The TransPot scientific aim is to obtain an unmatched depth of understanding of prostate cancer in order to improve the prognosis and treatment of lethal prostate cancer. This will include:
• Providing important insights into the mechanisms driving treatment resistant prostate cancer (including CRPC);
• Identifying novel targets for therapy;
• Developing and validating predictive models for disease progression and prognosis;
• Providing superior, clinically relevant tools and biomarkers for personalising and optimising CRPC therapy.
The TransPot project will incorporate the latest research technologies to understand lethal prostate cancer, and the network has been assembled to include leading academic and non-academic researchers, together giving an unrivalled expertise in the field. It will offer an innovative training programme for early stage researchers (ESRs) to ensure that they can effectively operate in today's multi-disciplinary programmes in scientific research. The training will include both scientific and transferable skill sets. Additionally, the network will focus on promoting their research to the whole of society to raise awareness and understanding of PC. This will include events such as open days and outreach events to schools, as well as communicating research advances and results via the media and social media. Advances achieved will facilitate personalised targeted medicine in treating lethal PC, and will impact beyond the scientific community by improving the well-being of advanced PC patients.
The central aim of TransPot is to provide a multi-disciplinary research training programme integrating cancer biology and systems medicine to train young researchers to become future successful leaders in cancer research. It will provide ESRs with a solid base for a career in translational research in either academia or industry. For this purpose, our programme consists of individual (interconnected) research projects. Supplementing these projects is a broad range of network-wide training activities including:
• Local training activities: PhD programme activities at host institute, local seminars and public engagement activities;
• Network-wide events: meetings, annual training events (see table below);
• Secondments: research-focused study visits to other labs (both academic and industrial) within the consortium of 1-2 months duration in order to gain specific skills sets;
• International conferences, peer-reviewed publications and thesis preparation.
Our ESRs will benefit from both generic and specific research skills, many of which are transferable, incorporating a wide range of topics: cancer biology, novel PC models, high-content screening, mathematical and computational modelling, as well as generic skills including intellectual property, gender issues, research integrity, entrepreneurship, communication and management.
ESR 1, Rafael Sanchez Martinez - Role of SLFN5 in Castration Resistant Prostate Cancer
Androgen deprivation therapy is used in the clinic to treat prostate cancer, as the cancer cells are dependent on androgens for their growth and survival. Castration resistant prostate cancer (CRPC) is developed as a resistance to this treatment, which causes the cancer to progress even in the absence of androgens. To do this, prostate cancer cells develop a wide array of biological changes that allow them to be refractory to this therapy. In order to study the inner workings of this process, a model study that analysed and compared the in vivo proteome of androgen dependent and castration resistant prostate cancer lines, was carried out to identify key players in this process. It was from this study human Schlafen 5 (SLFN5) emerged as a target of interest.
My goal is to validate the results of this initial study, proving the increased presence of SLFN5 in different CRPC cell lines. From there, my aim is to study the role of SLFN5 in CRPC, and I will work to determine if it is a key player in the development of resistance. I will also investigate which biochemical processesSLFN5 affects in order to exert is function, as well as which processes lead to its emergence in CRPC.
ESR 2, Parmveer Singh - Defining master regulators of human prostate epithelial differentiation from pluripotent stem cells
Prostate cancer is one of the leading causes of death among men in developed countries. Advances in uncovering the mechanisms of this disease have been hindered due to the absence of a sufficient human model that incorporates the complexity and variability of the disease. Recently, in vitro prostate organoids have emerged as an advantageous model system to study prostate cancer. Currently, prostate organoids are primarily produced using primary prostate tissue. Patient-derived organoids can allow for high-throughput testing of therapies and predict the response of a patient to specific drugs. However, the process of prostate organoid generation from patient tissue is inefficient and requires access to fresh biopsies. An alternative is using patient-derived induced pluripotent stem cells (iPSCs). Stem cells first are first differentiated into prostate tissue. Since the factors leading to prostate induction are not known, this step requires the use of inductive mesenchymal tissue. In this project, I am attempting to produce prostate organoids from iPSCs using seminal vesicle mesenchyme and the ventral mesenchymal pad (VMP). Furthermore, through RNA sequencing of prostate organoids at different stages of development, I will search for regulators of prostate differentiation, which could then be synthetically expressed for prostate tissue differentiation.
ESR 3, Syed Umbreen - Modelling androgen receptor mediated gene regulatory networks to predict CRPC
I am working on prostate cancer, which is the most commonly diagnosed disease in men in the United Kingdom. Out of many drivers, androgen receptor (AR) is one of the well-studied and known drivers of prostate cancer. The disease becomes chiefly metastatic once patients develop resistance against castration and the mechanisms of resistance are associated with the AR signalling pathway, which therefore becomes the key pathway target for treating metastatic prostate cancer. In addition, AR regulates the expression of genes involved in growth and survival e.g., PSA that is the most commonly used biomarker for the screening of prostate cancer. However, PSA testing is so economical and quick that there are chances of over diagnosis leading to unnecessary biopsies and treatment. My project focusses on finding better biomarkers that could predict response to treatment and disease progression. This involves mapping AR recruitment to the genome using ChIP-seq and identifying changes in the structure of chromatin using ATAC-seq in various stages of the disease and in response to the treatment in various clinically relevant prostate cancer models.
ESR4, Syeda Afshan - Organotypic tissue models that recapitulate tumour-host interactions in CRPC
The incidence of prostate cancer (PCa) has been rising in the last decades. Although basic research, medical diagnosis and therapy of PCa have advanced, there are still challenges in the diagnosis and treatment of late stage PCa. The lack of relevant in vitro and in vivo tumour models still impairs functional target validation and there is also a lack of tools for high content, phenotypic screening. It is now widely accepted that three-dimensional models better mimic the in vivo growth of tumour cells and provide more informative readouts. Our research group has developed 3D organoids, 3D co-cultures and patient derived explant cultures and xenografts (PDX). I aim to apply these models to study PCa progression. I plan to determine the role of FGF Receptor signalling in progression of PCa. The focus will be on their function in the tumour/stroma interaction and the tumour microenvironment.
Among the 5 FGFRs, I have short-listed two of them as the receptors of interest in PCa and their interaction with androgen receptor (AR) as basis of our future studies in collaboration with Prof. Ian Mills, The Queen's University of Belfast. I am working with AR expressing PCa cell lines to perform the preliminary studies to understand the functions of these FGFRs with respect to AR and then study the same using 3D co-culture models and patient derived primary samples.
ESR 5, Annelies van Hemelryk - Drug screening using 3D cultured therapy-resistant PDX and validation in PDX tissue slices of PC
|The treatment options for advanced and castration resistant prostate cancer (CRPC) have expanded drastically over the last decades and several new therapeutics are to be expected. Yet, the optimal treatment strategy regarding first line and subsequent treatments for (progressive) CRPC remains unclear, giving rise to interpatient heterogeneity in resistance profiles. To date, preclinical (CR)PC models are scarce and insufficiently represent current clinical disease. To overcome this shortcoming, I aim to establish an extensive in vitro 3D organoid panel that allows to test, develop, optimise and potentially personalise treatment strategies. So far, I have been able to establish long term prostate cancer cell line derived and short term PDX and patient derived organoid cultures, the latter being characterised at the moment. Next to this, I'm using the established prostate cancer cell line derived organoids to set up image based high throughput drug screen protocols in order to identify novel candidate targets and lead compounds for (CR)PC therapy. Ultimately, I would like to perform early drug screens on short term primary CRPC patient derived organoids, as in vitro testing of subsequent treatment options might predict patient responses and therefore allow guidance of clinical decision making.|
ESR 6, Marouane Kdadra - Identification of biomarkers in urine by metabolite profiling
The overall goal of my research project is to identify and validate urinary metabolite biomarkers and biomarker networks, i.e. high-dimensional classifiers, associated with prostate cancer. The basis for the project is an NMR based urinary metabolite analysis. Typically a three step approach was outlined, including (1) discovery and identification of biomarkers networks for PC aggressiveness, (2) optimization and validation of candidate networks in an independent cohort and (3) finally to test these biomarkers as a prognostic urinary metabolic panel for CRPC.
So far, metabolic signatures were identified that are differently expressed in low grade prostate cancer (indolent PC, Gleason score ≤6, n=55) compared to higher tumour grades (aggressive PC, GS ≥7a, n=211) in a training cohort of n=266 PC after radical prostatectomy from 6 different centres. In 10-fold cross-validation diagnostic accuracy of robust model candidates were in the range of AUC 0.70 to 0.74. Currently the identification of the metabolites causing the differently expressed NMR signals is ongoing, using spike-in experiment. In cooperation with TransPot Partners, these metabolomics data will be correlated in systems biology approaches with open-source proteomics and transcriptomics data to predict affected pathways using established pathway analysis tools. Subsequently, predicted protein changes will be validated in tissue by targeted proteomic analysis. Finally, obtained functional targets will be validated both in vitro and in vivo.
ESR7, Anna Mantsiou - Identification of tissue markers by proteomic analysis
During the first 14 months of my PhD in TransPot, multiple activities were undertaken to characterise protein changes associated with CRPC which collectively, have already provided some promising results currently under further investigation. Firstly, a systematic literature review was performed aiming to compile the existing information on proteomics analysis of PCa tissue in association to Castrate Resistant Prostate Cancer (CRPC). In the proteomics studies performed over the past 10 years, a small number of highly reproducible findings have been reported, including protein biomarkers that have been independently validated in more than one studies. However, many studies lacked adequate validation of findings and relied on relatively lower resolution proteomics techniques in comparison to current state of the art.
In the context of identifying effective drug combinations for the treatment of CRPC, proteomics analysis of prostate tissue from xenograft models treated with different drugs was performed. The animal model experiments have highlighted various protein changes and molecular processes associated with treatment sensitive or resistant PCa and they are currently under further evaluation.
Importantly, methods for the analysis of archival formalin-fixed paraffin-embedded (FFPE) tissue were established, that promise to accelerate further CRPC investigations. FFPE is a challenging material for proteomics due to the difficulty to obtain efficient and repeatable extraction of full length proteins, resulting in lack of standard operating procedures for handling such samples. To facilitate investigation of scarce CRPC tissue samples, an extensive protocol optimisation was performed, targeting the reproducible analysis of FFPE tissues. Initial experiments were performed using archival mouse kidney FFPE tissues and the respective fresh-frozen (FF) samples. The amount and quality of the extracted proteome was evaluated by SDS-page and GeLC-MS/MS. A pilot study then followed to test the efficacy of the optimized protocol to analyse FFPE clinical prostate samples.
ESR 8, Mina Sattari – Applying long non-coding RNA (IncRNA) to predict CRPC in a systems context
Prostate cancer (PCa) is a leading cause of cancer-related death of men globally. While some types of PCa grow slowly, other types may progress to a lethal castration-resistant prostate cancer (CRPC). Thus, there is a critical need to identify biomarkers that could distinguish the potentially lethal PCas from the indolent ones. Accumulating evidence suggests that long non-coding RNAs (lncRNAs) play an important role in PCa. However, discovery and functional characterization of lncRNAs in PCa needs further investigation. In my project, I am providing a view of the transcriptome offered by next-generation sequencing platforms to explore lncRNAs from PCa tissue samples, including untreated PCas, local CRPCs, and metastatic CRPCs. I will also integrate publicly available datasets, especially data from The Cancer Genome Atlas (TCGA), for analysing the expression profiles of lncRNAs. So far, I have done basic quality control and differential expression analyses, using the DESeq2 Bioconductor package. My investigations into this area are still ongoing. It is expected that the results of the current study will improve our knowledge on the roles of lncRNAs in PCa, their possible use as biomarkers, and as therapeutic targets for CRPC.
ESR 9, Mario Cangiano - miRNA in treatment-resistant CRPC
My project has been based on the investigation of RNAseq data generated by UGLA from isogenic pairs of human prostate cancer cell lines (namely CWR22-22RV1, LNCAP-LNCAPAI and VCAP-VCAP pairs in triplicates) grown in vivo in androgen naïve or castration resistant conditions, respectively. The first step of my analysis was the identification of master regulators of castration resistance xenografts from a gene regulatory network enriched by differentially expressed genes.
The inferred network was validated using RNAseq expression profiles from TCGA-PRAD (prostate adenocarcinoma) repository. Progression free time points have been used to evaluate the prognostic ability of the enriched regulons in identifying separated classes of likelihood of progression. TF-genes relationships inferred from xenografts were preserved when translated into human data.
Next step foresees the adoption of the set of proteomics profiles (matched to the RNAseq data used earlier) to reconstruct an orthografts-specific protein-protein interaction network that will help finding signaling convergence nodes to validate as prognostic markers.
ESR 10, Cathal McKinney - Validation of a MEKK and DDRD gene expression signature in preclinical models
The main objective of my project is to distinguish robust molecular subtypes of prostate cancer by using a meta-analytical approach to gene expression subtyping. In my project I will evaluate any existing methods of meta-analytical based subtyping and subsequently develop my own meta-analytical subtyping approach which incorporates in silico methodologies developed by my lab group (an R package or a pipeline).
I will determine the prognostic utility of identified subgroups and perform network and pathway analysis to distinguish the unique biological processes of each subgroup. I aim to identify mutations, copy number alterations and methylation marks, and to score a number of pre-existing assays developed by the Almac group in the identified subtypes.
Following the completion of my 1st year, I have identified a suitable meta-clustering package. I adapted published scripts and I developed the code of the package to ensure it corresponded to its updated dependencies. I demonstrated that subtypes generated from the available gene expression datasets were not robust to perturbation, which was in contrast to the proof-of-concept analysis. Currently, I am investigating the use of compositional ratios, the impact of intratumoural heterogeneity and correction at the individual dataset level.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 721746