McPhail, Ellen Darcy

Abstract Patients with immunoglobulin M (IgM) light chain (AL) amyloidosis have a distinct clinical presentation compared with those with non-IgM amyloidosis. We hypothesized that differential immunoglobulin light-chain variable region (IGVL) gene usage may explain the differences in organ involvement, because IGVL usage correlates with organ tropism. IGVL usage was evaluated by mass spectrometry of amyloid deposits (IgM, n = 45; non-IgM, n = 391) and differed across the 2 groups. In the λ family, LV2-08 (13% vs 2%; P < .001) and LV2-14 (36% vs 10%; P < .001) usage was more common in IgM vs non-IgM amyloidosis, whereas LV1-44 (0% vs 10%; P = .02) and LV6-57 (2% vs 18%; P = .004) usage was less common. In the κ family, there was a trend toward higher KV4-01 (11% vs 4%; P = .06) usage in IgM amyloidosis. IGVL usage correlated with disease characteristics/organ tropism. LV2-14 (more common in IgM amyloidosis) has historically been associated with peripheral nerve involvement and lower light chain burden, which were more frequent in IgM amyloidosis. LV1-44 (less common in IgM), associated with cardiac involvement, was less frequent in IgM patients. LV6-57 (less common in IgM) is associated with t(11;14), which was less frequent in IgM patients. In conclusion, IGVL gene usage differs in patients with IgM vs non-IgM amyloidosis and may explain the distinct clinical presentation.

Abstract Aims Cardiac scintigraphy, a non-invasive technique for diagnosing ATTR cardiac amyloidosis, lacks specificity in patients with concomitant monoclonal gammopathy (up to 40% of cases). For these patients, amyloid type is often established by endomyocardial biopsy (EMB), which has clinical risk. This study aimed to investigate the frequency of ATTR in amyloid-positive tendon/synovium, urinary bladder, and prostate biopsies, sites for which prior biopsy specimens might exist for patients suspected of having cardiac amyloidosis, and, when available, determine the amyloid type concordance rate with other anatomic sites and provide clinical data regarding subsequent development of cardiac amyloidosis. Methods and results We queried our reference laboratory database of 19,298 amyloid specimens from myriad anatomic sites typed by mass spectrometry-based proteomics (LC-MS/MS) to investigate the frequency of ATTR amyloid in tendon/synovium, urinary bladder, and prostate. The amyloid type was ATTR in 104/138 (75.4%) tendon/synovium, 173/453 (38.0%) urinary bladder, and 27/81 (33.3%) prostate samples. Of 62 patients with available clinical data, 12 (19%) had bona fide ATTR cardiac amyloidosis prior to/concomitant with the non-cardiac site biopsy. Of the remaining 14 with follow-up, 8 developed bona fide and 2 probable cardiac amyloidosis; at last follow-up 4 had no evidence of cardiac amyloidosis. Fourteen of 16 patients (87.5%) for whom we typed both non-cardiac and cardiac sites had concordant amyloid types. There were 2 discordant cases (prostate = ASem1/heart = AL and urinary bladder = AL/heart = ATTR); only the latter is potentially clinically consequential. Conclusions In patients suspected of having cardiac amyloidosis based on cardiac scintigraphy, LC-MS/MS typing of Congophilic deposits in pre-existing biopsy specimens from non-cardiac sites may help establish the cardiac amyloid type, obviating the need for EMB. However, if the amyloid type identified in the non-cardiac site is not in keeping with other clinical features, then EMB for typing the cardiac amyloid might be indicated.

Anaplastic large cell lymphomas (ALCLs) are broadly classified into ALK-positive and ALK-negative. ALK-negative ALCL is composed of DUSP22-rearranged, TP63-rearranged, and triple-negative cases. While lymphoid enhancer–binding factor (LEF1) plays a crucial role in T-cell maturation, limited data exist on its expression in T-cell lymphomas, including ALCL. We characterized the expression of LEF1 in ALCL by immunohistochemistry. LEF1 nuclear expression in the neoplastic cells was graded as negative (0), weak (1+), intermediate (2+), or strong (3+), with the percentage of LEF1-positive neoplastic cells recorded. A total of 45 ALCL cases were evaluated, of which 16 were DUSP22-rearranged. About 93.8% (15/16) DUSP22-rearranged cases showed strong expression of LEF1 in >75% tumor cells, compared with 3.4% (1/29) non–DUSP22-rearranged ALCL (P<0.0001). The striking association of LEF1 protein overexpression with DUPS22 rearrangement in ALCL was further confirmed by a gene expression profiling study which revealed significantly higher LEF1 expression in DUSP22-rearranged ALCL compared with other ALCL subtypes (P=0.0001). Although LEF1 is a nuclear mediator of the Wnt/β-catenin pathway, CTNNB1 RNA and protein levels were not overexpressed in LEF1-positive cases, suggesting the LEF1 overexpression in ALCL may not be involved in the Wnt/β-catenin pathway. The strong and uniform LEF1 expression pattern has a high positive predictive value (93.8%) and high negative predictive value (96%) for DUSP22 rearrangement in ALK-negative ALCL. The combination of characteristic morphologic and molecular features of DUSP22-rearranged cases with the high LEF1 expression further emphasizes that DUSP22-rearranged ALCL represents a distinct clinicopathologic subset of ALCL.

Introduction: Anaplastic large cell lymphomas (ALCLs) are CD30-positive T-cell lymphomas classified into ALK-positive and ALK-negative. The ALK-negative ALCLs are a genetically heterogeneous group that includes DUSP22-rearranged, TP63-rearranged and triple-negative cases. The DUSP22-rearranged ALCLs characteristically grow in a diffuse sheet-like pattern and are composed of intermediate-sized monomorphous hallmark cells, admixed with smaller cells with prominent nuclear indentations and occasional central nuclear pseudo inclusions (King RL et al, Am J Surg Pathol. 2016). Further, DUSP22-rearranged ALCL is a molecularly distinct subset of ALCL with a unique immunogenic phenotype that portends a favorable prognosis (Luchtel RA et al. Blood 2018). Lymphoid enhancer binding factor (LEF1) is a crucial nuclear mediator of the Wnt/ β-catenin pathway and a transcription factor involved in T-cell maturation. However, limited data exists on the association of LEF1 in T-cell lymphomas, including ALCL. The aims of this study were to explore LEF1 expression by immunohistochemistry in different subsets of ALCLs and to analyze the association of Wnt/ β-catenin pathway in this cohort. Methods: After approval by the institutional review board, we screened pathology reports from January 1, 1995 to December 31, 2019 and identified patients with a diagnosis of ALCL using our institutional database. Details on fluorescence in situ hybridization studies in ALK-negative cases were documented. Immunostaining for LEF1 and β catenin was performed on all cases where formalin fixed paraffin embedded tissue was available. The presence or absence of β catenin nuclear staining was recorded. LEF1 nuclear staining in the neoplastic ALCL cells was graded as negative (0), weak (1+), intermediate (2+) or strong (3+) and the percentage of LEF1-positive neoplastic cells was detailed (Figure 1). LEF1 scoring was performed by manual counting of the stained nuclei out of all tumor nuclei by two pathologists (A.R. and M.S.) and the grading was reviewed by another pathologist (P.J.K.). Results: We evaluated 45 ALCL cases, including 41 (91.1%) ALK-negative and 4 (8.9%) ALK-positive ALCLs. Among the ALK-negative ALCLs, there were16 DUSP22-rearranged, and 25 non-DUSP22-rearanged (7 TP63-rearranged and 18 triple-negative) cases. Fifteen (94%) of 16 DUSP22-rearranged ALCLs showed strong expression of LEF1 in &gt;75% tumor cells (Figure 2). In contrast, this strong and uniform LEF1 overexpression was only detected in 1 of 29 (3%) of the remaining ALCL cases (P &lt;.0001) (Table 1). We previously characterized a distinctive molecular signature in ALK-negative ALCLs with DUSP22-rearrangements using gene expression microarray analysis on frozen ALCL tissue samples (Luchtel RA et al. Blood 2018). On examining LEF1 gene expression in this previously published dataset, DUSP22 -rearranged cases had significantly higher LEF1 expression than any other genetic subtype (triple-negative, TP63-rearranged, or ALK-positive). Overall, mean LEF1 expression was 6.3-fold higher in ALCLs with DUSP22 rearrangements than in ALCLs without DUSP22-rearrangements (P=0.0001). LEF1 is a coactivator of the Wnt/β-catenin pathway; therefore, it is reasonable to hypothesize the overexpression of LEF1 may promote Wnt/β-catenin signaling. However, all LEF1-positive cases were negative for β-catenin by immunohistochemistry, suggesting that LEF1 overexpression is independent of the Wnt/β-catenin pathway signaling in these cases. Conclusions: There is a striking association between high LEF1 expression and DUSP22-rearrangements in ALCLs. The strong and uniform LEF1 expression pattern has a high positive predictive value (94%) and high negative predictive value (96%) for DUSP22- rearrangement in ALK-negative ALCLs (Table 2). The combination of characteristic morphologic and molecular features of DUSP22-rearranged cases with the unique LEF1 expression further emphasizes that DUSP22-rearranged ALCL represents a distinct clinicopathologic subset of ALCL. Disclosures No relevant conflicts of interest to declare.

AimsTo characterise the clinicopathological features of amyloidosis due to EGF‐containing fibulin‐like extracellular matrix protein 1 (EFEMP1), a newly described amyloid type.Methods and resultsWe identified cases by searching the Mayo Clinic amyloid liquid chromatography and tandem mass spectrometry typing database for specimens with the universal amyloid signature proteins, abundant EFEMP1 spectra and absence of other specific amyloid precursor proteins. We also developed an immunohistochemical stain for EFEMP1 applicable to formalin‐fixed tissue sections and performed electron microscopy in one case. We identified 33 specimens from 32 patients with EFEMP1 amyloid. Most patients were female (91%) with a mean age of 75 years, and most specimens (94%) were from the bowel. EFEMP1 amyloid was incidentally identified in specimens biopsied/resected for a variety of clinical indications. In bowel specimens, EFEMP1 amyloid involved blood vessels and interstitium of the lamina propria, submucosa and/or muscularis propria. Although the EFEMP1 deposits were weakly to moderately Congo red‐positive with absent to weak birefringence, they were strongly positive for EFEMP1 by immunohistochemistry, had the characteristic fibrillar ultrastructure of amyloid and were readily identified by mass spectrometry.ConclusionsEFEMP1 amyloid is a recently described novel amyloid type that predominantly affects the bowel of elderly females. Because EFEMP1 amyloid is only weakly Congo red‐positive, it may be overlooked without a high index of suspicion. However, its characteristic microanatomical distribution is highlighted by immunohistochemistry and its identity is readily confirmed by mass spectrometry. Based on its distinctive features, we propose that EFEMP1 amyloidosis be considered a new amyloid type.

Key Points An incidental histologic diagnosis of DLBCL was identified during temporary interruption of ibrutinib treatment in patients with CLL. In contrast to an aggressive clinical course typical of Richter transformation, these patients responded to reinitiation of ibrutinib alone.

Abstract Systemic amyloidosis is a rare protein misfolding and deposition disorder leading to progressive organ failure. There are over 15 types of systemic amyloidosis, each caused by a different precursor protein which promotes amyloid formation and tissue deposition. Amyloidosis can be acquired or hereditary and can affect various organs, including the heart, kidneys, liver, nerves, gastrointestinal tract, lungs, muscles, skin and soft tissues. Symptoms are usually insidious and nonspecific resulting in diagnostic delay. The field of amyloidosis has seen significant improvements over the past decade in diagnostic accuracy, prognosis prediction and management. The advent of mass spectrometry‐based shotgun proteomics has revolutionized amyloid typing and has led to the discovery of new amyloid types. Accurate typing of the precursor protein is of paramount importance as the type dictates a specific management approach. In this article, we review each type of systemic amyloidosis to provide the practitioner with practical tools to improve diagnosis and management of these rare disorders.

Introduction: The clinical presentation and frequency of organ involvement are quite distinct in patients with IgM amyloidosis compared to patients with non-IgM amyloidosis, with less heart involvement and more nerve, soft tissue and lung involvement in IgM patients. It has been observed that immunoglobulin light chain variable region (IGVL) gene and gene family of the plasma cell clone impact phenotypic manifestations in AL amyloidosis, particularly organ tropism. (Kourelis et al. Blood. 2017;129). Given the variability in clinical features amongst patients with IgM vs. non IgM AL amyloidosis, we hypothesized that IGVL gene usage would be different between these two groups. Methods: Patients with newly diagnosed IgM and non-IgM amyloidosis seen at our institution from 01/2006 to 12/2015 were identified. IGVL gene usage was assessed by liquid chromatography tandem mass spectrometry (LC/MS/MS) as previously described. (Vrana et al Blood 2009;Dasari et al J of Proteome Res; Kourelis et al. Blood. 2017). Mass spectrometry data for IGVL analysis was obtained from clinically available data. LC/MS/MS was performed on available archival specimens when such data was not available clinically. Results: Patients with newly diagnosed amyloidosis who had sufficient mass spectrometry data on fat aspirate or tissue biopsy for IGVL gene usage identification were included (IgM AL, N=44 and Non-IgM AL, N=391). Clinical Features: As expected, differences were noted in baseline characteristics of patients with IgM vs. non-IgM AL amyloidosis. Patients with IgM amyloidosis were older (68 vs. 64 years, p=0.04) and more likely to have kappa as the involved light chain (34% vs. 25%, p=0.2), though the difference did not reach statistical significance. IgM amyloidosis patients had lower difference in involved and uninvolved free light chains (dFLC) (15.5 vs. 25.8 mg/dL, p=0.01) and lower NTProBNP levels (median: 1987 vs. 2655 pg/mL, p=0.02) compared with non-IgM amyloid patients. Presence of t(11;14) was less common by FISH (30% vs. 48%, p=0.1), though the difference did not reach statistical significance. Patterns of organ involvement were different, with heart involvement being less frequent in IgM patients (64% vs. 76%), while peripheral nerve (30% vs. 18%, p=0.05), soft tissue (41% vs. 21%, p=0.005) and lung involvement (7% vs. 3%, p=0.1) were more common in patients with IgM vs. non-IgM AL amyloidosis. There was no difference in liver (9% vs. 15%), kidney (50% vs. 51%) GI (23% vs. 25%) or autonomic nervous system involvement (18% vs. 14%). IGVL Gene Usage: As shown in Figure 1, IGVL usage differed across the two groups. In the lambda group, LV2-08 (14% vs. 2%, p<0.001) and LV2-14 (36% vs. 10%, p<0.001) were more common in IgM amyloid patients, while LV1-44 (0% vs. 10%, p=0.02) and LV6-57 (2% vs. 18%, p=0.004) were less common in the IgM group. There was also a trend towards less frequent involvement of LV3-01 (2% vs. 12%, p=0.07) in IgM amyloid patients. In the kappa group, KV4-01 (11% vs. 4%, p=0.04) was more common in patients with IgM amyloidosis. Correlation Between IGVL Gene usage and Clinical Features: IGVL gene usage correlated with disease characteristics and differences in the clinical presentation of patients across the two groups. LV2-14 was more commonly seen in IgM amyloid patients (above). It has been previously associated with a higher frequency of peripheral nerve involvement (Kourelis et al. Blood 2017), which was more frequent in IgM group. LV2-14 is also associated with low dFLC levels, which were noted to be lower in the IgM AL cohort. LV1-44 has been associated with higher likelihood of cardiac involvement. LV1-44 was seen less frequently in the IgM amyloid patients, who also had lower rates of cardiac involvement. LV6-57 has been historically associated with higher rates of t(11;14). It was less common in IgM AL patients, who also had lower rates of t(11;14). Conclusions: IGVL gene usage differed significantly in patients with IgM vs. non-IgM amyloidosis, correlating with differences in disease characteristics and organ involvement. This is the first study to our knowledge to report IGVL gene usage differences in IgM vs. non-IgM AL amyloidosis. Differences in IGVL gene usage may explain the distinct clinical presentation seen in IgM amyloidosis, the biology of which has so far remained poorly understood. *SS and SD contributed equally Disclosures Dasari: The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Dispenzieri:Alnylam: Research Funding; Celgene: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Consultancy; Akcea: Consultancy; Intellia: Consultancy. Murray:The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding. Gertz:Prothena: Honoraria; Alnylam: Honoraria; Ionis: Honoraria; Janssen: Honoraria; Spectrum: Honoraria, Research Funding; Celgene: Honoraria.

Introduction: Current classifications of low-grade B-cell lymphomas (LGL), including splenic marginal zone lymphomas (SMZL), nodal marginal zone lymphomas (NMZL), and Lymphoplasmacytic lymphomas (LPL) are based on a mixture of clinical features and morphologic, immunophenotypic, and genetic findings from the tumor biopsy specimen. While this approach to classification makes pathologic diagnosis more precise, the corresponding clinical impact for the timing and choice of treatment is limited, and differentiating between cases can be challenging. Although LGL are considered indolent and the 10-year overall survival (OS) is about 80%, 70% of cases will eventually require treatment and approximately 30% of patients display a more aggressive phenotype and have a poor prognosis. Consequently, further investigations into the driving genetic, biological, and immune mechanisms of LGL are essential for early identification of high-risk patients and design of personalized treatments. Materials and Methods: RNA-seq was performed on 63 newly diagnosed LGL patient samples from the Mayo Clinic/University of Iowa Lymphoma SPORE: SMZL (N=48), NMZL (N=6), LPL (N=5), MALT (N=2), and BCL (N=2) as well as 5 normal memory B cell controls (CD19+CD27+). For identification of biologic clusters, filtered RNA-seq data was analyzed using the Non-negative Matrix Factorization (NMF) clustering tool from the Broad Institute against the normal samples. Each cluster was analyzed for differential gene expression. This analysis generated cluster-specific T values for each gene. Genes that were significantly associated with a cluster (FDR<0.05) were analyzed for genetic ontology. Cibersort was used to deconvolute the tumor microenvironment (TME). We performed whole exome sequencing (WES) analyses on 60/63 matched LGL to characterize mutations and copy number alterations. Results: WES revealed the presence of previously reported mutations in genes such as MYD88, SPEN, NOTCH, and KLF2 as well as copy number alterations 7q31.2, BCL6, TNFAIP3, and BCL2. NMF clustering of RNA-seq data resulted in a best fit of 5 clusters, named LGL1 through LGL4 and "Normals". Pathologic subtypes were not exclusive to a specific cluster. Differential RNA expression analysis resulted in gene sets that were differentially expressed in each cluster. A cluster signature of the top 1% of associated genes (N=94) was created for each. LGL1 genes were associated with higher BCR signaling. LGL2 was characterized by regulatory dysfunction, particularly concerning mitochondrial integrity. LGL3 genes were associated with high TME and NOTCH and STAT signaling components. Specifically, LGL3 was associated with high CD8+ T-cell and M2 macrophage TME presence. LGL4 was distinguished by the presence of aggressive genetic programs related to B-cell activation such as NF-kB and IRF4. Consequently, LGL4 patients had significantly poorer rates of event-free survival (EFS) (P=0.029) and OS (P=0.006) when compared against the other groups. The top 1% gene signature (N=94) was next validated against an independent cohort of 84 LGL samples with gene expression data (SMZL [N=34], NZML [N=24], and LPL [N=24]). A similar cluster pattern emerged, with pathological subtypes distributing across LGL classifications based on respective transcriptomic signatures. Association of DNA variants within each cluster was also analyzed, but due to the low frequency of genomic variants detected in LGL, nothing was significantly associated. However, we did see an enrichment of BCL2, BCL6, and TNFAIP3 alterations in LGL4 and an overall lower driver gene mutation frequency in LGL2. Conclusions: Gaining a greater understanding of LGL based on their genetic, biologic, and immune profiles will establish a valuable platform for clinicians to identify high-risk cases and make better therapeutic decisions. Using a large cohort of well-annotated cases we identify novel clusters of LGL that present unique genomic and clinical profiles. Our study lays the groundwork for a precision therapy approach in LGL in which DNA or RNA profiles can be used to identify patients early in treatment who may not benefit from the current standard of care and who would benefit from closer monitoring and targeted agents. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding.