Author

R. C. Rockne

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 8 Bioengineering· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 10 Approach:deep learning· 7 Organism:human· 7 Assay:bulk RNA-seq· 6 Research Focus:cell type annotation· 5 Research Focus:trajectory inference· 5 Research Focus:differential expression· 4 Organism:mouse· 3

Frequent Co-Authors

S. Branciamore· 5 shared Y.-H. Kuo· 4 shared A. B. Brummer· 3 shared C. E. Brown· 3 shared D. O'Meally· 3 shared G. Marcucci· 3 shared H. Cho· 3 shared M. Gutova· 3 shared

Activity by Year

2017-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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5 shared papersCancer Biology

S. Branciamore

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Author profile →

10 papers

Recent·Most cited

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood

B. Orcutt-Jahns, J. R. Lima Junior, R. C. Rockne, A. Matache, S. Branciamore, E. Hung, A. S. Rodin, P. P. Lee, A. S. Meyer

The paper profiles cytokine signaling responses in breast cancer patients and finds coordinated dysregulation across pathways and cell types, suggesting immune cell reprogramming and immunosuppression.

bioRxiv · Oct 31, 2023Cancer Biology

State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia

D. E. Frankhouser, R. C. Rockne, L. Uechi, D. Zhao, S. Branciamore, D. O'Meally, J. Irizarry, L. Ghoda, H. Ali, J. Trent, S. Forman, Y.-H. Fu, Y.-H. Kuo, B. Zhang, G. Marcucci

The paper uses transcriptome-based state-transition models to predict CML disease progression and treatment response using time-sequential blood samples from mice.

bioRxiv · Oct 11, 2023Cancer Biology

Model discovery approach enables non-invasive measurement of intra-tumoral fluid transport in dynamic MRI

R. T. Woodall, C. C. Esparza, M. Gutova, M. Wang, J. Cunningham-Reynolds, A. B. Brummer, C. Stine, C. Brown, J. M. Munson, R. C. Rockne

A physics-informed computational method measures fluid dynamics in tumors, showing differences in fluid velocity between breast and brain cancer tissues.

bioRxiv · Aug 28, 2023 · PreprintBioengineering

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

A. B. Brummer, A. Xella, R. Woodall, V. Adhikarla, H. Cho, M. B. Gutova, C. E. Brown, R. C. Rockne

This paper applies sparse identification of non-linear dynamics (SINDy) to discover and interpret CAR T-cell interaction dynamics with glioblastoma cells using latent variables.

bioRxiv · Sep 22, 2022Cancer Biology

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

A. B. Brummer, X. Yang, E. Ma, M. Gutova, C. E. Brown, R. C. Rockne

This paper uses a mathematical model to show that dexamethasone destabilizes CAR T-cell efficacy in glioblastoma by altering coexistence dynamics and introducing critical thresholds.

bioRxiv · Oct 1, 2021Cancer Biology

State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia

D. E. Frankhouser, D. O'Meally, S. Branciamore, L. Uechi, H. Qin, X. Wu, N. Carlesso, G. Marcucci, Y.-H. Kuo, R. C. Rockne

This study uses time-sequential miRNA expression data from a mouse AML model to identify state transitions during leukemogenesis and shows the model predicts time to AML development.

bioRxiv · Mar 2, 2021 · PreprintCancer Biology

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data

P. Sahoo, X. Yang, D. Abler, D. Maestrini, V. Adhikarla, D. Frankhouser, H. Cho, V. Machuca, D. Wang, M. Barish, M. Gutova, S. Branciamore, C. E. Brown, R. C. Rockne

This paper uses a mathematical model to analyze CAR T-cell killing dynamics in glioma, showing dose-dependent effects on proliferation and exhaustion.

bioRxiv · Nov 9, 2019Immunology

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development

R. C. Rockne, S. Branciamore, J. Qi, D. Frankhouser, D. O'Meally, W.-K. Hua, G. J. Cook, L. Zhang, E. Carnahan, A. Marom, H. Wu, D. Maestrini, X. Wu, Y.-C. Yuan, Z. Liu, , G. Marcucci

The paper models leukemia transcriptome dynamics in a state-space to identify critical points that predict disease development.

bioRxiv · Jun 4, 2019 · PreprintCancer Biology

From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response

J. A. Gallaher, S. C. Massey, A. C. Hawkins-Daarud, S. S. Noticewala, R. C. Rockne, S. K. Johnston, L. Gonzalez-Cuyar, J. Juliano, O. Gil, K. R. Swanson, P. Canoll, A. R. A. Anderson

This paper develops a hybrid agent-based model to study how intratumor heterogeneity affects glioblastoma growth and treatment response using multiscale data and mathematical frameworks.

bioRxiv · May 26, 2019Cancer Biology

Modeling acute myeloid leukemia in a continuum of differentiation states

H. Cho, K. Ayers, L. de Pillis, Y.-H. Kuo, J. Park, A. Radunskaya, R. C. Rockne

The paper models cellular differentiation in acute myeloid leukemia using a mathematical framework based on single-cell RNA sequencing data and graph-based partial differential equations.

bioRxiv · Dec 21, 2017Cancer Biology