RESUMEN
Immune checkpoint inhibitors have emerged as a valuable therapeutic strategy in cancer treatment. Pembrolizumab is an inhibitor of programmed cell death protein 1 (PD-1) and its ligands 1 (PD-L1) and 2 (PD-L2). Disrupting the interaction between PD-L1 expressed on the cancer cell and PD-1 transmembrane protein on immune cells results in reactivation of T cell-mediated cellular immunity. This immune modulation has increased the risk of autoimmune adverse events, which can affect any organ system. Here, we present a case of delayed immune checkpoint inhibitor-induced vitiligo in a 74-year-old female with recurrent metastatic esophageal carcinoma who remains in remission more than five years after initiation of pembrolizumab.
RESUMEN
BACKGROUND: Recombination rates vary at the level of the species, population and individual. Now recognized as a transient feature of the genome, recombination rates at a given locus can change markedly over time. Existing inferential methods, predominantly based on linkage disequilibrium patterns, return a long-term average estimate of past recombination rates. Such estimates can be misleading, but no analytical framework to infer recombination rates that have changed over time is currently available. RESULTS: We apply coalescent modeling in conjunction with a suite of summary statistics to show that the recombination history of a locus can be reconstructed from a time series of genetic samples. More usefully, we describe a new method, based on n-tuple dataset subsampling, to infer past changes in recombination rate from DNA sequences taken at a single time point. This subsampling strategy can correctly assign simulated loci to constant, increasing and decreasing recombination models with an accuracy of 84%. CONCLUSIONS: While providing an important stepping-stone to determining past recombination rates, n-tuple subsampling still exhibits a moderate error rate. Theoretical limitations indicated by coalescent theory suggest that highly accurate inference of past recombination rates will remain challenging. Nevertheless, we show for the first time that reconstructing historic recombination rates is possible in principle.
