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

The spectrum of cell death in sarcoma

The balance between cell death and cell survival is a highly coordinated process by which cells break down and remove unnecessary or harmful materials in a controlled, highly regulated, and compartmentalized manner. Cell exposure to various stresses, such as oxygen starvation, a lack of nutrients, or exposure to radiation, can initiate autophagy. Autophagy is a carefully orchestrated process with multiple steps, each regulated by specific genes and proteins. Autophagy proteins impact cellular maintenance and cell fate in response to stress, and targeting this process is one of the most promising methods of anti-tumor therapy. It is currently not fully understood how autophagy affects different types of tumor cells, which makes it challenging to predict outcomes when this process is manipulated. In this review, we will explore the mechanisms of autophagy and investigate it as a potential and promising therapeutic target for aggressive sarcomas

CYCLOPS simulations of actinomycin D and paclitaxel combinations.

<p><b>(a)</b> Actinomycin D dose-response are shown for malignant (left) and normal (right) cells. <b>(b)</b> Combination dose-response for actinomycin D+paclitaxel for simultaneous administration (24 hours) and <b>(c)</b> when delaying actinomycin D by 12 hours (malignant left, normal right). Arrows highlight magnitude of antagonistic effect when adding paclitaxel to 30nM actinomycin D.</p

Simulations of palbociclib and gemcitabine effects.

<p><b>(a)</b> palbociclib effects on malignant (left) and normal (right) at several concentrations (“proportion cells” correspond to the increase in number of cells i.e. 10 = 10 times more cells). <b>(b)</b> Modulation of the cell cycle distribution (left malignant, right normal, arrows indicate changes with increasing concentrations). <b>(c)</b> gemcitabine effects on malignant (left) and normal (right) at several concentrations. <b>(d)</b> Simulated dose-response surface for the palbociclib+gemcitabine combination. The arrow shows the rise of antagonistic effect with normal cells when increasing palbociclib concentration. <b>(e)</b> gemcitabine delayed administration protocol. (f) Effects of varying the time delay on normal (blue line, shown as % control) and malignant cells (red line). The ratio of normal to malignant is shown in green.</p

Simulation in CYCLOPS of cell cycle and ligand modulation.

<p>EGF stimulation of malignant <b>(a)</b> and normal cells <b>(b)</b>. “Proportion cells” correspond to the increase in number of cells i.e. 10 = 10 times more cells.</p

Properties of the cell lines modelled.

<p>Most values were from the ATCC website.[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005529#pcbi.1005529.ref055" target="_blank">55</a>] It should be noted that the cytokinetic properties of cell lines vary substantially according to culture medium, concentration of serum and growth factors, inoculum density and oxygen and CO₂ concentration. The values shown are the ones used in CYCLOPS and may be regarded as typical values for early-stage cultures at low cell density under standard levels of O2 and CO₂.</p

Diagram of global processes and signalling pathways modelled in CYCLOPS.

<p><b>(a)</b> Cell cycle partition, checkpoint and apoptosis process. <b>(b)</b> Signalling processes modelled in the G1-S and <b>(c)</b> spindle assembly checkpoints. <b>(d)</b> Signalling modelled in the MAP kinase and <b>(e)</b> apoptosis pathways.</p

Paclitaxel modulation of the cell cycle distribution.

<p>24hours paclitaxel treatment was simulated and the resulting cell cycle distribution is shown at 12, 24 and 27 hours (3 hours after wash-out) for <b>(a)</b> normal and <b>(b)</b> malignant cells. Plain bars show the reference cell cycle distribution prior to treatment while broken-line bars show the distribution evolution over time.</p

Matrix Drug Screen Identifies Synergistic Drug Combinations to Augment SMAC Mimetic Activity in Ovarian Cancer

Inhibitor of apoptosis (IAP) proteins are frequently upregulated in ovarian cancer, resulting in the evasion of apoptosis and enhanced cellular survival. Birinapant, a synthetic second mitochondrial activator of caspases (SMAC) mimetic, suppresses the functions of IAP proteins in order to enhance apoptotic pathways and facilitate tumor death. Despite on-target activity, however, pre-clinical trials of single-agent birinapant have exhibited minimal activity in the recurrent ovarian cancer setting. To augment the therapeutic potential of birinapant, we utilized a high-throughput screening matrix to identify synergistic drug combinations. Of those combinations identified, birinapant plus docetaxel was selected for further evaluation, given its remarkable synergy both in vitro and in vivo. We showed that this synergy results from multiple convergent pathways to include increased caspase activation, docetaxel-mediated TNF-&alpha; upregulation, alternative NF-kB signaling, and birinapant-induced microtubule stabilization. These findings provide a rationale for the integration of birinapant and docetaxel in a phase 2 clinical trial for recurrent ovarian cancer where treatment options are often limited and minimally effective