the second and third most frequent cancer types, respectively, after colorectal cancer [2].
Although mutated KRAS is the major oncogenic driver in PDAC and is an attractive treatment target, no effective treatments have been identified for patients with KRAS-mutant PDAC, with the exception of the very rare KRAS G12C mutation. Sotorasib, which targets the KRAS G12C mutation, has shown an anticancer activity in patients with KRAS G12C mutated non-small cell lung cancer. The KRAS G12C mutation is also found in a small fraction of pancreatic cancers, and sotorasib has shown efficacy in pancreatic cancer in a phase I study.
Other recent studies have indicated that KRAS-wild type pancreatic cancer has various targetable alterations [3,4,5]. Additionally, homologous recombination deficiency (HRD) is common genomic abnormality. BRCA1 and BRCA2 are key proteins involved in homologous recombination (HR) and play important roles in DNA double-strand break repair. Hence, PDAC with HRD is reportedly more sensitive to platinum-based chemotherapy, which induces a DNA-damaging effect [6]. Other HR-related genes include PALB2, ATM, and ATR. A meta-analysis reported the pooled prevalence of HR-related gene mutations (germline and somatic mutations) in PDAC as follows: BRCA1, 0.9%; BRCA2, 3.5%; PALB2, 0.2%; ATM, 2.2%; and ATM, 0.2% [7]. Maintenance therapy with olaparib, a PARP inhibitor, demonstrated efficacy for platinum-sensitive PDAC with BRCA pathological variants in a phase III study (the POLO trial) [8]. In addition, pembrolizumab, developed for microsatellite instability-high solid tumors [9], and entrectinib, developed for NTRK fusion gene mutations across multiple tumor types [10], have also shown efficacy for pancreaticobiliary cancers.
In the Know Your Tumor® Project study conducted in the United States, 26% of patients with pancreatic cancer underwent CGP testing and had actionable mutations useful for treatment selection. Additionally, the overall survival of patients who received a drug that matched the actionable mutation was 31 months, which was significantly superior to 18.1 months in the control group (hazard ratio: 0.42, p < 0.01) [11].
As KRAS mutations are found in approximately 95% of cases of pancreatic cancer, it is considered that matched therapy (therapy that matches the genetic abnormality) is indicated in a small number of cases, but these results suggest that NGS testing to find actionable mutations should be performed early in the treatment process. Based on this information, the 2019 National Comprehensive Cancer Network (NCCN) clinical guideline recommends germline testing, tumor/somatic gene profiling, and microsatellite instability/mismatch repair analysis in patients with PDAC [12].

3. Comprehensive Genome Profiling of Bile Duct Cancers

Whole-genome sequencing studies in biliary tract cancer have revealed genomic alterations in several oncogenes and tumor suppressor genes, mainly in KRAS, TP53, CDKN2A, and SMAD4 [13,14,15]. Nakamura et al. revealed that several genetic mutations commonly occur in biliary tract cancers, while others occur according to the cancer site (e.g., intrahepatic/extrahepatic bile duct and gallbladder), in a comprehensive genetic analysis, including a whole-transcriptome sequence analysis [13]. According to their report, common mutations in biliary tract cancers, including gallbladder cancer, were detected in TP53, BRCA1, BRCA2, and PIK3CA; intrahepatic and extrahepatic duct-shared mutations were detected in KRAS, SMAD4, ARID1A, and GNAS; intrahepatic duct-specific mutations were detected in FGFR2 fusion, IDH1/2, EPHA2, and BAP1 genes; and extrahepatic duct-specific mutations were detected in PRKACA/PRKACB fusion, ELF3, and ARID1B genes. Jain et al. [16] reviewed the molecular profiles of bile duct cancers and indicated the frequencies of several major gene mutations, as follows: TP53, 44–28% in Asians and 6% in Caucasians; KRAS, 12–17%; SMAD4, 6–20%; CDKN2A, 4–5%; BRCA1/2, 3–5%; and PIK3CA, 3–5%. FGFR2 inhibitors Pemigatinib [17] and Fucibatinib [18] have been shown to be effective in cases of FGFR2 fusion gene abnormalities. Pemigatinib showed a favorable response rate of 35.5% (95% CI: 26.5–45.4) in a global phase II clinical trial (FIGHT-202 study) [17]. Fucibatinib also showed a good response rate of 42% (95% confidence interval, 32 to 52).
In the PRELUDE study [19], which investigated the expression rate of the FGFR2 fusion gene in biliary tract cancers in Japan, the FGFR2 fusion gene was found in 7.4% (20/272) of intrahepatic cholangiocarcinoma and 3.6% (3/83) of hilar cholangiocarcinoma, and was associated with young age (≤65 years: p = 0.070) and HCV/HBV infection (p = 0.037). In addition, ivosidenib, an IDH1 inhibitor, is available for the treatment of IDH1 mutations, which are considered positive in 10–20% of intrahepatic cholangiocarcinoma.
A phase III comparative study (ClarIDHy study) [20], ivosidenib significantly prolonged progression-free survival versus placebo (median 2.7 vs. 1.4 months, HR 0.37, 95%).
In the 5th edition of the WHO classification revised in 2019, it classified intrahepatic cholangiocarcinoma into two types: large duct type and small duct type.
This classification is reported to reflect the findings of both types, as well as pathological features and molecular profile.
In terms of the molecular profile, the large duct type has KRAS mutations and SMAD4 deletions are more frequent, whereas the small duct type has a higher frequency of IDH1 mutation and FGFR2 fusion abnormality.
The recognition of these two categories based on imaging findings and pathological features will help to improve the efficiency of the diagnosis and treatment of small ductal carcinoma.
In addition, in biliary tract cancer, there have been other responses such as dabrafenib + trametinib (response efficiency 47%) in BRAF V600E (response rate 47%) and pertuzumab + trastuzumab (response rate 23%) in HER2-positive patients (response rate 23%).
The development of niraparib for BRCA1/2 mutations and olaparib for patients with other DNA repair gene mutations is also underway. There are many promising target-drug combinations for treating biliary tract cancer.

4. Comprehensive Genome Profiling of across Tumor Types

Drugs targeting rare alterations found in different solid tumors, such as microsatellite instability-high (MSI-H), a high tumor mutation burden (TMB-H), and NTRK fusions, have obtained approval across tumor types [21]. Tumor MSI status and TMB have been shown to play a significant role in the therapeutic efficacy of immune checkpoint inhibitors [22]. The Food and Drug Administration approved pembrolizumab, an antibody against the programmed cell death-1 (PD-1) protein, for the treatment of patients with MSI-H solid tumors in 2017 and TMB-H solid tumors in 2020 [23], but it is limited to a small number of cases [22]. In pancreatic cancer, the frequency of MSI-H is as low as 2.5% [24]. The rate of MSI-H biliary cancer is reported to be 1–3% [25,26].

5. Results of Genomic Medicine Using EUS-TA in Pancreaticobiliary Cancers

Currently, two types of CGP tests for tumor-tissue specimens are covered by insurance in Japan: the OncoguideTM NCC OncoPanel System (NCCOP) and the Foundation One CDx Cancer Genome Profile (F-One). To perform precision medicine using EUS-TA specimens, it is necessary to be aware of the differences between these two panel tests (Table 1).
The major clinical differences between NCCOP and F-One are the presence/absence of an analysis of germline mutations and the number of genes analyzed. Furthermore, a major difference of importance to endoscopists is the difference in criteria regarding the amount of tissue required (tissue section area and recommended tumor cell content). As shown in Table 1, F-One requires a larger tumor cell area than NCCOP. Although F-One searches for a large number of genes, it also requires a larger amount of tissue (>25 mm2), which is a limitation of EUS-TA. In contrast, NCCOP only requires a tissue area of >4 mm2, and small EUS-TA specimens are more easily submitted.
Recently, liquid biopsy testing of blood and other body-fluid specimens has been developed as a new analytic method [27,28,29], and the F-One Liquid CDx Cancer Genome Profile [30] test for blood specimens is now covered by insurance programs in Japan. Although the liquid biopsy test is very useful for pancreaticobiliary cancers, for which the collection of samples for genetic testing is difficult, its sensitivity for detecting KRAS abnormalities is lower than that of the test using tumor tissue specimens [31], and tumor tissue specimens are superior in terms of certainty. The FGFR2 fusion gene is the most common genetic abnormality found in intrahepatic cholangiocarcinoma, and its detection using liquid biopsy is low. Therefore, in pancreatic and biliary tract cancers, it is still desirable to submit specimens from tissues as much as possible in order to identify the gene abnormality that is the target of treatment.
Table 2 summarizes the differences between the tumor and liquid tests. For patients with a promising prognosis, cancer genomic testing using tumor tissue samples is the first choice.
For CGP testing, DNA is extracted from a formalin-fixed paraffin-embedded (FFPE) specimen of the tumor and then analyzed. In recurrent cases, resected specimens can be used as an FFPE sample (in principle, within 3 years); however, for unresectable PDAC, CGP testing is usually performed on tissue samples obtained by EUS-TA or percutaneous liver biopsy in cases with liver metastases. Although a liver biopsy often provides a sufficient amount of the tumor for the panel test [32], in patients with locally advanced PDAC, lung metastases, peritoneal dissemination only, or in whom a liver biopsy is difficult to perform, tissue samples from EUS-TA are needed. However, PDAC is representative of low-cellularity tumors with abundant stromal components, and the tumor cell content is reported to be 5–20% [33], rendering CGP testing a relatively difficult task. Furthermore, EUS-TA specimens also contain various contaminants (e.g., gastrointestinal epithelium and stroma), which certainly degrade the quality of the specimen for genome analysis. Thus, the feasibility of CGP testing using EUS-TA specimens has attracted much interest.
In recent years, several studies have reported on CGP analysis using FFPE samples obtained by EUS-TA in pancreatic tumors [32,34,35,36,37]; these studies are summarized in Table 3. The success rates of the panel tests varied markedly, ranging 39.2–100%. This is due to the different types of panel tests used and the lack of uniform standards for the tumor-containing cell rate, tissue area, and DNA content required for the tests.
The amount of input DNA required for NGS depends on the platform, ranging ~10–300 ng. It is estimated that approximately 2000 tumor cells are needed to obtain 10 ng of DNA [38]. Regarding the amount of DNA extracted, Park et al. reported that the mean extracted DNA amounts in the NGS success and failure groups were 540 ng and 142 ng, respectively, and the success rate of the NGS analysis was improved from 57.4% (109/190) to 76.2% (109/143) when the amount of extracted DNA was more than 50 ng [37].
Hence, a sufficient amount and concentration of extracted DNA improves the success rate of the NGS analysis.
Only one study (number of cases > 10) on CGP testing using EUS-TA for biliary tract cancer has been reported. This is because compared with pancreatic lesions, bile duct lesions are more difficult to puncture, and there are fewer conditions in which they can be punctured. Hirata et al. [39] performed cancer gene panel testing for 21 cases of biliary tract cancer using EUS-TA samples, with 50 cancer genes being the analysis target. This retrospective study achieved deep sequencing coverage and identified pathogenic alterations in 95.2% (20/21) of the patients with biliary tract cancer using EUS fine-needle aspiration (EUS-FNA) samples. However, the analysis target of 50 cancer genes was small compared with the usual number in CGP testing, which may have been the reason for the good results. However, EUS-TA may be useful for CGP testing in patients with suspected biliary tract cancer, as in PDAC.

6. Tips for Improving the Success Rate of CGP Analysis with EUS-TA in Pancreaticobiliary Tract Cancers

Figure 1 shows the flow of the gene panel testing using EUS-TA. From EUS-TA to CGP testing, there are five main processes, each of which is important to obtain a high success rate: (1) EUS-TA, (2) specimen processing and formalin fixation, (3) slide preparation, (4) pre-check by pathologist, and (5) submission to CGP. The factors affecting these processes are discussed below.

6.1. Selection of Needle Type/Size for EUS-TA

Various preanalytical factors, including tumor cellularity, tumor fraction, and tumor viability, are associated with DNA quality; therefore, specimen collection and processing methods, including the selected needle type and size, are important for this reason.
In recent years, puncture needles with a unique needle tip shape, known as fine needle biopsy (FNB) needles, have emerged to improve the tissue specimen volume. Several types of needles are available, including Francine and fork-tip needles. These needles are similar to conventional FNA needles in that they use the puncture aspiration method, but they differ in tip shape, which increases the amount of tissue sampled. The difference in the amount of tissue sampled with FNA and FNB needles has been verified in several publications, and it has been reported that the FNB needle has a significantly higher success rate when CGP analysis is the primary objective [35,40,41,42]. Based on these results, the FNB needle is now recommended for EUS-TA considering CGP [43,44,45]. In addition, according to a recent network meta-analysis, Francine and fork-tip needles, among the FNB needles, show the best performance for tissue collection, and it is recommended that one of these two types be selected for CGP purposes as well [46].
Regarding needle size, Park et al. compared 19-G/22-G needles to 25-G needles and found [37] that a 19-G/22-G diameter needle was more appropriate for NGS (success rate: 63.2% vs. 38.8%, p = 0.003). Furthermore, in a randomized controlled trial of 25-G FNA and 19-G/22-G FNB needles, the percentage of patients who fulfilled F-One analysis criteria with a single puncture was greater with 19-G/22-G FNB needles (78%) compared with 25-G FNA needles (14%) [40]. Thus, the success rate of CGP analysis is low for 25-G needles. EUS-FNB and needle size are considered predictors of successful NGS in PDAC. Several studies comparing the adequate tissue rate of NGS analysis in PDAC between EUS-FNA and EUS-FNB have found that EUS-FNB is more suitable for NGS analysis (EUS-FNA: 14–66.9% vs. EUS-FNB: 70.4–90.9%) [32,35,40]. According to Kindel et al., the mean DNA concentrations in PDAC by FNB and FNA needles were 5.930 (standard deviation [SD] 0.881) µg/mL vs. 3.365 (SD 0.788) µg/mL, respectively (p = 0.01). Significantly more DNA was obtained using the FNB needle. Furthermore, the success rate of NGS analysis in PDAC is better with 19-G or 22-G than with 25-G needle (odds ratio [OR] 2.19, 95% confidence interval [CI]: 1.08–4.47) [37]. There are five types of suction methods for EUS-TA: “no suction”, “slow-pull technique”, “dry suction”, “modified wet suction”, and “wet suction.” According to a recent network meta-analysis [47], the “no suction” technique was significantly inferior to the other techniques. Consequently, “modified wet suction” resulted as the best technique (SUCRA 0.90). Based on these results, “modified wet suction” might be recommended for NGS.
Ikeda et al. [48] retrospectively investigated the percentage of cases achieving NCCOP analysis criteria (153 cases in total; FNB 19-G needle: 75 cases, FNB 22-G needle: 43 cases, and FNA 22-G needle: 35 cases). Overall, only 39.0% (60/153) of cases met the NCCOP analysis criteria. The percentage meeting criteria was significantly higher with 19-G FNB needles (56.0% [42/75]) than with other needles (22-G FNB: 32.6% [14/43] and 22-G FNA: 11.4% [4/35]) (p < 0.01). Furthermore, FNB (OR = 3.91, 95% CI: 1.17–13.0, p = 0.0267) and 19-G needles (OR = 2.44, 95% CI: 1.12–5.30), p = 0.0247) were independent factors contributing to the achievement of the NCCOP analysis criteria. Based on these results, the 19-G FNB needle is the first choice for unresectable (UR) pancreatic cancer in our institution for cases with CGP analysis in mind. However, as the needle size becomes larger, puncturing becomes more difficult and the risk of complications increases; thus, 22-G FNB (or FNA) is used for UR pancreatic cancer and resectable/borderline resectable pancreatic cancer that is not scheduled for CGP analysis (Figure 2).
A recent study indicated that the percentage of patients who achieved genomic criteria in EUS-TA specimens of primary pancreatic cancer (37.1% [53/143]) was significantly lower than the percentage of patients who achieved genomic criteria in EUS-TA specimens from metastatic sites (liver metastases or lymph nodes) (70% [7/10]) (p = 0.049). Thus, collecting samples from liver metastases for EUS-TA in anticipation of CGP testing may be recommended.
A prospective study [49] with a primary endpoint of the percentage of specimens obtained by EUS-TA using a 19-G FNB needle achieving NCCOP analysis criteria reported that the proportion of patients meeting the predefined criteria to be considered valid was 63.6% (95% CI: 47.22–80.05). The median tumor cell content was 60% (range: 7.5–85). Tissue sizes were ≤4 mm2 and 4–16 mm2 in 10 (30.3%) and 23 (69.7%) patients, respectively. Moreover, none of the patients met the F-One analysis suitability criteria. The samples were actually submitted to NCCOP analysis in 36.4% (12/33) of cases, of which 100% (12/12) were evaluable. The mean DNA content was 1050.7 ng (SD 684.2). Currently, there is no clear indication regarding the appropriate number of punctures for CGP. However, it has been suggested that the use of macroscopic on-site evaluation (MOSE) may contribute to a reduction in the number of EUS-TA punctures [50], and this may be applied to CGP as well.
There are also some important considerations regarding pre-analytical specimen-processing methods. FFPE DNA extraction methods may cause DNA fragmentation and chemical modification. Therefore, the formalin fixation time should be shortened for small specimens, such as EUS-FNA specimens. In addition, the quality of nucleic acids deteriorates with time and the most recent specimen is the most suitable for NGS.

6.2. Specimen Processing and Formalin Fixation

Although the optimal specimen-processing method for EUS-TA with CGP testing has not yet been established, the following three points are important in EUS-TA. (1) Tissues collected from biopsies should be promptly immersed in a fixative solution for fixation. (2) A 10% neutral buffered formalin solution should be used in the composition of the formalin fixative. (3) Formalin fixation should be performed for 6–48 h [51].
In real-world practice, if EUS-TA is performed before a long holiday, over fixation of formalin is a concern, which is especially relevant to point out (3). Therefore, it is preferable to avoid performing EUS-TA before a long holiday, if possible, when considering CGP analysis.

6.3. Pathologist’s Criteria for Genome Analysis (Pre-Check)

The pathologist’s determination of whether the required criteria for analysis have been met (commonly called the pre-check) is based on the tumor cell content and tissue section area. This pre-check plays a gatekeeping role, before submission to the actual CGP examination. This is because CGP testing is very expensive; if CGP testing cannot be performed due to an insufficient specimen volume, the patient burden will be high. Additionally, from the standpoint of medical resource use, the pre-check should be strictly required in terms of testing conditions. Thus, the pre-check may be extremely strict.
Additionally, specific criteria for EUS-TA are lacking. The current pre-check based on the tissue area and tumor content does not conform to the criteria for EUS-TA specimens, rendering it difficult to make a judgment. Thus, the pre-check may need to be revised in the future with criteria specifically for EUS-TA. This issue should be clarified in a multicenter prospective study.

7. Conclusions

We described genomic diagnosing using EUS-TA in pancreatic cancers. In the past, EUS-FNA was primarily used for diagnosis; however, EUS-FNB, which can collect a larger amount of tissue, has become the mainstream specimen collection method, as a larger amount of tissue is required for EUS-TA in the current era of genomic medicine. Although the amount of DNA required for panel testing is expected to decrease as genomic medicine advances in the future, endoscopists still face numerous demands at this time. To respond to these demands and contribute to the patient’s cancer treatment to the best extent, it is necessary for endoscopists to make continuous efforts to study and collect information on a daily basis.

8. Future Directions

CGP will become more advanced and require less tissue. It is also expected that CGP testing will become possible with smaller amounts of DNA, such as with the development of gene panel tests specific for biliopancreatic cancer. At this point, however, it is important to utilize many EUS-TA techniques and devices to improve the success rate of CGP testing with EUS-TA.

Author Contributions

Conceptualization, S.H., Y.N., Y.M., Y.K., K.T., C.M. and T.O.; methodology, S.H., Y.N., Y.M., Y.K., K.T. and C.M.; writing—review and editing, S.H.; supervision, Y.N., Y.M., Y.K., K.T., C.M. and T.O.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.


This work was supported in part by The National Cancer Center Research and Development Fund (2022-A-16).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Sunami, K.; Ichikawa, H.; Kubo, T.; Kato, M.; Fujiwara, Y.; Shimomura, A.; Koyama, T.; Kakishima, H.; Kitami, M.; Matsushita, H.; et al. Feasibility and utility of a panel testing for 114 cancer-associated genes in a clinical setting: A hospital-based study. Cancer Sci. 2019, 110, 1480–1490. [Google Scholar] [CrossRef] [PubMed][Green Version]
  2. Kohno, T.; Kato, M.; Kohsaka, S.; Sudo, T.; Tamai, I.; Shiraishi, Y.; Okuma, Y.; Ogasawara, D.; Suzuki, T.; Yoshida, T.; et al. C-CAT: The National Datacenter for Cancer Genomic Medicine in Japan. Cancer Discov. 2022, 12, 2509–2515. [Google Scholar] [CrossRef] [PubMed]
  3. Philip, P.A.; Azar, I.; Xiu, J.; Hall, M.J.; Hendifar, A.E.; Lou, E.; Hwang, J.J.; Gong, J.; Feldman, R.; Ellis, M.; et al. Molecular Characterization of KRAS Wild-type Tumors in Patients with Pancreatic Adenocarcinoma. Clin. Cancer Res. 2022, 28, 2704–2714. [Google Scholar] [CrossRef] [PubMed]
  4. Turpin, A.; Neuzillet, C.; Colle, E.; Dusetti, N.; Nicolle, R.; Cros, J.; de Mestier, L.; Bachet, J.B.; Hammel, P. Therapeutic advances in metastatic pancreatic cancer: A focus on targeted therapies. Ther. Adv. Med. Oncol. 2022, 14, 17588359221118019. [Google Scholar] [CrossRef]
  5. Luchini, C.; Paolino, G.; Mattiolo, P.; Piredda, M.L.; Cavaliere, A.; Gaule, M.; Melisi, D.; Salvia, R.; Malleo, G.; Shin, J.I.; et al. KRAS wild-type pancreatic ductal adenocarcinoma: Molecular pathology and therapeutic opportunities. J. Exp. Clin. Cancer Res. 2020, 39, 227. [Google Scholar] [CrossRef]
  6. Wattenberg, M.M.; Asch, D.; Yu, S.; O’Dwyer, P.J.; Domchek, S.M.; Nathanson, K.L.; Rosen, M.A.; Beatty, G.L.; Siegelman, E.S.; Reiss, K.A. Platinum response characteristics of patients with pancreatic ductal adenocarcinoma and a germline BRCA1, BRCA2 or PALB2 mutation. Br. J. Cancer 2020, 122, 333–339. [Google Scholar] [CrossRef]
  7. Casolino, R.; Paiella, S.; Azzolina, D.; Beer, P.A.; Corbo, V.; Lorenzoni, G.; Gregori, D.; Golan, T.; Braconi, C.; Froeling, F.E.M.; et al. Homologous Recombination Deficiency in Pancreatic Cancer: A Systematic Review and Prevalence Meta-Analysis. J. Clin. Oncol. 2021, 39, 2617–2631. [Google Scholar] [CrossRef]
  8. 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. [Google Scholar] [CrossRef]
  9. 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. [Google Scholar] [CrossRef]
  10. Doebele, R.C.; Drilon, A.; Paz-Ares, L.; Siena, S.; Shaw, A.T.; Farago, A.F.; Blakely, C.M.; Seto, T.; Cho, B.C.; Tosi, D.; et al. Entrectinib in patients with advanced or metastatic NTRK fusion-positive solid tumours: Integrated analysis of three phase 1-2 trials. Lancet Oncol. 2020, 21, 271–282. [Google Scholar] [CrossRef]
  11. Canto, M.I.; Almario, J.A.; Schulick, R.D.; Yeo, C.J.; Klein, A.; Blackford, A.; Shin, E.J.; Sanyal, A.; Yenokyan, G.; Lennon, A.M.; et al. Risk of Neoplastic Progression in Individuals at High Risk for Pancreatic Cancer Undergoing Long-term Surveillance. Gastroenterology 2018, 155, 740–751.e2. [Google Scholar] [CrossRef] [PubMed][Green Version]
  12. NCCN. NCCN Clinical Guideline in Oncology; NCCN: Plymouth Meeting, PA, USA, 2022. [Google Scholar]
  13. Nakamura, H.; Arai, Y.; Totoki, Y.; Shirota, T.; Elzawahry, A.; Kato, M.; Hama, N.; Hosoda, F.; Urushidate, T.; Ohashi, S.; et al. Genomic spectra of biliary tract cancer. Nat. Genet. 2015, 47, 1003–1010. [Google Scholar] [CrossRef] [PubMed]
  14. Lowery, M.A.; Jordan, E.J.; Basturk, O.; Ptashkin, R.N.; Zehir, A.; Berger, M.F.; Leach, T.; Herbst, B.; Askan, G.; Maynard, H.; et al. Real-Time Genomic Profiling of Pancreatic Ductal Adenocarcinoma: Potential Actionability and Correlation with Clinical Phenotype. Clin. Cancer Res. 2017, 23, 6094–6100. [Google Scholar] [CrossRef] [PubMed][Green Version]
  15. Lowery, M.A.; Ptashkin, R.; Jordan, E.; Berger, M.F.; Zehir, A.; Capanu, M.; Kemeny, N.E.; O’Reilly, E.M.; El-Dika, I.; Jarnagin, W.R.; et al. Comprehensive Molecular Profiling of Intrahepatic and Extrahepatic Cholangiocarcinomas: Potential Targets for Intervention. Clin. Cancer Res. 2018, 24, 4154–4161. [Google Scholar] [CrossRef] [PubMed][Green Version]
  16. Jain, A.; Javle, M. Molecular profiling of biliary tract cancer: A target rich disease. J. Gastrointest. Oncol. 2016, 7, 797–803. [Google Scholar] [CrossRef][Green Version]
  17. 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. [Google Scholar] [CrossRef]
  18. Goyal, L.; Meric-Bernstam, F.; Hollebecque, A.; Valle, J.W.; Morizane, C.; Karasic, T.B.; Abrams, T.A.; Furuse, J.; Kelley, R.K.; Cassier, P.A.; et al. Futibatinib for FGFR2-Rearranged Intrahepatic Cholangiocarcinoma. N. Engl. J. Med. 2023, 388, 228–239. [Google Scholar] [CrossRef]
  19. Maruki, Y.; Morizane, C.; Arai, Y.; Ikeda, M.; Ueno, M.; Ioka, T.; Naganuma, A.; Furukawa, M.; Mizuno, N.; Uwagawa, T.; et al. Correction to: Molecular detection and clinicopathological characteristics of advanced/recurrent biliary tract carcinomas harboring the FGFR2 rearrangements: A prospective observational study (PRELUDE Study). J. Gastroenterol. 2021, 56, 297. [Google Scholar] [CrossRef]
  20. 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. [Google Scholar] [CrossRef]
  21. Mosele, F.; Remon, J.; Mateo, J.; Westphalen, C.B.; Barlesi, F.; Lolkema, M.P.; Normanno, N.; Scarpa, A.; Robson, M.; Meric-Bernstam, F.; et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: A report from the ESMO Precision Medicine Working Group. Ann. Oncol. 2020, 31, 1491–1505. [Google Scholar] [CrossRef]
  22. Yamai, T.; Ikezawa, K.; Sugimoto, N.; Urabe, M.; Kai, Y.; Takada, R.; Nakabori, T.; Uehara, H.; Kawamura, T.; Kunimasa, K.; et al. Utility of Comprehensive Genomic Profiling Tests for Patients with Incurable Pancreatic Cancer in Clinical Practice. Cancers 2023, 15, 970. [Google Scholar] [CrossRef] [PubMed]
  23. Prasad, V.; Kaestner, V.; Mailankody, S. Cancer Drugs Approved Based on Biomarkers and Not Tumor Type-FDA Approval of Pembrolizumab for Mismatch Repair-Deficient Solid Cancers. JAMA Oncol. 2018, 4, 157–158. [Google Scholar] [CrossRef] [PubMed]
  24. Luchini, C.; Brosens, L.A.A.; Wood, L.D.; Chatterjee, D.; Shin, J.I.; Sciammarella, C.; Fiadone, G.; Malleo, G.; Salvia, R.; Kryklyva, V.; et al. Comprehensive characterisation of pancreatic ductal adenocarcinoma with microsatellite instability: Histology, molecular pathology and clinical implications. Gut 2021, 70, 148–156. [Google Scholar] [PubMed]
  25. Lee, V.; Murphy, A.; Le, D.T.; Diaz, L.A., Jr. Mismatch Repair Deficiency and Response to Immune Checkpoint Blockade. Oncologist 2016, 21, 1200–1211. [Google Scholar] [CrossRef][Green Version]
  26. Eso, Y.; Shimizu, T.; Takeda, H.; Takai, A.; Marusawa, H. Microsatellite instability and immune checkpoint inhibitors: Toward precision medicine against gastrointestinal and hepatobiliary cancers. J. Gastroenterol. 2020, 55, 15–26. [Google Scholar] [CrossRef][Green Version]
  27. Herreros-Villanueva, M.; Bujanda, L.; Ruiz-Rebollo, L.; Torremocha, R.; Ramos, R.; Martín, R.; Artigas, M.C. Circulating tumor DNA tracking in patients with pancreatic cancer using next-generation sequencing. Gastroenterol. Hepatol. 2022, 45, 637–644. [Google Scholar] [CrossRef]
  28. Macgregor-Das, A.; Yu, J.; Tamura, K.; Abe, T.; Suenaga, M.; Shindo, K.; Borges, M.; Koi, C.; Kohi, S.; Sadakari, Y.; et al. Detection of Circulating Tumor DNA in Patients with Pancreatic Cancer Using Digital Next-Generation Sequencing. J. Mol. Diagn. 2020, 22, 748–756. [Google Scholar] [CrossRef]
  29. Nikas, I.P.; Mountzios, G.; Sydney, G.I.; Ioakim, K.J.; Won, J.K.; Papageorgis, P. Evaluating Pancreatic and Biliary Neoplasms with Small Biopsy-Based Next Generation Sequencing (NGS): Doing More with Less. Cancers 2022, 14, 397. [Google Scholar] [CrossRef]
  30. Woodhouse, R.; Li, M.; Hughes, J.; Delfosse, D.; Skoletsky, J.; Ma, P.; Meng, W.; Dewal, N.; Milbury, C.; Clark, T.; et al. Clinical and analytical validation of FoundationOne Liquid CDx, a novel 324-Gene cfDNA-based comprehensive genomic profiling assay for cancers of solid tumor origin. PLoS ONE 2020, 15, e0237802. [Google Scholar] [CrossRef]
  31. Nakamura, Y.; Taniguchi, H.; Ikeda, M.; Bando, H.; Kato, K.; Morizane, C.; Esaki, T.; Komatsu, Y.; Kawamoto, Y.; Takahashi, N.; et al. Clinical utility of circulating tumor DNA sequencing in advanced gastrointestinal cancer: SCRUM-Japan GI-SCREEN and GOZILA studies. Nat. Med. 2020, 26, 1859–1864. [Google Scholar] [CrossRef]
  32. Larson, B.K.; Tuli, R.; Jamil, L.H.; Lo, S.K.; Deng, N.; Hendifar, A.E. Utility of Endoscopic Ultrasound-Guided Biopsy for Next-Generation Sequencing of Pancreatic Exocrine Malignancies. Pancreas 2018, 47, 990–995. [Google Scholar] [CrossRef] [PubMed]
  33. Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma. Cancer Cell 2017, 32, 185–203.e13. [CrossRef] [PubMed][Green Version]
  34. Carrara, S.; Soldà, G.; Di Leo, M.; Rahal, D.; Peano, C.; Giunta, M.; Lamonaca, L.; Auriemma, F.; Anderloni, A.; Fugazza, A.; et al. Side-by-side comparison of next-generation sequencing, cytology, and histology in diagnosing locally advanced pancreatic adenocarcinoma. Gastrointest. Endosc. 2021, 93, 597–604.e5. [Google Scholar] [CrossRef]
  35. Elhanafi, S.; Mahmud, N.; Vergara, N.; Kochman, M.L.; Das, K.K.; Ginsberg, G.G.; Rajala, M.; Chandrasekhara, V. Comparison of endoscopic ultrasound tissue acquisition methods for genomic analysis of pancreatic cancer. J. Gastroenterol. Hepatol. 2019, 34, 907–913. [Google Scholar] [CrossRef]
  36. Gleeson, F.C.; Kerr, S.E.; Kipp, B.R.; Voss, J.S.; Minot, D.M.; Tu, Z.J.; Henry, M.R.; Graham, R.P.; Vasmatzis, G.; Cheville, J.C.; et al. Targeted next generation sequencing of endoscopic ultrasound acquired cytology from ampullary and pancreatic adenocarcinoma has the potential to aid patient stratification for optimal therapy selection. Oncotarget 2016, 7, 54526–54536. [Google Scholar] [CrossRef] [PubMed][Green Version]
  37. Park, J.K.; Lee, J.H.; Noh, D.H.; Park, J.K.; Lee, K.T.; Lee, J.K.; Lee, K.H.; Jang, K.T.; Cho, J. Factors of Endoscopic Ultrasound-Guided Tissue Acquisition for Successful Next-Generation Sequencing in Pancreatic Ductal Adenocarcinoma. Gut Liver 2020, 14, 387–394. [Google Scholar] [CrossRef][Green Version]
  38. Chen, H.; Luthra, R.; Goswami, R.S.; Singh, R.R.; Roy-Chowdhuri, S. Analysis of Pre-Analytic Factors Affecting the Success of Clinical Next-Generation Sequencing of Solid Organ Malignancies. Cancers 2015, 7, 1699–1715. [Google Scholar] [CrossRef]
  39. Hirata, K.; Kuwatani, M.; Suda, G.; Ishikawa, M.; Sugiura, R.; Kato, S.; Kawakubo, K.; Sakamoto, N. A Novel Approach for the Genetic Analysis of Biliary Tract Cancer Specimens Obtained Through Endoscopic Ultrasound-Guided Fine Needle Aspiration Using Targeted Amplicon Sequencing. Clin. Transl. Gastroenterol. 2019, 10, e00022. [Google Scholar]
  40. Kandel, P.; Nassar, A.; Gomez, V.; Raimondo, M.; Woodward, T.A.; Crook, J.E.; Fares, N.S.; Wallace, M.B. Comparison of endoscopic ultrasound-guided fine-needle biopsy versus fine-needle aspiration for genomic profiling and DNA yield in pancreatic cancer: A randomized crossover trial. Endoscopy 2021, 53, 376–382. [Google Scholar] [CrossRef]
  41. Wang, J.; Zhao, S.; Chen, Y.; Jia, R.; Zhang, X. Endoscopic ultrasound guided fine needle aspiration versus endoscopic ultrasound guided fine needle biopsy in sampling pancreatic masses: A meta-analysis. Medicine 2017, 96, e7452. [Google Scholar] [CrossRef]
  42. Asokkumar, R.; Yung Ka, C.; Loh, T.; Kah Ling, L.; Gek San, T.; Ying, H.; Tan, D.; Khor, C.; Lim, T.; Soetikno, R. Comparison of tissue and molecular yield between fine-needle biopsy (FNB) and fine-needle aspiration (FNA): A randomized study. Endosc. Int. Open 2019, 7, e955–e963. [Google Scholar] [CrossRef]
  43. Imaoka, H.; Sasaki, M.; Hashimoto, Y.; Watanabe, K.; Ikeda, M. New Era of Endoscopic Ultrasound-Guided Tissue Acquisition: Next-Generation Sequencing by Endoscopic Ultrasound-Guided Sampling for Pancreatic Cancer. J. Clin. Med. 2019, 8, 1173. [Google Scholar] [CrossRef] [PubMed][Green Version]
  44. Tanaka, H.; Matsusaki, S. The Utility of Endoscopic-Ultrasonography-Guided Tissue Acquisition for Solid Pancreatic Lesions. Diagnostics 2022, 12, 753. [Google Scholar] [CrossRef] [PubMed]
  45. Ashida, R.; Kitano, M. Endoscopic ultrasound-guided tissue acquisition for pancreatic ductal adenocarcinoma in the era of precision medicine. Dig. Endosc. 2022, 34, 1329–1339. [Google Scholar] [CrossRef] [PubMed]
  46. Gkolfakis, P.; Crinò, S.F.; Tziatzios, G.; Ramai, D.; Papaefthymiou, A.; Papanikolaou, I.S.; Triantafyllou, K.; Arvanitakis, M.; Lisotti, A.; Fusaroli, P.; et al. Comparative diagnostic performance of end-cutting fine-needle biopsy needles for EUS tissue sampling of solid pancreatic masses: A network meta-analysis. Gastrointest. Endosc. 2022, 95, 1067–1077.e15. [Google Scholar] [CrossRef]
  47. Facciorusso, A.; Crinò, S.F.; Ramai, D.; Madhu, D.; Fugazza, A.; Carrara, S.; Spadaccini, M.; Mangiavillano, B.; Gkolfakis, P.; Mohan, B.P.; et al. Comparative Diagnostic Performance of Different Techniques for Endoscopic Ultrasound-Guided Fine-Needle Biopsy of Solid Pancreatic Masses: A Network Meta-analysis. Gastrointest. Endosc. 2023; online ahead of print. [Google Scholar] [CrossRef]
  48. Ikeda, G.; Hijioka, S.; Nagashio, Y.; Maruki, Y.; Ohba, A.; Hisada, Y.; Yoshinari, M.; Harai, S.; Kitamura, H.; Koga, T.; et al. Fine-needle biopsy with 19G needle is effective in combination with endoscopic ultrasound-guided tissue acquisition for genomic profiling of unresectable pancreatic cancer. Dig. Endosc. 2022, 35, 124–133. [Google Scholar] [CrossRef] [PubMed]
  49. Hisada, Y.; Hijioka, S.; Ikeda, G.; Maehara, K.; Hashimoto, T.; Kitamura, H.; Harai, S.; Yoshinari, M.; Kawasaki, Y.; Murashima, Y.; et al. Proportion of unresectable pancreatic cancer specimens obtained by endoscopic ultrasound-guided tissue acquisition meeting the OncoGuide™ NCC Oncopanel System analysis suitability criteria: A single-arm, phase II clinical trial. J. Gastroenterol. 2022, 57, 990–998. [Google Scholar] [CrossRef]
  50. Iwashita, T.; Yasuda, I.; Mukai, T.; Doi, S.; Nakashima, M.; Uemura, S.; Mabuchi, M.; Shimizu, M.; Hatano, Y.; Hara, A.; et al. Macroscopic on-site quality evaluation of biopsy specimens to improve the diagnostic accuracy during EUS-guided FNA using a 19-gauge needle for solid lesions: A single-center prospective pilot study (MOSE study). Gastrointest. Endosc. 2015, 81, 177–185. [Google Scholar] [CrossRef]
  51. Hatanaka, Y.; Kuwata, T.; Morii, E.; Kanai, Y.; Ichikawa, H.; Kubo, T.; Hatanaka, K.C.; Sakai, K.; Nishio, K.; Fujii, S.; et al. The Japanese Society of Pathology Practical Guidelines on the handling of pathological tissue samples for cancer genomic medicine. Pathol. Int. 2021, 71, 725–740. [Google Scholar] [CrossRef]
Figure 1. Process from EUS-TA to CGP analysis and discussion points. CGP: comprehensive genome profile; EUS-TA: endoscopic ultrasound-guided tissue acquisition.
Figure 1. Process from EUS-TA to CGP analysis and discussion points. CGP: comprehensive genome profile; EUS-TA: endoscopic ultrasound-guided tissue acquisition.
Diagnostics 13 01275 g001
Figure 2. EUS-TA recommendations. For resectable/borderline resectable (BR) pancreatic cancer, 22-G FNB is the first choice because the main focus is on a benign/malignant diagnosis; however, 22-G FNA is selected in cases of difficult puncture. For unresectable pancreatic cancer and when CGP analysis is considered, 19-G FNB is the first choice; if CGP analysis is not planned, 22-G FNB is selected based on puncture performance and safety. CGP: comprehensive genome profile.
Figure 2. EUS-TA recommendations. For resectable/borderline resectable (BR) pancreatic cancer, 22-G FNB is the first choice because the main focus is on a benign/malignant diagnosis; however, 22-G FNA is selected in cases of difficult puncture. For unresectable pancreatic cancer and when CGP analysis is considered, 19-G FNB is the first choice; if CGP analysis is not planned, 22-G FNB is selected based on puncture performance and safety. CGP: comprehensive genome profile.
Diagnostics 13 01275 g002
Table 1. Differences between two cancer gene panel tests.
Table 1. Differences between two cancer gene panel tests.
Test NameNCC Onco PanelFoundation One CDx
Number of genes analyzed114 genetic variants
12 fusion genes
324 gene mutations
36 fusion genes
Determination of microsatellite instabilityNoYes
Determination of tumor mutation burdenYesYes
Analysis of germline mutationsPossibleImpossible
Specimen usedTumor specimen (FFPE)
Normal (peripheral blood)
Tumor specimen (FFPE)
Required area
(tissue section area and number of slides required)
In principle, 16 mm2 or more (4 mm2 is acceptable)
5–10 µm sheets
25 mm2 or more
5–10 µm sheets
Specimen eligibility criteria
(recommended tumor cell content)
Tumor cell content ≥ 20%
DNA input amount: 200 ng
Tumor cell content ≥ 20%
(Optimal 30% or more)
FFPE: formalin-fixed paraffin-embedded.
Table 2. Differences between EUS-TA and liquid biopsy panel analyses.
Table 2. Differences between EUS-TA and liquid biopsy panel analyses.
EUS-TA (Tissue)Liquid Biopsy (Plasma)
Turnaround timeLong (7–8 weeks from the time of tissue collection)As short as 3–4 weeks
KRAS mutation detection rate83–88%Approximately 60%
Other advantagesApplications in RNA, whole exon, and whole genome analysesOvercomes the problem of intra-tumor heterogeneity
Application in the early detection of small residual lesions
DisadvantagesDifficulty in specimen collection, risk of complications,
specimen degradation over time,
and heterogeneity issues within tumors
Results depend on the timing of specimen collection, tumor cell content cannot be evaluated in advance,
limited sensitivity in detecting fusion genes and copy number, and uncertainty in determining TMB and MSI.
EUS-TA: endoscopic ultrasound-guided tissue acquisition; MSI: microsatellite instability; TMB: tumor mutation burden.
Table 3. Success rate of cancer genomic testing using EUS-TA FFPE for pancreatic tumors.
Table 3. Success rate of cancer genomic testing using EUS-TA FFPE for pancreatic tumors.
First AuthorDesignnType of TumorNeedle Size/TypeTypes of Gene Panel TestsMinimum Amount of DNA RequiredRequirementPercentage Meeting NGS Analysis Criteria/Successful RateDNA Amount
DNA Concentration
Judgment MethodFrequency of
Genomic Alternations
Young G
N/ACustom panel
(287 genes)
50 ngTumor cell content ≥ 20%100% (23/23)N/AActual
Panel Inspection
KRAS 83% (15/18)
CDKN2A 44% (8/18)
Gleeson FC
papillary carcinoma
N/AHuman Comprehensive
Cancer GeneRead
DNAseq Targeted Panel
V2 (160 genes)
DNA concentration:
5 ng/μL
Tumor cell content ≥ 20%61.7% (29/47)66.9 ng/μL
(range 9.3–164)
Panel Inspection
KRAS 93.1% (27/29)
TP53 72.4% (21/29)
SMAD4 31% (9/29)
GNAS 10.3% (3/29)
Elhanafi S
Retrospective167Adenoca.22G FNA (n = 145)
22G FNB (n = 22)
TruSeq Amplicon Cancer Panel (47 genes)N/ATumor cell content ≥ 10%70.10%
22G FNB (90.9%)
22G FNA (66.9%)
N/AAnalysis Criteria
KRAS 88% (22/25)
TP53 68% (17/25)
SMAD4 16% (4/25)
Larson BK
Acinar cell carcinoma
EUS-FNA (n = 7)
EUS-FNB (n = 54)
Others (n = 15)
(324 genes)
N/ATissue slicing area ≥ 25 mm2
Tumor cell content ≥ 20%
EUS-FNA 42.9%
EUS-FNB 70.4%
N/AAnalysis Criteria
Park JK
Cancer SCANTM v1
(83 genes)
50 ngN/A57.4% (109/190)NGS success:
1.42 ± 1.57 μg
NGS failure:
0.54 ± 1.70 μg
Panel Inspection
KRAS 78.9% (86/109)
TP53 60.6% (66/109)
SMAD4 30.3% (33/109)
CKDN2A 25.7% (28/109)
Ishizawa T
Retrospective26Adenoca.EZ shot 2 22G
ProCore 22G
AmpliSeq Comprehensive Cancer Panel
(409 genes)
N/AN/A100% (26/26)mean 171 ng
(range 34–478)
Panel Inspection
KRAS 92% (24/26)
TP53 50% (13/26)
SMAD4 31% (8/26)
CDKN2A 15% (4/26)
Kandel P
FNB 22G or 19G
(324 genes)
10 ngTissue slicing area ≥ 25 mm2
Tumor cell content ≥ 20%
25G FNA (14%)
FNB 22G/19G (78%)
mean 3.365 ng/μL
mean 5.930 ng/μL
Analysis Criteria
Carrara S
Prospective33Adenoca.Acquire 22GAmpliSeq Comprehensive Panel v3
161 genes
DNA concentration:
3.3 ng/μL
N/A97.0% (32/33)N/AActual
Panel Inspection
KRAS 94% (30/32)
TP53 78% (25/32)
SMAD4 13% (4/32)
CDKN2A 9% (3/32)
GNAS 9% (3/32)
Habib JR
Retrospective56Adenoca.N/AAmpliseq Custom Panel
9 genes
DNA concentration:
3.3 ng/μL
N/A100% (56/56)N/AActual
Panel Inspection
KRAS 85.7% (48/56)
TP53 32.1% (18/56)
SMAD4 3.6% (2/56)
CKDN2A 3.6% (2/56)
Ikeda G
Retrospective153Adenoca.19G FNB (n = 75)
22G FNB (n = 43)
22G FNA (n = 35)
NCC Oncopanel
(126 genes)
200 ngTissue slicing area ≥ 5 mm2
Tumor cell content ≥ 20%
39.0% (60/153 cases)
19G FNB (56%)
22G FNB (32.6%)
22G FNA (11.4%)
19G FNB1062.1 ng
22G FNB 411.32 ng
Analysis Criteria
KRAS 93.3% (28/30)
TP53 76.7% (23/30)
SMAD4 30.0% (9/30)
BRCA 10% (3/30)
Prospective33Adenoca.19G FNB
(Top Gain)
NCC Oncopanel
(126 genes)
200 ngTissue slicing area ≥ 5 mm2
Tumor cell content ≥ 20%
63.6% (12/33)N/AAnalysis Criteria
KRAS 100% (12/12)
TP53 66.7% (8/12)
SMAD4 66.7% (8/12)
GNAS 33.3% (4/12)
BRCA 25% (3/12)
Adenoca.: adenocarcinoma; EUS-TA: endoscopic ultrasound-guided tissue acquisition; FFPE: formalin-fixed paraffin-embedded; FNA: fine needle aspiration; FNB: fine needle biopsy; NEC: neuroendocrine carcinomas; NET: neuroendocrine tumor; NGS: next generation sequencing; PDAC: pancreatic ductal adenocarcinoma.
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Hijioka, S.; Nagashio, Y.; Maruki, Y.; Kawasaki, Y.; Takeshita, K.; Morizane, C.; Okusaka, T. Endoscopic Ultrasound-Guided Tissue Acquisition of Pancreaticobiliary Cancer Aiming for a Comprehensive Genome Profile. Diagnostics 2023, 13, 1275.

AMA Style

Hijioka S, Nagashio Y, Maruki Y, Kawasaki Y, Takeshita K, Morizane C, Okusaka T. Endoscopic Ultrasound-Guided Tissue Acquisition of Pancreaticobiliary Cancer Aiming for a Comprehensive Genome Profile. Diagnostics. 2023; 13(7):1275.

Chicago/Turabian Style

Hijioka, Susumu, Yoshikuni Nagashio, Yuta Maruki, Yuki Kawasaki, Kotaro Takeshita, Chigusa Morizane, and Takuji Okusaka. 2023. "Endoscopic Ultrasound-Guided Tissue Acquisition of Pancreaticobiliary Cancer Aiming for a Comprehensive Genome Profile" Diagnostics 13, no. 7: 1275.

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