Ian S. Goldlust

High-throughput combinatorial screening identifies drugs that cooperate with ibrutinib to kill activated B-cell-like diffuse large B-cell lymphoma cells.

Significance The treatment of cancer is highly reliant on drug combinations. Next-generation, targeted therapeutics are demonstrating interesting single-agent activities in clinical trials; however, the discovery of companion drugs through iterative clinical trial-and-error is not a tenable mechanism to prioritize clinically important combinations for these agents. Herein we describe the results of a large, high-throughput combination screen of the Bruton’s tyrosine kinase inhibitor ibrutinib versus a library of nearly 500 approved and investigational drugs. Multiple ibrutinib combinations were discovered through this study that can be prioritized for clinical examination. The clinical development of drug combinations is typically achieved through trial-and-error or via insight gained through a detailed molecular understanding of dysregulated signaling pathways in a specific cancer type. Unbiased small-molecule combination (matrix) screening represents a high-throughput means to explore hundreds and even thousands of drug–drug pairs for potential investigation and translation. Here, we describe a high-throughput screening platform capable of testing compounds in pairwise matrix blocks for the rapid and systematic identification of synergistic, additive, and antagonistic drug combinations. We use this platform to define potential therapeutic combinations for the activated B-cell–like subtype (ABC) of diffuse large B-cell lymphoma (DLBCL). We identify drugs with synergy, additivity, and antagonism with the Bruton’s tyrosine kinase inhibitor ibrutinib, which targets the chronic active B-cell receptor signaling that characterizes ABC DLBCL. Ibrutinib interacted favorably with a wide range of compounds, including inhibitors of the PI3K-AKT-mammalian target of rapamycin signaling cascade, other B-cell receptor pathway inhibitors, Bcl-2 family inhibitors, and several components of chemotherapy that is the standard of care for DLBCL.

Say No to DMSO: Dimethylsulfoxide Inactivates Cisplatin, Carboplatin, and Other Platinum Complexes

The platinum drugs cisplatin, carboplatin, and oxaliplatin are highly utilized in the clinic and as a consequence are extensively studied in the laboratory setting. In this study, we examined the literature and found a significant number of studies (11%-34%) in prominent cancer journals utilizing cisplatin dissolved in DMSO. However, dissolving cisplatin in DMSO for laboratory-based studies results in ligand displacement and changes to the structure of the complex. We examined the effect of DMSO on platinum complexes, including cisplatin, carboplatin, and oxaliplatin, finding that DMSO reacted with the complexes, inhibited their cytotoxicity and their ability to initiate cell death. These results render a substantial portion of the literature on cisplatin uninterpretable. Raising awareness of this significant issue in the cancer biology community is critical, and we make recommendations on appropriate solvation of platinum drugs for research.

Subtelomeric hotspots of aberrant 5-hydroxymethylcytosine-mediated epigenetic modifications during reprogramming to pluripotency

Mammalian somatic cells can be directly reprogrammed into induced pluripotent stem cells (iPSCs) by introducing defined sets of transcription factors. Somatic cell reprogramming involves epigenomic reconfiguration, conferring iPSCs with characteristics similar to embryonic stem cells (ESCs). Human ESCs (hESCs) contain 5-hydroxymethylcytosine (5hmC), which is generated through the oxidation of 5-methylcytosine by the TET enzyme family. Here we show that 5hmC levels increase significantly during reprogramming to human iPSCs mainly owing to TET1 activation, and this hydroxymethylation change is critical for optimal epigenetic reprogramming, but does not compromise primed pluripotency. Compared with hESCs, we find that iPSCs tend to form large-scale (100 kb–1.3 Mb) aberrant reprogramming hotspots in subtelomeric regions, most of which exhibit incomplete hydroxymethylation on CG sites. Strikingly, these 5hmC aberrant hotspots largely coincide (∼ 80%) with aberrant iPSC–ESC non-CG methylation regions. Our results suggest that TET1-mediated 5hmC modification could contribute to the epigenetic variation of iPSCs and iPSC–hESC differences.

An automated fitting procedure and software for dose-response curves with multiphasic features

In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straight-forward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe dose-response curves with multiphasic features. The resulting general model enables interpreting each phase of the dose-response as an independent dose-dependent process. We developed an algorithm which automatically generates and ranks dose-response models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing dose-response curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 dose-response curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit dose-response curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data.

Mouse model implicates GNB3 duplication in a childhood obesity syndrome

Significance We describe a genomic disorder that causes obesity, intellectual disability, and seizures. Children with this syndrome carry an unbalanced chromosome translocation that results in the duplication of over 100 genes, including G protein β3 (GNB3). Although GNB3 polymorphisms have been associated with obesity, hypertension, and diabetes, the mechanism of GNB3 pathogenesis is unknown. We created a transgenic mouse model that carries a duplication of GNB3, weighs significantly more than wild-type mice, and has excess abdominal fat. GNB3 is highly expressed in the brain and may be important for signaling related to satiety and/or metabolism. Obesity is a highly heritable condition and a risk factor for other diseases, including type 2 diabetes, cardiovascular disease, hypertension, and cancer. Recently, genomic copy number variation (CNV) has been implicated in cases of early onset obesity that may be comorbid with intellectual disability. Here, we describe a recurrent CNV that causes a syndrome associated with intellectual disability, seizures, macrocephaly, and obesity. This unbalanced chromosome translocation leads to duplication of over 100 genes on chromosome 12, including the obesity candidate gene G protein β3 (GNB3). We generated a transgenic mouse model that carries an extra copy of GNB3, weighs significantly more than its wild-type littermates, and has excess intraabdominal fat accumulation. GNB3 is highly expressed in the brain, consistent with G-protein signaling involved in satiety and/or metabolism. These functional data connect GNB3 duplication and overexpression to elevated body mass index and provide evidence for a genetic syndrome caused by a recurrent CNV.

Density matters: comparison of array platforms for detection of copy-number variation and copy-neutral abnormalities

Purpose:A combination of oligonucleotide and single-nucleotide polymorphism probes on the same array platform can detect copy-number abnormalities and copy-neutral aberrations such as uniparental disomy and long stretches of homozygosity. The single-nucleotide polymorphism probe density in commercially available platforms varies widely, which may affect the detection of copy-neutral abnormalities.Methods:We evaluated the ability of array platforms with low (Oxford Gene Technology CytoSure ISCA uniparental disomy), mid-range (Agilent custom array), and high (Affymetrix CytoScan HD) single-nucleotide polymorphism probe density to detect copy-number variation, mosaicism, uniparental isodisomy, and absence of heterozygosity in 50 clinical samples.Results:All platforms reliably detected copy-number variation, mosaicism, and uniparental isodisomy; however, absence-of-heterozygosity detection varied significantly. The low-density array called absence-of-heterozygosity regions not confirmed by the other platforms and also overestimated the length of true absence-of-heterozygosity regions. Furthermore, the low- and mid-density platforms failed to detect some small absence-of-heterozygosity regions that were identified by the high-density platform.Conclusion:Variation in single-nucleotide polymorphism density can lead to major discrepancies in the detection of and confidence in copy-neutral abnormalities. Although suitable for uniparental disomy detection, copy-number plus single-nucleotide polymorphism arrays with 30,000 or fewer unique single-nucleotide polymorphism probes miscall absence-of-heterozygosity regions due to identity by descent.Genet Med 15 9, 706–712.Genetics in Medicine (2013); 15 9, 706–712. doi:10.1038/gim.2013.36

Type I cytokines synergize with oncogene inhibition to induce tumor growth arrest

Acquavella, Clever, and colleagues show that IFNγ and TNFα synergize with vemurafenib to induce tumor growth arrest, supporting further study of the intersection between immunologic and oncogenic signaling in cancer cells and of treatment strategies combining vemurafenib and T-cell–based immunotherapy. Both targeted inhibition of oncogenic driver mutations and immune-based therapies show efficacy in treatment of patients with metastatic cancer, but responses can be either short lived or incompletely effective. Oncogene inhibition can augment the efficacy of immune-based therapy, but mechanisms by which these two interventions might cooperate are incompletely resolved. Using a novel transplantable BRAFV600E-mutant murine melanoma model (SB-3123), we explored potential mechanisms of synergy between the selective BRAFV600E inhibitor vemurafenib and adoptive cell transfer (ACT)–based immunotherapy. We found that vemurafenib cooperated with ACT to delay melanoma progression without significantly affecting tumor infiltration or effector function of endogenous or adoptively transferred CD8+ T cells, as previously observed. Instead, we found that the T-cell cytokines IFNγ and TNFα synergized with vemurafenib to induce cell-cycle arrest of tumor cells in vitro. This combinatorial effect was recapitulated in human melanoma–derived cell lines and was restricted to cancers bearing a BRAFV600E mutation. Molecular profiling of treated SB-3123 indicated that the provision of vemurafenib promoted the sensitization of SB-3123 to the antiproliferative effects of T-cell effector cytokines. The unexpected finding that immune cytokines synergize with oncogene inhibitors to induce growth arrest has major implications for understanding cancer biology at the intersection of oncogenic and immune signaling and provides a basis for design of combinatorial therapeutic approaches for patients with metastatic cancer. Cancer Immunol Res; 3(1); 37–47. ©2014 AACR. See related commentary by Riddell, p. 23

Large-scale pharmacological profiling of 3D tumor models of cancer cells

The discovery of chemotherapeutic agents for the treatment of cancer commonly uses cell proliferation assays in which cells grow as two-dimensional (2D) monolayers. Compounds identified using 2D monolayer assays often fail to advance during clinical development, most likely because these assays do not reproduce the cellular complexity of tumors and their microenvironment in vivo. The use of three-dimensional (3D) cellular systems have been explored as enabling more predictive in vitro tumor models for drug discovery. To date, small-scale screens have demonstrated that pharmacological responses tend to differ between 2D and 3D cancer cell growth models. However, the limited scope of screens using 3D models has not provided a clear delineation of the cellular pathways and processes that differentially regulate cell survival and death in the different in vitro tumor models. Here we sought to further understand the differences in pharmacological responses between cancer tumor cells grown in different conditions by profiling a large collection of 1912 chemotherapeutic agents. We compared pharmacological responses obtained from cells cultured in traditional 2D monolayer conditions with those responses obtained from cells forming spheres versus cells already in 3D spheres. The target annotation of the compound library screened enabled the identification of those key cellular pathways and processes that when modulated by drugs induced cell death in all growth conditions or selectively in the different cell growth models. In addition, we also show that many of the compounds targeting these key cellular functions can be combined to produce synergistic cytotoxic effects, which in many cases differ in the magnitude of their synergism depending on the cellular model and cell type. The results from this work provide a high-throughput screening framework to profile the responses of drugs both as single agents and in pairwise combinations in 3D sphere models of cancer cells.

Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy

The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed “cyclotherapy”. Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs.

mQC: A Heuristic Quality-Control Metric for High-Throughput Drug Combination Screening.

Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most reported HTS QC metrics are designed for plate level or single well level analysis. With the advent of high throughput combination screening there is a need for QC metrics that quantify the quality of combination response matrices. We introduce a predictive, interpretable, matrix-level QC metric, mQC, based on a mix of data-derived and heuristic features. mQC accurately reproduces the expert assessment of combination response quality and correctly identifies unreliable response matrices that can lead to erroneous or misleading characterization of synergy. When combined with the plate-level QC metric, Z’, mQC provides a more appropriate determination of the quality of a drug combination screen. Retrospective analysis on a number of completed combination screens further shows that mQC is able to identify problematic screens whereas plate-level QC was not able to. In conclusion, our data indicates that mQC is a reliable QC filter that can be used to identify problematic drug combinations matrices and prevent further analysis on erroneously active combinations as well as for troubleshooting failed screens. The R source code of mQC is available at http://matrix.ncats.nih.gov/mQC.