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The New England Journal of Statistics in Data Science


Investigating Ecological Interactions in the Tumor Microenvironment Using Joint Species Distribution Models for Point Patterns
Volume 2, Issue 3 (2024), pp. 296–310
Pub. online: 19 June 2024      Type: Methodology Article      Open accessOpen Access
Area: Cancer Research
Accepted
23 April 2024
Published
19 June 2024

Abstract

The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample. Such models are potentially useful for investigating the spatial relationships between different cell types in the tumor microenvironment, but so far studies of the TME using cell-resolution imaging have been largely limited to spatial descriptive statistics. However, MGPP models have many advantages over descriptive statistics, such as uncertainty quantification, incorporation of multiple covariates and the ability to make predictions. In this paper, we describe and apply a previously developed MGPP method, the saturated pairwise interaction Gibbs point process model, to a publicly available multiplexed imaging dataset obtained from colorectal cancer patients. Importantly, we show how these methods can be used as joint species distribution models (JSDMs) to precisely frame and answer many relevant questions related to the ecology of the tumor microenvironment.

References

[1] 
Boyk, A., Wojas-Krawczyk, K., Krawczyk, P., Milanowski and Tumor, J. (June 2022). Microenvironment—A Short Review of Cellular and Interaction Diversity. Biology 11(6), 929. Publisher: Multidisciplinary Digital Publishing Institute issn: 2079-7737. https://www.mdpi.com/2079-7737/11/6/929 (2023).
[2] 
De Visser, K. E. and Joyce, J. A. (Mar. 13, 2023) The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell 41, 374–403. issn: 1878-3686.
[3] 
Boedtkjer, E. and Pedersen, S. F. (Feb. 10, 2020). The Acidic Tumor Microenvironment as a Driver of Cancer. Annual Review of Physiology 82, 103–126 issn: 1545-1585.
[4] 
Petrova, V., Annicchiarico-Petruzzelli, M., Melino, G. and Amelio, I. (Jan. 24, 2018) The hypoxic tumour microenvironment. Oncogenesis 7(1), 1–13. Publisher: Nature Publishing Group, issn: 2157-9024. https://www.nature.com/articles/s41389-017-0011-9 (2023).
[5] 
Li, Y., Zhao, L., Li, Hypoxia, X. -F. and the Tumor Microenvironment. (Aug. 5, 2021). Technology in Cancer Research & Treatment 20, 15330338211036304. issn: 1533-0346. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358492/ (2023).
[6] 
Tan, K., Naylor and Tumour, M. J. (May 13, 2022). Microenvironment-Immune Cell Interactions Influencing Breast Cancer Heterogeneity and Disease Progression. Frontiers in Oncology 12, 876451. issn: 2234-943X. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138702/ (2023).
[7] 
Sun, Y. (Sept. 28, 2016). Tumor microenvironment and cancer therapy resistance. Cancer Letters 380, 205–215. issn: 0304-3835. https://www.sciencedirect.com/science/article/pii/S0304383515005121 (2023).
[8] 
Ni, Y. et al. (May 20, 2021). The Role of Tumor-Stroma Interactions in Drug Resistance Within Tumor Microenvironment. Frontiers in Cell and Developmental Biology 9, 637675. issn: 2296-634X. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173135/ (2023).
[9] 
Li, X. et al. (Oct. 7, 2021). Crosstalk Between the Tumor Microenvironment and Cancer Cells: A Promising Predictive Biomarker for Immune Checkpoint Inhibitors. Frontiers in Cell and Developmental Biology 9, 738373. issn: 2296-634X. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529050/ (2023).
[10] 
Pienta, K. J., McGregor, N., Axelrod, R. and Axelrod, D. E. (Dec. 2008). Ecological Therapy for Cancer: Defining Tumors Using an Ecosystem Paradigm Suggests New Opportunities for Novel Cancer Treatments. Translational Oncology 1, 158–164. issn: 1936-5233. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2582164/ (2021).
[11] 
Adler, F. R., Amend, S. R., Baratchart, E. and Whelan, C. J. (Feb. 10, 2022). Editorial: From Ecology to Cancer Biology and Back Again. Frontiers in Ecology and Evolution 10, 840375. issn: 2296-701X. https://www.frontiersin.org/articles/10.3389/fevo.2022.840375/full (2022).
[12] 
Somarelli, J. A. (Apr. 28, 2021). The Hallmarks of Cancer as Ecologically Driven Phenotypes. Frontiers in Ecology and Evolution 9, 661583. issn: 2296-701X. https://www.frontiersin.org/articles/10.3389/fevo.2021.661583/full (2022).
[13] 
Thomas, F., Roche, B., Giraudeau, M., Hamede, R. and Ujvari, B. (2020). The interface between ecology, evolution, and cancer: More than ever a relevant research direction for both oncologists and ecologists. Evolutionary Applications 13, 1545–1549. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/eva.13031, issn: 1752-4571. https://onlinelibrary.wiley.com/doi/abs/10.1111/eva.13031 (2021).
[14] 
Reynolds, B. A., Oli, M. W. and Oli, M. K. (2020). Eco-oncology: Applying ecological principles to understand and manage cancer. Ecology and Evolution 10, 8538–8553. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.6590, issn: 2045-7758. https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.6590 (2021).
[15] 
Van Dam, S., Baars, M. J. D., Vercoulen and Multiplex, Y. (Jan. 2022). Tissue Imaging: Spatial Revelations in the Tumor Microenvironment. Cancers 14(13), 3170. Publisher: Multidisciplinary Digital Publishing Institute, issn: 2072-6694. https://www.mdpi.com/2072-6694/14/13/3170 (2023).
[16] 
Schürch, C. M. et al. (Sept. 3, 2020). Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front. Cell 182, 1341–1359.e19. issn: 1097-4172.
[17] 
Black, S. et al. (Aug. 2021). CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nature Protocols 16, 3802–3835. issn: 1750-2799. https://www.nature.com/articles/s41596-021-00556-8 (2021).
[18] 
Baddeley, A., Turner and spatstat, R. (Jan. 26, 2005). An R Package for Analyzing Spatial Point Patterns. Journal of Statistical Software 12, 1–42. issn: 1548-7660. https://doi.org/10.18637/jss.v012.i06 (2023).
[19] 
Fortin, M. -J., Dale, M. R. and Ver Hoef, J. M. (2016). In Wiley StatsRef: Statistics Reference Online, 1–13. John Wiley & Sons, Ltd, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118445112.stat07766.pub2, isbn: 978-1-118-44511-2. https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118445112.stat07766.pub2 (2023).
[20] 
Ben-Said, M. (Aug. 5, 2021). Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review. Ecological Processes 10, 56. issn: 2192-1709. https://doi.org/10.1186/s13717-021-00314-4 (2023).
[21] 
Velázquez, E., Martínez, I., Getzin, S., Moloney, K. A. and Wiegand, T. (2016). An evaluation of the state of spatial point pattern analysis in ecology. Ecography. 39, 1042–1055. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.01579, issn: 1600-0587. https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.01579 (2023).
[22] 
Canete, N. P. et al. (May 26, 2022). spicyR: spatial analysis of in situ cytometry data in R. Bioinformatics 38, 3099–3105. issn: 1367-4803. https://doi.org/10.1093/bioinformatics/btac268 (2023).
[23] 
Vu, T. et al. (June 15, 2022). SPF: A spatial and functional data analytic approach to cell imaging data. PLOS Computational Biology 18, e1009486. Publisher: Public Library of Science, issn: 1553-7358. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009486 (2023).
[24] 
Creed, J. H. et al. (Dec. 7, 2021). spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data. Bioinformatics 37, 4584–4586. issn: 1367-4803. https://doi.org/10.1093/bioinformatics/btab757 (2023).
[25] 
Behanova, A., Klemm, A. and Wählby, C. (Jan. 28, 2022). Spatial Statistics for Understanding Tissue Organization. Frontiers in Physiology 13, 832417. issn: 1664-042X. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837270/ (2023).
[26] 
Yuan, Z. et al. (Nov. 28, 2022). SOTIP is a versatile method for microenvironment modeling with spatial omics data. Nature Communications 13(1), 7330. Publisher: Nature Publishing Group, issn: 2041-1723. https://www.nature.com/articles/s41467-022-34867-5 (2023).
[27] 
Tanevski, J., Flores, R. O. R., Gabor, A., Schapiro, D. and Saez-Rodriguez, J. (Apr. 14, 2022). Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biology 23, 97. issn: 1474-760X. https://doi.org/10.1186/s13059-022-02663-5 (2023).
[28] 
Galon, J. et al. (Oct. 3, 2012). Cancer classification using the Immunoscore: a worldwide task force. Journal of Translational Medicine 10, 205. issn: 1479-5876. https://doi.org/10.1186/1479-5876-10-205 (2023).
[29] 
Allam, M. et al. (Sept. 1, 2022). Spatially variant immune infiltration scoring in human cancer tissues. npj Precision Oncology 6(1), 1–21. Publisher: Nature Publishing Group. issn: 2397-768X. https://www.nature.com/articles/s41698-022-00305-4 (2023).
[30] 
Tikhonov, G. et al. (2020). Joint species distribution modelling with the r-package Hmsc. Methods in Ecology and Evolution 11, 442–447. _eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13345, issn: 2041-210X. https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13345 (2023).
[31] 
Tikhonov, G., Abrego, N., Dunson, D. and Ovaskainen, O. (2017). Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context. Methods in Ecology and Evolution 8, 443–452. _eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12723, issn: 2041-210X. https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12723 (2023).
[32] 
Ovaskainen, O., Roy, D. B., Fox, R. and Anderson, B. J. (2016). Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution 7, 428–436. _eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12502, issn: 2041-210X. https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12502 (2023).
[33] 
Wilkinson, D. P., Golding, N., Guillera-Arroita, G., Tingley, R. and McCarthy, M. A. (2021). Defining and evaluating predictions of joint species distribution models. Methods in Ecology and Evolution 12, 394–404. _eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13518, issn: 2041-210X. https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13518 (2023).
[34] 
Clark, J. S., Gelfand, A. E., Woodall, C. W. and Zhu, K. (2014). More than the sum of the parts: forest climate response from joint species distribution models. Ecological Applications 24, 990–999. _eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/13-1015.1, issn: 1939-5582. https://onlinelibrary.wiley.com/doi/abs/10.1890/13-1015.1 (2023).
[35] 
Stoyan, D. and Penttinen, A. (2000). Recent Applications of Point Process Methods in Forestry Statistics. Statistical Science 15, 61–78. Publisher: Institute of Mathematical Statistics, issn: 0883-4237. https://www.jstor.org/stable/2676677 (2023). https://doi.org/10.1214/ss/1009212674. MR1842237
[36] 
Law, R. et al. (2009). Ecological information from spatial patterns of plants: insights from point process theory. Journal of Ecology 97, 616–628. _eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2745.2009.01510.x, issn: 1365-2745. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2745.2009.01510.x (2023).
[37] 
Illian, J. B. et al. (2013). Fitting complex ecological point process models with integrated nested Laplace approximation. Methods in Ecology and Evolution 4, 305–315. _eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210x.12017, issn: 2041-210X. https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210x.12017 (2023).
[38] 
Illian, J. B., Møller, J. and Waagepetersen, R. P. (Sept. 1, 2009). Hierarchical spatial point process analysis for a plant community with high biodiversity. Environmental and Ecological Statistics 16, 389–405. issn: 1573-3009. https://doi.org/10.1007/s10651-007-0070-8 (2023). https://doi.org/10.1007/s10651-007-0070-8. MR2749847
[39] 
Renner, I. W. et al. (2015). Point process models for presence-only analysis. Methods in Ecology and Evolution 6, 366–379. _eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12352, issn: 2041-210X. https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12352 (2023).
[40] 
Baddeley, A., Rubak, E. and Turner, R. (Nov. 11, 2015). Spatial Point Patterns: Methodology and Applications with R. CRC Press. Google-Books-ID: rGbmCgAAQBAJ. 830 pp. isbn: 978-1-4822-1021-7.
[41] 
Rajala, T., Murrell, D. J., Olhede and Detecting, S. C. (Nov. 1, 2018). Multivariate Interactions in Spatial Point Patterns with Gibbs Models and Variable Selection. Journal of the Royal Statistical Society Series C: Applied Statistics 67, 1237–1273. issn: 0035-9254, 1467-9876. https://academic.oup.com/jrsssc/article/67/5/1237/7058395 (2023). https://doi.org/10.1111/rssc.12281. MR3873707
[42] 
Flint, I., Golding, N., Vesk, P., Wang, Y. and Xia, A. (Nov. 1, 2022). The Saturated Pairwise Interaction Gibbs Point Process as a Joint Species Distribution Model. Journal of the Royal Statistical Society Series C: Applied Statistics 71, 1721–1752. issn: 0035-9254. https://doi.org/10.1111/rssc.12596 (2023). https://doi.org/10.1111/rssc.12596. MR4511129
[43] 
Goltsev, Y. et al. (Aug. 9, 2018). Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging. Cell 174, 968–981.e15. issn: 0092-8674. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6086938/ (2023).
[44] 
Geyer, C. (1999). (O. Barndorff-Nielsen, W. Kendall and M. Van Lieshout, eds.) In Stochastic Geometry: Likelihood and Computation, chapter 3. Monographs on Statistics and Applied Probability 80, 79–140. Chapman and Hall/CRC, isbn: 978-0-203-73827–978-0-203-6. https://doi.org/10.1007/978-1-4899-3210-5. MR1317097
[45] 
Baddeley, A. J. and van Lieshout, M. N. M. (Dec. 1, 1995). Area-interaction point processes. Annals of the Institute of Statistical Mathematics 47, 601–619. issn: 1572-9052. https://doi.org/10.1007/BF01856536 (2024). https://doi.org/10.1007/BF01856536. MR1370279
[46] 
Illian, J. B. and Hendrichsen, D. K. (2010). Gibbs point process models with mixed effects. Environmetrics 21, 341–353. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.1008, issn: 1099-095X. https://onlinelibrary.wiley.com/doi/abs/10.1002/env.1008 (2024). https://doi.org/10.1002/env.1008. MR2842247
[47] 
King, R., Illian, J. B., King, S. E., Nightingale, G. F. and Hendrichsen, D. K. (Dec. 1, 2012). A Bayesian Approach to Fitting Gibbs Processes with Temporal Random Effects. Journal of Agricultural, Biological, and Environmental Statistics 17. 601–622. issn: 1537-2693. https://doi.org/10.1007/s13253-012-0111-0 (2024). https://doi.org/10.1007/s13253-012-0111-0. MR3041887
[48] 
Baddeley, A., Coeurjolly, J. -F., Rubak, E. and Waagepetersen, R. (2014). Logistic regression for spatial Gibbs point processes. Biometrika 101, 377–392. Publisher: [Oxford University Press, Biometrika Trust], issn: 0006-3444. https://www.jstor.org/stable/43305620 (2024). https://doi.org/10.1093/biomet/ast060. MR3215354
[49] 
Spiekerman, C. F., Lin and Marginal, D. Y. (1998). Regression Models for Multivariate Failure Time Data. Journal of the American Statistical Association 93, 1164–1175. Publisher: [American Statistical Association, Taylor & Francis, Ltd.], issn: 0162-1459. https://www.jstor.org/stable/2669859 (2023). https://doi.org/10.2307/2669859. MR1649210

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© 2024 New England Statistical Society
Open access article under the CC BY license.

Keywords
Tumor microenvironment Spatial point patterns Joint species distribution models

Funding
The authors were funded by the following grants during the course of this research: Advanced Proteogenomics of Cancer (T32 CA140044), R37 CA214955-01A1, NSF DMS-2152776 and MICDE Catalyst grants.

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