A New Method to Understanding Breast Cancer Tumors at the Cellular Level

Researchers have developed a new computational method to better understand what goes on inside a breast cancer tumor. Experts from Baylor College of Medicine, the University of Pittsburgh Cancer Institute, RainDance Technologies, Inc., and Mayo Clinic College of Medicine and Science worked together to form this new strategy which enabled them to identify different cell types in the tumor and to reveal how the interactions between cancer and normal cells may affect whether the tumor grows or shrinks.

This revelation was first published in Cell Reports.

“We can now look at the individual cell types that constitute the tumor,” said Vitor Onuchic, molecular and human genetics graduate student in a Baylor video. “Instead of looking at the tumor as a whole we can look at what constitutes the elements of the tumor and look at the changes in the epigenomic profile; meaning, what becomes activated or inactivated as the cancer progresses within each of these cell types instead of just looking at the whole tumor tissue.”

With the new computational approach the researchers are able to look at how these cells interact in their native microenvironment without having to physically separate the different cell types.

The research team developed epigenomic deconvolution (EDec), a method that infers cell type composition of complex tissues as well as DNA methylation and gene transcription profiles of constituent cell type. By using this method, they were able to estimate the level of immune cell infiltration within the tumor in a large collection of breast tumor samples from the Cancer Genome Atlas. They saw that for particular subtypes of breast tumors, a higher level of immune cell infiltration resulted in better patient survival.

In addition, they also discovered that a shift from adipose to fibrotic tissue creates a microenvironment rich in compounds that feed the growth of cancer cells- less adipose stroma tends to display lower levels of mitochondrial activity and is associated with cancerous cells with higher levels of oxidative metabolism.

“In addition, our approach allows us to look at a large number of tumor samples,” said Onuchic. “We can reanalyze numerous data we already generated from tumors and extract different types of information from the same dataset without having to go back and process the original samples.”

Understanding the cellular composition and the cell interactions that affect tumor death or growth may provide ample opportunities for future targeted therapies.