Reid, Brett M.

Abstract Introduction: Cilta-cel is a BCMA-directed CAR T-cell therapy that has shown remarkable efficacy in patients with RRMM. As real-world (RW) experience with cilta-cel continues to expand, and with the recent FDA guidance reducing the recommended proximity to the CAR T center from four to two weeks post-infusion, there is growing interest in identifying patients at low risk for severe early complications who may be safely managed in the outpatient setting and potentially ‘discharged’ home earlier. Methods Data were collected from 769 patients with RRMM treated with cilta-cel at 15 institutions in the U.S. via the Multiple Myeloma Immunotherapy Consortium.Logistic regression was used to identify predictors of patients who could be managed in the outpatient setting (low-risk group). Based on expert consensus within the Consortium, low-risk patients were defined as: grade ≤1 CRS, no ICANS, and no IEC-HS. The remaining patients were categorized as high-risk. We evaluated baseline characteristics with missingness < 20% as predictors, including age, sex, race, and ethnicity, ECOG performance status (PS) at time of lymphodepletion (LD), extramedullary disease, bone marrow plasma cell (BMPC) percentage, high-risk cytogenetics, bridging therapy including response, penta-refractory status, number of prior lines of therapy, prior anti-BCMA therapy, baseline C-reactive protein (CRP), and baseline ferritin. The cohort was restricted to patients with complete data on all predictors (n=455) and split into a training and testing cohort (80/20 split, n=364/91). Bidirectional stepwise regression was used to build the predictive model using variables with significance of p<0.15 selected for entry. The best model was selected based on the AIC (Akaike Information Criterion) and a significance level of p<0.05. Results Among the 455 patients with complete data included in the final model, 290 (64%) were categorized as low-risk and 165 (36%) as high-risk. Compared to the low-risk group, high-risk patients were more likely to have an ECOG PS ≥2 at time of LD (20% vs 9%, p<0.001), high-risk cytogenetics (47% vs 35%, p=0.009), and penta-refractory disease status (34% vs 24%, p=0.031). High-risk patients also had a higher median BMPC percentage (15% vs 5%, p<0.001), and higher baseline pre-LD CRP and ferritin. Overall response rates were high, at 95% vs 90% (p=0.047) for the low vs high risk groups, with complete response or better achieved in 73% vs 66% of patients, respectively (p=0.14). With a median follow-up of approximately 11 months, median progression-free survival was not reached (95% CI: 27-NA) in the low-risk group and 18.2 months (95% CI: 13.1-NA) in the high-risk group. In terms of safety, non-ICANS neurotoxicity (NINT) occurred less frequently in the low-risk group (6.6% vs 13%, p=0.015). Specifically, Parkinsonism was observed in 1.7% vs 6.1% (p=0.013), and cranial nerve palsy in 4.5% vs 5.5% (p=0.7), respectively. Non-relapse mortality (NRM) was lower in the low-risk group (4.2%) compared to the high-risk group (14%; p < 0.001). The most common causes of NRM in the low- and high-risk groups were acute toxicities (0% vs 36%, including CRS, IEC-HS, and ICANS), delayed toxicities (0% vs 8%, including colitis, NINT, and Parkinsonism), infections (50% vs 48%), and other unrelated causes (50% vs 8%). The best model to predict low-risk patients that could be managed in the outpatient settingincluded BMPC percentage and ECOG PS at LD. In the training cohort, patients with a higher pre-LD BMPC percentage and an ECOG PS ³2 were less likely to be in the low-risk group(OR per 1-unit change in BMPC percentage = 0.99, 95% CI=0.98-0.99, p<0.001 and OR for ECOG PS 2-4 = 0.38, 95% CI=0.20-0.72, p=0.003). The AUC for this model was 0.663 in the training cohort and 0.659 in the testing cohort. Conclusion In this large, multi-institutional cohort of patients with RRMM treated with cilta-cel, we identified clinical factors associated with lower risk for early severe toxicities, supporting outpatient administration and the potential for earlier discharge in selected patients, particularly those who are fit and have low disease burden. Our findings provide a practical framework to guide risk stratification and outpatient CAR T management strategies, particularly in light of evolving regulatory guidance and growing RW experience with cilta-cel.

Abstract Introduction: Ciltacabtagene autoleucel (cilta-cel) is a highly effective BCMA-redirected CAR T-cell therapy for RRMM. While the pivotal CARTITUDE-1 trial established a target dose of 0.75 x 10^6 CAR+ viable T-cells/kg, the approved cell dose is 0.5-1.0 x 10^6 and the optimal dosing strategy to balance efficacy and toxicity remains unclear. This question is of interest given that a dose-response association has been demonstrated for idecabtagene vicleucel (a related BCMA-directed CAR T product). It has also been hypothesized that lower doses of cilta-cel may be associated with reduced toxicity. This study aims to evaluate the association between cilta-cel dose and key efficacy and toxicity outcomes using a large, real-world dataset from the US Multiple Myeloma Immunotherapy Consortium. Methods: We conducted a multicenter, retrospective analysis of 751 RRMM patients treated with commercial cilta-cel across 15 US academic centers. The administered cilta-cel dose (million cells/kg) was categorized into four groups: low (<0.5), standard-low (0.5 to <0.6), standard (0.6 to <0.8), and standard-high (0.8 to 1.0). We compared incidence rates of cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), delayed neurotoxicity (DNT), and immune effector cell-associated hemophagocytic lymphohistiocytosis-like syndrome (IEC-HS) across these dose categories using logistic regression. Best response rates and progression-free survival (PFS), estimated using the Kaplan-Meier method, were also compared, with differences assessed using the log-rank test for the latter. Results presented are derived from multivariable models adjusting for extramedullary disease, ECOG status, penta-refractory disease and receipt of bridging therapy. Results: Among 751 infused patients, the dose distribution was: <0.5 (n=33), 0.5 to <0.6 (n=131), 0.6 to <0.8 (n=550), and 0.8 to 1.0 (n=37). The four dose groups were well-balanced in terms of baseline patient and disease characteristics, including age, high-risk cytogenetics, and extramedullary disease, with the exception of t(14;16), which was more common in the standard-low and standard dose groups (p=0.006). There were no statistically significant differences in the rates of any grade CRS (p=0.11), any grade ICANS (p=0.84), grade ≥3 ICANS (p=0.36), duration of CRS (p=0.18) or ICANS (p=0.64), or IEC-HS (p=0.16) across the four dose categories. Time to onset of CRS was shorter for the standard-high dose compared to the low dose (6 days vs. 8 days; p=0.08). Importantly, there was no significant difference in the rate of DNT (3.0%, 9.2%, 8.8%, 5.4%; p=0.24). Similarly, there was no significant difference in overall response rates (97%, 96%, 96%, 88%; p=0.50) or complete response rates (75%, 65%, 75%, 66%; p=0.10) by dose. The median follow-up was 12.3 months (IQR: 6.9, 20.5). For patients who received a standard-low dose (0.5, 0.6), the median PFS was 17.5 (IQR: 13.5, NR) months. For the standard dose range of (0.6, 0.8), the median PFS was 28.7 (95% CI: 19.8, NR) months, while the median PFS for the standard-high dose (0.8, 1.0) was not reached. For the low dose range of <0.5, the median follow up was only 8.7 months, which did not allow reliable estimation of the median PFS. While the overall difference in PFS among the groups did not reach statistical significance by the log rank test, there was a clear trend toward improved PFS with higher cell doses in the multivariable model, with outcomes clearly ordered in the same direction as the cell dose categories. A similar trend was also evident in the rates of 12-month PFS which showed an improvement with increasing dose (62% ,67%, 73%, 80%). Conclusions: In this large, real-world cohort of RRMM patients treated with cilta-cel, a lower cell dose was not associated with reduced incidence or severity of CRS, ICANS, DNT, or IEC-HS. Conversely, a dose-response correlation with PFS was observed, with higher doses trending toward longer median and 12-month PFS. It remains unknown if a lower cell dose in the commercial setting reflects poor T cell quality, yet these findings suggest that dose reduction of cilta-cel may not be an effective strategy to mitigate toxicities and might be associated with a trend toward lower efficacy. Therefore, the standard target dose should be utilized to maximize patient outcomes and further prospective dose exploration is warranted.

Abstract Background: IEC-HS is a potentially life-threatening complication following CAR-T therapy in RRMM. Reliable diagnostic biomarkers are needed to help expeditiously identify cases to initiate prompt treatment and supportive care. Serum ferritin, a routinely available laboratory parameter, is commonly elevated in IEC-HS and has been used to identify cases. However, ferritin can also be elevated in cytokine release syndrome (CRS) or secondary to baseline tumor-related inflammation. The optimal diagnostic threshold for post-CAR-T ferritin in IEC-HS in RRMM has not been previously established. Methods: This study used data from 15 centers within the US Multiple Myeloma Immunotherapy Consortium to identify the optimal post-CAR-T ferritin threshold to distinguish RRMM patients (pts) with and without a diagnosis of IEC-HS. Receiver operating characteristic (ROC) analysis was used to determine the optimal threshold based on Youden's Index. The performance of the threshold was evaluated overall and stratified by CAR-T product (cilta-cel, ide-cel). Additional analyses included the temporal relationship of peak ferritin and IEC-HS diagnosis, the evaluation of IEC-HS criteria according to ferritin levels, comparison of IEC-HS criteria co-occurrence, and comprehensive baseline characteristic comparisons across ferritin strata. Results: Of 1502 RRMM CAR-T recipients (ide-cel, n=712; cilta-cel, n=790), 74 patients (4.9%) had a provider-determined diagnosis of IEC-HS. A post-CAR-T ferritin threshold of 7470 ng/mL was identified as optimal for distinguishing pts with and without IEC-HS (AUC 0.938). Sensitivity (0.904) and specificity (0.934) were both very high, indicating high discriminatory ability. Stratified ROC analyses showed near-identical optimal thresholds for cilta-cel (7450 ng/mL; sensitivity 0.885, specificity 0.936, AUC 0.94) and ide-cel (7470 ng/mL; sensitivity 0.952, specificity 0.933, AUC 0.95), indicating minimal impact of CAR-T product on ferritin's prognostic utility. Baseline pt characteristics (N=1,413) differed significantly by ferritin threshold. Pts above the ferritin threshold of 7470 (N=154) were younger (median 64 vs 67 years; p=0.004), more frequently male (65% vs 56%; p=0.043), had higher baseline ferritin (1,300 vs 187; p<0.001), higher ECOG at apheresis (p=0.001), more likely penta-refractory (p=0.016), had higher baseline bone marrow plasma cell burden (p<0.001), and had higher R-ISS scores (p<0.001). Evaluation of IEC-HS criteria (Hines et al, TCT 2023) demonstrated significantly higher rates of laboratory and clinical criteria, including hepatic transaminase elevation (43% vs 15%), hypofibrinogenemia (53% vs 0%), cytopenias (95% vs 81%), hypertriglyceridemia (43% vs 8%), ICANS (52% vs 23%), renal insufficiency (20% vs 0%), and soluble IL-2 receptor elevations (47% vs 4%), in pts with ferritin >7500 ng/mL compared to those with lower ferritin. Heatmap visualization revealed distinct patterns of co-occurring criteria in high- and low-ferritin groups. Assessment of the timing of peak ferritin and IEC-HS diagnosis among IEC-HS cases revealed a median difference of 0 days (IQR: -1, 0), suggesting simultaneous occurrence. Median time to max ferritin was 9 days (IQR 8,12) in IEC-HS pts compared to 8 days (IQR 5,10) in non-IEC-HS pts (Wilcoxon p<0.05). Conclusions: In this analysis of over 1500 patients, a post-CAR-T ferritin level of 7500 ng/mL robustly identifies RRMM pts with IEC-HS following either cilta-cel or ide-cel. Pts exceeding this threshold demonstrate marked enrichment in IEC-HS diagnostic criteria and significant differences in baseline characteristics, warranting further study of the role of ferritin in IEC-HS diagnosis and early intervention.

Abstract Introduction: IEC-HS is an emerging and potentially life-threatening complication in multiple myeloma (MM) patients (pts) treated with CAR-T therapies. We conducted a retrospective, multi-institutional analysis to define the features and outcomes of IEC-HS after ide-cel or cilta-cel in the real-world setting. Methods: This study used data from 15 centers within the US Multiple Myeloma Immunotherapy Consortium for patients with relapsed/refractory MM (RRMM) who received commercial cilta-cel or ide-cel. IEC-HS was defined by each center based on diagnostic criteria (Hines et al, TCT 2023). Kaplan-Meier and multivariable Cox models were used for time-to-event outcomes while logistic regression was used for categorical outcomes. Results: Of 1502 RRMM pts who received either ide-cel (n=712) or cilta-cel (n=790), 74 patients (4.9%) had a provider-determined diagnosis of IEC-HS. Product-specific incidences were 2.9% for ide-cel and 6.7% for cilta-cel. The median time from CAR-T infusion to IEC-HS onset was 9.5 days (range 2-37 days). IEC-HS-related clinical findings (Hines et al, 2024) included cytopenias (95%), elevated soluble IL-2R levels (79%), fever (76%), elevated LDH (71%, median 1,800 U/L), hypofibrinogenemia (70%), hypertriglyceridemia (65%), ICANS (63%), hepatic transaminase elevation (56%), pulmonary involvement (46%), renal insufficiency (29%), hyperbilirubinemia (22%), and hemophagocytosis in bone marrow (6%). The median interval from peak CRS to IEC-HS onset was 4 days (IQR 1–6). Some variables, such as splenomegaly and coagulopathy, were not systematically collected. Nearly all pts received pharmacologic intervention: corticosteroids (94%, n=59), anakinra (76%, n=48), tocilizumab (71%, n=45), emapalumab (13%, n=8), ruxolitinib (16%, n=10), or etoposide (3%, n=2). Only one pt (2%) did not receive any IEC-HS therapy. Among those evaluable for outcomes, 58% (n=35) improved, 12% (n=7) developed further CAR-T-related complications (e.g., recurrent IEC-HS, delayed neurotoxicity), and 30% (n=18) had no therapeutic response and died. Compared to non-IEC-HS pts (n=1,428), those with IEC-HS were younger (median age 63 vs 66), male (61% vs 57%), and more frequently received cilta-cel (72% vs 52%, all p<0.01). They had higher rates of triple-class (81%) and penta-refractory (43%) disease, greater pre-treatment bone marrow plasma cell burden (40% vs 6%), and higher baseline ferritin (1058 vs 208 ng/mL). Prior autologous SCT was less common, and advanced R-ISS stage was more prevalent. IEC-HS was associated with dramatically worse PFS and OS: with cilta-cel, median PFS was 6.5 months (mo) with IEC-HS (vs 33.1 without); for ide-cel, 8.9 vs 11.4 mo. Median OS for cilta-cel IEC-HS pts was 9.6 mo (not reached in non-IEC-HS pts); for ide-cel IEC-HS, 19.6 mo (vs 40.8 mo). Multivariable analysis confirmed IEC-HS as an independent predictor of shorter PFS (HR 1.88, 95% CI 1.29–2.74, p=0.0011) and OS (HR 2.30, 95% CI 1.39–3.83, p=0.0013). Notably, IEC-HS was not associated with lower response rates – either best CR (63.9% vs 64.0%, p=0.71) or ORR (91.8% vs 90.9%, p=0.97). Cilta-cel pts had a higher probability of achieving a deep (CR) response, regardless of IEC-HS status. Grade > 3 CRS occurred in 14% (n=3/21) of ide-cel and 15% (n=8/52) of cilta-cel IEC-HS pts, versus 2% (n=12/691) and 2% (n=13/737) in non-IEC-HS pts, respectively. Any-grade ICANS was observed in 67% (n=14/21) of ide-cel and 55% (n=29/52) of cilta-cel IEC-HS cases, compared to 26% (n=179/690) and 20% (n=148/738) in non-IEC-HS. Grade ≥3 ICANS occurred in 29% (n=6/21) of ide-cel and 26% (n=14/52) of cilta-cel IEC-HS cases, relative to 4% (n=25/690) and 4% (n=27/738) in pts without IEC-HS. Severe infections were also more common: 71% (n=15/21) of ide-cel and 53% (n=28/52) of cilta-cel IEC-HS pts experienced at least one infection, compared to 31% (n=214/691) and 29% (n=217/737) in non-IEC-HS groups. Conclusions: IEC-HS is an uncommon but severe and early complication after CAR T-cell therapy for RRMM. Despite similar ORRs with or without IEC-HS, IEC-HS was associated with significantly worse PFS and OS for both ide-cel and cilta-cel. These findings support the need for vigilant monitoring and potential preemptive treatment strategies to better manage this syndrome.

Abstract Background: Real-world data suggest that safety and efficacy of ide-cel and cilta-cel mirror data from pivotal trials, yet older adults remain underrepresented. We report outcomes of the largest cohort of RRMM patients aged ≥75 years receiving CAR-T and compare them with propensity-matched younger patients. Methods: We retrospectively analyzed RRMM patients receiving standard-of-care BCMA CAR-T at 15 centers from the US Myeloma Immunotherapy Consortium between May 2021 and December 2024. Patients ≥ 75 years were 2:1 propensity-matched to patients < 75 based on CAR-T product, sex, performance status, extramedullary disease, baseline ferritin, penta-refractory status, bridging therapy, prior autologous SCT and lines of therapy. Continuous outcomes were analyzed with multivariable linear regression, binary outcomes with logistic regression, and time-to-event endpoints with Cox proportional-hazards models. We estimated average treatment effects via G-computation, using cluster-robust standard errors to account for the matched sets. Results: After 2:1 propensity matching, the cohort included 194 patients ≥ 75 years (cilta-cel: 64 and ide-cel: 130) and 388 patients < 75 years (cilta-cel: 128, ide-cel: 260). Median ages were 78 (range: 75-90) for the older cohort vs 65 (range: 35-74) for the younger cohort. The older cohort had a higher frequency of t(14;16) (9% vs. 3%, p = 0.005), lower baseline ferritin (151 vs. 196 ng/mL, P = 0.046), and lower likelihood of prior ASCT (57% vs. 71%, P = 0.001). Median prior lines of therapy were 5. Median follow-up time for ≥ 75 and < 75 groups for cilta-cel was 11.5 and 11 months and for ide-cel was 16 and 24.3 months, respectively. ORR did not differ amongst the older vs younger patients: cilta-cel (97% vs. 97%) and ide-cel (90% vs. 88%).For patients ≥ 75 years, median PFS with cilta-cel was 28.7 months (95% CI: 13.3–NR) and with ide-cel was 16.4 months (95% CI: 12.1–22.4). In patients < 75 years, median PFS was not reached for cilta-cel but was 11.5 months (95% CI: 9.6–12.7) for ide-cel, reflecting inferior PFS with ide-cel in the younger group (HR 0.4, 95% CI 0.3–0.6). On sensitivity analysis by Cox proportional hazards model, we did not observe any statistically significant differences in PFS in older vs younger patients receiving cilta-cel, although older patients paradoxically had better PFS with ide-cel compared to their matched younger cohort. In the cilta-cel cohort, HR for PFS for older vs younger patients was 1.48 (95% CI:0.77-2.84; p=0.2) and in the ide-cel cohort it was 0.7 (95% CI: 0.5, 0.98; p=0.04). Median OS was reached only in the < 75-year ide-cel cohort at 40 months (95% CI: 32.5–43.5). Rates of CRS were similar, with any-grade (grade ≥3) CRS seen in 74% vs 78% (0% vs. 0.8%) of cilta-cel recipients and 86% vs. 87% (3.1% vs. 1.5%) of ide-cel recipients in the older vs younger groups, respectively. ICANS rates were 21% vs. 20% for cilta-cel (p = 0.79) and 29% vs 20% for ide-cel (p = 0.07), with similar rates of grade ≥ 3 ICANS. In the cilta-cel cohort, Parkinsonian delayed neurotoxicity (DNT) occurred in 3/62 (4.8%) older patients and 6/128 (4.7%) younger patients. Similarly, other non-Parkinsonian DNT rates were similar across age groups, with 6.4% vs 5.5% in older vs younger cilta-cel cohort and 0% vs 1% in the older vs younger ide-cel cohort. Rates of IEC-HS, and infections were comparable across the cohorts. There was no difference in hospital length of stay and ICU admission rate between products within either age cohort. The overall non-relapse mortality (NRM) was 6.7% for patients ≥ 75 years and 6.2% in patients < 75 years. For cilta-cel, NRM in older vs younger cohort was 9.4% vs 4.7% (p= 0.96) and for ide-cel it was 5.4% vs 6.9%, respectively (p= 0.65). Conclusion: Appropriately selected older adults (≥75 years) receiving CAR-T therapy for RRMM can derive a similar efficacy benefit with CAR-T as a propensity-matched younger cohort, without any increased signal of toxicity including CRS, ICANS or delayed NT. NRM was numerically greater, but not statistically significant, in older adults receiving cilta-cel. Median PFS with cilta-cel and ide-cel in older adults were 28.7 months and 16.4 months with median OS not reached and 40 months, respectively. Older adults, ≥75 years old, should not be excluded from CAR-T therapy solely due to advanced age. Overall, cilta-cel was not associated with worsened toxicities in older adults and may offer better PFS.

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 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: Identifying dysregulated gene expression in ovarian cancers has been limited by a deficit of available normal tissues. Here, we generated the largest set of high-grade serous ovarian cancer (HGSOC) tumors with normal precursor tissues for transcriptome analyses to identify lncRNA expression patterns, provide insight into their cellular contexts and functions, and assess their relevance to HGSOC pathogenesis and genetic susceptibility. Methods: We performed RNA sequencing on 220 primary HGSOCs and 116 benign epithelia (micro-dissected fallopian tube, ovarian surface, and inclusion cyst epithelia), and combined samples with 428 HGSOCs from TCGA, 60 HGSOCs from a prior study, and 180 bulk ovary tissues from GTEx. Reads were processed with a uniform bioinformatic and quality control pipeline, combined and batch corrected. Normalized TPM expression values were compared using the Wilcoxon rank sum test with >2-fold change and FDR <1% considered statistically significant. We computed weighted gene co-expression networks for each tissue type and tested for enrichment of differentially expressed genes (DEGs) and gene ontologies within co-expression modules. Results: Transcriptomes included 27,700 expressed genes (8,202 lncRNAs) across 706 HGSOCs, 180 bulk ovary, and 88 ovarian epithelia tissues. Most genes were expressed in all tissues, though 5% each showed HGSOC- or normal tissue-specific expression which comprised ≥50% lncRNA. Comparing HGSOCs to normal tissues identified 11,804 DEGs with 4,522 lncRNAs (DElncRNA) of which ~50% were tissue-specific. DEGs included MUC16 and multiple GWAS/TWAS implicated susceptibility genes including RAD51, BRIP1, BNC2, TIPARP-AS1, PRC1, KANSL1, ANKLE1, CHMP4C, ESRP2, and CCNE1. The most highly expressed DElncRNA in HGSOC were upregulated RMRP (P=1.4 × 10−39), SNHG1 (P=3.0 × 10−27), and HAGLR (P=2.0 × 10−24) at the HOXD risk locus. Lower proportions of lncRNA were seen in the HGSOC co-expression modules compared to those in normal tissues and less (3% vs. 15%) HGSOC modules were considered strongly lncRNA regulated (>50% lncRNA in module). Tissue-specific DElncRNA tended toward upregulation in HGSOC compared to ovarian epithelia with enriched modules associated with cell cycle regulation (P=1.5 × 10−38); and toward downregulation in HGSOC compared to bulk ovary with enriched modules associated with chemokine signaling/response (P=1.7 × 10−113). Conclusions: The lower proportion of lncRNA involved in HGSOC co-expression networks indicates a marked loss of regulation by lncRNA. Preliminary interrogation of HGSOC-dysregulated lncRNA expression revealed tissue-specific differences that highlight unique biological pathways in precursor epithelia and the ovarian microenvironment that contribute to HGSOC pathogenesis. Our results provide additional support for previously nominated risk genes. eQTL analysis has recently been completed and integration with GWAS are underway to elucidate lncRNA and lncRNA-regulated susceptibility genes and will be presented at the conference. Citation Format: Brett M. Reid, Ann Chen, Zhihua Chen, Florian A. Karreth, Peter Kanetsky, Jennifer B. Permuth, Ozlen Saglam, Jamie K. Teer, Xiaoqing Yu, Simon Gayther, Ellen Goode, Paul Pharoah, Thomas A. Sellers, Kate Lawrenson, Jonathan Tyrer. Patterns of lncRNA-regulated gene expression in high-grade serous ovarian carcinomas [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A004.

Abstract Purpose: Identifying dysregulated gene expression in ovarian cancers has been limited by a deficit of available normal tissues. Here, we generated the largest set of high-grade serous ovarian cancer (HGSOC) tumors with normal precursor tissues for transcriptome analyses. Methods: We performed RNA sequencing on 220 primary HGSOCs and 116 benign epithelia (micro-dissected fallopian tube, ovarian surface, and inclusion cysts), and combined samples with 428 HGSOCs from TCGA, 60 HGSOCs from a prior study, and 180 bulk ovary tissues from GTEx. Raw reads were processed with a uniform bioinformatic and quality control pipeline; combined data were batch corrected. We tested for differences in median normalized CPM expression values using the Wilcoxon rank sum test with >2-fold change and a false discovery rate <1% considered statistically significant. We also conducted weighted gene co-expression network analysis in each tissue type. The hypergeometric test was used for enrichment of differentially expressed genes (DEGs) and gene ontologies within modules. Results: Transcriptomes comprised 27,700 expressed genes (8,202 lncRNAs) across 706 HGSOCs, 180 bulk ovary, and 88 ovarian epithelia tissues. Most (~90%) genes were expressed in all tissues; 5% each showed HGSOC- or normal tissue-specific expression and ≥50% were lncRNA. Comparing HGSOCs to ovarian epithelia and to bulk ovary identified 11,804 DEGs with 4,522 lncRNAs (DElncRNA) of which ~50% were tissue-specific. DEGs included MUC16 and multiple GWAS/TWAS implicated susceptibility genes including RAD51, BRIP1, BNC2, TIPARP-AS1, PRC1, KANSL1, ANKLE1, CHMP4C, ESRP2, and CCNE1. The most highly expressed DElncRNA in HGSOC were upregulated RMRP (P=1.4x10-39), SNHG1 (P=3.0x10-27), and HAGLR (P=2.0x10-24) at the HOXD risk locus. The highest expressed DElncRNA in precursor tissues was the HGSOC-downregulated MEG3 (P=1.7x10-67). DEGs were enriched in HGSOC co-expression modules associated with immune response, cell motility/localization, cell cycle regulation, angiogenesis, reproductive development, transcriptional regulation, and metabolic processes. Tissue-specific DElncRNA tended toward upregulation compared to ovarian epithelia with enriched modules associated with cell cycle regulation (hub=BUB1B; top DElncRNA TRPM2-AS, P=1.5x10-38); and toward downregulation compared to bulk ovary with enriched modules associated with chemokine signaling/response (hub= GADD45B, top DElncRNA RP11-87P3.1, P=1.7x10-113). Conclusion: HGSOC-dysregulated lncRNA expression revealed tissue-specific differences that highlight unique biological pathways in precursor epithelia and the ovarian microenvironment that contribute to HGSOC pathogenesis. Our results provide additional evidence to support previously nominated risk genes. Integration with eQTL and GWAS are underway to further elucidate novel HGSOC susceptibility genes. Citation Format: Brett M. Reid, Ann Chen, Zhihua Chen, Florian A. Karreth, Peter Kanetsky, Jennifer B. Permuth, Ozlen Saglam, Jamie Teer, Xiaoqing Yu, Simon Gayther, Ellen Goode, Paul Pharoah, Thomas A. Sellers, Kate Lawrenson. Patterns of dysregulated coding and noncoding gene expression in high-grade serous ovarian carcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6065.

CMML is a lethal myeloid neoplasm with no therapies that improve its dismal prognosis. Inhibition of BET family members has been proposed as a therapeutic strategy based on preclinical data identifying BRD4 as a therapeutic target in acute myeloid leukemia. However, despite potent on-target transcriptional remodeling, early phase clinical trials have demonstrated only modest activity secondary to a variety of resistance mechanisms. In ovarian cancer BET inhibitor (BETi) treated cells, compensatory upregulation and addiction to pro-survival kinase networks have been observed. Given that over 50% of CMML cases have mutations upregulating kinase signaling, we hypothesized that BETi resistance is mediated by these networks in CMML and can be targeted therapeutically. We tested this hypothesis by performing a limited screen of kinase inhibitors alone and in combination with the IC20 of the BETi INCB54329 in 8 human leukemia cell lines. This screen revealed that the IC50 of the PIM inhibitor (PIMi) INCB53914 decreased after co-treatment with BETi in a majority of the leukemia cell lines tested. Synergy was validated chemically in U937, TF1 and SKM1 leukemia cells using other selective inhibitors of BET and PIM. We next assessed the activity of the BET-PIM combination in 14-day colony formation assays with 10 unique CMML bone marrow mononuclear cell (BM-MNCs) patient samples(Fig. 1A). These studies revealed that combination therapy significantly suppressed clonogenicity versus BMNCs treated with vehicle or single drug alone. Finally, this synergy was validated in vivo in 36 patient derived xenografts (PDX) from 3 CMML patients, as manifest by reduced leukemic burden/engraftment in CMML PDX treated with combination therapy(Fig. 1B). To explore the mechanism by which BETi and PIMi therapeutically synergize we treated U937 and SKM1 leukemia cells with INCB54329 and measured mRNA and protein levels for all PIM isoforms. Surprisingly, we identified that PIM1 was increased following treatment with INCB54329, other BETi, or a JQ1-derived PROTAC (Fig. 1C). PIM1 upregulation was also manifest in INCB54329 persistor U937 leukemia cells generated by daily BETi treatment for 6 weeks. Testing across a broader panel of leukemia cell lines revealed an inverse correlation between PIM1 induction and decrease in the IC50 of PIMi following BETi treatment, suggesting PIM1 upregulation confers sensitivity to combination therapy. Consistent with this, isogenic SKM1 leukemia cells engineered to overexpress PIM1 were resistant to INCB54329 and were more sensitive to INCB53914 versus controls cells. Recent studies have demonstrated that inhibitory miRNAs, especially those located near super-enhancers, are suppressed by BET inhibition. Given that several miRNAs are known to control PIM1 expression, we hypothesized that paradoxical PIM1 upregulation following BETi treatment was due to down-regulation of select miRNAs. To test this, we treated our leukemia cell models with broad inhibitors of miRNA activity (i.e., AGO and Dicer inhibitors) and observed a dose dependent increase in PIM1 levels similar to that seen with BET inhibition(Fig. 1Di). Further, integrating public H3K27 CHIP-seq and miRNA super enhancer datasets and using computational prediction algorithms, we identified 6 candidate miRNAs that could regulate PIM1 and were predicted to be controlled by BET inhibitors. Of these, only miR-33a levels were reduced in a dose dependent manner in SKM1 cells by BETi treatment(Fig. 1Dii). This was confirmed by genetically silencing all BET proteins, which suppressed miR-33a levels in SKM1 leukemia cells. Finally, miR-33a mimics (but not control miRNAs) abolished BETi-induced upregulation of PIM1(Fig. 1Diii). Collectively, these studies established BET and PIM inhibition as a novel and potent combination therapy for CMML that is mediated by miR-33a-dependent upregulation of PIM1(Fig. 1E). Disclosures Liu: Incyte Corporation: Employment. Patnaik:Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees. Lancet:Daiichi Sankyo: Consultancy, Other: fees for non-CME/CE services ; Agios, Biopath, Biosight, Boehringer Inglheim, Celator, Celgene, Janssen, Jazz Pharmaceuticals, Karyopharm, Novartis: Consultancy; Pfizer: Consultancy, Research Funding. Komrokji:Novartis: Speakers Bureau; JAZZ: Speakers Bureau; JAZZ: Consultancy; Agios: Consultancy; Incyte: Consultancy; DSI: Consultancy; pfizer: Consultancy; celgene: Consultancy. Epling-Burnette:Incyte Corporation: Research Funding. List:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Haura:Incyte Corporation: Research Funding. Reuther:Incyte Corporation: Research Funding. Koblish:Incyte Corporation: Employment.