Prognostic Value of Gleason Score at Positive Surgical Margin in Prostate Cancer: A Systematic Review and Meta-analysis

Controversies in prostate cancer management: Consensus recommendations from experts in northern Spain

In recent years, various aspects of prostate cancer (PC) management have undergone significant changes, including the implementation of therapeutic strategies such as the use of new hormonal agents like abiraterone, apalutamide, enzalutamide or darolutamide and the incorporation of next generation imaging techniques (NGI). However, the evidence regarding the role of NGI and the therapeutic decision-making based on their findings is not solid. Following the methodology of the Advanced Prostate Cancer Consensus Conference (APCCC), a multidisciplinary expert consensus was developed to address controversial questions concerning the use of NGI and clinical management in four priority scenarios: localized PC, PC after radical prostatectomy, PC after radiotherapy with curative intent, and metastatic hormone-sensitive PC. This consensus represents the opinions of medical oncology, radiation oncology and urology physicians and provides useful recommendations for clinical practice.

Time-dependent personalized prognostic analysis by machine learning in biochemical recurrence after radical prostatectomy: a retrospective cohort study

BACKGROUND

For biochemical recurrence following radical prostatectomy for prostate cancer, treatments such as radiation therapy and androgen deprivation therapy are administered. To diagnose postoperative recurrence as early as possible and to intervene with treatment at the appropriate time, it is essential to accurately predict recurrence after radical prostatectomy. However, postoperative recurrence involves numerous patient-related factors, making its prediction challenging. The purpose of this study is to accurately predict the timing of biochemical recurrence after radical prostatectomy and to analyze the risk factors for follow-up of high-risk patients and early detection of recurrence.

METHODS

We utilized the machine learning survival analysis model called the Random Survival Forest utilizing the 58 clinical factors from 548 patients who underwent radical prostatectomy at Chiba University Hospital. To visualize prognostic factors and assess accuracy of the time course probability, we employed SurvSHAP(t) and time-dependent Area Under Cureve(AUC).

RESULTS

The time-dependent AUC of RSF was 0.785, which outperformed the Cox proportional hazards model (0.704), the Cancer of the Prostate Risk Assessment (CAPRA) score (0.710), and the D'Amico score (0.658). The key prognostic factors for early recurrence were Gleason score(GS), Seminal vesicle invasion(SV), and PSA. The contribution of PSA to recurrence decreases after the first year, while SV and GS increase over time.

CONCLUSION

Our prognostic model analyzed the time-dependent relationship between the timing of recurrence and prognostic factors. Our study achieved personalized prognosis analysis and its rationale after radical prostatectomy by employing machine learning prognostic model. This prognostic model contributes to the early detection of recurrence by enabling clinicians to conduct appropriate follow-ups for high-risk patients.

Machine learning discrimination of Gleason scores below GG3 and above GG4 for HSPC patients diagnosis

This study aims to develop machine learning (ML)-assisted models for analyzing datasets related to Gleason scores in prostate cancer, conducting statistical analyses on the datasets, and identifying meaningful features. We retrospectively collected data from 717 hormone-sensitive prostate cancer (HSPC) patients at Yunnan Cancer Hospital. Of these, data from 526 patients were used for modeling. Seven auxiliary models were established using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Extreme gradient boosting tree (XGBoost), Adaptive Boosting (Adaboost), and artificial neural network (ANN) based on 21 clinical biochemical indicators and features. Evaluation metrics included accuracy (ACC), precision (PRE), specificity (SPE), sensitivity (SEN) or regression rate(Recall), and f1 score. Evaluation metrics for the models primarily included ACC, PRE, SPE, SEN or Recall, f1 score, and area under the curve(AUC). Evaluation metrics were visualized using confusion matrices and ROC curves. Among the ensemble learning methods, RF, XGBoost, and Adaboost performed the best. RF achieved a training dataset score of 0.769 (95% CI: 0.759-0.835) and a testing dataset score of 0.755 (95% CI: 0.660-0.760) (AUC: 0.786, 95%CI: 0.722-0.803), while XGBoost achieved a training dataset score of 0.755 (95% CI: 95%CI: 0.711-0.809) and a testing dataset score of 0.745 (95% CI: 0.660-0.764) (AUC: 0.777, 95% CI: 0.726-0.798). Adaboost scored 0.789 on the training dataset (95% CI: 0.782-0.857) and 0.774 on the testing dataset (95% CI: 0.651-0.774) (AUC: 0.799, 95% CI: 0.703-0.802). In terms of feature importance (FI) in ensemble learning, Bone metastases at first visit, prostatic volume, age, and T1-T2 have significant proportions in RF's FI. fPSA, TPSA, and tumor burden have significant proportions in Adaboost's FI, while f/TPSA, LDH, and testosterone have the highest proportions in XGBoost. Our findings indicate that ensemble learning methods demonstrate good performance in classifying HSPC patient data, with TNM staging and fPSA being important classification indicators. These discoveries provide valuable references for distinguishing different Gleason scores, facilitating more accurate patient assessments and personalized treatment plans.

CCT6A dysregulation in surgical prostate cancer patients: association with disease features, treatment information, and prognosis

OBJECTIVE

Chaperonin-containing tailless complex polypeptide 1 subunit 6A (CCT6A) involves several solid cancers' development and progression, while its clinical utility in prostate cancer management is rarely revealed. Consequently, the present study intended to investigate the linkage of CCT6A with disease features, treatment information, and prognosis of surgical prostate cancer patients.

METHODS

CCT6A in 220 surgical prostate cancer patients was determined via immunohistochemistry. Additionally, survival analyses on data from the public databases were performed to validate the prognostic value of CCT6A further.

RESULTS

CCT6A expression was upregulated in tumor tissue than in adjacent tissue (P < 0.001). Increased CCT6A was related to elevated Gleason score (P < 0.001) and pathological T stage (P = 0.029). CCT6A was increased in patients with positive surgical margin status (vs. negative) (P = 0.029) and patients with adjuvant external-beam radiation therapy (vs. no) (P = 0.001). Concerning the prognostic value, high tumor CCT6A was linked with shortened disease-free survival (DFS) (P = 0.009), which was also validated through further Cox's proportional hazard regression model analyses (hazard ratio: 2.695, 95% CI: 1.086-6.683, P = 0.032), whereas CCT6A was not correlated with overall survival (OS) (P > 0.050). Additionally, the Gene Expression Profiling Interactive Analysis database indicated that high tumor CCT6A was related to shortened DFS (P = 0.036), but it was not associated with OS (P > 0.050); meanwhile, the Human Protein Atlas database suggested that high tumor CCT6A was linked with reduced OS (P = 0.048).

CONCLUSION

Tumor CCT6A high expression correlates with the elevated Gleason score, pathological T stage, and shortened DFS in surgical prostate cancer patients.

ERG Status at the Margin Is Associated With Biochemical Recurrence After Radical Prostatectomy With Positive Surgical Margins

Positive surgical margins at radical prostatectomy are associated with an increased risk of biochemical recurrence (BCR). However, there is considerable variability in outcomes, suggesting that molecular biomarkers-when assessed specifically at the margin tumor tissue-may be useful to stratify prognosis in this group. We used a case-cohort design for the outcome of BCR, selecting 215 patients from a cohort of 813 patients undergoing prostatectomy treated at the Johns Hopkins from 2008 to 2017 with positive margins and available clinical data. Tissue microarrays were created from the tumor adjacent to the positive margin and stained for PTEN, ERG, and Ki-67. Cases were scored dichotomously (PTEN and ERG) or by the Ki-67 staining index using previously validated protocols. The analysis used Cox proportional hazards models weighted for the case-cohort design. Overall, 20% (37/185) of evaluable cases had PTEN loss and 38% (71/185) had ERG expression, and the median Ki-67 expression was 0.42%. In multivariable analysis adjusting for the CAPRA-S score, adjuvant radiation, and grade group at the positive margin, ERG-positive tumors were associated with a higher risk of BCR compared to those that were ERGnegative (hazard ratio [HR], 2.4; 95% CI, 1.2-4.9; P = .012) regardless of PTEN status at the margin, and adding ERG to clinicopathologic variables increased the concordance index from 0.827 to 0.847. PTEN loss was associated with an increased risk of BCR on univariable analysis (HR, 3.19; 95% CI, 1.72-5.92; P = .0002), but this association did not remain after adjusting for clinicopathologic variables (HR, 1.06; 95% CI, 0.49-2.29; P = .890). Thus, in the setting of prostate tumors with positive surgical margins after prostatectomy, ERG-positive tumors with or without PTEN loss at the positive margin are associated with a significantly higher risk of BCR after adjusting for clinicopathologic variables. If validated, ERG status may be helpful in decision-making surrounding adjuvant therapy after prostatectomy.

The Effect of Adverse Surgical Margins on the Risk of Biochemical Recurrence after Robotic-Assisted Radical Prostatectomy

Positive surgical margins (PSM) after radical prostatectomy are associated with a greater risk of biochemical recurrence (BCR). However, not all PSM harbour the same prognosis for recurrence. We aim to determine the impact of different PSM characteristics and their coexistence on the risk of BCR. This retrospective study included 333 patients that underwent robotic-assisted radical prostatectomy for prostate cancer between 2015−2020 at a single institution. The effect of PSM and their adverse characteristics on the risk of BCR was assessed using Cox proportional hazard models. Kaplan−Meier was used to represent BCR-free survival stratified by margin status. With a median follow-up of 34.5 months, patients with PSM had a higher incidence of BCR, higher risk of relapse and lower BCR-free survival than negative margins (p < 0.001). We established as adverse characteristics: PSM length ≥ 3 mm, multifocality and Gleason at margin > 3. PSM ≥ 3 mm or multifocal PSM were associated with an increased risk for BCR compared to favourable margins (HR 3.50; 95% CI 2.05−5.95, p < 0.001 and HR 2.18; 95% CI 1.09−4.37, p = 0.028, respectively). The coexistence of these two adverse features in the PSM also conferred a higher risk for biochemical relapse and lower BCR-free survival. Adverse Gleason in the margin did not confer a higher risk for BCR than non-adverse margins in our models. We concluded that PSM are an independent predictor for BCR and that the presence of adverse characteristics, such as length and focality, and their coexistence in the PSM are associated with a greater risk of recurrence. Nevertheless, subclassifying PSM with adverse features did not enhance the model’s predictive performance in our cohort.

Identification of novel prostate cancer genes in patients stratified by Gleason classification: role of antitumoral genes

Prostate cancer (PCa) is a tumor with a great heterogeneity, both at a molecular and clinical level. Despite its global good prognosis, cases can vary from indolent to lethal metastatic and scientific efforts are aimed to discern those with worse outcomes. Current prognostic markers, as Gleason score, fall short when it comes to distinguishing these cases. Identification of new early biomarkers to enable a better PCa distinction and classification remains a challenge. In order to identify new genes implicated in PCa progression we conducted several differential gene expression analyses over paired samples comparing primary PCa tissue against healthy prostatic tissue of PCa patients. The results obtained show that this approach is a serious alternative to overcome patient heterogeneity. We were able to identify 250 genes whose expression varies along with tissue differentiation—healthy to tumor tissue, 161 of these genes are described here for the first time to be related to PCa. The further manual curation of these genes allowed to annotate 39 genes with antitumoral activity, 22 of them described for the first time to be related to PCa proliferation and metastasis. These findings could be replicated in different cohorts for most genes. Results obtained considering paired differential expression, functional annotation and replication results point to: CGREF1, UNC5A, C16orf74, LGR6, IGSF1, QPRT and CA14 as possible new early markers in PCa. These genes may prevent the progression of the disease and their expression should be studied in patients with different outcomes.

Improving the prediction of biochemical recurrence after radical prostatectomy with the addition of detailed pathology of the positive surgical margin and cribriform growth

The risk on biochemical recurrence (BCR) after radical prostatectomy (RP) is usually estimated using PSA and pathological stage and grading including the presence of positive surgical margins (PSM). Objective was to investigate whether the presence of cribriform growth in the primary tumor, Grade Group (GG) at the PSM, and length of the PSM have added value in the prognostication. We analyzed data of 835 patients initially treated with RP between 2000 and 2017. Cox regression models were developed to compare the baseline model (PSA, pT-stage, pN-stage, GG at RP, and presence of PSM) with an extended model (adding the presence of cribriform growth, length and GG at the PSM) using the likelihood ratio test. Discrimination was assessed at internal validation by the time-dependent area under the receiver operating characteristic curve (AUC) at 3- and 5-year. A total of 224 men experienced BCR. Median follow-up for men without BCR was 50.4 months (interquartile range, IQR 11.9-95.5). The extended model had a significant better fit, χ²(4) = 31.0, p < 0.001 than the baseline model. The AUC of the 3- and 5-year extended model was 0.85 (95% CI 0.81-0.88) compared to 0.83 (95% CI 0.79-0.87) for the baseline model. Importantly, the presence of cribriform growth in the primary tumor, and GG ≥ 2 at PSM were associated with a higher risk on BCR. In conclusion, the addition of pathological variables improved the prediction of the risk on BCR after RP slightly. However, the clinical implications of this model are important.

Factors affecting biochemical recurrence of prostate cancer after radical prostatectomy in patients with positive and negative surgical margin

Purpose

To investigate the clinical and pathological predictive factors affecting biochemical recurrence (BCR) after radical prostatectomy (RP) in patients with positive and negative surgical margin (SM).

Methods

Patients who underwent RP were retrospectively reviewed for the study. Demographic, clinical, pathological and oncological data were evaluated. All data were compared between patients with positive SM and negative SM to detect factors associated with SM status. Later, patients were divided into two groups as BCR-negative and BCR-positive groups. Data were separately compared between BCR groups for all patients, SM-negative and SM-positive patients, respectively.

Results

A total of 254 patients with a mean age of 63.5 years and the mean prostate-specific antigen of 10.9 ng/ml were evaluated in the study. SM positivity was found to be an independent prognostic factor for BCR (p = 0.013, Odds Ratio (OR): 0.267, 95% Confidence Interval (CI): 0.094-0.755). In SM-positive patients, biopsy Gleason Score and International Society of Urological Pathology grade were found to be independent predictive factors for BCR (p < 0.05). However, only tumor to SM distance (TSMD) was found to be an independent risk factor for BCR (p = 0.024) in SM-negative patients. The predictive cutoff value of the TSMD was found to be 75 μm for BCR (100% sensitivity and 63.9% specificity) (AUC = 0.803, p = 0.024). Although all of 46 patients with >75 μm TSMD were recurrence free, 5 of 31 patients with <75 μm TSMD had BCR (p = 0.009; OR: 0.839 CI: 0.719-0.979).

Conclusion

High Gleason Score and International Society of Urological Pathology grade of biopsy were found to be associated with BCR in SM-positive patients. For SM-negative patients, only TSMD was found to be associated with BCR after RP.