Use of preclinical models for malignant pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is an aggressive cancer most commonly caused by prior exposure to asbestos. Median survival is 12-18 months, since surgery is ineffective and chemotherapy offers minimal benefit. Preclinical models that faithfully recapitulate the genomic and histopathological features of cancer are critical for the development of new treatments. The most commonly used models of MPM are two-dimensional cell lines established from primary tumours or pleural fluid. While these have provided some important insights into MPM biology, these cell models have significant limitations. In order to address some of these limitations, spheroids and microfluidic chips have more recently been used to investigate the role of the three-dimensional environment in MPM. Efforts have also been made to develop animal models of MPM, including asbestos-induced murine tumour models, MPM-prone genetically modified mice and patient-derived xenografts. Here, we discuss the available in vitro and in vivo models of MPM and highlight their strengths and limitations. We discuss how newer technologies, such as the tumour-derived organoids, might allow us to address the limitations of existing models and aid in the identification of effective treatments for this challenging-to-treat disease.MS and JO are supported by BLF-Papworth Fellowships from the British Lung Foundation and the Victor Dahdaleh Foundation. MJG and HEF is supported by the British Lung Foundation and Wellcome Trust grant 206194. RCR is supported by the Cambridge Biomedical Research Centre, Cancer Research UK Cambridge Centre, British Lung Foundation and Royal Papworth Hospital. SJM is supported by the Medical Research Council, British Lung Foundation, Cambridge BRC, Royal Papworth Hospital, and the Alpha1-Foundatio

Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity

How Prosecutors and Defense Attorneys Differ in Their Use of Neuroscience Evidence

Much of the public debate surrounding the intersection of neuroscience and criminal law is based on assumptions about how prosecutors and defense attorneys differ in their use of neuroscience evidence. For example, according to some commentators, the defense’s use of neuroscience evidence will abdicate criminals of all responsibility for their offenses. In contrast, the prosecution’s use of that same evidence will unfairly punish the most vulnerable defendants as unfixable future dangers to society. This “double- edged sword” view of neuroscience evidence is important for flagging concerns about the law’s construction of criminal responsibility and punishment: it demonstrates that the same information about the defendant can either be mitigating or aggravating depending on who is raising it. Yet empirical assessments of legal decisions reveal a far more nuanced reality, showing that public beliefs about the impact of neuroscience on the criminal law can often be wrong. This Article takes an evidence-based and multidisciplinary approach to examining how courts respond to neuroscience evidence in capital cases when the defense presents it to argue that the defendant’s mental state at the time of the crime was below the given legal requisite due to some neurologic or cognitive deficiency

LIM kinase inhibitors disrupt mitotic microtubule organization and impair tumor cell proliferation

The actin and microtubule cytoskeletons are critically important for cancer cell proliferation, and drugs that target microtubules are widely-used cancer therapies. However, their utility is compromised by toxicities due to dose and exposure. To overcome these issues, we characterized how inhibition of the actin and microtubule cytoskeleton regulatory LIM kinases could be used in drug combinations to increase efficacy. A previously-described LIMK inhibitor (LIMKi) induced dose-dependent microtubule alterations that resulted in significant mitotic defects, and increased the cytotoxic potency of microtubule polymerization inhibitors. By combining LIMKi with 366 compounds from the GSK Published Kinase Inhibitor Set, effective combinations were identified with kinase inhibitors including EGFR, p38 and Raf. These findings encouraged a drug discovery effort that led to development of CRT0105446 and CRT0105950, which potently block LIMK1 and LIMK2 activity in vitro, and inhibit cofilin phosphorylation and increase αTubulin acetylation in cells. CRT0105446 and CRT0105950 were screened against 656 cancer cell lines, and rhabdomyosarcoma, neuroblastoma and kidney cancer cells were identified as significantly sensitive to both LIMK inhibitors. These large-scale screens have identified effective LIMK inhibitor drug combinations and sensitive cancer types. In addition, the LIMK inhibitory compounds CRT0105446 and CRT0105950 will enable further development of LIMK-targeted cancer therapy

Combinations of PARP Inhibitors with Temozolomide Drive PARP1 Trapping and Apoptosis in Ewing's Sarcoma.

Ewing's sarcoma is a malignant pediatric bone tumor with a poor prognosis for patients with metastatic or recurrent disease. Ewing's sarcoma cells are acutely hypersensitive to poly (ADP-ribose) polymerase (PARP) inhibition and this is being evaluated in clinical trials, although the mechanism of hypersensitivity has not been directly addressed. PARP inhibitors have efficacy in tumors with BRCA1/2 mutations, which confer deficiency in DNA double-strand break (DSB) repair by homologous recombination (HR). This drives dependence on PARP1/2 due to their function in DNA single-strand break (SSB) repair. PARP inhibitors are also cytotoxic through inhibiting PARP1/2 auto-PARylation, blocking PARP1/2 release from substrate DNA. Here, we show that PARP inhibitor sensitivity in Ewing's sarcoma cells is not through an apparent defect in DNA repair by HR, but through hypersensitivity to trapped PARP1-DNA complexes. This drives accumulation of DNA damage during replication, ultimately leading to apoptosis. We also show that the activity of PARP inhibitors is potentiated by temozolomide in Ewing's sarcoma cells and is associated with enhanced trapping of PARP1-DNA complexes. Furthermore, through mining of large-scale drug sensitivity datasets, we identify a subset of glioma, neuroblastoma and melanoma cell lines as hypersensitive to the combination of temozolomide and PARP inhibition, potentially identifying new avenues for therapeutic intervention. These data provide insights into the anti-cancer activity of PARP inhibitors with implications for the design of treatment for Ewing's sarcoma patients with PARP inhibitors.Research in the M.J.G. laboratory is supported by grants from the Wellcome Trust (086357 and 102696/Z/13/Z; http://www.wellcome.ac.uk/Funding). Research in the S.P.J. laboratory is funded by Cancer Research UK Program Grant C6/A11224 (http://www.cancerresearchuk.org/funding-for-researchers/our-funding-schemes), the European Research Council (http://erc.europa.eu/funding-and-grants)and the European Community Seventh Framework Program grant agreement no. HEALTH-F2-2010-259893 (DDResponse). Core infrastructure funding was provided by Cancer Research UK Grant C6946/A14492 and Wellcome Trust Grant WT092096. S.P.J. receives a salary from the University of Cambridge, supplemented by Cancer Research UK. J.T. was funded by the European Community Seventh Framework Program grant agreement no. HEALTH-F2-2010-259893 (DDResponse). U.M. is supported by a Cancer Research UK Clinician Scientist Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.014098

Thresholds for identifying pathological intracranial pressure in paediatric traumatic brain injury.

Intracranial pressure (ICP) monitoring forms an integral part of the management of severe traumatic brain injury (TBI) in children. The prediction of elevated ICP from imaging is important when deciding on whether to implement invasive ICP monitoring for a patient. However, the radiological markers of pathologically elevated ICP have not been specifically validated in paediatric studies. Here in, we describe an objective, non-invasive, quantitative method of stratifying which patients are likely to require invasive monitoring. A retrospective review of patients admitted to Cambridge University Hospital's Paediatric Intensive Care Unit between January 2009 and December 2016 with a TBI requiring invasive neurosurgical monitoring was performed. Radiological biomarkers of TBI (basal cistern volume, ventricular volume, volume of extra-axial haematomas) from CT scans were measured and correlated with epochs of continuous high frequency variables of pressure monitoring around the time of imaging. 38 patients were identified. Basal cistern volume was found to correlate significantly with opening ICP (r = -0.53, p < 0.001). The optimal threshold of basal cistern volume for predicting high ICP ([Formula: see text]20 mmHg) was a relative volume of 0.0055 (sensitivity 79%, specificity 80%). Ventricular volume and extra-axial haematoma volume did not correlate significantly with opening ICP. Our results show that the features of pathologically elevated ICP in children may differ considerably from those validated in adults. The development of quantitative parameters can help to predict which patients would most benefit from invasive neurosurgical monitoring and we present a novel radiological threshold for this.We gratefully acknowledge financial support as follows. Research support: the Medical Research Council (MRC, Grant Nos. G0600986 ID79068 and G1002277 ID98489) and the National Institute for Health Research Biomedical Research Centre (NIHR BRC) Cambridge (Neuroscience Theme; Brain Injury and Repair Theme). Authors’ support: Peter J Hutchinson – NIHR Research Professorship, Academy of Medical Sciences/Health Foundation Senior Surgical Scientist Fellowship, NIHR Global Health Research Group on Neurotrauma, and NIHR Cambridge BRC. Joseph Donnelly is supported by a Woolf Fisher Scholarship. MC- NIHR BRC

Genomics of Drug Sensitivity in Cancer (GDSC): a Resource for Therapeutic Biomarker Discovery in Cancer Cells

Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies

Combinatorial CRISPR screen identifies fitness effects of gene paralogues.

Genetic redundancy has evolved as a way for human cells to survive the loss of genes that are single copy and essential in other organisms, but also allows tumours to survive despite having highly rearranged genomes. In this study we CRISPR screen 1191 gene pairs, including paralogues and known and predicted synthetic lethal interactions to identify 105 gene combinations whose co-disruption results in a loss of cellular fitness. 27 pairs influence fitness across multiple cell lines including the paralogues FAM50A/FAM50B, two genes of unknown function. Silencing of FAM50B occurs across a range of tumour types and in this context disruption of FAM50A reduces cellular fitness whilst promoting micronucleus formation and extensive perturbation of transcriptional programmes. Our studies reveal the fitness effects of FAM50A/FAM50B in cancer cells

Limited release of previously-frozen C and increased new peat formation after thaw in permafrost peatlands

Permafrost stores globally significant amounts of carbon (C) which may start to decompose and be released to the atmosphere in form of carbon dioxide (CO 2 ) and methane (CH 4 ) as global warming promotes extensive thaw. This permafrost carbon feedback to climate is currently considered to be the most important carbon-cycle feedback missing from climate models. Predicting the magnitude of the feedback requires a better understanding of how differences in environmental conditions post-thaw, particularly hydrological conditions, control the rate at which C is released to the atmosphere. In the sporadic and discontinuous permafrost regions of north-west Canada, we measured the rates and sources of C released from relatively undisturbed ecosystems, and compared these with forests experiencing thaw following wildfire (well-drained, oxic conditions) and collapsing peat plateau sites (water-logged, anoxic conditions). Using radiocarbon analyses, we detected substantial contributions of deep soil layers and/or previously-frozen sources in our well-drained sites. In contrast, no loss of previously-frozen C as CO 2 was detected on average from collapsed peat plateaus regardless of time since thaw and despite the much larger stores of available C that were exposed. Furthermore, greater rates of new peat formation resulted in these soils becoming stronger C sinks and this greater rate of uptake appeared to compensate for a large proportion of the increase in CH 4 emissions from the collapse wetlands. We conclude that in the ecosystems we studied, changes in soil moisture and oxygen availability may be even more important than previously predicted in determining the effect of permafrost thaw on ecosystem C balance and, thus, it is essential to monitor, and simulate accurately, regional changes in surface wetness

Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets.

Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyze data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger Institutes. Despite significant differences in experimental protocols and reagents, we find that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one data set are recovered in the other. Through further analysis and replication experiments at each institute, we show that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings