Frontiers in Genetics (Apr 2018)

Identification and Validation of a Diagnostic and Prognostic Multi-Gene Biomarker Panel for Pancreatic Ductal Adenocarcinoma

  • Hagen Klett,
  • Hagen Klett,
  • Hagen Klett,
  • Hannah Fuellgraf,
  • Hannah Fuellgraf,
  • Hannah Fuellgraf,
  • Ella Levit-Zerdoun,
  • Ella Levit-Zerdoun,
  • Ella Levit-Zerdoun,
  • Saskia Hussung,
  • Saskia Hussung,
  • Silke Kowar,
  • Simon Küsters,
  • Peter Bronsert,
  • Peter Bronsert,
  • Peter Bronsert,
  • Peter Bronsert,
  • Peter Bronsert,
  • Martin Werner,
  • Martin Werner,
  • Martin Werner,
  • Martin Werner,
  • Martin Werner,
  • Uwe Wittel,
  • Ralph Fritsch,
  • Ralph Fritsch,
  • Ralph Fritsch,
  • Hauke Busch,
  • Hauke Busch,
  • Melanie Boerries,
  • Melanie Boerries,
  • Melanie Boerries,
  • Melanie Boerries

DOI
https://doi.org/10.3389/fgene.2018.00108
Journal volume & issue
Vol. 9

Abstract

Read online

Late diagnosis and systemic dissemination essentially contribute to the invariably poor prognosis of pancreatic ductal adenocarcinoma (PDAC). Therefore, the development of diagnostic biomarkers for PDAC are urgently needed to improve patient stratification and outcome in the clinic. By studying the transcriptomes of independent PDAC patient cohorts of tumor and non-tumor tissues, we identified 81 robustly regulated genes, through a novel, generally applicable meta-analysis. Using consensus clustering on co-expression values revealed four distinct clusters with genes originating from exocrine/endocrine pancreas, stromal and tumor cells. Three clusters were strongly associated with survival of PDAC patients based on TCGA database underlining the prognostic potential of the identified genes. With the added information of impact of survival and the robustness within the meta-analysis, we extracted a 17-gene subset for further validation. We show that it did not only discriminate PDAC from non-tumor tissue and stroma in fresh-frozen as well as formalin-fixed paraffin embedded samples, but also detected pancreatic precursor lesions and singled out pancreatitis samples. Moreover, the classifier discriminated PDAC from other cancers in the TCGA database. In addition, we experimentally validated the classifier in PDAC patients on transcript level using qPCR and exemplify the usage on protein level for three proteins (AHNAK2, LAMC2, TFF1) using immunohistochemistry and for two secreted proteins (TFF1, SERPINB5) using ELISA-based protein detection in blood-plasma. In conclusion, we present a novel robust diagnostic and prognostic gene signature for PDAC with future potential applicability in the clinic.

Keywords