King, Rebecca L.

Abstract Objectives To summarize cases submitted to the 2019 Society for Hematopathology/European Association for Haematopathology Workshop under the category of myeloid/lymphoid neoplasms with eosinophilia and PDGFRA, PDGFRB, or FGFR1 or with PCM1-JAK2 rearrangements, focusing on recent updates and relevant practice findings. Methods The cases were summarized according to their respective gene rearrangement to illustrate the spectrum of clinical, laboratory, and histopathology manifestations and to explore the appropriate molecular genetic tests. Results Disease presentations were heterogeneous, including myeloproliferative neoplasms (MPNs), myelodysplastic syndromes (MDSs), MDS/MPN, acute myeloid leukemia, acute B- or T-lymphoblastic lymphoma/acute lymphoblastic lymphoma (ALL/LBL), or mixed-lineage neoplasms. Frequent extramedullary involvement occurred. Eosinophilia was common but not invariably present. With the advancement of RNA sequencing, cryptic rearrangements were recognized in genes other than PDGFRA. Additional somatic mutations were more frequent in the FGFR1-rearranged cases. Cases with B-ALL presentations differed from Philadelphia-like B-ALL by the presence of an underlying MPN. Cases with FLT3 and ABL1 rearrangements could be potential candidates for future inclusion in this category. Conclusions Accurate diagnosis and classification of this category of myeloid/lymphoid neoplasms has important therapeutic implications. With the large number of submitted cases, we expand our understanding of these rare neoplasms and improve our ability to diagnose these genetically defined disorders.

Abstract Objectives The 2019 Society for Hematopathology and European Association for Haematopathology Workshop reviewed the spectrum of neoplastic, nonneoplastic, and borderline entities associated with reactive eosinophilia in tissue. Methods The workshop panel reviewed 46 cases covered in 2 workshop sessions. Results The 46 cases were presented with their consensus diagnoses during the workshop. Reactive eosinophilia in lymph nodes and other tissues may be accompanied by or be distinct from peripheral blood eosinophilia. Reactive etiologies included inflammatory disorders such as Kimura disease and IgG4-related disease, which may show overlapping pathologic features and reactions to infectious agents and hypersensitivity (covered in a separate review). Hodgkin, T-cell, and B-cell lymphomas and histiocytic neoplasms can result in reactive eosinophilia. The spectrum of these diseases is discussed and illustrated through representative cases. Conclusions Reactive eosinophilia in lymph nodes and tissues may be related to both nonneoplastic and neoplastic lymphoid proliferations and histiocytic and nonhematolymphoid processes. Understanding the differential diagnosis of reactive eosinophilia and the potential for overlapping clinical and pathologic findings is critical in reaching the correct diagnosis so that patients can be treated appropriately.

Abstract Objectives Pure erythroid leukemia (PEL) is exceptionally rare in the pediatric setting. Four pediatric PEL cases with t(1;16)(p31;q24) NFIA-CBFA2T3 were reported previously. We present a case of an infant with PEL presenting with erythroblastic sarcoma and harboring a novel t(1;8)(p31.3;q21.3) NFIA-RUNX1T1 fusion detected by RNA sequencing and conventional karyotype. Methods Bone marrow (BM) and abdominal mass biopsies from the patient were evaluated with extensive immunohistochemical, flow cytometric, cytogenetic, and molecular studies. Results The patient was a female infant who presented between 2 and 5 months of age with cytopenias and an enlarging abdominal mass. Blasts in the BM and abdominal mass expressed CD71 and CD117 with focal expression of CD43, E-cadherin, epithelial membrane antigen, and hemoglobin A. They were negative for additional myeloid, lymphoid, and nonhematolymphoid markers. These findings were most consistent with PEL and erythroblastic sarcoma. RNA sequencing revealed the novel NFIA-RUNX1T1 fusion. Conclusions Along with the previously reported PELs with NFIA-CBFA2T3 fusions, we describe a subset of PELs that occur in children, that frequently display extramedullary disease, and that harbor rearrangements of NFIA with core binding factor genes. We hypothesize that, together, these cases represent a rare but distinct clinicopathologic group of pediatric PELs with recurrent genetic abnormality.

Abstract Objectives The 2019 Workshop of the Society for Hematopathology/European Association for Haematopathology received and reviewed cases covering the spectrum of mastocytosis and related diseases, including morphologic mimics, focusing on recent updates and relevant findings for pathologists. Methods The workshop panel reviewed 99 cases of cutaneous and systemic mastocytosis (SM) and SM and associated hematologic neoplasms (SM-AHN). Results Despite a common theme of KIT mutation (particularly D816V), mastocytosis is a heterogeneous neoplasm with a wide variety of presentations. This spectrum, including rare subtypes and extramedullary organ involvement, is discussed and illustrated by representative cases. Conclusions In the age of targeted treatment aimed at KIT, the accurate diagnosis and classification of mastocytosis has major implications for therapy and further interventions. Understanding the clinical, pathologic, and genetic findings of mastocytosis is crucial for selecting the proper tests to perform and subsequent arrival at a correct diagnosis in this rare disease.

Abstract Purpose: TNF-related apoptosis inducing ligand (TRAIL) expression by immune cells contributes to antitumor immunity. A naturally occurring splice variant of TRAIL, called TRAILshort, antagonizes TRAIL-dependent cell killing. It is unknown whether tumor cells express TRAILshort and if it impacts antitumor immunity. Experimental Design: We used an unbiased informatics approach to identify TRAILshort expression in primary human cancers, and validated those results with IHC and ISH. TRAILshort-specific mAbs were used to determine the effect of TRAILshort on tumor cell sensitivity to TRAIL, and to immune effector cell dependent killing of autologous primary tumors. Results: As many as 40% of primary human tumors express TRAILshort by both RNA sequencing and IHC analysis. By ISH, TRAILshort expression is present in tumor cells and not bystander cells. TRAILshort inhibition enhances cancer cell lines sensitivity to TRAIL-dependent killing both in vitro and in immunodeficient xenograft mouse models. Immune effector cells isolated from patients with B-cell malignancies killed more autologous tumor cells in the presence compared with the absence of TRAILshort antibody (P < 0.05). Conclusions: These results identify TRAILshort in primary human malignancies, and suggest that TRAILshort blockade can augment the effector function of autologous immune effector cells. See related commentary by de Miguel and Pardo, p. 5546

8550 Background: Waldenström Macroglobulinemia (WM) is a rare lymphoplasmacytic malignancy characterized by the presence of a recurrent point mutation in the MYD88 gene (MYD88L265P) in 80-95% of cases. Patients with MYD88WT genotype comprise a small subset that responds poorly to ibrutinib and other Bruton tyrosine kinase inhibitors. We examined the characteristics and outcome of WM patients with MYD88WT genotype predominantly treated with non-BTK inhibitor based therapies. Methods: Patients with a diagnosis of WM seen at Mayo Clinic, Rochester, between 1996 and 2018 were included. Their characteristics and outcomes were assessed from the time of active disease. Marrow MYD88 genotyping was assessed with an allele specific PCR assay (analytic sensitivity 1%). Categorical and continuous variables were compared using Chi square and Wilcoxon tests, respectively. Time-to-event analyses were performed using Kaplan Meier test. Results: Of 986 patients with active WM, MYD88 genotype data were available in 331 (34 %) patients; 72 (22%) and 260 (78%) patients harbored MYD88WT and MYD88L265P genotypes, respectively. The median follow-up was 5.8 years (95% CI: 5.0-6.5 years) from active WM; 6 years MYD88WT vs 5.4 years for MYD88L265P cohort. Median age was 63 years and 66 years in the MYD88WT and MYD88L265P cohorts, respectively (p = 0.07) with 46% and 53% patients being ≥65 years of age, respectively (p = 0.36). Pre-treatment marrow lymphoplasmacytic (LPL) infiltrate (median 40% for MYD88WT vs 60% for MYD88L265P; p = 0.001) and beta-2 microglobulin (median 3 µg/mL for MYD88WT vs 3.9 µg/mL for MYD88L265P; p = 0.02) were lower in the MYD88WT compared to the MYD88L265P cohort; other laboratory parameters at active disease were comparable. Per IPSSWM prognostic criteria, MYD88WT had fewer patients in the high risk group (18% for MYD88WT vs 42% MYD88L265P; p = 0.03). Patients with MYD88WT had higher likelihood of histological transformation [18% for MYD88WT vs 4% for MYD88L265P; odds ratio 5.8 (95% CI: 2.5-13.5; p < 0.0001)]. Among patients with treatment data available, only 35 (11%) patients received ibrutinib. The 5-year overall survival (OS) from active disease was comparable (85% in MYD88WT vs 82% in MYD88L265P cohort; p = 0.7). Conclusions: MYD88WT genotype in WM is associated with lower marrow LPL infiltration, lower likelihood of high-risk IPSSWM categorization and a higher likelihood of histological transformation in comparison to MYD88L265P mutant subpopulation. MYD88 genotype does not affect the OS from active disease in predominantly non-BTK inhibitor treated patients.

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p&lt;0.01) and D8 (p&lt;0.0001) had significantly worse survival outcomes than the rest of the population. D4 comprised 21% of the MER ndDLBCL replication cohort (cohort 2) with a median event-free survival (mEFS) of 38.2 months and a median overall survival (mOS) of 80.3 months. D8 comprised 5% of the cohort with a mEFS of 7.5 months and a mOS of 12.1 months. The remaining 6 subgroups were standard risk, with mEFS ranging from 82.1 months to not reached, and none reaching mOS. The subgroups were not uniquely defined by previously known molecular classification methods such as COO or double hit signature (DHITsig), nor by clinical risk factors such as age or international prognostic index (IPI). Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p&lt;0.005). Transcriptomic analysis revealed a lower abundance of immune infiltration. D4 was associated with high IPI, with 49% of D4 having IPI&gt;2, compared to 33% of non-D4 with IPI&gt;2 (p&lt;0.05). D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p&lt;0.0001). Transcriptomic analysis revealed low expression of immune response and cytokine signaling pathways, consistent with the low abundance of immune cells in D8. This subgroup consisted of 63% DHITsig positive patients. Although only 20% of all DHITsig positive patients were in D8, these D8 DHITsig patients showed significantly worse survival than non-D8 DHITsig patients (mEFS 11.3 months vs. not reached, p&lt;0.0001). In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Nowakowski:Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.