Morgan, Gareth J.

Abstract Studies have reported conflicting findings regarding the contribution of germline variants or somatic genomic drivers to racial disparities in multiple myeloma. To comprehensively investigate somatic drivers in relation to inherited genetics in multiple myeloma, we combined newly sequenced whole-genome sequencing data with publicly available datasets (total n = 1,286). Overall, we did not identify germline or somatic genomic differences that explain the different risk of developing multiple myeloma between patients with genetic similarity to African (AFR) or European (EUR) reference populations. A difference in the detectability and timing of APOBEC-associated and germinal center mutational activity was observed. Integrating epidemiologic data and mutational signature-based temporal estimates, we challenge the assumption that individuals in the AFR group develop multiple myeloma at a younger age. Finally, we demonstrate that, with equal access to efficacious therapies, patients in the AFR and EUR groups have equivalent clinical outcomes. Significance: Multiple myeloma is reported to occur at higher rates in individuals who self-identify as non-Hispanic Black. In this large dataset, genomic drivers occur at the same rate among ancestry groups, except for APOBEC mutagenesis. With equivalent therapy, clinical outcomes did not differ for patients grouped by genetic ancestry similarity.

Abstract Background: Multiple myeloma (MM) is always preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage or high tumor cell burden (i.e. SLiM criteria, SV Rajkumar Lancet Onc 2014), as morphology and cytogenetics cannot reliably distinguish them. Methods: To identify genomic features underlying clinical behavior in MGUS and SMM, a total of 374 patients with SMM (n= 290) or MGUS (n=84) with available whole exome (whole exome sequencing, WES, n=190) or whole genome sequencing (whole genome sequencing WGS, n=184) were included in the study. Overall, none of the patients had evidence of SLiM or CRAB criteria at the time of the biopsy. Based on the center of origin and availability of WES and WGS, 227 and 97 patients were used to form our study training and validation set. The study was designed in two parts: 1) defining a new classification for MM precursor conditions; 2) integrating genomics to existing prognostic score to improve accuracy in identifying patients at imminent risk of progression. Overall, 68 patients in the training set were either lost to follow-up or enrolled in a clinical trial and they were only included in the classification part of the study. Results: By leveraging the distribution of established MM-defining genomic events (Maura et al. JCO 2024) in the training set, we developed a workflow that differentiates MGUS and SMM into two genomically distinct entities: one with evidence of neoplastic transformation (i.e., genomic MM) and one without (i.e., genomic MGUS). Overall, 39% of MGUS and 91% of SMM cases had genomic evidence of neoplastic transformation in the training set (i.e., genomic MM). When we applied the same workflow and classification to the validation set, 46% and 83% of MGUS and SMM had evidence of neoplastic transformation, respectively. Importantly, none of the patients classified as genomic-MGUS progressed to MM in either the training or validation cohorts, with a median follow-up of 40 and 61 months, respectively. Moreover, all patients with IMWG 2/20/20 high risk SMM were classified as genomic MM in both the training and validation set. Interestingly, 5 out of 62 (8%) patients enrolled in early intervention trials for high risk SMM were genomically classified as genomic-MGUS. Overall, these data demonstrated that the MM genomic background and neoplastic transformation can be acquired very early in time, even before the SMM phase, in line with what observed in solid tumors. To avoid overestimating the prognostic impact of myeloma-defining genomic events—which are, by definition, enriched in the genomic-MM group compared to genomic-MGUS—we restricted our prognostic analysis to patients in whom transformation has already occurred (genomic-MM). In fact, distinct genomic drivers may predict malignant transformation but not necessarily the risk of imminent progression to MM once transformation has occurred. After running 5-fold cross validations corrected for IMWG 2/20/20, only presence of RAS mutations, genomic events involving MYC, presence of neoplastic copy number variant (CNV) signatures and APOBEC mutagenesis were predictive of early progression in the training set. Adding the presence of high-risk myeloma-defining genomic events to the IMWG 2/20/20 model (i.e., genomic-IMWG) significantly improves the accuracy of predicting MM progression in both the training (c-index 0.69 vs. 0.74) and validation sets (c-index 0.74 vs. 0.79), compared to the IMWG 2/20/20 alone. Conclusion: By leveraging comprehensive genomic profiling integrated with the clinical IMWG 2/20/20 risk score, we propose a novel classification and prognostication system for MM precursor conditions, which will better define patients who may benefit from early therapeutic intervention versus watch-and-wait monitoring.

Abstract Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group. Availability and implementation AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

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).

Introduction The pathogenic role of somatic non-coding (nc) single nucleotide variants (SNVs) is underexplored in multiple myeloma (MM) and additional characterization of these regions has the potential to provide significant new insights into disease progression. To investigate the role of ncSNVs and their relationship with MM pathogenesis, we have characterized a novel series of WGS from 294 cases of MGUS, SMM, and MM using computational tools optimized to identify functional driver SNVs in these regions. We hypothesize the ncSNVs that co-localize to sites of super-enhancers (SEs) active in plasma cells (PC) are sites of high functional impact SNVs and these mutations lead to the dysregulated expression of genes that should be silent at the terminally differentiated stage of PCs, providing a selective advantage that shapes the fitness of the MM genomic landscape. Methods We analyzed 294 high-coverage matched tumor/normal pairs (80x tumor and 40x normal) derived from 21 MGUS, 86 SMM, 167 NDMM and 40 RRMM. In addition to previously sequenced samples, newly sequenced patients were obtained from NYU Langone Health, Memorial Sloan Kettering Cancer Center, University of Alabama at Birmingham and University of Miami following IRB-approved informed consent. DNA was extracted from CD138+ purified bone marrow plasma cells using standard approaches. We used the MGP1000 (https://github.com/pblaney/mgp1000) to process sequencing data which employs a union-consensus mechanism for determining the final set of variants. We collected enhancer elements (E/SEs) active in GC B-cells, ABC-DLBCL and GBC-DLBCL (Bal, et al. Nature, 2022) and PC specific E/SEs (Loven, et al. Cell 2013). FunSeq2 was used to score ncSNVs. GRAM was used to predict how variants alter TF binding and gene expression in a subset of 6 cases with paired WGS in our series and will expand this analysis on a validation set of 263 NDMM and 93 RRMM cases from University-Hospital Heidelberg enrolled in the GMMG-HD6 clinical trial. Results We identified 1,952,232 high-quality SNVs with 18,199 (0.93%) accumulating in 280 focal sites we termed “mutationally enriched regions” or MERs. To discern SNVs in MERs with the highest potential for imparting a selective advantage, we clustered mutations based on the density of FunSeq2 impact scores which uses reference data contexts on sequence conservation, modification of transcription factor (TF) binding, and gene linkage to regulatory elements and network hubs from pan-cancer studies. We show that MERs are enriched for ncSNVs with high impact scores (mean=2.57) compared to random similarly-sized regions of the genome (Fisher's exact test P&lt;9.7e-12, OR=10.10 [3.88-37.40]). The high impact ncSNVs (hi-ncSNVs) in MERs (n=220) account for 8.09% of all hi-ncSNVs and are present in 46.3% of cases (136/294) with some patients having up to 7. The proportion of cases carrying a MER hi-ncSNVs was similar across disease stages (MGUS=42.9%, SMM=46.5%, MM=40.7%, and RR=47.5%). Analysis of the VAF of MER hi-ncSNVs shows a minor shift towards more clonal versus subclonal mutations (53.6% vs 46.4% VAF&gt;=0.25) suggesting some high impact mutations are acquired in the GC but additional hi-ncSNV are acquired later in PC development, providing evidence that hi-ncSNVs accumulate in MERs throughout disease progression. We mapped these hi-ncSNVs to associated genes then intersected the loci with E/SEs active in GCBs and PCs to determine their functional context. This approach identified 12 genes affected by hi-ncSNVs including the previously identified nc-drivers NEAT1, MIR142, ZCCHC7, and IGLL5 as well as the PC differentiation genes IRF8, BACH2, proliferation genes RHOH, SPRED2, known coding MM drivers CCND1, ZFP36L1, and chr1q genes BTG2 and CROCC. We predicted the effect of ncSNVs on expression through modulation of TF binding at this set of putative nc drivers in MM and identified a trend towards increase in expression of SPRED2, CCND1, NEAT1, and IGLL5 compared to non-mutated cases (P=0.089). We will present data on the validation of this set of genes on a larger set of data derived from the Heidelberg group. Conclusions We identified a set of 12 putative nc driver genes in MM that modify expression and have the potential to significantly alter the pathogenesis of MM by providing a selective survival advantage. We will present the impact of these by clinical stage and on outcome.

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.

PURPOSE Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. METHODS To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. RESULTS Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression–based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922 ) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. CONCLUSION Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

Summary: The multiple myeloma treatment landscape has changed dramatically. This change, paralleled by an increase in scientific knowledge, has resulted in significant improvement in survival. However, heterogeneity remains in clinical outcomes, with a proportion of patients not benefiting from current approaches and continuing to have a poor prognosis. A significant proportion of the variability in outcome can be predicted on the basis of clinical and biochemical parameters and tumor-acquired genetic variants, allowing for risk stratification and a more personalized approach to therapy. This article discusses the principles that can enable the rational and effective development of therapeutic approaches for high-risk multiple myeloma.

Introduction: Bortezomib has been reported to favourably impact the outcomes of t(4;14) and del(17p) in multiple myeloma (MM), but its impact on gain 1q (+1q) is unknown. Methods: To address this, 250 patients treated with bortezomib-based induction were analysed. All myeloma samples had fluorescence in situ hybridization (FISH) performed on CD138-sorted bone marrow aspirate, and plasma cells were analysed using DNA probes specific for the following chromosomal aberrations: del(13q14), del(17p), t(14;16), t(4;14), and +1q. Presence of +1q was defined as the presence of at least three copies of 1q21 at the cut off level of 20% of bone marrow plasma cells. Results: +1q identified in 167 (66.8%) and associated with t(4;14) and high lactate dehydrogenase (LDH). +1q was not associated with response rate but shorter event-free survival (EFS) (median EFS 35 vs 55 months, p = 0.05) and overall survival (OS) (median OS 74 vs 168 months, p = 0.00025). Copy number and clone size did not impact survival. Multivariate analysis showed +1q was an independent adverse factor for OS together with International Staging System (ISS)3, high LDH, del(17p) and t(4;14). When a risk score of 1 was assigned to each independent adverse factor, OS was shortened incrementally by a risk score from 0 to 4. Post-relapse/progression survival was inferior in those with +1q (median 60 vs 118 months, p = 0.000316). Autologous stem cell transplantation (ASCT) improved OS for those with +1q (median OS 96 vs 49 months, p = 0.000069). Conclusion: +1q is an adverse factor for OS in MM uniformly treated with bortezomib-based induction but was partially mitigated by ASCT. A risk scoring system comprising +1q, LDH, high-risk FISH, and ISS is a potential tool for risk stratification in MM.