Cell and extracellular matrix growth theory and its implications for tumorigenesis

In situtumor model for longitudinal in silico imaging trials

Objective.In this article, we introduce a computational model for simulating the growth of breast cancer lesions accounting for the stiffness of surrounding anatomical structures.Approach.In our model, ligaments are classified as the most rigid structures while the softer parts of the breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly proliferating tumor cells are met with differential resistance. It is found that these cells are likely to circumvent stiffer terrains such as ligaments, instead electing to proliferate preferentially within the more yielding confines of the breast's soft topography. By manipulating the interstitial tumor pressure in direct proportion to the elastic constants of the tissues surrounding the tumor, this model thus creates the potential for realizing a database of unique lesion morphology sculpted by the distinctive topography of each local anatomical infrastructure. We modeled the growth of simulated lesions within volumes extracted from fatty breast models, developed by Graffet alwith a resolution of 50μm generated with the open-source and readily available Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) imaging pipeline. To visualize and validate the realism of the lesion models, we leveraged the imaging component of the VICTRE pipeline, which replicates the siemens mammomat inspiration mammography system in a digital format. This system was instrumental in generating digital mammogram (DM) images for each breast model containing the simulated lesions.Results.By utilizing the DM images, we were able to effectively illustrate the imaging characteristics of the lesions as they integrated with the anatomical backgrounds. Our research also involved a reader study that compared 25 simulated DM regions of interest (ROIs) with inserted lesions from our models with DM ROIs from the DDSM dataset containing real manifestations of breast cancer. In general the simulation time for the lesions was approximately 2.5 hours, but it varied depending on the lesion's local environment.Significance.The lesion growth model will facilitate and enhance longitudinal in silico trials investigating the progression of breast cancer.

Deep silicon photon-counting CT: A first simulation-based study for assessing perceptual benefits across diverse anatomies

OBJECTIVES

To assess perceptual benefits provided by the improved spatial resolution and noise performance of deep silicon photon-counting CT (Si-PCCT) over conventional energy-integrating CT (ECT) using polychromatic images for various clinical tasks and anatomical regions.

MATERIALS AND METHODS

Anthropomorphic, computational models were developed for lungs, liver, inner ear, and head-and-neck (H&N) anatomies. These regions included specific abnormalities such as lesions in the lungs and liver, and calcified plaques in the carotid arteries. The anatomical models were imaged using a scanner-specific CT simulation platform (DukeSim) modeling a Si-PCCT prototype and a conventional ECT system at matched dose levels. The simulated polychromatic projections were reconstructed with matched in-plane resolutions using manufacturer-specific software. The reconstructed pairs of images were scored by radiologists to gauge the task-specific perceptual benefits provided by Si-PCCT compared to ECT based on visualization of anatomical and image quality features. The scores were standardized as z-scores for minimizing inter-observer variability and compared between the systems for evidence of statistically significant improvement (one-sided Wilcoxon rank-sum test with a significance level of 0.05) in perceptual performance for Si-PCCT.

RESULTS

Si-PCCT offered favorable image quality and improved visualization capabilities, leading to mean improvements in task-specific perceptual performance over ECT for most tasks. The improvements for Si-PCCT were statistically significant for the visualization of lung lesion (0.08 ± 0.89 vs. 0.90 ± 0.48), liver lesion (-0.64 ± 0.37 vs. 0.95 ± 0.55), and soft tissue structures (-0.47 ± 0.90 vs. 0.33 ± 1.24) and cochlea (-0.47 ± 0.80 vs. 0.38 ± 0.62) in inner ear.

CONCLUSIONS

Si-PCCT exhibited mean improvements in task-specific perceptual performance over ECT for most clinical tasks considered in this study, with statistically significant improvement for 6/20 tasks. The perceptual performance of Si-PCCT is expected to improve further with availability of spectral information and reconstruction kernels optimized for high resolution provided by smaller pixel size of Si-PCCT. The outcomes of this study indicate the positive potential of Si-PCCT for benefiting routine clinical practice through improved image quality and visualization capabilities.

Development and CT image-domain validation of a computational lung lesion model for use in virtual imaging trials

PURPOSE

Computational abnormalities (e.g., lesion models) for use in medical imaging simulation studies are frequently generated using data collected from clinical images. Although this approach allows for highly-customizable lesion detectability studies on clinical computed tomography (CT) data, the ground-truth lesion models produced with this method do not provide a sufficiently realistic lesion morphology for use with current anthropomorphic simulation studies. This work is intended to demonstrate that the new anatomically-informed lesion model presented here is not inferior to the previous lesion model under CT imaging, and can therefore provide a more biologically-informed model for use with simulated CT imaging studies.

METHODS

The lesion model was simulated initially from a seed cell with 10 μm diameter placed in an anatomical location within segmented lung CT and was allowed to reproduce locally within the available solid angle in discrete time-intervals (corresponding to synchronous cell cycles) up to a size of ∼200 μm in diameter. Daughter cells of generation G were allowed also to reproduce on the next available time-step given sufficient space. At lesion sizes beyond 200 μm in diameter, the health of subregions of cells were tracked with a Markov chain technique, indicating which regions were likely to continue growing, which were likely stable, and which were likely to develop necrosis given their proximity to anatomical features and other lesion cells. For lesion sizes beyond 500 μm, the lesion was represented with three nested, triangulated surfaces (corresponding to proliferating, dormant, and necrotic regions), indicating how discrete volumes of the lesion were behaving at a particular time. Lesions were then assigned smoothly-varying material properties based on their cellular level health in each region, resulting in a multi-material lesion model. The lesions produced with this model were then voxelized and placed into lung CT images for comparison with both prior work and clinical data. This model was subject to an observer study in which cardiothoracic imaging radiologists assessed the realism of both clinical and synthetic lesions in CT images.

RESULTS

The useable outputs of this work were voxel- or surface-based, validated, computational lesions, at a scale clearly visible on clinical CT (3-4 cm). Analysis of the observer study results indicated that the computationally-generated lesions were indistinguishable from clinical lesions (AUC = 0.49, 95% CI = [0.36, 0.61]) and non-inferior to an earlier image-based lesion model-indicating the advantage of the model for use in both hybrid CT images and in simulated CT imaging of the lungs.

CONCLUSIONS

Results indicated the non-inferiority of this model as compared to previous methods, indicating the utility of the model for use in both hybrid CT images and in simulated CT imaging.

The Role of the Matrix Metalloproteinase-9 Gene in Tumor Development and Metastasis: A Narrative Review

Matrix metalloproteinase-9 (MMP-9) is one of the widely studied enzymes of the extracellular matrix which can degrade various matrix biomolecules. The gene coding for this enzyme has been found to be associated with various multifactorial diseases, including cancer. More specifically, the expression of MMP-9 and polymorphisms of its gene have been found to be correlated with the formation and the invasiveness of different types of cancer. Hence, the latter gene can potentially be used both as a clinical genetic marker and a possible target in anticancer therapy. The present minireview explores the role of the MMP-9 gene in the process of tumor formation, growth, and metastasis and presents an overview of the polymorphisms of the gene associated with cancer as well as its regulation mechanisms, to provide an insight into the potential clinical applications. Nevertheless, further clinical trials and research are still required to reach more valuable conclusions for the clinical implications of the recent findings.

Wiskott-Aldrich syndrome gene as a prognostic biomarker correlated with immune infiltrates in clear cell renal cell carcinoma

Introduction

The abnormal expression of the Wiskott-Aldrich syndrome protein (WASP) encoded by the Wiskott-Aldrich syndrome (WAS) gene has been implicated in tumor invasion and immune regulation. However, prognostic implications of WAS and its correlation tumor infiltrating in renal clear cell carcinoma (ccRCC) is not clear cut.

Methods

The correlation between WAS expression, clinicopathological variables and clinical outcomes were evaluated using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Tumor Immune Estimation Resource (TIMER), UALCAN, Gene Expression Profiling Interaction Analysis (GEPIA), Kaplan-Meier (KM) plotter and other databases. Furthermore, we assessed the transcription expression of WAS in renal cancer tissues, various renal carcinoma cell lines and human renal tubular cells (HK2) using quantitative polymerase chain reaction (qPCR). A comprehensive analysis of multiple databases including TIMER, GEPIA, TISIDB, ESTIMATE algorithm, and CIBERSORT algorithm were performed to determine the correlation between WAS and tumor infiltrating immune cells in ccRCC.

Results

The results displayed an increase in WAS mRNA level in ccRCC compared to normal tissue. WAS protein level was found highly expressed in cancer tissues, particularly within renal tumor cells via the human protein atlas (HPA). Interestingly, we found that elevated WAS expression was significantly positively correlated with the infiltration of CD8+ T cells, B cells, Monocytes, Neutrophils, Macrophages, T cell regulation, NK cells, and Dendritic cells in ccRCC. Bioinformatics demonstrated a strong correlation between WAS expression and 42 immune checkpoints, including the T cell exhaustion gene PD-1, which is critical for exploring immunotherapy for ccRCC. We revealed that patients with high WAS expression were less sensitive to immunotherapy medications.

Conclusion

In conclusion, our study identified that WAS was a prognostic biomarker and correlated with immune infiltrates in ccRCC.