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Introduction ABBV-383 is a fully human, silenced Fc region IgG4 monoclonal, BCMA × CD3 T-cell-engaging bispecific antibody with 2 BCMA-binding domains and a low-affinity CD3-binding domain. ABBV-383 has shown promising activity following intravenous (IV) infusion every three weeks (Q3W) as monotherapy in patients (pts) with RRMM in an ongoing phase 1 study (D'Souza A, et al. J Clin Oncol. 2022 Nov 1;40(31):3576-3586; Voorhees et al. Blood 2022;140[suppl 1]:4401). Herein, preliminary pharmacokinetic (PK) and immunogenicity results from Q3W regimens of this study and impact of soluble BCMA (sBCMA) on ABBV-383 PK are reported. Methods In the phase 1 study (NCT03933735), adult pts with RRMM, who were previously exposed to ≥3 prior multiple myeloma agents including ≥1 proteasome inhibitor, immunomodulatory drug, and anti-CD38 monoclonal antibody, received Q3W IV infusion at doses 0.025 - 120 mg in escalation (ESC) phase, and at 20 mg, 40 mg, and 60 mg in expansion (EXP) phase. Serial blood samples for PK characterization were collected in cycles 1, 3, and 6 along with sparse/periodic samples throughout the study. Blood samples were also collected throughout the study to detect anti-drug antibodies (ADAs) for immunogenicity characterization and to determine sBCMA concentrations. Appropriate validated assays were used to determine ‘total’ (unbound and bound to sBCMA) and ‘free’ (unbound and partially bound to sBCMA) ABBV-383 serum concentrations, to detect/determine ADAs/titers, and to determine total sBCMA concentrations. ‘Total’ and ‘free’ ABBV-383 serum concentrations from cycles 1, 3, and 6 were used to calculate individual PK parameters (peak concentration [C max], trough concentration [C trough], area under the concentration-time curve [AUC], terminal phase elimination half-life [t 1/2], clearance [CL], and steady state volume of distribution [V ss]) using non-compartmental analysis in Phoenix WinNonlin Version 8.2. Immunogenicity was summarized using ADA/titer data. Impact of baseline/pre-dose sBCMA concentrations on ‘total’ and ‘free’ ABBV-383 PK parameters from cycles 1, 3, and 6 was explored. Results ‘Total’ and ‘free’ ABBV-383 intensive PK data (cycles 1, 3, and 6) were available from 169 and 157 pts, respectively, representing Q3W regimens in ESC and EXP cohorts and ADA data were available from 186 evaluable subjects. Preliminary PK results suggest that ‘total’ and ‘free’ ABBV-383 exhibited approximately dose-proportional PK between 5.4-120 mg Q3W. Estimated t 1/2 for ‘total’ and ‘free’ ABBV-383 ranged between 11-12 days and 8-11 days, respectively, with steady state achieved by cycle 4 in Q3W EXP cohorts. Estimated CL and V ss were consistent with those of a typical IgG4 antibody in humans. Across EXP Q3W regimens, accumulation (cycle 3 vs. cycle 1) was 30%-60% in C max and 60%-177% in C trough for ‘free’ and ‘total’ ABBV-383. ABBV-383 exposures (AUC tau and C trough) of ‘total’ ABBV-383 were 28-74% higher than those representing ‘free’ ABBV-383 among the EXP Q3W regimens with no/minimal observed differences in C max (≤21%). Higher sBCMA levels prior to treatment appeared to reduce ‘free’ ABBV-383 PK exposures with pronounced reduction at lower doses (e.g., 20 mg) and in cycle 1, however the effect diminished in later cycles (cycles 3 and 5). Preliminary assessment of immunogenicity indicated an ADA incidence of 3.8% (7/186) with low titers ranging between 11.7 and 187 titer units and no impact on PK. Conclusions ABBV-383 exhibited a PK profile with relatively long half-life and low incidence of immunogenicity. Overall, sBCMA impacted (reduced) the ‘free’ ABBV-383 (pharmacologically active component) PK exposures only, with pronounced effect at lower doses and the effect is consistent with understanding of treatment dependent sBCMA dynamics over time. Determination of both ‘total’ and ‘free’ ABBV-383 serum concentrations allowed to demonstrate such underlying impact of sBCMA on PK.

Background: Skeletal related events (SREs) including pathologic fractures, surgery or radiation to bone, and spinal cord compression contribute to diminished quality of life in patients with multiple myeloma. Bone modifying agents (BMA) such as denosumab or the bisphosphonates zoledronic acid and pamidronate, are used to inhibit osteoclast activity to prevent SREs. The Bone Working Group of the International Myeloma Working Group suggests monthly administration of zoledronic acid for 12 months in newly diagnosed myeloma patients. This is followed by an option to discontinue or decrease administration frequency to every 3, 6, or 12 months if a very good partial response or better is achieved. The ideal start time of BMA from diagnosis and the duration of administration has not been adequately established to optimize reduction of SREs. Methods: This was a retrospective, single-center study that included newly diagnosed myeloma patients treated at the Mayo Clinic between 9/1/2018 and 9/1/2020 who had received at least one dose of a BMA. The primary objective was to evaluate the impact of BMA dosing frequency and duration on SREs. Secondary objectives included time to BMA initiation, time to relapse or progressions following BMA start, and safety. Data was summarized using frequencies and percentages for categorical data, and medians and interquartile ranges (IQR) for continuous data. A spline plot and cut-point analysis were used to assess the association of time from multiple myeloma diagnosis to BMA initiation with skeletal related outcomes. Rates of SREs were estimated using the Aalen-Johansen method, where death was treated as a competing risk. Associations of factors with SREs were assessed using Cox proportional hazards regressions, and these associations were summarized using hazard ratios (HR) and corresponding 95% confidence intervals (CI). Variables that could change throughout follow-up including BMA frequency, cumulative BMA dose, and duration of BMA were treated as time-dependent covariates in the Cox proportional hazards models, where the information for these variables was updated each time an additional dose of BMA was given. All analyses were performed using SAS version 9.4 software (SAS Institute, Inc.; Cary, NC) and R version 4.2.2 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria). Results: Sixty-seven patients were included with a median age of 65 years old. The most frequently used BMA was zoledronic acid (88.1%), followed by pamidronate (7.5%), and denosumab (4.5%). Fifty-five patients (82.1%) had bone disease at baseline. Within 2 years of starting BMA, SREs occurred in 42.2% of patients with baseline bone disease and 8.3% of patients without (p=0.054). Median time to BMA initiation after diagnosis was 45 days (IQR 14-105) in patients with baseline bone disease and 87 (IQR 32-496) in patients without. Among the 26 patients with a SRE, median time from start of BMA to SRE was 170 days (IQR 32-496). No significant association was found between time to initiation and development of a SRE (Figure 1). BMA dosing frequencies of every 3 months and 6 months had a HR for SREs of 1.67 and 0.47, respectively compared to every 1 month, (P-values >0.25). After excluding patients without baseline bone disease, compared to every 1 month dosing, dosing every 3 months and every 6 months increased the risk of SRE, although the differences were not statistically significant (3 months: HR=1.92, 95% CI 0.76-4.88, p=0.17; and 6 months: HR=1.26, 95% CI 0.14-10.94, p=0.84). Patients without bone disease at baseline remained on BMAs for a median of 1.8 years while patients with bone disease received BMAs for a median of 2 years. There was no statistically significant difference found with varying duration or cumulative dose and SRE development (Table 1). Relapse or progression occurred in 32.1% of patients within 2 years of starting BMA, while death occurred in 7.5% of patients. Osteonecrosis of the jaw occurred in 1 patient with bone disease at baseline who received zoledronic acid every 3 months for a duration of 596 days. Conclusions: No significant association between time of BMA initiation, dosing frequency, or duration on development of SREs were observed. This suggests delaying the start time of BMAs to allow for dental clearance and initial treatment would not increase risk of SREs. Prospective trials are needed to confirm these findings.

BACKGROUND Several studies have highlighted the poor outcomes observed among patients with relapsed/refractory multiple myeloma (MM), particularly those with triple-class refractory (TCR; refractory to at least one PI, one IMiD, and one anti-CD38 mAb) disease. The aim of this study was to describe the longitudinal changes in quality of life (QOL) and other patient-reported outcomes (PROs) among TCR MM patients in real-world clinical practice who initiate a new line-of-therapy. METHODS A prospective, multi-site observational study of patients with TCR MM was conducted through the Mayo Clinic system. Patients initiating a new post-TCR therapy (defined as the index date) were followed for a 12-month observation period during which PRO surveys were administered for the first 6 months. For patients initiating a CAR-T therapy, a survey assessment was conducted at apheresis, lymphodepletion chemotherapy, and every month after T-cell infusion until Month 6. All other patients received a survey assessment before index and then every month thereafter until Month 6. PRO measures included the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ-C30; including the Global Health Score [GHS]), the EORTC Multiple Myeloma Questionnaire (QLQ-MY20; including the Disease Symptoms [DS] domain score), the EORTC QLQ for Chemotherapy-Induced Peripheral Neuropathy (CIPN20), the EuroQoL-5 Dimensions (EQ-5D), the Patient Global Impression of Severity (PGIS), and the Patient Global Impression of Change (PGIC). Patient surveys were supplemented with retrospective chart reviews conducted 3 months and 12 months following the index date to obtain clinical information. Descriptive data were reported at each timepoint separately for patients who received CAR-T and non-CAR-T index therapies. The study is ongoing and this abstract reflects all patient visit data as of April 2023. RESULTS N=55 patients (60% male, 96% White, mean age 66 years) were included in this interim analysis. The median time interval between becoming TCR and the index date was 3.9 months (interquartile range [IQR]: 1.7, 15.5). Most patients had an ECOG score of 0 or 1 (53% and 38%, respectively) and were R-ISS I or II (36% and 54%, respectively); 53% of patients had high-risk cytogenetics and 20% had extramedullary disease. The median (IQR) number of prior treatment lines was 4 (4.0, 5.5). N=24 patients initiated a CAR-T therapy as their index therapy (equally split between ide-cel and cilta-cel); the remaining N=31 patients predominantly initiated a treatment regimen containing an IMiD (52%), a PI (55%), an anti-CD38 antibody (13%), or an alkylating agent (36%). Among CAR-T patients, QOL and disease-related symptoms worsened from baseline through Month 2-3 (eg, mean GHS was 65.9 at apheresis, 64.9 at lymphodepletion, and 53.3 at Month 2 [higher scores indicate better QOL]; mean DS domain score from the QLQ-MY20 was 24.1 at apheresis, 23.4 at lymphodepletion, and 33.7 at Month 2 [higher scores indicate more symptoms]; see Table 1). These decrements were followed by improvement back to baseline levels and, in some cases, improvement above baseline levels (eg, GHS was 67.9 and 65.0 at Months 4 and 5, respectively; DS was 18.3 and 17.8 at Months 4 and 5), though few patients had sufficient follow up to analyze QOL at later timepoints. Based on the PGIC, 77.8% of patients reported being “a little better” or “much better” as early as Month 1, though this number declined over time. Among non-CAR-T patients, QOL largely plateaued (GHS was 62.2 at baseline and 58.3 at Month 6), though disease symptoms improved over time (DS was 28.6 at baseline and 24.5 at Month 6; pain was 42.3 at baseline and 29.2 at Month 6). Based on the PGIC, 34.7% of patients reported being “a little better” or “much better” at Month 1, which improved to 50% by Month 6. CONCLUSIONS In this interim analysis of an ongoing prospective observational study, QOL and symptom measures generally worsened over the first few months for CAR-T patients in the real-world though improved to baseline levels and beyond at later time points. Patients did, however, directly report improvement in their disease state as early as Month 1 based on the PGIC. Non-CART patients generally showed a QOL plateau with some modest improvement in disease symptoms which was also reflected in their self-reported assessment of disease state improvement based on the PGIC.

Background: The management of multiple myeloma (MM) continues to evolve with the emergence of new classes of therapy, most recently including BCMA-targeted therapies. Considering the expanding MM treatment armamentarium, we sought to identify contemporary practice patterns among healthcare professionals (HCPs) for MM and compare them with those of experts using an online decision support tool. Methods: An online decision support tool was developed with input from 5 experts providing therapy recommendations for 192 unique patient case scenarios based on considerations including disease setting, treatment history, access to therapy, and specific comorbidities. HCP tool users entered specific patient criteria to define a case along with their intended management for that case. The tool then showed the 5 expert recommendations for that same case scenario, and users were asked if the recommendations impacted their intended approach. An analysis of expert recommendations and user-selected therapy was performed. Results: Between April 2023 and July 2023, 207 patient cases were entered by 144 participating HCPs. HCP and expert treatment recommendations were frequently discordant, regardless of treatment setting (Table). The highest concordance between HCP and expert recommendations (47%; P <.0001) was in the induction setting (n = 111). In this setting, experts were more likely to recommend quadruplet regimens compared with HCPs (62% vs 27%, respectively; P <.0001). Treatment selection concordance was lower in the maintenance (n = 17; 24%; P <.0001) and relapsed/refractory settings (n = 79; 20%; P <.0001). The lowest concordance (10%; P <.0001) between HCP and expert treatment recommendations was noted in the relapsed/refractory setting specifically among patients who were refractory to earlier lines of therapy (n = 41; P <.0001). For patients who are refractory to earlier lines of therapy, experts were more likely to recommend an anti-CD38-based combination regimen compared with HCPs (81% vs 32; P <.0001). When recommending treatment for patients with triple- or penta-refractory disease, experts were significantly more likely to recommend BCMA-targeted agents (83% vs 39%; P <.0001). For HCPs whose treatment plan did not match expert recommendations in these scenarios, 53% of respondents indicated that expert recommendations confirmed or changed their intended therapy, but 31% indicated that there were barriers to implementing those recommendations. Conclusions: These data suggest ongoing challenges with incorporating the newest therapies and combinations across the spectrum of MM into patient care, particularly multidrug regimens in the induction and maintenance settings and BCMA-targeted therapies in those with triple- or penta-refractory disease. Continued education and development of resources for HCPs, including online decision support tools, may be increasingly important as the treatment of MM continues to grow in complexity.

Multiple myeloma is the second-most common hematological malignancy in the US that remains a challenging disease to cure despite recent advancements in treatment strategies. Drug resistance is a major cause of concern in myeloma chemotherapy. We have created an optimization-regularization-based computational prediction method called secDrug that identified several novel secondary drug (‘secDrug‘) combinations against drug-resistant myeloma (relapsed/refractory multiple myeloma or RRMM). Top among our predicted secDrugs includes the survivin inhibitor, YM155. Survivin is a member of the inhibitor of apoptosis protein (IAP) family that has dual roles in mitosis and apoptosis. YM155 inhibits survivin at the transcriptional level by inhibiting the survivin gene promoter. Several studies have demonstrated the efficacy of YM155 in solid tumors and heme malignancies, including myeloma. However, the precise mechanism of action is not yet known. MM cell lines express high levels of IAPs. We observed high in vitro cytotoxicity of YM155 as a single agent (IC 50 range 0.41 to 12.85nM) as well as in combination with the primary drugs like proteasome inhibitors and Immunomodulatory drugs (IMiDs) against >15 human myeloma cell lines representing innate/refractory and acquired/emerging (relapse) resistance. Next, we explored the molecular mechanisms underlying the efficacy of YM155 using pre-vs-post-treatment tumor whole-transcriptome analysis (RNAseq). The top differentially regulated genes include DDIT3 (CHOP), ATF3, TXNDC5 SEC24D, and DHX15 ( Figure 1A). Ingenuity Pathway Analysis (IPA) predicted that, in addition to regulating apoptosis, survivin inhibition in myeloma significantly upregulated unfolded protein response and downregulated vehicle trafficking and genes associated with immune cell function. Further, we performed single-cell transcriptomics (scRNAseq; 10X Genomics) analysis to explore treatment-induced changes to the subclonal architecture. Our scRNAseq results revealed a distinct shift in clusters representing myeloma single-cell subpopulations following YM155 treatment ( Figure 1B). For example, Cluster 1 (53.11%), characterized by high expression of cell survival markers Ki67 and MYC, was pre-dominant in untreated myeloma cells, while Cluster 4 (52.19%) was the primary subclone in post-treatment tumors. Cluster 3 was shared between the untreated and treated groups. Detailed distinctions between each single-cell cluster will be presented. Furthermore, our scRNAseq data alluded to the benefits of combining survivin-inhibitors and Hsp90-inhibitors based on the expression of drug target genes. Hsp90 inhibitors indirectly target survivin by disrupting the interaction between survivin and Hsp90, resulting in a destabilized survivin protein that ultimately results in survivin degradation. In fact, we have earlier shown the effectiveness of Hsp90 inhibitors (17-AAG, AICAR, and CCT018159) as potent secDrugs against RRMM. Finally, high-dimensional mass cytometry or CyTOF analysis using heavy metal-conjugated proteins in patient bone marrow-derived primary myeloma cells ( ex vivo model system) showed that YM155 induced dose-dependent reduction of several protein markers implicated in cell survival and chemotherapeutic resistance in multiple myeloma, including myc, Ki-67, CD28, CD81, and CD274 (PD-L1). Previous studies have shown that CD28 and CD81 are high-risk immunophenotypes associated with significantly worse prognosis in patients with myeloma. PD-L1 (Programmed death-ligand 1) is a cell-surface glycoprotein expressed in a large number of malignancies that is involved in the termination of immune response and immune evasion. An earlier study has shown that YM155 enhances daratumumab-mediated cellular lysis of multiple myeloma cells. Our RNAseq and CyTOF results thus suggest a potential basis of synergy between YM155 with immunotherapies approved for myeloma, which we will test further. Our approach thus integrates in silico prediction with single-cell multi-ome analysis to identify molecular mechanisms potentially underlying subclonal response to novel combination therapy candidates (secDrugs) for the treatment of RRMM.

Background: It has been recently demonstrated that altered lipid metabolism involving increased free fatty acids (FFAs) uptake supports the growth and proliferation of MM cells (Panaroni et al. Blood 2022; Farell et al. Elife 2023). Whether the availability of FFAs in the bone marrow (BM) microenvironment is associated with the pathogenesis of MM from MGUS has not been previously evaluated. Thus, we evaluated if individual species of FAs or FA-containing lipids such as triacylglycerols (TAGs), diacylglycerols (DAGs), and monoacylglycerols (MAGs) are differentially present between the BM microenvironment of MGUS and MM patients. Methods: Lipid profiles of BM plasma samples (reflective of the BM microenvironment) from patients with a spectrum of plasma cell disorders (22 MGUS and 24 MM (15 newly diagnosed and 9 relapsed/refractory)) were acquired by liquid chromatography (LC)/tandem mass spectrometry (MS), and individual lipid species were quantified based on single-point quantification using 55 isotopically labeled internal standards. Concentration-based lipid species data was analyzed by the web-based tool MetaboAnalyst 5.0. Data was normalized by quantile normalization, log transformed and scaled by Pareto scaling. p values <0.05 were considered significant. Results: A total of 993 distinct lipid species were quantified by the MS-based targeted lipidomics assessment. Of these lipid species, the levels of individual mid-chain (≤50 carbon chain length) and long-chain (≥52 carbon chain length) polyunsaturated TAGs were the most differentially present in the BM plasma of MM compared to MGUS patients (Figure 1A). Even though a principal component analysis (PCA) involving all lipid species showed moderate separation of MM from MGUS patients, five MGUS patients (categorized as MGUS-other (MGO)) who clustered more towards MM patients compared to the remaining MGUS patients had a distinct pattern of individual mid and long-chain polyunsaturated TAG levels that were comparable to MM patients (Figure 1B). Furthermore, the proliferation rate of clonal plasma cells in the BM of these MM and MGUS as measured by the S-phase% was found to be directly correlated with the level of long-chain polyunsaturated TAGs. Conclusions: Mid and long-chain polyunsaturated TAGs are present in lower and higher levels, respectively, in the BM microenvironment of MM compared to MGUS patients. These findings continue to support the importance of FA-containing TAGs and other lipids in the pathogenesis of MM. Future confirmatory studies evaluating the biological significance of these differences are required to determine if they can be exploited for diagnostic and therapeutic purposes.

Myeloma is well known to have significant genetic heterogeneity among patients' tumors, affecting both progression as well as therapeutic responses. Moreover, subclonal diversity can lead to selective outgrowths that are influenced by new mutations, as well as the bone marrow microenvironment and therapeutic exposures. The ras family of genes (N -and K-) are the most common mutations (40-50% ) in myeloma. Notably, ras mutations occur among a variety of other co-existing chromosomal abnormalities. Ras mutations were found to be more commonly associated with cyclin D1 high expression (e.g., resulting from 11;14 translocations). We and others have also shown that rasmutations may not be disease-initiating but may arise during disease progression and relapse; or are subclonal events that impart selective growth advantage and emerging drug resistance. There is evidence that activating mutations (codons 12,13, 61) in K- ras may have different clinical impact than activating mutations in N- ras yet there are conflicting clinical outcomes that may be due to therapies analyzed or differences in other co-existing genetic mutations. Previous studies using transfection of genes with targeted mutations show random integration and often in multiple copies, thus, limiting interpretations. We chose to create N ras61 and K ras12 mutations by CRISPR-cas directed edits in a single endogenous ras allele in a myeloma cell line (FLAM76) that initially had two wild type alleles (and an 11;14 translocation). We then compared the impact of these edits on growth, signaling, gene expression, and drug responses. We find both the mutN ras and mutK ras edited cells have increased proliferation compared to wild type. However, we find that the N ras mutation increases MAPK signaling and downstream gene activation to a greater extent than the K rasmutation. Based on RNAseq analysis, a MAPK gene activation score was higher in mutN ras edited cells than mutK ras edited cells. Further, we see distinct gene expression patterns between mutN ras versus mutK ras edited cells (See heat map Fig) impacting distinct pathways. Notably, mutNras cells have much higher NOXA expression, increasing sensitivity to apoptosis-inducing drugs (e.g., proteasome inhibitors); and Kras induces anti-apoptotic Bcl-xL, reducing sensitivity. We have conducted a high throughput drug screen with 84 drugs, and while some drugs are active against both mutN ras and mutK ras edits (Proteasome inhibitors), we have identified drugs with differential response to K versus N ras mutations. For example, mutK ras FLAM76 cells are more resistant to proteasome inhibitors; and particularly more resistant to HSP inhibitors and HDAC inhibitors compared to wild type or mutN ras cells. In contrast, mutK ras cells are more sensitive to PikFyve and IL-12 inhibitors than mutNras or wild type cells. Gene expression analysis shows that mutKras cells result in more downregulation of apoptotic genes and higher upregulation of cell survival genes, as well as ER stress pathway genes. Our results suggest that treating myelomas containing K versus N ras activating mutations may benefit from understanding distinct pathway activations and targeting selective drug therapies. We are currently testing new pan-Ras inhibitors to determine if there are distinct responses between our N- and K- ras edited cells, and we are expanding testing to a wider panel of myeloma cell lines containing ras mutations in the context of other genetic abnormalities. Details of gene expression distinctions, pathways, and drug responses that distinguish mutN ras and mutK ras in myeloma will be presented.

Background ABBV-383 is an off-the-shelf, fully human B-cell maturation antigen (BMCA) X CD3 bispecific antibody with bivalent BCMA domain and low CD3 affinity that targets both BCMA-expressing multiple myeloma (MM) cells and CD3-expressing T cells. ABBV-383 has shown promising activity in an ongoing phase 1 study in patients (pts) with relapsed/refractory (RR) MM ( Voorhees et al. ASH 2022; Weisel et al. EHA 2023). Here, we report the safety and efficacy results from the ABBV-383 IV Q3W at 20 mg, 40 mg and 60 mg dose levels. Methods The phase 1 dose escalation (ESC) and expansion (EXP) study (NCT03933735) of ABBV-383 enrolled pts with RRMM with ≥3 prior lines of therapy (LOT), including a PI, IMiD, and an anti-CD38 mAb with no prior BCMA exposure. After exploring 14 dose levels in dose ESC, 3 dose levels were further explored in dose EXP including ABBV-383 Q3W IV 20 mg, 40 mg, and 60 mg, until disease progression/unacceptable toxicity. The primary objectives were to determine safety/tolerability, PK/PD, and RP2D. The secondary objective was to evaluate clinical activity. Responses were investigator assessed per IMWG 2016 criteria and adverse events (AEs) by CTCAE v5.0. Results As of 17May2023, 220 pts were treated with ABBV-383, 73 pts in the dose ESC and 147 in the dose EXP with 32 pts at 20 mg, 55 pts at 40 mg, and 61 pts at 60 mg Q3W. Median duration of follow up was 7.1 (0.8-36.9), 12.2 (1.3-34.4) and 24.2 (0.6-33.4) months (mo) in the 20 mg, 40 mg and 60 mg dose levels with 25%, 36% and 21% pts continuing treatment (tx), respectively. Median age for all pts was 68 yrs (range, 35-92 yrs), Median prior LOT was 5 (range, 3-23), and 80% were triple-class refractory. Baseline characteristics are summarized in Table 1 by dose level. Treatment-emergent (TE) AEs were reported in 97% (78% ≥G3) of pts at 20 mg, 100% (84% ≥G3) of pts at 40 mg, and 100% (82% ≥G3) of pts at 60 mg. CRS, the most common TEAE, occurred in 50% (34% G1/13% G2/3% G3) of pts at 20 mg, 71%(45% G1/25% G2/0% ≥G3) of pts at 40 mg, and 70%(51% G1/18% G2/2% G3) of pts at 60mg. At the 3 dose levels, CRS occurring after the first dose had median time to onset of 1 day (range, 1-2) and median duration was 2 days (range, 1-10). ICANS occurred in 3% (3% G1/0% ≥G2) of pts at 20 mg, 5% (2 % G1/4% G2/0% ≥G3) of pts at 40 mg, and 5% (3% G2/2% G3) of pts at 60 mg. No G4/5 CRS or ICANS occurred. Infections occurred in 56% (25% ≥G3) of pts at 20 mg, 71% (26% ≥G3) of pts at 40 mg, and 57% (34% ≥G3) of pts at 60 mg; most frequently (≥10%) reported were upper respiratory tract infections (15%, G3/4 1%), pneumonia (14%, G3/4 12%), urinary tract infections (12%, G3/4 3%), and COVID-19 (11%, G3/4 1%). At 20 mg, 40 mg, and 60 mg, the incidence of neutropenia was 41% (25% ≥G3), 42% (31% ≥G3), and 43% (34% ≥G3); anemia 50% (25% ≥G3), 55% (31% ≥G3), and 38% (13% ≥G3); thrombocytopenia 44% (28% ≥G3), 36% (16% ≥G3) and 26% (13% ≥G3), respectively. The tx discontinuation due to AEs was 6%, 11%, 10% in 20 mg, 40 mg and 60 mg, respectively, with no single PT reported in >2 pts. Clinical responses are shown in Figure 1. The median time to ≥VGPR was 0.7mo (0.7-5.8) at 20 mg, 1.0mo (0.7-10.4) at 40 mg, and 1.4 mo (0.7-7.8) at 60 mg. The median DOR was 9.7 mo (3.5-not reached (NR)) at 20 mg, and NR at 40 mg and 60 mg. The Kaplan-Meier (KM) estimates for DOR at 12 mo was 42% at 20 mg, 70% at 40 mg, 66% at 60 mg. The mPFS was 3.8 mo (2.4-7.9) at 20mg, 13.7mo (5.0-NR) at 40 mg, and 11.2 mo (4.8-NR) at 60 mg. The KM estimates for PFS at 12 mo was 19% at 20 mg, 51% at 40 mg, 49% at 60 mg. The median OS was 21.6 mo (7.9-NR) at 20 mg, and NR at 40 mg and 60 mg. ABBV-383 at 20 mg, 40 mg, and 60 mg led to rapid and transient production of key proinflammatory cytokines (IL-6, IL8, IFN-γ, and TNF-α), and reduction of soluble BCMA levels over time that were associated with disease response. ABBV-383 also promoted T-cell activation and proliferation with upregulation of CD69 and KI67 markers on peripheral CD4 and CD8 T cells in evaluable pts. Conclusions ABBV-383 showed a manageable safety profile at dosages of 20 mg, 40 mg, and 60 mg IV Q3W, with low incidence of TEAEs leading to discontinuation. Also, extended Q3W dosing interval with no step-up dosing schedule of ABBV-383 may provide enhanced convenience. However, 20 mg yielded numerically lower responses when compared to 40 mg and 60 mg. At 40 mg and 60 mg, ABBV-383 monotherapy yielded deep and durable responses with mPFS of 13.7 mo and 11.2 mo, and 12-month DOR of 70% and 66% (mDOR NR in pts with ≥CR), respectively. These results in heavily pretreated RRMM pts support further clinical evaluation.

Background: Waldenström Macroglobulinemia (WM) is an indolent lymphoma with heterogeneous outcomes. The recently developed, Revised International Prognostic Scoring System for WM (rIPSS-WM) did not examine the prognostic impact of MYD88L265P, a somatic alteration present in most patients with WM. Moreover, rIPSS-WM could only be partially validated in an external cohort. Here, we report the performance of rIPSS-WM and propose a refined prognostic model with external validation. Methods: We reviewed medical records of treatment-naïve patients with active WM consecutively seen at Mayo Clinic, Rochester (MCR) between 01/01/1996 and 12/31/2017. We applied the rIPSS-WM model in patients with data available for all required parameters, including age, serum beta-2 microglobulin (β2M), serum lactate dehydrogenase (LDH) and serum albumin at diagnosis to assess the prognostic utility of this model. We then identified the dichotomized clinical and laboratory parameters prognostic for overall survival (OS) by univariate analyses (UVA) in our cohort. The significant parameters were analyzed to independently predict OS in a multivariable analysis (MVA) and to create a prognostic model, which was subsequently validated in an independent multi-institutional cohort. Results: We identified 889 patients with active WM at MCR, with a median follow-up of 8.2 [95% confidence interval (CI): 7.5-9] years. The estimated median OS for this cohort was 10.6 (95%CI: 9.7-11.4) years. Patients with available data in this cohort for the validation of rIPSS-WM (n=241) were risk stratified into very low (n=46), low (n=64), intermediate (n=58), high (n=46) and very-high risk groups (n=27) per rIPSS-WM, with the respective 5-year OS rates of 96%, 76%, 72%, 77% and 32%, demonstrating overlapping survival curves for the low, intermediate and the high-risk groups. Because rIPSS-WM could not be validated, we used the data from the MCR cohort and identified age at diagnosis of active WM, LDH >upper limit of normal (ULN), β2M >3µg/mL and albumin <3.5g/dL as statistically significant prognostic markers for OS on UVA. Notably, the presence of MYD88 L265P did not impact OS [median 11.4 years (95%CI: 10.1-not reached (NR) for MYD88 L265P genotype (n=231) versus 10 years (95% CI: 8.1-NR) for MYD88 WT genotype (n=65); p=0.33]. On MVA, LDH >ULN (HR 2.34), serum albumin <3.5 g/dL (HR 1.5) and age groups 65-75 years (HR 1.4) and >75 years (HR 2.6) were independently prognostic. Based on comparable HRs, we assigned a score of 1 point each to serum albumin <3.5 g/dL and age group 66-75 years and similarly, 2 points each for age >75 years and serum LDH >ULN per their impact on OS. Patients with the composite scores of 0, 1 and 2 were categorized as low risk, low- intermediate risk and intermediate risk, respectively. Due to similar OS for the scores of 3 to 5, these were combined to form the high-risk group of our proposed model: Modified Staging System for WM (MSS-WM). The median OS was 14.6 years [95%CI: 9.1 years-NR] for low-risk (score 0, n=71, 21%), 11.2 years (95% CI: 9.1-15.2 years) for low-intermediate risk (score 1, n=110, 32%), 8.1 years (95% CI: 6.4-12.2) for intermediate risk (score 2, n=81, 24%) and 5.5 years (95 % CI: 3.9-11 years) for the high-risk group (score ≥3, n=79, 23%); p<0.0001. In this derivation cohort, the 5-year OS rate was 93%, 82%, 69% and 55% and 10-year OS rate was 60%, 53%, 45% and 30% for the low, low-intermediate, intermediate, and high-risk cohort, respectively ( Figure 1A). We also validated MSS-WM using competing risk analysis (p=0.001) and in the cohort of rituximab treated patients (p<0.0001). We then used data from an independent series of 335 treatment-naïve symptomatic patients from academic institutions (n=5) across the US and Europe for external validation of the MSS-WM model. The median follow-up for the validation cohort was 6.3 years (95% CI: 5.5-7.2 years). The 5-year OS was 93%, 90%, 75% and 57% and the estimated 10-year OS was 91%, 80%, 64% and 35% for the low risk (n= 86, 26%), low-intermediate (n=107, 32%), intermediate (n=86, 26%) and high-risk (n=59, 18%) cohorts, respectively (p<0.0001, Figure 1B). Conclusions: Our proposed model, MSS-WM, is a simple, clinically useful, externally validated model that reliably risk stratifies previously untreated patients with active WM into four groups that have distinct outcomes based on the composite scores derived from the patients' age, serum albumin and serum LDH at diagnosis.

Introduction Newly-diagnosed multiple myeloma (MM) patients often share tumor genetic abnormalities associated with a high risk of progression and poor survival. We hypothesized that single-cell transcriptomic characterization of the tumor immune microenvironment (TIME) may be combined with tumor-based risk stratification to uncover novel disease biology mechanisms associated with clinical outcomes and improve risk stratification. Methods We performed single-cell RNA sequencing of 361 CD138 negative sorted bone marrow mononuclear cells (BMMCs) samples from 263 MM patients enrolled at time of diagnosis in the Multiple Myeloma Research Foundation CoMMpass study (NCT01454297). Specimens were analyzed at baseline (n=263) across a network of five collaborating academic research centers following assay harmonization. Genetic factors that determine risk were defined as one or more of the following gain- or loss-of-function events derived from baseline genomic sequencing data: del17p14, translocations of the Ig locus with the MAF/A/B or WHSC1/MMSET/NSD2 loci, and gain of chromosome 1q21. Statistical modeling strategies were used to assess single-cell populations or RNA signatures associations with known risk factors or disease progression (FDR<0.05). Results The study generated high-quality single-cell profiles of 731,643 cells from 123 high-risk and 107 standard-risk MM patients based on genetics. Based on distinct transcriptome profiles, BMMCs formed 106 clusters corresponding to 5 major compartments: NK and T cells, B cells, erythrocytes, myeloid cells, and plasma cells. Our preliminary differential abundance analysis comparing high- vs standard-risk patients at baseline uncovered significant differences in the TIME associated with genetic risk. The high-risk group was identified as enriched in effector CD8+, and CD4+ T cells. In contrast, the high-risk group exhibited depletion of naive CD8+ and interferon-associated CD4+ and CD8+ T as compared to the standard-risk group. Within subclusters in the T and NK cells compartment, a higher cytotoxicity signature was observed in high-risk patients, characterized by the expression of granzymes and perforin. Finally, our preliminary survival and progression analysis associated a higher frequency of IDO1-expressing macrophages in worst progression patients, while better outcomes were associated with CD8+ T and B cells expressing androgen response and IL6/STAT3/EMT programs, respectively. Conclusions Current approaches for risk stratification are based on bulk measurements of tumor's genetic or epigenetics features, which do not capture the diversity and dynamics of the TIME. Through single-cell transcriptomics, we were able to capture, in high granularity, the TIME and correlate specific immune cell populations and phenotypes with relapse risk and poor prognostic outcomes. These results suggest that immune subpopulations may be an essential novel aspect for improving current risk stratification models.