The combination of single-cell techniques and machine learning uncovers an association between cancer cells and the immune system.
A blood test procedure. Image credits: Max Pixel, CC0 Public Domain
Researchers from the University of Helsinki and Aalto University have shown that the body’s immune system attacks itself in a rare type of blood cancer. The discovery could lead to improved treatments and a more complex understanding of the immune system’s role in other cancers.
Current treatment methods for large granular lymphocyte (LGL) leukemia, a rare type of blood cancer, are based on the understanding that cancer cells attack the body’s tissues. Prior research has focused on the study of these rogue cells, which may lead to a better understanding of the disease.
Single-cell technologies allow analysis of individual cells and comparison of normal cells with tumor cells (purple). Image credit: Claudeau Cotta / Aalto University
‘Our research group showed ten years ago that LGL cancer cells normally have mutations in the STAT3 gene, which is now the leading cause of disease diagnosis worldwide,’ says Professor of Translational Hematology Satu Mustajoki from the University of Helsinki. is used.
Although rarely fatal, blood cancer causes a number of chronic symptoms, including an increased risk of infection, anemia, and joint pain. The challenge so far has been that patients show a mixed response to treatment.
‘Current treatment methods target cancer cells and their vulnerabilities,’ explains Jani Huhtanen from the University of Helsinki and Aalto. ‘It is impossible to evaluate which patients will respond to treatment, because in some patients, the amount of activated cancer cells is reduced, yet symptoms persist, and for others, it is the opposite.’
Satu Mustajoki’s research group took a step back from conventional thinking and examined the role of other cells in the immune system. They used the latest single-cell techniques combined with machine learning models developed by Aalto University. This enabled the group to uncover an adverse interaction between the body’s immune system and blood cancer cells.
“In these patients, the immune system becomes more active and signals the tumor cells to grow and provide them with a favorable environment,” says Dipaburn Bhattacharyya, a doctoral researcher at the University of Helsinki.
The research group demonstrated that in this type of leukemia, it is not only the cancer cells isolated from other cancer cells in different patients, but also the immune system as a whole. The discovery could have important implications for current treatment methods.
“Our research may explain the observed discrepancy between LGL cancer cells and symptoms,” Huhtenen elaborated. ‘The immune system is cooperating with the cancer cells. Therefore, future treatments should target the entire immune system – not just cancer cells – to enhance patients’ quality of life.’
Through the Looking Glass with Machine Learning
Distinguishing normal cells associated with the immune system from blood cancer cells is no easy task, and traditional methods have hit the wall. In LGL leukemia, the cancer cells are similar to the normal T cells found in the blood. To address this challenge the group employed single-cell techniques and computational life sciences. They were able to differentiate cancer cells from normal T cells for the first time and compare them with each other.
‘The single-cell technique opens up completely new avenues for research,’ says Tina Kelka, dossent of immunology from the University of Helsinki.
These technologies can quantify key receptor proteins in immune cells, helping researchers better understand the role of the immune system in LGL leukemia and other diseases. These receptors determine what types of cancer cells or pathogens the cell can fight, but advanced machine learning tools must analyze the data.
‘Several different machine learning-based computational techniques were needed in this study. “The latest statistical machine learning and artificial intelligence methods have proven effective in single-cell data analysis,” says Harry Lahdesmaki, professor of computational biology and machine learning at Aalto University.
The machine learning component also includes an open-source machine learning model developed by Aalto’s Computational Systems Biology Group, which was also used to study the SARS-CoV-2 coronavirus in 2021.
‘This is the most interesting aspect of medical research, which is undergoing a significant computational transition,’ explains Huhtenen, who is working on a doctoral thesis at the University of Helsinki and Aalto’s Department of Computer Science. ‘These computational methods allow us to access medical data without any preconceptions and see where it takes us.’
The research group is looking to investigate the role of the immune system in other types of cancer, which may cover up one of the most serious health problems.
Source: Aalto University
