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. 2015 Feb 24;10(2):e0117818.
doi: 10.1371/journal.pone.0117818. eCollection 2015.

mRNA profiling reveals determinants of trastuzumab efficiency in HER2-positive breast cancer

Silvia von der Heyde  1 , Steve Wagner  2 , Alexander Czerny  3 , Manuel Nietert  3 , Fabian Ludewig  4 , Gabriela Salinas-Riester  4 , Dorit Arlt  2 , Tim Beißbarth  3
Affiliations

mRNA profiling reveals determinants of trastuzumab efficiency in HER2-positive breast cancer

Silvia von der Heyde et al. PLoS One. .

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Development of BTR50 cells.
Trastuzumab resistant cells were developed by culturing parental BT474 cells in the presence of 20/50 μg trastuzumab for around 6 months.
Fig 2
Fig 2. BT474 cells have acquired resistance to trastuzumab.
(a) Proliferation rate of BT474 (parental) and trastuzumab resistant BT474 (BTR50) cells treated with 20 μg/ml trastuzumab. Proliferation rates were measured daily over 7 days by a luciferase-based viability assay. (b) Sensitivity of BT474 (parental) and trastuzumab resistant BTR50 cells towards increasing concentrations of trastuzumab. Cell viability was determined by a luciferase-based viability assay after 7 days of treatment.
Fig 3
Fig 3. Fold Changes of DE genes (BT474 plus trastuzumab vs. BT474).
The barchart displays the log2 fold changes of validated candidate genes, which significantly changed their expression in BT474 after trastuzumab treatment. The positive values indicate an upregulation upon drug treatment. Black bars denote values resulting from RNA-Seq analysis. Gray bars denote values resulting from RT-qPCR analysis.
Fig 4
Fig 4. CAV1 expression is raised upon trastuzumab treatment.
The boxplot displays CAV1 gene expression (log2) of seven patients treated with trastuzumab for one year in addition to neoadjuvant chemotherapy (red) and four patients treated with neoadjuvant chemotherapy only (gray). The patient data samples were selected from the transNOAH breast cancer trial (GEO series GSE50948).
Fig 5
Fig 5. Fold Changes of DE genes (BT474 vs. HCC1954).
The barchart displays the log2 fold changes of validated candidate genes, which showed significant differences in their expression in BT474 and HCC1954, respectively. Positive values indicate an upregulation in BT474. Negative values indicate an upregulation in HCC1954. Black bars denote values resulting from RNA-Seq analysis. Gray bars denote values resulting from RT-qPCR analysis.
Fig 6
Fig 6. Principle Component Analysis (PCA) of all measured samples.
The plot displays the result of a PCA on the pairwise normalized RNA-Seq expression values of all 23367 annotated genes in the samples BT474, BTR50 and HCC1954 with and without trastuzumab (T) treatment. Same colors denote that the samples belong to the same conducted statistical test and thus were normalized in pair. The five two sample tests included BT474 vs. HCC1954, BT474 vs. BTR50, HCC1954+T vs. HCC1954, BTR50+T vs. BTR50, and BT474+T vs. BT474.
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This work was supported by the German Federal Ministry of Education and Research in the platform MedSys via the project BreastSys (grant 0315396A, URL http://www.ams.med.uni-goettingen.de/breastsys/), in the platform e:Bio via the project MetastaSys (grant 0316173A, URL http://www.ams.med.unigoettingen.de/metastasys/) and in the platform e:Med via the project Her2Low (grant 031A429C, URL http://www.ams.med.uni-goettingen.de/her2low/). TB received the mentioned funding. Above that SvdH, MN, SW and DA were funded by BreastSys. AC was funded by MetastaSys. The authors also acknowledge support by the Open Access Publication Funds of the Göttingen University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.