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Abstract Background: Prolonged cytopenias are a well-recognized complication following CAR-T therapy, leading to increased morbidity and healthcare utilization. Autologous SCB offers a potential strategy to promote hematologic recovery. We conducted a retrospective multi-institutional analysis across 18 centers comparing outcomes of SCB versus supportive care alone in RRMM patients who experienced prolonged cytopenias >30 days after CAR-T infusion. Methods: Patients were included if they received SCB within 1 year after commercial CAR-T between 6/2021 and 3/2024. To identify matched controls, we reviewed other CAR-T recipients from the same consortium and selected those with an absolute neutrophil count (ANC) of <0.72x109/L and platelet count of <21K at day 30 post-infusion-thresholds representing the 75th percentile of cytopenia severity in the SCB cohort. These criteria ensured control patients had comparable hematologic toxicity but without SCB. Hematologic recovery in the SCB cohort was assessed using the CIBMTR engraftment criteria. For the non-SCB (nSCB) group, cell counts were analyzed cross-sectionally at day 90 post-CAR-T and compared to the SCB cohort with Kruskal Wallis tests. Progression-free survival (PFS) and overall survival (OS) were evaluated using Kaplan Meier methods. Results: Of the 590 CAR-T recipients, 91 patients (15.4%) developed prolonged cytopenias; 39 patients received SCB and 52 did not. SCB patients more frequently had ISS stage III disease (23.1% vs. 11.5%), exrtamedullary disease (59% vs. 46.2%) and penta-refractory status (43.6% vs. 34.6%). The median number of prior lines of therapy was 6 in both cohorts. Thirteen patients (33.3%) in the SCB and 11 (21.2%) in the nSCB received high intensity infusional chemotherapy (DCEP/PACE/HyperCVAD). Pre-CAR-T cytopenias tended to be less frequent in the SCB vs. nSCB cohort: ANC <1000 in 5.1% vs. 13.5%, Hgb <8 in 23.1% vs. 21.2%, and platelets <50K in 28.2% vs. 40.4%. Median plasmacytosis in the bone marrow prior to CAR-T in the SCB group was 35% and 47.5% in the nSCB group. In the SCB cohort, 2 patients harbored DNMT3A mutations, one of whom had CCUS. Ide-cel was administered in 64.8% and cilta-cel in 35.2% of all patients. The rates and grades of cytokine release syndrome did not differ between groups. Six patients (15.4%) had HLH in the SCB group vs. none in the control group. All (100%) SCB patients received growth factor support; 58% also received TPO agonists. In the nSCB group, 43 patients (82.7%) received growth factor, and 16 patients (30.8%) received TPO agonists. Out of 24 pre-SCB bone marrow biopsies available, only 1 patient had relapsed MM while the remainder had hypocellular marrows. Median CD34+ cell dose was 2.93 million/kg (range: 1.76-23.55) administered at a median of 53 days post-CAR-T (range 24-265). All but one patient (97.4%) in the SCB group achieved hematologic recovery; median time to recovery was 24 days (range 9-87). No new toxicities attributable to SCB were observed. On day 90 post CAR-T infusion, SCB patients had higher median ANC (2.12 vs. 1.59x109/L, p=0.277), Hgb (10.6 vs. 8.7g/dL, p=0.002), and platelet counts (135 vs. 35K/L (p<0.001). Minimal residual disease negativity was achieved in 82.4% (SCB) vs. 83.3% (nSCB). Infections occured in 53.8% and 48.1% of SCB and nSCB cohorts. All serious infections occurred prior to SCB. There were 12 deaths in the SCB group (7 from disease progression, 2 from infection, 1 from neurotoxicity (NT), and 1 unknown) and 27 in the nSCB group (22 from progression, 3 from infection, 1 from HLH, 1 from NT). After a median follow-up period of 12.6 months in the SCB cohort and 11.6 months in the nSCB arm, the mPFS was 11.0 months and 8.2 months, respectively. The mPFS was 10.6 months (SCB) and 5.8 months (nSCB) (p=0.216), and 11.0 months (SCB) and 10.9 months (nSCB), for ide-cel and cilta-cel treated-patients, respectively. Median OS was not reached vs.12.3 months in the SCB vs. nSCB groups, respectively. Conclusion: Overall, SCB was safe and effective in promoting rapid hematologic recovery in nearly all patients. Compared to controls, SCB recipeints had significantly higher Hgb and platelet levels by day 90 post-CAR-T infusion. SCB was associated wih a near doubling of PFS in patients who received ide-cel, though interpretation is limited by non-randomized design and small sample size. Further study is needed to optimize paient selection, timing, and integration of SCB into CAR-T care.

Abstract Introduction: Ciltacabtagene autoleucel (cilta-cel) has shown high efficacy in relapsed MM, but further efforts are needed to mitigate non-ICANS delayed neurotoxicity (DNT) and non-relapse mortality (NRM). Identifying risk factors associated with DNT and NRM may help with risk mitigation by aiding the development of interventions for potentially modifiable risk factors and also help with clinical decision making. Methods: In this multi-center retrospective study from the US MM Immunotherapy Consortium, we evaluated 761 patients treated at 15 centers receiving standard of care cilta-cel for relapsed MM between May 2022 to December 2024. Risk factors for DNT, particularly Parkinsonism and NRM were evaluated in by univariable and multivariable analysis. Any NRM events occurring after disease progression were censored for NRM evaluation. Results: The median age of the cohort was 65 years old (range: 30-88), median prior lines of therapy (pLoT) were 5 (range: 1-23). Cilta-cel was used in earlier relapse (1-3 pLOT) in 16% (n=120) patients. High-risk cytogenetics [del 17p, t(14;16) or t(4;14)] were present in 39%, with extramedullary disease (EMD) in 27% and R-ISS stage III in 18% of patients, respectively. Majority of patients (86%) received bridging therapy with response to bridging (≥ partial response, PR) seen in 33% of patients, respectively. Median follow up was 10.1 months, with response rate of 92% and CR rate of 70%. DNT was seen in 10% (n=75) of patients, with distribution as follows: Parkinsonism in 2.9% (n=22), cranial nerve palsy in 4.6% (n=35) and other DNT in 2.4% (n=18), with some patients manifesting more than one type of DNT. Risk of DNT was higher in patients who did not respond to bridging therapy (Any DNT: 12% vs 6%; Parkinsonism: 5% vs 0.5%, both p<0.05) Of the 22 cases of Parkinsonism, 21 patients (95%) did not respond to bridging, even though they achieved a post CAR-T response with ORR of 91% and ≥ CR in 68%. Absolute lymphocyte count (ALC), a surrogate measure of CAR expansion was higher in patients who developed DNT, particularly Parkinsonism, including ALC at days 7,14,21,28 and as peak ALC in the first month post cilta-cel (p<0.05 for all). Median peak ALC for patients with vs without Parkinsonism was: 5.88 vs 1.17/uL (p<0.001). We evaluated risk of Parkinsonism with various thresholds of peak ALC expansion. Amongst patients who developed Parkinsonism vs. not, peak ALC was as follows, > 1000/uL: 100% vs 57%, > 2500/uL: 73% vs 19% and > 3000/uL: 68% vs 14% (all p<0.001). The absolute risk of Parkinsonism with ALC > 3000 vs ≤ 3000/uL was 12% vs 1%, p<0.001; for ALC 2500 vs ≤ 2500uL was 9% vs 1%, p<0.001. On multivariable analysis, peak ALC > 3000/uL (OR: 12.7, p<0.001) and non-response to bridging therapy (OR: 9.9, p=0.03) were independent risk factors for developing Parkinsonism after cilta-cel. After cilta-cel, 1 and 2 year NRM estimates were 9% and 10%, respectively. Amongst 63 (8%) patients experiencing NRM events, the most common cause of NRM was infectious complications (35, 56%), followed by acute immune-mediated adverse events (AEs) like CRS, ICANS, IEC-HS in 14 (22%), delayed immune mediated AEs like DNT and colitis in 6 (9.5%), second cancers in 5 (8%) and other causes in 3 (5%) patients, respectively. When evaluating the timing of NRM events, the risk of infection related mortality remained relatively stable in the first year, though immune mediated toxicty deaths were uncommon after 6 months, while SPMs were uncommon within the first 6 months. On multivariable analysis, non-response to bridging (HR 2.41, p=0.046), poor performance status ≥ 2, high-risk cytogenetics and age ≥ 70 years were independent predictors for NRM. Conclusion: In a large cohort of over 750 patients receiving cilta-cel, we identified potentially modifiable risk factors for Parkinsonism and NRM. Non-response to bridging therapy and peak ALC > 3000/uL were independent predictors of developing Parkinsonism. Peak ALC of > 2500/uL and > 3000/uL were associated with 9% and 12% absolute risk of Parkinsonism. This indicates that peak ALC of 2500/uL or 3000/uL in the first month after cilta-cel may be a potential biomarker to identify patients for pre-emptive interventions such as steroids that can suppress rapid CAR expansion and perhaps decrease the risk of Parkinsonism. In addition, we need to focus on effective bridging strategies to further decrease the risk of Parkinsonism, as well as NRM with cilta-cel.

Abstract Introduction Marginal zone lymphoma (MZL) is an indolent and heterogenous disease. Several prognostic models have been proposed to assess outcomes in MZL, including FLIPI, IPI (primarily designed for overall survival [OS]), and MZL-specific scores such as MALT-IPI (event-free survival [EFS]) and MZL-IPI (progression-free survival). FLIPI24 is a recently developed prognostic index for patients with follicular lymphoma (FL) treated with first-line (1L) immunochemotherapy (IC), identifying a high-risk population with inferior survival and increased risk of lymphoma-related death. FLIPI24 has also demonstrated robust performance in the entire FL population, including patients under observation. This study aimed to evaluate the prognostic utility of FLIPI24 in patients with MZL and to compare its performance with other established indices (FLIPI, MZL-IPI, IPI, MALT-IPI). Methods We pooled harmonized data from newly diagnosed MZL patients with complete available FLIPI24 variables (age, white blood cell count [WBC], hemoglobin [HGB], serum lactate dehydrogenase [LDH], and beta-2-microglobulin [B2M] levels) treated with 1L IC. Data were collected from three prospective international cohorts: NiHiL (Czech Republic; n=897; enrolled 2005–2023), LEO (USA; n=62; 2015–2020), and MER (USA; n=32; 2010–2015), forming a pooled cohort of 991 1L IC-treated patients. The analysis was then extended to include all newly diagnosed MZL patients (n=2,116), regardless of treatment. The primary endpoint in the 1L IC-treated cohort was EFS at 24 months (EFS24), and for all MZL patients, it was OS. Secondary endpoints included OS24 for 1L IC treated cohort, lymphoma-related mortality by FLIPI24 risk groups, and comparative prognostic performance of FLIPI, IPI, and MALT-IPI, by C-statistics; performance of MZL-IPI was assessed on a subgroup of NiHiL patients with available data (n=877 all MZL, n=485 1L IC-treated). Results Among the 991 1L IC-treated patients, the median age at diagnosis was 66 years (range 22–94); 47% were male; 11% had ECOG performance status ≥2, 68% clinical stage III–IV, 42% elevated LDH, 40% IPI 3–5, 42% FLIPI 3–5, and 62% MALT-IPI 2–3. The median B2M level was 2.8 mg/L, WBC 7.0 × 10⁹/L, and HGB 13.1 g/dL. MZL subtypes were: 64% extranodal (EMZL), 22% nodal (NMZL), and 14% splenic (SMZL). Most common 1L IC regimens were R-CVP (46%), R-CHOP-like (32%), R-chlorambucil (9%), and B-R (9%). FLIPI24 stratified patients into low- (18%), intermediate- (37%), and high-risk (45%) groups. High-risk patients were older (median 54 vs 65 vs 71 years for low-, intermediate-, and high-risk groups, respectively; P<0.01), had more frequent adverse baseline features, and were more likely to have the SMZL subtype (2% vs 7% vs 25% for low-, intermediate-, and high-risk groups, respectively; P<0.01). After a median follow-up of 7.2 years, EFS24 was 89%, 85%, and 65% (P<0.01) across low-, intermediate-, and high-risk groups; OS at 2 years was 98%, 97%, and 79%; and OS at 5 years was 96%, 91%, and 62%, resp. (P<0.01). The 5-year cumulative incidence of lymphoma-related death was 13% in high-risk vs 6% in low-/intermediate-risk groups. FLIPI24 showed better prognostic accuracy for EFS compared with FLIPI, MZL-IPI, IPI and MALT-IPI (C-index: 0.640 vs 0.630, 0.622, 0.620 and 0.603, resp.), as well as for OS (C-index: 0.710 vs 0.672, 0.641, 0.663, 0.652, resp.). Prognostic performance was consistent across MZL subtypes. In the extended cohort of 2,116 newly diagnosed MZL patients (47% receiving IC, 17% rituximab monotherapy, 11% radiotherapy, 8% surgery, 2% antibiotics; 15% observed), FLIPI24 stratified patients into low- (24%), intermediate- (39%), and high-risk (37%) groups. OS at 2 year was 99%, 97%, and 83%, at 5 years it was 96%, 91%, and 62%, resp. (P<0.01). The 5-year cumulative incidence of lymphoma-related death was 12% in high-risk vs 4% in low-/intermediate-risk patients. FLIPI24 outperformed other indices (OS C-index: 0.754 vs MZL-IPI 0.654, FLIPI 0.700, MALT-IPI 0.694, IPI 0.699). Conclusion FLIPI24 demonstrates strong and consistent prognostic performance across all MZL subtypes, identifying ~40% of patients as high-risk with significantly worse survival and increased lymphoma-related mortality. The FLIPI24 outperforms FLIPI, MZL-IPI, IPI, and MALT-IPI in predicting outcomes and may serve as a practical tool to guide risk-adapted management and selection of patients for novel therapies in MZL. Funding: NU21-03-00411, P50 CA97274, U01 CA195568.

Abstract Introduction The International Prognostic Index (IPI, NEJM, 1993) has guided risk stratification in large B-cell lymphoma (LBCL) for over three decades. Its simplicity and clinical utility have driven its widespread adoption, even in the era of precision medicine. However, the prognostic value of the IPI and its components may not be uniform across the age spectrum of LBCL patients. We evaluated the contemporary prognostic performance of the IPI in a large international cohort, focusing on age-stratified performance and the relative contributions of its individual components, grouped as patient- and disease-related factors. Methods We harmonized and pooled data from 6,941 patients with newly diagnosed systemic LBCL treated with R-CHOP-like regimens (R-CHOP, R-miniCHOP, DA-EPOCH-R, R-CHOEP, R-CHOP+X) with complete IPI data from three prospective cohorts: NiHiL (Czech Republic, n=4,590; 2010–2023), LEO (USA, n=1,789; 2015–2020), and MER (USA, n=562; 2010–2015). IPI components included age, ECOG performance status (PS) as patient-related, and Ann Arbor clinical stage, serum lactate dehydrogenase (LDH), and extranodal (EN) involvement as disease-related factors. Prognostic performance was evaluated using multivariable Cox regression models and C-statistics. Age-stratified analysis was performed for: ≤40 years (7%; n=492), 41–60 years (28%; n=1,909), 61–80 years (59%; n=4,080), and >80 years (7%; n=460). The primary endpoint was overall survival (OS). Results In the pooled cohort, the median age was 66 years (range 18–95). Compared to the original IPI cohort (n=3,273), our cohort was older (age >60 years: 66% vs 41%) but otherwise comparable: ECOG PS 2–4 (25% vs 24%), clinical stage III–IV (63% vs 66%), elevated LDH (60% vs 52%), and >1 EN involvement (32% vs 30%). In multivariable analysis, age (≤60 vs >60 years) was the strongest predictor of OS (HR=2.78; vs HR=1.96 in original IPI report), followed by ECOG PS (HR=2.07; vs 1.80), clinical stage (HR=1.50; vs 1.47), LDH (HR=1.41; vs 1.85, all P<0.01); EN involvement was not significant (P=0.50, HR=1.03 vs 1.48). Risk group distribution shifted from the original IPI: 28% were low-risk (0–1 factor; vs 35%), 23% low-intermediate (2; vs 27%), 24% high-intermediate (3; vs 22%), and 25% high-risk (4–5; vs 16%). Corresponding 2-year OS rates were 95%, 86%, 78%, and 63%, superior to those of the original IPI cohort (87%, 67%, 55%, 44%; pre-rituximab era). Age was associated with an increasing risk of mortality, with a more pronounced rise beyond 60 years. In a piecewise Cox model, the HR per year was 1.04 for ages ≤60 (linear), 1.05 for 61–69 (accelerated), and 1.06 for ≥70 years (exponential, all P<0.01). ECOG PS retained prognostic significance across age groups: HRs were 1.87 (P=0.06) for ≤40 years, 1.63 (P<0.01) for 41–60, 2.22 (P<0.01) for 61–80, and 1.43 (P<0.01) for >80 years. The strength of other IPI components declined with age. Clinical stage showed decreasing impact: HR 2.08 (P=0.09), 1.97 (P<0.01), 1.49 (P<0.01), and 1.31 (P=0.05). LDH was significant only up to age 80 years: HRs were 2.07 (P=0.04), 1.96 (P<0.01), 1.32 (P<0.01), and 1.21 (P=0.13). EN involvement was non-significant across all groups: HRs were 1.37 (P=0.34), 1.26 (P=0.05), 0.98 (P=0.74), and 1.06 (P=0.67). The IPI C-statistics was 0.676 overall and declined with age: ≤40 years (0.708), 41–60 years (0.683), 61–80 years (0.641), and >80 years (0.598). We next stratified the IPI factors into patient- (age, ECOG PS) and disease-related (clinical stage, LDH, EN involvement). In patients ≤40 years, disease-related factors outperformed patient-related (C-index 0.693 vs 0.602), as in the 41–60 group (C-index 0.677 vs 0.589). In patients aged 61–80, predictive values were comparable (0.613 vs 0.622). In >80-year-olds, both declined substantially (0.580 vs 0.568). Conclusion The IPI remains a valuable prognostic tool in LBCL, particularly among younger patients. However, its overall predictive accuracy declines with age. In patients ≤60 years old, prognosis is primarily driven by disease-related factors, suggesting a role for molecular classifiers to enhance risk stratification. In older patients, incorporation of additional patient-level factors, such as comorbidities and nutritional status could be considered. These findings underscore the need for more individualized prognostic tools for LBCL patients. Funding: NU21-03-00411, P50 CA97274, U01 CA195568, Charles University Haematology-Oncology Cooperatio Program.

Abstract Introduction Ciltacabtagene autoleucel (cilta-cel) has demonstrated unprecedented response rates and progression-free survival (PFS) in relapsed/refractory multiple myeloma (RRMM), but a proportion of patients experience early relapse. Identifying these patients is essential to guide future therapeutic strategies, such as enhanced bridging therapy or consolidation, while avoiding overtreatment in those likely to achieve durable disease control. Methods We conducted a multi-center study of patients treated with cilta-cel at 15 U.S. academic centers. Outcomes were evaluated based on high-risk (HR) features, including functional HR disease (FHR; relapse within 18 months of first-line therapy initiation), true extramedullary disease (EMD), traditional HR cytogenetics as defined in CARTITUDE-1 [deletion 17p, t(4;14), t(14;16)], and a 2024 IMS-IMWG-adapted HR definition (deletion 17p; t(4;14) or t(14;16) with gain 1q or deletion 1p; and gain 1q with deletion 1p). Multivariable Cox regression was used to examine the association of HR features (FHR, adapted IMS-IMWG HR, and EMD, individually and collectively) with PFS while adjusting for baseline ferritin, prior BCMA, and ECOG performance status (PS ≥2). Among patients with ≥18 months of follow-up, we evaluated predictors of early relapse (≤18 months post infusion) using logistic regression. Results Our cohort included 598 patients treated with cilta-cel between 5/2022 and 12/2024 who had available data on all HR features. The median age was 65 years (range 33-83), and the median prior lines of therapy (pLOT) was 5 (range: 1-18, 84% with >3 pLOT); 8% had received prior BCMA-directed therapy, and 27% were penta-refractory. Most patients (88%) received bridging therapy and 30% achieved ≥ partial response. In our cohort, 33% (n=195) had FHR, 43% (n=258) had traditional HR cytogenetics, 42% (n=253) had adapted IMS-IMWG HR disease, and 11% (n=64) had EMD. Median follow up was 10.6 months (range 0.4-34.1). Overall response rate (ORR) and complete response (CR) rates were lower in patients with vs without HR features: FHR (ORR: 89% vs 94%, CR: 61% vs 75%), traditional HR (ORR: 90% vs 94%, CR: 65% vs 74%), adapted IMS-IMWG HR (ORR: 91% vs 94%, CR: 64% vs 75%) and EMD (ORR: 77% vs 94%, CR: 61% vs 71%). Corresponding 12-month PFS was also lower in patients with vs without HR features: FHR (55% vs 79%), traditional HR (63% vs 78%), adapted IMS-IMWG HR (61% vs 80%) and EMD (59% vs 73%). ORR, CR rate, and 12-month PFS in patients without any HR features compared to those with ≥1 HR feature were 96% vs 90%, 81% vs 64%, and 83% vs 65%, respectively. In multivariable models, each HR feature (FHR, adapted IMS-IMWG HR, and EMD) was independently associated with inferior PFS after adjusting for baseline ferritin, prior BCMA, and ECOG PS ≥ 2. However, because early relapse cannot always be identified a priori based on traditional risk factors, we sought to identify predictors of relapse within 18 months of cilta-cel in a subset of patients with ≥18 months of follow-up (N=247; infused by 12/2023). Notably, while FHR (OR=3.04, 95% CI=1.59-5.89) and adapted IMS-IMWG HR (OR=2.07, 95% CI=1.10-3.89) were independently associated with odds of relapse within 18 months, EMD was not (OR=0.90, 95% CI=0.32, 2.40) when accounting for prior BCMA, ECOG PS ≥ 2, and baseline ferritin. We also used a time-varying Cox model to examine each HR feature associated with PFS at two time periods, <18 and ≥18 months, while adjusting for baseline ferritin, ECOG PS, and prior BCMA. We confirmed that FHR and adapted IMS-IMWG HR were both associated with relapse <18 months (HR=2.05, 95% CI=1.48-2.83 and HR=1.56, 95% CI=1.11-2.19) but not in the ≥18-month time period (HR=0.70, 95% CI=0.19-2.59 and HR=0.76, 95% CI=0.24-2.40). The magnitude of the association for EMD with PFS was similar across both time periods (<18 months HR=1.67, 95% CI=1.09-2.57 and ≥18 months HR=2.17, 95% CI=0.45-10.40). Conclusion While cilta-cel leads to high response rates and durable PFS in RRMM, a subset of patients with FHR and adapted IMS-IMWG HR remains at increased risk for early relapse. In this large multi-center cohort, we describe PFS outcomes across high-risk subgroups and identify key predictors of relapse within 18 months of cilta-cel. These findings inform risk-adapted therapeutic strategies and interventional trials, including bridging, consolidation, or maintenance approaches, to improve outcomes in the highest-risk RRMM patients.

Abstract Introduction: The CARTITUDE-4 study (NCT04181827), which enrolled patients with lenalidomide-refractory multiple myeloma (MM) after 1–3 prior lines of therapy (pLOT), demonstrated a significant benefit of ciltacabtagene autoleucel (cilta-cel) over established triplet regimens. However, the benefit of cilta-cel for patients with standard-risk cytogenetics remains less defined. Here, we report outcomes in patients with standard-risk cytogenetics from the intent-to-treat and as-treated populations in CARTITUDE-4. Methods: InCARTITUDE-4, patients randomized to the cilta-cel arm underwent apheresis and bridging treatment with either pomalidomide, bortezomib, and dexamethasone (PVd) or daratumumab, pomalidomide, and dexamethasone (DPd), followed by lymphodepletion therapy with cyclophosphamide and fludarabine, and then a single cilta-cel infusion. Progression-free survival (PFS) was assessed using a validated computerized algorithm. The intent-to-treat population included 208 patients; 32 patients progressed or died on bridging therapy, resulting in an as-treated population of 176 patients. Patients with high-risk cytogenetics, defined as del(17p), t(14;16), t(4;14), gain/amp(1q) (n=105), or with unknown cytogenetics (n=12), were excluded from the as-treated analysis. The 12-month minimal residual disease (MRD)-negative complete response (CR) was defined per the International Myeloma Working Group criteria as the proportion of participants with CR or better prior to and at 12 months (±3 months), achieving MRD-negative status at 12 months (+3 months) after cilta-cel infusion, as determined by next-generation sequencing (10-5), prior to progressive disease or subsequent anti-myeloma therapy. For the as-treated population, PFS rates were measured from the time of cilta-cel infusion. Results: In CARTITUDE-4, in the intent-to-treat population, at a median follow-up of 33.6 months, patients with standard-risk cytogenetics had a 30-month PFS rate (95% CI) of 71.0% (58.8–80.2) in the cilta-cel arm (N=69) vs 43.2% (31.3–54.5) in the standard-of-care (SOC) arm (N=70). Patients with standard-risk cytogenetics in the as-treated population (n=59) had a 30-month PFS rate of 80.5% (95% CI, 67.2–88.8). In CARTITUDE-1 (NCT03548207), which evaluated cilta-cel in patients with heavily pretreated relapsed/refractory MM (RRMM; ≥3 pLOT), the 30-month PFS rate among patients with standard-risk cytogenetics (negative for del(17p), t(14;16), or t(4;14); n=68) was 59.9% (95% CI, 47.2–70.5). In the CARTITUDE-4 as-treated population with standard-risk cytogenetics, 8 PFS events occurred within 1 year and 4 PFS events beyond 1 year of cilta-cel infusion. Twenty-six patients achieved MRD-negative CR at 12 months after cilta-cel; 100.0% of these patients were progression free at 30 months. Fourteen patients were not evaluable for MRD due to: calibration failure (n=12), no sample availability for testing (n=1), or indeterminate results post baseline (n=1). Conclusions: The PFS rate at 2.5 years for patients with standard-risk RRMM was higher in CARTITUDE-4 compared with CARTITUDE-1, supporting the use of cilta-cel as early as second line in the treatment course. In CARTITUDE-4 (as-treated population), 80.0% of patients with standard-risk cytogenetics were progression free and off treatment at 2.5 years. In patients with standard-risk disease who achieved MRD-negative CR at 1 year, this rate increased to 100.0%. The low rate of progression events in cilta-cel-treated patients with standard-risk cytogenetics shows the profound benefit of a single cilta-cel infusion in this population.

Abstract Background: Although progression-free survival (PFS) in early-stage (ES) classic Hodgkin Lymphoma (cHL) is high, there remains a subset of patients (pts) at higher risk of worse outcomes, particularly in those with unfavorable risk disease. Leveraging pt-level data from within the HoLISTIC (Hodgkin Lymphoma International STudy for Individual Care) Consortium, the E-HIPI was developed in clinical trial pts and validated in real-world registry pts to predict 2-year (y) PFS (Rodday. NEJM Evidence 2025). Continuous, objective, and readily measurable pre-treatment variables were considered, with sex, maximum tumor diameter, albumin, and hemoglobin included as significant predictors. Using the E-HIPI, interactive calculators for pt risk, comparison, and stratification were created (https://rtools.mayo.edu/holistic_ehipi/). Although the E-HIPI was developed and validated in both unfavorable and favorable ES cHL pts, the clinical predictors are primarily markers of unfavorable disease. In the development and initial validation cohorts, 2y PFS was 93.7% and 90.3%, respectively, the C-statistic was 0.63 in both cohorts, calibration was strong with slight underprediction of events in the validation cohort, and the E-HIPI outperformed the historic binary classification of patients as favorable or unfavorable using EORTC criteria. However, since pts in the original E-HIPI development and validation were primarily treated with ABVD chemotherapy protocols, additional validation is needed, particularly among pts with unfavorable ES cHL who were treated initially with more intensive chemotherapeutic platforms (e.g., BEACOPP-based). Thus, we conducted an external validation of the E-HIPI in pts treated on recent unfavorable ES cHL GHSG trials. Methods: The validation cohort included 2367 adults (ages 18 to 60y) with newly-diagnosed unfavorable cHL from randomized phase 3 trials conducted by the GHSG (HD14 von Tresckow. JCO 2012; and HD17 Borchmann. Lancet 2021). We calculated the predicted probability of 2y PFS events based on the E-HIPI model equation (scale 1-100%). Model performance was assessed using discrimination and calibration. Discrimination, which is the model's ability to separate high and low risk pts, was assessed using Harrell's C-statistic. Calibration, which is the model's ability to accurately predict absolute risk, was assessed by comparing observed and predicted probabilities of 2y PFS events within quintiles of predicted probabilities and estimating calibration-in-the-large (predicted minus observed event rate) and the calibration slope. Results: For pts treated on unfavorable GHSG trials, mean age was 33y, 95% had stage II disease, mean baseline E-HIPI score (i.e., predicted 2y PFS event rate) was 6.0% (SD=2.3%), and the observed 2y PFS rate was 96.2% (95%CI: 95.4-97.0%). The C-statistic was 0.65 (95% CI: 0.59-0.70), calibration-in-the-large was 0.02, and the calibration slope was 1.08 (95% CI: 0.52, 1.64). Within the lowest risk E-HIPI quintile, the observed 2y PFS event rate was 1.6%, compared with 6.1% in the highest risk quintile. Conclusions: In external validation of the recently published E-HIPI in pts with cHL treated on unfavorable GHSG trials using more intensive BEACOPP-based protocols, discrimination was similar to the original development and validation cohorts. Collectively, the E-HIPI is a treatment-agnostic prediction tool across varied treatment regimens for unfavorable ES cHL pts. Better 2y PFS in the GHSG trials than in the E-HIPI development and initial validation cohorts may have contributed to mild misclassification, likely reflecting differences in treatment regimens. This may be resolved with recalibration or incorporating granular treatment variables into future models. Additional research is needed to understand E-HIPI performance in pts with favorable disease who have even lower event rates as well as to more fully predict individualized pt outcomes across varied chemotherapy platforms and radiation received for all ES cHL pts.

Abstract Introduction The LEO Comorbidity Index (LCI), comprising ten equally weighted comorbidity categories (respiratory, cardiovascular, digestive, hepatic, renal, autoimmune, diabetes, cancer history, HIV, and stroke), has demonstrated prognostic utility in patients with non-Hodgkin lymphomas. However, its predictive accuracy may vary with increasing age, due to age-related rises in comorbidity burden and physiological vulnerability. To investigate these dynamics, we evaluated the age-stratified predictive performance of LCI in patients with large B-cell lymphoma (LBCL), with a focused analysis on individuals aged 80+ years. We also examined which specific comorbidities most strongly influenced overall survival (OS) and developed a simplified, clinically applicable index tailored to this population. Methods We first included adults ≥40 years with newly diagnosed LBCL and recorded comorbidities from the LEO registry (USA, 2015–2020; n=1,652) to assess age-stratified predictive performance of LCI using the C-index. Next, we focused on patients 80+ years (n=263), pooled from the LEO cohort (n=147) and NiHiL registry (restricted to General Hospital, Prague, Czech Republic, 2010–2023; n=116). Comorbidities were collected from patient self-reports (LEO) and/or medical records (LEO/NiHiL). Univariate and multivariate Cox models were used. The primary endpoint was OS. Results Among the total LBCL population (n=1,652), LCI predictive performance improved by age category with C-index for patients <65 of 0.584, 65–79 years 0.579, and ≥80 years 0.647, indicating stronger discrimination in the 80+ group. Subsequent analysis focused on patients aged 80+ years (n=263; median age 83). Within this cohort, 34% had ECOG performance status (PS) 2–4, 66% stage III–IV disease, 59% elevated lactate dehydrogenase, 35% >1 extranodal site involved, and 58% International Prognostic Index (IPI) score of 3–5. A total of 377 comorbidities were reported, averaging 1.43 per patient (1.22 in LEO; 1.61 in NiHiL). LCI distribution was: 0 points in 24% (n=62), 1 point in 33% (n=88), and 2–5 points in 43% (n=113). Higher LCI scores had worse ECOG PS 2–4: 19% (LCI=0), 33% (LCI=1), and 43% (LCI ≥2; P=0.01), and inferior OS (median 6.9 years LCI=0, 3.0 years LCI=1, and 2.8 years LCI≥2, resp.; P=0.02). Presence of ≥1 comorbidity was associated with shorter OS compared to no comorbidities (HR=1.67, P=0.01). The LCI was an independent predictor of OS after adjusting for age (HR=1.18, P=0.01, C-index 0.623), IPI (HR=1.16, P=0.03, C-index 0.628) as well as for recently proposed SENIOR-IPI (HR=1.14, P=0.049, C-index 0.665). When evaluating individual comorbidities, heart disease (HR=1.54, P=0.01), stroke (HR=1.89, P=0.03), and recent malignancy (HR=1.99, P=0.02) were independent predictors of OS. Based on these findings, we developed a simplified LCI (sLCI) for use in 80+ patients incorporating only the three comorbidities (1 point each). Patients were categorized as sLCI: 0 (49%, n=129), 1 (44%, n=115), and 2–3 points (7%, n=19). sLCI scores 0, 1, 2–3 were associated with ECOG PS 2–4 27%, 37%, and 63%, respectively (P=0.01) and inferior OS: median 5.3 years (0), 2.5 years (1), and 1.4 years (2–3; P<0.01). sLCI remained an independent and strong predictor of OS after adjusting for age (HR=1.71, P<0.01, C-index 0.636), IPI (HR=1.66, P<0.01, C-index 0.635), and SENIOR-IPI (HR=1.60, P<0.04, C-index 0.659). The results were consistent in both NiHiL and LEO cohorts and remained significant among patients treated with anthracyclines (sLCI adjusted for age: HR=1.58, P<0.01, C-index 0.607; for IPI: HR=1.53, P=0.01, C-index 0.623; for SENIOR-IPI: HR=1.51, P=0.01, C-index 0.633). Conclusion Comorbidity burden becomes increasingly prognostic with higher age in LBCL patients. In the 80+ individuals, comorbidities – particularly heart disease, recent cancer, and stroke – have an independent impact on OS. While the original LCI captures the impact of broadly defined comorbidities, its prognostic utility attenuates in the 80+ patients, likely reflecting limited relevance of indolent chronic conditions in this population's constrained survival horizon. The sLCI, focused on the most prognostically relevant comorbidities, offers practical risk stratification. Incorporation of sLCI into clinical decision-making – alongside other prognostic indices – could better identify high-risk individuals and guide treatment strategies. Funding: NU21-03-00411, U01 CA195568.

Abstract Background Accurate reconstruction of electronic health record (EHR) treatment sequences is essential for leveraging real-world data (RWD) in clinical research and trial-eligibility platforms in multiple myeloma (MM). However, inconsistencies in RWD treatment documentation and the complexity of line-of-treatment (LOT) assignments in MM hinder reproducibility and limit comparisons against published trials. To address these limitations, we developed HEAL-MM, an automated rule-based algorithm that systematically parses structured medication data to calculate LOTs and segment treatment into standardized, clinically meaningful phases. The algorithm is designed to be interpretable, adaptable to evolving therapeutic frameworks, and compatible with widely adopted EHR data standards. Here, we report its real-world validation against expert-adjudicated treatment sequences to assess concordance, identify edge-case failure points, and support RWD integration. Methods A stratified cohort of 100 MM patients (9,136 structured treatment events) was curated from 114 unique healthcare systems compiled from the multi-institutional HealthTree Foundation harmonized registry. Cohort selection was randomized to ensure coverage across diagnostic eras (pre-2010, 2015–2020, 2022–2025) and treatment exposures, including CAR T-cell therapies, bispecific antibodies, and anti-CD38-based regimens. HEAL-MM sequenced MM regimens chronologically and applied predefined logic rules to identify LOT transitions, the initiation of a new therapeutic course based on drug changes, treatment gaps, and procedural anchors such as stem cell transplant or CAR T. Within each LOT, the algorithm segments and labels therapy into phases, clinically distinct intervals (e.g., induction and maintenance) using rule-based clinical logic. Outputs were benchmarked against expert-labeled adjudications. Concordance was evaluated using concordance index (C-index), stratified by patient, LOT depth and diagnostic era. Results: HEAL-MM demonstrated high concordance with expert-labeled treatment sequences: 0.94 for LOT identification, 0.98 for treatment phase segmentation, and 0.96 for phase label assignment. Achieving complete agreement with all expert annotations in 90% of patients for LOT assignment, 94% for the number of treatment phases identified, 77% for the specific labels applied to each phase, and 75% across all three dimensions simultaneously. Among discordant patients, the absolute difference in final LOT count was 0.5 ± 0.9. Performance was highest in early lines (LOTs 1–3; C-index: 0.97), with seven patients showing discrepancies at this stage. Concordance declined in later lines (LOT ≥4; C-index: 0.87), where 50% of the discordant cases (n=4) were attributable to misclassifications in earlier LOTs, such as medication restarts following >3-month gaps, demonstrating the compounding effect of early errors on longitudinal accuracy. Temporal stratification revealed improved accuracy in post-2020 cases (C-index: 0.94) compared to 2015–2020 cohorts (0.91), reflecting the increasing adoption of structured treatment standards and EHR data quality improvements. To assess HEAL-MM performance at clinically significant junctures, we analyzed treatment transitions that initiated a new LOT or marked a change in phase, achieving C-indices of 0.97 for LOT transitions, 0.97 for treatment phase shifts, and 0.94 for phase label assignment. Conclusions: HEAL-MM provides a validated, scalable solution for standardizing MM treatment structures in RWD environments, as it accurately detects the exact treatment entry of a new LOT 97% of the time. This rule-based framework enables reproducible LOT and phase assignment across heterogeneous EHR systems, supporting real-time cohort identification across institutions and EHR platforms. The algorithm's modular design allows for continuous integration of novel therapeutic categories and evolving treatment logic. While the algorithm maintained high fidelity overall, early-stage misclassifications consistent with increased heterogeneity and documentation ambiguity occasionally propagated downstream, underscoring the compounding effect of upstream errors in longitudinal reconstruction. With its generalizable structure and compatibility with industry-standard data models, HEAL-MM lays the foundation for structured treatment reconstruction in MM across RWD infrastructure and serves as a transferable blueprint for other hematologic malignancies.

Abstract Background Patients (pts) with plasma cell leukemia (PCL) were excluded from key trials, so we assessed their outcomes with standard of care bispecific antibodies (BsAbs). Methods Our multicenter retrospective US Myeloma consortium study included PCL pts treated with BsAbs. PCL was defined as ≥5% circulating plasma cells, including primary PCL, secondary PCL, historical PCL, and active PCL within 30 days of BsAb initiation. Endpoints were toxicity rates, overall response (ORR), progression-free survival (PFS), and overall survival (OS). Results Our study involved 122 PCL pts from 15 US centers with: 27% primary PCL, 65% secondary PCL, 51% active PCL, 53% women, median age of 65 years (range 31-83), 77% white, 15% black, 60% high-risk cytogenetics, 45% high bone marrow burden (≥50% plasma cells), 5% CNS myelomatosis, and 20% EMD. Median prior lines of therapy (tx) were 6 (IQR 5-9), 94% triple-refractory, 57% penta-refractory, 73% prior transplant, and 57% prior BCMA tx (70% in talquetamab group, 41% in BCMA BsAb group). Treatments included 4 cohorts: 37% teclistamab, 11% elranatamab, 42% talquetamab definitive tx, and 11% talquetamab bridge to CAR-T. BsAb was used either as monotherapy (94%) or in combination (6%) with other agents. CRS grade (G) 1 was 41%, G2 14.8%, G3 2.5%, and G4 1.6%, while ICANS G1 was 8.2%, G2 7.4%, G3 3.3%, and G4 1.6%. Infections occurred in 34% and median hospitalization was 10 days (IQR 8-15). Of 110 evaluable pts, ORR was 54.5%, with 24.5% CR, 13.6% VGPR, and 16.4% PR. The ORR was 60.8% for talquetamab definitive tx (21.6% CR, 23.5% VGPR, 15.7% PR), 46.2% for elranatamab (38.5% CR, 7.7% PR), and 33.3% for teclistamab (20% CR, 4.4% VGPR, 8.9% PR). Talquetamab bridge to CAR-T in 12 evaluable pts had a pre-CAR-T day 30 ORR of 58.3% (16.7% CR, 8.3% VGPR, 33.3% PR). With a median follow-up of 8.3 months (mo), the median (m) PFS was 3.2 mo, and mOS was 9.2 mo, with similar outcomes between primary vs. secondary PCL (p=0.37 for PFS, p=0.18 for OS). Talquetamab definitive tx had better outcomes with a mPFS of 5.5 mo, mOS of 11.5 mo, 10-mo PFS of 31.2%, and 10-mo OS of 55.7%, and further improved when used as a bridge to CAR-T, with unreached mPFS, unreached mOS, 10-mo PFS of 53.8%, and 10-mo OS of 62.7%. BCMA BsAbs had inferior outcomes, with teclistamab yielding a mPFS of 1.2 mo, mOS of 8.1 mo, 10-mo PFS of 15.3%, and 10-mo OS of 44.3%, while elranatamab showed a mPFS of 1.6 mo and mOS of 3.6 mo (p=0.007 for PFS, p=0.023 for OS). In BCMA naive pts, BCMA BsAbs' outcomes remained inferior to talquetamab with a mPFS of 1.2 mo and mOS of 3.4 mo (p=0.003, 0.001). Sixty pts with active PCL had a poor mPFS (1.6 vs. 4.4 mo, p=0.036) and mOS (5.3 vs. 10.7 mo, p=0.004) compared to pts with only historical PCL. However, in active PCL, talquetamab definitive tx (n=29) showed superior outcomes with a mPFS of 6.9 mo and mOS of 12.2 mo (p=0.003, 0.012). Talquetamab bridge to CAR-T (n=3) showed a mPFS of 3.2 mo and mOS of 5.2 mo. BCMA BsAbs had inferior outcomes: teclistamab with a mPFS of 0.7 mo and mOS of 1.4 mo, while elranatamab showed a mPFS of 1.0 mo and mOS of 3.1 mo (p=0.003, 0.012). In BCMA naive pts, BCMA BsAbs' efficacy remained poor with a mPFS of 0.7 mo and mOS of 1.4 mo (p=0.006, 0.002). While in pts with only historical PCL (without active PCL), BCMA BsAbs in BCMA naive pts led to similar outcomes with a mPFS of 5.2 mo and mOS of 8.5 mo, compared to a mPFS of 3.7 mo and mOS of 10.4 mo with talquetamab (p=0.90, 0.86). Multivariable analysis (MVA) associated active PCL with worse PFS (p=0.035, HR=1.93, CI 1.05-3.55) and OS (p=0.017, HR=2.3, CI 1.16-4.60), talquetamab with improved PFS (p=0.009, HR=0.41, CI 0.21-0.81) and OS (p=0.012, HR=0.40, CI 0.19-0.82), and talquetamab bridge to CAR-T with improved PFS (p=0.042, HR 0.28, CI 0.08-0.95) without reaching statistical significance for OS (p=0.11, HR 0.29, HR 0.07-1.3). MVA results remained similar when the talquetamab bridge to CAR-T cohort was excluded. Conclusions Despite PCL's poor prognosis, talquetamab was superior to BCMA BsAbs in active PCL, with a mPFS of 6.9 mo and mOS of 12.2 mo, aligning with the broader cohort. BCMA BsAbs had poor efficacy in active PCL, even in BCMA naive pts, with a mPFS of 0.7 mo and mOS of 1.4 mo. In historical PCL without active PCL, BCMA BsAbs in BCMA-naive pts showed comparable outcomes to talquetamab. In the overall cohort, talquetamab bridge to CAR-T improved PFS without reaching significance for OS benefit in MVA.