SUMMARY DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ~20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy.

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 *

SARS-CoV-2 is the cause of the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. Using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as of the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.

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.