Researchers led by Universitätsmedizin Frankfurt and Goethe University Frankfurt announced on July 3 that they have identified how particularly aggressive forms of lymphoma can be recognized. By combining genetic and proteomic analyses, the scientists identified biological characteristics of tumors, especially in high-risk patients for whom standard therapy offers little chance of cure. The researchers said that such patients could receive alternative, more effective therapies directly in the future. Experimental laboratory research also provided initial clues to potential therapeutic targets.
Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive form of lymphoma worldwide, with more than 150,000 new cases each year. After diagnosis, patients typically receive a standard treatment regimen consisting of a therapeutic antibody and chemotherapy (R-CHOP or Pola-R-CHP), which cures nearly two-thirds of patients. However, more than one-third experience relapse or resistance to therapy and may require alternative treatments such as CAR T-cell therapy.
The effectiveness of standard therapy varies due to significant molecular heterogeneity within DLBCL. Researchers have been searching for molecular tumor characteristics that would allow them to distinguish between different DLBCL subtypes and treat them more specifically. To date, extensive genetic investigation has led to classification systems distinguishing subtypes based on genetic alterations and gene expression patterns.
An international research team led by Goethe University Frankfurt, Universitätsmedizin Frankfurt, the German Cancer Consortium (DKTK), and the Frankfurt Cancer Institute has now identified novel tumor characteristics beyond genetics that characterize DLBCL tumors. These features may enable identification of high-risk patients who are unlikely to respond successfully to standard therapy.
To achieve this breakthrough, researchers analyzed tumor samples from 478 patients by examining mutations in tumors as well as gene expression levels. They also determined which proteins were produced in tumor cells through proteomic analysis. The data were evaluated using artificial intelligence models developed by Professor Florian Büttner's team at Goethe University Frankfurt: "Our model demonstrates how interpretable machine learning can reveal relationships across different molecular layers: we succeeded in correlating mutation and protein patterns with treatment outcomes." This enabled classification into groups describing disease biology while providing insights into potential therapeutic options; findings were validated using high-resolution single-cell analyses.
Dr. Julius Enssle explained, "We can now much better understand the biological characteristics of DLBCL tumors that determine patients' clinical prognosis and are independent of previously established risk factors." According to Enssle, tumors classified as PG4 are centered around the gene MYC—driving cell growth—and feature suppressed cytotoxic T cell function: "The tumors of high-risk patients are immunologically 'cold' - in particular, the function of cytotoxic T cells is suppressed..."
Building on these findings, pharmacological inhibition targeting MYC programs selectively eliminated cultured PG4 lymphoma cells: "This has enabled us to identify potential targets for the development of precision diagnostics and therapies," Enssle said. Professor Thomas Oellerich concluded, "Although there is still a long way to go, we have taken an important step toward personalized medicine for aggressive lymphoma...our findings may help identify high-risk patients earlier and tailor their treatment more precisely."
