Author

R. A. Beckman

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· 4

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:clustering· 1

Frequent Co-Authors

A. R. Akhmetzhanov· 1 shared B. Kohrn· 1 shared C. Chen· 1 shared C.-H. Yeang· 1 shared D. Guinn· 1 shared D. Mobin· 1 shared D. Parashar· 1 shared E. H. Ahn· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 9 mutual co-author links

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1 shared paperCancer Biology

A. R. Akhmetzhanov

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4 papers

Recent·Most cited

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

S. Bahnassy, H. Stires, L. Jin, S. Tam, D. Mobin, M. Balachandran, M. B. Podar, M. D. McCoy, R. A. Beckman, R. B. Riggins

The paper develops two in vitro models of endocrine therapy-resistant HER2+/ER+ breast cancer and identifies lipid metabolism and ferroptosis as therapeutic vulnerabilities.

bioRxiv · Aug 21, 2023Cancer Biology

Efficiency of a Randomized Confirmatory Basket Trial Design Constrained to Control the False Positive Rate by Indication

L. He, Y. Ren, H. Chen, D. Guinn, D. Parashar, C. Chen, S. Yuan, V. Korostyshevskiy, R. A. Beckman

This paper evaluates a randomized basket trial design that controls false positive rate by indication, showing improved efficiency compared to parallel studies.

bioRxiv · Jul 22, 2020 · PreprintCancer Biology

Unexpectedly High Subclonal Mutational Diversity in Human Colorectal Cancer and Its Significance

L. A. Loeb, B. Kohrn, K. J. Loubet-Senear, Y. J. Dunn, E. H. Ahn, J. N. O'Sullivan, J. J. Salk, M. P. Bronner, R. A. Beckman

This study quantifies subclonal mutations in colorectal cancer using Duplex Sequencing, revealing high mutation rates and confirming neutral evolution in tumors.

bioRxiv · Jun 21, 2019Cancer Biology

Modelling bistable tumour population dynamics to design effective treatment strategies

A. R. Akhmetzhanov, J. W. Kim, R. Sullivan, R. A. Beckman, P. Tamayo, C.-H. Yeang

This study models bistable tumour population dynamics to evaluate treatment strategies, finding that periodic treatments outperform static ones and optimal control theory-based treatments are most effective.

bioRxiv · Apr 8, 2019Cancer Biology