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TP53 mutational status is predictive of pazopanib response in advanced sarcomas.

K. Koehler,1 D. Liebner,1,2 and J. L. Chen1,2,*

1Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus
2Division of Bioinformatics, Department of Biomedical Informatics, The Ohio State University, Columbus, USA

Ann Oncol. 2016 Mar; 27(3): 539–543.
Published online 2015 Dec 8.

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Predicting Doxorubicin Response to Liposarcoma Using a Novel Genomic Matching Algorithm 

Timothy Makkar, James L. Chen, MD

Department of Biomedical Informatics, The Ohio State University Wexner Medical Center

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Opportunities for Patient Matching Algorithms to Improve Patient Care in Oncology 

Travis Johnson, David Liebner, and James L. Chen

All authors: The Ohio State University, Columbus, OH

JCO Clinical Cancer Informatics 2017 :1, 1-8

 

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Automating predictions of cancer patient outcomes

Jeffrey Spitzner, PhD1; Trevor Allen, BA1; Guy Jacks, BA1;James L Chen, MD1,2

1MatchTX, LLC, Columbus, Ohio

2Dept of Biomedical Informatics and Internal Medicine, The Ohio State University, Columbus, Ohio

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The MatchTx algorithms are able to predict the response of patients with advanced sarcomas to cancer drug pazopanib (a VEGFR inhibitor) based on the set of TP53 genetic mutations in the patient population. Taken from:

Ann Oncol. 2016 Mar;27(3):539-43. doi: 10.1093/annonc/mdv598. Epub 2015 Dec 8.

TP53 mutational status is predictive of pazopanib response in advanced sarcomas.

PFS with VEGFR inhibition based on TP53 status
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