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dc.contributor.advisorSchneider, Phillipen
dc.contributor.advisorCropp, Cherylen
dc.contributor.authorAaron Pavlik*
dc.contributor.authorSchneider, Phillip*
dc.contributor.authorCropp, Cheryl*
dc.date.accessioned2016-06-22T17:43:10Z
dc.date.available2016-06-22T17:43:10Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10150/614155
dc.descriptionClass of 2015 Abstracten
dc.description.abstractObjectives: The aim of this study was to develop a computational process capable of hypothesizing potential chemotherapeutic agents for the treatment of skin cutaneous melanoma given an annotated chemotherapy molecular target database and patient-specific genetic tumor profiles. Methods: Aberrational profiles for a total of 246 melanoma patients indexed by the Cancer Genome Atlas (TCGA) for whom complete somatic mutational, mRNA expression, and protein expression data was available were queried against an annotated targeted therapy database using Visual Basic for Applications and Python in conjunction with Microsoft Excel. Identities of positively and negatively associated therapy-profile matches were collected and ranked. Results: Subjects included in the analysis were predominantly Caucasian (93%), non-Hispanic (95.9%), female (59%), and characterized as having stage III clinical disease (37.4%). The most frequently occurring positive and negative therapy associations were determined to be 17-AAG (tanespimycin; 42.3%) and sorafenib (41.9%), respectively. Mean total therapy hypotheses per patient did not differ significantly with regard to either positive or negative associations (p=0.1951 and 0.4739 by one-way ANOVA, respectively) when stratified by clinical melanoma stage. Conclusions: The developed process does not appear to offer discernably different therapy hypotheses amongst clinical stages of cutaneous melanoma based upon genetic data alone. The therapy-matching algorithm may be useful in quickly retrieving potential therapy hypotheses based upon the genetic characteristics of one or many subjects specified by the user.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author.en
dc.subjectTherapiesen
dc.subjectCancer Genome Atlas (TCGA)en
dc.subjectDrug Databaseen
dc.subjectCutaneous Melanomaen
dc.subject.meshMelanoma
dc.subject.meshMolecular Targeted Therapy
dc.subject.meshAntineoplastic Agents
dc.titleProposing Molecularly Targeted Therapies Using an Annotated Drug Database Querying Algorithm in Cutaneous Melanomaen_US
dc.typetexten
dc.typeElectronic Reporten
dc.contributor.departmentCollege of Pharmacy, The University of Arizonaen
dc.description.collectioninformationThis item is part of the Pharmacy Student Research Projects collection, made available by the College of Pharmacy and the University Libraries at the University of Arizona. For more information about items in this collection, please contact Jennifer Martin, Associate Librarian and Clinical Instructor, Pharmacy Practice and Science, jenmartin@email.arizona.edu.en
html.description.abstractObjectives: The aim of this study was to develop a computational process capable of hypothesizing potential chemotherapeutic agents for the treatment of skin cutaneous melanoma given an annotated chemotherapy molecular target database and patient-specific genetic tumor profiles. Methods: Aberrational profiles for a total of 246 melanoma patients indexed by the Cancer Genome Atlas (TCGA) for whom complete somatic mutational, mRNA expression, and protein expression data was available were queried against an annotated targeted therapy database using Visual Basic for Applications and Python in conjunction with Microsoft Excel. Identities of positively and negatively associated therapy-profile matches were collected and ranked. Results: Subjects included in the analysis were predominantly Caucasian (93%), non-Hispanic (95.9%), female (59%), and characterized as having stage III clinical disease (37.4%). The most frequently occurring positive and negative therapy associations were determined to be 17-AAG (tanespimycin; 42.3%) and sorafenib (41.9%), respectively. Mean total therapy hypotheses per patient did not differ significantly with regard to either positive or negative associations (p=0.1951 and 0.4739 by one-way ANOVA, respectively) when stratified by clinical melanoma stage. Conclusions: The developed process does not appear to offer discernably different therapy hypotheses amongst clinical stages of cutaneous melanoma based upon genetic data alone. The therapy-matching algorithm may be useful in quickly retrieving potential therapy hypotheses based upon the genetic characteristics of one or many subjects specified by the user.


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