Researchers have utilized sophisticated imaging techniques and a computational method to explore the interactions among immune cells. Their findings indicate that the way immune cells interact near breast cancer or melanoma can provide insights into immune responses to these cancers as well as predict patient outcomes.
The growth of a tumor is influenced not only by the cancer cells it contains but also by the surrounding tissue, which affects its biological behavior. A team from The Jackson Laboratory (JAX) has merged advanced imaging methods with a novel computational approach to investigate immune cell interactions in unprecedented detail. This research indicates that immune cell interactions near breast cancer or melanoma can serve as indicators for immune responses and patient prognoses.
Led by Jeffrey Chuang, a JAX professor and the study’s senior author, the study found a correlation between the frequency of interactions between two specific immune cell types and the survival rates of breast cancer patients. This research was conducted in collaboration with JAX professor Karolina Palucka, who is also the director of the JAX Cancer Center. Their findings were recently published in the advanced online issue of Communications Biology.
“Scientists have long believed that understanding the complex community of immune cells, blood vessels, and signaling molecules surrounding tumors could provide insights into cancer growth, progression, and treatment response,” stated Chuang. “Our new methodology allows us to quantify the spatial distributions and interactions of cells and molecules in a way that hasn’t been achievable with conventional imaging techniques.”
Immune molecules
Immune cells communicate through critical signaling proteins that form physical ‘synapses.’ This transfer of materials from inside the cell to its surface aids in coordinating immune responses against both pathogens and tumors.
To investigate immune cell interactions within the tumor microenvironment—the area surrounding a tumor—scientists typically isolate these cells to analyze their gene activity. Another method involves tagging specific proteins with fluorescent markers to visualize their presence in cells based on the brightness of the fluorescence.
However, these methods do not reveal if proteins are present on the cell surface at a synapse, which is essential for cell-to-cell interactions.
“An immune cell might express a signaling protein but not utilize it on its surface for interacting with other cells for several reasons,” explained Zichao Sam Liu, a graduate student in the Chuang lab and co-first author alongside Drs. Victor Wang and Jan Martinek. “Simply knowing that a protein is present in a cell provides only part of the insight, not the whole picture.”
Chuang, Liu, and their team aimed to leverage existing microscopy data to investigate how signaling molecules cluster at immune synapses, thus providing a more comprehensive view of immune interactions.
A new analysis technique
The researchers developed a technique called Computational Immune Synapse Analysis (CISA), which identifies both the physical contacts between cells and whether key molecules are concentrated at those contact points.
This technique analyzes images of immune cells by emphasizing their edges and potential immune synapses, then comparing these to the localization of tagged molecules.
Focusing on T cells within the immune system, the researchers demonstrated that CISA could pinpoint interactions between T cells and other immune cell types in human melanoma samples.
“Observing a preferential localization of specific proteins at cell-cell interfaces suggests the formation of synapses, implying interactions,” said Liu. “For the first time, our method allows quantification in a native tumor environment.”
Further experiments indicated that synapses between T cells and macrophages—cells that engulf pathogens and tumor cells—were linked to enhanced T cell proliferation.
Clinical lessons
The research team subsequently investigated whether the interactions between immune cells in breast cancer samples correlated with cancer progression. They discovered that stronger connections between T cells and B cells (another type of immune cell) correlated with improved survival outcomes for patients. This finding could lead to innovative methods of predicting patient outcomes, identifying individuals likely to benefit from immune treatments, and even the creation of new immunotherapies.
The primary objective of CISA is to uncover new patterns in cell interactions that hold biological significance. Chuang’s research group has made the image analysis platform publicly available for other researchers and believes it can be applied to analyze interactions between any cell types. It is also adaptable for different image types; the melanoma samples were captured using histocytometry, while the breast cancer samples utilized imaging mass cytometry (IMC).
“We have designed this to be broadly applicable and user-friendly,” Liu noted.
Chang and Liu plan to extend this method to other tumor types and immune cell types to further explore the tumor microenvironment and its influence on cancer.
