Characterisation of Collagen Re-Modelling in Localised Prostate Cancer Using Second-Generation Harmonic Imaging and Transrectal Ultrasound Shear Wave Elastography

Prostate cancer has a poor prognosis and high mortality rate due to metastases. Extracellular matrix (ECM) re-modelling and stroma composition have been linked to cancer progression, including key components of cell migration, tumour metastasis, and tissue modulus. Moreover, collagens are one of the most significant components of the extracellular matrix and have been ascribed to many aspects of neoplastic transformation. This study characterises collagen re-modelling around localised prostate cancer using the second harmonic generation of collagen (SHG), genotyping and ultrasound shear wave elastography (USWE) measured modulus in men with clinically localised prostate cancer. Tempo-sequence assay for gene expression of COL1A1 and COL3A1 was used to confirm the expression of collagen. Second-harmonic generation imaging and genotyping of ECM around prostate cancer showed changes in content, orientation, and type of collagen according to Gleason grades (cancer aggressivity), and this correlated with the tissue modulus measured by USWE in kilopascals. Furthermore, there were clear differences between collagen orientation and type around normal and cancer tissues

Proliferative effect of the calcium channel blocker Nifedipine on human embryonic kidney cells

Background: Numerous epidemiological studies have shown a positive as well as negative association between chronic use of calcium channel blockers and the increased risk of developing cancer. However, these associations were enmeshed with controversies in the absence of laboratory based studies to back up those claims. The aim was to determine in mechanistic terms the association between the long-term administrations of nifedipineand increased risk of developing cancer with the aid of human embryonic kidney (HEK293) cell line.Methods: Cell counting using the Trypan blue dye exclusion and 3-[4, 5-Dimethylthiazol-2-yl]-2, 5-diphenyl-tetrazolium bromide (MTT) assays were used to investigate the effect of nifedipine on the growth pattern of HEK293 cells.Results: Nifedipine had a proliferative effect on HEK293 cells growth and this proliferation is more profound at low concentrations of nifedipine than high concentrations and the proliferation was statistically significant (p<0.01).Conclusions: The chronic use of nifedipine is associated with increased proliferation of cells with concomitant elevation of polyamines concentration and elevated polyamine levels have been implicated in many malignant transformations and hence, these provide possible explanation on the link between long term use of nifedipine and development of some human cancers

Prediction of clinically significant cancer using radiomics features of pre-biopsy of multiparametric MRi in men suspected of prostate cancer

SIMPLE SUMMARY: Radiomics is the field of computer-based medical image analysis that incorporates various radiological imaging features, such as texture and shape parameters, from scans to derive algorithms. These mathematical algorithms have the potential to predict the biological characteristics of disease. In this study, we obtained quantitative imaging texture features of pre-biopsy multiparametric MRI of men suspected of prostate cancer and extracted from the T2WI and ADC images focusing on gray-level co-occurrence matrices (GLCM). These were correlated with the Gleason score of the histopathology of radical prostatectomy specimen, including the prediction of clinically significant prostate cancer. The knowledge gained through this prospective protocol-based study should facilitate establishing that GLCM texture features alone can be used as a biomarker for predicting the presence of clinically significant PCa. ABSTRACT: Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer

Multimodality Characterization of Cancer-Associated Fibroblasts in Tumor Microenvironment and Its Correlation With Ultrasound Shear Wave-Measured Tissue Stiffness in Localized Prostate Cancer

INTRODUCTION: Growing evidence suggests that the tumor microenvironment (TME) represented by cellular and acellular components plays a key role in the multistep process of metastases and response to therapies. However, imaging and molecular characterization of the TME in prostate cancer (PCa) and its role in predicting aggressive tumor behavior and disease progression is largely unexplored. The study explores the PCa TME through the characterization of cancer-associated fibroblasts (CAFs) using both immunohistochemistry (IHC) and genomics approaches. This is then correlated with transrectal ultrasound shear wave elastography (USWE)-measured tissue stiffness. PATIENTS AND METHODS: Thirty patients with clinically localized PCa undergoing radical prostatectomy for different risk categories of tumor (low, intermediate, and high) defined by Gleason score (GS) were prospectively recruited into this study. Prostatic tissue stiffness was measured using USWE prior to surgery. The CAFs within the TME were identified by IHC using a panel of six antibodies (FAP, SMAα, FSP1, CD36, PDGFRα, and PDGFRβ) as well as gene expression profiling using TempO-sequence analysis. Whether the pattern and degree of immunohistochemical positivity (measured by Quick score method) and expression of genes characterizing CAFs were correlated with USWE- and GS-measured tissue stiffnesses were tested using Spearman’s rank correlation and Pearson correlation. RESULTS: There was a statistically significant correlation between GS of cancers, the pattern of staining for CAFs by immunohistochemical staining, and tissue stiffness measured in kPa using USWE (p < 0.001). Significant differences were also observed in immunohistochemical staining patterns between normal prostate and prostatic cancerous tissue. PDGFRβ and SMAα immunostaining scores increased linearly with increasing the USWE stiffness and the GS of PCa. There was a significant positive correlation between increasing tissue stiffness in tumor stroma and SMAα and PDGFRβ gene expression in the fibromuscular stroma (p < 0.001). CONCLUSION: USWE-measured tissue stiffness correlates with increased SMAα and PDGFRβ expressing CAFs and PCa GSs. This mechanistic correlation could be used for predicting the upgrading of GS from biopsies to radical surgery and response to novel treatments

Prediction of Clinically Significant Cancer Using Radiomics Features of Pre-Biopsy of Multiparametric MRI in Men Suspected of Prostate Cancer

Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal&ndash;Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm&ndash;Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859&ndash;0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p &lt; 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer

Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer

Objectives: To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. Materials and Methods: Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson’s coefficients and survival analysis using Kaplan–Meier estimators were performed. Results: The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively. Conclusion: This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients