RESUMEN
Cancer is the second leading cause of death in the United States, killing more than 600.000 people each year.1 Despite several screening programs available, cancer diagnosis is often made incidentally during imaging studies performed for other reasons. Once the diagnosis is made, treatment assessment and surveillance of these patients heavily rely on radiological tools. Computed tomography (CT) in particular is one of the most commonly ordered modalities due to wide availability even in the most remote locations, and fast results. However, conventional CT often cannot definitively characterize a neoplastic lesion unless it was tailored toward answering a specific question. Furthermore, characterizing small lesions can be difficult with CT. An innovative technique called dual-energy CT (DECT) offers solutions to some of the challenges of conventional CT in oncological imaging.
Asunto(s)
Neoplasias , Tomografía Computarizada por Rayos X , Humanos , Neoplasias/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodosRESUMEN
Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.