Liquid biopsies are an integral part of the diagnosis of cancer. Here, we have extended previous validation studies of a new targeted NGS panel to include the detection of copy number amplifications (CNAs), fusions, and exon skipping variants. Detection of these gene classes included specimens from clinical and healthy donors and cell lines (fusions: ROS1, EML4-ALK, NTRK1), (exon skipping: MET exon 14), and (CNAs: HER2, CDK6, EGFR, MYC and MET). The limit of detection (LOD) for fusion/skipping was 42 copies (QC threshold = 3 copies) and was verified using 3 additional fusion/skipping variants. LOD for CNAs was 1.40-fold-change (QC threshold = 1.15-fold change) and was verified with 3 additional CNAs. In precision studies, all fusion/skipping variants were detected in all runs and all days of testing (n=18/18; 100%); average CV for repeatability was 20.5% (range 8.7% - 34.8%), and for reproducibility was 20.8% (range 15.7% - 30.5%). For CNAs, 28/29 (96.6%) copy gains were detected. For CNAs, average CV of 1.85% (range 0% to 5.49%) for repeatability and 6.59% (range 1.65% to 9.22%) for reproducibility. The test panel meets the criteria for being highly sensitive and specific and extends its utility for the detection of clinically relevant variants in cancer.

Cancers arise from the accumulation of somatic genome mutations, with varying contributions of intrinsic (i.e. genetic predisposition) and extrinsic (i.e. environmental) factors. For the understanding of malignant clones, precise information about their genomic composition has to be correlated with morphological, clinical and individual features, in the context of the available medical knowledge. Rapid improvements in molecular profiling techniques, the accumulation of large amount of data in genomic alterations in human malignancies and the expansion of bioinformatic tools and methodologies have facilitated the understanding of the molecular changes during oncogenesis, and their correlation with clinico-pathological phenotypes. Far beyond a limited set of "driver" genes, oncogenomic profiling has identified a large variety of somatic mutations; and whole genome sequencing studies of healthy individuals have improved the knowledge of heritable genome variation. Nevertheless, main challenges arise from the skewed representation of individuals from varying population backgrounds in biomedical studies, and also through the limited extend in which some cancer entities are represented in the scientific literature. Content analyses of oncogenomic publications could provide guidance for the planning and support of future studies aiming at filling prominent knowledge gaps.

Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes' spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, H3K9me3 and H3K27me3 large domains, HiC compartments and lamina associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNAs) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.