Warsame, Rahma

Abstract Background: MASS-FIX is a screening method for serum and urine monoclonal proteins in multiple myeloma and related plasma cell disorders, which uses immunoglobulin enrichment coupled with matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MALDI-TOF). In addition to superior sensitivity over conventional gel-based techniques, MASS-FIX can distinguish therapeutic monoclonal antibodies (MoAb) from patient's M protein. As the utilization of therapeutic MoAbs increases, it is essential to understand the persistence pattern of these therapeutic antibodies in the serum. We designed this study to evaluate the duration of daratumumab detection by MASS-FIX in the serum of treated patients. Methods: We used a prospectively maintained database at Mayo clinic to identify patients with multiple myeloma and related plasma cell disorders who were treated with a daratumumab-containing regimen anytime during their disease course and had serial MASS-FIX data available after discontinuation of daratumumab. A univariate analysis was performed to assess for factors that may impact the clearance of daratumumab. Results: We included 125 patients with plasma cell disorders who received daratumumab as first or subsequent line of treatment between March 15 th, 2016, and March 4 th, 2020. The median age was 60.2 years and 57% were male. The most common diagnoses were multiple myeloma (70%) and light chain amyloidosis (18%). Daratumumab-based treatments were initiated after a median of 28.8 (IQR: 6.4-76.3) months from initial diagnosis. The most common regimen used was daratumumab, bortezomib and dexamethasone (23%); 26% underwent transplant after daratumumab-based induction. The median duration of treatment with a daratumumab-based regimen was 208 (IQR: 99-479) days. The median follow-up from the time of daratumumab discontinuation was 457 (95% CI: 346-NR) days. By last follow up, daratumumab was not detected by MASS-FIX in 93 (74%) patients but remained detectable in 32 (26%) patients. The median time from daratumumab discontinuation to disappearance of daratumumab by MASS-FIX was 160 (IQR: 107-233) days. On univariate analysis, the presence of ≥0.5 grams of urine protein was associated with earlier disappearance of daratumumab on MASS-FIX [risk ratio (RR): 2.0, P=0.02). The median time from daratumumab discontinuation to disappearance of daratumumab on MASS-FIX was 116 (95%CI: 76-160) days in patients with urine protein ≥0.5 grams and 203 (95%CI: 162-216) days in patients with urine protein <0.5 grams (P=0.02). There was no association between the time to disappearance of daratumumab by MASS-FIX and old age ≥70 (RR: 0.9, P=0.81], male gender (RR: 0.9, P=0.60), eGFR <60 (RR: 1.0, P=0.98), daratumumab schedule (every 1/2 weeks vs >2weeks) (RR: 1.0, P=0.97), treatment duration (<200 days vs ≥200 days) ( RR: 1.0, P=0.95), or transplantation status (RR: 1.0, P=0.98). Conclusion: The therapeutic monoclonal antibody daratumumab remains detectable in the serum of treated patients by MASS-FIX for several months after discontinuation and the duration varies between individual patients. This data has implications for diagnostic and monitoring testing and may provide guidance for reuse of daratumumab in clinical trials and practice. Proteinuria is associated with earlier disappearance of daratumumab by MASS-FIX and may have implications in patients with amyloidosis and monoclonal immunoglobulin deposition disease (MIDD). Further studies are needed to identify additional factors associated with the timing of disappearance. Disclosures Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Dingli: Alexion: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Janssen: Consultancy; Sanofi: Consultancy; GSK: Consultancy. Kumar: Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Merck: Research Funding; Roche-Genentech: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract INTRODUCTION POEMS syndrome is a paraneoplastic disorder that results in multi-organ disease, and Vascular endothelial growth factor (VEGF) is associated with disease activity. There is evidence that the cytokine Interleukin- 6 (IL-6) stimulates VEGF production in several cancer cell lines. It has also been suggested that patients with POEMS syndrome experience symptoms improvement when IL-6 levels are low. Our study aims to understand the role of IL-6 and whether it is a contributor to disease activity in POEMS syndrome. METHODS We conducted a retrospective study of patients (pts) seen at Mayo Clinic within 1 year of diagnosis for POEMS syndrome with an IL-6 level within 3 months of diagnosis between 01/01/2011-05/31/2021 were included. Our database comprised 358 pts,7 were excluded because did not have official diagnosis of POEMS syndrome. Two hundred and ninety-nine pts did not have IL-6 testing within 90 days of diagnosis and were excluded. Clinical data was abstracted from our electronic medical record. Descriptive statistics were used for IL-6 levels. The majority of IL-6 laboratory testing for pts utilized the same reference range. A minority of pts had alternate reference ranges; thus, all IL-6 values and reference ranges were fold-corrected to have a unified reference to determine elevation in IL-6. High IL-6 was considered any value above the normal range, also analyzed pts in quartiles and deciles. Hematologic, VEGF and PET response criteria were utilized to group pts into responders or non-responders. Time to progression (TTP) and overall survival (OS) was calculated using the Kaplan Meier method. Differences between curves were by log rank. Statistical analysis was conducted via JMP software package (SAS, Cary, NC, USA). Univariate analysis was done to determine the prognostic value of IL-6. Statistical significance is considered with p-values <0.05. RESULTS 52 patients from among 352 met criteria and were included for analysis. Twenty-one patients (40%) had elevated IL-6 levels (IQR 5.67, 22.6) at the time of diagnosis. The fold elevation about normal was typically not very high, with a median of 3.1-fold elevation (IQR 1.3, 4.4; and range 1.0, 8.3). The percentage of males in the high IL-6 versus the normal Il-6 group were 90% versus 65%, p=0.03. Compared to normal IL-6, those with elevated IL-6 values had more instances of hepatomegaly (43% vs 16%, p=0.03), ascites (28% vs 6%, p=0 .04), abnormal DLCO (26% vs 3%, p=0.03), mixed bone lesions (57% vs 29%, p= 0.04), and lower serum albumin (range 2.3-3.5 vs 2.5-4.5 g/dL, p=0.0008).There was a trend for lower VEGF values among the high IL-6 group, but this did not meet statistical significance (median 250 vs 438 pg/mL). There was no significant difference in the time to progression between pts with high versus normal IL-6 levels (HR 1.36; p=0.456). Overall survival was significantly longer in pts with normal IL-6 compared to pts with high IL-6 levels (Median OS 67.4 vs 47.8 months, p=0.02). Forty-two pts were evaluated for VEGF response after treatment; there was a significant improvement in time to progression (85.5 vs 14.9 months, p=0.03) in pts who were VEGF responders. Among pts evaluable for a hematologic response (n=15), time to progression in pts with baseline high IL6 was longer in hematologic responders compared to non-responders (p=0.02). Conclusion Although OS was longer for pts with normal IL-6, elevated IL-6 at the time of diagnosis does not prevent pts from achieving disease remission. The study demonstrates that response to treatment rather than interleukin-6 levels in pts with POEMS syndrome is more prognostic. This study is an important initial step into understanding the utility of IL-6 in POEMS syndrome. Figure 1 Figure 1. Disclosures Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria. Kumar: Oncopeptides: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Merck: Research Funding; Antengene: Consultancy, Honoraria; Tenebio: Research Funding; Novartis: Research Funding; Roche-Genentech: Consultancy, Research Funding; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Dingli: GSK: Consultancy; Novartis: Research Funding; Janssen: Consultancy; Sanofi: Consultancy; Apellis: Consultancy; Alexion: Consultancy. Lin: Janssen: Consultancy, Research Funding; Merck: Research Funding; Novartis: Consultancy; Legend: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Vineti: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Sorrento: Consultancy; Gamida Cell: Consultancy; Takeda: Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dispenzieri: Pfizer: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding.

Abstract Introduction: Smoldering multiple myeloma (SMM) prognostication models routinely used in clinical practice were developed for use at diagnosis. However, retrospective studies have shown that in SMM patients the risk of progression to multiple myeloma (MM) decreases over time. Therefore, this study assessed whether the Mayo 2018 and IMWG 2020 scores could be used dynamically to risk stratify patients post-diagnosis, and whether they could identify SMM patients with evolving disease. Methods: We retrospectively studied 704 SMM patients diagnosed between January 2000 to January 2020. Patients with a baseline FLCr ≥100 and involved FLC ≥10 mg/dL or baseline bone marrow plasma cells ≥60% were excluded. We collected serial laboratory data and available pathology data and re-applied the Mayo 2018 and IMWG 2020 SMM risk stratification models at annual landmark timepoints up to 5 years post SMM diagnosis. Survival analyses were performed using the Kaplan-Meier method. Time to progression (TTP) was defined as time from diagnosis or landmark timepoint to treatment initiation for MM or systemic AL amyloidosis. Cox proportional hazards models were used to estimate hazard ratios. A two-sided p-value <0.05 was considered statistically significant. Results: At SMM diagnosis, 271 (38%) patients were low risk, 228 (32%) were intermediate risk, and 205 (29%) were high risk per the Mayo 2018 score. Applying the IMWG 2020 score at diagnosis, 90 (34%) of patients were low risk, 111 (42%) were low-intermediate risk, 54 (21%) were intermediate risk, and 9 (3%) were high risk. The Mayo 2018 and IMWG 2020 risk scores was re-assessed annually post-diagnosis in patients without progression (respective sample sizes: n=430 and n=197 at year 1 landmark, n=326 and n=143 at year 2 landmark, n=260 and n=106 at year 3 landmark, n=203 and n=73 patients at year 4 landmark). The Mayo 2018 and IMWG 2020 models reliably stratified patients based on progression risk post diagnosis (as shown in Figure 1 and Figure 2, respectively). As shown in Figure 1, if only diagnostic values were used in risk categorization with the Mayo 2018 model, the TTP between risk categories became less distinct over time. However, when follow-up laboratory values were used to re-stratify patients, the TTP between risk categories was more consistent over time. The respective 2-year progression risk in Mayo 2018 high-risk versus IMWG 2020 intermediate-high risk patients was 51% versus 62% at the 1-year landmark, 60% versus 65% at the 2-year landmark, 47% versus 62% at the 3-year landmark, and 47% versus 45% at the 4-year landmark. To assess the prognostic significance of an increase in SMM risk category, we stratified patients based on whether the SMM risk category at follow-up had increased versus was stable or decreased compared to the baseline SMM risk stratification. We found that patients evolving to a risk category during follow up consistently had an increased risk of progression compared to patients with a stable/decreased risk categorization using both the Mayo 2018 (HR 3.34, 95% CI 2.04-5.48, p<0.001 at 2-year landmark; HR 3.41, 95% CI 2.05-5.68, p<0.001 at 3-year landmark; HR 2.69, 95% CI 1.34-5.38, p=0.005 at 4-year landmark) and IMWG 2020 (HR 2.32, 95% CI 1.42-3.77, p<0.001 at 1-year landmark; HR 3.10, 95% CI 1.69-5.70, p<0.001 at 2-year landmark; HR 2.50, 95% CI 1.30-4.78, p=0.004 at 3-year landmark; HR 2.26, 95% CI 0.91-5.60, p=0.072 at 4-year landmark) risk models. However, patients categorized as Mayo 2018 high-risk at follow-up had a similar risk of progression regardless of whether the baseline risk categorization was low-intermediate versus high-risk (HR 0.87, 95% CI 0.51-1.48, p=0.606 at 2-year landmark; HR 1.01, 95% CI 0.59-1.73, p=0.972 at 3-year landmark; HR 0.75, 95% CI 0.34-1.65, p=0.467 at 4-year landmark). Conclusions: Our findings support the use of the Mayo 2018 and IMWG 2020 scores post-diagnosis. We showed that patients migrating to a higher risk category have an increased risk of progression. This suggests that if patients evolve to a high-risk score during follow-up, they should be considered for an early intervention treatment approach. Figure 1 Figure 1. Disclosures Kapoor: Sanofi: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Karyopharm: Consultancy; Cellectar: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dispenzieri: Pfizer: Research Funding; Janssen: Consultancy, Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy. Gertz: Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Ionis Pharmaceuticals: Other: Advisory Board. Dingli: Janssen: Consultancy; Sanofi: Consultancy; Apellis: Consultancy; Novartis: Research Funding; Alexion: Consultancy; GSK: Consultancy. Kumar: Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Antengene: Consultancy, Honoraria; Oncopeptides: Consultancy; Beigene: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: The use of immunotherapeutic agents in multiple myeloma (MM) has shown improvements in clinical outcomes, emphasizing the role the host immune system plays in disease control. In recognition of this relationship, efforts aimed at identifying biomarkers of immune surveillance in MM have identified prognostic value in the peripheral absolute lymphocyte (ALC) and absolute monocyte (AMC) counts at various stages of disease and following autologous stem cell transplant (ASCT), with lower ALC and AMC serving as a surrogate for immune dysregulation and correlating with inferior progression free (PFS) and overall survival (OS). With ASCT remaining the standard of care in the treatment of MM, we sought to examine whether CD3 content of the apheresis product during stem cell collection could be used as a biomarker for immune competence and whether it influences outcomes. Methods: A retrospective study was conducted on 1086 MM patients who underwent stem cell (CD34) collection with available CD3 data and subsequent ASCT at our institution. We recorded the total absolute CD3 and CD34 cell count upon completion of mobilization, with a higher CD3 count serving as a surrogate for immune competence. In addition to investigating the absolute CD3 count, we calculated the CD3/CD34 ratio given variation in the CD34 goals. Patients were dichotomized based on whether their absolute CD3 and CD3/CD34 ratio values were above or below medians. A Kaplan-Meier model was used to compare median PFS and OS between groups. A cox proportional hazards model was used to determine its prognostic impact, adjusting for ISS stage 3, high risk FISH, achievement of CR near day 100, and maintenance therapy received post-ASCT. Results: The median length of follow-up from date of ASCT for the entire cohort (N=1086) was 34.1 months (range: 0.26-157 months) and the median time from stem cell collection to ASCT was 9 days (range: 3-3143 days). The most common mobilizing regimen was Neupogen and plerixafor (N=425, 39%) and 694 (64%) patients received ASCT in the first line setting. The median absolute total CD3 count was 4.3 x 10^6/kg (range: 0.1-21.9) with 542 patients above and 544 patients at or below the median. The median absolute total CD34 count was 8.53 x 10^6/kg (range: 0.2-37.0) and median CD3/CD34 ratio was 0.50 (range: 0.01-15), with 536 patients above and 550 patients at or below the median ratio. The median PFS among patients with a CD3 count > 4.25 x 10^6/kg was 23.1 vs. 16.9 months among patients with CD3 count ≤ 4.25 x 10^6/kg (p<0.0001) and median OS was 65.3 months vs. 41.6 months (p<0.0001), respectively. A similar trend was observed when dividing patients based on CD3/CD34 ratio, with median PFS of 22.4 vs. 17.5 months (p=.0003) and median OS of 61.5 vs. 45.7 months (p=.0001), both favoring the cohort with a CD3/CD34 ratio > 0.5 (Figure 1). The prognostic value among patients with a CD3/CD34 ratio > 0.5 was retained after adjusting for ISS stage III, high risk FISH features, and use of maintenance therapy as follows: PFS HR: 0.73 (95% CI: 0.62-0.86; p=0.0002); OS 0.71 (95% CI: 0.59-0.88; p=0.001). Conclusions: Our study demonstrates that patients who have higher CD3 content in their apheresis product have better PFS and OS following ASCT. These findings reveal a possible role for using absolute CD3 and CD3/CD34 ratios in the apheresis product as a surrogate marker for immune competence and in predicting clinical outcomes. Figure 1 Figure 1. Disclosures Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Ionis Pharmaceuticals: Other: Advisory Board. Dingli: GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Alexion: Consultancy; Apellis: Consultancy; Novartis: Research Funding. Dispenzieri: Takeda: Research Funding; Pfizer: Research Funding; Alnylam: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Kapoor: Sanofi: Research Funding; Takeda: Research Funding; Ichnos Sciences: Research Funding; Glaxo SmithKline: Research Funding; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; Karyopharm: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding. Kumar: Novartis: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Tenebio: Research Funding; Amgen: Consultancy, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; BMS: Consultancy, Research Funding; Oncopeptides: Consultancy; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Achievement of a complete response (CR) to therapy in multiple myeloma (MM) early in disease course correlates with improvement in clinical outcomes, reflective of therapeutically sensitive disease biology. While depth of response is known to impact overall survival (OS) and progression-free survival (PFS), the effect of known prognostic variables at baseline including FISH risk and International Staging System (ISS) score outside of clinical trial settings is not well described. We sought to determine the impact of known prognostic variables in MM including FISH and ISS risk in the context of NDMM patients achieving a CR to therapy and to also examine additional predictors of outcome among this cohort. Methods: A retrospective study was conducted on 1869 NDMM patients who had ≥ 2 consecutive monoclonal protein immunofixation (IFE) studies in the serum and urine available within 24 months from diagnosis. We identified the number of patients who had ≥ 2 negative serum and urine IFE as a surrogate for achieving CR (N=461), since a bone marrow biopsy is not routinely performed to confirm CR in the non-trial setting. A Kaplan-Meier model was used to compare median PFS and OS between patients achieving a CR vs. non-CR and a multivariate analysis was performed to adjust for known prognostic variables. A cox proportional hazards model was used to determine the prognostic impact of baseline FISH (high risk vs. standard risk) and ISS scores (3 vs.1 and 2) on PFS and OS, among patients achieving a CR vs. non-CR, and identify additional prognostically significant variables within the CR cohort. Results: The median length of follow-up for the entire cohort (n=1869) was 64 months (range: 1-199). The median time from diagnosis to achievement of CR among the CR cohort was 9.8 months (0.20-23.8 months). The median PFS for CR (n=461) vs. non-CR (n=1408) patients was 42.0 vs. 29.3 months (p<.0001) and median OS was 124.4 vs. 85.6 months (p<.0001), respectively (Figure 1). The impact of achieving a CR on outcomes was retained after adjusting for FISH, ISS, age, sex, autologous stem cell transplant, and involved heavy chain as follows: PFS HR 0.57 (0.43-0.76; p=0.0001); OS HR 0.56 (0.43-0.74; p<0.0001). The baseline FISH and ISS risk on PFS and OS comparing CR vs. non-CR patients are shown in Table 1, with both variables having statistically insignificant impact on PFS and OS among the CR cohort. We examined several variables present at baseline and at the time of CR to determine their impact on PFS and OS, including age, sex, and non-IgG involved heavy chain (baseline), and thrombocytopenia (<150,000), hypoalbuminemia (<3.5 g/dL), and immunoparesis (at time of CR). The following variables were found as inferior predictors on PFS within the CR cohort with hazard ratios as follows: male gender, HR 1.36 (1.05-1.75; p=0.02) and non-IgG involved heavy chain, HR 2.2 (1.55-3.2).The following variables were found as predictors for inferior outcomes on OS within the CR cohort with hazard ratios as follows: Age> 75 years, HR 2.7 (1.54-4.8; p=.0005); male gender, HR 1.5 (1.1-2.02; p=.008); serum albumin <3.5, HR 1.54 (1.1-2.08; p=.004); and non-IgG involved heavy chain, HR 1.75 (1.23-2.47; p=.002). On multivariate analysis, age, hypoalbuminemia, and non-IgG involved heavy chain retained prognostic significance for OS. Conclusion: Attainment of an early CR in NDMM confers improvement in PFS and OS. Our study confirms these findings and suggests that achievement of CR neutralizes the impact of baseline FISH and ISS risk on PFS and OS. Among the CR cohort, age> 75 years, male sex, non-IgG involved heavy chain, and hypoalbuminemia at the time of CR predict for inferior overall survival. Figure 1 Figure 1. Disclosures Dingli: Sanofi: Consultancy; Alexion: Consultancy; Novartis: Research Funding; GSK: Consultancy; Apellis: Consultancy; Janssen: Consultancy. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Dispenzieri: Alnylam: Research Funding; Pfizer: Research Funding; Takeda: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Lin: Vineti: Consultancy; Bluebird Bio: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Juno: Consultancy; Merck: Research Funding; Sorrento: Consultancy; Celgene: Consultancy, Research Funding; Gamida Cell: Consultancy; Novartis: Consultancy; Takeda: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Legend: Consultancy. Kumar: Merck: Research Funding; Antengene: Consultancy, Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Tenebio: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Roche-Genentech: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background Multiple Myeloma (MM) remains incurable with current approaches and eventually relapses in nearly all patients. At relapse, patients may present with increasing serum and/or urine monoclonal (M) protein or with clinical features suggestive of progression. The outcomes of different progression patterns need to be better understood to provide insight about the optimal time to intervene with salvage therapy. This study aimed to describe the prevalence and outcome of biochemical (BP) versus clinical progression (CP) in a real-world setting. Methods We retrospectively assessed 1347 relapsed MM patients seen at Mayo Clinic between February 2001 and December 2018. CP was defined as per IMWG criteria (increase ≥50% in size or new plasmacytomas/bone lesions, hypercalcemia ≥11.5 mg/dl, decrease in hemoglobin ≥2 g/dl or to ≤10 g/dl, rise in serum creatinine ≥2g/dl, hyperviscosity), while BP was defined as increase in serum or urine M protein leading to initiation of a new line of therapy without meeting criteria for CP. Aggressive relapse was defined as new extramedullary disease (EMD), plasma cell leukemia or hyperviscosity requiring plasmapheresis. The overall survival (OS) was defined as the time from 1 st relapse to last follow-up or death. Time to next treatment (TTNT) was defined as the time between initiation of 2 nd and 3 rd line therapy. Results Prevalence of different patterns of relapse is presented in Table 1. The majority of progressions were BP (60.4%); among the CP (39.6%), the most common presentation was a new bone lesion. Similar proportions of BP and CP patients were diagnosed before 2013 (70% and 73%; p=0.2). The median time from diagnosis to 1 st relapse was 22.8 months (range, 0.4-154.7) and was shorter in the CP group in comparison to BP (20.8 [range, 1.0-152.1] vs 23.7 [range, 0.4-154.7] months, p=0.001). With the exception of ISS stage, patients in the CP group had higher risk features at MM diagnosis including: high-risk (HR) FISH ([4;14], t[14;16], t[14;20], deletion 17p or p53 mutation; 29% vs 20%, p=0.01); elevated LDH (23% vs 17%, p=0.03); and EMD (19% vs 10%, p<0.001). CP patients more often had non-secretory disease at baseline (6% vs 2%, p<0.001). At 1 st line, patients with eventual CP were less likely to have received novel agents (78% vs 85%, p<0.001), upfront autologous stem cell transplantation (ASCT, 43% vs 51%, p=0.004), and lenalidomide-based maintenance (11% vs 16%, p=0.01). While patients in the CP group were less likely to have achieved very good partial remission (VGPR) or better to the 1 st line therapy (53% vs 60%, p=0.01), the percentage of patients who achieved CR was similar between the groups (22% vs 24%, p=0.4). Relapse while on primary treatment/maintenance was more prevalent in the CP (46% vs 40%, p=0.04). On multivariable logistic regression model male sex, plasma cell labeling index (PCLI) ≥2% and EMD at diagnosis were associated with higher risk of CP, whereas achieving VGPR or better after 1 st line treatment was associated with decreased risk of CP. Patients with CP had shorter median TTNT from 2 nd line therapy compared to BP (9.6 vs 17.0 months, p<0.001) as well as shorter median OS from 1 st relapse (26.2 vs 59.4 months, p<0.001; Figure 1). Amongst patients with CP, those presenting with typical "aggressive" versus "CRAB" features at relapse had a significantly shorter median TTNT (6.0 vs 11.0 months, p<0.001) and OS (17.2 vs 28.1 months, p<0.001). Using a multivariable Cox proportional hazard model, CP (vs BP) was a predictor of shorter TTNT from 2 nd line therapy (HR 1.40, 95% CI 1.20-1.62, p<0.001), along with ISS stage III, HR FISH at diagnosis and relapse while on primary treatment/maintenance, whereas the use of upfront ASCT was a predictor of longer TTNT from 2 nd line therapy. CP was also a predictor of shorter OS from 1 st relapse (HR 1.53, 95% CI 1.29-1.81, p<0.001) along with age >65 years, male sex, ISS stage III, HR FISH at diagnosis, EMD at diagnosis and relapse while on primary treatment/maintenance. Conclusions Patients with clinical features at relapse have a worse prognosis after 1 st relapse than patients who relapsed solely biochemically. Approximately half of CP presented while on active treatment, which suggests that even patients monitored closely may develop CRAB features at relapse. Factors that help predict the occurrence of CP include male sex, PCLI ≥2% and EMD at diagnosis. Patients with deeper response to 1 st line treatment are less likely to develop CP. Figure 1 Figure 1. Disclosures Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dispenzieri: Sorrento Therapeutics: Consultancy; Pfizer: Research Funding; Oncopeptides: Consultancy; Janssen: Consultancy, Research Funding; Takeda: Research Funding; Alnylam: Research Funding. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Dingli: Apellis: Consultancy; Sanofi: Consultancy; GSK: Consultancy; Novartis: Research Funding; Alexion: Consultancy; Janssen: Consultancy. Kumar: Antengene: Consultancy, Honoraria; Sanofi: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Tenebio: Research Funding; Beigene: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Carsgen: Research Funding; Oncopeptides: Consultancy; Roche-Genentech: Consultancy, Research Funding; Novartis: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding.

Abstract Background Multiple myeloma (MM) is an incurable disease, and its prognosis is largely influenced by clinicopathological features, response to therapy, and relapse characteristics. Despite novel agents and the attempts to individualize treatment strategies based on baseline risk stratification, significant variations in progression-free survivals (PFS) and overall survivals (OS) are observed between patients. We examined the outcomes of MM patients stratified according to timing of first relapse into early (<2 years) and late (>5 years). Methods We retrospectively assessed 1441 MM patients seen at Mayo Clinic between 2003 and 2018. Patients were 18 years or older and had at least one disease relapse that required an additional line of treatment. The study cohort was divided into three groups based on time of first relapse: early relapse (<2 years from diagnosis), relapse between 2 to 5 years from diagnosis, and late relapse (>5 years from diagnosis). The independent predictors of early/late relapse were identified using a forward stepwise multivariate logistic regression. Odds ratios in multivariate models were adjusted for age and sex. Results Early relapse has been recognized in 758 patients (52.6%), relapse between 2-5 years in 561 patients (38.9%), late relapse in 122 patients (8.5%). Six patients had a PFS >10 years. In comparison to patients with late relapse, patients with early relapse were older (median 63 vs 61 years, p=0.04), more frequently ISS stage III (40% vs 20%, p<0.001) had higher bone marrow plasma cell infiltration (median 60% vs 40%, p<0.001), and were more likely to have high-risk (HR) FISH (defined as translocation t[4;14], t[14;16], t[14;20], deletion 17p or p53 mutation; 28% vs 11%, p<0.001). At diagnosis, early relapse group more often presented with anemia (35% vs 21%, p=0.004), hypercalcemia (15% vs 5%, p<0.001), renal insufficiency (19% vs 5%, p<0.001) and higher serum beta-2-microglobulin (median 4.4 vs 3.4 mg/l, p<0.001). In terms of first line treatment, novel agents use was higher in early relapse group in comparison to late relapse group (82% vs 72%, p=0.007). Early relapse patients more often received PI-based therapy (30% vs 10%, p<0.001) or PI+IMID-based therapy (22% vs 12%, p=0.01), whereas late relapse patients received more often IMID-based therapy (63% vs 37%, p<0.001). No differences in the maintenance therapy were observed. Early relapse patients were less frequently treated with upfront autologous stem cell transplantation (ASCT, 35% vs 60%, p<0.001). Progression on active treatment/maintenance was observed more often in the early relapse group (55% vs 18%, p<0.001). On multivariable logistic regression model early relapse (vs all remaining patients) was predicted by HR cytogenetic features (odds ratio [OR] 3.06, 95% confidence interval [CI] 1.71-5.46, p<0.001), non-IgG isotype disease (OR 2.17, 95% CI 1.33-3.54, p=0.02), a non-ASCT pathway (OR 2.78, 95% CI 1.71-4.52, p<0.01), and by achieving less than a very good partial remission (VGPR; OR 3.23, 95% CI 1.96-5.35, p<0.01). The only factor associated with decreased chances of late relapse (vs all remaining patients) on multivariate logistic regression model was the presence of HR FISH features (OR 0.18, 95% CI 0.04-0.82, p=0.03). Median PFS from first relapse for the whole population was 13.9 months (95% CI 12.9-15.1), median OS from first relapse - 44.6 months (95% CI 41.7-183.0). Early relapse group exhibited worse median PFS and OS from first relapse (median PFS 9.1 months; median OS 26.6 months) than patients who relapsed 2-5 years after diagnosis (median PFS 18.5 months; OS 71.9 months), or late relapse group (median PFS 31.6 months; median OS 87.8 months; p<0.001) (Figure 1). Conclusions Early relapse (<2 years) is an indicator for shorter duration of response to subsequent treatments, and worse OS. Treatment with upfront ASCT and achieving VGPR or better after first line therapy lower the risk of early relapse. The only parameter that is predictive for both early and late relapse is HR FISH features. Although factors that predict worse survival in MM are well defined, further studies are needed to identify predictors of a more indolent disease course so that future therapeutic approaches can be tailored to each individual. Figure 1 Figure 1. Disclosures Kapoor: AbbVie: Research Funding; Glaxo SmithKline: Research Funding; Takeda: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding. Dispenzieri: Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Ionis Pharmaceuticals: Other: Advisory Board; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria. Dingli: Alexion: Consultancy; Janssen: Consultancy; Novartis: Research Funding; GSK: Consultancy; Sanofi: Consultancy; Apellis: Consultancy. Kumar: Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Tenebio: Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Antengene: Consultancy, Honoraria; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Self-reported financial hardship (FH) amongst cancer patients is increasingly becoming a challenge for patients, caregivers, and healthcare providers. FH not only leads to financial struggles, significant lifestyle changes, and emotional distress, but also contributes to treatment noncompliance, affecting clinical outcomes. As treatment costs rise, it is crucial to develop efficient methods to proactively identify and alleviate FH in hematology practice. One potential approach is utilizing automated processes to identify those at highest risk of FH. At Mayo Clinic, screening for FH involves using a single financial strain question 'How hard is it for you to pay for the very basics like food, housing, medical care, and heating?' which all cancer patients answer annually as part of the institution's Social Determinants of Health (SDOH) assessment. Answers are on a five-point scale including not hard at all, not very hard, somewhat hard, hard, and very hard. In this study, we assess the prevalence and predictors for FH (denoted by a response of "Very hard" "Hard" or "Somewhat hard") amongst the Mayo Clinic hematologic malignancy patient population. Our study objective was to determine if this automated process could identify those at risk for FH. Methods: Patients who received care for hematologic malignancies (lymphoma, leukemia, plasma disorders, myelodysplastic/myeloproliferative disorders, and other heme malignancies) at any of the Mayo Clinic cancer centers (Minnesota, Arizona, and Florida) and who had completed the SDOH screen at least once were included in this study. The electronic medical record (EMR) and Mayo Clinic Cancer Registry were utilized to extract demographic and disease variables. Patient's home zip code was used to determine rural/urban residence, distance from cancer center, and the Area Deprivation Index (ADI), a measure of socioeconomic disadvantage based on home zip code (ranging from 1-100, with 100 representing the most disadvantaged). Multivariable logistic regression modeling was used to examine predictor variables for FH in this patient population. Results: The final cohort included 10,024 patients from 2018 to 2020. Median age was 64.6 years (IQR 58.1,73.7), 58% were male, and 79% married. Race/ethnicity composition was 94% White (n=9,268), 2.5% Black (n=246), 0.4% American Indian/Alaskan Native (44), and 4% Hispanic (n=360). Fifty-six percent of patients had Medicare and 41% had commercial insurance. Fifty percent were retired, 40% were working/students, and 72% were urban residents. Mean ADI was 41.2. Fifty-six percent of patients had lymphomas, 23.5% had plasma cell disorders, 8.5% had leukemias, 6.8% had other hematological malignancies, and 5.5% had myelodysplastic/myeloproliferative neoplasms. FH was reported by 12.8% (n=1286) of the patients. Table 1 shows the results of the multivariable model. A significantly higher likelihood of endorsing FH was noted in Hispanic vs non-Hispanics, Black and American Indian/Alaskan Native groups vs whites, Disabled/Unemployed vs working, Medicaid, Medicare, and Self-Pay groups vs commercial insurance, higher ADI (5 th quintile vs 1 st), and myelodysplastic/myeloproliferative disorder and other hematologic malignancy vs lymphoma patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusion: Our study used automated data extraction from the EMR to efficiently identify predictors of FH in hematologic cancer patients. Employing a dichotomized and automated "flag" for FH, particularly if incorporated in the EMR, could ease the identification of SDOH issues, facilitate timely connection to appropriate resources, and help provide better patient-centered care. Figure 1 Figure 1. Disclosures Ailawadhi: Sanofi: Consultancy; Cellectar: Research Funding; Karyopharm: Consultancy; Ascentage: Research Funding; Genentech: Consultancy; Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Beigene: Consultancy; GSK: Consultancy, Research Funding; AbbVie: Consultancy; Medimmune: Research Funding; Pharmacyclics: Consultancy, Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Xencor: Research Funding. Fonseca: OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy; BMS: Consultancy; Mayo Clinic in Arizona: Current Employment; Aduro: Consultancy; AbbVie: Consultancy; GSK: Consultancy; Merck: Consultancy; Juno: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Novartis: Consultancy; Bayer: Consultancy; Celgene: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy. Griffin: Exact Sciences: Research Funding.

Abstract Background: Treatment for multiple myeloma (MM) patients refractory to proteasome inhibitors (PIs), immunomodulatory agents (IMiDs), and monoclonal antibodies remains an unmet need. Belantamab mafodotin is a first in class antibody drug conjugates (ADCs) in MM utilizing a humanized monoclonal antibody targeting B cell maturation antigen (BCMA) conjugated to a microtubule-disrupting monomethyl auristatin F. The efficacy of belantamab in relapsed refractory MM (RRMM) patients has been reported in several randomized clinical trials. However, clinical trials have restricted patients' eligibilty. We report the "real world" efficacy and safety of belanatmab in patients treated at all three Mayo Clinic sites. Methods: We retrospectively reviewed all consecutive MM patients that received at least one dose of belantamab outside a clinical trial. The primary endpoints were PFS and OS, and the secondary endpoints included overall response rates (ORR) and safety. Results: Thirty-seven RRMM patients were treated with belantamab between September 2020 and June 2021. The median age at diagnosis was 61 (range 37-83) and 64% were male. The median age at belantamab administration was 67 (IQR 59-74) and 14 patients (38%) were 70 years or older. Fifteen of 34 patients (44%) had high-risk FISH and 13 patients of 33 (39%) had ISS 3. Two patients had a creatinine of >2.5 mg/dL, and one patient was on dialysis. All three received full dose belantmab (2.5 mg/kg). The median time from diagnosis to belantamab initiation was 7 years (IQR 4-11), and the median prior lines of therapy was 8 (IQR 7-11). Twenty-seven patients (73%) underwent an autologous stem cell transplantation prior to belantamab therapy, and all patients were penta refractory (IMiD, PI, and anti CD38). Seven patients (19%) received CAR-T therapy prior to belantamab. The median number of belantamab doses administered was 3 (range 1-6). Thirty-one (84%) received belantamab monotherapy and 6 patients (16%) received belantamab in combination [pomalidomide (n=3), cyclophosphamide (n=2), or thalidomide (n=1)]. The overall response (ORR) rate to belantamab was 33% (out of 36 evaluable patients). Two patients (6%) achieved complete response (CR), 3 patients (8%) achieved very good partial response (VGPR), and 7 patients (19%) achieved partial response (PR). At data cutoff 20 patients (54%) are alive. Seventeen patients died of MM complications. The median follow-up was 6 months (IQR 3.5-7). The median PFS and OS for the whole cohort was 2 months (95% CI 1-3) and 7 months (95% CI 3--NR), respectively. Two patients died within the first cycle of belantamab (due to PD), and overall, 12 died within 100 days of belantamab therapy, all due to disease progression. Eleven patients (30%) were hospitalized during belantamab therapy. The reasons for hospitalization were related to MM in 4 patients (one patient with malignant ascites, one with hypercalcemia, 2 patients for pain management). Three patients were hospitalized due to thrombocytopenia (related to disease), and one due to an infusion-related reaction (IRR). Other reasons for hospitalization were infection (soft tissue abscess, n=1), proctitis (n=1) and tumor lysis (n=1). Keratopathy developed in 16 patients (43%). Keratopathy was grade 1 in 6 patients, grade 2 in 7 patients, and grade 3 in 3 patients. Only one patient had corneal erosions and 6 patients reported decreased visual acuity, (1 grade 3 keratopathy, 4 had grade 2 keratopathy, and 1 had grade 1 keratopathy). Two patients had IRR. Two patients reported gaining 3.5 kg each after therapy with belantamab was discontinued. Conclusions: The response rates are similar to those reported in randomized clinical trials. The role of belantamab in the era of other BCMA directed therapy (CAR-T, BiTEs) remains unclear. Disclosures Dispenzieri: Janssen: Consultancy, Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Alnylam: Research Funding; Takeda: Research Funding. Kumar: Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Oncopeptides: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Dingli: Novartis: Research Funding; GSK: Consultancy; Janssen: Consultancy; Sanofi: Consultancy; Alexion: Consultancy; Apellis: Consultancy. Kapoor: Cellectar: Consultancy; BeiGene: Consultancy; Sanofi: Consultancy; Ichnos Sciences: Research Funding; Karyopharm: Consultancy; Amgen: Research Funding; Pharmacyclics: Consultancy; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Ionis Pharmaceuticals: Other: Advisory Board; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Aurora Biopharma: Other: Stock option.

Abstract Introduction: Chimeric antigen receptor T-cell (CAR-T) therapy is an effective treatment that has transformed hematologic malignancy management. However, its significant short-term toxicities, mainly cytokine release syndrome (CRS) and neurotoxicity, require frequent monitoring almost universally achieved via prolonged inpatient admissions. We have reported our institution's specialized, multi-disciplinary hospital-based outpatient (HBO) CAR-T service and experience (Bansal et.al, ASCO 2021). To enhance the HBO practice, we implemented a remote patient monitoring (RPM) program, comprised of in-home, electronic health record-integrated technology to monitor vital signs and neurologic symptoms for 30 days post CAR-T infusion. The RPM program's triaging algorithm generates alerts based on predetermined parameters to guide escalation of care if needed. In this report, we describe the characteristics of RPM program patients (pts), focusing on the alerts received within 48 hours (h) of first hospitalization in the post-CAR-T setting. Methods: Pts undergoing commercial CAR-T cell therapy at Mayo Clinic Rochester were enrolled in the RPM program as part of our standard of care from 06/2020 to 05/2021. Pts with detailed RPM data available were included in this analysis. Patient characteristics, health care utilization, inpatient interventions, and RPM alert data were reviewed. Wilcoxon test was used for continuous variables, chi-squared test was used for categorical variables. Results: Baseline characteristics are summarized in Table 1. Of 20 pts in this analysis, 13 had RPM alerts in the 48h preceding the first hospitalization post CAR-T. For the remaining 7 pts without RPM alerts, 3 were hospitalized within 24h of CAR-T infusion without significant RPM data, 1 patient hospitalized with acute pancreatitis (day +8 post CAR-T infusion), 1 for doubling of CRP (day +10), 1 for acute pain crisis (day +5), and 1 with neutropenic fever (day +5) with not reporting RPM data. The median RPM program duration was 18 days (range 3-32) which accounts for interruptions in the RPM monitoring while pts are hospitalized. The median number of data points per patient was 231 (range 22-532), with a median of 20 alerts per patient (range 2-81). The median missing data points per patient were 20 (10%) (range 0-95). Of the 13 pts with RPM alerts within 48h of first hospitalization, alerts met our definition of urgent criteria in 6 pts [temperature (temp) > 100F (n=3), temp > 100F + tachycardia (n=1), temp > 100F + hypoxia (n=1), and hypoxia (n=1)]. In the remaining 7 pts, the RPM alerts met our routine criteria definition [temp 99-100 (n=2), tachycardia + hypertension + weight loss (n=2), hypertension (n=2), new incontinence (n=1)]. CRS was reported in 12 pts [grade 1-2 (n=11), grade 3-4 (n=1)] and neurotoxicity in 8 pts [grade 1-2 (n=7), grade 3-4 (n=1)]. The median post CAR-T first hospitalization day was day +4 (range day +2-10), Figure 1. The median time from the last RPM alert to hospitalization was 5.8h for pts meeting urgent criteria vs 10.8h for those meeting routine criteria (p=0.13). Tocilizumab was used in 5 pts during the first hospitalization [3 (50%) in the urgent criteria group and 2 (28%) in the routine criteria group], with a median time from admission to tocilizumab of 12h in the urgent criteria group vs 43h in the routine criteria group (p=0.05). Early hospitalization (≤ 3 days) post CAR-T infusion was associated with later need for tocilizumab [median 51h (range 30-57) vs. 11.5h (range 6-33) from admission, p=0.03] than late hospitalization (> 4 days post CAR-T). Upon admission, oxygen supplementation was started on both pts with hypoxia noted in the RPM alerts. Only 1 patient (routine criteria group) required use of vasopressors while hospitalized, initiated more than 48h after admission. Conclusion: A CAR-T RPM program can identify adverse symptoms and vital sign changes in CAR-T pts managed in the outpatient setting. In this pilot implementation, the CAR-T RPM triaging algorithm identified pts requiring expedited hospitalization and facilitated early initiation of tocilizumab. The intermittent monitoring and proportion of missing data are the main limitations of the RPM in the acute care setting post-CAR-T infusion. Incorporation of wearable devices for continuous remote monitoring is being explored as a mitigating strategy to these limitations. Figure 1 Figure 1. Disclosures Paludo: Karyopharm: Research Funding. Dingli: Apellis: Consultancy; Janssen: Consultancy; GSK: Consultancy; Novartis: Research Funding; Sanofi: Consultancy; Alexion: Consultancy. Kapoor: Glaxo SmithKline: Research Funding; Sanofi: Consultancy; Amgen: Research Funding; AbbVie: Research Funding; Sanofi: Research Funding; Karyopharm: Research Funding; Ichnos Sciences: Research Funding; Pharmacyclics: Consultancy; Takeda: Research Funding; Regeneron Pharmaceuticals: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy. Gertz: Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria. Wang: InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kenderian: Humanigen, Inc.: Consultancy, Honoraria, Research Funding. Kumar: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Oncopeptides: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Carsgen: Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Bluebird Bio: Consultancy; Beigene: Consultancy; Tenebio: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Bennani: Purdue Pharma: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Vividion: Other: Advisory Board; Kymera: Other: Advisory Board; Verastem: Other: Advisory Board. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Lin: Merck: Research Funding; Vineti: Consultancy; Gamida Cell: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Legend: Consultancy; Celgene: Consultancy, Research Funding; Sorrento: Consultancy; Takeda: Research Funding; Janssen: Consultancy, Research Funding; Novartis: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding.