Cancer Systems Biology

We have been developing Moonlight, a framework based on the integration of different biological data to identify driver genes in cancer and classify them in tumor suppressors and oncogenes. The framework is also used to identify dual role genes, i.e., those genes that can act both as tumor suppressors or oncogenes depending on the cellular context. 
The research that we carry out with Moonlight is accompanied by other approaches for clustering and stratification of the samples depending on their gene expression, genomics, or proteomics signatures. As an example, we have been developing CAMPP (CAncer bioMarker Prediction Pipeline,  for the analyses of different sources of cancer data. CAMPP includes data-management tools for missing value imputation, quality checks, normalization protocols, data clustering, differential expression, survival, and network-based analyses.

We aim to provide an atlas of cancer alterations affecting the protein products that can also be consulted to prioritize variants for experimental or functional studies.

We aim to go beyond the available tools to predict pathogenic mutations based on sequence or simple structural features. These methods do not provide enough details about the mechanisms related to the alterations and how we can counteract them.

In our workflow, we aim to comprehensively account for different aspects that mutational events alter in terms of protein structure, dynamics, and function. For examples, the variants are ranked and classified according to the alteration at the level of stability of the protein product, changes in post-translational regulations, interactome, also including allosteric changes that are the results of the alteration at a distal site with respect to the functional one. As an example, we applied the workflow to the study of a key autopaghy protein and its cancer-related mutations.

In this work, we explore the emerging concept of the Germline-Somatic continuum using Next Generation Sequencing (NGS) data obtained from cohorts of Scandinavian children diagnosed with cancer. According to the Germline-Somatic continuum view, low-penetrance germline mutations cooperate with a number of somatic mutations and they can have additive effects, ultimately inducing cancer onset and development.  To this goal, we have been analyzing germline, tumor, and somatic variants by Whole Genome Sequencing (WGS), RNAseq, and WGS+RNAseq, respectively. We aim to provide a framework to make sense of cancer genomic data and identifying the specific damaged pathways and molecular dependencies towards precision medicine. This research area is carried out  in close collaboration with the Disease Data Intelligence (DDI) group in the Section.