Prospects and challenges of ovarian cancer organoids in chemotherapy research (Review)
- Authors:
- Weijia Zhang
- Yuqing Ding
- Hui He
- Keming Chen
- Qingsong Zeng
- Xiaoming Cao
- Ying Xiang
- Hai Zeng
-
Affiliations: Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei 434023, P.R. China, Department of Gynecology and Obstetrics, First Affiliated Hospital of Yangtze University, Jingzhou, Hubei 434023, P.R. China, Department of Cell Biology and Medical Genetics, School of Basic Medicine, Health Science Center, Yangtze University, Jingzhou, Hubei 434023, P.R. China - Published online on: February 24, 2025 https://doi.org/10.3892/ol.2025.14944
- Article Number: 198
-
Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
This article is mentioned in:
Abstract
 |
 |
|
Kuroki L and Guntupalli SR: Treatment of epithelial ovarian cancer. BMJ. 371:m37732020. View Article : Google Scholar : PubMed/NCBI | |
|
Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I and Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 74:229–263. 2024. View Article : Google Scholar : PubMed/NCBI | |
|
Matulonis UA, Sood AK, Fallowfield L, Howitt BE, Sehouli J and Karlan BY: Ovarian cancer. Nat Rev Dis Primers. 2:160612016. View Article : Google Scholar : PubMed/NCBI | |
|
American Cancer Society: Cancer Facts and Figures 2023. American Cancer Society; Atlanta, GA: 2023 | |
|
Liberto JM, Chen SY, Shih IM, Wang TH, Wang TL and Pisanic TR II: Current and emerging methods for ovarian cancer screening and diagnostics: A comprehensive review. Cancers (Basel). 14:28852022. View Article : Google Scholar : PubMed/NCBI | |
|
Kikuchi Y, Kita T, Takano M, Kudoh K and Yamamoto K: Treatment options in the management of ovarian cancer. Expert Opin Pharmacother. 6:743–754. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Bookman MA: First-line chemotherapy in epithelial ovarian cancer. Clin Obstet Gynecol. 55:96–113. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Ozols RF, Bundy BN, Greer BE, Fowler JM, Clarke-Pearson D, Burger RA, Mannel RS, DeGeest K, Hartenbach EM and Baergen R: Phase III trial of carboplatin and paclitaxel compared with cisplatin and paclitaxel in patients with optimally resected stage III ovarian cancer: A gynecologic oncology group study. J Clin Oncol. 41:4077–4083. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Kyrgiou M, Salanti G, Pavlidis N, Paraskevaidis E and Ioannidis JP: Survival benefits with diverse chemotherapy regimens for ovarian cancer: Meta-analysis of multiple treatments. J Natl Cancer Inst. 98:1655–1663. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Kim SI, Cho J, Lee EJ, Park S, Park SJ, Seol A, Lee N, Yim GW, Lee M, Lim W, et al: Selection of patients with ovarian cancer who may show survival benefit from hyperthermic intraperitoneal chemotherapy: A systematic review and meta-analysis. Medicine (Baltimore). 98:e183552019. View Article : Google Scholar : PubMed/NCBI | |
|
Ozols RF: Challenges for chemotherapy in ovarian cancer. Ann Oncol. 17 (Suppl 5):v181–v187. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Fung-Kee-Fung M, Oliver T, Elit L, Oza A, Hirte HW and Bryson P: Optimal chemotherapy treatment for women with recurrent ovarian cancer. Curr Oncol. 14:195–208. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Pokhriyal R, Hariprasad R, Kumar L and Hariprasad G: Chemotherapy resistance in advanced ovarian cancer patients. Biomark Cancer. 11:1179299×198608152019. View Article : Google Scholar : PubMed/NCBI | |
|
Cornelison R, Llaneza DC and Landen CN: Emerging therapeutics to overcome chemoresistance in epithelial ovarian cancer: A mini-review. Int J Mol Sci. 18:21712017. View Article : Google Scholar : PubMed/NCBI | |
|
Baker BM and Chen CS: Deconstructing the third dimension: How 3D culture microenvironments alter cellular cues. J Cell Sci. 125:3015–3024. 2012.PubMed/NCBI | |
|
Zhang Z, Bédard E, Luo Y, Wang H, Deng S, Kelvin D and Zhong R: Animal models in xenotransplantation. Expert Opin Investig Drugs. 9:2051–2068. 2000. View Article : Google Scholar : PubMed/NCBI | |
|
Bertotti A, Migliardi G, Galimi F, Sassi F, Torti D, Isella C, Corà D, Di Nicolantonio F, Buscarino M, Petti C, et al: A molecularly annotated platform of patient-derived xenografts (‘xenopatients’) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1:508–523. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
DeRose YS, Wang G, Lin YC, Bernard PS, Buys SS, Ebbert MT, Factor R, Matsen C, Milash BA, Nelson E, et al: Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat Med. 17:1514–1520. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, Balgobind AV, Wind K, Gracanin A, Begthel H, et al: A living biobank of breast cancer organoids captures disease heterogeneity. Cell. 172:373–386.e310. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Zanoni M, Cortesi M, Zamagni A, Arienti C, Pignatta S and Tesei A: Modeling neoplastic disease with spheroids and organoids. J Hematol Oncol. 13:972020. View Article : Google Scholar : PubMed/NCBI | |
|
Graham O, Rodriguez J, van Biljon L, Fashemi B, Graham E, Fuh K, Khabele D and Mullen M: Generation and culturing of high-grade serous ovarian cancer patient-derived organoids. J Vis Exp. 6:1912023. | |
|
Yani W, Qi J, Yuchen Z and Haiyan Z: Application of organoids technology in drug sensitivity test of ovarian cancer. J Int Obstet Gynecol. 49:181–185. 2022. | |
|
Yujie S, Hong Y, Jia L and Ying X: Application prospects on organoid culture system in drug screening and treatment target for ovarian cancer. J Chin Oncol. 28:1042–1045. 2022.(In Chinese). | |
|
Jianjun G, Wei Q, Hao W and Xiangyu Z: Application and prospect of organoid technique in cancer research. Chin J Tissue Engineering Res. 23:1136–1141. 2019.(In Chinese). | |
|
Aihara A, Abe N, Saruhashi K, Kanaki T and Nishino T: Novel 3-D cell culture system for in vitro evaluation of anticancer drugs under anchorage-independent conditions. Cancer Sci. 107:1858–1866. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Ben-David U, Ha G, Tseng YY, Greenwald NF, Oh C, Shih J, McFarland JM, Wong B, Boehm JS, Beroukhim R and Golub TR: Patient-derived xenografts undergo mouse-specific tumor evolution. Nat Genet. 49:1567–1575. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Byrne AT, Alférez DG, Amant F, Annibali D, Arribas J, Biankin AV, Bruna A, Budinská E, Caldas C, Chang DK, et al: Interrogating open issues in cancer medicine with patient-derived xenografts. Nat Rev Cancer. 17:6322017. View Article : Google Scholar : PubMed/NCBI | |
|
Sachs N and Clevers H: Organoid cultures the analysis of cancer phenotypes. Curr Opin Genet Dev. 24:68–73. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Bleijs M, van de Wetering M, Clevers H and Drost J: Xenograft and organoid model systems in cancer research. EMBO J. 38:e1016542019. View Article : Google Scholar : PubMed/NCBI | |
|
Wensink GE, Elias SG, Mullenders J, Koopman M, Boj SF, Kranenburg OW and Roodhart JML: Patient-derived organoids as a predictive biomarker for treatment response in cancer patients. NPJ Precis Oncol. 5:302021. View Article : Google Scholar : PubMed/NCBI | |
|
Perkhofer L, Frappart PO, Müller M and Kleger A: Importance of organoids for personalized medicine. Per Med. 15:461–465. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Rossi G, Manfrin A and Lutolf MP: Progress and potential in organoid research. Nat Rev Genet. 19:671–687. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Tsang SI, Hassan AA, To SKY and Wong AST: Experimental models for ovarian cancer research. Exp Cell Res. 416:1131502022. View Article : Google Scholar : PubMed/NCBI | |
|
Yang J, Huang S, Cheng S, Jin Y, Zhang N and Wang Y: Application of ovarian cancer organoids in precision medicine: Key challenges and current opportunities. Front Cell Dev Biol. 9:7014292021. View Article : Google Scholar : PubMed/NCBI | |
|
Aboulkheyr Es H, Montazeri L, Aref AR, Vosough M and Baharvand H: Personalized cancer medicine: An organoid approach. Trends Biotechnol. 36:358–371. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Maenhoudt N, Defraye C, Boretto M, Jan Z, Heremans R, Boeckx B, Hermans F, Arijs I, Cox B, Van Nieuwenhuysen E, et al: developing organoids from ovarian cancer as experimental and preclinical models. Stem Cell Reports. 14:717–729. 2020. View Article : Google Scholar : PubMed/NCBI | |
|
Kopper O, de Witte CJ, Lõhmussaar K, Valle-Inclan JE, Hami N, Kester L, Balgobind AV, Korving J, Proost N, Begthel H, et al: An organoid platform for ovarian cancer captures intra- and interpatient heterogeneity. Nat Med. 25:838–849. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Pauli C, Hopkins BD, Prandi D, Shaw R, Fedrizzi T, Sboner A, Sailer V, Augello M, Puca L, Rosati R, et al: Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7:462–477. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Jabs J, Zickgraf FM, Park J, Wagner S, Jiang X, Jechow K, Kleinheinz K, Toprak UH, Schneider MA, Meister M, et al: Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations. Mol Syst Biol. 13:9552017. View Article : Google Scholar : PubMed/NCBI | |
|
Maru Y, Tanaka N, Itami M and Hippo Y: Efficient use of patient-derived organoids as a preclinical model for gynecologic tumors. Gynecol Oncol. 154:189–198. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Hill SJ, Decker B, Roberts EA, Horowitz NS, Muto MG, Worley MJ Jr, Feltmate CM, Nucci MR, Swisher EM, Nguyen H, et al: Prediction of DNA repair inhibitor response in short-term patient-derived ovarian cancer organoids. Cancer Discov. 8:1404–1421. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Nanki Y, Chiyoda T, Hirasawa A, Ookubo A, Itoh M, Ueno M, Akahane T, Kameyama K, Yamagami W, Kataoka F and Aoki D: Patient-derived ovarian cancer organoids capture the genomic profiles of primary tumours applicable for drug sensitivity and resistance testing. Sci Rep. 10:125812020. View Article : Google Scholar : PubMed/NCBI | |
|
Hoffmann K, Berger H, Kulbe H, Thillainadarasan S, Mollenkopf HJ, Zemojtel T, Taube E, Darb-Esfahani S, Mangler M, Sehouli J, et al: Stable expansion of high-grade serous ovarian cancer organoids requires a low-Wnt environment. EMBO J. 39:e1040132020. View Article : Google Scholar : PubMed/NCBI | |
|
Kondo J and Inoue M: application of cancer organoid model for drug screening and personalized therapy. Cells. 8:4702019. View Article : Google Scholar : PubMed/NCBI | |
|
Antoni D, Burckel H, Josset E and Noel G: Three-dimensional cell culture: A breakthrough in vivo. Int J Mol Sci. 16:5517–5527. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Kimlin LC, Casagrande G and Virador VM: In vitro three-dimensional (3D) models in cancer research: An update. Mol Carcinog. 52:167–182. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Shoemaker RH: The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer. 6:813–823. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Rizvanov AA, Yalvaç ME, Shafigullina AK, Salafutdinov II, Blatt NL, Sahin F, Kiyasov AP and Palotás A: Interaction and self-organization of human mesenchymal stem cells and neuro-blastoma SH-SY5Y cells under co-culture conditions: A novel system for modeling cancer cell micro-environment. Eur J Pharm Biopharm. 76:253–259. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Enmon RM Jr, O'Connor KC, Lacks DJ, Schwartz DK and Dotson RS: Dynamics of spheroid self-assembly in liquid-overlay culture of DU 145 human prostate cancer cells. Biotechnol Bioeng. 72:579–591. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Westhouse RA: Safety assessment considerations and strategies for targeted small molecule cancer therapeutics in drug discovery. Toxicol Pathol. 38:165–168. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Wong CC, Cheng KW and Rigas B: Preclinical predictors of anticancer drug efficacy: Critical assessment with emphasis on whether nanomolar potency should be required of candidate agents. J Pharmacol Exp Ther. 341:572–578. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Ravi M, Paramesh V, Kaviya SR, Anuradha E and Solomon FD: 3D cell culture systems: Advantages and applications. J Cell Physiol. 230:16–26. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Jamieson LE, Harrison DJ and Campbell CJ: Chemical analysis of multicellular tumour spheroids. Analyst. 140:3910–3920. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Beningo KA, Dembo M and Wang Yl: Responses of fibroblasts to anchorage of dorsal extracellular matrix receptors. Proc Natl Acad Sci USA. 101:18024–18029. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Sambale F, Lavrentieva A, Stahl F, Blume C, Stiesch M, Kasper C, Bahnemann D and Scheper T: Three dimensional spheroid cell culture for nanoparticle safety testing. J Biotechnol. 205:120–129. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Jarockyte G, Dapkute D, Karabanovas V, Daugmaudis JV, Ivanauskas F and Rotomskis R: 3D cellular spheroids as tools for understanding carboxylated quantum dot behavior in tumors. Biochim Biophys Acta Gen Subj. 1862:914–923. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Mehta G, Hsiao AY, Ingram M, Luker GD and Takayama S: Opportunities and challenges for use of tumor spheroids as models to test drug delivery and efficacy. J Control Release. 164:192–204. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Souza AG, Silva IBB, Campos-Fernandez E, Barcelos LS, Souza JB, Marangoni K, Goulart LR and Alonso-Goulart V: Comparative assay of 2D and 3D cell culture models: Proliferation, gene expression and anticancer drug response. Curr Pharm Des. 24:1689–1694. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Breslin S and O'Driscoll L: The relevance of using 3D cell cultures, in addition to 2D monolayer cultures, when evaluating breast cancer drug sensitivity and resistance. Oncotarget. 7:45745–45756. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Verjans ET, Doijen J, Luyten W, Landuyt B and Schoofs L: Three-dimensional cell culture models for anticancer drug screening: Worth the effort? J Cell Physiol. 233:2993–3003. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Hirst J, Pathak HB, Hyter S, Pessetto ZY, Ly T, Graw S, Koestler DC, Krieg AJ, Roby KF and Godwin AK: Licofelone enhances the efficacy of paclitaxel in ovarian cancer by reversing drug resistance and tumor stem-like properties. Cancer Res. 78:4370–4385. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Cavarzerani E, Caligiuri I, Bartoletti M, Canzonieri V and Rizzolio F: 3D dynamic cultures of HGSOC organoids to model innovative and standard therapies. Front Bioeng Biotechnol. 11:11353742023. View Article : Google Scholar : PubMed/NCBI | |
|
Samson DJ, Seidenfeld J, Ziegler K and Aronson N: Chemotherapy sensitivity and resistance assays: A systematic review. J Clin Oncol. 22:3618–3630. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Brooks EA, Galarza S, Gencoglu MF, Cornelison RC, Munson JM and Peyton SR: Applicability of drug response metrics for cancer studies using biomaterials. Philos Trans R Soc Lond B Biol Sci. 374:201802262019. View Article : Google Scholar : PubMed/NCBI | |
|
Brodeur MN, Simeone K, Leclerc-Deslauniers K, Fleury H, Carmona E, Provencher DM and Mes-Masson AM: Carboplatin response in preclinical models for ovarian cancer: Comparison of 2D monolayers, spheroids, ex vivo tumors and in vivo models. Sci Rep. 11:181832021. View Article : Google Scholar : PubMed/NCBI | |
|
Thorel L, Morice PM, Paysant H, Florent R, Babin G, Thomine C, Perréard M, Abeilard E, Giffard F, Brotin E, et al: Comparative analysis of response to treatments and molecular features of tumor-derived organoids versus cell lines and PDX derived from the same ovarian clear cell carcinoma. J Exp Clin Cancer Res. 42:2602023. View Article : Google Scholar : PubMed/NCBI | |
|
Loessner D, Stok KS, Lutolf MP, Hutmacher DW, Clements JA and Rizzi SC: Bioengineered 3D platform to explore cell-ECM interactions and drug resistance of epithelial ovarian cancer cells. Biomaterials. 31:8494–8506. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Tofani LB, Abriata JP, Luiz MT, Marchetti JM and Swiech K: Establishment and characterization of an in vitro 3D ovarian cancer model for drug screening assays. Biotechnol Prog. 36:e30342020. View Article : Google Scholar : PubMed/NCBI | |
|
Bi J, Newtson AM, Zhang Y, Devor EJ, Samuelson MI, Thiel KW and Leslie KK: Successful patient-derived organoid culture of gynecologic cancers for disease modeling and drug sensitivity testing. Cancers (Basel). 13:29012021. View Article : Google Scholar : PubMed/NCBI | |
|
Garcia J, Hurwitz HI, Sandler AB, Miles D, Coleman RL, Deurloo R and Chinot OL: Bevacizumab (Avastin®) in cancer treatment: A review of 15 years of clinical experience and future outlook. Cancer Treat Rev. 86:1020172020. View Article : Google Scholar : PubMed/NCBI | |
|
Cohen MH, Gootenberg J, Keegan P and Pazdur R: FDA drug approval summary: Bevacizumab plus FOLFOX4 as second-line treatment of colorectal cancer. Oncologist. 12:356–361. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Govindaraju S and Yun K: Synthesis of gold nanomaterials and their cancer-related biomedical applications: An update 3. Biotech. 8:1132018.PubMed/NCBI | |
|
Oliva P, Decio A, Castiglioni V, Bassi A, Pesenti E, Cesca M, Scanziani E, Belotti D and Giavazzi R: Cisplatin plus paclitaxel and maintenance of bevacizumab on tumour progression, dissemination, and survival of ovarian carcinoma xenograft models. Br J Cancer. 107:360–369. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Yang H, Wang Y and Wang P, Zhang N and Wang P: Tumor organoids for cancer research and personalized medicine. Cancer Biol Med. 19:319–332. 2021.PubMed/NCBI | |
|
Seidlitz T, Koo BK and Stange DE: Gastric organoids-an in vitro model system for the study of gastric development and road to personalized medicine. Cell Death Differ. 28:68–83. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Rivenbark AG, O'Connor SM and Coleman WB: Molecular and cellular heterogeneity in breast cancer: Challenges for personalized medicine. Am J Pathol. 183:1113–1124. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Verma M: Personalized medicine and cancer. J Pers Med. 2:1–14. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Offit K: Personalized medicine: New genomics, old lessons. Hum Genet. 130:3–14. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Peres LC, Risch H, Terry KL, Webb PM, Goodman MT, Wu AH, Alberg AJ, Bandera EV, Barnholtz-Sloan J, Bondy ML, et al: Racial/ethnic differences in the epidemiology of ovarian cancer: A pooled analysis of 12 case-control studies. Int J Epidemiol. 47:10112018. View Article : Google Scholar : PubMed/NCBI | |
|
Nero C, Vizzielli G, Lorusso D, Cesari E, Daniele G, Loverro M, Scambia G and Sette C: Patient-derived organoids and high grade serous ovarian cancer: From disease modeling to personalized medicine. J Exp Clin Cancer Res. 40:1162021. View Article : Google Scholar : PubMed/NCBI | |
|
Lui G, Richardson A, Chatterjee P, Pollastro M, Lints M, Peretti D, Rosati R, Appleyard L, Durenberger G, Diaz R, et al: Functional drug screening of organoids from ovarian cancer patients demonstrates clinical and genomic concordance and identifies novel therapeutic vulnerabilities. Cancer Res. 81:534. 2021. View Article : Google Scholar | |
|
Phan N, Hong JJ, Tofig B, Mapua M, Elashoff D, Moatamed NA, Huang J, Memarzadeh S, Damoiseaux R and Soragni A: A simple high-throughput approach identifies actionable drug sensitivities in patient-derived tumor organoids. Commun Biol. 2:782019. View Article : Google Scholar : PubMed/NCBI | |
|
Åkerlund E, Gudoityte G, Moussaud-Lamodière E, Lind O, Bwanika HC, Lehti K, Salehi S, Carlson J, Wallin E, Fernebro J, et al: The drug efficacy testing in 3D cultures platform identifies effective drugs for ovarian cancer patients. NPJ Precis Oncol. 7:1112023. View Article : Google Scholar : PubMed/NCBI | |
|
Clark J, Fotopoulou C, Cunnea P and Krell J: Novel ex vivo models of epithelial ovarian cancer: The future of biomarker and therapeutic research. Front Oncol. 12:8372332022. View Article : Google Scholar : PubMed/NCBI | |
|
Compadre AJ, van Biljon LN, Valentine MC, Llop-Guevara A, Graham E, Fashemi B, Herencia-Ropero A, Kotnik EN, Cooper I, Harrington SP, et al: RAD51 foci as a biomarker predictive of platinum chemotherapy response in ovarian cancer. Clin Cancer Res. 29:2466–2479. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Ceccaldi R, Rondinelli B and D'Andrea AD: Repair pathway choices and consequences at the double-strand break. Trends Cell Biol. 26:52–64. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Ito K, Murayama Y, Kurokawa Y, Kanamaru S, Kokabu Y, Maki T, Mikawa T, Argunhan B, Tsubouchi H, Ikeguchi M, et al: Real-time tracking reveals catalytic roles for the two DNA binding sites of Rad51. Nat Commun. 11:29502020. View Article : Google Scholar : PubMed/NCBI | |
|
Wilson AJ, Stubbs M, Liu P, Ruggeri B and Khabele D: The BET inhibitor INCB054329 reduces homologous recombination efficiency and augments PARP inhibitor activity in ovarian cancer. Gynecol Oncol. 149:575–584. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Pellegrino B, Herencia-Ropero A, Llop-Guevara A, Pedretti F, Moles-Fernández A, Viaplana C, Villacampa G, Guzmán M, Rodríguez O, Grueso J, et al: Preclinical in vivo validation of the RAD51 test for identification of homologous recombination-deficient tumors and patient stratification. Cancer Res. 82:1646–1657. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Mukhopadhyay A, Elattar A, Cerbinskaite A, Wilkinson SJ, Drew Y, Kyle S, Los G, Hostomsky Z, Edmondson RJ and Curtin NJ: Development of a functional assay for homologous recombination status in primary cultures of epithelial ovarian tumor and correlation with sensitivity to poly(ADP-ribose) polymerase inhibitors. Clin Cancer Res. 16:2344–2351. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Shah MM, Dobbin ZC, Nowsheen S, Wielgos M, Katre AA, Alvarez RD, Konstantinopoulos PA, Yang ES and Landen CN: An ex vivo assay of XRT-induced Rad51 foci formation predicts response to PARP-inhibition in ovarian cancer. Gynecol Oncol. 134:331–337. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Meijer TG, Verkaik NS, Sieuwerts AM, van Riet J, Naipal KAT, van Deurzen CHM, den Bakker MA, Sleddens HFBM, Dubbink HJ, den Toom TD, et al: Functional ex vivo assay reveals homologous recombination deficiency in breast cancer beyond BRCA gene defects. Clin Cancer Res. 24:6277–6287. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
van Wijk LM, Vermeulen S, Meijers M, van Diest MF, Ter Haar NT, de Jonge MM, Solleveld-Westerink N, van Wezel T, van Gent DC, Kroep JR, et al: The RECAP test rapidly and reliably identifies homologous recombination-deficient ovarian carcinomas. Cancers (Basel). 12:28052020. View Article : Google Scholar : PubMed/NCBI | |
|
Liu JF, Palakurthi S, Zeng Q, Zhou S, Ivanova E, Huang W, Zervantonakis IK, Selfors LM, Shen Y, Pritchard CC, et al: Establishment of patient-derived tumor xenograft models of epithelial ovarian cancer for preclinical evaluation of novel therapeutics. Clin Cancer Res. 23:1263–1273. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Cybulska P, Stewart JM, Sayad A, Virtanen C, Shaw PA, Clarke B, Stickle N, Bernardini MQ and Neel B: A genomically characterized collection of high-grade serous ovarian cancer xenografts for preclinical testing. Am J Pathol. 188:1120–1131. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT, Caldas C, Clarke RB, de Jong S, Jonkers J, Mælandsmo GM, et al: Patient-derived xenograft models: An emerging platform for translational cancer research. Cancer Discov. 4:998–1013. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
de Witte CJ, Valle-Inclan JE, Hami N, Lõhmussaar K, Kopper O, Vreuls CPH, Jonges GN, van Diest P, Nguyen L, Clevers H, et al: Patient-Derived ovarian cancer organoids mimic clinical response and exhibit heterogeneous inter- and intrapatient drug responses. Cell Rep. 31:1077622020. View Article : Google Scholar : PubMed/NCBI | |
|
Zhou Z, Cong L and Cong X: Patient-Derived organoids in precision medicine: Drug screening, organoid-on-a-chip and living organoid biobank. Front Oncol. 11:7621842021. View Article : Google Scholar : PubMed/NCBI | |
|
Narita Y, Kitazoe Y, Kurihara Y, Okuhara Y, Takamatsu K, Saito N and Doi Y: Increase or decrease of HDL-cholesterol concentrations during pravastatin treatment depending on the pre-treatment HDL cholesterol levels. Eur J Clin Pharmacol. 52:461–463. 1997. View Article : Google Scholar : PubMed/NCBI | |
|
Mosiewicz KA, Kolb L, van der Vlies AJ, Martino MM, Lienemann PS, Hubbell JA, Ehrbar M and Lutolf MP: In situ cell manipulation through enzymatic hydrogel photopatterning. Nat Mater. 12:1072–1078. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Verduin M, Hoeben A, De Ruysscher D and Vooijs M: Patient-derived cancer organoids as predictors of treatment response. Front Oncol. 11:6419802021. View Article : Google Scholar : PubMed/NCBI | |
|
Ahn SI, Sei YJ, Park HJ, Kim J, Ryu Y, Choi JJ, Sung HJ, MacDonald TJ, Levey AI and Kim YT: Microengineered human blood-brain barrier platform for understanding nanoparticle transport mechanisms. Nat Commun. 11:1752020. View Article : Google Scholar : PubMed/NCBI | |
|
Nagle PW, Plukker JTM, Muijs CT, van Luijk P and Coppes RP: Patient-derived tumor organoids for prediction of cancer treatment response. Semin Cancer Biol. 53:258–264. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Psilopatis I, Sykaras AG, Mandrakis G, Vrettou K and Theocharis S: Patient-derived organoids: The beginning of a new era in ovarian cancer disease modeling and drug sensitivity testing. Biomedicines. 11:12022. View Article : Google Scholar : PubMed/NCBI | |
|
Kenny PA, Lee GY, Myers CA, Neve RM, Semeiks JR, Spellman PT, Lorenz K, Lee EH, Barcellos-Hoff MH, Petersen OW, et al: The morphologies of breast cancer cell lines in three-dimensional assays correlate with their profiles of gene expression. Mol Oncol. 1:84–96. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Hughes CS, Postovit LM and Lajoie GA: Matrigel: A complex protein mixture required for optimal growth of cell culture. Proteomics. 10:1886–1890. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Chan WS, Mo X, Ip PPC and Tse KY: Patient-derived organoid culture in epithelial ovarian cancers-Techniques, applications, and future perspectives. Cancer Med. 12:19714–19731. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Mandrycky C, Wang Z, Kim K and Kim DH: 3D bioprinting for engineering complex tissues. Biotechnol Adv. 34:422–434. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Annett S, Moore G, Short A, Marshall A, McCrudden C, Yakkundi A, Das S, McCluggage WG, Nelson L, Harley I, et al: FKBPL-based peptide, ALM201, targets angiogenesis and cancer stem cells in ovarian cancer. Br J Cancer. 122:361–371. 2020. View Article : Google Scholar : PubMed/NCBI | |
|
Baka Z, Godier C, Lamy L, Mallick A, Gribova V, Figarol A, Bezdetnaya L, Chateau A, Magne Z, Stiefel M, et al: A coculture based, 3D bioprinted ovarian tumor model combining cancer cells and cancer associated fibroblasts. Macromol Biosci. 23:e22004342023. View Article : Google Scholar : PubMed/NCBI | |
|
Xu F, Celli J, Rizvi I, Moon S, Hasan T and Demirci U: A three-dimensional in vitro ovarian cancer coculture model using a high-throughput cell patterning platform. Biotechnol J. 6:204–212. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Driehuis E and Clevers H: CRISPR/Cas 9 genome editing and its applications in organoids. Am J Physiol Gastrointest Liver Physiol. 312:G257–G265. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Neal JT and Kuo CJ: Organoids as models for neoplastic transformation. Ann Rev Pathol. 11:199–220. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Lõhmussaar K, Kopper O, Korving J, Begthel H, Vreuls CPH, van Es JH and Clevers H: Assessing the origin of high-grade serous ovarian cancer using CRISPR-modification of mouse organoids. Nat Commun. 11:26602020. View Article : Google Scholar : PubMed/NCBI | |
|
Dumont S, Jan Z, Heremans R, Van Gorp T, Vergote I and Timmerman D: Organoids of epithelial ovarian cancer as an emerging preclinical in vitro tool: A review. J Ovarian Res. 12:1052019. View Article : Google Scholar : PubMed/NCBI | |
|
Li Z, Gu H, Xu X, Tian Y, Huang X and Du Y: Unveiling the novel immune and molecular signatures of ovarian cancer: Insights and innovations from single-cell sequencing. Front Immunol. 14:12880272023. View Article : Google Scholar : PubMed/NCBI | |
|
Wan C, Keany MP, Dong H, Al-Alem LF, Pandya UM, Lazo S, Boehnke K, Lynch KN, Xu R, Zarrella DT, et al: Enhanced efficacy of simultaneous PD-1 and PD-L1 immune checkpoint blockade in high-grade serous ovarian cancer. Cancer Res. 81:158–173. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Gonzalez VD, Samusik N, Chen TJ, Savig ES, Aghaeepour N, Quigley DA, Huang YW, Giangarrà V, Borowsky AD, Hubbard NE, et al: Commonly occurring cell subsets in high-grade serous ovarian tumors identified by single-cell mass cytometry. Cell Rep. 22:1875–1888. 2018. View Article : Google Scholar : PubMed/NCBI |
