Rajaraman, A. | bioRxiv Science

bioRxiv2020

A molecular map of lymph node blood vascular endothelium at single cell resolution

Blood vascular endothelial cells (BECs) control the immune response by regulating immune cell recruitment, metabolite exchange and blood flow in lymphoid tissues. However, the diversity of BEC and their origins during immune angiogenesis remain poorly understood. Here we profile transcriptomes of BEC from mouse peripheral lymph nodes and map key phenotypes to the vasculature. Our analysis identifies multiple novel subsets including a venous population whose gene signature predicts an unexpectedly selective role in myeloid cell (vs lymphocyte) recruitment to the medulla, confirmed by 2 photon videomicroscopy. We define five phenotypes of capillary lining BEC including a capillary resident regenerative population (CRP) that displays stem cell and migratory gene signatures and contributes to homeostatic BEC turnover and to vascular neogenesis after immunization. Trajectory analyses reveal retention of developmental programs along a progression of cellular phenotypes from CRP to mature venous and arterial BEC subsets. Overall, our single cell atlas provides a molecular blueprint of the lymph node blood vasculature and defines subset specialization for immune cell recruitment and vascular homeostasis.

immunology↗

bioRxiv2019

Improving personalized prediction of cancer prognoses with clonal evolution models

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, known as mutator phenotypes, which act in different combinations or degrees in different cancers. Here we explore the question of how and to which degree different mutator phenotypes act in a cancer predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational preferences, followed by a machine learning frame-work to identify key features predictive of progression. We build phylogenies tracing the evolution of subclones of cells in tumor tissues using a variety of somatic genomic alterations, including single nucleotide variations, copy number alterations, and structural variations. We demonstrate that mutation preference features derived from the phylogenies are predictive of clinical outcomes of cancer progression - overall survival and disease-free survival - based on the analyses on breast invasive carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. We further show that mutational phenotypes have predictive power even after accounting for traditional clinical and driver-centric predictors of progression. These results confirm the power of mutational phenotypes as an independent class of predictive biomarkers and suggest a strategy for enhancing the predictive power of conventional clinical or driver-centric genomic features.

cancer biology↗