Citations

Abstract The diagnosis of T‐cell neoplasms remains one of the most challenging areas in hematopathology due to the immunophenotypic heterogeneity and subtle aberrancies often present in these entities. This “Best Practice” manuscript provides a practical framework for laboratories to design, validate, and interpret immunophenotyping studies of immature and mature T‐cell neoplasms. We outline the utility of key antigens in the screening and classification of T‐cell lymphomas/leukemia including TRBC1 and TRBC2. Analytical strategies using the “difference from normal” method and template‐based gating are discussed, along with validation considerations aligned with CLSI H62 guidelines. By integrating these principles into laboratory workflows, this manuscript aims to standardize and improve the assessment of T‐cell neoplasms across diverse clinical settings.

ABSTRACT The World Health Organization (WHO) 5th Edition and International Consensus Classification (ICC) have continued to delineate more genetically defined acute leukemias. Both recognize genetic aberrations associated with myelodysplastic syndromes/neoplasms (MDS) as defining MDS‐related AML (AML‐MR). Mutations in RUNX1 are included in this category in the ICC, but not WHO. The current classifications are unclear on how acute leukemias with ambiguous lineage (ALAL), including those of mixed lineage (MPAL) and those without a defined lineage (AUL), should be categorized in the presence of such mutations. Our multi‐institutional study aims to determine the significance of RUNX1 mutation in ALAL compared to de novo AML. Newly diagnosed MPAL, AUL, and AML with RUNX1 mutations were collected. ALAL with MDS‐related cytogenetics, BCR::ABL1 , KMT2A rearrangements, or ZNF384 or BCL11b abnormalities were excluded. We analyzed 17 cases of ALAL and 71 cases of AML that met inclusion criteria. ALAL‐ RUNX1 and AML‐ RUNX1 showed overlapping mutational landscapes with frequent comutations in MDS‐associated genes. No ALAL‐ RUNX1 or AML‐ RUNX1 cases showed concomitant TP53 mutation. ALAL‐ RUNX1 had more bone marrow blasts and more karyotypic abnormalities; however, neither impacted survival, nor did RUNX1 variant allele frequency (VAF). Survival analysis revealed similarly poor outcomes between them. AML‐directed therapies and allogeneic hematopoietic stem cell transplant trended toward improved survival in ALAL‐ RUNX1; however, it did not reach significance. Our results suggest ALAL‐ RUNX1 is associated with younger age, higher blasts, and more karyotypic abnormalities, but has similar clinical and genetic features and outcomes to AML‐ RUNX1 . Our findings suggest, like other MR mutations, RUNX1 ‐mutated ALALs should be included within AML‐MR.

Abstract Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155 884 cells per case, on average, (87% cellularity reduction), whereas preserving small MRD populations (median, 100% retention for MRD of <1%) and allowing for true percentage MRD estimates using a correction factor. A deep neural network cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score of 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve [AUC] of 0.98) or B-lymphoid cells (AUC of 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (standard deviation of ±0.57); with 100% positive agreement with conventional analysis (for MRD of ≥0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance and has the potential of facilitating B-ALL MRD analysis by more clinical laboratories.

SummaryIn individuals with high‐count monoclonal B‐cell lymphocytosis (MBL), we investigated if lymphadenopathy or splenomegaly found by imaging was associated with shorter time to first chronic lymphocytic leukaemia (CLL) therapy (TTFT) and overall survival (OS). Individuals with MBL seen at Mayo Clinic (2002–2019) were retrospectively divided into three cohorts based on imaging studies within 1 year of diagnosis: no imaging studies (Cohort A); imaging with no evidence of lymphadenopathy/splenomegaly (Cohort B); imaging with evidence of lymphadenopathy/splenomegaly (Cohort C). We compared baseline characteristics, TTFT and OS across the MBL cohorts to a cohort of individuals with small lymphocytic lymphoma (SLL). A total of 1078 patients were included: 640 with MBL and 438 with SLL. Compared to Cohort B, individuals in Cohort C were more likely to have unmutated immunoglobulin heavy chain variable region (IGHV) (43% vs. 25% p = 0.016), high‐risk fluorescence in situ hybridization (FISH) (del17p and del11q in 15% vs. 4%, p = 0.038) and higher expression of CD38 (31% vs. 16%; p = 0.011). After adjusting for sex and CLL‐International Prognostic Index (IPI), lymphadenopathy/splenomegaly was associated with a shorter TTFT (hazard ratio [HR] = 2.04, 95% confidence interval [CI]: 1.02–4.04, p = 0.042) but not OS (HR = 1.09, 95% CI: 0.62–1.92, p = 0.775). Lymphadenopathy/splenomegaly on imaging in individuals with high‐count MBL is associated with a more unfavourable risk profile and shorter time to first CLL‐directed therapy.

Background: Flow cytometric detection of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL) is complex, time-consuming, and subject to inter-operator variability. Deep neural networks (DNNs) offer potential for standardization and efficiency improvement, but require rigorous validation and monitoring for safe clinical implementation. Methods: We evaluated a DNN-assisted human-in-the-loop approach for CLL MRD detection. Initial validation included method comparison against manual analysis (n = 240), precision studies, and analytical sensitivity verification. Post-implementation monitoring comprised four components: daily electronic quality control, input data drift detection, error analysis, and attribute acceptance sampling. Laboratory efficiency was assessed through a timing study of 161 cases analyzed by five technologists. Results: Method comparison demonstrated 97.5% concordance with manual analysis for qualitative classification (sensitivity 100%, specificity 95%) and excellent correlation for quantitative assessment (r = 0.99, Deming slope = 0.99). Precision studies confirmed high repeatability and within-laboratory precision across multiple operators. Analytical sensitivity was verified at 0.002% MRD. Post-implementation monitoring identified 2.97% of cases (26/874) with input data drift, primarily high-burden CLL and non-CLL neoplasms. Error analysis showed the DNN alone achieved 97% sensitivity compared to human-in-the-loop-reviewed results, with 13 missed cases (1.5%) showing atypical immunophenotypes. Attribute acceptance sampling confirmed 98.8% of reported negative cases were true negatives. The DNN-assisted workflow reduced average analysis time by 60.3% compared to manual analysis (4.2 ± 2.3 vs. 10.5 ± 5.8 min). Conclusions: The implementation of a DNN-assisted approach for CLL MRD detection in a clinical laboratory provides diagnostic performance equivalent to expert manual analysis while substantially reducing analysis time. Comprehensive performance monitoring ensures ongoing safety and effectiveness in routine clinical practice. This approach provides a model for responsible AI integration in clinical laboratories, balancing automation benefits with expert oversight.

Background/Objectives: Acute myeloid leukemia (AML) is a biologically heterogeneous malignancy with a variable prognosis. Despite many patients achieving complete remission, relapse remains common, underscoring the need for effective prognostic markers. Measurable residual disease (MRD) has emerged as a critical prognostic indicator, associated with higher relapse risk and shorter survival. This study reports on our initial experience of MRD detection by flow cytometry in 385 bone marrow samples from 126 AML patients. Methods: The flow cytometry MRD assay, validated according to stringent consensus recommendations, consists of a 3-tube, 10-color panel incorporating a broad spectrum of lineage differentiation markers. Analytical specificity, sensitivity, precision, and reproducibility were evaluated, demonstrating the assay’s robustness. Results: The results reveal distinct immunophenotypic aberrancies in all AML cases, with consistent identification of aberrant immunophenotypes in follow-up specimens. AML MRD was detected in 32 out of 126 patients (25%) and in 77 out of 385 analyses (20%), with a median aberrant blast percentage of 1.87% (range, 0.01–12). A change in immunophenotype was documented in 21% of the MRD-positive cases. MRD positivity detected in the first sample studied was associated with reduced overall survival (HR: 5.153; p < 0.0001). Conclusions: Our findings support the integration of flow cytometric MRD analysis into routine clinical practice to enhance risk stratification and treatment planning for AML patients, as currently recommended by professional guidelines.

Background: Flow cytometry (FC) is widely utilized for the identification of minimal residual disease (MRD) in B acute lymphoblastic leukemia (BALL), providing an essential laboratory indicator for prognostic assessment and therapeutic management. Unfortunately, the high level of expertise required, long manual analysis time, and complexity of FC data files limit the offering of this testing to selected reference laboratories. Methods: We developed an artificial intelligence (AI) pipeline to automatically enhance unaltered FC files, minimizing the expertise and manual analysis time needed to detect BALL-MRD on a single-tube 10-color panel (CD10, CD19, CD20, CD22, CD24, CD34, CD38, CD45, CD58, CD66c). Raw FC files corresponding to peripheral blood or bone marrow specimens from 171 BALL MRD positive cases (MRD &lt; 5%, median = 0.11%, 107 patients), and 89 MRD-negative cases (63 patients) were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline on a cloud environment (Google Vertex AI). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), dimensionality reduction (UMAP), cluster-based anomaly detection compared to negative controls (24 bone marrow aspirates and 7 peripheral bloods), and cluster-informed downsampling with preservation of low-event subsets. In addition, a deep neural network (DNN) trained on expert-defined subpopulations from negative controls was included for automatic gating of normal subsets (Tensorflow v2). AI-enhanced data files were analyzed using a general purpose flow cytometry analysis software (Kaluza v 3.5, Beckman Coulter), and %MRD estimates compared to reported results based on expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells per case to be manually analyzed from 1.2 million to 155,884 cells on average (87% cellularity reduction); resulting in a smaller FC data file (from 64.5 MB to 13.7 MB; 79% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 100% retention for both MRD&lt;0.01% and MRD 0.01-1%). True number of events were accurately estimated on gated subsets using an “upsampling factor” parameter [observed # events x mean (upsampling factor)]. Gating of normal subsets was completely automated using a DNN classifier parameter. Identification of MRD was aided by an AI-generated “aberrancy scale” parameter that discriminated BALL (median: 991) from both benign mononuclear cells (median: 486, p &lt; 0.0001) and background B-lymphoid cells (median: 379, p &lt; 0.0001), with a performance (AUC: 0.98 and 0.94 for mononuclear and B-lymphoid cells, respectively) superior to CD20 (0.54 and 0.69), CD38 (0.60 and 0.81), CD45 (0.75 and 0.74), CD58 (0.52 and 0.89), CD66c (0.56 and 0.72) and all other antigens studied. The use of AI-enhanced files reduced manual analysis time from 14.16 minutes (SD = 3.7) to 1.05 minutes (SD = 0.6) per case, on average (p &lt; 0.0001) (93% reduction). BALL MRD was detected in all positive cases above clinically relevant threshold (≥0.01%, as recommended by NCCN guidelines), and in 28 of 32 (88%) cases below this threshold, with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 1.18, intercept = 0.04%, R squared = 0.92, P &lt; 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw BALL-MRD FC data into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of BALL-MRD in clinical diagnostics, reducing number of cells analyzed by 87% and manual analysis time by 93%, without impacting test performance. Moreover, CCADDAS can be utilized for any labroatory-developed BALL-MRD assay, and its small-sized export is compatible with any clinical FC software, computing platform and analysis strategy. Adoption of CCADDAS is likely to facilitate the implementation of BALL-MRD FC analysis by more clinical laboratories.