The roles of patient-derived xenograft models and artificial intelligence toward precision medicine

Abstract

Patient-derived xenografts (PDX) involve transplanting patient cells or tissues into immunodeficient mice, offering superior disease models compared with cell line xenografts and genetically engineered mice. In contrast to traditional cell-line xenografts and genetically engineered mice, PDX models harbor the molecular and biologic features from the original patient tumor and are generationally stable. This high fidelity makes PDX models particularly suitable for preclinical and coclinical drug testing, therefore better predicting therapeutic efficacy. Although PDX models are becoming more useful, the several factors influencing their reliability and predictive power are not well understood. Several existing studies have looked into the possibility that PDX models could be important in enhancing our knowledge with regard to tumor genetics, biomarker discovery, and personalized medicine; however, a number of problems still need to be addressed, such as the high cost and time-consuming processes involved, together with the variability in tumor take rates. This review addresses these gaps by detailing the methodologies to generate PDX models, their application in cancer research, and their advantages over other models. Further, it elaborates on how artificial intelligence and machine learning were incorporated into PDX studies to fast-track therapeutic evaluation. This review is an overview of the progress that has been done so far in using PDX models for cancer research and shows their potential to be further improved in improving our understanding of oncogenesis.

Keywords

artificial intelligence / cancer biology / nanodrug delivery / patient-derived xenografts / PDX model / personalized medicine / tumor genetics / tumor modeling

References

[1] Kurtova AV, Xiao J, Mo Q, et al. Blocking PGE2-induced tumour repopulation abrogates bladder cancer chemoresistance. Nature. 2015; 517(7533): 209-213.

[2] Ericsson AC, Crim MJ, Franklin CL. A brief history of animal modeling. Mo Med. 2013; 110(3): 201-205.

[3] Aguilar-Lazcano CA, Espinosa-Curiel IE, Ríos-Martínez JA, Madera-Ramírez FA, Pérez-Espinosa H. Machine learning-based sensor data fusion for animal monitoring: scoping review. Sensors (Basel). 2023; 23(12): 5732.

[4] Abdolahi S, Ghazvinian Z, Muhammadnejad S, Saleh M, Asadzadeh Aghdaei H, Baghaei K. Patient-derived xenograft (PDX) models, applications and challenges in cancer research. J Transl Med. 2022; 20(1).

[5] Rygaard J, Povlsen CO. Heterotransplantation of a human malignant tumour to “Nude” mice. Acta Pathol Microbiol Scand. 1969; 77: 758-760.

[6] Johnson JI, Decker S, Zaharevitz D, et al. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br J Cancer. 2001; 84(10): 1424-1431.

[7] Daniel VC, Marchionni L, Hierman JS, et al. A primary xenograft model of small-cell lung cancer reveals irreversible changes in gene expression imposed by culture in vitro. Cancer Res. 2009; 69(8): 3364-3373.

[8] Wang Y, Ding X, Wang S, et al. Antitumor effect of FGFR inhibitors on a novel cholangiocarcinoma patient derived xenograft mouse model endogenously expressing an FGFR2-CCDC6 fusion protein. Cancer Lett. 2016; 380(1): 163-173.

[9] Inoue T, Terada N, Kobayashi T, Ogawa O. Patient-derived xenografts as in vivo models for research in urological malignancies. Nat Rev Urol. 2017; 14(5): 267-283.

[10] Abdolahi S, Ghazvinian Z, Muhammadnejad S, Saleh M, Asadzadeh Aghdaei H, Baghaei K. Patient-derived xenograft (PDX) models, applications and challenges in cancer research. J Transl Med. 2022; 20(1): 206.

[11] Fichtner I, Slisow W, Gill J, et al. Anticancer drug response and expression of molecular markers in early-passage xenotransplanted colon carcinomas. Eur J Cancer. 2004; 40(2): 298-307.

[12] Cho SY. Patient-derived xenografts as compatible models for precision oncology. Lab Anim Res. 2020; 36(1).

[13] Behrens D, Pfohl U, Conrad T, et al. Establishment and thorough characterization of xenograft (PDX) models derived from patients with pancreatic cancer for molecular analyses and chemosensitivity testing. Cancers (Basel). 2023; 15(24): 5753.

[14] Mattar M, McCarthy CR, Kulick AR, Qeriqi B, Guzman S, de Stanchina E. Establishing and maintaining an extensive library of patient-derived xenograft models. Front Oncol. 2018; 8(FEB).

[15] Kuracha MR, Thomas P, Loggie BW, Govindarajan V. Patient-derived xenograft mouse models of pseudomyxoma peritonei recapitulate the human inflammatory tumor microenvironment. Cancer Med. 2016; 5(4): 711-719.

[16] Fu X, Guadagni F, Hoffman RM. A metastatic nude-mouse model of human pancreatic cancer constructed orthotopically with histologically intact patient specimens. Proc Natl Acad Sci USA. 1992; 89(12): 5645-5649.

[17] Rogojina A, Klesse LJ, Butler E, et al. Comprehensive characterization of patient-derived xenograft models of pediatric leukemia. iScience. 2023; 26(11): 108171.

[18] Chiorazzi M, Martinek J, Krasnick B, et al. Autologous humanized PDX modeling for immuno-oncology recapitulates features of the human tumor microenvironment. J Immunother Cancer. 2023; 11(7): e006921.

[19] Puig I, Chicote I, Tenbaum SP, et al. A personalized preclinical model to evaluate the metastatic potential of patient-derived colon cancer initiating cells. Clin Cancer Res. 2013; 19(24): 6787-6801.

[20] Kuwata T, Yanagihara K, Iino Y, et al. Establishment of novel gastric cancer patient-derived xenografts and cell lines: pathological comparison between primary tumor, patient-derived, and cell-line derived xenografts. Cells. 2019; 8(6): 585.

[21] Fichtner I, Rolff J, Soong R, et al. Establishment of patient-derived non-small cell lung cancer xenografts as models for the identification of predictive biomarkers. Clin Cancer Res. 2008; 14(20): 6456-6468.

[22] DeRose YS, Gligorich KM, Wang G, et al. Patient-derived models of human breast cancer: protocols for in vitro and in vivo applications in tumor biology and translational medicine. Curr Protoc Pharmacol. 2013; 60(1).

[23] Ricci F, Bizzaro F, Cesca M, et al. Patient-derived ovarian tumor xenografts recapitulate human clinicopathology and genetic alterations. Cancer Res. 2014; 74(23): 6980-6990.

[24] Alkema NG, Tomar T, Duiker EW, et al. Biobanking of patient and patient-derived xenograft ovarian tumour tissue: efficient preservation with low and high fetal calf serum based methods. Sci Rep. 2015; 5(1): 14495.

[25] Jin J, Yoshimura K, Sewastjanow-Silva M, Song S, Ajani JA. Challenges and prospects of patient-derived xenografts for cancer research. Cancers (Basel). 2023; 15(17): 4352.

[26] Anderson WC, Boyd MB, Aguilar J, et al. Initiation and characterization of small cell lung cancer patient-derived xenografts from ultrasound-guided transbronchial needle aspirates. Chan D, ed. PLoS One. 2015; 10(5): e0125255.

[27] Zuber TJ. Punch biopsy of the skin. Am Fam Physician. 2002; 65(6): 1155-1158. 1161-1162, 1164.

[28] Turk DJ, Kozarek RA, Botoman VA, Patterson DJ, Ball TJ. Disposable endoscopic biopsy forceps: comparison with standard forceps of sample size and adequacy of specimen. J Clin Gastroenterol. 1991; 13(1): 76-78.

[29] Jang SY, Bae HI, Lee IK, Park HK, Cho CM. Successful xenograft of endoscopic ultrasound-guided fine-needle aspiration specimen from human extrahepatic cholangiocarcinoma into an immunodeficient mouse. Gut Liver. 2015; 9(6): 805.

[30] Embuscado EE, Laheru D, Ricci F, et al. Immortalizing the complexity of cancer metastasis: genetic features of lethal metastatic pancreatic cancer obtained from rapid autopsy. Cancer Biol Ther. 2005; 4(5): 548-554.

[31] Yu M, Stott S, Toner M, Maheswaran S, Haber DA. Circulating tumor cells: approaches to isolation and characterization. J Cell Biol. 2011; 192(3): 373-382.

[32] Burgenske DM, Monsma DJ, MacKeigan JP, Patient-Derived Xenograft Models of Colorectal Cancer: Procedures for Engraftment and Propagation. In: 2018: 307-314.

[33] Yoshida GJ. Applications of patient-derived tumor xenograft models and tumor organoids. J Hematol Oncol. 2020; 13(1): 1-16.

[34] Jiang Y, Zhao J, Zhang Y, et al. Establishment of lung cancer patient-derived xenograft models and primary cell lines for lung cancer study. J Transl Med. 2018; 16(1): 138.

[35] Deng M, Jiang Z, Li Y, et al. Effective elimination of adult B-lineage acute lymphoblastic leukemia by disulfiram/copper complex in vitro and in vivo in patient-derived xenograft models. Oncotarget. 2016; 7(50): 82200-82212.

[36] Clarkson B, Ohkita T, Ota K, Fried J. Studies of cellular proliferation in human leukemia. I. Estimation of growth rates of leukemic and normal hematopoietic cells in two adults with acute leukemia given single injections of tritiated thymidine *. J Clin Invest. 1967; 46(4): 506-529.

[37] Collins AT, Lang SH. A systematic review of the validity of patient derived xenograft (PDX) models: the implications for translational research and personalised medicine. PeerJ. 2018; 2018(11): 1-22.

[38] Koga Y, Ochiai A. Systematic review of patient-derived xenograft models for preclinical studies of anti-cancer drugs in solid tumors. Cells. 2019; 8(5): 418.

[39] Shi C, Chen X, Tan D. Development of patient-derived xenograft models of prostate cancer for maintaining tumor heterogeneity. Transl Androl Urol. 2019; 8(5): 519-528.

[40] Furukawa T, Kubota T, Watanabe M, et al. A metastatic model of human colon cancer constructed using cecal implantation of cancer tissue in nude mice. Surg Today. 1993; 23(5): 420-423.

[41] Xu C, Li X, Liu P, Li M, Luo F. Patientderived xenograft mouse models: a high fidelity tool for individualized medicine (Review). Oncol Lett. 2018; 17(1): 3-10.

[42] Kim MP, Evans DB, Wang H, Abbruzzese JL, Fleming JB, Gallick GE. Generation of orthotopic and heterotopic human pancreatic cancer xenografts in immunodeficient mice. Nat Protoc. 2009; 4(11): 1670-1680.

[43] Li G. Patient-derived xenograft models for oncology drug discovery. J Cancer Metastasis Treat. 2015; 0(0): 0.

[44] Wilding JL, Bodmer WF. Cancer cell lines for drug discovery and development. Cancer Res. 2014; 74(9): 2377-2384.

[45] Jung J, Seol HS, Chang S. The generation and application of patient-derived xenograft model for cancer research. Cancer Res Treat. 2018; 50(1): 1-10.

[46] Miyamoto S, Tanaka T, Hirosuna K, et al. Validation of a patient-derived xenograft model for cervical cancer based on genomic and phenotypic characterization. Cancers (Basel). 2022; 14(12): 2969.

[47] Reyal F, Guyader C, Decraene C, et al. Molecular profiling of patient-derived breast cancer xenografts. Breast Cancer Res. 2012; 14(1): R11.

[48] DeRose YS, Wang G, Lin YC, et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat Med. 2011; 17(11): 1514-1520.

[49] Lebedeva A, Shaykhutdinova Y, Seriak D, et al. Incidental germline findings during molecular profiling of tumor tissues for precision oncology: molecular survey and methodological obstacles. J Transl Med. 2022; 20(1): 29.

[50] Zou S, Ye M, Zhang J an JiH, Chen Y, Zhu X. Establishment and genetically characterization of patient-derived xenograft models of cervical cancer. BMC Med Genomics. 2022; 15(1): 1-11.

[51] Cronan MR. In the thick of it: formation of the tuberculous granuloma and its effects on host and therapeutic responses. Front Immunol. 2022; 13.

[52] Morton CL, Houghton PJ. Establishment of human tumor xenografts in immunodeficient mice. Nat Protoc. 2007; 2(2): 247-250.

[53] Das S, Dey MK, Devireddy R, Gartia MR. Biomarkers in cancer detection, diagnosis, and prognosis. Sensors (Basel). 2024; 24(1): 37.

[54] Kuwata T, Yanagihara K, Iino Y, et al. Establishment of novel gastric cancer patient-derived xenografts and cell lines: pathological comparison between primary tumor, patient-derived, and cell-line derived xenografts. Cells. 2019; 8(6): 585.

[55] Liu Y, Wu W, Cai C, Zhang H, Shen H, Han Y. Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduct Target Ther. 2023; 8(1).

[56] Schott CR, Koehne AL, Sayles LC, et al. Osteosarcoma PDX-derived cell line models for preclinical drug evaluation demonstrate metastasis inhibition by dinaciclib through a genome-targeted approach. Clin Cancer Res. 2024; 30(4): OF1-OF16.

[57] Sausville EA, Burger AM. Contributions of human tumor xenografts to anticancer drug development. Cancer Res. 2006; 66(7): 3351-3354.

[58] Fujii E, Kato A, Suzuki M. Patient-derived xenograft (PDX) models: characteristics and points to consider for the process of establishment. J Toxicol Pathol. 2020; 33(3): 153-160.

[59] Piña-Sánchez P, Chávez-González A, Ruiz-Tachiquín M, et al. Cancer biology, epidemiology, and treatment in the 21st century: current status and future challenges from a biomedical perspective. Cancer Control. 2021; 28: 1-21.

[60] Shah SP, Morin RD, Khattra J, et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature. 2009; 461(7265): 809-813.

[61] Campbell PJ, Pleasance ED, Stephens PJ, et al. Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing. Proc Natl Acad Sci. 2008; 105(35): 13081-13086.

[62] Kreso A, O’Brien CA, van Galen P, et al. Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science (1979). 2013; 339(6119): 543-548.

[63] Liu W, Cui Y, Zheng X, Yu K, Sun G. Application status and future prospects of the PDX model in lung cancer. Front Oncol. 2023; 13: 1098581.

[64] Marusyk A, Tabassum DP, Altrock PM, Almendro V, Michor F, Polyak K. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature. 2014; 514(7520): 54-58.

[65] Jacobo JM, Donnella HJ, Sobti S, Kaushik S, Goga A, Bandyopadhyay S. An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer. Sci Rep. 2024; 14(1): 1-15.

[66] Ebinger S, Özdemir EZ, Ziegenhain C, et al. Characterization of rare, dormant, and therapy-resistant cells in acute lymphoblastic leukemia. Cancer Cell. 2016; 30(6): 849-862.

[67] Uzozie AC, Ergin EK, Rolf N, et al. PDX models reflect the proteome landscape of pediatric acute lymphoblastic leukemia but divert in select pathways. J Exp Clin Cancer Res. 2021; 40(1).

[68] Klco JM, Spencer DH, Miller CA, et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell. 2014; 25(3): 379-392.

[69] Guillen KP, Fujita M, Butterfield AJ, et al. A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology. Nature Cancer. 2022; 3(2): 232-250.

[70] Ding L, Ellis MJ, Li S, et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature. 2010; 464(7291): 999-1005.

[71] Lee EM, Yee D, Busfield SJ, et al. Efficacy of an Fc-modified anti-CD123 antibody (CSL362) combined with chemotherapy in xenograft models of acute myelogenous leukemia in immunodeficient mice. Haematologica. 2015; 100(7): 914-926.

[72] Sykes DB, Kfoury YS, Mercier FE, et al. Inhibition of dihydroorotate dehydrogenase overcomes differentiation blockade in acute myeloid leukemia. Cell. 2016; 167(1): 171-186.e15. e15.

[73] Hahn CK, Berchuck JE, Ross KN, et al. Proteomic and genetic approaches identify Syk as an AML target. Cancer Cell. 2009; 16(4): 281-294.

[74] Etchin J, Montero J, Berezovskaya A, et al. Activity of a selective inhibitor of nuclear export, selinexor (KPT-330), against AML-initiating cells engrafted into immunosuppressed NSG mice. Leukemia. 2016; 30(1): 0-0.

[75] Veitonmäki N, Hansson M, Zhan F, et al. A human ICAM-1 antibody isolated by a function-first approach has potent macrophage-dependent antimyeloma activity in vivo. Cancer Cell. 2013; 23(4): 502-515.

[76] Wu SC, Li LS, Kopp N, et al. Activity of the type II JAK2 inhibitor CHZ868 in B cell acute lymphoblastic leukemia. Cancer Cell. 2015; 28(1): 29-41.

[77] Solit DB, Garraway LA, Pratilas CA, et al. BRAF mutation predicts sensitivity to MEK inhibition. Nature. 2006; 439(7074): 358-362.

[78] Lawson DA, Bhakta NR, Kessenbrock K, et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature. 2015; 526(7571): 131-135.

[79] El Ayachi I, Fatima I, Wend P, et al. The WNT10B network is associated with survival and metastases in chemoresistant triple-negative breast cancer. Cancer Res. 2019; 79(5): 982-993.

[80] Yu J, Qin B, Moyer AM, et al. DNA methyltransferase expression in triple-negative breast cancer predicts sensitivity to decitabine. J Clin Invest. 2018; 128(6): 2376-2388.

[81] Wang CJ, Tong PJ, Zhu MY. The combinational therapy of trastuzumab and cetuximab inhibits tumor growth in a patient-derived tumor xenograft model of gastric cancer. Clin Transl Oncol. 2016; 18(5): 507-514.

[82] Siu IM, Ruzevick J, Zhao Q, et al. Erlotinib inhibits growth of a patient-derived chordoma xenograft. Rutteman GR, ed. PLoS One. 2013; 8(11): e78895.

[83] Misale S, Bozic I, Tong J, et al. Vertical suppression of the EGFR pathway prevents onset of resistance in colorectal cancers. Nat Commun. 2015; 6(1): 8305.

[84] Jin K, Li G, Cui B, et al. Assessment of a novel VEGF targeted agent using patient-derived tumor tissue xenograft models of colon carcinoma with lymphatic and hepatic metastases. Welm AL, ed. PLoS One. 2011; 6(12): e28384.

[85] JIN K, LAN H, CAO F, et al. Differential response to EGFR-and VEGF-targeted therapies in patient-derived tumor tissue xenograft models of colon carcinoma and related metastases. Int J Oncol. 2012; 41(2): 583-588.

[86] Herrero A, Pinto A, Colón-Bolea P, et al. Small molecule inhibition of ERK dimerization prevents tumorigenesis by RAS-ERK pathway oncogenes. Cancer Cell. 2015; 28(2): 170-182.

[87] Storm EE, Durinck S, de Sousa e Melo F, et al. Targeting PTPRK-RSPO3 colon tumours promotes differentiation and loss of stem-cell function. Nature. 2016; 529(7584): 97-100.

[88] Wu X, Zhang J, Zhen R, et al. Trastuzumab anti-tumor efficacy in patient-derived esophageal squamous cell carcinoma xenograft (PDECX) mouse models. J Transl Med. 2012; 10(1): 180.

[89] Lu J, Li G, He K, et al. Luteolin exerts a marked antitumor effect in cMet-overexpressing patient-derived tumor xenograft models of gastric cancer. J Transl Med. 2015; 13(1): 42.

[90] Li J, Davies BR, Han S, et al. The AKT inhibitor AZD5363 is selectively active in PI3KCA mutant gastric cancer, and sensitizes a patient-derived gastric cancer xenograft model with PTEN loss to Taxotere. J Transl Med. 2013; 11(1): 241.

[91] Martin P, Stewart E, Pham NA, et al. Cetuximab inhibits T790M-Mediated resistance to epidermal growth factor receptor tyrosine kinase inhibitor in a lung adenocarcinoma patient-derived xenograft mouse model. Clin Lung Cancer. 2016; 17(5): 375-383.e2. e2.

[92] Weeden CE, Holik AZ, Young RJ, et al. Cisplatin increases sensitivity to FGFR inhibition in patient-derived xenograft models of lung squamous cell carcinoma. Mol Cancer Ther. 2017; 16(8): 1610-1622.

[93] Gong K, Guo G, Gerber DE, et al. TNF-driven adaptive response mediates resistance to EGFR inhibition in lung cancer. J Clin Invest. 2018; 128(6): 2500-2518.

[94] Han B, Park D, Li R, et al. Small-molecule Bcl2 BH4 antagonist for lung cancer therapy. Cancer Cell. 2015; 27(6): 852-863.

[95] Sugimoto K, Hayakawa F, Shimada S, et al. Discovery of a drug targeting microenvironmental support for lymphoma cells by screening using patient-derived xenograft cells. Sci Rep. 2015; 5(1): 13054.

[96] Garner EF, Williams AP, Stafman LL, et al. FTY720 decreases tumorigenesis in group 3 medulloblastoma patient-derived xenografts. Sci Rep. 2018; 8(1): 6913.

[97] Yamamoto M, Zhao M, Hiroshima Y, et al. Efficacy of tumor-targeting salmonella A1-R on a melanoma patient-derived orthotopic xenograft (PDOX) nude-mouse model. Mattei F. PLoS One. 2016; 11(8): e0160882.

[98] Einarsdottir BO, Karlsson J, Söderberg EMV, et al. A patient-derived xenograft pre-clinical trial reveals treatment responses and a resistance mechanism to karonudib in metastatic melanoma. Cell Death Dis. 2018; 9(8): 810.

[99] Talebi A, Dehairs J, Rambow F, et al. Sustained SREBP-1-dependent lipogenesis as a key mediator of resistance to BRAF-targeted therapy. Nat Commun. 2018; 9(1): 2500.

[100] Gad H, Koolmeister T, Jemth AS, et al. MTH1 inhibition eradicates cancer by preventing sanitation of the dNTP pool. Nature. 2014; 508(7495): 215-221.

[101] Mason JM, Lin DCC, Wei X, et al. Functional characterization of CFI-400945, a Polo-like kinase 4 inhibitor, as a potential anticancer agent. Cancer Cell. 2014; 26(2): 163-176.

[102] Girotti MR, Lopes F, Preece N, et al. Paradox-breaking RAF inhibitors that also target SRC are effective in drug-resistant BRAF mutant melanoma. Cancer Cell. 2015; 27(1): 85-96.

[103] Chauhan D, Tian Z, Nicholson B, et al. A small molecule inhibitor of ubiquitin-specific protease-7 induces apoptosis in multiple myeloma cells and overcomes bortezomib resistance. Cancer Cell. 2012; 22(3): 345-358.

[104] Ham J, Costa C, Sano R, et al. Exploitation of the apoptosis-primed state of MYCN-amplified neuroblastoma to develop a potent and specific targeted therapy combination. Cancer Cell. 2016; 29(2): 159-172.

[105] Chao MP, Alizadeh AA, Tang C, et al. Anti-CD47 antibody synergizes with rituximab to promote phagocytosis and eradicate non-Hodgkin lymphoma. Cell. 2010; 142(5): 699-713.

[106] Thomas EK, Cancelas JA, Chae HD, et al. Rac guanosine triphosphatases represent integrating molecular therapeutic targets for BCR-ABL-induced myeloproliferative disease. Cancer Cell. 2007; 12(5): 467-478.

[107] Torphy RJ, Tignanelli CJ, Kamande JW, et al. Circulating tumor cells as a biomarker of response to treatment in patient-derived xenograft mouse models of pancreatic adenocarcinoma. Kyprianou N, ed. PLoS One. 2014; 9(2): e89474.

[108] Zhou H, Qian W, Uckun FM, et al. IGF1 receptor targeted theranostic nanoparticles for targeted and image-guided therapy of pancreatic cancer. ACS Nano. 2015; 9(8): 7976-7991.

[109] Witkiewicz AK, Balaji U, Eslinger C, et al. Integrated patient-derived models delineate individualized therapeutic vulnerabilities of pancreatic cancer. Cell Rep. 2016; 16(7): 2017-2031.

[110] Rajeshkumar NV, Yabuuchi S, Pai SG, et al. Treatment of pancreatic cancer patient–derived xenograft panel with metabolic inhibitors reveals efficacy of phenformin. Clin Cancer Res. 2017; 23(18): 5639-5647.

[111] Andersen RJ, Mawji NR, Wang J, et al. Regression of castrate-recurrent prostate cancer by a small-molecule inhibitor of the amino-terminus domain of the androgen receptor. Cancer Cell. 2010; 17(6): 535-546.

[112] Yoshida T, Kinoshita H, Segawa T, et al. Antiandrogen bicalutamide promotes tumor growth in a novel androgen-dependent prostate cancer xenograft model derived from a bicalutamide-treated patient. Cancer Res. 2005; 65(21): 9611-9616.

[113] Chen W, Hill H, Christie A, et al. Targeting renal cell carcinoma with a HIF-2 antagonist. Nature. 2016; 539(7627): 112-117.

[114] Murakami T, DeLong J, Eilber FC, et al. Tumor-targeting Salmonella typhimurium A1-R in combination with doxorubicin eradicate soft tissue sarcoma in a patient-derived orthotopic xenograft (PDOX) model. Oncotarget. 2016; 7(11): 12783-12790.

[115] Mohammad HP, Smitheman KN, Kamat CD, et al. A DNA hypomethylation signature predicts antitumor activity of LSD1 inhibitors in SCLC. Cancer Cell. 2015; 28(1): 57-69.

[116] Wang Y, Feng L, Liu H, et al. Abstract 4236: pDX-derived organoids (PDXO) are valuable tools to unveil the shortcomings of new anti-cancer drug candidates. Cancer Res. 2024; 84(6): 4236-4236. Supplement.

[117] Loskutov J, Oswald E, Wedeken L, Reinhard CJ, Schueler J, Regenbrecht CR. Abstract 240: mix and match—A comprehensive pipeline for matched patient-derived PDX and PD3D® modelsfor cancer research and preclinical drug development. Cancer Res. 2024; 84(6): 240-240. Supplement.

[118] Tanaka R, Kimura K, Tani N, et al. Abstract C068: construction of preclinical study model for optimal anticancer drug selection using PDX model in pancreatic cancer. Cancer Res. 2024; 84(2): C068-C068. Supplement.

[119] Kinose Y, Kasuya K, Wang Y, et al. Abstract B059: a patient-derived xenograft (PDX) orthotopic mouse model of rare gynecologic mesonephric-like adenocarcinoma as a platform of personalized medicine. Cancer Res. 2024; 84(5): B059-B059. Supplement_2.

[120] Kim Y, Lee S, Cho S, et al. High-throughput functional evaluation of human cancer-associated mutations using base editors. Nat Biotechnol. 2022; 40(6): 874.

[121] Berger AH, Brooks AN, Wu X, et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell. 2016; 30(2): 214-228.

[122] Strupp C, Corvaro M, Cohen SM, et al. Increased cell proliferation as a key event in chemical carcinogenesis: application in an integrated approach for the testing and assessment of non-genotoxic carcinogenesis. Int J Mol Sci. 2023; 24(17): 13246.

[123] Pearson AT, Finkel KA, Warner KA, et al. Patient-derived xenograft (PDX) tumors increase growth rate with time. Oncotarget. 2016; 7(7): 7993-8005.

[124] Hanselmann RG, Welter C. Origin of cancer: cell work is the key to understanding cancer initiation and progression. Front Cell Dev Biol. 2022; 10.

[125] Atashzar MR, Baharlou R, Karami J, et al. Cancer stem cells: a review from origin to therapeutic implications. J Cell Physiol. 2020; 235(2): 790-803.

[126] Rezayatmand H, Razmkhah M, Razeghian-Jahromi I. Drug resistance in cancer therapy: the Pandora’s Box of cancer stem cells. Stem Cell Res Ther. 2022; 13(1): 181.

[127] Singh SK, Hawkins C, Clarke ID, et al. Identification of human brain tumour initiating cells. Nature. 2004; 432(7015): 396-401.

[128] Liu H, Patel MR, Prescher JA, et al. Cancer stem cells from human breast tumors are involved in spontaneous metastases in orthotopic mouse models. Proc Natl Acad Sci USA. 2010; 107(42): 18115-18120.

[129] Gill RPK, Gantchev J, Villarreal AM, et al. Understanding cell lines, patient-derived xenograft and genetically engineered mouse models used to study cutaneous T-cell lymphoma. Cells. 2022; 11(4): 593.

[130] Riolo G, Cantara S, Marzocchi C, Ricci C. miRNA targets: from prediction tools to experimental validation. Methods Protoc. 2021; 4(1): 1-20.

[131] Nucera S, Giustacchini A, Boccalatte F, et al. miRNA-126 orchestrates an oncogenic program in B cell precursor acute lymphoblastic leukemia. Cancer Cell. 2016; 29(6): 905-921.

[132] Voloshin N, Tyurin-Kuzmin P, Karagyaur M, Akopyan Z, Kulebyakin K. Practical use of immortalized cells in medicine: current advances and future perspectives. Int J Mol Sci. 2023; 24(16): 12716.

[133] Gillet JP, Calcagno AM, Varma S, et al. Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc Natl Acad Sci USA. 2011; 108(46): 18708-18713.

[134] Ellis LM, Fidler IJ. Finding the tumor copycat: therapy fails, patients don’t. Nat Med. 2010; 16(9): 974-975.

[135] Sarhadi VK, Armengol G. Molecular biomarkers in cancer. Biomolecules. 2022; 12(8): 1021.

[136] Bradford JR, Wappett M, Beran G, et al. Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumor and stromal specific biomarkers. Oncotarget. 2016; 7(15): 20773-20787.

[137] Zhao Q, Zhou H, Liu Q, et al. Prognostic value of the expression of cancer stem cell-related markers CD133 and CD44 in hepatocellular carcinoma: from patients to patient-derived tumor xenograft models. Oncotarget. 2016; 7(30): 47431-47443.

[138] Huang JL, Oshi M, Endo I, Takabe K. Clinical relevance of stem cell surface markers CD133, CD24, and CD44 in colorectal cancer. Am J Cancer Res. 2021; 11(10): 5141.

[139] Skowron KB, Pitroda SP, Namm JP, et al. Basal tumor cell isolation and patient-derived xenograft engraftment identify high-risk clinical bladder cancers. Sci Rep. 2016; 6(1): 35854.

[140] Gardner EE, Lok BH, Schneeberger VE, et al. Chemosensitive relapse in small cell lung cancer proceeds through an EZH2-SLFN11 axis. Cancer Cell. 2017; 31(2): 286-299.

[141] Fujii E, Kato A, Suzuki M. Patient-derived xenograft (PDX) models: characteristics and points to consider for the process of establishment. J Toxicol Pathol. 2020; 33(3): 153.

[142] Wang Z, Zhang H, Xu S, Liu Z, Cheng Q. The adaptive transition of glioblastoma stem cells and its implications on treatments. Signal Transduct Target Ther. 2021; 6(1).

[143] Bagó JR, Alfonso-Pecchio A, Okolie O, et al. Therapeutically engineered induced neural stem cells are tumour-homing and inhibit progression of glioblastoma. Nat Commun. 2016; 7(1): 10593.

[144] Garcia PL, Miller AL, Yoon KJ. Patient-derived xenograft models of pancreatic cancer: overview and comparison with other types of models. Cancers (Basel). 2020; 12(5): 1327.

[145] Yano S, Hiroshima Y, Maawy A, et al. Color-coding cancer and stromal cells with genetic reporters in a patient-derived orthotopic xenograft (PDOX) model of pancreatic cancer enhances fluorescence-guided surgery. Cancer Gene Ther. 2015; 22(7): 344-350.

[146] Hiroshima Y, Maawy A, Metildi CA, et al. Successful fluorescence-guided surgery on human colon cancer patient-derived orthotopic xenograft mouse models using a fluorophore-conjugated anti-CEA antibody and a portable imaging system. J Laparoendosc Adv Surg Tech. 2014; 24(4): 241-247.

[147] Suetsugu A, Katz M, Fleming J, et al. Multi-color palette of fluorescent proteins for imaging the tumor microenvironment of orthotopic tumorgraft mouse models of clinical pancreatic cancer specimens. J Cell Biochem. 2012; 113(7): 2290-2295.

[148] Willey CD, Gilbert AN, Anderson JC, Gillespie GY. Patient-derived xenografts as a model system for radiation research. Semin Radiat Oncol. 2015; 25(4): 273-280.

[149] Hutchinson L, Kirk R. High drug attrition rates—where are we going wrong?. Nat Rev Clin Oncol. 2011; 8(4): 189-190.

[150] Tentler JJ, Tan AC, Weekes CD, et al. Patient-derived tumour xenografts as models for oncology drug development. Nat Rev Clin Oncol. 2012; 9(6): 338-350.

[151] Whittle JR, Lewis MT, Lindeman GJ, Visvader JE. Patient-derived xenograft models of breast cancer and their predictive power. Breast Cancer Res. 2015; 17(1): 17.

[152] Murayama T, Gotoh N. Patient-derived xenograft models of breast cancer and their application. Cells. 2019; 8(6): 621.

[153] Izumchenko E, Meir J, Bedi A, Wysocki P, Hoque M, Sidransky D. Patient-derived xenografts as tools in pharmaceutical development. Clin Pharmacol Ther. 2016; 99(6): 612-621.

[154] Galluzzi L, Aryankalayil MJ, Coleman CN, Formenti SC. Emerging evidence for adapting radiotherapy to immunotherapy. Nat Rev Clin Oncol. 2023; 20(8): 543-557.

[155] Mokhtari RB, Homayouni TS, Baluch N, et al. Combination therapy in combating cancer. Oncotarget. 2017; 8(23): 38022-38043.

[156] Goel S, Wang Q, Watt AC, et al. Overcoming therapeutic resistance in HER2-positive breast cancers with CDK4/6 inhibitors. Cancer Cell. 2016; 29(3): 255-269.

[157] Vora SR, Juric D, Kim N, et al. CDK 4/6 inhibitors sensitize PIK3CA mutant breast cancer to PI3K inhibitors. Cancer Cell. 2014; 26(1): 136-149.

[158] Murayama T, Gotoh N. Patient-derived xenograft models of breast cancer and their application. Cells. 2019; 8(6): 621.

[159] Kumari R, Xu X, Li HQX. Translational and clinical relevance of PDX-derived organoid models in oncology drug discovery and development. Curr Protoc. 2022; 2(7).

[160] Navarro-Yepes J, Kettner NM, Rao X, et al. Abemaciclib is effective in palbociclib-resistant hormone receptor-positive metastatic breast cancers. Cancer Res. 2023; 83(19): 3264-3283.

[161] Topp MD, Hartley L, Cook M, et al. Molecular correlates of platinum response in human high-grade serous ovarian cancer patient-derived xenografts. Mol Oncol. 2014; 8(3): 656-668.

[162] Marangoni E, Vincent-Salomon A, Auger N, et al. A new model of patient tumor-derived breast cancer xenografts for preclinical assays. Clin Cancer Res. 2007; 13(13): 3989-3998.

[163] Zhang X, Lewis MT. Establishment of patient-derived xenograft (PDX) models of human breast cancer. Curr Protoc Mouse Biol. 2013; 3(1): 21-29.

[164] Sivanand S, Pena-Llopis S, Zhao H, et al. A validated tumorgraft model reveals activity of dovitinib against renal cell carcinoma. Sci Transl Med. 2012; 4(137): 137ra75-137ra75.

[165] Hidalgo M, Bruckheimer E, Rajeshkumar NV, et al. A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol Cancer Ther. 2011; 10(8): 1311-1316.

[166] Hidalgo M, Amant F, Biankin AVV, et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 2014; 4(9): 998-1013.

[167] Julien S, Merino-Trigo A, Lacroix L, et al. Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer. Clin Cancer Res. 2012; 18(19): 5314-5328.

[168] Koga Y, Ochiai A. Systematic review of patient-derived xenograft models for preclinical studies of anti-cancer drugs in solid tumors. Cells. 2019; 8(5): 418.

[169] Clohessy JG, Pandolfi PP. Mouse hospital and co-clinical trial project—from bench to bedside. Nat Rev Clin Oncol. 2015; 12(8): 491-498.

[170] Heid I, Steiger K, Trajkovic-Arsic M, et al. Co-clinical assessment of tumor cellularity in pancreatic cancer. Clin Cancer Res. 2017; 23(6): 1461-1470.

[171] Owonikoko TK, Zhang G, Kim HS, et al. Patient-derived xenografts faithfully replicated clinical outcome in a phase II co-clinical trial of arsenic trioxide in relapsed small cell lung cancer. J Transl Med. 2016; 14(1): 111.

[172] Lanigan TM, Kopera HC, Saunders TL. Principles of genetic engineering. Genes (Basel). 2020; 11(3): 291.

[173] Wang J, Chen C, Wang L, et al. Patient-derived tumor organoids: new progress and opportunities to facilitate precision cancer immunotherapy. Front Oncol. 2022; 12.

[174] Xue B, Schüler J, Harrod CM, Lashuk K, Bomya Z, Hribar KC. A novel hydrogel-based 3D in vitro tumor panel of 30 PDX models incorporates tumor, stromal and immune cell compartments of the TME for the screening of oncology and immuno-therapies. Cells. 2023; 12(8): 1145.

[175] Östman A. The tumor microenvironment controls drug sensitivity. Nat Med. 2012; 18(9): 1332-1334.

[176] Goto T. Patient-derived tumor xenograft models: toward the establishment of precision cancer medicine. J Pers Med. 2020; 10(3): 1-14.

[177] Kim Y, Kim D, Cao B, Carvajal R, Kim M. PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients. BMC Bioinf. 2020; 21(1).

[178] Martinez-Ruiz L, López-Rodríguez A, Florido J, et al. Patient-derived tumor models in cancer research: evaluation of the oncostatic effects of melatonin. Biomed Pharmacother. 2023; 167: 115581.

[179] Walters DM, Stokes JB, Adair SJ, et al. Clinical, molecular and genetic validation of a murine orthotopic xenograft model of pancreatic adenocarcinoma using fresh human specimens. PLoS One. 2013; 8(10): e77065.

[180] Mantovani A, Marchesi F, Malesci A, Laghi L. Europe PMC funders group tumor-associated macrophages as treatment targets in oncology. Nat Rev Clin Oncol. 2018; 14(7): 399-416.

[181] Choi N, Kim K, Shin KH, et al. Malignant and borderline phyllodes tumors of the breast: a multicenter study of 362 patients (KROG 16-08). Breast Cancer Res Treat. 2018; 171(2): 335-344.

[182] Cassidy JW, Caldas C, Bruna A. Europe PMC funders group maintaining tumour heterogeneity in patient-derived tumour xenografts. Cancer Res. 2016; 75(15): 2963-2968.

[183] Bartucci M, Ferrari AC, Kim IY, Ploss A, Yarmush M, Sabaawy HE. Personalized medicine approaches in prostate cancer employing patient derived 3D organoids and humanized mice. Front Cell Dev Biol. 2016; 4(JUN): 1-8.

[184] Pompili L, Porru M, Caruso C, Biroccio A, Leonetti C. Patient-derived xenografts: a relevant preclinical model for drug development. J Exp Clin Cancer Res. 2016; 35(1): 1-8.

[185] Sileni VC, Pigozzo J, Ascierto PA, et al. Efficacy and safety of ipilimumab in elderly patients with pretreated advanced melanoma treated at Italian centres through the expanded access programme. J Exp Clin Cancer Res. 2014; 33(1): 1-7.

[186] Khalil DN, Smith EL, Brentjens RJ, Wolchok JD. The future of cancer treatment: immunomodulation, CARs and combination immunotherapy. Nat Rev Clin Oncol. 2016; 13(5): 273-290.

[187] Abdelrahim M, Esmail A, Abudayyeh A, et al. Transplant oncology: an evolving field in cancer care. Cancers (Basel). 2021; 13(19): 4911.

[188] Courtney D, Davey MG, Moloney BM, et al. Breast cancer recurrence: factors impacting occurrence and survival. Ir J Med Sci. 2022; 191(6): 2501.

[189] Mattar M, Abdel-Wahab O, de Stanchina E. Acquisition and storage of clinical samples to establish PDX models. Patient Derived Tumor Xenograft Models: Promise, Potential and Practice. 2017: 109-118. Published online January 1.

[190] Pate A, Emsley R, Sperrin M, Martin GP, van Staa T. Impact of sample size on the stability of risk scores from clinical prediction models: a case study in cardiovascular disease. Diagn Progn Res. 2020; 4(1).

[191] Kamili A, Gifford AJ, Li N, et al. Accelerating development of high-risk neuroblastoma patient-derived xenograft models for preclinical testing and personalised therapy. Br J Cancer. 2020; 122(5): 680-691.

[192] Huang M, Ramsey S, Xue W, Xie J, Pellissier J, Briggs A. Conceptual framework and methodological challenges for modeling effectiveness in oncology treatment sequence models. Pharmacoeconomics. 2022; 40(3): 257-268.

[193] Ben-David U, Ha G, Tseng YY, et al. Patient-derived xenografts undergo mouse-specific tumor evolution. Nat Genet. 2017; 49(11): 1567-1575.

[194] Wiekmeijer AS, Pike-Overzet K, Brugman MH, et al. Sustained engraftment of cryopreserved human bone marrow CD34 + cells in young adult NSG mice. Biores Open Access. 2014; 3(3): 110-116.

[195] Garralda E, Paz K, Lopez-Casas PP, et al. Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment. Clin Cancer Res. 2014; 20(9): 2476-2484.

[196] Jung HY, Kim TH, Lee JE, et al. PDX models of human lung squamous cell carcinoma: consideration of factors in preclinical and co-clinical applications. J Transl Med. 2020; 18(1).

[197] Shi M, Wang Y, Lin D, Wang Y. Patient-derived xenograft models of neuroendocrine prostate cancer. Cancer Lett. 2022; 525: 160-169.

[198] Lee RY, Wu Y, Goh D, et al. Application of artificial intelligence to in vitro tumor modeling and characterization of the tumor microenvironment. Adv Healthc Mater. 2023; 12(14).

[199] Trebeschi S, Drago SG, Birkbak NJ, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol. 2019; 30(6): 998-1004.

[200] Maniatis A, Bourou S, Anastasakis Z, Psychogios K. VOXReality: immersive XR experiences combining language and vision AI models. AHFE International. 2023; 70.

[201] Li J, Mi W, Guo Y, et al. Artificial intelligence for histological subtype classification of breast cancer: combining multi-scale feature maps and the recurrent attention model. Histopathology. 2022; 80(5): 836-846.

[202] Data from Next-Generation Sequencing Analysis and Algorithms for PDX and CDX Models. Published online April 3, 2023.

[203] Gao C, Killeen BD, Hu Y, et al. SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments. Published online June 13, 2022.

[204] Panfilova AS, Turdakov DY. Applying explainable artificial intelligence methods to models for diagnosing personal traits and cognitive abilities by social network data. Sci Rep. 2024; 14(1): 5369.

[205] D’Amore B, Smolinski-Zhao S, Daye D, Uppot RN. Role of machine learning and artificial intelligence in interventional oncology. Curr Oncol Rep. 2021; 23(6).

[206] Fabelo H, Halicek M, Ortega S, et al. Deep learning-based framework for in vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors (Basel). 2019; 19(4): 920.

[207] Farahmand S, Fernandez AI, Ahmed FS, et al. Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer. Mod Pathol. 2022; 35(1): 44-51.

[208] Johannet P, Coudray N, Donnelly DM, et al. Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma. Clin Cancer Res. 2021; 27(1): 131-140.

[209] Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine learning and deep learning in medical imaging: intelligent imaging. J Med Imaging Radiat Sci. 2019; 50(4): 477-487.

[210] Foroughi pour A, White BS, Park J, Sheridan TB, Chuang JH. Deep learning features encode interpretable morphologies within histological images. Sci Rep. 2022; 12(1).

[211] White BS, Woo XY, Koc S, et al. A pan-cancer patient-derived xenograft histology image repository with genomic and pathologic annotations enables deep learning analysis. Cancer Res. 2024; 84(13): 2060-2072.

[212] Gao H, Korn JM, Ferretti S, et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat Med. 2015; 21(11): 1318-1325.

[213] Koc K, Ekmekcioğlu Ö, Gurgun AP. Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods. Engineering, Construction and Architectural Management. 2023; 30(9): 4486-4517.

[214] Kim Y, Kim D, Cao B, Carvajal R, Kim M. PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients. BMC Bioinf. 2020; 21(1): 288.

[215] Koc S, Lloyd MW, Grover JW, et al. PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery. NAR Cancer. 2022; 4(2).

[216] Cully M. Cancer: xenograft encyclopaedia identifies drug combination opportunities. Nat Rev Drug Discov. 2015; 14(12): 818-819.

[217] Su AI, Wiltshire T, Batalov S, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci. 2004; 101(16): 6062-6067.

[218] Okazawa Y, Mizukoshi K, Koyama Y, et al. High-sensitivity detection of micrometastases generated by GFP lentivirus-transduced organoids cultured from a patient-derived colon tumor. Journal of Visualized Experiments. 2018; 14(136): 57374.

[219] Rashid MBMA, Chow EKH. Artificial intelligence-driven designer drug combinations: from drug development to personalized medicine. SLAS Technol. 2019; 24(1): 124-125.

[220] Zhao Y, Zeng D, Rush AJ, Kosorok MR. Estimating individualized treatment rules using outcome weighted learning. J Am Stat Assoc. 2012; 107(499): 1106-1118.

[221] Ren S, Jin Y, Chen Y, Shen B. CRPMKB: a knowledge base of cancer risk prediction models for systematic comparison and personalized applications. Bioinformatics. 2022; 38(6): 1669-1676.

[222] Huh D, Matthews BD, Mammoto A, Montoya-Zavala M, Yuan Hsin H, Ingber DE. Reconstituting organ-level lung functions on a chip. Science (1979). 2010; 328(5986): 1662-1668.

[223] Murphy SV, Atala A. 3D bioprinting of tissues and organs. Nat Biotechnol. 2014; 32(8): 773-785.

[224] Galarraga JH, Kwon MY, Burdick JA. 3D bioprinting via an in situ crosslinking technique towards engineering cartilage tissue. Sci Rep. 2019; 9(1).

[225] Lama J, Buhidma Y, Fletcher EJR, Duty S. Animal models of Parkinson’s disease: a guide to selecting the optimal model for your research. Neuronal Signal. 2021; 5(4): 20210026.

[226] Zhang YS, Arneri A, Bersini S, et al. Bioprinting 3D microfibrous scaffolds for engineering endothelialized myocardium and heart-on-a-chip. Biomaterials. 2016; 110: 45-59.

[227] Yarali E, Mirzaali MJ, Ghalayaniesfahani A, et al. 4D Printing for Biomedical Applications. Adv Mater. 2024; 36(31): 2402301.

[228] Ma C, Peng Y, Li H, Chen W. Organ-on-a-Chip: a new paradigm for drug development. Trends Pharmacol Sci. 2021; 42(2): 119.

[229] Siolas D, Hannon GJ. Patient-derived tumor xenografts: transforming clinical samples into mouse models. Cancer Res. 2013; 73(17): 5315-5319.

[230] Kamiyama T, Yokoo H, Furukawa JI, et al. Identification of novel serum biomarkers of hepatocellular carcinoma using glycomic analysis. Hepatology. 2013; 57(6): 2314-2325.

[231] Yachida S, Zhong Y, Patrascu R, et al. Establishment and characterization of a new cell line, A99, from a primary small cell carcinoma of the pancreas. Pancreas. 2011; 40(6): 905-910.

[232] Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012; 366(10): 883-892.

[233] Liu W, Cui Y, Zheng X, Yu K, Sun G. Application status and future prospects of the PDX model in lung cancer. Front Oncol. 2023; 13.

[234] Zhu X, Xu X, Zhang B, et al. Individualized therapy based on the combination of mini-PDX and NGS for a patient with metastatic AFP-producing and HER-2 amplified gastric cancer. Oncol Lett. 2022; 24(5).

[235] Kim IG, Park SA, Lee SH, et al. Transplantation of a 3D-printed tracheal graft combined with iPS cell-derived MSCs and chondrocytes. Sci Rep. 2020; 10(1).

[236] Vidal L, Kampleitner C, Krissian S, et al. Regeneration of segmental defects in metatarsus of sheep with vascularized and customized 3D-printed calcium phosphate scaffolds. Sci Rep. 2020; 10(1).

[237] Hidalgo M, Amant F, Biankin AV, et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 2014; 4(9): 998-1013.

RIGHTS & PERMISSIONS

2024 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.