Citations

Purpose: Transgender and gender non-binary (TGNB) individuals face numerous inequalities in healthcare and there is substantial work to be done in fostering TGNB culturally competent care in radiology. A radiology department’s online presence and use of gender-inclusive language are essential in promoting an environment of equity, diversity, and inclusion (EDI). The naming of radiology fellowships and continuing medical education (CME) courses with terminology such as “Women’s Imaging” indicates a lack of inclusivity to TGNB patients and providers, which could result in suboptimal patient care. Methods: We conducted a cross-sectional analysis of all institutions in Canada and the United States (US) offering training in Breast Imaging, Women’s Imaging, or Breast and Body Imaging. Data was collected from each institution’s radiology department website pertaining to fellowship names, EDI involvement, and CME courses. Results: 8 Canadian and 71 US radiology fellowships were identified. 75% of Canadian and 90% of US fellowships had gender-inclusive names. One (12.5%) Canadian and 29 (41%) US institutions had EDI Committees mentioned on their websites. Among institutions publicly displaying CME courses about breast/body or women’s imaging, gender-inclusive names were used in only 1 (25%) of the Canadian CME courses, compared to 81% of the US institutions. Conclusions: Most institutions in Canada and the US have gender-inclusive names for their radiology fellowships pertaining to breast and body imaging. However, there is much opportunity to and arguably the responsibility for institutions in both countries to increase the impact and visibility of their EDI efforts through creation of department-specific committees and CME courses.

AbstractObjectiveDual leucine zipper kinase (DLK), which regulates the c‐Jun N‐terminal kinase pathway involved in axon degeneration and apoptosis following neuronal injury, is a potential therapeutic target in amyotrophic lateral sclerosis (ALS). This first‐in‐human study investigated safety, tolerability, and pharmacokinetics (PK) of oral GDC‐0134, a small‐molecule DLK inhibitor. Plasma neurofilament light chain (NFL) levels were explored in GDC‐0134‐treated ALS patients and DLK conditional knockout (cKO) mice.MethodsThe study included placebo‐controlled, single and multiple ascending‐dose (SAD; MAD) stages, and an open‐label safety expansion (OLE) with adaptive dosing for up to 48 weeks.ResultsForty‐nine patients were enrolled. GDC‐0134 (up to 1200 mg daily) was well tolerated in the SAD and MAD stages, with no serious adverse events (SAEs). In the OLE, three study drug‐related SAEs occurred: thrombocytopenia, dysesthesia (both Grade 3), and optic ischemic neuropathy (Grade 4); Grade ≤2 sensory neurological AEs led to dose reductions/discontinuations. GDC‐0134 exposure was dose‐proportional (median half‐life = 84 h). Patients showed GDC‐0134 exposure‐dependent plasma NFL elevations; DLK cKO mice also exhibited plasma NFL compared to wild‐type littermates.InterpretationThis trial characterized GDC‐0134 safety and PK, but no adequately tolerated dose was identified. NFL elevations in GDC‐0134‐treated patients and DLK cKO mice raised questions about interpretation of biomarkers affected by both disease and on‐target drug effects. The safety profile of GDC‐0134 was considered unacceptable and led to discontinuation of further drug development for ALS. Further work is necessary to understand relationships between neuroprotective and potentially therapeutic effects of DLK knockout/inhibition and NFL changes in patients with ALS.

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is highly contagious and causes coronavirus disease 2019 (COVID-19). Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is the most accurate and reliable molecular assay to detect active SARS-CoV-2 infection. However, a rapid increase in test subjects has created a global bottleneck in testing capacity. Given that efficient nucleic acid extraction greatly affects reliable and accurate testing results, we compared three extraction platforms: MagNA Pure 96 DNA and Viral NA Small Volume kit on MagNA Pure 96 (Roche, Basel, Switzerland), careGENETM Viral/Pathogen HiFi Nucleic Acid Isolation kit (WELLS BIO Inc., Seoul, Korea) on KingFisher Flex (Thermo Fisher Scientific, Rocklin, CA, USA), and SGRespiTM Pure kit (Seegene Inc., Seoul, Korea) on Maelstrom 9600 (Taiwan Advanced Nanotech Inc., Taoyuan, Taiwan). RNA was extracted from 245 residual respiratory specimens from the different types of samples (i.e., NPS, sputum, and saliva) using three different kits. The 95% limits of detection of median tissue culture infectious dose per milliliter (TCID50/mL) for the MagNA Pure 96, KingFisher Flex, and Maelstrom 9600 were 0.37–3.15 × 101, 0.41–3.62 × 101, and 0.33–1.98 × 101, respectively. The KingFisher Flex platform exhibited 99.2% sensitivity and 100% specificity, whereas Maelstrom 9600 exhibited 98.3–100% sensitivity and 100% specificity. Bland–Altman analysis revealed a 95.2% concordance between MagNA Pure 96 and KingFisher Flex and 95.4% concordance between MagNA Pure 96 and Maelstrom 9600, indicating that all three platforms provided statistically reliable results. This suggests that two modifying platforms, KingFisher Flex and Maelstrom 9600, are accurate and scalable extraction platforms for large-scale SARS-CoV-2 clinical detection and could help the management of COVID-19 patients.

Abstract Purpose: Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers. Experimental Design: We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors. Results: We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1–high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases. Conclusions: Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.

Background The epidemiology of ventricular arrhythmias (VAs) in patients with advanced heart failure (HF) is not well defined. Methods and Results Residents of Olmsted County, Minnesota, with advanced HF from 2007 to 2017 were identified using the 2018 European Society of Cardiology criteria. Billing codes were used to capture VAs; severe VAs requiring emergency care were defined as events associated with emergency department visits or hospitalizations. The cumulative incidence of VAs postadvanced HF was estimated with the Kaplan–Meier method. Multivariable Cox analyses were used to determine the following: (1) Predictors of severe VAs postadvanced HF; and (2) Impact of severe VAs on mortality. Of 936 patients with advanced HF, 261 (27.9%) had a history of VA. The 1‐year cumulative incidence of severe VAs postadvanced HF was 5.4%. Prior VAs (hazard ratio [HR] 2.22 [95% CI, 1.26–3.89], P =0.006) and left ventricular ejection fraction <40% (HR, 3.79 [95% CI, 1.72–8.39], P <0.001) were independently associated with increased severe VA risk postadvanced HF. New‐onset severe VAs were associated with increased mortality (HR, 4.41 [95% CI, 2.80–6.94]; P <0.001), whereas severe VAs in patients with prior VAs had no significant association with mortality risk (HR, 1.08 [95% CI, 0.65–1.78]; P =0.77). Severe VAs were associated with increased mortality in patients without implantable cardioverter defibrillators (HR, 4.89 [95% CI, 2.89–8.26]; P <0.001), but not in patients with implantable cardioverter defibrillators (HR, 1.42 [95% CI, 0.92–2.19]; P =0.11). Conclusions Patients with left ventricular ejection fraction <40% and prior VAs have increased risk of severe VA postadvanced HF. New‐onset severe VAs or severe VAs without implantable cardioverter defibrillators postadvanced HF are associated with increased mortality.

Abstract This international guideline proposes improving clozapine package inserts worldwide by using ancestry-based dosing and titration. Adverse drug reaction (ADR) databases suggest that clozapine is the third most toxic drug in the United States (US), and it produces four times higher worldwide pneumonia mortality than that by agranulocytosis or myocarditis. For trough steady-state clozapine serum concentrations, the therapeutic reference range is narrow, from 350 to 600 ng/mL with the potential for toxicity and ADRs as concentrations increase. Clozapine is mainly metabolized by CYP1A2 (female non-smokers, the lowest dose; male smokers, the highest dose). Poor metabolizer status through phenotypic conversion is associated with co-prescription of inhibitors (including oral contraceptives and valproate), obesity, or inflammation with C-reactive protein (CRP) elevations. The Asian population (Pakistan to Japan) or the Americas’ original inhabitants have lower CYP1A2 activity and require lower clozapine doses to reach concentrations of 350 ng/mL. In the US, daily doses of 300–600 mg/day are recommended. Slow personalized titration may prevent early ADRs (including syncope, myocarditis, and pneumonia). This guideline defines six personalized titration schedules for inpatients: 1) ancestry from Asia or the original people from the Americas with lower metabolism (obesity or valproate) needing minimum therapeutic dosages of 75–150 mg/day, 2) ancestry from Asia or the original people from the Americas with average metabolism needing 175–300 mg/day, 3) European/Western Asian ancestry with lower metabolism (obesity or valproate) needing 100–200 mg/day, 4) European/Western Asian ancestry with average metabolism needing 250–400 mg/day, 5) in the US with ancestries other than from Asia or the original people from the Americas with lower clozapine metabolism (obesity or valproate) needing 150–300 mg/day, and 6) in the US with ancestries other than from Asia or the original people from the Americas with average clozapine metabolism needing 300–600 mg/day. Baseline and weekly CRP monitoring for at least four weeks is required to identify any inflammation, including inflammation secondary to clozapine rapid titration.

AbstractBackgroundData regarding ventricular tachycardia (VT) or premature ventricular complex (PVC) ablation following mitral valve surgery (MVS) is limited. Catheter ablation (CA) can be challenging given perivalvular substrate in the setting of mitral annuloplasty or prosthetic valves.ObjectiveTo investigate the characteristics, safety, and outcomes of radiofrequency CA in patients with prior MVS and ventricular arrhythmias (VA).MethodsWe identified consecutive patients with prior MVS who underwent CA for VT or PVC between January 2013 and December 2018. We investigated the mechanism of arrhythmia, ablation approach, peri‐operative complications, and outcomes.ResultsIn our cohort, 31 patients (77% men, mean age 62.3 ± 10.8 years, left ventricular ejection fraction 39.2 ± 13.9%) with prior MVS underwent CA (16 VT; 15 PVC). Access to the left ventricle was via transseptal approach in 17 patients, and a retrograde aortic approach was used in 13 patients. A combined transseptal and retrograde aortic approach was used in one patient, and a percutaneous epicardial approach was combined with trans‐septal approach in one patient. Heterogenous scar regions were present in 94% of VT patients and scar‐related reentry was the dominant mechanism of VT. Forty‐seven percent of PVC patients had abnormal substrate at the site targeted for ablation. Clinical VA substrates involved the peri‐mitral area in six patients with VT and five patients with PVC ablation. No procedure‐related complications were reported. The overall recurrence‐free rate at 1‐year was 72.2%; 67% in the VT group and 78% in the PVC group. No arrhythmia‐related death was documented on long‐term follow‐up.ConclusionCA of VAs can be performed safely and effectively in patients with MVS.

Abstract Introduction: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology. However, these biological subgroups overlap clinically. While R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard of care for treating patients with DLBCL, predicting which patients will not benefit from such therapy is important so that alternative therapy or clinical trials can be considered. Most of the studies stratifying patients select biomarkers first, then explore how these biomarkers can stratify patients based on outcome. We explored the potential of using machine learning to first group patients with DLBCL based on survival, then isolating the biomarkers necessary for predicting these survival subgroups. Methods: RNA was extracted from tissue paraffin blocks from 379 R-CHOP treated patients with de novo DLBCL, and from 247 patients with extranodal DLBCL. A targeted hybrid capture RNA panel of 1408 genes was used for next generation sequencing (NGS). Sequencing was performed using an Illumina NextSeq 550 System platform. Ten million reads per sample in a single run were required, and the read length was 2 × 150 bp. An expression profile was generated from the sequencing coverage profile of each individual sample using Cufflinks. A machine learning system was developed to classify patients into four groups based on their overall survival. This machine learning approach based on Naïve Bayesian algorithm was also used to discover the relevant subset of genes with which to classify patients into each of the four survival groups. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict the survival group for each patient. Results: Using machine learning, patients were first divided into two groups: short survival (S) and long survival (L). To refine this model, we used the same approach and divided the patients in each group into two subgroups, generating four groups: long survival in the long group (LL), short survival in the long group (LS), long survival in the short group (SL), and short survival in the short group (SS). The hazard ratio for this model was 0.174 (confidence interval: 0.120-0.251), and P-value &lt;0.0001. After defining these four groups, a machine learning algorithm was used to discover the biomarkers from the expression data of the 1408 genes from NGS data. To reduce the effects of noise and avoid overfitting, we employed a 12-step cross validation to obtain a robust measure. For an individual gene, a generalized Naïve Bayesian classifier was constructed on the training of one of the 12 subsets and tested on the other 11 testing subsets. This allowed us to limit the prediction process to 60 genes for each separation step. Using the selected biomarkers, we classified the patients in the original set (379 patients) into LL, LS, SL, and SS groups and then evaluated the survival pattern of these groups. As shown in Fig. 1A, the selected biomarkers predicted survival as expected in the overall survival groups prior to biomarker selection. For additional validation of the system, we used the selected biomarkers to classify a completely new set of 247 samples of patients with extranodal DLBCL. As shown in Fig. 1B, these selected biomarkers successfully predicted the overall survival in this group of patients with an HR of 0.530 (confidence interval: 0.234-1.197, P=0.005). This classification correlated with cell of origin classification, TP53 mutation status, MYC expression, and IRF4 expression. However, in a multivariate analysis, only TP53 mutation was independent in predicting prognosis (P=0.005) and age (below or over 60) (P=0.01) along with the survival grouping (P&lt;0.000001). Conclusions: Using a novel machine learning approach with the expression levels of 180 genes, we developed a model that can reliably stratify patients with DLBCL treated with R-CHOP into four survival subgroups. This model can be used to identify patients who may not respond well to R-CHOP to be considered for alternative therapy and clinical trials. Figure 1 Figure 1. Disclosures Hsi: AbbVie Inc, Eli Lilly: Research Funding. Ferreri: Ospedale San Raffaele srl: Patents & Royalties; BMS: Research Funding; Pfizer: Research Funding; Beigene: Research Funding; Hutchison Medipharma: Research Funding; Amgen: Research Funding; Genmab: Research Funding; ADC Therapeutics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; PletixaPharm: Membership on an entity's Board of Directors or advisory committees; x Incyte: Membership on an entity's Board of Directors or advisory committees; Adienne: Membership on an entity's Board of Directors or advisory committees. Piris: Millenium/Takeda, EUSA, Jansen, NanoString, Kyowa Kirin, Gilead and Celgene.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Winter: BMS: Other: Husband: Data and Safety Monitoring Board; Actinium Pharma: Consultancy; Janssen: Other: Husband: Consultancy; Agios: Other: Husband: Consultancy; Gilead: Other: Husband: Consultancy; Epizyme: Other: Husband: Data and Safety Monitoring Board; Ariad/Takeda: Other: Husband: Data and Safety Monitoring Board; Merck: Consultancy, Honoraria, Research Funding; Novartis: Other: Husband: Consultancy, Data and Safety Monitoring Board; Karyopharm (Curio Science): Honoraria.

Abstract Abstract text: Background/Introduction: Chronic Myelomonocytic Leukemia (CMML) is an uncommon MDS/MPN overlap syndrome that has historically been included under the umbrella of myelodysplastic syndromes (MDS) for clinical trial and treatment. As a result, DNA methyltransferase inhibitors (DNMTi) such as decitabine or azacitidine have been the established standard of care for the treatment of CMML. The oral bioavailability of these agents has been limited due to rapid degradation by cytidine deaminase (CDA) in the gut and liver so treatment has required intravenous infusion or subcutaneous injections daily for 5-7 days every month (m) adding significant burden to older cancer patients due to daily time commitment and travel to treatment centers. In the context of pandemic SARS-CoV-2, parenteral therapy also increases contact with medical settings with increased infection risk. Oral decitabine 35 mg/cedazuridine 100 mg (ASTX727) is an oral fixed dose combination of decitabine and the CDA inhibitor cedazuridine that produced equivalent exposure (99%; 90% CI 93% to 106%) to IV decitabine 20 mg/m 2 in a randomized cross-over study (Garcia-Manero et al, ASH 2019), and Median overall survival (mOS) for the entire study population in the ASCERTAIN study was approximately 32 months (Savona, 2021). Here, we present outcome data for this study for the enrolled subpopulation of patients with CMML. Methods: We used a randomized cross over design in which patients were randomized in the first 2 cycles 1:1 to either Sequence A: (decitabine 35 mg/ cedazuridine 100 mg in Cycle 1 followed by IV decitabine at 20 mg/m 2 in Cycle 2), or Sequence B: (IV decitabine in Cycle 1 followed by oral decitabine/cedazuridine in Cycle 2). We conducted an intra-patient comparison of decitabine PK (primary PK endpoint: decitabine AUC equivalence over 5 days of dosing). Cycles were repeated every 28 days (unless delays were needed). All patients received oral decitabine/cedazuridine in Cycles 3 and above until disease progression or unacceptable toxicity. Patients were eligible per the FDA-approved label of IV decitabine (MDS patients by FAB classification including CMML, or MDS IPSS Intermediate-1, 2 or high-risk patients). Clinical endpoints were best response according to International Working Group (IWG) 2006 response criteria, transfusion independence for at least 8 or 16 consecutive weeks, overall survival, and safety. Adverse events (AEs) were graded by Common Terminology Criteria for Adverse Events (CTCAE) v 4.03. Results: Of the 133 patients enrolled and treated in ASCERTAIN, 16 (12%) had a diagnosis of CMML with demographics and as follows: median age 71.5 years, 69% Male/31% Female, median weight 87kg (range 65-124), 25% ECOG 0, 75% ECOG 1. Population disease characteristics were: 19% poor or intermediate risk cytogenetics, with median baseline hemoglobin 90 g/L, neutrophils 1.27 X 10 9/L, platelets 84 x 10 9/L, bone marrow blasts 5%, with 38% RBC transfusion dependent. Patients received a median of 7 cycles of therapy (range 3-24). Treatment-emergent adverse events of CTCAE Grade 3 or higher in &gt; 10% of patients, independent of relationship to ASTX727, were cytopenias (neutropenia [69%], thrombocytopenia [63%], anemia [56%], leukopenia [19%]), febrile neutropenia (31%), fatigue (13%). Two patients (12.5%) had Complete Responses (CR), 8 (50%) had marrow CR ([mCR], including 3 (19%) with hematologic improvement (HI); Overall Response rate (ORR) [CR + PR+ mCR + HI] was 75%. Of six patients with baseline RBC transfusion dependence 3 (50%) became transfusion independent. Leukemia-free survival was 28.2 months and after a median follow up of more than 33 months, median overall survival had not been reached. Two patients (13%) went on to Hematopoietic Stem Cell Transplant (HCT). Conclusions: In the overall study, oral decitabine/cedazuridine delivered equivalent PK exposure to 5 days of IV decitabine 20mg/m 2 with a resultant clinical activity safety and efficacy profile in CMML patients consistent with the published literature (e.g Zeidan, et al 2017) and the Phase 2 experience. The use of oral decitabine/cedazuridine is a reasonable approach in CMML patients. References: Garcia-Manero, et al ASH 2019 Savona, et al, Int. MDS Symposium, 2021 Zeidan, et al, Cancer 2017: 3754-3762. Figure 1 Figure 1. Disclosures Savona: Geron: Consultancy, Membership on an entity's Board of Directors or advisory committees; CTI: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; BMS-Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; NOVARTIS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Taiho: Consultancy, Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; ALX Oncology: Research Funding; Astex: Research Funding; Incyte: Research Funding. McCloskey: Pfizer: Consultancy; Takeda: Consultancy, Speakers Bureau; Incyte: Speakers Bureau; Novartis: Consultancy; COTA: Other: Equity Ownership; BMS: Honoraria, Speakers Bureau; Amgen: Speakers Bureau; Jazz: Consultancy, Speakers Bureau. Griffiths: Boston Biomedical: Consultancy; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Taiho Oncology: Consultancy, Honoraria; Genentech: Research Funding; Astex Pharmaceuticals: Honoraria, Research Funding; Takeda Oncology: Consultancy, Honoraria; Novartis: Honoraria; Apellis Pharmaceuticals: Research Funding; Alexion Pharmaceuticals: Consultancy, Research Funding. Yee: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Forma Therapeutics: Research Funding; Geron: Research Funding; Shattuck Labs: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb/Celgene: Membership on an entity's Board of Directors or advisory committees; F. Hoffmann La Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Honoraria; Janssen: Research Funding; Onconova: Research Funding; Genentech: Research Funding; Otsuka: Membership on an entity's Board of Directors or advisory committees; MedImmune: Research Funding; Jazz: Research Funding; Astex: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; TaiHo: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Paladin: Membership on an entity's Board of Directors or advisory committees. Zeidan: BeyondSpring: Consultancy; Janssen: Consultancy; Boehringer Ingelheim: Consultancy, Research Funding; BioCryst: Other: Clinical Trial Committees; AstraZeneca: Consultancy; Pfizer: Other: Travel support, Research Funding; Kura: Consultancy, Other: Clinical Trial Committees; Incyte: Consultancy, Research Funding; Ionis: Consultancy; Daiichi Sankyo: Consultancy; Epizyme: Consultancy; Novartis: Consultancy, Other: Clinical Trial Committees, Travel support, Research Funding; Loxo Oncology: Consultancy, Other: Clinical Trial Committees; Genentech: Consultancy; Geron: Other: Clinical Trial Committees; Cardiff Oncology: Consultancy, Other: Travel support, Research Funding; BMS: Consultancy, Other: Clinical Trial Committees, Research Funding; Gilead: Consultancy, Other: Clinical Trial Committees; Aprea: Consultancy, Research Funding; Astellas: Consultancy; Astex: Research Funding; Jazz: Consultancy; Jasper: Consultancy; Amgen: Consultancy, Research Funding; Agios: Consultancy; ADC Therapeutics: Research Funding; Acceleron: Consultancy, Research Funding; AbbVie: Consultancy, Other: Clinical Trial Committees, Research Funding. Al-Kali: Novartis: Research Funding; Astex: Other: Research support to institution. Patel: Agios: Membership on an entity's Board of Directors or advisory committees; Celgene-BMS: Membership on an entity's Board of Directors or advisory committees; PVI: Honoraria. Sabloff: Takeda: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; TaiHo: Membership on an entity's Board of Directors or advisory committees; Jaxx: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; ROCHE: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Dao: Astex Pharmaceuticals, Inc.: Current Employment. Fazal: Janssen Oncology: Consultancy, Honoraria, Speakers Bureau; Taiho Pharmaceuticals: Consultancy, Honoraria, Speakers Bureau; Gilead Sciences: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau; Agios: Consultancy, Honoraria, Speakers Bureau; Sanofi Genzyme: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Speakers Bureau; Glaxo Smith Kline: Consultancy, Honoraria, Speakers Bureau; AMGEN: Consultancy, Honoraria, Speakers Bureau; Incyte: Consultancy, Honoraria, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Honoraria, Speakers Bureau; Bristol Myers Squibb: Consultancy, Honoraria, Speakers Bureau; Stemline Therapeutics: Consultancy, Honoraria, Speakers Bureau; Karyopharm Pharmaceuticals: Consultancy, Honoraria, Speakers Bureau. Odenike: Celgene, Incyte, AstraZeneca, Astex, NS Pharma, AbbVie, Gilead, Janssen, Oncotherapy, Agios, CTI/Baxalta, Aprea: Research Funding; AbbVie, Celgene, Impact Biomedicines, Novartis, Taiho Oncology, Takeda: Consultancy. Kantarjian: Ipsen Pharmaceuticals: Honoraria; Astra Zeneca: Honoraria; Astellas Health: Honoraria; Aptitude Health: Honoraria; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Jazz: Research Funding; Immunogen: Research Funding; Daiichi-Sankyo: Research Funding; BMS: Research Funding; Ascentage: Research Funding; Amgen: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; KAHR Medical Ltd: Honoraria; NOVA Research: Honoraria; Precision Biosciences: Honoraria; Taiho Pharmaceutical Canada: Honoraria. DeZern: Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Taiho: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees. Roboz: Janssen: Research Funding; AbbVie: Consultancy; Actinium: Consultancy; Agios: Consultancy; Amgen: Consultancy; Astex: Consultancy; Astellas: Consultancy; AstraZeneca: Consultancy; Bayer: Consultancy; Blueprint Medicines: Consultancy; Bristol Myers Squibb: Consultancy; Celgene: Consultancy; Daiichi Sankyo: Consultancy; Glaxo SmithKline: Consultancy; Helsinn: Consultancy; Janssen: Consultancy; Jasper Therapeutics: Consultancy; Jazz: Consultancy; MEI Pharma - IDMC Chair: Consultancy; Mesoblast: Consultancy; Novartis: Consultancy; Otsuka: Consultancy; Pfizer: Consultancy; Roche/Genentech: Consultancy. Busque: Novartis: Consultancy. Leber: Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AMGEN: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TaiHo: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Otsuka: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Hao: Astex Pharmaceuticals, Inc.: Current Employment. Keer: Astex Pharmaceuticals, Inc.: Current Employment. Azab: Astex Pharmaceuticals, Inc.: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction: Multiple Myeloma (MM) is an incurable plasma cell malignancy commonly preceded by the asymptomatic stage smoldering multiple myeloma (SMM). MM is characterized with significant genomic heterogeneity of chromosomal gains and losses (CNVs), translocations, and point mutations (SNVs); alterations that are also observed in SMM patients. However, current SMM risk models rely solely on clinical markers and do not accurately capture progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase risk stratification precision in SMM. Methods: We obtained a total of 214 patient samples at SMM diagnosis. We performed whole-exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture was done on 48 additional tumors. Upon binarization of DNA features, we performed consensus non-negative matrix factorization to identify distinct molecular clusters. We then trained a random forest classifier on translocations, SNVs, and CNVs. The predicted clinical outcomes for the molecular subtypes were further validated in an independent SMM cohort of 74 patients. Results: We identified six genomic subtypes, four with hyperdiploidy (&gt;48 chromosomes, HMC, HKR, HNT, HNF) and two with IgH translocations (FMD, CND) (Table 1). In multivariate analysis accounting for IMWG (20-2-20) clinical risk stages, high-risk (HMC, FMD, HKR) and intermediate-risk (HNT, HNF) genetic subtypes were independent predictors of progression (Hazards ratio [HR]: 3.8 and 5.5, P = 0.016 and 0.001, respectively). The low-risk, CND subtype harboring translocation (11;14) was enriched for the previously defined CD-2 MM signature defined by the B cell markers CD20 and CD79A (FDR = 0.003 ), showed upregulation of CCND1, E2F1, and E2F7 (FDR = 0.01, 0.0004, 0.08), and was enriched for G2M checkpoint, heme metabolism, and monocyte cell signature (FDR = 0.003, 0.003, 0.003, respectively). The FMD subtype with IgH translocations (4;14) and (14;16) was enriched for P53, mTORC1, unfolded protein signaling pathways and plasmacytoid dendritic cell signatures (FDR = 0.01, 0.005, 0.008, respectively). The HKR tumors were enriched for inflammatory cytokine signaling, MYC target genes, T regulatory cell signature, and the MM proliferative (PR) signatures (FDR = 0.02, 0.03, 0.007, 0.02, respectively). The APOBEC mutational signature was enriched in HMC and FMD tumors (P = 0.005), while there was no statistical difference across subtypes in the AID signature. The median follow-up for the primary cohort is 7.1 years. Median TTP for patients in HMC, FMD, and HKR was 3.8, 2.6, and 2.2 years, respectively; TTP for HNT and HNF was 4.3 and 5.2, respectively, while it was 11 years in CND patients (P = 0.007). Moreover, by analyzing the changes in MM clinical biomarkers over time, we found that patients from high-risk subgroups had higher odds of developing evolving hemoglobin and monoclonal protein levels over time (P = 0.01 and 0.002, respectively); Moreover, the absolute increase in M-protein was significantly higher in patients from the high-risk genetic subtypes at one, two, and five years from diagnosis (P = 0.001, 0.03, and 0,01, respectively). Applying the classifier to the external cohort replicated our findings where intermediate and high-risk genetic subgroups conferred increased risk of progression to MM in multivariate analysis after accounting for IMWG staging (HR: 5.5 and 9.8, P = 0.04 and 0.005, respectively). Interestingly, within the intermediate-risk clinical group in the primary cohort, patients in the high-risk genetic subgroups had increased risk of progression (HR: 5.2, 95% CI 1.5 - 17.3, P = 0.007). In the validation cohort, these patients also had an increased risk of progression to MM (HR: 6.7, 95% CI 1.2 - 38.3, P = 0.03), indicating that molecular classification improves the clinical risk-stratification models. Conclusion: We identified and validated in an independent dataset six SMM molecular subgroups with distinct DNA alterations, transcriptional profiles, dysregulated pathways, and risks of progression to active MM. Our results underscore the importance of molecular classification in addition to clinical evaluation in better identifying high-risk SMM patients. Moreover, these subgroups may be used to identify tumor vulnerabilities and target them with precision medicine efforts. Figure 1 Figure 1. Disclosures Bustoros: Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria. Casneuf: Janssen: Current Employment. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Davies: Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Beigene: Honoraria; Janssen: Honoraria. Bergsagel: Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; GSK: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Yong: BMS: Research Funding; Autolus: Research Funding; Takeda: Honoraria; Janssen: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; GSK: Honoraria; Amgen: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Getz: IBM, Pharmacyclics: Research Funding; Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees. 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.