Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.
10553 Background: Loss of PTEN function has been widely reported to cause up-regulation of the PI3K/AKT signalling pathway resulting in increased cell growth, proliferation and survival. More recently it has been reported that PTEN null cells demonstrate genomic instability and have increased production of reactive oxygen species (ROS) and oxidative stress induced DNA damage. Ataxia Telangiectasia Mutated (ATM) is the primary response kinase, which responds to stalled DNA replication and DNA double strand breaks due to oxidative DNA damage. Methods: A metagene representing ATM activation was generated from cell line data and used to perform hierarchical clustering analysis of public DNA microarray profiling datasets of breast cancer, ovarian cancer and glioblastoma with known PTEN IHC/mutation status. Furthermore, we ask if ATM activation may be therapeutically exploited in PTEN null tumours using ATM specific siRNA and compounds in 2 PTEN isogenic cell line model systems. Results: We show that PTEN null cells have elevated levels of ROS, DNA damage and have endogenous activation of ATM, an enzyme important in responding to DNA damage resulting from oxidative stress. We hypothesised that PTEN deficient tumours may rely on ATM enzyme for survival. To investigate this we generated a 189-gene list representing ATM activation and used this to perform hierarchical clustering analysis of a breast cancer DNA microarray dataset. This list was able to significantly cluster tumours with known loss of PTEN expression (p=0.004). Furthermore, this gene list was able to segregate PTEN null/mutant tumours from PTEN wild-type tumours in 2 independent datasets of glioblastoma and ovarian cancer (p=0.015 and p=0.012). In addition, we found that inhibition of ATM using the selective inhibitor KU-55933 caused DNA damage, cell cycle arrest and apoptosis specifically in PTEN deficient cells when compared to PTEN wild-type cells. Conclusions: These observations suggest that ATM may represent a therapeutic target in PTEN deficient tumours and furthermore ATM activation may be the basis of a biomarker of PTEN status in human cancers.
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