Summary 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.

2 SUMMARYWe 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.

The creation of genome-wide libraries for CRISPR knockout (CRISPRko), interference (CRISPRi), and activation (CRISPRa) has enabled the systematic interrogation of gene function. Here, we show that our recently-described CRISPRko library (Brunello) is more effective than previously published libraries at distinguishing essential and non-essential genes, providing approximately the same perturbation-level performance improvement over GeCKO libraries as GeCKO provided over RNAi. Additionally, we present genome-wide libraries for CRISPRi (Dolcetto) and CRISPRa (Calabrese), and show in negative selection screens that Dolcetto, with fewer sgRNAs per gene, outperforms existing CRISPRi libraries and achieves comparable performance to CRISPRko in detecting essential genes. We also perform positive selection CRISPRa screens and demonstrate that Calabrese outperforms the SAM approach at identifying vemurafenib resistance genes. We further compare CRISPRa to genome-scale libraries of open reading frames (ORFs). Together, these libraries represent a suite of genome-wide tools to efficiently interrogate gene function with multiple modalities.

Unlike most tumor suppressor genes, the most common genetic alterations in tumor protein p53 (TP53) are missense mutations. Mutant p53 protein is often abundantly expressed in cancers and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53 wild-type and null cell lines. We found that loss or dominant-negative inhibition of wild-type p53 function reliably enhanced cellular fitness. By integrating these data with the Catalog of Somatic Mutations in Cancer (COSMIC) mutational signatures database, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.

Introduction: Novel targeted therapeutics have transformed the care of subsets of patients with cancer. In pediatric malignancies, however, with simple tumor genomes and infrequent targetable mutations, there have been few new FDA-approved targeted drugs. The cyclin-dependent kinase (CDK)4/6 pathway recently emerged as a dependency in Ewing sarcoma. Given the heightened efficacy of this class with targeted drug combinations in other cancers, as well as the propensity of resistance to emerge with single agents, we aimed to identify genes mediating resistance to CDK4/6 inhibitors and biologically relevant combinations for use in Ewing. Experimental Design: We performed a genome-scale open reading frame (ORF) screen in two Ewing cell lines sensitive to CDK4/6 inhibitors to identify genes conferring resistance. Concurrently, we established resistance to a CDK4/6 inhibitor in a Ewing cell line. Results: The ORF screen revealed IGF1R as a gene whose overexpression promoted drug escape. We also found elevated levels of phospho-IGF1R in our resistant Ewing cell line, supporting the relevance of IGF1R signaling to acquired resistance. In a small-molecule screen, an IGF1R inhibitor scored as synergistic with CDK4/6 inhibitor treatment. The combination of CDK4/6 and IGF1R inhibitors were synergistic in vitro and active in mouse models. Mechanistically, this combination more profoundly repressed cell cycle and PI3K/mTOR signaling than either single drug perturbation. Conclusions: Taken together, these results suggest that IGF1R activation is an escape mechanism to CDK4/6 inhibitors in Ewing sarcoma and that dual targeting of CDK4/6 and IGF1R provides a candidate synergistic combination for clinical application in this disease.