Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics.
Journal
NPJ precision oncology
ISSN: 2397-768X
Titre abrégé: NPJ Precis Oncol
Pays: England
ID NLM: 101708166
Informations de publication
Date de publication:
10 Jan 2024
10 Jan 2024
Historique:
received:
24
02
2023
accepted:
04
12
2023
medline:
11
1
2024
pubmed:
11
1
2024
entrez:
10
1
2024
Statut:
epublish
Résumé
The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signals, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our study illustrates the potential of ST to resolve CRC molecular heterogeneity and thereby help advance personalized therapy.
Identifiants
pubmed: 38200223
doi: 10.1038/s41698-023-00488-4
pii: 10.1038/s41698-023-00488-4
doi:
Types de publication
Journal Article
Langues
eng
Pagination
10Informations de copyright
© 2024. The Author(s).
Références
Biller, L. H. & Schrag, D. Diagnosis and treatment of metastatic colorectal cancer: a review. JAMA 325, 669–685 (2021).
pubmed: 33591350
doi: 10.1001/jama.2021.0106
Wang, W. et al. Molecular subtyping of colorectal cancer: recent progress, new challenges and emerging opportunities. Semin. Cancer Biol. 55, 37–52 (2019).
pubmed: 29775690
doi: 10.1016/j.semcancer.2018.05.002
Okita, A. et al. Consensus molecular subtypes classification of colorectal cancer as a predictive factor for chemotherapeutic efficacy against metastatic colorectal cancer. Oncotarget 9, 18698–18711 (2018).
pubmed: 29721154
pmcid: 5922348
doi: 10.18632/oncotarget.24617
Chan, D. K. H. & Buczacki, S. J. A. Tumour heterogeneity and evolutionary dynamics in colorectal cancer. Oncogenesis 10, 1–9 (2021).
doi: 10.1038/s41389-021-00342-x
Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).
pubmed: 26457759
pmcid: 4636487
doi: 10.1038/nm.3967
Lee, H.-O. et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nat. Genet. 52, 594–603 (2020).
pubmed: 32451460
doi: 10.1038/s41588-020-0636-z
Khaliq, A. M. et al. Refining colorectal cancer classification and clinical stratification through a single-cell atlas. Genome Biol. 23, 1–30 (2022).
Joanito, I. et al. Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer. Nat. Genet. 54, 963–975 (2022).
pubmed: 35773407
pmcid: 9279158
doi: 10.1038/s41588-022-01100-4
Cañellas-Socias, A. et al. Metastatic recurrence in colorectal cancer arises from residual EMP1
pubmed: 36352230
doi: 10.1038/s41586-022-05402-9
Chowdhury, S. et al. Implications of intratumor heterogeneity on consensus molecular subtype (CMS) in colorectal cancer. Cancers 13, 4923 (2021).
Andersson, A. et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat. Commun. 12, 1–14 (2021).
doi: 10.1038/s41467-021-26271-2
Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 1–13 (2018).
doi: 10.1038/s41467-018-04724-5
Hunter, M. V., Moncada, R., Weiss, J. M., Yanai, I. & White, R. M. Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface. Nat. Commun. 12, 1–16 (2021).
doi: 10.1038/s41467-021-26614-z
Wu, Y. et al. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov. 12, 134–153 (2022).
pubmed: 34417225
doi: 10.1158/2159-8290.CD-21-0316
Peng, Z., Ye, M., Ding, H., Feng, Z. & Hu, K. Spatial transcriptomics atlas reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment components in colorectal cancer. J. Transl. Med. 20, 302 (2022).
pubmed: 35794563
pmcid: 9258101
doi: 10.1186/s12967-022-03510-8
Qi, J. et al. Single-cell and spatial analysis reveal interaction of FAP fibroblasts and SPP1 macrophages in colorectal cancer. Nat. Commun. 13, 1742 (2022).
pubmed: 35365629
pmcid: 8976074
doi: 10.1038/s41467-022-29366-6
Zhang, R. et al. Spatial transcriptome unveils a discontinuous inflammatory pattern in proficient mismatch repair colorectal adenocarcinoma. Fundam. Res. https://doi.org/10.1016/j.fmre.2022.01.036 (2022).
Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01139-4 (2022).
Mevizou, R., Sirvent, A. & Roche, S. Control of tyrosine kinase signalling by small adaptors in colorectal cancer. Cancers 11, 669 (2019).
Nunez, S. K. et al. Identification of gene co-expression networks associated with consensus molecular subtype-1 of colorectal cancer. Cancers 13, 5824 (2021).
García-Aranda, M. & Redondo, M. Targeting receptor kinases in colorectal cancer. Cancers 11, 433 (2019).
Rebersek, M. Consensus molecular subtypes (CMS) in metastatic colorectal cancer - personalized medicine decision. Radiol. Oncol. 54, 272–277 (2020).
pubmed: 32463385
pmcid: 7409603
doi: 10.2478/raon-2020-0031
Orouji, E. et al. Chromatin state dynamics confers specific therapeutic strategies in enhancer subtypes of colorectal cancer. Gut 71, 938–949 (2022).
pubmed: 34059508
doi: 10.1136/gutjnl-2020-322835
Martin, T. A. et al. NUPR1 and its potential role in cancer and pathological conditions (Review). Int. J. Oncol. 58, 21 (2021).
Shi, X., Young, C. D., Zhou, H. & Wang, X. Transforming growth factor-β signaling in fibrotic diseases and cancer-associated fibroblasts. Biomolecules 10, 1666 (2020).
Lin, Y., Xu, J. & Lan, H. Tumor-associated macrophages in tumor metastasis: biological roles and clinical therapeutic applications. J. Hematol. Oncol. 12, 76 (2019).
pubmed: 31300030
pmcid: 6626377
doi: 10.1186/s13045-019-0760-3
Thanki, K. et al. Consensus molecular subtypes of colorectal cancer and their clinical implications. Int Biol. Biomed. J. 3, 105–111 (2017).
pubmed: 28825047
pmcid: 5557054
Naito, T. et al. Mesenchymal stem cells induce tumor stroma formation and epithelial‑mesenchymal transition through SPARC expression in colorectal cancer. Oncol. Rep. 45, 104 (2021).
Ran, H. et al. Stearoyl-CoA desaturase-1 promotes colorectal cancer metastasis in response to glucose by suppressing PTEN. J. Exp. Clin. Cancer Res. 37, 54 (2018).
pubmed: 29530061
pmcid: 5848567
doi: 10.1186/s13046-018-0711-9
Syed, V. TGF-β Signaling in Cancer. J. Cell. Biochem. 117, 1279–1287 (2016).
pubmed: 26774024
doi: 10.1002/jcb.25496
Tanevski, J., Flores, R. O. R., Gabor, A., Schapiro, D. & Saez-Rodriguez, J. Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biol. 23, 97 (2022).
pubmed: 35422018
pmcid: 9011939
doi: 10.1186/s13059-022-02663-5
Neill, T., Schaefer, L. & Iozzo, R. V. Decorin: a guardian from the matrix. Am. J. Pathol. 181, 380–387 (2012).
pubmed: 22735579
pmcid: 3409438
doi: 10.1016/j.ajpath.2012.04.029
Deves, C. et al. Analysis of select members of the E26 (ETS) transcription factors family in colorectal cancer. Virchows Arch. 458, 421–430 (2011).
pubmed: 21318373
doi: 10.1007/s00428-011-1053-6
Gİrgİn, B., KaradaĞ-Alpaslan, M. & KocabaŞ, F. Oncogenic and tumor suppressor function of MEIS and associated factors. Turk. J. Biol. 44, 328–355 (2020).
pubmed: 33402862
pmcid: 7759197
doi: 10.3906/biy-2006-25
Du, B., Gao, W., Qin, Y., Zhong, J. & Zhang, Z. Study on the role of transcription factor SPI1 in the development of glioma. Chin. Neurosurg. J. 8, 7 (2022).
pubmed: 35361282
pmcid: 8973577
doi: 10.1186/s41016-022-00276-2
Nie, X., Liu, H., Liu, L., Wang, Y.-D. & Chen, W.-D. Emerging Roles of Wnt Ligands in Human Colorectal Cancer. Front. Oncol. 10, 1341 (2020).
pubmed: 32923386
pmcid: 7456893
doi: 10.3389/fonc.2020.01341
Guillermin, O. et al. Wnt and Src signals converge on YAP-TEAD to drive intestinal regeneration. EMBO J. 40, e105770 (2021).
pubmed: 33950519
pmcid: 8246259
doi: 10.15252/embj.2020105770
Koch, M. et al. CD36-mediated activation of endothelial cell apoptosis by an N-terminal recombinant fragment of thrombospondin-2 inhibits breast cancer growth and metastasis in vivo. Breast Cancer Res. Treat. 128, 337–346 (2011).
pubmed: 20714802
doi: 10.1007/s10549-010-1085-7
Page-McCaw, A., Ewald, A. J. & Werb, Z. Matrix metalloproteinases and the regulation of tissue remodelling. Nat. Rev. Mol. Cell Biol. 8, 221–233 (2007).
pubmed: 17318226
pmcid: 2760082
doi: 10.1038/nrm2125
Zhang, J., Sud, S., Mizutani, K., Gyetko, M. R. & Pienta, K. J. Activation of urokinase plasminogen activator and its receptor axis is essential for macrophage infiltration in a prostate cancer mouse model. Neoplasia 13, 23–30 (2011).
pubmed: 21245937
pmcid: 3022425
doi: 10.1593/neo.10728
Liu, M. et al. Transcription factor c-Maf is a checkpoint that programs macrophages in lung cancer. J. Clin. Invest. 130, 2081–2096 (2020).
pubmed: 31945018
pmcid: 7108920
doi: 10.1172/JCI131335
Hara, T. & Tanegashima, K. CXCL14 antagonizes the CXCL12-CXCR4 signaling axis. Biomol. Concepts 5, 167–173 (2014).
pubmed: 25372750
doi: 10.1515/bmc-2014-0007
Reszegi, A. et al. The protective role of decorin in hepatic metastasis of colorectal carcinoma. Biomolecules 10, 1199 (2020).
Fontana, E., Eason, K., Cervantes, A., Salazar, R. & Sadanandam, A. Context matters-consensus molecular subtypes of colorectal cancer as biomarkers for clinical trials. Ann. Oncol. 30, 520–527 (2019).
pubmed: 30796810
pmcid: 6503627
doi: 10.1093/annonc/mdz052
Dunne, P. D. et al. Challenging the cancer molecular stratification dogma: intratumoral heterogeneity undermines consensus molecular subtypes and potential diagnostic value in colorectal cancer. Clin. Cancer Res. 22, 4095–4104 (2016).
pubmed: 27151745
doi: 10.1158/1078-0432.CCR-16-0032
Eide, P. W., Bruun, J., Lothe, R. A. & Sveen, A. CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models. Sci. Rep. 7, 1–8 (2017).
doi: 10.1038/s41598-017-16747-x
Herrera, M. et al. Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients. Mol. Cancer 20, 73 (2021).
pubmed: 33926453
pmcid: 8082938
doi: 10.1186/s12943-021-01367-x
Zhong, Z. A., Michalski, M. N., Stevens, P. D., Sall, E. A. & Williams, B. O. Regulation of Wnt receptor activity: Implications for therapeutic development in colon cancer. J. Biol. Chem. 296, 100782 (2021).
pubmed: 34000297
pmcid: 8214085
doi: 10.1016/j.jbc.2021.100782
Tsukiyama, T. et al. Molecular role of RNF43 in canonical and noncanonical Wnt signaling. Mol. Cell. Biol. 35, 2007–2023 (2015).
pubmed: 25825523
pmcid: 4420922
doi: 10.1128/MCB.00159-15
Thasler, W. E. et al. Charitable state-controlled foundation human tissue and cell research: ethic and legal aspects in the supply of surgically removed human tissue for research in the academic and commercial sector in Germany. Cell Tissue Bank. 4, 49–56 (2003).
pubmed: 15256870
doi: 10.1023/A:1026392429112
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 1–7 (2017).
doi: 10.1038/s41598-017-17204-5
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020).
pubmed: 31792435
doi: 10.1038/s41592-019-0654-x
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423
pmcid: 6927181
doi: 10.1186/s13059-019-1874-1
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
pubmed: 31740819
pmcid: 6884693
doi: 10.1038/s41592-019-0619-0
Schubert, M. et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat. Commun. 9, 20 (2018).
pubmed: 29295995
pmcid: 5750219
doi: 10.1038/s41467-017-02391-6
Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838–847 (2016).
pubmed: 27322546
pmcid: 5040167
doi: 10.1038/ng.3593
Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. & Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res. 29, 1363–1375 (2019).
pubmed: 31340985
pmcid: 6673718
doi: 10.1101/gr.240663.118
Gonzalez, I., Déjean, S., Martin, P. & Baccini, A. CCA: AnRPackage to extend canonical correlation analysis. J. Stat. Softw. 23, 1–14 (2008).
McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).
pubmed: 28088763
pmcid: 5408845
doi: 10.1093/bioinformatics/btw777
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00935-2 (2021)
Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).
pubmed: 27909575
pmcid: 5112579
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
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Türei, D. et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol. 17, e9923 (2021).
pubmed: 33749993
pmcid: 7983032
doi: 10.15252/msb.20209923
Dimitrov, D. et al. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat. Commun. 13, 1–13 (2022).
doi: 10.1038/s41467-022-30755-0
Kolde, R., Laur, S., Adler, P. & Vilo, J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics 28, 573–580 (2012).
pubmed: 22247279
pmcid: 3278763
doi: 10.1093/bioinformatics/btr709
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
pubmed: 14597658
pmcid: 403769
doi: 10.1101/gr.1239303
Chen, B. et al. Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps. Cell 184, 6262–6280.e26 (2021).
pubmed: 34910928
pmcid: 8941949
doi: 10.1016/j.cell.2021.11.031
Kosinski, C. et al. Gene expression patterns of human colon tops and basal crypts and BMP antagonists as intestinal stem cell niche factors. Proc. Natl Acad. Sci. USA. 104, 15418–15423 (2007).
pubmed: 17881565
pmcid: 2000506
doi: 10.1073/pnas.0707210104
Yuan, H. et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 47, D900–D908 (2019).
pubmed: 30329142
doi: 10.1093/nar/gky939
Seo, M.-K., Kang, H. & Kim, S. Tumor microenvironment-aware, single-transcriptome prediction of microsatellite instability in colorectal cancer using meta-analysis. Sci. Rep. 12, 6283 (2022).
pubmed: 35428835
pmcid: 9012745
doi: 10.1038/s41598-022-10182-3