An algorithm developed by A*STAR, to analyze which genes are turned on and off in individual cancer cells within a tumor is being used to examine the different cell types in bowel cancers. The findings challenge an established concept of cancer development.
The heterogeneity of cells in tumors is a major obstacle in cancer therapy, explains Shyam Prabhakar from the A*STAR Genome Institute of Singapore who collaborated with the National Cancer Center Singapore and other research centers. “We hope that our novel approach to understanding the basic biology of tumors will lead to new ideas for treatment,” says Prabhakar, adding that he anticipates this approach could be applied to other types of cancer in future.
The raw data for the algorithm comes from RNA sequencing, which characterizes the RNA molecules that control protein synthesis. While this technique has previously been used to study cancer, most researchers have focused on mixed cell samples, missing the crucial distinctions between different types of cell in a tumor. Looking at single cells provides much finer detail about the subtypes of cells present, and may offer the knowledge clinicians need to determine the most appropriate treatments for particular patients.
“We were surprised that existing algorithms for defining different cell types did not work well,” says Prabhakar. This led the researchers to develop a new algorithm which substantially improved the accuracy of clustering cells into specific types based on their gene activities.
The next surprise was that the results challenged an established dogma of cancer biology which suggests that tumor development often involves epithelial cells changing into mesenchymal cells. “We found no evidence that this was happening,” says Prabhakar, explaining that the results show that in some cases the genes in pre-existing non-cancerous mesenchymal cells simply become more active, rather than epithelial cells undergoing the textbook “epithelial-mesenchymal transition”. If this insight applies to other types of cancers, then it will rewrite current textbook versions of cancer development.
“We would like to connect what we are seeing at the single cell level to something that clinicians really care about,” says Prabhakar. He hopes that single-cell analysis might reveal why some cells are resistant to chemotherapy or immunotherapy and why some cells metastasize and spread to other parts of the body.
“Drug resistance and metastasis are the killer aspects of cancer,” Prabhakar adds. Understanding these processes is the crucial first step toward preventing them.
The A*STAR-affiliated researchers contributing to this research are from the Genome Institute of Singapore and the Institute for Infocomm Research. For more information about the team’s research, please visit the Computational and Systems Biology webpage.