About

Ritu Agarwal is the Wm. Polk Carey Distinguished Professor of Information Systems and Health at the Johns Hopkins Carey Business School. She is also the founding co-director of the Center for Digital Health and Artificial Intelligence (CDHAI). Her expertise lies in the strategic use of information technology, digital transformation of healthcare, health analytics, and artificial intelligence applications in health. Prior to her current role, she served as a Distinguished University Professor and the Robert H. Smith Dean’s Chair of Information Systems at the University of Maryland's Robert H. Smith School of Business, where she was also the founding director of the Center for Health Information and Decision Systems (CHIDS). Her research focuses on applying advanced digital technologies to healthcare practice and delivery, exploring behavioral, psychological, and social processes that influence healthcare interventions. She has published extensively in leading journals, testified before national health committees, and her work has been featured in major media outlets. Agarwal has been recognized with numerous awards and honors, including being named a Fellow of the Association for Information Systems and INFORMS, and receiving lifetime achievement awards. She has held significant leadership roles in professional organizations and has contributed to shaping research and policy in health informatics and digital health.

Research topics

• Biology
• Internal medicine
• Medicine
• Genetics
• Computational biology
• Bioinformatics
• Computer Science
• Pathology
• Library science
• Endocrinology
• Data science
• Oncology

Selected publications

• Abstract 4681: Human Cancer Models Initiative (HCMI): A community resource of next-generation cancer models and associated data

  Cancer Research · 2023 · 6 citations

  • Computer Science
  • Computer Science
  • Medicine

  Abstract The Human Cancer Models Initiative (HCMI) is an international consortium founded by National Cancer Institute (NCI), Cancer Research UK, Wellcome Sanger Institute, and the foundation Hubrecht Organoid Technology. The initiative has generated patient derived Next-generation Cancer Models (NGCMs) from diverse tumor types and subtypes including rare adult and pediatric cancers as a community resource. HCMI addresses deficiencies in traditional cell lines models by collecting patients’ clinical data, as well as the genomes and transcriptomes of the parent tumor, case-matched normal tissue, and the derived next-generation cancer model. NCI’s Center for Cancer Genomics (CCG) sponsors four Cancer Model Development Centers (CMDCs) who are managed by Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc. CCG also supports the downstream model development pipeline. The CMDCs are tasked with generating HCMI NGCMs. The model-associated clinical data are submitted to the Clinical Data Center. The models, their associated tumor, and normal samples are processed at the Biospecimen Processing Center (BPC). The nucleic acids isolated at BPC are sent to the Genomic Characterization Centers for molecular characterization. All biospecimen, clinical, and molecular characterization data are quality controlled and submitted to NCI’s Genomic Data Commons (GDC) for the research community. The HCMI models and culture protocols are made available to the research community through a single third-party distributor. The HCMI Searchable Catalog (https://hcmi-searchable-catalog.nci.nih.gov/) is an online resource that allows users to query and identify available models using various data elements including clinical and molecular characterization data, including WGS, WXS, RNA-seq, and methylation array. To date, over 250 HCMI models are available to query on the Searchable Catalog and are available to the research community through the NCI designated model distributor, ATCC. These models have been derived from several cancer types including glioblastoma, colorectal, pediatric, gastroesophageal, pancreatic, and more. Biospecimen, clinical, and molecular characterization data are available for over 100 models at NCI’s GDC, with additional cases released as the data completes the HCMI pipeline. Data, tools, and resources generated by CCG initiatives are made publicly available via the CCG website and GDC. The CCG website also provides available data types, data usage policies and guides to access data (https://www.cancer.gov/about-nci/organization/ccg). Citation Format: Eva Tonsing-Carter, Rachana Agarwal, Cindy W. Kyi, Julyann Perez-Mayoral, Conrado T. Soria, Jean Claude Zenklusen. Human Cancer Models Initiative (HCMI): A community resource of next-generation cancer models and associated data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4681.

  DOI

• Supplementary Figure 2 from A Multicenter, Double-Blinded Validation Study of Methylation Biomarkers for Progression Prediction in Barrett's Esophagus

  2023

  • Internal medicine
  • Medicine
  • Oncology

  Supplementary Figure 2 from A Multicenter, Double-Blinded Validation Study of Methylation Biomarkers for Progression Prediction in Barrett's Esophagus

  PublisherDOI

• Effect of saroglitazar 2 mg and 4 mg on glycemic control, lipid profile and cardiovascular disease risk in patients with type 2 diabetes mellitus: a 56-week, randomized, double blind, phase 3 study (PRESS XII study)

  Cardiovascular Diabetology · 2020 · 47 citations

  • Medicine
  • Internal medicine
  • Endocrinology

  BACKGROUND: The potential for PPAR agonists to positively affect risk of cardiovascular disease in patients with type 2 diabetes (T2DM) is of persistent attention. The PRESS XII study primarily aimed to evaluate the efficacy and safety of saroglitazar (2 mg and 4 mg) as compared to pioglitazone 30 mg on glycemic control in patients with type 2 diabetes mellitus. METHODS: In this randomized double-blind study, patients with T2DM [glycosylated hemoglobin (HbA1c) ≥ 7.5%] were enrolled from 39 sites in India. Patients received once-daily doses of either saroglitazar or pioglitazone (1:1:1 allocation ratio) for a total of 24 weeks. Patients were continued in a double blind extension period for an additional 32 weeks. Efficacy evaluations of glycemic parameters [HbA1c (Primary endpoint at week 24), FPG and PPG] and other lipid parameters (TG, LDL-C, VLDL-C, HDL-C, TC, Non HDL-C, Apo A1 and Apo B) were conducted at week 12, 24 and 56 and compared to the baseline levels. The efficacy analyses were performed by using paired t-test and ANCOVA model. RESULTS: A total of 1155 patients were enrolled in this study. The baseline characteristics were similar between the three treatment groups. The within group mean (± SD) change in HbA1c (%) from baseline of the saroglitazar (2 mg and 4 mg) and pioglitazone treatment groups at week 24 were: - 1.38 ± 1.99 for saroglitazar 2 mg; - 1.47 ± 1.92 for saroglitazar 4 mg and - 1.41 ± 1.86 for pioglitazone, respectively. Statistically significant reduction from baseline in HbA1c was observed in each treatment group at week 24 with p-value < 0.016. There was a significant reduction in TG, LDL-C, VLDL-C, TC and Non HDL-C with a significant increase in HDL-C from baseline levels (< 0.016). Most of the AE's were 'mild' to 'moderate' in severity and were resolved by the completion of the study. CONCLUSIONS: Saroglitazar effectively improved glycemic control and lipid parameters over 56 weeks in patients of T2DM receiving background metformin therapy and has a promising potential to reduce the cardiovascular risk in T2DM patients. Trial registration CTRI/2015/09/006203, dated 22/09/2015.

  DOI

• The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution

  Cell · 2020 · 581 citations

  • Biology
  • Computational biology
  • Bioinformatics

  Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

  DOI

Frequent coauthors

• Stephen J. Meltzer

  Sidney Kimmel Comprehensive Cancer Center

  120 shared

• Yulan Cheng

  Sir Run Run Shaw Hospital

  112 shared

• Mary E. Barcus

  Leidos Biomedical Research Inc. (United States)

  108 shared

• Conrado Soria

  Leidos (United States)

  108 shared

• Yuriko Mori

  105 shared

• Alexandru Olaru

  105 shared

• John Abraham

  105 shared

• Hana M. Odeh

  University of Pennsylvania

  104 shared

Awards & honors

• Fellow of the Association for Information Systems (2011)
• Distinguished Fellow from the Information Systems Society of…
• Distinguished University Professor (2017)
• LEO Award for Lifetime Achievement from the Association for…
• President’s Medal at the University of Maryland (2021)

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