Weiqing Wang

The RAF inhibitor PLX4032 inhibits ERK signaling and tumor cell proliferation in a V600E BRAF-selective manner

Tumors with mutant BRAF and some with mutant RAS are dependent upon ERK signaling for proliferation, and their growth is suppressed by MAPK/ERK kinase (MEK) inhibitors. In contrast, tumor cells with human EGF receptor (HER) kinase activation proliferate in a MEK-independent manner. These findings have led to the development of RAF and MEK inhibitors as anticancer agents. Like MEK inhibitors, the RAF inhibitor PLX4032 inhibits the proliferation of BRAFV600E tumor cells but not that of HER kinase-dependent tumors. However, tumors with RAS mutation that are sensitive to MEK inhibition are insensitive to PLX4032. MEK inhibitors inhibit ERK phosphorylation in all normal and tumor cells, whereas PLX4032 inhibits ERK signaling only in tumor cells expressing BRAFV600E. In contrast, the drug activates MEK and ERK phosphorylation in cells with wild-type BRAF. In BRAFV600E tumor cells, MEK and RAF inhibitors affect the expression of a common set of genes. PLX4032 inhibits ERK signaling output in mutant BRAF cells, whereas it transiently activates the expression of these genes in tumor cells with wild-type RAF. Thus, PLX4032 inhibits ERK signaling output in a mutant BRAF-selective manner. These data explain why the drug selectively inhibits the growth of mutant BRAF tumors and suggest that it will not cause toxicity resulting from the inhibition of ERK signaling in normal cells. This selectivity may lead to a broader therapeutic index and help explain the greater antitumor activity observed with this drug than with MEK inhibitors.

Models from experiments: combinatorial drug perturbations of cancer cells

We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built‐in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho‐proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.

Off-target effects dominate a large-scale RNAi screen for modulators of the TGF-β pathway and reveal microRNA regulation of TGFBR2

BackgroundRNA interference (RNAi) screens have been used to identify novel components of signal-transduction pathways in a variety of organisms. We performed a small interfering (si)RNA screen for novel members of the transforming growth factor (TGF)-β pathway in a human keratinocyte cell line. The TGF-β pathway is integral to mammalian cell proliferation and survival, and aberrant TGF-β responses have been strongly implicated in cancer.ResultsWe assayed how strongly single siRNAs targeting each of 6,000 genes affect the nuclear translocation of a green fluorescent protein (GFP)-SMAD2 reporter fusion protein. Surprisingly, we found no novel TGF-β pathway members, but we did find dominant off-target effects. All siRNA hits, whatever their intended direct target, reduced the mRNA levels of two known upstream pathway components, the TGF-β receptors 1 and 2 (TGFBR1 and TGFBR2), via micro (mi)RNA-like off-target effects. The scale of these off-target effects was remarkable, with at least 1% of the sequences in the unbiased siRNA library having measurable off-target effects on one of these two genes. It seems that relatively minor reductions of message levels via off-target effects can have dominant effects on an assay, if the pathway output is very dose-sensitive to levels of particular pathway components. In search of mechanistic details, we identified multiple miRNA-like sequence characteristics that correlated with the off-target effects. Based on these results, we identified miR-20a, miR-34a and miR-373 as miRNAs that inhibit TGFBR2 expression.ConclusionsOur findings point to potential improvements for miRNA/siRNA target prediction methods, and suggest that the type II TGF-β receptor is regulated by multiple miRNAs. We also conclude that the risk of obtaining misleading results in siRNA screens using large libraries with single-assay readout is substantial. Control and rescue experiments are essential in the interpretation of such screens, and improvements to the methods to reduce or predict RNAi off-target effects would be beneficial.

Perturbation biology: inferring signaling networks in cellular systems.

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs. DOI: http://dx.doi.org/10.7554/eLife.04640.001

Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles

Motivation: Somatic homozygous deletions of chromosomal regions in cancer, while not necessarily oncogenic, may lead to therapeutic vulnerabilities specific to cancer cells compared with normal cells. A recently reported example is the loss of one of the two isoenzymes in glioblastoma cancer cells such that the use of a specific inhibitor selectively inhibited growth of the cancer cells, which had become fully dependent on the second isoenzyme. We have now made use of the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer Genome Atlas (TCGA) and of tumor-derived cell lines in the Cancer Cell Line Encyclopedia, as well as the availability of integrated pathway information systems, such as Pathway Commons, to systematically search for a comprehensive set of such epistatic vulnerabilities. Results: Based on homozygous deletions affecting metabolic enzymes in 16 TCGA cancer studies and 972 cancer cell lines, we identified 4104 candidate metabolic vulnerabilities present in 1019 tumor samples and 482 cell lines. Up to 44% of these vulnerabilities can be targeted with at least one Food and Drug Administration-approved drug. We suggest focused experiments to test these vulnerabilities and clinical trials based on personalized genomic profiles of those that pass preclinical filters. We conclude that genomic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy. Availability and implementation: A web-based tool for exploring all vulnerabilities and their details is available at http://cbio.mskcc.org/cancergenomics/statius/ along with supplemental data files. Contact: statius@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online.

Spatial Normalization of Reverse Phase Protein Array Data

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src.

PiHelper: an open source framework for drug-target and antibody-target data

Motivation: The interaction between drugs and their targets, often proteins, and between antibodies and their targets, is important for planning and analyzing investigational and therapeutic interventions in many biological systems. Although drug-target and antibody-target datasets are available in separate databases, they are not publicly available in an integrated bioinformatics resource. As medical therapeutics, especially in cancer, increasingly uses targeted drugs and measures their effects on biomolecular profiles, there is an unmet need for a user-friendly toolset that allows researchers to comprehensively and conveniently access and query information about drugs, antibodies and their targets. Summary: The PiHelper framework integrates human drug-target and antibody-target associations from publicly available resources to help meet the needs of researchers in systems pharmacology, perturbation biology and proteomics. PiHelper has utilities to (i) import drug- and antibody-target information; (ii) search the associations either programmatically or through a web user interface (UI); (iii) visualize the data interactively in a network; and (iv) export relationships for use in publications or other analysis tools. Availability: PiHelper is a free software under the GNU Lesser General Public License (LGPL) v3.0. Source code and documentation are at http://bit.ly/pihelper. We plan to coordinate contributions from the community by managing future releases. Contact: pihelper@cbio.mskcc.org

Perturbation biology models predict c-Myc as an effective co-target in RAF inhibitor resistant melanoma cells

Systematic prediction of cellular response to perturbations is a central challenge in biology, both for mechanistic explanations and for the design of effective therapeutic interventions. We addressed this challenge using a computational/experimental method, termed perturbation biology, which combines high-throughput (phospho)proteomic and phenotypic response profiles to targeted perturbations, prior information from signaling databases and network inference algorithms from statistical physics. The resulting network models are computationally executed to predict the effects of tens of thousands of untested perturbations. We report cell type-specific network models of signaling in RAF-inhibitor resistant melanoma cells based on data from 89 combinatorial perturbation conditions and 143 readouts per condition. Quantitative simulations predicted c-Myc as an effective co-target with BRAF or MEK. Experiments showed that co-targeting c-Myc, using the BET bromodomain inhibitor JQ1, and the RAF/MEK pathway, using kinase inhibitors is both effective and synergistic in this context. We propose these combinations as pre-clinical candidates to prevent or overcome RAF inhibitor resistance in melanoma.

Protein Profiling In Cancer Cell Lines And Tumor Tissue Using Reverse Phase Protein Arrays

Reverse phase protein array (RPPA) technology is an antibody-based high-throughput assay for protein profiling of biological specimens that allows for many measurements with very small amounts of cell lysate. Here, we report the sensitivity, reproducibility, and accuracy of a particular RPPA platform called Zeptosens. We customized the RPPA protocol for our in-house setup, and measured more than 80 total protein and phospho-protein levels in various cancer samples, including cell lines, organoids, tumor chunks, core needle biopsies, and laser-capture microdissected tissue samples. We discuss pros and cons of the RPPA platform, and describe results from profiling 15 cancer cell line cells using RPPA.