E Fountzilas1, K Markou2, K Vlachtsis2, A Nikolaou2, P Arapantoni-Dadioti3, E Ntoula3, G Tassopoulos4, M Bobos5, P Konstantinopoulos1, G Fountzilas6, D Spentzos7. 1. Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA. 2. Department of Otorhinolaryngology, "AHEPA" Hospital, Aristotle University of Thessaloniki School of Medicine, Thessaloniki. 3. Department of Pathology. 4. Department of Otorhinolaryngology, "Metaxa" Cancer Hospital, Piraeus. 5. Laboratory of Molecular Oncology, Hellenic Foundation for Cancer Research, Thessaloniki. 6. Department of Medical Oncology, "Papageorgiou" Hospital, Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece. 7. Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA. Electronic address: dspentzo@bidmc.harvard.edu.
Abstract
BACKGROUND: Despite improvement in therapeutic techniques, patients with early-stage laryngeal cancer still recur after treatment. Gene expression prognostic models could suggest which of these patients would be more appropriate for testing adjuvant strategies. MATERIALS AND METHODS: Expression profiling using whole-genome DASL arrays was carried out on 56 formalin-fixed paraffin-embedded tumor samples of patients with early-stage laryngeal cancer. We split the samples into a training and a validation set. Using the supervised principal components survival analysis in the first cohort, we identified gene expression profiles that predict the risk of recurrence. These profiles were then validated in an independent cohort. RESULTS: Gene models comprising different number of genes identified a subgroup of patients who were at high risk of recurrence. Of these, the best prognostic model distinguished between a high- and a low-risk group (log-rank P<0.005). The prognostic value of this model was reproduced in the validation cohort (median disease-free survival: 38 versus 161 months, log-rank P=0.018), hazard ratio=5.19 (95% confidence interval 1.14-23.57, P<0.05). CONCLUSIONS: We have identified gene expression prognostic models that can refine the estimation of a patient's risk of recurrence. These findings, if further validated, should aid in patient stratification for testing adjuvant treatment strategies.
BACKGROUND: Despite improvement in therapeutic techniques, patients with early-stage laryngeal cancer still recur after treatment. Gene expression prognostic models could suggest which of these patients would be more appropriate for testing adjuvant strategies. MATERIALS AND METHODS: Expression profiling using whole-genome DASL arrays was carried out on 56 formalin-fixed paraffin-embedded tumor samples of patients with early-stage laryngeal cancer. We split the samples into a training and a validation set. Using the supervised principal components survival analysis in the first cohort, we identified gene expression profiles that predict the risk of recurrence. These profiles were then validated in an independent cohort. RESULTS: Gene models comprising different number of genes identified a subgroup of patients who were at high risk of recurrence. Of these, the best prognostic model distinguished between a high- and a low-risk group (log-rank P<0.005). The prognostic value of this model was reproduced in the validation cohort (median disease-free survival: 38 versus 161 months, log-rank P=0.018), hazard ratio=5.19 (95% confidence interval 1.14-23.57, P<0.05). CONCLUSIONS: We have identified gene expression prognostic models that can refine the estimation of a patient's risk of recurrence. These findings, if further validated, should aid in patient stratification for testing adjuvant treatment strategies.
Authors: Ahmedin Jemal; Rebecca Siegel; Elizabeth Ward; Taylor Murray; Jiaquan Xu; Michael J Thun Journal: CA Cancer J Clin Date: 2007 Jan-Feb Impact factor: 508.702
Authors: James E Korkola; Jane Houldsworth; Darren R Feldman; Adam B Olshen; Li-Xuan Qin; Sujata Patil; Victor E Reuter; George J Bosl; R S K Chaganti Journal: J Clin Oncol Date: 2009-09-21 Impact factor: 44.544
Authors: Matthew A Ginos; Grier P Page; Bryan S Michalowicz; Ketan J Patel; Sonja E Volker; Stefan E Pambuccian; Frank G Ondrey; George L Adams; Patrick M Gaffney Journal: Cancer Res Date: 2004-01-01 Impact factor: 12.701
Authors: D Dionysopoulos; K Pavlakis; V Kotoula; E Fountzilas; K Markou; I Karasmanis; N Angouridakis; A Nikolaou; K T Kalogeras; G Fountzilas Journal: Strahlenther Onkol Date: 2013-02-13 Impact factor: 3.621
Authors: Elena Fountzilas; Vassiliki Kotoula; Nikolaos Angouridakis; Ilias Karasmanis; Ralph M Wirtz; Anastasia G Eleftheraki; Elke Veltrup; Konstantinos Markou; Angelos Nikolaou; Dimitrios Pectasides; George Fountzilas Journal: PLoS One Date: 2013-08-09 Impact factor: 3.240
Authors: Douglas W Mahoney; Terry M Therneau; S Keith Anderson; Jin Jen; Jean-Pierre A Kocher; Monica M Reinholz; Edith A Perez; Jeanette E Eckel-Passow Journal: BMC Res Notes Date: 2013-01-30