Tumour evolution and microenvironment interactions in 2D and 3D space.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
Oct 2024
Oct 2024
Historique:
received:
02
11
2023
accepted:
19
09
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
ppublish
Résumé
To study the spatial interactions among cancer and non-cancer cells
Identifiants
pubmed: 39478210
doi: 10.1038/s41586-024-08087-4
pii: 10.1038/s41586-024-08087-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1178-1186Informations de copyright
© 2024. The Author(s).
Références
Fu, T. et al. Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response. J. Hematol. Oncol. 14, 98 (2021).
pubmed: 34172088
pmcid: 8234625
doi: 10.1186/s13045-021-01103-4
Schmitt, M. W., Loeb, L. A. & Salk, J. J. The influence of subclonal resistance mutations on targeted cancer therapy. Nat. Rev. Clin. Oncol. 13, 335–347 (2016).
pubmed: 26483300
doi: 10.1038/nrclinonc.2015.175
Roper, N. et al. Clonal evolution and heterogeneity of osimertinib acquired resistance mechanisms in EGFR mutant lung cancer. Cell Rep. Med. 1, 100007 (2020).
pubmed: 32483558
pmcid: 7263628
doi: 10.1016/j.xcrm.2020.100007
Trédan, O., Galmarini, C. M., Patel, K. & Tannock, I. F. Drug resistance and the solid tumor microenvironment. J. Natl Cancer Inst. 99, 1441–1454 (2007).
pubmed: 17895480
doi: 10.1093/jnci/djm135
Qu, Y. et al. Tumor microenvironment-driven non-cell-autonomous resistance to antineoplastic treatment. Mol. Cancer 18, 69 (2019).
pubmed: 30927928
pmcid: 6441162
doi: 10.1186/s12943-019-0992-4
Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).
pubmed: 22237025
pmcid: 3267864
doi: 10.1038/nature10738
Yang, D. et al. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 185, 1905–1923.e25 (2022).
pubmed: 35523183
pmcid: 9452598
doi: 10.1016/j.cell.2022.04.015
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
pubmed: 27365449
doi: 10.1126/science.aaf2403
Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).
pubmed: 34381231
pmcid: 8475179
doi: 10.1038/s41586-021-03634-9
Cui Zhou, D. et al. Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer. Nat. Genet. 54, 1390–1405 (2022).
pubmed: 35995947
pmcid: 9470535
doi: 10.1038/s41588-022-01157-1
Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).
pubmed: 34215862
pmcid: 8647621
doi: 10.1038/s41596-021-00556-8
Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).
pubmed: 22258609
pmcid: 3367003
doi: 10.1038/nature10762
Ding, L., Raphael, B. J., Chen, F. & Wendl, M. C. Advances for studying clonal evolution in cancer. Cancer Lett. 340, 212–219 (2013).
pubmed: 23353056
doi: 10.1016/j.canlet.2012.12.028
Erickson, A. et al. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 608, 360–367 (2022).
pubmed: 35948708
pmcid: 9365699
doi: 10.1038/s41586-022-05023-2
Lomakin, A. et al. Spatial genomics maps the structure, nature and evolution of cancer clones. Nature 611, 594–602 (2022).
pubmed: 36352222
pmcid: 9668746
doi: 10.1038/s41586-022-05425-2
Di Maggio, F. & El-Shakankery, K. H. Desmoplasia and biophysics in pancreatic ductal adenocarcinoma: can we learn from breast cancer? Pancreas 49, 313–325 (2020).
pubmed: 32168249
doi: 10.1097/MPA.0000000000001504
Chen, H., Liu, H. & Qing, G. Targeting oncogenic Myc as a strategy for cancer treatment. Signal. Transduct. Target Ther. 3, 5 (2018).
pubmed: 29527331
pmcid: 5837124
doi: 10.1038/s41392-018-0008-7
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).
pubmed: 33603203
doi: 10.1038/s41587-021-00830-w
Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).
pubmed: 24113773
doi: 10.1038/ncomms3612
Gencheva, R. & Arnér, E. S. J. Thioredoxin reductase inhibition for cancer therapy. Annu. Rev. Pharmacol. Toxicol. 62, 177–196 (2022).
pubmed: 34449246
doi: 10.1146/annurev-pharmtox-052220-102509
Jiang, Y. et al. A systematic analysis of C5ORF46 in gastrointestinal tumors as a potential prognostic and immunological biomarker. Front. Genet. 13, 926943 (2022).
pubmed: 35991552
pmcid: 9389054
doi: 10.3389/fgene.2022.926943
Han, L., Li, Z., Jiang, Y., Jiang, Z. & Tang, L. SNHG29 regulates miR-223-3p/CTNND1 axis to promote glioblastoma progression via Wnt/β-catenin signaling pathway. Cancer Cell Int. 19, 345 (2019).
pubmed: 31889897
pmcid: 6924063
doi: 10.1186/s12935-019-1057-x
Huang, C. K., Sun, Y., Lv, L. & Ping, Y. ENO1 and cancer. Mol. Ther. Oncolytics 24, 288–298 (2022).
pubmed: 35434271
pmcid: 8987341
doi: 10.1016/j.omto.2021.12.026
Zhang, X. et al. Thymosin beta 10 is a key regulator of tumorigenesis and metastasis and a novel serum marker in breast cancer. Breast Cancer Res 19, 15 (2017).
pubmed: 28179017
pmcid: 5299657
doi: 10.1186/s13058-016-0785-2
Chen, R. H. et al. Tumor cell-secreted ISG15 promotes tumor cell migration and immune suppression by inducing the macrophage M2-like phenotype. Front. Immunol. 11, 594775 (2020).
pubmed: 33424843
pmcid: 7785797
doi: 10.3389/fimmu.2020.594775
Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).
pubmed: 33597522
pmcid: 7889871
doi: 10.1038/s41467-021-21246-9
Filippou, P. S., Karagiannis, G. S. & Constantinidou, A. Midkine (MDK) growth factor: a key player in cancer progression and a promising therapeutic target. Oncogene 39, 2040–2054 (2020).
pubmed: 31801970
doi: 10.1038/s41388-019-1124-8
Sick, E. et al. Activation of CD47 receptors causes proliferation of human astrocytoma but not normal astrocytes via an Akt-dependent pathway. Glia 59, 308–319 (2011).
pubmed: 21125662
doi: 10.1002/glia.21102
Isenberg, J. S., Frazier, W. A. & Roberts, D. D. Thrombospondin-1: a physiological regulator of nitric oxide signaling. Cell. Mol. Life Sci. 65, 728–742 (2008).
pubmed: 18193160
pmcid: 2562780
doi: 10.1007/s00018-007-7488-x
Jeanne, A. et al. Identification of TAX2 peptide as a new unpredicted anti-cancer agent. Oncotarget 6, 17981–18000 (2015).
pubmed: 26046793
pmcid: 4627230
doi: 10.18632/oncotarget.4025
Wang, Y., Hu, L., Zheng, Y. & Guo, L. HMGA1 in cancer: cancer classification by location. J. Cell. Mol. Med. 23, 2293–2302 (2019).
pubmed: 30614613
pmcid: 6433663
doi: 10.1111/jcmm.14082
Huang, R., Huang, D., Dai, W. & Yang, F. Overexpression of HMGA1 correlates with the malignant status and prognosis of breast cancer. Mol. Cell. Biochem. 404, 251–257 (2015).
pubmed: 25772486
doi: 10.1007/s11010-015-2384-4
Mitselou, A. et al. Predictive role of thymidine phosphorylase expression in patients with colorectal cancer and its association with angiogenesis-related proteins and extracellular matrix components. In Vivo 26, 1057–1067 (2012).
pubmed: 23160694
Baris, A., Fraile-Bethencourt, E., Eubanks, J., Khou, S. & Anand, S. Thymidine phosphorylase facilitates retinoic acid inducible gene-I induced endothelial dysfunction. Cell Death Dis. 14, 294 (2023).
pubmed: 37100811
pmcid: 10131517
doi: 10.1038/s41419-023-05821-0
Schmidt, M. et al. Prognostic impact of immunoglobulin kappa C (IGKC) in early breast cancer. Cancers 13, 3626 (2021).
pubmed: 34298839
pmcid: 8304855
doi: 10.3390/cancers13143626
Wang, J. et al. CCL19 has potential to be a potential prognostic biomarker and a modulator of tumor immune microenvironment (TIME) of breast cancer: a comprehensive analysis based on TCGA database. Aging 14, 4158–4175 (2022).
pubmed: 35550569
pmcid: 9134962
doi: 10.18632/aging.204081
Nishimura, T. et al. Evolutionary histories of breast cancer and related clones. Nature 620, 607–614 (2023).
pubmed: 37495687
pmcid: 10432280
doi: 10.1038/s41586-023-06333-9
Dang, H. X. et al. The clonal evolution of metastatic colorectal cancer. Sci. Adv. 6, eaay9691 (2020).
pubmed: 32577507
pmcid: 7286679
doi: 10.1126/sciadv.aay9691
Simeonov, K. P. et al. Single-cell lineage tracing of metastatic cancer reveals selection of hybrid EMT states. Cancer Cell 39, 1150–1162.e9 (2021).
pubmed: 34115987
pmcid: 8782207
doi: 10.1016/j.ccell.2021.05.005
Chen, H. N. et al. Genomic evolution and diverse models of systemic metastases in colorectal cancer. Gut 71, 322–332 (2022).
pubmed: 33632712
doi: 10.1136/gutjnl-2020-323703
Leung, M. L. et al. Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer. Genome Res. 27, 1287–1299 (2017).
pubmed: 28546418
pmcid: 5538546
doi: 10.1101/gr.209973.116
Hu, Z. et al. Quantitative evidence for early metastatic seeding in colorectal cancer. Nat. Genet. 51, 1113–1122 (2019).
pubmed: 31209394
pmcid: 6982526
doi: 10.1038/s41588-019-0423-x
Tang, J. et al. Single-cell exome sequencing reveals multiple subclones in metastatic colorectal carcinoma. Genome Med. 13, 148 (2021).
pubmed: 34507604
pmcid: 8434739
doi: 10.1186/s13073-021-00962-3
Poos, A. M. et al. Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis. Blood 142, 1633–1646 (2023).
pubmed: 37390336
pmcid: 10733835
doi: 10.1182/blood.2023019758
Braxton, A. M. et al. 3D genomic mapping reveals multifocality of human pancreatic precancers. Nature 629, 679–687 (2024).
pubmed: 38693266
doi: 10.1038/s41586-024-07359-3
Herndon, J., Fields, R., Zhou, D. C. & Ding, L. Biospecimen collection and processing 2.0. protocols.io https://doi.org/10.17504/protocols.io.bszynf7w (2021).
Wu, Y. et al. Epigenetic and transcriptomic characterization reveals progression markers and essential pathways in clear cell renal cell carcinoma. Nat. Commun. 14, 1681 (2023).
pubmed: 36973268
pmcid: 10042888
doi: 10.1038/s41467-023-37211-7
Houston, A., Chen, S. & Chen, F. Spatial transcriptomics for OCT using 10x Genomics Visium. protocols.io https://doi.org/10.17504/protocols.io.x54v9d3opg3e/v1 (2023).
Houston, A., Chen, S. & Chen, F. Spatial transcriptomics for FFPE utilizing 10x Genomics Visium. protocols.io https://doi.org/10.17504/protocols.io.kxygx95ezg8j/v1 (2023).
Caravan, W., Jayasinghe, R. & Al Deen, N. N. WU sn-prep protocol for solid tumors—snRNA protocol v2.8. protocols.io https://doi.org/10.17504/protocols.io.14egn7w6zv5d/v1 (2022).
Jayasinghe, R., Caravan, W., Houston, A. & AlDeen, N. N. WU sn-prep protocol for solid tumors—joint snRNA+ATAC v2.9. protocols.io https://doi.org/10.17504/protocols.io.261gednx7v47/v1 (2023).
Jayasinghe, R., Ding, L. & Chen, F. WU sc-prep protocol for solid tumors v2.1. protocols.io https://doi.org/10.17504/protocols.io.bsnqnddw (2021).
Jayasinghe, R., Ding, L., Chen, F. & Satok. Bulk DNA extraction (Ding Lab). protocols.io https://doi.org/10.17504/protocols.io.bsnhndb6 (2021).
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
pubmed: 30013048
doi: 10.1038/s41592-018-0051-x
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
pubmed: 23396013
pmcid: 3833702
doi: 10.1038/nbt.2514
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
pubmed: 22300766
pmcid: 3290792
doi: 10.1101/gr.129684.111
Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).
pubmed: 19561018
pmcid: 2781750
doi: 10.1093/bioinformatics/btp394
Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).
pubmed: 29625053
pmcid: 6029450
doi: 10.1016/j.cell.2018.02.060
Li, Y. et al. Pan-cancer proteogenomics connects oncogenic drivers to functional states. Cell 186, 3921–3944.e25 (2023).
pubmed: 37582357
doi: 10.1016/j.cell.2023.07.014
Terekhanova, N. V. et al. Epigenetic regulation during cancer transitions across 11 tumour types. Nature 623, 432–441 (2023).
pubmed: 37914932
pmcid: 10632147
doi: 10.1038/s41586-023-06682-5
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
doi: 10.1101/gr.107524.110
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pubmed: 20110278
doi: 10.1093/bioinformatics/btq033
Ma, C. et al. Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2024.03.09.584244 (2024).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
pubmed: 26771021
pmcid: 4707969
doi: 10.1016/j.cels.2015.12.004
Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).
pubmed: 29195078
doi: 10.1016/j.cell.2017.10.049
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
pubmed: 27141961
pmcid: 4987924
doi: 10.1093/nar/gkw377
Liu, B. et al. An entropy-based metric for assessing the purity of single cell populations. Nat. Commun. 11, 3155 (2020).
pubmed: 32572028
pmcid: 7308400
doi: 10.1038/s41467-020-16904-3
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
pubmed: 34062119
doi: 10.1016/j.cell.2021.04.048
Cang, Z. et al. Screening cell–cell communication in spatial transcriptomics via collective optimal transport. Nat. Methods 20, 218–228 (2023).
pubmed: 36690742
pmcid: 9911355
doi: 10.1038/s41592-022-01728-4
Liu, X., Zeira, R. & Raphael, B. J. Partial alignment of multislice spatially resolved transcriptomics data. Genome Res. 33, 1124–1132 (2023).
Bogovic J. A., Hanslovsky P., Wong A., Saalfeld S. in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 1123–1126 (2016)
Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. Preprint at https://doi.org/10.48550/arXiv.2010.11929 (2021).
He, K. et al. in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 15979–15988 (IEEE, 2022).
Pérez-García, F., Sparks, R. & Ourselin, S. TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Comput. Methods Programs Biomed. 208, 106236 (2021).
pubmed: 34311413
pmcid: 8542803
doi: 10.1016/j.cmpb.2021.106236
Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555–565 (2022).
pubmed: 34795433
doi: 10.1038/s41587-021-01094-0
Lewiner, T., Lopes, H., Vieira, A. W. & Tavares, G. Efficient implementation of marching cubes’ cases with topological guarantees. J. Graph. Tools https://doi.org/10.1080/10867651.2003.10487582 (2012).
Lorensen, W. E. & Cline, H. E. Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Comp. Graph. 21, 163–169 (1987).
doi: 10.1145/37402.37422