With the advent of novel, highly effective therapies for multiple myeloma (MM), classical serologic monitoring appears insufficient for response assessment and prediction of relapse. Moreover, serologic studies in MM are hampered by interference of therapeutic antibodies. The detection of malignant plasma cell clones by next generation sequencing (NGS) or multiparameter flow cytometry (MFC) circumvents these difficulties and can be performed in the peripheral blood (pB) by targeting circulating cell-free DNA (cfDNA) or circulating plasma cells (CPCs), thus also avoiding an invasive sampling procedure. Here, we applied NGS of VJ light chain (LC) rearrangements in cfDNA and MFC of magnetically-enriched CD138-positive CPCs (me-MFC) to investigate disease burden in unselected MM patients. Sequencing was successful for 114/130 (87.7%) cfDNA samples and me-MFC results were analyzable for 196/205 (95.6%) samples. MM clones were detectable in 38.9% of samples taken at initial diagnosis or relapse (ID/RD), but only in 11.8% of samples taken during complete remission (CR). Circulating MM plasma cells were present in 83.3% of ID/RD samples and 9.9% of CR samples. Residual disease assessment by NGS or me-MFC in samples taken during very good partial remission or CR was 80% concordant. Notably, 4/4 (NGS) and 5/8 (me-MFC) positive CR samples were from patients with oligo- or non-secretory myeloma. The time to progression was shorter if there was evidence of residual myeloma in the pB. Together, our findings indicate that our two novel analytical approaches accurately indicate the course of MM and may be particularly valuable for monitoring patients with serologically non-trackable disease.

Minimal residual disease (MRD) detection is a strong predictor for survival and relapse in acute myeloid leukemia (AML). MRD can be either determined by molecular assessment strategies or via multiparameter flow cytometry. The degree of bone marrow (BM) dilution with peripheral blood (PB) increases with aspiration volume causing consecutive underestimation of the residual AML blast amount. In order to prevent false‐negative MRD results, we developed Cinderella, a simple automated method for one‐tube simultaneous measurement of hemodilution in BM samples and MRD level. The explainable artificial intelligence (XAI) Cinderella was trained and validated with the digital raw data of a flow cytometric “8‐color” AML‐MRD antibody panel in 126 BM and 23 PB samples from 35 patients. Cinderella predicted PB dilution with high accordance compared to the results of the Holdrinet formula (Pearson's correlation coefficient r = 0.94, R² = 0.89, p < 0.001). Unlike conventional neuronal networks Cinderella calculated the distributions of 12 different cell populations that were assigned to true hematopoietic counterparts as a human in the loop (HIL) approach. Besides characteristic BM cells such as myelocytes and myeloid progenitor cells the XAI identified discriminating populations, which were not specific for BM or PB (e.g., T cell/NK cell subpopulations and CD45 negative cells) and considered their frequency differences. Thus, Cinderella represents a HIL‐XAI algorithm capable to calculate the degree of hemodilution in BM samples with an AML MRD immunophenotype panel. It is explicable, transparent, and paves a simple way to prevent false negative MRD reports.

Background: The International Prognostic Index (IPI) is applied to predict the outcome of chronic lymphocytic leukemia (CLL) with five prognostic factors, including genetic analysis. We investigated whether multiparameter flow cytometry (MPFC) data of CLL samples could predict the outcome by methods of explainable artificial intelligence (XAI). Further, XAI should explain the results based on distinctive cell populations in MPFC dot plots. Methods: We analyzed MPFC data from the peripheral blood of 157 patients with CLL. The ALPODS XAI algorithm was used to identify cell populations that were predictive of inferior outcomes (death, failure of first-line treatment). The diagnostic ability of each XAI population was evaluated with receiver operating characteristic (ROC) curves. Results: ALPODS defined 17 populations with higher ability than the CLL-IPI to classify clinical outcomes (ROC: area under curve (AUC) 0.95 vs. 0.78). The best single classifier was an XAI population consisting of CD4+ T cells (AUC 0.78; 95% CI 0.70–0.86; p < 0.0001). Patients with low CD4+ T cells had an inferior outcome. The addition of the CD4+ T-cell population enhanced the predictive ability of the CLL-IPI (AUC 0.83; 95% CI 0.77–0.90; p < 0.0001). Conclusions: The ALPODS XAI algorithm detected highly predictive cell populations in CLL that may be able to refine conventional prognostic scores such as IPI.

Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data.

Autoimmune hemolytic anemia (AIHA) can be life-threatening, if hemoglobin (Hb) levels continue to decline after established treatments with glucocorticoids, rituximab, intravenous immunoglobulins, and plasmapheresis. Impaired regulatory T cells (Treg) are proposed to alleviate AIHA development through decreased binding of CTLA-4 to antigen-presenting cells. Abatacept is a fusion protein with a CTLA-4 domain and is approved for use in rheumatoid arthritis. It mimics the immunosuppressive CTLA-4 effect of Treg. Thus, application of abatacept in refractory AIHA might be reasonable. A 54-year-old woman with known AIHA was admitted to our clinic due to therapy-refractory hemoglobin decrease to 4.0 g/dl. Previously, multiple courses of glucocorticoids, rituximab, azathioprine, mycophenolate mofetil, cyclophosphamide, bortezomib, and a splenectomy failed to stop or stabilize hemoglobin levels and hemolysis. A new immunosuppressive therapy with cyclosporine was initiated and erythropoiesis was stimulated with darbepoetin alfa. Again, therapy failed even though we tried to support immunosuppressive therapy by reducing the amount of pathogenic antibody through plasmapheresis. We stopped the treatment with cyclosporine and applied abatacept instead. After seven days hemoglobin stabilized at 4.3 g/dl and no further red blood cells transfusions were necessary. About one month later hemolysis aggravated again and azathioprine was added to the ongoing abatacept treatment. Finally, the combination of abatacept and azathioprine led to a long-lasting increase of the Hb level above 11 g/dl six months later. Abatacept can be applied to overcome therapy refractory autoimmune hemolytic anemia but should be combined with an additional immunosuppressive medication such as azathioprine.