Neuhauser, Claudia

Cancer therapy remains challenging due to the myriad presentations of the disease and the vast genetic diversity of tumors that continuously evolve and often become resistant to therapy. Viruses can be engineered to specifically infect, replicate, and kill tumor cells (tumor virotherapy). Moreover, the viruses can be “armed” with therapeutic genes to enhance their oncolytic effect. Using viruses to treat cancer is exciting and novel and in principle can be used for a broad variety of tumors. However, the approach is distinctly different from other cancer therapies since success depends on establishment of an infection within the tumor and ongoing propagation of the oncolytic virus within the tumor itself. Therefore, the target itself amplifies the therapy. This introduces complex dynamics especially when the immune system is taken into consideration as well as the physical and other biological barriers to virus growth. Understanding these dynamics not only requires mathematical and computational models but also approaches for the noninvasive monitoring of the virus and tumor populations. In this perspective, we discuss strategies and current results to achieve this important goal of understanding these dynamics in pursuit of optimization of oncolytic virotherapy.

Quantitative activity through the TCR-proximal protein network specifies thymic positive versus negative selection.

Abstract The use of replication-competent viruses as oncolytic agents is rapidly expanding, with several oncolytic viruses approved for cancer therapy. As responses to therapy are highly variable, understanding the dynamics of therapy is critical for optimal application of virotherapy in practice. Although mathematical models have been developed to understand the dynamics of tumor virotherapy, a scarcity of in vivo data has made difficult parametrization of these models. To tackle this problem, we studied the in vitro and in vivo spread of two oncolytic measles viruses that induce expression of the sodium iodide symporter (NIS) in cells. NIS expression enabled infected cells to concentrate radioactive isotopes that could be reproducibly and quantitatively imaged using SPECT/CT. We observed a strong linear relationship in vitro between infectious virus particles, viral N and NIS gene expression, and radioactive isotope uptake. In vivo radioisotope uptake was highly correlated with viral N and NIS gene expression. Similar expression patterns between viral N and NIS gene expression in vitro and in vivo implied that the oncolytic virus behaved similarly in both scenarios. Significant titers of viable virus were consistently isolated from tumors explanted from mice that had been injected with oncolytic measle viruses. We observed a weaker but positive in vivo relationship between radioisotope uptake and the viable virus titer recovered from tumors; this was likely due to anisotropies in the viral distribution in vivo. These data suggest that methods that enable quantitation of in vivo anisotropies are required for continuing development of oncolytic virotherapy. Significance: These findings address a fundamental gap in our knowledge of oncolytic virotherapy by presenting technology that gives insight into the behavior of oncolytic viruses in vivo. Cancer Res; 78(20); 5992–6000. ©2018 AACR.

Abstract Human immunity exhibits remarkable heterogeneity among individuals, which engenders variable responses to immune perturbations in human populations. Population studies reveal that, in addition to interindividual heterogeneity, systemic immune signatures display longitudinal stability within individuals, and these signatures may reliably dictate how given individuals respond to immune perturbations. We hypothesize that analyzing relationships among these signatures at the population level may uncover baseline immune phenotypes that correspond with response outcomes to immune stimuli. To test this, we quantified global gene expression in peripheral blood CD4+ cells from healthy individuals at baseline and following CD3/CD28 stimulation at two time points 1 mo apart. Systemic CD4+ cell baseline and poststimulation molecular immune response signatures (MIRS) were defined by identifying genes expressed at levels that were stable between time points within individuals and differential among individuals in each state. Iterative differential gene expression analyses between all possible phenotypic groupings of at least three individuals using the baseline and stimulated MIRS gene sets revealed shared baseline and response phenotypic groupings, indicating the baseline MIRS contained determinants of immune responsiveness. Furthermore, significant numbers of shared phenotype-defining sets of determinants were identified in baseline data across independent healthy cohorts. Combining the cohorts and repeating the analyses resulted in identification of over 6000 baseline immune phenotypic groups, implying that the MIRS concept may be useful in many immune perturbation contexts. These findings demonstrate that patterns in complex gene expression variability can be used to define immune phenotypes and discover determinants of immune responsiveness.

A multiplex antibody-mediated capture technique reveals patient-specific protein complexes.