Warren Kibbe

Overview:

Warren A. Kibbe, PhD, is chief for Translational Biomedical Informatics in the Department of Biostatistics and Bioinformatics and Chief Data Officer for the Duke Cancer Institute. He joined the Duke University School of Medicine in August after serving as the acting deputy director of the National Cancer Institute (NCI) and director of the NCI’s Center for Biomedical Informatics and Information Technology where he oversaw 60 federal employees and more than 600 contractors, and served as an acting Deputy Director for NCI. As an acting Deputy Director, Dr. Kibbe was involved in the myriad of activities that NCI oversees as a research organization, as a convening body for cancer research, and as a major funder of cancer research, funding nearly $4B US annually in cancer research throughout the United States. 

Positions:

Professor in Biostatistics and Bioinformatics

Biostatistics & Bioinformatics
School of Medicine

Chief, Division of Translational Biomedical Informatics

Biostatistics & Bioinformatics
School of Medicine

Chief Data Officer, DCI

Biostatistics & Bioinformatics
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 1990

California Institute of Technology

Grants:

IPA NCI/NIH - Warren Kibbe

Administered By
Biostatistics & Bioinformatics
Awarded By
National Cancer Institute
Role
Research Associate
Start Date
End Date

Publications:

The YPT protein family in yeast

© 1993 by Taylor & Francis. GTP-binding proteins of the Ypt family are members of the ras superfamily of proteins. The first example of a YPT gene, YPT1, was cloned and sequenced as part of the actin-β-tubulin gene cluster in the yeast Saccharomyces cerevisiae and the homology of the Ypt1 protein (Ypt1p) with ras proteins was immediately noted. 1 The subsequent identification by cDNA cloning of mammalian proteins that are strikingly similar in primary structure to the yeast Ypt1p 2, 3 suggested the existence of a larger family of evolutionarily conserved proteins distinct from ras gene products. In fact, multiple members of the Ypt family have been found in the evolutionarily distant yeasts S. cerevisiae and Schizosaccharomyces pombe. 1, 4 - 7 The existence in mammals of 20 or more Ypt-related proteins, designated Rab, 2, 3, 8 - 10 signifies the importance of this still-growing family.
Authors
Kibbe, WA; Hengst, L; Gallwitz, D
MLA Citation
Kibbe, W. A., et al. “The YPT protein family in yeast.” The Ras Superfamily of GTPases, 2017, pp. 367–85. Scopus, doi:10.1201/9780203709931.
URI
https://scholars.duke.edu/individual/pub1451313
Source
scopus
Published Date
Start Page
367
End Page
385
DOI
10.1201/9780203709931

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics.
Authors
Barnholtz-Sloan, JS; Rollison, DE; Basu, A; Borowsky, AD; Bui, A; DiGiovanna, J; Garcia-Closas, M; Genkinger, JM; Gerke, T; Induni, M; Lacey, JV; Mirel, L; Permuth, JB; Saltz, J; Shenkman, EA; Ulrich, CM; Zheng, WJ; Nadaf, S; Kibbe, WA
MLA Citation
Barnholtz-Sloan, Jill S., et al. “Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.Jco Clin Cancer Inform, vol. 4, Feb. 2020, pp. 108–16. Pubmed, doi:10.1200/CCI.19.00166.
URI
https://scholars.duke.edu/individual/pub1447707
PMID
32078367
Source
pubmed
Published In
Jco Clinical Cancer Informatics
Volume
4
Published Date
Start Page
108
End Page
116
DOI
10.1200/CCI.19.00166

Investigating sources of inaccuracy in wearable optical heart rate sensors.

As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.
Authors
Bent, B; Goldstein, BA; Kibbe, WA; Dunn, JP
MLA Citation
Bent, Brinnae, et al. “Investigating sources of inaccuracy in wearable optical heart rate sensors.Npj Digit Med, vol. 3, 2020, p. 18. Pubmed, doi:10.1038/s41746-020-0226-6.
URI
https://scholars.duke.edu/individual/pub1431475
PMID
32047863
Source
pubmed
Published In
Npj Digital Medicine
Volume
3
Published Date
Start Page
18
DOI
10.1038/s41746-020-0226-6

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

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.
Authors
Rozenblatt-Rosen, O; Regev, A; Oberdoerffer, P; Nawy, T; Hupalowska, A; Rood, JE; Ashenberg, O; Cerami, E; Coffey, RJ; Demir, E; Ding, L; Esplin, ED; Ford, JM; Goecks, J; Ghosh, S; Gray, JW; Guinney, J; Hanlon, SE; Hughes, SK; Hwang, ES; Iacobuzio-Donahue, CA; Jané-Valbuena, J; Johnson, BE; Lau, KS; Lively, T; Mazzilli, SA; Pe'er, D; Santagata, S; Shalek, AK; Schapiro, D; Snyder, MP; Sorger, PK; Spira, AE; Srivastava, S; Tan, K; West, RB; Williams, EH; Human Tumor Atlas Network,
MLA Citation
Rozenblatt-Rosen, Orit, et al. “The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.Cell, vol. 181, no. 2, Apr. 2020, pp. 236–49. Pubmed, doi:10.1016/j.cell.2020.03.053.
URI
https://scholars.duke.edu/individual/pub1437796
PMID
32302568
Source
pubmed
Published In
Cell
Volume
181
Published Date
Start Page
236
End Page
249
DOI
10.1016/j.cell.2020.03.053

THE MOLECULAR ANALYSIS FOR THERAPY CHOICE (NCI-MATCH) TRIAL: LESSONS for GENOMIC TRIAL DESIGN.

BACKGROUND: The proportion of tumors of various histologies that may respond to drugs targeted to molecular alterations is unknown. NCI-MATCH, a collaboration between ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) and the National Cancer Institute (NCI), was initiated to find efficacy signals by matching patients with refractory malignancies to treatment targeted to potential tumor molecular drivers regardless of cancer histology. METHODS: Trial development required assumptions about molecular target prevalence, accrual rates, treatment eligibility, enrollment rates, as well as consideration of logistical requirements. Central tumor profiling was performed with an investigational Next Generation DNA targeted Sequencing assay (NGS) of alterations in 143 genes, and protein expression of PTEN, MLH1, MSH2 and Rb. Treatments were allocated with a validated computational platform (MATCHBOX). A pre-planned interim analysis evaluated assumptions and feasibility in this novel trial. RESULTS: At interim analysis, accrual was robust, tumor biopsies were safe (< 1% severe events), and profiling success was 87.3%. Actionable molecular alteration frequency met expectations, but assignment and enrollment lagged due to histology exclusions and mismatch of resources to demand. To address this lag, we revised estimates of mutation frequencies, increased screening sample size, added treatments and improved assay throughput and efficiency (93.9% completion and 14-day turnaround). CONCLUSIONS: The experiences in the design and implementation of the NCI-MATCH trial suggest that profiling from fresh tumor biopsies and assigning treatment can be performed efficiently in a large national network trial. The success of such trials necessitates a broad screening approach and many treatment options easily accessible to patients.
Authors
Flaherty, KT; Gray, R; Chen, A; Li, S; Patton, D; Hamilton, SR; Williams, PM; Mitchell, EP; Iafrate, AJ; Sklar, J; Harris, LN; McShane, LM; Rubinstein, LV; Sims, DJ; Routbort, M; Coffey, B; Fu, T; Zwiebel, JA; Little, RF; Marinucci, D; Catalano, R; Magnan, R; Kibbe, W; Weil, C; Tricoli, JV; Alexander, B; Kumar, S; Schwartz, GK; Meric-Bernstam, F; Lih, C-J; McCaskill-Stevens, W; Caimi, P; Takebe, N; Datta, V; Arteaga, CL; Abrams, JS; Comis, R; O'Dwyer, PJ; Conley, BA; NCI-MATCH Team,
MLA Citation
Flaherty, Keith T., et al. “THE MOLECULAR ANALYSIS FOR THERAPY CHOICE (NCI-MATCH) TRIAL: LESSONS for GENOMIC TRIAL DESIGN.J Natl Cancer Inst, Jan. 2020. Pubmed, doi:10.1093/jnci/djz245.
URI
https://scholars.duke.edu/individual/pub1428061
PMID
31922567
Source
pubmed
Published In
J Natl Cancer Inst
Published Date
DOI
10.1093/jnci/djz245

Research Areas:

Bioinformatics
Clinical medicine--Research
Informatics
Medical Informatics
Software
Software Design
Software Validation