title: A generic cycling hypoxia-derived prognostic gene signature: application to breast cancer profiling.
abstract: BACKGROUND
  Temporal and local fluctuations in O2 in tumors require adaptive mechanisms to support cancer cell survival and proliferation. The transcriptome associated with cycling hypoxia (CycHyp) could thus represent a prognostic biomarker of cancer progression.
  
  
  METHODS
  We exposed 20 tumor cell lines to repeated periods of hypoxia/reoxygenation to determine a transcriptomic CycHyp signature and used clinical data sets from 2,150 breast cancer patients to estimate a prognostic Cox proportional hazard model to assess its prognostic performance.
  
  
  RESULTS
  The CycHyp prognostic potential was validated in patients independently of the receptor status of the tumors. The discriminating capacity of the CycHyp signature was further increased in the ER+ HER2- patient populations including those with a node negative status under treatment (HR=3.16) or not (HR=5.54). The CycHyp prognostic signature outperformed a signature derived from continuous hypoxia and major prognostic metagenes (P<0.001). The CycHyp signature could also identify ER+HER2 node-negative breast cancer patients at high risk based on clinicopathologic criteria but who could have been spared from chemotherapy and inversely those patients classified at low risk based but who presented a negative outcome.
  
  
  CONCLUSIONS
  The CycHyp signature is prognostic of breast cancer and offers a unique decision making tool to complement anatomopathologic evaluation.
authors.affiliation: Institut de Recherche Expérimentale et Clinique (IREC), Pole of Pharmacology and Therapeutics (FATH), Université catholique de Louvain, Brussels, Belgium. These authors contribued equally to this work.
authors.forename: Romain
authors.initials: R
authors.lastname: Boidot
authors.affiliation: Machine Learning Group, Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, Louvain-la-Neuve, Belgium. These authors contribued equally to this work.
authors.forename: Samuel
authors.initials: S
authors.lastname: Branders
authors.affiliation: Machine Learning Group, Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, Louvain-la-Neuve, Belgium.
authors.forename: Thibault
authors.initials: T
authors.lastname: Helleputte
authors.affiliation: Institut de Recherche Expérimentale et Clinique (IREC), Pole of Pharmacology and Therapeutics (FATH), Université catholique de Louvain, Brussels, Belgium.
authors.forename: Laila Illan
authors.initials: LI
authors.lastname: Rubio
authors.affiliation: Machine Learning Group, Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, Louvain-la-Neuve, Belgium.
authors.forename: Pierre
authors.initials: P
authors.lastname: Dupont
authors.affiliation: Institut de Recherche Expérimentale et Clinique (IREC), Pole of Pharmacology and Therapeutics (FATH), Université catholique de Louvain, Brussels, Belgium.
authors.forename: Olivier
authors.initials: O
authors.lastname: Feron
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism* / Q000473:pathology; D015687:Cell Hypoxia / Q000235:genetics; D049109:Cell Proliferation / Q000235:genetics; D002470:Cell Survival / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D011379:Prognosis; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism
chemical_list: D012333:RNA, Messenger