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REVIEW ARTICLE

Integrating patient-derived organoids and multi-omics to decode spatiotemporal therapeutic resistance

Cize Gao1 ,  Jianing Chen1 ,  Leilei Wu2 ,  Boyue Pang1 ,  Chunxia Su1*
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1 Department of Comprehensive Oncology Center, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, China
2 Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
OR 2026, 2(1), 026020002 https://doi.org/10.36922/OR026020002
Received: 10 January 2026 | Revised: 13 February 2026 | Accepted: 24 February 2026 | Published online: 9 March 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Intratumoral heterogeneity, alongside the dynamic evolution of cancer cells, continues to pose principal obstacles to efficacious cancer therapies. Although patient-derived organoids (PDOs) represent the gold standard for recapitulating patient-specific histopathology, their full utility emerges only through integration with high-resolution molecular profiling. This review synthesizes recent progress in combining PDOs with multi-omics approaches—including genomics, single-cell transcriptomics, and spatial omics—to elucidate the underpinnings of therapeutic resistance. We initially explore how organoid genomic profiling can monitor clonal dynamics and subclonal selection under therapeutic pressure. Next, we underscore the contributions of single-cell RNA sequencing in delineating transcriptional plasticity, detecting infrequent drug-tolerant persister populations, and charting nongenetic adaptive pathways. Of particular importance, we address the nascent domain of spatial transcriptomics, which preserves the structural integrity of organoids to uncover how proximate cell–cell interactions and niche elements shield tumor cells from therapeutic insult. Through the fusion of these multifaceted datasets, we advance a novel paradigm that frames resistance not as a fixed binary state, but as a spatiotemporally evolving adaptive phenomenon. The review culminates in delineating the translational promise of this synergistic paradigm for devising combination regimens that concurrently address genetic aberrations and adaptive microenvironments.

Keywords
Patient-derived organoids
Multi-omics profiling
Therapeutic resistance
Spatiotemporal heterogeneity
Clonal evolution
Single-cell RNA sequencing
Funding
This work was supported by the Key Special Projects of the “14th Five-Year Plan” of the National Key R&D Program of China (2023YFC2508601, 2023YFC2508604, and 2023YFC2508605), the National Natural Science Foundation of China (grant numbers: 82072568, 82373320), the Shanghai Shenkang Hospital Development Center (grant number: SHDC12020110), the Shanghai Shenkang Development Research Physician Project (grant number: SHDC2022CRD048), and the Tongji University Medicine-X Interdisciplinary Research Initiative (2025–0554–ZD–08).
Conflict of interest
The authors declare that they have no competing interests.
References
  1. Ge M, Chen XY, Huang P, et al. Understanding and overcoming multidrug resistance in cancer. Nat Rev Clin Oncol. 2025;22(10):760-780. doi: 10.1038/s41571-025-01059-1

 

  1. Lei ZN, Tian Q, Teng QX, et al. Understanding and targeting resistance mechanisms in cancer. MedComm. 2023;4(3):e265. doi: 10.1002/mco2.265

 

  1. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15(2):81-94. doi: 10.1038/nrclinonc.2017.166

 

  1. Gatenby R, Brown J. The Evolution and Ecology of Resistance in Cancer Therapy. Cold Spring Harb Perspect Med. 2018;8(3):a033415. doi: 10.1101/cshperspect.a033415

 

  1. Milne A, Marusyk A, Maini PK, Anderson ARA, Picco N. The role of environmentally mediated drug resistance in facilitating the spatial distribution of residual disease. Commun Biol. 2025;8(1):1189. doi: 10.1038/s42003-025-08585-9

 

  1. He W, McCoy MD, Riggins RB, Beckman RA, Yeang CH. Personalized cancer treatment strategies incorporating irreversible and reversible drug resistance mechanisms. NPJ Syst Biol Appl. 2025;11(1):70. doi: 10.1038/s41540-025-00547-5

 

  1. Soragni A, Knudsen ES, O’Connor TN, et al. Acquired resistance in cancer: towards targeted therapeutic strategies. Nat Rev Cancer. 2025;25(8):613-633. doi: 10.1038/s41568-025-00824-9

 

  1. Goyal Y, Busch GT, Pillai M, et al. Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells. Nature. 2023;620(7974):651-659. doi: 10.1038/s41586-023-06342-8

 

  1. Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell. 2017;168(4):707-723. doi: 10.1016/j.cell.2017.01.017

 

  1. Colonna G. Overcoming Barriers in Cancer Biology Research: Current Limitations and Solutions. Cancers. 2025;17(13):2102. doi: 10.3390/cancers17132102

 

  1. Thorel L, Perreard M, Florent R, et al. Patient-derived tumor organoids: a new avenue for preclinical research and precision medicine in oncology. Exp Mol Med. 2024;56(7):1531-1551. doi: 10.1038/s12276-024-01272-5

 

  1. Li P, Huang M, Li M, et al. Combining molecular characteristics and therapeutic analysis of PDOs predict clinical responses and guide PDAC personalized treatment. J Exp Clin Cancer Res. 2025;44(1):72. doi: 10.1186/s13046-025-03332-8

 

  1. Akerlund E, Gudoityte G, Moussaud-Lamodiere E, et al. The drug efficacy testing in 3D cultures platform identifies effective drugs for ovarian cancer patients. NPJ Precis Oncol. 2023;7(1):111. doi: 10.1038/s41698-023-00463-z

 

  1. Nadalin F, Marzi MJ, Pirra Piscazzi M, et al. Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution. Nat Commun. 2024;15(1):7609. doi: 10.1038/s41467-024-51424-4

 

  1. Peng Z, Lv X, Sun H, Zhao L, Huang S. 3D tumor cultures for drug resistance and screening development in clinical applications. Mol Cancer. 2025;24(1):93. doi: 10.1186/s12943-025-02281-2

 

  1. Dong M, Wang L, Hu N, Rao Y, Wang Z, Zhang Y. Integration of multi-omics approaches in exploring intra-tumoral heterogeneity. Cancer Cell Int. 2025;25(1):317. doi: 10.1186/s12935-025-03944-2

 

  1. Wang E, Xiang K, Zhang Y, Wang XF. Patient-derived organoids (PDOs) and PDO-derived xenografts (PDOXs): New opportunities in establishing faithful pre-clinical cancer models. J Natl Cancer Cent. 2022;2(4):263-276. doi: 10.1016/j.jncc.2022.10.001

 

  1. Roelofsen LM, Voabil P, de Bruijn M, et al. Protocol for ex vivo culture of patient-derived tumor fragments. STAR Protoc. 2023;4(2):102282. doi: 10.1016/j.xpro.2023.102282

 

  1. Bleijs M, van de Wetering M, Clevers H, Drost J. Xenograft and organoid model systems in cancer research. EMBO J. 2019;38(15):e101654. doi: 10.15252/embj.2019101654

 

  1. Rathje F, Klingler S, Aberger F. Organoids for Modeling (Colorectal) Cancer in a Dish. Cancers. 2022; 14(21):5416. doi: 10.3390/cancers14215416

 

  1. Wu Y, Sha Y, Guo X, Gao L, Huang J, Liu SB. Organoid models: applications and research advances in colorectal cancer. Front Oncol. 2025;15:1432506. doi: 10.3389/fonc.2025.1432506

 

  1. Choi W, Kim YH, Woo SM, et al. Establishment of Patient-Derived Organoids Using Ascitic or Pleural Fluid from Cancer Patients. Cancer Res Treat. 2023;55(4):1077-1086. doi: 10.4143/crt.2022.1630

 

  1. Chang Dong Yeo, Young-Pil Yun, Dong Hyuck Ahn, et al. Therapeutic applications of three-dimensional organoid models in lung cancer. Organoid. 2021;1:1-11. doi: 10.51335/organoid.2021.1.e6

 

  1. Zheng X, Zhang X, Yu S. Organoids derived from metastatic cancers: Present and future. Heliyon. 2024;10(9):e30457. doi: 10.1016/j.heliyon.2024.e30457

 

  1. C T Wang, J L Zhou, G L Lin, et al. Advances in three-dimensional tumor models for colorectal cancer. Chin J Oncol. 2023;45(6):464-470. doi: 10.3760/cma.j.cn112152-20220928-00661

 

  1. Qin X, Cardoso Rodriguez F, Sufi J, Vlckova P, Claus J, Tape CJ. An oncogenic phenoscape of colonic stem cell polarization. Cell. 2023;186(25):5554-5568.e18. doi: 10.1016/j.cell.2023.11.004

 

  1. Yan HHN, Siu HC, Law S, et al. A Comprehensive Human Gastric Cancer Organoid Biobank Captures Tumor Subtype Heterogeneity and Enables Therapeutic Screening. Cell Stem Cell. 2018;23(6):882-897.e11. doi: 10.1016/j.stem.2018.09.016

 

  1. Yin Y, Zeng S, Zhang H, et al. Patient-derived gastric cancer organoids model heterogeneity and stroma-mediated chemoresistance in poorly cohesive carcinoma. Front Mol Biosci. 2025;12:1631168. doi: 10.3389/fmolb.2025.1631168

 

  1. Morceau F, El-Khoury V, Lee K, Berna MJ, Kwon YJ. Pancreatic Cancer Organoids: Modeling Disease and Guiding Therapy. Cancers. 2025; 17(23):3850. doi: 10.3390/cancers17233850

 

  1. Schuth S, Le Blanc S, Krieger TG, et al. Patient-specific modeling of stroma-mediated chemoresistance of pancreatic cancer using a three-dimensional organoid-fibroblast co-culture system. J Exp Clin Cancer Res. 2022;41(1):312. doi: 10.1186/s13046-022-02519-7

 

  1. Zhu Y, Tang S, Yuan Q, et al. Integrated characterization of hepatobiliary tumor organoids provides a potential landscape of pharmacogenomic interactions. Cell Rep Med. 2024;5(2):101375. doi: 10.1016/j.xcrm.2023.101375

 

  1. Lin J, Rao D, Zhang M, Gao Q. Metabolic reprogramming in the tumor microenvironment of liver cancer. J Hematol Oncol. 2024;17(1):6. doi: 10.1186/s13045-024-01527-8

 

  1. Giron-Michel J, Padelli M, Oberlin E, Guenou H, Duclos-Vallee JC. State-of-the-Art Liver Cancer Organoids: Modeling Cancer Stem Cell Heterogeneity for Personalized Treatment. Bio Drugs. 2025;39(2):237-260. doi: 10.1007/s40259-024-00702-0

 

  1. Tang W, Tian X. Patient-derived organoids in non-small cell lung cancer: advances in drug sensitivity testing. Front Pharmacol. 2025;16:1639268. doi: 10.3389/fphar.2025.1639268

 

  1. Li X, Huang T. Advances in the intrinsic signaling pathway interactions and clinical translation of HR+/HER2+ breast cancer. Breast Cancer. 2025;32(6):1216-1243. doi: 10.1007/s12282-025-01779-3

 

  1. Bhatia S, Kramer M, Russo S, et al. Patient-Derived Triple-Negative Breast Cancer Organoids Provide Robust Model Systems That Recapitulate Tumor Intrinsic Characteristics. Cancer Res. 2022;82(7):1174-1192. doi: 10.1158/0008-5472.CAN-21-2807

 

  1. Rao X, Qiao Z, Yang Y, et al. Unveiling Epigenetic Vulnerabilities in Triple-Negative Breast Cancer through 3D Organoid Drug Screening. Pharmaceuticals. 2024;17(2):225. doi: 10.3390/ph17020225

 

  1. Nomdedeu-Sancho G, Gorkun A, Mahajan N, et al. In Vitro Three-Dimensional (3D) Models for Melanoma Immunotherapy. Cancers. 2023;15(24):5779. doi: 10.3390/cancers15245779

 

  1. De Cock L, Palubeckaite I, Bersani F, et al. Establishment of patient-derived 3D in vitro models of sarcomas: literature review and guidelines on behalf of the FORTRESS working group. Neoplasia. 2025;65:101171. doi: 10.1016/j.neo.2025.101171

 

  1. Silvestri G, Chatterjee A. Rebuilding the Marrow In Vitro: Translational Advances in the 3D Modeling of Blood Cancers. Onco. 2025;5(4):51. doi: 10.3390/onco5040051

 

  1. He X, Jiang Y, Zhang L, et al. Patient-derived organoids as a platform for drug screening in metastatic colorectal cancer. Front Bioeng Biotechnol. 2023;11:1190637. doi: 10.3389/fbioe.2023.1190637

 

  1. Feng QS, Shan XF, Yau V, Cai ZG, Xie S. Facilitation of Tumor Stroma-Targeted Therapy: Model Difficulty and Co-Culture Organoid Method. Pharmaceuticals. 2025;18(1):62. doi: 10.3390/ph18010062

 

  1. Khan AO, Rodriguez-Romera A, Reyat JS, et al. Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies. Cancer Discov. 2023;13(2):364-385. doi: 10.1158/2159-8290.CD-22-0199

 

  1. Mattioda C, Voena C, Ciardelli G, Mattu C. In Vitro 3D Models of Haematological Malignancies: Current Trends and the Road Ahead? Cells. 2025;14(1):38. doi: 10.3390/cells14010038

 

  1. Neal JT, Li X, Zhu J, et al. Organoid Modeling of the Tumor Immune Microenvironment. Cell. 2018;175(7):1972-1988.e16. doi: 10.1016/j.cell.2018.11.021

 

  1. Saeki S, Kumegawa K, Takahashi Y, et al. Transcriptomic intratumor heterogeneity of breast cancer patient-derived organoids may reflect the unique biological features of the tumor of origin. Breast Cancer Res. 2023;25(1):21. doi: 10.1186/s13058-023-01617-4

 

  1. Ou L, Liu S, Wang H, et al. Patient-derived melanoma organoid models facilitate the assessment of immunotherapies. EBioMedicine. 2023;92:104614. doi: 10.1016/j.ebiom.2023.104614

 

  1. Zeng G, Yu Y, Wang M, et al. Advancing cancer research through organoid technology. J Transl Med. 2024;22(1):1007. doi: 10.1186/s12967-024-05824-1

 

  1. Verduin M, Hoosemans L, Vanmechelen M, et al. Patient-derived glioblastoma organoids reflect tumor heterogeneity and treatment sensitivity. Neurooncol Adv. 2023;5(1):vdad152. doi: 10.1093/noajnl/vdad152

 

  1. Pantazopoulou V, Kubota CS, Ogawa S, et al. Experimental models of pancreas cancer: what has been the impact for precision medicine? J Clin Investig. 2025;135(16). doi: 10.1172/JCI191945

 

  1. Nikeghbal P, Zamanian D, Burke D, Steinkamp MP. Organoid models established from primary tumors and patient-derived xenograft tumors reflect platinum sensitivity of ovarian cancer patients. BMC Cancer. 2025;25(1):1459. doi: 10.1186/s12885-025-14811-8

 

  1. Coppo R, Inoue M. Patient-Derived Tumor Organoids to Model Cancer Cell Plasticity and Overcome Therapeutic Resistance. Cells. 2025;14(18). doi: 10.3390/cells14181464

 

  1. Liu X, Du X, Wang X, Duan H, Wang D, Chen L, et al. Cancer-Associated Fibroblasts induce aggressive stem cell phenotype and chemoresistance in cervical tumoroid-fibroblast co-culture system via upregulating CK17 expression. Res Sq. Preprint online August 8, 2024. doi: 10.21203/rs.3.rs-4741458/v1

 

  1. Yang C, Yang L, Feng Y, et al. Modeling methods of different tumor organoids and their application in tumor drug resistance research. Cancer Drug Resist. 2025;8:32. doi: 10.20517/cdr.2025.34

 

  1. Kratz JD, Rehman S, Johnson KA, et al. Subclonal response heterogeneity to define cancer organoid therapeutic sensitivity. Sci Rep. 2025;15(1):12072. doi: 10.1038/s41598-025-96204-2

 

  1. Franca GS, Baron M, King BR, et al. Cellular adaptation to cancer therapy along a resistance continuum. Nature. 2024;631(8022):876-883. doi: 10.1038/s41586-024-07690-9

 

  1. Li T, Yin J, Hao Y, et al. Single-cell sequencing and organoids: applications in organ development and disease. Mol Biomed. 2025;6(1):134. doi: 10.1186/s43556-025-00364-6

 

  1. Kim SY, Kim SM, Lim S, et al. Modeling Clinical Responses to Targeted Therapies by Patient-Derived Organoids of Advanced Lung Adenocarcinoma. Clin Cancer Res. 2021;27(15):4397-4409. doi: 10.1158/1078-0432.CCR-20-5026

 

  1. Chew CA, Wun CM, Lee YF, Chee CE, Ho KY, Bonney GK. Functional Precision in Pancreatic Cancer: Redefining Biomarkers with Patient-Derived Organoids. Int J Mol Sci. 2025;26(18). doi: 10.3390/ijms26189083

 

  1. Minoli M, Cantore T, Hanhart D, et al. Bladder cancer organoids as a functional system to model different disease stages and therapy response. Nat Commun. 2023;14(1):2214. doi: 10.1038/s41467-023-37696-2

 

  1. Lee D, Kim Y, Kang DH, Chung C. Modeling Drug and Radiation Resistance with Patient-Derived Organoids: Recent Progress, Unmet Needs, and Future Directions for Lung Cancer. Cells. 2025;14(24):1994. doi: 10.3390/cells14241994

 

  1. Chitrangi S, Vaity P, Jamdar A, Bhatt S. Patient-derived organoids for precision oncology: a platform to facilitate clinical decision making. BMC Cancer. 2023;23(1):689. doi: 10.1186/s12885-023-11078-9

 

  1. Zhou Z, Cong L, Cong X. Patient-Derived Organoids in Precision Medicine: Drug Screening, Organoid-on-a-Chip and Living Organoid Biobank. Front Oncol. 2021;11:762184. doi: 10.3389/fonc.2021.762184

 

  1. Kabeer F, Tran H, Andronescu M, et al. Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers. Genome Biol. 2024;25(1):191. doi: 10.1186/s13059-024-03318-3

 

  1. Li H, Xu W, Cheng W, Yu G, Tang D. Drug-tolerant persister cell in cancer: reversibility, microenvironmental interplay, and therapeutic strategies. Front Pharmacol. 2025;16:1612089. doi: 10.3389/fphar.2025.1612089

 

  1. Dhanyamraju PK, Schell TD, Amin S, Robertson GP. Drug-Tolerant Persister Cells in Cancer Therapy Resistance. Cancer Res. 2022;82(14):2503-2514. doi: 10.1158/0008-5472.CAN-21-3844

 

  1. Nojima Y, Yao R, Suzuki T. Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer. Biochim Biophys Acta Mol Basis Dis. 2025;1871(3):167693. doi: 10.1016/j.bbadis.2025.167693

 

  1. Pu Y, Li L, Peng H, et al. Drug-tolerant persister cells in cancer: the cutting edges and future directions. Nat Rev Clin Oncol. 2023;20(11):799-813. doi: 10.1038/s41571-023-00815-5

 

  1. Eyler CE, Matsunaga H, Hovestadt V, Vantine SJ, van Galen P, Bernstein BE. Single-cell lineage analysis reveals genetic and epigenetic interplay in glioblastoma drug resistance. Genome Biol. 2020;21(1):174. doi: 10.1186/s13059-020-02085-1

 

  1. Yang D, Zhang X, Hu Z, et al. Organoid-based single cell sequencing revealed the lineage evolution during docetaxel treatment in gastric cancer. Cancer Lett. 2025;619:217617. doi: 10.1016/j.canlet.2025.217617

 

  1. Chen G, Gong T, Wang Z, et al. Colorectal cancer organoid models uncover oxaliplatin-resistant mechanisms at single cell resolution. Cell Oncol. 2022;45(6):1155-1167. doi: 10.1007/s13402-022-00705-5

 

  1. Rao A, Barkley D, Franca GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature. 2021;596(7871):211-220. doi: 10.1038/s41586-021-03634-9

 

  1. Tong L, Cui W, Zhang B, et al. Patient-derived organoids in precision cancer medicine. Med. 2024;5(11):1351-1377. doi: 10.1016/j.medj.2024.08.010

 

  1. Hunter MV, Moncada R, Weiss JM, Yanai I, White RM. Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface. Nat Commun. 2021;12(1):6278. doi: 10.1038/s41467-021-26614-z

 

  1. Cilento MA, Sweeney CJ, Butler LM. Spatial transcriptomics in cancer research and potential clinical impact: a narrative review. J Cancer Res Clin Oncol. 2024;150(6):296. doi: 10.1007/s00432-024-05816-0

 

  1. Zhu J, Zhang K, Chen Y, et al. Progress of single-cell RNA sequencing combined with spatial transcriptomics in tumour microenvironment and treatment of pancreatic cancer. J Transl Med. 2024;22(1):563. doi: 10.1186/s12967-024-05307-3

 

  1. Wu L, Yan J, Bai Y, et al. An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte-tumor cell crosstalk, local immunosuppression and tumor-progression. Cell Res. 2023;33(8):585-603. doi: 10.1038/s41422-023-00831-1

 

  1. Ren P, Zhang R, Wang Y, et al. Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors. Nat Commun. 2025;16(1):9232. doi: 10.1038/s41467-025-64292-3

 

  1. Li H, Chen Y, Kaster J, et al. Clonal dynamics shaped by diverse drug-tolerant persister states in melanoma resistance. bioRxiv. Preprint online September 19, 2025. doi: 10.1101/2025.09.16.676608

 

  1. Xiao J, Yu X, Meng F, et al. Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer. Cell Death Dis. 2024;15(5):326. doi: 10.1038/s41419-024-06598-6

 

  1. Hsieh WC, Budiarto BR, Wang YF, et al. Spatial multi-omics analyses of the tumor immunemicroenvironment. J Biomed Sci. 2022;29(1):96. doi: 10.1186/s12929-022-00879-y

 

  1. Wu H, Zhang Q, Cao Z, et al. Integrated spatial omics of metabolic reprogramming and the tumor microenvironment in pancreatic cancer. iScience. 2025;28(6):112681. doi: 10.1016/j.isci.2025.112681

 

  1. Chen F, Fu Y, Bai G, Qiu J, Hua K. Multi-omics dissection of tumor microenvironment-mediated drug resistance: Mechanisms and therapeutic reprogramming. Front Pharmacol. 2025;16:1634413. doi: 10.3389/fphar.2025.1634413

 

  1. Boe RH, Triandafillou CG, Lazcano R, Wargo JA, Raj A. Spatial transcriptomics reveals influence of microenvironment on intrinsic fates in melanoma therapy resistance. bioRxiv. Preprint online 2024. doi: 10.1101/2024.06.30.601416

 

  1. Zhu Q, Balasubramanian A, Asirvatham JR, et al. Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in Black American and White American patients with TNBC. Nat Commun. 2025;16(1):6584. doi: 10.1038/s41467-025-61034-3

 

  1. Wimmer RA, Leopoldi A, Aichinger M, et al. Human blood vessel organoids as a model of diabetic vasculopathy. Nature. 2019;565(7740):505-510. doi: 10.1038/s41586-018-0858-8

 

  1. Yuki K, Cheng N, Nakano M, Kuo CJ. Organoid Models of Tumor Immunology. Trends Immunol. 2020;41(8):652-664. doi: 10.1016/j.it.2020.06.010

 

  1. Grebenyuk S, Ranga A. Engineering Organoid Vascularization. Front Bioeng Biotechnol. 2019;7:39. doi: 10.3389/fbioe.2019.00039

 

  1. Haque MR, Wessel CR, Leary DD, Wang C, Bhushan A, Bishehsari F. Patient-derived pancreatic cancer-on-a-chip recapitulates the tumor microenvironment. Microsyst Nanoeng. 2022;8:36. doi: 10.1038/s41378-022-00370-6

 

  1. Dijkstra KK, Cattaneo CM, Weeber F, et al. Generation of Tumor-Reactive T Cells by Co-culture of Peripheral Blood Lymphocytes and Tumor Organoids. Cell. 2018;174(6):1586-1598.e12. doi: 10.1016/j.cell.2018.07.009

 

  1. Yan H, Shi J, Dai Y, et al. Technique integration of single-cell RNA sequencing with spatially resolved transcriptomics in the tumor microenvironment. Cancer Cell Int. 2022;22(1):155. doi: 10.1186/s12935-022-02580-4

 

  1. Chelebian E, Avenel C, Wahlby C. Combining spatial transcriptomics with tissue morphology. Nat Commun. 2025;16(1):4452. doi: 10.1038/s41467-025-58989-8

 

  1. Li S, Wang Z, Huang HD. Deciphering ovarian cancer heterogeneity through spatial transcriptomics, single-cell profiling, and copy number variations. PLoS ONE. 2025;20(3):e0317115. doi: 10.1371/journal.pone.0317115

 

  1. Cheng X, Peng T, Chu T, et al. Application of single-cell and spatial omics in deciphering cellular hallmarks of cancer drug response and resistance. J Hematol Oncol. 2025;18(1):70. doi: 10.1186/s13045-025-01722-1

 

  1. Luo J, Fu J, Lu Z, Tu J. Deep learning in integrating spatial transcriptomics with other modalities. Brief Bioinform. 2024;26(1):bbae719. doi: 10.1093/bib/bbae719

 

  1. Jain MS, Polanski K, Conde CD, et al. MultiMAP: dimensionality reduction and integration of multimodal data. Genome Biol. 2021;22(1):346. doi: 10.1186/s13059-021-02565-y

 

  1. Yuan J, Yu J, Yi Q, Ye Z, Xu P, Liu W. SpateCV: cross-modality alignment regularization of cell types improves spatial gene imputation for spatial transcriptomics. J Transl Med. 2025;23(1):1188. doi: 10.1186/s12967-025-07245-0

 

  1. Ascolani G, Angaroni F, Maspero D, et al. LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution. BMC Bioinform. 2023;24(1):99. doi: 10.1186/s12859-023-05221-3

 

  1. Ramazzotti D, Graudenzi A, De Sano L, Antoniotti M, Caravagna G. Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. BMC Bioinform. 2019;20(1):210. doi: 10.1186/s12859-019-2795-4

 

  1. He X, Liu X, Zuo F, Shi H, Jing J. Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semin Cancer Biol. 2023;88:187-200. doi: 10.1016/j.semcancer.2022.12.009

 

  1. Tang Z, Chen G, Chen S, Yao J, You L, Chen CY. Modal-nexus auto-encoder for multi-modality cellular data integration and imputation. Nat Commun. 2024;15(1):9021. doi: 10.1038/s41467-024-53355-6

 

  1. Yang P, Jin K, Yao Y, et al. Spatial integration of multi-omics single-cell data with SIMO. Nat Commun. 2025;16(1):1265. doi: 10.1038/s41467-025-56523-4

 

  1. Pernik M, Bird C, Traylor J, et al. Patient-derived cancer organoids for precision oncology treatment. Preprints. 2021. doi: 10.20944/preprints202103.0114.v1

 

  1. Lv J, Du X, Wang M, Su J, Wei Y, Xu C. Construction of tumor organoids and their application to cancer research and therapy. Theranostics. 2024;14(3):1101-1125. doi: 10.7150/thno.91362

 

  1. Avci CB, Bagca BG, Shademan B, Takanlou LS, Takanlou MS, Nourazarian A. The future of cancer therapy: exploring the potential of patient-derived organoids in drug development. Front Cell Dev Biol. 2024;12:1401504. doi: 10.3389/fcell.2024.1401504

 

  1. Seppala TT, Zimmerman JW, Sereni E, et al. Patient-derived Organoid Pharmacotyping is a Clinically Tractable Strategy for Precision Medicine in Pancreatic Cancer. Ann Surg. 2020;272(3):427-435. doi: 10.1097/SLA.0000000000004200

 

  1. Chen KY, Srinivasan T, Lin C, et al. Single-Cell Transcriptomics Reveals Heterogeneity and Drug Response of Human Colorectal Cancer Organoids. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:2378-2381. doi: 10.1109/EMBC.2018.8512784

 

  1. Luan Q, Pulido I, Isagirre A, et al. Deciphering fibroblast-induced drug resistance in non-small cell lung carcinoma through patient-derived organoids in agarose microwells. Lab Chip. 2024;24(7):2025-2038. doi: 10.1039/d3lc01044a

 

  1. Xiang D, He A, Zhou R, et al. Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development. Theranostics. 2024;14(8):3300-3316. doi: 10.7150/thno.96027

 

  1. Betge J, Rindtorff N, Sauer J, et al. The drug-induced phenotypic landscape of colorectal cancer organoids. Nat Commun. 2022;13(1):3135. doi: 10.1038/s41467-022-30722-9

 

  1. Kondo J, Ekawa T, Endo H, et al. High‐throughput screening in colorectal cancer tissue‐originated spheroids. Cancer Sci. 2018;110(1):345-355. doi: 10.1111/cas.13843

 

  1. Sun Z, Huang S, Jiang P, Hu P. DTF: Deep Tensor Factorization for predicting anticancer drug synergy. Bioinformatics. 2020;36(16):4483-4489. doi: 10.1093/bioinformatics/btaa287

 

  1. Liu Y, Gan Y, AiErken N, et al. Combining Organoid Models with Next-Generation Sequencing to Reveal Tumor Heterogeneity and Predict Therapeutic Response in Breast Cancer. J Oncol. 2022;2022:9390912. doi: 10.1155/2022/9390912

 

  1. Ermini L, Lipsa A, Hau A-C, et al. Integrative multi-omics combined with functional pharmacological profiling in patient-derived organoids identifies personalized therapeutic vulnerabilities of adult high-grade gliomas. bioRxiv. Preprint online 2025. doi: 10.1101/2025.09.09.675145

 

  1. Ramzy GM, Norkin M, Koessler T, et al. Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma. J Exp Clin Cancer Res. 2023;42(1):79. doi: 10.1186/s13046-023-02650-z

 

  1. Bae J, Choi YS, Cho G, Jang SJ. The Patient-Derived Cancer Organoids: Promises and Challenges as Platforms for Cancer Discovery. Cancers. 2022;14(9):2144. doi: 10.3390/cancers14092144

 

  1. Tiriac H, Belleau P, Engle DD, et al. Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer. Cancer Discov. 2018;8(9):1112-1129. doi: 10.1158/2159-8290.CD-18-0349

 

  1. Geyer M, Schreyer D, Gaul LM, Pfeffer S, Pilarsky C, Queiroz K. A microfluidic-based PDAC organoid system reveals the impact of hypoxia in response to treatment. Cell Death Discov. 2023;9(1):20.doi: 10.1038/s41420-023-01334-z

 

  1. Papaccio F, Garcia-Mico B, Gimeno-Valiente F, et al. “Proteotranscriptomic analysis of advanced colorectal cancer patient derived organoids for drug sensitivity prediction”. J Exp Clin Cancer Res. 2023;42(1). doi: 10.1186/s13046-022-02591-z

 

  1. Ji S, Feng L, Fu Z, et al. Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology. Sci Transl Med. 2023;15(706):eadg3358. doi: 10.1126/scitranslmed.adg3358

 

  1. Yang H, Cheng J, Zhuang H, et al. Pharmacogenomic profiling of intra-tumor heterogeneity using a large organoid biobank of liver cancer. Cancer Cell. 2024;42(4):535-551.e8. doi: 10.1016/j.ccell.2024.03.004

 

  1. Du L, Dong J, Du S. Organoid models for T cell-based immunotherapy in hepatobiliary cancers. Hepatobiliary Surg Nutr. 2025;14(6):1042-1044. doi: 10.21037/hbsn-2025-aw-863

 

  1. Du Y, Ding X, Ye Y. The spatial multi-omics revolution in cancer therapy: Precision redefined. Cell Rep Med. 2024;5(9):101740. doi: 10.1016/j.xcrm.2024.101740

 

  1. Wu Z, Boen J, Jindal S, et al. Spatial multi-omics and deep learning reveal fingerprints of immunotherapy response and resistance in hepatocellular carcinoma. bioRxiv. 2025. doi: 10.1101/2025.06.11.656869

 

  1. Kim J, Yong SH, Jang G, et al. Spatial profiling of non-small cell lung cancer provides insights into tumorigenesis and immunotherapy response. Commun Biol. 2024;7(1):930. doi: 10.1038/s42003-024-06568-w

 

  1. Yang H, Xiao X, Zeng L, et al. Integrating cfDNA liquid biopsy and organoid-based drug screening reveals PI3K signaling as a promising therapeutic target in colorectal cancer. J Transl Med. 2024;22(1):132. doi: 10.1186/s12967-023-04675-6

 

  1. Foo MA, You M, Chan SL, et al. Clinical translation of patient-derived tumour organoids- bottlenecks and strategies. Biomark Res. 2022;10(1):10. doi: 10.1186/s40364-022-00356-6

 

  1. de Kort WWB, Millen R, Driehuis E, Devriese LA, van Es RJJ, Willems SM. Clinicopathological Factors as Predictors for Establishment of Patient Derived Head and Neck Squamous Cell Carcinoma Organoids. Head Neck Pathol.2024;18(1):59. doi: 10.1007/s12105-024-01658-x

 

  1. Shiihara M, Furukawa T. Application of Patient-Derived Cancer Organoids to Personalized Medicine. J Pers Med. 2022;12(5):789. doi: 10.3390/jpm12050789

 

  1. Yao N, Jing N, Lin J, et al. Patient-derived tumor organoids for cancer immunotherapy: culture techniques and clinical application. Investig New Drugs. 2025;43(2):394-404. doi: 10.1007/s10637-025-01523-w

 

  1. Sun CP, Lan HR, Fang XL, Yang XY, Jin KT. Organoid Models for Precision Cancer Immunotherapy. Front Immunol. 2022;13:770465. doi: 10.3389/fimmu.2022.770465

 

  1. Ohlund D, Handly-Santana A, Biffi G, et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J Exp Med. 2017;214(3):579-596. doi: 10.1084/jem.20162024

 

  1. Homan KA, Gupta N, Kroll KT, et al. Flow-enhanced vascularization and maturation of kidney organoids in vitro. Nat Methods. 2019;16(3):255-262. doi: 10.1038/s41592-019-0325-y

 

  1. Shirure VS, Bi Y, Curtis MB, et al. Tumor-on-a-chip platform to investigate progression and drug sensitivity in cell lines and patient-derived organoids. Lab Chip. 2018;18(23):3687-3702. doi: 10.1039/c8lc00596f

 

  1. Li K, He Y, Jin X, Jin K, Qian J. Reproducible extracellular matrices for tumor organoid culture: challenges and opportunities. J Transl Med. 2025;23(1):497. doi: 10.1186/s12967-025-06349-x

 

  1. Qu S, Xu R, Yi G, et al. Patient-derived organoids in human cancer: a platform for fundamental research and precision medicine. Mol Biomed. 2024;5(1):6. doi: 10.1186/s43556-023-00165-9

 

  1. Xin-Yi J, Yan-Ran W, Pin-Ru D, Shi-Yi Q, Hai-Tao J. Organoids in cancer therapies: a comprehensive review. Front Bioeng Biotechnol. 2025;13:1607488. doi: 10.3389/fbioe.2025.1607488

 

  1. Bar-Ephraim YE, Kretzschmar K, Clevers H. Organoids in immunological research. Nat Rev Immunol. 2020;20(5):279-293. doi: 10.1038/s41577-019-0248-y

 

  1. Dengxu T, Yifan M, Ke L. Reshaping Intercellular Interactions: Empowering the Exploration of Disease Mechanisms and Therapies Using Organoid Co-Culture Models. Lab Anim Comp Med. 2025;45:309-317. doi: 10.12300/j.issn.1674-5817.2024.164

 

  1. Kim R, Chang W. Endothelial-to-Mesenchymal Transition in Health and Disease: Molecular Insights and Therapeutic Implications. Int J Mol Sci. 2025;26(23):11724. doi: 10.3390/ijms262311724

 

  1. Madan A, Saini R, Dhiman N, Juan S-H, Satapathy MK. Organoids as Next-Generation Models for Tumor Heterogeneity, Personalized Therapy, and Cancer Research: Advancements, Applications, and Future Directions. Organoids. 2025;4(4):23. doi: 10.3390/organoids4040023

 

  1. Gao J, Luo H, Wang S, et al. Renal cell carcinoma organoids for precision medicine: bridging the gap between models and patients. J Transl Med. 2025;23(1):1152. doi: 10.1186/s12967-025-06949-7

 

  1. Heo YJ, Hwa C, Lee GH, Park JM, An JY. Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes. Mol Cells. 2021;44(7):433-443. doi: 10.14348/molcells.2021.0042
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Organoid Research, Electronic ISSN: 3082-8503 Published by AccScience Publishing
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