KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer-a targeted molecular approach

Exploratory analyses of the associations between Ki-67 expression, lymph node metastasis, and prognosis in patients with esophageal squamous cell cancer

Background

The relationships between Ki-67/MKI67 expression, lymph node metastasis (LNM), vascular invasion (VI), and perineural invasion (PI) in esophageal squamous cell cancer (ESCC) remain unclear. This retrospective cohort study was performed to evaluate the prognostic value of Ki-67 expression and its association with LNM in patients with resected ESCC.

Methods

The analysis included 168 patients with ESCC with available Ki-67 protein expression data. The patients were divided into Ki-67 high-expression group (Ki-67 High, 93 cases) and Ki-67 low-expression (Ki-67 Low, 75 cases) groups. Associations between Ki-67 expression and ESCC pathological features was assessed using chi-square test. Overall survival (OS) was compared between the two groups using Kaplan-Meier survival analysis and Cox proportional hazards model.

Results

Median follow-up duration was 33.5 months (range 3.0-60.0 months). High Ki-67 expression was significantly associated with poor OS in patients with ESCC compared to that of the low-expression in both univariate (hazard ratios (HR) = 3.42, 95% CI [2.22-5.27], P < 0.001) and multivariate analyses (HR = 1.98, 95% CI [1.33-2.94], P < 0.001). Furthermore, high Ki-67 expression was significantly associated with an increased risk of LNM (χ 2 = 11.219, P = 0.011), VI (χ 2 = 6.359, P = 0.012), and PI (χ 2 = 8.877, P = 0.003).

Conclusions

High Ki-67 protein expression is associated with poor prognosis in ESCC. Increased Ki-67 expression significantly increases the risk of LNM, VI, and PI in ESCC, and thus may serve as an indication for adjuvant therapy in ESCC management.

High-fat diet impact on prostate gland from adiponectin knockout mice: Morphometric, metabolic and inflammatory cytokines analyses

AIMS

Obesity is a global public health issue, and some studies have linked it to an increased risk of prostatic diseases. This study aimed to evaluate the effects of a high-fat diet on metabolic parameters and prostate morphology in wild-type (WT) and adiponectin knockout (KO) mice.

MAIN METHODS

Male WT and KO mice were fed a control diet (CD) or high-fat diet (HFD) for 6 months. Serum metabolic parameters, inflammatory cytokines in epididymal fat tissue, dorsal prostatic lobe morphometry and histopathology were analyzed.

KEY FINDINGS

CD WT and CD KO mice did not exhibit altered metabolic or prostatic parameters. However, HFD WT mice showed altered glucose and insulin tolerance even without excessive weight gain. On the other hand, HFD KO mice developed obesity, with an increase in low-density lipoprotein (11.8 ± 5.1 vs. 31.4 ± 3.6 mg/dL), high-density lipoprotein (73.4 ± 7.4 vs. 103.4 ± 2.5 mg/dL), and total cholesterol levels (126.2 ± 16.1 vs. 294.6 ± 23.2 mg/dL), a decrease in insulin levels (28.7 ± 12.2 vs. 4.6 ± 2.3 μIU/mL), and glucose and insulin resistance. We also observed that HFD KO animals display an increase in inflammatory cytokines, such as IL6, IL1β, and IL1RA. The dorsal prostate from HFD KO animals also presented significant increases in the mast cells (1.9 ± 0,7 vs. 5,3 ± 1.5 cells/field) and Ki67 index (2.91 ± 0.6 vs. 4.7 ± 0.4 %).

SIGNIFICANCE

The above findings highlight the complex interactions between adiponectin, metabolism, malnutrition, and prostate health. Metabolic deregulation combined with adipose inflammation potentially induces a proliferative and inflammatory microenvironment in the prostate gland under conditions of low adiponectin production, potentially impairing prostate morphophysiology in the context of obesity and aging.

Investigating Ras homolog gene family member C (RhoC) and Ki67 expression following external beam radiation therapy show increased RhoC expression in relapsing prostate cancer xenografts

Ras homolog gene family member C (RhoC) is a GTPase involved in cell migration, implicated in epithelial-mesenchymal transition and treatment resistance and metastasis of cancer. For example, RhoC has been shown to be involved in resistance to radiation in cervical carcinoma. Here, the effect of X-ray irradiation on RhoC expression in prostate cancer (PCa) xenografts was investigated in both xenografts in regression and relapse. Male BALB/cAnNRj-Foxn1nu/nu mice were inoculated with 4-6 million LNCaP-FGC cells and established xenografts were irradiated with X-rays (200 kV, 1 Gymin-1), 5, 10 or 15 Gy using a Gulmay Medical X-ray system. Expression of RhoC and Ki67, a known proliferation marker, was investigated in xenografts, given 15 Gy, 7 days (midst response as measured by size) or 3 weeks (relapse) post irradiation. Staining was quantified using the Halo software (v2.3.2089.34) with the Indica Labs - cytonuclear v1.6 algorithm. RhoC and Ki67 staining was divided into weak, medium, and strong staining and the percentage of cells stained, single and dual staining, was quantified. The HALO software was further used to classify the tissue in each section so that analysis of RhoC and Ki67 expression in cancer cells, stroma and necrotic areas could be done separately. The results showed that RhoC expression in cancer and stroma cells was significantly higher in relapsed xenografts than in those in regression. This was not seen for Ki67 staining, where the percentage of stained cells were the same in regressing and relapsing tumors. RhoC could be a useful biomarker to confirm relapse following external beam radiation therapy.

A Study of Ki-67 Immunostaining in Prostate Carcinomas and Its Correlation with Gleason's Score and Prognosis: An Experience at a Tertiary Centre in the Himalayan Foothills

Prostate cancer is a significant cause of cancer-related mortality among men worldwide, necessitating the exploration of prognostic biomarkers to aid in accurate risk assessment and treatment decision-making. This cross-sectional study aimed to comprehensively evaluate the role of Ki-67 as a prognostic marker in prostate cancer by examining its association with clinicopathological parameters. A total of 102 archived cases of prostate core biopsy specimens, histopathologically reported as prostate carcinoma, were included in this study. Histopathological grading was conducted using Gleason's scoring and grading system based on morphology. The statistical software "R" was utilized for data analysis. Kruskal-Wallis test and Fisher's exact test were employed to analyze the association between Ki-67 expression and clinicopathological parameters. The study revealed significant correlations between Ki-67 expression and various clinicopathological parameters in prostate cancer cases. High Ki-67 expression levels were associated with higher Gleason scores, increased incidence of perineural invasion, advanced T stages, lymph node metastasis, presence of distant metastasis, and higher prognostic stage groups. The findings of this cross-sectional study support the potential of Ki-67 as a prognostic marker in prostate cancer. The significant associations observed between Ki-67 expression and clinicopathological parameters indicate its usefulness in risk stratification and treatment decision-making. The incorporation of histopathological grading, including Gleason scoring, and analysis of perineural invasion strengthens the validity of the study. Ki-67, in combination with morphological assessments, provides valuable prognostic information for prostate cancer patients.

Trps1 predicts poor prognosis in advanced high grade serous ovarian carcinoma

TRPS1 is aberrantly expressed in a variety of tumors, including breast, prostate, and gastric cancers, and is strongly associated with tumorigenesis or prognosis. However, the role of TRPS1 in high grade serous ovarian carcinoma (HGSC) is unknown. We investigated the relationship between TRPS1 expression and clinicopathology in HGSC patients. The tumor-related regulatory mechanisms of TRPS1 was explored through in vivo and vitro experiments. The results showed that TRPS1 was highly expressed in HGSC compared to normal tissues. It was also linked to the cell proliferation index Ki67 and poor prognosis. In vivo experiments showed that knockdown of TRPS1 could inhibit tumor growth. In vitro experiments, knockdown of TRPS1 inhibited the proliferation of ovarian cancer cells. TRPS1 exerted its regulatory role as a transcription factor, binding to the PSAT1 promoter and promoting the expression of PSAT1 gene. Meanwhile, PSAT1 was positively correlated with CCND1 expression. These results suggest that TRPS1 affects HGSC proliferation and cell cycle by regulating PSAT1 and thus CCND1 expression.

Differences in the expression of the phosphatase PTP-1B in patients with localized prostate cancer with and without adverse pathological features

Introduction

Patients with adverse pathological features (APF) at radical prostatectomy (RP) for prostate cancer (PC) are candidates for adjuvant treatment. Clinicians lack reliable markers to predict these APF preoperatively. Protein tyrosine phosphatase 1B (PTP-1B) is involved in migration and invasion of PC, and its expression could predict presence of APF. Our aim was to compare PTP-1B expression in patients with and without APF, and to explore PTP-1B expression as an independent prognostic factor.

Methods

Tissue microarrays (TMAs) were constructed using RP archival specimens for immunohistochemical staining of PTP-1B; expression was reported with a standardized score (0-9). We compared median PTP-1B score between cases with and without APF. We constructed two logistic regression models, one to identify the independence of PTP-1B score from biologically associated variables (metformin use and type 2 diabetes mellitus [T2DM]) and the second to seek independence of known risk factors (Gleason score and prostate specific antigen [PSA]).

Results

A total of 73 specimens were suitable for TMA construction. Forty-four (60%) patients had APF. The median PTP-1B score was higher in those with APF: 8 (5-9) vs 5 (3-8) (p=0.026). In the logistic regression model including T2DM and metformin use, the PTP-1B score maintained statistical significance (OR 1.21, 95% CI 1.01-1.45, p=0.037). In the model including PSA and Gleason score; the PTP-1B score showed no independence (OR 1.68, 95% CI 0.97-1.41, p=0.11). The area under the curve to predict APF for the PTP-1B score was 0.65 (95% CI 0.52-0.78, p=0.03), for PSA+Gleason 0.71 (95% CI 0.59-0.82, p=0.03), and for PSA+Gleason+PTP-1B score 0.73 (95% CI 0.61-0.84, p=0.001).

Discussion

Patients with APF after RP have a higher expression of PTP-1B than those without APF, even after adjusting for T2DM and metformin exposure. PTP-1B has a good accuracy for predicting APF but does not add to known prognostic factors.

Marker Ki-67 is a potential biomarker for the diagnosis and prognosis of prostate cancer based on two cohorts

BACKGROUND

Prostate cancer (PCa) is a widespread malignancy, predominantly affecting elderly males, and current methods for diagnosis and treatment of this disease continue to fall short. The marker Ki-67 (MKI67) has been previously demonstrated to correlate with the proliferation and metastasis of various cancer cells, including those of PCa. Hence, verifying the association between MKI67 and the diagnosis and prognosis of PCa, using bioinformatics databases and clinical data analysis, carries significant clinical implications.

AIM

To explore the diagnostic and prognostic efficacy of antigens identified by MKI67 expression in PCa.

METHODS

For cohort 1, the efficacy of MKI67 diagnosis was evaluated using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. For cohort 2, the diagnostic and prognostic power of MKI67 expression was further validated using data from 271 patients with clinical PCa.

RESULTS

In cohort 1, MKI67 expression was correlated with prostate-specific antigen (PSA), Gleason Score, T stage, and N stage. The receiver operating characteristic (ROC) curve showed a strong diagnostic ability, and the Kaplan-Meier method demonstrated that MKI67 expression was negatively associated with the progression-free interval (PFI). The time-ROC curve displayed a weak prognostic capability for MKI67 expression in PCa. In cohort 2, MKI67 expression was significantly related to the Gleason Score, T stage, and N stage; however, it was negatively associated with the PFI. The time-ROC curve revealed the stronger prognostic capability of MKI67 in patients with PCa. Multivariate COX regression analysis was performed to select risk factors, including PSA level, N stage, and MKI67 expression. A nomogram was established to predict the 3-year PFI.

CONCLUSION

MKI67 expression was positively associated with the Gleason Score, T stage, and N stage and showed a strong diagnostic and prognostic ability in PCa.

MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer

PURPOSE

The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa.

METHODS

A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices.

RESULTS

The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r = 0.382, p < 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230).

CONCLUSION

The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method.

Artificial Intelligence in Urooncology: What We Have and What We Expect

INTRODUCTION

Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers.

METHODOLOGY

We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors.

RESULTS

Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors.

CONCLUSIONS

AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.

Development and validation of a nomogram to predict the recurrence of eyelid sebaceous gland carcinoma

PURPOSE

Eyelid sebaceous gland carcinoma (SGC) is a malignancy with fatal risk, high recurrence rate, and pagetoid spread. Thus, recurrence risk prediction and prompt treatment are extremely important. This study aimed to develop a nomogram to predict SGC recurrence based on potential risk factors.

METHODS

We conducted a retrospective study to train and test a nomogram based on the clinical data of 391 patients across our hospital (304) and other grass-roots hospitals (87). After Cox regression, predictors included in the nomogram were selected, and sensitivity, specificity, concordance index (C-index), etc., were calculated to test their discrimination ability.

RESULTS

After a median follow-up period of 4.12 years, SGC recurred in 52 (17.11%) patients. The 1-, 2-, and 5-year recurrence-free survival rates were 88.3%, 85.4%, and 81.6%, respectively. We examined five risk factors, such as lymph node metastasis at initial diagnosis (hazard ratio [HR], 2.260; 95% confidence interval [CI], 1.021-5.007), Ki67 (HR, 1.036; 95% CI, 1.020-1.052), histology differentiation degree (HR, 2.274; 95% CI, 1.063-4.865), conjunctival pagetoid infiltration (HR, 2.100; 95% CI, 1.0058-4.167), and orbital involvement (HR, 4.764; 95% CI, 1.436-15.803). The model had good discrimination in both internal and external test sets. The model had good discrimination in both internal and external test sets. The sensitivity of the internal test and external test set were 0.722 and 0.806, respectively, and specificity of the internal test and external test set were 0.886 and 0.893, respectively.

CONCLUSION

We examined the potential risk factors for eyelid SGC recurrence and constructed a nomogram, which complements the TNM system in terms of prediction, indicating that our nomogram has the potential to reach clinical significance. This nomogram has the potential to assist healthcare practitioners in promptly detecting patients who are at an elevated risk and in tailoring clinical interventions to meet their individualized needs.

Identification and validation of DLX4 as a prognostic and diagnostic biomarker for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a lethal cancer, and biomarkers for exact diagnosis and predicting prognosis are urgently needed. The present study aimed to determine the roles of distal-less homeobox (DLX) family genes in ccRCC. The clinicopathological and mRNA expression data of patients with ccRCC were derived from The Cancer Genome Atlas database. Kaplan-Meier curves, univariate and multivariate Cox hazard analyses, in addition to receiver operator characteristic curves were used to evaluate the prognostic and diagnostic values. A single-sample gene set enrichment analysis was used to quantify the infiltration levels of immune cells. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry were conducted to examine the expression levels of DLX4 in tumor and adjacent tissue; the results demonstrated that DLX4 was highly expressed in ccRCC tissues compared with normal renal tissues. Furthermore, DLX4 expression was associated with tumor stage and grade. High proportions of males, advanced pathological stage, higher tumor grade and T, N and M stage were also observed in the high DLX4 expression group. Patients with the high DLX4 expression levels tended to have lower overall survival and disease-free survival rates compared with those with low DLX4 expression. DLX4 expression also showed favorable diagnostic efficiency in ccRCC patients. Based on functional enrichment analysis, cell cycle related pathways, epithelial-mesenchymal transition, glycolysis and inflammatory response were associated with the expression levels of DLX4. Furthermore, DLX4 expression was revealed to be associated with tumor immunosuppressive microenvironment. Overall, the expression level of DLX4 may be considered a novel prognostic indicator in ccRCC and a specific diagnostic biomarker for patients with ccRCC.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

DLX2 Is a Potential Immune-Related Prognostic Indicator Associated with Remodeling of Tumor Microenvironment in Lung Squamous Cell Carcinoma: An Integrated Bioinformatical Analysis

BACKGROUND

It is still an unmet clinical need to identify potent biomarkers for immunotherapy on patients with lung squamous cell carcinoma (LUSC).

METHODS

In this study, we explored the differentially expressed genes (DEGs) that were simultaneously correlated with four pathways (i.e. CD8+ αβT cell proliferation/differentiation/activation pathways and ferroptosis pathway) and possibly related to the remodeling of tumor microenvironment via the TCGA-LUSC dataset. Besides, four GEO datasets (GSE157009, GSE157010, GSE19188, and GSE126045) and IMvigor210 dataset were utilized for confirmation and validation.

RESULTS

The co-downregulated DEG DLX2 was selected for further analysis. Function enrichment analysis revealed that low-expression of DLX2 was closely related to various immune-related pathways like T/B/NK cell mediated immunity, interferon gamma/alpha response, and various autoimmune disease. DLX2-downregulated group was enriched in more immune-activating cells and lower tumor immune dysfunction and exclusion (TIDE) score. Via the Cancer Immunome Atlas (TCIA) database, lower expression of DLX2 was also found to be associated with better IPS score of PD-1/PD-L1 blockade (p < 0.001) as well as CTLA-4 combined with PD-1/PD-L1 blockade (p < 0.001). Furthermore, patients in DLX2-low group were found to have significant longer median OS than those in DLX2-high group in IMvigor210 dataset (10.8 vs 7.4 months; hazard ratio [HR]=0.74, 95% confidence interval [95%CI] 0.57-0.96; p = 0.024).

CONCLUSIONS

Our study on an integrated bioinformatical analysis implied that DLX2 could be served as a promising indicator for remodeling tumor microenvironment status and predicting ICI response of patients with LUSC.

Diffuse pattern, orbital invasion, perineural invasion and Ki-67 are associated with nodal metastasis in patients with eyelid sebaceous carcinoma

Background

Metastasis dominates the prognosis of eyelid sebaceous carcinoma (SC). This study aimed to explore risk factors for nodal metastasis and develop a nomogram to predict nodal metastasis in patients with eyelid SC.

Methods

A retrospective case–control study was performed, comprising 320 patients with eyelid SC. Cox analyses were employed to investigate predictors of metastasis-free survival (MFS), and a nomogram was established and validated by the bootstrap method.

Results

Forty patients (12.5%) developed nodal metastasis during a median follow-up of 48.0 months, and the median period between the initial treatment and first nodal metastasis was 18.5 months (range 6.0–80.0 months). The 1-year, 3-year and 5-year nodal metastasis rates were 5.5%, 12.5% and 15.4%, respectively. Diffuse pattern (HR: 4.34, 95% CI 1.75 to 10.76, p=0.002), orbital invasion at presentation (HR: 3.22, 95% CI 1.42 to 7.33, p=0.005), perineural invasion (HR: 3.24, 95% CI 1.11 to 9.49, p=0.032) and high Ki-67 percentage (HR: 1.03, 95% CI 1.01 to 1.05, p<0.001) were identified as independent risk factors for nodal metastasis. A nomogram that integrated these four factors had a C-index of 0.785, demonstrating a strong power in predicting nodal metastasis of eyelid SC.

Conclusions

We identified risk factors for nodal metastasis and developed a nomogram to provide individualised estimates of nodal metastasis for eyelid SC patients and guide postoperative management. This nomogram contained clinicopathological factors besides the T category of the TNM staging system and suggesting great clinical value.

Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation

The classic prostate cancer (PCa) diagnostic pathway that is based on prostate-specific antigen (PSA) levels and the findings of digital rectal examination followed by systematic biopsy has shown multiple limitations. The use of multiparametric MRI (mpMRI) is now widely accepted in men with clinical suspicion for PCa. In addition, clinical information, PSA density, risk calculators, and genomic and other "omics" biomarkers are being used to improve risk stratification. On the basis of mpMRI and MRI-targeted biopsies (MRI-TBx), new diagnostic pathways have been established, aiming to improve the limitations of the classic diagnostic approach. However, these pathways still show limitations associated with mpMRI and MRI-TBx. Definitive PCa diagnosis is made on the basis of histopathologic Gleason grading, which has demonstrated an excellent correlation with clinical outcomes. However, Gleason grading is done subjectively by pathologists and involves poor reproducibility, and PCa may have a heterogeneous distribution of histologic patterns. Thus, important discrepancies persist between biopsy tumor grading and final whole-organ pathologic assessment after radical prostatectomy. PCa offers a unique opportunity to establish a real radiologic-pathologic correlation, as whole-mount radical prostatectomy specimens permit a complete spatial relationship with mpMRI. Artificial intelligence is increasingly being applied to radiologic and pathologic images to improve clinical accuracy and efficiency in PCa diagnosis. This review delineates current PCa diagnostic pathways, with a focus on the role of mpMRI, MRI-TBx, and pathologic analysis. An overview of the expected improvements in PCa diagnosis derived from the use of artificial intelligence, integrated radiologic-pathologic systems, and decision support tools for multidisciplinary teams is provided. An invited commentary by Purysko is available online. Online supplemental material is available for this article. ^©RSNA, 2021.

Engineered hypoxia-responding Escherichia coli carrying cardiac peptide genes, suppresses tumor growth, angiogenesis and metastasis in vivo

Development of engineered non-pathogenic bacteria, capable of expressing anti-cancer proteins under tumor-specific conditions, is an ideal approach for selectively eradicating proliferating cancer cells. Herein, using an engineered hypoxia responding nirB promoter, we developed an engineered Escherichia coli BW25133 strain capable of expressing cardiac peptides and GFP signaling protein under hypoxic condition for spatiotemporal targeting of mice mammary tumors. Following determination of the in vitro cytotoxicity profile of the engineered bacteria, selective accumulation of bacteria in tumor microenvironment was studied 48 h after tail vein injection of 108 cfu bacteria in animals. For in vivo evaluation of antitumoral activities, mice with establishment mammary tumors received 3 consecutive intravenous injections of transformed bacteria with 4-day intervals and alterations in expression of tumor growth, invasion and angiogenesis specific biomarkers (Ki-67, VEGFR, CD31and MMP9 respectively), as well as fold changes in concentration of proinflammatory cytokines were examined at the end of the 24-day study period. Intravenously injected bacteria could selectively accumulate in tumor site and temporally express GFP and cardiac peptides in response to hypoxia, enhancing survival rate of tumor bearing mice, suppressing tumor growth rate and expression of MMP-9, VEGFR2, CD31 and Ki67 biomarkers. Applied engineered bacteria could also significantly reduce concentrations of IL-1β, IL-6, GC-SF, IL-12 and TNF-α proinflammatory cytokines while increasing those of IL-10, IL-17A and INF-γ. Overall, administration of hypoxia-responding E. coli bacteria, carrying cardiac peptide expression construct could effectively suppress tumor growth, angiogenesis, invasion and metastasis and enhance overall survival of mice bearing mammary tumors.

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

BACKGROUND

Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.

METHODS

The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.

RESULTS

The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.

CONCLUSION

ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

A Systematic Review of Artificial Intelligence in Prostate Cancer

The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.

Oligometastatic Prostate Adenocarcinoma. Clinical-Pathologic Study of a Histologically Under-Recognized Prostate Cancer

The clinical parameters and the histological and immunohistochemical findings of a prospective protocolized series of 27 prostate carcinoma patients with oligometastatic disease followed homogeneously were analyzed. Lymph nodes (81.5%) and bones (18.5%) were the only metastatic sites. Local control after metastatic directed treatment was achieved in 22 (81.5%) patients. A total of 8 (29.6%) patients developed castration-resistant prostate cancer. Seventeen (63%) patients presented with non-organ confined disease. The Gleason index 8-10 was the most frequently observed (12 cases, 44.4%) combined grade. Positive immunostainings were detected with androgen receptor (100%), PGP 9.5 (74%), ERG (40.7%), chromogranin A (29.6%), and synaptophysin (18.5%) antibodies. The Ki-67 index value > 5% was observed in 15% of the cases. L1CAM immunostaining was negative in all cases. Fisher exact test showed that successful local control of metastases was associated to mild inflammation, organ confined disease, Ki-67 index < 5%, and Gleason index 3 + 3. A castration resistant status was associated with severe inflammation, atrophy, a Gleason index higher than 3 + 3, Ki-67 index ≥ 5%, and positive PGP 9.5, chromogranin A, and synaptophysin immunostainings. In conclusion, oligometastatic prostate adenocarcinoma does not have a specific clinical-pathologic profile. However, some histologic and immunohistochemical parameters of routine use may help with making therapeutic decisions.

Integrated Analysis of Distant Metastasis-Associated Genes and Potential Drugs in Colon Adenocarcinoma

Background: Most colon adenocarcinoma (COAD) patients die of distant metastasis, though there are some therapies for metastatic COAD. However, the genes exclusively expressed in metastatic COAD remain unclear. This study aims to identify prognosis-related genes associated with distant metastasis and develop therapeutic strategies for COAD patients.

Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA; n = 514) cohort were analyzed as a discovery dataset. Next, the data from the GEPIA database and PROGgeneV2 database were used to validate our analysis. Key genes were identified based on the differential miRNA and mRNA expression with respect to M stage. The potential drugs targeting candidate differentially expressed genes (DEGs) were also investigated.

Results: A total of 127 significantly DEGs in patients with distant metastasis compared with patients without distant metastasis were identified. Then, four prognosis-related genes (LEP, DLX2, CLSTN2, and REG3A) were selected based on clustering analysis and survival analysis. Finally, three compounds targeting the candidate DEGs, including ajmaline, TTNPB, and dydrogesterone, were predicted to be potential drugs for COAD.

Conclusions: This study revealed that distant metastasis in COAD is associated with a specific group of genes, and three existing drugs may suppress the distant metastasis of COAD.

Single nucleotide polymorphisms within the Wnt pathway predict the risk of bone metastasis in patients with non-small cell lung cancer

The Wingless-type (Wnt) signaling pathway plays an important role in the development and progression of cancer. This study aimed to evaluate the relationship between single nucleotide polymorphisms (SNPs) in the Wnt pathway and the risk of bone metastasis in patients with non-small cell lung cancer (NSCLC). We collected 500 blood samples from patients with NSCLC and genotyped eight SNPs from four core genes (WNT2, AXIN1, CTNNB1 and APC) present within the WNT pathway. Moreover, we assessed the potential relationship of these genes with bone metastasis development. Our results showed that the AC/AA genotype of CTNNB1: rs1880481 was associated with a decreased risk of bone metastasis. Polymorphisms with an HR of < 1 had a cumulative protective impact on the risk of bone metastasis. Furthermore, patients with the AC/AA genotype of CTNNB1: rs1880481 was associated with Karnofsky performance status score, squamous cell carcinoma antigen and Ki-67 proliferation index. Lastly, patients with the AC/AA genotype of CTNNB1: rs1880481 had significantly longer median progression free survival time than those with the CC genotype. In conclusion, SNPs within the Wnt signaling pathway are associated with a decreased risk of bone metastasis, and may be valuable biomarkers for bone metastasis in patients with NSCLC.

Ki-67 is an independent predictor of prostate cancer death in routine needle biopsy samples: proving utility for routine assessments

Standard clinical parameters fail to accurately differentiate indolent from aggressive prostate cancer. Our previous studies showed that immunohistochemical testing for Ki-67 improved prediction of prostate cancer death in a previous cohort of conservatively treated clinically localized prostate cancer. However there is a need for validation of usage with whole biopsy sections rather than tissue micro-arrays for use in routine diagnostics. Prostate cancer biopsy cases were identified in the UK, between 1990 and 2003, treated conservatively. Tumor extent and prostate-specific antigen (PSA) serum measurements were available. Biopsy cases were centrally reviewed by three uropathologists and Gleason conformed to contemporary ISUP 2014 criteria. Follow-up was through cancer registries up until 2012. Deaths were divided into those from prostate cancer and those from other causes. The percentage of Ki-67 in tumor cells was evaluated by immunohistochemistry on whole biopsy sections and was available for 756 patients. This percentage was used in analysis of cancer specific survival using a Cox proportional hazards model. In univariate analysis, the interquartile hazard ratio (HR) (95% confidence intervals) for continuous Ki-67 was 1.68 (1.49, 1.89), χ₁² = 47.975, P < 0.001. In grade groups 1 and 2, continuous Ki-67 was a statistically significant predictor of time to death from prostate cancer, HR (95% CI) = 1.97 (1.34, 2.88), χ₁² = 9.017, p = 0.003. In multivariate analysis, continuous Ki-67 added significant predictive information to that provided by grade groups, extent of disease and serum PSA, HR (95% CI) = 1.34 (1.16, 1.54), Δχ₁² = 13.703, P < 0.001. We now advocate the introduction of Ki-67 as a viable and practicable prognostic biomarker in clinical practice. The association of Ki-67 with mortality was highest in grade groups 1 and 2, showing that Ki-67 can be used as a routine biomarker in patients being considered for active surveillance.

Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer

BACKGROUND

Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states.

RESULTS

In this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study.

CONCLUSIONS

Metabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena.

Actinidia chinensis Planch root extract attenuates proliferation and metastasis of hepatocellular carcinoma by inhibiting epithelial-mesenchymal transition

ETHNO-PHARMACOLOGICAL RELEVANCE

Numerous studies have demonstrated the potent anticancer activity of various Chinese herbs. Actinidia chinensis Planch root (acRoots), a traditional Chinese medicine, functions as an antitumor and detoxifying agent and plays a role in diuresis and hemostasis. Treatment with acRoots confers strong inhibition of tumor growth in various forms of cancer. Here, we evaluated the anticancer activity and molecular mechanisms of Actinidia chinensis Planch root extract (acRoots) on hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Our previous study used mRNA chip analyses to identify the genes regulated by acRoots. Further analyses of the altered genes identified a key regulator of genes in response to acRoots. Here, the effects of acRoots on HCC cell proliferation, migration, invasion, and apoptosis were evaluated by cell counting, Transwell and apoptosis assays. In addition, the in vivo anti-HCC effects of acRoots were investigated using an HCC animal model. The expression of a key regulator of genes in response to acRoots was analyzed using quantitative polymerase chain reaction and western blotting.

RESULTS

Treatment with acRoots (10 mg/mL) had no cytotoxicity in L02 cells and had a positive effect on L02 cell viability; however, it significantly inhibited HCC cell proliferation. Treatment with acRoots downregulated DLX2 gene expression in HCC cells, and high DLX2 expression was associated with advanced stage and poor prognosis in patients with HCC. Treatment with acRoots inhibited proliferation, invasion and migration, clonality, and the epithelial-to-mesenchymal transition, and promoted the apoptosis of HCC cells by downregulating DLX2 expression. HCC cells with higher DLX2 expression were more sensitive to acRoots.

CONCLUSIONS

acRoots inhibited the malignant biological behavior of HCC cells via regulation of the epithelial-mesenchymal transition (EMT) by DLX2.

Oncogenic Metabolism Acts as a Prerequisite Step for Induction of Cancer Metastasis and Cancer Stem Cell Phenotype

Metastasis is a major obstacle to the efficient and successful treatment of cancer. Initiation of metastasis requires epithelial-mesenchymal transition (EMT) that is regulated by several transcription factors, including Snail and ZEB1/2. EMT is closely linked to the acquisition of cancer stem cell (CSC) properties and chemoresistance, which contribute to tumor malignancy. Tumor suppressor p53 inhibits EMT and metastasis by negatively regulating several EMT-inducing transcription factors and regulatory molecules; thus, its inhibition is crucial in EMT, invasion, metastasis, and stemness. Metabolic alterations are another hallmark of cancer. Most cancer cells are more dependent on glycolysis than on mitochondrial oxidative phosphorylation for their energy production, even in the presence of oxygen. Cancer cells enhance other oncogenic metabolic pathways, such as glutamine metabolism, pentose phosphate pathway, and the synthesis of fatty acids and cholesterol. Metabolic reprogramming in cancer is regulated by the activation of oncogenes or loss of tumor suppressors that contribute to tumor progression. Oncogenic metabolism has been recently linked closely with the induction of EMT or CSC phenotypes by the induction of several metabolic enzyme genes. In addition, several transcription factors and molecules involved in EMT or CSCs, including Snail, Dlx-2, HIF-1α, STAT3, TGF-β, Wnt, and Akt, regulate oncogenic metabolism. Moreover, p53 induces metabolic change by directly regulating several metabolic enzymes. The collective data indicate the importance of oncogenic metabolism in the regulation of EMT, cell invasion and metastasis, and adoption of the CSC phenotype, which all contribute to malignant transformation and tumor development. In this review, we highlight the oncogenic metabolism as a key regulator of EMT and CSC, which is related with tumor progression involving metastasis and chemoresistance. Targeting oncometabolism might be a promising strategy for the development of effective anticancer therapy.

Disruption of MEK/ERK/c-Myc signaling radiosensitizes prostate cancer cells in vitro and in vivo

PURPOSE

Prostate cancer (PCa) cell radioresistance causes the failure of radiation therapy (RT) in localized or locally advanced disease. The aberrant accumulation of c-Myc oncoprotein, known to promote PCa onset and progression, may be due to the control of gene transcription and/or MEK/ERK-regulated protein stabilization. Here, we investigated the role of MEK/ERK signaling in PCa.

METHODS

LnCAP, 22Rv1, DU145, and PC3 PCa cell lines were used in in vitro and in vivo experiments. U0126, trametinib MEK/ERK inhibitors, and c-Myc shRNAs were used. Radiation was delivered using an x-6 MV photon linear accelerator. U0126 in vivo activity alone or in combination with irradiation was determined in murine xenografts.

RESULTS

Inhibition of MEK/ERK signaling down-regulated c-Myc protein in PCa cell lines to varying extents by affecting expression of RNA and protein, which in turn determined radiosensitization in in vitro and in vivo xenograft models of PCa cells. The crucial role played by c-Myc in the MEK/ERK pathways was demonstrated in 22Rv1 cells by the silencing of c-Myc by means of short hairpin mRNA, which yielded effects resembling the targeting of MEK/ERK signaling. The clinically approved compound trametinib used in vitro yielded the same effects as U0126 on growth and C-Myc expression. Notably, U0126 and trametinib induced a drastic down-regulation of BMX, which is known to prevent apoptosis in cancer cells.

CONCLUSIONS

The results of our study suggest that signal transduction-based therapy can, by disrupting the MEK/ERK/c-Myc axis, reduce human PCa radioresistance caused by increased c-Myc expression in vivo and in vitro and restores apoptosis signals.

Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test

BACKGROUND

Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.

METHODS

A prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan-Meier, and hazards ratio.

RESULTS

In the training cohort (n = 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76-0.86], HR:6.7, [95% CI: 3.59-12.45], p < 0.00001. Results were confirmed in validation (n = 284) with a C-index 0.77 [95% CI: 0.72-0.81], HR = 5.4, [95% CI: 2.74-10.52], p < 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.

CONCLUSIONS

Precise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.

The organic ester O,O'-diethyl-(S,S)-ethylenediamine-N,N'-di-2-(3-cyclohexyl)propanoate dihydrochloride attenuates murine breast cancer growth and metastasis

Pharmacological treatment of cancer is mostly limited by drug-toxicity and resistance. It has been noticed that new organic ester ligand, O,O’-diethyl-(S,S)-ethylenediamine-N,N’-di-2-(3-cyclohexyl)propanoate dihydrochloride (named DE-EDCP) showed effective cytotoxic capacities against several human and mouse cancer cell lines. However, its effects on tumor growth and metastasis are unexplored. The aim of present study was to examine the ability of DE-EDCP to inhibit 4T1 murine breast cancer growth and progression and to explore possible molecular mechanisms. DE-EDCP exhibited significant tumoricidal activity on human and murine breast cancer cell lines. Further, marked reduction of murine breast cancer growth and progression by DE-EDCP was shown. DE-EDCP exhibits fewer side-effects compared to cisplatin as a conventional chemotherapeutic. Results obtained from in vivo and in vitro experiments indicate that DE-EDCP induces apoptosis and inhibits proliferation of 4T1 cells. DE-EDCP increases percentage of 4T1 cells in late apoptosis, expression of pro-apoptotic Bax and caspase-3, while decreases expression of anti-apoptotic Bcl-2. DE-EDCP treatment increased the percentage of TUNEL-positive nuclei and reduced Ki-67 expression in breast cancer tissue. DE-EDCP decreased expression of cyclin D3 and Ki-67, increased expression of cyclin-dependent kinase inhibitors p16, p21 and p27 and arrested 4T1 cells in G0/G1 cell cycle phase. Expression of STAT3 and downstream regulated molecules, NANOG and SOX2, was reduced in 4T1 cells after DE-EDCP treatment.

In conclusion, DE-EDCP impairs breast cancer growth and progression by triggering cancer cell death and inhibition of cancer cell proliferation. DE-EDCP might be of interest in the development of the new anticancer agent.

Is Ki67 prognostic for aggressive prostate cancer? A multicenter real-world study

AIM

To test if Ki67 expression is prognostic for biochemical recurrence (BCR) after radical prostatectomy (RP).

METHODS

Ki67 immunohistochemistry was performed on tissue microarrays constructed from specimens obtained from 464 men undergoing RP at the Durham and West LA Veterans Affairs Hospitals. Hazard ratios (HR) for Ki67 expression and time to BCR were estimated using Cox regression.

RESULTS

Ki67 was associated with more recent surgery year (p < 0.001), positive margins (p = 0.001) and extracapsular extension (p < 0.001). In center-stratified analyses, the adjusted HR for Ki67 expression and BCR approached statistical significance for west LA (HR: 1.54; p = 0.06), but not Durham (HR: 1.10; p = 0.74).

CONCLUSION

This multi-institutional 'real-world' study provides limited evidence for the prognostic role of Ki67 in predicting outcome after RP.

The prognostic value of Ki67 in ovarian high-grade serous carcinoma: an 11-year cohort study of Chinese patients

OBJECTIVE

This study sought to assess the prognostic role of Ki67 in primary ovarian high-grade serous carcinoma (HGSC) and to determine whether Ki67 expression can predict responsiveness to platinum and paclitaxel chemotherapy.

RESULTS

A total of 318 women were included in the analysis and the median follow-up time was 48 months (range, 3-150 months). Ki67 proliferation indices ranged from 3% to 95% with a median of 40%. Using 40% as the cut-off value for the Ki67 index, we classified 141 patients as having low Ki67 expression and 177 patients as having high Ki67 expression. Low Ki67 expression was a predictor of platinum resistance (hazard ratio (HR) 2.85, 95% CI 1.43-5.98, P < 0.001). In the Kaplan-Meier analysis, comparisons of patients with low versus high Ki67 expression demonstrated that low Ki67 expression was significantly associated with decreased progression-free survival (PFS) (22% vs. 34% for 5-year PFS, P < 0.001) and decreased overall survival (OS) (31% vs. 55%, P < 0.001). Multivariate analysis indicated that low Ki67 expression was associated with decreased PFS (HR 2.98, 95% CI 1.75-6.56, P < 0.001) and decreased OS (HR 1.74, 95% CI 1.38-5.01, P = 0.003).

MATERIALS AND METHODS

A retrospective study of patients with stage I-IV primary ovarian HGSC was conducted from January 1, 2002, to December 31, 2012. Ki67 levels were measured via immunohistochemistry (IHC) and analyzed with respect to clinicopathological factors, and a survival analysis was performed.

CONCLUSIONS

HGSC appears to be a heterogeneous disease with different clinical outcomes. Low Ki67 expression (< 40%) in HGSC is significantly associated with platinum resistance and decreased survival.

High immunoexpression of Ki67, EZH2, and SMYD3 in diagnostic prostate biopsies independently predicts outcome in patients with prostate cancer

INTRODUCTION

Overtreatment is a major concern in patients with prostate cancer (PCa). Prognostic biomarkers discriminating indolent from aggressive disease in prostate biopsy are urgently needed. We aimed to evaluate the prognostic value of Ki67, EZH2, LSD1, and SMYD3 immunoexpression in diagnostic biopsies from a cohort of PCa patients with long term follow-up.

MATERIALS AND METHODS

A series of 189 consecutive prostate biopsies diagnosed with PCa (1997-2001) in a cancer center was included in the study, with follow-up last updated in November 2016. Biopsies were reviewed and graded according to 2016 WHO criteria. Immunohistochemistry was performed in the most representative block. Nuclear staining was assessed using digital image analysis. Study outcomes included disease-specific, disease-free, and progression-free survival. Statistical analysis was tabulated using SPSS version 22.0. Survival curves and hazard ratios (HRs) were estimated using Kaplan-Meyer and Cox-regression models, respectively. Statistical significance was set at P<0.05.

RESULTS

The proportion of patients who completed the study was 177/189 (94%). In univariable analysis, high Ki67, EZH2, and SMYD3 immunoexpression associated with significantly worse disease-specific survival (HR = 1.86, 95% CI: 1.05-3.29; HR = 1.87, 95% CI: 1.10-3.27; HR = 2.68, 95% CI: 1.02-7.92). In multivariable analysis, the 3 biomarkers displayed significantly worse DSS adjusted for CAPRA score (HR = 1.78, 95% CI: 1.01-3.16; HR = 1.93, 95% CI: 1.12-3.32; HR = 2.71, 95% CI: 1.04-7.10). Among patients with low/intermediate risk CAPRA score, high Ki67 immunoexpression identified those more prone to experience disease recurrence (HR = 9.20, 95% CI: 1.27-66.44) and progression (HR = 2.97, 95% CI: 1.05-8.43).

CONCLUSIONS

High Ki67, EZH2, and SMYD3 immunoexpression, adjusted for standard clinicopathological parameters, independently predicts outcome in patients with PCa, at diagnosis. This might assist in discriminating indolent from aggressive PCa, improving treatment selection.

Epigenetic Signature: A New Player as Predictor of Clinically Significant Prostate Cancer (PCa) in Patients on Active Surveillance (AS)

Widespread prostate-specific antigen (PSA) testing notably increased the number of prostate cancer (PCa) diagnoses. However, about 30% of these patients have low-risk tumors that are not lethal and remain asymptomatic during their lifetime. Overtreatment of such patients may reduce quality of life and increase healthcare costs. Active surveillance (AS) has become an accepted alternative to immediate treatment in selected men with low-risk PCa. Despite much progress in recent years toward identifying the best candidates for AS in recent years, the greatest risk remains the possibility of misclassification of the cancer or missing a high-risk cancer. This is particularly worrisome in men with a life expectancy of greater than 10–15 years. The Prostate Cancer Research International Active Surveillance (PRIAS) study showed that, in addition to age and PSA at diagnosis, both PSA density (PSA-D) and the number of positive cores at diagnosis (two compared with one) are the strongest predictors for reclassification biopsy or switching to deferred treatment. However, there is still no consensus upon guidelines for placing patients on AS. Each institution has its own protocol for AS that is based on PRIAS criteria. Many different variables have been proposed as tools to enrol patients in AS: PSA-D, the percentage of freePSA, and the extent of cancer on biopsy (number of positive cores or percentage of core involvement). More recently, the Prostate Health Index (PHI), the 4 Kallikrein (4K) score, and other patient factors, such as age, race, and family history, have been investigated as tools able to predict clinically significant PCa. Recently, some reports suggested that epigenetic mapping differs significantly between cancer patients and healthy subjects. These findings indicated as future prospect the use of epigenetic markers to identify PCa patients with low-grade disease, who are likely candidates for AS. This review explores literature data about the potential of epigenetic markers as predictors of clinically significant disease.

Rapid and Short-term Extracellular Matrix-mediated In Vitro Culturing of Tumor and Nontumor Human Primary Prostate Cells From Fresh Radical Prostatectomy Tissue

OBJECTIVE

To culture prostate cells from fresh biopsy core samples from radical prostatectomy (RP) tissue. Further, given the genetic heterogeneity of prostate cells, the ability to culture single cells from primary prostate tissue may be of importance toward enabling single-cell characterization of primary prostate tissue via molecular and cellular phenotypic biomarkers.

METHODS

A total of 260 consecutive tissue samples from RPs were collected between October 2014 and January 2016, transported at 4°C in serum-free media to an off-site central laboratory, dissociated, and cultured. A culture protocol, including a proprietary extracellular matrix formulation (ECMf), was developed that supports rapid and short-term single-cell culture of primary human prostate cells derived from fresh RP samples.

RESULTS

A total of 251 samples, derived from RP samples, yielded primary human tumor and nontumor prostate cells. Cultured cells on ECMf exhibit (1) survival after transport from the operating room to the off-site centralized laboratory, (2) robust (>80%) adhesion and survival, and (3) expression of different cell-type-specific markers. Cells derived from samples of increasing Gleason score exhibited a greater number of focal adhesions and more focal adhesion activation as measured by phospho-focal adhesion kinase (Y397) immunofluorescence when patient-derived cells were cultured on ECMf. Increased Ki67 immunofluorescence levels were observed in cells derived from cancerous RP tissue when compared to noncancerous RP tissue.

CONCLUSION

By utilizing a unique and defined extracellular matrix protein formulation, tumor and nontumor cells derived from primary human prostate tissue can be rapidly cultured and analyzed within 72 hours after harvesting from RP tissue.