Researchers from Children’s Hospital of Philadelphia (CHOP) have created a new AI-powered algorithm to understand how different cells organize in tissues and communicate with each other. The algorithm was tested on cancer tissues to show how cells interact to resist therapy. The findings were published in the journal Nature Methods.

The concept of tissue cellular neighborhoods (TCNs) was proposed to describe units where different cell types work together to support tissue functions. The huge amount of data needed an advanced AI algorithm to interpret and test the models and hypotheses.

The deep-learning-based CytoCommunity algorithm was developed to identify TCNs based on cell identities, spatial distributions, and patient data. By using CytoCommunity on breast and colorectal cancer data, the algorithm revealed new fibroblast-enriched and granulocyte-enriched TCNs specific to high-risk breast cancer and colorectal cancer.

The next step is to apply this algorithm to healthy and diseased tissue data from research consortia. This study was supported by several grants.

The research will help determine which genetic pathways may be involved at the cellular and molecular levels and may be associated with responses to certain therapies.

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