Binder, Moritz

Introduction: Clonal dendritic cell (DC) aggregates in chronic myelomonocytic leukemia (CMML) are seen in approximately 20-30% patients at diagnosis. Previously, these were thought to be comprised of CD123+ plasmacytoid DCs but our data has suggested that these are heterogenous and comprise of myeloid DCs, monocytes, and myeloid-derived suppressor cells. Furthermore, these aggregates uniformly express immune checkpoints such as indoleamine 2,3-dioxygenase-1 (IDO), associate with an IDO-specific systemic impact (increased trytophan catabolism), and expanded regulatory T cell population in CMML, suggesting a role in inducing immune tolerance. DC aggregates are also associated with increased risk of acute myeloid leukemia (AML) transformation and resistance to hypomethylating agents, highlighting that they are important for disease progression in CMML (Mangaonkar A et al. Blood Adv 2020). We performed this study to decipher the architecture of a CMML DC aggregate to understand the complex inter-cellular interactions, and assess their impact on the adaptive immune system. Methods: In this study, we used archival fresh-frozen paraffin-embedded (FFPE) bone marrow biopsy specimens obtained from CMML patients, and isolated monocytes (CD14+ magnetic bead sorting) and lymphocytes (CD14-ve fraction) from untreated CMML and age-matched normal control (NC) peripheral blood mononuclear cells (PBMCs). DCs were cultured from monocytes using a commercial protocol. Spatial architecture of the CMML BM microenvironment was interrogated through multiplex imaging of FFPE samples by co-detection by indexing (CODEX) using 30 markers (CCR6, CD107a, CD11c, CD20, CD21, CD31, CD3e, CD4, CD44, CD45, CD45RO CD56, CD68, CD8, E-Cadherin, FoxP3, HLA-DR, CD14, CD16, CD11b, CD33, CD15, CD66b, CD123, CD303, CD34, CD163 and IDO). Single-cell transcriptomic signatures were analyzed using the 10X single cell Fixed RNA profiling platform, and validated using bulk RNA seq (Illumina stranded mRNA prep) from isolated and cultured cells. Impact of DCs on lymphocytes was assessed via autologous mixed DC-T culture experiments. Results: BM FFPE samples from 5 CMML cases (3 with DC aggregates) were included for multiplex imaging via CODEX. Results confirm the significant interactions of CD8+ and CD4+ T cells with CD123+/HLA-DR+ DCs within a CMML DC aggregate (Fig. 1A). Upon quantitative assessment of cell-cell interactions, IDO+/CD8+T ( P=0.006), IDO+/MDSC ( P=0.005), and IDO+/macrophage ( P=0.001) interactions were significantly increased in CMML samples with versus without DC aggregates . Specific findings were validated using single-stain immunohistochemistry and multiplex immunoflourescence staining. Next, in order to assess transcriptomic signatures within BM at the single-cell level, we chose two cases; CMML (BM aspirate blast% 1, WBC 49.6 x 10 9/L, ASXL1/GATA2/SETBP1/SRSF2 mutations) and AML transformed from CMML (BM aspirate blast% 81, WBC 152 x 10 9/L, NPM1/DNMT3A/TET2 mutations). Approximately, 10,000 cells were obtained and analyzed with cell type annotation through human PBMC reference (Azimuth). Results show an expanded regulatory T cell population in AML compared to CMML (UMAP plot, Fig. 1B), suggestive of immune tolerance. In order to validate findings and perform functional experiments, we used sorted monocytes and cutured DCs from CMML and NC. DC phenotype was confirmed both through visual inspection and flow cytometry, and clonality was confirmed through Sanger sequencing (for previously identified mutations). Differential gene expression analysis through bulk RNAseq showed significant differences between CMML and NC lymphocytes with immunoinhibitory receptor, LILRB2 (log2fold change=7.05, adjusted P=0.0002) being one of the top five significantly upregulated genes, further confirming immune tolerance. Finally, mixed autologous DC-lymphocyte reaction (1:4 ratio) experiments demonstrated that DCs induce T cell tolerance in CMML (expansion of exhausted PD1/TIM3/LAG3+ CD8 T cells) when compared to NC. Conclusion: Clonal dendritic cell aggregates within the CMML bone marrow microenviroment interact with T cells and induce immune tolerance. This represents a novel mechanism of disease progression in CMML with potential therapeutic targets (IDO, LILRB2, and/or CD123).

Background: Relapsed DLBCL occurs in up to 40% of patients and has a poor prognosis. Current prognostic tools such as the international prognostic index (IPI) and newer forms (NCCN-IPI) only include baseline clinical features. PET/CT features have not yet been incorporated into prognostic risk models despite being essential for staging and response assessment. Deep learning computer algorithms have consistently demonstrated the ability to analyze imaging data and identify features that are not readily apparent to the human eye. Using a deep learning computer vision model that automatically extracted radiomic features from PET/CTs, we examined the association of changes between baseline PET and interim PET (PET2) radiomic features on survival outcomes and compared the prognostic performance with NCCN-IPI. Methods: Adult patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) between 2005 and 2015 and enrolled in the prospective MER cohort were included. Native DICOM images from FDG PET/CT scans at baseline and interim PET (PET2, after 2-3 cycles of chemotherapy) were preprocessed for regularity and then segmented using the nnU-Net architecture with an external lymphoma-specific model. Feature extraction and standard uptake value (SUV) characterization were accomplished with PyRadiomics and NiBabel. Our computational method included no manual segmentation correction. Individual radiomics features (n=115) and the % change above (Δ High) and below (Δ low) 50 th quartile from baseline to PET2 were evaluated for association with event free survival (EFS) and overall survival (OS) using a univariate Cox regression model and by Kaplan Meier analysis. EFS was defined as time from PET2 to first systemic relapse or death from any cause. OS was defined as time from PET2 to death from any cause. Additionally, Spearman and Pearson correlation matrices were used to guide feature selection while minimizing collinearity. We then assessed the association of the highest scoring radiomics features with EFS in a multivariate cox regression model. Results: A total of 506 patients had baseline and PET2 scans available for radiomic feature extraction. Median age at diagnosis was 61 years (range 18-90), 43% were females. NCCN-IPI composition and outcomes of our cohort was comparable to the previously published NCCN cohort, with patient distribution and 5-year OS as follows: Low (9%, 5-y OS 83%), Low-intermediate (38%, 5-y OS 49%), High-intermediate (41%, 5-y OS 31%), High (11%, 5-y OS 12%). At a median follow up of 81 months,there were 308 events, and272 patients had died.Several radiomics features were associated with EFS including change in tumor surface area (SA Δ) (HR: 1.75, 95% CI 1.36-2.24, p<0.005, median of 26,927 mm 2, Figure 1A) and change in total metabolic tumor volume (TMTV Δ) (HR: 1.64, 95% CI 1.27-2.10, p<0.005, median of 120,473 mm 3, Figure 1B). As a reference, NCCN-IPI univariate Cox regression analysis of EFS produced a Harrell's C-statistic (c) of 0.624. Single radiomics features of SA Δ and TMTV Δ individually produced unadjusted c-statistics of 0.616 and 0.614 respectively. Combinations of radiomics features (SA Δ, TMTV Δ and NCCN-IPI classification) demonstrated cumulative benefit (c=0.652). Conclusion: Automatic PET-CT radiomic feature extraction using deep learning models is feasible among patients with newly diagnosed DLBCL. The change in tumor SA and TMTV appear to be promising radiomic biomarkers. A low decline in either tumor SA or TMTV by PET2 is associated with higher risk of relapse and poor survival outcomes. As individual characteristics, these features are similar prognostic indicators of EFS as the NCCN-IPI. Further investigation into risk stratification when combining radiomics with clinical and genomic variables is warranted. Figure 1. Event free survival for DLBCL patients with A) a change in tumor surface area by PET2 less than 50% percentile (SA_Δ Low) and greater than 50 th percentile (SA_Δ High) and B) a change in TMTV by PET2 less than 50% percentile (TMTV_Δ Low) and greater than 50 th percentile (TMTV_Δ High).

Abstract The approach to patients with high-risk smoldering multiple myeloma (SMM) varies among clinicians; while some advocate early intervention, others reserve treatment at progression to multiple myeloma (MM). We aimed to describe the myeloma-defining events (MDEs) and clinical presentations leading to MM diagnosis among SMM patients seen at our institution. We included 406 patients diagnosed with SMM between 2013-2022, seen at Mayo Clinic, Rochester, MN. The 2018 Mayo 20/2/20 criteria were used for risk stratification. Median follow-up was 3.9 years. Among high-risk patients who did not receive treatment in the SMM phase (n=71), 51 progressed by last follow-up; the MDEs included: bone lesions(37%), anemia(35%), hypercalcemia(8%), and renal failure(6%); 24% met MM criteria based on marrow plasmacytosis (≥60%) and/or free light chain ratio (>100); 45% had clinically significant MDEs (hypercalcemia, renal insufficiency, and/or bone lesions). MM diagnosis was made based on surveillance labs/imaging(45%), testing obtained due to provider suspicion for progression(14%), bone pain(20%), and hospitalization/ED presentations due to MM complications/symptoms(4%). The presentation was undocumented in 14%. A high proportion (45%) of patients with high-risk SMM on active surveillance develop end-organ damage at progression. About a quarter of patients who progress to MM are not diagnosed based on routine interval surveillance testing.

Abstract Most patients with solitary bone plasmacytomas (SBP) progress to multiple myeloma (MM) after definitive radiation therapy as their primary treatment. Whether the presence of high-risk (HR) cytogenetic abnormalities by fluorescence in situ hybridization (FISH) in the clonal plasma cells, obtained either directly from the diagnostic SBP tissue or the corresponding bone marrow examination at the time of diagnosis, is associated with a shorter time to progression (TTP) to MM is unknown. This study evaluated all patients diagnosed with SBP at the Mayo Clinic from January 2012 to July 2022. The presence of del(17p), t(14;16), t(4;14), or +1q (gain or amplification) by FISH in clonal plasma cells was defined as HR. A total of 114 patients were included in this cohort, and baseline FISH was available for 55 patients (48%), of which 22 were classified as HR (40%). The median TTP to MM for patients with SBP and HR FISH was 8 months (95% confidence interval [CI], 6.3-26) compared with 42 months (95% CI, 25-not reached [NR]) in patients with SBP without HR FISH (P < .001). In a multivariate analysis, only HR FISH was a significant predictor for shorter TTP to MM, independent of minimal marrow involvement and an abnormal serum free light chain ratio at diagnosis. Deletion (17p) and gain 1q abnormalities were the most common FISH abnormalities responsible for the short TTP to MM. Thus, assessing for HR FISH abnormalities in clonal plasma cells derived from either the diagnostic SBP tissue or the staging bone marrow examination of patients with newly diagnosed SBP is feasible and prognostic for a shorter TTP to MM.

Abstract Extramedullary multiple myeloma (EMM) can present either at initial diagnosis (de novo) or at disease relapse (secondary) and confers an aggressive clinical course. Limited data exist for choosing the optimal therapy for EMM and this remains an area of unmet clinical need. After excluding paraskeletal multiple myeloma and primary plasma cell leukemia, we identified 204 (68%) patients with secondary EMM and 95 (32%) with de novo EMM between January 01, 2000 and 31 December, 2021. The median overall survival (OS) was 0.7 (95% CI: 0.6–0.9) years for secondary EMM and 3.6 (95%CI: 2.4–5.6) years for de novo EMM. The median progression‐free survival (PFS) with initial therapy was 2.9 months (95% CI: 2.4–3.2 months) for secondary EMM and 12.9 months (95% CI: 6.7–18 months) for de novo EMM. Patients with secondary EMM treated with CAR‐T therapy ( n = 20) achieved a partial response (PR) or better in 75% with a median PFS of 4.9 months (3.1 months‐not reached; NR). Patients with EMM treated with bispecific antibodies ( n = 12) achieved a ≥ PR in 33%, with a median PFS of 2.9 months (95%CI: 2.2 months‐NR). In a matched cohort, multivariate logistic regression analysis demonstrated younger age at diagnosis, 1q duplication, and t (4;14) at diagnosis of MM to be independent predictors of development of secondary EMM. Presence of EMM was independently associated with inferior OS in the matched cohorts for both de novo (HR 2.9 [95% CI: 1.6–5.4], p = .0007) and secondary EMM (HR 1.5 [95% CI: 1.1–2], p = .001).

AbstractIn this phase 1/2 study, carfilzomib was added to high‐dose melphalan conditioning prior to autologous stem cell transplantation (ASCT) in patients with multiple myeloma that had been treated with ≤2 prior lines of therapy. Carfilzomib was escalated at doses of 27, 36, 45, and 56 mg/m2 on days −6, −5, −2, and −1 before ASCT in the phase 1 component of the study. In addition, all the patients received melphalan 100 mg/m2 on days −4 and −3. The primary endpoint of the phase 1 component was to identify the maximum tolerated dose, and the primary endpoint of the phase 2 component was the rates of complete response (≥CR) at 1 year after ASCT. The phase 1 dose escalation cohort included 14 patients, and 35 patients were included in the phase 2 cohort. The maximum tested dose was 56 mg/m2 (MTD). The median time from diagnosis to study enrollment was 5.8 (range 3.4–88.4) months, and 16% of patients had obtained a ≥CR prior to ASCT. The best response within 1 year after ASCT was a ≥ CR rate in 22% for the entire cohort, and 22% for patients treated at the MTD. The ≥VGPR rates improved from 41% before ASCT to 77% by 1 year after ASCT. One patient had a grade 3 renal adverse event, and renal function returned to baseline with supportive care. The rate of grade 3–4 cardiovascular toxicity was 16%. The addition of carfilzomib to melphalan conditioning was safe and resulted in deep responses after ASCT.