Cancer therapy resistance from a spatial-omics perspective

Abstract

Cancer therapy resistance (CTR) remains a significant challenge in oncology. Traditional methods like imaging, liquid biopsies and conventional omics analyses provide valuable insights, but lack the spatial resolution to fully characterise heterogeneity of tumour and the tumour microenvironment (TME). Recent advancements in spatial omics technologies offer unprecedented insights into the spatial organisation of tumours and TME. In this review, we summarise current methodologies for CTR research and highlight how spatial omics technologies and computational methods are revolutionising our understanding of CTR mechanisms. We also summarise recent studies leveraging spatial omics to uncover novel insights into CTR across various cancer types and therapies and discuss future opportunities.

Keywords

cancer therapy resistance / spatial omics

References

[1] Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024; 74: 229-263.

[2] Chen S, Cao Z, Prettner K, et al. Estimates and projections of the global economic cost of 29 cancers in 204 countries and territories from 2020 to 2050. JAMA Oncol. 2023; 9: 465-472.

[3] Cancer chemotherapy: a critical analysis of its 60 years of history. Crit Rev Oncol Hematol. 2012; 84: 181-199.

[4] Chabner BA, Roberts TG. Chemotherapy and the war on cancer. Nat Rev Cancer. 2005; 5: 65-72.

[5] Moore AR, Rosenberg SC, McCormick F, Malek S. RAS-targeted therapies: is the undruggable drugged? Nat Rev Drug Discov. 2020; 19: 533-552.

[6] Lee YT, Tan YJ, Oon CE. Molecular targeted therapy: treating cancer with specificity. Eur J Pharmacol. 2018; 834: 188-196.

[7] Fukumura D, Kloepper J, Amoozgar Z, Duda DG, Jain RK. Enhancing cancer immunotherapy using antiangiogenics: opportunities and challenges. Nat Rev Clin Oncol. 2018; 15: 325-340.

[8] Meric-Bernstam F, Larkin J, Tabernero J, Bonini C. Enhancing anti-tumour efficacy with immunotherapy combinations. Lancet. 2021; 397: 1010-1022.

[9] Vasan N, Baselga J, Hyman DM. A view on drug resistance in cancer. Nature. 2019; 575: 299-309.

[10] Lei Z-N, Tian Q, Teng Q-X, et al. Understanding and targeting resistance mechanisms in cancer. MedComm. 2023; 4: e265.

[11] Moses L, Pachter L. Museum of spatial transcriptomics. Nat Methods. 2022; 19: 534-546.

[12] Tian L, Chen F, Macosko EZ. The expanding vistas of spatial transcriptomics. Nat Biotechnol. 2023; 41: 773-782.

[13] Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet. 2023; 24: 494-515.

[14] Carstens JL, Krishnan SN, Rao A, et al. Spatial multiplexing and omics. Nat Rev Methods Primer. 2024; 4: 1-19.

[15] Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell. 2024; 187: 4488-4519.

[16] Planque M, Igelmann S, Ferreira Campos AM, Fendt S-M. Spatial metabolomics principles and application to cancer research. Curr Opin Chem Biol. 2023; 76: 102362.

[17] Min X, Zhao Y, Yu M, et al. Spatially resolved metabolomics: from metabolite mapping to function visualising. Clin Transl Med. 2024; 14: e70031.

[18] Ståhl PL, Salmén F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016; 353: 78-82.

[19] Merritt CR, Ong GT, Church SE, et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol. 2020; 38: 586-599.

[20] Chen Ao, Liao S, Cheng M, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022; 185: 1777-1792.e21.

[21] Stickels RR, Murray E, Kumar P, et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol. 2021; 39: 313-319.

[22] Russell AJC, Weir JA, Nadaf NM, et al. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature. 2024; 625: 101-109.

[23] Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015; 348: aaa6090.

[24] He S, Bhatt R, Brown C, et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol. 2022; 40: 1794-1806.

[25] Ke R, Mignardi M, Pacureanu A, et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat Methods. 2013; 10: 857-860.

[26] Gerdes MJ, Sevinsky CJ, Sood A, et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc Natl Acad Sci USA. 2013; 110: 11982-11987.

[27] Rivest F, Eroglu D, Pelz B, et al. Fully automated sequential immunofluorescence (seqIF) for hyperplex spatial proteomics. Sci Rep. 2023; 13: 16994.

[28] Kinkhabwala A, Herbel C, Pankratz J, et al. MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors. Sci Rep. 2022; 12: 1911.

[29] Black S, Phillips D, Hickey JW, et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc. 2021; 16: 3802-3835.

[30] Goltsev Y, Samusik N, Kennedy-Darling J, et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell. 2018; 174: 968-981.e15.

[31] Giesen C, Wang HAO, Schapiro D, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 2014; 11: 417-422.

[32] Angelo M, Bendall SC, Finck R, et al. Multiplexed ion beam imaging of human breast tumors. Nat Med. 2014; 20: 436-442.

[33] Wang G, Heijs B, Kostidis S, et al. Analyzing cell-type-specific dynamics of metabolism in kidney repair. Nat Metab. 2022; 4: 1109-1118.

[34] You Y, Fu Y, Li L, et al. Systematic comparison of sequencing-based spatial transcriptomic methods. Nat Methods. 2024; 21: 1743-1754.

[35] Miao Z, Tian T, Chen W, et al. Spatial resolved transcriptomics: computational insights into gene transcription across tissue and organ architecture in diverse applications. Innov Life. 2024;100097.

[36] Mi H, Sivagnanam S, Ho WJ, et al. Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology. Brief Bioinform. 2024; 25: bbae421.

[37] Pang M, Roy TK, Wu X, Tan K. CelloType: a unified model for segmentation and classification of tissue images. Nat Methods. 2024; 1-10.

[38] He Y, Tang X, Huang J, et al. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nat Commun. 2021; 12: 5909.

[39] Park J, Choi W, Tiesmeyer S, et al. Cell segmentation-free inference of cell types from in situ transcriptomics data. Nat Commun. 2021; 12: 3545.

[40] Petukhov V, Xu RJ, Soldatov RA, et al. Cell segmentation in imaging-based spatial transcriptomics. Nat Biotechnol. 2022; 40: 345-354.

[41] Wang Y, Wang W, Liu D, Hou W, Zhou T, Ji Z. GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging. Genome Biol. 2023; 24: 235.

[42] Zhong Y, Ren X. Cell segmentation and gene imputation for imaging-based spatial transcriptomics. 2023. Preprint at

[43] Chen H, Li D, Bar-Joseph Z. SCS: cell segmentation for high-resolution spatial transcriptomics. Nat Methods. 2023; 20: 1237-1243.

[44] Fu X, Lin Y, Lin DM, et al. BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data. Nat Commun. 2024; 15: 509.

[45] Bhuva DD, Tan CW, Salim A, et al. Library size confounds biology in spatial transcriptomics data. Genome Biol. 2024; 25: 99.

[46] Atta L, Clifton K, Anant M, Aihara G, Fan J. Gene count normalization in single-cell imaging-based spatially resolved transcriptomics. Genome Biol. 2024; 25: 153.

[47] Shen R, Liu L, Wu Z, et al. Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding. Nat Commun. 2022; 13: 7640.

[48] Biancalani T, Scalia G, Buffoni L, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods. 2021; 18: 1352-1362.

[49] Mages S, Moriel N, Avraham-Davidi I, et al. TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics. Nat Biotechnol. 2023; 41: 1465-1473.

[50] Lopez R, Li B, Keren-Shaul H, et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat Biotechnol. 2022; 40: 1360-1369.

[51] Ma Y, Zhou X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol. 2022; 40: 1349-1359.

[52] Kleshchevnikov V, Shmatko A, Dann E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol. 2022; 40: 661-671.

[53] Li H, Zhou J, Li Z, et al. A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics. Nat Commun. 2023; 14: 1548.

[54] Liu Y, Yang C. Computational methods for alignment and integration of spatially resolved transcriptomics data. Comput Struct Biotechnol J. 2024; 23: 1094-1105.

[55] Li Z, Zhou X. BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies. Genome Biol. 2022; 23: 168.

[56] Duan B, Chen S, Cheng X, Liu Q. Multi-slice spatial transcriptome domain analysis with SpaDo. Genome Biol. 2024; 25: 73.

[57] Yuan Z. MENDER: fast and scalable tissue structure identification in spatial omics data. Nat Commun. 2024; 15: 207.

[58] Qiu X, Zhu DY, Lu Y, et al. Spatiotemporal modeling of molecular holograms. Cell. 2024;S0092867424011590.

[59] Li H, Lin Y, He W, et al. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nat Commun. 2024; 15: 6048.

[60] Clifton K, Anant M, Aihara G, et al. STalign: alignment of spatial transcriptomics data using diffeomorphic metric mapping. Nat Commun. 2023; 14: 8123.

[61] Jones A, Townes FW, Li D, Engelhardt BE. Alignment of spatial genomics data using deep Gaussian processes. Nat Methods. 2023; 20: 1379-1387.

[62] Zeira R, Land M, Strzalkowski A, Raphael BJ. Alignment and integration of spatial transcriptomics data. Nat Methods. 2022; 19: 567-575.

[63] Hu Y, Xie M, Li Y, et al. Benchmarking clustering, alignment, and integration methods for spatial transcriptomics. Genome Biol. 2024; 25: 212.

[64] Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol. 2023; 95: 25-41.

[65] Dries R, Zhu Q, Dong R, et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 2021; 22: 78.

[66] Miller BF, Bambah-Mukku D, Dulac C, Zhuang X, Fan J. Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities. Genome Res. 2021; 31: 1843.

[67] Weber LM, Saha A, Datta A, Hansen KD, Hicks SC. nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes. Nat Commun. 2023; 14: 4059.

[68] Hao M, Hua K, Zhang X. SOMDE: a scalable method for identifying spatially variable genes with self-organizing map. Bioinformatics. 2021; 37: 4392-4398.

[69] Zhu J, Sun S, Zhou X. SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biol. 2021; 22: 184.

[70] Svensson V, Teichmann SA, Stegle O. SpatialDE: identification of spatially variable genes. Nat Methods. 2018; 15: 343-346.

[71] Chen C, Kim HJ, Yang P. Evaluating spatially variable gene detection methods for spatial transcriptomics data. Genome Biol. 2024; 25: 18.

[72] Zhu J, Sabatti C Integrative Spatial Single-cell Analysis with Graph-based Feature Learning. 2020. 2020.08.12.248971 Preprint at

[73] Pham D, Tan X, Balderson B, et al. Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues. Nat Commun. 2023; 14: 7739.

[74] Hu J, Li X, Coleman K, et al. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods. 2021; 18: 1342-1351.

[75] Zhao E, Stone MR, Ren X, et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat Biotechnol. 2021; 39: 1375-1384.

[76] Fu H, Xu H, Chong K, et al. Unsupervised Spatially Embedded Deep Representation of Spatial Transcriptomics. 2021. 2021.06.15.448542 Preprint at

[77] Li J, Chen S, Pan X, Yuan Y, Shen H-B. Cell clustering for spatial transcriptomics data with graph neural networks. Nat Comput Sci. 2022; 2: 399-408.

[78] Cang Z. SCAN-IT: Domain Segmentation of Spatial Transcriptomics Images by Graph Neural Network.

[79] Dong K, Zhang S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat Commun. 2022; 13: 1739.

[80] Ren H, Walker BL, Cang Z, Nie Q. Identifying multicellular spatiotemporal organization of cells with SpaceFlow. Nat Commun. 2022; 13: 4076.

[81] Zong Y, Yu T, Wang X, Wang Y, Hu Z, Li Y. conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics. 2022. 2022.01.14.476408 Preprint at

[82] Li Z, Zhou X. BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies. Genome Biol. 2022; 23: 168.

[83] Long Y, Ang KS, Li M, et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat Commun. 2023; 14: 1155.

[84] Yuan Z, Zhao F, Lin S, et al. Benchmarking spatial clustering methods with spatially resolved transcriptomics data. Nat Methods. 2024; 1-11.

[85] Birk S, Bonafonte-Pardàs I, Feriz AM, et al. Quantitative characterization of cell niches in spatially resolved omics data. Nat Genet. 2025; 1-13.

[86] Singhal V, Chou N, Lee J, et al. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat Genet. 2024; 56: 431-441.

[87] Cui Y, Yuan Z. 1 Scalable Condition-Relevant Cell Niche Analysis of Spatial 2 Omics Data with Taichi.

[88] Hu Y, Rong J, Xu Y, et al. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nat Methods. 2024; 1-12.

[89] Cang Z, Zhao Y, Almet AA, et al. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat Methods. 2023; 20: 218-228.

[90] Arnol D, Schapiro D, Bodenmiller B, Saez-Rodriguez J, Stegle O. Modeling cell-cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 2019; 29: 202-211.e6.

[91] Cang Z, Nie Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat Commun. 2020; 11: 2084.

[92] Yuan Y, Bar-Joseph Z. GCNG: Graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 2020; 21: 300.

[93] Tanevski J, Flores ROR, Gabor A, Schapiro D, Saez-Rodriguez J. Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biol. 2022; 23: 97.

[94] Jerby-Arnon L, Regev A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat Biotechnol. 2022; 40: 1467-1477.

[95] Ghazanfar S, Lin Y, Su X, et al. Investigating higher order interactions in single cell data with scHOT. Nat Methods. 2020; 17: 799.

[96] Li D, Ding J, Bar-Joseph Z. Identifying signaling genes in spatial single-cell expression data. Bioinformatics. 2020; 37: 968.

[97] Kueckelhaus J, Frerich S, Kada-Benotmane J, et al. Inferring histology-associated gene expression gradients in spatial transcriptomic studies. Nat Commun. 2024; 15: 7280.

[98] Tubelleza R, Kilgallon A, Tan CW, Monkman J, Fraser J, Kulasinghe A. PRISM: A Python Package for Interactive and Integrated Analysis of Multiplexed Tissue Microarrays. 2024. 2024.12.23.630034 Preprint at

[99] Alexander Wolf F, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018; 19: 15.

[100] Palla G, Spitzer H, Klein M, et al. Squidpy: a scalable framework for spatial omics analysis. Nat Methods. 2022; 19: 171-178.

[101] Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: A generalist algorithm for cellular segmentation. Nat Methods. 2021; 18: 100-106.

[102] Greenwald NF, Miller G, Moen E, et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol. 2022; 40: 555-565.

[103] Tan Y, Kempchen TN, Becker M, et al. SPACEc: A Streamlined, Interactive Python Workflow for Multiplexed Image Processing and Analysis. 2024. Preprint at

[104] Brbić M, Cao K, Hickey JW, et al. Annotation of spatially resolved single-cell data with STELLAR. Nat Methods. 2022; 19: 1411-1418.

[105] Pielawski N, Andersson A, Avenel C, et al. TissUUmaps 3: improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data. Heliyon. 2023; 9.

[106] Li X, Pechuan-Jorge X, Risom T, et al. SPEX: A Modular End-to-End Platform for High-Plex Tissue Spatial Omics Analysis. 2022. Preprint at

[107] Kamel M, Sarangi A, Senin P, et al. SpatialOne: end-to-end analysis of visium data at scale. Bioinformatics. 2024; 40: btae509.

[108] Graham S, Vu QD, Raza SEA, et al. Hover-Net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med Image Anal. 2019; 58: 101563.

[109] Kamel M, Song Y, Solbas A, et al. ENACT: end-to-end analysis of visium high definition (HD) data. Bioinformatics. 2025; 41: btaf094.

[110] Weigert M, Schmidt U. Nuclei instance segmentation and classification in histopathology images with stardist. in 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC) 1-4 (IEEE, Kolkata, India, 2022).

[111] Nouri N, Gaglia G, Kurlovs AH, De Rinaldis E, Savova V. A marker gene-based method for identifying the cell-type of origin from single-cell RNA sequencing data. MethodsX. 2023; 10: 102196.

[112] Zhang AW, O'Flanagan C, Chavez EA, et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat Methods. 2019; 16: 1007-1015.

[113] Conde CD, Xu C, Jarvis LB, et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science. 2022; 376(6594): eabl5197.

[114] Shen X, Zuo L, Ye Z, et al. Inferring cell trajectories of spatial transcriptomics via optimal transport analysis. Cell Syst. 2025; 16: 101194.

[115] Anand U, Dey A, Chandel AKS, et al. Cancer chemotherapy and beyond: current status, drug candidates, associated risks and progress in targeted therapeutics. Genes Dis. 2023; 10: 1367-1401.

[116] Kulasinghe A, Monkman J, Shah ET, Matigian N, Adams MN, O'Byrne K. Spatial profiling identifies prognostic features of response to adjuvant therapy in triple negative breast cancer (TNBC). Front Oncol. 2022; 11: 798296.

[117] Donati B, Reggiani F, Torricelli F, et al. Spatial distribution of immune cells drives resistance to neoadjuvant chemotherapy in triple-negative breast cancer. Cancer Immunol Res. 2023.

[118] Agostini A, Guerriero I, Piro G, et al. Talniflumate abrogates mucin immune suppressive barrier improving efficacy of gemcitabine and nab-paclitaxel treatment in pancreatic cancer. J Transl Med. 2023; 21: 843.

[119] Mori Y, Okimoto Y, Sakai H, et al. Targeting PDGF signaling of cancer-associated fibroblasts blocks feedback activation of HIF-1α and tumor progression of clear cell ovarian cancer. Cell Rep Med. 2024; 5(5): 101532.

[120] Wu Y, Yang S, Ma J, et al. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov. 2022; 12: 134-153.

[121] Cheng X, Yang F, Li Y, et al. The crosstalk role of CDKN2A between tumor progression and cuproptosis resistance in colorectal cancer. Aging (Albany NY). 2024; 16: 10512-10538.

[122] Shiau C, Cao J, Gong D, et al. Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment. Nat Genet. 2024; 56(11): 2466-2478.

[123] Lemaitre L, Adeniji N, Suresh A, et al. Spatial analysis reveals targetable macrophage-mediated mechanisms of immune evasion in hepatocellular carcinoma minimal residual disease. Nat Cancer. 2024; 5(10): 1534-1556.

[124] Kats I, Simovic-Lorenz M, Schreiber HS, et al. Spatio-temporal transcriptomics of chromothriptic SHH-medulloblastoma identifies multiple genetic clones that resist treatment and drive relapse. Nat Commun. 2024; 15(1): 10370.

[125] Smirnov P, Przybilla MJ, Simovic-Lorenz M, et al. Multi-omic and single-cell profiling of chromothriptic medulloblastoma reveals genomic and transcriptomic consequences of genome instability. Nat Commun. 2024; 15: 10183.

[126] Zhou X, Han J, Zuo A, et al. THBS2 + cancer-associated fibroblasts promote EMT leading to oxaliplatin resistance via COL8A1-mediated PI3K/AKT activation in colorectal cancer. Mol Cancer. 2024; 23(1): 282.

[127] Wu Y, Yang S, Ma J, et al. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov. 2022; 12: 134-153.

[128] Barkley D, Moncada R, Pour M, et al. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat Genet. 2022; 54: 1192-1201.

[129] Cheng H-Y, Hsieh C-H, Lin Po-H, et al. Snail-regulated exosomal microRNA-21 suppresses NLRP3 inflammasome activity to enhance cisplatin resistance. J Immunother Cancer. 2022; 10: e004832.

[130] Kiviaho A, Eerola SK, Kallio HML, et al. Single cell and spatial transcriptomics highlight the interaction of club-like cells with immunosuppressive myeloid cells in prostate cancer. Nat Commun. 2024; 15: 9949.

[131] Huang FW, Song H, Weinstein HNw, et al. Club-like cells in proliferative inflammatory atrophy of the prostate. J Pathol. 2023; 261: 85-95.

[132] Carbone C, De Luca R, Puca E, et al. Antibody-based delivery of interleukin-2 modulates the immunosuppressive tumor microenvironment and achieves cure in pancreatic ductal adenocarcinoma syngeneic mice. J Exp Clin Cancer Res CR. 2025; 44: 7.

[133] Patel R, Klein P, Tiersten A, Sparano JA. An emerging generation of endocrine therapies in breast cancer: a clinical perspective. Npj Breast Cancer. 2023; 9: 1-12.

[134] Sella T, Weiss A, Mittendorf EA, et al. Neoadjuvant endocrine therapy in clinical practice: a review. JAMA Oncol. 2021; 7: 1700-1708.

[135] Rasha F, Sharma M, Pruitt K. Mechanisms of endocrine therapy resistance in breast cancer. Mol Cell Endocrinol. 2021; 532: 111322.

[136] Franzoi MA, Agostinetto E, Perachino M, et al. Evidence-based approaches for the management of side-effects of adjuvant endocrine therapy in patients with breast cancer. Lancet Oncol. 2021; 22: e303-e313.

[137] Zhang KM, Zhao DC, Li ZY, et al. Inactivated cGAS-STING signaling facilitates endocrine resistance by forming a positive feedback loop with AKT kinase in ER+HER2- breast cancer. Adv Sci (Weinh). 2024; 11(35): e2403592.

[138] Elosua-Bayes M, Nieto P, Mereu E, Gut I, Heyn H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 2021; 49: e50.

[139] Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013; 14: 7.

[140] Romero R, Chu T, González Robles TJ, et al. The neuroendocrine transition in prostate cancer is dynamic and dependent on ASCL1. Nat Cancer. 2024; 5(11): 1641-1659.

[141] Ku SYu, Rosario S, Wang Y, et al. Rb1 and Trp53 cooperate to suppress prostate cancer lineage plasticity, metastasis, and antiandrogen resistance. Science. 2017; 355: 78-83.

[142] Tsimberidou A-M. Targeted therapy in cancer. Cancer Chemother Pharmacol. 2015; 76: 1113-1132.

[143] Labrie M, Brugge JS, Mills GB, Zervantonakis IK. Therapy resistance: opportunities created by adaptive responses to targeted therapies in cancer. Nat Rev Cancer. 2022; 22: 323-339.

[144] Jussila AR, Haensel D, Gaddam S, Oro AE. Acquisition of drug resistance in basal cell nevus syndrome tumors through basal to squamous cell carcinoma transition. J Invest Dermatol. 2023. S0022-202X(23)03199-8.

[145] Li NY, Zhang W, Haensel D, et al. Basal-to-inflammatory transition and tumor resistance via crosstalk with a pro-inflammatory stromal niche. Nat Commun. 2024; 15: 8134.

[146] Zhou W, Su M, Jiang T, et al. SORC: an integrated spatial omics resource in cancer. Nucleic Acids Res. 2024; 52: D1429-D1437.

[147] Xu Z, Wang W, Yang T, et al. STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization. Nucleic Acids Res. 2024; 52: D1053-D1061.

[148] Zhang Y, Kang T, Wang Y, et al. Low level of tumor necrosis factor α in tumor microenvironment maintains self-renewal of glioma stem cells by Vasorin-mediated glycolysis. Neuro-Oncol. 2024;noae147.

[149] Izumi M, Fujii M, Kobayashi IS, et al. Integrative single-cell RNA-seq and spatial transcriptomics analyses reveal diverse apoptosis-related gene expression profiles in EGFR-mutated lung cancer. Cell Death Dis. 2024; 15: 1-11.

[150] Li Y, Xie T, Wang S, et al. Mechanism exploration and model construction for small cell transformation in EGFR-mutant lung adenocarcinomas. Signal Transduct Target Ther. 2024; 9: 261.

[151] Wang J, Sun N, Kunzke T, et al. Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer. Br J Cancer. 2024; 130(6): 1036-1045.

[152] Bouchard G, Zhang W, Ilerten I, et al. A quantitative spatial cell-cell colocalizations framework enabling comparisons between in vitro assembloids and pathological specimens. Nat Commun. 2025; 16: 1392.

[153] Rubinstein JC, Domanskyi S, Sheridan TB, et al. Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma. Cancer Res. 2024.

[154] Hong J, Bae S, Cavinato L, et al. Deciphering the effects of radiopharmaceutical therapy in the tumor microenvironment of prostate cancer: an in-silico exploration with spatial transcriptomics. Theranostics. 2024; 14: 7122-7139.

[155] Billan S, Kaidar-Person O, Gil Z. Treatment after progression in the era of immunotherapy. Lancet Oncol. 2020; 21: e463-e476.

[156] Liu C, Yang M, Zhang D, Chen M, Zhu D. Clinical cancer immunotherapy: current progress and prospects. Front Immunol. 2022; 13.

[157] Sun S, Wang K, Guo D, et al. Identification of the key DNA damage response genes for predicting immunotherapy and chemotherapy efficacy in lung adenocarcinoma based on bulk, single-cell RNA sequencing, and spatial transcriptomics. Comput Biol Med. 2024; 171: 108078.

[158] Lozar T, Laklouk I, Golfinos AE, et al. Stress keratin 17 is a predictive biomarker inversely associated with response to immune check-point blockade in head and neck squamous cell carcinomas and beyond. Cancers. 2023; 15: 4905.

[159] Xia S, Chen L, Yu M, et al. Genetic and therapeutic heterogeneity shape the baseline and longitudinal immune ecosystem of ovarian clear cell carcinoma. J Immunother Cancer. 2024; 12: e010069.

[160] Jin S, Plikus MV, Nie Q. CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics. Nat Protoc. 2024; 1-40.

[161] Jhaveri N, Ben Cheikh B, Nikulina N, et al. Mapping the spatial proteome of head and neck tumors: key immune mediators and metabolic determinants in the tumor microenvironment. GEN Biotechnol. 2023; 2: 418-434.

[162] Larroquette M, Guegan J-P, Besse B, et al. Spatial transcriptomics of macrophage infiltration in non-small cell lung cancer reveals determinants of sensitivity and resistance to anti-PD1/PD-L1 antibodies. J Immunother Cancer. 2022; 10: e003890.

[163] Berrell N et al. Spatial resolution of the head and neck cancer tumor microenvironment to identify tumor and stromal features associated with therapy response. Immunol Cell Biol. n/a,.

[164] Hu D, Shen Xu, Gao P, et al. Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine. EPMA J. 2024; 15: 321-343.

[165] Liu Y, Xun Z, Ma K, et al. Identification of a tumour immune barrier in the HCC microenvironment that determines the efficacy of immunotherapy. J Hepatol. 2023; 78: 770-782.

[166] Quek C, Pratapa A, Bai X, et al. Single-cell spatial multiomics reveals tumor microenvironment vulnerabilities in cancer resistance to immunotherapy. Cell Rep. 2024; 43(7): 114392.

[167] Monkman J, Moradi A, Yunis J, et al. Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging. J Transl Med. 2024; 22(1): 239.

[168] Cui X, Gu X, Li D, et al. Tertiary lymphoid structures as a biomarker in immunotherapy and beyond: advancing towards clinical application. Cancer Lett. 2025; 613: 217491.

[169] MacFawn IP, Magnon G, Gorecki G, et al. The activity of tertiary lymphoid structures in high grade serous ovarian cancer is governed by site, stroma, and cellular interactions. Cancer Cell. 2024; 42: 1864-1881.e5.

[170] Zhang Y, Liu G, Zeng Q, et al. CCL19-producing fibroblasts promote tertiary lymphoid structure formation enhancing anti-tumor IgG response in colorectal cancer liver metastasis. Cancer Cell. 2024; 42: 1370-1385.e9.

[171] Peyraud F, Guégan JP, Rey C, et al. Spatially resolved transcriptomics reveal the determinants of primary resistance to immunotherapy in NSCLC with mature tertiary lymphoid structures. Cell Rep Med. 2025; 6(2): 101934.

[172] Yan Y, Sun D, Hu J, et al. Multi-omic profiling highlights factors associated with resistance to immuno-chemotherapy in non-small-cell lung cancer. Nat Genet. 2025; 57(1): 126-139.

[173] Wang X, Venet D, Lifrange F, et al. Spatial transcriptomics reveals substantial heterogeneity in triple-negative breast cancer with potential clinical implications. Nat Commun. 2024; 15: 10232.

[174] Lin J, Jiang S, Chen B, et al. Tertiary lymphoid structures are linked to enhanced antitumor immunity and better prognosis in muscle-invasive bladder cancer. Adv Sci Weinh Baden-Wurtt Ger. 2025; 12: e2410998.

[175] Molina-Alejandre M, Perea F, Calvo V, et al. Perioperative chemoimmunotherapy induces strong immune responses and long-term survival in patients with HLA class I-deficient non-small cell lung cancer. J Immunother Cancer. 2024; 12: e009762.

[176] Davidson G, Helleux A, Vano YA, et al. Mesenchymal-like tumor cells and myofibroblastic cancer-associated fibroblasts are associated with progression and immunotherapy response of clear cell renal cell carcinoma. Cancer Res. 2023; 83(17): 2952-2969.

[177] Di Modugno F, Di Carlo A, Spada S, et al. Tumoral and stromal hMENA isoforms impact tertiary lymphoid structure localization in lung cancer and predict immune checkpoint blockade response in patients with cancer. EBioMedicine. 2024; 101: 105003.

[178] Meylan M, Petitprez F, Becht E, et al. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity. 2022; 55: 527-541.e5.

[179] Zhang J, Peng Q, Fan J, et al. Single-cell and spatial transcriptomics reveal SPP1-CD44 signaling drives primary resistance to immune checkpoint inhibitors in RCC. J Transl Med. 2024; 22: 1157.

[180] Naei VY, Tubelleza R, Monkman J, et al. Spatial interaction mapping of PD-1/PD-L1 in head and neck cancer reveals the role of macrophage-tumour barriers associated with immunotherapy response. J Transl Med. 2025; 23(1): 177.

[181] Zhang S, Yuan L, Danilova L, et al. Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence. Genome Med. 2023; 15: 72.

[182] Wu SZ, Al-Eryani G, Roden DL, et al. A single-cell and spatially resolved atlas of human breast cancers. Nat Genet. 2021; 53: 1334-1347.

[183] Salachan PV, Rasmussen M, Ulhøi BP, Jensen JB, Borre M, Sørensen KD. Spatial whole transcriptome profiling of primary tumor from patients with metastatic prostate cancer. Int J Cancer. 2023; 153: 2055-2067.

[184] Fan J, Lu F, Qin T, et al. Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance. Nat Genet. 2023; 1-14.

[185] Wang H, Liang Y, Liu Z, et al. POSTN+ cancer-associated fibroblasts determine the efficacy of immunotherapy in hepatocellular carcinoma. J Immunother Cancer. 2024; 12: e008721.

[186] Schaer DJ, Schulthess-Lutz N, Baselgia L, et al. Hemorrhage-activated NRF2 in tumor-associated macrophages drives cancer growth, invasion, and immunotherapy resistance. J Clin Invest. 2023.

[187] Xun Z, Zhou H, Shen M, et al. Identification of hypoxia-ALCAMhigh macrophage- exhausted T cell axis in tumor microenvironment remodeling for immunotherapy resistance. Adv Sci Weinh Baden-Wurtt Ger. 2024; 11: e2309885.

[188] Qi J, Sun H, Zhang Y, et al. Single-cell and spatial analysis reveal interaction of FAP+ fibroblasts and SPP1+ macrophages in colorectal cancer. Nat Commun. 2022; 13: 1742.

[189] Ji AL, Rubin AJ, Thrane K, et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell. 2020; 182: 497-514.e22.

[190] Fan G et al. The co-location of MARCO+ tumor-associated macrophages and CTSE+ tumor cells determined the poor prognosis in intrahepatic cholangiocarcinoma. Hepatol Baltim Md. 2024.

[191] Aung TN, Warrell J, Martinez-Morilla S, et al. Spatially informed gene signatures for response to immunotherapy in melanoma. Clin Cancer Res. 2024; 30: 3520-3532.

[192] Soupir AC, Hayes MT, Peak TC, et al. Increased spatial coupling of integrin and collagen IV in the immunoresistant clear-cell renal-cell carcinoma tumor microenvironment. Genome Biol. 2024; 25(1): 308. Published 2024 Dec 5. https://doi.org/10.1186/s13059-024-03435-z

[193] Liu Y, Yang M, Deng Y, et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell. 2020; 183: 1665-1681.e18.

[194] Jiang F, Zhou X, Qian Y, et al. Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development. Nat Methods. 2023; 20: 1048-1057.

[195] Zhao T, Chiang ZD, Morriss JW, et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature. 2022; 601: 85-91.

[196] Liu S, Iorgulescu JB, Li S, et al. Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response. Immunity. 2022; 55: 1940-1952.e5.

[197] Vickovic S, Lötstedt B, Klughammer J, et al. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat Commun. 2022; 13: 795.

[198] Liu Y, DiStasio M, Su G, et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat Biotechnol. 2023; 41: 1405-1409.

[199] Zhang Di, Deng Y, Kukanja P, et al. Spatial epigenome-transcriptome co-profiling of mammalian tissues. Nature. 2023; 616: 113-122.

[200] Long Y, Ang KS, Sethi R, et al. Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nat Methods. 2024; 1-10.

[201] Coleman K, Schroeder A, Loth M, et al. Resolving tissue complexity by multimodal spatial omics modeling with MISO. Nat Methods. 2025; 1-9.

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