Lawrence Bronk

Three-dimensional tissue culture based on magnetic cell levitation

Cell culture is an essential tool in drug discovery, tissue engineering and stem cell research. Conventional tissue culture produces two-dimensional cell growth with gene expression, signalling and morphology that can be different from those found in vivo, and this compromises its clinical relevance. Here, we report a three-dimensional tissue culture based on magnetic levitation of cells in the presence of a hydrogel consisting of gold, magnetic iron oxide nanoparticles and filamentous bacteriophage. By spatially controlling the magnetic field, the geometry of the cell mass can be manipulated, and multicellular clustering of different cell types in co-culture can be achieved. Magnetically levitated human glioblastoma cells showed similar protein expression profiles to those observed in human tumour xenografts. Taken together, these results indicate that levitated three-dimensional culture with magnetized phage-based hydrogels more closely recapitulates in vivo protein expression and may be more feasible for long-term multicellular studies.

Enhanced relative biological effectiveness of proton radiotherapy in tumor cells with internalized gold nanoparticles

The development and use of sensitizing agents to improve the effectiveness of radiotherapy have long been sought to improve our ability to treat cancer. In this letter, we have studied the relative biological effectiveness of proton beam radiotherapy on prostate tumor cells with and without internalized gold nanoparticles. The effectiveness of proton radiotherapy for the killing of prostate tumor cells was increased by approximately 15%-20% for those cells containing internalized gold nanoparticles.

Spatial mapping of the biologic effectiveness of scanned particle beams: towards biologically optimized particle therapy

The physical properties of particles used in radiation therapy, such as protons, have been well characterized, and their dose distributions are superior to photon-based treatments. However, proton therapy may also have inherent biologic advantages that have not been capitalized on. Unlike photon beams, the linear energy transfer (LET) and hence biologic effectiveness of particle beams varies along the beam path. Selective placement of areas of high effectiveness could enhance tumor cell kill and simultaneously spare normal tissues. However, previous methods for mapping spatial variations in biologic effectiveness are time-consuming and often yield inconsistent results with large uncertainties. Thus the data needed to accurately model relative biological effectiveness to guide novel treatment planning approaches are limited. We used Monte Carlo modeling and high-content automated clonogenic survival assays to spatially map the biologic effectiveness of scanned proton beams with high accuracy and throughput while minimizing biological uncertainties. We found that the relationship between cell kill, dose, and LET, is complex and non-unique. Measured biologic effects were substantially greater than in most previous reports, and non-linear surviving fraction response was observed even for the highest LET values. Extension of this approach could generate data needed to optimize proton therapy plans incorporating variable RBE.

From combinatorial peptide selection to drug prototype (II): Targeting the epidermal growth factor receptor pathway

The epidermal growth factor receptor (EGFR), a tyrosine kinase, is central to human tumorigenesis. Typically, three classes of drugs inhibit tyrosine kinase pathways: blocking antibodies, small kinase inhibitors, and soluble ligand receptor traps/decoys. Only the first two types of EGFR-binding inhibitory drugs are clinically available; notably, no EGFR decoy has yet been developed. Here we identify small molecules mimicking EGFR and that functionally behave as soluble decoys for EGF and TGFα, ligands that would otherwise activate downstream signaling. After combinatorial library selection on EGFR ligands, a panel of binding peptides was narrowed by structure–function analysis. The most active motif was CVRAC (EGFR 283–287), which is necessary and sufficient for specific EGFR ligand binding. Finally, a synthetic retro-inverted derivative, D(CARVC), became our preclinical prototype of choice. This study reveals an EGFR-decoy drug candidate with translational potential.

Analysis of the track- and dose-averaged LET and LET spectra in proton therapy using the GEANT4 Monte Carlo code

PURPOSE The motivation of this study was to find and eliminate the cause of errors in dose-averaged linear energy transfer (LET) calculations from therapeutic protons in small targets, such as biological cell layers, calculated using the geant 4 Monte Carlo code. Furthermore, the purpose was also to provide a recommendation to select an appropriate LET quantity from geant 4 simulations to correlate with biological effectiveness of therapeutic protons. METHODS The authors developed a particle tracking step based strategy to calculate the average LET quantities (track-averaged LET, LETt and dose-averaged LET, LETd) using geant 4 for different tracking step size limits. A step size limit refers to the maximally allowable tracking step length. The authors investigated how the tracking step size limit influenced the calculated LETt and LETd of protons with six different step limits ranging from 1 to 500 μm in a water phantom irradiated by a 79.7-MeV clinical proton beam. In addition, the authors analyzed the detailed stochastic energy deposition information including fluence spectra and dose spectra of the energy-deposition-per-step of protons. As a reference, the authors also calculated the averaged LET and analyzed the LET spectra combining the Monte Carlo method and the deterministic method. Relative biological effectiveness (RBE) calculations were performed to illustrate the impact of different LET calculation methods on the RBE-weighted dose. RESULTS Simulation results showed that the step limit effect was small for LETt but significant for LETd. This resulted from differences in the energy-deposition-per-step between the fluence spectra and dose spectra at different depths in the phantom. Using the Monte Carlo particle tracking method in geant 4 can result in incorrect LETd calculation results in the dose plateau region for small step limits. The erroneous LETd results can be attributed to the algorithm to determine fluctuations in energy deposition along the tracking step in geant 4. The incorrect LETd values lead to substantial differences in the calculated RBE. CONCLUSIONS When the geant 4 particle tracking method is used to calculate the average LET values within targets with a small step limit, such as smaller than 500 μm, the authors recommend the use of LETt in the dose plateau region and LETd around the Bragg peak. For a large step limit, i.e., 500 μm, LETd is recommended along the whole Bragg curve. The transition point depends on beam parameters and can be found by determining the location where the gradient of the ratio of LETd and LETt becomes positive.

Radiobiological issues in proton therapy

Abstract Background: The relative biological effectiveness (RBE) for particle therapy is a complex function of particle type, radiation dose, linear energy transfer (LET), cell type, endpoint, etc. In the clinical practice of proton therapy, the RBE is assumed to have a fixed value of 1.1. This assumption, along with the effects of physical uncertainties, may mean that the biologically effective dose distributions received by the patient may be significantly different from what is seen on treatment plans. This may contribute to unforeseen toxicities and/or failure to control the disease. Variability of Proton RBE: It has been shown experimentally that proton RBE varies significantly along the beam path, especially near the end of the particle range. While there is now an increasing acceptance that proton RBE is variable, there is an ongoing debate about whether to change the current clinical practice. Clinical Evidence: A rationale against the change is the uncertainty in the models of variable RBE. Secondly, so far there is no clear clinical evidence of the harm of assuming proton RBE to be 1.1. It is conceivable, however, that the evidence is masked partially by physical uncertainties. It is, therefore, plausible that reduction in uncertainties and their incorporation in the estimation of dose actually delivered may isolate and reveal the variability of RBE in clinical practice. Nevertheless, clinical evidence of RBE variability is slowly emerging as more patients are treated with protons and their response data are analyzed. Modelling and Incorporation of RBE in the Optimization of Proton Therapy: The improvement in the knowledge of RBE could lead to better understanding of outcomes of proton therapy and in the improvement of models to predict RBE. Prospectively, the incorporation of such models in the optimization of intensity-modulated proton therapy could lead to improvements in the therapeutic ratio of proton therapy.

Optimization of Monte Carlo particle transport parameters and validation of a novel high throughput experimental setup to measure the biological effects of particle beams

Purpose: Accurate modeling of the relative biological effectiveness (RBE) of particle beams requires increased systematic in vitro studies with human cell lines with care towards minimizing uncertainties in biologic assays as well as physical parameters. In this study, we describe a novel high‐throughput experimental setup and an optimized parameterization of the Monte Carlo (MC) simulation technique that is universally applicable for accurate determination of RBE of clinical ion beams. Clonogenic cell‐survival measurements on a human lung cancer cell line (H460) are presented using proton irradiation. Methods: Experiments were performed at the Heidelberg Ion Therapy Center (HIT) with support from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg, Germany using a mono‐energetic horizontal proton beam. A custom‐made variable range selector was designed for the horizontal beam line using the Geant4 MC toolkit. This unique setup enabled a high‐throughput clonogenic assay investigation of multiple, well defined dose and linear energy transfer (LETs) per irradiation for human lung cancer cells (H460) cultured in a 96‐well plate. Sensitivity studies based on application of different physics lists in conjunction with different electromagnetic constructors and production threshold values to the MC simulations were undertaken for accurate assessment of the calculated dose and the dose‐averaged LET (LETd). These studies were extended to helium and carbon ion beams. Results: Sensitivity analysis of the MC parameterization revealed substantial dependence of the dose and LETd values on both the choice of physics list and the production threshold values. While the dose and LETd calculations using FTFP_BERT_LIV, FTFP_BERT_EMZ, FTFP_BERT_PEN and QGSP_BIC_EMY physics lists agree well with each other for all three ions, they show large differences when compared to the FTFP_BERT physics list with the default electromagnetic constructor. For carbon ions, the dose corresponding to the largest LETd value is observed to differ by as much as 78% between FTFP_BERT and FTFP_BERT_LIV. Furthermore, between the production threshold of 700 μm and 5 μm, proton dose varies by as much as 19% corresponding to the largest LETd value sampled in the current investigation. Based on the sensitivity studies, the FTFP_BERT physics list with the low energy Livermore electromagnetic constructor and a production threshold of 5 μm was employed for determining accurate dose and LETd. The optimized MC parameterization results in a different LETd dependence of the RBE curve for 10% SF of the H460 cell line irradiated with proton beam when compared with the results from a previous study using the same cell line. When the MC parameters are kept consistent between the studies, the proton RBE results agree well with each other within the experimental uncertainties. Conclusions: A custom high‐throughput, high‐accuracy experimental design for accurate in vitro cell survival measurements was employed at a horizontal beam line. High sensitivity of the physics‐based optimization establishes the importance of accurate MC parameterization and hence the conditioning of the MC system on a case‐by‐case basis. The proton RBE results from current investigations are observed to agree with a previous measurement made under different experimental conditions. This establishes the consistency of our experimental findings across different experiments and institutions.

On the synergistic effects of ligand-mediated and phage-intrinsic properties during in vivo selection.

Phage display has been used as a powerful tool in the discovery and characterization of ligand-receptor complexes that can be utilized for therapeutic applications as well as to elucidate disease mechanisms. While the basic properties of phage itself have been well described, the behavior of phage in an in vivo setting is not as well understood due to the complexity of the system. Here, we take a dual approach in describing the biophysical mechanisms and properties that contribute to the efficacy of in vivo phage targeting. We begin by considering the interaction between phage and target by applying a kinetic model of ligand-receptor complexation and internalization. The multivalent display of peptides on the pIII capsid of phage is also discussed as an augmenting factor in the binding affinity of phage-displayed peptides to cellular targets accessible in a microenvironment of interest. Lastly, we examine the physical properties of the total phage particle that facilitate improved delivery and targeting in vivo compared to free peptides.

A model for relative biological effectiveness of therapeutic proton beams based on a global fit of cell survival data

We introduce an approach for global fitting of the recently published high-throughput and high accuracy clonogenic cell-survival data for therapeutic scanned proton beams. Our fitting procedure accounts for the correlation between the cell-survival, the absorbed (physical) dose and the proton linear energy transfer (LET). The fitting polynomials and constraints have been constructed upon generalization of the microdosimetric kinetic model (gMKM) adapted to account for the low energy and high lineal-energy spectrum of the beam where the current radiobiological models may underestimate the reported relative biological effectiveness (RBE). The parameters (α, β) of the linear-quadratic (LQ) model calculated by the presented method reveal a smooth transition from low to high LETs which is an advantage of the current method over methods previously employed to fit the same clonogenic data. Finally, the presented approach provides insight into underlying microscopic mechanisms which, with future study, may help to elucidate radiobiological responses along the Bragg curve and resolve discrepancies between experimental data and current RBE models.

WE-H-BRA-01: BEST IN PHYSICS (THERAPY): Nano-Dosimetric Kinetic Model for Variable Relative Biological Effectiveness of Proton and Ion Beams

PURPOSE Recent clonogenic cell survival and γH2AX studies suggest proton relative biological effectiveness (RBE) may be a non-linear function of linear energy transfer (LET) in the distal edge of the Bragg peak and beyond. We sought to develop a multiscale model to account for non-linear response phenomena to aid in the optimization of intensity-modulated proton therapy. METHODS The model is based on first-principle simulations of proton track structures, including secondary ions, and an analytical derivation of the dependence on particle LET of the linear-quadratic (LQ) model parameters α and β. The derived formulas are an extension of the microdosimetric kinetic (MK) model that captures dissipative track structures and non-Poissonian distribution of DNA damage at the distal edge of the Bragg peak and beyond. Monte Carlo simulations were performed to confirm the non-linear dose-response characteristics arising from the non-Poisson distribution of initial DNA damage. RESULTS In contrast to low LET segments of the proton depth dose, from the beam entrance to the Bragg peak, strong deviations from non-dissipative track structures and Poisson distribution in the ionization events in the Bragg peak distal edge govern the non-linear cell response and result in the transformation α=(1+c_1 L) α_x+2(c_0 L+c_2 L^2)(1+c_1 L) β_x and β=(1+c_1 L)^2 β_x. Here L is the charged particle LET, and c_0,c_1, and c_2 are functions of microscopic parameters and can be served as fitting parameters to the cell-survival data. In the low LET limit c_1, and c_2 are negligible hence the linear model proposed and used by Wilkins-Oelfke for the proton treatment planning system can be retrieved. The present model fits well the recent clonogenic survival data measured recently in our group in MDACC. CONCLUSION The present hybrid method provides higher accuracy in calculating the RBE-weighted dose in the target and normal tissues.