Tumor-Induced IL-6 Reprograms Host Metabolism to Suppress Anti-tumor Immunity

In patients with cancer, the wasting syndrome, cachexia, is associated with caloric deficiency. Here, we describe tumor-induced alterations of the host metabolic response to caloric deficiency that cause intratumoral immune suppression. In pre-cachectic mice with transplanted colorectal cancer or autochthonous pancreatic ductal adenocarcinoma (PDA), we find that IL-6 reduces the hepatic ketogenic potential through suppression of PPARalpha, the transcriptional master regulator of ketogenesis. When these mice are challenged with caloric deficiency, the resulting relative hypoketonemia triggers a marked rise in glucocorticoid levels. Multiple intratumoral immune pathways are suppressed by this hormonal stress response. Moreover, administering corticosterone to elevate plasma corticosterone to a level that is lower than that occurring in cachectic mice abolishes the response of mouse PDA to an immunotherapy that has advanced to clinical trials. Therefore, tumor-induced IL-6 impairs the ketogenic response to reduced caloric intake, resulting in a systemic metabolic stress response that blocks anti-cancer immunotherapy.We also thank the University of Cambridge, Cancer Research UK, the CRUK Cambridge Institute Core Facilities, and Hutchison Whampoa Limited. This work was also supported by the Lustgarten Foundation for Pancreatic Cancer Research, the Ludwig Institute for Cancer Research, the NIHR Biomedical Research Centre, and the Cambridge ECMC. T.R.F. was supported by the Rosetrees Trust and the Cambridge School of Clinical Medicine’s MB/PhD Programme, T.J. was supported by the Wellcome Trust Translational Medicine and Therapeutics Programme and the University of Cambridge Department of Oncology (RJAG/076), C.M.C. was supported by the Cambridge University Hospitals NHS Foundation Trust, E.W.R. was supported by the CRI Irvington Postdoctoral Fellowship Program, and A.P.C. was supported by the Medical Research Council (MRC) Metabolic Diseases Unit (MRC_MC_UU_12012/1). D.T.F. is a Distinguished Scholar of the Lustgarten Foundation

Combenefit: an interactive platform for the analysis and visualization of drug combinations.

MOTIVATION: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. RESULTS: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. AVAILABILITY AND IMPLEMENTATION: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This work has been supported by the Cancer Research UK grant C14303/A17197This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/bioinformatics/btw23

Paclitaxel and CYC3, an aurora kinase A inhibitor, synergise in pancreatic cancer cells but not bone marrow precursor cells.

BACKGROUND: Amplification of aurora kinase A (AK-A) overrides the mitotic spindle assembly checkpoint, inducing resistance to taxanes. RNA interference targeting AK-A in human pancreatic cancer cell lines enhanced taxane chemosensitivity. In this study, a novel AK-A inhibitor, CYC3, was investigated in pancreatic cancer cell lines, in combination with paclitaxel. METHODS: Western blot, flow cytometry and immunostaining were used to investigate the specificity of CYC3. Sulforhodamine B staining, time-lapse microscopy and colony-formation assays were employed to evaluate the cytotoxic effect of CYC3 and paclitaxel. Human colony-forming unit of granulocyte and macrophage (CFU-GM) cells were used to compare the effect in tumour and normal tissue. RESULTS: CYC3 was shown to be a specific AK-A inhibitor. Three nanomolar paclitaxel (growth inhibition 50% (GI(50)) 3 nM in PANC-1, 5.1 nM in MIA PaCa-2) in combination with 1 μM CYC3 (GI(50) 1.1 μM in MIA PaCa2 and 2 μM in PANC-1) was synergistic in inhibiting pancreatic cell growth and causing mitotic arrest, achieving similar effects to 10-fold higher concentrations of paclitaxel (30 nM). In CFU-GM cells, the effect of the combination was simply additive, displaying significantly less myelotoxicity compared with high concentrations of paclitaxel (30 nM; 60-70% vs 100% inhibition). CONCLUSION: The combination of lower doses of paclitaxel and CYC3 merits further investigation with the potential for an improved therapeutic index in vivo

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

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.This work was funded by Cancer Research UK grant C14303/A17197.This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/srep1470

Combenefit: an interactive platform for the analysis and visualization of drug combinations.

Motivation: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. Results: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. Availability and Implementation: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). Contact: [email protected]. Supplementary information:Supplementary data are available at Bioinformatics online

A quantitative FastFUCCI assay defines cell cycle dynamics at a single-cell level

The fluorescence ubiquitination-based cell cycle indicator (FUCCI) is a powerful tool for use in live cells but current FUCCI-based assays have limited throughput in terms of image processing and quantification. Here, we developed a lentiviral system that rapidly introduced FUCCI transgenes into cells by using an all-in-one expression cassette, FastFUCCI. The approach alleviated the need for sequential transduction and characterisation, improving labelling efficiency. We coupled the system to an automated imaging workflow capable of handling large datasets. The integrated assay enabled analyses of single-cell readouts at high spatiotemporal resolution. With the assay, we captured in detail the cell cycle alterations induced by antimitotic agents. We found that treated cells accumulated at G2 or M phase but eventually advanced through mitosis into the next interphase, where the majority of cell death occurred, irrespective of the preceding mitotic phenotype. Some cells appeared viable after mitotic slippage, and a fraction of them subsequently re-entered S phase. Accordingly, we found evidence that targeting the DNA replication origin activity sensitised cells to paclitaxel. In summary, we demonstrate the utility of the FastFUCCI assay for quantifying spatiotemporal dynamics and identify its potential in preclinical drug development.We acknowledge the support from the University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited

Cancer immunotherapy trial registrations increase exponentially but chronic immunosuppressive glucocorticoid therapy may compromise outcomes

This work was supported by the Wellcome Trust Translational Medicine and Therapeutics Programme [RJAG/076 to TJ], Cancer Research UK and the Cambridge Translational Medicine and Therapeutics Academic Clinical Fellowship Programme (to CMC)

Cancer Immunotherapy Trials Underutilize Immune Response Monitoring

Immune‐related radiological and biomarker monitoring in cancer immunotherapy trials permits interrogation of efficacy and reasons for therapeutic failure. We report the results from a cross‐sectional analysis of response monitoring in 685 T‐cell checkpoint‐targeted cancer immunotherapy trials in solid malignancies, as registered on the U.S. National Institutes of Health trial registry by October 2016. Immune‐related radiological response criteria were registered for only 25% of clinical trials. Only 38% of trials registered an exploratory immunological biomarker, and registration of immunological biomarkers has decreased over the last 15 years. We suggest that increasing the utilization of immune‐related response monitoring across cancer immunotherapy trials will improve analysis of outcomes and facilitate translational efforts to extend the benefit of immunotherapy to a greater proportion of patients with cancer.This work was supported by the Wellcome Trust Translational Medicine and Therapeutics Programme [RJAG/076 to T.J], by Cancer Research UK [C42738/A24868 to TJ], and the Cambridge Translational Medicine and Therapeutics Academic Clinical Fellowship Programme (C.M.C)

A Highly Porous Metal-Organic Framework System to Deliver Payloads for Gene Knockdown

© 2019 Elsevier Inc. Gene knockdown is an advantageous therapeutic strategy to lower dangerous genetic over-expression. However, the molecules responsible for initiating this process are unstable. Porous nanoparticles called metal-organic frameworks can encapsulate, protect, and deliver these compounds efficaciously without the need for chemical modifications—commonly done to enhance stability. By applying this platform technology, this work demonstrates the successful reduction in expression of a gene by avoiding retention and subsequent degradation in cellular compartments.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (NanoMOFdeli), ERC-2016-COG 726380, and (SUPUVIR) no. 722380. M.H.T. thanks the Gates Cambridge Trust for funding, S. Haddad for helpful discussions, and A. Li for assistance with data visualization. D.F.-J. thanks the Royal Society for funding through a University Research Fellowship. S.B.d.Q.F., F.M.R., and D.I.J. were funded by Cancer Research UK Senior Group Leader Grant CRUK/A15678. O.K.F. gratefully acknowledges DTRA for financial support (grant HDTRA-1-14-1-0014). C.F.K. acknowledges funding from the UK Engineering and Physical Sciences Research Council (grants EP/L015889/1 and EP/H018301/1), the Wellcome Trust (grants 3-3249/Z/16/Z and 089703/Z/09/Z) and the UK Medical Research Council (grants MR/K015850/1 and MR/K02292X/1), and Infinitus (China) Ltd. Computational work was supported by the Cambridge High Performance Computing Cluster, Darwin