Research ArticleExperimental Studies

Feasibility of Gastric Tumor Xenograft (GTX)-derived Cell Lines for Individualized Anti-cancer Drug Screening

SUNG EUN OH, MI YUN OH, SU MI KIM, SUN YOUNG KIM, JI YEONG AN, JUN HO LEE, TAE SUNG SOHN, JAE MOON BAE and MIN-GEW CHOI
Anticancer Research June 2022, 42 (6) 2883-2891; DOI: https://doi.org/10.21873/anticanres.15770

Abstract

Background/Aim: Because there are ongoing efforts to identify and develop novel drugs in the treatment of refractory gastric cancer, it is necessary to develop effective preclinical studies. Here, the preclinical efficacy of gastric tumor xenograft (GTX)-derived cell line models for the personalized treatment of gastric cancer was investigated. Materials and Methods: Anti-cancer drugs were scanned with high-throughput screening (HTS) using pre-established GTX-derived cell lines. The efficacy of a selected drug (afatinib) was re-confirmed in vivo and intracellular signaling pathways were investigated in xenograft tumor cell lysates using western blotting. Validation studies, such as cell proliferation and caspase activity assays, were also conducted in vitro with GTX-derived cell lines. Results: HTS indicated that afatinib was effective in one of the five GTX-derived cell lines (GTX-087). A xenograft mouse model was established from GTX-087, and administration of afatinib at 1 mg/20 g body weight/day per oral resulted in tumor-suppressive activity in vivo. The RAS-ERK pathway was inactivated by an increase in Bax and cleaved caspase-3 in this xenograft model. In vitro cell proliferation assay also revealed that afatinib was able to suppress the growth of the GTX-087 cell line. Caspase activity assay confirmed that afatinib had an apoptotic role on GTX-087 and showed that caspase-3/7 activity increased in a time dependent manner. Conclusion: The GTX-derived cell line model might be useful for estimating novel drug responses and could be an alternative to patient-derived xenograft animal models.

Key Words:

According to GLOBOCAN, the incidence of gastric cancer in Korea is the highest in the world (1). Gastric cancer is also the most commonly diagnosed cancer in Korea (2). Between 2011 and 2015, patients with gastric cancer showed outstanding improvement in 5-year survival rates (75.4%) (2). However, the prognosis is poor in advanced cases, such as unresectable, metastatic, or recurrent cancer. Therefore, there are ongoing efforts to identify and develop novel drugs in the treatment of refractory gastric cancer (3).

In clinical practice, systemic therapy for advanced gastric cancer is mainly composed of 5-fluorouracil and platinum-based agents. Trastuzumab (Herceptin) is added in patients with HER2 overexpression (4). Those with a poor response to first-line agents are considered candidates for second-line therapy, which includes novel target therapy (5, 6). Unfortunately, there are not many second-line drugs to choose from (3). In addition, the response to these drugs is unpredictable. Therefore, preclinical studies using in vitro and in vivo models are important for new drug development (7). Prediction of anti-tumor response to selected novel drugs in individual patients would also be extremely useful (8).

With this background and the need for effective preclinical studies, a patient-derived gastric cancer cell line model (PDC) was established and has been used for in vitro studies (9, 10). This cell line is less expensive and requires fewer resources than in vivo animal models (11). However, PDC is also limited by confounding factors, such as the tumor microenvironment, pharmacokinetics, and metabolism, leading to discordant results between in vitro and in vivo drug responses. Therefore, to verify the complex biologic effect of a drug, an in vivo study is required after in vitro screening (12). However, in the case of patient-derived xenografts (PDXs), high throughputs for various drugs are not feasible because of the large number of animals and test compounds required; this volume significantly increases the time, space, and cost involved (13).

A previous study demonstrated that xenografts lead to more efficient cancer cell line establishment than fresh tumors (in PDX-derived cell lines versus PDCs) (14). Moreover, cell lines derived from the same tumor fragment using both methods showed similarity in major phenotypic and genotypic characteristics. In the case of gastric cancer, PDX (defined as gastric tumor xenograft, GTX)-derived cell lines were established and were reported to have histological and molecular features consistent with those of the primary tumors (15).

The aim of this study is to investigate whether a GTX-derived cell line model analyzed with high-throughput screening (HTS) could efficiently replace the GTX animal model.

Materials and Methods

Chemicals and antibodies. Afatinib was synthesized by Selleckchem (BIBW2992). The antibodies against epidermal growth factor receptor (EGFR), phospho-EGFR (Tyr1173), HER-2, phospho-HER-2 (Thy1221), ERK1 and phospho-ERK1 (Tyr204), Bax and Bcl-xl were purchased from Cell Signaling Technology (Beverly, CA, USA). GAPDH was purchased from Chemicon International (Temecula, CA, USA).

Establishment of GTX-derived cell lines. The xenograft tumors originated from specimens surgically resected from patients with gastric cancer. The five cell lines (GTX-085, GTX-087, GTX-103, GTX-116 and GTX-139) used in our study were obtained from pathologically proven gastric carcinomas using the methods previously described (15) at the Gastric Cancer Center of Samsung Medical Center. The xenograft tumor tissues were mechanically and enzymatically dissociated. After washing with phosphate-buffered saline (PBS), the minced tissue was mixed with an enzyme cocktail of 0.4 mg/ml collagenase (Gibco, Waltham, MA, USA), 0.2 mg/ml dispase, and 0.5 mg/mL Dnase I. The cell lines were maintained in RPMI 1640 media (Hyclone, Logan, UT, USA) supplemented with 10% fetal bovine serum (FBS; Gibco) in an atmosphere of 5% CO2 at 37°C. The cells were incubated for two hours with shaking.

Cryopreservation of GTX-derived cell lines. Cells were collected in the exponential growth phase and centrifuged at 600 g for 5 min. The cells were then washed twice with PBS and resuspended at a concentration of 1×106 cells/ml in an ice-cooled CELL-BANKER (Zenoaq, Fukushima, Japan). They were then transferred to cryotube vials (Corning, Merck KGaA, Darmstadt, Germany). The samples were cooled at –130°C in a mechanical deep freeze.

Chemical screening and analysis of GTX-derived cell lines. HTS was performed to identify novel drugs that may be effective in all five cell lines. The GTX-derived cell lines were cultured and incubated for six days in serum-free media. Next, they were dissociated into single cells and seeded into 384-well plates with technical duplicates. The GTX-derived cell lines were treated with 43 drugs that target major oncogenetic signaling molecules (SeleckChem, Houston, TX, USA).

Xenografts in a murine model. Seven- to eight-week-old female Balb/c nude mice (OrientBio, Seoul, Republic of Korea) were cared for in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The GTX-derived cell line (GTX-087) was resuspended in a 1:1 mixture of PBS and Matrigel (BD Bio-Science, San Jose, CA, USA) to produce a final volume of 100 μl. This mixture, including GTX-087 (2×106 cells/50 μl) cells, was then injected into the flanks of the mice. When the tumor width reached 2.5-5 mm, eight animals were randomly selected and placed in the control (n=4) and treatment (n=4) groups. After regrouping, the mice were treated with afatinib (1 mg/20 g/day per oral) daily for 28 days. Tumor volume and body weight were monitored two times per week during treatment. The tumors were measured along two diameters using calipers. The tumor volume was calculated as follows: V=π/6[(D + d)/2] (4, 16), where D and d were the largest and smallest diameters, respectively. The results from two mice were excluded from statistical analysis due to unusual results. The use of animals in this study was reviewed and approved (SMC 20141230007) by the ethics committee of Institutional Animal Care and Use Committee (IACUC) in accordance with the institution’s rules and regulations. The study was carried out in compliance with the ARRIVE guidelines.

Western blot analysis. Cancer cells and adjacent tissues from the xenograft models were lysed in RIPA buffer and M-PER mammalian Protein Extract Reagent (Thermo) containing phosphatase inhibitors (Phosphatase Inhibitor Cocktail I and II; Sigma-Aldrich). Each cell lysate (25-100 μg) was separated by SDS-PAGE, and then transferred to nitrocellulose membranes (Whatman, Dassel, Germany) and incubated with the appropriate antibodies. Protein bands were detected using an Enhanced Chemiluminescence Blotting Analysis System from GE Healthcare (Chalfont St Giles, UK). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as the endogenous control in western blot analysis.

Cell proliferation assay with GTX-derived cell lines. First, 3×103 GTX-derived cells were seeded on a 96-well plate. The next day, the cells were treated with afatinib for 24 h. Cell viability was determined using a CellTiter 96 non-radioactive cell proliferation assay kit (Promega, Madison, WI, USA) as indicated by the manufacturer. The 50% inhibitory concentration (IC50) of the drug was determined from the survival curve.

Caspase activity assay with a GTX-derived cell line. The pre-selected GTX-derived cell line was cultured in RPMI-1640 medium supplemented with 10% FBS, 2 mM glutamine, and 1% penicillin-streptomycin in an atmosphere of 5% CO2 at 37°C. After the cells were cultured, we seeded them (3×103 cells/well) in white 96-well plates (Thermo Scientific, Rochester, NY, USA) at a total volume of 100 μl/well. The following day, cell supernatants were replaced by afatinib diluted in RPMI (Hyclone) media and incubated at 37°C in 5% CO2. After incubation, we added an equal volume of Caspase-Glo 3/7 assay reagent (Promega) and mixed it gently on an orbital shaker for about 30 s, and further incubated at 37°C in 5% CO2 for 60 min before reading. Luminescence was measured after 1 h with a Microplate Reader Mithras LB-943 (luminometer).

Statistical analysis. The Mann-Whitney U-test was used to compare the mean tumor volumes, and the quantitative protein expression between the control and treatment groups. The results were expressed with mean values. Variables with p-Values <0.05 were considered statistically significant. Statistical analysis was performed using SPSS version 25.0 for Windows (SPSS, Chicago, IL, USA).

Results

The study protocol is shown in Figure 1.

Figure 1.
Figure 1.

The experimental protocol for evaluating the efficacy of GTX-derived cell lines for anti-cancer drug screening. In the red box, we demonstrate the platform for anti-cancer drug screening. We followed a protocol used in a previous study for the establishment of GTX-derived cell lines. The preformed GTX-derived cell lines (GTX-085, GTX-087, GTX-103, GTX-116, and GTX-139) were used for HTS. To validate this newly established preclinical platform and demonstrate that it was a reliable and feasible preclinical model, we further conducted in vivo and in vitro (blue box) studies. In the in vivo study, we established a xenograft from the selected GTX-derived cell line (GTX-087) and treated it with the selected anti-cancer drug (afatinib). The retrieved tumor was used for western blotting analysis of apoptotic related proteins. In the in vitro study, we performed cell proliferation and caspase activity assays to confirm the tumor-suppressive and apoptotic effects of afatinib. HTS, High throughput screening; GTX, gastric tumor xenograft; PDX, patient-derived xenograft.

We selected GTX-087 as the main cell line, and afatinib as the investigational drug according to the results of HTS. The GTX-087 line demonstrated a more significant decrease in viability (less than 50% of cell viability) after EGFR target drugs (gefitinib, afatinib, and neratinib) than the other four cell lines (Figure 2).

Figure 2.
Figure 2.

HTS of 43 drugs in GTX-derived cell lines. This newly established preclinical platform, based on HTS, was used on five GTX-derived cell lines (GTX-085, GTX-087, GTX-103, GTX-116, and GTX-139). The responses of the cells to the 43 cytotoxic drugs were investigated. Among the cells, the GTX-087 line showed <50% cell viability to the EGFR target drugs (gefitinib, afatinib, and neratinib). HTS, High throughput screening; GTX, gastric tumor xenograft.

In the in vivo study using xenografts that were established from GTX-087, there was a trend where tumor volume increased less in the afatinib treatment group than it did in the control group (Figure 3). The tumor progressed and the volume increased by >60% (from baseline volume) in the control group, but only 28% in the treatment group. Afatinib demonstrated a tumor-suppressive effect in the xenografts. However, the number of mice was low, and the statistical significance was not reached.

Figure 3.
Figure 3.

Change of tumor volume in vivo (expressed as mean value and standard error bars). The afatinib treatment group showed slower cancer progression than the control group, indicating that afatinib has a tumor-suppressive effect.

The expression patterns of both EGFR and HER2 were evaluated after the tumors were removed from the xenografts (Figure 4A). There were no significant differences in the expression of EGFR and HER2 between the study groups. There was also no decrease in EGFR expression in the treatment group (Figure 4B).

Figure 4.
Figure 4.

Western blot analysis of EGFR, HER2 expression, and intracellular proteins. (A) The pattern of EGFR and HER2 expression did not significantly vary between the control and afatinib treatment groups. (B) Quantitative relative mean values of EGFR and HER2 from western blot analysis. (C) EGFR intracellular signaling proteins were identified using western blot analysis. The p-ERK/total ERK level was lower in the treatment group than in the control group. (D) The expression of apoptosis-inducing proteins (Bax and cleaved caspase-3) was higher in the treatment group than in the control group. (E) Quantitative relative mean values of intracellular proteins from the western blot analysis. Relative p-ERK levels: p-ERK/T-ERK; relative Bax levels: Bax/Bcl-xL; relative cleaved caspase-3 levels: cleaved caspase-3/GAPDH.

The intracellular signaling pathway of EGFR was investigated further in the xenograft tumor. The ratio of phosphorylated ERK (p-ERK) to total ERK level was lower in the treatment group than in the control group (Figure 4C and E). In addition, the treatment group demonstrated higher expression of Bax and cleaved caspase-3, which induce cancer cell death (apoptosis), than the control group. In contrast, the mean value of the pro/anti-apoptotic proteins (Bax/Bcl-xL) in the control group was lower than in the treatment group (Figure 4D and E).

In the cell proliferation assay using GTX-087, the number of viable cancer cells decreased as afatinib concentration increased (Figure 5A). We confirmed that afatinib had a tumor-suppressive effect using this assay. The IC50 dose of afatinib was 2.5 μg. Caspase levels were assessed at various time intervals ranging from 1 to 7 h post-afatinib treatment (2.5 μg). The results showed that levels of caspase activation increased in a time-dependent manner (Figure 5B). Luminescence showed an increase at 1 h, peak level at 3 h, and loss of activity after 5 h.

Figure 5.
Figure 5.

Cell proliferation assay and the caspase activity in GTX-087 treated with afatinib. (A) The cancer cell number [presented as mean value (%) with standard error bars] decreased as the drug dosage increased. The 50% inhibitory concentration (IC50) of afatinib was 2.5 μg (*significantly different at p<0.05). The cell number (%) on the y-axis was calculated as: (cell number in test dose/number in control) ×100. (B) Time course of afatinib-induced caspase activity in vitro. Caspase induction was measured in afatinib (2.5 μg)-treated cell cultures (GTX-087) using a Caspase-Glo 3/7 assay kit (Promega), and the results are shown as mean luminescence [measured in relative light units (RLU)] ± standard error for 3 replicate wells. *Significantly different at p<0.05.

Discussion

We have demonstrated that a newly established preclinical platform that scans multiple GTX-derived cell lines via HTS is effective in preclinical studies of drug selection. In experiments validating this platform, the tumor-suppressive and apoptotic effects of afatinib were shown both in vivo and in vitro. We predicted that inhibition of the EGFR pathway might play a role in afatinib’s mechanism of tumor cell death.

EGFR is part of the receptor tyrosine kinase (RTK) family. This family of proteins has been shown to play important roles in carcinogenesis (17). Although there is strong evidence of EGFR involvement, phase III trials have not found the promising results that were previously found in phase II and preclinical studies (18). This preclinical study showed that afatinib, an EGFR inhibitor, suppressed tumor growth by inhibiting its intracellular pathway and inducing apoptosis regardless of the level of EGFR and HER2 expression. In contrast to other experimental results, we did not find a significant change in EGFR and HER2 expression between the groups (19). Therefore, further studies are needed to explain this inconsistency.

Among the preclinical models currently being used to evaluate the efficacy of novel anti-cancer agents, PDCs are one of the most fundamental systems (13). To establish a more precise physiological platform with gastric cancer cell lines, HTS is necessary to test multiple drug candidates (20). During the screening process, multiple cancer cell lines with diverse genetic characteristics are tested with a multitude of novel drugs at different concentrations. Therefore, the HTS method must accommodate a large number of screening tests. However, there are concerns that PDCs may not be equivalent to the original tumor in scientific investigations (21). Radical changes in gene expression profiles occur during long periods of cell culture (22). Also, the lack of heterogeneity and tumor microenvironment might affect the complex pharmacodynamics of novel drugs. To address such concerns, PDX was introduced and has become an essential tool in overcoming the limitations of PDC.

PDXs can be established by directly transplanting surgical samples from cancer patients into immunocompromised mice. PDXs formed using tumor tissue might reflect the human tumor biological characteristics more precisely than a general xenograft formed from tumor cell lines (23). Several studies have shown that PDXs from tumor tissue maintain the histopathologic properties and genetic patterns of the tumor (24, 25). However, an immunosuppressed mouse does not produce a tumor microenvironment that is comparable to that of a human. In addition, humanized mice are difficult to produce and, consequently, expensive to obtain (26). Furthermore, the engraftment rate may differ according to the implanted site and the tumor origin (27). A critical disadvantage of PDX is that it requires more than four months to establish. Considering that patients with multidrug-resistant tumors typically survive less than a year (11), using PDX to assess a novel drug response in such patients is impractical. Therefore, clinicians and researchers in biomedical engineering have turned their attention to a new and improved PDC model (28).

Because performing HTS with PDX for various drugs is not feasible because of the large number of animals and test compounds required (13), and as PDCs are more technically difficult to directly establish from cancer tissues than xenograft-derived cell lines (14), we used GTX-derived cell lines established using a method described previously (15). Additionally, GTX could be extended to PDX animal models for further in vivo drug testing. These GTX-derived cell lines were shown to be unique and related to the primary tissue at the DNA level, as shown by fingerprinting analysis. Furthermore, they mirrored the histochemical and biochemical features of primary gastric tumors (15). In this study, we validated that the efficacy and action mechanism of a drug on a GTX-derived cell line was similar both in vivo and in vitro. Our results suggest that GTX-derived cell lines are a viable and advantageous model for preclinical testing.

This study is limited by the disadvantages of the cancer cell line model, as described. This screening technology did not produce a similar tumor microenvironment to that of an in vivo study, which is an important factor in pharmacokinetics. Factors such as the extracellular matrix, fluid shear stress, pH, and hypoxia have been investigated individually with regard to their role in drug sensitivity. However, the combination of these factors and their synergistic effects remain unclear with regard to treatment response. Therefore, the interaction between cancer cells and the stroma must be studied further (20). Using the tremendous amount of previous lab results, researchers could create a universal library of well-established GTX-derived cell lines and numerous novel agents to develop a more accurate screening platform. In addition, further experiments are needed to determine whether chemo-sensitivity is reproducible and to compare the results of preclinical and clinical trials. Lastly, as it was difficult to apply appropriate statistics because of a small number of xenografts, only tumor volume was compared in the in vivo study and only the amount of proteins compared in western blot analysis.

Conclusion

In conclusion, GTX-derived cell line analysis via the HTS method can be an effective drug screening tool that may provide a more convenient preclinical option than the PDX model with regard to time, cost and effort. In addition, GTX-derived cell lines can be established more easily and efficiently than PDCs. In patients with refractory gastric cancer and a poor prognosis, we recommend using this GTX-derived cell line model to identify potentially effective drugs. An in vivo xenograft model could be established simultaneously with individualized treatment of the patient, to confirm the drug effects and to further investigate the interaction between cancer cells and the drug.

Acknowledgements

This research was supported by a grant from the Korean Health Technology R&D Project through the Korean Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI14C3418).

Footnotes

  • Authors’ Contributions

    Min-Gew Choi contributed to the conception of this study and revised it critically. Sung Eun Oh and Mi Yun Oh collected and analyzed the data and drafted the manuscript. Mi Yun Oh, Su Mi Kim, and Sun Young Kim conducted the main experiment. Ji Yeong An, Jun Ho Lee, Tae Sung Sohn and Jae Moon Bae ensured that questions related to the accuracy or integrity of all parts of the work were investigated and resolved appropriately. All Authors approved the final version of the manuscript to be published.

  • Conflicts of Interest

    The Authors have no conflicts of interest to declare in relation to this study,

  • Received March 10, 2022.
  • Revision received April 16, 2022.
  • Accepted April 21, 2022.

References

  1. ↵
    2. Ferlay J ,
    3. Soerjomataram I ,
    4. Ervik M ,
    5. Dikshit R ,
    6. Eser S ,
    7. Mathers C ,
    8. Rebelo M ,
    9. Parkin DM ,
    10. Forman D and
    11. Bray F
    : GLOBOCAN 2012 v1.0: cancer incidence and mortality worldwide, 2013. Available at: https://gco.iarc.fr/ [Last accessed on May 17, 2021]
  2. ↵
    2. Jung KW ,
    3. Won YJ ,
    4. Kong HJ ,
    5. Lee ES and Community of Population-Based Regional Cancer Registries
    : Cancer statistics in Korea: Incidence, mortality, survival, and prevalence in 2015. Cancer Res Treat 50(2): 303-316, 2018. PMID: 29566481. DOI: 10.4143/crt.2018.143
    OpenUrlCrossRefPubMed
  3. ↵
    2. Jou E and
    3. Rajdev L
    : Current and emerging therapies in unresectable and recurrent gastric cancer. World J Gastroenterol 22(20): 4812-4823, 2016. PMID: 27239108. DOI: 10.3748/wjg.v22.i20.4812
    OpenUrlCrossRefPubMed
  4. ↵
    2. Ajani JA ,
    3. D’Amico TA ,
    4. Bentrem DJ ,
    5. Chao J ,
    6. Cooke D ,
    7. Corvera C ,
    8. Das P ,
    9. Enzinger PC ,
    10. Enzler T ,
    11. Fanta P ,
    12. Farjah F ,
    13. Gerdes H ,
    14. Gibson MK ,
    15. Hochwald S ,
    16. Hofstetter WL ,
    17. Ilson DH ,
    18. Keswani RN ,
    19. Kim S ,
    20. Kleinberg LR ,
    21. Klempner SJ ,
    22. Lacy J ,
    23. Ly QP ,
    24. Matkowskyj KA ,
    25. McNamara M ,
    26. Mulcahy MF ,
    27. Outlaw D ,
    28. Park H ,
    29. Perry KA ,
    30. Pimiento J ,
    31. Poultsides GA ,
    32. Reznik S ,
    33. Roses RE ,
    34. Strong VE ,
    35. Su S ,
    36. Wang HL ,
    37. Wiesner G ,
    38. Willett CG ,
    39. Yakoub D ,
    40. Yoon H ,
    41. McMillian N and
    42. Pluchino LA
    : Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 20(2): 167-192, 2022. PMID: 35130500. DOI: 10.6004/jnccn.2022.0008
    OpenUrlCrossRefPubMed
  5. ↵
    2. Fuchs CS ,
    3. Tomasek J ,
    4. Yong CJ ,
    5. Dumitru F ,
    6. Passalacqua R ,
    7. Goswami C ,
    8. Safran H ,
    9. Dos Santos LV ,
    10. Aprile G ,
    11. Ferry DR ,
    12. Melichar B ,
    13. Tehfe M ,
    14. Topuzov E ,
    15. Zalcberg JR ,
    16. Chau I ,
    17. Campbell W ,
    18. Sivanandan C ,
    19. Pikiel J ,
    20. Koshiji M ,
    21. Hsu Y ,
    22. Liepa AM ,
    23. Gao L ,
    24. Schwartz JD ,
    25. Tabernero J and REGARD Trial Investigators
    : Ramucirumab monotherapy for previously treated advanced gastric or gastro-oesophageal junction adenocarcinoma (REGARD): an international, randomised, multicentre, placebo-controlled, phase 3 trial. Lancet 383(9911): 31-39, 2014. PMID: 24094768. DOI: 10.1016/S0140-6736(13)61719-5
    OpenUrlCrossRefPubMed
  6. ↵
    2. Wilke H ,
    3. Muro K ,
    4. Van Cutsem E ,
    5. Oh SC ,
    6. Bodoky G ,
    7. Shimada Y ,
    8. Hironaka S ,
    9. Sugimoto N ,
    10. Lipatov O ,
    11. Kim TY ,
    12. Cunningham D ,
    13. Rougier P ,
    14. Komatsu Y ,
    15. Ajani J ,
    16. Emig M ,
    17. Carlesi R ,
    18. Ferry D ,
    19. Chandrawansa K ,
    20. Schwartz JD ,
    21. Ohtsu A and RAINBOW Study Group
    : Ramucirumab plus paclitaxel versus placebo plus paclitaxel in patients with previously treated advanced gastric or gastro-oesophageal junction adenocarcinoma (RAINBOW): a double-blind, randomised phase 3 trial. Lancet Oncol 15(11): 1224-1235, 2014. PMID: 25240821. DOI: 10.1016/S1470-2045(14)70420-6
    OpenUrlCrossRefPubMed
  7. ↵
    2. Cristescu R ,
    3. Lee J ,
    4. Nebozhyn M ,
    5. Kim KM ,
    6. Ting JC ,
    7. Wong SS ,
    8. Liu J ,
    9. Yue YG ,
    10. Wang J ,
    11. Yu K ,
    12. Ye XS ,
    13. Do IG ,
    14. Liu S ,
    15. Gong L ,
    16. Fu J ,
    17. Jin JG ,
    18. Choi MG ,
    19. Sohn TS ,
    20. Lee JH ,
    21. Bae JM ,
    22. Kim ST ,
    23. Park SH ,
    24. Sohn I ,
    25. Jung SH ,
    26. Tan P ,
    27. Chen R ,
    28. Hardwick J ,
    29. Kang WK ,
    30. Ayers M ,
    31. Hongyue D ,
    32. Reinhard C ,
    33. Loboda A ,
    34. Kim S and
    35. Aggarwal A
    : Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med 21(5): 449-456, 2015. PMID: 25894828. DOI: 10.1038/nm.3850
    OpenUrlCrossRefPubMed
  8. ↵
    2. Chang HR ,
    3. Park HS ,
    4. Ahn YZ ,
    5. Nam S ,
    6. Jung HR ,
    7. Park S ,
    8. Lee SJ ,
    9. Balch C ,
    10. Powis G ,
    11. Ku JL and
    12. Kim YH
    : Improving gastric cancer preclinical studies using diverse in vitro and in vivo model systems. BMC Cancer 16: 200, 2016. PMID: 26955870. DOI: 10.1186/s12885-016-2232-2
    OpenUrlCrossRefPubMed
  9. ↵
    2. Ku JL and
    3. Park JG
    : Biology of SNU cell lines. Cancer Res Treat 37(1): 1-19, 2005. PMID: 19956504. DOI: 10.4143/crt.2005.37.1.1
    OpenUrlCrossRefPubMed
  10. ↵
    2. Park JG ,
    3. Frucht H ,
    4. LaRocca RV ,
    5. Bliss DP Jr ,
    6. Kurita Y ,
    7. Chen TR ,
    8. Henslee JG ,
    9. Trepel JB ,
    10. Jensen RT and
    11. Johnson BE
    : Characteristics of cell lines established from human gastric carcinoma. Cancer Res 50(9): 2773-2780, 1990. PMID: 2158397.
    OpenUrlAbstract/FREE Full Text
  11. ↵
    2. Lee JY ,
    3. Kim SY ,
    4. Park C ,
    5. Kim NK ,
    6. Jang J ,
    7. Park K ,
    8. Yi JH ,
    9. Hong M ,
    10. Ahn T ,
    11. Rath O ,
    12. Schueler J ,
    13. Kim ST ,
    14. Do IG ,
    15. Lee S ,
    16. Park SH ,
    17. Ji YI ,
    18. Kim D ,
    19. Park JO ,
    20. Park YS ,
    21. Kang WK ,
    22. Kim KM ,
    23. Park WY ,
    24. Lim HY and
    25. Lee J
    : Patient-derived cell models as preclinical tools for genome-directed targeted therapy. Oncotarget 6(28): 25619-25630, 2015. PMID: 26296973. DOI: 10.18632/oncotarget.4627
    OpenUrlCrossRefPubMed
  12. ↵
    2. Phillips RM ,
    3. Bibby MC and
    4. Double JA
    : A critical appraisal of the predictive value of in vitro chemosensitivity assays. J Natl Cancer Inst 82(18): 1457-1468, 1990. PMID: 2202838. DOI: 10.1093/jnci/82.18.1457
    OpenUrlCrossRefPubMed
  13. ↵
    2. Sharma SV ,
    3. Haber DA and
    4. Settleman J
    : Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents. Nat Rev Cancer 10(4): 241-253, 2010. PMID: 20300105. DOI: 10.1038/nrc2820
    OpenUrlCrossRefPubMed
  14. ↵
    2. Dangles-Marie V ,
    3. Pocard M ,
    4. Richon S ,
    5. Weiswald LB ,
    6. Assayag F ,
    7. Saulnier P ,
    8. Judde JG ,
    9. Janneau JL ,
    10. Auger N ,
    11. Validire P ,
    12. Dutrillaux B ,
    13. Praz F ,
    14. Bellet D and
    15. Poupon MF
    : Establishment of human colon cancer cell lines from fresh tumors versus xenografts: comparison of success rate and cell line features. Cancer Res 67(1): 398-407, 2007. PMID: 17210723. DOI: 10.1158/0008-5472.CAN-06-0594
    OpenUrlAbstract/FREE Full Text
  15. ↵
    2. Song K ,
    3. Park J ,
    4. Kim H ,
    5. Kang M and
    6. Kim S
    : Establishment of highly tumorigenic human gastric carcinoma cell lines from xenograft tumors in mice. Biomedical Science Letters 23(3): 238-250, 2017. DOI: 10.15616/BSL.2017.23.3.238
    OpenUrlCrossRef
  16. ↵
    2. Chen LM ,
    3. Liang YJ ,
    4. Ruan JW ,
    5. Ding Y ,
    6. Wang XW ,
    7. Shi Z ,
    8. Gu LQ ,
    9. Yang XP and
    10. Fu LW
    : Reversal of P-gp mediated multidrug resistance in-vitro and in-vivo by FG020318. J Pharm Pharmacol 56(8): 1061-1066, 2004. PMID: 15285852. DOI: 10.1211/0022357043879
    OpenUrlCrossRefPubMed
  17. ↵
    2. Robinson DR ,
    3. Wu YM and
    4. Lin SF
    : The protein tyrosine kinase family of the human genome. Oncogene 19(49): 5548-5557, 2000. PMID: 11114734. DOI: 10.1038/sj.onc.1203957
    OpenUrlCrossRefPubMed
  18. ↵
    2. Aprile G ,
    3. Giampieri R ,
    4. Bonotto M ,
    5. Bittoni A ,
    6. Ongaro E ,
    7. Cardellino GG ,
    8. Graziano F ,
    9. Giuliani F ,
    10. Fasola G ,
    11. Cascinu S and
    12. Scartozzi M
    : The challenge of targeted therapies for gastric cancer patients: the beginning of a long journey. Expert Opin Investig Drugs 23(7): 925-942, 2014. PMID: 24806575. DOI: 10.1517/13543784.2014.912631
    OpenUrlCrossRefPubMed
  19. ↵
    2. Janjigian YY ,
    3. Viola-Villegas N ,
    4. Holland JP ,
    5. Divilov V ,
    6. Carlin SD ,
    7. Gomes-DaGama EM ,
    8. Chiosis G ,
    9. Carbonetti G ,
    10. de Stanchina E and
    11. Lewis JS
    : Monitoring afatinib treatment in HER2-positive gastric cancer with 18F-FDG and 89Zr-trastuzumab PET. J Nucl Med 54(6): 936-943, 2013. PMID: 23578997. DOI: 10.2967/jnumed.112.110239
    OpenUrlAbstract/FREE Full Text
  20. ↵
    2. Jo Y ,
    3. Choi N ,
    4. Kim K ,
    5. Koo HJ ,
    6. Choi J and
    7. Kim HN
    : Chemoresistance of cancer cells: Requirements of tumor microenvironment-mimicking in vitro models in anti-cancer drug development. Theranostics 8(19): 5259-5275, 2018. PMID: 30555545. DOI: 10.7150/thno.29098
    OpenUrlCrossRefPubMed
  21. ↵
    2. Gillet JP ,
    3. Varma S and
    4. Gottesman MM
    : The clinical relevance of cancer cell lines. J Natl Cancer Inst 105(7): 452-458, 2013. PMID: 23434901. DOI: 10.1093/jnci/djt007
    OpenUrlCrossRefPubMed
  22. ↵
    2. Gillet JP ,
    3. Calcagno AM ,
    4. Varma S ,
    5. Marino M ,
    6. Green LJ ,
    7. Vora MI ,
    8. Patel C ,
    9. Orina JN ,
    10. Eliseeva TA ,
    11. Singal V ,
    12. Padmanabhan R ,
    13. Davidson B ,
    14. Ganapathi R ,
    15. Sood AK ,
    16. Rueda BR ,
    17. Ambudkar SV and
    18. Gottesman MM
    : Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anticancer drug resistance. Proc Natl Acad Sci USA 108(46): 18708-18713, 2011. PMID: 22068913. DOI: 10.1073/pnas.1111840108
    OpenUrlAbstract/FREE Full Text
  23. ↵
    2. Jin K ,
    3. Teng L ,
    4. Shen Y ,
    5. He K ,
    6. Xu Z and
    7. Li G
    : Patient-derived human tumour tissue xenografts in immunodeficient mice: a systematic review. Clin Transl Oncol 12(7): 473-480, 2010. PMID: 20615824. DOI: 10.1007/s12094-010-0540-6
    OpenUrlCrossRefPubMed
  24. ↵
    2. Cho YB ,
    3. Hong HK ,
    4. Choi YL ,
    5. Oh E ,
    6. Joo KM ,
    7. Jin J ,
    8. Nam DH ,
    9. Ko YH and
    10. Lee WY
    : Colorectal cancer patient-derived xenografted tumors maintain characteristic features of the original tumors. J Surg Res 187(2): 502-509, 2014. PMID: 24332554. DOI: 10.1016/j.jss.2013.11.010
    OpenUrlCrossRefPubMed
  25. ↵
    2. Joo KM ,
    3. Kim J ,
    4. Jin J ,
    5. Kim M ,
    6. Seol HJ ,
    7. Muradov J ,
    8. Yang H ,
    9. Choi YL ,
    10. Park WY ,
    11. Kong DS ,
    12. Lee JI ,
    13. Ko YH ,
    14. Woo HG ,
    15. Lee J ,
    16. Kim S and
    17. Nam DH
    : Patient-specific orthotopic glioblastoma xenograft models recapitulate the histopathology and biology of human glioblastomas in situ. Cell Rep 3(1): 260-273, 2013. PMID: 23333277. DOI: 10.1016/j.celrep.2012.12.013
    OpenUrlCrossRefPubMed
  26. ↵
    2. Richmond A and
    3. Su Y
    : Mouse xenograft models vs GEM models for human cancer therapeutics. Dis Model Mech 1(2-3): 78-82, 2008. PMID: 19048064. DOI: 10.1242/dmm.000976
    OpenUrlFREE Full Text
  27. ↵
    2. Hidalgo M ,
    3. Amant F ,
    4. Biankin AV ,
    5. Budinská E ,
    6. Byrne AT ,
    7. Caldas C ,
    8. Clarke RB ,
    9. de Jong S ,
    10. Jonkers J ,
    11. Mælandsmo GM ,
    12. Roman-Roman S ,
    13. Seoane J ,
    14. Trusolino L and
    15. Villanueva A
    : Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov 4(9): 998-1013, 2014. PMID: 25185190. DOI: 10.1158/2159-8290.CD-14-0001
    OpenUrlAbstract/FREE Full Text
  28. ↵
    2. Mitra A ,
    3. Mishra L and
    4. Li S
    : Technologies for deriving primary tumor cells for use in personalized cancer therapy. Trends Biotechnol 31(6): 347-354, 2013. PMID: 23597659. DOI: 10.1016/j.tibtech.2013.03.006
    OpenUrlCrossRefPubMed
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Feasibility of Gastric Tumor Xenograft (GTX)-derived Cell Lines for Individualized Anti-cancer Drug Screening
SUNG EUN OH, MI YUN OH, SU MI KIM, SUN YOUNG KIM, JI YEONG AN, JUN HO LEE, TAE SUNG SOHN, JAE MOON BAE, MIN-GEW CHOI
Anticancer Research Jun 2022, 42 (6) 2883-2891; DOI: 10.21873/anticanres.15770
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