Dataset Information

EMT Markers in Locally-Advanced Prostate Cancer: Predicting Recurrence?

ABSTRACT: Background: Prostate cancer (PCa) is the second most frequent cause of cancer-related death in men worldwide. It is a heterogeneous disease at molecular and clinical levels which makes its prognosis and treatment outcome hard to predict. The epithelial-to-mesenchymal transition (EMT) marks a key step in the invasion and malignant progression of PCa. We sought to assess the co-expression of epithelial cytokeratin 8 (CK8) and mesenchymal vimentin (Vim) in locally-advanced PCa as indicators of EMT and consequently predictors of the progression status of the disease. Methods: Co-expression of CK8 and Vim was evaluated by immunofluorescence (IF) on paraffin-embedded tissue sections of 122 patients with PCa who underwent radical prostatectomies between 1998 and 2016 at the American University of Beirut Medical Center (AUBMC). EMT score was calculated accordingly and then correlated with the patients' clinicopathological parameters and PSA failure. Results: The co-expression of CK8/Vim (EMT score), was associated with increasing Gleason group. A highly significant linear association was detected wherein higher Gleason group was associated with higher mean EMT score. In addition, the median estimated biochemical recurrence-free survival for patients with < 25% EMT score was almost double that of patients with more than 25%. The validity of this score for prediction of prognosis was further demonstrated using cox regression model. Our data also confirmed that the EMT score can predict PSA failure irrespective of Gleason group, pathological stage, or surgical margins. Conclusion: This study suggests that assessment of molecular markers of EMT, particularly CK8 and Vim, in radical prostatectomy specimens, in addition to conventional clinicopathological prognostic parameters, can aid in the development of a novel system for predicting the prognosis of locally-advanced PCa.

SUBMITTER: Cheaito KA

PROVIDER: S-EPMC6421270 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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EMT Markers in Locally-Advanced Prostate Cancer: Predicting Recurrence?

Cheaito Katia A KA Bahmad Hisham F HF Hadadeh Ola O Saleh Eman E Dagher Christelle C Hammoud Miza Salim MS Shahait Mohammad M Mrad Zaki Abou ZA Nassif Samer S Tawil Ayman A Bulbul Muhammad M Khauli Raja R Wazzan Wassim W Nasr Rami R Shamseddine Ali A Temraz Sally S El-Sabban Marwan E ME El-Hajj Albert A Mukherji Deborah D Abou-Kheir Wassim W

Frontiers in oncology 20190311

<b>Background:</b> Prostate cancer (PCa) is the second most frequent cause of cancer-related death in men worldwide. It is a heterogeneous disease at molecular and clinical levels which makes its prognosis and treatment outcome hard to predict. The epithelial-to-mesenchymal transition (EMT) marks a key step in the invasion and malignant progression of PCa. We sought to assess the co-expression of epithelial cytokeratin 8 (CK8) and mesenchymal vimentin (Vim) in locally-advanced PCa as indicators ...[more]

PMID: 30915272

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Project description:BackgroundProstate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa.MethodsWe systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers.ResultsBased on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups.ConclusionOverall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

Project description:Background The treatment strategy for locally advanced rectal cancer (LARC) has recently expanded from total mesorectal excision to additional neoadjuvant chemoradiotherapy (nCRT) and/or systemic chemotherapy (NAC). Data on disease recurrence after each treatment strategy are limited. Methods Clinical stage II to III rectal cancer patients who underwent curative surgery between July 2005 and February 2021 were analyzed. The cumulative incidence and site of first recurrence were assessed. The median follow-up duration was 4.6 years. Results Among the 332 patients, we performed nCRT and NAC in 15.4% (N=51) and 14.8% (N=49), respectively. The overall recurrence rate was 23.5% (N=78). Although several differences in tumor stage or location were observed, there was no significant difference in the rate among the surgery alone (N=54, 23.3%), nCRT (N=11, 21.6%), and NAC (N=13, 26.5%) groups. In this cohort, the local recurrence rate (18.4%) was higher than the rate of distant metastasis in the NAC group (14.3%). All patients with recurrence in the nCRT group had distant metastases (N=11: one patient had distant and local recurrences simultaneously). For pathological stage 0-I, the recurrence rate was higher in the nCRT and NAC groups than in the surgery-alone group (nCRT, 10.0%; NAC, 15.4%; and surgery-alone, 2.0%). Curative-intent resection of distant-only recurrences significantly improved patients’ overall survival (hazard ratio [95% confidence interval], 0.34 [0.14–0.84]), which was consistent even when stratified according to neoadjuvant treatment. Regardless of neoadjuvant treatment, >80% of recurrences occurred in the first 2.2 years, and 98.7% within 5 years after surgery. Conclusion Regardless of neoadjuvant treatment, detecting distant metastases with intensive surveillance, particularly in the first 2 years after surgery, is important. Also, even if neoadjuvant treatment can downstage LARC to pathological stage 0-I, careful follow-up is needed. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-023-03136-0.

Project description:Background The first sign of metastatic prostate cancer after radical prostatectomy is rising PSA levels in the blood, termed biochemical recurrence. The prediction of recurrence relies mainly on the morphological assessment of prostate cancer using the Gleason grading system. However, in this system, within-grade morphological patterns and subtle histopathological features are currently omitted, leaving a significant amount of prognostic potential unexplored. Methods To discover additional prognostic information using artificial intelligence, we trained a deep learning system to predict biochemical recurrence from tissue in H&E-stained microarray cores directly. We developed a morphological biomarker using convolutional neural networks leveraging a nested case-control study of 685 patients and validated on an independent cohort of 204 patients. We use concept-based explainability methods to interpret the learned tissue patterns. Results The biomarker provides a strong correlation with biochemical recurrence in two sets (n = 182 and n = 204) from separate institutions. Concept-based explanations provided tissue patterns interpretable by pathologists. Conclusions These results show that the model finds predictive power in the tissue beyond the morphological ISUP grading. Plain language summary To determine the prognosis of patients with prostate cancer, several clinical factors are taken into account. One of these is the cancer grade, assigned by a pathologist based on the cancer’s appearance under a microscope. The grade ranges from 1 to 5, where 5 is the most aggressive tumour type. This study explored whether deep learning—a technique in which computer software learns patterns from multiple examples—can learn to predict the risk of patients’ cancers recurring from microscopic images of the tumours. We show, on two clinical datasets from different institutions, that such a system can help to better predict prognosis, beyond the information provided by grade alone. In the future, this type of method could help clinicians to predict the prognosis of individual prostate cancer patients. Pinckaers et al. develop a deep learning system to predict biochemical recurrence in prostate cancer patients treated with radical prostatectomy. The authors’ morphological biomarker provides predictive power beyond traditional Gleason grading, based on analysis of two clinical datasets from different institutions.

Project description:BackgroundProstate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.MethodsData were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.ResultsThe weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.ConclusionIn summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.

Dataset Information

EMT Markers in Locally-Advanced Prostate Cancer: Predicting Recurrence?

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EMT Markers in Locally-Advanced Prostate Cancer: Predicting Recurrence?

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