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Teema : geeniohjatun syövän hoidon työryhmä

Breast cancer is the leading cause of cancer-related deaths in women due to distinct cancer subtypes associated with early recurrence and aggressive metastatic progression. High lipoprotein-associated phospholipase A2 (PLA2G7) expression has previously been associated with aggressive disease and metastasis in prostate cancer. Here, we explore the expression pattern and functional role of PLA2G7 in breast cancer. First, a bioinformatic analysis of genome-wide gene expression data from 970 breast samples was carried out to evaluate the expression pattern of PLA2G7 mRNA in breast cancer. Second, the expression profile of PLA2G7 was studied in 1042 breast cancer samples including 89 matched lymph node metastasis samples using immunohistochemistry. Third, the effect of PLA2G7 silencing on genome-wide gene expression profile was studied and validated in cultured breast cancer cells expressing PLA2G7 at high level. Last, the expression pattern of PLA2G7 mRNA was investigated in 24 nonmalignant tissue samples and 65 primary and 7 metastatic tumour samples derived from various organs using qRT-PCR. The results from clinical breast cancer samples indicated that PLA2G7 is overexpressed in a subset of breast cancer samples compared to its expression in benign breast tissue samples and that high PLA2G7 expression associated with hormone receptor negativity as well as with poor prognosis in a subset of breast cancer samples. In vitro functional studies highlighted the putative role of PLA2G7 in the regulation of epithelial-mesenchymal transition (EMT)-related signalling pathways, vimentin and E-cadherin protein expression as well as cell migration in cultured breast cancer cells. Furthermore, supporting the findings in breast and prostate cancer, high PLA2G7 mRNA expression was associated with metastatic cancer in four additional organs of origin. In conclusion, our results indicate that PLA2G7 is highly expressed in a subset of metastatic and aggressive breast cancers and in metastatic samples of various tissues of origin and promotes EMT and migration in cultured breast cancer cells.

Prostate cancers form a heterogeneous group of diseases and there is a need for novel biomarkers, and for more efficient and targeted methods of treatment. In this thesis, the potential of microarray data, RNA interference (RNAi) and compound screens were utilized in order to identify novel biomarkers, drug targets and drugs for future personalized prostate cancer therapeutics. First, a bioinformatic mRNA expression analysis covering 9873 human tissue and cell samples, including 349 prostate cancer and 147 normal prostate samples, was used to distinguish in silico prevalidated putative prostate cancer biomarkers and drug targets. Second, RNAi based high-throughput (HT) functional profiling of 295 prostate and prostate cancer tissue specific genes was performed in cultured prostate cancer cells. Third, a HT compound screen approach using a library of 4910 drugs and drug-like molecules was exploited to identify potential drugs inhibiting prostate cancer cell growth. Nine candidate drug targets, with biomarker potential, and one cancer selective compound were validated in vitro and in vivo. In addition to androgen receptor (AR) signaling, endoplasmic reticulum (ER) function, arachidonic acid (AA) pathway, redox homeostasis and mitosis were identified as vital processes in prostate cancer cells. ERG oncogene positive cancer cells exhibited sensitivity to induction of oxidative and ER stress, whereas advanced and castrate-resistant prostate cancer (CRPC) could be potentially targeted through AR signaling and mitosis. In conclusion, this thesis illustrates the power of systems biological data analysis in the discovery of potential vulnerabilities present in prostate cancer cells, as well as novel options for personalized cancer management.

Our laboratory performs high-throughput screening (HTS) experiments using siRNA, miRNA and compound libraries on cancer-cell lines. We utilize three technologies: plate-based screens with different assays as endpoints, transfected cell arrays with up to four antibody endpoints, and protein lysate arrays allowing many immunostaining endpoints. In addition, we profile samples using at least three commercially available microarray technologies: gene expression using Affymetrix arrays, miRNA expression using Agilent arrays, and gene copy numbers (aCGH) using Agilent arrays. This enables us to study the same biological sample from multiple angles which provides a unique integrated opportunity to understand cancer. The database design presented here is based on the idea that different high-throughput technologies can be integrated at the gene level.