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Precision medicine: study of deregulated functional networks in neoplasms by integrated proteomic and metabolomic analysis

Dr Aldo Profumo


The cancer treatment is increasingly oriented towards the personalization of care for the patient. The availability of biomarkers that in an individual can highlight the tendency to develop a neoplasm or its progression, can address towards more appropriate treatments. Likewise, the metabolomic profile can also play a role in the implementation of Personalized Medicine. Although “omics” techniques for studying the genome and transcriptome of tumors have generated a large amount of data, to date the obtained results have not had an effective impact in the clinical setting for improving the management of cancer patients. Essentially, two are the main causes of this failure: i) the peculiar phenotypic heterogeneity of the tumors, for which it is necessary to identify the specific protein expression profiles for the different cell types and how these are altered in particular stages of disease development ; ii) the low availability of proteomic studies evaluating the whole repertoire of proteins expressed in transformed cells in order to build functional networks that allow to identify the driver proteins associated with a pathological condition that can be evaluated as potential biomarkers and/or therapeutic targets.

Hypothesis and significance

Cancer, like many other pathological phenomena, is now recognized to be a multifactorial event, and therefore related to various events that contribute to the onset of the disease. From this point of view, it is difficult to state that the evaluation of a single bioanalyte can provide all the answers necessary to make a correct diagnosis, establish a prognosis or even select the best treatment. Most of the recent studies are therefore aimed at identifying a panel of biomarkers able to characterize a particular pathological condition. In recent years, there has been a gradual shift from traditional diagnostic tests, such as CEA, alpha fetal protein or CA125, to molecular “signatures” consisting of the simultaneous measurement of different parameters (mRNA microarrays, a multiplex technology widely used in medicine and molecular biology, are a classic example of this new approach).
There are numerous studies, based on the use of these techniques, aimed at identifying new biomarkers related to cancer. However, although micro-arrays are able to study numerous genes at the same time (up to a some thousand), few “critical” genes have yet been identified. Furthermore, it is necessary to consider that tumor tissue usually contains cells with different degrees of differentiation and may be “contaminated” by other cell types (for example immunocompetent cells) which contribute to create the tumor micro-environment. The simultaneous presence of different cell species makes the identification of tumor samples suitable for the investigation problematic. Finally, it is not possible to apply these techniques to tissue samples taken from healthy subjects in an attempt to identify a genomic “signature” that can be correlated with an organ predisposition to develop a specific neoplasm. This explains the growing interest in proteomics techniques.
The studies on the proteome allow, in fact, to work not only on tissue samples, but also on plasma or other biological liquids (easily obtainable, even more than once), using relatively small quantities of material. Proteomics, which seeks to characterize the protein expression profile in a well-defined tissue, cell or biological fluid, is a very broad field that includes: the identification of proteins, the determination of their structure, the study of post-translational modifications and of the interactions between proteins, the expression and purification of proteins.

Significance and Innovation

Despite multimodal approaches and new therapeutic resources have improved oncologic patients’ care, to date the precision medicine showed inadequacy in several neoplasm treatments on absence of guidelines on target molecules selection based on clinical evidence. The proposed study, by an innovative approach, aims to integrate proteomic and metabolomic data with clinical information, retrieved from database online, to improve individualized treatment by the use of new diagnostics and therapeutics approaches that target biomarker associated with patient characteristics. For this purpose, the molecular data reflecting the biological contexts (e.g. normal, tumoral, metastatic tissue) and their change over the time (e.g. disease progression, chemo/radio-resistance insurgence) will be integrate with all data types based on functional proteins interactions and then associated with patient pathophysiology to build functional network representing the tissue specific effects of cancer evolution. This approach will allow us identified the key proteins in the network that can be used for research and for clinical care guideline creation.

Translational relevance and impact for the National Health System (SSN)

The main goal of our project is to expand the biological information related to different tumor forms in order to identify proteins that may play a crucial role in the phenotypic transitions observed in cancer progression, providing the scientific rationale for the development of new and more effective strategies for personalized disease management in the clinical setting.
The development of methodology able to signaling, in an early and safe way, the risk for developing a neoplasm or the propensity to progress for an existing ones, could allow the adaptation of the correct therapy to the single patient avoiding useless, ineffective and debilitating treatments. Moreover, the integration of new scientific information with available information on drugs pharmacokinetics, safety and manufacturing, is able to explore drug repurposing opportunities in a tailored, personalized manner. Taken together, the potential results obtained with this study may represents not only a new opportunity for oncologic patients but also a cost-efficient approach for the SSN.