1269P - Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic...

Date 11 September 2017
Event ESMO 2017 Congress
Session Poster display session
Topics Cancer in Adolescents
Diagnostics
Pancreatic Cancer
Translational Research
Presenter Octavio Caba
Citation Annals of Oncology (2017) 28 (suppl_5): v449-v452. 10.1093/annonc/mdx378
Authors O. Caba1, A.L. Irigoyen2, C. Jimenez-Luna3, M. Benavides4, F.M. Ortuño5, C. Guillen-Ponce6, I. Rojas5, E. Aranda Aguilar7, J.C. Prados3
  • 1Health Sciences Department, University of Jaén, 23071 - Jaen/ES
  • 2Medical Oncology, Hospital Virgen de la Salud, 49004 - Toledo/ES
  • 3Human Anatomy And Embriology, University of Granada, 18016 - Granada/ES
  • 4Medical Oncology, Hospital Universitario Regional y Virgen de la Victoria, 29010 - Malaga/ES
  • 5Computer Architecture And Computer Technology, University of Granada, 18071 - Granada/ES
  • 6Medical Oncology, Hospital Universitario Ramon y Cajal, 28031 - Madrid/ES
  • 7Medical Oncology, University Hospital Reina Sofia. CIBERONC Instituto de Salud Carlos III, 14004 - Cordoba/ES

Abstract

Background

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology.

Methods

Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R.

Results

After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors.

Conclusions

The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Clinical trial identification

Legal entity responsible for the study

Grupo de Tratamiento de Tumores Digestivos

Funding

Grupo de Tratamiento de Tumores Digestivos

Disclosure

M. Benavides: Received honoraria from Roche and honoraria for advisory role from Roche. C. Guillen-Ponce: Received honoraria from Roche; honoraria for advisory role from Roche Pharma, Bayer, Merck Serono, Sanofi Aventis, Celgene y Novocure; other remuneration from Celgene, Roche Pharma, Sanofi Aventis, Merck Serono. E. Aranda Aguilar: Received honoraria for advisory role from Amgen, Bayer, Celgene, Merck, Roche, Sanofi. All other authors have declared no conflicts of interest.