Joshua J Meeks

About

Joshua J Meeks is a Professor of Urology and an Associate Professor in Urology, Biochemistry and Molecular Genetics at Northwestern University Feinberg School of Medicine. He is affiliated with the Department of Urology and is involved in research and clinical work related to urology. His professional profile is associated with the Northwestern Medicine system, and he is part of various research institutes including the Center for Genetic Medicine, Northwestern University Clinical and Translational Sciences Institute (NUCATS), Robert H. Lurie Comprehensive Cancer Center, Simpson Querrey Institute for Epigenetics, among others. His work focuses on urology, with an emphasis on research areas such as urologic cancers, regenerative medicine, and health services and outcomes research.

Research topics

• Computational biology
• Biology
• Computer Science
• Internal medicine
• Medicine
• Environmental health
• Genetics
• Pathology
• Cancer research
• Bioinformatics

Selected publications

• Epidemiology of Bladder Cancer in 2023: A Systematic Review of Risk Factors

  European Urology · 2023 · 453 citations

  CONTEXT: Bladder cancer (BC) is common worldwide and poses a significant public health challenge. External risk factors and the wider exposome (totality of exposure from external and internal factors) contribute significantly to the development of BC. Therefore, establishing a clear understanding of these risk factors is the key to prevention. OBJECTIVE: To perform an up-to-date systematic review of BC's epidemiology and external risk factors. EVIDENCE ACQUISITION: Two reviewers (I.J. and S.O.)…

  DOI

• An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer

  Nature Communications · 2021 · 370 citations

  The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC (n = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond establishe…

  DOI

• Immunosuppressive IDO in Cancer: Mechanisms of Action, Animal Models, and Targeting Strategies

  Frontiers in Immunology · 2020 · 222 citations

  study of IDO, we also review current transgenic animal modeling systems while highlighting three new constructs recently created by our group. This work converges on the central premise that maximal immunotherapeutic efficacy in subjects with advanced cancer requires both IDO enzyme- and non-enzyme-neutralization, which is not adequately addressed by available IDO-targeting pharmacologic approaches at this time.

  DOI

• Circulating tumor DNA (ctDNA) in patients with muscle-invasive bladder cancer (MIBC) who received perioperative durvalumab (D) in NIAGARA.

  Journal of Clinical Oncology · 2025-05-28 · 31 citations

  article

  4503 Background: In the phase 3 NIAGARA trial (NCT03732677) of patients (pts) with cisplatin-eligible MIBC, addition of perioperative D to neoadjuvant chemotherapy (NAC) demonstrated a statistically significant and clinically meaningful improvement in event-free survival (EFS) and overall survival compared with NAC alone, and a 10% higher pathological complete response (pCR) rate, with a manageable safety profile and no impact on the feasibility of surgery. Here, we report a planned exploratory…

  PublisherDOI

• Predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer via interpretable multimodal deep learning

  npj Digital Medicine · 2025-03-22 · 26 citations

  articleOpen access

  Building accurate prediction models and identifying predictive biomarkers for treatment response in Muscle-Invasive Bladder Cancer (MIBC) are essential for improving patient survival but remain challenging due to tumor heterogeneity, despite numerous related studies. To address this unmet need, we developed an interpretable Graph-based Multimodal Late Fusion (GMLF) deep learning framework. Integrating histopathology and cell type data from standard H&E images with gene expression profiles derive…

  PublisherOA PDFDOI

Recent grants

• Epigenetic Regulation of Immune Evasion in Bladder Cancer

  NIH · 2016–2028

Frequent coauthors

• Jeff M. Michalski

  Washington University in St. Louis

  119 shared

• Noah M. Hahn

  Johns Hopkins Medicine

  85 shared

• Seth P. Lerner

  Baylor College of Medicine

  82 shared

• Thomas W. Flaig

  University of Colorado Cancer Center

  81 shared

• Jason A. Efstathiou

  Harvard University

  81 shared

• Elizabeth R. Plimack

  Fox Chase Cancer Center

  79 shared

• Terence W. Friedlander

  UCSF Helen Diller Family Comprehensive Cancer Center

  75 shared

• Harry W. Herr

  Memorial Sloan Kettering Cancer Center

  75 shared

Education

• M.D.

  Northwestern University Feinberg School of Medicine

• B.S.

  University of Illinois at Chicago

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