PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Systematic Use of Computational Methods Allows Stratifying Treatment Responders in Glioblastoma Multiforme
Riku Louhimo, Viljami Aittomäki, Ali Faisal, Marko Laakso, Ping Chen, Kristian Ovaska, Erkka Valo, Vladimir Rogojin, Samuel Kaski and Sampsa Hautaniemi
In: Proceedings of CAMDA 2011, Critical Assessment of Massive Data Analysis(2011).

Abstract

Cancers are complex diseases whose comprehensive characterization requires genome-scale molecular data at several levels from genetics to transcriptomics and clinical data. We use our recently published Anduril framework and introduce novel approaches, such as dependency analysis, to identify key variables at miRNA, copy number variation, expression, methylation and pathway level in glioblastoma multiforme (GBM) progression and drug resistance. We also present methods to identify characteristics of clinically relevant subgroups, such as patients treated with temozolomide drug and patients with an EGFRvIII mutation, which is a constitutively active variant of EGFR. Our results identify several novel genomic regions and transcript profiles that may contribute to GBM progression and drug resistance. All results and Anduril scripts are available at http://csbi.ltdk.helsinki.fi/camda/.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9171
Deposited By:Samuel Kaski
Deposited On:21 February 2012