Maclachlan, Kylee

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 Chimeric antigen receptor T-cell (CART) and T-cell engager (TCE) therapies targeting B-cell maturation antigen (BCMA) are transforming the treatment landscape for relapsed multiple myeloma (MM). However, despite impressive initial response rates, most patients eventually relapse. To investigate this unmet medical need, we applied whole-genome sequencing (WGS) to MM cells from cohorts of 102 relapsed patients treated with anti-BCMA CART and TCE therapies. Several genomic alterations were associated with clinical outcomes, particularly primary refractoriness, including high genomic complexity and mutations in genes regulating plasma cell identity, which predicted resistance to therapy. Single-cell RNA sequencing further revealed that MM cells from refractory patients exhibited high proliferation signatures and reduced expression of TNFRSF17 (encoding BCMA), while were less enriched for plasma cell–associated transcriptional programs, a phenomenon we term “plasma cell identity escape.” This profile was strongly associated with immune dysregulation of CD8 T cells including increased activation and exhaustion. This evolution of MM toward a more proliferative and lineage-divergent state, refractory to the anti-BCMA T-cell redirecting therapies, was functionally validated in preclinical MM mouse models. Collectively, our results comprehensively define the cellular and molecular mechanisms underlying primary resistance to anti-BCMA therapies.

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 Consumption of a Western diet and high body mass index (BMI) are risk factors for progression from premalignant phenotypes to multiple myeloma, a hematologic cancer. In the NUTRIVENTION trial (NCT04920084), we administered a high-fiber, plant-based diet (HFPBD; meals for 12 weeks, coaching for 24 weeks) to 23 participants with myeloma precursor states and elevated BMI. The intervention was feasible and improved quality of life and modifiable risk factors: metabolic (BMI, insulin resistance), microbiome (diversity, composition), and immune (inflammation, monocyte subsets). Disease progression trajectory improved (n = 2) or was stable. Findings were translated to Vk*MYC mice modeling the myeloma precursor state, in which a high-fiber diet (HFD) delayed disease progression through improved metabolism and microbiome composition, leading to increased short-chain fatty acid production that reinvigorated antitumor immunity and inhibited tumor growth. These effects from fiber consumption were independent of calorie restriction and weight loss. A HFD is a low-risk intervention that may delay progression to myeloma. Significance: A HFPBD in participants with precursor plasma cell disorders and an elevated BMI improved metabolic, microbiome, and immune biomarkers of disease. In a subset, it may delay progression to myeloma. In mouse models, a HFD delayed progression to myeloma independent of calorie restriction. See related commentary by Fairfield and Reagan, p. 623

Multiple myeloma (MM) is consistently 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 as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

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.

Introduction Poor diet quality, elevated body mass index (BMI), insulin resistance, microbiome dysbiosis, inflammation as well as immune dysfunction have all been implicated in progression from monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM) to multiple myeloma (MM). Plant-based diets have been associated with reduced risk of MGUS and MM in epidemiological studies. Whether a dietary intervention can impact these modifiable risk factors and delay disease progression has not been investigated. Methods We conducted a pilot, single-arm trial providing controlled high fiber plant based dietary (HFPBD) intervention for 12 weeks and health coaching for 24 weeks in 20 patients with MGUS/SMM with BMI≥25 (NCT04920084). The primary endpoint was feasibility (adherence and BMI reduction). Secondary and exploratory endpoints included quality of life, metabolic markers (insulin, adiponectin, leptin, LDL cholesterol), microbiome (diversity and composition by 16S and shotgun sequencing) and immune profiling in peripheral blood (flow cytometry and Olink inflammation panel) and bone marrow (scRNAseq and ATACseq). We also conducted an in vivo study in transgenic Vk*MYC mice fed a high fiber or control diet at the phase of mouse(m)SMM. The mice were monitored for the progression to active mMM. Differences in microbiome and immune response profiles between high fiber- and control-treated mice were assessed via microbiome 16S sequencing and flow cytometry. Results The HFPBD intervention was safe, feasible, improved quality of life and addressed modifiable risk factors - metabolic profile (improved BMI, insulin resistance, adiponectin leptin ratio), microbiome profile (increased alpha-diversity and butyrate producers) and immune (decreased inflammation and increased anti-inflammatory classical monocyte) subsets. A reduction in long-term progression trajectory was observed in 2 patients. Consistently, the high fiber diet delayed progression from mSMM to mMM in Vk*MYC mice increasing median progression-free survival from 12 weeks in the control arm to 30 weeks in the high fiber diet intervention arm. Additionally, in the intervention arm 40% of mice did not progress to mMM during the study period whereas all mice in the control arm progressed. Both human and mouse data showed that HFPBD modulated gut microbiota composition favoring the expansion of butyrate-producing bacteria. Additionally, short-chain fatty acids were increased in the feces of mice fed a high fiber diet. Integrated analysis from human bone marrow and peripheral blood analysis indicated that the dietary intervention reduced inflammatory biomarkers and skewed the immune response towards T helper and CD14+ monocytes. Consistently, the bone marrow of Vk*MYC mice fed high fiber diet was more infiltrated by IFNg-producing T lymphocytes while displaying less exhausted T cells and immunosuppressive myeloid cells. Conclusion This is the first interventional clinical trial and in vivo study to show that a HFPBD intervention may delay progression from MGUS/SMM to MM. To our knowledge there has been no similar dietary interventional study in hematological cancers or solid tumors. Together our in vivo and clinical data support the beneficial anti-inflammatory role of a HFPBD providing a link between diet, microbiota, and immune modulation to delay disease progression in MGUS/SMM.

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<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>=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.

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.