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High prognostic potential of a transcriptomic signature related to cycling hypoxia: direct application to breast cancer profiling

ABSTRACT: Purpose. Temporal and local fluctuations in oxygen levels observed within tumors represent stressful conditions requiring adaptive mechanisms that provide tumor cells with phenotypic alterations to survive and proliferate in this hostile environment. The analysis of the transcriptome associated with such cycling hypoxia could thus represent a prognostic biomarker of cancer progression. Patients and Methods. We exposed 20 cell lines derived from various tissues to repeated periods of hypoxia/reoxygenation in order to determine a transcriptomic signature of cycling hypoxia (CycHyp). We then used clinical data sets from 2,150 patients with primary breast cancer to estimate a prognostic Cox proportional hazard model and to assess the prognostic performance of the CycHyp signature on independent samples. Results. The prognostic potential of the CycHyp signature was validated in patients independently of the receptor status of the tumors (HR=1.97; p = 1.8e-12). The discriminating capacity of the CycHyp signature was further increased in the ER+ HER2- patient populations (HR = 2.34, p = 9e-12) including those with a node negative status receiving or not a treatment (HR = 3.32 and 5.51; p= 5.61e-10 and 8.15e-11, respectively). We also documented the capacity of the CycHyp signature to outperform existing prognostic gene signatures with significantly higher BCR and concordance index. We also showed that the CycHyp signature could identify ER-positive node-negative breast cancer patients at high risk based on conventional clinico-pathologic criteria but who could have been spared from chemotherapy and inversely those patients classified at low risk based on the same criteria but who presented a negative outcome. Conclusion. This study demonstrates that a gene signature derived from the transcriptomic adaptation of tumor cells to cycling hypoxia is prognostic of breast cancer and offers a unique decision making tool to complement the discrimination of breast cancer patients based on anatomo-pathological evaluation. The prognostic value of CycHyp further confirms the link between cycling hypoxia and cancer progression, and thereby paves the way for a broad applicability to evaluate cancer patient outcomes.

ORGANISM(S): Homo sapiens

PROVIDER: GSE42416 | GEO | 2015/11/16

SECONDARY ACCESSION(S): PRJNA181352

REPOSITORIES: GEO

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Project description:TP53 mutations are a poor prognostic factor in breast cancers. This study sets out to identify the gene set that determine expression signature of the TP53 status (TP53 signature) and to correlate it with clinical outcome. Using comprehensive expression analysis and DNA sequencing of the TP53 gene in 38 Japanese breast cancer patients, we have isolated a gene set of 33 genes from differentially expressed genes in the learning set (n=26), depending on the TP53 status. Predictive accuracy of TP53 status by gene expression profile was 83.3% in the test set (n=12). As independent external datasets, two published datasets were introduced for validation of TP53 status prediction (251 Swedish samples) and survival analysis (both the Swedish and 295 Dutch samples). TP53 signature has the ability to predict recurrence-free survival (RFS) of 29 stage I and II Japanese breast cancers (log rank, P = 0.032), and RFS, overall survival of two independently published datasets (log rank, both P < 0.0001). Multivariate analysis has shown an independent and significant prognostic factor with a hazard ratio (HR) for recurrence and survival in two external datasets (P < 0.0001). The TP53 signature is also a strong prognostic factor in the subgroups: estrogen-receptor positive, lymph node (LN) positive and negative, intermediate/high risk in St. Gallen criteria, and high risk in National Cancer Institute (NCI) criteria (log rank, P < 0.0001). TP53 signature is a reliable and independent predictor of the outcome of disease in operated breast cancer. Experiment Overall Design: Microarray hybridizations (Agilent: Whole Human Genome Oligo Microarray; 41k unique probe) were carried out with 1Î¼g Cy3 labeled cRNA and 1 Î¼g Cy5 labeled cRNA, both prepared from each sample and reference pool, respectively. Experiment Overall Design: Fluorescent intensities of scanned images were quantified by ArrayVision Ver.8 rev.4 (Imaging research). Experiment Overall Design: The gene expression data was quantified and analyzed by GeneSpring 6.2 (Silicon Genetics). To identify the TP53 status predictor gene set, a Wilcoxon rank sum test along with Benjamini and Hochberg false discovery rate (FDR) was used.

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High prognostic potential of a transcriptomic signature related to cycling hypoxia: direct application to breast cancer profiling

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