Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA295975 | ENA |
REPOSITORIES: ENA
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Project description:Prostate cancer is the second most occurring cancer in men worldwide, and with the advances made with screening for prostate-specific antigen, it has been prone to early diagnosis and over-treatment. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. The model includes pathways such as androgen receptor, MAPK, Wnt, NFkB, PI3K/AKT, MAPK, mTOR, SHH, the cell cycle, the epithelial-mesenchymal transition (EMT), apoptosis and DNA damage pathways. The final model accounts for 133 nodes and 449 edges. We applied a methodology to personalise this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients, using TCGA and GDSC datasets.
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Project description:Current prostate cancer prognostic models are based on pre-treatment prostate-specific antigen (PSA) levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict clinical disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. We analyzed a Swedish Watchful Waiting cohort (1977â1999) with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL) method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP) samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer or developed metastases and men who survived more than 10 years without metastases (lethals and indolents, respectively). Several models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. We reasoned that tumor sampling might preclude the identification of the dominant metastatic nodule. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Thus the determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses preventing the development of molecular biomarkers for prostate cancer progression. 281 cases from the population-based Swedish-Watchful Waiting cohort. The cohort consists of men with localized prostate cancer (clinical stage T1-T2, Mx, N0); Training set: first 186 samples; Validation cohort: remaining 95 cases from the same population.
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