Advanced Glycation End Products Impair Cardiac Atrial Appendage Stem Cells Properties

Background: During myocardial infarction (MI), billions of cardiomyocytes are lost. The optimal therapy should effectively replace damaged cardiomyocytes, possibly with stem cells able to engraft and differentiate into adult functional cardiomyocytes. As such, cardiac atrial appendage stem cells (CASCs) are suitable candidates. However, the presence of elevated levels of advanced glycation end products (AGEs) in cardiac regions where CASCs are transplanted may affect their regenerative potential. In this study, we examine whether and how AGEs alter CASCs properties in vitro. Methods and Results: CASCs in culture were exposed to ranging AGEs concentrations (50 µg/mL to 400 µg/mL). CASCs survival, proliferation, and migration capacity were significantly decreased after 72 h of AGEs exposure. Apoptosis significantly increased with rising AGEs concentration. The harmful effects of these AGEs were partially blunted by pre-incubation with a receptor for AGEs (RAGE) inhibitor (25 µM FPS-ZM1), indicating the involvement of RAGE in the observed negative effects. Conclusion: AGEs have a time- and concentration-dependent negative effect on CASCs survival, proliferation, migration, and apoptosis in vitro, partially mediated through RAGE activation. Whether anti-AGEs therapies are an effective treatment in the setting of stem cell therapy after MI warrants further examination

The Dynamics of Nucleotide Variants in the Progression from Low–Intermediate Myeloma Precursor Conditions to Multiple Myeloma: Studying Serial Samples with a Targeted Sequencing Approach

Multiple myeloma (MM), or Kahler’s disease, is an incurable plasma cell (PC) cancer in the bone marrow (BM). This malignancy is preceded by one or more asymptomatic precursor conditions, monoclonal gammopathy of undetermined significance (MGUS) and/or smoldering multiple myeloma (SMM). The molecular mechanisms and exact cause of this progression are still not completely understood. In this study, the mutational profile underlying the progression from low–intermediate risk myeloma precursor conditions to MM was studied in serial BM smears. A custom capture-based sequencing platform was developed, including 81 myeloma-related genes. The clonal evolution of single nucleotide variants and short insertions and deletions was studied in serial BM smears from 21 progressed precursor patients with a median time of progression of six years. From the 21 patients, four patients had no variation in one of the 81 studied genes. Interestingly, in 16 of the 17 other patients, at least one variant present in MM was also detected in its precursor BM, even years before progression. Here, the variants were present in the pre-stage at a median of 62 months before progression to MM. Studying these paired BM samples contributes to the knowledge of the evolutionary genetic landscape and provides additional insight into the mutational behavior of mutant clones over time throughout progression

Whole-Genome Sequencing Reveals Evidence of Two Biologically and Clinically Distinct Entities: Progressive Versus Stable Myeloma Precursor Disease

Introduction Multiple myeloma (MM) is consistently preceded by an asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM). Here, we present the first comprehensive whole-genome sequencing (WGS) analysis of patients with MGUS and SMM. Methods To characterize the genomic landscape of myeloma precursor disease (i.e. SMM and MGUS) we performed WGS of CD138-positive bone marrow mononuclear samples from 32 patients with MGUS (N=18) and SMM (N=14), respectively. For cases with low cellularity resulting in low amounts of extracted DNA (N=15), we used the low-input enzymatic fragmentation-based library preparation method (Lee-Six et al, Nature 2019). Myeloma precursor disease samples were compared with 80 WGS of patients with MM. All WGSs (N=112) were investigated using computational tools available at the Wellcome Sanger Institute. Results After a median follow up of 29 months (range: 2-177), 17 (53%) patients with myeloma precursor disease progressed to MM (13 SMM and 4 MGUS). To interrogate the genomic differences between progressive versus stable myeloma precursor disease we first characterized the single base substitution (SBS) signature landscape. Across the entire cohort of plasma cell disorders, all main MM mutational signatures were identified: aging (SBS1 and SBS5), AID (SBS9), SBS8, SBS18, and APOBEC (SBS2 and SBS13). Interestingly, only 2/15 (13%) stable myeloma precursor disease cases showed evidence of APOBEC activity, while 14/17 (82%) and 68/80 (85%) patients with progressive myeloma precursor disease (p=0.0058) and MM (p=0.004), respectively, had APOBEC mutational activity. The two stable cases with detectable APOBEC were characterized by a high APOBEC3A:3B ratio, a feature which defines a group of MAF-translocated MM patients whose pathogenesis is characterized by intense and early APOBEC activity (Rustad et al Nat Comm 2020) and is distinct from the canonical ~1:1 APOBEC3A:3B mutational activity observed in most cases. When exploring the cytogenetic landscape, no differences were found between progressive myeloma precursor disease and MM cases. Compared to progressors and to MM, patients with stable myeloma precursor disease were characterized by a significantly lower prevalence of known recurrent MM aneuploidies (i.e. gain1q, del6q, del8p, gain 8q24, del16q) (p<0.001). This observation was validated using SNP array copy number data from 78 and 161 stable myeloma precursor disease and MM patients, respectively. To further characterize differences between progressive versus stable myeloma precursor disease, we leveraged the comprehensive WGS resolution to explore the distribution and prevalence of structural variants (SV). Interestingly, stable cases were characterized by low prevalence of SV, SV hotspots, and complex events, in particular chromothripsis and templated insertions (both p<0.01). In contrast, progressors showed a genome wide distribution and high prevalence of SV and complex events similar to the one observed in MM. To rule out that the absence of key WGS-MM defining events among stable cases would reflect a sample collection time bias, we leveraged our recently developed molecular-clock approach (Rustad et al. Nat Comm 2020). Notably, this approach is based on pre- and post-chromosomal gain SBS5 and SBS1 mutational burden, designed to estimate the time of cancer initiation. Stable myeloma precursor disease showed a significantly different temporal pattern, where multi-gain events were acquired later in life compared to progressive myeloma precursor disease and MM cases. Conclusions In summary, we were able to comprehensively interrogate for the first time the whole genome landscape of myeloma precursor disease. We provide novel evidence of two biologically and clinically distinct entities: (1) progressive myeloma precursor disease, which represents a clonal entity where most of the genomic drivers have been already acquired, conferring an extremely high risk of progression to MM; and (2) stable myeloma precursor disease, which does not harbor most of the key genomic MM hallmarks and follows an indolent clinical outcome. Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Dogan:Roche: Consultancy, Research Funding; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; EUSA Pharma: Consultancy; National Cancer Institute: Research Funding; AbbVie: Consultancy. Landgren:Pfizer: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Merck: Other; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Merck: Other; Seattle Genetics: Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. Bolli:Celgene: Honoraria; Janssen: Honoraria