Kern, Wolfgang

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

Abstract Clinical flow cytometry laboratories are facing rising test volumes, greater assay complexity, and increasing requirements for quality control and assay validation. In response, the International Clinical Cytometry Society (ICCS) conducted a workload survey in early 2023 to gather updated information on assay volumes, complexity, staffing, and technology. Data analysis focused on identifying correlations between length of time to introduce new assays and other factors as a means to gain insight about laboratories that seem to be either adapting or struggling. Flow cytometry assays were categorized into 3 levels of technical/interpretative complexity: high (e.g., measurable/minimal residual disease (MRD assays)), moderate (e.g., leukemia/lymphoma assays (Assays L&L ), excluding MRD assays), and low (e.g., CD4 count). Annual assays per staff member were calculated according to staff involved in case sign‐out (Staff Signout ) or other laboratory operations (Staff LabOps ). Respondents were from 101 laboratories in the United States (69.3%), Canada (4.0%), and other countries (26.7%). Low, moderate, and high technical/interpretative complexity assays were performed in 85.1%, 97.0%, and 47.5% of all laboratories, respectively. Median annual total assays (Assays Total ) per laboratory were 3515 and, based on complexity, were 1518.5 (low), 1808.8 (moderate), and 350 (high). Among all laboratories, the median time (interquartile range) to introduce new Assays L&L was 6 mos. (4–12 mos.), to introduce MRD assays was 11 mos. (5–12 mos.), and to validate/go‐live with new cytometers was 8 mos. (4–12 mos.); these times positively correlated with each other. This study confirmed significantly increased workload since the prior ICCS 2013 workload survey with a concurrent decrease in Staff LabOps . Faster introduction of new assays correlated with other successes, including quicker validation of and going live with new cytometers. Among all laboratories, those that performed myeloid MRD assays versus those that did not were also found to be faster to introduce new assays. The need for sufficient staffing has been emphasized because laboratories with both higher annual volumes of myeloma MRD assays and higher ratios of Assays Total per Staff LabOps were slower to introduce new assays. “Lack of staff and/or time dedicated or protected for assay development” and, more generally, “staff number” were the most commonly identified major barriers for new assay development, with the former specifically linked to slower introduction of new assays among all laboratories.

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.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P&lt;.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P&lt;.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P&lt;.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P&lt;.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

AbstractThe Clinical and Laboratory Standards Institute (CLSI) H62—Validation of Assays Performed by Flow Cytometry guideline, released in 2021, provides recommendations for platform workflow and quality system essentials, instrument setup and standardization, assay development and optimization and fit‐for‐purpose analytical method validation. In addition, CLSI H62 includes some recommendations for the validation strategies after a validated flow cytometric method has been modified. This manuscript builds on those recommendations and discusses the impact of different types of assay modifications on assay performance. Recommendations regarding which validation parameters to evaluate depending on the type of modification are provided. The impact of assay modification on the assay's intended use is discussed. When recommending minor deviations from the CLSI H62 process for a laboratory‐initiated assay revision (e.g., specimen numbers for sensitivity, specificity, or precision studies), a rationale based on expert opinion is provided with the understanding that not every laboratory, assay type, and circumstance can be comprehensively addressed in this paper. These recommendations are meant as a practical recommendation and are not intended to be restrictive, prescriptive, or understood as necessarily sufficient to meet every specific requirement from regulatory bodies (e.g., FDA or New York State Department of Health).

Abstract Transcriptome sequencing has identified multiple subtypes of B-progenitor acute lymphoblastic leukemia (B-ALL) of prognostic significance, but a minority of cases lack a known genetic driver. Here, we used integrated whole-genome (WGS) and -transcriptome sequencing (RNA-seq), enhancer mapping, and chromatin topology analysis to identify previously unrecognized genomic drivers in B-ALL. Newly diagnosed (n = 3221) and relapsed (n = 177) B-ALL cases with tumor RNA-seq were studied. WGS was performed to detect mutations, structural variants, and copy number alterations. Integrated analysis of histone 3 lysine 27 acetylation and chromatin looping was performed using HiChIP. We identified a subset of 17 newly diagnosed and 5 relapsed B-ALL cases with a distinct gene expression profile and 2 universal and unique genomic alterations resulting from aberrant recombination-activating gene activation: a focal deletion downstream of PAN3 at 13q12.2 resulting in CDX2 deregulation by the PAN3 enhancer and a focal deletion of exons 18-21 of UBTF at 17q21.31 resulting in a chimeric fusion, UBTF::ATXN7L3. A subset of cases also had rearrangement and increased expression of the PAX5 gene, which is otherwise uncommon in B-ALL. Patients were more commonly female and young adult with median age 35 (range,12-70 years). The immunophenotype was characterized by CD10 negativity and immunoglobulin M positivity. Among 16 patients with known clinical response, 9 (56.3%) had high-risk features including relapse (n = 4) or minimal residual disease &gt;1% at the end of remission induction (n = 5). CDX2-deregulated, UBTF::ATXN7L3 rearranged (CDX2/UBTF) B-ALL is a high-risk subtype of leukemia in young adults for which novel therapeutic approaches are required.

Abstract Lineage-ambiguous leukemias are high-risk malignancies of poorly understood genetic basis. Here, we describe a distinct subgroup of acute leukemia with expression of myeloid, T lymphoid, and stem cell markers driven by aberrant allele-specific deregulation of BCL11B, a master transcription factor responsible for thymic T-lineage commitment and specification. Mechanistically, this deregulation was driven by chromosomal rearrangements that juxtapose BCL11B to superenhancers active in hematopoietic progenitors, or focal amplifications that generate a superenhancer from a noncoding element distal to BCL11B. Chromatin conformation analyses demonstrated long-range interactions of rearranged enhancers with the expressed BCL11B allele and association of BCL11B with activated hematopoietic progenitor cell cis-regulatory elements, suggesting BCL11B is aberrantly co-opted into a gene regulatory network that drives transformation by maintaining a progenitor state. These data support a role for ectopic BCL11B expression in primitive hematopoietic cells mediated by enhancer hijacking as an oncogenic driver of human lineage-ambiguous leukemia. Significance: Lineage-ambiguous leukemias pose significant diagnostic and therapeutic challenges due to a poorly understood molecular and cellular basis. We identify oncogenic deregulation of BCL11B driven by diverse structural alterations, including de novo superenhancer generation, as the driving feature of a subset of lineage-ambiguous leukemias that transcend current diagnostic boundaries. This article is highlighted in the In This Issue feature, p. 2659