Successful tumors are able to evade the immune system, which is otherwise capable of killing transformed cells. Therapies that prevent this evasion have become revolutionary treatments for incurable cancers. This presentation will focus on our recent work targeting immune suppressive Siglec receptors and their sialylated glycan ligands, which are abundant within the cancer glycocalyx. We found that Siglec-ligand interactions can confer resistance to antibody-dependent cell cytotoxicity mediated by monoclonal antibody cancer drugs such as Herceptin. Based on this, we designed biotherapeutic molecules termed antibody-enzyme conjugates that selectively remove sialic acids from tumor cells and render them susceptible to immune cell killing. Editing the cancer cell glycocalyx with antibody-enzyme conjugates represents a new approach to cancer immune therapy.

Human immunity relies on coordinated responses of ensembles of cells from various types and functional states. Over the past several decades, substantial work has been done to catalog the cell types, states, and interactions that inform these behaviors. The emerging field of single cell genomics has the potential to make important contributions to this body of work by providing a way to systematically map previously unknown subsets of cells, predict their functionality, and ultimately modulate their relative abundance so as to favorably tune the emerging immune response. However, while data sets produced by single cell genomics hold unique information, they also pose unique challenges, from idiosyncratic technical limitations to difficulty of interpretability. 

In this talk, I will present some of the computational tools that we have developed in my group in order to address these challenges and extract knowledge from the observed variability between cells. I will describe these tools in the context of two applications  - studying the role of transcriptional diversity during innate immune response to HIV-1 infection, and exploring the effect of metabolic diversity on the function of T helper cells in autoimmune settings.

Microenvironmental conditions contribute to the pathogenesis of cancer, and aberrant extracellular matrix (ECM) remodeling plays a key role in this process. However, our understanding of the specific mechanisms by which the ECM promotes cancer is relatively limited. Our lab focuses on the integration of materials science, tissue engineering, and cancer biology approaches to test the role of ECM biological and physical properties in cancer initiation and progression. More specifically, we characterize the effect of tumors on ECM composition, structure, and mechanics and investigate the relevance of these changes to tumor cell behavior both in vitro and in vivo. Additionally, we are evaluating whether obesity, a condition commonly associated with an increased risk and worse clinical prognosis for cancer, may promote tumorigenesis by mimicking tumor-like ECM dynamics.

Synthetic lethality refers to a genetic interaction where single gene defects are compatible with cell viability, but combinations result in cell death. The synthetic lethality paradigm provides a new approach to find targeted therapies for cancers with “undruggable” mutations – using inhibitors of the synthetic lethal (SL) partners of cancer mutations to selectively kill cancer cells. An example of a synthetic lethality-based therapy to reach the clinic is the targeting of BRCA1- and BRCA2-deficient tumor cells with PARP inhibitors. Today, there is increased interest in identifying SL partners of other cancer mutations in the hope of discovering new therapeutic options for untreatable cancers. However, current approaches for identifying mutation-specific SL partners rely on cell line-based screens, that are adversely affected by (i) the limited representation of newly discovered mutations in existing cell lines, and (ii) the artificiality of in vitro screening conditions, which cannot fully capture in vivo tumor evolution in the tumor microenvironment.

In this talk, I will describe MiSL (Mining Synthetic Lethals) - a novel computational tool for discovering SL partners of cancer-specific alterations by mining primary human tumor data. I will discuss MiSL in the context of two key aspects of precision cancer therapy: (1) discovery of novel drug targets, and (2) identification of predictive biomarkers of targeted drugs.

Single cell RNA-seq (scRNA-seq) has emerged as a transformative tool to characterize heterogeneous systems, yet translating the wealth of data available into biological insight remains challenging. However, the diversity of technologies, modalities, and systems underlying new datasets, presents exciting opportunities for data integration. I will discuss new experimental and computational methods that allow for the integrated learning of cellular states from multimodal measurements, as well as an approach to “align” shared subpopulations across scRNA-seq datasets to facilitate integrated or comparative analysis.

Appreciation is increasing for the influence of complex cell-cell interactions in tissue microenvironments on physiology, pathology, and treatment responses associated with inflammatory and malignant disease.  These interactions include among diverse immune cell types as well as between immune cells and the various resident tissue cells types.  New therapeutic approaches directed toward modulating these interactions are becoming available, including small molecule drug-, antibody-, cytokine-, and cell-based interventions, but predictive understanding of when and how these treatments may or may not be effective is limited at present.  In this seminar, I will present recent efforts by our laboratory in partnership with collaborators aimed at addressing this challenge using multi-variate systems analysis closely integrating experiment and computational modeling. 

Cancer cells within tumors exist in specialized niches in the microenvironment. Metastasis is the major cause of poor outcome in breast cancer. The tumor cells are are heterogeneous and can have properties as divergent as self-renewal, tumor initiation and repopulation potential, dormancy, evasion of cell death and metastasis. The microenvironment is critical for regulating multiple aspects of tumor progression and metastasis. Understanding the processes involved in metastasis is critical for developing new preventative and therapeutic strategies We have analyzed the matrix environments and its function in tumor progression using in vivo imaging, genetic models of breast cancer, patient-derived xenografts and organotypic cultures. We have found that the matrix environment and the innate immune cells responding to the primary tumor and infiltrating the metastatic sites are critical for regulating tumor invasion and metastasis. Indeed, the inflammatory microenvironment of metastatic sites was distinct from that of the parental tumor and the normal tissue, even before metastasis takes hold. We showed at the single-cell level that early stage metastatic cells possess a distinct stem-like gene expression signature. Metastatic cells from low-burden tissues were distinct due to their increased expression of basal, stem cell, pro-survival, and dormancy-associated genes. In contrast, cells from macrometastases were similar to primary tumor cells, which were more differentiated and heterogeneous. We found that metastases can be inhibited selectively compared to primary tumors by targeting the extracellular matrix, myeloid cells in the microenvironment or the metastatic cells themselves. Thus, metastatic niches may be sensitive to novel therapies and targeting them may prevent metastatic disease from advancing and presenting a poor outcome.

When and why do individual cells in a population behave differently from the rest? Cancer is a disease replete with examples of single cells behaving badly—for instance, when treated with anti-cancer therapies, most cells die but a few rare cells remain and continue to grow despite treatment, presenting a major barrier to cure. Mostly, people think resistance is due to genetic differences in those rare cells, but we found that non-genetic differences can also lead to resistance. We will talk about tools we are developing to find these non-genetically distinct rare cell subpopulations both prospectively and retrospectively, thus allowing us to connect single cell profiles to phenotypic differences. We also describe recent efforts to manipulate these single cell behaviors. Together, these results might help us develop new quantitative approaches to the problem of therapy resistance in cancer, and may help answer outstanding conceptual questions in single cell biology in general.

Cancer is a complex adaptive system orchestrated by the interactions between tumor cells and their microenvironment. In particular, cancer-associated fibroblasts (CAFs), the dominant cellular component of the tumor stroma, are often associated with a poor prognosis and play an important role in tumor progression for a number of cancers. While significant literature has highlighted the influence of CAFs on cancer cell phenotypes including tumor cell proliferation and invasion, the role of CAF heterogeneity on treatment response remains largely understudied. Additionally, preclinical treatment studies repeatedly focus on drug-induced changes to tumor cells with little investigation into the impact on surrounding stromal cells. To advance our biological understanding of cancer and improve treatment efficacy, we are utilizing quantitative high-content imaging coupled with more physiologically-relevant patient-derived model systems to illuminate the dynamic interactions between cancer cells and their microenvironment. These studies are aimed at increasing our understanding of the functional and therapeutic utility of CAFs by leveraging expertise across disciplines. In this presentation, I will highlight several imaging-based workflows to phenotypically profile heterocellular populations across spatiotemporal scales. Using these approaches, we have identified cancer-associated fibroblasts as a potent-source of environment mediated drug resistance in colorectal cancer.

Until very recently most cancer biologist operated with the assumption that the most common route to metastasis involved cells of the primary tumor transforming to a motile single-cell phenotype via complete EMT (the epithelial-mesenchymal transition). This change allowed them to migrate individually to distant organs, eventually leading to clonal growths in other locations. But, a new more nuanced picture has been emerging, based on advanced measurements and on computational systems biology approaches. It has now been realized that cells can readily adopt states with hybrid properties, use these properties to move collectively and cooperatively, and reach distant niches as highly metastatic clusters. This talk will focus on the accumulating evidence for this revised perspective and on open questions currently under investigation.

During mammalian organogenesis, the cells of the three germ layers transform into an embryo that includes most major internal and external organs. The key regulators of developmental defects can be studied during this critical window, but conventional approaches lack the throughput and resolution to obtain a global view of the molecular states and trajectories of a rapidly diversifying and expanding number of cell types. Here we set out to investigate the transcriptional dynamics of mouse development during organogenesis at single cell resolution. With sci-RNA-seq3, an improved single cell combinatorial indexing-based protocol, we profiled ~2 million cells derived from 61 mouse embryos staged between 9.5 and 13.5 days of gestation (E9.5 to E13.5; 10 to 15 replicates per time-point). We identify hundreds of expanding, contracting and transient cell types, many of which are only detected because of the depth of cellular coverage obtained here, and define the corresponding sets of cell type-specific marker genes, several of which we validate by whole mount in situ hybridization. With Monocle 3, we delineate and annotate 56 single cell developmental trajectories of mouse organogenesis. We explore the dynamics of proliferation and gene expression within cell types over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle. These data comprise a foundational resource for mammalian developmental biology, and are made available in a way that will facilitate their ongoing annotation by the research community.