Citations

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 :COVID-19 adversely affects individuals with cancer. Several studies have found that seroconversion rates among patients with hematologic malignancies are suboptimal when compared to patients without cancer. Among patients with hematologic malignancies, seroconversion rates also appear to be influenced by recent treatment and the type of treatment they have received. Patients with non-Hodgkin lymphoma (NHL) and multiple myeloma (MM) are immunocompromised due to impaired humoral and cellular immunity in addition to prescribed immunosuppressive therapy. Chimeric antigen receptor T-cell (CAR T) therapy is now widely used for NHL and MM, but little is known about seroconversion rates after COVID-19 vaccination among these populations. Current national guidelines recommend COVID-19 vaccination to be offered to CAR T recipients as early as three months thereafter. We retrospectively evaluated SARS-CoV-2 spike-binding IgG antibody levels following COVID-19 vaccination among NHL and MM CAR T therapy recipients. Methods:This retrospective study was conducted at three Mayo Clinic sites on NHL and MM patients that received CAR T infusions from Sept 2016 to June 2021. Baseline characteristics were ascertained from medical records. All NHL and MM patients who had received CAR T at any point and were alive at the time that the COVID-19 vaccine first became available were eligible for inclusion for antibody response evaluation. For antibody response to vaccination, antibody spike values > 0.80 U/mL were considered positive. Results: Out of 104 CAR T infusions, 73 patients are alive at the time of this submission. We have had 7 patients with known COVID-19 pre-CAR T and all 7 are currently alive (5 have antibody titers and 2 have not been tested yet). Nineteen patients developed known COVID infection post-CAR T (13 alive and 6 deceased). The mortality of COVID post-CAR T in our sample was 31.5%. Furthermore, of the 13 patients that survived COVID-19, they received CAR T an average of 416 days prior to COVID-19 infection (median = 337, range = 54 - 1406); the 6 patients who died from COVID-19 had received CAR T an average of 250 days prior to COVID-19 infection (median = 164, range = 7 - 846). All 6 deceased patients did not receive COVID-19 vaccination pre-CAR T. Out of 17 CAR T patients tested for antibody spike titers post COVID-19 vaccination, 76.4% were able to mount an antibody response. More patients with MM had a higher titer response to the vaccine (>250 U/mL) compared to the NHL counterparts (0.80-249 U/mL). All patients that received the vaccine, regardless of antibody response, were alive at the time of this submission. Conclusions:The majority of CAR T recipients with NHL and MM are able to mount an antibody response following COVID-19 vaccination in our relatively small sample. The frequency of seroconversion among CAR T recipients seems to be similar to patients with hematologic malignancy who had received a hematopoietic cell transplant reported elsewhere. These findings are limited by our small sample size and may be influenced by the timing of vaccination relative to CAR T. Furthermore, almost half of our patients received IVIG post CAR T which could potentially cause false positive antibody results as pooled immunoglobulin preparations may contain COVID-19 antibodies from vaccinated healthy donors. To better understand the characteristics of the immunologic response against SARS-CoV-2 in patients post-CAR T, larger multicenter studies exploring both humoral and cellular immunity will be needed. JEWN, MI and JM are co-first authors and PV, HM and AR are co-senior authors. Figure 1 Figure 1. Disclosures Munoz: Physicians' Education Resource: Honoraria; Seattle Genetics: Honoraria; Bayer: Research Funding; Gilead/Kite Pharma: Research Funding; Celgene: Research Funding; Merck: Research Funding; Portola: Research Funding; Incyte: Research Funding; Genentech: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Research Funding; Janssen: Research Funding; Millennium: Research Funding; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Targeted Oncology: Honoraria; OncView: Honoraria; Kyowa: Honoraria. Bergsagel: Oncopeptides: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; GSK: Consultancy, Honoraria. Wang: Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; InnoCare: Research Funding; Novartis: Research Funding; MorphoSys: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. Fonseca: Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy; Amgen: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Mayo Clinic in Arizona: Current Employment; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy. Palmer: Sierra Oncology: Consultancy, Research Funding; CTI BioPharma: Consultancy, Research Funding; Protagonist: Consultancy, Research Funding; Incyte: Research Funding; PharmaEssentia: Research Funding. Dingli: Novartis: Research Funding; GSK: Consultancy; Apellis: Consultancy; Alexion: Consultancy; Sanofi: Consultancy; Janssen: Consultancy. Kapoor: Sanofi: Research Funding; AbbVie: Research Funding; Takeda: Research Funding; 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. Kumar: Roche-Genentech: Consultancy, Research Funding; Oncopeptides: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Beigene: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Adaptive: 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; Tenebio: Research Funding; Merck: Research Funding; Carsgen: 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; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; Sanofi: Research Funding. Paludo: Karyopharm: Research Funding. Bennani: Kymera: Other: Advisory Board; Vividion: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Purdue Pharma: 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: Kite, a Gilead Company: Consultancy, Research Funding; Merck: Research Funding; Gamida Cell: Consultancy; Takeda: Research Funding; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Janssen: Consultancy, Research Funding; Sorrento: Consultancy; Legend: Consultancy; Vineti: Consultancy. Murthy: CRISPR Therapeutics: Research Funding.

6594 Background: Patient reported financial hardship (FH) in cancer care is a growing challenge for patients, their caregivers and healthcare providers. As treatment costs escalate, it is imperative to develop effective strategies to proactively recognize and mitigate FH within oncology practice. Using automated processes to screen and refer patients to appropriate resources is a potential option. 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?’ completed by all cancer patients annually as part of the Social Determinants of Health (SDOH) assessment. In this study, we describe the prevalence and predictors for FH (denoted by the answer ‘hard and very hard’) in our patient population. Methods: Patients receiving cancer care at the three Mayo Clinic sites (Minnesota, Arizona, and Florida) who completed the FH screen at least once were included in this study. Demographics (age, gender, race/ ethnicity, insurance, employment status, marital status, and zip code) and disease state data for included patients was extracted from the EMR and Mayo Clinic Cancer Registry. Disease state was categorized by type of cancer (hematological or solid malignancy) and cancer stage. Zip code was used to derive median income, rural/urban residence and distance from the cancer center. Multivariable logistic regression models were utilized to examine factors associated with FH. Results: The final study cohort included 31,969 patients with median age 66 years (IQR 57,73), 51% females, and 76% married. Race/ethnicity composition was 93% White, 3% Black, and 4% Hispanic. 52% of patients had Medicare and 43% had commercial insurance. Other notable factors included 48% retired, 41% working/ students, 76% married, and 72% urban residents. Median time from cancer diagnosis was 1.1 year (IQR 0.1, 3.8) and median income was $64,406 (IQR 53,067, 82,038). 31% of patients had hematological malignancies, 20% of the cancers for which staging information was available were metastatic. FH was reported by 4% (n = 1194) of the patients. A significantly higher likelihood of endorsing FH (p < 0.001 for all) was noted in Hispanic (OR 1.64), Black (OR 1.84), American Indian/Alaskan native (OR 2.02), below median income (OR 1.48), rural (OR 1.17), self-pay (OR 2.77), Medicaid (OR 2.29), Medicare (OR 1.43), unemployed/disabled (OR 2.39), single (OR 2.07), or divorced (OR 2.43) patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusions: Our study successfully leveraged the EMR to identify key sociodemographic groups more likely to report FH. An electronic trigger to flag such patients at high-risk of FH and proactively address FH is currently being developed.

Abstract Background:Racial disparities in multiple myeloma (MM) outcomes are well established. Clinical characteristics and cytogenetic abnormalities dictate outcomes in MM but historically they have been mostly defined in White patients with paucity of information regarding racial-ethnic minorities. Hispanics are the fastest growing racial-ethnic subgroup in United States and have the worst overall survival (OS) in MM but differences in disease presentation among Hispanics compared to Whites, the historical control, are undefined. Methods: MM patients seen at Mayo Clinic in Florida (MCF) and Arizona (MCA) between 1/1/2000-06/15/2017 were included. Patient and disease characteristics including gender, age at diagnosis, bone marrow (BM) plasmacytosis, laboratory data [M spike, creatinine (Cr), beta-2-microglobulin (B2M), Hemoglobin (Hb), calcium (Ca)], MM subtype [IgG, IgA, light chain (LC), other], LC subtype (κ, λ, biclonal), presence of lytic bone lesions, disease stage (DS, ISS, R-ISS), mSMRT cytogenetic risk category, presence of individual cytogenetic mutations (deletions, translocations, gains, trisomy/hyperdiploidy) was collected. Comparisons between Hispanic study cohort characteristics and historical control were made by Chi-square, Fischer's exact and Signed rank tests, where applicable, using data from previous publications [Kyle et al, 2003 (n=1027) and Fonseca et al, 2003 (n=351)]. Patients with missing data on a certain clinical or disease feature were not included in the statistical analysis of that specific characteristic. Results: A total of 1705 MM patients were screened and 266 Hispanic patients seen at either site between the specified dates were identified for the analysis. These included equal number of males and females (n=133 each) with a median age of 58 years (range 25-87). IgG disease was seen in 52% with IgA and LC subtypes in 23% each. LC subtype was κ in 67%, λ in 31% and biclonal in 2% cases. Lytic and extramedullary disease were noted in 71% and 44% patients, respectively. DS and R-ISS stage at the time of MM diagnosis was not available in most patients while ISS stage among 150 patients showed stage 1, 2 and 3 in 48%, 28% and 24% patients, respectively. mSMART risk categories were present in 226 patients and included high, intermediate and standard risk in 12%, 11% and 77% patients, respectively. Of these 226 patients, cytogenetic disease prior to starting any MM treatment was available in 153 patients with similar risk category distribution. Median follow up for the whole Hispanic cohort was 38.6 months with a median OS 10.7 years (Figure 1). Comparison with historical control datasets which were 97% or greater comprised of White patients showed some significant differences for the Hispanic cohort including gender distribution, median age, BM plasmacytosis at the time of diagnosis, median Hb, Cr or M spike at presentation, light chain subtype, presence of lytic bone disease and certain cytogenetic alterations including t(4;14) and del13q/monosomy (Table 1). We did not note differences in high-risk cytogenetics between Hispanics and historical White controls. Conclusions: We report the first description of clinical and cytogenetic characteristics of Hispanic MM patients. The current and historical datasets that we have used are all from Mayo Clinic experience, thus making reasonable comparisons possible. We were able to confirm previous reports of demographic differences in Hispanics e.g., younger age at diagnosis. While some characteristics were significantly different, the clinical and cytogenetic differences we note do not clearly explain the inferior OS noted for Hispanics in population-based studies. Significant differences that we report warrant continued exploration in larger institutional and public databases in addition to a continued look into healthcare access and utilization. An ongoing collaborative analysis with data from Mayo Clinic, University of Miami and University of Southern California, Los Angeles is ongoing. Disclosures Fonseca: AMGEN: Consultancy; Jansen: Consultancy; Mayo Clinic & Dr Fonseca: Patents & Royalties: Prognostication of myeloma via FISH, ~$2000/year; Celgene Corporation: Consultancy, Research Funding; Merck: Consultancy; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Bayer: Consultancy; Bristol-Myers Squibb: Consultancy; Takeda: Consultancy; Sanofi: Consultancy; Pharmacyclics: Consultancy; Novartis: Consultancy. Kelly: Pharmacyclics: Honoraria; Abbvie: Honoraria; Amgen: Honoraria; Jannsen: Honoraria. Sher: LAM Therapeutics, Inc: Research Funding. Ailawadhi: Pharmacyclics: Research Funding; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Novartis: Consultancy, Honoraria.

Multiple Myeloma (MM) is a hematological malignancy resulting in abnormal plasma cells proliferating in the bone marrow. One of the most common treatment strategies for newly diagnosed MM is the combination of induction therapy followed by autologous stem cell transplant (ASCT). Due to the underlying disease and/or the effect of the treatment regimens administered, immunosuppression is a common clinical consequence. It has been shown that ASCT recipients, treated with traditional compounds, have a reduction in the levels of antibody titers to vaccine-preventable diseases such as measles, mumps and rubella (MMR), between one and four years post-transplant. Therefore, re-vaccination is recommended at least two years after ASCT. Therapeutic options have expanded in the past decade, with the introduction of novel agents that have significantly improved MM patient outcomes; however, this may also arguably have new and different implications on a patient's immune system. In this study we sought to analyze the presence of IgG antibodies against MMR in 110 patients with MM post-ASCT. All patients received ASCT between 2014 and 2019 at Mayo Clinic Arizona. Plasma samples were collected approximately 100 days after ASCT (median 92 days) and the antibody titers were tested using the Bio-Rad MMR IgG multiplex flow immunoassay. Sample antibody index values were compared with the assay-specific calibration to determine positivity. For a control population, we utilized the results of fully vaccinated and presumptively immune healthcare workers (HCWs) that were evaluated for the presence of antibodies to measles (n=199), mumps (n=197) and rubella (n=209), using the same method, instrument and laboratory. Overall, 70% of the MM patients were positive for antibodies against measles, compared with 77.4% of HCWs. For mumps, the MM cohort had a positivity of 49.1% versus 84.8% of HCWs. Finally, rubella antibodies were found in 64.5% and 83.7% of MM and HWCs, respectively. Next, we performed testing on serial samples collected from 45 MM patients, comparing the presence of MMR antibodies pre-ASCT (median 130 days) and post-ASCT. The number of patients with positive titers detected pre- and post-transplant was unchanged for each of the three viruses. In summary, our findings do not indicate a significant reduction in relative MMR antibody levels in ASCT recipients. This suggests that earlier re-vaccination is not required post-ASCT in the era of novel MM compounds. However, further validation studies in larger cohorts are necessary prior to considering a change in current vaccination guidelines. Disclosures Braggio: DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Fonseca:Janssen: Consultancy; Takeda: Consultancy; Novartis: Consultancy; Bayer: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Pharmacyclics: Consultancy; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy; GSK: Consultancy; AbbVie: Consultancy; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees.

Introduction: Daratumumab (D) is an anti-CD38 monoclonal antibody, used frequently in combination with immunomodulatory drugs (IMiDs), lenalidomide (Len) and pomalidomide (Pom). There are no studies comparing DRd to DPd in Len-exposed or refractory multiple myeloma (MM). We present the Mayo Clinic experience of DPd and DRd utilization and outcomes. Methods: We identified consecutive MM pts who received D, between 1/2015 and 4/2019 at Mayo Clinic. We included pts who were exposed (at least one full cycle, 21/28 days) or refractory (progression on full dose or maintenance Len during treatment or within 60 days of stopping Len) to Len prior to D and received 1 to 4 prior lines of therapy. Only Pts who received DRd and DPd were included. Results: Out of 411 evaluable pts 162 met the inclusion criteria; 67 were Len exposed, but not refractory and 95 were Len refractory. Pts' characteristics are shown in Table 1. DRd was used in 76 (47%) and DPd in 86 (53%) pts. Majority of pts (105; 65%) received DPd or DRd in the 2nd or 3rd line and 104 pts (64%) are still alive. In Len refractory pts, the median time from last dose of Len to D initiation was 1 mths (interquartile range 0-6.5). Seventy pts (43%) had high-risk cytogenetics (HR). Only 11 pts (7%) underwent autologous hematopoietic cell transplant after DPd or DRd regimen. There was no significant difference between pts who received DRd and DPd in terms of age (&gt;65 years), gender, race, proportion exposed or refractory to Bortezomib, number of prior lines of treatment (&gt;2), or HR status (p&gt;0.05 for all). Pts who received DPd were 4 times more likely to be Len refractory compared to pts who received DRd, p=0.0008. Median follow up time for the whole cohort from initial diagnosis was 92 mths (CI 95%; 79.5-107), while from D start date was 30 mths (CI 95%; 27-34). Median PFS for the whole cohort was 21 mths (CI 95%; 15-25) and median OS was 138 mths (CI 95%; 111-193). In Len exposed but not refractory pts, n=67, the overall response rate (ORR) was 82% in the DRd group compared to 68% in the DPd group, p=0.2. There was no difference in median PFS; 25.5 mths (CI 95%; 23-NR) for DRd compared to 25 mths (CI 95%; 5-NR) for DPd, p=0.37. Similarly, there was no difference in median OS for DRd compared to DPd; NR mths (CI 95%; 138-NR) and 145.5 mths (CI 95%; 70-NR), respectively, p=0.06. In the 48 Len exposed but refractory pts who received DRd or DPd in 2nd or 3rd line, there was no difference in median PFS; 39 mths (CI 95%; 23-NR) for DRd compared to 21 mths (CI 95%; 3-NR) for DPd, p=0.11. Similarly, there was no difference in median OS for DRd compared to DPd; NR mths (CI 95%; NR-NR) and 145.5 mths (CI 95%; 36-NR), respectively, p=0.13. In Len refractory pts only, n=95, the ORR was 84% in the DRd group compared to 58% in the DPd group, p=0.009. There was no difference in median PFS; 25 mths (CI 95%; 13-32) for DRd compared to 9 mths (CI 95%; 5-15) for DPd, p=0.18. Similarly, there was no difference in median OS for DRd compared to DPd; 103 mths (CI 95%; 78-NR) and 106 mths (CI 95%; 66-183), respectively, p=0.5. In the 57 Len refractory pts who received DRd or DPd in 2nd or 3rd line, there was no difference in median PFS; 25 mths (CI 95%; 13-32) for DRd compared to 13 mths (CI 95%; 5-NR) for DPd, p=08. Similarly, there was no difference in median OS for DRd compared to DPd; 103 mths (CI 95%; 69-NR) and 105 mths (CI 95%; 47-NR), respectively, p=0.35. In the 16 Len exposed but not refractory pts with HR MM who received DRd or DPd in 2nd or 3rd line, the median PFS was better in the DRd group; 24.4 mths (CI 95%; 12-NR) compared to 3.3 mths (CI 95%; 1.9-27.5) for DPd, p=0.0093. However, there was no difference in median OS for DRd compared to DPd; NR mths (CI 95%; 51-NR) and NR mths (CI 95%; 18-NR), respectively, p=0.13. In the 29 Len refractory pts with HR MM who received DRd or DPd in 2nd or 3rd line, there was no statistically significant difference in median PFS; 18 mths (CI 95%; 6-43) for DRd compared to 6 mths (CI 95%; 4-13) for DPd, p=0.22.Similarly, there was no difference in median OS for DRd compared to DPd; 103 mths (CI 95%; 47-NR) and 53 mths (CI 95%; 26-105), respectively, p=0.2. Conclusions: D+IMiD regimens after prior Len exposure/refractoriness are commonly used in real-world, despite the lack of data from prospective clinical trials. Combining D with IMiDs in Len exposed or refractory pts is efficacious, whether the IMiD is Len or Pom. Our study shows that regardless of prior Len use, DRd is at least equivalent to DPd. Prospective studies should be done to confirm these observations. Disclosures Alhaj Moustafa: Acrotech: Consultancy. Larsen:Janssen Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kapoor:Janssen: Research Funding; GlaxoSmithKline: Research Funding; Cellectar: Consultancy; Celgene: Honoraria; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Consultancy, Research Funding. Fonseca:Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy; GSK: Consultancy; AbbVie: Consultancy; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Dingli:Karyopharm Therapeutics: Research Funding; Rigel: Consultancy; Bristol Myers Squibb: Research Funding; Millenium: Consultancy; Sanofi-Genzyme: Consultancy; Janssen: Consultancy; Alexion: Consultancy; Apellis: Consultancy. Ailawadhi:Celgene: Honoraria; Amgen: Research Funding; Takeda: Honoraria; Janssen: Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; BMS: Research Funding; Medimmune: Research Funding; Phosplatin: Research Funding.

Introduction: New targets and treatment modalities are needed for multiple myeloma (MM). Fc receptor-homolog 5 (FcRH5) is a type I membrane protein that is expressed on B cells and plasma cells, and is found on myeloma cells with near 100% prevalence. BFCR4350A, a humanized immunoglobulin G-based T-cell-engaging bispecific antibody (bsAb), targets the most membrane-proximal domain of FcRH5 on myeloma cells and CD3 on T cells. Dual binding facilitates efficient immunological synapse formation, resulting in T-cell activation and potent killing of myeloma cells (Li et al. Cancer Cell 2017). GO39775 (NCT03275103) is an ongoing, multicenter Phase I trial evaluating the safety, activity, pharmacodynamics, and pharmacokinetics of BFCR4350A monotherapy in pts with relapsed/refractory (R/R) MM. We present dose-escalation data from the single step-up dosing cohort (Arm A). Methods: All pts have R/R MM for which no established therapy is available, appropriate, or tolerated. Prior exposure to CAR-T cells, T-cell engaging bsAbs, and antibody-drug conjugates (ADCs), including those targeting BCMA, is allowed. In dose-escalation, pts receive BFCR4350A by IV infusion in 21-day cycles (Q3W). In Arm A, a single step-up dose is used in Cycle (C) 1 to mitigate the risk for cytokine release syndrome (CRS), with the step dose (0.05-3.6mg) given on C1 Day (D) 1 and the target dose (0.15-132mg) given on C1D8, and on D1 of each subsequent cycle. Results: At cut-off (April 13, 2020), 51 pts (median age: 62.0 years [range: 33-80]; high-risk [HR] cytogenetics [1q21, t(4;14), t(14;16), or del(17p)]: 28 pts) had been enrolled into Arm A. Median number of prior lines of therapy was 6 (range: 2-15). Prior treatments included: proteasome inhibitors (PIs), 100% (94.1% refractory); immunomodulatory drugs (IMiDs), 100% (98.0% refractory); anti-CD38 mAbs, 78.4% (92.5% refractory); autologous stem cell transplant, 86.3%. Overall, 66.7% of pts were triple-class refractory (≥1 PI, ≥1 IMiD, and ≥1 anti-CD38 mAb), and 94.1% of pts were refractory to their last therapy. At cut-off, 46/51 pts were evaluable for efficacy. Responses were observed at the 3.6/20mg dose level and above, in 15/29 pts (51.7%) (Table). Responses included 3 stringent CRs, 3 CRs, 4 VGPRs, and 5 PRs (Table). At the 3.6/20mg dose level and above, responses were observed in pts with HR cytogenetics (9/17), triple-class refractory disease (10/20), and prior exposure to anti-CD38 mAbs (11/22), CAR-Ts (2/3), or ADCs (2/2). Duration of response data are evolving, with 6/15 pts in response for &gt;6 months at cut-off. Median follow-up for safety was 6.2 months (range: 0.2-26.3 months). Almost all pts (49/51) had ≥1 treatment-related AE. The most common treatment-related AE was CRS (Lee et al. 2014 criteria; 38/51 pts, 74.5%). CRS was Grade (Gr) 1 in 20 pts (39.2%), Gr 2 in 17 pts (33.3%), and Gr 3 in 1 pt (2%) (due to Gr 4 transaminase elevation). CRS was most common in C1 (38 pts) and was uncommon or absent in subsequent cycles (4 pts). Most CRS events (49/58, 84.5%) resolved within 2 days. 18/38 (47.3%) pts with CRS received tocilizumab and/or steroids. Other treatment-related AEs in ≥5 pts were neutropenia and lymphocyte count decreased (6 pts each, 11.8%), aspartate aminotransferase increased and platelet count decreased (5 pts each, 9.8%). Treatment-related Gr 3-4 AEs (20 pts, 39.2%) in ≥3 pts were lymphocyte count decreased (6 pts, 11.8%), neutropenia (5 pts, 9.8%), anemia and platelet count decreased (3 pts each, 5.9%). No treatment-related Gr 5 (fatal) AEs were observed. Treatment-related AEs leading to withdrawal of treatment were uncommon (1 pt, 2.0%). One DLT (Gr 3 pneumonia) was observed in the 3.6/90mg cohort, but the MTD was not reached. BFCR4350A PK was linear across the active dose levels tested and the estimated half-life was supportive of the Q3W dosing regimen. Conclusions: BFCR4350A monotherapy demonstrates promising activity in heavily pre-treated R/R MM, with deep and durable responses observed in pts with HR cytogenetics, triple-class refractory disease, and/or prior exposure to anti-CD38 mAbs, CAR-Ts, or ADCs. Toxicity was manageable, with C1 single step-up dosing effectively mitigating the risk for severe CRS and allowing escalation to clinically active doses. Updated data will be presented. Disclosures Cohen: Novartis: Other: Patents/Intellectual property licensed, Research Funding; Bristol-Myers Squibb: 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; Takeda,: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Genentech/Roche: Membership on an entity's Board of Directors or advisory committees. Harrison:Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; F. Hoffmann-La Roche: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: wrt panobinostat; BMS: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Haemalogix: Consultancy; Janssen: Honoraria; CRISPR Therapeutics: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Krishnan:Sutro: Membership on an entity's Board of Directors or advisory committees; Z Predicta: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Regeneron: Consultancy; Takeda: Speakers Bureau; Amgen: Speakers Bureau; BMS/Celgene: Consultancy, Other: Stock BMS, Speakers Bureau; Sanofi: Consultancy. Fonseca:Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy; GSK: Consultancy; AbbVie: Consultancy; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Forsberg:Celgene: Speakers Bureau; Genentech, Inc., Sanofi, Karyopharm, Abbvie: Research Funding. Spencer:AbbVie, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Consultancy; Amgen, Celgene, Haemalogix, Janssen, Servier and Takeda: Research Funding; AbbVie, Amgen, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Honoraria; Celgene, Janssen and Takeda: Speakers Bureau. Berdeja:BMS: Consultancy, Research Funding; Glenmark: Research Funding; Genentech, Inc.: Research Funding; Bioclinica: Consultancy; Teva: Research Funding; Vivolux: Research Funding; Abbvie: Research Funding; Cellularity: Research Funding; Acetylon: Research Funding; CURIS: Research Funding; Legend: Consultancy; Lilly: Research Funding; Constellation: Research Funding; EMD Sorono: Research Funding; Celgene: Consultancy, Research Funding; Novartis: Research Funding; Janssen: Consultancy, Research Funding; Prothena: Consultancy; Poseida: Research Funding; Amgen: Consultancy, Research Funding; Servier: Consultancy; Takeda: Consultancy, Research Funding; Bluebird: Research Funding; Karyopharm: Consultancy; Kite Pharma: Consultancy; CRISPR Therapeutics: Consultancy, Research Funding; Kesios: Research Funding. Li:Genentech, Inc./ F. Hoffmann-La Roche: Current Employment. Choeurng:Genentech, Inc.: Current Employment, Current equity holder in publicly-traded company. Vaze:Genentech, Inc./ F. Hoffmann-La Roche: Current Employment, Current equity holder in publicly-traded company. Samineni:Genentech, Inc.: Current Employment, Current equity holder in publicly-traded company. Sumiyoshi:Genentech, Inc.: Current Employment, Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months. Cooper:Genentech, Inc./ F. Hoffmann-La Roche: Current Employment, Current equity holder in publicly-traded company. Fine:Genentech, Inc.: Current Employment; F. Hoffmann-La Roche: Current equity holder in publicly-traded company. Trudel:Janssen: Honoraria, Research Funding; Sanofi: Honoraria; GSK: Consultancy, Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Karyopharm: Honoraria; AstraZeneca: Honoraria; BMS: Consultancy, Honoraria, Research Funding; Genentech, Inc.: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Honoraria. OffLabel Disclosure: BFCR4350A is a humanized IgG-based T-cell-engaging bispecific antibody that targets the most membrane-proximal domain of FcRH5 on myeloma cells and CD3 on T cells. Dual binding facilitates efficient immunological synapse formation, resulting in T-cell activation and killing of myeloma cells. BFCR4350A is an investigational agent.

Introduction: Response assessment at day 100 post Autologous Stem Cell Transplant (ASCT) is associated with long-term relapsed free survival (RFS) and overall survival (OS) in multiple myeloma (MM). The International Myeloma Working Group (IMWG) are the preferred criteria to define best response to treatment and define relapse. In the last years, response assessment has incorporated minimal residual disease (MRD) status -associated with improved RFS and OS (Munshi et al); and PET/CT combined with clinical characteristics -also associated with favorable outcomes (Zamagni et al. NCT01910987; MMY3033). The 2016 IMWG MRD criteria, combined imaging (PET/CT) plus next-generation sequencing (NGS) MRD-negative to define complete response (CR). To our knowledge, there is limited data examining the correlation and prognostic value of MRD and FDG-PET/CT at day 100 post ASCT in MM. IN this study, we aimed to determine the prognostic valued of MRD by NGS combined with PET/CT in RFS and OS status after high dose chemotherapy and ASCT in MM. Methods: Patients who underwent ASCT for MM at Mayo Clinic Arizona and had MRD and PET/CT data were included in the study. Clinical data was obtained via retrospective chart review. Cytogenetic risk (CyR) was classified using the mSMART criteria . Disease and ASCT related characteristics were compared by MRD status. MRD was measured by NGS on bone marrow aspirates using the previosly validated clonoSEQ ® Assay (Adaptive Biotechnologies Corporation, Seattle, USA) tracking the IgH, IgK and IgL rearrangements at a minimum sensitivity level of 10-5. MRD was defined by residual clonal cells per million nucleated cells as: negative= 0, borderline= 1-5, positive &gt;5. PET/CT scans were performed locally at baseline and at day 100. Comparisons were performed using the chi-square test for categorical variables, Wilcoxon rank-sum test for continuos variables, McNemar's test and Cohens's Kappa for agreement measures. Results: A total of 103 patients had matched MRD and PET/CT assessment around day 100 (+/-9 days) and were included in the analysis. Median age at diagnosis was 62 years (range, 54-66 years), 71 patients (68.9%) were men. CyR was standard risk in 49 (47.6%), high-risk in 39 (37.9%) and unknown in 15 (14.6%) patients. Most 75 (72.8%) patients were MRD positive, 16 (15.5%) were MRD negative, and 12 (11.7%) borderline. The median main MRD clone detected was 64 (range 0-91,874). 70 patients (68%) and 33 (32%) had a negative and positive PET/CT respectively. The median follow-up time was 18 months (range, 13-31 months). At the time of data analysis, 10 patients (9.7%) had relapsed and only 4 (3.9%) had died. There was a high-correlation between MRD status and PET/CT, 31 patients (93.9%) with positive PET/CT were also MRD positive (p=0.0027). There were no statistical differences between PET/CT and CyR (p=0.95). We analyzed the correlation using the FREQ procedure (McNemars's test); there was a strong association between positive PET/CT and positive MRD in 31/33 patients (93.9%, high sensitivity), and low association for negative PET/CT the negative/borderline MRD in 26/70 (37.1%, low specificity; p&lt;0.001). The agreement measure between the PET/CT and MRD using negative/borderline combined had a kappa of 0.23 (95% CI 0.11, 0.35) indicating a fair agreement beyond chance (Figure 1). PET/CT-CT was a statistically significant predictor of worse RFS (HR 3.53, 95%CI: 1.02-12.24, p&lt;0.0337) and OS (HR 11.38, 95%CI: 1.18-109.56, p&lt;0.0078) (Figure 2-3, respectively). MRD was not predictive of neither RFS (HR 1.72, 95%CI: 0.36-8.14, p&lt;0.49) or OS (p&lt;0.16). Conclusions: In conclusion, we demonstrate that the combination of MRD by NGS (clonoSEQ ®) and PET/CT at day 100 are complementary and have a high sensitivity (true positive rate) and fair correlation of agreement but low specificity (true negative rate). PET/CT was the best most sensitive technique to prognosticate RFS and OS. We did not find prognostic correlation of MRD with RFS and OS. However, our findings might be confounded by the low risk of relapse and death, a longer follow-up may demonstrate clinically important differences. Our results add evidence that MRD plus PET/CT improve the definition of CR in MM patients post ASCT. Prospective studies are needed to elucidate the optimal timing and role of combined MRD, PET/CT with other prognostic markers of clinical outcomes. Disclosures Larsen: Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees. Fonseca:Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Oncopeptides: Consultancy; GSK: Consultancy; AbbVie: Consultancy; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy.

8521 Background: Several clinical and molecular prognostic factors (e.g, International Staging System [ISS] stage, plasma cell labeling index, genomic models) exist for multiple myeloma (MM). We hypothesized that exploiting gene signatures representative of oncogenic pathway deregulation (i.e., Ras, Myc, etc.), would improve MM prognostication and also aid with the identification of novel therapeutic targets. Methods: Using a discovery cohort (n=47) of patients with MM and corresponding gene expression data, we built upon current molecular risk-stratification and devised a Bayesian genomic (“metagene”) model for prognosis. We validated that model in an independent patient cohort (n=207). Finally, we incorporated ISS staging and clinical variables to construct a combined Clinico-Genomic Risk Stratification System. We further validated the combined model in a separate cohort (n=72), in a blinded manner. Results: Using gene signatures predictive of oncogenic pathway activation in the discovery cohort, we identified specific patterns (metagenes) of signaling pathway activation with prognostic relevance. In an independent validation cohort, this metagene-based model accurately predicted event free survival (EFS) independently of ISS (multivariate hazard ratio [HR] 3.4 for ISS stage, and 5.4 for the metagene model, p=0.002). Using multivariate risk modeling, we incorporated ISS staging and the metagene model into a Clinico-Genomic System and successfully stratified the validation cohort into three groups (low, intermediate, and high risk) with markedly different EFS (HR 4.2 for intermediate risk and 14.0 for high risk vs. the low risk cohort, p<0.0001). In an additional blinded validation, the Clinico-Genomic System again accurately predicted median overall survival (68.7 [low risk] vs 24.7 [intermediate risk] vs 18.7 months [high risk], p<0.0001); more accurately than either ISS or other reported genomic models. Conclusions: A combined Clinico-Genomic Risk Stratification System, building on patterns of oncogenic pathway activation and ISS staging system, improves upon current prognostic models in MM and identifies novel pathway targets for future therapeutic consideration. No significant financial relationships to disclose.