The Role of TCR Engagement Strength in Antitumor CD8 T Cell Exhaustion

Durability of an antitumor immune response is mediated in part by the persistence of progenitor exhausted CD8 T cells (Tpex). Tpex serve as a source for the pool of effector T cells that tend to have a short lifespan or eventually become dysfunctional in uncleared tumors. In contrast to the short-lived feature of their daughter cells, Tpex are able to preserve their quantity and developmental potential through a process of self-renewal. Previous research has demonstrated that Tpex can even self-renew in the absence of their cognate antigen. However, it remains unknown how T cell receptor (TCR) engagement impacts the self-renewal capacity of Tpex in settings of continued antigen exposure. Given that many cancer cells lack antigens capable of effectively engaging with TCRs, it is important to understand whether and how TCR engagement strength regulates the preservation of Tpex and their terminal differentiation during tumor progression. To investigate this, I established multiple murine tumor models, including Lewis lung carcinoma, which elicit an optimal or attenuated TCR signal in CD8 T cells. Longitu- dinal phenotyping and single-cell transcriptomics of tumor-specific T cells revealed that formation and maintenance of the Tpex reservoir in tumor-draining lymph nodes (tdLN) is dependent on optimal TCR engagement. Despite the generation of central memory-like T cells in tdLN under suboptimal priming, replenishment of tumor-infiltrating Tpex is abrogated. Moreover, adoptive transfer of optimally primed Tpex into a tumor setting with attenuated TCR stimulation significantly accelerates their terminal differentiation. This TCR-reinforced Tpex development and self-renewal is coupled to proximal position- ing to dendritic cell niches and epigenetic imprinting that involves increased chromatin accessibility at Egr2 and Tcf1 target loci. Collectively, my dissertation work reveals a previously unappreciated and coun- terintuitive role of TCR stimulation in preserving Tpex and highlights TCR-dependent self-renewal of Tpex during tumor progression. These results provide fundamental insight into T cell exhaustion and have important implications for the development of cancer vaccines that target tumor-epitopes with varying TCR binding avidities

On A Simpler and Faster Derivation of Single Use Reliability Mean and Variance for Model-Based Statistical Testing

Markov chain usage-based statistical testing has proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-toend reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper continues our earlier work on a simpler and faster derivation of the single use reliability mean, and proposes a new derivation of the single use reliability variance by applying a well-known theorem and eliminating the need to compute the second moments of arc failure probabilities. Our new results complete a new analysis that could be shown to be simpler, faster, and more direct while also rendering a more intuitive explanation. Our new theory is illustrated with three simple Markov chain usage models with manual derivations and experimental results

Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and is available on CRAN.Comment: The original title dated back to May 2015 is "Bootstrap Tests on High Dimensional Covariance Matrices with Applications to Understanding Gene Clustering