Marco Mina

The discovery of cancer-associated alterations has primarily focused on genetic variants. Nonetheless, altered epigenomes contribute to deregulate transcription and promote oncogenic pathways. Here, we designed an algorithmic approach (RESET) to identify aberrant DNA methylation and associated cis-transcriptional changes across >6,000 human tumors. Tumors exhibiting mutations of chromatin remodeling factors and Wnt signaling displayed DNA methylation instability, characterized by numerous hyper- and hypo-methylated loci. Most silenced and enhanced genes coalesced in specific pathways including apoptosis, DNA repair, and cell metabolism. Cancer-germline antigens (CG) were frequently epigenomically enhanced and their expression correlated with response to anti-PD-1, but not anti-CTLA4, in skin melanoma. Finally, we demonstrated the potential of our approach to explore DNA methylation changes in pediatric tumors, which frequently lack genetic drivers and exhibit epigenomic modifications. Our results provide a pan-cancer map of aberrant DNA methylation to inform functional and therapeutic studies.

The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.

infiltrating T cells that associates with favorable clinical outcome in MYCN-amplified tumors, improving patient survival when combined with the v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN) status. These findings support the hypothesis that infiltrating T cells influence the behavior of neuroblastoma and might be of clinical importance for the treatment of patients.