Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments

Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this...

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Published inPLoS computational biology Vol. 20; no. 6; p. e1011361
Main Authors VanderDoes, Jeremy, Marceaux, Claire, Yokote, Kenta, Asselin-Labat, Marie-Liesse, Rice, Gregory, Hywood, Jack D
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 14.06.2024
Public Library of Science (PLoS)
Subjects
Biology and Life Sciences
Breast cancer
Cancer
Cancer therapies
Care and treatment
Cell culture
Cell interaction
Cell interactions
Data analysis
Datasets
Development and progression
Expected values
Functionals
Geospatial data
Immune system
Immunotherapy
Information management
Ion beams
Lung cancer
Machine learning
Medical research
Medicine and Health Sciences
Medicine, Experimental
Methods
Microenvironments
Neutrophils
Physical Sciences
Predictions
Prognosis
Research and Analysis Methods
Scholarships & fellowships
Spatial data
Spatial distribution
Statistics
Tumor cells
Tumor microenvironment
Tumors
Canada
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Abstract Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.
AbstractList Tumor microenvironments (TMEs) contain vast amounts of information on patient’s cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells . Spatial data on the tumor microenvironment (TME) are becoming more prevalent. Existing methods to interrogate such data often have several limitations: (1) they can rely on estimating the spatial relationships among cells by examining simple counts of cells within a single radius, (2) they may not come with ways to evaluate the statistical significance of any findings, or (3) they model individual interactions independently of other interactions. Our approach leverages techniques in spatial statistics and uses a benchmark ensemble machine learning method to address each of these deficiencies; it (1) uses K functions to encode the relative densities of cells over all radii up to a user-selected maximum radius, (2) employs permutation and cross-validation to evaluate the statistical significance of any findings on the spatial interactions in the TME, and (3) models multiple interactions simultaneously. Our approach is freely available with an R implementation called funkycells . In the analysis of two real data sets, we have seen that the method performs well, and gives the expected results. We think this will be a robust tool for researchers looking to interrogate TME data.
Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.
Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.
Audience Academic
Author Marceaux, Claire
Asselin-Labat, Marie-Liesse
Hywood, Jack D
Yokote, Kenta
VanderDoes, Jeremy
Rice, Gregory
AuthorAffiliation 1 Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
3 Department of Medical Biology, The University of Melbourne, Parkville, Australia
4 Department of Anatomical Pathology, Royal Melbourne Hospital, Parkville, Australia
2 Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
University of Connecticut School of Medicine, UNITED STATES
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Snippet Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor...
Tumor microenvironments (TMEs) contain vast amounts of information on patient’s cancer through their cellular composition and the spatial distribution of tumor...
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SourceType Open Website
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StartPage e1011361
SubjectTerms Biology and Life Sciences
Breast cancer
Cancer
Cancer therapies
Care and treatment
Cell culture
Cell interaction
Cell interactions
Data analysis
Datasets
Development and progression
Expected values
Functionals
Geospatial data
Immune system
Immunotherapy
Information management
Ion beams
Lung cancer
Machine learning
Medical research
Medicine and Health Sciences
Medicine, Experimental
Methods
Microenvironments
Neutrophils
Physical Sciences
Predictions
Prognosis
Research and Analysis Methods
Scholarships & fellowships
Spatial data
Spatial distribution
Statistics
Tumor cells
Tumor microenvironment
Tumors
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Title Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments
URI https://www.ncbi.nlm.nih.gov/pubmed/38875302
https://www.proquest.com/docview/3086942700
https://www.proquest.com/docview/3068755848
https://pubmed.ncbi.nlm.nih.gov/PMC11210873
https://doaj.org/article/cb32ee8d35394b8791d94581ec832a80
http://dx.doi.org/10.1371/journal.pcbi.1011361
Volume 20
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