Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references. / The human microbiome is essential for health and has been implicated in many diseases. DNA sequencing has enabled the detailed characterization of these human-associated microbial communities, leading to a rapid expansion in studies investigating the human microbiome. In this thesis, I describe multiple projects which overcome various data analysis challenges to extract useful clinical insights from microbiome data. In the first project, I present an analysis of lung, stomach, and oropharyngeal microbiomes. I leverage data collected from multiple sites per patient to identify aspiration-associated changes in the relationships between these communities, discovering new properties of the aerodigestive microbiome and suggesting new approaches for treatment. In the second project, I perform a meta-analysis of case-control gut microbiome datasets with standard data processing and analysis methods. / I find consistent patterns characterizing disease-associated microbiome changes and a set of shared associations which could inform clinical treatment and therapeutic development approaches for different microbiome-mediated diseases. Enabled by this work, in the third project I contribute to the development of a method to correct for batch effects in case-control microbiome studies. In the fourth project, I describe a framework for rational donor selection in fecal microbiota transplant clinical trials in which knowledge derived from clinical and basic science research is used to inform which donor is selected for fecal transplants, increasing the likelihood of successful trials. Finally, I present preliminary results analyzing the microbiome and metabolome of residential sewage as a novel platform for community-level public health surveillance. / Together, these projects demonstrate a variety of approaches to mine the human microbiome for clinically-relevant insights and suggests multiple avenues forward for translating findings from microbiome data analyses into clinical and public health impact. / by Claire Marie Noëlle Duvallet. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 151-167). / Medulloblastoma (MB) is a chiefly pediatric cancer of the cerebellum that has been studied extensively using genomic, epigenomic, and transcriptomic data. It comprises at least four molecularly distinct subgroups: WNT, SHH, Group 3, and Group 4. Despite the detailed characterization of MB, many disease-driving events remain to be elucidated and therapeutic targets to be nominated. In this thesis, we describe three studies that contribute to a better understanding of this devastating disease: First, we describe a study that aims to fully describe the genomic landscape in the largest medulloblastoma cohort to date, using 491 sequenced MB tumors and 1,256 epigenetically analyzed cases. This work describes subgroup-specific driver alterations including previously unappreciated actionable targets; and, based on epigenetic data, identifies further heterogeneity within Group 3 and Group 4 tumors. Second, we focus on the proteomes and phospho-proteomes of 45 medulloblastoma samples. / We identified distinct pathways associated with two subsets of SHH tumors that showed robustly distinct proteomes, but similar transcriptomes, and found post-translational modifications of MYC that are associated with poor outcomes in Group 3 tumors. We also found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. This study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies. Third, we characterize the metabolomic space of MB on largely the same 45 tumors as used in the proteome-focused study. Here, we present preliminary insights from derived from integrative network and other analyses. We find that MB consensus subgroups are preserved in metabolic space, and that certain classes of metabolites are elevated in MYC-activated MB. / We also show that, similar to other cancers, a previously described gain-of-function mutation in IDH1 may cause elevated 2-hydroxyglutarate levels in MB. The work described in this thesis significantly enhances previous knowledge of medulloblastoma and its subgroups, and provides insights that may aid in the development of medulloblastoma therapies in the near future. / by Tobias Ehrenberger. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 128-137). / Effective cancer treatment of the future requires incorporating diverse and innovative aspects of immunity to fight against cancer, accounting for pharmacokinetic and temporal barriers of therapeutics, and engineering approaches to understand and improve upon current immunotherapies. This thesis addresses these challenges in three projects utilizing the Wittrup Lab's quantitative, engineering approach to protein-based cancer immunotherapy. In the first project, I attempted to harness the potency of allergic reactions against cancer by designing IgE class antibodies against two mouse tumor antigens and comparing them with traditional IgG antibodies. These IgE antibodies elicited modest or no tumor control, and limited efficacy could be due to fast pharmacokinetic clearance, absence of human-like allergic effector cells in mice, or tumor-suppressive effects from mast cells responding to IgE. / The second project described in this thesis focused on synchronizing combination immunotherapies with the temporal progression of the anti-cancer immune response. In this work, anti-tumor antibodies were combined with the cytokines interleukin 2 (IL2) and interferon alpha (IFNa). The order of administration of these therapies decoupled strong efficacy from dose-limiting toxicity in two tumor models. Given before IFN[alpha], IL2 activated natural killer cells and heightened their responsiveness to subsequent IFN[alpha], which was ultimately toxic and unnecessary for therapeutic efficacy. This project's proof of concept that efficacy and toxicity could be unlinked in immunotherapy began to establish a framework to use for rational combination therapy treatment schedule design, with the goal of treating with each agent when that piece of the immune system is active. / Finally, the third project used the Wittrup Lab's system of yeast surface display to engineer novel antibodies against the checkpoint blockade target cytotoxic T lymphocyte associated protein 4 (CTLA-4) as tools to improve understanding of the anti-CTLA-4 mechanism of action against cancer. Although the first wave of antibodies made had favorable characteristics against CTLA-4 as a soluble target, they bound a CTLA-4 epitope too close to the cell surface and so could not be used for therapeutic studies. Next generation sequencing on the yeast libraries identified alternative CTLA-4 binding antibody sequences, and these will be tested in future mechanistic and therapeutic studies. / by Adrienne Marie Rothschilds. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 153-173). / The field of synthetic biology aims to control cellular behavior using programmable gene circuits. Generally these gene circuits sense molecular biomarkers, process these inputs and execute a desired calculated response. This is especially relevant for gene and cell therapies where integrating multiple disease-related inputs and/or sophisticated control could lead to safer and more effective approaches. While mammalian synthetic biology has made great progress, few gene circuit-based therapies have entered the clinic. Regulatory issues aside, this lag may be due to several technical impediments. First, the computing part of circuits is often accomplished via transcriptional regulation, which presents challenges as we move toward the clinic. Second, the field relies on a limited set of sensors; the detection of other types of disease biomarkers will help robustly identify cell state. / Finally, the design cycle currently used to develop gene circuits is laborious and slow, which is not suitable for clinical development, especially applications in personalized medicine. In this thesis I describe how I address these three limitations. I develop a new posttranscriptional regulation platform based on RNA cleavage that I term "PERSIST" (Programmable Endonucleolytic RNA Scission-Induced Stability Tuning). CRISPR-specific endonucleases are adapted as RNA-level regulators for the platform and we demonstrate several genetic devices including cascades, feedback, logic functions and a bistable switch. I explore sensor designs for relevant biomolecules including mRNAs, miRNAs and proteins via the PERSIST and other platforms. Finally, I present a "poly-transfection" method, associated advanced data analysis pipelines, and computational models that make circuit engineering faster and more predictive. / Taken together, the expanded RNA toolkit that the PERSIST platform offers as well as advancements in sensing and circuit design will enable the more straightforward creation of robust gene circuits for gene and cell therapies. / by Breanna Elizabeth DiAndreth. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 190-200). / The transcription factor Myc is a basic helix-loop-helix leucine zipper (bHLHLZ) protein with crucial roles in regulating normal cellular processes, but its transcriptional activity is deregulated in a majority of human cancers. Myc transcriptional activity is dependent on dimerization with its obligate partner Max, another bHLHLZ transcription factor. Max also forms homodimers as well as heterodimers with other proteins including the Mxd family of proteins, creating a dynamic network of protein-protein interactions to regulate transcriptional programs. Despite the significance of this network, the arsenal of chemical probes to interrogate these proteins in cancer biology remains limited. Here, we utilized small molecule microarrays and luciferase-based reporter assays to identify compounds that bind Max and modulate Myc transcriptional activity. We discovered the small molecule KI-MS2-008, which stabilizes the Max homodimer while reducing Myc protein and Myc-regulated transcript levels. KI-MS2-008 also decreases viable cancer cell growth in a Myc-dependent manner and suppresses tumor growth in mouse models of Myc-driven cancers. In a cancer cell line model treated with KI-MS2-008, the equilibrium of protein-protein interactions shifts toward a transcriptionally repressed state over time by recruiting Mxd4 and other repressive machinery to Max. This study establishes that perturbing Max dimerization with small molecules is a tractable approach to targeting Myc activity in cancer. / by Andrew Chen. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 147-168). / Cell-cell communication between malignant, immune, and stromal cells influences many aspects of in vivo tumor biology, including tumorigenesis, tumor progression, and therapeutic resistance. As a result, targeting receptor-ligand interactions, for instance with immune check-point inhibitors, can provide significant benefit for patients. However, our knowledge of this complex network of cell-cell interactions in a tumor microenvironment is still incomplete, and there is a need for systematic approaches to study cell-cell communication. This thesis presents computational approaches for characterizing cell-cell communication networks in three different experimental studies. In the first study, we modeled metastatic triple negative breast cancer in the liver using a microphysiological system and identified inflammatory cytokines secreted by the microenvironment that result in the proliferation of dormant metastases. In the second study, we used single-cell RNA sequencing (scRNA-seq) to quantify receptor-ligand interactions in six syngeneic mouse tumor models. To identify specific receptor-ligand interactions that predict tumor growth rate and immune infiltration, we used receptor-ligand interactions as features in regression models. For the third study, we extended our scRNA-seq approach to include inferences of single-cell signaling pathway and transcription factor activity. We then identified protein-protein interaction networks that connect extra-cellular receptor-ligand interactions to intra-cellular signal transduction pathways. Using this approach, we compared inflammatory versus genetic models of colorectal cancer and identified cancer-associated-fibroblasts as drivers of a partial epithelial-to-mesenchymal transition in tumor cells via MAPK1 and MAPK14 signaling. Overall, the methods developed in this thesis provide a foundational computational framework for constructing "multi-scale" models of communication networks in multi-cellular tissues. / by Manu Prajapati Kumar. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 171-181). / Vaccination against infectious diseases has long been heralded as one of the greatest advancements in public health, yet its application to other clinical indications has fallen short of expectations. In this thesis, we apply engineering principles to develop more potent vaccines in the treatment of cancer and autoimmunity. Both major components of molecular vaccines, antigen and adjuvant, are independently explored as a part of this work. Our antigen studies sought to improve the delivery of peptide epitopes to lymphoid organs by fusing epitopes to inert protein carriers with defined pharmacokinetic properties. To promote anti-tumor immunity, we found that antigen carriers should 1) protect peptide cargo from proteolytic degradation, 2) be appropriately bulky to drain into the lymphatics, and 3) be rapidly cleared once in the blood to prevent tolerization at distal poorly inflamed organs. / Applying these principles, we identified transthyretin as an optimal delivery protein, and demonstrated efficacy against a number of clinically relevant antigens. Because our protein-epitope fusion approach is fully recombinant in nature, we were able to convert our protein vaccines into nucleic acid modalities, including plasmid DNA and self-replicating RNA, which are significantly easier and cheaper to manufacture at scale. Finally, we applied our learnings to purposefully induce tolerization in the treatment of autoimmunity, and found that albumin is a particularly efficacious antigen carrier protein for this application due to its extended half-life. On the adjuvant front, we attempted to engineer novel Toll-like receptor 3 (TLR3) agonists via yeast surface display. Although we successfully developed high affinity TLR3 binders, all tested clones failed to agonize TLR3 despite the utilization of several multimerization strategies. / Separately, in an effort to better understand adjuvant biology, we conducted a detailed mechanistic study of lipo-CpG, a particularly potent amphiphilic CpG variant previously developed by the Irvine lab. We uncovered a cascade of inflammatory signals originating from monocytes that facilitates the induction of high magnitude T cell responses, largely by acting in trans rather than directly on the antigen-presenting cell. Overall, these studies have elucidated a number of design principles that should aid in the engineering of next generation vaccines to better treat cancer and autoimmunity. / by Naveen K. Mehta. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 93-108). / Kinase inhibitors targeting the MAPK pathway are often limited by lack of durable clinical responses or, in many cancer types, lack of even initial responses. While great headway has been made on characterizing mechanisms of resistance, understanding the full influence of complex intercellular interactions on drug resistance remains a challenge. Here, we combine computation with experiment to investigate the cellular and molecular contributions of the tumor microenvironment to MAPK inhibitor response. First, we employ a computational framework using published bulk and single-cell patient gene expression data to investigate immune cell correlates of MAPK inhibitor resistance, and subsequently quantify potential intercellular ligand-receptor interactions between cell populations of interest. Next, we use multiplex proteomic immunoassays and co-culture experiments to characterize the impact of these interactions on tumor-intrinsic bypass signaling and phenotype. To assess the in vivo relevance of these multicellular and multidirectional signaling networks, we develop an intravital imaging strategy to monitor the influence of tumor-associated macrophages on cancer cell kinase activity dynamics. Finally, we rationally design a nanotherapy to exploit inhibitor-induced immunomodulation and crosstalk. Overall, we present a paradigm to systematically dissect signaling pathways between tumors and their microenvironments, validate these interactions in various models of disease, and design therapeutic strategies to target them. / by Stephanie Joy Wang. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references. / Chimeric antigen receptor (CAR) T cells are a promising cancer therapeutic, as they can specifically redirect the cytotoxic function of a T cell to a chosen target of interest. CAR T cells have been successful in clinical trials against hematological cancers, but have experienced low efficacy against solid tumors for a number of reasons, including a paucity of tumor-specific antigens to target and a highly immunosuppressive solid tumor microenvironment. In chapter 2 of this thesis, we develop a strategy to target multiple solid tumor types through markers in their microenvironment. The use of single domain antibody (VHH)-based CAR T cells that recognize these markers circumvents the need for tumor-specific targets. Chapter 3 will describe methods to overcome the immunosuppressive microenvironment of solid tumors. Here, we have developed VHH-secreting CAR T cells that can modulate additional aspects of the tumor microenvironment, including the engagement of the innate immune system through secretion of a VHH against an inhibitor of phagocytosis. We show that this strategy of VHH-secretion by CAR T cells can lead to significant benefits in outcome. We also demonstrate that delivery of therapeutics by CAR T cells can improve the safety profile of the therapeutic. Chapter 4 of this thesis explores strategies to increase the targeting capacity of CAR T cells by building logic-gated CARs. Finally, chapter 5 will describe work in imaging CAR T cells specifically, longitudinally, and non-invasively through PET imaging. Our results demonstrate the flexibility of VHHs in CAR T cell engineering and the potential of VHH-based CAR T cells to target the tumor microenvironment, modulate the tumor microenvironment, and treat solid tumors. / by Yushu Joy Xie. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, May, 2020 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 111-130). / Much of the diversity we observe in cellular and organismal phenotypes can be attributed to epigenetic and genetic variation. DNA provides the instructions for life, while epigenetic modifications regulate which parts of the genetic information contained in DNA can be read out in a given cell and how this information is interpreted. In recent years, epigenetic and genetic variation has been profiled on a large scale with sequencing-based assays, generating many datasets to be explored. In this thesis, I present three projects which apply computational techniques to identify and characterize epigenetic mechanisms that may contribute to the regulation of phenotypic variance. First, we mine a dataset charactering the epigenomes of diverse cell types in order to discover signatures of adult stem cell differentiation. / We identify a novel marker of the multipotent state, a chromatin state characterized by the histone marks H3K36me3 and H3K9me3, and describe biological processes that may be linked to the loss of this chromatin state in fully differentiated cell types. Next, I present what we learned from profiling the epigenetic state of cells before and after transplantation into Xenopus oocytes, a process that transcriptionally reprograms the cells. This analysis elucidates how the initial epigenetic state of a cell influences the success of cellular reprogramming and identifies transcription factors that help regulate this process. Finally, we integrate studies measuring the effects of genetic variants on disease with studies measuring the effects of genetic variants on transcriptional and epigenetic activity. This identifies specific mechanisms underlying disease processes, and demonstrates that transcriptional and epigenetic mechanisms may independently contribute to disease pathogenesis. / Together, these projects demonstrate the biological insights that can be gained from epigenetic profiling, and expand our understanding of the potential effects of epigenetic modifications. / by Khoi Thien Nguyen. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering