Genomic Archive Research Articles

The UCSC Genome Browser database: 2025 update.

The UCSC Genome Browser (https://genome.ucsc.edu) is a widely utilized web-based tool for visualization and analysis of genomic data, encompassing over 4000 assemblies from diverse organisms. Since its release in 2001, it has become an essential resource for genomics and bioinformatics research. Annotation data available on Genome Browser includes both internally created and maintained tracks as well as custom tracks and track hubs provided by the research community. This last year's updates include over 25 new annotation tracks such as the gnomAD 4.1 track on the human GRCh38/hg38 assembly, the addition of three new public hubs, and significant expansions to the Genome Archive[GenArk) system for interacting with the enormous variety of assemblies. We have also made improvements to our interface, including updates to the browser graphic page, such as a new popup dialog feature that now displays item details without requiring navigation away from the main Genome Browser page. GenePred tracks have been upgraded with right-click options for zooming and precise navigation, along with enhanced mouseOver functions. Additional improvements include a new grouping feature for track hubs and hub description info links. A new tutorial focusing on Clinical Genetics has also been added to the UCSC Genome Browser.

DDBJ update in 2024: the DDBJ Group Cloud service for sharing pre-publication data.

The Bioinformation and DNA Data Bank of Japan Center (DDBJ Center, https://www.ddbj.nig.ac.jp) provides public databases that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), the DDBJ Center accepts and distributes nucleotide sequence data ranging from raw reads to assembled and annotated sequences with the National Center for Biotechnology Information and the European Bioinformatics Institute. Besides INSDC databases, the DDBJ Center provides databases for functional genomics (Genomic Expression Archive), metabolomics (MetaboBank), human genetic variations (TogoVar-repository) and human genetic and phenotypic data (Japanese Genotype-phenotype Archive). These database systems have been built on the National Institute of Genetics supercomputer, which is also a platform for the DDBJ Group Cloud (DGC) services for sharing and analysis of pre-publication data among research groups. This paper reports recent updates on the databases and the services of the DDBJ Center, highlighting the DGC service.

Best practices for genetic and genomic data archiving.

Genetic and genomic data are collected for a vast array of scientific and applied purposes. Despite mandates for public archiving, data are typically used only by the generating authors. The reuse of genetic and genomic datasets remains uncommon because it is difficult, if not impossible, due to non-standard archiving practices and lack of contextual metadata. But as the new field of macrogenetics is demonstrating, if genetic data and their metadata were more accessible and FAIR (findable, accessible, interoperable and reusable) compliant, they could be reused for many additional purposes. We discuss the main challenges with existing genetic and genomic data archives, and suggest best practices for archiving genetic and genomic data. Recognizing that this is a longstanding issue due to little formal data management training within the fields of ecology and evolution, we highlight steps that research institutions and publishers could take to improve data archiving.

New Insights and Approach Toward the Genetic Diversity and Strain Typing of Erwinia pyrifoliae Based on rsxC, an Electron Transport Gene.

Erwinia pyrifoliae, a causal agent of black shoot blight in apple and pear trees, is a plant pathogenic bacterium first reported in South Korea. The symptoms of black shoot blight are very similar to those of the fire blight disease in apple and pear trees caused by E. amylovora, as E. pyrifoliae has a genetically very close relationship with E. amylovora. Recently, there have been reports that E. pyrifoliae causes disease in European strawberries, resulting in severe fruit loss that aroused great concern about its spread, distribution, and host range. Therefore, it is essential to establish a trustworthy approach to understanding the distribution patterns of E. pyrifoliae based on the genetic background to strengthen the barrier of potential spreading risks, although advanced methods have been provided to accurately detect E. pyrifoliae and E. amylovora. Consequently, this study discovered a noble and noteworthy gene, rsxC, capable of providing the pathogen genotype by comparing E. pyrifoliae genomic sequences in the international representative genome archive. Different numbers of 40-unit amino acid repeats in this gene among the strains induced intraspecific traits in RsxC. By comparing their repeat pattern, E. pyrifoliae isolates were divided into two main groups, branching into several clades via sequence alignment of 35 E. pyrifoliae isolates from various apple orchards from 2020 to 2021 in South Korea. The newly discovered quadraginta amino acid repeat within this gene would be a valuable genetic touchstone for determining the genotype and distribution pattern of E. pyrifoliae strains, ultimately leading to exploring their evolution. The function of amino acid repeats and the biological significance of strains need to be elucidated further.

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model.

The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. The parameter ratio Dw/ρ(P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

DDBJ update in 2023: the MetaboBank for metabolomics data and associated metadata.

The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) provides database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), DDBJ accepts and distributes nucleotide sequence data as well as their study and sample information along with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute (EBI). Besides INSDC databases, the DDBJ Center provides databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank) and human genetic and phenotypic data (JGA: Japanese Genotype-phenotype Archive). These database systems have been built on the National Institute of Genetics (NIG) supercomputer, which is also open for domestic life science researchers to analyze large-scale sequence data. This paper reports recent updates on the archival databases and the services of the DDBJ Center, highlighting the newly redesigned MetaboBank. MetaboBank uses BioProject and BioSample in its metadata description making it suitable for multi-omics large studies. Its collaboration with MetaboLights at EBI brings synergy in locating and reusing public data.

NIMG-17. TOWARDS IMAGE - BASED PERSONALIZATION OF GLIOBLASTOMA THERAPY: A COMPREHENSIVE VALIDATION STUDY OF A NOVEL, DEEP LEARNING - DRIVEN TUMOR GROWTH MODEL FOR INDIVIDUALIZED RADIOTHERAPY

Abstract BACKGROUND The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment planning. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy by estimating the tumor cell density in the whole brain. Here, we performed biological and clinical validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. METHODS 124 patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model that is adjusted to the patient’s anatomy using deep learning. To further evaluate clinical potential, we performed the same growth modeling on preoperative MRI data from 30 patients of our institution and compared model-derived tumor volumes (that serve as alternative clinical target volumes) and recurrence coverage with standard radiotherapy plans. RESULTS The parameter ratio Dw/ρ (p &amp;lt; 0.05 in TCGA) as well as the simulated tumor volume (0.5 tumor cell density) (p &amp;lt; 0.05 in TCGA/UCSF) were significantly inversely correlated with overall survival. We found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter ρ, proving its biological plausibility. Depending on the cutoff value for tumor cell density, we observed significant improvement of recurrence coverage without significantly increased total radiation volume utilizing model-derived target volumes instead of standard radiation plans. More precisely, a cutoff value of 0.5 tumor cell density yielded the best results here. CONCLUSIONS Identifying a significant correlation between computed growth parameters, and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve accuracy of radiation planning in the near future.

DNA Data Bank of Japan (DDBJ) update report 2022.

The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) maintains database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), our primary mission is to collect and distribute nucleotide sequence data, as well as their study and sample information, in collaboration with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute. In addition to INSDC resources, the Center operates databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank), and human genetic and phenotypic data (JGA: Japanese Genotype-Phenotype Archive). These databases are built on the supercomputer of the National Institute of Genetics, whose remaining computational capacity is actively utilized by domestic researchers for large-scale biological data analyses. Here, we report our recent updates and the activities of our services.

Building a Genome Archiving and Communication System Integrated into a Health Information Systems.

The use of next-generation sequencing technologies in clinical practice has increased the volume of information that must be stored, processed, and interpreted. In this work, a description of the implementation of a genomic communication and archiving system (GACS) is presented. This GACS will allow us to store, share and search the genomic files and genetic variants obtained as a result of genetic laboratory tests.

Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication.

To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified). Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data. Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812). Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis. • Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance.

Race, Genealogy, and the Genomic Archive in Post-apartheid South Africa

From the early 2000s onward, scientists, politicians, and intellectuals have presented the South African gene pool as a new archive for the new nation, suggesting a non-racial unity in diversity through common human origins. In this discourse, population genomics and genetic ancestry allude to metaphors of shared kinship to overcome the legacies of race. However, a focus on the underlying practices of measuring and classification reveals how the genomic archive is implicated in the history of apartheid and its racialized subjectivities. Similarly, individual interpretations of genetic ancestry show that race is constantly brought forth in this archival process. The genomic archive interweaves measuring practices in the sciences with the politics of social and biographical experience—a relationship that is at the heart of genetic genealogies.

DNA Data Bank of Japan (DDBJ) update report 2021.

The Bioinformation and DDBJ (DNA Data Bank of Japan) Center (DDBJ Center; https://www.ddbj.nig.ac.jp) operates archival databases that collect nucleotide sequences, study and sample information, and distribute them without access restriction to progress life science research as a member of the International Nucleotide Sequence Database Collaboration (INSDC), in collaboration with the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute. Besides the INSDC databases, the DDBJ Center also provides the Genomic Expression Archive for functional genomics data and the Japanese Genotype-phenotype Archive for human data requiring controlled access. Additionally, the DDBJ Center started a new public repository, MetaboBank, for experimental raw data and metadata from metabolomics research in October 2020. In response to the COVID-19 pandemic, the DDBJ Center openly shares SARS-CoV-2 genome sequences in collaboration with Shizuoka Prefecture and Keio University. The operation of DDBJ is based on the National Institute of Genetics (NIG) supercomputer, which is open for large-scale sequence data analysis for life science researchers. This paper reports recent updates on the archival databases and the services of DDBJ.

Rare cases of medulloblastoma with hypermutation.

BackgroundMedulloblastoma is the most common malignant brain tumor of childhood and is considered a tumor with low mutational burden (~1 Mut/Mb). Therefore, though the medulloblastoma genomes have been extensively characterized in literature, reports on potential hypermutations and underlying mutagenic processes in medulloblastomas are limited.AimIn this report, we studied the landscape of mutational burden in primary and recurrent medulloblastoma. Furthermore, we wanted to understand the differences in underlying mutagenic mechanisms in medulloblastoma with low and high mutational burdens.MethodsFifty‐three primary and recurrent medulloblastoma genomic sequence were downloaded from the European Genome Archive as BAM files. Thirty‐three cases were obtained from formalin‐fixed paraffin‐embedded tissues from pathology diagnostic archives of Spectrum Health and Cooperative Human Tissue Network. Somatic mutations were called using Mutect2, following best practices guidelines for Genome Analysis Toolkit V4. Mutational signatures were analyzed using deconstructSigs.ResultsWe identified nine medulloblastoma cases with high mutational burden (>5 Mut/Mb). Of them, five cases met the criteria of hypermutation (>10Mut/Mb), two of the five tumors had canonical mutations in the POLE proof‐reading domain, where a large proportion of mutations in these tumor genomes contributed to signature 10. The hypermutated cases also demonstrated mutational signatures 14, 15, and 21, indicating the role of mis match repair deficiency in their mutagenesis. Of the four known molecular subgroups in medulloblastoma–SHH, WNT, Group 3, and Group 4—both the POLE‐mutated cases belonged to the SHH subgroup. This report identifies rare cases of hypermutation in medulloblastoma driven by defects in DNA repair mechanisms.ConclusionHypermutation in medulloblastoma can impact therapeutic decisions, especially at recurrence in otherwise fatal high risk SHH‐medulloblastomas. A defect in DNA repair leading to SHH ‐medulloblastoma is yet another important mechanism that should be further investigated in the genesis of these tumors. Therefore, this report provides important scientific and clinical rationale for future research looking for incidence of hypermutation in large cohorts of medulloblastoma patients.

Abstract PO-076: Unsupervised learning of image embeddings enables new opportunities to extract novel information from digital pathology H&amp;E images

Abstract Introduction: In digital pathology, large amounts of time and money are spent annotating whole slide images (WSIs). By using an AI algorithm trained on thousands of WSIs, it is possible to remove the process of annotating from the workflow whilst simultaneously finding potential imaging features that correlate with drug response, molecular phenotypes, histologic features (e.g. Tertiary Lymphoid Structures), inflammation at the tumor-stroma interface, artifacts, etc. Method: A pipeline was constructed for ingesting WSI from the set of diagnostic slides available in the cancer genome archive (TCGA). The WSIs were masked for tissue areas and divided into patches extracted at multiple magnification levels. These patches were used to train a U-Net (initialised with a pre-trained ResNet34) to complete a self-supervised in-painting task and evaluated with perceptual loss using VGG16. After training, embeddings were extracted from the bottleneck layer of the U-Net, and these were evaluated for their capacity to cluster according to tissue type and magnification level. Results: The trained embeddings showed strong clustering by tissue type, magnification level, and separation of artefacts. The results were qualitatively evaluated by pathologist with consensus and exceeded the performance of a baseline pre-trained ImageNet model. Conclusion: These results highlight a novel methodology for AI algorithm development that removes the need for numerous pathologists to annotate pathology images by hand and converts their role to reviewing and quality-checking images. AI algorithms based on this approach can have a significant impact in the pathologic evaluation of tissue for oncology clinical trials by extracting a richer set of features than glass microscopy alone would permit. Furthermore, self-supervised methods, such as in-painting allow for the training of embeddings that encapsulate a richer understanding of the data and are consequently more repurposable than supervised/labelled approaches. Citation Format: Jason Hipp, Mona Xu, Lucas Bordeaux, Feng Gu, Carlos Pedrinaci, Khan Baykaner. Unsupervised learning of image embeddings enables new opportunities to extract novel information from digital pathology H&amp;E images [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-076.

A secure system for genomics clinical decision support.

We developed a prototype genomic archiving and communications system to securely store genome data and provide clinical decision support (CDS). This system operates on a client-server model. The client encrypts the data, and the server stores data and performs the computations necessary for CDS. Computations are directly performed on encrypted data, and the client decrypts results. The server cannot decrypt inputs or outputs, which provides strong guarantees of security. We have validated our system with three genomics-based CDS applications. The results demonstrate that it is possible to resolve a long-standing dilemma in genomic data privacy and accessibility, by using a principled cryptographical framework and a mathematical representation of genome data and CDS questions.

The EBiSC iPSC bank for disease studies

The European Bank for induced Pluripotent Stem Cells (EBiSC), a non-profit repository for storage, banking, Quality Control (QC) and subsequent distribution of research-grade human induced Pluripotent Stem Cell (iPSC) lines, has centralised iPSC lines generated internationally across >35 disease areas and made them available to users via the EBiSC Catalogue, for research use (cells.ebisc.org/). Comprehensive datasets are accessible prior to purchase detailing the disease background of the original tissue sample, background of iPSC reprogramming and cell line characterisation data. EBiSC also performs robust QC screening to ensure supply of reliable, well-characterised iPSC lines, compliant with ISO9001:2015 principles. Whole Genome Sequencing data for specific iPSC lines can be downloaded from the European Genome Archive, subject to application to the EBiSC Data Access Committee. The EBiSC Access and Use Agreement, required to be completed prior to shipping, can be downloaded from the website along with specific Cell Line Information Packs; together these documents clarify how EBiSC lines can be used for research and detail any specific Third Party Obligations and/or restrictions for use which may apply. A protocol for how to culture and monitor iPSC lines including implementation of routine cell line screening is also available. A second project phase will continue collecting iPSC lines generated internationally, provide iPSC derived differentiated products using improved automation strategies for upscaling and develop the current services provided by EBiSC, including iPSC reprogramming, gene-editing and characterisation.

All of gene expression (AOE): An integrated index for public gene expression databases.

Gene expression data have been archived as microarray and RNA-seq datasets in two public databases, Gene Expression Omnibus (GEO) and ArrayExpress (AE). In 2018, the DNA DataBank of Japan started a similar repository called the Genomic Expression Archive (GEA). These databases are useful resources for the functional interpretation of genes, but have been separately maintained and may lack RNA-seq data, while the original sequence data are available in the Sequence Read Archive (SRA). We constructed an index for those gene expression data repositories, called All Of gene Expression (AOE), to integrate publicly available gene expression data. The web interface of AOE can graphically query data in addition to the application programming interface. By collecting gene expression data from RNA-seq in the SRA, AOE also includes data not included in GEO and AE. AOE is accessible as a search tool from the GEA website and is freely available at https://aoe.dbcls.jp/.

Comprehensive gene expression analysis for exploring the association between glucose metabolism and differentiation of thyroid cancer

BackgroundThe principle of loss of iodine uptake and increased glucose metabolism according to dedifferentiation of thyroid cancer is clinically assessed by imaging. Though these biological properties are widely applied to appropriate iodine therapy, the understanding of the genomic background of this principle is still lacking. We investigated the association between glucose metabolism and differentiation in advanced thyroid cancer as well as papillary thyroid cancer (PTC).MethodsWe used RNA sequencing of 505 patients with PTC obtained from the Cancer Genome Archives and microarray data of poorly-differentiated and anaplastic thyroid cancer (PDTC/ATC). The signatures of GLUT and glycolysis were estimated to assess glucose metabolic profiles. The glucose metabolic profiles were associated with tumor differentiation score (TDS) and BRAFV600E mutation status. In addition, survival analysis of glucose metabolic profiles was performed for predicting recurrence-free survival.ResultsIn PTC, the glycolysis signature was positively correlated with TDS, while the GLUT signature was inversely correlated with TDS. These correlations were significantly stronger in the BRAFV600E negative group than the positive group. Meanwhile, both GLUT and glycolysis signatures were negatively correlated with TDS in advanced thyroid cancer. The high glycolysis signature was significantly associated with poor prognosis in PTC in spite of high TDS. The glucose metabolic profiles are intricately associated with tumor differentiation in PTC and PDTC/ATC.ConclusionsAs glycolysis was an independent prognostic marker, we suggest that the glucose metabolism features of thyroid cancer could be another biological progression marker different from differentiation and provide clinical implications for risk stratification.Trial registrationNot applicable.

A Pharmacogenomics Clinical Decision Support Service Based on FHIR and CDS Hooks.

Pharmacogenomics (PGx) is often considered a low-hanging fruit for genomics-electronic health record (EHR) integrations, and many have expressed the notion that drug-gene interaction checking might one day become as much a commodity in EHRs as drug-drug and drug-allergy checking. In addition, the U.S. Office of the National Coordinator has recognized the trend toward storing complete sequencing data outside the EHR in a Genomic Archiving and Communication System (GACS) and has emphasized the need for "pilots that test Fast Healthcare Interoperability Resources (FHIR) Genomics for GACS integration with EHRs." We sought to develop a PGx clinical decision support (CDS) service, leveraging the emerging FHIR and CDS Hooks standards, and based on an assumption that pharmacogene sequencing data would be stored alongside the EHR in a GACS. We developed a PGx CDS service as a functional prototype. The service is triggered by a medication order in the EHR. When evoked, the service looks for relevant genetic data in a GACS and returns corresponding recommendations back to the ordering clinician. Where the patient has no genetic data on file, the service can recommend pretreatment genetic testing where applicable. Overall, we were able to meet our objectives and deploy a functional prototype, interfaced with a commercial EHR. We identified several areas where FHIR or CDS Hooks lacked necessary semantics or have implementation ambiguity. Primary FHIR challenges included multiple ways to say the same thing, which exacerbated the complexity of variant to allele conversion and lack of representation of deoxyribonucleic acid region(s) studied. Primary CDS Hooks challenges included the complexity of executing an authenticated query against one system (GACS) upon being triggered by a different system (the EHR), and limitations in the types of actionable recommendations that can be returned to the EHR. In conclusion, we have found that PGx CDS based on FHIR and CDS Hooks appears to represent a promising means of genomics-EHR integration. More real-world testing along with a set of use-case driven GACS interface requirements will push us closer to the U.S. National Human Genome Research Institute vision of a plug-in PGx app.

Whole Genome DNA Methylation Analysis of Commpass Identifies Biomarkers of Multiple Myeloma Survival

Multiple myeloma is a malignancy of terminally differentiated, antibody secreting B cells known as plasma cells. Normal B cell differentiation and cell fate are coupled to epigenetic and transcriptional reprogramming, including a proliferation-dependent global loss of DNA methylation (Barwick et al., 2016, 2018). However, relatively little is known about the epigenetic changes that underlie myelomagenesis and how these may contribute to pathogenesis. To this end, we are analyzing the DNA methylome of myeloma specimens from the MMRF CoMMpass trial (NCT01454297), which has already characterized the mutational, structural, and transcriptional landscape of nearly 1,000 myelomas from newly diagnosed patients.CoMMpass specimens were obtained from a centralized biobank with approval from the CoMMpass Tissue Use Committee and Emory IRB. DNA isolated from CD138+ myeloma specimens was subjected to reduced representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS). In total, DNA methylation was derived for over 24 million CpGs with an average of 18x coverage. WGBS data from normal B cells and plasma cells was obtained with permission from the BluePrint project (Agirre et al., 2015) via the European Genome Archive. DNA methylation levels were associated with PFS and OS using a cox proportional regression.We have determined the DNA methylome for 36 primary myeloma specimens and an additional 84 specimens are currently being sequenced. Relative to normal B cells that had an average DNA methylation level of 89.1%, plasma cells and myelomas exhibited a progressive demethylation with mean levels of 71.3% and 43.7%, respectively. While this is consistent with previous observations (Agirre et al., 2015; Salhia et al., 2010), WGBS revealed that myeloma in particular was characterized by large hypomethylated domains. These large hypomethylated domains encompassed genes that were devoid of gene expression whereas DNA methylation remained unchanged in the bodies of genes that were highly expressed. Although the majority of these hypomethylated domains were common across myelomas, there existed many regions where methylation levels varied between myelomas and these differences commonly corresponded with local gene expression differences. To understand if these specific patterns of DNA methylation were indicative of disease pathogenesis, DNA methylation levels were compared to PFS and OS. This identified 2,594 regions where the level of DNA methylation was prognostic of outcome (P≤0.001). Reduced DNA methylation corresponded with poor outcome at 88.5% (N=2,298) of these regions, which included loci proximal to cell cycle genes such as MYC, E2F3, CCND1, and CCNE1. Only 11.5% (N=296) of regions associated with outcome had higher levels of DNA methylation associated with poor prognosis. These regions tended to be proximal to genes involved in B cell receptor signaling, such as PLCG2 and VAV2. Although the expression of several of these genes was also prognostic of survival, the majority were not, indicating that the epigenetic state contains a unique prognostic value.These data indicate that myeloma undergoes profound epigenetic remodeling that is co-ordinate with changes in gene expression. Perhaps the most striking feature were megabase domains of hypomethylation. That DNA methylation was preferentially retained in the bodies of expressed genes suggests that a molecular mechanism and/or cellular selection occurs to maintain methylation at genes whose expression is required for myeloma cell survival. Despite the small number (N=36) of myeloma specimens analyzed thus far, the large number of regions associated with survival indicates the potential prognostic value of DNA methylation in myeloma. Furthermore, DNA methylation indicative of outcome only partially overlapped with the prognostic value of gene expression, indicating DNA methylation has independent value as a biomarker of outcome in myeloma. This may be due, in part, to the fact that DNA methylation is a very stable modification that not only reflects the current gene expression program, but is also indicative of the cell history and potential. Integrative genetic, epigenetic, and transcriptional analysis from WGBS of 120 CoMMpass myeloma specimens will be presented, including matched baseline and relapsed specimens from 25 patients. DisclosuresLonial:Amgen: Research Funding. Boise:Abbvie: Consultancy; AstraZeneca: Honoraria.