Making Sense Of Cancer Data: Implications For Personalized Medicine And Cancer Biology
| dc.contributor.advisor | Sander, Chris | |
| dc.contributor.author | Aksoy, Bulent | |
| dc.date.accessioned | 2019-03-26T17:05:18Z | |
| dc.date.available | 2019-03-27T06:01:20Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | In the very near future, all cancer patients coming into the clinics will have their genomic material profiled and we will need computational approaches that can make sense out of these data sets to enable more effective cancer therapies based on a patient's genomic profiling results. Here, we will first introduce computational utilities that we have been developing to facilitate cancer genomics studies. These will include: PiHelper, an open source framework for drug-target and antibody-target data; cBioPortal, a web-based tool that provides visualization, analysis and download of large-scale cancer genomics data sets; and Pathway Commons, a network biology resource that acts as a convenient point of access to biological pathway information collected from public pathway databases. We will then give two examples to how these resources can be used in conjunction with large-scale cancer genomics profiling projects, in particular the Cancer Genome Atlas (TCGA). First, we will describe our work involving prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles, where we show that random passenger genomic events can create patient-specific therapeutic vulnerabilities that can be exploited by targeted drugs. Second, we will show how comprehensive analysis of cancer genomics data sets can reveal interesting biological insights about specific alteration events. In particular, we will describe our computational characterization of cancer-associated recurrent mutations in RNase III domains of DICER1, again using the TCGA data set. | |
| dc.identifier.uri | https://hdl.handle.net/1813/64669 | |
| dc.language.iso | en_US | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Bioinformatics | |
| dc.subject | Cancer | |
| dc.subject | Computational Biology | |
| dc.subject | Genetics | |
| dc.subject | Genomics | |
| dc.subject | Systems Biology | |
| dc.title | Making Sense Of Cancer Data: Implications For Personalized Medicine And Cancer Biology | |
| dc.type | dissertation or thesis | |
| thesis.degree.discipline | Physiology, Biophysics & Systems Biology | |
| thesis.degree.grantor | Weill Cornell Graduate School of Medical Sciences | |
| thesis.degree.level | Doctor of Philosophy |
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