Scientific publications
Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterations
Aramburu A (1), Zudaire I (2), Pajares MJ (2,3,4), Agorreta J (2,3,4), Orta A (2), Lozano MD (5,4), Gúrpide A (6,4), Gómez-Román J (7), Martinez-Climent JA (8,4), Jassem J (9), Skrzypski M (9), Suraokar M (10), Behrens C (11), Wistuba II (10,11), Pio R (12,13,14), Rubio A (15), Montuenga LM (16,17,18).
(1) Group of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastian, Spain.
(2) Laboratory of Biomarkers, Program in Solid Tumors and Biomarkers, Center for Applied Medical Research, University of Navarra, Pio XII, 55, 31008, Pamplona, Spain.
(3) Department of Histology and Pathology, School of Medicine, University of Navarra, Pamplona, Spain.
(4) Navarra's Health Research Institute (IDISNA), Pamplona, Spain.
(5) Department of Pathology, Clinica Universidad de Navarra, Pamplona, Spain.
(6) Department of Oncology, Clinica Universidad de Navarra, Pamplona, Spain.
(7) Department of Pathology, Marques de Valdecilla University Hospital, School of Medicine, University of Cantabria, Santander, Spain.
(8) Program in Hemato-Oncology, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.
(9) Department of Oncology and Radiotherapy, Medical University of Gdańsk, Gdańsk, Poland.
(10) Department of Translational Molecular Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
(11) Department of Thoracic/Head and Neck Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
(12) Laboratory of Biomarkers, Program in Solid Tumors and Biomarkers, Center for Applied Medical Research, University of Navarra, Pio XII, 55, 31008, Pamplona, Spain.
(13) Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain.
(14) Navarra's Health Research Institute (IDISNA), Pamplona, Spain.
(15) Group of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastian, Spain.
(16) Laboratory of Biomarkers, Program in Solid Tumors and Biomarkers, Center for Applied Medical Research, University of Navarra, Pio XII, 55, 31008, Pamplona, Spain.
(17) Department of Histology and Pathology, School of Medicine, University of Navarra, Pamplona, Spain.
(18) Navarra's Health Research Institute (IDISNA), Pamplona, Spain.
BACKGROUND:
The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies.
RESULTS:
A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures.
The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both:
a) significant correlation between copy number and gene expression, and
b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning.
These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n = 73 for ADC, n = 97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P = 0.008 for ADC and P = 0.019 for SCC) and outperforms both the clinical models (P = 0.060 for ADC and P = 0.121 for SCC) and the genomic models applied separately (P = 0.350 for ADC and P = 0.269 for SCC).
CONCLUSION:
The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on tumor DNA that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.
CITATION BMC Genomics. 2015 Oct 6;16:752. doi: 10.1186/s12864-015-1935-0
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