Abstract
Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.
Highlights
Metastases at distant sites is the major cause of death in breast cancer patients [1]
We propose synergistic network (SyNet), a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data
To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a Network-based outcome predictors (NOPs) model
Summary
Metastases at distant sites (e.g. in bone, lung, liver and brain) is the major cause of death in breast cancer patients [1]. Randomized clinical trials showed that at least 40% of these patients survive without chemotherapy and unnecessarily suffer from the toxic side effect of this treatment [3, 4]. For this reason, substantial efforts have been made to derive molecular classifiers that can predict clinical outcome based on gene expression profiles obtained from the primary tumor at the time of diagnosis [5, 6]. An important shortcoming in molecular classification is that ‘cross-study’ generalization is often poor [7] This means that prediction performance decreases dramatically when a classifier trained on one patient cohort is applied to another one [8]. This lack of consistency casts doubt on whether the identified signatures capture true ‘driver’ mechanisms of the disease or rather subsidiary ‘passenger’ effects [12]
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