Eden Deng

All Publications

• Integrative analysis of lung adenocarcinoma across diverse ethnicities and exposures. Cancer cell Satpathy, S., Clark, N. M., Chen, Y. J., Hosseini, N., Chang, Y. H., Hsiao, Y., Lei, J. T., Petralia, F., Chen, J. S., Geffen, Y., Heiman, D. I., Paul, I., Cho, H., Hollenberg, M., Marino, G. B., Lin, K. T., Mannan, R., White, C. J., Allen, J., Avanessian, S. C., Kane, M. H., Wolfe, A., Kinarivala, M., Liu, W., Anand, S., Lin, M. W., Haines, M., Bergstrom, E. J., Hussey, G., Li, G. X., Mani, D. C., Fang, H., Jaehnig, E. J., Keshishian, H., Miller, B., Su, K. Y., Hsiao, Y. J., Hsu, H. H., Hsieh, M. S., Hsu, K. H., Monovoukas, A., Gohsman, S., Thorup, J. R., Deng, Y., Akiyama, Y., Deng, E., Sheng-Wen Chen, E., Krek, A., Espinoza, R., Ma, W., Charytonowicz, D., Sebra, R., Lin, J. H., Chen, Y. S., Hsu, Y. C., Lin, Z. S., Chen, K. C., Yeh, C. W., Wang, Y. T., Lazar, A. J., Mesri, M., An, E., Zhang, X., Clauser, K. R., Fenyö, D., Chinnaiyan, A. M., Zhang, B., Ding, L., Ruggles, K., Newton, C., Zhang, H., Wang, P., Hostetter, G., Omenn, G. S., Kumar-Sinha, C., Thiagarajan, M., Govindan, R., Paik, P., Parolia, A., Li, Q. K., Ma'ayan, A., Getz, G. A., Dhanasekaran, S. M., Robles, A. I., Chang, G. C., Yang, P. C., Yu, S. L., Chen, H. Y., Nesvizhskii, A. I., Carr, S. A., Mani, D. R., Cieslik, M. P., Chen, Y. J., Gillette, M. A. 2025

  Abstract

  Lung adenocarcinomas (LUAD) are a pressing global health problem with enduring lethality and rapidly shifting epidemiology. Proteogenomic studies integrating proteomics and post-translational modifications with genomics can identify clinical strata and oncogenic mechanisms, but have been underpowered to examine effects of ethnicity, smoking and environmental exposures, or sex on this heterogeneous disease. This comprehensive proteogenomic analysis of LUAD tumors and matched normal adjacent tissues from 406 patients across diverse geographic and demographic backgrounds explores the impact of understudied driver mutations, prognostic role of chromosomal instability, patterns of immune signaling, differential and sex-specific effects of endogenous mutagens and environmental carcinogens, and pathobiology of early-stage tumors with "late-like" characteristics. Candidate protein biomarkers are proposed for unstable tumors with highly fragmented genomes and for carcinogen exposures, and a LUAD subtype-specific atlas of therapeutic vulnerabilities is presented. These observations and the associated data resource advance the objective of precision management strategies for this devastating disease.

  View details for DOI 10.1016/j.ccell.2025.07.011

  View details for PubMedID 40749670

  View details for PubMedCentralID PMC12393171

• Temporal Trends in the Utilization and Survival Outcomes of Lobar, Segmental, and Wedge Resection for Early-Stage NSCLC, 2004 to 2020. JTO clinical and research reports Deng, E. Z., Wang, X., Zhang, J., Stinchcombe, T. E., Yang, C. J., Altorki, N. 2025; 6 (3): 100794

  Abstract

  Although lobectomy has long been the standard of surgical treatment for early-stage NSCLC, segmental and wedge resections have become another option often used over the past two decades.To examine the trends over time in the utilization, quality, and overall survival (OS) differences of lobectomy, segmentectomy, and wedge resection, we performed an observational, population-level study of 76,466 patients with T1 or T2 N0M0 NSCLC tumors 2 cm or less in size in the National Cancer Database, from 2004 to 2020. To compare the OS of the three treatments, we used inverse probability of treatment weighting to analyze a subgroup of cases with nodal examination and minimal comorbidity burden.From 2004 to 2020, the use of lobectomy decreased from 75.2% to 67.6% of resections, wedge remained relatively stable (20.5%-22.8%), and segmentectomy increased from 4.3% to 9.7%. The likelihood of nodal assessments and negative margins has increased for all treatments. Younger patients, patients with low comorbidity burden, and patients with smaller tumors have become increasingly likely to receive segmental and wedge resections. Five-year OS of segmentectomy (80.6%, 95% confidence interval [CI]: 78.1%-83.2%) remained noninferior to lobectomy (83.6%, 95% CI: 83.1%-84.1%]), whereas wedge resection was inferior until 2016 to 2019 (five-y OS = 79.9%, 95% CI: 75.9%-83.8%).Sublobar resections, particularly segmentectomies, have increased in frequency and quality. The growing use of sublobar resections for younger and healthier patients highlights the need for additional clinical evidence demonstrating whether these trends do indeed lead to better outcomes.

  View details for DOI 10.1016/j.jtocrr.2025.100794

  View details for PubMedID 39996091

  View details for PubMedCentralID PMC11849078

• Protocol for using Multiomics2Targets to identify targets and driver kinases for cancer cohorts profiled with multi-omics assays. STAR protocols Marino, G. B., Deng, E. Z., Clarke, D. J., Diamant, I., Resnick, A. C., Ma, W., Wang, P., Ma'ayan, A. 2024; 5 (4): 103457

  Abstract

  The availability of multi-omics data applied to profile cancer cohorts is rapidly increasing. Here, we present a protocol for Multiomics2Targets, a computational pipeline that can identify driver cell signaling pathways, protein kinases, and cell-surface targets for immunotherapy. We describe steps for preparing the data, uploading files, and tuning parameters. We then detail procedures for running the workflow, visualizing the results, and exporting and sharing reports containing the analysis. For complete details on the use and execution of this protocol, please refer to Deng et al.1.

  View details for DOI 10.1016/j.xpro.2024.103457

  View details for PubMedID 39565691

  View details for PubMedCentralID PMC11617449

• RummaGEO: Automatic mining of human and mouse gene sets from GEO. Patterns (New York, N.Y.) Marino, G. B., Clarke, D. J., Lachmann, A., Deng, E. Z., Ma'ayan, A. 2024; 5 (10): 101072

  Abstract

  The Gene Expression Omnibus (GEO) has millions of samples from thousands of studies. While users of GEO can search the metadata describing studies, there is a need for methods to search GEO at the data level. RummaGEO is a gene expression signature search engine for human and mouse RNA sequencing perturbation studies extracted from GEO. To develop RummaGEO, we automatically identified groups of samples and computed differential expressions to extract gene sets from each study. The contents of RummaGEO are served for gene set, PubMed, and metadata search. Next, we analyzed the contents of RummaGEO to identify patterns and perform global analyses. Overall, RummaGEO provides a resource that is enabling users to identify relevant GEO studies based on their own gene expression results. Users of RummaGEO can incorporate RummaGEO into their analysis workflows for integrative analyses and hypothesis generation.

  View details for DOI 10.1016/j.patter.2024.101072

  View details for PubMedID 39569206

  View details for PubMedCentralID PMC11573963

• Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics. Cell reports methods Deng, E. Z., Marino, G. B., Clarke, D. J., Diamant, I., Resnick, A. C., Ma, W., Wang, P., Ma'ayan, A. 2024; 4 (8): 100839

  Abstract

  The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.

  View details for DOI 10.1016/j.crmeth.2024.100839

  View details for PubMedID 39127042

  View details for PubMedCentralID PMC11384097

• Rummagene: massive mining of gene sets from supporting materials of biomedical research publications. Communications biology Clarke, D. J., Marino, G. B., Deng, E. Z., Xie, Z., Evangelista, J. E., Ma'ayan, A. 2024; 7 (1): 482

  Abstract

  Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .

  View details for DOI 10.1038/s42003-024-06177-7

  View details for PubMedID 38643247

  View details for PubMedCentralID PMC11032387

• Pan-cancer proteogenomics characterization of tumor immunity. Cell Petralia, F., Ma, W., Yaron, T. M., Caruso, F. P., Tignor, N., Wang, J. M., Charytonowicz, D., Johnson, J. L., Huntsman, E. M., Marino, G. B., Calinawan, A., Evangelista, J. E., Selvan, M. E., Chowdhury, S., Rykunov, D., Krek, A., Song, X., Turhan, B., Christianson, K. E., Lewis, D. A., Deng, E. Z., Clarke, D. J., Whiteaker, J. R., Kennedy, J. J., Zhao, L., Segura, R. L., Batra, H., Raso, M. G., Parra, E. R., Soundararajan, R., Tang, X., Li, Y., Yi, X., Satpathy, S., Wang, Y., Wiznerowicz, M., González-Robles, T. J., Iavarone, A., Gosline, S. J., Reva, B., Robles, A. I., Nesvizhskii, A. I., Mani, D. R., Gillette, M. A., Klein, R. J., Cieslik, M., Zhang, B., Paulovich, A. G., Sebra, R., Gümüş, Z. H., Hostetter, G., Fenyö, D., Omenn, G. S., Cantley, L. C., Ma'ayan, A., Lazar, A. J., Ceccarelli, M., Wang, P. 2024; 187 (5): 1255-1277.e27

  Abstract

  Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

  View details for DOI 10.1016/j.cell.2024.01.027

  View details for PubMedID 38359819

  View details for PubMedCentralID PMC10988632

• GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery. Nucleic acids research Marino, G. B., Ngai, M., Clarke, D. J., Fleishman, R. H., Deng, E. Z., Xie, Z., Ahmed, N., Ma'ayan, A. 2023; 51 (W1): W213-W224

  Abstract

  Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.

  View details for DOI 10.1093/nar/gkad399

  View details for PubMedID 37166966

  View details for PubMedCentralID PMC10320068

• Computational screen to identify potential targets for immunotherapeutic identification and removal of senescence cells. Aging cell Deng, E. Z., Fleishman, R. H., Xie, Z., Marino, G. B., Clarke, D. J., Ma'ayan, A. 2023; 22 (6): e13809

  Abstract

  To prioritize gene and protein candidates that may enable the selective identification and removal of senescent cells, we compared gene expression signatures from replicative senescent cells to transcriptomics and proteomics atlases of normal human tissues and cell types. RNA-seq samples from in vitro senescent cells (6 studies, 13 conditions) were analyzed for identifying targets at the gene and transcript levels that are highly expressed in senescent cells compared to their expression in normal human tissues and cell types. A gene set made of 301 genes called SenoRanger was established based on consensus analysis across studies and backgrounds. Of the identified senescence-associated targets, 29% of the genes in SenoRanger are also highly differentially expressed in aged tissues from GTEx. The SenoRanger gene set includes previously known as well as novel senescence-associated genes. Pathway analysis that connected the SenoRanger genes to their functional annotations confirms their potential role in several aging and senescence-related processes. Overall, SenoRanger provides solid hypotheses about potentially useful targets for identifying and removing senescence cells.

  View details for DOI 10.1111/acel.13809

  View details for PubMedID 37082798

  View details for PubMedCentralID PMC10265163

• Replication of neural responses to monetary incentives and exploration of reward-influenced network connectivity in fibromyalgia. Neuroimage. Reports Park, S. H., Deng, E. Z., Baker, A. K., MacNiven, K. H., Knutson, B., Martucci, K. T. 2022; 2 (4)

  Abstract

  Neuroimaging research has begun to implicate alterations of brain reward systems in chronic pain. Previously, using functional magnetic resonance imaging (fMRI) and a monetary incentive delay (MID) task, Martucci et al. (2018) showed that neural responses to reward anticipation and outcome are altered in fibromyalgia. In the present study, we aimed to test the replicability of these altered neural responses to reward in a separate fibromyalgia cohort. In addition, the present study was conducted at a distinct U.S. location but involved a similar study design. For the present study, 20 patients with fibromyalgia and 20 healthy controls participated in MID task fMRI scan procedures and completed clinical/psychological questionnaires. fMRI analyses comparing patient and control groups revealed a consistent trend of main results which were largely similar to the prior reported results. Specifically, in the replication fibromyalgia cohort, medial prefrontal cortex (MPFC) response was reduced during gain anticipation and was increased during no-loss (non-punishment) outcome compared to controls. Also consistent with previous findings, the nucleus accumbens response to gain anticipation did not differ in patients vs. controls. Further, results from similarly-designed behavioral, correlational, and exploratory analyses were complementary to previous findings. Finally, a novel network-based functional connectivity analysis of the MID task fMRI data across patients vs. controls implied enhanced connectivity within the default mode network in participants with fibromyalgia. Together, based on replicating prior univariate results and new network-based functional connectivity analyses of MID task fMRI data, we provide further evidence of altered brain reward responses, particularly in the MPFC response to reward outcomes, in patients with fibromyalgia.

  View details for DOI 10.1016/j.ynirp.2022.100147

  View details for PubMedID 36618964

  View details for PubMedCentralID PMC9815752

• Evaluation by Survival Analysis of Cold Pain Tolerance in Patients with Fibromyalgia and Opioid Use. Journal of pain research Deng, E. Z., Weikel, D. P., Martucci, K. T. 2022; 15: 2783-2799

  Abstract

  The cold pressor test (CPT) is a clinical pain research method used to measure cold pain tolerance. During this test, participants immerse an extremity (ie, hand or foot) into cold water for as long as tolerable. The duration of the test (traditionally up to an experimentally imposed cut-off at 2 minutes) indicates the amount of cold pain tolerance by the participant. Prior research studies have investigated cold pain tolerance in patients with chronic pain. However, few of these studies have used survival analysis, which allows for proper handling of data censoring and is therefore, an optimal statistical method for CPT data analysis. The goal of the present study was to use survival analysis to evaluate cold pain tolerance in patients with fibromyalgia. Furthermore, we aimed to model relationships between psychological and clinical variables as well as opioid medication use and cold pain tolerance.A total of 85 patients with fibromyalgia (42 who were taking opioids) and 47 healthy pain-free controls provided CPT and questionnaire data (collected across 2 study sites) for a case-control study. We used survival analysis using Cox regression to evaluate group differences (patients vs controls) in cold pain tolerance and to evaluate cold pain tolerance relationships with psychological, clinical, and medication use.As compared to healthy controls, patients with fibromyalgia exhibited significantly lower CPT survival (HR = 2.17, 95% CI: [1.42, 3.31], p = 0.00035). As indicated by Cox regression models, the significant group difference in CPT survival did not relate to our selected psychological and clinical measures (p > 0.05). The groups of non-opioid-taking patients and healthy controls showed consistent CPT survival across study sites. However, patients taking opioid pain medications showed differences in CPT survival across study sites.By using survival analysis, an optimal method for time-to-event pain measures such as the CPT, we confirmed previously identified reductions in cold pain tolerance in patients with fibromyalgia. While our selected psychological and clinical measures were not significantly associated with cold pain tolerance, our data suggest that opioid medication use may impart greater cold pain tolerance in some patients.

  View details for DOI 10.2147/JPR.S368805

  View details for PubMedID 36111289

  View details for PubMedCentralID PMC9470281