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iwNHL 2025 | The integration of evolving molecular insights in diagnostic algorithms for lymphoid malignancies
Laurence de Leval, MD, PhD, Lausanne University Hospital, Lausanne, Switzerland, comments on the integration of evolving molecular insights in diagnostic algorithms for lymphoid malignancies. She notes that while sequencing data is increasingly available, its full potential is not yet being utilized to inform patient care. Dr de Leval highlights the need for further application of data analysis and artificial intelligence (AI) to unlock insights from multidimensional data. This interview took place at the 22nd International Workshop on Non-Hodgkin Lymphoma (iwNHL 2025), held in Cambridge, MA.
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Transcript
Of course, our knowledge is expanding. The molecular characterization of tumors is not anymore just a theoretical exercise to publish data, but it has really entered the diagnostic labs and it’s a helpful guide to support or to orient some diagnoses. So in the community, I’m speaking for myself, but in many academic or even private institutions, sequencing is increasingly done for lymphoid malignancies and in particular for T-cell lymphomas...
Of course, our knowledge is expanding. The molecular characterization of tumors is not anymore just a theoretical exercise to publish data, but it has really entered the diagnostic labs and it’s a helpful guide to support or to orient some diagnoses. So in the community, I’m speaking for myself, but in many academic or even private institutions, sequencing is increasingly done for lymphoid malignancies and in particular for T-cell lymphomas. It generates a lot of data because of heterogeneity, the panels are somewhat extended. For the moment, it’s certainly helpful to the pathologists to support some diagnoses. We’ve heard a lot this morning about follicular helper T-cell lymphomas. They’re typically associated with a particular mutation landscape. So that’s helpful to the pathologists. Now, I feel probably we utilize only a small proportion of the data. I’m not aware that the information generated is really used to change the care or the treatment of the patient. But certainly for the future, PTCL is a field where AI and data science, data analysis, would be useful to be applied in order to get some insights from this integration of multidimensional data. Very exciting also to hear about the importance of microenvironment in those diseases and certainly there’s an open space there for spatial biology techniques and these data are starting to be generated.
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