Formalizing biological knowledge about molecular mechanisms involved in cancer

The knowledge about molecular signaling mechanisms is dispersed in thousands of publications, precluding application of methods and algorithms developed in the field of bioinformatics and systems biology. Formalizing this knowledge is one of the major missions of systems biology today. With this aim we created the Atlas of Cancer Signaling Network resource (ACSN). ACSN depicts signaling pathways frequently implicated in cancer, including multiple crosstalk and regulatory circuits between molecular processes. The content of ACSN is represented as a seamless geographic-like map browsable by the Google Maps engine and semantic zooming. The associated blog provides a forum for commenting and curating the ACSN maps (Kuperstein et al, Oncogenesis, 2015). These features are supported by NaviCell, an interactive web-based environment for navigation, curation and data visualization (Bonnet et al, NAR, 2015; Kuperstein et al, BMC SysBio, 2013).

In order to make the constructed atlas useful tool, we develop appropriate methods for data analysis in the context of biological maps. The integrated NaviCell web-based tool box in the ACSN resource, allows importing and visualizing heterogeneous omics data on top of the ACSN maps and to perform functional analysis of the maps (Bonnet et al, NAR, 2015). NaviCom tool can automatically generate ACSN-based molecular portraits of cancer using multi-level omics data from cancer data resources as cBioPortal (Dorel et al, Database (Oxford), 2017).

These tools are provided as a user-friendly platform accessible for end users and non-computational biologists and clinicians which facilitates formulating scientific hypotheses from data and helps decision-making regarding the diagnosis and the treatment schemes for patients.

The clinical implication of knowledge formalization and data analysis activity of the team is to reveal specific mechanisms deregulated in each patient, accordingly to this, to stratify patients and to suggest personalized treatment (Dorel et al, 2015).