Co-transfer of tumor-specific effector and memory CD8+ T cells enhances the efficacy of adoptive melanoma immunotherapy in a mouse model

Abstract Background Adoptive cell transfer (ACT) is a promising cancer immunotherapeutic strategy that remains ineffective for a large subset of patients. ACT with memory CD8+ T cells (Tmem) has been shown to have superior efficacy compared to traditional ACT with effector CD8+ T cells (Teff). Teff and Tmem have complementary physiological advantages for immunotherapy, but previous publications have not examined ACT using a combination of Teff and Tmem. Methods Splenocytes harvested from Ly5.1+/C57BL/6 mice during and after infection with lymphocytic choriomeningitis virus (LCMV) were used to generate bona fide effector and memory CD8+ T cells specific for the LCMV epitope peptide GP33. Congenic Ly5.2+/C57BL/6 mice were inoculated with B16F10 melanoma cells transfected to express very low levels of GP33, then treated with ACT 7 days later with GP33-specific Teff, Tmem, or a combination of Teff + Tmem. Results Inhibition of melanoma growth was strongest in mice receiving combinatorial ACT. Although combinatorial ACT and memory ACT resulted in maximal intratumoral infiltration of CD8+ T cells, combinatorial ACT induced stronger infiltration of endogenous CD8+ T cells than Tmem ACT and a stronger systemic T cell responsiveness to tumor antigen. In vitro assays revealed rapid but transient melanoma inhibition with Teff and gradual but prolonged melanoma inhibition with Tmem; the addition of Tmem enhanced the ability of Teff to inhibit melanoma in a manner that could be reproduced using conditioned media from activated Tmem and blocked by the addition of anti-IL-2 blocking antibody. Conclusions These findings suggest that a novel combinatorial approach that takes advantage of the unique and complementary strengths of tumor-specific Teff and Tmem may be a way to optimize the efficacy of adoptive immunotherapy.https://deepblue.lib.umich.edu/bitstream/2027.42/143864/1/40425_2018_Article_358.pd

Determining crystal structures through crowdsourcing and coursework

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality