Decoding Heterogenous Single-cell Perturbation Responses.

Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets.

Genome-wide CRISPR screens identify combinations of candidate latency reversing agents for targeting the latent HIV-1 reservoir.

Reversing HIV-1 latency promotes killing of infected cells and is essential for cure strategies; however, no single latency reversing agent (LRA) or LRA combination have been shown to reduce HIV-1 latent reservoir size in persons living with HIV-1 (PLWH). Here, we describe an approach to systematically identify LRA combinations to reactivate latent HIV-1 using genome-wide CRISPR screens. Screens on cells treated with suboptimal concentrations of an LRA can identify host genes whose knockout enhances viral gene expression. Therefore, inhibitors of these genes should synergize with the LRA. We tested this approach using AZD5582, an activator of the noncanonical nuclear factor κB (ncNF-κB) pathway, as an LRA and identified histone deacetylase 2 (HDAC2) and bromodomain-containing protein 2 (BRD2), part of the bromodomain and extra-terminal motif (BET) protein family targeted by BET inhibitors, as potential targets. Using CD4+ T cells from PLWH, we confirmed synergy between AZD5582 and several HDAC inhibitors and between AZD5582 and the BET inhibitor, JQ1. A reciprocal screen using suboptimal concentrations of an HDAC inhibitor as an LRA identified BRD2 and ncNF-κB regulators, especially BIRC2, as synergistic candidates for use in combination with HDAC inhibition. Moreover, we identified and validated additional synergistic drug candidates in latency cell line cells and primary lymphocytes isolated from PLWH. Specifically, the knockout of genes encoding CYLD or YPEL5 displayed synergy with existing LRAs in inducing HIV mRNAs. Our study provides insights into the roles of host factors in HIV-1 reactivation and validates a system for identifying drug combinations for HIV-1 latency reversal.

High-content CRISPR screening.

CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development - with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen.

CRISP-view: a database of functional genetic screens spanning multiple phenotypes.

High-throughput genetic screening based on CRISPR/Cas9 or RNA-interference (RNAi) enables the exploration of genes associated with the phenotype of interest on a large scale. The rapid accumulation of public available genetic screening data provides a wealth of knowledge about genotype-to-phenotype relationships and a valuable resource for the systematic analysis of gene functions. Here we present CRISP-view, a comprehensive database of CRISPR/Cas9 and RNAi screening datasets that span multiple phenotypes, including in vitro and in vivo cell proliferation and viability, response to cancer immunotherapy, virus response, protein expression, etc. By 22 September 2020, CRISP-view has collected 10 321 human samples and 825 mouse samples from 167 papers. All the datasets have been curated, annotated, and processed by a standard MAGeCK-VISPR analysis pipeline with quality control (QC) metrics. We also developed a user-friendly webserver to visualize, explore, and search these datasets. The webserver is freely available at http://crispview.weililab.org.

Identification of an immunotherapy-responsive molecular subtype of bladder cancer.

BACKGROUND: Although various molecular subtypes of bladder cancer (BC) have been investigated, most of these studies have focused on muscle-invasive BC (MIBC). A few studies have investigated non-muscle-invasive BC (NMIBC) or NMIBC and MIBC together, but none has classified progressive NMIBC or immune checkpoint inhibitor (ICI)-based therapeutic responses in early-stage BC patients. METHODS: A total of 1,934 samples from seven patient cohorts were used. We performed unsupervised hierarchical clustering to stratify patients into distinct subgroups and constructed a classifier by applying SAM/PAM algorithms. We then investigated the association between molecular subtypes and immunotherapy responsiveness using various statistical methods. FINDINGS: We explored large-scale genomic datasets encompassing NMIBC and MIBC, redefining four distinct molecular subtypes, including a subgroup containing progressive NMIBC and MIBC with poor prognosis that would benefit from ICI treatment. This subgroup showed poor progression-free survival with the distinct features of high mutation load, activated cell cycle, and inhibited TGFß signalling. Importantly, we verified that BC patients with this subtype were significantly responsive to an anti-PD-L1 agent in the IMvigor210 cohort. INTERPRETATION: Our results reveal an immunotherapeutic option for ICI treatment of highly progressive NMIBC and MIBC with poor prognosis. FUNDING: This research was supported by the National Research Foundation of Korea grant funded by the Korean government, a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea, and a grant from the KRIBB Research Initiative Program.

A gene expression signature of FOXM1 predicts the prognosis of hepatocellular carcinoma.

FOXM1 (Forkhead box M1) is a key regulator of tumorigenesis. Previous studies demonstrated that FOXM1 overexpression was strongly correlated with poor prognosis in various cancers, including hepatocellular carcinoma (HCC). In this study, we examined an association between the gene expression signature of FOXM1 and HCC patient outcome. The co-expressed gene set of FOXM1, which is significantly associated with the prognosis of HCC patients, was identified by analyzing the gene expression profiles of 100 patients with HCC, and this gene set was validated in two independent HCC patient cohorts (n=573). In multivariate analysis, the co-expressed gene set of FOXM1 was the most significant prognostic factor for overall survival in patients with HCC (hazard ratio=1.706, 95% confidence intervals=1.176-2.475, P=0.005). We also analyzed different types of cancer, including pancreatic adenocarcinoma, lung adenocarcinoma, breast carcinoma and bladder urothelial carcinoma, to verify the association between the co-expressed gene set of FOXM1 and patient prognosis, and we found a consistent prognostic significance, regardless of tumor type. Finally, we identified a putative signaling pathway in which miR-34a acts as an upstream regulator of the FOXM1-MYC signaling network; this pathway may be ultimately responsible for the poor prognosis of HCC patients. The prognostic subgroups defined by the gene expression signature of FOXM1 could help predict high-risk patients and may guide selection of the best treatment strategy.

Bioinformatic identification of prognostic signature defined by copy number alteration and expression of CCNE1 in non-muscle invasive bladder cancer.

Non-muscle invasive bladder cancer (NMIBC) patients frequently fail to respond to treatment and experience disease progression because of their clinical and biological diversity. In this study, we identify a prognostic molecular signature for predicting the heterogeneity of NMIBC by using an integrative analysis of copy number and gene expression data. We analyzed the copy number and gene expression profiles of 404 patients with bladder cancer obtained from The Cancer Genome Atlas (TCGA) consortium. Of the 14 molecules with significant copy number alterations that were previously reported, 13 were significantly correlated with copy number and expression changes. Prognostic gene sets based on the 13 genes were developed, and their prognostic values were verified in three independent patient cohorts (n=501). Among them, a signature of CCNE1 and its coexpressed genes was significantly associated with disease progression and validated in the independent cohorts. The CCNE1 signature was an independent risk factor based on the result of a multivariate analysis (hazard ratio=6.849, 95% confidence interval=1.613-29.092, P=0.009). Finally, gene network and upstream regulator analyses revealed that NMIBC progression is potentially mediated by CCND1-CCNE1-SP1 pathways. The prognostic molecular signature defined by copy number and expression changes of CCNE1 suggests a novel diagnostic tool for predicting the likelihood of NMIBC progression.