Algorithm Shines Light on Cancer Cell Neighborhoods
Why do some cancer cells respond well to treatment whereas others seem to stealthily evade therapy? Maybe it’s the “neighborhood” they live in, that is, the microenvironment surrounding these cells.
By looking closely at those cancer cell neighborhoods, we can learn what goes into building tissues, the types of cells that are present, and the way these cells communicate.
If you want to know more about these tissue cellular neighborhoods (TCNs), and how they help cells to form and interact, you’ll be interested in a new NIH-funded study from scientists at the Children’s Hospital of Philadelphia (CHOP).
The researchers developed a new algorithm, called CytoCommunity, to identify and map TCNs and track their characteristics.
Read more about this study in Nature Methods. You can find the CytoCommunity algorithms at GitHub and Zeonodo.
Unlike other TCN-detection methods, you can apply CytoCommunity with or without supervision. With supervision using patient clinical data, the researchers were able to identify TCNs associated with tumors that had a higher tendency to develop resistance to therapy. When unsupervised, they found CytoCommunity was superior in identifying TCNs of varying sizes from different tissues.
Dr. Kai Tan, corresponding author of the study, noted, “Today’s powerful technologies can map the microenvironment at the genomic, transcriptomic, and proteomic levels; however, we lack the analytical tools to make sense of these maps. By using CytoCommunity, we can efficiently analyze these spatial omics maps, giving us detailed information on how cancer cells interact with their microenvironment to evade therapy.”
According to NCI Program Officer, Dr. Miguel R. Ossandon, “The algorithm in this project further empowers our studies of cell-cell interactions, giving us a clearer idea of how cells organize themselves to support tissue functions. The way cells interact with their microenvironment can either regulate or promote how tumors grow and spread. By better understanding these mechanisms, we can develop new and more effective treatment strategies.”