Lau, Mai Chan, et al. "Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment". PLOS Computational Biology 21.2 (2025): e1012707.
Abstract:
We have developed a computational tool called the Tumor-Immune Partitioning andClustering (TIPC) algorithm, designed to reveal the intricate organization of immunecells within the tumor microenvironment. Traditionally, studies have mainly focusedon counting these cells or examining their proximity to one another. Those approachesoften underestimate the complex roles of immune cells in tumors. With TIPC, we haveuncovered distinct patterns in the arrangement of immune cells across different tumorregions. This advancement has enabled us to identify tumor subtypes that were previous-ly undetectable with existing methods. Our new method can determine which tumorsare likely to have longer survival rates or respond better to immunotherapy, based on the layout of immune cells rather than merely their numbers. This breakthrough has signifi-cant implications for cancer research, highlighting the importance of understanding thespatial patterns of immune cells. Such knowledge is crucial for selecting appropriate pa-tients for specific treatments and for assessing the potential effectiveness of immunother-apy. By tailoring treatment plans to the unique cellular landscapes of each tumor, we canpotentially improve outcomes and provide more personalized and effective cancer care.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research is supported by core funding from: Bioinformatics Institute; Singapore Immunology Network
Grant Reference no. :
This research / project is supported by the National Medical Research Council - Open Fund– Young Individual Research
Grant Reference no. : OFYIRG23jan-0049