Liewei Wang - US grants

[unreadable] DESCRIPTION (provided by applicant): This proposal represents an application for an NCI Transition Career Development Award (K22) on behalf of Liewei Wang, M.D.-Ph.D. Dr. Wang has laboratory-based Cancer Research training in pharmacogenomics -- with an emphasis on the pharmacogenomics of purine antimetabolites such as 6-mercaptopurine. The applicant initiated her independent research career approximately a year ago, and this proposal builds on her training in Cancer Research to focus on pharmacogenomic studies of the pyrimidine antineoplastic antimetabolite, gemcitabine. The proposed studies will take advantage of the outstanding environment at the Mayo Clinic and a series of NIH-Mayo initiatives, including the NIH Comprehensive Cancer Center, the NIH Pharmacogenetics Research Network (PGRN) grant and the NIH Pancreatic Cancer SPORE. The applicant proposes to utilize a "Human Variation Panel" cell line model system that expresses virtually all of the genes encoding proteins that participate in "the gemcitabine pathway", i.e., drug transport, metabolism, activation and targets. This Human Variation Panel consists of 203 cell lines obtained from three different ethnic groups. The applicant has obtained in-depth resequencing data for these genes in all 203 cell lines, as well as basal gene expression array data and genome-wide SNP data. She has also generated gemcitabine cytotoxicity data for the cell lines that will make it possible to perform both gemcitabine pathway-based and genome-wide SNP pharmacogenomic genotype-phenotype association analyses. Hypotheses generated with this model system will then be tested using DNA samples from patients with pancreatic cancer who were treated with gemcitabine, and candidate genes/SNPs identified with the cell lines and/or patient samples will be characterized functionally in the laboratory. These studies will not only utilize modern statistical genetic and high throughput genomic techniques to test the hypothesis that genetic variation in germline DNA might contribute to gemcitabine sensitivity and/or resistance, but will also serve to build on the applicant's training in Cancer Research to make it possible for her to gain additional training in statistical genetics with Dr. Daniel Schaid as her Mentor. Therefore, the proposed studies will provide an ideal "Transition Career Development" path to extend her pharmacogenomic studies of antineoplastic drugs and to make it possible for her to submit a future R01 Cancer Research grant. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): The cytidine analogue gemcitabine is first line chemotherapy for the treatment of pancreatic cancer, and it has also shown promising results in the treatment of breast cancer and non-small cell lung cancer. Gemcitabine has its effect as a result of a "pathway" that includes drug transporters, enzymes catalyzing drug activation and inactivation, and drug targets. However, very little is known with regard to determinants of variation in gemcitabine response, especially single nucleotide polymorphisms (SNPs) in genes outside of the pathway described by our current knowledge of this drug. In order to identify additional genes of importance for variation in gemcitabine response, we have used 300 Human Variation Panel lymphoblastoid cell line as a model system for common genetic variation to perform genome-wide expression association studies to identify genes with expression levels that were significantly associated with variation in gemcitabine cytotoxicity (IC50 values). One top candidate gene, FKBP5, a gene encoding a 51 kDa immunophilin, was shown to affect the apoptotic pathway in response to gemcitabine. Specifically, lower expression of FKBP5 was associated with resistance to gemcitabine-induced cytotoxicity. We also demonstrated an inhibitory role for FKBP5 in AKT phosphorylation. As a result, we hypothesize that FKBP5 affects gemcitabine response by negatively regulating AKT activation and that genetic variation associated with FKBP5 gene expression and protein function might contribute significantly to variation in gemcitabine response. In this application, we propose to determine mechanisms by which FKBP5 regulates AKT activation, followed by testing the role of FKBP5 in gemcitabine response using mice models and tumor samples from pancreatic cancer patients treated with gemcitabine. In addition, we will also determine gene sequence variation that is associated with FKBP5 gene expression and response to gemcitabine using 300 lymphoblastoid cell lines, followed by performing functional genomic studies with these SNPs. Finally, we will perform a genotype-phenotype correlation study with DNA from pancreatic cancer patients to determine whether SNPs that affect FKBP5 expression and/or protein function might influence response to gemcitabine when used to treat pancreatic cancer. In summary, this comprehensive series of experiments will enhance our understanding of mechanisms of gemcitabine resistance and may identify biomarkers that might help predict gemcitabine response in the treatment of pancreatic cancer. PUBLIC HEALTH RELEVANCE: The cytidine analogue gemcitabine is first line chemotherapy for the treatment of pancreatic cancer. However, very little is known with regard to determinants of variation in gemcitabine response, especially single nucleotide polymorphisms (SNPs) in genes outside of the "pathway" described by our current knowledge of the metabolism and "targets" for this drug. In order to identify additional genes of importance for variation in gemcitabine response, we have used 300 Human Variation Panel lymphoblastoid cell lines as a model system, together with genome-wide approaches to identify one top candidate gene, FKBP5, for which expression was significantly associated with gemcitabine sensitivity. In this application, based on extensive preliminary data, we propose to investigate mechanisms by which FKBP5 regulates response to gemcitabine and to identify genetic variation in FKBP5 that might be used as a biomarker to help predict gemcitabine response in the treatment of pancreatic cancer.

DESCRIPTION (provided by applicant): This proposal represents a request for continued funding of the Mayo Clinic Pharmacogenomics Research Network (PGRN) grant Pharmacogenetics of Phase II Drug Metabolizing Enzymes. The Mayo PGRN is an integrated, multidisciplinary, pharmacogenomic research effort based on a decades-long focus at Mayo on the pharmacogenetics of phase II (conjugating) drug metabolizing enzymes. The Mayo PGRN began by applying a genotype-to-phenotype research strategy that included, sequentially, gene resequencing, functional genomic, mechanistic and translational studies. During the present funding cycle, the Mayo PGRN has also incorporated the use of genome-wide techniques and pharmacogenomic model systems, with a special emphasis on functional mechanisms responsible for genetic effects on drug response. We have used that approach to study the pharmacogenomics ofthe endocrine therapy of breast cancer and selective serotonin reuptake inhibitor (SSRI) therapy of depression - research that grew out of the contribution of phase II enzymes to the biotransformation of the estrogens that play such an important role in breast cancer and biotransformation ofthe neurotransmitters that are central to the pathophysiology and treatment of depression. Recently, we have performed pharmacogenomic genome-wide association (GWA) studies of breast cancer, and we will soon perform similar studies of the SSRI therapy of depression. We propose to continue this genome- wide focus during the next funding cycle, with both clinical and model system GWA studies of the drug therapy of breast cancer and depression, always including replication as well as functional and mechanistic studies. We also propose two Network Resources, one designed to provide access to Next Generation DNA sequencing for all PGRN Centers and the other focused on pharmacogenomic ontology. In summary, the studies in this application build on Mayo PGRN strengths in DNA sequencing and functional genomics - while incorporating genome-wide techniques - to provide insight into the role of inheritance in variation in the efficacy and side effects of drugs used to treat breast cancer and depression. RELEVANCE: Breast cancer is the most frequent cancer of women and depression is the most common major psychiatric illness. Drugs are available to treat both of these serious illnesses, but many patients fail to respond and some suffer serious adverse drug reactions. The Mayo Clinic PGRN will apply modern pharmacogenomic techniques to help make it possible to individualize the drug therapy of breast cancer and depression.

ABSTRACT This proposal represents a request for continued funding of the NIH-sponsored Clinical Pharmacology T32 Fellowship Training Program at the Mayo Clinic. The foundation for this program is strong training in state-of-the-art biomedical research as applied to human-drug interactions. The Mayo Clinical Pharmacology training experience includes a curriculum that systematically exposes Trainees to critical aspects of the science that underlies Clinical Pharmacology. However, beyond the formal curriculum, at the heart of the training is an outstanding individual research experience within a supportive mentoring environment. Clinical Pharmacology is a ?bridge discipline? devoted to studies of the interaction between drugs and biological systems. However, within the context of the ongoing ?revolution? that is occurring in biomedical science, a revolution that promises to transform medical practice, Clinical Pharmacology lies at the confluence of molecular pharmacology, genomics and other ?omics? disciplines. In addition, increasingly, bioinformatics, systems pharmacology and novel computational methods are also becoming critical compotents of Clinical Pharmacology?with an ultimate goal of truly ?individualized? and rational drug therapy. Specifically, Clinical Pharmacology seeks to enhance our understanding of the molecular basis for drug response and the application of that information at the translational interface to make it possible to tailor drug therapy to both the underlying disease process and the unique characteristics of each individual patient. Our ability to take advantage of the opportunity represented by the dramatic advances that are occurring in biomedical science to achieve that ?ultimate goal? will require that we train a new generation of Clinical Pharmacologists in ?Systems Clinical Pharmacology?. Large, comprehensive, integrated academic medical centers like the Mayo Clinic are ideally positioned to train this new generation of Clinical Pharmacologists. Mayo is able to do that because of its long history of supporting and performing outstanding basic and clinical medical research and of integrating the two. Mayo also has a long tradition of contining contributions to the discipline of Clinical Pharmacology as well as decades of experience in successfully recruiting and training both physician scientists and laboratory-based translational scientists in Clinical Pharmacology. During the next funding cycle, the Mayo Clinical Pharmacology Fellowship Training Program will continue to emphasize strong laboratory-based research training in a supportive mentored environment joined with a strong and evolving curriuculum and systematic exposure to novel developments in clinical science, with an emphasis on the rapidly advancing nature of biomedical science?all directed toward the goal of preparing each Fellow enrolled in the Program to become a future leader in Clinical Pharmacology.

The proposed ?I/UCRC for Computing and Genomics - An Essential Partnership for Biology Breakthroughs? proposed by the U. of Illinois at Urbana-Champaign, the U. of Chicago, and Mayo Clinic will enhance the research, education, and entrepreneurship while performing the important technology transfer by bringing together an interdisciplinary team of industry partners from computer systems, health care/pharmaceuticals, and life sciences working in collaboration with genomic experts to address the colossal big-data challenge. The application of genomics across the life sciences industry is currently challenged by an inadequate ability to generate, interpret, and apply genomic data quickly and accurately for a wide variety of applications. One challenge has been that of integrating thought and market leaders across what had historically been orthogonal industries: those involved with computer sciences, and those involved with biological sciences. With the advent of Next Generation Sequencing technology, those industries are now interdependent and have a critical need to synthesize and coordinate activities at the interface of computing and genomics. The participating sites propose to establish a collaborative environment that improves the applicability, timeliness, efficiency, and accuracy of the computational infrastructure to address the pressing genome-based challenges. The CompGen consortium?s vision is to engineer and optimize computing systems needed by industry for genome analysis.

The CompGen Center will address the experimental process for genomic data. A variety of questions on health and social problems will be addressable, enabling much needed biological and healthcare breakthroughs. Outcomes will enrich research infrastructure, develop next generation of leaders in engineering and science, improve the quality of workforce, and involve international partners. Collaborations will produce artifacts such as new algorithms, optimizations, and statistical models, in turn driving the design of the computing enterprise. The goal is to generalize those artifacts to drive the design and evaluation of computational models and hardware/software co-designed architectures, tightly coupled with new memory and computing technologies for scalability and accuracy.

? DESCRIPTION (provided by applicant): Breast cancer is the most common cancer and the second leading cause of cancer death in women. A better understanding of breast cancer etiology would help us to better prevent and treat this disease. The LBK1-AMPK pathway is a central regulator of energy metabolism, and misregulation of this pathway has been implicated in cancers, including breast cancer. Indirect AMPK activators such as metformin have shown beneficial effect in breast cancer prevention and treatment. However, other than LKB1 mutations, how this pathway might be misregulated in breast cancer remains unclear. In this application, based on our extensive Preliminary Data, we propose to characterize several factors based on their newly identified roles in AMPK regulation and in turn, on breast cancer and treatment response. The first factor is called tumor suppressor protein 52 (TPD52) which we found to negatively regulate AMPK. TPD52 is known to overexpress in HER2+ breast cancer and, together with HER2, to promote tumor growth. The AMPK pathway is also known to be involved in anti-HER2 response. Furthermore, we found that AMPK regulates USP10. USP10 can enhance the stability of two important tumor suppressors, p53 and SIRT6, so our finding has established a new downstream pathway to AMPK. It is reported that SIRT6 can also activate AMPK. Therefore, we have identified a new axis of AMPK regulation with TPD52 negatively regulating AMPK with the AMPK downstream factors, UPS10/SIRT6 forming a positive feedback loop, further activating AMPK, all of which would be important in energy metabolism and response to metabolic stress induced by metformin. Metformin response is heterogeneous; therefore our findings could shed light on the underlying mechanisms. Our preliminary data indicated that cells with TPD52 overexpression were more sensitive to metformin as well as combined metformin+HER2 inhibitors. This is consistent with previous studies indicating that metformin can inhibit cell proliferation and increase patient survival in HER2+ breast cancer. Based on these preliminary findings, we hypothesize that the AMPK-USP10 axis contributes to the metabolic function of AMPK; TPD52, by negatively regulating AMPK, can promote misregulation of energy metabolism and contribute to breast cancer development. Drugs like metformin would be more effective in patients with TPD52 overexpression and might be used in combination with HER2 inhibitors in HER2+ cancers. Therefore, in this application, we propose to determine how TPD52 regulates the AMPK pathway and how AMPK might regulate SIRT6 and p53 through the regulation of USP10 as well as the impact of this regulation on response to biguanides and anti-HER2 therapy using cell lines, breast tumor samples, and breast cancer patient derived xenografts. In summary, these studies would help us to understand how this new axis TPD52-AMPK-USP10-SIRT6/p53 contributes to tumorigenesis and treatment response.

Project Summary Endocrine therapy plays a preeminent role in the management of the majority (about two thirds) of women with breast cancer whose tumors have the target, the estrogen receptor (ER). A recent meta-analysis showed that aromatase inhibitors (AIs) were superior to tamoxifen as adjuvant therapy in early-stage disease, but despite this superiority, about one-fifth of women had recurrence by 10 years. In addition to variability in outcomes, there is also a marked variability in tolerance, which can adversely impact adherence to treatment. The mechanism of action of AIs is inhibition of aromatase, thereby suppressing estrogen synthesis and reducing the ligand for the ER. The assumption is that all AIs produce sufficient estrogen suppression. However, our Preliminary Data showed marked variation in estradiol and estrone levels before and while on treatment with the AI anastrozole. However, it remains unknown whether the degree of estrogen suppression, or how to best quantify it, is related to degree of clinical benefit of these agents. We also have Preliminary Data from GWAS, using germline DNA from patients receiving AIs, showing that the variability in AI-related adverse events and outcomes is related to variation in host (germline) genetics. Furthermore, our preliminary findings with SNPs and genes identified to be associated with estrogen suppression by AIs and breast events (recurrences) provide a strong rational to test the hypothesis that genetic variation plays an important role in AI response, and this effect might be through the regulation of estrogen suppression, the mechanism of AI action. Of particular interest is that our functional studies of these genetic variants and genes also showed a SNP- and individual AI-dependent regulation of the expression of the aromatase gene. This novel finding has potentially important implications for precision AI treatment. Therefore, in this current proposal, we propose to take advantage of the extensive resources and Preliminary Data we have obtained. These include three major multi-center clinical trials involving all three third-generation AIs (anastrozole, exemestane, letrozole) for which we already have genome-wide genotyping available: 1) our own M3 study of anastrozole alone with estrogen levels pre and on anastrozole and anastrozole and anastrozole metabolite concentrations, 2) the MA.27 trial, comparing anastrozole and exemestane, from which we have published two GWAS relating to adverse events, and 3) the PreFace, a single-arm letrozole trial. MA.27 and PreFace have clinical follow-up data as well as biospecimens before and on AI treatment that would allow us to determine if the degree of estrogen suppression correlates with clinical AI treatment outcomes and, with genotyping data, the common or AI specific SNPs associated with these two phenotypes. We will then test the SNPs related to estrogen suppression in a prospective trial. Of crucial importance, we will also perform functional studies of those SNPs to elucidate mechanisms by which they might affect estrogen levels and AI response. These findings would have direct implications for the majority of women with breast cancer and would contribute to precision endocrine therapy with the AIs.

The application of genomics across the life sciences industries is currently challenged by an inadequate ability to generate, interpret, and apply genomic data quickly and accurately for a wide variety of applications. Major Innovations in the applicability, timeliness, efficiency, and accuracy of computational genomic methods are needed, and these innovations will develop best when an interdisciplinary team of scientists, engineers, and physicians from academia and industry, spanning computer systems, health care/pharmaceuticals, and life sciences, work together. The Mayo Clinic and the University of Illinois at Urbana-Champaign (UIUC) are building on their longstanding collaboration to form the Center for Computational Biotechnology and Genomic Medicine (CCBGM), which will bring together their excellence in computing, genomic biology, and patient-specific individualized medicine. Working closely with industry, the CCBGM's multidisciplinary teams will use the power of computational genomics to advance pressing societal issues, such as enabling patient-specific cancer treatment, understanding and modifying microbial communities in diverse environments related to human health and agriculture, and supporting humanity's rapidly expanding need for food by improving the efficiency of plant and animal agriculture. The CCBGM will leverage Mayo Clinic's first-rate medical research - both basic and translational - across virtually all medical disciplines. Mayo Clinic generates a large quantity of big data sets from patients, biological experiments, and animal models. The goal is for these data sets to be linked to diseases and therapies to help understand the mechanisms involved in disease processes and in treatment resistance. Through this Center, state-of-the-art computational analytical tools will be built to help improve health care in an efficient and cost-saving manner through precision medicine. These tools will also be extended into many other areas of life science to improve product quality and safety. At the same time, educational efforts to include students, fellows, and junior faculty within the Center activities will also help build the nation's next generation of scientists and entrepreneurs, especially minority and women in the area of STEM (science, technology, engineering, and mathematics).


The CCBGM will bring together an interdisciplinary team to address the colossal genomic data challenge. Academia/industry partnerships will enhance research, education, and entrepreneurship while performing important technology transfer. The Center will achieve transformational computing innovations on three fronts. (1) It will innovate computing and data management to deal with issues of scaling to the ever-growing volume, velocity, and variety of genomic data. It will concentrate initially on scaling the computation of epistatic interactions (interactions between two or more genes or DNA variants) in genome-wide association study data, generating lists of genomic features that are maximally predictive of phenotypes, and information-compression algorithms for genomic data storage and transfer. (2) It will revolutionize the generation of actionable intelligence from multimodal structured and unstructured data, to generate knowledge from big data. The emphasis will be on the processing and integration of genomic and multi-omic data, and on the merging of unstructured phenotypic data with information from curated data sources (e.g., electronic medical records, annotation databases). The integration of these diverse data types will improve discovery research, predictive genomics, diagnostics, prognostics, and theranostics. Application areas include targeted cancer therapy, pharmacogenomics, crop improvement, and predictive microbiome analysis. (3) It will achieve systems innovation by designing computer systems especially suited for computational genomics, providing unprecedented speed and energy efficiency while preserving the accuracy of the analytics. The systems will be used to quantify and improve the accuracy of detecting genomic variation and, more generally, to optimize computing architectures for the execution of genome analysis workflows.

Variation in drug response could affect both the efficacy and toxicity of virtually all drugs. Adverse drug reactions are the 4th leading cause of death in the United States. Therefore, to understand factors that might contribute to this variation and to use that information to help maximize drug efficacy and to minimize side effects would represent a major advance. Host genetics, among many factors, contributes significantly to variation in drug response. Genetic variation within genes encoding proteins determining drug concentrations, so called pharmacokinetic pathways, and proteins determining the effect of the drug, so called pharmacodynamics, can both influence drug response. The well-studied Phase I metabolism enzymes, the cytochromes P450 (CYPs), are highly genetically polymorphic. Many CYP genes contain variants with known clinical utility and have been incorporated into FDA drug labeling or relabeling. Among the CYP family genes, CYP3A4, CYP2C9 and CYP2C19, taken together, metabolize more than 50% of all drugs. The regulation of these genes is of great interest and importance from both basic scientific and clinical points of view. Even though SNPs and copy number variation (CNV) that cis-regulate CYP gene function have been well-studied, they do not explain all of the inter-individual variability in the function of these genes. Previous evidence indicates the functional significance of the trans-regulation of CYPs through genetic variation in transcription factors, microRNAs or epigenetic regulation. These findings serve to emphasize the crucial need to identify mechanisms underlying the transcriptional regulation of CYP genes and to identify genomic alterations responsible for variation in these regulatory mechanisms which, in turn, contribute to variation in CYP gene function and?ultimately--drug response. As a result, enhancing our basic knowledge of the transcription of CYPs would help to us build more comprehensive regulatory networks for CYP gene expression and function, and this knowledge would enhance our ability to individualize drug therapy. In this application, our extensive preliminary data have shown that a novel family of proteins, the TSPYL family, can function as transcription factors, contributing significantly to regulation of the expression of CYP2C and 3A family members. Our Preliminary Data showed that a functional SNP in TSPYL1 can influence in vitro level and clinical response of abiraterone, a drug that is metabolized by CYP 3A4. Here, we propose to study mechanisms by which TSPYL family members might regulate CYP gene expression as well as the contribution of genetic variation that either cis or trans-regulates TSPYL genes to inter-individual variation in CYP gene expression and in drug response phenotypes. We believe that our novel finding could add another comprehensive layer to our understanding of the transcription regulation of CYPs, which could, in turn, contribute significantly to understanding of variation in drug response.

Clinicians (workers) rate electronic health record (EHR) systems (human-technology frontier), used to review and document patient's health status and enter orders for drug prescription (work), an 'F' for usability. Specifically, the EHR is often seen as a barrier to care, rather than a tool to facilitate high quality care. This is due, in part, to high volumes of EHR alerts that are automatically generated and must be addressed when prescribing medications to treat conditions (e.g., depression). Such alerts, which typically address potential adverse reactions ranging from life threatening to minor reactions, are based on population studies and are not patient-specific. Current EHR alerts also only advise what "not to do" and do not offer guidance (representing a significant knowledge gap) as to "what to do" (e.g., which alternative medication(s) should be considered instead). As a result, clinicians spend substantial amounts of time dealing with unhelpful EHR alerts (contributing to high work stress and burnout) and employ a costly "trial-and-error" approach to selecting drugs. Clinicians need a technology interface that facilitates care - one that seamlessly provides an estimated likelihood of efficacy and adverse drug reactions of a given medication for a particular patient. A "patient-specific drug EHR alert" would advance patient care (faster remission from depression), foster shared decision-making between clinicians and patients (more information readily available to individualizing therapy), and reduce worker stress and risk of burnout (improved human-technology frontier by improving EHR usability). This project is of significant public health importance given that new drugs are discovered at unprecedented rates and clinical evidence continues to accumulate showing that several genetic tests developed to individualize therapy have improved patient outcomes and demonstrated significant savings in healthcare costs. Education activities include a curriculum development for a new course on fundamentals of machine learning and genomic medicine. The researchers will also involve undergraduate and underrepresented community in the proposed research activities.

The overarching goal of this project is to facilitate the integration of machine learning-based predictive analytics into EHR systems that use genomic and clinical data to tailor therapy for patients. The following objectives help achieve the overarching goal: (1) Develop a multi-task machine learning model that can simultaneously predict efficacy and associated adverse reactions to drug therapy, using patient's genomic, clinical and sociodemographic data. Different predictive approaches such as task clustering and task relation will be explored to provide the best predictive performance. This technology is enabled by the use of patient data from Mayo Clinic Biobank and clinical trials, and will be validated in a prospective patient cohort in routine practice at Mayo Clinic's Rochester and Florida campuses; (2) Conduct a "system usability study" to demonstrate that "patient-specific drug response profile" (i.e., efficacy and adverse reactions) improves EHR usability, which translates into reduced work stress, and perceived added value by clinicians; and (3) Establish clinician perceptions of added value in genomic technologies designed to individualize therapy, thereby characterizing facilitators and barriers of genomic-tailored EHR drug alerts. As a case study, this project will focus on antidepressant drugs used to treat major depressive disorder, leveraging data from over 10,000 patients in the Mayo Clinic Biobank.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Triple negative breast cancer (TNBC) [estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) negative breast cancer is an aggressive subtype of breast cancer for which there are no approved targeted therapies. While standard chemotherapy reduces the risk of a disease event, patients with residual TNBC after neoadjuvant chemotherapy have a high risk of locoregional recurrence despite surgical resection and aggressive postoperative radiotherapy. Therefore, better understanding mechanisms of TNBC progression and identifying novel treatment approaches for patients who have progressed on standard treatment are of great needs. PD-L1 is overexpressed in TNBC, relative to normal breast tissue and other breast cancer subtypes. Aberrant PD-L1 expression on tumors is an important means of evading elimination by its host immune system. The binding of programmed death ligand 1 (PD-L1) to its receptor, programmed cell death protein 1 (PD-1) transmits signals that inhibit T- cell activation. Therefore, abrogating the PD-1/PD-L1 interaction with therapeutic antibodies has been explored as a means to enhance antitumor immunity. Although the extracellular role of PD-L1 in the regulation of T-cell responses has been well studied, potential intracellular functions of PD-L1 in cancer remain largely unknown. Surprisingly, we have found that TNBC proliferation requires PD-L1 and a subset of PD-L1 localizes in the nucleus and interacts with cohesin, a protein complex that is important for appropriate chromosome alignment and segregation during the cell cycle. Our Preliminary Data suggest that PD-L1 directly regulates cohesion function in TNBC. Knocking down PD-L1 dramatically causes incomplete chromosome segregation and inhibits TNBC cell proliferation, while has no effect on normal cells. The central hypothesis being tested in this proposal is that PD-L1 regulates cell cycle and chromosomal stability in triple negative breast cancer (TNBC), and targeting the intracellular/nuclear function of PD-L1 or pathways (mitosis and cohesin) regulated by PD-L1 is of therapeutic use. We propose to test this central hypothesis in the following Specific Aims: Aim 1, Determine the role of PD-L1 in regulation of cell cycle, genomic stability, and tumor cell proliferation by studying the exact mechanisms by which nuclear PD-L1 might regulate cohesion. Aim 2, Study the regulation of PD-L1 during cell cycle and mitosis. Aim 3, Evaluate the inhibition of PD-L1 nuclear function on chromosome segregation, tumor growth and response to radiochemotherapy both in vitro and in animal models. The overall impact from the successful completion of this work will be a more complete understanding of the role of PD-L1 in cancer pathogenesis. In addition, our work will lead to the design of more rational and effective combination therapies for TNBC patients by defining novel strategies that not only enhance cancer therapy by inhibiting mitosis but also unleash the antitumor activity of the patient?s immune system.