Lahoud, Oscar

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

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.

Abstract Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P < .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment.

High dose chemotherapy followed by autologous hematopoietic cell transplantation (auto HCT) is a standard therapy for patients with plasma cell disorders (PCD). A barrier to improving candidate selection for auto HCT is appraisal of risk for transplant related complications. Electrocardiography (ECG) is a standard pre-HCT test to evaluate the potential for arrhythmia and structural heart disease. We hypothesized that an artificial intelligence (AI) based interpretation of ECG waveform data (AI-ECG) could enhance risk prediction for cardiac-specific and other outcomes following auto HCT for patients with PCD. To test this hypothesis, we used several AI-ECG tools developed using a deep-learning based approach that were trained to predict comorbidity (Attia et al, Lancet 2019 & Grogan et al, Mayo Clin Proc 2021). Model outputs include a scaled prediction value indicating increasing risk for atrial fibrillation/flutter (AF), reduced ejection fraction (EF), or cardiac amyloidosis. We evaluated this tool in 9,225 ECGs obtained from 849 patients (median 6 ECGs/patient, inter-quartile range: 3-12) who underwent first or subsequent auto HCT at Memorial Sloan Kettering Cancer Center between 2016-2022 and who had a diagnosis of multiple myeloma (N = 810), amyloidosis (N = 37), or other PCD (N = 2). The endpoints were to determine whether baseline AI-ECG prediction of AF risk correlated with peri-transplant AF events, and to explore whether model outputs correlated with survival and other cardiac clinical outcomes. The mean age of patients was 61.0 years (range 23.7 - 81.2) and median follow up was 2.7 years post auto HCT. Contributing cardiovascular risk factors included a prior history of AF (N = 101, 11.9%), coronary or cerebrovascular disease (N = 95, 11.2%), and diabetes mellitus (N = 98, 11.5%). Reduced dose melphalan (< 200 mg/m 2) was administered in 26%. The HCT-comorbidity index (HCT-CI) was high risk (≥3) in 52.7%. The one-year overall survival and progression-free survival in the whole cohort was 94% (95% confidence interval (95% CI): 92 - 95%) and 86% (84 - 89%), respectively. Among patients not in AF at pre HCT evaluation, the 90-day cumulative incidence of AF was 5.6% (4.2 - 7.3%). In univariable competing risk regression, baseline pre HCT AI-ECG appraisal of AF risk was strongly correlated with incidence AF in patients who were in sinus rhythm before HCT (hazard ratio (HR): 25.1, 95% CI: 11.7 - 53.7, p < 0.001). In a multivariable model adjusting for patient age and HCT-CI, AI-ECG AF predicted risk remained associated with incidence of post HCT AF (HR: 13.4, 95% CI: 5.9 - 30.4, p < 0.001). Considering a decision threshold identified during original model training, AI-ECG predicted AF events with an accuracy of 75% (Mcnemar's p < 2 x 10 -16). The precision, recall, and F1-score were 0.94, 0.77, and 0.85, respectively. AI-ECG predicted age correlated highly with actual patient age (Spearman rho = 0.59, p < 2 x 10 -16). Amyloid risk scores were highly correlated with a known clinical history of amyloidosis (median 0.45 versus 0.05, Wilcoxon rank-sum p < 0.001). Patients with EF < 50% had greater prediction scores for cardiomyopathy compared to patients with normal EF (median 0.23 versus 0.01, Wilcoxon rank-sum p <0.001). In univariable analysis, increasing AI-ECG predicted age (HR 1.02, CI: 1.003 - 1.04, p = 0.02) and predicted AF risk score (HR: 3.2, CI: 1.4 - 7.2, p = 0.006) were associated with risk for death, whereas predicted amyloid score and predicted low EF were not associated with decreased survival. In multivariable analysis of all patients that considered patient age, hematopoietic comorbidity index, and remission status at HCT, increasing predicted AF score trended towards association with increased risk for death (HR: 2.3, 95%CI: 1.0 - 5.5, p = 0.059). Predicted AF score correlated highly with the HCT-CI and patient age. Finally, we examined whether risk prediction for low EF correlated with peri-HCT fluid overload (FO) defined by absolute weight gain. The incidence of ≥ 10% weight gain (grade 2 FO) and ≥ 20% weight gain (grade 3 FO) at 180 days post HCT was 7.4% (95% CI: 5.8%, 9.3%) and 1.0% (95% CI: 0.5%, 1.8%), respectively. Predicted low EF was not associated with development of either grade 2 or grade 3 FO in this population. In summary, AI-ECG predicts episodes of AF in patients undergoing auto HCT for PCD and may augment transplant risk appraisal when considered in conjunction with other measures of comorbidity.

Background: Diffuse large B-cell lymphoma (DLBCL) is frequently curable after frontline therapy, however, relapses can occur after achievement of a PET-negative complete remission (CR). Early detection of relapse may be associated with improved outcomes with second-line curative intent therapy or novel therapies. Controversy exists regarding the utility of post-therapy surveillance imaging which is associated with patient (pt) anxiety, radiation exposure, false-positive results, and cost. A retrospective study found that serial minimal residual disease (MRD) monitoring during DLBCL surveillance could identify pts at risk of relapse before clinical evidence of disease (Roschewski, Lancet Oncol, 2015). In this multicenter prospective study, we assessed whether a next-generation sequencing (NGS)-based MRD assay could be used for early detection of molecular relapse in DLBCL. Methods: Eligible pts with DLBCL or high-grade B-cell lymphoma (HGBCL) who received anthracycline-containing chemotherapy were enrolled across five cancer centers. In pts who achieved a PET-negative CR, serial peripheral blood samples were obtained every 3 months (mos) and CT scans every 6 mos for 2 years (yrs) post-treatment. The clonoSEQ® next generation sequencing (NGS) MRD assay (Adaptive Biotechnologies, Seattle, WA) that leverages multiplex PCR followed by NGS to identify and track rearrangements of IgH, V-J, D-J and IgK/L loci and translocations in Bcl1/2-IgH was used. MRD positive was defined as any detectable rearrangement and MRD negative as no evidence of rearrangement. We also reported MRD positivity above the limit of detection (LOD) (observations required to have 95% reproducibility). Interim futility analysis was performed after approximately 50% of anticipated relapses occurred to assess the preliminary sensitivity of the assay. Progression-free survival (PFS) was defined as time from treatment start date to relapse or death. Results: 501 pts were enrolled and 401 were evaluable (pre-treatment tumor pathology available, completed frontline treatment, and achieved PET-negative CR at end-of-treatment). Baseline characteristics were median age of 62 yrs (range 19-95), male sex 58%, advanced stage 61%, and poor-risk by R-IPI 40% (Table 1). Histologies included DLBCL, NOS (86%), primary mediastinal B-cell lymphoma (6%), HGBCL (5%), T-cell rich B-cell lymphoma (1%), and other (1%). Pts received regimens including RCHOP x 6 cycles (36%), REPOCH x 6 cycles (20%), clinical trial (15%), combined modality therapy (RCHOP+RT) (10%), and other RCHOP variations (19%). Among the 401 evaluable pts, 44 relapses have occurred as of June 1, 2020. In 47% of pts (18/38), clinical relapse was detected using surveillance imaging alone (typically CT CAP with IV contrast) in an otherwise asymptomatic pt with a normal physical exam and laboratory evaluation. Clinical relapse was detected by clinical symptoms alone in 11% (4/28), by the evaluation of an oncologist alone in 0%, and by imaging and clinical symptoms and/or evaluation by an oncologist in 42% (16/38) (missing data in 6 pts). With a median follow-up of 2.2 yrs, the 2-yr PFS was 88.1% (84.8, 91.4) (Figure 1). Advanced age, advanced stage, and poor-risk R-IPI were associated with inferior PFS. Of the 44 relapses, 43 pts were included in the interim analysis and tumor-specific clonotypes were identified in 39 pts (91%). In 56% (22/39), the MRD assay was positive at or before clinical relapse. In 38% (15/39), the MRD assay was positive above the limit of detection at or prior to clinical relapse, with only 10 relapses detected more than 3 mos prior to relapse, reflecting the poor clinical sensitivity of the assay. The NGS-MRD assay was more sensitive in the acellular (plasma cell-free DNA) than in the cellular (circulating leukocytes) compartment. Results: Overall outcomes are excellent for DLBCL and HGBCL pts who achieve PET-negative complete remission. In interim analysis, surveillance MRD assessment using the clonoSEQ® assay fails to consistently identify pts at risk of recurrence before clinical evidence of relapse in DLBCL. In a prospective analysis, a substantial proportion (47%) of clinical relapses are detected by imaging in asymptomatic pts, supporting the value of CT surveillance imaging in DLBCL, particularly for pts with advanced stage or high-risk disease. Additional analyses are forthcoming for MRD assessment in all pts, including those in remission. Disclosures Kumar: Celgene: Research Funding; Pharmacyclics: Research Funding; Adaptive Biotechnologies,: Research Funding; AbbVie: Research Funding; Seattle Genetics: Research Funding; Celgene: Honoraria, Other: Honoraria for Advisory Board; Astra Zeneca: Honoraria, Other: Honoraria for Advisory Board; Kite Pharmaceuticals: Honoraria, Other: Honoraria for Advisory Board. Westin:Kite: Consultancy, Research Funding; Curis: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Amgen: Consultancy; Astra Zeneca: Consultancy, Research Funding; Morphosys: Consultancy, Research Funding; 47: Research Funding; Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding. Nowakowski:Curis: Consultancy; Seattle Genetics: Consultancy; Nanostrings: Research Funding; MorphoSys: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding; Kite: Consultancy; Denovo: Consultancy; Kymera: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other. Lossos:Verastem: Consultancy, Honoraria; Seattle Genetics: Consultancy, Other; NCI: Research Funding; Stanford University: Patents & Royalties; Janssen Biotech: Honoraria; Janssen Scientific: Consultancy, Other. Batlevi:Life Sci, GLG, Juno/Celgene, Seattle Genetics, Kite: Consultancy; Janssen, Novartis, Epizyme, Xynomics, Bayer, Autolus, Roche/Genentech: Research Funding. Drullinsky:Roche: Research Funding; Novartis: Research Funding. Gerecitano:Janssen: Current Employment. Hamlin:Celgene: Consultancy; Karyopharm: Consultancy; Juno Therapeutics: Consultancy; Portola: Research Funding; Molecular Templates: Research Funding; Incyte: Research Funding; J&J Pharmaceuticals: Research Funding; Portola Pharmaceutics: Consultancy. Horwitz:ASTEX: Consultancy; Affirmed: Consultancy; Vividion Therapeutics: Consultancy; Verastem: Consultancy, Research Funding; Myeloid Therapeutics: Consultancy; Miragen: Consultancy; Kura Oncology: Consultancy; Janssen: Consultancy; GlaxoSmithKline: Consultancy; Daiichi Sankyo: Research Funding; C4 Therapeutics: Consultancy; Beigene: Consultancy; Portola: Consultancy, Research Funding; Mundipharma: Consultancy; Innate Pharma: Consultancy; Corvus: Consultancy; Trillium: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Millenium/Takeda: Consultancy, Research Funding; Kyowa Hakka Kirin: Consultancy, Research Funding; Infinity/Verastem: Research Funding; Forty Seven: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Aileron: Consultancy, Research Funding; ADCT Therapeutics: Consultancy, Research Funding. Jacob:Adaptive Biotechnologies: Current Employment, Current equity holder in publicly-traded company. Joffe:AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees. von Keudell:Bayer: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding. Lahoud:MorphoSys: Other: Advisory Board. Matasar:Bayer: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Rocket Medical: Consultancy, Research Funding; Teva: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Daiichi Sankyo: Consultancy; IGM Biosciences: Research Funding; Janssen: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Immunovaccine Technologies: Honoraria, Research Funding; Merck: Consultancy; Juno Therapeutics: Consultancy; Genentech, Inc.: Consultancy, Honoraria, Research Funding. Mullins:Adaptive Biotechnologies: Current Employment, Other: shareholder. Noy:Pharmacyclics: Research Funding; NIH: Research Funding; Rafael Pharma: Research Funding; Janssen: Consultancy; Pharmacyclics: Consultancy; Medscape: Consultancy; Targeted Oncology: Consultancy; Morphosys: Consultancy. Straus:Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees; ASH: Other: Conference in December 2019 on HL to other physicians during ASH; Elsevier: Membership on an entity's Board of Directors or advisory committees, Other: CME writer; OncLive: Speakers Bureau; Takeda Pharmaceuticals: Research Funding, Speakers Bureau; NY Lymphoma Rounds: Consultancy; Targeted Oncology: Consultancy, Speakers Bureau; Imedex, Inc.: Speakers Bureau; Karyopharm Therapeutics: Membership on an entity's Board of Directors or advisory committees. Younes:Takeda: Consultancy; HCM: Consultancy; AstraZeneca: Current Employment; Novartis: Consultancy; Daiichi Sankyo: Consultancy; Janssen: Consultancy; Epizyme: Consultancy; BMS: Consultancy; BioPath: Consultancy; Curis: Consultancy. Zelenetz:MEI Pharma: Research Funding; MorphoSys: Research Funding; Sandoz: Research Funding; Celgene: Research Funding; Roche: Research Funding; Gilead: Research Funding; Genentech/Roche: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Novartis: Consultancy; Adaptive Biotechnology: Consultancy; BeiGene: Membership on an entity's Board of Directors or advisory committees.