Mitigation of off-target toxicity in CRISPR-Cas9 screens for essential non-coding elements.

Pooled CRISPR-Cas9 screens are a powerful method for functionally characterizing regulatory elements in the non-coding genome, but off-target effects in these experiments have not been systematically evaluated. Here, we investigate Cas9, dCas9, and CRISPRi/a off-target activity in screens for essential regulatory elements. The sgRNAs with the largest effects in genome-scale screens for essential CTCF loop anchors in K562 cells were not single guide RNAs (sgRNAs) that disrupted gene expression near the on-target CTCF anchor. Rather, these sgRNAs had high off-target activity that, while only weakly correlated with absolute off-target site number, could be predicted by the recently developed GuideScan specificity score. Screens conducted in parallel with CRISPRi/a, which do not induce double-stranded DNA breaks, revealed that a distinct set of off-targets also cause strong confounding fitness effects with these epigenome-editing tools. Promisingly, filtering of CRISPRi libraries using GuideScan specificity scores removed these confounded sgRNAs and enabled identification of essential regulatory elements.

Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays.

The relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ∼500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

Discovery of common and rare genetic risk variants for colorectal cancer.

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10-8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.

Intertumoral Heterogeneity in SCLC Is Influenced by the Cell Type of Origin.

The extent to which early events shape tumor evolution is largely uncharacterized, even though a better understanding of these early events may help identify key vulnerabilities in advanced tumors. Here, using genetically defined mouse models of small cell lung cancer (SCLC), we uncovered distinct metastatic programs attributable to the cell type of origin. In one model, tumors gain metastatic ability through amplification of the transcription factor NFIB and a widespread increase in chromatin accessibility, whereas in the other model, tumors become metastatic in the absence of NFIB-driven chromatin alterations. Gene-expression and chromatin accessibility analyses identify distinct mechanisms as well as markers predictive of metastatic progression in both groups. Underlying the difference between the two programs was the cell type of origin of the tumors, with NFIB-independent metastases arising from mature neuroendocrine cells. Our findings underscore the importance of the identity of cell type of origin in influencing tumor evolution and metastatic mechanisms.Significance: We show that SCLC can arise from different cell types of origin, which profoundly influences the eventual genetic and epigenetic changes that enable metastatic progression. Understanding intertumoral heterogeneity in SCLC, and across cancer types, may illuminate mechanisms of tumor progression and uncover how the cell type of origin affects tumor evolution. Cancer Discov; 8(10); 1316-31. ©2018 AACR. See related commentary by Pozo et al., p. 1216 This article is highlighted in the In This Issue feature, p. 1195.

Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map.

The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss-of-function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that microRNA (miRNA)-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a consensus gene signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 single guide RNAs (sgRNAs) in 6 cells lines and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function.

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Enrichment of colorectal cancer associations in functional regions: Insight for using epigenomics data in the analysis of whole genome sequence-imputed GWAS data.

BACKGROUND: The evaluation of less frequent genetic variants and their effect on complex disease pose new challenges for genomic research. To investigate whether epigenetic data can be used to inform aggregate rare-variant association methods (RVAM), we assessed whether variants more significantly associated with colorectal cancer (CRC) were preferentially located in non-coding regulatory regions, and whether enrichment was specific to colorectal tissues. METHODS: Active regulatory elements (ARE) were mapped using data from 127 tissues and cell-types from NIH Roadmap Epigenomics and Encyclopedia of DNA Elements (ENCODE) projects. We investigated whether CRC association p-values were more significant for common variants inside versus outside AREs, or 2) inside colorectal (CR) AREs versus AREs of other tissues and cell-types. We employed an integrative epigenomic RVAM for variants with allele frequency <1%. Gene sets were defined as ARE variants within 200 kilobases of a transcription start site (TSS) using either CR ARE or ARE from non-digestive tissues. CRC-set association p-values were used to evaluate enrichment of less frequent variant associations in CR ARE versus non-digestive ARE. RESULTS: ARE from 126/127 tissues and cell-types were significantly enriched for stronger CRC-variant associations. Strongest enrichment was observed for digestive tissues and immune cell types. CR-specific ARE were also enriched for stronger CRC-variant associations compared to ARE combined across non-digestive tissues (p-value = 9.6 × 10-4). Additionally, we found enrichment of stronger CRC association p-values for rare variant sets of CR ARE compared to non-digestive ARE (p-value = 0.029). CONCLUSIONS: Integrative epigenomic RVAM may enable discovery of less frequent variants associated with CRC, and ARE of digestive and immune tissues are most informative. Although distance-based aggregation of less frequent variants in CR ARE surrounding TSS showed modest enrichment, future association studies would likely benefit from joint analysis of transcriptomes and epigenomes to better link regulatory variation with target genes.

An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues.

We present Omni-ATAC, an improved ATAC-seq protocol for chromatin accessibility profiling that works across multiple applications with substantial improvement of signal-to-background ratio and information content. The Omni-ATAC protocol generates chromatin accessibility profiles from archival frozen tissue samples and 50-µm sections, revealing the activities of disease-associated DNA elements in distinct human brain structures. The Omni-ATAC protocol enables the interrogation of personal regulomes in tissue context and translational studies.

Molecular definition of a metastatic lung cancer state reveals a targetable CD109-Janus kinase-Stat axis.

Lung cancer is the leading cause of cancer deaths worldwide, with the majority of mortality resulting from metastatic spread. However, the molecular mechanism by which cancer cells acquire the ability to disseminate from primary tumors, seed distant organs, and grow into tissue-destructive metastases remains incompletely understood. We combined tumor barcoding in a mouse model of human lung adenocarcinoma with unbiased genomic approaches to identify a transcriptional program that confers metastatic ability and predicts patient survival. Small-scale in vivo screening identified several genes, including Cd109, that encode novel pro-metastatic factors. We uncovered signaling mediated by Janus kinases (Jaks) and the transcription factor Stat3 as a critical, pharmacologically targetable effector of CD109-driven lung cancer metastasis. In summary, by coupling the systematic genomic analysis of purified cancer cells in distinct malignant states from mouse models with extensive human validation, we uncovered several key regulators of metastatic ability, including an actionable pro-metastatic CD109-Jak-Stat3 axis.

Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution.

We define the chromatin accessibility and transcriptional landscapes in 13 human primary blood cell types that span the hematopoietic hierarchy. Exploiting the finding that the enhancer landscape better reflects cell identity than mRNA levels, we enable 'enhancer cytometry' for enumeration of pure cell types from complex populations. We identify regulators governing hematopoietic differentiation and further show the lineage ontogeny of genetic elements linked to diverse human diseases. In acute myeloid leukemia (AML), chromatin accessibility uncovers unique regulatory evolution in cancer cells with a progressively increasing mutation burden. Single AML cells exhibit distinctive mixed regulome profiles corresponding to disparate developmental stages. A method to account for this regulatory heterogeneity identified cancer-specific deviations and implicated HOX factors as key regulators of preleukemic hematopoietic stem cell characteristics. Thus, regulome dynamics can provide diverse insights into hematopoietic development and disease.

An Arntl2-Driven Secretome Enables Lung Adenocarcinoma Metastatic Self-Sufficiency.

The ability of cancer cells to establish lethal metastatic lesions requires the survival and expansion of single cancer cells at distant sites. The factors controlling the clonal growth ability of individual cancer cells remain poorly understood. Here, we show that high expression of the transcription factor ARNTL2 predicts poor lung adenocarcinoma patient outcome. Arntl2 is required for metastatic ability in vivo and clonal growth in cell culture. Arntl2 drives metastatic self-sufficiency by orchestrating the expression of a complex pro-metastatic secretome. We identify Clock as an Arntl2 partner and functionally validate the matricellular protein Smoc2 as a pro-metastatic secreted factor. These findings shed light on the molecular mechanisms that enable single cancer cells to form allochthonous tumors in foreign tissue environments.