work plan

The research field of metabolomics has made considerable technical advances in recent years. However, in contrast to functional genomics and proteomics, these advances have not been translated to the field of patient-oriented cancer research, so far. Therefore, the METAcancer consortium has been strategically built to cover the major technical approaches of metabolomics and to link these approaches with knowledge in clinical pathology and clinical research.

The aim of METAcancer is to identify metabolic pathways which are perturbed in breast cancer tissue and either classify the tumor type or provide information on treatment efficacy through these metabolic changes.

METAcancer's integrated approach combines metabolomic data from patient samples gained under the project with existing transcriptomic data pools for bioinformatic interpretation. This will lead to a validated predictive diagnostic system to be used for in vivo screening of breast tumors to support individualized treatment therapy in order to minimize therapies with toxic or non-effective chemotherapeutics. 
Below a graphical survey on the general METAcancer workflow.

The first half of the three years project duration is dedicated to the metabolic analysis of tumor tissue:
The basis for our investigations is the METAcancer biobank (Work Package 1) containing a large number of clinically well-annotated fresh frozen tumor samples as well as paraffin embedded samples and serum samples from clinical studies. The biobank will be continuously updated throughout the project. As all three techniques for metabolic profiling can be applied to very small tumor samples, it is possible to divide the samples in three parts and to analyze each sample by all three methods. To ensure that all samples are comparable to each other, an SOP-based histopathological quality control will be performed to confirm the presence of an adequate number of tumor cells as well as the absence of necrosis in each of the three subsamples.

The samples will be analyzed by different analyzing platforms (GC-MS (Work Package 2), NMR spectroscopy (Work Package 3) and LC-MS (Work Package 4)) in parallel. These high throughput technologies are in particular suited to detect significant predictive alterations in cancer metabolic profiles. Potential new targets or metabolic signatures can be identified by data base analyses and by mass spectrometry. Target signatures and clinical information will be handled by bioinformatic data management. The results of the metabolic analysis will be stored in a central database and new techniques for identification of metabolites will be applied (Work Package 6). Click on the Figure to get a survey on  the METAcancer samples work flow.

The combined statistical analysis resulting from the work packages 2, 3 and 4 will lead to the identification of those areas of cellular metabolism that are most relevant to the progression of cancer and can be used for diagnostic biomarkers as well as new therapeutic targets. The bioinformatic analysis will be conducted by all METAcancer partners.

These partners have actively contributed as chairs and participants of the Metabolomics Standards Initiative (MSI) of the Metabolomics Society towards establishing minimum standards in reporting metabolomic experiments. We believe that standardized reports are mandatory for validating biomarkers and disseminating results through databases, which lay the ground for establishing these biomarkers as diagnostic tools. Synergy effects will arise from the analysis of the same samples on different metabolomics platforms (NMR, GC-MS, LC-MS), as well as from the analysis of different patient cohorts on the same platform. The result is a complementary view on the biological mechanism underlying metabolic changes in cancer and the opportunity for a safe validation of the detected predictive signatures on independent data sets.

In parallel, a large scale data mining analysis (Work Package 5) of all available transcriptomic datasets in breast cancer will be performed based on previous work of the partners, that will be focused on those areas of that have been identified by the metabolomics approach. We will combine the available state-of-the-art technology for metabolic profiling. The originality of the METAcancer approach will be the first-time application of these combined technologies to large scale analysis of patient samples in the field of translational research in breast cancer. Furthermore, we will use state-of-the-art datasets of breast cancer transcriptomics, but go beyond the state-of-the-art by connecting them to new metabolic profiling results generated by our consortium. We believe, that the results of metabolic analysis of cancer tissue should be interpreted in
the context of cellular networks that consists of metabolites, proteins and nucleic acids. These other levels of ?omics will contribute substantially to the understanding of molecular changes in tumor tissue, the selection of biomarkers and the development of new therapeutic strategies.

The second 18 months of the project as well as the second part of the major Work Packages will be dedicated to the validation of the results of the metabolic analysis, the interconnection between metabolomics and transcriptomics as well as the focused analysis of protein markers and mRNA markers.

For a further validation of the results, additional frozen samples from the biobank will be used for the investigation of metabolic biomarker signatures. For connection to protein alterations, the biobank has collections of thousands of pretherapeutic breast cancer samples from neoadjuvant clinical trials, which can be used for protein analysis using tissue microarrays as well as for mRNA analysis using established protocols for isolation of mRNA from FFPE tissue. Key proteins that may be involved in the metabolic alterations will be investigated by in-situ proteomics using TMA-based immunohistochemistry in independent datasets from these clinical studies that enable us to link metabolic alterations to chemotherapy response. In addition, frozen samples will be used for focused transcriptomics using a bead-based technique that allow the detection of several hundreds of markers. These investigations on the interconnection between metabolomics and transcriptomics will be focused and based on hypotheses that come from the results of the metabolic profiling.

A further important element of the general strategy in the last 18 months is the planning of a clinical study for validation of metabolic signatures that will be conducted as a translational sub-study to neoadjuvant treatment concepts.

Three of the eight METAcancer partners are Small or Medium Enterprises. This early embedding of companies will allow the translation and development of strategies to utilize commercially the knowledge achieved during the project.




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