Project description:The transcriptomic heterogeneity of the prostate cancer was tested by profiling histologically distinct but equally graded (Gleason score 4+5=9/10) cancer nodules from a surgically removed prostate cancer. We found that not only that the genes were differently regulated in the two nodules but also that expression fluctuations were differently controlled and the gene networks differently remodeled.
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.
Project description:Prostate cancer (PCa) disseminated tumor cells (DTC) in the bone marrow (BM) can remain dormant for prolonged periods before recurrence. Our aim was to characterize individual prostate DTC, analyze tumor cell heterogeneity, and identify markers of tumor dormancy. Custom Agilent 44K whole human genome expression oligonucleotide microarrays were used to profile single disseminated tumor cells isolated from bone marrow (BM) samples of four patients with no evidence of disease (NED) upon follow-up and six advanced disease (ADV) prostate cancer patients. Essentially, a two-step selection process was employed, in which anti-CD45 and anti-CD61 conjugated to immunomagnetic beads were used for negative selection, and anti-HEA was used for positive selection. Cells were then fluorescently stained for BerEP4, counter stained with RPE anti-CD45, and individually selected (10 single cells each per patient) under fluorescent light using a micropipette system for further analysis. RNA was amplified using the WT-Oviation one-direct system and hybridized against a common reference pool of prostate tumor cell lines.
Project description:Prostate cancer (PCa) disseminated tumor cells (DTC) in the bone marrow (BM) can remain dormant for prolonged periods before recurrence. Our aim was to characterize individual prostate DTC, analyze tumor cell heterogeneity, and identify markers of tumor dormancy.
Project description:Intra-tumor heterogeneity (ITH) has been studied at the morphologic, genomic, and transcriptomic level, but not proteomic level. Recent advances in mass spectrometric (MS) proteome quantification techniques, exemplified by SWATH-MS, a massively parallel targeting method, now also support precise quantitative proteomic comparisons across multiple samples, thus identifying molecular and implied functional differences. Here we used SWATH-MS to analyze the proteome profiles of a set of fresh-frozen prostate tissue samples derived from radical prostatectomy specimens. A high confidence set of 1,906 proteins were consistently quantified across 60 biopsy-level tissue samples from three prostatectomy patients, each consisting of 1.0 mm punch biopsies from histologically malignant (acinar and ductal adenocarcinoma) and matching benign prostatic hyperplasia tissues. The quantitative protein profiles allowed independent quantification of the degree of intra-tumor heterogeneity for each protein in benign and malignant tissues. We found that while majority of the proteins showed comparably low intra-tumor variability, 122 proteins were highly variable in malignant and/or matching benign tissues. We observed that proteins that varied between patients or tissue types also tended to be highly variable within prostate tissues, suggesting that these variability patterns will be a critical selection criterion in future protein biomarker studies. The data also permitted investigation of the heterogeneity of multiple biochemical pathways. The high variability of several of the pathways, including Glypican-1 network, alpha-linolenic acid metabolism and celecoxib pathway, explained contradictory results regarding them in the literature. In conclusion, we demonstrated a methodology for investigating proteomic intra-tumor heterogeneity from biopsy-level tissue samples, and quantified the degree of intra-tumor heterogeneity of 1,906 proteins in prostate tumors. The method and data presented here have advanced our understanding of tumor biology and offered critical insights for future biomarker development.