A landscape of response to drug combinations in non-small cell lung cancer.

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy.

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets.

BACKGROUND: Drug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools. RESULTS: We describe a statistical and visualization framework for the analysis of large-scale drug combination screens. We developed an approach suitable for datasets with large number of drugs pairs even if small number of data points are available per drug pair. We demonstrate our approach using a systematic screen of all possible pairs among 108 cancer drugs applied to melanoma cell lines. In this dataset only two dose-response data points per drug pair and two data points per single drug test were available. We used a Bliss-based linear model, effectively borrowing data from the drug pairs to obtain robust estimations of the singlet viabilities, consequently yielding better estimates of drug synergy. Our method improves data consistency across dosing thus likely reducing the number of false positives. The approach allows to compute p values accounting for standard errors of the modeled singlets and combination viabilities. We further develop a synergy specificity score that distinguishes specific synergies from those arising with promiscuous drugs. Finally, we developed a summarized interactive visualization in a web application, providing efficient access to any of the 439,000 data points in the combination matrix ( http://www.cmtlab.org:3000/combo_app.html ). The code of the analysis and the web application is available at https://github.com/arnaudmgh/synergy-screen . CONCLUSIONS: We show that statistical modeling of single drug response from drug combination data can help determine significance of synergy and antagonism in drug combination screens with few data point per drug pair. We provide a web application for the rapid exploration of large combinatorial drug screen. All codes are available to the community, as a resource for further analysis of published data and for analysis of other drug screens.

Quasimesenchymal phenotype predicts systemic metastasis in pancreatic ductal adenocarcinoma.

Metastasis following surgical resection is a leading cause of mortality in pancreatic ductal adenocarcinoma. Epithelial-mesenchymal transition is thought to play an important role in metastasis, although its clinical relevance in metastasis remains uncertain. We evaluated a panel of RNA in-situ hybridization probes for epithelial-mesenchymal transition-related genes expressed in circulating tumor cells. We assessed the predictive value of this panel for metastasis in pancreatic ductal adenocarcinoma and, to determine if the phenotype is generalizable between cancers, in colonic adenocarcinoma. One hundred fifty-eight pancreatic ductal adenocarcinomas and 205 colonic adenocarcinomas were classified as epithelial or quasimesenchymal phenotype using dual colorimetric RNA-in-situ hybridization. SMAD4 expression on pancreatic ductal adenocarcinomas was assessed by immunohistochemistry. Pancreatic ductal adenocarcinomas with quasimesenchymal phenotype had a significantly shorter disease-specific survival (P = 0.031) and metastasis-free survival (P = 0.0001) than those with an epithelial phenotype. Pancreatic ductal adenocarcinomas with SMAD4 loss also had lower disease-specific survival (P = 0.041) and metastasis-free survival (P = 0.001) than those with intact SMAD4. However, the quasimesenchymal phenotype proved a more robust predictor of metastases-area under the curve for quasimesenchymal = 0.8; SMAD4 = 0.6. The quasimesenchymal phenotype also predicted metastasis-free survival (P = 0.004) in colonic adenocarcinoma. Epithelial-mesenchymal transition defined two phenotypes with distinct metastatic capabilities-epithelial phenotype tumors with predominantly organ-confined disease and quasimesenchymal phenotype with high risk of metastatic disease in two epithelial malignancies. Collectively, this work validates the relevance of epithelial-mesenchymal transition in human gastrointestinal tumors.

Harnessing synthetic lethality to predict the response to cancer treatment.

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

Differential Effector Engagement by Oncogenic KRAS.

KRAS can bind numerous effector proteins, which activate different downstream signaling events. The best known are RAF, phosphatidylinositide (PI)-3' kinase, and RalGDS families, but many additional direct and indirect effectors have been reported. We have assessed how these effectors contribute to several major phenotypes in a quantitative way, using an arrayed combinatorial siRNA screen in which we knocked down 41 KRAS effectors nodes in 92 cell lines. We show that every cell line has a unique combination of effector dependencies, but in spite of this heterogeneity, we were able to identify two major subtypes of KRAS mutant cancers of the lung, pancreas, and large intestine, which reflect different KRAS effector engagement and opportunities for therapeutic intervention.

Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities.

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

Preclinical Anticancer Efficacy of BET Bromodomain Inhibitors Is Determined by the Apoptotic Response.

Small-molecule inhibitors of the bromodomain and extraterminal (BET) family of proteins are being tested in clinical trials for a variety of cancers, but patient selection strategies remain limited. This challenge is partly attributed to the heterogeneous responses elicited by BET inhibition (BETi), including cellular differentiation, senescence, and death. In this study, we performed phenotypic and gene-expression analyses of treatment-naive and engineered tolerant cell lines representing human melanoma and leukemia to elucidate the dominant features defining response to BETi. We found that de novo and acquired tolerance to BETi is driven by the robustness of the apoptotic response, and that genetic or pharmacologic manipulation of the apoptotic signaling network can modify the phenotypic response to BETi. We further reveal that the expression signatures of the apoptotic genes BCL2, BCL2L1, and BAD significantly predict response to BETi. Taken together, our findings highlight the apoptotic program as a determinant of response to BETi, and provide a molecular basis for patient stratification and combination therapy development.

Landscape of Targeted Anti-Cancer Drug Synergies in Melanoma Identifies a Novel BRAF-VEGFR/PDGFR Combination Treatment.

A newer generation of anti-cancer drugs targeting underlying somatic genetic driver events have resulted in high single-agent or single-pathway response rates in selected patients, but few patients achieve complete responses and a sizeable fraction of patients relapse within a year. Thus, there is a pressing need for identification of combinations of targeted agents which induce more complete responses and prevent disease progression. We describe the results of a combination screen of an unprecedented scale in mammalian cells performed using a collection of targeted, clinically tractable agents across a large panel of melanoma cell lines. We find that even the most synergistic drug pairs are effective only in a discrete number of cell lines, underlying a strong context dependency for synergy, with strong, widespread synergies often corresponding to non-specific or off-target drug effects such as multidrug resistance protein 1 (MDR1) transporter inhibition. We identified drugs sensitizing cell lines that are BRAFV600E mutant but intrinsically resistant to BRAF inhibitor PLX4720, including the vascular endothelial growth factor receptor/kinase insert domain receptor (VEGFR/KDR) and platelet derived growth factor receptor (PDGFR) family inhibitor cediranib. The combination of cediranib and PLX4720 induced apoptosis in vitro and tumor regression in animal models. This synergistic interaction is likely due to engagement of multiple receptor tyrosine kinases (RTKs), demonstrating the potential of drug- rather than gene-specific combination discovery approaches. Patients with elevated biopsy KDR expression showed decreased progression free survival in trials of mitogen-activated protein kinase (MAPK) kinase pathway inhibitors. Thus, high-throughput unbiased screening of targeted drug combinations, with appropriate library selection and mechanistic follow-up, can yield clinically-actionable drug combinations.

The WTX Tumor Suppressor Interacts with the Transcriptional Corepressor TRIM28.

WTX encodes a tumor suppressor implicated in the pediatric kidney cancer Wilms tumor and in mesenchymal differentiation with potentially distinct functions in the cytoplasm, at the plasma membrane, and in the nucleus. Although modulating components of the WNT signaling pathway is a proposed function for cytoplasmic and membrane-bound WTX, its nuclear properties are not well understood. Here we report that the transcriptional corepressor TRIM28 is the major binding partner for nuclear WTX. WTX interacted with the coiled coil domain of TRIM28 required for its binding to Krüppel-associated box domains of transcription factors and for its chromatin recruitment through its own coiled coil and proline-rich domains. Knockdown of endogenous WTX reduced the recruitment of TRIM28 to a chromatinized reporter sequence and its ability to repress a target transcript. In mouse embryonic stem cells where TRIM28 plays a major role in repressing endogenous retroviruses and long interspersed elements, knockdown of either TRIM28 or WTX combined with single molecule RNA sequencing revealed a highly significant shared set of differentially regulated transcripts, including derepression of non-coding repetitive sequences and their neighboring protein encoding genes (p < 1e-20). In mesenchymal precursor cells, depletion of WTX and TRIM28 resulted in analogous ß-catenin-independent defects in adipogenic and osteogenic differentiation, and knockdown of WTX reduced TRIM28 binding to Pparγ promoter. Together, the physical and functional interaction between WTX and TRIM28 suggests that the nuclear fraction of WTX plays a role in epigenetic silencing, an effect that may contribute to its function as a regulator of cellular differentiation and tumorigenesis.

Patient-derived models of acquired resistance can identify effective drug combinations for cancer.

Targeted cancer therapies have produced substantial clinical responses, but most tumors develop resistance to these drugs. Here, we describe a pharmacogenomic platform that facilitates rapid discovery of drug combinations that can overcome resistance. We established cell culture models derived from biopsy samples of lung cancer patients whose disease had progressed while on treatment with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors and then subjected these cells to genetic analyses and a pharmacological screen. Multiple effective drug combinations were identified. For example, the combination of ALK and MAPK kinase (MEK) inhibitors was active in an ALK-positive resistant tumor that had developed a MAP2K1 activating mutation, and the combination of EGFR and fibroblast growth factor receptor (FGFR) inhibitors was active in an EGFR mutant resistant cancer with a mutation in FGFR3. Combined ALK and SRC (pp60c-src) inhibition was effective in several ALK-driven patient-derived models, a result not predicted by genetic analysis alone. With further refinements, this strategy could help direct therapeutic choices for individual patients.

Targeting transcription regulation in cancer with a covalent CDK7 inhibitor.

Tumour oncogenes include transcription factors that co-opt the general transcriptional machinery to sustain the oncogenic state, but direct pharmacological inhibition of transcription factors has so far proven difficult. However, the transcriptional machinery contains various enzymatic cofactors that can be targeted for the development of new therapeutic candidates, including cyclin-dependent kinases (CDKs). Here we present the discovery and characterization of a covalent CDK7 inhibitor, THZ1, which has the unprecedented ability to target a remote cysteine residue located outside of the canonical kinase domain, providing an unanticipated means of achieving selectivity for CDK7. Cancer cell-line profiling indicates that a subset of cancer cell lines, including human T-cell acute lymphoblastic leukaemia (T-ALL), have exceptional sensitivity to THZ1. Genome-wide analysis in Jurkat T-ALL cells shows that THZ1 disproportionally affects transcription of RUNX1 and suggests that sensitivity to THZ1 may be due to vulnerability conferred by the RUNX1 super-enhancer and the key role of RUNX1 in the core transcriptional regulatory circuitry of these tumour cells. Pharmacological modulation of CDK7 kinase activity may thus provide an approach to identify and treat tumour types that are dependent on transcription for maintenance of the oncogenic state.

dREAM co-operates with insulator-binding proteins and regulates expression at divergently paired genes.

dREAM complexes represent the predominant form of E2F/RBF repressor complexes in Drosophila. dREAM associates with thousands of sites in the fly genome but its mechanism of action is unknown. To understand the genomic context in which dREAM acts we examined the distribution and localization of Drosophila E2F and dREAM proteins. Here we report a striking and unexpected overlap between dE2F2/dREAM sites and binding sites for the insulator-binding proteins CP190 and Beaf-32. Genetic assays show that these components functionally co-operate and chromatin immunoprecipitation experiments on mutant animals demonstrate that dE2F2 is important for association of CP190 with chromatin. dE2F2/dREAM binding sites are enriched at divergently transcribed genes, and the majority of genes upregulated by dE2F2 depletion represent the repressed half of a differentially expressed, divergently transcribed pair of genes. Analysis of mutant animals confirms that dREAM and CP190 are similarly required for transcriptional integrity at these gene pairs and suggest that dREAM functions in concert with CP190 to establish boundaries between repressed/activated genes. Consistent with the idea that dREAM co-operates with insulator-binding proteins, genomic regions bound by dREAM possess enhancer-blocking activity that depends on multiple dREAM components. These findings suggest that dREAM functions in the organization of transcriptional domains.

Distinct, strict requirements for Gfi-1b in adult bone marrow red cell and platelet generation.

The zinc finger transcriptional repressor Gfi-1b is essential for erythroid and megakaryocytic development in the embryo. Its roles in the maintenance of bone marrow erythropoiesis and thrombopoiesis have not been defined. We investigated Gfi-1b's adult functions using a loxP-flanked Gfi-1b allele in combination with a novel doxycycline-inducible Cre transgene that efficiently mediates recombination in the bone marrow. We reveal strict, lineage-intrinsic requirements for continuous adult Gfi-1b expression at two distinct critical stages of erythropoiesis and megakaryopoiesis. Induced disruption of Gfi-1b was lethal within 3 wk with severely reduced hemoglobin levels and platelet counts. The erythroid lineage was arrested early in bipotential progenitors, which did not give rise to mature erythroid cells in vitro or in vivo. Yet Gfi-1b(-/-) progenitors had initiated the erythroid program as they expressed many lineage-restricted genes, including Klf1/Eklf and Erythropoietin receptor. In contrast, the megakaryocytic lineage developed beyond the progenitor stage in Gfi-1b's absence and was arrested at the promegakaryocyte stage, after nuclear polyploidization, but before cytoplasmic maturation. Genome-wide analyses revealed that Gfi-1b directly regulates a wide spectrum of megakaryocytic and erythroid genes, predominantly repressing their expression. Together our study establishes Gfi-1b as a master transcriptional repressor of adult erythropoiesis and thrombopoiesis.

Bending modes of DNA directly addressed by cryo-electron microscopy of DNA minicircles.

We use cryo-electron microscopy (cryo-EM) to study the 3D shapes of 94-bp-long DNA minicircles and address the question of whether cyclization of such short DNA molecules necessitates the formation of sharp, localized kinks in DNA or whether the necessary bending can be redistributed and accomplished within the limits of the elastic, standard model of DNA flexibility. By comparing the shapes of covalently closed, nicked and gapped DNA minicircles, we conclude that 94-bp-long covalently closed and nicked DNA minicircles do not show sharp kinks while gapped DNA molecules, containing very flexible single-stranded regions, do show sharp kinks. We corroborate the results of cryo-EM studies by using Bal31 nuclease to probe for the existence of kinks in 94-bp-long minicircles.

Probabilistic base calling of Solexa sequencing data.

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.

3D reconstruction and comparison of shapes of DNA minicircles observed by cryo-electron microscopy.

We use cryo-electron microscopy to compare 3D shapes of 158 bp long DNA minicircles that differ only in the sequence within an 18 bp block containing either a TATA box or a catabolite activator protein binding site. We present a sorting algorithm that correlates the reconstructed shapes and groups them into distinct categories. We conclude that the presence of the TATA box sequence, which is believed to be easily bent, does not significantly affect the observed shapes.