Author

B. Ramaswamy

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· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Approach:experimental assay· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell type annotation· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

J. Datta· 2 shared M. A. Cherian· 2 shared M. Lustberg· 2 shared N. Willingham· 2 shared R. K. Ganju· 2 shared R. Wesolowski· 2 shared C. C. Coss· 1 shared C. E. Bennett· 1 shared

Activity by Year

2019-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersCancer Biology

J. Datta

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Activity of Estrogen Receptor β Agonists in Therapy-Resistant Estrogen Receptor-Positive Breast Cancer

J. Datta, N. Willingham, J. Manouchehri, P. Schnell, M. Sheth, J. J. David, M. Kassem, T. A. Wilson, H. S. Radomska, C. C. Coss, C. E. Bennett, R. K. Ganju, S. D. Sardesai, M. Lustberg, B. Ramaswamy, , M. A. Cherian

This study shows that selective ERβ agonists reduce viability of ER+ breast cancer cells and suggest their potential as a therapeutic strategy when combined with ER blockade.

bioRxiv · Jan 14, 2022Cancer Biology

Augmentation of extracellular ATP synergizes with chemotherapy in triple negative breast cancer

J. M. Manouchehri, J. Datta, N. Willingham, R. Wesolowski, D. A. Stover, R. K. Ganju, W. Carson, B. Ramaswamy, M. A. Cherian

This study shows that increasing extracellular ATP through P2RX channels and inhibiting ATPases enhances chemotherapy effectiveness in triple-negative breast cancer cells.

bioRxiv · Jan 11, 2022Cancer Biology

Machine learning predicts rapid relapse of triple negative breast cancer

Y. Zhang, W. Nock, M. Wyse, Z. Weber, E. Adams, S. Stockard, D. Tallman, S. Asad, E. P. Winer, N. U. Lin, M. Cherian, M. Lustberg, B. Ramaswamy, S. Sardesai, J. Vandeusen, , D. G. Stover

The paper uses machine learning to predict rapid relapse of triple-negative breast cancer based on clinical and genomic features.

bioRxiv · Apr 21, 2019Cancer Biology