Kumar, Shaji

Background: Contemporary questions about treatment and outcomes of patients (pts) living with multiple myeloma (MM) demand efficient and timely evidence generation methods, where real-world/ everyday practice data can play an important role. Relevant potential applications include understanding and addressing disparities in treatment access, mitigating toxicities of novel therapies while maintaining quality of life, appropriately sequencing treatments within long-term management strategies, and disseminating and appropriately using surrogate endpoints in practice, such as minimal residual disease. However, existing real-world data (RWD) repositories are limited by an inability to implement interventions in response to issues identified through retrospective data analysis, which is pivotal for the use of newer therapies. The ASH Research Collaborative (ASH RC) MM Data Hub Program was developed to address these gaps. Methods: We developed a multicenter network of US-based tertiary academic medical centers, engaging site clinicians, patients, biostatisticians, informaticians, and data scientists. Initial activities included constructing a shared real-world MM data model comprised of consensus core data elements. Pt data from each site, dating back to at least 2019, is currently being entered through electronic case report forms, batch uploaded utilizing existing institutional data repositories, and soon direct electronic health record import in standard data acquisition formats. Data are aggregated in the Data Hub and shared in a confidential Site Portal with each site. The Portal allows sites to track their own pts, analyze outcomes, and identify ways to improve the quality of care provided to individuals living with MM. We also developed an innovative evidence generation learning community of site clinicians who meet monthly and collaborate on the “COVID-19 OutcomeS in Myeloma and the Impact of VaCcines (COSMIC)” study, a prospective, decentralized observational study that rapidly accrued pts and collects relevant practice information. The group also shares best practices in study conduct to accelerate study enrollment and execution. Results: The MM Data Hub Program now contains twelve centers: Memorial Sloan Kettering Cancer Center, Mayo Clinic, Icahn School of Medicine at Mount Sinai, University of California San Francisco, University of California Davis, Medical College of Wisconsin, Barbara Ann Karmanos Cancer Institute, University of North Carolina Medical Center at Chapel Hill, Emory University Hospital, Washington University School of Medicine in St. Louis, Dana-Farber Cancer Institute, and Atrium Health. From this Network, six centers have contributed data from 8,824 pts (2,825 with sufficiently complete data are included in this analysis). The median age at time of MM diagnosis is 63, and of the 627 deceased pts, the median age at death is 70.6 years, with median time to death from first MM diagnosis of 6 years. The proportions of pts by decade of diagnoses are 34.9% from 2020-2024; 57.6% from 2010-2019; 7.3% from 2000-2009; and 0.9% prior to 2000. 83.1% of pts had a diagnosis of plasma cell myeloma (non-light chain), and 14.4% of pts had a diagnosis of light chain myeloma. 42.3% of pts are female, and the self-reported patient races are: 85.0% white; 9.2% black or African; and 4.3% all other races (race was not documented in 1.5% of pts). 2.8% of pts are of Hispanic ethnicity. Participant data files are currently undergoing completion and cleaning procedures; the analysis will include updated results at the meeting, and a Network-wide analytic data set will be available for initial retrospective data analyses in early 2025. The COSMIC study opened for enrollment on January 22, 2024, and completed enrollment of 200 pts on May 17, 2024. Conclusions: The ASH RC Data Hub MM Program launched in 2023 and is comprised of 12 sites that collectively have contributed participant files for 8,824 pts thus far. Utilizing the structure of the Network, we successfully implemented and established the feasibility of collecting data directly from pts electronically to monitor SARS-CoV-2 vaccines and COVID-19 related outcomes. The Program serves as a successful proof-of-concept model, providing an enduring RWD resource and a prospective evidence generation platform for the myeloma community.

Introduction: Considerable variation has been observed in the prevalence of monoclonal gammopathies across the globe. Although the prevalence of monoclonal gammopathy of undetermined significance (MGUS) has been evaluated in two hospital-based studies in India, no data exists on its prevalence in community-based settings amongst the rural Indian population. Objectives: To determine the prevalence of MGUS in a rural Indian population in a community-based setting. Methods: A cross-sectional study was conducted across 58 agrarian villages/hamlets of a tribal (aboriginals) district and 8 agrarian villages of a Non-Tribal district of Western India spreading over 2299 square miles (sq-mi), as part of SIMPLe (Screening Intervention for Myeloma and Prevention of Lifestyle diseases) study. For the complete study population, demographic details, medical history, and blood samples were collected after a thorough medical examination. Hemograms and serum protein electrophoresis (SPEP) were performed in all individuals. SPEP was performed using on-site capillary zone electrophoresis. Serum immunofixation electrophoresis (SIFE), and biochemistry (liver and renal function tests) were performed for those with abnormal SPEP graphs (those with M spike and those with abnormal bulges in beta-2 or gamma region suspicious of MGUS). Patients with monoclonal protein on SPEP or SIFE were further assessed for any smoldering/ multiple myeloma features. The data was analyzed using JMP ver. 17.2.0. Results: A total of 10,024 individuals were screened and data was analyzed for 9283 (8527 Tribal + 756 Non-tribal) individuals. The prevalence of MGUS in the study population was 62 (0.66%) and was 1.79% above the age of 55y (62 of 3447w). The median age of individuals with MGUS was 65y (IQR 59-72) with male preponderance. None of the patients satisfied the criteria for smoldering or multiple myeloma using IMWG criteria. The median Hb of the study population was 12g/dL (IQR 11.4-13). Among those with MGUS, the distribution of heavy and light chains was IgG - 83.33%, IgA - 16.67%, with none having IgM/ IgD paraprotein, Kappa - 50%, and Lambda - 50%. Two individuals only had light chain MGUS. There was no statistically significant difference between the tribal and non-tribal populations (aged >45y) in the incidence of MGUS (1.798% Vs. 1.745%, p=0.78), total protein levels (7.71 vs. 7.19 g/dL, p=0.87) and albumin-globulin ratio (1.54 vs 1.62, p=0.77) with numerically more patients having polyclonal gammaglobulinemia in the tribal population. The low Hb reflected the statistically lower Hb levels in the tribal population (9.1±4.2 Vs. 12.39±1.8g/dL, p-0.021) owing to concurrent hemoglobinopathies (sickle cell/ thalassemia traits). Conclusion: The prevalence of MGUS in the rural population of Western India is low at 0.66%, rising to 1.79% in individuals over 55 years, predominantly affecting older males, with IgG MGUS being the most common type. There are no significant differences in MGUS prevalence, total protein levels, or Albumin-globulin ratios between tribal and non-tribal populations over 45 years. Our study comprehensively describes the prevalence of MGUS in rural community settings in India. The background prevalence appears to be lower compared to the Western (both USA ~3% and European~5%) population.

Background: Beside fibril-forming proteins, amyloid deposits contain other proteins which can influence organ toxicity, disease initiation and clearance. We investigated the association between the amyloid plaque proteomic signature of diagnostic renal biopsies from patients achieving deep and sustained hematologic remissions and renal organ response in AL amyloidosis. Methods: Retrospective study of newly diagnosed AL amyloidosis patients between 2010-2020. Patients were eligible if; (1) they had a baseline renal biopsy which had undergone laser micro-dissection and mass spectrometry, (2) were assessable for renal response [RenR, non-selective proteinuria ≥1g /24 hours, predominantly albumin], (3) were not on renal replacement therapy and did not experience renal progression within 3-months of diagnosis and (4) had RenR measurements, at least twice annually in the first year from treatment and annually thereafter. Patients who did not obtain a renal complete response (RenCR) required a hematologic very good partial response (VGPR) or better within 6 months of treatment initiation, sustained for a minimum of 6 months. Hematological and renal responses were graded according to consensus criteria. Overrepresentation analyses of differentially expressed proteins were performed using the WebGestalt 2019 and the reactome pathway database. A false detection rate corrected p value <0.05 was used to identify significantly overrepresented proteins and pathways. Results: We defined two organ response categories, to capture the extreme ends of the spectrum: renal suboptimal responders (n=24), patients for whom the best renal response was ≤partial response (RenPR), and renal complete responders (n=29), patients who obtained a RenCR at best response. Overall, the median follow-up was 6.7 years (IQR: 5.4-9.2), 6.6 years (IQR: 5.4-8.1) for complete responders, and 8.4 years (IQR: 6.1-11.3) for suboptimal responders. Between renal suboptimal responders and complete responders there was no significant difference in median age (63 vs 63 years, p=0.7), lambda light chain isotype (66% vs 79%, p=0.3), first-line therapy received and renal stage (Stage III: 14% vs 8%, p=0.9). The median time to RenCR was 52-months (IQR: 31-63). Protein pathways over-represented in renal complete responders included 14-3-3 proteins (YWHAG, YWHAH, YWHAQ, YWHAZ), early components of the complement cascade and complement regulatory proteins (C3, C4A, C4B, C8A, complement factor H), keratins, cytoskeletal proteins (tubulin, actin, filamen) and collagen IV. Protein pathways over-represented in renal suboptimal responders included terminal/effector components of the complement cascade (C5, C6, C8B, C9), amyloid signature proteins (ApoA1 and E, vitronectin, clusterin), proteoglycans (heparan sulfate proteoglycan), transgelin and metallopeptidase inhibitor 3. Proteins with the highest-fold increase in renal complete responders included serine protease inhibitor (SERPINA3) and F-box and leucine rich repeat protein (FBXL6), 9.0 x 106 & 7.7-fold, respectively. Proteins with the highest-fold increase in renal suboptimal responders included complement 6 and 8B, 3.5 & 3.6-fold, respectively. Conclusion: This is the first study to identify proteomic determinants of organ response in AL amyloidosis. Molecular chaperones involved in protein aggregate homeostasis, such as the 14-3-3 proteins are upregulated in renal complete responders, similar to what we have shown in our prior work. This suggests that organ responders may have a more favorable microenvironment which “primes” them for organ response after the amyloidogenic light chain has been cleared from the circulation. Renal suboptimal responders (≤RenPR) have elevated levels of terminal complement proteins including membrane attack complex proteins and reduced levels of early components or complement regulatory proteins, which may be responsible for reno-toxic and reno-protective effects, respectively.

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: Multiple myeloma (MM) involves the proliferation of monoclonal plasma cells (PCs) in the bone marrow. The plasma cell labeling index (PCLI), indicating the percentage of PCs in the S-phase, is a key prognostic factor in newly diagnosed multiple myeloma (NDMM) patients. Methods: A retrospective study at Mayo Clinic analyzed bone marrow Affymetrix microarray datasets from 136 NDMM patients (2001-2012). Data normalization and quality assessment were followed by differential gene expression analysis using limma-voom, adjusted for age, BMPC percentage, and disease stage (RISS). Selected genes were dichotomized by median and subjected to logistic regression for high/low PCLI. Data from 594 patients with a Proliferation Index at baseline was pooled from the MMRF (CoMMpass) dataset for validation after normalization across assays. Results: The baseline characteristics of the patients are as follows: 62 (46%) were female and 74 (54%) were male, with a mean age at diagnosis of 61.6 ± 11.5 years. RISS staging included 36 patients (26.5%) at stage 1, 32 (23.5%) at stage 2, 29 (21.32%) at stage 3, and 39 patients (28.67%) with unknown stage. PCLI distribution showed 113 patients (83.7%) with <2%, and 23 (16.3%) ≥2%. Differential Analysis: The differential analyses reveal several critical insights. In the primary continuous differential analysis, 21,367 genes were analyzed, identifying 654 as differentially expressed with an FDR-adjusted p-value ≤0.05. Of these, 481 genes were upregulated (top 5: SPC25, TRAT1, PBK, E2F8, ASPM) and 173 were downregulated (top 5: SPARC, CD24, MYOF, CXCL12, VCAM1). Univariate and multivariate stratified analyses showed significant findings: 668 genes in age stratification, 395 genes in BMPC% stratification, and 536 genes in RISS stratification. In multivariate analysis, 408 genes were significant with an FDR-adjusted p-value <0.05 for PCLI and ≥0.05 for BMPC%, age, and RISS. 3-Fold Internal Validation: 240 significant genes were identified in fold 1, 25 in fold 2, and 167 in fold 3, with 21 genes common across all folds (e.g., MCM10, GTSE1, UBE2S, NCAPG, FOXM1, STMN1, NEK2, CENPF, MND1, HJURP, E2F8, TOP2A, SKA1, CCNB2, CCNF, CHAF1A, PBK, EXO1, AURKB, CDCA2, DHFR). 19 genes were common in the sensitivity analysis by excluding RISS 3. 20 genes were common in categorical analysis (Categories <1%, 1-2%, 2-3%, 3-4%, >4%). 3 Gene-Based Proliferation Score (3GPS): On univariate logistic regression analysis of 21 genes for high/low PCLI, 17 significant genes were identified. In the step-wise multivariate analysis, CDCA2 (OR=11.56), DHFR (OR=9.6), and CENPF (OR=6.11) were significant. Log-odds were used as weights. For the high expression(median and above) of gene as 1 and low expression as 0: 3GPS=2.4*CDCA2+ 2.3*DHFR +1.8*CENPF A cut-off of 2.4 was chosen as CDCA2 had the highest positive concordance rate (90.91%) versus DHFR (86.36%) and CENPF (77.27%). External Validation: In the CoMMpass dataset, the sensitivity and specificity for the presence of high expression of any of the 3 genes with ≥2 Proliferation Index (PI) were 92.4% and 73.8%, respectively (AUC=83.1%). The median 3GPS was 0 for <2 PI (n=238) and 6.5 for ≥2 PI (n=356). The sensitivity and specificity for the 3GPS in predicting ≥2 PI were 82.6% and 93.6%, respectively (AUC=88.1%). Pathway Analysis: Significant pathways using Reactome include polo-like kinase mediated events in G2/M transition, condensation of prometaphase chromosomes in metaphase, resolution of sister chromatid cohesion in anaphase, and APC-Cdc20 mediated degradation of Nek2A for regulation of the cell cycle. p53 signaling, cancer pathways, and microtubule regulation were also mapped. Survival Analysis: In our cohort, for CDCA2 quartiles (Q)3 and Q4, for CENPF Q3 and Q4, and for DHFR Q4 were associated with inferior overall survival. 3GPS ≥2.4 was associated with inferior survival of 60.28 months versus 111.5 months for <2.4 (p<0.01). In the validation dataset, median survival was not reached at 100 months (vs. high 79.7, p<0.01). The multivariate Cox model found 3GPS score ≥2.4 as an independent predictor (HR=1.64, 95% CI: 1.23 - 2.19), along with ECOG status, ISS stage, gender, age, and whether the patient underwent Stem Cell Transplant. Conclusion: CDCA2, DHFR, and CENPF are associated with ≥ 2% PCLI. The 3GPS score can serve as a measure of gene-based proliferation index. Prospective validation is needed for clinical practice.

Aim: The definition of high-risk (HR) in Multiple Myeloma (MM) is ever evolving with the discovery of new genomic abnormalities and increasing therapeutic options. There are various stratification models incorporating laboratory and genetic features with different definitions of high-risk and varied emphasis on individual risk factors. Recognized high-risk cytogenetic abnormalities (HRCA) include t(4;14), t(14;16), t(14;20), del(17p) with recent inclusion of del(1p) and gain/amplification (amp) of 1q (+1q) . In addition to del(17p), the presence of biallelic del(1p) and ≥2 HRCAs (“double-” or “triple-hit”) appear to confer poorer outcomes. A new definition proposed by the International Myeloma society (IMS) defines HR as the presence of del(17p) and/or TP53 mutation; an IgH translocation - t(4;14), t(4;16) or t(14;20) along with +1q and/or del(1p); monoallelic del(1p32) along with +1q; bi-allelic del(1p32); or β2 microglobulin ≥5.5 with normal creatinine (<1.2mg/dL). Herein, we report real-world outcomes on these HRCAs and the new IMS consensus definition of HR MM. Method: We retrospectively reviewed newly diagnosed MM patients from the Australian and New Zealand Myeloma and Related Diseases Registry across 4 Australian states. Patients with FISH and/or SNP microarray testing for IgH translocations, 1q21 (CKS1B), del(1p32) (CDKN2C) and del(17p) (TP53) were identified. Positive threshold was 30%. We used Kaplan-Meier methods to compare progression free survival (PFS) and overall survival (OS) between groups and first performed univariate analysis (UA) for each HRCA. We next analyzed patients with gain1q vs amp1q and monoallelic vs biallelic del(1p). In the final analysis, two risk groups were formed: cases meeting the new IMS-defined HR criteria were considered IMS-HR and the rest were considered standard risk (IMS-SR). Indeterminate cases with missing variables that could influence risk classification were excluded. Results: Of 839 newly diagnosed patients (pts) included, 31% had ISS (International staging system) stage 3 disease. High-risk IgH translocations t(4;14) and t(14;16) were present in 8% and 4%. Secondary HRCAs del(17p), del(1p), gain1q and amp1q were present in 9%, 14%, 30% and 11% respectively. In this cohort, 41% received ASCT. Of the transplant eligible pts, 41% received triplet therapy (PI, IMiD and corticosteroid) and 57% received PI-based induction. Of the transplant ineligible pts, 31% received triplet therapy, 47% PI-based and 19% IMiD-based therapy. Frontline Anti-CD38 antibody use was <1%. Median follow up was 35 months[m] (IQR 18-60). On UA, the risk of progression and/or death was increased with ISS III vs I, amp1q, del(17p), t(14;16), del(1p) and gain1q in descending order by hazard ratio. The risk remained significant in ISS III, del(17p), amp1q and gain1q after multi-variate analysis (MVA). The risk of death was increased with ISS III vs I, del(17p), amp1q, del(1p) and t(14;16), ISS II vs I in descending order by hazard ratio. After MVA, the risk remained significant for ISS stage III, del(17p), amp1q and t(14;16). Of the 117 pts with del(1p32), 108 had monoallelic and 9 had biallelic loss. Pts with biallelic del(1p32) had an inferior PFS (median 16.3 vs 23.2m, p<0.001) and OS (median 24.8m vs NR, p<0.001) compared to those with monoallelic del(1p32). In pts with 1q+, pts with amp1q had an inferior PFS (median 16.3 vs 27.5m, p<0.001) and OS (median 37.0m vs NR, p<0.001) vs pts with gain1q. Based on the new definition of HR myeloma - 385 pts met the criteria for IMS-HR and 216 for IMS-SR. There were 238 indeterminate cases due to missing data. The IMS-HR group had an inferior PFS (median 20.2 vs 39.7m, p<0.001) and OS (median 44.2m vs NR, p<0.001) when compared to IMS-SR. Noting that the IMS-HR group had a higher median age, poorer performance status and lower rates of ASCT use, adjusted analysis for these variables still showed an inferior PFS (aHR 1.68; 95% CI 1.30-2.16, p<0.001) and OS [aHR 1.72; 95% CI 1.21-2.44, p=0.002). Conclusion: ISS III, del(17p), amp1q are the strongest predictor of OS and PFS. Differentiating between monoallelic vs biallelic del(132p) and gain vs amp1q21 has important prognostic value. The IMS- HR definition which takes into account del(1p), “double-” and “triple-hit” disease predicts for a group of patients with a PFS of ~20 months and inferior OS, and provides a standardized approach to risk stratification in MM.

Background: Chimeric antigen receptor T-cell (CAR-T) therapy has shown substantial promise in treatment of hematological malignancies, including non-Hodgkin Lymphoma (NHL). A subset of patients develop prolonged cytopenia, comprising quality of life, increasing healthcare utilization, and imparting considerable morbidity and mortality. Therapy-related myeloid neoplasms (t-MN), typically seen in patients who receive DNA-damaging agents such as radiotherapy and alkylators, have also been observed in CAR-T recipients. CAR-Hematotox score predicts the development of hematotoxicity in CAR-T recipients using laboratory parameters obtained at lymphodepleting (LD) chemotherapy, a timepoint when the patient has already gone through leukapheresis thus committed to the CAR-T process. Therefore, it is critical to identify patients at the highest risk of prolonged cytopenia and/or t-MN prior to apheresis, making it possible for a better triage of those who will proceed with CAR-T and those who might benefit from other strategies. Methods: A retrospective analysis was conducted in NHL patients who received commercial CAR-T products between January 2018 and March 2024 at Mayo Clinic Rochester, Arizona and Florida. Clinical and laboratory data including age, hemoglobin (Hb), absolute neutrophil count (ANC), platelet (PLT), ferritin, and LDH, obtained at the initial CAR-T evaluation, typically within 1 week of leukapheresis, were used for analysis. Prior lines of therapies including autologous stem cell transplant (ASCT) were collected. Prolonged cytopenias were defined as Hb < 8 g/dL, ANC < 0.5 x 109/L, and/or PLT < 50 x 109/L at 3 months after CAR-T. t-MN was diagnosed per WHO 2016. For prolonged cytopenia, the optimal cutoff points for continuous variables were determined by maximizing the sum of sensitivity and specificity. Logistic regression was then used to determine the factors associated with cytopenia at 3 months post-CAR-T. For t-MN, maximally selected log-rank statistics was used to determine the optimal cutoffs. We used competing risk analysis to determine the factors associated with development of t-MN. Death without t-MN was considered a competing risk. For both analyses, the factors with P < 0.1 in univariate analysis (UVA) were included in multivariate analysis (MVA). Results: Among the 387 NHL patients, with a median follow-up of 11 months (95% CI 8.7 - 12.2 months), 56 (14.4%) developed (41, 10.5% with 1 cytopenia; 14, 3.6% with 2 and 1, 0.2% with cytopenia in all 3 lineages), 20 (5.2%) developed and 315 (81.2%) did not have either. Factors associated with prolonged cytopenia: were age >, PLT ≤ 150, ferritin ≥ 600, and LDH ≥ 250. Univariate analysis identified baseline Hb, ANC, PLT and ferritin to be significantly associated with prolonged cytopenia. MVA identified Hb ≤ 11 (HR = 1.98; 95% CI 1.03-3.85, P = 0.042) and PLT ≤ (HR = 2.56; 95% CI 1.39-4.78; P = 0.003) to be associated with the development of prolonged cytopenia. Factors associated with t-MN:were age > 65 years, Hb ≤ 9 g/dl, ANC < 3.8, PLT ≤ 80, Univariate analysis identified age, Hb, ANC, PLT, ferritin, and ≥ 3 prior lines of treatment to be significantly associated with t-MN. On MVA, age ≥ 65 (HR = 3.91; 95% CI 1.52-10.03; P = 0.004), Hb ≤ 9 (HR = 3.49; 95% CI 1.38-8.85; P = 0.008), ferritin ≥ 300 (HR = 3.06 ; 95% CI 1.08-8.67; P = 0.035) and ≥ 3 prior lines of therapy (HR = 3.25; 95% CI 1.33-7.94 ; P = 0.010) were associated with t-MN development. Association of prolonged cytopenia with t-MN development: Prolonged cytopenia was noted in 45% of patients who developed t-MN and 15% of those who did not (P = 0.002). Cumulative incidence of t-MN at 1 year was 3.3% and the presence of prolonged cytopenia was associated with a higher 1of t-MN (HR 8.93, 95% CI 2.15 - 37.1, P = 0.003). Conclusions: Readily accessible clinical and lab data such as blood count and inflammatory markers at the time of CAR-T evaluation were associated with the development of prolonged cytopenias. Similarly, advanced age, anemia, high ferritin levels and history of ≥3 lines of therapy were associated with the development of t-MN. These findings, when validated in larger dataset, can be used for patient counseling and therapy selection by identifying patients at high-risk of developing hematotoxicity and/or t-MN development.

Background: The COVID-19 pandemic had an outsized impact on individuals with immune compromise, such as multiple myeloma, leading to increased rates of severe COVID, hospitalization, and death in these groups. The pandemic also demonstrated the need for rapid and efficient evidence generation to address rapidly changing medical needs and accelerated the use of innovative trial designs to accomplish this goal. Methods: We designed and established a prospective, decentralized study within a site-based provider network, COVID-19 OutcomeS in Myeloma and the Impact of VaCcines (COSMIC), for patients with multiple myeloma at ASH Research Collaborative sites to monitor outcomes related to COVID-19 infection and vaccination. The direct-to-patient approach was accomplished using a study application accessed with a QR code provided to patients during usual clinical care. All study activities occurred within the patient portal at Baseline, Day 30, and Day 180, minimizing burden on site personnel and staff. Study activities included eligibility confirmation, consenting, electronic health record (EHR) data permissions, and patient-reported outcome (PRO) surveys. PRO data (EORTC QLQ-C30, EORTC QLQ MY-20) were made available to clinicians through the portal to visualize results from their patients. Patients shared if they received a COVID-19 vaccination or booster and if they had a COVID-19 infection. Patients with a history of COVID-19 infection included method of confirmation and if hospitalization was required. Patients were provided compensation for participation. Automated text and email messages were sent at three separate intervals: 24 hours, Day 8, and Day 15 for participants who initiated but did not complete study activities. A personal email reminder per participant was sent for those whose second study visit window was approaching. ASH Research Collaborative clinicians met regularly to share best practices related to enrollment and study conduct, thereby creating an innovative evidence generation learning community. We studied the feasibility of data collection in a decentralized format by focusing on the number of participants who interacted with the system, those who consented, and those who completed baseline PRO assessments. Results: The COSMIC study rapidly accrued and consented 200 patients from eight participating ASH Research Collaborative sites over four months (1/22/2024 through 5/17/2024): 69% self-reported as non-Hispanic White (NHW). Of enrolled participants, 57.5% opted to permission sharing of their EHR data, which included disease and treatment-related clinical data, laboratory results, pathology reports, and imaging reports. A total of 196 patients (98%) provided demographic and COVID-related disease and vaccine data at baseline; nearly all of these patients (n=195) provided baseline PRO data, and 150 patients provided further information at Day 30 at the time of this analysis. Those who did not provide Day 30 data were not significantly different from those who did in terms of age (median: 68 [no Day 30 data] vs. 67 [Day 30 data] years; p=0.44), sex (41% [no Day 30 data] vs. 47% [Day 30 data] Female; p=0.47), or race (65% [no Day 30 data] vs. 71% [Day 30 data] NHW; p=0.60). A higher proportion of Hispanic patients were retained at Day 30 (13/15=87%) vs. 78% of non-Hispanic patients or those who declined reporting ethnicity (2/7=29%; p=0.007). COVID-19 vaccination data were submitted by 196 patients, 186 of whom had ever received a COVID-19 vaccination. Data on number, type, brand, and dates for these vaccinations were provided. Conclusions: The COSMIC study conducted by the ASH Research Collaborative was able to rapidly accrue a diverse cohort of patients using a decentralized workflow within a site-based provider network, with high consent and compliance rates. This platform, and the innovative evidence generation learning community of ASH Research Collaborative investigators and patients, sets a precedent to address a variety of real-world questions related to disease management, infections, vaccinations, and PROs in this population of patients with multiple myeloma.

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.