Decision-making system for oncologic diagnosis&treatment based on rational multi-omic analyses for breast cancer patient

The present project aims to the development of a platform for medical decision making from the integration of clinical data, massive sequencing of exomas and metabolic of the analysis of liquid biopsies of patients with breast cancer with a computational system to improve the precision medicine

Breast cancer is the most common form of cancer in the world. There are several histological and molecular subtypes which require constant clinical evaluations and individualized treatments. Once detected, the key to an optimal treatment is the correct diagnostic and characterization of the disease. An important factor in treatment is also the early detection of breast cancer, in both primary and relapsed tumors. The latter substantially conditions the patient's chance of survival. Also, prior knowledge of the degree of response to treatment is another key factor when scheduling antitumor therapy. Despite all advances and knowledge in this disease, breast cancer still presents a high mortality, since approximately 30% of the patients have recurrence or metastasize. Therefore, it is necessary to continue investigating new methods of early diagnosis linked to individualized therapies based on a better knowledge of molecular profiles. Precision medicine. As a result of multiple technological and practical advances, high-throughput sequencing can now be incorporated into standard clinical practice. For professionals related to clinical oncology, understanding the potential and limitations of the nucleic acid sequencing will be crucial in this new era of precision and personalized medicine. Therefore, new generation technologies and their particular application in the medical field. This entails enormous benefits, but also new difficulties, such as handling huge amounts of data. This issue has brought the Big Data concept, that refers to the storage and procedures used to find repetitive patterns within that data. In this context, the present project aims to develop a platform for medical decision-making based on the integration of clinical data, genomic, transcriptomic and metabolic analyses and metabolic analysis of liquid biopsies of breast cancer patients. It is expected that this novel tool will help to the early diagnosis, as well as to decide the optimal individualized therapy to predict the response to treatment of breast cancer patients. The main hypothesis is based on the presence of alterations in the normal metabolic processes that occur in cancer cells and that do not occur in healthy cells. This causes changes in normal serum concentrations of certain metabolites that can be readily detected and analyzed from blood samples. This platform has been called Oncoprecise, described as an integrative platform for omico-type analysis, bioinformatic processing of data and inclusion of artificial intelligence. This platform will be developed in three stages for 30 months. The first stage will develop a back-end for the collection and processing of patient data in an automated manner. The second stage will develop a report generator, automated front-end including artificial intelligence. The third stage validates the usability of the platform and its packaging to be used in Chile and Spain. The model proposed by Oncoprecise incorporates modern techniques of computational biology and Big data, such as Deep learning and Machine learning, which allow us to gather functional relations directly from raw data, obtaining predictive models that allow to identify, for example the efficacy of a particular drug on a particular type of cancer from a set of genetic, epigenetic and additional clinical data. In order to accelerate calculation processes, Oncoprecise will use a new computational cluster. This cluster has the computing power necessary to execute complex tasks in a high performance with about of 420 Tflops. Thus, Oncoprecise offers a solution tool for breast tumor clinical management, integrating modern molecular and bioinformatics techniques, such as metabolomics, high-throughput sequencing and Big data techniques, for automation of data processing. finally, it offers a specialized, transferable and easy-to-understand information integration solution that, to our knowledge, unprecedented in both participating countries. The decision-making system for Oncologic diagnosis and treatment based on rational multi-omic analyses for breast cancer patients would be available by the end of 2019 to be applied in both Spain and Chile.
Project ID: 
11 244
Start date: 
Project Duration: 
Project costs: 
510 000.00€
Technological Area: 
Diagnostics, Diagnosis
Market Area: 

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