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Multi-omics molecular treatment targets for Prostate Cancer

Periodic Reporting for period 1 - PCaProTreat (Multi-omics molecular treatment targets for Prostate Cancer)

Periodo di rendicontazione: 2018-04-01 al 2020-03-31

Clinical problem: Prostate Cancer (PCa) is the second most commonly diagnosed cancer among men. PCa patients represent a diverse group of individuals, with many presenting indolent disease, (unlikely to progress in the absence of treatment), and others having aggressive, life-threatening disease. Discrimination between these two groups is crucial to prevent over-treatment of patients with indolent disease and under-treatment of those with lethal disease. Lack of effective therapies for advanced disease is reflected in the 5-year survival rate that is decreasing from 100% (for patients with localized stage) to 29% (for patients with advanced stage). Even though, scientific advancements have led to the approval of new drugs for advanced PCa, the low response rate and development of resistance remains a barrier to improve on therapeutic outcomes.

Challenges: Progress has been made in understanding biology of PCa progression. However, development of new drugs is hindered by high molecular complexity, heterogeneity and development of resistance, impacting the treatment response. The problem seems to be multifactorial, with “sub-optimal” selection of the drug targets, being a key issue. The latter is to a large extent a result of insufficient knowledge of the molecular pathophysiology, (e.g. cross- linking of the molecular pathways), assessment of biological relevance and underestimation of disease heterogeneity.

Societal Implications: PCaProTreat was designed to face existing therapeutic challenges and address pressing needs related to PCa burden. The aim is to improve our understanding of molecular changes associated with PCa progression to define novel drug targets based on the molecular pathophysiology. Thus, PCaProTreat has the potential to revolutionise PCa management, and ultimately improve patient outcome and quality of life. The specific PCaProTreat objectives were:
1. Establishment of PCa knowledgebase with features related to PCa progression.
2. Characterisation of the molecular landscape of PCa progression through the integration of multi-source (tissue, urine, and seminal plasma) and multi-omics data, complemented with literature-mined data.
3. Definition of biological processes/ pathways and their regulatory elements responsible for disease progression (best suited drug targets), followed by their validation.
4. In silico prediction of therapeutic agents based on profiling data sets.

Conclusions: PCaProTreat defined molecular-driven drug targets/ potential therapeutic agents that directly targets molecular mechanisms underlying the disease. The research and training activities boosted the IF fellow career in the industry sector.
Publicly available, previously published and newly collected multi-omics data and literature-mined features were cross-correlated and analysed using bioinformatics tools to decipher molecular alterations related to PCa progression. This led to the generation of a knowledgebase of disease-relevant molecular alterations, molecular processes/ pathways with increased validity. Placing the data in the context of the tumour biology revealed key elements driving PCa progression.

Specifically, multi-omics analysis was performed based on seminal plasma (n=80), urinary peptidomics (n=823), tissue proteomics (n=104), and transcriptomics data (n=1,707). In brief, peptidomics analysis allowed for identification of 16 seminal plasma-derived and 91 urinary peptides that were significantly correlated with disease progression (p<0.05 Spearman's correlation). The former (among others) were fragments of most abundant seminal plasma proteins (semenogelins, lactotransferrin) and the latter were mostly collagen fragments, likely reflecting changes in the extracellular matrix remodelling, a crucial process for cancer progression. Tissue proteomics resulted in an identification of a total of 5,324 proteins, of which 2,802 were found to be significantly correlated with PCa progression; whereas the transcriptomics data (a part of Prostate Cancer Transcriptome Atlas) revealed 12,862 genes being associated with progression. 8,591 molecular features associated with PCa were also retrieved from literature and public resources. To shortlist the most credible molecular changes, the above data were cross-correlated. This gave rise to 392 proteins (a molecular signature) exhibiting the same direction of the association with disease progression across different data traits. Among them, 257 (66%) were confirmed to be associated with PCa based on literature.

Subsequently, bioinformatics analysis was conducted to decipher the biological function and pathways behind the cross-correlated data. The vast majority of the predicted pathways/ processes were linked to metabolism. Oxidative phosphorylation, branched-chain amino acids degradation, fatty acid β-oxidation I, Pyrimidine/ Purine biosynthesis, were predicted (among others) as activated, and reflected the metabolic hallmarks of PCa. Potential therapeutic agents were predicted using Connectivity Map (CMap), leading to the identification of 68 drugs/ compounds (p<0.05) that could potentially reverse the disease phenotype. Among 15 most promising findings, 7 were novel in the context of PCa. Cross-correlation of the results from the CMap and pathway analysis, revealed that 32 protein changes within the metabolic pathways are reversed by at least 3 novel therapeutic agents. Out of those, 10 proteins were involved in at least 2 pathways, and those were defined as potential drug targets and considered for validation. Three out of ten potential drug targets (i.e. mitochondrial acetyl-CoA acetyltransferase, peroxisomal multifunctional enzyme type 2, low molecular weight phosphotyrosine protein phosphatase) have been previously studied by immunohistochemistry (IHC), confirming the validity of the findings.

In parallel, dissemination to multi-target groups (e.g. scientists, pharma companies) was actively followed via multiple routes including e.g. scientific publications, presentations to meetings, project website, and direct contacts. Among other achievements, PCaProTreat was selected to be part of Innovation Radar, a Pilot study on Innovation launched by the European Commission. The IF fellow also edited a Special Issue in Proteomics Clinical Application, entitled “Clinical Proteomics on the Way towards Implementation”. The Project created a solid base for future exploitation of the findings in pre-clinical models. The initiation of new collaborative research projects is ongoing. PCaProTreat analytical pipeline may be also transformed into a service, supporting definition of molecularly-driven drugs/ therapeutic agents. In parallel, extensive training was followed to strengthen the competitiveness and the position of the IF fellow in the scientific community. These achievements were enabled by the smooth integration of the IF fellow in the Host team, and their fruitful collaboration.
PCaProTreat contributes and moves beyond the state-of-the-art by comprehensive characterisation of the molecular landscape of PCa progression and development of molecularly-driven drug targets/ therapeutic agents linked to pathophysiology and mechanisms underlying the disease. The results have potential to overcome the main stumbling blocks limiting the access to new therapies. It is anticipated that patients using drugs defined based on molecular pathophysiology will likely have a better treatment response.
Overview of PCaProTreat Approach