The effort to personalize treatment plans for cancer patients involves the identification of drug treatments that can effectively target the disease while minimizing the likelihood of adverse reactions. In this study, the gene-expression profile of 810 cancer cell lines and their response data to 368 small molecules from the Cancer Therapeutics Research Portal (CTRP) are analyzed to identify pathways with significant rewiring between genes, or differential gene dependency, between sensitive and non-sensitive cell lines. Identified pathways and their corresponding differential dependency networks are further analyzed to discover essentiality and specificity mediators of cell line response to drugs/compounds. For analysis we use the previously published method EDDY (Evaluation of Differential DependencY). EDDY first constructs likelihood distributions of gene-dependency networks, aided by known gene-gene interaction, for two given conditions, for example, sensitive cell lines vs. non-sensitive cell lines. These sets of networks yield a divergence value between two distributions of network likelihoods that can be assessed for significance using permutation tests. Resulting differential dependency networks are then further analyzed to identify genes, termed mediators, which may play important roles in biological signaling in certain cell lines that are sensitive or non-sensitive to the drugs. Establishing statistical correspondence between compounds and mediators can improve understanding of known gene dependencies associated with drug response while also discovering new dependencies. Millions of compute hours resulted in thousands of these statistical discoveries. EDDY identified 8,811 statistically significant pathways leading to 26,822 compound-pathway-mediator triplets. By incorporating STITCH and STRING databases, we could construct evidence networks for 14,415 compound-pathway-mediator triplets for support. The results of this analysis are presented in a searchable website to aid researchers in studying potential molecular mechanisms underlying cells’ drug response as well as in designing experiments for the purpose of personalized treatment regimens.
Phenotypic cell-based screening is a powerful approach to small-molecule discovery, but a major challenge of this strategy lies in determining the intracellular target and mechanism of action (MoA) for validated hits. Here, we show that the small-molecule BRD0476, a novel suppressor of pancreatic β-cell apoptosis, inhibits interferon-gamma (IFN-γ)-induced Janus kinase 2 (JAK2) and signal transducer and activation of transcription 1 (STAT1) signaling to promote β-cell survival. However, unlike common JAK-STAT pathway inhibitors, BRD0476 inhibits JAK-STAT signaling without suppressing the kinase activity of any JAK. Rather, we identified the deubiquitinase ubiquitin-specific peptidase 9X (USP9X) as an intracellular target, using a quantitative proteomic analysis in rat β cells. RNAi-mediated and CRISPR/Cas9 knockdown mimicked the effects of BRD0476, and reverse chemical genetics using a known inhibitor of USP9X blocked JAK-STAT signaling without suppressing JAK activity. Site-directed mutagenesis of a putative ubiquitination site on JAK2 mitigated BRD0476 activity, suggesting a competition between phosphorylation and ubiquitination to explain small-molecule MoA. These results demonstrate that phenotypic screening, followed by comprehensive MoA efforts, can provide novel mechanistic insights into ostensibly well-understood cell signaling pathways. Furthermore, these results uncover USP9X as a potential target for regulating JAK2 activity in cellular inflammation.
Novel therapeutic approaches are urgently required for multiple myeloma (MM). We used a phenotypic screening approach using co-cultures of MM cells with bone marrow stromal cells to identify compounds that overcome stromal resistance. One such compound, BRD9876, displayed selectivity over normal hematopoietic progenitors and was discovered to be an unusual ATP non-competitive kinesin-5 (Eg5) inhibitor. A novel mutation caused resistance, suggesting a binding site distinct from known Eg5 inhibitors, and BRD9876 inhibited only microtubule-bound Eg5. Eg5 phosphorylation, which increases microtubule binding, uniquely enhanced BRD9876 activity. MM cells have greater phosphorylated Eg5 than hematopoietic cells, consistent with increased vulnerability specifically to BRD9876's mode of action. Thus, differences in Eg5-microtubule binding between malignant and normal blood cells may be exploited to treat multiple myeloma. Additional steps are required for further therapeutic development but our results indicate that unbiased chemical biology approaches can identify therapeutic strategies unanticipated by prior knowledge of protein targets.
Identifying therapeutic targets in rare cancers remains challenging due to the paucity of established models to perform preclinical studies. As a proof-of-concept, we developed a patient-derived cancer cell line, CLF-PED-015-T, from a paediatric patient with a rare undifferentiated sarcoma. Here, we confirm that this cell line recapitulates the histology and harbours the majority of the somatic genetic alterations found in a metastatic lesion isolated at first relapse. We then perform pooled CRISPR-Cas9 and RNAi loss-of-function screens and a small-molecule screen focused on druggable cancer targets. Integrating these three complementary and orthogonal methods, we identify CDK4 and XPO1 as potential therapeutic targets in this cancer, which has no known alterations in these genes. These observations establish an approach that integrates new patient-derived models, functional genomics and chemical screens to facilitate the discovery of targets in rare cancers.
Identifying therapeutic targets in rare cancers is challenging due to the lack of relevant pre-clinical models. Here, the authors generate a cancer cell line from a paediatric patient with a rare undifferentiated sarcoma and through functional genomics and chemical screens identified CDK4 and XPO1 as potential therapeutic targets in this cancer.
Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with ~19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters, and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.
High cancer death rates indicate the need for new anti-cancer therapeutic agents. Approaches to discover new cancer drugs include target-based drug discovery and phenotypic screening. Here, we identified phosphodiesterase 3A modulators as cell-selective cancer cytotoxic compounds by phenotypic compound library screening and target deconvolution by predictive chemogenomics. We found that sensitivity to 6-(4-(diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one, or DNMDP, across 766 cancer cell lines correlates with expression of the phosphodiesterase 3A gene, PDE3A. Like DNMDP, a subset of known PDE3A inhibitors kill selected cancer cells while others do not. Furthermore, PDE3A depletion leads to DNMDP resistance. We demonstrated that DNMDP binding to PDE3A promotes an interaction between PDE3A and Schlafen 12 (SLFN12), suggesting a neomorphic activity. Co-expression of SLFN12 with PDE3A correlates with DNMDP sensitivity, while depletion of SLFN12 results in decreased DNMDP sensitivity. Our results implicate PDE3A modulators as candidate cancer therapeutic agents and demonstrate the power of predictive chemogenomics in small-molecule discovery.
Small-molecule probes can illuminate biological processes and aid in the assessment of emerging therapeutic targets by perturbing biological systems in a manner distinct from other experimental approaches. Despite the tremendous promise of chemical tools for investigating biology and disease, small-molecule probes were unavailable for most targets and pathways as recently as a decade ago. In 2005, the U.S. National Institutes of Health launched the decade-long Molecular Libraries Program with the intent of innovating in and broadening access to small-molecule science. This Perspective describes how novel small-molecule probes identified through the program are enabling the exploration of biological pathways and therapeutic hypotheses not otherwise testable. These experiences illustrate how small-molecule probes can help bridge the chasm between biological research and the development of medicines, but also highlight the need to innovate the science of therapeutic discovery.
Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2).
Radiotherapy is not currently informed by the genetic composition of an individual patient's tumour. To identify genetic features regulating survival after DNA damage, here we conduct large-scale profiling of cellular survival after exposure to radiation in a diverse collection of 533 genetically annotated human tumour cell lines. We show that sensitivity to radiation is characterized by significant variation across and within lineages. We combine results from our platform with genomic features to identify parameters that predict radiation sensitivity. We identify somatic copy number alterations, gene mutations and the basal expression of individual genes and gene sets that correlate with the radiation survival, revealing new insights into the genetic basis of tumour cellular response to DNA damage. These results demonstrate the diversity of tumour cellular response to ionizing radiation and establish multiple lines of evidence that new genetic features regulating cellular response after DNA damage can be identified.
The variability in patient response to radiation treatment is difficult to predict. Here, using more than 500 cell lines the authors measure response to radiation exposure and a large panel of compounds, and show that response can be predicted by genetic alterations of the cells.
Over the past decade, tremendous progress in high-throughput small-molecule screening methods has facilitated the rapid expansion of phenotype-based data. Parallel advances in genomic characterization methods have complemented these efforts by providing a growing list of annotated cell line features. Together, these developments have paved the way for feature-based identification of novel, exploitable cellular dependencies, subsequently expanding our therapeutic toolkit in cancer and other diseases. Here, we provide an overview of the evolution of phenotypic small-molecule profiling and discuss the most significant and recent profiling and analytical efforts, their impact on the field, and their clinical ramifications. We additionally provide a perspective for future developments in phenotypic profiling efforts guided by genomic science.
cell-line profiling; genotype-phenotype; small molecule
There is an urgent need in oncology to link molecular aberrations in tumors with therapeutics that can be administered in a personalized fashion. One approach identifies synthetic-lethal genetic interactions or dependencies that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated a systematic and quantitative chemical-genetic interaction map that charts the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model complex cellular contexts. Applied to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene including resistance to AKT/PI3K pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic-lethal manner, providing new drug and biomarker pairs for clinical investigation. This scalable approach enables the prediction of drug responses from patient data and can accelerate the development of new genotype-directed therapies.
systems biology; synthetic lethal; genetic interactions; networks
The small-molecule probes STF-31
and its analogue compound 146 were discovered while searching for
compounds that kill VHL-deficient renal cell carcinoma cell lines
selectively and have been reported to act via direct inhibition of
the glucose transporter GLUT1. We profiled the sensitivity of 679
cancer cell lines to STF-31 and found that the pattern of response
is tightly correlated with sensitivity to three different inhibitors
of nicotinamide phosphoribosyltransferase (NAMPT). We also performed
whole-exome next-generation sequencing of compound 146-resistant HCT116
clones and identified a recurrent NAMPT-H191R mutation. Ectopic expression
of NAMPT-H191R conferred resistance to both STF-31 and compound 146
in cell lines. We further demonstrated that both STF-31 and compound
146 inhibit the enzymatic activity of NAMPT in a biochemical assay
in vitro. Together, our cancer-cell profiling and genomic approaches
identify NAMPT inhibition as a critical mechanism by which STF-31-like
compounds inhibit cancer cells.
Recent industry-academic partnerships involve collaboration across disciplines, locations, and organizations using publicly funded “open-access” and proprietary commercial data sources. These require effective integration of chemical and biological information from diverse data sources, presenting key informatics, personnel, and organizational challenges. BARD (BioAssay Research Database) was conceived to address these challenges and to serve as a community-wide resource and intuitive web portal for public-sector chemical biology data. Its initial focus is to enable scientists to more effectively use the NIH Roadmap Molecular Libraries Program (MLP) data generated from 3-year pilot and 6-year production phases of the Molecular Libraries Probe Production Centers Network (MLPCN), currently in its final year. BARD evolves the current data standards through structured assay and result annotations that leverage the BioAssay Ontology (BAO) and other industry-standard ontologies, and a core hierarchy of assay definition terms and data standards defined specifically for small-molecule assay data. We have initially focused on migrating the highest-value MLP data into BARD and bringing it up to this new standard. We review the technical and organizational challenges overcome by the inter-disciplinary BARD team, veterans of public and private sector data-integration projects, collaborating to describe (functional specifications), design (technical specifications), and implement this next-generation software solution.
chemical and biological data and database; public data sources; “open innovation”; PubChem; web portal; data standards; definitions; assay protocols; data migration; analytical and transactional processing; data warehouse; visualization; community adoption
Ferroptosis is a form of nonapoptotic cell death for which key regulators remain unknown. We sought a common mediator for the lethality of 12 ferroptosisinducing small molecules. We used targeted metabolomic profiling to discover that depletion of glutathione causes inactivation of glutathione peroxidases (GPXs) in response to one class of compounds and a chemoproteomics strategy to discover that GPX4 is directly inhibited by a second class of compounds. GPX4 overexpression and knockdown modulated the lethality of 12 ferroptosis inducers, but not of 11 compounds with other lethal mechanisms. In addition, two representative ferroptosis inducers prevented tumor growth in xenograft mouse tumor models. Sensitivity profiling in 177 cancer cell lines revealed that diffuse large B cell lymphomas and renal cell carcinomas are particularly susceptible to GPX4-regulated ferroptosis. Thus, GPX4 is an essential regulator of ferroptotic cancer cell death.
Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules’ effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound’s mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test datasets, as well as open-source implementations of the various methods in a common software framework.
phenotypic screening; high-content screening; image-based screening; drug profiling
The high rate of clinical response to protein kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell-line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: 1) associate with specific cancer-genomic alterations and 2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene β-catenin with sensitivity to the Bcl2-family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and accelerate discovery of drugs matched to patients by their cancer genotype and lineage.
The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.
Efforts to develop more effective therapies for acute leukemia may benefit from high-throughput screening systems that reflect the complex physiology of the disease, including leukemia stem cells (LSCs) and supportive interactions with the bone-marrow microenvironment. The therapeutic targeting of LSCs is challenging because LSCs are highly similar to normal hematopoietic stem and progenitor cells (HSPCs) and are protected by stromal cells in vivo. We screened 14,718 compounds in a leukemia-stroma co-culture system for inhibition of cobblestone formation, a cellular behavior associated with stem-cell function. Among those that inhibited malignant cells but spared HSPCs was the cholesterol-lowering drug lovastatin. Lovastatin showed anti-LSC activity in vitro and in an in vivo bone marrow transplantation model. Mechanistic studies demonstrated that the effect was on-target, via inhibition of HMGCoA reductase. These results illustrate the power of merging physiologically-relevant models with high-throughput screening.
Type-1 diabetes (T1D) is an autoimmune disease in which insulin-secreting pancreatic beta cells are destroyed by the immune system. An emerging strategy to regenerate beta-cell mass is through transdifferentiation of pancreatic alpha cells to beta cells. We previously reported two small molecules, BRD7389 and GW8510, that induce insulin expression in a mouse alpha cell line and provide a glimpse into potential intermediate cell states in beta-cell reprogramming from alpha cells. These small-molecule studies suggested that inhibition of kinases in particular may induce the expression of several beta-cell markers in alpha cells. To identify potential lineage reprogramming protein targets, we compared the transcriptome, proteome, and phosphoproteome of alpha cells, beta cells, and compound-treated alpha cells. Our phosphoproteomic analysis indicated that two kinases, BRSK1 and CAMKK2, exhibit decreased phosphorylation in beta cells compared to alpha cells, and in compound-treated alpha cells compared to DMSO-treated alpha cells. Knock-down of these kinases in alpha cells resulted in expression of key beta-cell markers. These results provide evidence that perturbation of the kinome may be important for lineage reprogramming of alpha cells to beta cells.
T cell acute lymphoblastic leukemia (T-ALL) is an aggressive cancer that is frequently associated with activating mutations in NOTCH1 and dysregulation of MYC. Here, we performed 2 complementary screens to identify FDA-approved drugs and drug-like small molecules with activity against T-ALL. We developed a zebrafish system to screen small molecules for toxic activity toward MYC-overexpressing thymocytes and used a human T-ALL cell line to screen for small molecules that synergize with Notch inhibitors. We identified the antipsychotic drug perphenazine in both screens due to its ability to induce apoptosis in fish, mouse, and human T-ALL cells. Using ligand-affinity chromatography coupled with mass spectrometry, we identified protein phosphatase 2A (PP2A) as a perphenazine target. T-ALL cell lines treated with perphenazine exhibited rapid dephosphorylation of multiple PP2A substrates and subsequent apoptosis. Moreover, shRNA knockdown of specific PP2A subunits attenuated perphenazine activity, indicating that PP2A mediates the drug’s antileukemic activity. Finally, human T-ALLs treated with perphenazine exhibited suppressed cell growth and dephosphorylation of PP2A targets in vitro and in vivo. Our findings provide a mechanistic explanation for the recurring identification of phenothiazines as a class of drugs with anticancer effects. Furthermore, these data suggest that pharmacologic PP2A activation in T-ALL and other cancers driven by hyperphosphorylated PP2A substrates has therapeutic potential.
Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery.
The mechanism by which cells decide to skip mitosis to become polyploid is largely undefined. Here we used a high-content image-based screen to identify small-molecule probes that induce polyploidization of megakaryocytic leukemia cells and serve as perturbagens to help understand this process. We found that dimethylfasudil (diMF, H-1152P) selectively increased polyploidization, mature cell-surface marker expression, and apoptosis of malignant megakaryocytes. A broadly applicable, highly integrated target identification approach employing proteomic and shRNA screening revealed that a major target of diMF is Aurora A kinase (AURKA), which has not been studied extensively in megakaryocytes. Moreover, we discovered that MLN8237 (Alisertib), a selective inhibitor of AURKA, induced polyploidization and expression of mature megakaryocyte markers in AMKL blasts and displayed potent anti-AMKL activity in vivo. This research provides the rationale to support clinical trials of MLN8237 and other inducers of polyploidization in AMKL. Finally, we have identified five networks of kinases that regulate the switch to polyploidy.
Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P = 1.4×10−9). Second, we demonstrate that subjects homozygous for the RA risk allele have ∼33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P = 10−9), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-κB transcription factor. Finally, we develop a high-throughput NF-κB luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA–approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-κB signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA.
A current challenge in human genetics is to follow-up “hits” from genome-wide association studies (GWAS) to guide drug discovery for complex traits. Previously, we identified a common variant in the CD40 locus as associated with risk of rheumatoid arthritis (RA). Here, we fine-map the CD40 signal of association through a combination of dense genotyping and exonic sequencing in large patient collections. Further, we demonstrate that the RA risk allele is a gain-of-function allele that increases the amount of CD40 on the surface of primary human B lymphocyte cells from healthy control individuals. Based on these observations, we develop a high-throughput assay to recapitulate the biology of the RA risk allele in a system suitable for a small molecule drug screen. After a series of primary screens and counter screens, we identify small molecules that inhibit CD40-mediated NF-kB signaling in human B cells. While this is only the first step towards a more comprehensive effort to identify CD40-specific inhibitors that may be used to treat RA, our study demonstrates a successful strategy to progress from a GWAS to a drug screen for complex traits such as RA.
Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the the number of confirmed hits. In contrast, “utility-aware” methods are informed by models of screeners’ preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims—by changing which hits are sent for confirmatory testing—to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than three-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.