The MLL MVZ at the 67th ASH Annual Meeting & Exposition

The 67th annual meeting of the American Society of Hematology (ASH) in Orlando was an important event for the MLL MVZ. Our experts presented new research findings at the meeting in the form of a workshop, three lectures, and 14 poster presentations.

"The integration of artificial intelligence and molecular precision diagnostics is revolutionizing hematology. Our contributions demonstrate how innovative technologies lead to better therapeutic decisions and allow us to share these advancements with the global community,” emphasizes Prof. Dr. Torsten Haferlach.

One of the highlights was the scientific workshop, "Translating ctDNA MRD for Patients with B-Cell Lymphoma," which was co-chaired by Professor Torsten Haferlach and Dr. Wencke Walter. The workshop focused on the latest developments in ctDNA analysis in B-cell lymphomas. Dr. Heiko Müller presented "Rolling Reporters," an innovative method for detecting clonal evolution in ctDNA that increases test intensity.

Furthermore, during a session hosted by Beckman Coulter Life Sciences, Professor Wolfgang Kern provided insights into investigating measurable residual disease (MRD) using flow cytometry in his presentation, "Studying Measurable Residual Disease by Phenotype and Genotype Using Flow Cytometry."

In a presentation, Dr. Wencke Walter described biallelic TET2 alterations in NPM1-mutated acute myeloid leukemia (AML) as a distinct subgroup. This subgroup differs significantly from cases of AML with NPM1 mutations but no biallelic TET2 alterations. This difference is due to the co-mutation pattern and poorer overall survival.

Several of our contributions focused on artificial intelligence (AI). Five posters demonstrated how machine learning and large language models support established diagnostic methods in hematology, making workflows more efficient:

• Flow Cytometry: Tsamadou et al. developed an AI-based classification model that can detect B-cell lymphomas with 99.3% accuracy, reducing processing time by up to 75%. In all cases, the AI-generated flow cytometric plots were considered equivalent to manual data analysis.
• Interphase Fluorescence In Situ Hybridization (FISH): Bode et al. presented a deep learning system for the automated interpretation of interphase FISH images. This system achieved accuracies of up to 93%. 
• Cytomorphology: Wuerf et al. presented the integration of LLMs into cytomorphological reporting. In 85% of cases, the LLM's findings required minimal or no revision, saving specialist staff 58% of their time.
• Molecular Genetics: Nadarajah et al. developed an automated classification system for rare variants with 86% accuracy that reduced analysis time from several minutes to seconds.
• Cytogenetics: Looser et al. developed a computer-assisted pipeline that translates International System for Human Cytogenetic Nomenclature (ISCN) karyotypes into a readable form. Then, it uses automated feature selection to create a prognostic model.

In addition to its work focused on AI, the MLL MVZ presented new findings on prognostics and therapies. 

Several studies have broadened our understanding of the molecular landscape of lymphatic neoplasms, which has implications for diagnosing and assessing the prognosis of these tumors:

More information on MLL MVZ research projects can be found on our website. Links to all contributions by MLL MVZ experts at this year's ASH conference are available here:

Mueller H et al. Rolling Reporters - A New Analytical Approach to Detect Clonal Evolution in ctDNA.

Wencke W et al. Bi-allelic TET2 alterations are frequently found in NPM1 mutated AML and constitute a distinct subgroup with unfavorable prognosis. https://ashpublications.org/blood/article/146/Supplement 1/339/551574/Bi-allelic-TET2-alterations-are-frequently-found

Kern W. Studying Measurable Residual Disease by Phenotype and Genotype Using Flow Cytometry.

Baldi et al. CLL with t(11;14)(q13;q32) or t(14:18)(q32:q21) – is this CLL or lymphoma?
https://ashpublications.org/blood/article/146/Supplement 1/2114/552193/CLL-with-t-11-14-q13-q32-or-t-14-18-q32-q21-is

Bode et al. Supporting routine diagnostics: AI for interpretation of interphase FISH images.
https://ashpublications.org/blood/article/146/Supplement 1/2577/551453/Supporting-routine-diagnostics-AI-for

Ecker et al. Determining the origin of TP53 mutations in patients with mature B-cell neoplasms is essential to distinguish lymphoma-related mutations from those due to clonal hematopoiesis, helping to guide treatment decisions.
https://ashpublications.org/blood/article/146/Supplement 1/5389/548576/Determining-the-origin-of-TP53-mutations-in?searchresult=1

Huber et al. The role of sole loss of chromosome y in myeloid neoplasms. https://ashpublications.org/blood/article/146/Supplement 1/1390/556430/The-role-of-sole-loss-of-chromosome-y-in-myeloid

Huber et al. MDS without clonal marker: When depth outperforms breadth. https://ashpublications.org/blood/article/146/Supplement 1/2071/551099/MDS-without-clonal-marker-When-depth-outperforms

Looser et al. ML-driven analysis of iscn karyotypes enables detection of novel prognostic markers in chronic lymphocytic leukemia (CLL).
https://ashpublications.org/blood/article/146/Supplement 1/6118/550435/ML-driven-analysis-of-iscn-karyotypes-enables?searchresult=1

Nadarajah et al. Automated multi-source data consensus classification of low-frequency variants in hematologic malignancies using transparent artificial intelligence.
https://ashpublications.org/blood/article/146/Supplement 1/2576/551454/Automated-multi-source-data-consensus

Ordemann et al. Additional genetic targets are present in the majority of AML patients with NPM1, KMT2A or NUP98 aberrations potentially impacting combination therapy with menin inhibitors.
https://ashpublications.org/blood/article/146/Supplement 1/5248/553489/Additional-genetic-targets-are-present-in-the

Schlieben et al. Enrichment of Rare Pathogenic Variants in Common Cancer Predisposition Genes in Lymphatic Malignancy: A Comprehensive Analysis of 2,138 Cases.
https://ashpublications.org/blood/article/146/Supplement 1/1767/549111/Enrichment-of-rare-pathogenic-variants-in-common

Stengel et al. The rearrangement partner and the presence of MYC mutations determines outcome of patients with MYC and BCL6 rearrangements.
https://ashpublications.org/blood/article/146/Supplement 1/1749/551480/The-rearrangement-partner-and-the-presence-of-MYC

Tsamadou C et al. Automated AI classification in clinical flow cytometry: Transforming B-cell lymphoma diagnostics.
https://ashpublications.org/blood/article/146/Supplement 1/6122/550431/Automated-AI-classification-in-clinical-flow

Walter W et al. Long-read single-cell isoform sequencing for cell type-specific detection of genomic rearrangement-dependent and -independent fusion transcripts.
https://ashpublications.org/blood/article/146/Supplement 1/6117/550436/Long-read-single-cell-isoform-sequencing-for-cell

Wobst et al. Multiple myeloma with concomitant chronic lymphocytic leukemia: Common or distinct clonal origin?
https://ashpublications.org/blood/article/146/Supplement 1/3981/548594/Multiple-myeloma-with-concomitant-chronic

Wuerf V et al. Unlocking New Frontiers in Leukemia Diagnostics Through Large Language Model–Driven Report Generation.
https://ashpublications.org/blood/article/146/Supplement 1/2574/551456/Unlocking-new-frontiers-in-leukemia-diagnostics