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Background: Anaplastic large cell lymphomas represent a group of T-cell lymphomas that have long been broadly classified by the presence or absence of ALK fusion proteins (ALK+ or ALK−, respectively). However, recent discovery of extensive additional molecular heterogeneity has both promoted refinement of disease classification and uncovered new opportunities for biomarker-guided targeted therapy. In addition to recognition of a new genetic subtype, ALK− ALCL with DUSP22 rearrangement (R), a new molecular classification has been proposed based on the recent discovery of 2 overarching molecular types of ALCL, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. BCL2 is a mitochondrial membrane protein that functions to inhibit the actions of pro-apoptotic proteins such as BAX and BAK and can be targeted clinically by BCL2 small molecule inhibitors. BCL2 has been reported to be expressed more frequently in ALK− ALCL than in ALK+ ALCL, but its expression in the context of current molecular findings is unknown. Methods: ALCLs were centrally reviewed and classified by current WHO/ICC criteria. BCL2 was performed using IHC and scored as positive if ≥30% of tumor cells showed staining. DUSP22-R was assessed by fluorescence in situ hybridization (FISH) using a breakapart probe set and a dual-color, dual-fusion (D) probe set for the most common DUSP22 translocation, t(6;7)(p25.3;q32.3). pSTAT3Y705 IHC, which is >90% accurate for Type I/II assignment based on gene expression, was performed and scored based on a previously validated cut-off of ≥30%. Results: We evaluated 217 ALCLs (148M/69F; age: mean, 57 y; range, 5-93 y), including 55 systemic ALK+ ALCLs, 127 systemic ALK− ALCLs, and 35 primary cutaneous (pc) ALCLs. Overall, 86/217 cases were positive for BCL2 (40%). BCL2 was positive in 67/127 ALK− ALCLs (53%) and 15/35 pcALCLs (43%; P=NS), but in only 4/51 ALK+ (7%; P<0.0001). DUSP22-R was present in 90 cases (63 ALK− ALCL and 27 pcALCL). BCL2 was positive in 57/90 cases with DUSP22-R (63%) compared to 29/127 without DUSP22-R (23%; P<0.0001). Among 85 cases with DUSP22-R and informative D-FISH testing, BCL2 was positive in 24/36 cases with t(6;7)(p25.3;q32.3) (67%) and 31/49 cases without t(6;7)(p25.3;q32.3) (63%; P=NS). Among 213 cases with informative pSTAT3Y705 staining, BCL2 was positive in 13/88 pSTAT3+ cases (Type I, 15%) and 70/125 pSTAT3− cases (Type II, 56%; P<0.0001). Conclusions: We identified a complex pattern of BCL2 expression across ALCLs that strongly correlates with a recently introduced molecular classification based on gene expression profiling. While our findings are in keeping with the previously reported low expression in ALK+ ALCL, we found that the higher expression in both systemic ALK− ALCL and pcALCL is particularly associated with DUSP22-R and the absence of pSTAT3Y705 expression (Type II ALCLs). The discovery of 2 overarching types of ALCL associated with the presence or absence JAK-STAT3 pathway activation represents a new conceptual framework for classifying ALCL and understanding its therapeutic vulnerabilities. Of note, previous data demonstrated that Type II ALCLs are enriched for epigenetic alterations and particularly overexpress the histone methyltransferase EZH2. Correlation between BCL2 expression and EZH2 expression also is observed in other lymphomas such as diffuse large B-cell lymphoma, where dual targeting with EZH2 and BCL2 inhibitors has been proposed. Thus, our findings provide further rationale for stratifying Type I and Type II ALCLs and identify BCL2 as a differentially expressed oncoprotein that might represent a target for precision therapy approaches.

Background: Anaplastic large cell lymphomas (ALCLs) represent a heterogeneous group of T-cell lymphomas that currently are classified by the presence or absence of ALK tyrosine kinase (TK) fusion genes (ALK+ or ALK−) and clinical presentation (systemic, cutaneous, or breast implant-associated). Two overarching molecular types of ALCL recently were discovered, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. Fusions involving non-ALK TK genes occur in some ALK− cases, but the fusion landscape of ALCL remains incompletely characterized. Methods: Expert consensus pathology review was conducted in the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). RNAseq was performed and fusions were identified using FusionCatcher. Here, we focused on recurrent in-frame coding fusions. Previously unreported fusions were validated by RT-PCR. Type I/II was assigned using a previously validated gene expression-based model. Genes with adjusted (adj) P<0.05 were considered differentially expressed. Overall survival (OS) was assessed for systemic ALCL, when available. Results: We evaluated 379 ALCLs (229M/150F; mean age, 56 y). Of 199 candidate fusions (excluding reciprocal events), 28 were recurrent and passed quality metrics. At least 1 of these 28 fusions was present in 150 cases (40%). ALK fusions were present in 106 ALK+ ALCLs (all Type I; P<0.0001). Of these, NPM1::ALK was seen in 68/106 (64%). Alternate partners (X::ALK) included ATIC (N=24) and CTLC, COL1A2, MSN, MYH9, RNF213, SATB1, TFH, TPM3, and TRAF1 (1-3 cases each). X::ALK was associated with older age (mean, 52 y) than NPM1::ALK (32 y; P<0.0001) and showed relative overexpression of 49 genes, including multiple activators of small GTPases such as CGNL1 (FC, 5.6; Padj=2.4×10-6), SRGAP1, ALS2, DOCK1, and NCKAP1. ALK expression was similar in cases with NPM1::ALK and X::ALK and there was no significant difference in OS. Non-ALK TK fusions were seen in 17/273 ALK− cases (6%), including TYK2 (N=9); JAK2 (N=6); and ROS1 (N=2). All were Type I ALCLs (P<0.0001). ALK− cases with non-ALK TK fusions overexpressed 38 genes compared to ALK− cases without TK fusions, including cytokines involved in the IL17 signaling pathway such as CXCL6 (FC, 12.5; Padj=4.2×10-3), CXCL1, and CSF3. We then assessed the relationship between ALK− cases with non-ALK TK fusions and ALK+ cases by separately comparing each subset to ALK− cases without TK fusions. FC values for ALK− cases with non-ALK TK fusions were significantly correlated with FC values for ALK+ ALCLs over the set of expressed genes (R=0.54; P<0.0001). At a median follow-up of 32 months, 0/4 systemic ALCL patients with non-ALK TK fusions had died, while 30% of those with ALK− ALCL without TK fusions had died, but this was not statistically significant (P=0.26). The other 13 ALK− cases with non-ALK TK fusions were either localized ALCLs or did not have outcome data available. Of 20 TP63 fusions, 17 (85%) were TBL1XR1::TP63 and 3 were X::TP63. As previously reported, TP63 fusions were associated with poor OS (median OS, 10 months; not reached for other ALK− ALCLs; P=0.007). Four ALCLs had a novel NCL::UBTF fusion involving genes encoding the nucleolar proteins nucleolin and nucleolar (upstream binding) transcription factor-1. Four cases each had PARG::BMS1 and TNK1::GPS2 (the latter seen only in Type I ALCLs). Conclusions: This large consortium-based analysis of coding fusions further elucidates the molecular distinction between Type I and Type II ALCLs. Both ALK and non-ALK TK fusions were seen exclusively in Type I ALCLs, which we have shown previously are enriched for JAK-STAT3 pathway genes and correlate strongly with pSTAT3Y705 positivity by immunohistochemistry. These fusions were not identified in Type II ALCLs, which previous data suggest are associated with epigenetic alterations. ALK+ ALCLs with NPM1::ALK and X::ALK showed differences in gene expression, suggesting biological differences. Furthermore, ALCLs with non-ALK TK fusions shared gene expression features with ALK+ ALCL. Clinical fusion detection could enhance molecular classification of Type I and Type II ALCLs and may guide precision therapy.

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.

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).

7533 Background: Maintenance treatment (rx) with lenalidomide (R) improves overall survival (OS) in multiple myeloma (MM). We report a retrospective cohort of patients (pts) with second primary B-cell acute lymphoblastic leukemia (ALL) diagnosed after R-rx for MM. Methods: We included pts who were exposed to at least 1 yr of R for MM rx prior to ALL dx. After approval by institutional review board, data were collected by chart review at participating centers using Redcap. OS was the primary endpoint. We used BlueSky statistics v10.3.1 for analysis. Results: Analysis included 33 ALL pts diagnosed from 2014-2022 at 7 sites. 52% pts were female. Median follow up was 9.6 yrs (95%CI,7.6-11.8) from MM dx and 3.2 yrs (CI, 2.1-4.7) from ALL dx. Pt characteristics at MM dx are given in Table. Induction rx included: proteasome inhibitor (PI) +immunomodulatory drug (IMiD) (43%), PI+alkylator (24%), IMiD (18%) & others (15%). 50% pts received autologous stem cell transplantation (ASCT). Median OS from MM dx was 11.2 yrs (CI, 8.9-NR). Time from MM dx and initial R rx to ALL dx were 5.0 yrs (range, 2.4-18.5) and 4.4 yrs (2.2-10.6) respectively. Median duration of R exposure before ALL dx was 3.8 yrs (1.6-10.4). Table shows characteristics of pts at B-ALL dx and genetic classification based on karyotyping (n=30), ALL FISH (n=28), chromosomal microarray (n=5) and next-generation sequencing (n=5). 31 pts received ALL-directed rx while 1 pt was initially managed with close observation as the pt attained CR on stopping R ( Lakshman et al; ASH 2019 ). Rx was unknown in 1 pt. Initial rx included: hyper-CVAD+/- rituximab- 49%, pediatric inspired protocols-18%, reduced intensity rx for frail pts- 21% and others- 12%. Response to first line rx were: CR- 56%, CRi- 28%, induction failure- 16%. 32% pts attained measurable residual disease (MRD) negativity. 48% pts underwent SCT for ALL. 14 pts died during follow up. 3-yr OS from ALL-dx was 50% (CI, 33%-76%). 3 pts required rx for MM after ALL diagnosis (2 had allo-SCT for ALL before MM relapse). Conclusions: Most pts had standard risk MM and were exposed to R for several years prior to dx of B-ALL, characterized by high incidence of hypodiploid karyotype; IKZF1 loss and TP53 deletions/mutations were detected in a subset of pts.MRD-adapted timely discontinuation of R in standard risk MM may decrease risk of second primary ALL. [Table: see text]