Citations

Introduction: Although endomyocardial biopsy is the gold standard, echocardiography, and Cardiac Magnetic Resonance (CMR) play a pivotal role in the diagnosis of cardiac amyloidosis. In this systematic review, we aim to estimate the diagnostic test accuracy (DTA) of CMR and echocardiography for cardiac amyloidosis. Methods: As part of the American Society of Hematology guidelines on the diagnosis of Amyloidosis, we searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception till January 2024 for relevant studies. Two reviewers independently performed title and abstract screening and full-text article screening on LASER Al and extracted relevant data using a piloted Excel sheet. We performed statistical pooling using Stata 18.0 software. We evaluated risk of bias using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of the evidence. We report diagnostic test accuracy as sensitivity (95% confidence interval) and specificity (95% confidence interval). Results: After screening 29,237, we included 9 studies assessing DTA of late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) using an endomyocardial biopsy (EMB) as a reference test in patients with amyloidosis (light chain and ATTR). The majority of studies didn't report on the type of amyloidosis where only 2 out of 9 were in patients with light chain amyloidosis. The pooled estimates for sensitivity and specificity of LGE are 0.95 (0.88. 0.98), and 0.81 (0.60, 0.92), respectively. We did a sensitivity analysis and included only LGE suggestive for amyloidosis which didn't change sensitivity but led to an increase in specificity for amyloidosis to 0.87 (0.76, 0.94), respectively. The pooled estimates were based on moderate certainty in the evidence. For 2-dimensional echocardiography, we addressed DTA for different echocardiographic findings and we did not limit the reference to EMB. A panel of experts judged whether the reference was acceptable or not. Acceptable reference was EMB alone or a combination of extracardiac biopsy plus cardiac imaging (CMR or echocardiography) with or without positive cardiac biomarkers (troponin or BNP or NT-proBNP). Six studies (n=2063) addressed interventricular septum thickness (IVS), sensitivity was 0.77 (0.69, 0.84) and specificity was 0.71 (0.60, 0.81). 4 out of 6 studies used 12 mm as a cutoff while the other 2 studies used 13 mm as a cutoff. Only 2 studies (n=1667) assessed the DTA of posterior wall thickness (PWT), with sensitivity ranging from 0.76 to 1.00, and specificity ranging from 0.84 to 0.89. Six studies (n=438) addressed diastolic dysfunction (grade 2,3), with a sensitivity of 0.71 (0.40, 0.90), and specificity of 0.75 (0.64, 0.84). 7 studies (n=472) reported DTA of only diastolic dysfunction grade 3. When compared to diastolic dysfunction (grade 2,3, sensitivity decreases to 0.42 (0 28. 0.58) and specificity increases to 0.89 (0.83. 0.94). For E/A ratio, only 2 studies (n=127) were included. Sensitivity ranged from 0.45 to 0.65, and specificity ranged from 0.85 to 0.98. Four studies (n=1596) yielded a sensitivity of 0.86 (0.65, 0.95) and specificity of 0.76 (0.55, 0.89) of left ventricular global longitudinal strain (GLS). All except one used -17% as a cutoff for GLS. For apical sparing on GLS, pooled estimates from 8 studies (n=2253) led to a sensitivity of 0.72 (0.64, 0.78) and specificity of 0.78 (0.64, 0.88). There was a variation of cutoff across studies. The certainty of evidence for echocardiographic findings was low to moderate. Conclusion: CMR is a more accurate test with better performance compared to echocardiography.

Introduction: Light chain (AL) amyloidosis is a clonal plasma cell disorder characterized by tissue deposition of misfolded immunoglobulin light chains in the form of amyloid, leading to organ dysfunction. Halting the production of the amyloidogenic free light chains by achieving a deep hematologic response (HemR) is key to improving organ function, however, variation in organ responses exists. In this work, we investigated variables associated with liver response (LivR) in a large cohort of patients with AL amyloidosis. Methods: Retrospective study of newly diagnosed AL amyloidosis patients with liver involvement seen at Mayo Clinic within 90 days of diagnosis between January 2000 and December 2021. Patients were eligible if they (1) had liver involvement, defined as a baseline alkaline phosphatase (ALP) ≥ 1.5 x upper limit of normal (ULN) and (2) had LivR measurements at least twice annually in the first year from treatment and annually afterward. LivR was defined as a >50% reduction in alkaline phosphatase (ALP) from baseline value or normalization of ALP. LivR was recorded at 6, 12, and 24 months from treatment initiation and at the best LivR achieved. Patients who died prior to landmark assessment were considered non-responders at subsequent milestone assessments. Covariates associated with LivR were identified by univariate and multivariable logistic regression analyses and a proportional subdistribution hazard (Fine-Gray) model for competing risk (treating death as a competing event). Multivariable logistic regression models for LivR were created using best subset selection, with calculation of odds ratio (OR) and 95% confidence interval (CI). Results: A total of 130 patients were included, with a median age of 60 years (IQR 53-66), male predominance (69%), and a median follow-up of 8.8 years. Sixty percent of patients had lambda isotype, the median ALP was 368 IU/mL (IQR 279-589), with 48% of patients having ALP ≥3 fold the upper limit of normal (ULN). Median bilirubin was 0.7 mg/dL (IQR 0.5-1.1). Forty-three percent of patients received front-line autologous stem cell transplant (ASCT) with or without prior induction therapy, and 5% received daratumumab-based therapy as their front-line treatment. Liver response rate increased with time, with 36 patients (28%) achieving a LivR at 6 months, 47 (36%) at 12 months, 63 (48%) at 24 months, and 94 (72%) at best response. The median time to liver response was 14 months (7.22-32.67). At the 24-month landmark, age <60 years, ALP ≥3 fold ULN, and front-line ASCT were significantly associated with achieving a LivR on univariate logistic regression. On univariate logistic regression at best liver response, age <60 years, ALP ≥3 fold the ULN, kappa isotype, ≥HemVGPR within 6 months, and front-line ASCT were significantly associated with obtaining a LivR. On multivariable logistic regression, at 24 months, ALP ≥3 folds of ULN (OR=2.9, 95% CI 1.4-6.3, P=0.004) was significantly associated with an increased likelihood of LivR. Achieving ≥HemVGPR and front-line ASCT were also associated with increased LivR likelihood, although not statistically significant (p=0.094, p=0.078, respectively). At best liver response, achieving ≥HemVGPR within 6 months (OR=5.2, 95% CI 1.9-14.8), kappa isotype (OR=3.2, 95% CI 1.1-10.0), ALP ≥3 folds of ULN (OR=3.3, 95% CI 1.3-9.9), and front-line ASCT (OR=5.3, 95% CI 1.9-16.7) were factors significantly associated with LivR. The Fine-Gray model demonstrated kappa isotype (HR=1.7), baseline ALP ≥3 folds of ULN (HR=2.1), ≥HemVGPR within 6 months (HR=1.9), and age <60 years at diagnosis (HR=1.9) to be positively associated with achieving LivR (P<0.05). Conclusions: In this large cohort of liver AL amyloidosis, LivR was achieved in 72% of patients, and amongst responders, the median time to response was 14 months from therapy initiation. Predictors of LivR included kappa isotype, deep hematological response in the first 6 months of initial therapy, ASCT usage, and higher baseline ALP levels. These findings better inform clinicians on the likelihood of achievement of liver response and may enable ways to improve its achievement.

Background: AL amyloidosis is a potentially fatal plasma cell dyscrasia. In a randomized phase 3 study (Kastritis E et al, NEJM 2021;385:46-58), the addition of daratumumab to cyclophosphamide, bortezomib and dexamethasone (Dara-CyBorD) significantly improved hematological and organ responses, and prolonged event-free survival (EFS). There was no overall survival (OS) difference at a median follow-up of 11.4 months. Methods: A retrospective study of AL amyloidosis patients seen at our institution between January 2018 and December 2022 who received upfront Dara-CyBorD or CyBorD. We excluded patients who received other forms of upfront therapy (including modifications of the above regimens, such as bortezomib-dexamethasone or daratumumab-dexamethasone). We compared the hematologic and organ responses, EFS and OS between the two treatment groups. Hematologic response was assessed at 2 and 6 months from therapy initiation, while cardiac and renal responses at 6- and 12-month landmarks. In all response evaluations, we included responses up to autologous stem cell transplantation (ASCT) if used or second-line therapy for any reason. For EFS analysis, an event was defined as death of any cause, hematological progression, end-stage cardiac or renal failure, or need for subsequent anti-plasma cell therapy in the absence of hematological progression. OS was calculated from the time of treatment initiation to death or last follow-up. Results: Among 641 newly diagnosed patients seen at our center between 2018 and 2022, 361 patients (56%) were included on intention-to-treat analysis [Dara-CyBorD =147 (41% of the cohort); CyBorD=214 (59%)]. There was no difference in the baseline characteristics between the treatment groups, including age, sex, difference between involved and uninvolved light chains, bone marrow plasma cells percentage, NT-proBNP and 24-hour proteinuria. The 2-month hematologic complete response (hemCR; 15.3% vs 4.4%, P<0.001) and very good partial response or better (≥hemVGPR; 62.2% vs 32.5%, respectively; P<0.001) were higher with Dara-CyBorD compared to CyBorD. At 6 months, the hemCR and ≥hemVGPR were again in favor of the Dara-CyBorD group compared to CyBorD (32.0% vs 13.7%, and 72.8% vs 46.7%, respectively; P≤0.002). The 6- and 12-month cardiac response rate in the Dara-CyBorD and CyBorD groups were 40.2% vs 22.6% (P=0.01) and 44.1% vs 23.2%, respectively (P=0.006). Cardiac ≥VGPR at the 6- and 12-month landmarks were statistically higher with Dara-CyBorD compared to CyBorD and increased in both arms between 6 and 12 months [20.5 % vs 9.3% (P=0.03) and 34.3 % vs 20.0% (P=0.04), respectively]. The 6-month and 12-month renal response to first-line therapy were numerically higher with Dara-CyBorD compared to CyBorD but were not statistically significant (48% vs 31.3%, P=0.10; 54.1% vs 30.1%, P=0.05). The 6 month and 12 months, renal ≥VGPR were numerically higher with Dara-CyBorD compared to CyBorD, again without statistical significance (26% vs 19.6%, P=0.63; 35.7% vs 17.2%, P=0.11, respectively). Treatment with Dara-CyBorD was significantly associated with lower 6-month mortality rate compared to CyBorD (8.8% vs 16.3%, P=0.04). With a median follow-up of 43.3 months (30 vs 59.4 months for Dara-CyBorD vs CyBorD, respectively; P<0.001), the two-year EFS was superior in the Dara-CyBorD group compared to CyBorD (69.1% vs 30.3%, hazard ratio (HR) 0.36, 95% confidence interval (CI), 0.26 to 0.48, P<0.001). The 2-year OS was also statistically higher in the Dara-CyBorD group compared to CyBorD (82.0% vs 69.6%, HR 0.53, 95% CI, 0.36 to 0.79, P=0.003). 148 patients (40.9%) proceeded to second-line therapy with a median of 5.1 months from first line therapy. To adjust for difference in follow-up time between the two treatment groups, we analyzed the second line therapy utilization at the 12-month mark which was statistically higher with CyBorD compared to Dara-CyBorD (42.9% vs 14.9%, P<0.001). Also, at 12 months, a higher proportion of patients treated with CyBorD received ASCT compared to Dara-CyBorD (24.3% vs 13.6%, P= 0.01). Conclusions: In this cohort of newly diagnosed AL amyloidosis patients in a large referral center, we confirmed higher hematological and organ response rates with Dara-CyBorD compared to CyBorD. We have also demonstrated that these higher response rates translate into a survival advantage of Dara-CyBorD over CyBorD.

Introduction The tumor ecosystem in multiple myeloma (MM) involves complex interactions among various cell types, including clonal plasma cells (PCs) and immune cells. Previous research has indicated that the bone marrow (BM) microenvironment's metabolite and lipid profiles differ between MM patients and those with precursor conditions. This study aims to explore if differences in cell composition, metabolic phenotype of clonal PCs, and the spatial organization of various cell types are linked to the early versus late progression of high-risk smoldering MM (SMM) to MM. Methods We identified high-risk SMM patients using the IMWG 20-2-20 criteria, focusing on early progressors (EP), who progressed to MM within one year, and later progressors (LP), who progressed after more than four years. Using a high-dimensional single-cell spatial imaging platform, CODEX (CO-Detection by indEXing), we identified clonal PCs and immune cell types as well as their metabolic phenotypes at the single-cell level, in paired FFPE BM samples at SMM diagnosis and at the time of progression to MM. Our validated panel included markers such as CD3e, CD4, CD8, CD14, CD20, CD31, CD38, CD45, CD66a, CD68, CD138, HLA-DR, ASCT2, pNRF2, GLUT1, LDHA, ATPA5, CPT1A, and Citrate synthase. We employed single-cell segmentation, artifact cleanup, signal thresholding, and spatial analysis. Repeated measure models accommodated multiple regions of interest (ROIs) per patient and accounted for potential intrapatient correlation, while Poisson models estimated incidence rate ratios (RR) of cell types. Spatial statistical models evaluated cell type segregation and integration at the time of SMM diagnosis. Results Single cell events were acquired from 6 unique SMM patients who had paired FFPE BM samples from the time of their SMM diagnosis and at the time of their progression to MM. There were 3 EP SMM patients (Time to Progression (TTP):8, 8, 10 mos) and 3 LP SMM patients (TTP: 51, 58, 117 mos). The median number of single cell events identified per patient slide sample was 104,390 (Range: 24,357 - 314,877). We identified 8 main cell types: PCs, CD4+ T-cells, CD8+ T-cells, B-cells, granulocytes, Monocyte/Macrophages and two groups of other immune cells. The PCs and the remainder of the non-PCs were further classified based on the expression of the following metabolic markers: ASCT2 (Glutamine importer), CPT1A (Fatty acid metabolism), GLUT1 (Glucose importer), LDHA (Glycolysis), Citrate synthase and ATPA5 (Oxidative Phosphorylation). There was a higher RR of granulocytes (RR 2.1, CI 1.6-2.8, p<0.001) in EP compared to LP samples at the time of SMM diagnosis, but a lower RR of CD4 T cells (RR 0.40, CI 0.3-0.6, p< 0.001). Focusing on metabolic phenotypes of the PCs, we observed higher incidence RR of most metabolic markers: ASCT2 (RR=1.77; p=0.032), ATPA5 (RR=1.25; p<0.001), CPT1a (RR=2.42; p=0.015), and LDHA (RR 2.13; p<0.001) in EP compared to LP samples at the time of SMM diagnosis. However, GLUT1 had a lower incidence RR in EP patients (RR=0.79; p<0.001). The same modeling for non-PCs at the time of SMM diagnosis yielded higher expression of LDHA (RR=1.98; p<0.001) and ASCT2 (RR=3.3; p=0.002). Using the x and y coordinates and the corresponding phenotype classifications, we performed spatial statistical modeling based on spatial point processes to generate aggregated measures of how different types of cells of interest interacted. CD4 T cells were more isolated in LPs compared to EPs (p =0.046). Segregation indices for PCs and CD4 T cells indicated that they were more integrated/collocated in EPs. Granulocytes were more isolated in EPs although the difference was not as pronounced (p= 0.07). PCs were more integrated with granulocytes (p=0.06) and B cells (p=0.07) in LP patients. Further analyses of paired samples at the time of progression to MM will be reported at the time of the presentation. Conclusion This study is the first to provide whole-slide, single-cell, spatial proteomic characterization of clonal PCs and immune cells in the FFPE BMs of high-risk SMM patients with varying progression times to MM. Distinct metabolic pathway markers in clonal PCs and their spatial relationships with immune cell subsets are closely associated with the risk of progression to MM. These findings illuminate the complex interactions between the metabolic states of clonal PCs and their spatial orientation relative to immune cells in the pathogenesis of MM.

Introduction: Ven is a BCL2 inhibitor that has shown clinical efficacy in RRMM patients (pts) particularly those that harbor t(11;14). Ven has been evaluated in clinical trials as a single agent, in combination with dexamethasone (dex), immunomodulatory drugs (IMiD), proteasome inhibitors (PI), and anti-CD38 monoclonal antibodies (MAb) yet its place in the therapeutic landscape remains in question. In this retrospective analysis, we aim to evaluate the real-world efficacy of Ven-based regimens in RRMM pts treated outside of a clinical trial across the 3-site MCCC. Methods: We retrospectively analyzed the medical records of pts with RRMM treated with Ven-based regimens between January 2015 and October 2023 at the MCCC. Refractoriness was defined by progression on or within 60 days of stopping treatment. Categorical variables were compared with Chi-square tests and continuous variables were compared with t-tests. Outcomes were estimated using the Kaplan-Meier method and curves were compared between the groups using log-rank tests. Results: 83 pts were included in the analysis. The median age was 71 (39-93), 63% were male, 93% were white, 27% had ISS III, 18% had high risk cytogenetics (HRCyto), 76% had t(11;14), the median prior lines of therapy (LOT) was 4 (1-15), 68% received ≥ 4 prior LOT, 87% received prior autologous transplant, 58% were triple-class refractory (TCR), 64%/23% were penta-drug exposed/refractory (PDE/R), 8% received prior BCMA-directed therapies (BDT), 58% received Ven+dex, 25% received Ven+PI +/-dex, 6 % received Ven+anti-CD38 MAb+/-dex and 11% received Ven+other. The median follow-up time from start of a Ven-based regimen was 29 months (mo). The median Ven dose was 800mg (20-1600) with 25% of pts receiving Ven ramp-up dosing. The overall response rate (ORR) to a Ven-based regimen was 58% and was 64% for pts with t(11;14) and 40% for pts without t(11;14); p=0.06. Ven-treated pts with HRCyto had inferior PFS (3mo) and OS (8mo) compared to patients who did not have HRCyto who had a PFS of 9mo (p=0.020) and OS of 80mo (p<0.001). The ORR for TCR and PDR pts were 52% and 42%, respectively. No significant ORR differences were noted across the different Ven-based regimens (p=0.32). The median PFS for pts with t(11;14) was 10mo compared to 2mo for pts w/o t(11;14); p=<0.001. The median OS for pts with t(11;14) was 80mo compared to 10mo for pts without t(11;14); p=0.001. Ven-treated pts who received 1-3 prior LOT has superior ORR, median PFS, and median OS; 89%, 25mo, and 99mo, respectively, compared to pts who received ≥4 prior LOT who achieved an ORR of 43% (p<0.001), median PFS of 5mo (p<0.001) and median OS of 25mo (p<0.001). Ven-treated pts that were TCR had inferior PFS (5mo) and OS (26mo) compared to pts who were not TCR who achieved a PFS of 12mo (p<0.001) and a median OS of 80mo (p=0.02).Ven-treated pts that were PDR had inferior PFS (4mo) and OS (22mo) compared to pts who were not PDR who achieved a PFS of 9mo (p<0.001) and a median OS of 80mo (p=0.02).Ven-treated pts who received prior BDT (n=7) achieved an ORR of 29% and had a median PFS and OS of 2mo and 6mo, respectively on a Ven-based therapy. 59 pts received additional therapy in the post Ven-progression setting. The ORR to post-Ven progression therapies was 53% (n=31/59), the median PFS was 9mo and median OS was 35mo. The most common post-Ven progression regimens used were PI-based (n=16; ORR=56%), anti-CD38 MAb-based (n=14; ORR=50%), BDT (n=12; ORR=83%), selinexor-based (n=3; ORR=0%) with BDT resulting in highest ORR (p=0.048) and PFS (p=0.005). Conclusions: Ven-based regimens are effective in heavily pre-treated pts with RRMM however they are more effective when used within 1-3 prior LOT, in pts with t(11;14), and in pts without HRCyto, TCR or PDR MM. BDT appear to be the most efficacious therapies in the post-Ven progression setting.

Introduction: Uniform assessment of response to treatment is crucial to managing myeloma and developing new therapies. Measurement of the monoclonal protein (s) secreted by the myeloma cells forms the cornerstone of disease assessment in MM and has primarily been performed by electrophoretic methods in the serum and urine, as well as assessment of free light chains in the serum. According to international myeloma working group (IMWG) guidelines, serial serum-free light chain (sFLC) is included in MM response assessment to define stringent complete response and for evaluating partial response (PR) or progressive disease (PD) in non/oligo-secretory MM. This study aims to determine the added value of serial sFLC measurements in monitoring response and progression in MM with measurable disease by comparing the kinetics of FLC with M spike measured by electrophoresis in serum or urine. Methods: This retrospective observational study included newly diagnosed MM patients evaluated at Mayo Clinic in Rochester between 2011-2021. All patients had measurable sFLC (involved FLC level ≥ 10mg/dL, with abnormal sFLC ratio) along with measurable M-protein by serum or urine protein electrophoresis at diagnosis. Serial measurements of sFLC and M-protein by electrophoresis (sPEP/uPEP) were prospectively collected from the electronic medical records at different time points. FLC-response and FLC-PD were evaluated based on IMWG criteria. Results: The study cohort included 839 patients, 52% with measurable M protein by electrophoresis in serum, 27% in both serum and urine, and 21% in urine-only. A significant association was observed between sFLC and electrophoretic response during the induction and at the time of best response (p <.001). Serum FLC showed a comparable response rate of 98% (≥ PR) at the time of best electrophoretic response (p <.001). Ninety-nine percent of early FLC responders eventually achieved an overall response by sPEP/uPEP (p <.001). In the urine-only group, 99% achieved at least a PR by uPEP, and all showed at least a PR based on sFLC. We observed comparable median percentage changes in serial dFLC and urine M-protein, with parallel trends (p <0.001). The correlation coefficients between the change of sFLC and urine M-protein showed a moderate/strong correlation with coefficients (r) of 0.791, 0.597, 0598, 0.545, after cycles 1, 2, 3, 4, respectively, (p < .001). The response for all cohorts was detected earlier by dFLC (median time 1.1 months, 95% CI: 1.06-1.17), in comparison with serum M-protein (1.5 months, 95% CI: 1.40-1.62) and urine M-protein (2.8 months, 95% CI: 2.42-3.10) with p < .001. In the urine-only group, earlier detection of response by dFLC (1.1 months, 95% CI: 1.00-1.2) was observed, compared to urine M-protein (2.4 months, 95% CI: 2.00-2.7) (p <.001). After a median follow-up of 52 (IQR 27.6, 72.3) months, 56% had PD: 62% by s-FLC, 56% by serum M-protein, and 24% by urine M-protein. Among patients with PD by uPEP, all except one patient met sFLC-PD. Within patients with PD, sFLC was the only detectable tumor marker in 12% of cases at the time of the second line of treatment. After four cycles of induction, there was no difference in PFS between VGPR response determined by sFLC, sPEP, and uPEP (p =0.538). The median second-PFS for patients with onlysFLC-PD was similar to those with urine M-protein PD with or without sFLC-PD (HR 1.28, 95% CI =0.77-2.13. p 0.334). However, the median overall survival (OS) from the first relapse was significantly better for patients with onlysFLC-PD than those with urine M-protein PD (HR 1.87, 95% CI =1.07-3.27. p 0.026). Conclusions: In this study, achieving sFLC-VGPR has a similar impact on PFS as electrophoretic-VGPR after induction, indicating that serial sFLC can potentially replace the requirement for urine M-protein response (< 100mg in 24-hours urine) as part of the electrophoretic-VGPR criteria, as required by IMWG. A similar response rate between sFLC and urine paraprotein confirms the current cutoffs (90% for urine and 50% for dFLC for VGPR) used for response categories for s-FLC. We also observed that in 12% of patients with PD, sFLC was the only marker of progression, highlighting the value of serial s-FLC in monitoring relapse. In conclusion, our study supports the integration of serial sFLC measurements for monitoring response and progression in MM, even with electrophoretic measurable disease.

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.

Introduction: HIV-associated diffuse large B-cell lymphoma (HIV-DLBCL) is a significant complication in HIV-positive individuals, presenting unique clinical challenges and a poorer prognosis compared to patients with de novo DLBCL. Epstein-Barr Virus (EBV) status is a critical factor influencing the pathogenesis, clinical behavior, and therapeutic responses in these lymphomas. Plasmablastic lymphomas (PBL) have shared some overlapping genomic abnormalities with HIV-DLBCL including MYC rearrangements. This study aims to elucidate the differences in the genetic profiles and pathway activations between EBV(+) and EBV(-) HIV-DLBCL through comprehensive genomic profiling. Methods: Cases of DLBCL were identified in patients living with HIV from a multi-institute cooperative group. Cases of Burkitt lymphoma and PBL were excluded. We performed whole exome sequencing (WES) on 106 tumors of HIV-DLBCL and RNA sequencing on 39 tumors and mapped the reads to hg38 reference genome using BWA and STAR, respectively. EBV status of cases was determined by in situ hybridization and/or reads mapped on EBV genome in sequencing data. Somatic mutations were identified using variant callers (Strelka2, Mutect2) with WES data. For those mutated genes in COSMIC, allele frequency cutoff was set at 0.03. Mutations with an artifact score larger than 0.7 in SOBDetector and those located in segmental duplications were further removed. They were annotated with Ensembl Variant Effect Predictor, manually checked, and plotted with maftools. RNAseq read counts aligned to each gene were obtained by featureCounts and DEseq2 was used for differential expression (DE) analysis between EBV positive and negative groups. Pathway analysis with the DE genes was performed using GSEA. FISH analysis was performed using MYC, BCL2, and BCL6 break-apart probes. Results: There were 41 cases of EBV(+) HIV-DLBCL and 65 cases of EBV(-) HIV-DLBCL. FISH of EBV(+) HIV-DLBCL identified 42% (3/7) with MYC rearrangement (MYCr), none with BCL2 rearrangements (BCL2r) (0/6), and 33% (2/6) positive for BCL6 rearrangement (BCL6r) with 1 tumor having a MYCr/BCL6r. For EBV(-) HIV-DLBCL, there were 35% with a MYCr (6/17), 18% with BCL2r (2/11), and 27% with a BCL6r (3/11) with 2 tumors with MYCr/BCL6r, and 1 tumor with MYCr/BCL2r/BCL6r. The most frequent mutations seen in EBV(+) HIV-DLBCL were STAT3 (32%), DDX3X (22%), and TP53 (15%). For EBV(-) HIV-DLBCL, the most frequent mutations were KMT2D (25%), TP53 (23%), GNA13 (22%), and TET2 (22%). Pathways that were most commonly associated with EBV(+) HIV-DLBCL were JAK/STAT (42%) (STAT3, JAK1) and NF-kB (39%)(DDX3X, TNFAIP3) while EBV(-) HIV-DLBCL was associated with epigenetic modifiers (62%)(KMT2D, TET2, EP300), JAK/STAT (45%)(SOCS1, JAK3) and PI3K/mTOR (40%)(GNA13, MTOR). Significant disparities between EBV(+) and EBV(-) were KMT2D (24% vs 50%, P=0.0025), TET2 (10% vs 26%, P=0.008), SOCS1 (29% vs 39%, P=0.04), DDX3X (15% vs 5%, P=0.03), and GNA13 (5% vs 24%, P=0.001). RAS mutations (KRAS, NRAS) were infrequent in HIV-DLBCL (EBV(+), 12%; EBV(-), 6%), in contrast to prior studies of PBL. Gene expression profiling showed enrichment of MYC targets, oxidative phosphorylation, and the mTORC pathway in EBV(+) as compared to EBV(-) HIV-DLBCL. Conclusion: EBV(+) HIV-DLBCL show a distinct mutational profile compared to EBV(-) HIV-DLBCL, with EBV(+) cases showing frequent mutations with STAT3 and DDX3X in contrast to EBV(-) cases with frequent mutations associated with epigenetic modification pathways (KMT2D, TET2). These genetic profiles underscore the need for tailored therapeutic approaches depending on EBV status. Although the analysis is limited and requires confirmation in a larger cohort, this is the largest study at present evaluating these tumors.

Introduction Peripheral T-cell lymphoma (PTCL) encompasses a heterogeneous group of clinically aggressive entities. ~30% of cases cannot be further classified and are designated as PTCL - not otherwise specified (NOS). We delineated two novel molecular subgroups among PTCL-NOS with distinct clinical and biological features: PTCL-GATA3 which is characterized by high expression of GATA3, a master regulator of T-helper-2-cell (TH2) differentiation, and its target genes (i.e., CCR4, IL4, IL13) and PTCL-TBX21 which is characterized by high expression of TBX21, a master regulator of TH1 differentiation, and its target genes (i.e., CXCR3, IFNG), with the GATA3 subtype showing significantly worse clinical outcomes (overall survival). Nonsense mutations in CCR4 are found predominately in PTCL-GATA3, often within the cytoplasmic domain and gain-of-function in nature. Methods Mutation and copy number data were generated from whole exome sequencing (TFH phenotype excluded following LLMPP pathology review; n = 153). Single nucleotide variants were determined using Mutect2 and Varsvan2 using in-house filtering. Copy number (CN) profiles were generated using CNVkit. Four CCR4 cytoplasmic domain mutants and controls (cytoplasmic domain knockout [CDKO], wild-type CCR4 overexpression (CCR4-WT), and empty vector [EV]) were transduced into healthy-donor CD4+ T cells (n = 2). Polarization studies were generated using TH1- or TH2-associated cytokine exposure. Surface plasmon resonance was used to determine the binding affinity of CCR4 proteins to CCL17 and CCL22. In silico docking studies were done through Schrödinger's BioLuminate. Antibody-dependent cell-mediated cytotoxicity (ADCC) assay was performed through co-incubation of NK92 (CD16 and CD56) and CCR4 edited primary CD4+ T-cells (stained with calcein AM) for 2 hours at a 5:1 NK:T cell ratio by 7-AAD flow cytometry. Results PTCL-GATA3 had a higher frequency of CN aberrations (e.g. TP53, PTEN CN loss) and a more aberrant genome (p < 0.001) than PTCL-TBX21, including mutations with genes associated with DNA repair and damage (TP53, ATM) and T-cell differentiation (CCR4). CCR4 mutations were virtually mutually exclusive with TP53 mutations, and these cases showed higher CCR4 mRNA and protein expression than CCR4-WT cases (p < 0.05). Primary CD4+ T-cells with ectopic expression of the CCR4 variants, especially CCR4-Q330X, did not require CD3/CD28 for proliferation, bypassing CD3/CD28 ligation for T-cell activation. In addition, proliferation rates were similar between TH2-conditional media and normal media, but proliferation and TBX21 expression in TH1-conditional media were inhibited, implying a novel role of CCR4 in T-cell receptor (TCR) activation and TH-differentiation. CCR4 variant proteins showed higher binding affinity for their natural ligands, CCL22 and CCL17, with mutants exhibiting tighter binding to both ligands (1.5- to 3-fold decrease in KD) by surface plasmon resonance and in silico modeling studies. The in silico models demonstrated mutant CCR4 bound mogamulizumab tighter than CCR4-WT, consistent with a higher ADCC-induced apoptosis rate, highest with CCR4-Q330X mutation. CCR4-Y331X exhibited a worse docking score compared to CCR4-WT and a lower ADCC-induced apoptosis rate. Conclusions Our findings reveal distinct genetic characterizations including aberrant TP53 and PI3K signaling and CCR4 mutations in PTCL-GATA3. These CCR4 mutations are shown to have a key role in PTCL-GATA3 pathogenesis by dysregulating TH-differentiation and aberrantly activating the TCR. CCR4 mutation promotes the TH2 phenotype by blocking TH1 differentiation. CCR4 mutant proteins demonstrated an increased binding affinity towards its known ligands, CCL17 and CCL22, and mogamulizumab, consistent with previous studies exhibiting mogamulizumab efficacy against CCR4 mutations. However, the most frequent CCR4 mutant, Y331X, showed worse binding compared to other CCR4 mutants or CCR4-WT, a mutation that showed Mogamulizumab-resistance in a prior CTCL study as well as in an in vitro ADCC assay. The results emphasize aberrant pathways implicated by genomic and clinical evaluation of two novel PTCL subtypes, as well as support the novel role of mutant CCR4 in enhancing T-cell lymphomagenesis and T-helper differentiation, highlighting multiple therapeutic targets.

Introduction Plasmablastic lymphoma (PBL) is a rare aggressive subtype of lymphoma. It is more common in HIV positive patients, and is often associated with Epstein-Barr virus (EBV) and MYC rearrangements. PBL is characterized by markers of plasmacytic differentiation including CD138 and MUM1/IRF4, and loss of B-cell markers such as CD20, which suggests a cell of origin similar to that of multiple myeloma (MM) or activated B-cell-like (ABC) diffuse large B-cell lymphoma (DLBCL). The standard of care for PBL is CHOP-based chemotherapy but this is frequently not curative. Other than EBV-negative status, which defines a high-risk group, no other biological features have been discovered that are associated with outcomes. Methods Here, we provide a comprehensive analysis of the genomic and transcriptomic landscape of PBL using a collection of 176 exomes and genomes (n=55 new, contributed by Lymphoma/Leukemia Molecular Profiling Project [LLMPP] sites and centrally reviewed by the LLMPP pathology panel [WHO 2016]; n=121 previously published [PMID: 33225311, 33951889, 34465776, 34714908]), 65 transcriptomes (n=44 new; n=21 published) and 37 in situ single cell transcriptomic profiles (CosMx Spatial Molecular Imager) from archival diagnostic tissue biopsies. Somatic mutations (SNVs/Indels) were identified using an ensemble of four variant callers (Strelka2, Lofreq, Mutect2, SAGE). Salmon and DESeq2 were used to identify differentially expressed genes. This multi-omics dataset was compared to other known B-cell malignancies including 92 MM, 238 Burkitt lymphomas (BL), and 208 DLBCLs. Cell segmentation of the CosMx data was performed using Baysor and downstream analyses were conducted using the Seurat workflow. For functional validation, cell viability assays (CellTiter-Glo Luminescent Assay) were conducted in the PBL cell line, PBL-1, and 4 control DLBCL cell lines. Results Within the cohort, 63% (106/169) of primary PBL cases were EBV-positive (EBV+) and 57% (82/144) harbored MYC rearrangements (MYC+). When stratifying patients by EBV and MYC rearrangement status, mutations in different genes were enriched in each group: STAT3 occurred in 55% (29/53) of EBV+/MYC+ PBLs; NOTCH1 in 19% (7/36) of EBV+/MYC-; MYC in 24% (6/25) and TP53 in 40% (10/25) of EBV-/MYC+ tumors; TET2 was mutated in 38% (9/24) and KRAS in 25% (6/24) of EBV-/MYC- PBLs. EBV-/MYC+ tumors represented a subgroup of patients with dismal outcomes. The 2-year overall survival (OS) of these patients was 22% compared to 61%, 78%, and 54% of EBV+/MYC+, EBV+/MYC-, and EBV-/MYC- PBL patients, respectively (log-rank test p-adj. < 0.05). Consistent with previous studies, we observed recurrent mutations affecting JAK-STAT (STAT3, SOCS1, SOCS3, DUSP2) in 41% of cases and RAS-RAF (NRAS, KRAS, BRAF) signaling in 38% of all PBLs. In contrast to ABC-DLBCL, very few mutations occurred in the NF-κB signalling pathway. Accordingly, PBL showed marked downregulation of genes involved in B-cell receptor and NF-κB signaling. Consistent with the lack of reliance on these pathways, functional analyses in PBL-1 showed resistance to treatment to ibrutinib as well as PI3K- and MALT1-inhibitors. Moreover, constitutive expression of the IκBα super-repressor did not affect viability of PBL-1 cells. In situ single cell transcriptomic data analyses showed a sparse tumor microenvironment (TME) in PBL, with the primary non-malignant cell types being, on average, 10% macrophages, 7% NK/T cells, and 4% stromal cells of all represented cell types. EBV+/MYC+ tumors displayed a more abundant immune cell population with higher levels of macrophages (median 12%) and NK/T cells (median 9%), whereas EBV-/MYC- tumors were composed of the lowest levels of immune cells (median 4% macrophages and 1% NK/T cells). With respect to malignant cell phenotypes, we noted a novel population of SPP1-expressing cells that were enriched in EBV+/MYC+ tumors and a population of CD44-expressing cells that were enriched in EBV+/MYC- tumors. Conclusion Here we present novel subgroups of PBL with unique biology and clinical outcomes. We show that PBL does not rely on B-cell receptor signaling, instead relying on potentially targetable vulnerabilities in JAK-STAT, NOTCH, and RAS-RAF signaling according to EBV positivity and MYC rearrangement status. Lastly, we provide insight into the TME of PBL and demonstrate a novel population of SPP1 and CD44 expressing malignant cells in EBV+ tumors.