Author

J. Eaton

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· 2 Cell Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:whole-genome sequencing· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Approach:machine learning (classical)· 1 Approach:mechanistic / dynamical model· 1

Frequent Co-Authors

R. Schwartz· 2 shared A. Rajaraman· 1 shared A. Whitt· 1 shared A. Zippelius· 1 shared B. Stamp· 1 shared C. Li· 1 shared H. Donninger· 1 shared H. Kim· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersCancer Biology

R. Schwartz

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Tumor-educated monocytes suppress T cells via adenosine and depletion of adenosine in the tumor microenvironment with adenosine deaminase enzyme promotes response to immunotherapy

O. S. Sarkar, H. Donninger, N. Al Rayyan, L. C. Chew, B. Stamp, X. Zhang, A. Whitt, C. Li, M. Hall, R. A. Mitchell, A. Zippelius, J. Eaton, J. A. Chesney, K. Yaddanapudi

This study shows that tumor-educated monocytes suppress T cells via adenosine and that depleting adenosine with PEG-ADA enhances immunotherapy response in preclinical models.

bioRxiv · Jun 22, 2022 · PreprintCell Biology

Improving personalized prediction of cancer prognoses with clonal evolution models

Y. Tao, A. Rajaraman, X. Cui, Z. Cui, J. Eaton, H. Kim, J. Ma, R. Schwartz

This study develops a computational framework to predict cancer progression using mutator phenotypes derived from tumor phylogenies, showing improved predictive power over traditional clinical and driver-centric features.

bioRxiv · Sep 18, 2019 · PreprintCancer Biology

Deconvolution and phylogeny inference of structural variations in tumor genomic samples

J. Eaton, J. Wang, R. Schwartz

This paper introduces a method to infer tumor phylogeny and deconvolute structural variations from bulk genomic data, showing it works well on simulated and breast cancer data.

bioRxiv · Jan 30, 2018Cancer Biology