Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes

The diagnostic work-up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected-MDS. The computational diagnostic workflow consists of methods for pre-processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient)

NK Cell Phenotype Is Associated With Response and Resistance to Daratumumab in Relapsed/Refractory Multiple Myeloma

The CD38-targeting antibody daratumumab has marked activity in multiple myeloma (MM). Natural killer (NK) cells play an important role during daratumumab therapy by mediating antibody-dependent cellular cytotoxicity via their FcγRIII receptor (CD16), but they are also rapidly decreased following initiation of daratumumab treatment. We characterized the NK cell phenotype at baseline and during daratumumab monotherapy by flow cytometry and cytometry by time of flight to assess its impact on response and development of resistance (DARA-ATRA study; NCT02751255). At baseline, nonresponding patients had a significantly lower proportion of CD16 + and granzyme B + NK cells, and higher frequency of TIM-3 + and HLA-DR + NK cells, consistent with a more activated/exhausted phenotype. These NK cell characteristics were also predictive of inferior progression-free survival and overall survival. Upon initiation of daratumumab treatment, NK cells were rapidly depleted. Persisting NK cells exhibited an activated and exhausted phenotype with reduced expression of CD16 and granzyme B, and increased expression of TIM-3 and HLA-DR. We observed that addition of healthy donor-derived purified NK cells to BM samples from patients with either primary or acquired daratumumab-resistance improved daratumumab-mediated MM cell killing. In conclusion, NK cell dysfunction plays a role in primary and acquired daratumumab resistance. This study supports the clinical evaluation of daratumumab combined with adoptive transfer of NK cells

Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes

Myelodysplastic syndromes (MDS) are a challenging group of diseases for clinicians and researchers, as both disease course and pathobiology are highly heterogeneous. In (suspected) MDS patients, multi-parameter flow cytometry can aid in establishing diagnosis, risk stratification and choice of therapy. This review addresses the developments and future directions of multi-parameter flow cytometry scores in MDS. Additionally, we propose an integrated diagnostic algorithm for suspected MDS

Computational analysis of flow cytometry data in hematological malignancies: future clinical practice?

PURPOSE OF REVIEW: This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies. RECENT FINDINGS: In recent years, computational analysis methods have been applied to clinical flow cytometry data of hematological malignancies with promising results. Most studies combined dimension reduction (principle component analysis) or clustering methods (FlowSOM, generalized mixture models) with machine learning classifiers (support vector machines, random forest). For diagnosis and classification of hematological malignancies, many studies have reported results concordant with manual expert analysis, including B-cell chronic lymphoid leukemia detection and acute leukemia classification. Other studies, e.g. concerning diagnosis of myelodysplastic syndromes and classification of lymphoma, have shown to be able to increase diagnostic accuracy. With respect to treatment response monitoring, studies have focused on, for example, computational minimal residual disease detection in multiple myeloma and posttreatment classification of healthy or diseased in acute myeloid leukemia. The results of these studies are encouraging, although accurate relapse prediction remains challenging. To facilitate clinical implementation, collaboration and (prospective) validation in multicenter setting are necessary. SUMMARY: Computational analysis methods for clinical flow cytometry data hold the potential to increase ease of use, objectivity and accuracy in the clinical work-up of hematological malignancies

Myelodysplastic neoplasms dissected into indolent, leukaemic and unfavourable subtypes by computational clustering of haematopoietic stem and progenitor cells

Myelodysplastic neoplasms (MDS) encompass haematological malignancies, which are characterised by dysplasia, ineffective haematopoiesis and the risk of progression towards acute myeloid leukaemia (AML). Myelodysplastic neoplasms are notorious for their heterogeneity: clinical outcomes range from a near-normal life expectancy to leukaemic transformation or premature death due to cytopenia. The Molecular International Prognostic Scoring System made progress in the dissection of MDS by clinical outcomes. To contribute to the risk stratification of MDS by immunophenotypic profiles, this study performed computational clustering of flow cytometry data of CD34+ cells in 67 MDS, 67 AML patients and 49 controls. Our data revealed heterogeneity also within the MDS-derived CD34+ compartment. In MDS, maintenance of lymphoid progenitors and megakaryocytic-erythroid progenitors predicted favourable outcomes, whereas expansion of granulocyte-monocyte progenitors increased the risk of leukaemic transformation. The proliferation of haematopoietic stem cells and common myeloid progenitors with downregulated CD44 expression, suggestive of impaired haematopoietic differentiation, characterised a distinct MDS subtype with a poor overall survival. This exploratory study demonstrates the prognostic value of known and previously unexplored CD34+ populations and suggests the feasibility of dissecting MDS into a more indolent, a leukaemic and another unfavourable subtype

The flow cytometry myeloid progenitor count: A reproducible parameter for diagnosis and prognosis of myelodysplastic syndromes

Background: The bone marrow blast count is central to the diagnosis and monitoring of myelodysplastic syndromes (MDS). It is an independent risk factor for worse prognosis whether based on the morphology blast count or the flow cytometry (FC) myeloid progenitor (MyP) count. It is a principal population in FC MDS analysis also because once defined; it provides significant contributions to the overall FC MDS score. Methods: We elected to investigate inter-analyst agreement for the most fundamental parameter of the FC MDS diagnostic score: the MyP count. A common gating strategy was agreed and used by seven cytometrists for blind analysis of 34 routine bone marrows sent for MDS work-up. Additionally, we compared the results with a computational approach. Results: Concordance was excellent: Intraclass correlation was 0.993 whether measuring %MyP of total cells or CD45+ cells, and no significant difference was observed between files from different centers or for samples with abnormal MyP phenotypes. Computational and manual results were similar. Applying the common strategy to individual laboratories' control cohorts produced similar MyP reference ranges across centers. Conclusion: The FC MyP count offers a reliable diagnostic and prognostic measurement in MDS. The use of manual and computational approaches side by side may allow for optimizing both strategies. Considering its known prognostic power, the MyP count could be considered a useful and reliable addition to existing prognostic scoring systems

The wider perspective: twenty years of clinical trials in myelodysplastic syndromes

Most patients with myelodysplastic syndromes (MDS) require therapeutic intervention. However, there are few approved treatments for MDS. To explore reasons, we searched clinicaltrials.gov and clinicaltrialsregister.eu for MDS trials from 2000 to 2020. We assessed which agents were under investigation and analysed clinical trial characteristics and continuation rates from phase I to II to III to approval. As such, we identified 384 unique agents in 426 phase I, 430 phase II and 48 phase III trials. Success rates for phase III trials and agents were low, and MDS trials took markedly longer to complete than the average clinical trial. Although success rates were higher when MDS-specific phase I trials were conducted, 52% of the agents had not been evaluated in a phase I trial for MDS. MDS trials often failed to include quality of life, an especially important outcome for older MDS patients. Our work identifies factors potentially contributing to the paucity of available agents for MDS. We suggest a framework to improve clinical research in MDS that might ultimately augment the number of available agents