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PathTracer: High-sensitivity detection of differential pathway activity in tumours.

Published version
Peer-reviewed

Type

Article

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Authors

Nygård, Ståle 
Lingjærde, Ole Christian 
Børresen-Dale, Anne-Lise 

Abstract

Gene expression profiling of tumours is an important source of information for cancer patient stratification. Detecting subtle alterations of gene expression remains a challenge, however. Here, we propose a novel tool for high-sensitivity detection of differential pathway activity in tumours. For a pathway defined by a collection of genes, the samples are projected onto a low-dimensional manifold in the subspace spanned by those genes. For each sample, a score is next found by calculating the distance between each projected sample and the projection of a subgroup of reference samples. Depending on the aim of the analysis and the available data, the reference samples may represent e.g. normal tissue or tumour samples with a particular genotype or phenotype. The proposed tool, PathTracer, is demonstrated on gene expression data from 1952 invasive breast cancer samples, 10 DCIS, 9 benign samples and 144 tumour adjacent normal breast tissue samples. PathTracer scores are shown to predict survival, clinical subtypes, cellular proliferation and genomic instability. Furthermore, predictions are shown to outperform those obtained with other comparable methods.

Description

Keywords

Breast Neoplasms, Computational Biology, Gene Expression Profiling, Mutation, Oligonucleotide Array Sequence Analysis, Tumor Suppressor Protein p53

Journal Title

Sci Rep

Conference Name

Journal ISSN

2045-2322
2045-2322

Volume Title

9

Publisher

Springer Science and Business Media LLC