Researchers led by Frankfurt Medical University and Goethe University Frankfurt have identified how particularly aggressive lymphomas are recognized. By combining genetic and proteomic analyses, scientists were able to identify the biological characteristics of tumors, especially in high-risk patients who have little chance of cure with standard treatments. In the future, such patients will have direct access to more effective alternative treatments. Additionally, laboratory studies have provided the first clues to potential therapeutic targets.
Diffuse large B-cell lymphoma (DLBCL), with more than 150,000 new cases worldwide each year, is the most common aggressive lymphoma. After diagnosis, patients usually receive a standard treatment regimen consisting of therapeutic antibodies and chemotherapy (R-CHOP or Pola-R-CHP), with almost two-thirds of patients likely to be cured. However, more than one-third of patients experience recurrence after treatment or their tumors do not respond to treatment, requiring alternative treatments such as CAR T-cell therapy.
The variable effectiveness of standard treatments is due to the considerable molecular heterogeneity of the disease. Therefore, researchers have long sought molecular tumor characteristics that could distinguish the different DLBCL subtypes and allow more specific treatment.
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To date, diffuse large B-cell lymphoma has been extensively studied at the genetic level. This has led to a classification system that distinguishes subtypes according to patterns of genetic changes and gene expression.
An international research team led by Goethe University Frankfurt, Frankfurt Medical University, the German Cancer Consortium (DKTK) and the Frankfurt Cancer Institute has identified new tumor characteristics that go beyond the genetics that characterize DLBCL tumors. In the future, these capabilities may allow us to identify high-risk patients for whom standard treatments are unlikely to be successful.
To accomplish this, the researchers analyzed tumor samples from 478 patients, looking for mutations within the tumors and the expression of each gene. Additionally, we determined which proteins are produced in tumor cells and in what quantities (proteomic analysis).
We then used AI models to evaluate these data and identify patterns within the dataset. Professor Florian Buettner from the Faculty of Medicine and Institute of Computer Science, the team that developed the machine learning model, explains:Our model shows how interpretable machine learning can reveal relationships across different molecular layers. We were able to correlate mutations and protein patterns with treatment outcomes.This allowed the research team to categorize patients into groups that explain the biology of the disease and provide insight into potential treatment options. The results were validated using high-resolution single-cell tumor analysis.
Characteristics of high-risk patients
“We’ve seen a lot of research in the past,” explains Dr. Julius Ensl, a physician-scientist at the University of Frankfurt and the National Institutes of Health, and one of the study’s three lead authors, along with biochemist Dr. Björn Heupl and computer scientist Arbor Kwok.We are now able to better understand the biological characteristics of DLBCL tumors that determine the clinical prognosis of patients and are independent of previously established risk factors. Our data indicate that different genetic mutations can lead to similar tumor cell characteristics in DLBCL, and these mechanisms are now more clearly understood. This is especially important for high-risk patients“According to Enssle, this group of tumors (referred to in the study as PG4 (proteogenotype 4)) is centered around the gene MYC, which promotes tumor cell growth and division. Additionally, there are very few immune cells present in the microenvironment of these tumors.”Tumors in high-risk patients are immunologically “cold” and, in particular, the ability of cytotoxic T cells, which normally recognize and eliminate tumor cells, is suppressed.”
Based on these findings, the research team was able to pharmacologically inhibit a molecular program involving MYC in cultured PG4 lymphoma cells, thereby selectively eliminating lymphoma cells. Ensl said: ”This allowed us to identify potential targets for the development of highly accurate diagnostics and treatments.” Professor Thomas Oehlerich, Head of the Second Faculty of Medicine at the University of Frankfurt and principal investigator of the study, believes:Although we still have a long way to go, we have taken an important step toward personalized medicine for advanced lymphoma. In the long term, our findings may help identify high-risk patients earlier and tailor treatment more precisely to the underlying tumor biology.. ”
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Goethe University Frankfurt
Reference magazines:
Ensl, J.C.; others. (2026) Pathogenesis of the prototypical form of diffuse large B-cell lymphoma. cancer cells. DOI: 10.1016/j.ccell.2026.05.008. https://www.sciencedirect.com/science/article/pii/S1535610826002539?via%3Dihub
