Molecular Mechanisms of Therapeutic Resistance in Cancer.

Development of therapeutic resistance limits the efficacy of current cancer treatment. Understanding the molecular basis for therapeutic resistance should facilitate the identification of actionable targets and development of new combination therapies for cancer patients. Yet the understanding of therapeutic resistance still remains incomplete. In this thesis, clinically relevant mouse models coupled with systematic genomic and imaging technologies are used to identify mechanisms driving resistance, which also formulate novel therapeutic paradigms for patients with drug-resistant tumors. In the first study, a genetically engineered mouse model of ovarian endometrioid adenocarcinoma (OEA) was utilized in combination with molecular imaging to understand mechanisms of chemoresistance in OEA. It was demonstrated that AKT signaling pathway was activated upon chemotherapy (cisplatin) administration, which protected cells from apoptosis and thereby leading to the development of resistance. In support of this observation, inhibition of AKT activity improved the efficacy of chemotherapy by enhanced induction of apoptosis. A second study was undertaken to develop a new understanding of the mechanistic basis for therapeutic resistance in glioblastoma using a patient derived xenograft model. An integrated transcriptome analysis revealed that chemoradioresistance was associated with an increased expression of genes involved in the mesenchymal and stem cell phenotype as well as a decreased expression of genes involved in cell death. TGF-β signaling was identified to be central to each of the mesenchymal/stem phenotype and therefore a critical player in modulating therapeutic resistance. In support, treatment with a TGF-β inhibitor partially restored the sensitivity to therapy in TMZ/IR resistant tumors. Overall, this thesis demonstrated the importance of the AKT and TGF-β signaling pathways in therapeutic resistance in a subset of ovarian cancer and glioblastoma patients, which provides clinical guidance for applying new combination therapies. It also demonstrates the concept that the combination of clinically relevant mouse models, molecular imaging and systematic genomic analysis can be used to derive novel insights into the dynamic signaling processes involved with gain of resistance. Future studies are needed to investigate if targeting these resistance mechanisms delays or prevents the development of resistance in treatment-naïve patients.PHDCellular and Molecular BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111378/1/hanxiaow_1.pd

Highly Efficient Regression for Scalable Person Re-Identification

Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available. This work proposes a truly scalable solution to re-id by addressing both problems. Specifically, a Highly Efficient Regression (HER) model is formulated by embedding the Fisher's criterion to a ridge regression model for very fast re-id model learning with scalable memory/storage usage. Importantly, this new HER model supports faster than real-time incremental model updates therefore making real-time active learning feasible in re-id with human-in-the-loop. Extensive experiments show that such a simple and fast model not only outperforms notably the state-of-the-art re-id methods, but also is more scalable to large data with additional benefits to active learning for reducing human labelling effort in re-id deployment

Dynamic Coalition Portfolio Selection with Recursive Utility

In this paper, we consider a dynamic coalition portfolio selection problem, with each agent's objective given by an Epstein--Zin recursive utility. To find a Pareto optimum, the coalition's problem is formulated as an optimization problem evolved by a multi-dimensional forward-backward SDE. Since the evolution system has a forward-backward structure, the problem is intrinsically time-inconsistent. With the dynamic-game point of view, we rigorously develop an approach to finding the equilibrium Pareto investment-consumption strategy. We find that the relationship between risk aversion and EIS has more influence on the coalition's problem than that on the one-agent problem. More interestingly, we show that the equilibrium Pareto consumption strategy associated with the recursive utility is much more effective than that associated with the CRRA expected utility, which highlights the feature of recursive utilities that the marginal benefit of consumption can depend on the future consumption

Linear-Quadratic Optimal Control for Backward Stochastic Differential Equations with Random Coefficients

This paper is concerned with a linear-quadratic (LQ, for short) optimal control problem for backward stochastic differential equations (BSDEs, for short), where the coefficients of the backward control system and the weighting matrices in the cost functional are allowed to be random. By a variational method, the optimality system, which is a coupled linear forward-backward stochastic differential equation (FBSDE, for short), is derived, and by a Hilbert space method, the unique solvability of the optimality system is obtained. In order to construct the optimal control, a new stochastic Riccati-type equation is introduced. It is proved that an adapted solution (possibly non-unique) to the Riccati equation exists and decouples the optimality system. With this solution, the optimal control is obtained in an explicit way