The proto-oncogene KRAS is mutated in a wide array of human cancers, most of which are aggressive and respond poorly to standard therapies. Although the identification of specific oncogenes has led to the development of clinically effective, molecularly targeted therapies in some cases, KRAS has remained refractory to this approach. A complementary strategy for targeting KRAS is to identify gene products that, when inhibited, result in cell death only in the presence of an oncogenic allele1,2. Here we have used systematic RNA interference (RNAi) to detect synthetic lethal partners of oncogenic KRAS and found that the non-canonical IκB kinase, TBK1, was selectively essential in cells that harbor mutant KRAS. Suppression of TBK1 induced apoptosis specifically in human cancer cell lines that depend on oncogenic KRAS expression. In these cells, TBK1 activated NF-κB anti-apoptotic signals involving cREL and BCL-XL that were essential for survival, providing mechanistic insights into this synthetic lethal interaction. These observations identify TBK1 and NF-κB signaling as essential in KRAS mutant tumors and establish a general approach for the rational identification of co-dependent pathways in cancer.

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.

Intratumor heterogeneity is a major clinical problem because tumor cell subtypes display variable sensitivity to therapeutics and may play different roles in progression. We previously characterized 2 cell populations in human breast tumors with distinct properties: CD44 + CD24 -cells that have stem cell-like characteristics, and CD44 -CD24 + cells that resemble more differentiated breast cancer cells. Here we identified 15 genes required for cell growth or proliferation in CD44 + CD24 -human breast cancer cells in a large-scale loss-of-function screen and found that inhibition of several of these (IL6, PTGIS, HAS1, CXCL3, and PFKFB3) reduced Stat3 activation. We found that the IL-6/JAK2/Stat3 pathway was preferentially active in CD44 + CD24 -breast cancer cells compared with other tumor cell types, and inhibition of JAK2 decreased their number and blocked growth of xenografts. Our results highlight the differences between distinct breast cancer cell types and identify targets such as JAK2 and Stat3 that may lead to more specific and effective breast cancer therapies.

Aberrant activation of the canonical Wnt/β-catenin pathway occurs in almost all colorectal cancers and contributes to their growth, invasion and survival. Although dysregulated β-catenin activity drives colon tumorigenesis, additional genetic perturbations are required to elaborate fully malignant disease. To identify genes that both modulate β-catenin activity and are essential for colon cancer cell proliferation, we conducted two loss-of-function screens in human colon cancer cells and compared genes identified in these screens with an analysis of copy-number alterations in colon cancer specimens. One of these genes, CDK8, which encodes a member of the mediator complex, is located at 13q12.13, a region of recurrent copy number gain in a substantial fraction of colon cancers. Suppression of CDK8 expression inhibited proliferation in colon cancer cells characterized by high levels of CDK8 and β-catenin hyperactivity. CDK8 kinase activity was necessary for β-catenin driven transformation and expression of several β-catenin transcriptional targets. Together these observations suggest that therapeutic interventions targeting CDK8 may confer clinical benefit in β-catenin-driven malignancies.Correspondence and Requests for materials should be addressed to W.C.H. (Email: william_hahn@dfci.harvard.edu).. The Wnt/β-catenin pathway is implicated in over 90% of colon cancers and in a fraction of other human malignancies. Loss of the tumor suppressor APC or activating CTNNB1 (β-catenin) mutations results in constitutive activity of the β-catenin-T cell factor (TCF) transcriptional complex, which drives adenoma formation 1,2 . Although mutations in TP53 or K-RAS cooperate with dysregulated β-catenin signaling to program a fully malignant phenotype 3 , these mutations are found in less than half of β-catenin-driven colon cancers 4 . NIH Public AccessTo identify oncogenes that modulate β-catenin-dependent transcription and regulate colon cancer cell proliferation, we conducted two RNAi-based loss-of-function screens. We engineered DLD1 colon cancer cells, which harbor APC deletions and depend on β-catenin for proliferation 5 , to stably express "TOPFLASH" β-catenin-luciferase and "FOPFLASH" mutant-Renilla reporter constructs 6,7 (DLD1 Rep ). Suppression of β-catenin expression in DLD1 Rep cells by three β-catenin-specific short hairpin RNAs (shRNA) markedly reduced the TOPFLASH/FOPFLASH ratio (Fig. 1a), confirming that reporter activity requires β-catenin expression. We then screened DLD1 Rep cells with a shRNA library containing 4849 shRNAs that target 1000 genes, including 95% of the human kinome 6 . We found 34 genes whose expression was necessary for β-catenin activity, including two known β-catenin regulators, CSNK1G3 8 and CSNK1E 9 ( Fig. 1b and Supplementary Table 1).In parallel, we performed an arrayed, kinase-enriched shRNA screen in another β-catenindependent colon cancer cell line, HCT116, to identify genes essential for cancer cell proliferation. We identified 166 candidate genes necessary for proliferatio...

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Summary During the course of a viral infection, viral proteins interact with an array of host proteins and pathways. Here we present a systematic strategy to elucidate the dynamic interactions between H1N1 influenza and its human host. A combination of yeast two hybrid analysis and genome-wide expression profiling implicated hundreds of human factors in mediating viral-host interactions. These factors were then examined functionally through depletion analyses in primary lung cells. The resulting data point to potential roles for some unanticipated host and viral proteins in viral infection and the host response, including a network of RNA binding proteins, components of WNT signaling and viral polymerase subunits. This multilayered approach provides a comprehensive and unbiased physical and regulatory model of influenza-host interactions, and demonstrates a general strategy for uncovering complex host-pathogen relationships.

Elucidation of the mutational landscape of human cancer has progressed rapidly and been accompanied by the development of therapeutics targeting mutant oncogenes. However, a comprehensive mapping of cancer dependencies has lagged behind and the discovery of therapeutic targets for counteracting tumor suppressor gene loss is needed. To identify vulnerabilities relevant to specific cancer subtypes, we conducted a large-scale RNAi screen in which viability effects of mRNA knockdown were assessed for 7,837 genes using an average of 20 shRNAs per gene in 398 cancer cell lines. We describe findings of this screen, outlining the classes of cancer dependency genes and their relationships to genetic, expression, and lineage features. In addition, we describe robust gene-interaction networks recapitulating both protein complexes and functional cooperation among complexes and pathways. This dataset along with a web portal is provided to the community to assist in the discovery and translation of new therapeutic approaches for cancer.