Detection of light chain (LC) monoclonal gammopathies (MGs) traditionally relies on serum free LC (FLC) kappa, lambda, and their ratio (kappa/lambda) reference ranges based on a mostly White population. We investigated FLC values in a racially diverse population by screening 10 035 individuals for heavy chain MG, identifying 9028 negative cases whose FLC were measured. Participants included 4149 from the PROMISE study (United States, n = 2383; South Africa, n = 1766) and 4879 from the Mass General Brigham Biobank, with 44% self-identifying as Black. Using standard FLC reference ranges, 1074 of 10 035 individuals (10.7%) were diagnosed with LC monoclonal gammopathy of undetermined significance (MGUS), with 99% being kappa-restricted. In the United States, 14.8% of Black and 4% of White individuals were diagnosed (P < .01). Among US participants of African (AFR) and European (EUR) genetic ancestry, 14.4% AFR and 2.9% EUR were diagnosed (P < .01). Among South Africans (100% Black), 27.8% were diagnosed using standard ranges. To avoid overdiagnosis, we propose a new kappa/lambda ratio reference range (0.686 to 2.10) for populations of AFR descent with normal renal function, with standard values for kappa and lambda being 7.97 to 77.50 mg/L and 6.20 to 49.20 mg/L, respectively. This reduces LC-MGUS overdiagnosis by 91% (10.7% vs 0.97%). Using the new reference, LC-MGUS accounts for 8.8% of MGUS cases, with 74% being kappa-restricted, consistent with LC myeloma rates. These findings highlight the importance of basing disease definitions, such as MGUS, on diverse populations. Adopting our proposed FLC reference values would reduce MGUS overdiagnosis among Black individuals, avoiding unnecessary financial, psychological, and medical consequences. This study includes data from NCT03689595.

AbstractEarly therapeutic intervention in high-risk smoldering multiple myeloma (HR-SMM) has shown benefits, however, no studies have assessed whether biochemical progression or response depth predicts long-term outcomes. The single-arm I-PRISM phase II trial (NCT02916771) evaluated ixazomib, lenalidomide, and dexamethasone in 55 patients with HR-SMM. The primary endpoint, median progression-free survival (PFS), was not reached (NR) (95% CI: 57.7–NR, median follow-up 50 months). The secondary endpoint, biochemical PFS, was 48.6 months (95% CI: 39.9–NR) and coincided with or preceded SLiM-CRAB in eight patients. For additional secondary objectives, the overall response rate was 93% with 31% achieving complete response (CR) and 45% very good partial response (VGPR) or better. CR correlated strongly with the absence of SLiM-CRAB and biochemical progression. MRD-negativity (10-5 sensitivity) predicted a 5-year biochemical PFS of 100% versus 40% in MRD-positive patients (p = 0.051), demonstrating that deep responses significantly improve time to progression. Exploratory single-cell RNA sequencing linked tumor MHC class I expression to proteasome inhibitor response, and a lower proportion of GZMB+ T cells within clonally expanded CD8+ T cells associated with suboptimal outcomes.

Background We previously demonstrated that monoclonal gammopathies can be detected in a population at risk for myeloma using mass spectrometry (MS), identifying two entities: monoclonal gammopathy of indeterminate potential (MGIP) and MS detected MGUS (MS-MGUS). However, it remains unclear whether these early clones progress to clinically detected MGUS or multiple myeloma (MM). To investigate this, we utilized archived blood samples from individuals in the screening arm of the National Cancer Institute Prostate, Lung, Colorectal, and Ovarian Cancer Screening (PLCO) Trial, collected up to 22 years before their clinical diagnosis with MGUS or MM. Methods Samples were obtained from healthy adults aged 55-74 enrolled between Nov 1993 and July 2001. We focused on participants who developed either MM or MGUS based on clinical records of serum protein electrophoresis (SPEP and IFX) and serum free light chain (sFLC) tests conducted by Landgren et al. We used matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) MS coupled with turbidimetry (EXENT and Optilite, Binding Site part of Thermo Fisher Scientific) to detect and quantify monoclonal peaks in the serum. Monoclonal peaks were categorized as previously described: MGIP (0.015-0.2 g/L) or MS-MGUS (>0.2 g/L). Participants were classified into: MS-MGUS, MGIP or Negative. Results We analyzed 2,219 serial samples from 509 participants (median 5 samples, range 1-6) who later developed MM (n=134; median 13 yrs [IQR: 10-17]) or MGUS (n=375: median 17 yrs [IQR: 13-19]). Among the 128 participants who progressed to MM (excluding 6 with light chain only disease), 123 (96%) had at least one sample positive for MGIP or MS-MGUS before clinical diagnosis. Of these, 90 cases (73%) showed an MS-MGUS peak, while 33 cases (27%) an MGIP peak at the earliest sample. MGIP (n=23) or MS-MGUS (n=31) was detected in 54 out of 56 patients (96%) who had available samples before the clinical MGUS diagnosis (median 3 yrs, range 1-6 yrs). A log-rank test showed no significant difference in time of progression to MM between individuals whose first sample had a monoclonal protein detected at concentrations of MGIP (median: 13, IQR 10-17 yrs) vs. MS-MGUS (median: 14, IQR 11-17 yrs) (p=0.92). We captured progression of the MGIP to MS-MGUS in 6 and 26 participants, before MM or MGUS clinical diagnosis, respectively. We leveraged the sensitivity of MS to determine the number of monoclonal peaks present in each sample, their isotype, and mass-to-change ratio and then followed those clones across time in the serial samples. The purpose of this was to gain insight into early clonal dynamics among expanded B cell/plasma cell clones and elucidate how these monoclonal populations compete over time. Several distinct patterns were observed in 422 individuals who had at least 2 positive samples serially tested. In 324/422 participants (77%), there was a dominant clone present in all samples and had minimal dynamic alterations over time. In 127 of the 324 (39%) participants with a persistent dominant clone, MS detected only one clone in the first sample, which remained dominant with follow up until clinical detection. For the remaining cases, 197/324 (61%) participants, MS demonstrated the presence of multiple monoclonal clones (median of 2 clones, range: 2-13) in which one clone remained dominant. In the remaining 98/422 (23%) participants, clonal dominance varied significantly over time with different clones becoming dominant at various timepoints. When clones were competing for dominance, IgM isotype clones were the first o be detected at the MGIP stages and were replaced by IgG clones later becoming the dominant clone in the majority (43%) of the observed cases during follow up over time. This phenomenon of transitioning clones was observed at similar rates among participants regardless of their progression to MGUS or MM. The clonal transition was similar in all cases regardless of the clone's concentration at first available sample (MGIP or MS-MGUS) or isotype (IgG, IgA, or IgM). Conclusion Our data demonstrates that monoclonal gammopathies detected by MS precede the clinical diagnosis of MGUS or MM by up to 22 years. In most cases, a dominant clone persists over time, while in 23%, there is notable clonal transition. Further investigation is needed to determine if these IgM to IgG changes constitute isotype class switch within the same B cell clone during plasma cell differentiation.

Abstract Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant condition of multiple myeloma with few known risk factors. The emergence of mass spectrometry (MS) for the detection of MGUS has provided new opportunities to evaluate its risk factors. In total, 2628 individuals at elevated risk for multiple myeloma were enrolled in a screening study and completed an exposure survey (PROMISE trial). Participant samples were screened by MS, and monoclonal proteins (M-proteins) with concentrations of ≥0.2 g/L were categorized as MS-MGUS. Multivariable logistic models evaluated associations between exposures and MS outcomes. Compared with normal weight (body mass index [BMI] of 18.5 to <25 kg/m2), obesity (BMI of ≥30 kg/m2) was associated with MS-MGUS, adjusting for age, sex, Black race, education, and income (odds ratio [OR], 1.73; 95% confidence interval [CI], 1.21-2.47; P = .003). High physical activity (≥73.5 metabolic equivalent of task (MET)-hours per week vs <10.5 MET-hours per week) had a decreased likelihood of MS-MGUS (OR, 0.45, 95% CI, 0.24-0.80; P = .009), whereas heavy smoking and short sleep had increased likelihood of MS-MGUS (>30 pack-years vs never smoker: OR, 2.19; 95% CI, 1.24-3.74; P = .005, and sleep <6 vs ≥6 hours per day: OR, 2.11; 95% CI, 1.26-3.42; P = .003). In the analysis of all MS-detected monoclonal gammopathies, which are inclusive of M-proteins with concentrations of <0.2 g/L, elevated BMI and smoking were associated with all MS-positive cases. Findings suggest MS-detected monoclonal gammopathies are associated with a broader range of modifiable risk factors than what has been previously identified. This trial was registered at www.clinicaltrials.gov as #NCT03689595.

Background The 20/2/20 model is the current gold standard to stratify smoldering multiple myeloma (SMM) patients at baseline into three subgroups (low, intermediate, and high) according to the risk of progression based on the free light chain ratio (FLCr), M-protein concentration, and percentage of bone marrow (BM) plasma cells (PC). Evolving patterns that may alter the risk of progression are not considered in this static model. We previously proposed the PANGEA model that allows for personalized risk prediction using FLCr, M-protein, creatinine, age, hemoglobin trajectory, and optionally BM PC. We developed an improved PANGEA 2.0 model that includes trajectory modeling of these biomarkers to capture evolving patterns and improve predictions of MM progression. Methods We conducted a retrospective review of clinical data from 1,431 participants diagnosed with SMM at 4 international sites (Dana-Farber Cancer Institute, Boston, US, n = 737; National and Kapodistrian University of Athens, Greece, n = 379; University College London, UK, n = 97; and University of Navarra, Spain, n = 218). Dana-Farber participants comprised a training cohort to identify biomarker trajectories and to develop the PANGEA 2.0 model. The model was validated on two international cohorts: validation cohort 1 included patients from Greece and the UK (n = 476) and validation cohort 2 included patients from Spain (n = 218). The longitudinal data collected from 2018-2024 included current values and historical trajectories of age, M-protein, FLCr, creatinine, and hemoglobin, as well as BM PC (optional). We used a systematic grid search with 5-fold cross-validation to determine optimal trajectory definitions for M-protein, FLCr, creatinine, and hemoglobin. For each, we evaluated seven binary trajectory definitions based on average increase over time (slopes) or recent increase from the previous visit on absolute or relative (%) increase scales, with varying thresholds and time periods. We used Cox regression models to create PANGEA 2.0 risk prediction models including the optimal trajectory variables. We compared the PANGEA 2.0 trajectory model with BM data to the 20/2/20 score at the last available time point by predictive accuracy (C-statistics) in the validation cohorts. Results Median follow-up of the training cohort was 3.5 years (IQR: 1.2 - 7.0 years), with a median of 5 visits per patient (IQR: 2 - 9 visits). Median age was 67 years, 53% were female, and 68%, 21%, and 12% had low, intermediate, and high-risk SMM at baseline per the 20/2/20 model. Thus far, 227 (19%) patients progressed to overt MM with a median time-to-progression of 3 years (IQR: 1.1 - 6.1 years). The BM PANGEA trajectory model improved predictions of SMM patients' progression risk with C-statistics of 0.86, 0.83, and 0.72 in the training cohort and validation cohorts 1 and 2 respectively, improving on the 20/2/20 model defined at the latest available time point (C-statistic: 0.77, 0.76, and 0.71). Importantly, in 33 (25%) cases of MM progressors who had increasing biomarker trajectories in validation cohort 1, the PANGEA 2.0 model accurately identified an increased risk of progression within 2 years while the 20/2/20 model classified them as low-risk (n=10) or intermediate-risk (n=23). In validation cohort 2, in 4 (44%) cases of MM progressors with increasing biomarker trajectories, PANGEA 2.0 accurately identified high-risk of progression while 20/2/20 classified them as intermediate-risk. Conclusion We developed the PANGEA 2.0 trajectory model to predict progression risk in SMM. In a large-scale, multicenter cohort with longitudinal follow-up, we demonstrated that adding trajectory information improved SMM risk prediction compared to the 20/2/20 model, particularly for patients with evolving biomarker values. We advocate adding these trajectories to 20/2/20 in a collaborative international study.

Introduction Multiple Myeloma (MM) precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM) have variable risk of progression to active MM, and identifying which patients may progress remains a clinical challenge. Deep proteome profiling of peripheral blood (PB) plasma may advance non-invasive precursor disease staging, monitoring and characterization. Here, we performed plasma proteomic profiling on patients across the MM disease continuum as well as progressive and stable disease to provide insights into MM disease biology and identify protein-based high-risk disease features that may improve prognostication. Methods We performed high-throughput profiling of >5400 proteins using the Olink® Explore HT library and Proximity Extension Assay (PEA) technology. We profiled 529 PB plasma samples from 485 individuals, including MGUS (n=100), SMM (n=203), MM (n=100), and healthy donors (HDs) (n=82). Sequential samples from SMM-MM progressors (n=32) and stable SMM-SMM non-progressors (n=32) were also profiled, where precursor samples ranged 1.04-6.91 years (median 2.33 years) prior to MM progression. T-tests, ANOVAs, and linear mixed effect (LME) models were used to identify significant proteins across disease stages, progression status, and time. Results were adjusted for multiple testing using Benjamini-Hochberg procedure. A subset of 86 individuals (13 HDs, 14 MGUS, 41 SMM, 18 MM) also had single-cell RNA sequencing (scRNA-seq) performed on tumor and immune cells from paired PB/BM collected at the same timepoint to enable cellular mapping of signals detected in plasma. Results We successfully captured significantly dysregulated proteins including proteins highly expressed on the surface of plasma cells, such as BCMA, SLAMF7 and CD38, highlighting the utility of PEA technology to monitor soluble levels of clinically relevant targets. Given SMM patients are clinically heterogeneous and can resemble MGUS or MM, we aimed to improve discrimination of disease states using biological features obtained from the plasma proteome. We developed a classifier using an Elastic Net model for each stage, where plasma proteins such as SLAMF7, BCMA, TACI, FCRL5 and others had the highest importance scores for the model. We demonstrated 97% SMM/MM samples could be identified from healthy samples (AUC=0.82), indicating our classifier could reliably screen disease-related cases. Moreover, 84% of SMM cases were correctly classified as clinically defined SMM, while misclassified samples were labelled as MM cases. Interestingly, the misclassified patients had consistently rising M-spike levels during clinical follow-up but without a shift in clinical stage or risk status, suggesting the plasma proteome may provide earlier indication of evolving disease. Since proteomic information may also hold value for improved risk stratification, we evaluated protein levels in precursor stage timepoint samples from SMM-MM progressors and stable SMM non-progressors. We identified a prognostic five-protein signature that was significantly elevated in SMM-MM progressors, of which BCMA and TACI were top proteins, as well as proteins vital for calcium homeostasis and integrin-mediated cell adhesion. BCMA and TACI levels also had a strong positive correlation with BM plasma cell infiltration, which suggests these proteins may be useful surrogates of BM tumor burden during routine blood-based assessment of precursor patients. Lastly, integrative analysis of scRNA-seq of tumor and immune cells was used to elucidate cell origin level information of the prognostic signature proteins. Four proteins were highly expressed in malignant versus non-malignant plasma cells, suggesting our prognostic signature partially provides a readout of plasma cell biology. Meanwhile, one protein was constitutively expressed on most leukocytes, suggesting that the interplay of other cell types in the immune microenvironment involved in progression may also be reflected in the plasma. Conclusion We performed the first comprehensive analysis of the plasma proteome of MM and its precursor conditions. Overall, we developed a classifier that utilizes plasma proteins alone to accurately classify disease stages and identified a prognostic protein signature associated with progressive disease.

Background Early treatment of patients with high-risk smoldering myeloma (HRSMM) has been shown to delay progression to multiple myeloma (MM), but not all patients respond well. Identifying biological predictors of response and resistance to treatment can help optimize treatment selection for patients and improve outcomes. Here, we identify tumor and immune biomarkers of response to ixazomib, lenalidomide, and dexamethasone treatment (I-PRISM study) that can be measured at diagnosis. Methods We performed single-cell RNA sequencing (scRNAseq), coupled with B cell receptor and T cell receptor sequencing, on CD138+ tumor and CD138- immune cells as well as whole genome sequencing (WGS) on tumor cells from the bone marrow of 22 HRSMM patients (14 biochemical progressors, 8 non-progressors) enrolled in the I-PRISM study and 11 healthy donors. Among progressors, 5 patients went on to develop myeloma by the SLiM-CRAB criteria. Results The median time to biochemical progression was 33 months and the median follow-up for non-progressors was 48 months. 35 patients were successfully cytogenetically classified by FISH; in the remaining patients (n=10), we inferred the presence of IgH translocations and copy number variants (CNVs) using scRNAseq and WGS. Translocations and CNVs inferred from scRNAseq showed excellent concordance with WGS data, suggesting that scRNAseq-based cytogenetic classification is reliable. Notably, scRNAseq identified additional CNVs (n=7) that were missed by FISH, highlighting its greater sensitivity. No significant association was observed between cytogenetics and response to treatment. Differential expression analysis of tumor cells identified by BCRseq revealed higher MHC-I gene expression in non-progressors compared to progressors, suggesting a potential link between MHC-I expression and response to therapy. We validated this observation using data from the PADIMAC study, where MM patients were treated with a different proteasome inhibitor (PI; Bortezomib) in a combination that did not include lenalidomide. This suggests a broader link between tumor-intrinsic MHC-I expression and response to proteasome inhibition (PI). Gene set enrichment analysis (GSEA) further revealed upregulated oxidative phosphorylation pathways in tumor cells from progressors, suggesting enhanced energy metabolism as a driver of progression post-treatment with PIs. Since cytotoxic CD8+ T cells recognize and eliminate cells presenting endogenous antigens on their surface via the MHC-I pathway, we investigated whether they show differences between progressors and non-progressors. By comparing the clonal expansion rate of two CD8+ T cell subsets, the earlier, memory-like granzyme GZMK+ compartment and the more terminally differentiated GZMB+ compartment, we found that progressors exhibited greater clonal expansion in the GZMK+ compartment than non-progressors. Notably, we previously showed that the GZMK+ compartment is the main source of PD-1 expression, a key T cell exhaustion marker, in patient bone marrow. Additionally, clonally expanded T cells from progressors showed a reduced proportion of the GZMB+ phenotype, suggesting their clonally expanded T cells may have a less mature cytotoxic profile compared to non-progressors. This may be related to the lower levels of MHC-I expression observed in tumor cells from progressors, which may indicate impaired tumor antigen presentation and cytotoxic T cell activation. Conclusions Our results highlight the role of the immune microenvironment and its complex interplay with tumor cells, potentially involving tumor antigen presentation and cytotoxic T cell activation, in response to PI-based regimens. Immune profiling may help to improve risk stratification of patients with HRSMM and MM and inform the selection of therapy for specific patients.

Background:Early therapeutic intervention in high-risk SMM (HR-SMM) has demonstrated benefit in previous studies of lenalidomide with or without dexamethasone. Triplets and quadruplet studies have been examined in this same population. However, to date, none of these studies examined the impact of depth of response on long-term outcomes of participants treated with lenalidomide-based therapy, and whether the use of the 20/2/20 model or the addition of genomic alterations can further define the population that would benefit the most from early therapeutic intervention. Here, we present the results of the phase II study of the combination of ixazomib, lenalidomide, and dexamethasone in patients with HR-SMM with long-term follow-up and baseline single-cell tumor and immune sequencing that help refine the population to be treated for early intervention studies. Methods:This is a phase II trial of ixazomib, lenalidomide, and dexamethasone (IRD) in HR-SMM. Patients received 9 cycles of induction therapy with ixazomib 4mg on days 1, 8, and 15; lenalidomide 25mg on days 1-21; and dexamethasone 40mg on days 1, 8, 15, and 22. The induction phase was followed by maintenance with ixazomib 4mg on days 1, 8, and 15; and lenalidomide 15mg d1-21 for 15 cycles for 24 months of treatment. The primary endpoint was progression-free survival after 2 years of therapy. Secondary endpoints included depth of response, biochemical progression, and correlative studies included single-cell RNA sequencing and/or whole-genome sequencing of the tumor and single-cell sequencing of immune cells at baseline. Results:Fifty-five patients, with a median age of 64, were enrolled in the study. The overall response rate was 93%, with 31% of patients achieving a complete response and 45% achieving a very good partial response or better. The most common grade 3 or greater treatment-related hematologic toxicities were neutropenia (16 patients; 29%), leukopenia (10 patients; 18%), lymphocytopenia (8 patients; 15%), and thrombocytopenia (4 patients; 7%). Non-hematologic grade 3 or greater toxicities included hypophosphatemia (7 patients; 13%), rash (5 patients; 9%), and hypokalemia (4 patients; 7%). After a median follow-up of 50 months, the median progression-free survival (PFS) was 48.6 months (95% CI: 39.9 - not reached; NR) and median overall survival has not been reached. Patients achieving VGPR or better had a significantly better progression-free survival (p<0.001) compared to those who did not achieve VGPR (median PFS 58.2 months vs. 31.3 months). Biochemical progression preceded or was concurrent with the development of SLiM-CRAB criteria in eight patients during follow-up, indicating that biochemical progression is a meaningful endpoint that correlates with the development of end-organ damage. High-risk 20/2/20 participants had the worst PFS compared to low- and intermediate-risk participants. The use of whole genome or single-cell sequencing of tumor cells identified high-risk aberrations that were not identified by FISH alone and aided in the identification of participants at risk of progression. scRNA-seq analysis revealed a positive correlation between MHC class I expression and response to proteasome inhibition and at the same time a decreased proportion of GZMB+ T cells within the clonally expanded CD8+ T cell population correlated with suboptimal response. Conclusions:Ixazomib, lenalidomide and dexamethasone in HR-SMM demonstrates significant clinical activity with an overall favorable safety profile. Achievement of VGPR or greater led to significant improvement in time to progression, suggesting that achieving deep response is beneficial in HR-SMM. Biochemical progression correlates with end-organ damage. Patients with high-risk FISH and lack of deep response had poor outcomes. ClinicalTrials.gov identifier: (NCT02916771).

Mutational properties. A, The maximum VAF attained by each of the 99 patients with CH. B, Distribution of VAF among different variants. C, Distribution of the types of single-nucleotide bp changes seen in all detected mutations.

The mutational spectrum of CH in 986 patients with multiple myeloma. A, The total number of patients harboring one or more mutations in each gene. B, Number of patients harboring mutations in one or two different genes. C, Commutation plot showing CH, tumor and germline mutations present in 129 patients: each column represents a single patient. The top row denotes the maximum VAF in each patient, with darker shades of red indicating higher VAF. The bar graph on the right designates the percentage of the different mutation subtypes for each gene out of all detected mutations.

Abstract Background The US FDA approved pembrolizumab for the treatment of patients (pts) with unresectable or metastatic tumors classified as TMB-high (⩾ 10 variants/Mb) and who have progressed on prior therapy. The degree to which TMB-high (TMB-H) generalizes as a biomarker in diverse ancestral populations remains unknown. Methods Using 3 clinical cohorts from the TCGA, Dana-Farber Cancer Institute (DFCI), and Memorial Sloan Kettering Cancer Center (MSKCC), genetic ancestry was determined for pts diagnosed with solid tumors. For pts in the TCGA cohort, TMB (mutations/Mb) was calculated using 2 methods: 1) Filtering out germline variants using paired normal tissue (gold standard) 2) Using publicly available reference panels to filter out germline variants, which in turn mirrors tumor-only TMB calling at DFCI. TCGA data was used to compute ancestry-specific calibration coefficients for panel tumor-only TMB with TMB paired (tumor/normal) as a benchmark. Ancestry-specific and cancer-specific TMB coefficients from TCGA were then projected onto the TMB estimates in the DFCI and MSKCC cohorts. Overall survival (OS) was estimated using the Kaplan-Meier method. Results In the TCGA cohort (N=3618 pts), germline contamination was associated with TMB (tumor-only) inflation across ancestral populations. TMB inflation was more pronounced in non-Europeans (non-EUR; 2.2 fold inflation) compared to EUR (1.5 fold inflation). We then computed calibration coefficients from the TCGA (methods) and projected them to the DFCI cohort (N=8,193) sequenced by a tumor-only panel. We computed raw TMB and calibrated TMB (TMB-c) for each of the pts at DFCI. Non-EUR with TMB-H tumors had significantly higher rates of false TMB-H (i.e., raw TMB-H corrected to TMB-c low) compared with EUR. Of 100 EUR, 21 would have false TMB-H versus 37 Asians and 44 Blacks. Among pts with non-small cell lung cancer treated with ICIs, those misclassified as TMB-high from tumor-only panels in DFCI and MSKCC cohorts did not associate with improved outcomes comapred to true TMB-high tumors. In the DFCI and MSKCC cohorts, higher TMB was associated with significantly longer OS in EUR but this was not the case in Blacks and Asians, although sample sizes were small. Conclusion: TMB estimates from tumor-only panels overclassified individuals into the TMB-high group due to germline contamination, and this bias was more pronounced in pts with Asian/African ancestry. Misclassifed TMB-H was associated with suboptimal survival outcomes compared to true TMB-H. Ancestry-aware tumor-only TMB calibration and ancestry-diverse biomarker studies are critical to ensure that existing disparities are not exacerbated in precision medicine. Citation Format: Amin Nassar, Elio Adib, Sarah Abou Alaiwi, Talal El Zarif, Stefan Groha, Elie W. Akl, Pier Vitale Nuzzo, Tarek H. Mouhieddine, Tomin Perea-Chamblee, Kodi Taraszka, Habib El-Khoury, Muhieddine Labban, Christopher Fong, Kanika S. Arora, Chris Labaki, Wenxin Xu, Guru P. Sonpavde, Robert I. Haddad, Kent W. Mouw, Marios Giannakis, Stephen Hodi, Noah Zaitlen, Adam Schoenfeld, Nikolaus Schultz, Michael F. Berger, Laura E. MacConaill, Guruprasad Ananda, David J. Kwiatkowski, Toni K. Choueiri, Deborah Schrag, Jian-Carrot Zhang, Alexander Gusev. Biomarker benchmarking across ancestral populations [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr IA050.

Introduction Multiple Myeloma (MM) develops from well-defined precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), where patients remain stable or may unknowingly rapidly progress. Bone marrow (BM) biopsies are not routine for precursor disease management, and precursor patients are limited to monitoring few proteins within peripheral blood (PB) for signs of progressive disease. Deep proteome profiling of PB circulating proteins may help track disease; however, the dynamic range of the plasma proteome has limited the depth of detection for MS-based proteomics without first depleting abundant proteins and fractionating the samples after digestion to peptides. Technological advancements in multiplex immunoassays with low cross-reactivity and off-target events have enabled plasma profiling for disease stage classification, defining high-risk disease features, and novel therapeutic target discovery. Here, we perform the first comprehensive plasma proteomic profiling study on patients across the MM disease continuum and longitudinal sequential samples from progressive and stable disease. Methods We carried out high-throughput plasma proteomic profiling for approximately 3000 proteins simultaneously using the Olink® Explore 3072 library and Proximity Extension Assay (PEA) technology. Targeted proteins are recognized by multiplexed, matched pairs of antibodies labelled with unique DNA oligonucleotides that upon binding come into proximity, hybridize, and are extended to generate a unique sequence for protein identification with NextGen DNA sequencing. We profiled 423 PB plasma samples from 348 individuals, including MGUS (n=67), SMM (n=179), MM (n=44), and healthy controls (n=58). Sequential samples from patients with progressive disease (n=27) and patients with stable disease with matched clinical follow-up time were also profiled. Precursor defined samples from progressors ranged 1.03-5.88 yrs prior to diagnosis and were untreated in the precursor setting, while MM disease samples were also collected prior to any active disease therapy. Patients had a median clinical follow-up time of 7.05 years. T-tests, ANOVAs, and a linear mixed effect (LME) model were used to identify proteins that change across disease stages, progression status, and time. Results were adjusted for multiple testing using the Benjamini-Hochberg Method. Results We identified 751 significantly dysregulated proteins with the majority upregulated in progressive disease. We captured circulating levels of proteins highly expressed on the surface of plasma cells, including CD38, SDC1, BCMA and SLAMF7, highlighting the utility of PEA technology to monitor clinically-relevant targets for which monoclonal antibodies, antibody-drug conjugates, CAR-T and BiTE therapies are being developed. We identified proteins that significantly distinguished MGUS, SMM, and NDMM from healthy donors (n=222, 423 and 494), where increasing B-cell maturation antigen (BCMA) was a significant classifier and positive control. Consistent with previous findings, baseline BCMA levels were also significantly elevated in SMM-MM progressors vs. SMM non-progressors, further supporting the potential utility of BCMA measurements during routine blood tests of precursor MM patients. Proinflammatory cytokines were also identified, including IL1, IL5, IL6, IL16, and IL18, known to create a BM environment that promotes malignant cell development by suppressing the microenvironment, promoting cellular adhesion, or increasing angiogenesis. Four novel proteins that are vital for calcium homeostasis and integrin-mediated cell adhesion significantly increased from healthy to MM and were also significantly elevated in SMM progressors vs. SMM non-progressors, nominating these proteins as candidate biomarkers of high-risk disease. Conclusion We performed the most comprehensive plasma proteomics study to date, which characterized disease stage proteomes and identified candidate high-risk disease biomarkers in longitudinal progressor samples. Further advancements are underway to validate the accuracy levels of the novel candidates, test the performance of a classification model that recognizes disease stage-specific proteins, and determine how best to integrate proteins into current MM risk stratification models.

Characteristics of the 8 patients who developed a second primary malignancy.

Introduction Multiple Myeloma (MM) is preceded by the precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), where some patients with precursor disease progress to active MM faster than others. BM biopsies are useful to monitor disease progression, however, they cannot be repeated often for continuous monitoring of tumor burden especially in the precursor disease management setting. Numerous studies have identified circulating tumor cell (CTC) levels in peripheral blood (PB) are a powerful biomarker of disease aggressiveness; CTC levels have associated with disease stage, PFS and OS in various trials and even outperformed BM to assess tumor burden in the SMM setting. Genomic profiling also demonstrated CTCs harbor the same copy number abnormalities, translocations and mutations as BM tumor cells, and CTCs can be serially monitored to detect the emergence of high-risk subclones in the blood. Further investigations of the molecular profile of both normal plasma cells (NPCs) and CTCs are needed to improve our understanding of myeloma pathogenesis and mechanisms of CTC dissemination across the MM disease continuum. Methods A cohort of 309 samples including CD138-enriched paired PB and BM aspirates and matched CD138- BM aspirates were collected from 103 individuals, including patients with MGUS (n=11), SMM (n=46), NDMM (n=17) and healthy donors (n=29). Malignant PCs from BM and PB underwent 5' single-cell RNA sequencing (scRNA-seq) and single-cell B-cell receptor sequencing (scBCR-seq) (10x Genomics), and CD138- BM immune cells underwent 5' scRNA-seq and single-cell T-cell receptor sequencing (scTCR-seq) to study immune alterations related the tumors' ability to circulate. To differentiate malignant from normal PCs, we used clonal V(D)J rearrangements. Differential expression (DE) and composition analyses were conducted using Wilcoxon's rank-sum tests. Results We successfully captured and profiled 774,647 BM tumor cells and 93,878 CTCs from the PB. The percent malignant plasma cells within the PB increased with disease stage, where NDMM patients had significantly more CTCs compared to MGUS patients (p=0.017) and low-risk SMM patients (p=0.0052). A median of 13.51%, 15.53%, and 18.34% CTCs were present from low (n=20), intermediate (n=10), and high-risk (n=16) SMM patients as defined by the International Myeloma Working Group's 2/20/20 criteria, suggesting sequencing-based CTC enumeration captures prognostically relevant differences in tumor burden. High expression of driver genes upregulated in patients with translocations, including CCND1, NSD2, and MAF, were detected in both BM tumor cells and CTCs in patients with t(11;14), t(4;14), and t(14;16) as identified by fluorescence in situ hybridization (FISH). In 5 patients with normal or inconclusive FISH results, we observed high levels of CCND2 and MAF in both BM and CTCs, indicating scRNA-seq can detect missed prognostically relevant cytogenetic abnormalities. A cytogenetic classifier model was developed to determine the accuracy of translocation calling using minimal numbers of CTCs, where the classifier's performance was able to consistently identify translocations downsampled to 50 CTCs. DE analysis of CTCs vs. BM tumor cells highlighted transcriptional similarity between BM and CTCs, validating their utility as a surrogate for analyzing BM tumor cells. DE analysis also revealed 8 genes significantly upregulated and 3 genes significantly downregulated in CTCs compared to BM tumor cells, providing novel insights into genes involved in PC circulatory potential. Pathway enrichment analysis revealed genes upregulated in CTCs were associated with epithelial mesenchymal transition, interferon response, and inflammation, consistent with CTC studies on MM patients, suggesting these pathways are dysregulated earlier in the disease continuum. Conclusions In the largest scRNA-seq study on CTCs to date, we demonstrate the utility of CTC-based molecular profiling for prognostication of patients with early-stage disease and provide novel insights into PC circulatory potential. Additional analyses are ongoing to gain further insight into intra-patient CTC heterogeneity and define high-risk disease CTC signatures that emerge throughout the MM disease continuum.

Introduction: Mass spectrometry (MS) technology holds great promise for the investigation of monoclonal proteins (M proteins) in peripheral blood. We present results from a multi-center clinical validation study using quantitative immunoprecipitation MS (QIP-MS). QIP-MS combines isotype-specific immunopurification with matrix-assisted laser-desorption ionization MS, and offers automated, sensitive detection, isotyping and quantification of M proteins. It reports the mass/charge ratio (m/z) of the involved light chain, which serves as molecular fingerprint for monitoring the M protein. Methods: The study included 460 diagnosed monoclonal gammopathy (MG) patients (160 multiple myeloma (MM), 112 smoldering MM (SMM), 120 monoclonal gammopathy of undetermined significance (MGUS), 47 Waldenström's Macroglobulinemia (WM), and 21 AL amyloidosis), and 170 disease controls for assessing diagnostic sensitivity and specificity. Sixty-four MM patients with 439 follow-up samples and 10 WM patients with 91 follow-up samples were included to evaluate the ability of QIP-MS to detect M protein changes related to treatment. Median follow up was 19 and 15 months, respectively. Serum samples were retrospectively analyzed at three sites. QIP-MS was carried out using the automated EXENT® solution (in development, The Binding Site, part of Thermo Fisher Scientific). The assay's diagnostic sensitivity and specificity were calculated based on categorizing results as positive or negative. A positive result was defined in baseline samples as the presence of an M protein which was either an intact immunoglobulin ≥0.200 g/L or a light chain only. Results by QIP-MS were also compared to serum protein electrophoresis (SPE) and immunofixation electrophoresis (IFE). QIP-MS response categories were defined based on M protein changes per international guidelines criteria, and compared to response categories assigned by the treating physician. Complete response (CR) was defined as absence of the M-peak that was observed at baseline, using isotype and m/z value of ±4 as criterion for identity. Results: The overall diagnostic sensitivity of QIP-MS in this study was 95.0%: 93.3% for MGUS; 100.0% for SMM; 94.4% for MM; 100.0% for WM; and 71.4% for AL amyloidosis. The diagnostic specificity of the assay was 68.2%. QIP-MS identified an M protein in more MG patients compared to SPE: 437 (95.0%) vs 398 (86.5%). The positivity rate by QIP-MS vs SPE was 93.3% vs 84.2% in MGUS; 100% vs 92.9% in SMM; 94.3% vs 85.0% in MM; 100% vs 100% in WM and 71.4% vs 47.6% in AL amyloidosis. Method comparison demonstrated a Passing-Bablok slope of 0.8 to 1.2 between QIP-MS and SPE for the quantification of M proteins for each disease group and for each isotype, except for monoclonal IgM and in WM (slope of 1.58). In SPE-positive MG patients, the overall concordance between QIP-MS and IFE for M protein isotype was 97%. The overall concordance rate between QIP-MS response categories and standard response assignment was 55% for MM and 56% for WM: 48% for progressive disease (PD); 63% for stable disease (SD); 46% for minimal response (MR); 71% for partial response (PR); 66% for very good partial response (VGPR); and 25% for CR in MM patients. Among 73 responses categorized as CR, QIP-MS produced a positive result for the original clone in 55 (75.3%) cases. Concordance rates in WM patients were 71% for PD%; 30% for SD; 38% for MR; and 74% for PR; no VGPR or CR were reported by either method. Conclusions: In this study, QIP-MS demonstrated the potential for same or superior diagnostic sensitivity compared to SPE, high concordance with IFE for the M protein isotype and good quantitative agreement with SPE measurements of the M proteins. It reported higher IgM values compared to SPE, likely due to reliance on turbidimetric immunoglobulin measurement for quantitations. Diagnostic specificity was impacted by the identification of minor M proteins, not detectable by SPE, and whose clinical significance requires further investigation. QIP-MS demonstrated “moderate” to “fair” agreement for response assignment in MM and WM, respectively, mostly due to the detection of residual M proteins in a significant proportion of patients in CR, in line with its enhanced analytical sensitivity. These data support the use of QIP-MS as an aid in the diagnosis and monitoring of MGs.

Abstract Clonal hematopoiesis (CH) at time of autologous stem cell transplant (ASCT) has been shown to be associated with decreased overall survival (OS) and progression-free survival (PFS) in patients with multiple myeloma not receiving immunomodulatory drugs (IMiD). However, the significance of CH in newly diagnosed patients, including transplant ineligible patients, and its effect on clonal evolution during multiple myeloma therapy in the era of novel agents, has not been well studied. Using our new algorithm to differentiate tumor and germline mutations from CH, we detected CH in approximately 10% of 986 patients with multiple myeloma from the Clinical Outcomes in MM to Personal Assessment of Genetic Profile (CoMMpass) cohort (40/529 transplanted and 59/457 non-transplanted patients). CH was associated with increased age, risk of recurrent bacterial infections and cardiovascular disease. CH at time of multiple myeloma diagnosis was not associated with inferior OS or PFS regardless of undergoing ASCT, and all patients benefited from IMiD-based therapies, irrespective of the presence of CH. Serial sampling of 52 patients revealed the emergence of CH over a median of 3 years of treatment, increasing its prevalence to 25%, mostly with DNMT3A mutations. Significance: Using our algorithm to differentiate tumor and germline mutations from CH mutations, we detected CH in approximately 10% of patients with newly diagnosed myeloma, including both transplant eligible and ineligible patients. Receiving IMiDs improved outcomes irrespective of CH status, but the prevalence of CH significantly rose throughout myeloma-directed therapy.

Depiction of cell mixtures for PB and BM samples.

Effect of IMiDs and CH on overall survival.

Abstract Background Current routine methodologies for monoclonal immunoglobulin measurements may not be sufficiently sensitive to reflect the depth of response seen in patients since the introduction of novel therapies; mass spectrometry may offer a valuable, sensitive alternative approach. Here we describe the preliminary analytical performance characteristics of the EXENT® solution (in development by The Binding Site, part of Thermo Fisher scientific) that combines specific immunoprecipitation steps and mass spectrometry for the identification and quantification of IgG, IgA and IgM intact monoclonal immunoglobulins. Each intact monoclonal immunoglobulin clone can be tracked using its unique m/z value. Methods The Lower Limit of Measuring Interval (LLMI) was established for each immunoglobulin type following EP17-A2:2012. Linearity studies were performed according to CLSI EP06-A2:2020 using high and low pools of IgG, IgA, and IgM monoclonal samples and with additional linearity testing below 1 g/L for each specificity to more effectively demonstrate low-end linearity. Within run, between run, between analyzer, between lot and total precision for M protein concentrations and for molecular mass (m/z) of the monoclonal peaks were assessed according to CLSI EP5-A3-2014. Interference was tested following ED3:2018 using 20 potential interferents against 7 samples including high and low IgG, IgA and IgM monoclonal samples. Results The initial indication of the EXENT solution performance, in development, are set out below. Results suggest an analytical sensitivity of the assays around 15 mg/L at the LLMI for all specificities. Linearity over a range of 0.014–88.9 g/L for IgG, 0.011–68.4 g/L for IgA, and 0.11–74.2 g/L for IgM. Coefficients of variation (CVs) for M protein concentrations in precision studies were <15% for all specificities and samples. Mass/charge values were within ±1.1 to ±1.5 m/z in total precision studies, and within ±2.7 to ±3.9 m/z in between lot precision studies, respectively, for M proteins with an m/z value ranging from 11 360.6 to 11 698.5 m/z. No significant interference effects were observed when testing the 20 interferents including intralipid (20 g/L), triglyceride (15 g/L), bilirubin (400 mg/L), rheumatoid factor (200 IU/mL) and haemoglobin (10 g/L). Conclusion The new EXENT solution demonstrates the potential for a wide measuring interval and the ability to detect M proteins with very low concentration. Also, it could provide stable and reproducible performance for the detection and typing of monoclonal immunoglobulins.

Classification of mutation origins. A, Scatterplot of observed VAFs in the PB and BM samples (n = 151). Colors specify the classification of each mutation by the winning model: CH (blue), germline (orange), or tumor (green). The scatterplot is in the log scale, with a small offset artificially labeled at y = 0 to visualize VAFs of 0 in select BM samples. B, Bar graph depicting total number of mutations classified as either CH, germline, or tumor.