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Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p  < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies. Artificial intelligence and machine learning promise to transform cancer therapies by accurately predicting the most appropriate drugs to treat individual patients. Here, the authors present an approach which uses omics data to produce ordered lists of drugs based on their effectiveness in decreasing cancer cell proliferation.

Aurora B kinase (AURKB) inhibitors have been trialled in a range of different tumour types but are not approved for any indication. Expression of the human papilloma virus (HPV) oncogenes and loss of retinoblastoma (RB) protein function has been reported to increase sensitivity to AURKB inhibitors but the mechanism of their contribution to sensitivity is poorly understood. Two commonly reported outcomes of AURKB inhibition are polyploidy and senescence, although their relationship is unclear. Here we have investigated the major cellular targets of the HPV E6 and E7, p53 and RB, to determine their contribution to AURKB inhibitor induced polyploidy and senescence. We demonstrate that polyploidy is a universal feature of AURKB inhibitor treatment in all cell types including normal primary cells, but the subsequent outcomes are controlled by RB and p53. We demonstrate that p53 by regulating p21 expression is required for an initial cell cycle arrest by inhibiting both CDK2 and CDK4 activity, but this arrest is only triggered after cells have undergone two failed mitosis and cytokinesis. However, cells can enter senescence in the absence of p53. RB is essential for AURKB inhibitor-induced senescence. AURKB inhibitor induces rapid hypophosphorylation of RB independent of inhibition of CDK2 or CDK4 kinases and p53. This work demonstrates that p53 activation determines the timing of senescence onset, but RB is indispensable for senescence.

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.

Polyploidy is a common outcome of chemotherapies, but there is conflicting evidence as to whether polyploidy is an adverse, benign or even favourable outcome. We show Aurora B kinase inhibitors efficiently promote polyploidy in many cell types, resulting in the cell cycle exit in RB and p53 functional cells, but hyper-polyploidy in cells with loss of RB and p53 function. These hyper-polyploid cells (>8n DNA content) are viable but have lost long-term proliferative potential in vitro and fail to form tumours in vivo. Investigation of mitosis in these cells revealed high numbers of centrosomes that were capable of supporting functional mitotic spindle poles, but these failed to progress to anaphase/telophase structures even when AURKB inhibitor was removed after 2–3 days. However, when AURKB inhibitor was removed after 1 day and cells had failed a single cytokinesis to become tetraploid, they retained colony forming ability and long-term proliferative potential. Mathematical modelling of the potential for polyploid cells to produce viable daughter cells demonstrated that cells with >8n DNA and >4 functional spindle poles approach zero probability of a viable daughter, supporting our experimental observations. These findings demonstrate that tetraploidy is tolerated by tumour cells, but higher ploidy states are incompatible with long-term proliferative potential. Model for AURKBi driven hyper-polyploid cells formation and fate. Aurora B inhibitor (AURKBi) treatment of RB+p53 defective cells efficiently promotes failed cell division. One failed cell division produces three possible outcomes, continued proliferation of the tetraploid daughter, cell death, or if AURKBi is continued, high polyploid states. Once cell have failed cell division >twice and have >8n DNA content they will continue to undergo rounds of endomitosis even in the absence of AURKBi to either become viable hyper-polyploid or die. The hyper-polyploid cells have no long-term proliferative potential.

Drug-induced cytopenias are a prevalent and significant issue that worsens clinical outcomes and hinders the effective treatment of cancer. While reductions in blood cell numbers are classically associated with traditional cytotoxic chemotherapies, they also occur with newer targeted small molecules and the factors that determine the hematotoxicity profiles of oncologic drugs are not fully understood. Here, we explore why some Aurora kinase inhibitors cause preferential neutropenia. By studying drug responses of healthy human hematopoietic cells in vitro and analyzing existing gene expression datasets, we provide evidence that the enhanced vulnerability of neutrophil-lineage cells to Aurora kinase inhibition is caused by early developmental changes in ATP-binding cassette (ABC) transporter expression. These data show that hematopoietic cell-intrinsic expression of ABC transporters may be an important factor that determines how some Aurora kinase inhibitors affect the bone marrow. Patients treated with Aurora kinase inhibitors experience dose-limiting neutropenia while other cytopenias are rare. Here, Chou et al . show that this cell-type specific side effect is partly explained by loss of drug efflux pump expression during neutrophil differentiation.

Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line. Viruses are like the Trojan horses of the biological world; they sneak their genetic code into a living cell and then hijack it, forcing that cell to produce their own viral proteins. Yet, if scientists replace the harmful genes in a virus with other genes, the virus can be transformed into a powerful tool for biology and medical science. For example, viruses can be turned into vaccines that prime the immune system to ward off future infections. Viruses could also be made to deliver the genetic code needed to repair faulty cells, and thus treat the cause of an illness from inside the body. Nevertheless, it is complicated to produce viruses like these on a large scale. The individual viruses in one batch can be very different shapes and sizes; they can also end up displaying different proteins on their outer surface – which is the part of the virus that our immune system will see first. To optimise the production of standardised viruses, scientists need a way to test the viruses throughout the manufacture process. At the moment, the best way to do this would be with electron microscopes. Yet these microscopes cannot tell exactly which proteins are in the outer surface of the virus. Also, these methods often need purified samples of virus, so cannot be used to look at the viruses until the final stage of production. Laine et al. now report a method that can test virus production at every step of the process. This new method uses a different type of microscopy called super-resolution imaging, which is quicker than electron microscopy and more able to deal with impurities, but can still see objects that are 500 times smaller than the width of a human hair. First, Laine et al. took pictures of many viruses with this new imaging technique, sorted the images into groups based on their appearance, and then trained computer algorithms with the pre-sorted groups (a technique called “supervised learning”). Next, the trained algorithms were shown new images of viruses and asked to classify them. The algorithms could separate images of a mixed population of viruses into six groups according to their shape and size, and then analyse each group in a specific way. For example, they would measure and report the length of filament-shaped viruses, the radius of spherical viruses and the length and width of rod-shaped viruses. The first set of test images were of Newcastle Disease Virus, which is currently under development as a treatment for cancer. But further testing revealed that the algorithm also works for the influenza virus, which is used to make flu vaccines. The algorithm could classify the viruses in pure and impure samples, and the imaging technique could handle over 200 viruses each second. This approach of combining super-resolution imaging with artificial intelligence could help scientists to understand what makes good vaccines and how best to optimise the production of viruses for medical purposes. It could also allow researchers to respond more rapidly to outbreaks of viral infections. The next step is to build this work into a system that can be used by the pharmaceutical industry.

XRCC4 and XLF are two structurally related proteins that function in DNA double-strand break (DSB) repair. Here, we identify human PAXX (PAralog of XRCC4 and XLF, also called C9orf142) as a new XRCC4 superfamily member and show that its crystal structure resembles that of XRCC4. PAXX interacts directly with the DSB-repair protein Ku and is recruited to DNA-damage sites in cells. Using RNA interference and CRISPR-Cas9 to generate PAXX–/– cells, we demonstrate that PAXX functions with XRCC4 and XLF to mediate DSB repair and cell survival in response to DSB-inducing agents. Finally, we reveal that PAXX promotes Ku-dependent DNA ligation in vitro and assembly of core nonhomologous end-joining (NHEJ) factors on damaged chromatin in cells. These findings identify PAXX as a new component of the NHEJ machinery.

Background Drugs targeting the spindle assembly checkpoint (SAC), such as inhibitors of Aurora kinase B (AURKB) and dual specific protein kinase TTK, are in different stages of clinical development. However, cell response to SAC abrogation is poorly understood and there are no markers for patient selection. Methods A panel of 53 tumor cell lines of different origins was used. The effects of drugs were analyzed by MTT and flow cytometry. Copy number status was determined by FISH and Q-PCR; mRNA expression by nCounter and RT-Q-PCR and protein expression by Western blotting. CRISPR-Cas9 technology was used for gene knock-out (KO) and a doxycycline-inducible pTRIPZ vector for ectopic expression. Finally, in vivo experiments were performed by implanting cultured cells or fragments of tumors into immunodeficient mice. Results Tumor cells and patient-derived xenografts (PDXs) sensitive to AURKB and TTK inhibitors consistently showed high expression levels of BH3-interacting domain death agonist (BID), while cell lines and PDXs with low BID were uniformly resistant. Gene silencing rendered BID-overexpressing cells insensitive to SAC abrogation while ectopic BID expression in BID-low cells significantly increased sensitivity. SAC abrogation induced activation of CASP-2, leading to cleavage of CASP-3 and extensive cell death only in presence of high levels of BID. Finally, a prevalence study revealed high BID mRNA in 6% of human solid tumors. Conclusions The fate of tumor cells after SAC abrogation is driven by an AURKB/ CASP-2 signaling mechanism, regulated by BID levels. Our results pave the way to clinically explore SAC-targeting drugs in tumors with high BID expression.