Development of a somatic variant registry in a National Cancer Center: towards Molecular Real World Data preparedness.

The Biomedical Research field is currently advancing to develop Clinical Trials and translational projects based on Real World Evidence. To make this transition feasible, clinical centers need to work toward Data Accessibility and Interoperability. This task is particularly challenging when applied to Genomics, that entered in routinary screening in the last years via mostly amplicon-based Next-Generation Sequencing panels. Said experiments produce up to hundreds of features per patient, and their summarized results are often stored in static clinical reports, making critical information inaccessible to automated access and Federated Search consortia. In this study, we present a reanalysis of 4620 solid tumor sequencing samples in five different histology settings. Furthermore, we describe all the Bioinformatics and Data Engineering processes that were put in place in order to create a Somatic Variant Registry able to deal with the large biotechnological variability of routinary Genomics Profiling.

ASPicDB: a database web tool for alternative splicing analysis.

Alternative splicing (AS) is a basic molecular phenomenon that increases the functional complexity of higher eukaryotic transcriptomes. Indeed, through AS individual gene loci can generate multiple RNAs from the same pre-mRNA. AS has been investigated in a variety of clinical and pathological studies, such as the transcriptome regulation in cancer. In human, recent works based on massive RNA sequencing indicate that >95 % of pre-mRNAs are processed to yield multiple transcripts. Given the biological relevance of AS, several computational efforts have been done leading to the implementation of novel algorithms and specific specialized databases. Here we describe the web application ASPicDB that allows the recovery of detailed biological information about the splicing mechanism. ASPicDB provides powerful querying systems to interrogate AS events at gene, transcript, and protein levels. Finally, ASPicDB includes web visualization instruments to browse and export results for further off-line analyses.

The transcriptional response of mammalian cancer cells to irradiation is dominated by a cell cycle signature which is strongly attenuated in non-cancer cells and tissues.

PURPOSE: Our goal was to identify genes showing a general transcriptional response to irradiation in mammalian cells and to analyze their response in function of dose, time and quality of irradiation and of cell type. MATERIALS AND METHODS: We used a modified MIAME (Minimal Information About Microarray Experiments) protocol to import microarray data from 177 different irradiation conditions in the Radiation Genes database and performed cut-off-based selections and hierarchical gene clustering. RESULTS: We identified a set of 29 genes which respond to a wide range of irradiation conditions in different cell types and tissues. Functional analysis of the negatively modulated genes revealed a dominant signature of mitotic cell cycle regulation which appears both dose and time-dependent. This signature is prominent in cancer cells and highly proliferating tissues but it is strongly attenuated in non cancer cells. CONCLUSIONS: The transcriptional response of mammalian cancer cells to irradiation is dominated by a mitotic cell cycle signature both dose and time-dependent. This core response, which is present in cancer cells and highly proliferating tissues such as skin, blood and lymph node, is weaker or absent in non-cancer cells and in liver and spleen. CDKN1A (cyclin-dependent kinase inhibitor 1A) appears as the most generally induced mammalian gene and its response (mostly dose- and time-independent) seems to go beyond the typical DNA damage response.

Radiation Genes: a database devoted to microarrays screenings revealing transcriptome alterations induced by ionizing radiation in mammalian cells.

The analysis of the great extent of data generated by using DNA microarrays technologies has shown that the transcriptional response to radiation can be considerably different depending on the quality, the dose range and dose rate of radiation, as well as the timing selected for the analysis. At present, it is very difficult to integrate data obtained under several experimental conditions in different biological systems to reach overall conclusions or build regulatory models which may be tested and validated. In fact, most available data is buried in different websites, public or private, in general or local repositories or in files included in published papers; it is often in various formats, which makes a wide comparison even more difficult. The Radiation Genes Database (http://www.caspur.it/RadiationGenes) collects microarrays data from various local and public repositories or from published papers and supplementary materials. The database classifies it in terms of significant variables, such as radiation quality, dose, dose rate and sampling timing, as to provide user-friendly tools to facilitate data integration and comparison.