Cole, Craig E.

The bone marrow immune microenvironment (BMME) is a critical factor in multiple myeloma (MM) pathogenesis. Despite higher incidence of MM among self-identified African Americans, the relationship between African genetic similarity and the BMME remains unclear. We present the largest analysis to date assessing the impact of Ancestry on the BMME in myeloma (N=320, including 64 with ≥50% African genetic similarity, AFR-High) leveraging our recently generated Immune Atlas. AFR-High was associated with enrichment of late T-effector cells, driven by rare cytotoxic CD4⁺ T cells, suggesting a distinct adaptive BMME. AFR-High exhibited expansion of late-differentiated adaptive NK cells with high cytotoxic GNLY and GZMH expression. AFR-High depicted reduced CD14⁺ monocytes, increased CD16⁺ non-classical monocytes, and expansion of macrophage-like monocyte populations with enrichment of metabolic adaptation, efferocytosis and phagocytic clearance programs, and immunoregulatory pathways. These findings reveal a shift from an innate inflammatory immune landscape toward adaptive cytotoxic and immunoregulatory remodeling associated with African genetic similarity in MM.

Abstract Background: Multiple myeloma is the second most common adult blood cancer and remains incurable, despite recent advances. The IMWG/IMS recently published updated risk criteria, Consensus Genomic Staging (CGS), combining genomic events with clinical measures to identify high-risk patients (Avet-Loiseau, et al. JCO 2025). We applied CGS risk criteria to baseline samples from the Multiple Myeloma Research Foundation CoMMpass Study (NCT01454297), a prospective, longitudinal observational study that enrolled more than1100 participants between 2011-2017. Methods: We assessed patient risk following CGS criteria. Criteria 1: del(17p), with a cutoff of >20% clonal fraction, and/or TP53 mutation. Criteria 2: an IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q+ and/or del(1p32). Criteria 3: monoallelic del(1p32) along with 1q+ or biallelic del(1p32). Criteria 4: beta-2 microglobulin >= 5.5 mg/L with normal creatinine. Patients meeting one or more criteria were classified as High Risk. To test if risk showed association with genetic ancestry, we grouped patients into those with high African genetic ancestry and those with low African genetic ancestry. Genetic ancestry was measured using ADMIXTURE in comparison to 1000 Genomes. Results: We evaluated the CGS risk status of 877 CoMMpass patients, finding 69.2% (573/828) to be Standard Risk (SR) and 30.8% (255/828) to be High Risk (HR). HR patients showed worse progression-free survival (median PFS 26.3 months vs. 42.5 months; log rank test, p < 0.0001) and overall survival (median OS 63.9 months vs. median not reached; p < 0.0001). 10.6% (91/858) of patients met Criteria 1, 10.7% (93/873) met Criteria 2, 5.6% (49/877) met Criteria 3, and 8.7% (73/835) met Criteria 4. We found 14.2% (123/867) of patients to have high African-like genetic ancestry (AFR-high) and 85.8% (744/867) to have low African-like genetic ancestry (AFR-low). We observed significantly fewer HR patients within the AFR-high group (20%, 23/115) than in the AFR-low group (32.4%, 231/712) (Fisher’s exact test, p = 0.0065). The AFR-high group met HR criteria at a lower rate across all criteria, but only with Criteria 3 was the difference significant (p-value = 0.03). Higher levels of creatinine in AFR-high patients may have impacted meeting the normal creatinine requirement of Criteria 4. Conclusions: HR patients comprise 30.8% of CoMMpass subjects and show significantly worse PFS and OS than SR patients. AFR-high patients met HR criteria at a lower rate than AFR-low patients according to the CGS criteria. Clinical trials that utilize CGS criteria to determine patient eligibility may be challenged to enroll a representative patient population, including AFR-high patients. Screening and eligibility criteria should be carefully considered to mitigate potential enrollment disparities. Citation Format: Steven M. Foltz, Chaitanya R. Acharya, Alexander Gout, Yuxin Jin, David E. Avigan, Ravi Vij, Shaji Kunnathu Kumar, Sagar Lonial, Hearn Cho, Katie Wozniak, Jonathan J. Keats, Craig Cole, John D. Carpten, George J. Mulligan. Multiple myeloma risk classification of CoMMpass using Consensus Genomic Staging identifies differences between patients with high and low African genetic ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3887.

Abstract Background: Social determinants of health (SDOH) are critical drivers of cancer care access and outcomes. In multiple myeloma (MM), access to cellular therapies including autologous stem cell transplantation and CAR-T therapy often depends on formal or informal caregiver support. Traditional SDOH measures (e.g., race, income, or education) are often used as proxies for access to care; however, these measures may overlook individual realities pertaining to cellular therapies where a caregiver is generally required. In response to feedback from a HealthTree Patient Advisory Board questioning the practice of inferring caregiver support, this study evaluated a simple survey question by a large cross-sectional survey. Methods: After development of a dedicated single-question (“What best describes your current caregiving support?”) through iterative feedback from the Patient Advisory Board and MM experts, four key answers were settled on: (1) cohabitation with dedicated caregiver(s) to support care (“family support”); (2) presence of non-cohabiting caregiver(s) to provide dedicated support (“external support”); (3) lack of dedicated caregiver, hence support is arranged when needed (“arrangeable support ”); or (4) lack of dedicated caregiver, hence finding any support is a struggle (“inadequate support”). We deployed this new demographic question from February through July 2025 through HealthTree. Responses were analyzed across SDOH variables using Fisher exact or chi square tests, and random forest classifiers were employed for predictive modeling. Results: Of 1231 respondents, 71% had dedicated caregivers (68%, n=839, with family support, and 3%, n=41, with external support). In contrast, 23% (n=287) of patients routinely arranged support while 5% (n=64) had inadequate support (struggle to find support). The inadequate-support group had significant demographic differences from the sample as a whole: a higher proportion were women (78% vs 56%, p = 0.04), Black (9% vs 4%, p < 0.01), lived >50 miles from their treatment center (17% vs 6%, p < 0.01), and had an annual income <$50,000 (17% vs 6%, p = 0.02). This group had a numerically higher proportion of patients whose employment status was disabled (12% vs 5%, p=0.08) as well. No proportion difference in age (67 ± 9 yr), military status, or severity of diagnosis - precursor disease (17%) v. multiple myeloma (81%). Rates of inadequate support were overall low in patients who lived with only their spouse (1%) or lived with their spouse & children (3%). In contrast, rates of inadequate support were higher (than 5%) in patients who lived alone (21%), divorced (20%), lived with adult children (14%), lived with friends (13%), separated (11%), or widowed (9%) (p < 0.001). However, the arrangeable-support group comprised 12% of patients who lived only with their spouse, 19% of patients who lived with their spouse and children, and 61% of patients who lived with their adult children (p < 0.001). Finally, a random forest classifier failed to identify any individual in the inadequate-support category, suggesting that unmeasured or more complex patterns may underlie this classification. Conclusions: To our knowledge, this is the largest analysis of caregiver support and its relationship with other SDOH in patients with MM to date. Despite the importance of caregiving to transplantation and CAR-T therapy, we found that 5% of patients with MM struggle to find caregiver support; this figure likely underestimates the true prevalence, given potential selection bias among patients who are digitally engaged through the HealthTree platform. Even among patients who lived with their spouses, an additional 10-20% of patients routinely had to arrange caregiving support on a visit-by-visit basis; for patients who lived with their adult children, this proportion was over 60%. The requirement to arrange caregiving support can create time toxicity and financial toxicity for patients, although these factors were not directly queried in our study. Finally, caregiver availability cannot be routinely inferred from traditional SDOH variables. Our data highlight the importance of including caregiver status as a standard demographic question in clinical and research settings. Further interventions are needed to improve caregiver support for vulnerable patients with MM as well as to reduce the duration of caregiver requirements for procedures such as CAR-T therapy.

BACKGROUND Aneuploidy, defined by abnormal copy number changes of chromosomes, contributes to genome instability in multiple myeloma and has potential prognostic impact. Previous research has explored transcriptional pathways affected by aneuploidy. We aim to evaluate aneuploidy, clinical prognosis, and gene expression in RRMM patients (pts) who participated in MMRF (Multiple Myeloma Research Foundation) sequencing study at University of Michigan. We further used gene set analysis to identify enrichment and variation of genetic pathways associated with aneuploidy and overall survival (OS). This was a pilot study in view of applying similar methods to larger populations. METHODS DNA and RNA materials were obtained from 51 RRMM pts at the time of disease relapse. Targeted sequencing was performed with the Onco1700 panel and RNA sequencing was performed with a capture protocol using Agilent SureSelect All Exon V4, followed by paired end sequencing. Copy number variation (CNV) was estimated using an in-house pipeline using matched normal samples. Arm-level aneuploidy was defined as copy number status (gain, loss or neutral) with maximal proportion for each chromosomal arm. Aneuploidy score was determined by total number of arm-level CNV aberrations, with the median score defining high and low aneuploidy groups. Survival analysis was performed using Kaplan-Meier (KM) and Cox regression. RNA-seq libraries were aligned with STAR aligner to the hg38 reference and read quantification were performed with featureCounts. We used gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) to obtain genetic pathway-level summaries for 50 hallmark pathways representative of cancer from the GSEA Molecular Signatures Database (https://www.gsea-msigdb.org/). Supervised GSEA was performed, based on differential analyses comparing gene expression profiles between high versus low aneuploidy groups to identify the differences in genetic pathways in RRMM pts. Unsupervised GSVA was applied to RNAseq count data of RRMM and newly-diagnosed multiple myeloma (NDMM) pts from the MMRF CoMMpass study (797 pts). Univariate Cox proportional hazard model was used to identify pathways having significant association with OS. RESULTS Arm scale aneuploidy analysis revealed high frequency of gains and losses in multiple chromosomes (Figure 1). RRMM pts with high aneuploidy scores had worse OS (median 15.9 months since study enrollment) compared to those with low scores (median not reached, p=0.027). Multivariate analysis demonstrated that high aneuploidy score is persistently associated with poor OS (hazard ratio (HR): 3.9, p=0.006), adjusting for t(4;14) and t(11;14). Hyperdiploidy (any whole chromosome carrying >2 copies) was identified in chromosomes 3, 5, 7, 9, 11, 19, 21, with frequency >20%. Pts with hyperdiploidy had better OS via KM (p=0.04). Using RNAseq gene expression data and GSEA analysis, we found enrichment of gene markers involving the pathways for targets of E2F transcription factors (Normalized Enrichment Score (NES) = 2.07), G2-M checkpoint in the cell division cycle (NES = 1.87), MYC proto-oncogene (NES = 1.93 and 1.85), and DNA repair (NES = 1.68) in RRMM pts with high aneuploidy compared against those with low scores (Benjamini-Hochberg adjusted p-value <0.001 for all pathways, Figure 2). GSVA analysis revealed 21 genetic pathways associated with survival in NDMM and RRMM pts, with 8, 2, and 11 pathways associated with OS in NDMM only, RRMM only, and both, respectively (Figure 3). Enrichment of several biological pathways involved with cellular growth, cell cycle regulation, and stress response (E2F, G2-M, MYC, DNA repair, mTORC1 signaling, UV radiation, mitotic spindle assembly, unfolded protein response, and peroxisome) are associated with poor survival (HR>1) in both NDMM and RRMM pts. Downregulation of KRAS signaling pathway is associated with better survival (HR<1) in both groups. CONCLUSION Aneuploidy, adjusted for chromosomal translocations, is an independent adverse prognostic factor for RRMM. Aneuploidy is correlated with dysregulation of major biological pathways involving cell cycle regulation, cellular division, oncogenes, and DNA damage repair. The majority of common pathways shared between RRMM and NDMM are linked to or reflective of genomic instability, and may explain association with poor survival. Disclosures Talpaz: IMAGO: Consultancy; Takeda: Research Funding; Novartis: Research Funding; Constellation Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Ye:Janssen: Research Funding; Portola: Research Funding; Millennium: Research Funding; Celgene: Research Funding; Sanofi: Research Funding; Karyopharm: Research Funding; Nektar: Research Funding; AbbVie: Research Funding.

8560 Background: Lenalidomide has shown activity in a wide range of hematological malignancies. We conducted a phase II trial of single-agent lenalidomide in indolent non-Hodgkin's lymphoma (NHL). Methods: Patients with relapsed or refractory indolent NHL were eligible, with no limit on the number of previous therapies. Oral lenalidomide 25 mg was self-administered once-daily on days 1–21 of every 28-day cycle for up to 52 weeks as tolerated, or until disease progression. The primary endpoint was overall response rate (ORR), with secondary endpoints of response duration, progression-free survival (PFS), and safety. Results: Forty-three patients were enrolled and were evaluable for response and safety. Patients had received a median of 3 prior systemic therapies (1–17) and half of all patients were refractory to their last therapy. The ORR was 23% (10/43), including a complete response (CR) or unconfirmed CR (CRu) rate of 7%. The median time to first response was 3.6 months (1.7–4.2) and the median time to CR or CRu was 4.2 months (1.9–11.1). Twenty-seven percent (6/22) of patients with follicular lymphoma grade 1 or 2, and 22% (4/18) of patients with small lymphocytic lymphoma responded to therapy. The median duration of response has not reached, but is longer than 16.5 months with 7 of 10 responses ongoing at 15–28 months. Median PFS was 4.4 months (2.5–10.4). Adverse events were consistent with the known safety profile of lenalidomide and manageable; the most common grade 3 or 4 adverse events were neutropenia (30% and 16%, respectively) and thrombocytopenia (14% and 5%, respectively). Conclusions: Oral lenalidomide monotherapy produces durable responses with manageable side effects in relapsed or refractory indolent NHL and warrants further investigation in the treatment of indolent NHL. [Table: see text]

Abstract Background: Patients with diffuse large B-cell lymphoma (DLBCL) not cured by first line R-CHOP chemotherapy or second line high-dose chemotherapy with autologous stem cell rescue have a poor prognosis and represent a clear unmet medical need. Lenalidomide (Revlimid®), an immunomodulatory drug, has activity in hematological malignancies including relapsed/refractory multiple myeloma, chronic lymphocytic leukemia and cutaneous T-cell lymphoma. Aim: To determine the activity and safety of lenalidomide monotherapy in relapsed/refractory DLBCL. Methods: Patients with relapsed/refractory DLBCL with measurable disease after at least 1 prior treatment regimen were eligible. Patients received 25 mg lenalidomide orally once daily on Days 1–21 every 28 days and continued therapy for 52 weeks as tolerated or until disease progression. Response and progression were evaluated using the IWLRC methodology. Univariate analyses using Fisher’s exact test was conducted to investigate and characterize associations of prognostic variables with response. Results: Twenty-six patients with DLBCL lymphoma were enrolled. The median age was 66 (45–86) and 13 were female. Median time from diagnosis to lenalidomide was 2.4 (0.5–12) years and median number of prior treatment regimens was 3 (1–6). Three patients (12%) exhibited an objective complete response (1 complete response (CR) and 2 complete responses unconfirmed (CRu)). Two patients had a partial response (PR) for an overall response rate of 19% and 7 had stable disease (SD). Response to lenalidomide was associated with low disease burden (33% for < 50 cm2v 0% for ≥ 50 cm2, P=0.06) and longer time from rituximab to lenalidomide treatment (33% for ≥ 230 days v 0% for < 230 days, P=0.05). Patients with favorable values for both these prognostic factors (N = 10) had a 50% response rate [RR] (30% CR/CRu RR) compared with a 0% RR for patients with unfavorable values (N = 16; P < 0.004) and a progression free survival (ongoing) of 7.4 months v 1.9 months. Although there were no responses in the poor prognosis group, 4 patients exhibited stable disease with an ongoing median PFS of > 4.2 months (> 3.6 - > 6.2). Eight patients (31%) required at least one dose reduction with a median time to first dose reduction of 1.8 months (0.4–2.9). One patient each (4%) had Grade 4 anemia, cauda equinine syndrome, febrile neutropenia, intermittent rash, lymphopenia, neutropenia, pneumonitis and thrombocytopenia. Most common Grade 3 adverse events were neutropenia (19%), thrombocytopenia (15%) and leukopenia (12%). Conclusion: The prognostic factors of tumor burden and time from last dose of rituximab appear to identify relapsed/refractory DLBCL patients with a high likelihood of response to lenalidomide oral monotherapy.

Abstract Background: Indolent non-Hodgkin’s lymphoma (NHL) has a median survival as long as 10 years but is rarely cured. Lenalidomide (Revlimid®), an immunomodulatory drug, is approved in the US for treatment of myelodysplastic syndromes associated with a del[q5] cytogenetic abnormality and is approved both in the US and the EU for the treatment of relapsed/refractory multiple myeloma. Lenalidomide also has activity in chronic lymphocytic leukemia and cutaneous T-cell lymphoma. Aim: This study was designed to assess the safety and efficacy of lenalidomide monotherapy in patients with relapsed/refractory indolent NHL. Methods: Patients with relapsed/refractory indolent NHL with measurable disease ≥ 2cm after at least 1 prior treatment regimen were eligible. Patients received 25 mg lenalidomide orally once daily on Days 1–21 every 28 days and continued therapy for 52 weeks as tolerated or until disease progression. Response and progression were evaluated using the IWLRC methodology. Results: Forty-three patients were enrolled in the study and included in this analysis. The median age was 63 (43–89) and 17 were female. Histology was small lymphocytic lymphoma [SLL] (n=18), follicular center lymphoma grades 1, 2 [FCL] (n=22), nodal marginal B-cell lymphoma (n=2) and extranodal marginal zone B-cell lymphoma of MALT type (n=1). Median time from diagnosis to lenalidomide treatment was 4.5 (0.4–24) years and median number of prior treatment regimens was 3 (1–15). Eleven of the 43 patients (26%) exhibited an objective response (2 complete responses (CR), 1 complete response unconfirmed (CRu), 8 partial responses (PR)), 15 had stable disease (SD), 13 had progressive disease (PD) and 4 were not evaluated. Responses included 4/18 SLL (22%) and 7/22 FCL (32%). Median time to response was 3.6 months (range 1.7–4.1). Progression free survival is 4.6 months for all patients, > 7.7 months (range 4.4 - > 13.5) for responding patients and ongoing. Sixteen patients (37%) required at least one dose reduction with a median time to first dose reduction of 2 months (0.5–9.6). The most common Grade 4 adverse event was neutropenia (14%) and the most common Grade 3 adverse events were neutropenia (21%) and thrombocytopenia (12%). Conclusion: Lenalidomide oral monotherapy is active with manageable side effects in relapsed/refractory indolent NHL.

Abstract Background: Lenalidomide (Revlimid®) has been reported to show activity in non-Hodgkin’s Lymphoma (NHL). However, prognostic factors predicting an individual patient’s response were unavailable. Aim: To investigate prognostic factors for lenalidomide response in patients with relapsed/refractory aggressive NHL. Methods: Patients with relapsed/refractory aggressive NHL with measurable disease ≥2 cm after at least 1 prior treatment regimen were eligible. Patients received 25 mg lenalidomide orally once daily on Days 1-21 every 28 days and continued therapy for 52 weeks as tolerated or until disease progression. Response and progression were evaluated using the IWLRC methodology. Univariate analyses using Fisher’s exact test and multivariate analyses using logistic regression were conducted to investigate associations of prognostic variables with response. Results: Forty-nine patients received lenalidomide oral monotherapy. The median age was 65 (23-86) and 24 were female. Histology was diffuse large B-cell lymphoma (n=26), follicular center lymphoma grade 3 (n=5), mantle cell lymphoma (n=15) and transformed (n=3). Median time from diagnosis was 2.7 (0.4-32) years and median number of prior treatment regimens was 4 (1-8). Forty-five (92%) of the patients had received prior rituximab and 63% were rituximab-refractory. Seventeen patients (35%) exhibited an objective response (2 complete responses, 4 complete responses unconfirmed and 11 partial responses). Response to lenalidomide was the same for patients who were refractory to rituximab (30%) and those who were not (31%). Three of 4 patients who were rituximab naive responded. Multivariate analysis including IPI, ECOG PS, stage, LDH, # of extranodal sites, age, sex, # of prior regimens and time from diagnosis identified only time since last rituximab and tumor burden as correlated with response. Response to lenalidomide was associated with low tumor burden (52% for <50 cm2v 0% for ≥50 cm2, P=0.033, odds ratio=10.9) and longer time from rituximab to lenalidomide treatment (52% for ≥ 230 days v 6% for <230 days, P=0.031, odds ratio=6.6). Rituximab depletes normal host B-cells and the kinetics for their loss and recovery are consistent with the timing of lenalidomide activity following rituximab. This may be a basis for this effect. However other tumor/host biological factors may also play a role. Patients with favorable values for both prognostic factors (N=24) had a 67% response rate [RR] compared to a 4% RR for patients with unfavorable values (N=25; P< 0.001) and a progression free survival (ongoing) of 7.5 months v 1.9 months. Although absolute lymphocyte count was not selected as a prognostic indicator of response in the multivariate setting, a trend was evident in the univariate analysis (42% for > 0.6 × 109/L v 9% for ≤0.6 × 109/L, P=0.071). Conclusion: Tumor burden and host immune competence may determine the response of relapsed/refractory aggressive NHL to lenalidomide monotherapy.

Abstract Background: Isatuximab (SAR650984) is a humanized IgG1 monoclonal antibody that binds selectively to a unique epitope on the human CD38 receptor. In the dose escalation part of the Phase I portion of the trial (NCT01749969), isatuximab ≥10 mg/kg IV given every other week (q2w) or 10 mg/kg weekly (qw) induced responses in 6/19 recipients (ORR 32%). Responses were noted at doses as low as 1 mg/kg q2w. The maximum tolerated dose (up to 20 mg/kg IV qw) was not reached. The most common treatment-emergent adverse events (TEAEs) were fatigue and nausea; few serious grade 3/4 TEAEs were reported (pneumonia 6%). The most common category of drug-related TEAE involved infusion-associated reactions (IARs) occurring in half of the patients, with grade 3/4 IARs in <5% of the patients. Based on promising Phase I activity, a Phase II dose finding study was added to investigate single-agent isatuximab in patients with relapsed or refractory myeloma (RRMM). Methods: In Phase II, eligible patients were originally randomly assigned (1:1:1) to one of 3 treatment groups: 3 mg/kg q2w, 10 mg/kg q2w, and 10 mg/kg q2w x 4 doses then q4w. A 4th treatment group was enrolled at a higher isatuximab dose with an optimized schedule of 20 mg/kg qw x 4 doses then q2w. Patient assignment was stratified by prior anti-myeloma therapy (Stratum 1: prior treatment with pomalidomide (POM) and/or carfilzomib (CAR); Stratum 2: no prior POM or CAR). Patients continued therapy until disease progression, unacceptable AEs or physician/patient decision. Eligible patients had measurable disease (serum M-protein >1 g/dL or urine M-protein ≥200 mg/24 h, or serum free light chain ≥10 mg/dL), prior exposure to ≥3 lines of anti-myeloma therapy or were double-refractory to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs), prior treatment with an alkylating agent, and adequate baseline bone marrow reserve and organ function. Enrollment of 24 patients was planned for each treatment group. An interim efficacy analysis is planned when all patients have completed ≥2 disease assessments, with central review of these data currently ongoing. Enrollment has now been completed and all patients who completed at least 1 cycle of isatuximab at the cutoff date (5/29/15) are included in this preliminary analysis. Results: Patient characteristics (n=96): median age 62.5 yrs (range 38-85); median time from initial diagnosis to study treatment 5.85 yrs (1.2-24.1); 76% had IgA/G MM; 24% had light-chain only disease; and 22%, 22%, and 44% had Stage I, II, and III disease, respectively. The median number of prior lines of therapy was 5 (2-14) with 100%, 99%, and 98% having prior exposure to PIs, IMiDs, and alkylator-containing therapy, respectively. Overall, 78% of patients had previously received POM and/or CAR; 55% had been exposed to both of these agents. At data cut-off, 36.5% of patients remained on therapy and 53.1%, 6.3%, and 4.2% had discontinued therapy due to disease progression, AEs, or other reason, respectively. The median number of cycles received was 3 (1-9), with a median duration of treatment of 11.7 wks. Approximately 17% of patients required at least 1 dose delay and 7.3% of isatuximab infusions were interrupted. TEAEs occurred in 93% of patients, with 51% being grade ≥3. Drug-related TEAEs occurred in 61% of patients (grade ≥3, 10%). The most common drug-related TEAEs (>5% of patients) were nausea (14.6%), chills (14.6%), dyspnea (13.5%), chest discomfort (10.4%), flushing (7.3%), headache (7.3%), cough (6.3%), and vomiting (5.2%). The majority of the symptoms occurred within 24 h of isatuximab infusion and were attributed to an IAR. IARs occurred in 50% of patients, were predominantly grade 1/2 (grade ≥3, 3.1%), and were mostly limited to Cycle 1 (2 patients had IARs in Cycle 2). Pneumonia (6.3%) and sepsis (5.2%) were the most common serious TEAEs. Six deaths have occurred within 30 days of last dose, 4 due to disease progression, and 2 due to serious AEs unrelated to study therapy (cardiopulmonary arrest and cerebral hemorrhage). Conclusion: Isatuximab administered to heavily pre-treated patients with RRMM appears well tolerated. IARs are common in Cycle 1, but typically do not recur. Results by treatment arm, including interim analysis response data, will be presented. Disclosures Martin: Sanofi: Research Funding. Richter:Takeda: Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Speakers Bureau; Amgen: Speakers Bureau; Novartis: Speakers Bureau. Vij:Millennium: Honoraria, Speakers Bureau; Onyx: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; BMS: Consultancy; Takeda: Consultancy, Research Funding; Novartis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Merck: Consultancy. Zonder:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS, Amgen, Prothena, Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Kaufman:Spectrum: Consultancy; Merck: Research Funding; Onyx: Consultancy, Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Millenium: Consultancy. Bensinger:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acetylon: Research Funding; Sanofi: 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, Speakers Bureau; Onyx: Research Funding, Speakers Bureau; Millenium: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dimopoulos:Novartis: Honoraria; Amgen: Honoraria; Genesis: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Onyx: Honoraria; Janssen-Cilag: Honoraria. San Miguel:Millennium, Celgene, Novartis, Janssen, Onyx, BMS, MSD: Membership on an entity's Board of Directors or advisory committees. Zimmerman:Onyx: Honoraria; Celgene: Honoraria, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Hari:Celgene: Consultancy; Takeda: Consultancy; BMS: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Spectrum: Consultancy; Sanofi: Consultancy. Gasparetto:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Honoraria; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Kumar:Skyline, Noxxon: Honoraria; Celgene, Millennium, Onyx, Janssen, Noxxon, Sanofi, BMS, Skyline: Consultancy; Celgene, Millennium, Onyx, Novartis, Janssen, Sanofi: Research Funding. Hsu:Sanofi: Employment. Charpentier:Sanofi: Employment. Strickland:Alexion Pharmaceuticals: Other: Advisory Board Particpation; Amgen: Other: Advisory Board Particpation; Daiichi-Sankyo: Other: Advisory Board Particpation; Boehringer-Ingelheim: Other: Advisory Board Particpation; Sunesis Pharmaceuticals: Other: Steering Committee and Advisory Board Participation. Mikhael:Sanofi: Research Funding; Onyx: Research Funding; Celgene: Research Funding; AbbVie: Research Funding.