A genome-wide association study of contralateral breast cancer in the Women's Environmental Cancer and Radiation Epidemiology Study

BACKGROUND

Contralateral breast cancer (CBC) is the most common second primary cancer diagnosed in breast cancer survivors, yet the understanding of the genetic susceptibility of CBC, particularly with respect to common variants, remains incomplete. This study aimed to investigate the genetic basis of CBC to better understand this malignancy.

FINDINGS

We performed a genome-wide association analysis in the Women's Environmental Cancer and Radiation Epidemiology (WECARE) Study of women with first breast cancer diagnosed at age < 55 years including 1161 with CBC who served as cases and 1668 with unilateral breast cancer (UBC) who served as controls. We observed two loci (rs59657211, 9q32, SLC31A2/FAM225A and rs3815096, 6p22.1, TRIM31) with suggestive genome-wide significant associations (P < 1 × 10-6). We also found an increased risk of CBC associated with a breast cancer-specific polygenic risk score (PRS) comprised of 239 known breast cancer susceptibility single nucleotide polymorphisms (SNPs) (rate ratio per 1-SD change: 1.25; 95% confidence interval 1.14-1.36, P < 0.0001). The protective effect of chemotherapy on CBC risk was statistically significant only among patients with an elevated PRS (Pheterogeneity = 0.04). The AUC that included the PRS and known breast cancer risk factors was significantly elevated.

CONCLUSIONS

The present GWAS identified two previously unreported loci with suggestive genome-wide significance. We also confirm that an elevated risk of CBC is associated with a comprehensive breast cancer susceptibility PRS that is independent of known breast cancer risk factors. These findings advance our understanding of genetic risk factors involved in CBC etiology.

Long-term p21 and p53 dynamics regulate the frequency of mitosis events and cell cycle arrest following radiation damage

Radiation exposure of healthy cells can halt cell cycle temporarily or permanently. In this work, we analyze the time evolution of p21 and p53 from two single cell datasets of retinal pigment epithelial cells exposed to several levels of radiation, and in particular, the effect of radiation on cell cycle arrest. Employing various quantification methods from signal processing, we show how p21 levels, and to a lesser extent p53 levels, dictate whether the cells are arrested in their cell cycle and how frequently these mitosis events are likely to occur. We observed that single cells exposed to the same dose of DNA damage exhibit heterogeneity in cellular outcomes and that the frequency of cell division is a more accurate monitor of cell damage rather than just radiation level. Finally, we show how heterogeneity in DNA damage signaling is manifested early in the response to radiation exposure level and has potential to predict long-term fate.

Transient Diffusion in Bi-Layer Composites With Mass Transfer Resistance: Exact Solution and Time Lag Analysis

Exact analytical and closed-form solutions to the transient diffusion in bi-layer composites with external mass transfer resistance are reported. Expressions for the concentrations and the mass permeated are derived in both the Laplace and time domains through the use of the Laplace transform Inversion Theorem. The lead and lag times, which are often of importance in the characterization of membranes and arise from the analysis of the asymptotic behavior of the mass permeated through the bi-layer composite, were also derived. The presented solutions are also compared to previously derived limiting cases of the diffusion in a bi-layer with an impermeable wall and constant concentrations at the upstream and downstream boundaries. Analysis of the time lag shows that this membrane property is independent of the direction of flow. Finally, an outline is provided of how these transient solutions in response to a step function increase in concentration can be used to derive more complex input conditions. The importance of adequately handling boundary layer effects has a wide array of applications such as the study of bi-layers undergoing phenomena of heat convection, gas film resistance, and absorption/desorption.

Distributed Implementation of Boolean Functions by Transcriptional Synthetic Circuits

Starting in the early 2000s, sophisticated technologies have been developed for the rational construction of synthetic genetic networks that implement specified logical functionalities. Despite impressive progress, however, the scaling necessary in order to achieve greater computational power has been hampered by many constraints, including repressor toxicity and the lack of large sets of mutually orthogonal repressors. As a consequence, a typical circuit contains no more than roughly seven repressor-based gates per cell. A possible way around this scalability problem is to distribute the computation among multiple cell types, each of which implements a small subcircuit, which communicate among themselves using diffusible small molecules (DSMs). Examples of DSMs are those employed by quorum sensing systems in bacteria. This paper focuses on systematic ways to implement this distributed approach, in the context of the evaluation of arbitrary Boolean functions. The unique characteristics of genetic circuits and the properties of DSMs require the development of new Boolean synthesis methods, distinct from those classically used in electronic circuit design. In this work, we propose a fast algorithm to synthesize distributed realizations for any Boolean function, under constraints on the number of gates per cell and the number of orthogonal DSMs. The method is based on an exact synthesis algorithm to find the minimal circuit per cell, which in turn allows us to build an extensive database of Boolean functions up to a given number of inputs. For concreteness, we will specifically focus on circuits of up to 4 inputs, which might represent, for example, two chemical inducers and two light inputs at different frequencies. Our method shows that, with a constraint of no more than seven gates per cell, the use of a single DSM increases the total number of realizable circuits by at least 7.58-fold compared to centralized computation. Moreover, when allowing two DSM's, one can realize 99.995% of all possible 4-input Boolean functions, still with at most 7 gates per cell. The methodology introduced here can be readily adapted to complement recent genetic circuit design automation software. A toolbox that uses the proposed algorithm was created and made available at https://github.com/sontaglab/DBC/.

Delicate Balances in Cancer Chemotherapy: Modeling Immune Recruitment and Emergence of Systemic Drug Resistance

Metronomic chemotherapy can drastically enhance immunogenic tumor cell death. However, the mechanisms responsible are still incompletely understood. Here, we develop a mathematical model to elucidate the underlying complex interactions between tumor growth, immune system activation, and therapy-mediated immunogenic cell death. Our model is conceptually simple, yet it provides a surprisingly excellent fit to empirical data obtained from a GL261 SCID mouse glioma model treated with cyclophosphamide on a metronomic schedule. The model includes terms representing immune recruitment as well as the emergence of drug resistance during prolonged metronomic treatments. Strikingly, a single fixed set of parameters, adjusted neither for individuals nor for drug schedule, recapitulates experimental data across various drug regimens remarkably well, including treatments administered at intervals ranging from 6 to 12 days. Additionally, the model predicts peak immune activation times, rediscovering experimental data that had not been used in parameter fitting or in model construction. Notably, the validated model suggests that immunostimulatory and immunosuppressive intermediates are responsible for the observed phenomena of resistance and immune cell recruitment, and thus for variation of responses with respect to different schedules of drug administration.

Modeling voxel-based Monte Carlo light transport with curved and oblique boundary surfaces

SIGNIFICANCE

Monte Carlo (MC) light transport simulations are most often performed in regularly spaced three-dimensional voxels, a type of data representation that naturally struggles to represent boundary surfaces with curvature and oblique angles. Not accounting properly for such boundaries with an index of refractivity, mismatches can lead to important inaccuracies, not only in the calculated angles of reflection and transmission but also in the amount of light that transmits through or reflects from these mismatched boundary surfaces.

AIM

A new MC light transport algorithm is introduced to deal with curvature and oblique angles of incidence when simulated photons encounter mismatched boundary surfaces.

APPROACH

The core of the proposed algorithm applies the efficient preprocessing step of calculating a gradient map of the mismatched boundaries, a smoothing step on this calculated 3D vector field to remove surface roughness due to discretization and an interpolation scheme to improve the handling of curvature.

RESULTS

Through simulations of light hitting the side of a sphere and going through a lens, the agreement of this approach with analytical solutions is shown to be strong.

CONCLUSIONS

The MC method introduced here has the advantage of requiring only slight implementation changes from the current state-of-the-art to accurately simulate mismatched boundaries and readily exploit the acceleration of general-purpose graphics processing units. A code implementation, mcxyzn, is made available and maintained at https://omlc.org/software/mc/mcxyzn/.

Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models

Significance: Functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brains. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy. Aim: We aim to highlight the importance of accurate anatomical modeling in the context of fNIRS and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis. Approach: We have developed a surface-based brain meshing pipeline that can produce significantly better brain mesh models, compared to conventional meshing techniques. It can convert segmented volumetric brain scans into multilayered surfaces and tetrahedral mesh models, with typical processing times of only a few minutes and broad utilities, such as in Monte Carlo or finite-element-based photon simulations for fNIRS studies. Results: A variety of high-quality brain mesh models have been successfully generated by processing publicly available brain atlases. In addition, we compare three brain anatomical models-the voxel-based brain segmentation, tetrahedral brain mesh, and layered-slab brain model-and demonstrate noticeable discrepancies in brain partial pathlengths when using approximated brain anatomies, ranging between - 1.5 % to 23% with the voxelated brain and 36% to 166% with the layered-slab brain. Conclusion: The generation and utility of high-quality brain meshes can lead to more accurate brain quantification in fNIRS studies. Our open-source meshing toolboxes "Brain2Mesh" and "Iso2Mesh" are freely available at http://mcx.space/brain2mesh.

Dual-grid mesh-based Monte Carlo algorithm for efficient photon transport simulations in complex three-dimensional media

The mesh-based Monte Carlo (MMC) method is an efficient algorithm to model light propagation inside tissues with complex boundaries, but choosing appropriate mesh density can be challenging. A fine mesh improves the spatial resolution of the output but requires more computation. We propose an improved MMC-dual-grid mesh-based Monte Carlo (DMMC)-to accelerate photon simulations using a coarsely tessellated tetrahedral mesh for ray-tracing computation and an independent voxelated grid for output data storage. The decoupling between ray-tracing and data storage grids allows us to simultaneously achieve faster simulations and improved output spatial accuracy. Furthermore, we developed an optimized ray-tracing technique to eliminate unnecessary ray-tetrahedron intersection tests in optically thick mesh elements. We validate the proposed algorithms using a complex heterogeneous domain and compare the solutions with those from MMC and voxel-based Monte Carlo. We found that DMMC with an unrefined constrained Delaunay tessellation of the boundary nodes yielded the highest speedup, ranging from 1.3  ×   to 2.9  ×   for various scattering settings, with nearly no loss in accuracy. In addition, the optimized ray-tracing technique offers excellent acceleration in high-scattering media, reducing the ray-tetrahedron test count by over 100-fold. Our DMMC software can be downloaded at http://mcx.space/mmc.

Selective photobiomodulation for emotion regulation: model-based dosimetry study

The transcranial photobiomodulation (t-PBM) technique is a promising approach for the treatment of a wide range of neuropsychiatric disorders, including disorders characterized by poor regulation of emotion such as major depressive disorder (MDD). We examine various approaches to deliver red and near-infrared light to the dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) in the human brain, both of which have shown strong relevance to the treatment of MDD. We apply our hardware-accelerated Monte Carlo simulations to systematically investigate the light penetration profiles using a standard adult brain atlas. To better deliver light to these regions-of-interest, we study, in particular, intranasal and transcranial illumination approaches. We find that transcranial illumination at the F3-F4 location (based on 10-20 system) provides excellent light delivery to the dlPFC, while a light source located in close proximity to the cribriform plate is well-suited for reaching the vmPFC, despite the fact that accessing the latter location may require a minimally invasive approach. Alternative noninvasive illumination strategies for reaching vmPFC are also studied and both transcranial illumination at the Fp1-FpZ-Fp2 location and intranasal illumination in the mid-nose region are shown to be valid. Different illumination wavelengths, ranging from 670 to 1064 nm, are studied and the amounts of light energy deposited to a wide range of brain regions are quantitatively compared. We find that 810 nm provided the overall highest energy delivery to the targeted regions. Although our simulations carried out on locations and wavelengths are not designed to be exhaustive, the proposed illumination strategies inform the design of t-PBM systems likely to improve brain emotion regulation, both in clinical research and practice.