Ghobrial, Irene M.

Despite significant improvements in survival of patients with multiple myeloma (MM), outcomes remain heterogeneous, and a significant proportion of patients experience suboptimal outcomes. Importantly, traditional prognostic factors based on data from patients treated with older therapies no longer capture prognosis accurately in the contemporary era of novel triplet or quadruplet therapies. Therefore, risk stratification requires refinement in the context of available and investigational treatment options in routine practice and clinical trials, respectively. The current identification of high-risk MM (HRMM) in routine practice is based on the Revised International Staging System, which stratifies patients using a combination of widely available serum biomarkers and chromosomal abnormalities assessed via fluorescence in situ hybridization. In recent years, a substantial body of evidence concerning additional clinical, biological, and molecular/genomic prognostic factors has accumulated, along with new MM risk stratification tools and consensus reports. The International Myeloma Society, along with the International Myeloma Working Group, convened an Expert Panel with the primary aim of revisiting the definition of HRMM and formulating a practical and data-driven consensus definition, based on new evidence from molecular/genomic assays, updated clinical data, and contemporary risk stratification concepts. The Panel proposes the following Consensus Genomic Staging (CGS) of HRMM which relies upon the presence of at least one of these abnormalities: (1) del(17p), with a cutoff of >20% clonal fraction, and/or TP53 mutation; (2) an IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q+ and/or del(1p32); (3) monoallelic del(1p32) along with 1q+ or biallelic del(1p32); or (4) β2 microglobulin ≥5.5 mg/L with normal creatinine (<1.2 mg/dL).

Abstract Accurate prediction of risk of progression from smoldering (SMM) to active multiple myeloma (MM) is paramount to individualized early therapeutic strategies with minimum risk of overtreatment. Current risk stratification models do not account for evolving biomarker trajectories. We assembled the largest cohort to date of 2,270 SMM patients from six international centers with longitudinal clinical and biological data to train and validate the PANGEA 2.0 risk models. Four evolving biomarkers were significantly associated with shorter time-to-progression: M-protein increase ≥0.2g/dL, involved:uninvolved serum free light chain ratio increase ≥20, creatinine increase >25%, and hemoglobin decrease ≥1.5g/dL. PANGEA 2.0 outperforms established models including the 20/2/20 and IMWG models by more accurately predicting progression (C-statistics=0.69–0.84), even without biomarker history (C-statistics=0.69–0.83) or recent bone marrow biopsy. PANGEA 2.0 is an easy-to-use, open-access tool (https://ghobrial.shinyapps.io/pangea_2_calculator) to improve and individualize SMM risk stratification. Validation tools are available to compare PANGEA 2.0 to established models (https://ghobrial.shinyapps.io/pangea_validation).

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 &gt;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 &gt;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.

Summary: While the current approach to precursor hematologic conditions is to “watch and wait,” this may change with the development of therapies that are safe and extend survival or delay the onset of symptomatic disease. The goal of future therapies in precursor hematologic conditions is to improve survival and prevent or delay the development of symptomatic disease while maximizing safety. Clinical trial considerations in this field include identifying an appropriate at-risk population, safety assessments, dose selection, primary and secondary trial endpoints including surrogate endpoints, control arms, and quality-of-life metrics, all of which may enable more precise benefit–risk assessment.

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 Since the publication of the Revised European-American Classification of Lymphoid Neoplasms in 1994, subsequent updates of the classification of lymphoid neoplasms have been generated through iterative international efforts to achieve broad consensus among hematopathologists, geneticists, molecular scientists, and clinicians. Significant progress has recently been made in the characterization of malignancies of the immune system, with many new insights provided by genomic studies. They have led to this proposal. We have followed the same process that was successfully used for the third and fourth editions of the World Health Organization Classification of Hematologic Neoplasms. The definition, recommended studies, and criteria for the diagnosis of many entities have been extensively refined. Some categories considered provisional have now been upgraded to definite entities. Terminology for some diseases has been revised to adapt nomenclature to the current knowledge of their biology, but these modifications have been restricted to well-justified situations. Major findings from recent genomic studies have impacted the conceptual framework and diagnostic criteria for many disease entities. These changes will have an impact on optimal clinical management. The conclusions of this work are summarized in this report as the proposed International Consensus Classification of mature lymphoid, histiocytic, and dendritic cell tumors.