The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability

Super enhancer acquisition drives expression of oncogenic PPP1R15B that regulates protein homeostasis in multiple myeloma

Multiple myeloma is a hematological malignancy arising from immunoglobulin-secreting plasma cells. It remains poorly understood how chromatin rewiring of regulatory elements contributes to tumorigenesis and therapy resistance in myeloma. Here we generate a high-resolution contact map of myeloma-associated super-enhancers by integrating H3K27ac ChIP-seq and HiChIP from myeloma cell lines, patient-derived myeloma cells and normal plasma cells. Our comprehensive transcriptomic and phenomic analyses prioritize candidate genes with biological and clinical implications in myeloma. We show that myeloma cells frequently acquire SE that transcriptionally activate an oncogene PPP1R15B, which encodes a regulatory subunit of the holophosphatase complex that dephosphorylates translation initiation factor eIF2α. Epigenetic silencing or knockdown of PPP1R15B activates pro-apoptotic eIF2α-ATF4-CHOP pathway, while inhibiting protein synthesis and immunoglobulin production. Pharmacological inhibition of PPP1R15B using Raphin1 potentiates the anti-myeloma effect of bortezomib. Our study reveals that myeloma cells are vulnerable to perturbation of PPP1R15B-dependent protein homeostasis, highlighting a promising therapeutic strategy.

Semimethylation is a feature of diffuse large B-cell lymphoma, and subgroups with poor prognosis are characterized by global hypomethylation and short telomere length

BACKGROUND

Large B-cell lymphoma (LBCL) is the most common lymphoma and is known to be a biologically heterogeneous disease regarding genetic, phenotypic, and clinical features. Although the prognosis is good, one-third has a primary refractory or relapsing disease which underscores the importance of developing predictive biological markers capable of identifying high- and low-risk patients. DNA methylation (DNAm) and telomere maintenance alterations are hallmarks of cancer and aging. Both these alterations may contribute to the heterogeneity of the disease, and potentially influence the prognosis of LBCL.

RESULTS

We studied the DNAm profiles (Infinium MethylationEPIC BeadChip) and relative telomere lengths (RTL) with qPCR of 93 LBCL cases: Diffuse large B-cell lymphoma not otherwise specified (DLBCL, n = 66), High-grade B-cell lymphoma (n = 7), Primary CNS lymphoma (n = 8), and transformation of indolent B-cell lymphoma (n = 12). There was a substantial methylation heterogeneity in DLBCL and other LBCL entities compared to normal cells and other B-cell neoplasms. LBCL cases had a particularly aberrant semimethylated pattern (0.15 ≤ β ≤ 0.8) with large intertumor variation and overall low hypermethylation (β > 0.8). DNAm patterns could not be used to distinguish between germinal center B-cell-like (GC) and non-GC DLBCL cases. In cases treated with R-CHOP-like regimens, a high percentage of global hypomethylation (β < 0.15) was in multivariable analysis associated with worse disease-specific survival (DSS) (HR 6.920, 95% CI 1.499-31.943) and progression-free survival (PFS) (HR 4.923, 95% CI 1.286-18.849) in DLBCL and with worse DSS (HR 5.147, 95% CI 1.239-21.388) in LBCL. These cases with a high percentage of global hypomethylation also had a higher degree of CpG island methylation, including islands in promoter-associated regions, than the cases with less hypomethylation. Additionally, telomere length was heterogenous in LBCL, with a subset of the DLBCL-GC cases accounting for the longest RTL. Short RTL was independently associated with worse DSS (HR 6.011, 95% CI 1.319-27.397) and PFS (HR 4.689, 95% CI 1.102-19.963) in LBCL treated with R-CHOP-like regimens.

CONCLUSION

We hypothesize that subclones with high global hypomethylation and hypermethylated CpG islands could have advantages in tumor progression, e.g. by inactivating tumor suppressor genes or promoting treatment resistance. Our findings suggest that cases with high global hypomethylation and thus poor prognosis could be candidates for alternative treatment regimens including hypomethylating drugs.

There's more to it: uncovering genomewide DNA methylation heterogeneity

Tweetable abstract Monitoring changes in methylation heterogeneity can be powerful in detecting disease progression early. This editorial highlights the importance of profiling methylation heterogeneity and identifies existing measures and research gaps.

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

Study on the Effect of EZH2 Inhibitor Combined with TIGIT Monoclonal Antibody against Multiple Myeloma Cells

EZH2, a member of the polycomb repressive complex 2, induces trimethylation of the downstream gene at the histone three lysine 27 (H3K27me3) position to inhibit tumor cell proliferation. Here, we showed that the apoptosis rate and apoptotic protein expression increased after EZH2 inhibition, whereas key molecules of the NF-κB signaling pathway and the downstream target genes were inhibited. Additionally, the expression of CD155, a TIGIT high-affinity ligand in multiple myeloma (MM) cells, was decreased by the mTOR signaling pathway. Furthermore, the combination of EZH2 inhibitor and TIGIT monoclonal antibody blockade enhanced the anti-tumor effect of natural killer cells. In summary, the EZH2 inhibitor not only plays an anti-tumor role as an epigenetic drug, but also enhances the anti-tumor effect of the TIGIT monoclonal antibody by affecting the TIGIT-CD155 axis between NK cells and MM cells, thus providing new ideas and theoretical basis for the treatment of MM patients.

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

Mechanistic Studies and a Retrospective Cohort Study: The Interaction between PPAR Agonists and Immunomodulatory Agents in Multiple Myeloma

Our previous study demonstrated that peroxisome proliferator-activated receptor (PPAR) agonists downregulated cereblon (CRBN) expression and reduced the anti-myeloma activity of lenalidomide in vitro and in vivo. We aimed to determine whether DNA methylation and protein degradation contribute to the effects of PPAR agonists. CRBN promoter methylation status was detected using methylation-specific polymerase chain reaction. The CRBN protein degradation rate was measured using a cycloheximide chase assay. Metabolomic analysis was performed in multiple myeloma (MM) cells treated with PPAR agonists and/or lenalidomide. Our retrospective study determined the effect of co-administration of PPAR agonists with immunomodulatory drugs on the outcomes of patients with MM. CpG islands of the CRBN promoter region became highly methylated upon treatment with PPAR agonists, whereas treatment with PPAR antagonists resulted in unmethylation. The CRBN protein was rapidly degraded after treatment with PPAR agonists. Lenalidomide and fenofibrate showed opposite effects on acylcarnitines and amino acids. Co-administration of immunomodulatory drugs and PPAR agonists was associated with inferior treatment responses and poor survival. Our study provides the first evidence that PPAR agonists reduce CRBN expression through various mechanisms including inducing methylation of CRBN promoter CpG island, enhancing CRBN protein degradation, and affecting metabolomics of MM cells.

Aberrant DNA methylation in multiple myeloma: A major obstacle or an opportunity?

Drug resistance (DR) of cancer cells leading to relapse is a huge problem nowadays to achieve long-lasting cures for cancer patients. This also holds true for the incurable hematological malignancy multiple myeloma (MM), which is characterized by the accumulation of malignant plasma cells in the bone marrow (BM). Although new treatment approaches combining immunomodulatory drugs, corticosteroids, proteasome inhibitors, alkylating agents, and monoclonal antibodies have significantly improved median life expectancy, MM remains incurable due to the development of DR, with the underlying mechanisms remaining largely ill-defined. It is well-known that MM is a heterogeneous disease, encompassing both genetic and epigenetic aberrations. In normal circumstances, epigenetic modifications, including DNA methylation and posttranslational histone modifications, play an important role in proper chromatin structure and transcriptional regulation. However, in MM, numerous epigenetic defects or so-called ‘epimutations’ have been observed and this especially at the level of DNA methylation. These include genome-wide DNA hypomethylation, locus specific hypermethylation and somatic mutations, copy number variations and/or deregulated expression patterns in DNA methylation modifiers and regulators. The aberrant DNA methylation patterns lead to reduced gene expression of tumor suppressor genes, genomic instability, DR, disease progression, and high-risk disease. In addition, the frequency of somatic mutations in the DNA methylation modifiers seems increased in relapsed patients, again suggesting a role in DR and relapse. In this review, we discuss the recent advances in understanding the involvement of aberrant DNA methylation patterns and/or DNA methylation modifiers in MM development, progression, and relapse. In addition, we discuss their involvement in MM cell plasticity, driving myeloma cells to a cancer stem cell state characterized by a more immature and drug-resistant phenotype. Finally, we briefly touch upon the potential of DNA methyltransferase inhibitors to prevent relapse after treatment with the current standard of care agents and/or new, promising (immuno) therapies.

Epialleles and epiallelic heterogeneity in hematological malignancies

DNA methylation has a well-established role in the pathogenesis, prognosis, and response to treatment in all the spectra of hematological malignancies. However, most of the data reported involve average DNA methylation observed in a sample. The emergence of bisulfite sequencing methods such as enhanced reduced representation that permit analyze adjacent CpGs led to exciting findings. Among these are the epialleles shift and the resulting epigenetic heterogeneity observed in leukemias and lymphomas. Epialleles seem to have an influential role as the cause of mutations that characterize leukemias, may stratify groups with different prognosis and response to treatment, and may be redistributed in the genome at different time points of the disease promoting activation of alternate transcriptional networks. Epiallelic shift may be responsible for the intratumor heterogeneity observed within the cells of the same tumor which increases with disease aggressiveness. It may also responsible for the interpatient heterogeneity explaining why blood cancers exhibit different behavior among different patients. Understanding better epiallelic conformation and the consequent chromatin conformational changes and the pathways that may be affected will permit deeper understanding of hematological malignancies pathogenesis and treatment.

Multiple Myeloma: Challenges Encountered and Future Options for Better Treatment

Multiple myeloma (MM) is a malignant hematological disease. The disease is characterized by the clonal proliferation of malignant plasma cells in the bone marrow. MM accounts for 1.3% of all malignancies and has been increasing in incidence all over the world. Various genetic abnormalities, mutations, and translocation, including epigenetic modifications, are known to contribute to the disease's pathophysiology. The prognosis is good if detected early, or else the outcome is very bad if distant metastasis has already occurred. Conventional treatment with drugs poses a challenge when there is drug resistance. In the present review, we discuss multiple myeloma and its treatment, drug resistance, the molecular basis of epigenetic regulation, the role of natural products in epigenetic regulators, diet, physical activity, addiction, and environmental pollutants, which may be beneficial for clinicians and researchers.

OUP accepted manuscript

Motivation

Intermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns.

Results

We proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest.

Availability and implementation

MeConcord is available at https://github.com/WangLabTHU/MeConcord.

Supplementary information

Supplementary data are available at Bioinformatics online.