|Single-cell technologies allow for the analysis of individual cells and the comparison of normal cells to tumour cells (purple). |
Credit: Claudiu Cotta
Researchers at the University of Helsinki and Aalto University have demonstrated that the body’s immune system attacks itself in a rare type of blood cancer. The finding could lead to improved treatment and a more intricate understanding of the immune system’s role in other cancers.
Current treatment methods for large granular lymphocyte (LGL) leukaemia, a rare type of blood cancer, are based on an understanding that the cancer cells attack the body’s own tissues. Prior research has focused on studying these rogue cells, making inroads to a better understanding of the disease.
‘Our research group demonstrated ten years ago that LGL cancer cells typically have a mutation in the STAT3 gene, a finding that is now used to diagnose this disease worldwide,’ says professor of translational hematology Satu Mustjoki from the University of Helsinki.
Although rarely fatal, blood cancer causes several chronic symptoms, including an increased infection risk, anemia and joint pain. The challenge so far has been that patients show a mixed response to treatment.
‘Current treatment methods have targeted the cancer cells and their vulnerabilities,’ explains Jani Huuhtanen of the University of Helsinki and Aalto University. ‘It’s impossible to evaluate which patients will respond to treatment, because in some patients the amount of active cancer cells decreases yet the symptoms remain, and for others it’s the opposite.’
Satu Mustjoki’s research group took a step back from established thinking and investigated the role of other cells in the immune system. They used the latest single-cell techniques combined with a machine learning model developed in cooperation with Aalto University. This enabled the group to unmask an adverse interaction between the body’s immune system and blood cancer cells.
‘The immune system in these patients is overactivated and keeps giving the tumor cells cues to keep growing, as well as providing them with a favorable environment,’ says doctoral researcher Dipabarna Bhattacharya from the University of Helsinki.
The research group demonstrated that in this type of leukemia, it’s not just the cancer cells that are distinct from other cancer cells in other patients, but the whole immune system. The finding could have important implications for current treatment methods.
‘Our research could explain the observed discrepancy between the LGL cancer cells and the symptoms,’ elaborates Huuhtanen. ‘The immune system has been collaborating with the cancer cells all this time, therefore future treatment should target the whole immune system – not only the cancer cells – to increase the patients’ quality of life.’
Through the looking glass with machine learning
Separating normal cells associated with the immune system from blood cancer cells is no easy feat, and traditional methods have hit a wall. In LGL leukemia, cancer cells bear a very close resemblance to normal T cells found in blood. To overcome this challenge, the group employed single-cell techniques and computational life sciences. They were able to separate cancer cells from normal T cells and compare them with each other for the first time.
'Single-cell techniques open up entirely new avenues for research,' says docent of immunology Tiina Kelkka from the University of Helsinki.
These technologies can quantify key receptor proteins in immune cells, which helps researchers better understand the role of the immune system in LGL leukemia and other diseases. These receptors determine what kind of cancer cells or pathogens the cell can fight against, but advanced machine learning tools are required to analyze the data.
‘Several different machine learning-based computational techniques were needed in this study. The latest methods from statistical machine learning and artificial intelligence have proven effective in single-cell data analysis,’ says Harri Lähdesmäki, professor of computational biology and machine learning at Aalto University.
The machine learning component involved 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 an important computational transition,’ explains Huuhtanen, who is working on his doctoral thesis at the Department of Computer Science at Aalto. ‘These computational methods open up the possibility to approach medical data without prior assumptions and see where it takes us.’
The research group have their eyes set on investigating the immune system’s role in other cancer types as well, which could lift the veil on one of the most important health problems of our time.
The study was published in the esteemed Nature Communications journal.
Source/Credit: Aalto University