Lightbody, Elizabeth D.

Abstract The development of targeted therapy for patients with multiple myeloma (MM) is hampered by the low frequency of actionable genetic abnormalities. Gain or amplification of chromosome 1q (1q+) is the most frequent arm-level copy number gain in patients with MM and is associated with higher risk of progression and death despite recent therapeutic advances. Thus, developing targeted therapy for patients with MM with 1q+ stands to benefit a large portion of patients in need of more effective management. Here, we used large-scale dependency screens and drug screens to systematically characterize the therapeutic vulnerabilities of MM with 1q+ and displayed increased sensitivity to myeloid cell leukemia-1 (MCL1) and phosphatidyl inositol 3-kinase (PI3K) inhibitors. Using single-cell RNA sequencing, we compared subclones with and without 1q+ within the same patient tumors and demonstrated that 1q+ is associated with higher levels of MCL1 and the PI3K pathway. Furthermore, by isolating isogenic clones with different copy number profiles for part of the chromosome 1q arm, we observed increased sensitivity to MCL1 and PI3K inhibitors with arm-level gain. Lastly, we demonstrated synergy between MCL1 and PI3K inhibitors and dissected their mechanism of action in MM with 1q+, uncovering a cytostatic effect. In conclusion, this study highlights that MM with 1q+ may present enhanced sensitivity to MCL1 and PI3K inhibitors, enabling their use at lower doses without sacrificing efficacy, and may thus accelerate the development of targeted therapy for patients with MM and 1q+.

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.

Introduction Genomic alterations are common in multiple myeloma precursor stages, monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, commonly used stratification systems do not include somatic alterations that play a role in the disease. To address this clinical need, we generated a comprehensive genomic map of MM and precursors by harmonizing and analyzing mostly WGS data from 1,030 patients. We used these data to define a simple MM-like score, that is able to discriminate which premalignant patients are at risk to progress. Methods We collected, enriched, and performed WGS of tumor plasma cells and matched normals from 138 participants with MGUS or SMM, including those in low, intermediate, and high-risk groups. We combined our precursor dataset with genomic profiles from MM (n=20) and external sources of 872 individuals with MGUS (n=18), SMM (n=62), and MM (n=792) for a cohort of 1,030 individuals with MM or a precursor condition. In 16 patients, we could perform WGS serially from the bone marrow or circulating tumor cells (n=39) and reassess the presence of mutations. Results To develop a simple score that reflects the similarity of precursor genomes to full-blown multiple myeloma disease, we searched among the 107 common driver events (occurring in >1% of patients) and initiating translocations for ones that are differently altered in precursor conditions versus MM. We found 26 differentially altered events: only MAF and MAFB translocations were enriched in MGUS/SMM whereas 24 events were enriched in MM, including known drivers MYC, KRAS, FAM46C, del(1p) and gain(1q). We then defined a simple “MM-like” score for each tumor as the number of MM-enriched drivers minus the number of MGUS-enriched drivers. As expected, the MM-like score in our cohort increased throughout MM development (MM-like score median of 1 in MGUS, 2 in SMM, and 3 in MM). Even among SMM patients, we found a gradual increase of the MM-like score from low- (median=1), to intermediate (median=2), to high-risk (median=3) groups that match the overall medians in MGUS, SMM and MM (p=2e-5). We tested whether the MM-like score can be used to estimate risk of progression. Among untreated SMM patients characterized with WGS, an MM-like score greater than 2 was associated with a higher likelihood of progression (HR=5.3, P=0.03). Using an independent dataset of 87 patients with SMM, we were able to validate that a MM-like score greater than 2 was indeed associated with a higher risk of progression (HR=3.3, P=5x10-5). 11 patients who did not progress and for whom we performed longitudinal WGS, has stable MM-like scores over time, while three patients clinically progressed and their MM-like score increased at the time of progression (due to newly detected mutations in KRAS and NRAS). For the two remaining patients, their MM-like score increased, and we observed growth of the subclone that acquired the additional MM-like driver. In the first patient with MGUS/low-risk SMM, a novel hyperdiploid clone was detected in addition to a TP53 bi-allelic hit and t(11;14) translocation and was shown to increase in clonal fraction with tumor burden (light chains +104% within three years). In the second non-progressor with an increasing MM-like score, a novel del(1p) was detected, and other subclones in the phylogenetic tree either shrank or were stable over three years. Finally, because the MM-like score can place tumors on a gradual axis of evolution towards a more genomically-mature tumor, we tested whether the MM-like score was also predictive of survival in patients treated with a newly diagnosed MM. To do so, we calculated the MM-like score in patients from the publicly available CoMMpass database. We found that an increased score significantly correlated with shorter progression-free survival (median PFS1: 3 years in high score, versus 5 years in low/intermediate score; p=1e-3) and overall survival (median OS: 4.5 years in high score, versus not reached in low/intermediate score; p=1e-5). Conclusion Overall, we demonstrate that it is possible to stratify precursor stages of MM by discovering drivers, detecting those that are associated with the stage of the disease, and defining a genomically-based score that can be used to assess risk of progression and is easy to implement in the clinic.

Abstract Multiple myeloma (MM) develops from well-defined precursor stages; however, invasive bone marrow (BM) biopsy limits screening and monitoring strategies for patients. We enumerated circulating tumor cells (CTC) from 261 patients (84 monoclonal gammopathy of undetermined significance, 155 smoldering multiple myeloma, and 22 MM), with neoplastic cells detected in 84%. We developed a novel approach, MinimuMM-seq, which enables the detection of translocations and copy-number abnormalities through whole-genome sequencing of highly pure CTCs. Application to CTCs in a cohort of 51 patients, 24 with paired BM, was able to detect 100% of clinically reported BM biopsy events and could replace molecular cytogenetics for diagnostic yield and risk classification. Longitudinal sampling of CTCs in 8 patients revealed major clones could be tracked in the blood, with clonal evolution and shifting dynamics of subclones over time. Our findings provide proof of concept that CTC detection and genomic profiling could be used clinically for monitoring and managing disease in MM. Significance: In this study, we established an approach enabling the enumeration and sequencing of CTCs to replace standard molecular cytogenetics. CTCs harbored the same pathognomonic MM abnormalities as BM plasma cells. Longitudinal sampling of serial CTCs was able to track clonal dynamics over time and detect the emergence of high-risk genetic subclones. This article is highlighted in the In This Issue feature, p. 247

Abstract Background Multiple myeloma (MM) evolves from monoclonal gammopathy of undetermined significance (MGUS), a clinically detectable but asymptomatic premalignant phase seen in ~3% of the general population 50 years of age or older. The prevalence of MGUS has not been described in a population at high risk of developing MM, specifically Black/African American (AA) individuals or first-degree relatives of patients with hematologic malignancies (HM). In 2019, we launched the first nationwide US screening study for individuals at high risk of MM to help better identify what population would benefit most from screening and early intervention for precursor MM stages. We aim to assess the prevalence of MGUS in a population at high risk of MM and characterize clinical variables of individuals who screen positive. Here, we report interim screening data on the first 2,960 participants. Methods Individuals aged 40 or older with an additional MM risk factor are eligible to be screened in the PROMISE Study. High-risk individuals include Black/AAs and those with a first-degree relative diagnosed with a hematologic malignancy or a precursor condition to MM. Blood from all participants was analyzed via serum protein electrophoresis, immunofixation, and Optilite® to measure the serum free light chains (sFLC), IgG, IgA and IgM. Results were returned to all participants, and those who tested positive for a monoclonal gammopathy (MGUS/SMM) were referred to a hematologist for clinical follow-up and invited to periodically complete epidemiologic exposure and psychosocial questionaries, including a 4-item cancer worry questionnaire and the RAND 36-item Short Form Survey (SF-36). To investigate the use of the higher-sensitivity matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) with the Optilite® IgG, IgA, IgM and sFLC results as a screening test for all participants, we rescreened 1,092 samples from PROMISE. The Binding Site Group proprietary software was used for the analysis of the combined MS/Optilite® results, allowing for the detection and quantification of M-protein. Heavy-Chain MGUS (HC-MGUS) was defined by the presence of one or more paired heavy and light chain monoclonal peaks detected by MS. Pairing was based on mass to charge ratio of identified peaks. To enrich the PROMISE cohort with Black/AA individuals, we identified and screened 1,868 Black/AA additional individuals from the Mass General Brigham (MGB) Biobank who met the PROMISE enrollment criteria. Screening was performed by MS/Optilite®, and results have not been returned to participants. Results We screened 2,960 participants with the combined MS/Optilite® approach. We report here the prevalence of HC-MGUS and plan on presenting the estimated rate of light chain MGUS in our cohort, at the meeting. We detected HC-MGUS in 9.6% (95% CI: 8.6-11%) of our cohort, with a prevalence of 10% (95% CI: 8.3-12%) in the PROMISE cohort and 9.4% (95% CI: 8.1-11%) in the MGB cohort (Table 1 and Figure 1). HC-MGUS prevalence increased with age in high-risk individuals from 4.9% (CI: 3.3-6.9%) for participants aged 40-49 to 13% (CI: 10-17%) in the 70-79 range (P < 1.2E-5). Among monoclonal HC-MGUS, we found 65% IgG, 18% IgM, and 18% IgA. M-spike was quantified in 97% of samples. Median M-spike concentration was, 0.058g/dL (max. 2.6g/dL) for IgG, 0.0043g/dL (max. 0.6g/dL) for IgM, and 0.067g/dL (max. 0.8g/dL) for IgA. In the Promise cohort, no significant change in cancer worry was observed across the pre- and post-screening interval among participants who screened positive (P = 0.52). Health-related quality of life, as measured by the SF-36, was not significantly different in screen-positive vs. screen-negative individuals for any of the eight subscales (all P > 0.20). Conclusions We present the largest dataset on monoclonal gammopathy prevalence and screening in individuals at high risk for MM, and more specifically the largest cohort of Black/AA, using a novel high-sensitivity testing approach. Our results confirm that older adults who are Black/AA or have a first-degree relative with an HM have a high prevalence MGUS and may benefit from precision screening approaches to allow for early detection and clinical intervention. Preliminary data on cancer worry and quality of life indicates that the psychosocial burden of screening in this population is likely minimal. Figure 1 Figure 1. Disclosures Sakrikar: The Binding Site: Current Employment. Krause: The Binding Site: Current Employment. Barnidge: The Binding Site: Current Employment. Bustoros: Takeda: Consultancy, Honoraria; Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau. Perkins: The Binding Site: Current Employment. Harding: The Binding Site: Current Employment, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Kapoor: Ichnos Sciences: Research Funding; Amgen: Research Funding; Pharmacyclics: Consultancy; Takeda: Research Funding; Sanofi: Consultancy; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; BeiGene: Consultancy; Sanofi: Research Funding; Karyopharm: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy. Mo: Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Epizyme: Consultancy; Eli Lilly: Consultancy; Janssen: Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees. Murray: The Binding Site: Patents & Royalties: Potential Royalties for use of mass spectrometry in M-protein detection. Getz: Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; IBM, Pharmacyclics: Research Funding. Marinac: JBF Legal: Consultancy; GRAIL Inc: Research Funding. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

AbstractThe development of novel agents has transformed the treatment paradigm for multiple myeloma, with minimal residual disease (MRD) negativity now achievable across the entire disease spectrum. Bone marrow–based technologies to assess MRD, including approaches using next-generation flow and next-generation sequencing, have provided real-time clinical tools for the sensitive detection and monitoring of MRD in patients with multiple myeloma. Complementary liquid biopsy–based assays are now quickly progressing with some, such as mass spectrometry methods, being very close to clinical use, while others utilizing nucleic acid–based technologies are still developing and will prove important to further our understanding of the biology of MRD. On the regulatory front, multiple retrospective individual patient and clinical trial level meta-analyses have already shown and will continue to assess the potential of MRD as a surrogate for patient outcome. Given all this progress, it is not surprising that a number of clinicians are now considering using MRD to inform real-world clinical care of patients across the spectrum from smoldering myeloma to relapsed refractory multiple myeloma, with each disease setting presenting key challenges and questions that will need to be addressed through clinical trials. The pace of advances in targeted and immune therapies in multiple myeloma is unprecedented, and novel MRD-driven biomarker strategies are essential to accelerate innovative clinical trials leading to regulatory approval of novel treatments and continued improvement in patient outcomes.