Computational genomics methods to probe cellular and microbial heterogeneity and interactions in cancer
Creators: name: Ghaddar, Bassel
Publisher: name: No Publisher Supplied
Year: 2023

description: Cancer is the result of genomic alterations that drive malignant cell growth in an environment that permits their flourishing and immune evasion. It is a disease in multicellular organism in which individual cells successively devolve into selfish cells with abnormal phenotypes and interactions with their environments. The heterogeneity in tumor microenvironments, which includes varying local physical properties, arrangements of normal and immune cells, and resident or colonizing microbiome, coupled with the diversity of genomic drivers and the co-evolution of this entire system, makes cancer a highly personalized and complex disease. A better understanding of the components in tumors and their interactions is critical to the design of new treatments and could aid in the clinical management of cancer patients. Next-generation sequencing technologies have enabled the widespread profiling of tumors at various scales. In this dissertation, I develop and apply three main computational methods to analyze heterogeneities and interactions in cancer primarily using single-cell RNA-sequencing (scRNA-seq) data.I developed Census, a biologically intuitive and fully automated cell-type identification method for scRNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. I use Census to identify disseminated pancreatic tumors cells in liver samples without evidence of metastasis and we characterize this rare cell-type.Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. I developed Neighbor-seq, a method to identify the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in scRNA-seq data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.Lastly, I developed SAHMI, a computational resource to identify truly present microbial nucleic acids and filter contaminants and spurious false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. In benchmark studies, SAHMI correctly identifies known microbial infections present in diverse tissues. I then use SAHMI to interrogate tumor-microbiome interactions in two human pancreatic cancer cohorts. I identify somatic-cell associated bacteria in a subset of tumors and their near absence in nonmalignant tissues. These bacteria predominantly pair with tumor cells, and their presence associates with cell-type specific gene expression and pathway activities, including cell motility and immune signaling. Modeling results indicate that tumor-infiltrating lymphocytes closely resemble T-cells from infected tissues. Finally, using multiple independent datasets, a signature of cell-associated bacteria predicts clinical prognosis.Collectively, the algorithms I developed shed light on cancer as a multicellular disease with complex cell-cell and cell-microbe interactions. These tools probe single-cell genomic data at three levels: the identity of individual cells, direct cell-cell interactions, and the recovery of microbial content and its association with host cells. I apply these to find subsets of pancreatic cancer with varying immune subtypes, microorganisms, and survival statistics. My work deepens our understanding of pancreatic cancer specifically, and my studies more generally advance methods to extract signal from complex and noisy biological datasets.