Abstract
Adenocarcinoma is the predominant histological subtype of lung cancer, the leading cause of cancer deaths in the world. At stage I, the tumor is cured by surgery alone in about 60% of cases. Markers are needed to stratify patients by prognostic outcomes and may help in devising more effective therapies for poor prognosis patients. To achieve this goal, we used an integrated strategy combining meta-analysis of published lung cancer microarray data with expression profiling from an experimental model. The resulting 80-gene model was tested on an independent cohort of patients using RT-PCR, resulting in a 10-gene predictive model that exhibited a prognostic accuracy of approximately 75% in stage I lung adenocarcinoma when tested on 2 additional independent cohorts. Thus, we have identified a predictive signature of limited size that can be analyzed by RT-PCR, a technology that is easy to implement in clinical laboratories.
| Original language | English |
|---|---|
| Pages (from-to) | 3436-3444 |
| Number of pages | 9 |
| Journal | Journal of Clinical Investigation |
| Volume | 117 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 1 2007 |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Survival prediction of stage I lung adenocarcinomas by expression of 10 genes. / Bianchi, Fabrizio; Nuciforo, Paolo; Vecchi, Manuela; Bernard, Loris; Tizzoni, Laura; Marchetti, Antonio; Buttitta, Fiamma; Felicioni, Lara; Nicassio, Francesco; Di Fiore, Pier Paolo.
In: Journal of Clinical Investigation, Vol. 117, No. 11, 01.11.2007, p. 3436-3444.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Survival prediction of stage I lung adenocarcinomas by expression of 10 genes
AU - Bianchi, Fabrizio
AU - Nuciforo, Paolo
AU - Vecchi, Manuela
AU - Bernard, Loris
AU - Tizzoni, Laura
AU - Marchetti, Antonio
AU - Buttitta, Fiamma
AU - Felicioni, Lara
AU - Nicassio, Francesco
AU - Di Fiore, Pier Paolo
PY - 2007/11/1
Y1 - 2007/11/1
N2 - Adenocarcinoma is the predominant histological subtype of lung cancer, the leading cause of cancer deaths in the world. At stage I, the tumor is cured by surgery alone in about 60% of cases. Markers are needed to stratify patients by prognostic outcomes and may help in devising more effective therapies for poor prognosis patients. To achieve this goal, we used an integrated strategy combining meta-analysis of published lung cancer microarray data with expression profiling from an experimental model. The resulting 80-gene model was tested on an independent cohort of patients using RT-PCR, resulting in a 10-gene predictive model that exhibited a prognostic accuracy of approximately 75% in stage I lung adenocarcinoma when tested on 2 additional independent cohorts. Thus, we have identified a predictive signature of limited size that can be analyzed by RT-PCR, a technology that is easy to implement in clinical laboratories.
AB - Adenocarcinoma is the predominant histological subtype of lung cancer, the leading cause of cancer deaths in the world. At stage I, the tumor is cured by surgery alone in about 60% of cases. Markers are needed to stratify patients by prognostic outcomes and may help in devising more effective therapies for poor prognosis patients. To achieve this goal, we used an integrated strategy combining meta-analysis of published lung cancer microarray data with expression profiling from an experimental model. The resulting 80-gene model was tested on an independent cohort of patients using RT-PCR, resulting in a 10-gene predictive model that exhibited a prognostic accuracy of approximately 75% in stage I lung adenocarcinoma when tested on 2 additional independent cohorts. Thus, we have identified a predictive signature of limited size that can be analyzed by RT-PCR, a technology that is easy to implement in clinical laboratories.
UR - http://www.scopus.com/inward/record.url?scp=36049000444&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36049000444&partnerID=8YFLogxK
U2 - 10.1172/JCI32007
DO - 10.1172/JCI32007
M3 - Article
C2 - 17948124
AN - SCOPUS:36049000444
VL - 117
SP - 3436
EP - 3444
JO - Journal of Clinical Investigation
JF - Journal of Clinical Investigation
SN - 0021-9738
IS - 11
ER -