(P 230) SARCDBASE: A TOOL FOR DETECTION OF GENETIC ALTERATIONS IN SARCOMA

Objective: Sarcomas are a diverse group of malignant tumors arising in the bone and soft tissues. For an increasing number of subtypes, there are already pathognomonic genetic alterations described. For some sarcomas, such alterations remain to be identified. The latter group primarily contains sarcomas with a massive amount of genomic copy number and structural alterations. This extreme number of alterations makes it difficult to discriminate those of biological importance from background noise. We have developed SarcDBase, a database-building method that matches genetic variants detected in high-resolution genomic and transcriptomic data from tumor biopsies with information on established biomarkers, eliminating the need for prior knowledge and manual screening of data.

Methods: The tool is divided into three main sections. Data analysis: DNA and RNA sequencing data are used to detect (i) structural variants, (ii) single nucleotide variants and insertions/deletions, (iii) genome-wide DNA copy number changes, and (iv) gene fusions. The latter step includes the tool NAFuse (https://github.com/ValeriaDifilippo/NAFuse), which is an integrated methodology that combines and compares the output of an RNA gene fusion detector with that of the BAM-file generated by whole genome DNA sequencing. In this way, gene fusions detected at both the RNA and DNA levels can be extracted. SarcDBase execution: a series of codes that use external sources, such as the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, the WHO classification of tumors (Soft Tissue and Bone Tumours), and other literature sources, to detect previously reported genetic alterations. SarcDBase also detects novel alterations of likely biological significance, such as new combinations of partner genes in gene fusions. SarcDBase visualization: an app in dash that helps the user to visualize and interpret the data with a graphical interphase and to have a dynamic visualization.

Results: The app in dash allows a dynamic navigation through the data and the user can navigate through multiple pages depending on the data available. Here, the performance of SarcDBase was tested on a diverse cohort of deep-sequenced sarcomas (n = 70). Several mutations of likely biological significance were identified. Among these, we found mutations that confirmed the diagnosis in some cases, such as a NAB2::STAT6 gene fusion (solitary fibrous tumor), an H3F3A p.G35W (giant cell tumor of bone), an SS18::SSX1 gene fusion (synovial sarcoma), a JAZF1::PHF1 gene fusion (endometrial stromal sarcoma) and a non-frameshift deletion of KIT (gastrointestinal stromal tumor). SarcDBase was also able to identify new genetic alterations of likely biological significance, such as novel combinations of partner genes in gene fusions.

Conclusion: SarcDBase is a useful tool that integrates genomic and transcriptomic information to extract previously reported and novel genetic alterations. It can be used on any deep sequencing data from tumor tissue and the graphical visualization part helps the user to evaluate the findings.