Treg Depletion Inhibits Efficacy of Cancer Immunotherapy: Implications for Clinical Trials

Regulatory T lymphocytes (Treg) infiltrate human glioblastoma (GBM); are involved in tumor progression and correlate with tumor grade. Transient elimination of Tregs using CD25 depleting antibodies (PC61) has been found to mediate GBM regression in preclinical models of brain tumors. Clinical trials that combine Treg depletion with tumor vaccination are underway to determine whether transient Treg depletion can enhance anti-tumor immune responses and improve long term survival in cancer patients.Using a syngeneic intracrabial glioblastoma (GBM) mouse model we show that systemic depletion of Tregs 15 days after tumor implantation using PC61 resulted in a decrease in Tregs present in tumors, draining lymph nodes and spleen and improved long-term survival (50% of mice survived >150 days). No improvement in survival was observed when Tregs were depleted 24 days after tumor implantation, suggesting that tumor burden is an important factor for determining efficacy of Treg depletion in clinical trials. In a T cell dependent model of brain tumor regression elicited by intratumoral delivery of adenoviral vectors (Ad) expressing Fms-like Tyrosine Kinase 3 ligand (Flt3L) and Herpes Simplex Type 1-Thymidine Kinase (TK) with ganciclovir (GCV), we demonstrate that administration of PC61 24 days after tumor implantation (7 days after treatment) inhibited T cell dependent tumor regression and long term survival. Further, depletion with PC61 completely inhibited clonal expansion of tumor antigen-specific T lymphocytes in response to the treatment.Our data demonstrate for the first time, that although Treg depletion inhibits the progression/eliminates GBM tumors, its efficacy is dependent on tumor burden. We conclude that this approach will be useful in a setting of minimal residual disease. Further, we also demonstrate that Treg depletion, using PC61 in combination with immunotherapy, inhibits clonal expansion of tumor antigen-specific T cells, suggesting that new, more specific targets to block Tregs will be necessary when used in combination with therapies that activate anti-tumor immunity

Quantitative Models of the Mechanisms That Control Genome-Wide Patterns of Transcription Factor Binding during Early Drosophila Development

Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ∼0.4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0.6–0.9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision