NDLI: Prognostic gene signatures for non-small-cell lung cancer

Content Provider	PubMed Central
Author	Boutros, Paul C. Lau, Suzanne K. Pintilie, Melania Liu, Ni Shepherd, Frances A. Der, Sandy D. Tsao, Ming-sound Penn, Linda Z. Igor, Jurisica
Abstract	Resectable non-small-cell lung cancer (NSCLC) patients have poor prognosis, with 30–50% relapsing within 5 years. Current staging criteria do not fully capture the complexity of this disease. Survival could be improved by identification of those early-stage patients who are most likely to benefit from adjuvant therapy. Molecular classification by using mRNA expression profiles has led to multiple, poorly overlapping signatures. We hypothesized that differing statistical methodologies contribute to this lack of overlap. To test this hypothesis, we analyzed our previously published quantitative RT-PCR dataset with a semisupervised method. A 6-gene signature was identified and validated in 4 independent public microarray datasets that represent a range of tumor histologies and stages. This result demonstrated that at least 2 prognostic signatures can be derived from this single dataset. We next estimated the total number of prognostic signatures in this dataset with a 10-million-signature permutation study. Our 6-gene signature was among the top 0.02% of signatures with maximum verifiability, reaffirming its efficacy. Importantly, this analysis identified 1,789 unique signatures, implying that our dataset contains >500,000 verifiable prognostic signatures for NSCLC. This result appears to rationalize the observed lack of overlap among reported NSCLC prognostic signatures.
Related Links	http://dx.doi.org/10.1073/pnas.0809444106
Ending Page	2828
Page Count	5
Starting Page	2824
File Format	PDF
ISSN	00278424
e-ISSN	10916490
Journal	Proceedings of the National Academy of Sciences of the United States of America
Issue Number	8
Volume Number	106
Language	English
Publisher	National Academy of Sciences
Publisher Date	2009-02-24
Access Restriction	Open
Rights Holder	National Academy of Sciences
Subject Keyword	General Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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