Anette Heller1, Matthias M Gaida2, D Männle3, Thomas Giese4, Aldo Scarpa5, John P Neoptolemos6, Thilo Hackert3, Oliver Strobel3, Jörg D Hoheisel7, Nathalia A Giese8, Andrea S Bauer7. 1. Department of Surgery, University Hospital Heidelberg, Heidelberg, Germany. Electronic address: anette.heller@med.uni-heidelberg.de. 2. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany. 3. Department of Surgery, University Hospital Heidelberg, Heidelberg, Germany. 4. Institute of Immunology, University Hospital Heidelberg, Heidelberg, Germany. 5. Department of Pathology and ARC-NET Research Centre, University of Verona, Verona, Italy. 6. National Institute for Health Research, Pancreas Biomedical Research Unit and the Liverpool Experimental Cancer Medicine Centre, Liverpool, UK. 7. Functional Genome Analysis, German Research Cancer Center, Heidelberg, Germany. 8. Department of Surgery, University Hospital Heidelberg, Heidelberg, Germany. Electronic address: nathalia.giese@med.uni-heidelberg.de.
Abstract
BACKGROUND/ OBJECTIVES: Meaningful profiling of pancreatic cancer samples is particularly challenging due to their complex cellular composition. Beyond tumor cells, surgical biopsies contain desmoplastic stroma with infiltrating inflammatory cells, adjacent normal parenchyma, and "non-pancreatic tissues". The risk of misinterpretation rises when the heterogeneous cancer tissues are sub-divided into smaller fragments for multiple analytic procedures. Pre-analytic histological evaluation is the best option to characterize pancreatic tissue samples. Our aim was to develop a complement or alternative procedure to determine the cellular composition of pancreatic cancerous biopsies, basing on intra-analytic molecular annotation. A standard process for sample stratification at a molecular level does not yet exist. Particularly in the case of retrospective or data depository-based studies, when hematoxylin-eosin stained sections are not available, it supports the correct interpretation of expression profiles. METHODS: A five-gene transcriptional signature (RNACellStrat) was defined that allows cell type-specific stratification of pancreatic tissues. Testing biopsy material from biobanks with this procedure demonstrated high correspondence of molecular (qRT-PCR and microarray) and histologic (hematoxylin-eosin stain) evaluations. RESULTS: Notably, about a quarter of randomly selected samples (tissue fragments) were exposed as inappropriate for subsequent clinico-pathological interpretation. CONCLUSIONS: Via immediate intra-analytical procedure, our RNA-based stratification RNACellStrat increases the accuracy and reliability of the conclusions drawn from diagnostic and prognostic molecular information.
BACKGROUND/ OBJECTIVES: Meaningful profiling of pancreatic cancer samples is particularly challenging due to their complex cellular composition. Beyond tumor cells, surgical biopsies contain desmoplastic stroma with infiltrating inflammatory cells, adjacent normal parenchyma, and "non-pancreatic tissues". The risk of misinterpretation rises when the heterogeneous cancer tissues are sub-divided into smaller fragments for multiple analytic procedures. Pre-analytic histological evaluation is the best option to characterize pancreatic tissue samples. Our aim was to develop a complement or alternative procedure to determine the cellular composition of pancreatic cancerous biopsies, basing on intra-analytic molecular annotation. A standard process for sample stratification at a molecular level does not yet exist. Particularly in the case of retrospective or data depository-based studies, when hematoxylin-eosin stained sections are not available, it supports the correct interpretation of expression profiles. METHODS: A five-gene transcriptional signature (RNACellStrat) was defined that allows cell type-specific stratification of pancreatic tissues. Testing biopsy material from biobanks with this procedure demonstrated high correspondence of molecular (qRT-PCR and microarray) and histologic (hematoxylin-eosin stain) evaluations. RESULTS: Notably, about a quarter of randomly selected samples (tissue fragments) were exposed as inappropriate for subsequent clinico-pathological interpretation. CONCLUSIONS: Via immediate intra-analytical procedure, our RNA-based stratification RNACellStrat increases the accuracy and reliability of the conclusions drawn from diagnostic and prognostic molecular information.
Authors: Benjamin Bian; Martin Bigonnet; Odile Gayet; Celine Loncle; Aurélie Maignan; Marine Gilabert; Vincent Moutardier; Stephane Garcia; Olivier Turrini; Jean-Robert Delpero; Marc Giovannini; Philippe Grandval; Mohamed Gasmi; Mehdi Ouaissi; Veronique Secq; Flora Poizat; Rémy Nicolle; Yuna Blum; Laetitia Marisa; Marion Rubis; Jean-Luc Raoul; James E Bradner; Jun Qi; Gwen Lomberk; Raul Urrutia; Andres Saul; Nelson Dusetti; Juan Iovanna Journal: EMBO Mol Med Date: 2017-04 Impact factor: 12.137
Authors: Matthias Neulinger-Muñoz; Dominik Schaack; Svetlana P Grekova; Andrea S Bauer; Thomas Giese; Gabriel A Salg; Elisa Espinet; Barbara Leuchs; Anette Heller; Jürg P F Nüesch; Miriam Schenk; Michael Volkmar; Nathalia A Giese Journal: Viruses Date: 2021-05-28 Impact factor: 5.048
Authors: Verena M Throm; David Männle; Thomas Giese; Andrea S Bauer; Matthias M Gaida; Juergen Kopitz; Thomas Bruckner; Konstanze Plaschke; Svetlana P Grekova; Klaus Felix; Thilo Hackert; Nathalia A Giese; Oliver Strobel Journal: Oncotarget Date: 2018-01-24