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SUMMARYThe phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

Deubiquitinating enzymes (DUBs), ~100 of which are found in human cells, are proteases that remove ubiquitin conjugates from proteins, thereby regulating protein turnover. They are involved in a wide range of cellular activities and are emerging therapeutic targets for cancer and other diseases. Drugs targeting USP1 and USP30 are in clinical development for cancer and kidney disease respectively. However, the majority of substrates and pathways regulated by DUBs remain unknown, impeding efforts to prioritize specific enzymes for research and drug development. To assemble a knowledgebase of DUB activities, co-dependent genes, and substrates, we combined targeted experiments using CRISPR libraries and inhibitors with systematic mining of functional genomic databases. Analysis of the Dependency Map, Connectivity Map, Cancer Cell Line Encyclopedia, and multiple protein-protein interaction databases yielded specific hypotheses about DUB function, a subset of which were confirmed in follow-on experiments. The data in this paper are browsable online in a newly developed DUB Portal and promise to improve understanding of DUBs as a family as well as the activities of incompletely characterized DUBs (e.g. USPL1 and USP32) and those already targeted with investigational cancer therapeutics (e.g. USP14, UCHL5, and USP7).

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.

We performed quantitative proteomics on 60 human-derived breast cancer cell lines to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

Drug response screens on panels of cell lines aimed at identifying markers of sensitivity or resistance have been limited in their successes. Unfortunately, the recent release of many such studies has been accompanied by concerns surrounding reproducibility. Since then, several publications have addressed these concerns by pointing out sources of variability and by suggesting better experimental methods as well as more robust analytical approaches. In the presented profiling effort, we integrated the latest advances in drug response measurement and focused on data diversity and quality rather than on breadth. We selected 32 breast cancer cell lines with a strong bias towards triple negative lines as well as 4 cell lines established from relevant patient-derived xenografts. We used high content microscopy to assay the phenotypic responses of the cell lines to a panel of 34 drugs made up largely of kinase inhibitors currently in the clinic along with some standard of care chemotherapeutics. The microscopy based drug response assay allowed us to measure drug potency, and to quantify the efficacy of the drugs in terms of growth inhibition and cell death. For the same cell lines, we used RNAseq to measure basal mRNA expression levels and shotgun mass spectrometry to measure endogenous protein levels. The completeness and controlled conditions under which these data sets were collected provide confidence in their integration. The complementarity of these multi-omics data has allowed us to address questions about the landscape of, particularly triple negative, breast cancer cell lines. Such questions include: where do the patient-derived lines lay among the established cell lines? and how different are the landscapes defined by drug response phenotypes, mRNA expression, and protein levels? We used network-based algorithms to identify eigenstates of signaling pathways related to genomic events, and further explored these states in the TCGA data. At the level of drug response, we have focused on important questions related to the clinical use of kinase inhibitors. In particular, we compared various CDK inhibitors in an effort to identify markers that are informative of response potency and efficacy. We have also looked at variability of the responses of the cell lines studied to multiple PI3K inhibitors that either target specific isoforms or all isoforms. Overall the data set that has been collected is a valuable resource for understanding drug response in triple negative breast cancer, and the transcriptomic and proteomic factors that influence it. Citation Format: Mills CE, Hafner MA, Sorger PK. Integration of transcriptomic, proteomic and drug response data in triple negative breast cancer cell lines and PDX models [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-08-02.

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS) is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.

Doublecortin-like kinase 1 (DCLK1) is critical for neurogenesis, but overexpression is also observed in multiple cancers and is associated with poor prognosis. Nevertheless, the function of DCLK1 in cancer, especially the context-dependent functions, are poorly understood. We present a "toolkit" that includes the DCLK1 inhibitor DCLK1-IN-1, a complementary DCLK1-IN-1-resistant mutation G532A, and kinase dead mutants D511N and D533N, which can be used to investigate signaling pathways regulated by DCLK1. Using a cancer cell line engineered to be DCLK1 dependent for growth and cell migration, we show that this toolkit can be used to discover associations between DCLK1 kinase activity and biological processes. In particular, we show an association between DCLK1 and RNA processing, including the identification of CDK11 as a potential substrate of DCLK1 using phosphoproteomics.

SUMMARY Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays., Graphical Abstract, In Brief Factors that impact the reproducibility of experimental data are poorly understood. Five NIH-LINCS centers performed the same set of drug-response measurements and compared results. Technical and biological variables that impact precision and reproducibility and are also sensitive to biological context were the most problematic.

The target profiles of many drugs are established early in their development and are not systematically revisited at the time of approval, raising the question whether human therapeutics with the same nominal targets but different chemical structures are functionally equivalent. In this paper we use five distinct phenotypic and biochemical assays to compare approved inhibitors of the cyclindependent kinases CDK4/6 (palbociclib, ribociclib, and abemaciclib) that are promising therapies for hormone-receptor positive breast cancer. We find that transcriptional, proteomic and phenotypic changes induced by the three drugs differ significantly and that abemaciclib has activates overlapping those of the older generation CDK inhibitor alvocidib. Abemaciclib’s activities arise from inhibition of kinases other than CDK4/6 including CDK2/Cyclin A/E and CDK1/Cyclin B. Our data suggest the potential for differential use in the clinic and argue that a multi-faceted experimental and computational approach is necessary to obtain a reliable picture of kinase inhibitor target spectrum.

SUMMARYThe target profiles of many drugs are established early in their development and are not systematically revisited at the time of FDA approval. Thus, it is often unclear whether therapeutics with the same nominal targets but different chemical structures are functionally equivalent. In this paper we use five different phenotypic and biochemical assays to compare approved inhibitors of cyclin-dependent kinases 4/6 – collectively regarded as breakthroughs in the treatment of hormone receptor-positive breast cancer. We find that transcriptional, proteomic and phenotypic changes induced by palbociclib, ribociclib, and abemaciclib differ significantly; abemaciclib in particular has advantageous activities partially overlapping those of alvocidib, an older polyselective CDK inhibitor. In cells and mice, abemaciclib inhibits kinases other than CDK4/6 including CDK2/Cyclin A/E – implicated in resistance to CDK4/6 inhibition – and CDK1/Cyclin B. The multi-faceted experimental and computational approaches described here therefore uncover under-appreciated differences in CDK4/6 inhibitor activities with potential importance in treating human patients.

I will describe new (unpublished) approaches to quantifying drug response at two points in the drug development pipeline: pre-clinical studies in cell lines and clinical trials of combination therapies in patient populations. Drug sensitivity and resistance in cell lines is conventionally quantified by IC50 or Emax values, but these measures suffer from a fundamental flaw when applied to growing cells: they are highly sensitive to cell division number, which varies with cell line, experimental condition, seeding density etc. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring important biological insights and interfering with biomarker discovery. I will describe alternative growth rate inhibition (GR) metrics that are insensitive to division number and can directly measure both endpoint sensitivity and adaptive drug resistance. Theory and experiments show that GR50 and GRmax are superior to IC50 and Emax for assessing the effects of drugs in dividing cells. GR metrics promise to improve our ability to score drug sensitivity in specific-derived tumor cells, improves data reproducibility, and increase the translational potential of pharmacogenomics data. In patients, combination therapy improves tumor control compared to monotherapy and the development of combinations is motivated in most cases by pre-clinical data on synergism in cell lines. However I will describe a different way in which combinations can provide clinical benefit. Based on analysis of between-patient variability in existing trial data and a large set patient derived tumor xenograft (PDX) mice published by Gao H, Korn JM, Ferretti S, et al. (Nature medicine 2015;21:1318-25), I will argue for a simple principle: in nearly two-thirds of cases analyzed, efficacious combinations work simply by improving the likelihood that a tumor will experience an outlier response to a single drug. Thus, even in the absence of additive or synergistic tumor inhibition, combinations are generally superior to monotherapies. Superiority by “independent action” provides a principle on which to design new combinations and a scientific rationale for the use of combination therapies in poorly understood cancers whenever toxicity is acceptable. These studies, at two different points of the drug discovery pipeline, illustrate the value of combining new first-principles theories about drug mechanism of action with “big data” that is increasingly available on pre-clinical and clinical drug response. Conversely, in the absence of conceptual innovation, we miss important mechanistic insights hidden in existing data. The purpose of such insights, when obtained, is drive new laboratory and real-world experiments. I will discuss how the process of coupling computation and experimentation works in practice. Citation Format: Peter K. Sorger, Marc Hafner, Mario Niepel, Caitlin Mills, Adam Palmer, Mohammed Fallahi Sichani. New concepts for quantifying the benefits of mono and combination therapy in an era of big data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5037. doi:10.1158/1538-7445.AM2017-5037