Hypoxia is a characteristic of breast tumours indicating poor prognosis. Based on the assumption that those genes which are up-regulated under hypoxia in cell-lines are expected to be predictors of poor prognosis in clinical data, many signatures of poor prognosis were identified. However, it was observed that cell line data do not always concur with clinical data, and therefore conclusions from cell line analysis should be considered with caution. As many transcriptomic cell-line datasets from hypoxia related contexts are available, integrative approaches which investigate these datasets collectively, while not ignoring clinical data, are required.
Approach & Results
In collaboration with Professor Adrian Harris and Dr Francesca Buffa (Oncology Department, University of Oxford), I followed a data-driven approach to search of a consistent signature of hypoxia. Therefore, I employed my developed bioinformatic frameworks to extract clusters of genes that are consistently co-expressed in sixteen breast cancer cell-line transcriptomic datasets. These datasets were all generated in hypoxia-related conditions despite being different in other properties. From this collection of genome-wide datasets, more than 1000 genes were automatically extracted as consistently co-regulated over all of the datasets, and some of which are still poorly understood and represent new potential targets of the hypoxia-induced factor (HIF), such as RSBN1 and KIAA0195.
Two main, anti-correlated, clusters were identified; the first is enriched with MYC targets participating in growth and proliferation, while the other is enriched with HIF targets directly participating in the hypoxia response. Surprisingly, in six clinical datasets, some sub-clusters of growth genes are found consistently positively correlated with hypoxia response genes, unlike the observation in cell lines. Moreover, the ability to predict bad prognosis by a combined signature of one sub-cluster of growth genes and one sub-cluster of hypoxia-induced genes appears to be comparable and perhaps greater than that of known hypoxia signatures.
This work demonstrates a novel clustering approach suitable to integrate data from diverse experimental set-ups. Its application to breast cancer cell-line datasets reveals new hypoxia-regulated signatures of genes which behave differently when in vitro (cell-line) data is compared with in vivo (clinical) data, and are of a prognostic value comparable or exceeding the state-of-the-art hypoxia signatures.
Basel Abu-Jamous, Francesca M. Buffa, Adrian L. Harris, Asoke K. Nandi. “In vitro downregulated hypoxia transcriptome is associated with poor prognosis in breast cancer”. Molecular Cancer, 2017, 16: 105. doi: 10.1186/s12943-017-0673-0. View online | Download PDF.