title: mRNA profiling reveals determinants of trastuzumab efficiency in HER2-positive breast cancer.
abstract: Intrinsic and acquired resistance to the monoclonal antibody drug trastuzumab is a major problem in the treatment of HER2-positive breast cancer. A deeper understanding of the underlying mechanisms could help to develop new agents. Our intention was to detect genes and single nucleotide polymorphisms (SNPs) affecting trastuzumab efficiency in cell culture. Three HER2-positive breast cancer cell lines with different resistance phenotypes were analyzed. We chose BT474 as model of trastuzumab sensitivity, HCC1954 as model of intrinsic resistance, and BTR50, derived from BT474, as model of acquired resistance. Based on RNA-Seq data, we performed differential expression analyses on these cell lines with and without trastuzumab treatment. Differentially expressed genes between the resistant cell lines and BT474 are expected to contribute to resistance. Differentially expressed genes between untreated and trastuzumab treated BT474 are expected to contribute to drug efficacy. To exclude false positives from the candidate gene set, we removed genes that were also differentially expressed between untreated and trastuzumab treated BTR50. We further searched for SNPs in the untreated cell lines which could contribute to trastuzumab resistance. The analysis resulted in 54 differentially expressed candidate genes that might be connected to trastuzumab efficiency. 90% of 40 selected candidates were validated by RT-qPCR. ALPP, CALCOCO1, CAV1, CYP1A2 and IGFBP3 were significantly higher expressed in the trastuzumab treated than in the untreated BT474 cell line. GDF15, IL8, LCN2, PTGS2 and 20 other genes were significantly higher expressed in HCC1954 than in BT474, while NCAM2, COLEC12, AFF3, TFF3, NRCAM, GREB1 and TFF1 were significantly lower expressed. Additionally, we inferred SNPs in HCC1954 for CAV1, PTGS2, IL8 and IGFBP3. The latter also had a variation in BTR50. 20% of the validated subset have already been mentioned in literature. For half of them we called and analyzed SNPs. These results contribute to a better understanding of trastuzumab action and resistance mechanisms.
authors.affiliation: Statistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany; IndivuTest GmbH, Hamburg, Germany.
authors.forename: Silvia
authors.initials: S
authors.lastname: von der Heyde
authors.affiliation: Division of Stem Cells and Cancer, German Cancer Research Center, Heidelberg, Germany; Division of Molecular Genome Analysis, German Cancer Research Center, Heidelberg, Germany.
authors.forename: Steve
authors.initials: S
authors.lastname: Wagner
authors.affiliation: Statistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Czerny
authors.affiliation: Statistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
authors.forename: Manuel
authors.initials: M
authors.lastname: Nietert
authors.affiliation: DNA Microarray and Deep-Sequencing Facility Göttingen, Department of Developmental Biochemistry, University of Göttingen, Göttingen, Germany.
authors.forename: Fabian
authors.initials: F
authors.lastname: Ludewig
authors.affiliation: DNA Microarray and Deep-Sequencing Facility Göttingen, Department of Developmental Biochemistry, University of Göttingen, Göttingen, Germany.
authors.forename: Gabriela
authors.initials: G
authors.lastname: Salinas-Riester
authors.affiliation: Division of Stem Cells and Cancer, German Cancer Research Center, Heidelberg, Germany; Division of Molecular Genome Analysis, German Cancer Research Center, Heidelberg, Germany.
authors.forename: Dorit
authors.initials: D
authors.lastname: Arlt
authors.affiliation: Statistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Beißbarth
mesh_terms: D061067:Antibodies, Monoclonal, Humanized / Q000494:pharmacology* / Q000627:therapeutic use; D000970:Antineoplastic Agents / Q000494:pharmacology* / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000473:pathology; D051242:Caveolin 1 / Q000235:genetics / Q000378:metabolism; D045744:Cell Line, Tumor; D002470:Cell Survival / Q000187:drug effects; D019008:Drug Resistance, Neoplasm / Q000187:drug effects; D005260:Female; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis; D012333:RNA, Messenger / Q000378:metabolism*; D060888:Real-Time Polymerase Chain Reaction; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D017423:Sequence Analysis, RNA; D000068878:Trastuzumab; D015854:Up-Regulation / Q000187:drug effects*
chemical_list: D061067:Antibodies, Monoclonal, Humanized; D000970:Antineoplastic Agents; D051242:Caveolin 1; D012333:RNA, Messenger; D018719:Erb-b2 Receptor Tyrosine Kinases; D000068878:Trastuzumab; C495540:CAV1 protein, human