Tumor 'floor plans' may predict immunotherapy response better than current biomarkers
Spatial transcriptomics is a powerful tool for creating a detailed map of not just how genes are expressed in a piece of tissue, but also where genetic activity takes place in different cells and locations within the tissue. This is especially valuable for studying tumors, which each have their own unique microenvironment that affects how they behave.
A new research study from the University of Chicago has developed a method to predict how tumors will respond to immunotherapy better than current biomarkers. This is achieved by using spatial transcriptomics, which creates detailed maps of tumor microenvironments, including where genetic activity takes place in different cells and locations within the tissue.
The study's lead author, Arjun Raman, compared the complexity of tumor microenvironments to flocks of birds, where individual cells interact and organize themselves to create subunits and meta subunits within the tumor. Raman and his team analyzed spatial transcriptomic data from 262 solid tumors, developing statistical formulas to describe the organization of tumor cells and genetic activity within the microenvironment.
This created a kind of floor plan for each tumor, with cells and genes clustering in different spatial groups. By comparing these floor plans using AI tools, Raman's team found that tumors generally share a stereotyped, hierarchical structure made of spatial groups. When compared at the level of spatial groups, rather than cell populations or genes, the study correctly predicted which non-small cell lung cancer patients would respond better to immunotherapy than the current standard-of-care biomarker.
Raman believes these tools can be applied to any type of tumor and is collaborating with other researchers to study ovarian cancer tumors and their response to chemotherapy.
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