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 improve 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 the understanding that the cancer cells attack the body’s tissues. Prior research has focused on studying these rogue cells, making inroads to understand the disease better.
Single-cell technologies allow for the analysis of individual cells and comparing normal cells to tumour cells (purple). Image credit: Claudiu Cotta
‘Our research group demonstrated ten years ago that LGL cancer cells typically have a mutation in the STAT3 gene, a finding now used to diagnose this disease worldwide,’ says professor of translational haematology Satu Mustjoki from the University of Helsinki.
Although rarely fatal, blood cancer causes several chronic symptoms, including an increased infection risk, anaemia 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 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 conventional 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 by 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 tumour cells cues to keep growing and providing them with a favourable environment,’ says doctoral researcher Dipabarna Bhattacharya from the University of Helsinki.
The research group demonstrated that in this type of leukaemia, it’s not just the cancer cells distinct from other cancer cells in different patients but also 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. 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 leukaemia, cancer cells bear a close resemblance to normal T cells found in the blood. The group employed single-cell techniques and computational life sciences to overcome this challenge. 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 immune system's role in LGL leukaemia and other diseases. These receptors determine what kind of cancer cells or pathogens the cell can fight against, but advanced machine learning tools must analyse 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 Harri Lähdesmäki, professor of computational biology and machine learning at Aalto University.
The machine learning component also 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 University of Helsinki and the Department of Computer Science Aalto. ‘These computational methods allow us to approach medical data without prior assumptions and see where it takes us.’
The research group has their eyes set on investigating the immune system’s role in other cancer types, which could lift the veil on one of the most critical health problems.
Source: Aalto University