Otteson, Gregory E.

Background: A deeper understanding of the cytogenetic features has enabled robust risk stratification for patients with newly diagnosed multiple myeloma (NDMM). However, heterogeneity persists in the currently available prognostic tools and there is further room for personalization. We assess the utility of PC-PRO, a test to measure the proportion of monotypic plasma cells in the synthetic phase (S-phase) of cell cycle, as a prognostic marker for NDMM. Methods: Patients with active MM diagnosed between January 2013 and January 2023 with a flow cytometry-based S-phase assessment at diagnosis were included. Flow cytometric immunophenotyping was performed using the following antibodies: CD19, CD38, CD45, CD138, cytoplasmic kappa and lambda immunoglobulin, and DAPI (4',6-diamidino-2-phenylindole). The percentage of plasma cells in S-phase was determined by measuring the proportion of cells with DNA content of the monotypic plasma cells between the G0/G1 and G2/M peaks. Plasma cell clonality was detected through demonstrating CD38 and CD138 positivity along with immunoglobulin light chain restriction, abnormality of CD19/ CD45 expression and/or ploidy difference by DAPI staining. High-risk cytogenetic abnormalities (HRCA) by FISH were defined as follows: presence of deletion 17p (del17p), t(4;14), t(14;16), t(14;20), 1q gain/amplification, 1p deletion (when available). Additionally, the second iteration of the revised ISS (R2-ISS), incorporating both laboratory and cytogenetic features as well an adaptation of the recently proposed International Myeloma Working Group (IMWG) Classification for high-risk disease, was utilized for risk stratification. All time-to-event analyses were performed using the Kaplan Meier method and compared using the log-rank test. Univariable and multivariable Cox proportional hazards analyses were performed to assess the independent prognostic impact of S-phase. Results: A total of 823 patients were included in the study. The median follow-up was 6 years [95% Confidence interval (CI): 5.5-6.3 years] and estimated median overall survival (OS) was 8.3 years. The median S-phase value for the cohort was 0.8 % (interquartile range: 0.5-1.4%), with 135 (16%) patients harboring an S-phase of ≥2% at diagnosis. Applying the R2-ISS prognostic model to our cohort, 11% (79/706) patients were stratified to the R2-ISS high-risk cohort; 93 patients (out of 788, 12%) had 2 or more HRCA at diagnosis. Patients with a S-phase ≥2% at diagnosis had a median OS of 3.9 years (95% CI: 2.9-5.7) vs. 9.2 years [95% CI: 7.9-not reached (NR)] for patients with a S-phase <2% [Hazard ratio (HR): 2.2 (95% CI: 1.7-2.8), p<0.0001]. The median OS was inferior for cohorts with an increasing S-phase value: S-phase of 0.1-1%: HR: 1 (standard), S-phase 1.1-3%: HR 1.7 (95% CI: 1.3-2.1), S-phase >3%: HR 3.0 (95% CI: 2.2-4.1)]. On a multivariable analysis including S-phase ≥2%, age at diagnosis of MM (>65 years) and R2-ISS high-risk status, S-phase ≥2% was an independent predictor of inferior OS [hazard ratio (HR) 1.9 (95% CI: 1.4-2.6), p<0.0001]. Age >65 years [HR 1.9 (95% CI: 1.5-2.5), p<0.0001] and R2-ISS high-risk status [HR 1.8 (1.3-2.6), p=0.001] were also independent predictors of OS. Within the sub cohort of patients with 2 or more of the listed high-risk cytogenetic features (n=93, 12%), a S-phase ≥2% (n=24) was associated with a worse median OS of 1.6 years (95% CI: 0.7-NR) vs. 6.2 years (95 % CI:3.3-NR) for S-phase <2% (p=0.002). Conclusion: Flow cytometry-based determination of the proportion of monotypic plasma cells in S-phase is an easy-to-study and powerful prognostic tool for NDMM. A S-phase of ≥2% at diagnosis is a predictor of OS independent of age and R2-ISS high-risk stage. An S-phase of ≥2% was able to identify a distinct group with markedly inferior outcomes even within the sub cohort of patients with 2 or more high-risk cytogenetic features.

Background: Flow cytometry (FC) is widely utilized for the identification of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL), 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 CLL MRD on a single-tube 10-color panel (CD5, CD19, CD20, CD22, CD38, CD43, CD45, CD200, kappa and lambda). Raw FC files corresponding to peripheral blood or bone marrow specimens from 166 CLL MRD positive cases (MRD < 5%, median = 0.17%, 122 patients), and 61 MRD-negative cases (45 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 (15 bone marrow aspirates and 14 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 1.1 million to 165,010 cells on average (85% cellularity reduction); resulting in a smaller FC data file (from 62.1 MB to 15.2 MB; 75% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 100% retention for both MRD<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. Rapid identification of suspected MRD was aided by an AI-generated “aberrancy scale” parameter that discriminated CLL MRD (mean ± 2SD: 969 ± 144) from background B-lymphoid cells (mean ± 2SD: 395 ± 401) (p<0.0001) (AUC = 0.98), with a performance superior to CD5 (AUC = 0.93), CD20 (AUC = 0.83), CD43 (AUC = 0.8) and CD200 (AUC = 0.7). Immunoglobulin light chain-positive CLL MRD was visually identified on a pre-calculated UMAP plot as a single-colored cluster, after dual coloring for kappa and lambda. The use of AI-enhanced files reduced manual analysis time from 11.6 minutes (SD = 4.5) to 0.8 minutes (SD = 0.4) per case, on average (p < 0.01) (93% reduction). CLL MRD was detected in all positive cases above clinically relevant threshold (≥0.01%, as recommended by NCCN guidelines), and in 17 of 21 (81%) cases below this threshold, with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 1.02, intercept = 0.001%, R squared = 0.98, P < 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw CLL-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 CLL-MRD in clinical diagnostics, reducing number of cells analyzed by 85% and manual analysis time by 93%, without impacting test performance. Moreover, CCADDAS can be utilized for any laboratory-developed CLL-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 CLL MRD FC analysis by more clinical laboratories.

Abstract The diagnosis of leukemic T-cell malignancies is often challenging, due to overlapping features with reactive T-cells and limitations of currently available T-cell clonality assays. Recently developed therapeutic antibodies specific for the mutually exclusive T-cell receptor constant β chain (TRBC)1 and TRBC2 isoforms provide a unique opportunity to assess for TRBC-restriction as a surrogate of clonality in the flow cytometric analysis of T-cell neoplasms. To demonstrate the diagnostic utility of this approach, we studied 135 clinical specimens with (50) or without (85) T-cell neoplasia, in addition to 29 blood samples from healthy donors. Dual TRBC1 and TRBC2 expression was studied within a comprehensive T-cell panel, in a fashion similar to the routine evaluation of kappa and lambda immunoglobulin light chains for the detection of clonal B-cells. Polytypic TRBC expression was demonstrated on total, CD4+ and CD8+ T-cells from all healthy donors; and by intracellular staining on benign T-cell precursors. All neoplastic T-cells were TRBC-restricted, except for 5 cases (10%) lacking TRBC expression. T-cell clones of uncertain significance were identified in 15 samples without T-cell malignancy (13%), and accounted for smaller subsets than neoplastic clones (median: 4.7% vs. 73% of lymphocytes, p < 0.0001). Single staining for TRBC1 produced spurious TRBC1-dim subsets in 21 clinical specimens (16%), all of which resolved with dual TRBC1/2 staining. Assessment of TRBC restriction by flow cytometry provides a rapid diagnostic method to detect clonal T-cells, and to accurately determine the targetable TRBC isoform expressed by T-cell malignancies.

Background: Identification of minimal residual disease (MRD) in acute myeloid leukemia (AML) is essential for assessing risk of relapse and guiding therapeutic management. While molecular assays have streamlined AML MRD detection, flow cytometric immunophenotyping (FCI) remains the only available test modality for approximately 50% of patients lacking traceable molecular MRD targets. Unfortunately, due to the high level of expertise required, long manual analysis time and complexity of AML MRD FCI data, only a few reference laboratories offer this as an orderable test. Methods: To facilitate the analysis of AML MRD by FCI, we devised an unsupervised machine learning (ML) pipeline that intakes raw FCI data, eliminates acquisition errors (FlowCut), performs state-of-the-art clustering (PARC) and dimensionality reduction (UMAP), estimates how aberrant or different-from-normal each cluster is compared to a cohort of controls (15 donor and 15 reactive marrow aspirates), downsamples clusters with high event counts without significant loss of low-event clusters, and corrects for downsampling using an upsampling factor. This CCADDAS (Clinically-intended Clustering of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline was containerized, deployed in a cloud environment (Google Cloud Vertex AI), and used to process raw FCI files from 50 AML MRD FCI assays (3-tube/10-color panel) with 0.1% to &lt;5% leukemic subsets, and 20 assays with no detectable leukemic subsets by prior conventional analysis. Machine learning-enhanced data files were analyzed using a commercially available FCI analysis software (Kaluza v 2.1, Beckman Coulter). Results: Cluster-informed downsampling reduced the total number of events needed to be analyzed from 1 million cells to 179,531 cells per tube on average (82% data reduction), while preserving low event subsets (clusters smaller than 5000 cells) and allowing for accurate quantitative estimates for any gated population [# events x mean (upsampling factor)]. MRD was detected in all positive cases, and absent on all negative cases when analyzed using the enhanced downsampled data (100% correlation with original data), with accurate quantitative estimates (Spearman r = 0.85). Analysis time was dramatically reduced from 73 minutes to 8 minutes per case on average (89% reduction of manual analysis time). Dimensionality reduction parameters simplified gating of lineage- and maturation-defined subsets. Cluster comparison against control samples facilitated the rapid identification of MRD subsets, based on a calculated aberrancy scale parameter that helped distinguish MRD blasts (mean ± 2SD: 0.85 ± 0.3) from benign blasts (mean ± 2SD: 0.26 ± 0.3) (p&lt;0.0001). Conclusion: We introduce CCADDAS, an unsupervised containerized cloud ML pipeline that enhances raw AML-MRD FCI data with ML annotations, cluster-informed downsampling and comparison to normal controls to create markedly smaller and software-agnostic FCI files. CCADDAS simplifies and accelerates detection of AML-MRD in clinical diagnostics, reducing number of cells analyzed by 82% and manual analysis time by 89%, without impacting test performance. Moreover, CCADDAS can be deployed on any laboratory-developed AML-MRD assay, and its small-sized export is compatible with any clinical FCI software, computing platform and analysis strategy. Adoption of CCADDAS is likely to facilitate the implementation of AML MRD FCI analysis by more clinical laboratories.

Abstract Objectives Fluorescence in situ hybridization (FISH) for plasma cell neoplasms (PCNs) requires plasma cell (PC) identification or purification strategies to optimize results. We compared the efficacy of cytoplasmic immunoglobulin FISH (cIg-FISH) and fluorescence-activated cell sorting FISH (FACS-FISH) in a clinical laboratory setting. Methods The FISH analysis results of 14,855 samples from individuals with a suspected PCN subjected to cytogenetic evaluation between 2019 and 2022 with cIg-FISH (n = 6917) or FACS-FISH (n = 7938) testing were analyzed. Results Fluorescence-activated cell sorting–FISH increased the detection rate of abnormalities in comparison with cIg-FISH, with abnormal results documented in 54% vs 50% of cases, respectively (P &lt; .001). It improved the detection of IGH::CCND1 (P &lt; .001), IGH::MAF (P &lt; .001), IGH::MAFB (P &lt; .001), other IGH rearrangements (P &lt; .001), and gains/amplifications of 1q (P &lt; .001), whereas the detection rates of IGH::FGFR3 fusions (P = .3), loss of 17p (P = .3), and other abnormalities, including hyperdiploidy (P = .5), were similar. Insufficient PC yield for FISH analysis was decreased between cIg-FISH and FACS-FISH (22% and 3% respectively, P &lt; .001). Flow cytometry allowed establishment of ploidy status in 91% of cases. In addition, FACS-FISH decreased analysis times, workload efforts, and operating costs. Conclusions Fluorescence-activated cell sorting–FISH is an efficient PC purification strategy that affords significant improvement in diagnostic yield and decreases workflow requirements in comparison with cIg-FISH.

Abstract Objectives Patients with clonal cytopenia of undetermined significance (CCUS) are at increased risk of developing myeloid neoplasia (MN). We evaluated whether a simple flow cytometry immunophenotyping (FCIP) assay could differentiate the risk of development of MN in patients with CCUS. Methods Bone marrow aspirates were assessed by FCIP panel in a cohort of 80 patients identified as having CCUS based on next-generation sequencing or cytogenetics from March 2015 to May 2020, with available samples. Flow cytometric assay included CD13/HLA-DR expression pattern on CD34-positive myeloblasts; CD13/CD16 pattern on maturing granulocytic precursors; and aberrant expression of CD2, CD7, or CD56 on CD34-positive myeloblasts. Relevant demographic, comorbidity, and clinical and laboratory data, including the type and extent of genetic abnormalities, were extracted from the electronic health record. Results In total, 17 (21%) patients with CCUS developed MN over the follow-up period (median survival follow-up, 28 months [95% confidence interval, 19-31]). Flow cytometry immunophenotyping abnormalities, including the aberrant pattern of CD13/HLA-DR expression, as detected at the time of the diagnosis of CCUS, were significantly associated with risk of developing MN (hazard ratio, 2.97; P = .006). Additional FCIP parameters associated with the development of MN included abnormal expression of CD7 on myeloblasts and the presence vs absence of any FCIP abnormality. Conclusions A simple FCIP approach that includes assessment of CD13/HLA-DR pattern on CD34-positive myeloblasts can be useful in identifying patients with CCUS at higher risk of developing MN.

Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that were trained on a 10-color CLL MRD panel from treated CLL patients, including DNN trained on the full cohort of 202 patients (F-DNN) and DNN trained on 138 patients with low-event cases (MRD < 1000 events) (L-DNN). A hybrid DNN approach was utilized, with F-DNN and L-DNN applied sequentially to cases. “Ground truth” classification of CLL MRD was confirmed by expert analysis. The hybrid DNN approach demonstrated an overall accuracy of 97.1% (95% CI: 84.7–99.9%) in an independent cohort of 34 unknown samples. When CLL cells were reported as a percentage of total white blood cells, there was excellent correlation between the DNN and expert analysis [r > 0.999; Passing–Bablok slope = 0.997 (95% CI: 0.988–0.999) and intercept = 0.001 (95% CI: 0.000–0.001)]. Gating time was dramatically reduced to 12 s/case by DNN from 15 min/case by the manual process. The proposed DNN demonstrated high accuracy in CLL MRD detection and significantly improved workflow efficiency. Additional clinical validation is needed before it can be fully integrated into the existing clinical laboratory practice.