Scientists have developed a new method to understand the influence of non-cancerous cells on cancer progression. The research, led by Sylvia Plevritis, PhD, chair of Stanford Medicine’s department of biomedical data science, introduces the “colocatome.” This tool documents interactions between malignant cells and their neighboring non-cancerous cells.
“Not all cells in a tumor are cancer cells – they’re not even always the most dominant cell type,” Plevritis stated. “There are many other cell types that support tumors.”
The study highlights how non-cancerous cells can affect tumor behavior, influencing growth rates and drug susceptibility. Gina Bouchard, PhD, an instructor of biomedical data science and lead author of the study published in Nature Communications, emphasized the importance of understanding this cellular ecosystem: “Understanding tumor biology is not only about cancer cells; there’s a whole ecosystem that needs to be studied.”
Researchers used experimental models of lung cancer analyzed with artificial intelligence to identify how non-cancerous cells organize around tumors. These findings were compared with patient tumor biopsies to validate the models’ accuracy.
Past studies by Plevritis indicated significant interactions between fibroblasts and cancer cells. Her experiments showed that fibroblasts could alter drug resistance in lung cancer treatments by changing spatial organization within tumors.
“That spatial reorganization appears to have given rise to drug-resistance,” said Plevritis. The team aims to use these insights for developing better treatment strategies by identifying specific colocalizations linked to drug resistance.
Future research will involve AI-driven analysis of spatial motifs across various cancers. “Then we can begin to see whether certain spatial motifs are shared between cancer types,” said Plevritis.
A researcher from the University of Oxford also contributed to this study funded by grants from the National Institute of Health and Les Fonds de Recherche du Québec. Stanford’s Department of Biomedical Data Sciences supported this work as well.
