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Genomic comparison between an in vitro three-dimensional culture model of melanoma and the original primary tumor

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Abstract

Three-dimensional (3D) melanoma culture is a personalized in vitro model that can be used for high-fidelity pre-clinical testing and validation of novel therapies. However, whether the genomic landscape of 3D cultures faithfully reflects the original primary tumor which remains unknown. The purpose of our study was to compare the genomic landscapes of 3D culture models with those of the original tumors. Patient-derived xenograft (PDX) tumors were established by engrafting fresh melanoma tissue from each patient. Then, a 3D culture model was generated using cryopreserved PDX tumors embedded in pre-gelled porcine skin decellularized extracellular matrix with normal human dermal fibroblasts. Using whole-exome sequencing, the genomic landscapes of 3D cultures, PDX tumors, and the original tumor were compared. We found that 91.4% of single-nucleotide variants in the original tumor were detected in the 3D culture and PDX samples. Putative melanoma driver mutations (BRAF p.V600E, CDKN2A p.R7*, ADAMTS1 p.Q572*) were consistently identified in both the original tumor and 3D culture samples. Genome-wide copy number alteration profiles were almost identical between the original tumor and 3D culture samples, including the driver events of ARID1B loss, BRAF gain, and CCND1 gain. In conclusion, our study revealed that the genomic profiles of the original tumor and our 3D culture model showed high concordance, indicating the reliability of our 3D culture model in reflecting the original characteristics of the tumor.

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Data availability

Raw sequencing data were deposited in the Sequence Read Archive (SRA) database (Project ID: PRJNA813613).

References

  1. Welch HG, Mazer BL, Adamson AS (2021) The rapid rise in cutaneous melanoma diagnoses. N Engl J Med 384:72–79

    Article  PubMed  Google Scholar 

  2. Curti BD, Faries MB (2021) Recent advances in the treatment of melanoma. N Engl J Med 384:2229–2240

    Article  CAS  PubMed  Google Scholar 

  3. Switzer B, Puzanov I, Skitzki JJ, Hamad L, Ernstoff MS (2022) Managing metastatic melanoma in 2022: a clinical review. JCO Oncol Practice 18:335–351

    Article  Google Scholar 

  4. Alzeeb G, Metges JP, Corcos L, Le Jossic-Corcos C: Three-Dimensional Culture Systems in Gastric Cancer Research. Cancers (Basel) 2020, 12.

  5. Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT, Caldas C, Clarke RB, de Jong S, Jonkers J, Mælandsmo GM et al (2014) Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov 4:998–1013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Herter-Sprie GS, Kung AL, Wong KK (2013) New cast for a new era: preclinical cancer drug development revisited. J Clin Invest 123:3639–3645

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. LeBlanc VG, Trinh DL, Aslanpour S, Hughes M, Livingstone D, Jin D, Ahn BY, Blough MD, Cairncross JG, Chan JA et al (2022) Single-cell landscapes of primary glioblastomas and matched explants and cell lines show variable retention of inter- and intratumor heterogeneity. Cancer Cell 40:379-392.e379

    Article  CAS  PubMed  Google Scholar 

  8. Powley IR, Patel M, Miles G, Pringle H, Howells L, Thomas A, Kettleborough C, Bryans J, Hammonds T, MacFarlane M, Pritchard C (2020) Patient-derived explants (PDEs) as a powerful preclinical platform for anti-cancer drug and biomarker discovery. Br J Cancer 122:735–744

    Article  PubMed  PubMed Central  Google Scholar 

  9. Templeton AR, Jeffery PL, Thomas PB, Perera MPJ, Ng G, Calabrese AR, Nicholls C, Mackenzie NJ, Wood J, Bray LJ et al (2021) Patient-derived explants as a precision medicine patient-proximal testing platform informing cancer management. Front Oncol 11:767697

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jeong YM, Bang C, Park M, Shin S, Yun S, Kim CM, Jeong G, Chung YJ, Yun WS, Lee JH, Jin S: 3D-Printed Collagen Scaffolds Promote Maintenance of Cryopreserved Patients-Derived Melanoma Explants. Cells 2021, 10.

  11. Pati F, Jang J, Ha DH, Won Kim S, Rhie JW, Shim JH, Kim DH, Cho DW (2014) Printing three-dimensional tissue analogues with decellularized extracellular matrix bioink. Nat Commun 5:3935

    Article  CAS  PubMed  Google Scholar 

  12. Ahn G, Min KH, Kim C, Lee JS, Kang D, Won JY, Cho DW, Kim JY, Jin S, Yun WS, Shim JH (2017) Precise stacking of decellularized extracellular matrix based 3D cell-laden constructs by a 3D cell printing system equipped with heating modules. Sci Rep 7:8624

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kim BS, Kwon YW, Kong JS, Park GT, Gao G, Han W, Kim MB, Lee H, Kim JH, Cho DW (2018) 3D cell printing of in vitro stabilized skin model and in vivo pre-vascularized skin patch using tissue-specific extracellular matrix bioink: A step towards advanced skin tissue engineering. Biomaterials 168:38–53

    Article  CAS  PubMed  Google Scholar 

  14. Won JY, Lee MH, Kim MJ, Min KH, Ahn G, Han JS, Jin S, Yun WS, Shim JH (2019) A potential dermal substitute using decellularized dermis extracellular matrix derived bio-ink. Artif Cells Nanomed Biotechnol 47:644–649

    Article  CAS  PubMed  Google Scholar 

  15. Park W, Gao G, Cho DW: Tissue-Specific Decellularized Extracellular Matrix Bioinks for Musculoskeletal Tissue Regeneration and Modeling Using 3D Bioprinting Technology. Int J Mol Sci 2021, 22.

  16. Kim YS, Shin S, Jung SH, Park YM, Park GS, Lee SH, Chung YJ (2022) Genomic progression of precancerous actinic keratosis to squamous cell carcinoma. J Invest Dermatol 142:528-538.e528

    Article  CAS  PubMed  Google Scholar 

  17. Van der Auwera GA OCB: Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (1st Edition). O'Reilly Media; 2020.

  18. Okonechnikov K, Conesa A, García-Alcalde F (2015) Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32:292–294

    Article  PubMed  PubMed Central  Google Scholar 

  19. Shen R, Seshan VE (2016) FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res 44:e131

    Article  PubMed  PubMed Central  Google Scholar 

  20. Jo SY, Kim E, Kim S (2019) Impact of mouse contamination in genomic profiling of patient-derived models and best practice for robust analysis. Genome Biol 20:231

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yang H, Wang K (2015) Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat Protoc 10:1556–1566

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Brash DE (2015) UV signature mutations. Photochem Photobiol 91:15–26

    Article  CAS  PubMed  Google Scholar 

  23. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H: Twelve years of SAMtools and BCFtools. Gigascience 2021, 10.

  24. Hodis E, Watson IR, Kryukov GV, Arold ST, Imielinski M, Theurillat JP, Nickerson E, Auclair D, Li L, Place C et al (2012) A landscape of driver mutations in melanoma. Cell 150:251–263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hayward NK, Wilmott JS, Waddell N, Johansson PA, Field MA, Nones K, Patch AM, Kakavand H, Alexandrov LB, Burke H et al (2017) Whole-genome landscapes of major melanoma subtypes. Nature 545:175–180

    Article  CAS  PubMed  Google Scholar 

  26. Zack TI, Schumacher SE, Carter SL, Cherniack AD, Saksena G, Tabak B, Lawrence MS, Zhsng CZ, Wala J, Mermel CH et al (2013) Pan-cancer patterns of somatic copy number alteration. Nat Genet 45:1134–1140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ben-David U, Ha G, Tseng Y-Y, Greenwald NF, Oh C, Shih J, McFarland JM, Wong B, Boehm JS, Beroukhim R, Golub TR (2017) Patient-derived xenografts undergo mouse-specific tumor evolution. Nat Genet 49:1567–1575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported by grants from National Research Foundation of Korea (2020R1C1C11011296, RS-2022-00165497, and 2019R1A5A2027588). This research was supported by a grant of the MD-Phd/Medical Scientist Training Program through the Korea Health Industry Development Institue (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea. The biospecimens for this study were provided by The Biobank of Seoul St. Mary's Hospital, the Catholic University of Korea. 

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Conception and design: Y.-J. Chung, C.-H. Bang Development of methodology: S. Jin, Y.-J. Chung, C.-H. Bang Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): All authors Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): All authors Writing, review, and/or revision of the manuscript: Y.-S. Kim, Y.-J. Chung, C.-H. Bang Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Y.-J. Chung, C.-H. Bang Study supervision: Y.-J. Chung, C.-H. Bang

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Correspondence to Yeun-Jun Chung or Chul Hwan Bang.

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Kim, YS., Park, M., Jin, S. et al. Genomic comparison between an in vitro three-dimensional culture model of melanoma and the original primary tumor. Arch Dermatol Res 315, 1225–1231 (2023). https://doi.org/10.1007/s00403-022-02502-4

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  • DOI: https://doi.org/10.1007/s00403-022-02502-4

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