Citations

LBA1551 Background: Traditional cancer therapy is primarily delivered in clinical settings, imposing logistical, emotional, and financial burdens, particularly on rural and underserved patients. While international studies have demonstrated the safety of home-based chemotherapy, no randomized U.S. trials have evaluated this approach. Mayo Clinic’s CCBW program aims to expand access to high-quality cancer care by providing in-home cancer treatment, lab testing, telemedicine, remote patient monitoring, command center and paramedic support. This pragmatic randomized controlled trial evaluates feasibility and impact of home-based versus in-clinic cancer directed therapy on patient preference, acceptability, safety, and patient-reported outcomes. Methods: Adult patients (ECOG 0-2) receiving chemotherapy or hormonal therapy at Mayo Clinic Florida were randomized 1:1 after confirmation of treatment tolerance in clinic to receive (1) home-based care for 24 weeks or (2) in-clinic care for 8 weeks followed by home-based care for 16 weeks. Target accrual is 200 patients. A planned interim analysis after 50 enrollments assessed accrual, data completeness, technical challenges, safety, and study outcomes. The primary endpoint is patient ratings of their overall cancer care experience (0–10 scale, CAHPS Cancer Care Survey) at 8 weeks. 200 patients provide 80% power to detect a 0.45 standard deviation difference in mean ratings between arms with a 2-sided 2-sample t-test (α=0.05) and 20% missing data rate. Secondary endpoints are patient preference, comfort with home infusions, treatment worthwhileness, functional/symptom assessments (EORTC QLQ-F17, PRO-CTCAE, GP5), grade ≥3 AEs, hospitalizations, and overall survival. Cost is a tertiary endpoint. The trial is IRB-approved (#23001719) and registered (NCT05969860). Results: Enrollment began 8/23/23. As of 2/21/25, 52 patients (26 per arm) were enrolled and 36 started protocol treatment. Common cancers were breast (38%), colorectal (21%), prostate (19%), and multiple myeloma (14%). 64% (28/44) of patients were female; 84% identified as White, 14% as Black, and 2% as multiple races. Median distance to clinic was 20 miles (range 2-66). After 8 weeks, patients in both arms rated their overall cancer care experience highly (mean [SD]=9.69 [0.85] versus 9.54 [0.66] for home-based vs. in-clinic care, n=26). After 24 weeks, 73% preferred home-based care, 18% had no preference, and 9% preferred in-clinic care; 100% felt comfortable with home infusions. No grade ≥3 AEs related to location of care have occurred. Conclusions: Early findings support the feasibility, safety, and high patient acceptability of home-based cancer therapy. No safety concerns related to care location were observed and most patients preferred home-based treatment. Ongoing accrual and analysis will assess clinical outcomes and cost implications. Clinical trial information: NCT05969860 .

e19503 Background: The uptake of BCMA directed ADCs in MM has been hindered by ocular toxicity. Belantamab mafodotin (BM) and MEDI2228 (MEDI) are BCMA directed monoclonal antibodies conjugated to the microtubule inhibitor, monomethyl auristatin F and the alkylator, pyrrolobenzodiazepine, respectively. Differences in the ADCs payload, antibody specificity and linker stability influence ocular toxicity. We investigated the patterns of ocular toxicity and tolerability of BCMA directed ADC therapy. Methods: Retrospective study of MM patients who commenced treatment with a commercial or investigational BCMA directed ADC at the Mayo Clinic between 2019-22. Results: 111 patients were included, the median age was 69 and median follow up 40 months. The median number of prior treatments was 6, 87% of patients were triple class refractory, 50% penta class refractory, 14% had prior BCMA directed therapy and 24% had extraosseous disease. Treatments included BM (n=56, 50%), BM in combination with other MM therapy (n=12, 11%) and MEDI (n=43, 39%). The overall response rate was 30% for BM and 44% for MEDI. The median OS and PFS for BM was 8 and 2 months, the median OS and PFS for MEDI was 11 and 5 months. The median number of ADC cycles was 2.5. Ocular toxicity is summarized in the table 1. Keratopathy was more common with BM (p=0.004). Only 4 MEDI patients developed keratopathy, all grade 1 and associated with dry eyes and/or photophobia. In contrast, 60% of BM patients developed keratopathy (13% grade 3-4 keratopathy). The median time to keratopathy onset was 1 month with BM, and 2.8 with MEDI. The median time until keratopathy was 4.6 months for BM. Loss of best-corrected visual acuity (VA) was significantly more common with BM (p<0.001). The median time to any decline in VA was 1.5 months for BM and 2.3 for MEDI. The median time until VA returned to baseline was 2.4 months for BM and 2.3 for MEDI. The frequency of dose delays for ocular toxicity was comparable between ADCs, as was the number of dose reductions (16% BM vs. 12% MEDI, p=0.5). The most common reasons for treatment discontinuation were progression or death (BM=69%, MEDI=44%) and ocular toxicity (BM=18%, MEDI=30%). Conclusions: Treatment interruptions, dose reductions and drug discontinuation are common with BCMA directed ADC therapy. Further research to optimize the administration schedule of these agents is warranted. BM and MEDI produce distinct patterns of ocular side effects, namely keratopathy and reduced VA with the former, and photophobia and dry eyes with the latter. BM N = 68 MEDI N = 43 p-value 2 Prophylactic steroid eye drops 4 (6%) 28 (68%) <0.001 Prophylactic lubricant eye drops 42 (63%) 33 (80%) 0.051 Max grade keratopathy 0.004 1-2 29 (47%) 4 (19%) 3-4 8 (13%) 0 (0%) VA equal or worse than 20/40 26 (68%) 6 (20%) <0.001 Days of ADC treatment 59 (33, 167) 68 (41, 105) 0.9 Dose delay for ocular toxicity 19 (28%) 8 (19%) 0.2 n (%); Median (IQR); 2 Chisq test; Wilcoxon rank sum.

e13622 Background: Chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized treatment of hematologic malignancies; however, severe toxicities like cytokine release syndrome (CRS) remain a limitation to broader utilization. Using a large dataset of patients treated with CAR-T, we developed a machine learning (ML) model leveraging multi-model patient characteristics to predict the risk of CRS onset with clinically meaningful lead times. Methods: A prospectively maintained dataset of CAR-T patients treated at Mayo Clinic was used. Baseline disease/patients characteristics, labs, ECG, echocardiogram, and vital signs (including data from remote patient monitoring program) were collected. A 24-hour rolling window was used to generate daily averages, maximum, minimum, and deltas for each parameter. ECG and echocardiogram data were forward-imputed; vital signs and labs were not imputed. Each day-post-infusion served as a potential event (CRS onset vs. no CRS). A gradient boosting classifier was trained and tested on a subset of patients (n = 351) using 1,000 bootstrap iterations, evaluating 24-, 48-, and 72-hour lead times. Shapley-based feature importance guided simplification to an 11-feature model, externally validated on an independent cohort (n = 51). Models’ performance assessed using the area under the receiver operating characteristic curve (AUC). Results: A total of 402 patients treated with CAR-T were examined. Median age was 64 years and 36% were female. Primary diagnosis was B-cell lymphoma (n = 240) or multiple myeloma (n = 162). A total of 226,332 vital sign measurements and 372,871 lab values, as well as ECG and echocardiogram data, were collected for 30 days post-infusion. Overall, 79.8% of patients experienced CRS within 30 days. The full ML model demonstrated AUCs of 0.90 (95% CI 0.86–0.93), 0.83 (95% CI 0.77–0.88), and 0.82 (95% CI 0.77–0.87) for 24-, 48-, and 72-hour predictions of CRS, respectively. Including “day of CRS onset post CAR-T” as a feature improved the 72-hour AUC to 0.87 (95% CI 0.83–0.90). A model restricted to vital signs alone showed AUCs of 0.88, 0.75, and 0.71 for 24-, 48-, and 72-hour horizons. An 11-feature model—focusing on temperature, blood pressure, fibrinogen, CRP, serum sodium level, QRS interval, and lymphocyte counts—maintained high accuracy at 24 hours (AUC 0.90, 95% CI 0.86–0.93) and was validated externally using an independent dataset with an AUC of 0.92. Conclusions: Our ML model provides a reliable approach for predicting if/when CRS will occur, with up to three days of lead time before clinical manifestation, offering an opportunity for proactive management. The robust performance of a parsimonious 11-feature model supports practical adoption across clinical settings and real-time risk stratification to enhance patient safety. Prospective validation and integration into clinical workflows will be critical to translating these findings into practice.

7522 Background: Movement and neurocognitive treatment emergent adverse events (MNTs), cranial nerve palsies (CNPs) and immune-effector cell enterocolitis (IEC-EC) are late onset toxicities associated with cilta-cel and are associated with significant morbidity and mortality. We aimed to identify predisposing risk factors to allow for formulating risk-reduction strategies. Methods: Retrospective study of MM pts who received FDA approved cilta-cel at Mayo Clinic between Feb 2022-Dec 2024. Interrogated factors included demographics, disease and treatment characteristics, and selected lab values at different time points. In odds ratio (OR) analyses, pts were divided by toxicity type (1) No delayed toxicity (2) MNT (3) IEC-EC without MNT (4) isolated CNP. Differences between pts in the MNT, IEC-EC and CNP groups were compared separately in a pairwise manner vs. those with no delayed toxicity. Results: Of 235 pts, 26 (11%) had delayed toxicity [MNT = 8 (3%), IEC-EC = 9 (4%), CNP = 14 (6%)]; 3 pts had both MNT/IEC-EC and 2 had both CNP/IEC-EC. Median onset from CAR-T infusion for MNT, IEC-EC and CNP was 19 (range 8-96), 106 (35-169) and 21 (7-43) days respectively. Of 164 evaluable pts, those who had ICANS had increased odds for any delayed toxicity (OR 4.7; 95% CI 1.6-13.8). Pts who had ICANS (OR 7.7; 1.6-36.9), HLH/MAS (OR 6.2; 1.1-6.7) and Ferritin >400mcg/L at time of lymphodepletion (OR 6.3; 1.5-32) had increased odds for MNTs. Pts who received alkylator-based bridging therapy had increased odds (OR 6.1; 1.1-35) for IEC-EC. Analysis of serial post-CAR infusion lab values showed that peak ALC was the most significant variable with differences between groups shown in table. Median time to peak ALC was 12 days (IQR 11-13). ROC analysis determined peak ALC >3 x 10 9 /L as a meaningful cut point. Pts with peak ALC >3.0 x 10 9 /L had increased odds of developing any delayed toxicity (OR 9.3; 3.5-29.3), MNT and CNP (table). Absolute risk of any delayed toxicity was 33% in pts with peak ALC >3 x 10 9 /L vs 5% without. At this threshold, number needed to treat was 3.6. Conclusions: Real-world data shows that cilta-cel associated delayed toxicity is seen in ~10% of patients, with specific toxicity rates between 4-8%. Factors associated with these toxicities predominantly occur in the post CAR-T infusion period. Given that ALC expansion in the first 2 weeks correlate with CAR-T expansion, peak ALC is potentially predictive of toxicity and 3 x 10 9 /L may be an interventional threshold for primary prophylaxis. Peak ALC by toxicity groups. No ToxN=138 MNTN=8 CNPN=12 IEC-ECN=6 p value Peak ALC (10 9 /L), median (IQR) 1.8 (1.0-4.1) 6.2 (4.8-14.4) 4.3 (3.2-6.7) 3.6 (1.9-5.2) <0.001 1 ALC >3 x 10 9 /L, n (%) 43 (31%) 7 (88%) 10 (83%) 4 (67%) <0.001 2 Odd Ratios vs no Tox 3 , 95% CI 15.5 (2.6-293.7) 11.0 (2.8-73.9) 4.4 (0.8-25.0) 1 Wilcoxon/Kruskal-Wallis Tests, 2 Chi square test, 3 Logistic regression analysis.

PURPOSE Teclistamab is initiated with a step-up dosing (SUD) schedule to mitigate the risk of cytokine release syndrome (CRS) and immune effector cell–associated neurotoxicity syndrome (ICANS). Early teclistamab users commonly received SUD in a hospital setting. This study aimed to evaluate safety and health care resource utilization (HRU) in real-world patients with multiple myeloma who initiated teclistamab SUD in an outpatient setting. METHODS This was a retrospective study using Mayo Clinic's electronic medical records from October 26, 2022, to October 31, 2023. Patient characteristics were summarized for all patients treated with teclistamab and separately for patients who started SUD outpatient. SUD pattern, safety, HRU, and post-SUD dosing schedule were described in patients with complete SUD. RESULTS At data cutoff, 65 patients received ≥1 teclistamab dose, including 58 patients who initiated SUD outpatient (median age, 69.2 years; male, 63.8%; White, 89.7%). Among 57 patients who completed SUD in an outpatient setting, all received premedications on the days of teclistamab administrations per label recommendation; 18 (31.6%) developed CRS (13 grade 1, four grade 2, and one grade 4) and two developed ICANS (one each with grade 2 and 4). All CRS and ICANS resolved with supportive care and all patients continued treatment. Eighteen patients were admitted to the hospital for CRS treatment, with a median CRS-related hospital stay of 2 days per admission. Most (60%) doses during SUD required <1 hour clinic time between administration and checkout. Post-SUD, clinic time for treatment doses decreased to <30 minutes for most doses (82%). CONCLUSION Outcomes of this study support outpatient administration as a safe and feasible option for teclistamab SUD to potentially reduce HRU and improve patient experiences.

Introduction: Myeloma therapeutics have tremendously improved in recent times, leading to unprecedented outcomes. Timely initiation of effective treatment is vital to improving survival and quality of life. Patient refusal to receive treatment is infrequent but certainly leads to increased morbidity/mortality. We studied factors associated with treatment refusal. Methods: The National Cancer Database (NCDB) was queried for adult patients diagnosed with MM between 2004-2021. Those who refused treatment for initial management despite physician's recommendation were identified. Patients with multiple primary malignancies or unknown treatment status were excluded. Patients' demographics and socioeconomic factors were collected. Kaplan-Meier and multivariate Cox regression were used to evaluate the likelihood of treatment refusal in the whole cohort and in each racial/ethnic group. Results: Out of 86,350 eligible patients with MM who were recommended to initiate therapy, 2,508 (2.9%) did not receive any treatment due to refusal. Of these, 52.6% were females, 64% were non-Hispanic White (NHW), 21.3% were non-Hispanic African American (NHAA), and 5.2% were Hispanic. On multivariate analysis, patients who were treated at comprehensive and integrated facilities were more likely to refuse treatment as compared to those at academic facilities (OR=1.36; 95% CI: 1.22-1.52, p&lt;0.001 and OR=1.22; 95% CI: 1.07-1.4, p=0.004, respectively). Patients on Medicare (OR=1.42; 95% CI: 1.16-1.73, p&lt;0.001), and presence of higher Charlson Comorbidity Index (CCI) also had a higher likelihood of refusing treatment. Lower refusal rates were seen for male gender (OR=0.83; 95% CI: 0.76-0.91, p&lt;0.001), NHW racial/ethnic group (as against NHAA; OR=0.34; 95% CI: 0.16-0.71, p=0.004), being insured (OR=0.69; 95% CI: 0.52-0.93, p=0.015), having private insurance (compared to Medicaid; OR=0.47; 95% CI: 0.38-0.59, p&lt;0.001) and having a higher income level (OR=0.82; 95% CI: 0.70-0.96, p=0.013). Among NHAA patients, 2.8% refused treatment and were more likely to have Medicare (OR=1.4; 95% CI: 1.03-1.9, p=0.032). Having private insurance (compared to Medicaid; OR=0.61; 95% CI: 0.43-0.88, p=0.008) had a lower likelihood of refusing treatment. Among NHW, 2.9% refused treatment and they were more likely to be treated at community, comprehensive and integrated facilities compared to academic facilities (OR=1.39; 95% CI: 1.12-1.73, p=0.003, OR=1.54; 95% CI: 1.33-1.77, p&lt;0.001 and OR=1.38; 95% CI: 1.16-1.64, p&lt;0.001, respectively), be from rural areas (OR=1.43; 95% CI: 1.05-1.96, p=0.025), and have a higher CCI. On the other hand, male gender (OR=0.79; 95% CI: 0.72-0.89, p&lt;0.001), private insurance (compared to Medicaid; OR=0.42; 95% CI: 0.30-0.59, p&lt;0.001) and higher income level (OR=0.80; 95% CI: 0.66-0.98, p=0.029) had a lower likelihood of refusing treatment. Among Hispanic patients, 2.3% refused treatment and were more likely to have Medicare (OR=1.96; 95% CI: 1.13-3.4, p=0.016), while those with private insurance (compared to Medicaid; OR=0.32; 95% CI: 0.14-0.73, p=0.007) had a lower likelihood of refusing treatment. Conclusions: This large real-world analysis highlights the intricate sociodemographic and economic factors that affect the decision of a MM to not proceed with the recommendation for treatment. (1) NHW are less likely to refuse treatment as compared to NHAA. (2) Insurance status is an important driver of refusal and transcends race/ethnicity. Furthermore, in this study, we highlight characteristics that are specific to different racial/ethnic groups like CCI which affect only certain racial/ethnic groups. This provides an opportunity to develop targeted interventions such that the benefit of therapeutic advancements can be realized by all.