Jevremović, Dragan

e20001 Background: High dose melphalan followed by autologous stem cell transplant (HDM-ASCT) remains the standard of care for transplant-eligible patients with newly diagnosed multiple myeloma (NDMM) despite advances in treatments. Achievement of complete response (CR) is associated with improved overall survival (OS) and progression-free survival (PFS); Minimal Residual Disease (MRD) negativity (-) using next generation flow cytometry with a sensitivity of 10-5 is strongly associated with improved PFS and OS. We investigated the rates of conversion to MRD(-), especially MRD(-) CR following HDM-ASCT in patients with NDMM. Methods: We retrospectively reviewed patients with NDMM who underwent early HDM-ASCT at Mayo Clinic Rochester from May 2018 to July 2019. Response assessment was conducted after induction and within 100 days post-ASCT based on 2016 IMWG criteria, except for using Mass-Fix instead of immunofixation. MRD was assessed in bone marrow using the established Euro Flow protocol with sensitivity of 10-5. The FISH assay was used to risk-stratify patients as high-risk (HR) and standard risk (SR) cytogenetics as per the mSMART guidelines. Primary outcome measure was the conversion to MRD(-) CR after ASCT. Secondary outcomes include the overall conversion to MRD(-) irrespective of IMWG response, the impact of HR cytogenetics and pre-ASCT IMWG response on conversion to MRD(-) CR. Results: Two-hundred and ten patients were included; 126 (60%) were male and median age 62 years (range 32–77 years). There were 78 (44%) patients with HR cytogenetics. Pre-ASCT, 23 patients (11%) achieved MRD(-) CR, and 66 (31%) patients achieved MRD(-) CR post ASCT. Of 187 patients not in MRD(-) CR pre-ASCT, 45 (24%) converted to MRD(-) CR. Table provides a breakdown of the conversion rate of pre-ASCT IMWG response category to MRD(-) CR post ASCT and patients with MRD(+) CR had the highest rate (78%) of conversion. The presence of HR cytogenetics did not impact rates of MRD(-) CR achievement post ASCT irrespective of pre-ASCT IMWG response (p = 1.0). Overall, irrespective of IMWG response, 43 (20%) patients were MRD(-) pre-ASCT (19 in VGPR, 24 in CR or better) and 102 (49%) patients were MRD(-) post-ASCT (36 in VGPR, 66 in CR or better). Among 85 patients in VGPR post-ASCT, 36 were MRD(-) of which 8 (22%) progressed, while 49 were MRD(+) of which 24 (49%) progressed (p = 0.014). Conclusions: Upfront HDM-ASCT in patients with NDMM leads to deeper responses with 24% converting to MRD(-) CR and more than doubling of the total rate of MRD(-) irrespective of IMWG response. [Table: see text]

7562 Background: Lymphoma is the 6th most common cancer and a top 10 cause of cancer deaths. To date, there are no blood test approaches to population screening, and accurate surveillance markers are limited. Methylated DNA markers (MDMs) are broadly informative for early detection of cancer but have not been extensively studied for lymphomas. We sought to discover and validate MDMs in DNA extracted from lymphoma tissue, then test these MDMs in archival blood plasma specimens. Methods: Reduced representation bisulfite sequencing (RRBS) was performed on DNA extracted from a discovery set of frozen tissues of classic Hodgkin lymphoma (CHL, n=24), non-Hodgkin lymphoma (NHL, n=78: T-cell-TCL (8); diffuse large B-cell-DLBCL (18); follicular-FL (12); mantle cell-MCL (20); marginal zone-MZL (15)), and lymphoma cell line samples (27); controls included benign lymph node (n=11) and healthy donor buffy coat (n=30). 30 MDMs were ranked by fold-change and AUC and used to design methylation-specific PCR assays for biological validation on independent DNA samples extracted from FFPE tissue from 13 normal lymph node and 63 lymphoma samples and 36 buffy coats from healthy patients. Target enrichment long-probe quantitative-amplified signal assays were developed for 16 MDMs and then assayed in plasma-extracted, bisulfite-converted DNA samples from 390 independent treatment-naïve lymphoma patients (100 CHL and 290 NHL:100 DLBCL, 73 FL, 41 MCL, 41 MZL, 35 TCL), and 210 controls without cancer. Lymphoma plasma samples and 159 controls were provided by the Lymphoma SPORE (CA97274); 51 controls came from a 7-county population archive. Classification of lymphoma cases vs controls was modeled with random forest method and cross-validated across 500 bootstrap samples of each dataset. Results: For MDMs tested in DNA from independent biological validation samples, cross-validated random forest models identified 60/63 cases (95% sensitivity) and 12/13 normal controls (92% specificity). In plasmas, a panel of 16 MDMs ( ZNF503, VWA5B1, HOXA9_5195, GABRG3, ITGA5, MAX_chr17_793, BNC1_2407, CDK20, MAX_chr4_184, TPBG, DNAH14, SYT2, CACNG8, FAM110B, and NRN1) detected 78% (95% CI, 74-82%) of lymphoma cases at 90% specificity. Excluding MZL and TCL, sensitivity increased to 84% (80-88%) including 26/49 (53% (38-67%)) of stage I and 59/71 (83% (71-90%)) of stage II cases. Conclusions: MDMs show promise to detect lymphoma. These markers could be evaluated as part of multicancer early detection testing and could also be evaluated as response markers to treatment and subsequent surveillance.[Table: see text]

e19010 Background: Mutated DEAD-box helicase 41 (m DDX41), and mutated GATA2 are germline mutations associated with familial predisposition syndromes. In this study, we compare the clinical characteristics and survival outcomes (OS) of m DDX41 and m GATA2 MN patients (pts). Methods: We retrospectively screened pts who had next-generation sequencing (NGS) (OncoHeme panel) performed at Mayo Clinic. 4,524 consecutive pts (2018-2021) were screened for DDX41 mutations and 3,872 for GATA2 mutations (2015-2020) and included 36 MN pts with m DDX41 genetic alterations, and 55 m GATA2 MN pts. m GATA2 cases were included at NGS date while m DDX41 were included at diagnosis date. Germline workup was not done in all cases. JMP 16.2.0 Software was used for statistical analysis. Results: Patient characteristics: The most common diagnosis was MDS (N = 22, 61% in m DDX41 N = 17, 31% in m GATA2 group; p = .0044). MDS/MPN overlap was seen in m GATA2 group only; (29% vs. 0%; p = .0004). Majority of pts were males with median age of 68 and 67 years for m DDX41 and m GATA2 pts; respectively (p = .7). m DDX41 pts had higher hemoglobin, platelets, and MCV (< .0001, 0.005, < .0001; respectively) and significantly lower white blood cells (WBC) count compared to m GATA2 pts (< .0001). All m DDX41-AML pts were (ELN) intermediate-risk, and 64% of m DDX41 MDS were intermediate risk (IPSS-R). In contrast, of m GATA2 pts 62% of AML were adverse risk and 44% of MDS very high risk. Majority of m DDX41 pts had normal karyotype (N = 32; 91% vs. N = 19, 37%; p < .0001), had isolated mutations (N = 23; 64%) and the most common co-mutations were DNMT3A (38%), ASXL1 (30%), JAK2 (23%). The majority of m GATA2 pts were co-mutated (96%) with a different co-mutation pattern ASXL1 (60%), SRSF2 (34%), RUNX1 (19%). Germline data: One m GATA2 pts had proven germline mutation, and 10/11 (91%) m DDX41 pts were confirmed Survival and progression in MDS/AML: After median follow-up of 30 months in MDS/AML, 7 (21%) m DDX41 and 23 (77%) m GATA2 pts died with superior OS in m DDX41 compared to m GATA2 pts with median OS of (136.7 vs. 6.8 months, p < .0001). Seven (31%) of 22 m DDX41 and 6 (35%) m GATA2 MDS pts progressed into AML with a median time to progression of (11.2 vs. 5.2 months, p = .045). The leukemia free survival (LFS) for m DDX41 MDS pts was significantly longer than LFS of m GATA2 MDS pts (24.4 vs. 6 months, p < .0001). Conclusions: We compare the outcomes of two unique mutations associated with germline predisposition. We found m DDX41 pts had fewer cytogenetic aberrations, no MDS/MPN overlap, and lower WBC count. Majority of m DDX41 MDS/AML pts were intermediate risk category, compared to predominance of adverse risk disease in m GATA2 pts, translating into better OS and LFS. This study is limited by the small size, lack of germline workup in all cases, and retrospective nature. However, it supports the favorable prognosis and indolent course of m DDX41 pts recently described.

Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that were trained on a 10-color CLL MRD panel from treated CLL patients, including DNN trained on the full cohort of 202 patients (F-DNN) and DNN trained on 138 patients with low-event cases (MRD < 1000 events) (L-DNN). A hybrid DNN approach was utilized, with F-DNN and L-DNN applied sequentially to cases. “Ground truth” classification of CLL MRD was confirmed by expert analysis. The hybrid DNN approach demonstrated an overall accuracy of 97.1% (95% CI: 84.7–99.9%) in an independent cohort of 34 unknown samples. When CLL cells were reported as a percentage of total white blood cells, there was excellent correlation between the DNN and expert analysis [r > 0.999; Passing–Bablok slope = 0.997 (95% CI: 0.988–0.999) and intercept = 0.001 (95% CI: 0.000–0.001)]. Gating time was dramatically reduced to 12 s/case by DNN from 15 min/case by the manual process. The proposed DNN demonstrated high accuracy in CLL MRD detection and significantly improved workflow efficiency. Additional clinical validation is needed before it can be fully integrated into the existing clinical laboratory practice.

AbstractAltered energy metabolism and changes in glycolytic and oxidative phosphorylation pathways are hallmarks of all cancer cells. The expression of select genes associated with the production of various enzymes and proteins involved in glycolysis and oxidative phosphorylation were assessed in the clonal plasma cells derived from patients with newly diagnosed multiple myeloma (NDMM) enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass data set. A scoring system consisting of assigning a point for every gene where their fragments per kilobase of transcript per million (FPKM) was above the median yielded a minimum of 0 and a maximum of 12 for the set of genes in the glycolytic and oxidative phosphorylation pathways to create a total energy metabolism molecular signature (EMMS) score. This EMMS score was independently associated with worse progression free survival (PFS) and overall survival (OS) outcomes of patients with NDMM. A higher EMMS score was more likely to be present in clonal plasma cells derived from Multiple myeloma (MM) patients than those from patients with monoclonal gammopathy of undetermined significance (MGUS). This was functionally confirmed by the clonal plasma cells from MM patients having a higher rate of mitochondrial and glycolysis‐derived ATP formation than clonal plasma cells from MGUS patients. Thus, this study provides evidence for the effect of energy metabolism within clonal plasma cells on pathogenesis and outcomes of patients with MM. Exploiting the energy‐producing metabolic pathways within clonal plasma cells for diagnostic and therapeutic purposes in MM should be explored in the future.

AbstractBackgroundMultiple myeloma (MM) measurable residual disease (MRD) evaluated by flow cytometry is a surrogate for progression‐free and overall survival in clinical trials. However, analysis and reporting between centers lack uniformity. We designed and evaluated a consensus protocol for MM MRD analysis to reduce inter‐laboratory variation in MM MRD reporting.MethodsSeventeen participants from 13 countries performed blinded analysis of the same eight de‐identified flow cytometry files from patients with/without MRD using their own method (Stage 1). A consensus gating protocol was then designed following survey and discussions, and the data re‐analyzed for MRD and other bone marrow cells (Stage 2). Inter‐laboratory variation using the consensus strategy was reassessed for another 10 cases and compared with earlier results (Stage 3).ResultsIn Stage 1, participants agreed on MRD+/MRD− status 89% and 68% of the time respectively. Inter‐observer variation was high for total numbers of analyzed cells, total and normal plasma cells (PCs), limit of detection, lower limit of quantification, and enumeration of cell populations that determine sample adequacy. The identification of abnormal PCs remained relatively consistent. By consensus method, average agreement on MRD− status improved to 74%. Better consistency enumerating all parameters among operators resulted in near‐unanimous agreement on sample adequacy.ConclusionUniform flow cytometry data analysis substantially reduced inter‐laboratory variation in reporting multiple components of the MM MRD assay. Adoption of a harmonized approach would meet an important need for conformity in reporting MM MRD for clinical trials, and wider acceptance of MM MRD as a surrogate clinical endpoint.