Time to biochemical relapse after radical prostatectomy and efficacy of salvage radiotherapy in patients with prostate cancer

Prostate‑specific antigen density and preoperative MRI findings as predictors of biochemical recurrence in high‑risk and very high‑risk prostate cancer

Patients with high-risk prostate cancer after prostatectomy have a particularly high chance of being diagnosed with biochemical recurrence (BCR). Patients with BCR have a greater risk of disease progression and mortality. The present retrospective observational study aimed to clarify the risk factors for the BCR of prostate cancer after radical prostatectomy in patients with high-risk and very high-risk prostate cancer. Patients diagnosed with prostate cancer who received radical prostatectomy in a single center from January 2009 to June 2020 were included in the study. Data from medical records were reviewed and the patients were followed up for ≥6 years. The primary outcome was BCR within 1 year after surgery. A total of 307 patients were included, with 187 in the high-risk group and 120 in the very high-risk group as classified by the National Comprehensive Cancer Network (NCCN) guidelines. Patients in the very high-risk group had a lower BCR-free survival rate compared with those in the high-risk group, with a high risk of BCR even if their PSA levels were initially undetectable after prostatectomy, and a high risk of postoperatively detectable PSA. In patients with undetectable PSA after prostatectomy, BCR was associated with the initial PSA density, imaging stage (T3aN0M0 and T3bN0M0), and pathologic stage (any N1). Postoperatively detectable PSA was associated with pathologic stage (T3bN0M0 and any N1) In conclusion, preoperative MRI imaging stage and PSA density are predictors for short-term BCR after prostatectomy. NCCN-defined high-risk patients with a high initial PSA density, imaging stage (T3aN0M0 and T3bN0M0), and pathologic stage (any N1) had a higher risk of BCR when compared with other patients with undetectable PSA, while those with pathologic stage (T3bN0M0 or any N1) displayed a higher risk of postoperatively detectable PSA. These findings may help urologists to identify patients for whom active therapeutic protocols are necessary.

Construction of DNA methylation-based nomogram for predicting biochemical-recurrence-free survival in prostate cancer

This study aimed to develop a DNA methylation-based nomogram for predicting biochemical recurrence in patients with prostate cancer. A DNA methylation signature was obtained via univariate, lasso, and stepwise multivariate Cox regression models. A 11-DNA methylation signature yielded a high evaluative performance for biochemical-recurrence-free survival. Cox regression analysis indicated that 11-DNA methylation signature and Gleason score served as independent risk factors. A nomogram was constructed based on the 11-DNA methylation signature and Gleason score, and C-index as well as the calibration plots demonstrated good performance and clinical application of the nomogram. A DNA methylation-associated nomogram serve as a prognosis stratification tool to predict the biochemical recurrence of prostate cancer patients after radical prostatectomy.

Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer

Objective

Post-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.

Methods

Five-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.

Results

The DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.

Conclusion

We developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.

Predictive Value of Circulating Tumor Cells Detected by ISET® in Patients with Non-Metastatic Prostate Cancer Undergoing Radical Prostatectomy

There is an unmet need for reliable biomarkers to predict prostate cancer recurrence after prostatectomy in order to better guide the choice of surgical treatment. We have evaluated the predictive value of the preoperative detection of Circulating Tumor Cells (CTC) for prostate cancer recurrence after surgery. A cohort of 108 patients with non-metastatic prostate adenocarcinoma undergoing radical prostatectomy was tested for the presence of CTC before prostatectomy using ISET®. Disease recurrence was assessed by the increase in serum PSA level after prostatectomy. The following factors were assessed for statistical association with prostate cancer recurrence: the presence of CTC, serum PSA, Gleason score, and pT stage using univariate and multivariate analyses, with a mean follow-up of 34.9 months. Prostate cancer recurrence was significantly associated with the presence of at least 1 CTC at the preoperative time point (p < 0.001; Predictive value = 0.83). Conversely, the absence of prostate cancer recurrence was significantly associated with the lack of CTC detection at diagnosis (Predictive value = 1). Our multivariate analysis shows that only CTC presence is an independent risk factor associated with prostate cancer recurrence after prostatectomy (p < 0.001). Our results suggest that CTC detection by ISET® before surgery is an interesting candidate predictive marker for cancer recurrence in patients with non-metastatic PCa.

A new risk stratification system of prostate cancer to identify high-risk biochemical recurrence patients

Background

Biochemical recurrence (BCR) is considered a decisive risk factor for clinical recurrence and the metastasis of prostate cancer (PCa). Therefore, we developed and validated a signature which could be used to accurately predict BCR risk and aid in the selection of PCa treatments.

Methods

A comprehensive genome-wide analysis of data concerning PCa from previous datasets of the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) was performed. Lasso and Cox regression analyses were performed to develop and validate a novel signature to help predict BCR risk. Moreover, a nomogram was constructed by combining the signature and clinical variables.

Results

A total of 977 patients were involved in the study. This consisted of patients from the TCGA (n=405), GSE21034 (n=131), GSE70770 (n=193) and GSE116918 (n=248) datasets. A 9-mRNA signature was identified in the TCGA dataset (composed of C9orf152, EPHX2, ASPM, MMP11, CENPF, KIF4A, COL1A1, ASPN, and FANCI) which was significantly associated with BCR (HR =3.72, 95% CI: 2.30-6.00, P<0.0001). This signature was validated in the GSE21034 (HR =7.54, 95% CI: 3.15-18.06, P=0.019), GSE70770 (HR =2.52, 95% CI: 1.50-4.22, P=0.0025) and GSE116918 datasets (HR =4.75, 95% CI: 2.51-9.02, P=0.0035). Multivariate Cox regression and stratified analysis showed that the 9-mRNA signature was a clinical factor independent of prostate-specific antigen (PSA), Gleason score (GS), or AJCC T staging. The mean AUC for 5-year BCR-free survival predictions of the 9-mRNA signature (0.81) was higher than the AUC for PSA, GS, or AJCC T staging (0.52-0.73). Furthermore, we combined the 9-mRNA signature with PSA, GS, or AJCC T staging and demonstrated that this could enhance prognostic accuracy.

Conclusions

The proposed 9-mRNA signature is a promising biomarker for predicting BCR-free survival in PCa. However, further controlled trials are needed to validate our results and explore a role in individualized management of PCa.

A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature

Within five to ten years after radical prostatectomy (RP), approximately 15–34% of prostate cancer (PCa) patients experience biochemical recurrence (BCR), which is defined as recurrence of serum levels of prostate-specific antigen >0.2 µg/L, indicating probable cancer recurrence. Models using clinicopathological variables for predicting this risk for patients lack accuracy. There is hope that new molecular biomarkers, like microRNAs (miRNAs), could be potential candidates to improve risk prediction. Therefore, we evaluated the BCR prognostic capability of 20 miRNAs, which were selected by a systematic literature review. MiRNA expressions were measured in formalin-fixed, paraffin-embedded (FFPE) tissue RP samples of 206 PCa patients by RT-qPCR. Univariate and multivariate Cox regression analyses were performed, to assess the independent prognostic potential of miRNAs. Internal validation was performed, using bootstrapping and the split-sample method. Five miRNAs (miR-30c-5p/31-5p/141-3p/148a-3p/miR-221-3p) were finally validated as independent prognostic biomarkers. Their prognostic ability and accuracy were evaluated using C-statistics of the obtained prognostic indices in the Cox regression, time-dependent receiver-operating characteristics, and decision curve analyses. Models of miRNAs, combined with relevant clinicopathological factors, were built. The five-miRNA-panel outperformed clinically established BCR scoring systems, while their combination significantly improved predictive power, based on clinicopathological factors alone. We conclude that this miRNA-based-predictor panel will be worth to be including in future studies.