Author

G. Marcucci

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

Signature Assays, Organisms & Methods

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Approach:statistical model· 5 Assay:bulk RNA-seq· 4 Approach:experimental assay· 4 Organism:human· 4 Research Focus:cell type annotation· 3 Organism:mouse· 3 Research Focus:trajectory inference· 3 Research Focus:cancer & tumor microenvironment· 2

Frequent Co-Authors

B. Zhang· 4 shared S. Branciamore· 4 shared Y.-H. Kuo· 4 shared D. O'Meally· 3 shared L. Ghoda· 3 shared N. Carlesso· 3 shared R. C. Rockne· 3 shared A. Chowdhury· 2 shared

Activity by Year

2017-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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4 shared papersCancer Biology

B. Zhang

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8 papers

Recent·Most cited

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

Cellular taxonomy of the preleukemic bone marrow niche of acute myeloid leukemia

C. Goda, R. Kulkarni, A. Rudich, M. Karunasiri, O. Balcioglu, Y. Al-Marrawi, E. Korn, S. Kanna, E. A. R. Garfinkle, B. Mundy-Bosse, B. Zhang, G. Marcucci, E. R. Mardis, R. Garzon, K. E. Miller, A. M. Dorrance

The paper identifies changes in the bone marrow microenvironment during preleukemic stages of AML, including an expanded CD55+ fibroblast population that supports preleukemic stem cells over normal HSCs.

bioRxiv · Sep 1, 2023Cancer Biology

Leveraging IFNγ/CD38 regulation to unmask and target leukemia stem cells in acute myelogenous leukemia

M. Murtadha, M. Park, Y. Zhu, E. Caserta, A. A. Dona, M. Singer, H. Vahed, T. Tandoh, A. Gonzalez, K. Ly, J. Sanchez, A. Chowdhury, A. Pozhitkov, L. Ghoda, L. Li, , F. Pichiorri

This paper develops a T cell engager that uses IFNγ to upregulate CD38 on leukemia stem cells, enabling their targeting and depletion in AML.

bioRxiv · Feb 27, 2023 · PreprintCancer 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

Daratumumab induces mechanisms of immune activation through CD38+ NK cell targeting

D. Viola, A. Dona, E. Caserta, E. Troadec, E. G. Gunes, F. Besi, T. McDonald, L. Ghoda, J. F. Sanchez, J. Khalife, M. Martella, C. Karanes, M. Htut, X. Wang, M. Rosenzweig, , F. Pichiorri

This study shows that daratumumab targets CD38+ NK cells to induce immune activation and anti-myeloma activity despite reducing total NK cell frequency.

bioRxiv · Nov 22, 2019Cancer Biology

The 14q32.31 DLK1-DIO3 MIR300 tumor suppressor promotes leukemogenesis by inducing cancer stem cell quiescence and inhibiting NK cell anti-cancer immunity.

G. Silvestri, R. Trotta, L. Stramucci, J. J. Ellis, J. G. Harb, P. Neviani, S. Wang, A.-K. Eisfeld, C. Walker, B. Zhang, K. Srutova, C. Gambacorti-Passerini, G. Pineda, C. H. M. Jamieson, F. Stagno, , D. Perrotti

bioRxiv · Jun 23, 2019Cancer Biology

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

Aging in a relativistic biological space-time

D. Maestrini, D. Abler, V. Adhikarla, S. Armenian, S. Branciamore, N. Carlesso, G. Marcucci, Y.-H. Kuo, P. Sahoo, R. Rockne

This paper proposes a theoretical framework modeling biological aging as trajectories in a mathematical space-time, with applications to cancer and aging rates.

bioRxiv · Dec 5, 2017Cancer Biology