RNA based individualized drug selection in breast cancer patients without patient-matched normal tissue
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Michael Forster1, Adam Mark1,2, Friederike Egberts3, Elisa Rosati1, Elke Rodriguez3, Martin Stanulla4, Dirk Bauerschlag5, Christian Schem6, Nicolai Maass5, Anu Amallraja7, Karla K. Murphy8, Bruce R. Prouse8, Raed A. Sulaiman8, Brandon M. Young7, Micaela Mathiak9, Georg Hemmrich-Stanisak1, David Ellinghaus1, Stephan Weidinger3, Philip Rosenstiel1, Norbert Arnold1,5, Brian Leyland-Jones7, Casey B. Williams7, Andre Franke1,* and Tobias Meißner7,*
1Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
2Current address: Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, CA, USA
3Department of Dermatology, Schleswig-Holstein University Hospital, Kiel, Germany
4Department of Pediatric Haematology and Oncology, Hannover Medical School, Hannover, Germany
5Department of Gynaecology and Obstetrics, Schleswig-Holstein University Hospital, Kiel, Germany
6Mammazentrum Hamburg, Hamburg, Germany
7Department of Molecular and Experimental Medicine, Avera Cancer Institute, Sioux Falls, SD, USA
8Pathology, Avera McKennan, Sioux Falls, SD, USA
9Department of Pathology, Schleswig-Holstein University Hospital, Kiel, Germany
*These authors share last authorship
Michael Forster, email: email@example.com
Keywords: breast cancer sequencing; normal RNA expression; drug selection; formalin fixed paraffin embedded; fresh frozen
Received: March 07, 2018 Accepted: August 04, 2018 Published: August 17, 2018
Background: While standard RNA expression tests stratify patients into risk groups, RNA-Seq can guide personalized drug selection based on expressed mutations, fusion genes, and differential expression (DE) between tumor and normal tissue. However, patient-matched normal tissue may be unavailable. Additionally, biological variability in normal tissue and technological biases may confound results. Therefore, we present normal expression reference data for two sequencing methods that are suitable for breast biopsies.
Results: We identified breast cancer related and drug related genes that are expressed uniformly across our normal samples. Large subsets of these genes are identical for formalin fixed paraffin embedded samples and fresh frozen samples. Adipocyte signatures were detected in frozen compared to formalin samples, prepared by surgeons and pathologists, respectively. Gene expression confounded by adipocytes was identified using fat tissue samples. Finally, immune repertoire statistics were obtained for healthy breast, tumor and fat tissues.
Conclusions: Our reference data can be used with patient tumor samples that are asservated and sequenced with a matching aforementioned method. Coefficients of variation are given for normal gene expression. Thus, potential drug selection can be based on confidently overexpressed genes and immune repertoire statistics.
Materials and Methods: Normal expression from formalin and frozen healthy breast tissue samples using Roche Kapa RiboErase (total RNA) (19 formalin, 9 frozen) and Illumina TruSeq RNA Access (targeted RNA-Seq, aka TruSeq RNA Exome) (11 formalin, 1 frozen), and fat tissue (6 frozen Access). Tumor DE using 10 formalin total RNA tumor samples and 1 frozen targeted RNA tumor sample.
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