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Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments.
VanderDoes, Jeremy; Marceaux, Claire; Yokote, Kenta; Asselin-Labat, Marie-Liesse; Rice, Gregory; Hywood, Jack D.
Afiliación
  • VanderDoes J; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
  • Marceaux C; Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
  • Yokote K; Department of Medical Biology, The University of Melbourne, Parkville, Australia.
  • Asselin-Labat ML; Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
  • Rice G; Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
  • Hywood JD; Department of Medical Biology, The University of Melbourne, Parkville, Australia.
PLoS Comput Biol ; 20(6): e1011361, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38875302
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Comunicación Celular / Microambiente Tumoral Límite: Female / Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Comunicación Celular / Microambiente Tumoral Límite: Female / Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos