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Introduction: With improving overall survival among MM patients, there is an increasing need to both understand the absolute risk and identify risk factors for developing a second primary malignancy (SPM). Understanding these risk and potential modulation factors may help guide decisions regarding treatment options, monitoring surveillance strategies, and patient counselling. Furthermore, understanding this baseline SPM risk will help contextualize the future SPM risk associated with newer therapeutics, including chimeric antigen receptor therapies. Thus, we conducted a population-based study with following objectives: 1) to evaluate the rate and distribution of SPMs, and 2) to identify risk factors including a history of prior malignancy associated with an increased risk of SPMs in patients with MM. Methods: We conducted a retrospective population-based study using data from Institute for Clinical Evaluative Sciences (ICES), an administrative database that captures all health records in the publicly funded health care system in Ontario, Canada. Adult patients treated for newly diagnosed MM between 2007-2022 were identified using ICD-O-3 code 9732/3 (MM). The list of secondary cancers (excluding non-melanoma skin cancers) were identified from pathology codes maintained in the Cancer Care Ontario Registry. Additional risk factors were also collected including a history and type of any pre-existing cancer (at least 1 year prior to MM diagnosis), treatment details including history of autologous stem cell transplantation (ASCT) and lenalidomide usage. The Kaplan-Meier method was used to estimate the median overall survival (OS). We estimated the cumulative incidence of secondary cancer accounting for death as a competing risk. To identify the association of prognostic factors on the risk of developing a SPM, we performed competing risks regression and estimated the subdistribution hazard ratios (sHR) accounting for death as a competing risk. Results: A total of 12123 patients . The median age at diagnosis was 70 (IQR 61-77) years, 6860 (57%) of our cohort had a male sex. In our 4511 (37%) received at least one ASCT, 8062 (67%) were lenalidomide exposed and 6917 (57%) were cyclophosphamide exposed at any time post MM diagnosis, and 3017 (16%) received oral melphalan based induction regimens. The median follow up for cohort was 4.53 (95% CI 4.4-4.7) years. A total of 2079 patients (17%) had a prior cancer diagnosis at least 1 year prior to their MM diagnosis. Of these patients, 665 (32%) had received chemotherapy and 553 (27%) had received radiation to treat their prior malignancy. The three most common prior cancers were: prostate cancer (25%), breast cancer (13%) and heme malignancies (4%).The median time from diagnosis of a prior malignancy to MM diagnosis was 6.4 (IQR 2.04-13.26) years. A total of 1070 (8.8%) of the cohort developed a SPM. The cumulative incidence of SPM post MM diagnosis, accounting for death as a competing risk, was 3.7% at 2 years, 7.6% at 5 years, and 11.5% at 10 years. The three most common SPMs were hematologic (21%), prostate (7%), and lung (7%) cancer. The three most common hematological SPMs were acute myeloid leukemia (37%), B-acute lymphoblastic leukemia (16%), and myelodysplastic syndrome (19%). The median OS following after any SPM diagnosis was 1.64 (95% CI 1.36-1.92) years, was 1.85 (95% CI 1.54-2.28) years for patients that developed a non-hematologic SPM, and was 1.05 (95% CI 0.88-1.40) years for patients that developed a hematologic SPM. Conclusion: This study represents one of the largest real-world cohort studies examining the rates, risk factor and outcomes of MM patients developing secondary cancers. We show that a prior malignancy diagnosis increased the risk of SPM after adjusting for ASCT and lenalidomide exposure, sex, and age at MM diagnosis. These data are important for patients and physicians to understand the risks associated with treatment of MM.

Introduction: Although not formally approved for the treatment of multiple myeloma (MM), the BCL2 inhibitor venetoclax (ven) has shown promising activity in patients (pts) with MM whose disease harbors t(11;14). The randomized BELLINI trial (VenVd vs Vd in unselected MM pts, Kumar Lancet Oncol 2020) demonstrated improved PFS, especially in the 35 patients with t(11;14) [HR 0.11], although worse OS in the intention to treat population due to increased treatment related mortality. The subsequent CANOVA trial (Vendex vs Pomdex in MM pts with t(11;14), Mateos IMS 2023) failed to show a PFS benefit (median PFS 9.9 mo vs 5.8 mo, p=0.237), leaving an uncertain future for ven in myeloma, despite the biological rationale and clinical activity. We therefore analyzed real world physician practice patterns using off-label ven for MM in the US. Methods: This retrospective observational study used data from the US nationwide Flatiron Health electronic health record (EHR)-derived de-identified longitudinal database. The Flatiron Health database contains structured and unstructured data curated via technology-enabled abstraction from approx. 280 cancer clinics and 800 unique sites of care. We included all patients in the Flatiron Health database with multiple myeloma (ICD-9 203.0x or ICD-10 C90.0x, confirmed with clinical review) who had received ven as part of any line of MM treatment. This included patients who commenced ven between 2016-2024. The primary endpoint was time to next treatment or death (TTNT) from ven initiation, with overall survival (OS) from ven initiation as the key secondary endpoint, both of which were estimated using the Kaplan-Meier method. Outcomes were compared using Cox proportional hazards model, using R v 4.3.0. Results: We included 208 patients who received ven for MM. They had a median of three prior lines of therapy (range 0-12), a median age of 67 years (Range 34-85 yrs), and 59% were male. Race/ethnicity breakdown was: Non-Hispanic White: 136 pts (72.7%), Black 36 (19.3%), Hispanic 10 (5.3%), Asian 5 (2.7%), other/unknown 21 (10.1%). Most had commercial insurance (188 pts, 91.3%) and 108 pts (52%) were cared for in academic settings while 100 (48%) were in community settings. ECOG performance status was 0 in 50 pts (24%), 1 in 80 pts (38.5%), ≥2 in 31 pts (14.9%) and unknown in 47 (22.6%). ISS Stage was I in 42 pts (20.2%), II in 43 pts (20.7%), III in 52 pts (25.0%) and unknown in 71 (34.1%). 148 pts had t(11;14) (71.2%), while 25 pts did not (12%) and 35 pts had unknown t(11;14) status (16.8%). Most pts received ven as part of a triplet combination (106 pts, 51%), followed by doublets (39, 18.8%; 21 ven+dex and 18 ven+non-dex doublet), quads (38, 18.3%), as a single agent (17, 8.2%) or in other combinations such as with multi-agent cytotoxic chemotherapy or in addition to quadruplet therapy (8, 3.9%). Most common first ven-containing regimens used were VenDd (34 pts), VenKd (31), VenVd (25), Vendex (21), VenDVd (13), VenDKd (7). Ven was used as part of ≥2 different regimens in 20 patients, in 10 of whom those regimens were non-consecutive. Median TTNT was 10.2 months (mo) in all comers, 12 mo in pts with t(11;14), 3.4 mo in pts without t(11;14) and 7.6 mo in pts with unknown t(11;14) status. Median OS was 27.6 mo in all comers, 30.5 mo in pts with t(11;14), 8.1 mo in pts without t(11;14) and 27.9 mo in pts with unknown t(11;14) status. Pts with t(11;14) receiving ven had a significantly longer TTNT than those without t(11;14) [median 12 mo vs 3.4 mo, HR 0.59, 95%CI 0.42-0.84, p=0.003]. and a significantly longer OS [median 30.5 mo vs 8.1 mo, HR 0.58, 95% CI 0.38 - 0.89, p=0.012]. Discussion: We evaluated US ven prescribing patterns in 208 pts with MM in the largest real world study of ven in MM to date. While it was mostly used in pts with t(11;14) (71%), there was occasional use in pts without t(11;14), and it was used in a wide variety of combination regimens. Ven achieved meaningful TTNT in some patients and both TTNT and OS were greater in pts with t(11;14) than those without. This suggest that despite mixed data from clinical trials, clinicians are using ven for MM patients in practice and further data is required to optimize its use and understand its best combination treatment partners in MM.

Introduction: CARTITUDE-4 is a phase 3 randomized controlled trial (RCT) assessing the efficacy and safety of CARVYKTI (ciltacabtagene autoleucel; cilta-cel) versus pomalidomide, bortezomib and dexamethasone (PVd) or daratumumab, pomalidomide and dexamethasone (DPd) in patients with relapsed and refractory multiple myeloma (RRMM) with one to three prior line(s) of therapy that included an immunomodulatory agent (IMiD) and a proteasome inhibitor (PI), and who are refractory to lenalidomide. Based on the latest reported results from CARTITUDE-4, cilta-cel demonstrated superiority over PVd and DPd on progression-free survival (PFS), overall survival (OS) and response rates (overall response rate [ORR], very good partial response [VGPR] or better, and complete response [CR] or better [CR or better]). In 2023, an initial analysis was performed to compare the efficacy outcomes between all apheresed patients from the cilta-cel arm in CARTITUDE-4 vs doublet (Pd and Kd) and daratumumab-based triplet (DKd and DVd) therapy regimens in the same target population using available patient level data. Data from an updated prespecified data-cut of CARTITUDE-4 with median follow-up of 34 months became available, allowing for an updated assessment of the comparative efficacy, including OS, between cilta-cel and these treatment regimens. Methods: Individual patient data were collected from the CARTITUDE-4 (cilta-cel), CASTOR (DVd), CANDOR (DKd and Kd), and APOLLO (Pd) RCTs, from which patients who met CARTITUDE-4 eligibility criteria were identified. As no patients in the comparative trials had prior treatment with anti-CD38, patients with prior exposure to anti-CD38 therapies in the cilta-cel cohort were excluded. Key prognostic baseline covariates including refractory status, ISS stage, presence of extramedullary disease (EMD), and time to progression on prior line, were adjusted for using inverse probability of treatment weighting (IPTW), with average treatment effect in the treated weights. Differences in baseline characteristics were assessed using standardized mean differences. PFS, OS and response rates (ORR, ≥VGPR and ≥CR) were compared between cilta-cel versus the comparator regimens. Relative efficacy for PFS and OS was estimated with Hazard ratios (HRs) and 95% confidence intervals (CIs) using weighted Cox proportional hazards regression. Response endpoints were analyzed using weighted logistic regression estimating response rate ratios (RRs) and 95% CIs. Sensitivity analyses using alternative statistical approaches and including additional covariates were explored. Results: After the exclusion of 53 patients with prior exposure to an anti-CD38 therapy, 155 patients in the cilta-cel arm remained, and were compared to patients treated with DVd (n=44), DKd (n=98), Kd (n=46) and Pd (n=92) who met the CARTITUDE-4 inclusion criteria. Baseline covariates were well balanced across the cohorts after IPTW. Patients from the cilta-cel arm showed significant improvements in PFS versus all comparators, with adjusted HRs ranging from 0.21 vs DVd to 0.58 vs DKd (all p ≤0.01). Also, OS was significantly longer for cilta-cel versus all other regimens, with adjusted HRs ranging between 0.31 vs Pd to 0.55 vs DKd (all p ≤0.05). While comparative estimates for ORR and VGPR did not change versus the previously reported results, the relative benefit for cilta-cel versus other regimens further increased on deeper levels of response, with adjusted RR for ≥CR from 2.71 vs DKd to 31.47 vs Pd (all p≤0.001). Treatment with DKd consistently yielded the best results out of all comparator regimens but results were still significantly in favor of cilta-cel. Results of cilta-cel benefit were generally consistent across sensitivity analyses. Conclusions: Results from this updated analysis confirm the previously observed significant superiority of PFS and response outcomes of cilta-cel, whilst showing significant OS benefit compared to commonly used treatment regimens for this patient population. These findings provide valuable information to support cilta-cel as a highly effective treatment for RRMM patients who received 1-3 prior lines of therapy, including a PI and IMiD, and who are refractory to lenalidomide, particularly in countries where standard of care may be different from DPd/PVd.

Introduction The major primary genetic events in multiple myeloma (MM) include translocations of immunoglobulin heavy chain (IgH) and a hyperdiploidy (HRD) karyotype (Kuehl, 2002). The main drivers of pathogenesis in HRD MM are unknown (Fonseca, 2003), therefore further research is needed in this subgroup. Translational research in HRD MM has relied on analysis of genomic and transcriptomic alterations, however changes at the genome and transcriptome level do not always correlate directionally with protein abundance. Because proteins are considered the main and final effectors of most cellular processes, proteogenomics can improve our understanding of the drivers of HRD MM. We employed it here to identify potential biomarkers in HRD MM and determine whether any given trisomy may lead to a selective advantage. Methods: Protein and mRNA were extracted from CD138+ cells from 47 HRD MM and 14 t(11;14) MM samples. RNA-seq data was processed using MAPR-Seq pipeline using default parameters and gene-wise raw counts were extracted for each sample. Proteomics data was processed using Specranaut and protein-wise intensities were extracted for each sample. A generalized linear model configured with negative binomial distribution was used to analyze gene-wise count data in edgeR software. Genes with an adjusted p-value of <=0.05 were considered significantly differentially expressed. Protein intensity data were normalized using trimmed mean of M-values and log2 transformed. A generalized linear model configured with Gaussian distribution was used to compare protein intensities between the groups. Proteins with an adjusted p-value <= 0.05 were considered significantly differentially expressed. Two separate rank lists for protein and mRNA data were generated for pathway analysis. Results When comparing our small cohort of HRD MM patient samples to those with t(11;14) MM, out of approximately 16,600 total transcripts and 9,700 proteins evaluated we identified approximately 2,100 significantly variant transcripts and 500 significantly variant proteins in HRD MM. GSEA pathway analysis of the transcriptome and proteome revealed significantly upregulated translational and ribosomal processing pathways in HRD MM. Transcriptional MYC target pathways were also significantly upregulated. RNAseq revealed significantly higher expression of MYC (Rank 24.5, p=5.3E-6) in HRD MM. It is known that patients with t(11;14) MM have increased expression of CCND1 (Kuehl and Bergsagel, 2012). As expected, we found that the HRD MM subgroup had significantly lower expression of CCND1 compared to the t(11;14) MM subgroup (Rank -79.9, p=2.8E-21) and this was also true at the protein level (Rank -21.7, p=2.2E-5). The most significantly abundant protein in the HRD MM subgroup compared to t(11;14) MM was BAHD1, which is a protein that targets H3K27me3 and recruits transcriptional co-repressors (Fan, 2020). BAHD1 has been shown to repress erythroid genes and may recruit HDAC5, which plays a role in osteoblast differentiation (Rank 72.3, p=2.9E-20; Xu, 2021). The second most abundant protein in HRD MM was CKS2, which binds to the catalytic subunit of cyclin-dependent kinases (CDKs) and therefore positively regulates cell proliferation, invasion, and migration and has been shown to be implicated in tumor progression in several solid tumors (Rank 72.3, p=2.9E-20; You, 2015). TRIM proteins promote tumorigenesis and cancer development. TRIM14 mediates cancer development through mediating the JAK/STAT, PI3K/AKT, NFkB, and p53 pathways and was found to be significantly higher in HRD MM (Rank 70.6, p=9.0E-20; Huang, 2022). We also found increased protein abundance of LAMP5 in HRD MM (Rank 20.1, p=6.0E-5). LAMP5 may play an important role in MM progression and has been shown to be highly expressed in patients with mixed-lineage leukemia and is associated with a poor prognosis (Wang, 2023; Gracia-Maldonado, 2022). We identified many proteins (not all discussed here) that have not been well described in HRD MM and thus may be biomarkers/targets of disease pathogenesis of this subgroup. Conclusion Proteins are the final effectors of most cellular processes. Proteogenomics can improve our understanding of the drivers of HRD MM. Using a proteogenomic approach we identified proteins that may lead to selective advantage and/or pathogenesis of trisomies in HRD MM.

Introduction The tumor ecosystem in multiple myeloma (MM) involves complex interactions among various cell types, including clonal plasma cells (PCs) and immune cells. Previous research has indicated that the bone marrow (BM) microenvironment's metabolite and lipid profiles differ between MM patients and those with precursor conditions. This study aims to explore if differences in cell composition, metabolic phenotype of clonal PCs, and the spatial organization of various cell types are linked to the early versus late progression of high-risk smoldering MM (SMM) to MM. Methods We identified high-risk SMM patients using the IMWG 20-2-20 criteria, focusing on early progressors (EP), who progressed to MM within one year, and later progressors (LP), who progressed after more than four years. Using a high-dimensional single-cell spatial imaging platform, CODEX (CO-Detection by indEXing), we identified clonal PCs and immune cell types as well as their metabolic phenotypes at the single-cell level, in paired FFPE BM samples at SMM diagnosis and at the time of progression to MM. Our validated panel included markers such as CD3e, CD4, CD8, CD14, CD20, CD31, CD38, CD45, CD66a, CD68, CD138, HLA-DR, ASCT2, pNRF2, GLUT1, LDHA, ATPA5, CPT1A, and Citrate synthase. We employed single-cell segmentation, artifact cleanup, signal thresholding, and spatial analysis. Repeated measure models accommodated multiple regions of interest (ROIs) per patient and accounted for potential intrapatient correlation, while Poisson models estimated incidence rate ratios (RR) of cell types. Spatial statistical models evaluated cell type segregation and integration at the time of SMM diagnosis. Results Single cell events were acquired from 6 unique SMM patients who had paired FFPE BM samples from the time of their SMM diagnosis and at the time of their progression to MM. There were 3 EP SMM patients (Time to Progression (TTP):8, 8, 10 mos) and 3 LP SMM patients (TTP: 51, 58, 117 mos). The median number of single cell events identified per patient slide sample was 104,390 (Range: 24,357 - 314,877). We identified 8 main cell types: PCs, CD4+ T-cells, CD8+ T-cells, B-cells, granulocytes, Monocyte/Macrophages and two groups of other immune cells. The PCs and the remainder of the non-PCs were further classified based on the expression of the following metabolic markers: ASCT2 (Glutamine importer), CPT1A (Fatty acid metabolism), GLUT1 (Glucose importer), LDHA (Glycolysis), Citrate synthase and ATPA5 (Oxidative Phosphorylation). There was a higher RR of granulocytes (RR 2.1, CI 1.6-2.8, p<0.001) in EP compared to LP samples at the time of SMM diagnosis, but a lower RR of CD4 T cells (RR 0.40, CI 0.3-0.6, p< 0.001). Focusing on metabolic phenotypes of the PCs, we observed higher incidence RR of most metabolic markers: ASCT2 (RR=1.77; p=0.032), ATPA5 (RR=1.25; p<0.001), CPT1a (RR=2.42; p=0.015), and LDHA (RR 2.13; p<0.001) in EP compared to LP samples at the time of SMM diagnosis. However, GLUT1 had a lower incidence RR in EP patients (RR=0.79; p<0.001). The same modeling for non-PCs at the time of SMM diagnosis yielded higher expression of LDHA (RR=1.98; p<0.001) and ASCT2 (RR=3.3; p=0.002). Using the x and y coordinates and the corresponding phenotype classifications, we performed spatial statistical modeling based on spatial point processes to generate aggregated measures of how different types of cells of interest interacted. CD4 T cells were more isolated in LPs compared to EPs (p =0.046). Segregation indices for PCs and CD4 T cells indicated that they were more integrated/collocated in EPs. Granulocytes were more isolated in EPs although the difference was not as pronounced (p= 0.07). PCs were more integrated with granulocytes (p=0.06) and B cells (p=0.07) in LP patients. Further analyses of paired samples at the time of progression to MM will be reported at the time of the presentation. Conclusion This study is the first to provide whole-slide, single-cell, spatial proteomic characterization of clonal PCs and immune cells in the FFPE BMs of high-risk SMM patients with varying progression times to MM. Distinct metabolic pathway markers in clonal PCs and their spatial relationships with immune cell subsets are closely associated with the risk of progression to MM. These findings illuminate the complex interactions between the metabolic states of clonal PCs and their spatial orientation relative to immune cells in the pathogenesis of MM.

Introduction: Ven is a BCL2 inhibitor that has shown clinical efficacy in RRMM patients (pts) particularly those that harbor t(11;14). Ven has been evaluated in clinical trials as a single agent, in combination with dexamethasone (dex), immunomodulatory drugs (IMiD), proteasome inhibitors (PI), and anti-CD38 monoclonal antibodies (MAb) yet its place in the therapeutic landscape remains in question. In this retrospective analysis, we aim to evaluate the real-world efficacy of Ven-based regimens in RRMM pts treated outside of a clinical trial across the 3-site MCCC. Methods: We retrospectively analyzed the medical records of pts with RRMM treated with Ven-based regimens between January 2015 and October 2023 at the MCCC. Refractoriness was defined by progression on or within 60 days of stopping treatment. Categorical variables were compared with Chi-square tests and continuous variables were compared with t-tests. Outcomes were estimated using the Kaplan-Meier method and curves were compared between the groups using log-rank tests. Results: 83 pts were included in the analysis. The median age was 71 (39-93), 63% were male, 93% were white, 27% had ISS III, 18% had high risk cytogenetics (HRCyto), 76% had t(11;14), the median prior lines of therapy (LOT) was 4 (1-15), 68% received ≥ 4 prior LOT, 87% received prior autologous transplant, 58% were triple-class refractory (TCR), 64%/23% were penta-drug exposed/refractory (PDE/R), 8% received prior BCMA-directed therapies (BDT), 58% received Ven+dex, 25% received Ven+PI +/-dex, 6 % received Ven+anti-CD38 MAb+/-dex and 11% received Ven+other. The median follow-up time from start of a Ven-based regimen was 29 months (mo). The median Ven dose was 800mg (20-1600) with 25% of pts receiving Ven ramp-up dosing. The overall response rate (ORR) to a Ven-based regimen was 58% and was 64% for pts with t(11;14) and 40% for pts without t(11;14); p=0.06. Ven-treated pts with HRCyto had inferior PFS (3mo) and OS (8mo) compared to patients who did not have HRCyto who had a PFS of 9mo (p=0.020) and OS of 80mo (p<0.001). The ORR for TCR and PDR pts were 52% and 42%, respectively. No significant ORR differences were noted across the different Ven-based regimens (p=0.32). The median PFS for pts with t(11;14) was 10mo compared to 2mo for pts w/o t(11;14); p=<0.001. The median OS for pts with t(11;14) was 80mo compared to 10mo for pts without t(11;14); p=0.001. Ven-treated pts who received 1-3 prior LOT has superior ORR, median PFS, and median OS; 89%, 25mo, and 99mo, respectively, compared to pts who received ≥4 prior LOT who achieved an ORR of 43% (p<0.001), median PFS of 5mo (p<0.001) and median OS of 25mo (p<0.001). Ven-treated pts that were TCR had inferior PFS (5mo) and OS (26mo) compared to pts who were not TCR who achieved a PFS of 12mo (p<0.001) and a median OS of 80mo (p=0.02).Ven-treated pts that were PDR had inferior PFS (4mo) and OS (22mo) compared to pts who were not PDR who achieved a PFS of 9mo (p<0.001) and a median OS of 80mo (p=0.02).Ven-treated pts who received prior BDT (n=7) achieved an ORR of 29% and had a median PFS and OS of 2mo and 6mo, respectively on a Ven-based therapy. 59 pts received additional therapy in the post Ven-progression setting. The ORR to post-Ven progression therapies was 53% (n=31/59), the median PFS was 9mo and median OS was 35mo. The most common post-Ven progression regimens used were PI-based (n=16; ORR=56%), anti-CD38 MAb-based (n=14; ORR=50%), BDT (n=12; ORR=83%), selinexor-based (n=3; ORR=0%) with BDT resulting in highest ORR (p=0.048) and PFS (p=0.005). Conclusions: Ven-based regimens are effective in heavily pre-treated pts with RRMM however they are more effective when used within 1-3 prior LOT, in pts with t(11;14), and in pts without HRCyto, TCR or PDR MM. BDT appear to be the most efficacious therapies in the post-Ven progression setting.

Introduction: Understanding patient preferences and decision-making processes is crucial in managing relapsed multiple myeloma (RRMM). Shared decision-making has become a key element of patient-centered care, allowing patients to collaborate with healthcare providers in choosing treatments that align with their health and quality of life goals. This study examines how the perceived severity of treatment side effects influences patient decision-making autonomy and communication patterns, providing insights into decision-making practices in hematology. Methods: A survey was conducted with 784 RRMM patients as of January 1, 2024, using HealthTree Cure Hub (PMID: 35271305). Among the participants, 333 experienced a relapse and responded to the question about how side effect severity affected their treatment choices. Of these, 54% (n=181) considered side effects to be very or extremely influential (VE), whereas 46% (n=152) regarded them as somewhat, slightly, or not influential at all (SSN). Participants evaluated these factors on 5-point Likert scales: 1 (very poor) to 5 (very good); 0 (not at all influential) to 4 (extremely influential). The survey assessed patient involvement in final treatment decisions, discussions with fellow patients, family or caregivers, and the time allocated and used for decision-making. Results: Patients in the VE group reported a better understanding of the likely side effects of treatment options compared to the SSN group (VE: 3.9±1.0; SSN: 3.6±1.0, p<0.05), with a higher proportion reporting a very good understanding (VE: 31%, n=45; SSN: 18%, n=29, p=0.036). A weak relationship confirmed VE patients engaged in additional side effect education before making a change in treatment decisions (r=0.164, p=0.004). VE patients also reported a better understanding of the balance between effectiveness and potential side effects of treatments than SSN patients (VE: 3.8±1.0; SSN: 3.5±1.1, p<0.05), with more VE patients reporting a very good understanding (29%, n=41 vs. 15%, n=24, p=0.006). A weak relationship confirmed that VE patients more closely evaluated the trade-offs of treatment options when choosing a new treatment regimen (r=0.197, p<0.001). Likely to achieve a better understanding, more VE patients talked with fellow patients (VE: 19%, n=24; SSN: 7%, n=11, p = 0.003) and rated these conversations as more influential than SSN (VE: 1.5±1.5; SSN: 1.1±1.4, p<0.05). Additionally, VE patients were more likely to discuss treatment options with family or caregivers before making a final decision (VE: 49%, n=61; SSN: 29%, n=43, p<0.05). Moreover, VE patients reported greater involvement in their doctor-patient communication approach (VE: 3.2±0.8; SSN: 2.8±0.8, p<0.05), with scores ranging from 1 (“...follow the doctor's suggestions”) to 4 (“...we discuss my preferences... and I make the final decision”). Furthermore, VE patients reported more self-determination in the treatment decision-making process than SSN patients, with more VE patients making the final treatment decision themselves rather than having their doctor decide (“I made the final decision” - VE: 66%, n=93; SSN: 52%, n=81; “My doctor made the final decision” - VE: 34%, n=47; SSN: 46%, n=72, p=0.013). VE patients reported being provided 1.5 more weeks of time to make a treatment decision than the SSN (4.8±8.9 v. 3.3±6.0 wks, p<0.05). Subsequently, VE patients reported using 79% more time than SSN patients to arrive at a final decision (VE: 2.5±6.0; SSN: 1.4±1.9 wks, p<0.05), likely attributed to time spent gaining a better understanding, having more conversations with patients and family, more shared decision making and self-determination in their treatment decision. Conclusion: The study highlights that RRMM patients who consider side effects to be very or extremely influential in their treatment decisions report being more proactive in understanding and discussing their treatment options. These patients engage more with healthcare providers, peers, and family members and are granted more time to make informed decisions. This emphasis on understanding and communication underscores the importance of personalized patient education and shared decision-making in the treatment of RRMM, especially for patients who are highly influenced by potential side effects. Together, these findings underscore the ongoing need for patient-centered care and education to optimize treatment choices.

Background: Fc receptor-homolog 5 (FcRH5) is a type I membrane protein that is expressed exclusively in the B-cell lineage, and at a higher level on multiple myeloma (MM) cells than on normal B cells. Cevostamab is a FcRH5xCD3 bispecific antibody that facilitates T cell-mediated killing of MM cells. In an ongoing Phase I dose-escalation and dose-expansion study (GO39775; NCT03275103), cevostamab monotherapy demonstrated target dose (TD)-dependent activity and manageable safety in patients (pts) with heavily pretreated RRMM (Trudel et al. ASH 2021). We report updated efficacy and safety data from pts who received cevostamab once every 3 weeks (Q3W) at the 160mg TD level. Methods: Eligible pts were those with RRMM for which no established therapy was available or appropriate. Cevostamab was initiated with Cycle (C) 1 single-step (SS) dosing, double-step (DS) dosing (Trudel et al. ASH 2021), or triple-step (TS) dosing (step doses on D1, D2 [or D3 or D4 depending on the emergence and resolution of cytokine release syndrome [CRS] with the previous administration], and D8, TD on D9 [or D10 or D11]). Cevostamab was then continued at the TD on D1 of each subsequent 21-day cycle. Cevostamab was continued for 17 cycles, unless disease progression or unacceptable toxicity occurred. Results: As of February 23, 2024, 167 pts (median age: 66 years, range: 40-90; ECOG PS: 0, 35% or 1, 65%; median time from first MM therapy: 6.3 years, range: 0.3-21.8) had received cevostamab at the 160mg TD level (SS: n=10; DS: n=97; TS: n=60); 28% of pts had extramedullary disease and 38% of pts with a conclusive assay result (38/100) had high-risk cytogenetics (t(4;14), t(14;16), or del(17p)). The median number of prior lines of therapy was 6 (range: 2-18). Almost all pts (95.8%) were triple-class refractory and 73.7% were penta-drug refractory; 85.0% were refractory to their last prior therapy. A majority (57%) of pts had received ≥1 prior B-cell maturation antigen (BCMA)-targeted therapy. Twenty four percent of pts had received ≥1 prior bispecific antibody. Median observation time was 11.3 months (range: 0.5-42.8). The overall response rate (ORR) in all pts who received cevostamab at the 160mg TD level was 43.1% (72/167 pts); 6.6% achieved a stringent complete response (CR), 6.6% a CR, 12.6% a very good partial response (VGPR), and 17.4% a partial response (PR). The VGPR or better rate was 25.7%. Median time to first response (PR or better) was 1.4 months (range: 0.6-4.6) and to best response was 2.4 months (range: 0.7-13.4). Median duration of response was 10.4 months (95% CI: 6.8, 10.5). ORR was 30.2% (29/96) in pts with ≥1 prior BCMA-targeted therapy and 60.6% (43/71) in those without. ORR was 30.0% (12/40) in pts with ≥1 prior bispecific antibody, 33.3% (20/60) with ≥1 prior chimeric antigen receptor (CAR) T-cell, and 41.2% (14/34) with ≥1 prior antibody-drug conjugate. Common (≥20%) adverse events (AEs) included CRS (74.3%; Grade [Gr] 1: 52.7%; Gr 2: 19.8%; Gr 3: 1.2%; Gr 4: 0.6%), neutropenia (31.1%; Gr 1-2: 3.0%; Gr 3: 12.0%; Gr 4: 16.2%), cough (30.5%; Gr 1-2: 29.9%; Gr 3: 0.6%), nausea (28.1%; Gr 1-2: 27.5%; Gr 3: 0.6%), diarrhea (24.0%; all Gr 1-2), anemia (23.4%; Gr 1-2: 5.4%; Gr 3: 17.4%; Gr 4: 0.6%), and fatigue (21.0%; Gr 1-2: 19.8%; Gr 3: 1.2%). Possible immune effector cell-associated neurotoxicity syndrome (ICANS) occurred in 13.2% of pts (Gr 1: 6.0%; Gr 2: 5.4%; Gr 3: 1.8%). Infections occurred in 53.9% of pts, with serious events and Gr ≥3 events in 22.2% and 19.2%, respectively. Treatment-related AEs leading to cevostamab discontinuation occurred in 11 pts (6.6%) and treatment-related Gr 5 (fatal) AEs in 3 pts (1.8%; hemophagocytic lymphohistiocytosis, n=2; disseminated intravascular coagulation and pseudomonal sepsis, n=1). In the 0.3/1.2/3.6/160mg TS cohort, CRS occurred in 19/30 pts (63.3%) and was all Gr 1 (46.7%) or Gr 2 (16.7%). Pts with CRS were managed with tocilizumab (47.4%), steroids (21.1%), or both agents (10.5%). No pts discontinued cevostamab due to CRS and all events were resolved at cut-off. Conclusions: Cevostamab demonstrates clinically meaningful activity and manageable safety at the 160mg TD level in pts with heavily pretreated RRMM. C1 TS dosing provides effective CRS mitigation. Cevostamab combination studies may use the 0.3/1.2/3.6mg TS regimen and the Q3W 160mg TD (or similar TD exposure).

Introduction Monoclonal antibodies (MoAbs) directed against CD38 are a mainstay in multiple myeloma (MM). Downregulation of CD38 expression is a resistance mechanism of antigenic escape, however, cell surface expression may return. While this tumor cell plasticity is likely driven by epigenetic mechanisms, it is unknown if MM cells can develop permanent resistance to anti-CD38 MoAb by acquiring genomic events leading to biallelic disruption of CD38 thus precluding re-treatment. Here, we report biallelic loss of CD38 as a recurrent mechanism of resistance, often via convergent evolution where distinct subclones are advantaged towards the same selective pressure. Methods We interrogated two sets of patients (Mayo Clinic; n = 31, Calgary University; n =19) with relapse following anti-CD38 MoAbs. Two additional cases of interest were selected from Heidelberg University. Whole genome sequencing (WGS, 60-100x) or whole exome sequencing (WXS), flow cytometry (flow), and bulk RNAseq were used to validate the effect of genomic events on CD38. Missense mutations predicted to affect CD38 MoAb binding were validated with site-directed mutagenesis of a CD38 plasmid, transduction into K562 MM cells, and CD38 MoAb binding assays. Results The prevalence of biallelic inactivation of CD38 at anti-CD38 MoAb relapse was 6% (3/50). An additional selected biallelic event was noted in the Heidelberg set. Each case is described in detail: MM-19 (Calgary) received daratumumab (Dara), lenalidomide, and dexamethasone (Dex), and then had a second exposure to Dara, following relapse. At this second relapse, WGS was performed on two independent extramedullary disease localizations in the liver. Both samples had unique, large deletions (>3 Mb) on chromosome 4 coupled with additional unique focal deletions involving CD38. Flow confirmed the presence of CD138+/CD38- plasma cell populations in both sites. A second case of convergent evolution was seen in a patient (Mayo) after seventh-line Dara/Len/Dex. WGS revealed a common deletion encompassing CD38. On the remaining allele, one subclone had a structural variation-mediated loss of CD38(cancer cell fraction; CCF 40%) while another harbored an L153H missense mutation (CCF 60%). Flow confirmed presence of two CD138+ clones; one with diminished CD38 expression. L153H was functionally validated to abrogate Dara binding, while maintaining CD38 expression. The third sample was collected after fifth-line Dara and sixth-line Dara/Bortezomib/Dex (Heidelberg) and represents another case of convergent evolution with a common loss of chromosome 4 and two subclones detected by WGS (P98Lfs*12, CCF 39% resulting in biallelic inactivation; R140G, CCF 61%). Bulk RNAseq revealed, in comparison to NDMM from CoMMpass, CD38 expression below the first decile. Notably, the R140G variant reduced binding affinity to Dara but not to Isatuximab (Isa). A fourth case (Mayo) relapsing after 6 months of Dara-based therapy similarly had a focal deletion of CD38 with an L18Sfs*16 frameshift deletion on the remaining allele associated with a 4.35-fold reduction in CD38 expression by RNAseq compared to pre-treatment. Interestingly, in 3/4 cases the CD38-negative clone emerged after a second exposure to anti-CD38 MoAb, suggesting emergence with the first exposure and then selection during the second. One other monoallelic event (C275Y; Heidelberg) was detected post-Isa. Copy number of the alternate allele is not known given targeted sequencing methodology, but the variant was seen to reduce binding affinity to both Dara and Isa. The prevalence of monoallelic loss of CD38 in newly diagnosed MM from MMRF CoMMpass was 7% (50/701) and two patients had mutations in CD38, but there were no cases of biallelic loss suggesting these events are driven by treatment pressure. Conclusion Distinction of the mode of CD38 loss is valuable for predicting outcome of re-challenge to anti-CD38 MoAbs. We see here an estimated 5-10% of anti-CD38 MoAb relapses are characterized by genomic antigen escape. Results here are in line with antigen escape to BCMA- and GPRC5D-directed immunotherapies (Lee et al, Nat Med 2023), suggesting that acquired antigen escape resulting from prolonged exposure to the selective pressures of immunotherapy is a recurrent mechanism of resistance in MM. Moreover, in select cases, non-overlapping resistance mutations provide rationale for switching agents at second exposure.

Introduction: Multiple myeloma (MM) with t(11;14)(q13;q32), resulting in CCND1;IGH rearrangement, remains the only subset amenable to targeted therapy with the BCL2 inhibitor, Venetoclax. Nevertheless, not all patients with this translocation respond to treatment and some progress early after initial response. The mechanisms behind this clinical heterogeneity are largely unknown. Methods: To investigate mechanisms of heterogeneity in response to Venetoclax, we interrogated 44 whole genome sequencing (WGS) and 24 whole exome sequencing data from a training cohort including 33 patients with t(11;14) MM treated with Venetoclax at the Mayo Clinic. In a validation cohort, we extracted single nucleotide variants (SNV) and indels from 17 MM patients with t(11;14) with available RNAseq data treated with Venetoclax at Emory University. Results: In the training cohort the median number of lines of therapy was 4 and Venetoclax drug regimens included combinations with dexamethasone, proteasome inhibitors, anti-CD38 agents, and immunomodulatory agents. After a median follow up of 30 months, the median progression free survival (PFS) was 9 months, and 13 patients (39%) achieved a complete remission (CR) to therapy, while 9 (27%) were refractory (i.e. no response or progressed within the first 60 days). For our driver discovery analysis, we integrated SNV, indels, copy number variants (CNV) and structural variants (SV). Deletion 17p (del17p) or TP53 mutation, MCL1 gain/amp, and hyper-APOBEC did not correlate with PFS in our cohort (p=0.59, p=0.71, and p=0.05, respectively). In contrast, mutations involving the MAPK pathway (NRAS, KRAS, BRAF, FGFR3) displayed the strongest association with short PFS, which was retained after adjusting for del17p/TP53 mutations, and 1q gain/amp (HR: 4.76, CI: 1.8-12.9, p=0.002). The association between suboptimal response and resistance to Venetoclax in MM patients with MAPK pathway mutations (n=7) was also observed in our validation cohort from Emory University (p=0.046). This is in line with what has been reported in functional studies in acute myeloid leukemia, where RAS pathway promotes MCL1 under the selective pressure of Venetoclax (Zhang et al. Signal Transduct Target Ther 2022). While MAPK mutations explained most cases of refractory patients (86%), the mechanisms through which initially responding patients subsequently progressed remain unclear. To investigate this, we interrogated 10 patients with pre- and post-treatment samples collected at disease progression (PD). We identified genomic events acquired/selected at PD of genes involved in the anti-apoptotic MCL1/BCL2 and MAPK pathways in 6/10 cases. One patient was refractory to Venetoclax and had double minutes involving BCL2, which caused multiple focal amplifications (>30 copies), likely reflecting extrachromosomal DNA. Another patient progressed with an acquired, focal deletion on BCL2L11. In two patients, the progressing clones harbored mutations in MAPK pathways (one BRAF and the other WNK2). Two patients who relapsed after sustained CR had major branching evolution where a previously undetectable focal deletion of PMAIP1 (NOXA) was seen at PD. Importantly, one of these deletions was a biallelic event. Venetoclax sensitivity assays conducted on PMAIP1 knockout in the MM t(11;14) cell line, KMS12PE, demonstrated significantly reduced cell death across all drug concentrations (p<0.05), validating the loss of NOXA as a mechanism of resistance to Venetoclax in MM. Finally, similarly with what was previously reported in chronic lymphocyte leukemia treated with Venetoclax (e.g., Herling et al Nat Comm 2018), CDKN2A/B monoallelic and biallelic focal deletions were selected post-Venetoclax, at PD in 4 patients while TP53 SNV/deletions were selected from subclonal to clonal in 2 patients at PD. Conclusion: In this study, we demonstrate that mutations involving the MAPK pathway identify a population of MM patients with t(11;14)(CCND1;IGH) who are unlikely to respond to Venetoclax. We also show that genomic events involving the MAPK and BCL2/MCL1 pathways are primarily responsible for disease progression after initial response. These findings highlight the critical role of comprehensive genomic profiling through WGS in identifying mechanisms of resistance to targeted therapies in MM, aiding patient selection for Venetoclax, and guiding the development of novel drug combinations.