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Accueil Tous les numéros Volume 38 / Numéro 11 (Novembre 2022) Med Sci (Paris), 38 11 (2022) 888-895 Références
Organoïdes
Open Access
Numéro
Med Sci (Paris)
Volume 38, Numéro 11, Novembre 2022
Organoïdes
Page(s) 888 - 895
Section M/S Revues
DOI https://doi.org/10.1051/medsci/2022149
Publié en ligne 30 novembre 2022
  1. Ancevski Hunter K, Socinski MA, Villaruz LC. PD-L1 Testing in Guiding Patient Selection for PD-1/PD-L1 Inhibitor Therapy in Lung Cancer. Mol Diagn Ther 2018 ; 22 : 1-10. [CrossRef] [PubMed] [Google Scholar]
  2. Bailey MH, Tokheim C, Porta-Pardo E, et al. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 2018 ; 174 : 1034-5. [CrossRef] [PubMed] [Google Scholar]
  3. Letai A, Bhola P, Welm AL. Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell 2022 ; 40 : 26-35. [CrossRef] [PubMed] [Google Scholar]
  4. Mirza MR, Monk BJ, Herrstedt J, et al. Niraparib Maintenance Therapy in Platinum-Sensitive, Recurrent Ovarian Cancer. N Engl J Med 2016 ; 375 : 2154-64. [CrossRef] [PubMed] [Google Scholar]
  5. Federici G, Soddu S. Variants of uncertain significance in the era of high-throughput genome sequencing: a lesson from breast and ovary cancers. J Exp Clin Cancer Res 2020 ; 39 : 46. [CrossRef] [PubMed] [Google Scholar]
  6. Alkema NG, Wisman GB, van der Zee AG, et al. Studying platinum sensitivity and resistance in high-grade serous ovarian cancer: Different models for different questions. Drug Resist Updat 2016 ; 24 : 55-69. [CrossRef] [PubMed] [Google Scholar]
  7. Ugurel S, Schadendorf D, Pfohler C, et al. In vitro drug sensitivity predicts response and survival after individualized sensitivity-directed chemotherapy in metastatic melanoma: a multicenter phase II trial of the Dermatologic Cooperative Oncology Group. Clin Cancer Res 2006 ; 12 : 5454-63. [CrossRef] [PubMed] [Google Scholar]
  8. Weiswald LB, Bellet D, Dangles-Marie V. Spherical cancer models in tumor biology. Neoplasia 2015 ; 17 : 1-15. [CrossRef] [PubMed] [Google Scholar]
  9. Lheureux S, N’Diaye M, Blanc-Fournier C, et al. Identification of predictive factors of response to the BH3-mimetic molecule ABT-737: an ex vivo experiment in human serous ovarian carcinoma. Int J Cancer 2015 ; 136 : E340-50. [CrossRef] [PubMed] [Google Scholar]
  10. Majumder B, Baraneedharan U, Thiyagarajan S, et al. Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. Nat Commun 2015 ; 6 : 6169. [CrossRef] [PubMed] [Google Scholar]
  11. Vaira V, Fedele G, Pyne S, et al. Preclinical model of organotypic culture for pharmacodynamic profiling of human tumors. Proc Natl Acad Sci U S A 2010 ; 107 : 8352-6. [CrossRef] [PubMed] [Google Scholar]
  12. Sachs N, Clevers H. Organoid cultures for the analysis of cancer phenotypes. Curr Opin Genet Dev 2014 ; 24 : 68-73. [CrossRef] [PubMed] [Google Scholar]
  13. Weeber F, Ooft SN, Dijkstra KK, Voest EE. Tumor Organoids as a Pre-clinical Cancer Model for Drug Discovery. Cell Chem Biol 2017 ; 24 : 1092-100. [CrossRef] [PubMed] [Google Scholar]
  14. Verduin M, Hoeben A, De Ruysscher D, Vooijs M. Patient-Derived Cancer Organoids as Predictors of Treatment Response. Front Oncol 2021 ; 11 : 641980. [CrossRef] [PubMed] [Google Scholar]
  15. Wensink GE, Elias SG, Mullenders J, et al. Patient-derived organoids as a predictive biomarker for treatment response in cancer patients. NPJ Precis Oncol 2021 ; 5 : 30. [CrossRef] [PubMed] [Google Scholar]
  16. Vlachogiannis G, Hedayat S, Vatsiou A, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 2018 ; 359 : 920-6. [CrossRef] [PubMed] [Google Scholar]
  17. Beutel AK, Schutte L, Scheible J, et al. A Prospective Feasibility Trial to Challenge Patient-Derived Pancreatic Cancer Organoids in Predicting Treatment Response. Cancers (Basel) 2021 ; 13. [Google Scholar]
  18. Park M, Kwon J, Kong J, et al. A Patient-Derived Organoid-Based Radiosensitivity Model for the Prediction of Radiation Responses in Patients with Rectal Cancer. Cancers (Basel) 2021 ; 13. [PubMed] [Google Scholar]
  19. Driehuis E, Kolders S, Spelier S, et al. Oral Mucosal Organoids as a Potential Platform for Personalized Cancer Therapy. Cancer Discov 2019 ; 9 : 852-71. [CrossRef] [PubMed] [Google Scholar]
  20. Yao Y, Xu X, Yang L, et al. Patient-Derived Organoids Predict Chemoradiation Responses of Locally Advanced Rectal Cancer. Cell Stem Cell 2020 ; 26 : 17-26 e6. [CrossRef] [PubMed] [Google Scholar]
  21. Votanopoulos KI, Forsythe S, Sivakumar H, et al. Model of Patient-Specific Immune-Enhanced Organoids for Immunotherapy Screening: Feasibility Study. Ann Surg Oncol 2020 ; 27 : 1956-67. [CrossRef] [PubMed] [Google Scholar]
  22. Forsythe SD, Erali RA, Sasikumar S, et al. Organoid Platform in Preclinical Investigation of Personalized Immunotherapy Efficacy in Appendiceal Cancer: Feasibility Study. Clin Cancer Res 2021 ; 27 : 5141-50. [CrossRef] [PubMed] [Google Scholar]
  23. Hill SJ, Decker B, Roberts EA, et al. Prediction of DNA Repair Inhibitor Response in Short-Term Patient-Derived Ovarian Cancer Organoids. Cancer Discov 2018 ; 8 : 1404-21. [CrossRef] [PubMed] [Google Scholar]
  24. Kopper O, de Witte CJ, Lohmussaar K, et al. An organoid platform for ovarian cancer captures intra- and interpatient heterogeneity. Nat Med 2019 ; 25 : 838-49. [CrossRef] [PubMed] [Google Scholar]
  25. Mauri G, Durinikova E, Amatu A, et al. Empowering Clinical Decision Making in Oligometastatic Colorectal Cancer: The Potential Role of Drug Screening of Patient-Derived Organoids. JCO Precis Oncol 2021 ; 5. [PubMed] [Google Scholar]
  26. Ooft SN, Weeber F, Dijkstra KK, et al. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci Transl Med 2019 ; 11. [PubMed] [Google Scholar]
  27. Ooft SN, Weeber F, Schipper L, et al. Prospective experimental treatment of colorectal cancer patients based on organoid drug responses. ESMO Open 2021 ; 6 : 100103. [CrossRef] [PubMed] [Google Scholar]
  28. Braun LM, Lagies S, Klar RFU, et al. Metabolic Profiling of Early and Late Recurrent Pancreatic Ductal Adenocarcinoma Using Patient-Derived Organoid Cultures. Cancers (Basel) 2020 ; 12. [PubMed] [Google Scholar]
  29. Kong J, Lee H, Kim D, et al. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat Commun 2020 ; 11 : 5485. [CrossRef] [PubMed] [Google Scholar]
  30. Li L, Knutsdottir H, Hui K, et al. Human primary liver cancer organoids reveal intratumor and interpatient drug response heterogeneity. JCI Insight 2019 ; 4. [PubMed] [Google Scholar]
  31. de Witte CJ, Espejo Valle-Inclan J, Hami N, et al. Patient-Derived Ovarian Cancer Organoids Mimic Clinical Response and Exhibit Heterogeneous Inter- and Intrapatient Drug Responses. Cell Rep 2020 ; 31 : 107762. [CrossRef] [PubMed] [Google Scholar]
  32. Tiriac H, Belleau P, Engle DD, et al. Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer. Cancer Discov 2018 ; 8 : 1112-29. [CrossRef] [PubMed] [Google Scholar]
  33. Kim M, Mun H, Sung CO, et al. Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening. Nat Commun 2019 ; 10 : 3991. [CrossRef] [PubMed] [Google Scholar]
  34. Dijkstra KK, Monkhorst K, Schipper LJ, et al. Challenges in Establishing Pure Lung Cancer Organoids Limit Their Utility for Personalized Medicine. Cell Rep 2020 ; 31 : 107588. [CrossRef] [PubMed] [Google Scholar]
  35. Colella G, Fazioli F, Gallo M, et al. Sarcoma Spheroids and Organoids-Promising Tools in the Era of Personalized Medicine. Int J Mol Sci 2018 ; 19. [Google Scholar]
  36. Driehuis E, Kretzschmar K, Clevers H. Establishment of patient-derived cancer organoids for drug-screening applications. Nat Protoc 2020 ; 15 : 3380-409. [CrossRef] [PubMed] [Google Scholar]
  37. Larsen BM, Kannan M, Langer LF, et al. A pan-cancer organoid platform for precision medicine. Cell Rep 2021 ; 36 : 109429. [CrossRef] [PubMed] [Google Scholar]
  38. Hu Y, Sui X, Song F, et al. Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week. Nat Commun 2021 ; 12 : 2581. [CrossRef] [PubMed] [Google Scholar]
  39. Larsen BM, Cancino A, Shaxted JM, Salahudeen AA. Protocol for drug screening of patient-derived tumor organoids using high-content fluorescent imaging. STAR Protoc 2022 ; 3 : 101407. [CrossRef] [PubMed] [Google Scholar]
  40. Cattaneo CM, Dijkstra KK, Fanchi LF, et al. Tumor organoid-T-cell coculture systems. Nat Protoc 2020 ; 15 : 15-39. [CrossRef] [PubMed] [Google Scholar]
  41. Tsai S, McOlash L, Palen K, et al. Development of primary human pancreatic cancer organoids, matched stromal and immune cells and 3D tumor microenvironment models. BMC Cancer 2018 ; 18 : 335. [CrossRef] [PubMed] [Google Scholar]
  42. Truelsen SLB, Mousavi N, Wei H, et al. The cancer angiogenesis co-culture assay: In vitro quantification of the angiogenic potential of tumoroids. PLoS One 2021 ; 16 : e0253258. [CrossRef] [PubMed] [Google Scholar]

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