Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.     

Characterization and the broad cross‐species applicability of 20 anonymous nuclear loci isolated from the Taiwan Hwamei (Garrulax taewanus)

We isolated 20 anonymous nuclear loci (8556 bp in total) from the Taiwan Hwamei (Garrulax taewanus), an endemic songbird of Taiwan. A panel of nine to 15 individuals with unknown relationship was used to characterize polymorphism of these loci. We identified 46 single nucleotide polymorphic sites (SNPs) in 15 polymorphic loci. Frequency of SNPs was one per every 186 bp in average. Nucleotide diversity, θ, ranged from 0.00054 to 0.00371 per locus. We also tested cross‐species applicability of these loci on 17 species from eight different passerine families. All 20 loci could be successfully amplified (ranged from one to 16 species, mean = 7.9 species).     

Effect of neurotensin on pancreatic function in man

The effect of neurotensin on submaximally-stimulated hepatobiliary and pancreatic secretion was studied in 6 healthy subjects. An intravenous infusion of neurotensin 1.4 ± 0.3 pmol/kg/min, designed to reproduce plasma neurotensin immunoreactivity levels within the physiological range, produced a significant increase in pancreatic bicarbonate output. Plasma concentrations of pancreatic polypeptide rose by 83 ± 16 pmol/l and were associated with a small reduction in trypsin, but no significant change in bilirubin outputs.     

Seasonality of Rotifers and Temperature in Lough Neagh,N. Ireland

Long runs of seasonal rotifer population data allow analysis of seasonal occurrence using mathematical tools. The application of Fourier analysis to a 15 year dataset describes seasonality in simple mathematical terms. This facilitates comparison of population expression with potential population driving variables and provides a basic modelling tool. Results show that annual patterns of occurrence and density have linkages with annual maximum and minimum environmental temperature, although the exact relationships are not clear.     

Takayasu's arteritis is associated with HLA-B*52, but not with HLA-B*51, in Turkey

Introduction

HLA-B*51 and HLA-B*52 are two close human leukocyte antigen (HLA) allele groups with minor amino acid differences. However, they are associated with two different vasculitides (HLA-B*51 in Behçet's disease and HLA-B*52 in Takayasu's arteritis (TAK)) and with major clinical and immunological differences. In this study, we aimed to screen a large cohort of TAK patients from Turkey for the presence of HLA-B*51 and HLA-B*52 as susceptibility and severity factors.

Methods

TAK patients (n = 330) followed at a total of 15 centers were included in the study. The mean age of the patients was 37.8 years, and 86% were women. DNA samples from the patients and healthy controls (HC; n = 210) were isolated, and the presence of HLA-B*51 or HLA-B*52 was screened for by using PCR with sequence-specific primers.

Results

We found a significant association of HLA-B*52 with TAK (20.9% vs HC = 6.7%, P = 0.000, OR = 3.7, 95% CI = 2.02 to 6.77). The distribution of HLA-B*51 did not differ between TAK patients and HCs (22.7% vs 24.8%, OR = 0.9, 95% CI = 0.60 to 1.34). The presence of HLA-B*52 decreased in late-onset patients (> 40 years of age; 12.0%, P = 0.024, OR = 0.43, 95% CI = 0.20 to 0.91). Patients with angiographic type I disease with limited aortic involvement also had a lower presence of HLA-B*52 compared to those with all other disease subtypes (13.1% vs 26%, P = 0.005, OR = 0.43, 95% CI = 0.23 to 0.78).

Conclusions

In this study, the previously reported association of TAK with HLA-B*52 in other populations was confirmed in patients from Turkey. The functional relevance of HLA-B*52 in TAK pathogenesis needs to be explored further.     

Myoglobin as a test case in the construction of space-filling models of proteins using dihedral angles

A method for construction of space-filling models of proteins of improved accuracy was developed from the dihedral angle technique proposed by Hauschka and Segal (P. V. Hauschka and D. M. Segal, Biopolymers, 4 , 1051, 1966). Tools were designed to facilitate the marking of accurate reference lines on component atoms and a simple method for cementing the fixed angles was devised. The torsion angle method was extended to include all side chains. The improved technique was used to assemble a model of sperm whale metmyoglobin from the x-ray data.     

Bayesian calibration of a stochastic,multiscale agent-based model for predicting in vitro tumor growth

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.