Autologous chimeric antigen receptor (CAR) T-cell therapies targeting CD19 have high efficacy in large B-cell lymphomas (LBCL), but long-term remissions are observed in less than half the patients and treatment-associated adverse events such as immune effector cell-associated neurotoxicity syndrome (ICANS) are a clinical challenge. We performed single-cell RNA sequencing with capture-based cell identification on autologous axicabtagene ciloleucel (axi-cel) anti-CD19 CAR T-cell infusion products to identify transcriptomic features associated with efficacy and toxicity in 24 patients with LBCL. Patients that achieved a complete response by PET/CT at their 3-month follow-up had 3-fold higher frequencies of CD8 T-cells expressing memory signatures compared to patients with partial response or progressive disease. Molecular response measured by cell-free DNA (cfDNA) sequencing at day 7 post-infusion was significantly associated with clinical response (p=0.008), and a signature of CD8 T-cell exhaustion was associated (q=2.8×10 −149 ) with a poor molecular response. Furthermore, a rare cell population *

Crosstalk between tumor cells and other cells within the tumor microenvironment (TME) plays a crucial role in tumor progression, metastases, and therapy resistance. We present iTALK, a computational approach to characterize and illustrate intercellular communication signals in the multicellular tumor ecosystem using single-cell RNA sequencing data. iTALK can in principle be used to dissect the complexity, diversity, and dynamics of cell-cell communication from a wide range of cellular processes.The TME has emerged as a key modulator of tumor progression, immune evasion, and emergence of the anti-tumor therapy resistance mechanisms 1, 2 . The TME includes a diversity of cell types such as tumor cells, a heterogeneous group of immune cells, and the nonimmune stromal components. Tumor cells orchestrate and interact dynamically with these non-tumor components, and the crosstalk between them is thought to provide key signals that can direct and promote tumor cell growth and migration. Through this intercellular communication, tumor cells can elicit profound phenotypic changes in other TME cells such as tumor-associated fibroblasts, macrophages and T cells, and reprogram the TME, in order to escape from immune surveillance to facilitate survival. Therefore, a better understanding of the cell-cell communication signals may help identify novel modulating therapeutic strategies for better patient advantage. However, this has been hampered by the lack of bioinformatics tools for efficient data analysis and visualization.Here, we present iTALK (identifying and illustrating alterations in intercellular signaling network; https://github.com/Coolgenome/iTALK), an open source R package designed to profile and visualize the ligand-receptor mediated intercellular cross-talk signals from singlecell RNA sequencing data (scRNA-seq) ( Fig. 1 and Online Methods). We demonstrated that iTALK can be successfully applied to scRNA-seq data to capture highly abundant ligandreceptor gene (or transcript) pairs, identify gains or losses of cellular interactions by comparative analysis, and track the dynamic changes of intercellular communication signals in longitudinal samples. Notably, functional annotation of ligand-receptor genes is automatically added with our curated iTALK ligand-receptor database, and the output can be visualized in different formats with our efficient data visualization tool, which is implemented as part of iTALK. This approach can be applied to data sets ranging from hundreds to hundreds of thousands of cells and is not limited by sequencing platforms. It is also noteworthy that, in addition to studying the TME, iTALK can also be applied to a wide range of biomedical research fields that involve cell-cell communication.

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The outbreak of severe acute respiratory syndrome (SARS) in 2003 marked the explosion of health information seeking online in China and the increasing emergence of Chinese health websites. There are both benefits and potential hazards of people's online health information seeking. This article intended to test part of Wilson's second model of information behavior, including source characteristics and activating mechanisms, and to identify the relationships among perceived access, perceived expertise credibility, reward assessment, Internet self-efficacy, and online health information-seeking behavior. Data were drawn from face-to-face surveys and an online survey of health information seekers (N = 393) in China. The results showed that source characteristics predicted activating mechanisms, which in turn predicted online health information-seeking behavior. Activating mechanisms, that is, reward assessment and Internet self-efficacy, mediated the relationship between source characteristics (i.e., access and credibility) and online health information-seeking behavior. Strategies for improving information access, expertise credibility, and Internet self-efficacy are discussed in order to maximize the benefits of online health information seeking and to minimize the potential harm.

The mechanisms driving therapeutic resistance and poor outcomes of mantle cell lymphoma (MCL) are incompletely understood. We characterize the cellular and molecular heterogeneity within and across patients and delineate the dynamic evolution of tumor and immune cell compartments at single cell resolution in longitudinal specimens from ibrutinib-sensitive patients and non-responders. Temporal activation of multiple cancer hallmark pathways and acquisition of 17q are observed in a refractory MCL. Multi-platform validation is performed at genomic and cellular levels in PDX models and larger patient cohorts. We demonstrate that due to 17q gain, BIRC5/survivin expression is upregulated in resistant MCL tumor cells and targeting BIRC5 results in marked tumor inhibition in preclinical models. In addition, we discover notable differences in the tumor microenvironment including progressive dampening of CD8+ T cells and aberrant cell-to-cell communication networks in refractory MCLs. This study reveals diverse and dynamic tumor and immune programs underlying therapy resistance in MCL.