scholarly journals SATB1, genomic instability and Gleason grading constitute a novel risk score for prostate cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph Dumke ◽  
Timo Gemoll ◽  
Martina Oberländer ◽  
Sandra Freitag-Wolf ◽  
Christoph Thorns ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Risk Stratification ◽  
Genomic Instability ◽  
Prostate Cancer Risk ◽  
Image Cytometry ◽  
Clinicopathological Parameters ◽  
Individual Risk ◽  
Clinical Risk ◽  
Satb1 Expression

AbstractCurrent prostate cancer risk classifications rely on clinicopathological parameters resulting in uncertainties for prognostication. To improve individual risk stratification, we examined the predictive value of selected proteins with respect to tumor heterogeneity and genomic instability. We assessed the degree of genomic instability in 50 radical prostatectomy specimens by DNA-Image-Cytometry and evaluated protein expression in related 199 tissue-microarray (TMA) cores. Immunohistochemical data of SATB1, SPIN1, TPM4, VIME and TBB5 were correlated with the degree of genomic instability, established clinical risk factors and overall survival. Genomic instability was associated with a GS ≥ 7 (p = 0.001) and worse overall survival (p = 0.008). A positive SATB1 expression was associated with a GS ≤ 6 (p = 0.040), genomic stability (p = 0.027), and was a predictor for increased overall survival (p = 0.023). High expression of SPIN1 was also associated with longer overall survival (p = 0.048) and lower preoperative PSA-values (p = 0.047). The combination of SATB1 expression, genomic instability, and GS lead to a novel Prostate Cancer Prediction Score (PCP-Score) which outperforms the current D’Amico et al. stratification for predicting overall survival. Low SATB1 expression, genomic instability and GS ≥ 7 were identified as markers for poor prognosis. Their combination overcomes current clinical risk stratification regimes.

scholarly journals Entering an era of radiogenomics in prostate cancer risk stratification

2018 ◽  
Vol 7 (S4) ◽  
pp. S443-S452 ◽  
Author(s):  
Nachiketh Soodana-Prakash ◽  
Radka Stoyanova ◽  
Abhishek Bhat ◽  
Maria C. Velasquez ◽  
Omer E. Kineish ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Prostate Cancer Risk

scholarly journals Pre-treatment risk stratification of prostate cancer patients:A critical review

2012 ◽  
Vol 6 (2) ◽  
Author(s):  
George Rodrigues ◽  
Padraig Warde ◽  
Tom Pickles ◽  
Juanita Crook ◽  
Michael Brundage ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Gleason Score ◽  
Critical Review ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Original Research ◽  
Pre Treatment

Introduction:  The use of accepted prostate cancer risk stratification groups based on prostate-specific antigen, T stage and Gleason score assists in therapeutic treatment decision-making, clinical trial design and outcome reporting. The utility of integrating novel prognostic factors into an updated risk stratification schema is an area of current debate. The purpose of this work is to critically review the available literature on novel pre-treatment prognostic factors and alternative prostate cancer risk stratification schema to assess the feasibility and need for changes to existing risk stratification systems. Methods:  A systematic literature search was conducted to identify original research publications and review articles on prognostic factors and risk stratification in prostate cancer. Search terms included risk stratification, risk assessment, prostate cancer or neoplasms, and prognostic factors. Abstracted information was assessed to draw conclusions regarding the potential utility of changes to existing risk stratification schema. Results:  The critical review identified three specific clinically relevant potential changes to the most commonly used three-group risk stratification system: (1) the creation of a very-low risk category; (2) the splitting of intermediate-risk into a low- and highintermediate risk groups; and (3) the clarification of the interface between intermediate- and high-risk disease. Novel pathological factors regarding high-grade cancer, subtypes of Gleason score 7 and percentage biopsy cores positive were also identified as potentially important risk-stratification factors. Conclusions:  Multiple studies of prognostic factors have been performed to create currently utilized prostate cancer risk stratification systems. We propose potential changes to existing systems.

Prostate cancer risk calculators for healthy population: A systematic review (Preprint)

2021 ◽  
Author(s):  
Antonio Bandala-Jacques ◽  
Kevin Daniel Castellanos Esquivel ◽  
Fernanda Pérez-Hurtado ◽  
Cristobal Hernández-Silva ◽  
Nancy Reynoso-Noverón
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Cancer Risk ◽  
Digital Rectal Examination ◽  
Prostate Cancer Risk ◽  
Individual Risk ◽  
Healthy Population ◽  
Risk Of Cancer ◽  
The Impact

BACKGROUND Screening for prostate cancer has long been a debated, complex topic. The use of risk calculators for prostate cancer is recommended for determining patients’ individual risk of cancer and the subsequent need for a prostate biopsy. These tools could lead to a better discrimination of patients in need of invasive diagnostic procedures and for optimized allocation of healthcare resources OBJECTIVE To systematically review available literature on current prostate cancer risk calculators’ performance in healthy population, by comparing the impact factor of individual items on different cohorts, and the models’ overall performance. METHODS We performed a systematic review of available prostate cancer risk calculators targeted at healthy population. We included studies published from January 2000 to March 2021 in English, Spanish, French, Portuguese or German. Two reviewers independently decided for or against inclusion based on abstracts. A third reviewer intervened in case of disagreements. From the selected titles, we extracted information regarding the purpose of the manuscript, the analyzed calculators, the population for which it was calibrated, the included risk factors, and the model’s overall accuracy. RESULTS We included a total of 18 calculators across 53 different manuscripts. The most commonly analyzed ones were they PCPT and ERSPC risk calculators, developed from North American and European cohorts, respectively. Both calculators provided high precision for the diagnosis of aggressive prostate cancer (AUC as high as 0.798 for PCPT and 0.91 for ERSPC). We found 9 calculators developed from scratch for specific populations, which reached diagnostic precisions as high as 0.938. The most commonly included risk factors in the calculators were age, PSA levels and digital rectal examination findings. Additional calculators included race and detailed personal and family history CONCLUSIONS Both the PCPR and the ERSPC risk calculators have been successfully adapted for cohorts other than the ones they were originally created for with no loss of diagnostic accuracy. Furthermore, designing calculators from scratch considering each population’s sociocultural differences has resulted in risk tools that can be well adapted to be valid in more patients. The best risk calculator for prostate cancer will be that which was has been calibrated for its intended population and can be easily reproduced and implemented CLINICALTRIAL CRD42021242110

scholarly journals Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2200 ◽  
Author(s):  
Ahmad Algohary ◽  
Rakesh Shiradkar ◽  
Shivani Pahwa ◽  
Andrei Purysko ◽  
Sadhna Verma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Operating Characteristic ◽  
Prostate Cancer Risk ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set ◽  
And Diffusion ◽  
Risk Categories

Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.

108 TEMPLATE TRANSPERINEAL PROSTATE MAPPING BIOPSIES FOR PROSTATE CANCER RISK STRATIFICATION AND CANCER LOCALIZATION

2010 ◽  
Vol 9 (2) ◽  
pp. 67
Author(s):  
P.B. Singh ◽  
H.U. Ahmed ◽  
D. Stevens ◽  
P. Gurung ◽  
A. Freeman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Prostate Cancer Risk

Is clinical stage T2c prostate cancer intermediate- or high-risk disease? Results from the SEARCH database.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 123-123
Author(s):  
Abhay A Singh ◽  
Leah Gerber ◽  
Stephen J. Freedland ◽  
William J Aronson ◽  
Martha K. Terris ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Predictive Accuracy ◽  
Cox Model ◽  
Clinical Stage ◽  
Prostate Cancer Risk ◽  
Clinical Staging ◽  
Intermediate Risk ◽  
Concordance Index

123 Background: Clinical stage T2c is a nebulous factor in the algorithm for prostate cancer risk stratification. According to D’Amico risk stratification cT2c is high-risk category where NCCN guidelines place this stage in intermediate-risk. As diagnostic work up with the use of MRI continues to escalate clinical staging may become more important. As cT2c represents a possible decision fork in treatment decisions we sought to investigate which risk group the clinical behavior of cT2c tumors more closely resembles. Methods: We retrospectively analyzed data from 1089 men who underwent radical prostatectomy (RP) from 1988 to 2009 who did not have low-risk CaP from the SEARCH database. We compared time to BCR between men with cT2c disease, those with intermediate-risk (PSA 10-20 ng/ml or Gleason sum (GS) =7), and those with high-risk (PSA>20 ng/ml, GS 8-10, cT3) using Cox regression models adjusting for age, race, year of RP, center, and percent cores positive. We also compared predictive accuracy of two Cox models wherein cT2c was considered either intermediate- or high-risk by calculating concordance index c. Results: A total of 68 men (3.4%) had cT2c tumors. After a median follow-up of 47.5 months, there was no difference in BCR risk between men with intermediate-risk CaP and those with cT2c tumors (HR=0.90; p=0.60). In contrast, there was a trend for men with high-risk CaP to have nearly 50% increased BCR risk compared to men with cT2c tumors (HR=1.50; 95% CI=0.97-2.30; p=0.07) which did not reach statistical significance. Concordance index c was higher in the Cox model wherein cT2c tumors were considered intermediate-risk (c=0.6147) as opposed to high-risk (c=0.6106). Conclusions: BCR risk for patients with clinical stage T2c was more comparable to men who had intermediate-risk CaP than men with high-risk. In addition, a model which incorporates cT2c disease as intermediate-risk has better predictive accuracy. These findings suggest men with cT2c disease should be offered treatment options for men with intermediate-risk CaP. As clinical staging more routinely incorporates MRI there is the potential to better identify bilateral organ-confined CaP and further establish risk classification.

Validation of cAMP phosphodiesterase-4D7 (PDE4D7) for its independent contribution to risk stratification in a prostate cancer patient cohort with longitudinal biological outcomes.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 5069-5069
Author(s):  
Jos Rijntjes ◽  
Marcia Alves de Inda ◽  
Dianne van Strijp ◽  
Eveline den Biezen-Timmermans ◽  
Anne van Brussel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Surgical Margin ◽  
Low Risk ◽  
Camp Phosphodiesterase ◽  
Historical Cohort ◽  
Clinical Risk ◽  
Psa Relapse ◽  
Biological Outcomes

5069 Background: In this study we present the retrospective validation of the prognostic prostate cancer biomarker PDE4D7 in predicting longitudinal biological outcomes in a historical cohort of radical prostatectomy patients. Methods: Biopsy punches from 550 patients were collected from a representative tumor area of FFPE surgical resections. RNA was extracted and PDE4D7 quantified by one-step RT-qPCR. PDE4D7 scores were calculated by normalization of PDE4D7 to the averaged expression of four reference genes. The independent prognostic value of the PDE4D7 scores were evaluated using uni- and multivariate Cox proportional hazard regression. Multivariate analyses were adjusted for clinical prognostic variables. Post-surgical outcomes tested were: PSA relapse, start of salvage treatment, progression to metastases, overall and prostate cancer specific mortality. Logistic regression was used to create a combined prognostic model of PDE4D7 with clinical risk and tested in outcome prediction. Results: The PDE4D7 score was significantly associated with time to PSA failure after prostatectomy (HR 0.53; 95% CI 0.41-0.67 for each unit increase; p < 1.0E-04). After adjustment for pathology Gleason, pT stage, surgical margin status, and seminal vesicle invasion the HR was 0.55 (95% CI 0.43-0.72; p < 1.0E-04). Patients with a high PDE4D7 score that were clinically classified as intermediate to high risk of progression were re-classified into a group with an average progression risk less than the average cohort risk of clinically very low risk patients. The maximum benefit, compared to Gleason score, was observed in the clinically intermediate favorable risk group. Combining clinical risk with PDE4D7 scores improved the overall risk stratification. Conclusions: The PDE4D7 score has potential to provide independent risk information and, in particular, to re-stratify patients with clinical intermediate to high risk characteristics to a very low risk profile.

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