Organoids for Precision Medicine in Malignant Pleural Mesothelioma: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Jason Zhu and Version 1 by Ren-Wang Peng.

MPM is an aggressive tumor originating from pleural mesothelial cells. A characteristic feature of the disease is the dominant prevalence of therapeutically intractable inactivating alterations in TSGs, making MPM one of the most difficult cancers to treat and the epitome of a cancer characterized by a significant lack of therapy options and an extremely poor prognosis (5-year survival rate of only 5% to 10%). Extensive interpatient heterogeneity poses another major challenge for targeted therapy of MPM, warranting stratified therapy for specific subgroups of MPM patients. Accurate preclinical models are critical for the discovery of new therapies and the development of personalized medicine. Organoids, an in vitro ‘organ-like’ 3D structure derived from patient tumor tissue that faithfully mimics the biology and complex architecture of cancer and largely overcomes the limitations of other existing models, are the next-generation tumor model. Although organoids have been successfully produced and used in many cancers, the development of MPM organoids is still in its infancy. 

  • mesothelioma
  • organoids
  • tumor model
  • drug screens

1. Introduction

Malignant mesothelioma is a rare but aggressive cancer type, arising from the mesothelium (the serosal outer linings) of the pleura, pericardium, peritoneum, and tunica vaginalis that cover the lung, heart, abdomen, and testes, respectively. Malignant pleural mesothelioma (MPM) accounts for 90% of all mesotheliomas, with the 5-year survival rate remaining 5% to 10% [1]. Exposure to asbestos is the most common cause of MPM with a latency period of 20 to 50 years [2]. Asbestiform fibers (erionite, winchite, magnesio-riebeckite, richterite, Libby asbestos, antigorite, and fluoro-edenite) causally relates to MPM [3]. Histologically, MPM is divided into four subtypes: epithelioid (50–60%), sarcomatoid (10%), biphasic (30–40%), and desmoplastic (<2%) [4], with epithelioid subtype associated with better survival compared with the other subtypes [1,5][1][5]. Molecularly, MPMs feature widespread mutations in TSGs, including BAP1, CDKN2A, and NF2, while driver mutations in oncogenes are rare, which poses a significant challenge for the development of targeted therapies against MPMs [6,7,8][6][7][8]. Platinum-based doublet chemotherapy is the standard first-line treatment for advanced MPM since 2003 [9], with effective second-line treatment that overcomes inevitable drug resistance still elusive [10]. Immunotherapy (e.g., immune checkpoint inhibitors, ICIs) has been recently approved as a new first-line treatment for unresectable MPM [11] because of the favorable benefit for patients compared with chemotherapy in clinical trials [11,12][11][12]. Consequently, novel therapeutic targets and strategies are urgently needed to effectively treat MPM [13,14,15,16][13][14][15][16]. Recent evidence reveals that MPM tumors are highly heterogeneous, which challenges one-size-fits-all strategies [17,18,19,20][17][18][19][20] and instead underscores the need for precision oncology-based personalized care of MPM patients.
Accurate preclinical models that faithfully recapitulate the genomic and histopathological features of MPMs are critical for identification and development of precision medicine [21]. Two-dimensional (2D) culture of MPM cell lines, established from primary tumors or pleural fluid [22], are the most-used models but have significant limitations, such as the lack of tissue architecture and complexity of in vivo biological processes [23]. Animal models of MPM have also been established, including asbestos-induced murine tumors, MPM-prone genetically modified mice, and patient-derived xenograft (PDX) models [24,25,26][24][25][26]. While 2D culture and the mouse models are useful, patient-derived organoids (PDOs), an in vitro culture of ‘organ-like’ three-dimensional (3D) structure that faithfully mimics the biology and complex architecture of the primary cancer, represent the next-generation tumor model with obvious advantages over other existing models [27]. Organoids have been successfully established in colorectal, gastrointestinal, pancreatic, prostate, liver, and brain cancers, but efforts to develop MPM organoids are still a largely unfulfilled endeavor [28,29,30][28][29][30].

2. Brief History and Current Status of Organoids

The term ‘organoid’ refers to mini-clusters of cells that self-organize and differentiate into functional cell types in vitro and recapitulate the structure and function of an organ in vivo (therefore, also called “mini-organs”) [31]. The organoid culture dates back to 1907, when H. V. Wilson first reported that sponge cells could self-organize to regenerate an entire organism in vitro [31,32][31][32]. A few decades later, researchers performed dissociation and re-aggregation experiments to generate different organs from stem cells of embryos in dishes [31,33][31][33]. With the development of stem cell research, such as the isolation of pluripotent stem cells (PSCs) and the generation of induced PSCs (iPSCs), organoid research progressed strikingly in the late 20th and early 21st centuries, as organoids can be generated from PSCs (embryonic and adult stem cells) and iPSCs [34,35,36][34][35][36]. In 2009, single leucine-rich repeat-containing G-protein-coupled receptor 5 (Lgr5)-expressing adult intestinal stem cells formed 3D intestinal organoids in Matrigel that self-organized and differentiated into crypt-villus structures without a mesenchymal niche; this was the first report of a 3D organoid culture derived from a single adult stem cell [37]. Since then, 3D organoid systems have attracted much attention and shown tremendous potential for modelling human cancers [38,39,40,41][38][39][40][41]. To date, organoids have been developed for many cancers, including colon cancer [42], gastrointestinal cancer [29], pancreatic cancer [43], prostate cancer [30[30][44],44], bladder cancer [45[45][46],46], liver cancer [47], breast cancer [48], and brain cancer [49].

3. PDOs and Precision Medicine for MPM

The heterogeneity of MPM tumor subpopulations [88,89][50][51] has led to the consensus call for the application of precision oncology to MPM, and PDOs provide an unprecedented platform for identifying and developing precision medicine strategies for this daunting disease. Treatment options for MPM are extremely limited, and patients do not have access to target therapies. Therefore, platinum-based chemotherapy, approved by the FDA in 2004, remains the standard of care. Recently, immunotherapy has also been approved, but only a fraction of MPM patients respond to treatment [86][52]. Identification of the molecular mechanisms underlying MPM pathogenesis and response to existing therapies promises to guide future development of precision medicine for MPM.

3.1. Personalized PDOs for Modelling MPM Heterogeneity

Molecular gradients, a measure of intra-tumor heterogeneity and of high prognostic value for patients, have recently been shown to improve MPM classification treatment [90][53]. Importantly, genetic alterations in TSGs stratify MPM patients into distinct groups that not only differ in molecular pathogenesis but also in therapy responses [67][54]. To precisely represent MPM heterogeneity, personalized PDOs are needed to model the disease for understanding the biology of the tumor and identifying precision oncology approaches [67][54]. Given the high fidelity of PDOs that recapitulate tumor heterogeneity cancers [28[28][49][55],49,91], a personalized PDO biobank of MPM that is amenable to translational and basic studies will provide unprecedented insights into the biology and therapeutic vulnerabilities of MPM.

3.2. MPM PDOs for Drug Screening

PDOs of many other cancers rates [92][56] have proven useful for drug screening and testing. Organoids of liver cancer are able to predict drug sensitivity or resistance in a patient-specific manner [47], as are lung cancer PDOs, which allow profiling of cancer patients’ response to drugs within a week [93][57]. A high-throughput screen of 2427 drugs using tissue-originated spheroid (CTOS), an ex vivo model from PDX tumors, was performed in colorectal cancer [62,94][58][59]. The automated devices—an organoid handler and a reagent dispenser—were used for this high-throughput screening. In order to obtain more tumor material for organoid culture, PDX tumors were used to generate organoids for drug screening in various cancers [91,95][55][60]. In ovarian cancer, it has been reported that patient-specific genomic alteration correlates with drug effects in organoids but not in 2D cell monolayers, suggesting that 3D organoids are a better model than 2D primary cells [96][61]. It may be necessary to add a 2D model, if possible, when drug testing is performed with organoids to show superiority in actual situations. Although drug screening with MPM PDOs has not yet been reported, personalized PDOs will be of particular importance for unbiased genetic and pharmacological studies to discover novel anti-MPM therapies in a manner tailored to individual patients. The fact that PDOs allow high-throughput drug screening will facilitate the subsequent selection of the most efficacious drug to treat MPM. An important consideration for such screening is to enable high-throughput drug testing, which requires multiple passages and the long-term culture of organoids, as has been described in lung and other cancers [97][62]. In a recent study, culture conditions for PDOs suitable for large-scale drug screening were systematically investigated [61][63]. The concept of drug repurposing has attracted considerable attention [62,98][58][64]. Under this framework, FDA-approved drugs are evaluated for their efficacy against cancer. The same concept can be applied to the identification of drug combinations that have been shown to be an effective strategy to overcome treatment resistance, as wresearchers have recently demonstrated [10]. Advances in high-throughput screening systems also enable rapid analysis of large numbers of drug compounds using automated machines to dispense cells and drugs, and to perform endpoint measurements [99][65].

3.3. MPM PDOs for Functional Genomics

The CRISPR/Cas9 gene editing system is a powerful platform for functional genomics, which has been successfully used for genome editing of colorectal cancer organoids [100][66] and other organoid models [66,101,102][67][68][69]. In particular, gene knockout in tumor organoids using CRISPR/Cas9 provides functional evidence for the main drivers of oncogenes in colorectal cancer and can be used to validate various therapeutic approaches [64][70]. The suitability of PDOs for functional genomics suggests that they can serve as clinically relevant models for MPM and enable unprecedented investigations to discover novel therapeutic targets and vulnerabilities, as well as strategies for developing precision medicine to treat MPM.

3.4. MPM PDOs for Other Applications

MPM PDOs are also useful for studying fundamental mechanisms of tumor development, progression, resistance to cancer therapies, and TME. Asbestos exposure is the major risk factor for MPM, but the mechanism underlying asbestos oncogenesis has not been fully understood [103][71]. Mouse models for asbestos-induced MPM have been developed, but the genetic profile is different from that of MPM patients because BAP1, NF2, or LATS2, which are frequently mutated in MPM patients, are not present in these mouse models [104][72]. Therefore, new models are needed to better understand the development and progression of MPM, and organoids derived from normal pleura may be a good option to study the pathogenic role of asbestos and to model the pathophysiology of MPM. Such organoids can be obtained from normal pleura or other autologous sources, such as iPSCs, as considerable progress has been made in the preparation of organoids from normal tissue [105,106][73][74]. Moreover, PDOs can be subjected to CRISPR/Cas9-mediated genomic editing to explore the molecular mechanisms underlying MPM out-growth, clone evolution, and drug resistance, as demonstrated in other cancers [65,101,102,107][68][69][75][76]. Epithelial-to-mesenchymal transition (EMT) plays a crucial role in MPM development, progression, and resistance to therapy [108[77][78],109], with the underlying mechanisms and key regulators largely unknown. As organoids are accessible to pharmacological and genetic perturbations, PDOs are a promising model to study the roles of EMT in MPM. Cancer cells actively and dynamically interact with the TME and this reciprocal interaction significantly influences tumor progression and drug response. MPM is known to have a tumor-promoting TME due to chronic inflammation. Immunotherapy has recently been approved by the FDA for advanced MPM, whereas unselected patients respond very differently to this therapy. Therefore, it is critical to understand the underlying mechanism of response or resistance to therapy to prospectively stratify subgroups of patients who will benefit from immunotherapy. With advances in organoid culture technology, the incorporation of immune components has been increasingly recognized and realized [72][79]. The TME of the original tumors can be modeled using air–liquid interface PDOs or microfluidic devices [53][80]. Alternatively, the TME can be reconstituted by adding purified immune populations from original tumors or peripheral blood into submerged tumor organoids [69][81]. Consequently, PDOs can be exploited to study not only cancer-cell-intrinsic mechanisms but also the dynamic interplay between cancer cells and the TME.

References

  1. Janes, S.M.; Alrifai, D.; Fennell, D.A. Perspectives on the Treatment of Malignant Pleural Mesothelioma. N. Engl. J. Med. 2021, 385, 1207–1218.
  2. Sekido, Y. Molecular pathogenesis of malignant mesothelioma. Carcinogenesis 2013, 34, 1413–1419.
  3. Filetti, V.; Vitale, E.; Broggi, G.; Hagnas, M.P.; Candido, S.; Spina, A.; Lombardo, C. Update of in vitro, in vivo and ex vivo fluoro-edenite effects on malignant mesothelioma: A systematic review (Review). Biomed. Rep. 2020, 13, 60.
  4. Scagliotti, G.V.; Bironzo, P.; Magnani, C.; Rossi, G.; Veltri, A.; Trisolini, R.; Rocco, G.; Ramella, S.; Grosso, F.; Marsico, V.A. Mesotelioma Pleurico; Associazione Italiana di Oncologia Medica: Milano, Italy, 2018; pp. 1–75.
  5. Yap, T.A.; Aerts, J.G.; Popat, S.; Fennell, D.A. Novel insights into mesothelioma biology and implications for therapy. Nat. Rev. Cancer 2017, 17, 475–488.
  6. Bueno, R.; Stawiski, E.W.; Goldstein, L.D.; Durinck, S.; De Rienzo, A.; Modrusan, Z.; Gnad, F.; Nguyen, T.T.; Jaiswal, B.S.; Chirieac, L.R.; et al. Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations. Nat. Genet. 2016, 48, 407–416.
  7. Guo, G.; Chmielecki, J.; Goparaju, C.; Heguy, A.; Dolgalev, I.; Carbone, M.; Seepo, S.; Meyerson, M.; Pass, H.I. Whole-exome sequencing reveals frequent genetic alterations in BAP1, NF2, CDKN2A, and CUL1 in malignant pleural mesothelioma. Cancer Res. 2015, 75, 264–269.
  8. Hmeljak, J.; Sanchez-Vega, F.; Hoadley, K.A.; Shih, J.; Stewart, C.; Heiman, D.; Tarpey, P.; Danilova, L.; Drill, E.; Gibb, E.A.; et al. Integrative Molecular Characterization of Malignant Pleural Mesothelioma. Cancer Discov. 2018, 8, 1548–1565.
  9. Vogelzang, N.J.; Rusthoven, J.J.; Symanowski, J.; Denham, C.; Kaukel, E.; Ruffie, P.; Gatzemeier, U.; Boyer, M.; Emri, S.; Manegold, C.; et al. Phase III Study of Pemetrexed in Combination With Cisplatin Versus Cisplatin Alone in Patients With Malignant Pleural Mesothelioma. J. Clin. Oncol. 2003, 21, 2636–2644.
  10. Xu, D.; Liang, S.Q.; Yang, H.; Bruggmann, R.; Berezowska, S.; Yang, Z.; Marti, T.M.; Hall, S.R.R.; Gao, Y.; Kocher, G.J.; et al. CRISPR Screening Identifies WEE1 as a Combination Target for Standard Chemotherapy in Malignant Pleural Mesothelioma. Mol. Cancer Ther. 2020, 19, 661–672.
  11. Baas, P.; Scherpereel, A.; Nowak, A.K.; Fujimoto, N.; Peters, S.; Tsao, A.S.; Mansfield, A.S.; Popat, S.; Jahan, T.; Antonia, S.; et al. First-line nivolumab plus ipilimumab in unresectable malignant pleural mesothelioma (CheckMate 743): A multicentre, randomised, open-label, phase 3 trial. Lancet 2021, 397, 375–386.
  12. Tsao, A.S.; Pass, H.I.; Rimner, A.; Mansfield, A.S. New Era for Malignant Pleural Mesothelioma: Updates on Therapeutic Options. J. Clin. Oncol. 2022, 40, 866–875.
  13. Napolitano, A.; Carbone, M. Malignant Mesothelioma: Time to Translate? Trends Cancer 2016, 2, 467–474.
  14. Bronte, G.; Incorvaia, L.; Rizzo, S.; Passiglia, F.; Galvano, A.; Rizzo, F.; Rolfo, C.; Fanale, D.; Listi, A.; Natoli, C.; et al. The resistance related to targeted therapy in malignant pleural mesothelioma: Why has not the target been hit yet? Crit. Rev. Oncol. Hematol. 2016, 107, 20–32.
  15. Xu, D.; Yang, H.; Schmid, R.A.; Peng, R.W. Therapeutic Landscape of Malignant Pleural Mesothelioma: Collateral Vulnerabilities and Evolutionary Dependencies in the Spotlight. Front. Oncol. 2020, 10, 579464.
  16. Yang, H.; Xu, D.; Schmid, R.A.; Peng, R.W. Biomarker-guided targeted and immunotherapies in malignant pleural mesothelioma. Ther. Adv. Med. Oncol. 2020, 12, 1758835920971421.
  17. Meiller, C.; Montagne, F.; Hirsch, T.Z.; Caruso, S.; de Wolf, J.; Bayard, Q.; Assie, J.B.; Meunier, L.; Blum, Y.; Quetel, L.; et al. Multi-site tumor sampling highlights molecular intra-tumor heterogeneity in malignant pleural mesothelioma. Genome Med. 2021, 13, 113.
  18. Politi, K.; Herbst, R.S. Lung cancer in the era of precision medicine. Clin. Cancer Res. 2015, 21, 2213–2220.
  19. El-Deiry, W.S.; Goldberg, R.M.; Lenz, H.J.; Shields, A.F.; Gibney, G.T.; Tan, A.R.; Brown, J.; Eisenberg, B.; Heath, E.I.; Phuphanich, S.; et al. The current state of molecular testing in the treatment of patients with solid tumors, 2019. CA Cancer J. Clin. 2019, 69, 305–343.
  20. Scherpereel, A.; Wallyn, F.; Albelda, S.M.; Munck, C. Novel therapies for malignant pleural mesothelioma. Lancet Oncol. 2018, 19, e161–e172.
  21. Shamseddin, M.; Obacz, J.; Garnett, M.J.; Rintoul, R.C.; Francies, H.E.; Marciniak, S.J. Use of preclinical models for malignant pleural mesothelioma. Thorax 2021, 76, 1154–1162.
  22. Kanellakis, N.I.; Asciak, R.; Hamid, M.A.; Yao, X.; McCole, M.; McGowan, S.; Seraia, E.; Hatch, S.; Hallifax, R.J.; Mercer, R.M.; et al. Patient-derived malignant pleural mesothelioma cell cultures: A tool to advance biomarker-driven treatments. Thorax 2020, 75, 1004–1008.
  23. Jensen, C.; Teng, Y. Is It Time to Start Transitioning From 2D to 3D Cell Culture? Front. Mol. Biosci. 2020, 7, 33.
  24. Altomare, D.A.; Vaslet, C.A.; Skele, K.L.; De Rienzo, A.; Devarajan, K.; Jhanwar, S.C.; McClatchey, A.I.; Kane, A.B.; Testa, J.R. A mouse model recapitulating molecular features of human mesothelioma. Cancer Res. 2005, 65, 8090–8095.
  25. Jongsma, J.; van Montfort, E.; Vooijs, M.; Zevenhoven, J.; Krimpenfort, P.; van der Valk, M.; van de Vijver, M.; Berns, A. A conditional mouse model for malignant mesothelioma. Cancer Cell 2008, 13, 261–271.
  26. Wu, L.; Allo, G.; John, T.; Li, M.; Tagawa, T.; Opitz, I.; Anraku, M.; Yun, Z.; Pintilie, M.; Pitcher, B.; et al. Patient-Derived Xenograft Establishment from Human Malignant Pleural Mesothelioma. Clin. Cancer Res. 2017, 23, 1060–1067.
  27. Drost, J.; Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 2018, 18, 407–418.
  28. Van de Wetering, M.; Francies, H.E.; Francis, J.M.; Bounova, G.; Iorio, F.; Pronk, A.; van Houdt, W.; van Gorp, J.; Taylor-Weiner, A.; Kester, L.; et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 2015, 161, 933–945.
  29. Vlachogiannis, G.; Hedayat, S.; Vatsiou, A.; Jamin, Y.; Fernández-Mateos, J.; Khan, K.; Lampis, A.; Eason, K.; Huntingford, I.; Burke, R.; et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 2018, 359, 920–926.
  30. Karkampouna, S.; La Manna, F.; Benjak, A.; Kiener, M.; De Menna, M.; Zoni, E.; Grosjean, J.; Klima, I.; Garofoli, A.; Bolis, M.; et al. Patient-derived xenografts and organoids model therapy response in prostate cancer. Nat. Commun. 2021, 12, 1117.
  31. Corro, C.; Novellasdemunt, L.; Li, V.S.W. A brief history of organoids. Am. J. Physiol. Cell Physiol. 2020, 319, C151–C165.
  32. Wilson, H.V. A New Method by Which Sponges May Be Artificially Reared. Science 1907, 25, 912–915.
  33. Weiss, P.; Taylor, A.C. Reconstitution of Complete Organs from Single-Cell Suspensions of Chick Embryos in Advanced Stages of Differentiation. Proc. Natl. Acad. Sci. USA 1960, 46, 1177–1185.
  34. Martin, G.R. Isolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells. Proc. Natl. Acad. Sci. USA 1981, 78, 7634–7638.
  35. Takahashi, K.; Tanabe, K.; Ohnuki, M.; Narita, M.; Ichisaka, T.; Tomoda, K.; Yamanaka, S. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 2007, 131, 861–872.
  36. Yu, J.; Vodyanik, M.A.; Smuga-Otto, K.; Antosiewicz-Bourget, J.; Frane, J.L.; Tian, S.; Nie, J.; Jonsdottir, G.A.; Ruotti, V.; Stewart, R.; et al. Induced Pluripotent Stem Cell Lines Derived from Human Somatic Cells. Science 2007, 318, 1917–1920.
  37. Sato, T.; Vries, R.G.; Snippert, H.J.; van de Wetering, M.; Barker, N.; Stange, D.E.; van Es, J.H.; Abo, A.; Kujala, P.; Peters, P.J.; et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 2009, 459, 262–265.
  38. Xu, H.; Lyu, X.; Yi, M.; Zhao, W.; Song, Y.; Wu, K. Organoid technology and applications in cancer research. J. Hematol. Oncol. 2018, 11, 116.
  39. Kuo, C.J.; Curtis, C. Organoids reveal cancer dynamics. Science 2018, 556, 441–442.
  40. Muthuswamy, S.K. Organoid Models of Cancer Explode with Possibilities. Cell Stem Cell 2018, 22, 290–291.
  41. Crespo, M.; Vilar, E.; Tsai, S.Y.; Chang, K.; Amin, S.; Srinivasan, T.; Zhang, T.; Pipalia, N.H.; Chen, H.J.; Witherspoon, M.; et al. Colonic organoids derived from human induced pluripotent stem cells for modeling colorectal cancer and drug testing. Nat. Med. 2017, 23, 878–884.
  42. Fujii, M.; Shimokawa, M.; Date, S.; Takano, A.; Matano, M.; Nanki, K.; Ohta, Y.; Toshimitsu, K.; Nakazato, Y.; Kawasaki, K.; et al. A Colorectal Tumor Organoid Library Demonstrates Progressive Loss of Niche Factor Requirements during Tumorigenesis. Cell Stem Cell 2016, 18, 827–838.
  43. Huang, L.; Holtzinger, A.; Jagan, I.; BeGora, M.; Lohse, I.; Ngai, N.; Nostro, C.; Wang, R.; Muthuswamy, L.B.; Crawford, H.C.; et al. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell- and patient-derived tumor organoids. Nat. Med. 2015, 21, 1364–1371.
  44. Gao, D.; Vela, I.; Sboner, A.; Iaquinta, P.J.; Karthaus, W.R.; Gopalan, A.; Dowling, C.; Wanjala, J.N.; Undvall, E.A.; Arora, V.K.; et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 2014, 159, 176–187.
  45. Lee, S.H.; Hu, W.; Matulay, J.T.; Silva, M.V.; Owczarek, T.B.; Kim, K.; Chua, C.W.; Barlow, L.J.; Kandoth, C.; Williams, A.B.; et al. Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer. Cell 2018, 173, 515–528.e17.
  46. Minoli, M.; Cantore, T.; Kiener, M.; Fedrizzi, T.; Manna, F.L.; Karkampouna, S.; Genitisch, V.; Rodriguez, A.; Klima, I.; Gasperini, P.; et al. Bladder cancer organoids as a functional system to model different disease stages and therapy response. BioRxiv 2022.
  47. Broutier, L.; Mastrogiovanni, G.; Verstegen, M.M.; Francies, H.E.; Gavarro, L.M.; Bradshaw, C.R.; Allen, G.E.; Arnes-Benito, R.; Sidorova, O.; Gaspersz, M.P.; et al. Human primary liver cancer-derived organoid cultures for disease modeling and drug screening. Nat. Med. 2017, 23, 1424–1435.
  48. Sachs, N.; de Ligt, J.; Kopper, O.; Gogola, E.; Bounova, G.; Weeber, F.; Balgobind, A.V.; Wind, K.; Gracanin, A.; Begthel, H.; et al. A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity. Cell 2018, 172, 373–386.e10.
  49. Velasco, S.; Kedaigle, A.J.; Simmons, S.K.; Nash, A.; Rocha, M.; Quadrato, G.; Paulsen, B.; Nguyen, L.; Adiconis, X.; Regev, A.; et al. Individual brain organoids reproducibly form cell diversity of the human cerebral cortex. Nature 2019, 570, 523–527.
  50. Blum, Y.; Meiller, C.; Quetel, L.; Elarouci, N.; Ayadi, M.; Tashtanbaeva, D.; Armenoult, L.; Montagne, F.; Tranchant, R.; Renier, A.; et al. Dissecting heterogeneity in malignant pleural mesothelioma through histo-molecular gradients for clinical applications. Nat. Commun. 2019, 10, 1333.
  51. Oehl, K.; Vrugt, B.; Opitz, I.; Meerang, M. Heterogeneity in Malignant Pleural Mesothelioma. Int. J. Mol. Sci. 2018, 19, 1603.
  52. Asciak, R.; Kanellakis, N.I.; Yao, X.; Abd Hamid, M.; Mercer, R.M.; Hassan, M.; Bedawi, E.O.; Dobson, M.; Fsadni, P.; Montefort, S.; et al. Pleural Fluid Has Pro-Growth Biological Properties Which Enable Cancer Cell Proliferation. Front. Oncol. 2021, 11, 658395.
  53. Oehl, K.; Vrugt, B.; Wagner, U.; Kirschner, M.B.; Meerang, M.; Weder, W.; Felley-Bosco, E.; Wollscheid, B.; Bankov, K.; Demes, M.C.; et al. Alterations in BAP1 Are Associated with Cisplatin Resistance through Inhibition of Apoptosis in Malignant Pleural Mesothelioma. Clin. Cancer Res. 2021, 27, 2277–2291.
  54. Neal, J.T.; Kuo, C.J. Organoids as Models for Neoplastic Transformation. Annu. Rev. Pathol. 2016, 11, 199–220.
  55. Huang, L.; Bockorny, B.; Paul, I.; Akshinthala, D.; Frappart, P.O.; Gandarilla, O.; Bose, A.; Sanchez-Gonzalez, V.; Rouse, E.E.; Lehoux, S.D.; et al. PDX-derived organoids model in vivo drug response and secrete biomarkers. JCI Insight 2020, 5, 1–20.
  56. Weeber, F.; Ooft, S.N.; Dijkstra, K.K.; Voest, E.E. Tumor Organoids as a Pre-clinical Cancer Model for Drug Discovery. Cell Chem. Biol. 2017, 24, 1092–1100.
  57. Hu, Y.; Sui, X.; Song, F.; Li, Y.; Li, K.; Chen, Z.; Yang, F.; Chen, X.; Zhang, Y.; Wang, X.; et al. Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week. Nat. Commun. 2021, 12, 2581.
  58. Kondo, J.; Inoue, M. Application of Cancer Organoid Model for Drug Screening and Personalized Therapy. Cells 2019, 8, 470.
  59. Kondo, J.; Ekawa, T.; Endo, H.; Yamazaki, K.; Tanaka, N.; Kukita, Y.; Okuyama, H.; Okami, J.; Imamura, F.; Ohue, M.; et al. High-throughput screening in colorectal cancer tissue-originated spheroids. Cancer Sci. 2019, 110, 345–355.
  60. Guillen, K.P.; Fujita, M.; Butterfield, A.J.; Scherer, S.D.; Bailey, M.H.; Chu, Z.; DeRose, Y.S.; Zhao, L.; Cortes-Sanchez, E.; Yang, C.-H.; et al. A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology. Nat Cancer. 2022, 3, 232–250.
  61. Jabs, J.; Zickgraf, F.M.; Park, J.; Wagner, S.; Jiang, X.; Jechow, K.; Kleinheinz, K.; Toprak, U.H.; Schneider, M.A.; Meister, M.; et al. Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations. Mol. Syst. Biol. 2017, 13, 955.
  62. Shi, R.; Radulovich, N.; Ng, C.; Liu, N.; Notsuda, H.; Cabanero, M.; Martins-Filho, S.N.; Raghavan, V.; Li, Q.; Mer, A.S.; et al. Organoid Cultures as Preclinical Models of Non-Small Cell Lung Cancer. Clin. Cancer Res. 2020, 26, 1162–1174.
  63. Driehuis, E.; Kretzschmar, K.; Clevers, H. Establishment of patient-derived cancer organoids for drug-screening applications. Nat. Protoc. 2020, 15, 3380–3409.
  64. Bertolini, F.; Sukhatme, V.P.; Bouche, G. Drug repurposing in oncology—Patient and health systems opportunities. Nat. Rev. Clin. Oncol. 2015, 12, 732–742.
  65. Noah, J.w. New developments and emerging trends in high throughput screening methods for lead compound identification. Int. J. High Throughput Screen. 2010, 1, 141–149.
  66. Matano, M.; Date, S.; Shimokawa, M.; Takano, A.; Fujii, M.; Ohta, Y.; Watanabe, T.; Kanai, T.; Sato, T. Modeling colorectal cancer using CRISPR-Cas9-mediated engineering of human intestinal organoids. Nat. Med. 2015, 21, 256–262.
  67. Wu, X.; Li, Z.; Zhang, H.; He, F.; Qiao, M.; Luo, H.; Zhang, J.; Zhang, M.; Mao, Y.; Wagstaff, W.; et al. Modeling colorectal tumorigenesis using the organoids derived from conditionally immortalized mouse intestinal crypt cells (ciMICs). Genes. Dis. 2021, 8, 814–826.
  68. Liu, J.; Li, P.; Wang, L.; Li, M.; Ge, Z.; Noordam, L.; Lieshout, R.; Verstegen, M.M.A.; Ma, B.; Su, J.; et al. Cancer-Associated Fibroblasts Provide a Stromal Niche for Liver Cancer Organoids That Confers Trophic Effects and Therapy Resistance. Cell Mol. Gastroenterol. Hepatol. 2021, 11, 407–431.
  69. Usui, T.; Sakurai, M.; Umata, K.; Elbadawy, M.; Ohama, T.; Yamawaki, H.; Hazama, S.; Takenouchi, H.; Nakajima, M.; Tsunedomi, R.; et al. Hedgehog Signals Mediate Anti-Cancer Drug Resistance in Three-Dimensional Primary Colorectal Cancer Organoid Culture. Int. J. Mol. Sci. 2018, 19, 1098.
  70. Takeda, H.; Kataoka, S.; Nakayama, M.; Ali, M.A.E.; Oshima, H.; Yamamoto, D.; Park, J.W.; Takegami, Y.; An, T.; Jenkins, N.A.; et al. CRISPR-Cas9-mediated gene knockout in intestinal tumor organoids provides functional validation for colorectal cancer driver genes. Proc. Natl. Acad. Sci. USA 2019, 116, 15635–15644.
  71. Zucali, P.A.; Ceresoli, G.L.; De Vincenzo, F.; Simonelli, M.; Lorenzi, E.; Gianoncelli, L.; Santoro, A. Advances in the biology of malignant pleural mesothelioma. Cancer Treat. Rev. 2011, 37, 543–558.
  72. Sneddon, S.; Patch, A.M.; Dick, I.M.; Kazakoff, S.; Pearson, J.V.; Waddell, N.; Allcock, R.J.N.; Holt, R.A.; Robinson, B.W.S.; Creaney, J. Whole exome sequencing of an asbestos-induced wild-type murine model of malignant mesothelioma. BMC Cancer 2017, 17, 396.
  73. Lancaster, M.A.; Knoblich, J.A. Organogenesis in a dish: Modeling development and disease using organoid technologies. Science 2014, 345, 1247125.
  74. Garreta, E.; Kamm, R.D.; Chuva de Sousa Lopes, S.M.; Lancaster, M.A.; Weiss, R.; Trepat, X.; Hyun, I.; Montserrat, N. Rethinking organoid technology through bioengineering. Nat. Mater. 2021, 20, 145–155.
  75. Lo, Y.H.; Kolahi, K.S.; Du, Y.; Chang, C.Y.; Krokhotin, A.; Nair, A.; Sobba, W.D.; Karlsson, K.; Jones, S.J.; Longacre, T.A.; et al. A CRISPR/Cas9-Engineered ARID1A-Deficient Human Gastric Cancer Organoid Model Reveals Essential and Nonessential Modes of Oncogenic Transformation. Cancer Discov. 2021, 11, 1562–1581.
  76. Michels, B.E.; Mosa, M.H.; Streibl, B.I.; Zhan, T.; Menche, C.; Abou-El-Ardat, K.; Darvishi, T.; Czlonka, E.; Wagner, S.; Winter, J.; et al. Pooled In Vitro and In Vivo CRISPR-Cas9 Screening Identifies Tumor Suppressors in Human Colon Organoids. Cell Stem Cell 2020, 26, 782–792.e87.
  77. Fassina, A.; Cappellesso, R.; Guzzardo, V.; Dalla Via, L.; Piccolo, S.; Ventura, L.; Fassan, M. Epithelial-mesenchymal transition in malignant mesothelioma. Mod. Pathol. 2012, 25, 86–99.
  78. Singh, A.S.; Heery, R.; Gray, S.G. In Silico and In Vitro Analyses of LncRNAs as Potential Regulators in the Transition from the Epithelioid to Sarcomatoid Histotype of Malignant Pleural Mesothelioma (MPM). Int. J. Mol. Sci. 2018, 19, 1297.
  79. Roper, J.; Tammela, T.; Cetinbas, N.M.; Akkad, A.; Roghanian, A.; Rickelt, S.; Almeqdadi, M.; Wu, K.; Oberli, M.A.; Sanchez-Rivera, F.J.; et al. In vivo genome editing and organoid transplantation models of colorectal cancer and metastasis. Nat. Biotechnol. 2017, 35, 569–576.
  80. Sontheimer-Phelps, A.; Hassell, B.A.; Ingber, D.E. Modelling cancer in microfluidic human organs-on-chips. Nat. Rev. Cancer 2019, 19, 65–81.
  81. Neal, J.T.; Li, X.; Zhu, J.; Giangarra, V.; Grzeskowiak, C.L.; Ju, J.; Liu, I.H.; Chiou, S.H.; Salahudeen, A.A.; Smith, A.R.; et al. Organoid Modeling of the Tumor Immune Microenvironment. Cell 2018, 175, 1972–1988.e16.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Video Production Service
Feedback