Arginine Depletion in Human Cancers.

Arginine is encoded by six different codons. Base pair changes in any of these codons can have a broad spectrum of effects including substitutions to twelve different amino acids, eighteen synonymous changes, and two stop codons. Four amino acids (histidine, cysteine, glutamine, and tryptophan) account for over 75% of amino acid substitutions of arginine. This suggests that a mutational bias, or "purifying selection", mechanism is at work. This bias appears to be driven by C > T and G > A transitions in four of the six arginine codons, a signature that is universal and independent of cancer tissue of origin or histology. Here, we provide a review of the available literature and reanalyze publicly available data from the Catalogue of Somatic Mutations in Cancer (COSMIC). Our analysis identifies several genes with an arginine substitution bias. These include known factors such as IDH1, as well as previously unreported genes, including four cancer driver genes (FGFR3, PPP6C, MAX, GNAQ). We propose that base pair substitution bias and amino acid physiology both play a role in purifying selection. This model may explain the documented arginine substitution bias in cancers.

Comprehensive Analysis of MEN1 Mutations and Their Role in Cancer.

MENIN is a scaffold protein encoded by the MEN1 gene that functions in multiple biological processes, including cell proliferation, migration, gene expression, and DNA damage repair. MEN1 is a tumor suppressor gene, and mutations that disrupts MEN1 function are common to many tumor types. Mutations within MEN1 may also be inherited (germline). Many of these inherited mutations are associated with a number of pathogenic syndromes of the parathyroid and pancreas, and some also predispose patients to hyperplasia. In this study, we cataloged the reported germline mutations from the ClinVar database and compared them with the somatic mutations detected in cancers from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. We then used statistical software to determine the probability of mutations being pathogenic or driver. Our data show that many confirmed germline mutations do not appear in tumor samples. Thus, most mutations that disable MEN1 function in tumors are somatic in nature. Furthermore, of the germline mutations that do appear in tumors, only a fraction has the potential to be pathogenic or driver mutations.

Applying Artificial Intelligence to Mitigate Effects of Patient Motion or Other Complicating Factors on Image Quality.

Artificial intelligence, particularly deep learning, offers several possibilities to improve the quality or speed of image acquisition in magnetic resonance imaging (MRI). In this article, we briefly review basic machine learning concepts and discuss commonly used neural network architectures for image-to-image translation. Recent examples in the literature describing application of machine learning techniques to clinical MR image acquisition or postprocessing are discussed. Machine learning can contribute to better image quality by improving spatial resolution, reducing image noise, and removing undesired motion or other artifacts. As patients occasionally are unable to tolerate lengthy acquisition times or gadolinium agents, machine learning can potentially assist MRI workflow and patient comfort by facilitating faster acquisitions or reducing exogenous contrast dosage. Although artificial intelligence approaches often have limitations, such as problems with generalizability or explainability, there is potential for these techniques to improve diagnostic utility, throughput, and patient experience in clinical MRI practice.