Anamorelin Induced Acute Hyperglycemia in a Patient with Advanced Pancreatic Cancer and Diabetes: A Case Report.

Anamorelin (ANAM) is a novel ghrelin receptor agonist for the treatment of cancer cachexia. In clinical trials of ANAM, glucose metabolism disorders as adverse effects were relatively frequent, however, when and how they occur remains unclear. Moreover, the safety in patients with pancreatic cancer and/or diabetes has not been clarified because most previous studies focused on patients with non-small cell lung cancer and had excluded patients with poorly controlled diabetes. Herein, a 66-year-old man with advanced pancreatic cancer and diabetes was administered ANAM, and acute hyperglycemia was developed and could be monitored by the self-monitoring of blood glucose (SMBG). Increasing the insulin dose failed to control hyperglycemia adequately, but the hyperglycemia ameliorated quickly after ANAM discontinuation. The continuous glucose monitoring (CGM) revealed that the sensor glucose levels had remained in the high range throughout the day during ANAM administration despite using 1.5 times more insulin. Our report is one of the few that describe the details of ANAM-induced hyperglycemia and provides important information for the safe and effective use of ANAM.

Quantification of Inter-Sample Differences in T-Cell Receptor Repertoires Using Sequence-Based Information.

Inter-sample comparisons of T-cell receptor (TCR) repertoires are crucial for gaining a better understanding of the immunological states determined by different collections of T cells from different donor sites, cell types, and genetic and pathological backgrounds. For quantitative comparison, most previous studies utilized conventional methods in ecology, which focus on TCR sequences that overlap between pairwise samples. Some recent studies attempted another approach that is categorized into Poisson abundance models using the abundance distribution of observed TCR sequences. However, these methods ignore the details of the measured sequences and are consequently unable to identify sub-repertoires that might have important contributions to the observed inter-sample differences. Moreover, the sparsity of sequence data due to the huge diversity of repertoires hampers the performance of these methods, especially when few overlapping sequences exist. In this paper, we propose a new approach for REpertoire COmparison in Low Dimensions (RECOLD) based on TCR sequence information, which can estimate the low-dimensional structure by embedding the pairwise sequence dissimilarities in high-dimensional sequence space. The inter-sample differences between repertoires are then quantified by information-theoretic measures among the distributions of data estimated in the embedded space. Using datasets of mouse and human TCR repertoires, we demonstrate that RECOLD can accurately identify the inter-sample hierarchical structures, which have a good correspondence with our intuitive understanding about sample conditions. Moreover, for the dataset of transgenic mice that have strong restrictions on the diversity of their repertoires, our estimated inter-sample structure was consistent with the structure estimated by previous methods based on abundance or overlapping sequence information. For the dataset of human healthy donors and Sézary syndrome patients, our method also showed robust estimation performance even under the condition of high sparsity in TCR sequences, while previous studies failed to estimate the structure. In addition, we identified the sequences that contribute to the pairwise-sample differences between the repertoires with the different genetic backgrounds of mice. Such identification of the sequences contributing to variation in immune cell repertoires may provide substantial insight for the development of new immunotherapies and vaccines.