12 research outputs found
Establishment of the Ba/F3 method to test the oncogenic potential of cancer gene mutations
No full textEstablishment of the Ba/F3 method to test the oncogenic potential of cancer gene mutations
No full textMaster i biomedisi
High-throughput screens identify microRNAs essential for HER2 positive breast cancer cell growth
No full textMicroRNAs (miRNAs) are non-coding RNAs regulating gene expression post-transcriptionally. We have characterized the role of miRNAs in regulating the human epidermal growth factor receptor 2 (HER2)-pathway in breast cancer. We performed miRNA gain-of-function assays by screening two HER2 amplified cell lines (KPL-4 and JIMT-1) with a miRNA mimic library consisting of 810 human miRNAs. The levels of HER2, phospho-AKT, phospho-ERK1/2, cell proliferation (Ki67) and apoptosis (cPARP) were analyzed with reverse-phase protein arrays. Rank product analyses identified 38 miRNAs (q <0.05) as inhibitors of HER2 signaling and cell growth, the most effective being miR-491-5p, miR-634, miR-637 and miR-342-5p. We also characterized miRNAs directly targeting HER2 and identified seven novel miRNAs (miR-552, miR-541, miR-193a-5p, miR-453, miR-134, miR-498, and miR-331-3p) as direct regulators of the HER2 3'UTR. We demonstrated the clinical relevance of the miRNAs and identified miR-342-5p and miR-744* as significantly down-regulated in HER2-positive breast tumors as compared to HER2-negative tumors from two cohorts of breast cancer patients (101 and 1302 cases). miR-342-5p specifically inhibited HER2-positive cell growth, as it had no effect on the growth of HER2-negative control cells in vitro. Furthermore, higher expression of miR-342-5p was associated with better survival in both breast cancer patient cohorts. In conclusion, we have identified miRNAs which are efficient negative regulators of the HER2 pathway that may play a role in vivo during breast cancer progression. These results give mechanistic insights in HER2 regulation which may open potential new strategies towards prevention and therapeutic inhibition of HER2-positive breast cancer
Drug-screening and genomic analyses of HER2-positive breast cancer cell lines reveal predictors for treatment response
No full textSandra Jernström,1,2 Vesa Hongisto,3 Suvi-Katri Leivonen,1,2 Eldri Undlien Due,1 Dagim Shiferaw Tadele,1 Henrik Edgren,4,5 Olli Kallioniemi,4 Merja Perälä,6 Gunhild Mari Mælandsmo,2,7,8 Kristine Kleivi Sahlberg,1,9 1Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 2KG Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway; 3Misvik Biology Oy, Turku, 4Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 5Medisapiens, Helsinki, Finland, 6VTT Technical Research Centre of Finland, Turku, Finland; 7Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; 8Institute of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, 9Department of Research, Vestre Viken Hospital Trust, Drammen, Norway Background: Approximately 15%–20% of all diagnosed breast cancers are characterized by amplified and overexpressed HER2 (= ErbB2). These breast cancers are aggressive and have a poor prognosis. Although improvements in treatment have been achieved after the introduction of trastuzumab and lapatinib, many patients do not benefit from these drugs. Therefore, in-depth understanding of the mechanisms behind the treatment responses is essential to find alternative therapeutic strategies. Materials and methods: Thirteen HER2 positive breast cancer cell lines were screened with 22 commercially available compounds, mainly targeting proteins in the ErbB2-signaling pathway, and molecular mechanisms related to treatment sensitivity were sought. Cell viability was measured, and treatment responses between the cell lines were compared. To search for response predictors and genomic and transcriptomic profiling, PIK3CA mutations and PTEN status were explored and molecular features associated with drug sensitivity sought. Results: The cell lines were divided into three groups according to the growth-retarding effect induced by trastuzumab and lapatinib. Interestingly, two cell lines insensitive to trastuzumab (KPL4 and SUM190PT) showed sensitivity to an Akt1/2 kinase inhibitor. These cell lines had mutation in PIK3CA and loss of PTEN, suggesting an activated and druggable Akt-signaling pathway. Expression levels of five genes (CDC42, MAPK8, PLCG1, PTK6, and PAK6) were suggested as predictors for the Akt1/2 kinase-inhibitor response. Conclusion: Targeting the Akt-signaling pathway shows promise in cell lines that do not respond to trastuzumab. In addition, our results indicate that several molecular features determine the growth-retarding effects induced by the drugs, suggesting that parameters other than HER2 amplification/expression should be included as markers for therapy decisions. Keywords: ErbB2, drug screening, gene expression, pharmacogenomics, predictor
Evaluation of arrayed primer extension for TP53 mutation detection in breast and ovarian carcinomas
No full textMutations in the tumor suppressor gene TP53 are associated with a wide range of different cancers and may have prognostic and therapeutic implications. Methods for rapid and sensitive detection of mutations in this gene are therefore required. In order to make screening more effective, a commercially available TP53 genotyping microarray from Asper Biotech has been constructed by arrayed primer extension (APEX). The present study is the first report that blindly evaluates the efficiency of the second generation APEX TP53 genotype chip outside the Asper laboratory and compares it to temporal temperature gradient electrophoresis (TTGE) and sequencing of TP53 for mutation detection in ovarian and breast cancer samples. All nucleotides in the TP53 gene from exon 2–9 are included on the chip by synthesis and application of sequence-specific oligonucleotides. The chip was validated by screening 48 breast and 11 ovarian cancer cases, all of which had previously been analyzed by TTGE and sequencing. APEX scored 17 of 20 sequence variants, missing one deletion, one insertion, and a missense mutation. Resequencing efficiency using APEX was 92% for both DNA strands and 99.5% for sense and/or antisense strand. We conclude that the APEX TP53 microarray is a robust, rapid, and comprehensive screening tool for sequence alterations in tumors
Drug-screening and genomic analyses of HER2-positive breast cancer cell lines reveal predictors for treatment response
No full textBackground
Approximately 15%–20% of all diagnosed breast cancers are characterized by amplified and overexpressed HER2 (= ErbB2). These breast cancers are aggressive and have a poor prognosis. Although improvements in treatment have been achieved after the introduction of trastuzumab and lapatinib, many patients do not benefit from these drugs. Therefore, in-depth understanding of the mechanisms behind the treatment responses is essential to find alternative therapeutic strategies.
Materials and methods
Thirteen HER2 positive breast cancer cell lines were screened with 22 commercially available compounds, mainly targeting proteins in the ErbB2-signaling pathway, and molecular mechanisms related to treatment sensitivity were sought. Cell viability was measured, and treatment responses between the cell lines were compared. To search for response predictors and genomic and transcriptomic profiling, PIK3CA mutations and PTEN status were explored and molecular features associated with drug sensitivity sought.
Results
The cell lines were divided into three groups according to the growth-retarding effect induced by trastuzumab and lapatinib. Interestingly, two cell lines insensitive to trastuzumab (KPL4 and SUM190PT) showed sensitivity to an Akt1/2 kinase inhibitor. These cell lines had mutation in PIK3CA and loss of PTEN, suggesting an activated and druggable Akt-signaling pathway. Expression levels of five genes (CDC42, MAPK8, PLCG1, PTK6, and PAK6) were suggested as predictors for the Akt1/2 kinase-inhibitor response.
Conclusion
Targeting the Akt-signaling pathway shows promise in cell lines that do not respond to trastuzumab. In addition, our results indicate that several molecular features determine the growth-retarding effects induced by the drugs, suggesting that parameters other than HER2 amplification/expression should be included as markers for therapy decisions
An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment
No full textHow mixtures of immune cells associate with cancer cell phenotype and affect pathogenesis is still unclear. In 15 breast cancer gene expression datasets, we invariably identify three clusters of patients with gradual levels of immune infiltration. The intermediate immune infiltration cluster (Cluster B) is associated with a worse prognosis independently of known clinicopathological features. Furthermore, immune clusters are associated with response to neoadjuvant chemotherapy. In silico dissection of the immune contexture of the clusters identified Cluster A as immune cold, Cluster C as immune hot while Cluster B has a pro-tumorigenic immune infiltration. Through phenotypical analysis, we find epithelial mesenchymal transition and proliferation associated with the immune clusters and mutually exclusive in breast cancers. Here, we describe immune clusters which improve the prognostic accuracy of immune contexture in breast cancer. Our discovery of a novel independent prognostic factor in breast cancer highlights a correlation between tumor phenotype and immune contexture
Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes
No full textBackground
The heterogeneous biology of breast cancer leads to high diversity in prognosis and response to treatment, even for patients with similar clinical diagnosis, histology, and stage of disease. Identifying mechanisms contributing to this heterogeneity may reveal new cancer targets or clinically relevant subgroups for treatment stratification. In this study, we have merged metabolite, protein, and gene expression data from breast cancer patients to examine the heterogeneity at a molecular level.
Methods
The study included primary tumor samples from 228 non-treated breast cancer patients. High-resolution magic-angle spinning magnetic resonance spectroscopy (HR MAS MRS) was performed to extract the tumors metabolic profiles further used for hierarchical cluster analysis resulting in three significantly different metabolic clusters (Mc1, Mc2, and Mc3). The clusters were further combined with gene and protein expression data.
Results
Our result revealed distinct differences in the metabolic profile of the three metabolic clusters. Among the most interesting differences, Mc1 had the highest levels of glycerophosphocholine (GPC) and phosphocholine (PCho), Mc2 had the highest levels of glucose, and Mc3 had the highest levels of lactate and alanine. Integrated pathway analysis of metabolite and gene expression data uncovered differences in glycolysis/gluconeogenesis and glycerophospholipid metabolism between the clusters. All three clusters had significant differences in the distribution of protein subtypes classified by the expression of breast cancer-related proteins. Genes related to collagens and extracellular matrix were downregulated in Mc1 and consequently upregulated in Mc2 and Mc3, underpinning the differences in protein subtypes within the metabolic clusters. Genetic subtypes were evenly distributed among the three metabolic clusters and could therefore contribute to additional explanation of breast cancer heterogeneity.
Conclusions
Three naturally occurring metabolic clusters of breast cancer were detected among primary tumors from non-treated breast cancer patients. The clusters expressed differences in breast cancer-related protein as well as genes related to extracellular matrix and metabolic pathways known to be aberrant in cancer. Analyses of metabolic activity combined with gene and protein expression provide new information about the heterogeneity of breast tumors and, importantly, the metabolic differences infer that the clusters may be susceptible to different metabolically targeted drugs
Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome.
No full textBACKGROUND: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. METHODS: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. RESULTS: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. CONCLUSIONS: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer
