Meta-analysis of predictive models to assess the clinical validity and utility for patient-centered medical decision making: application to the CAncer of the Prostate Risk Assessment (CAPRA)

Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer

Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34-7.3; HR = 2.24 and 95% CI = 1.28-3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11-4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution.

Differential outcomes of re-stratified high-risk prostate cancer patients treated with external beam radiation therapy plus high-dose-rate brachytherapy boost

Purpose

We report outcomes of high-risk prostate cancer (PCa) patients, initially classified according to a 3-tier NCCN classification system, treated with external beam radiation therapy (EBRT) and high-dose-rate brachytherapy boost (HDR-BT). Patients were analyzed based on a re-stratification of their risk grouping using CAPRA score and a newer 5-tier NCCN classification.

Material and methods

471 high-risk PCa patients treated with EBRT, HDR-BT, and androgen deprivation therapy (ADT) between 1999 and 2018 were included. Competing risk survival analyses to compare individuals with CAPRA scores < 6 vs. ≥ 6 for biochemical relapse (BCR) and metastasis incidence were conducted. Also, overall survival (OS) for both groups using Kaplan-Meier analysis was assessed. The same analyses were repeated using a 5-tier NCCN stratification comparing those classified as high-risk vs. very high-risk patients.

Results

The median age was 71 years, and the median follow-up period was 72 months. The whole cohort received an EQD2 of 74 Gy or greater, with a median EQD2 of 106.89 Gy. Both a CAPRA score ≥ 6 and belonging to the NCCN very high-risk group were associated with BCR, with subdistribution hazard ratios (sHRs) of 3.04 (p = 0.015) and 2.53 (p = 0.013), respectively. For metastasis incidence, both the CAPRA and NCCN groups had similar sHRs of 2.60 (p = 0.094) and 2.71 (p = 0.037), respectively. For 10-year OS, patients with CAPRA score ≥ 6 and belonging to the NCCN very high-risk group presented similar HRs of 2.11 (p = 0.005) and 2.10 (p = 0.002).

Conclusions

We showed that high-risk PCa patients classified according to the 3-tier NCCN system benefit from further stratification using the CAPRA score or the 5-tier NCCN stratification method. Patients with a CAPRA score ≥ 6 or classified as very high-risk demonstrate a higher hazard of BCR, metastasis, and death. These patients might benefit from further intensification of their investigations and treatment, based on ongoing research.

Novel Multiparametric Magnetic Resonance Imaging-Based Deep Learning and Clinical Parameter Integration for the Prediction of Long-Term Biochemical Recurrence-Free Survival in Prostate Cancer after Radical Prostatectomy

Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains the first sign of aggressive disease; hence, better assessment of potential long-term post-RP BCR-free survival is crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnetic resonance imaging (mpMRI) for predicting long-term post-RP BCR-free survival in PCa. A total of 437 patients with PCa who underwent mpMRI followed by RP between 2008 and 2009 were enrolled; radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced sequences by manually delineating the index tumors. Deep features from the same set of imaging were extracted using a deep neural network based on pretrained EfficentNet-B0. Here, we present a clinical model (six clinical variables), radiomics model, DL model (DLM-Deep feature), combined clinical-radiomics model (CRM-Multi), and combined clinical-DL model (CDLM-Deep feature) that were built using Cox models regularized with the least absolute shrinkage and selection operator. We compared their prognostic performances using stratified fivefold cross-validation. In a median follow-up of 61 months, 110/437 patients experienced BCR. CDLM-Deep feature achieved the best performance (hazard ratio [HR] = 7.72), followed by DLM-Deep feature (HR = 4.37) or RM-Multi (HR = 2.67). CRM-Multi performed moderately. Our results confirm the superior performance of our mpMRI-derived DL algorithm over conventional radiomics.

Prostate cancer screening: Continued controversies and novel biomarker advancements

Prostate cancer (PCa) screening remains one of the most controversial topics in clinical and public health. Despite being the second most common cancer in men worldwide, recommendations for screening using prostate-specific antigen (PSA) are unclear. Early detection and the resulting postscreening treatment lead to overdiagnosis and overtreatment of otherwise indolent cases. In addition, several unwanted harms are associated with PCa screening process. This literature review focuses on the limitations of PSA-specific PCa screening, reasons behind the screening controversy, and the novel biomarkers and advanced innovative methodologies that improve the limitations of traditional screening using PSA. With the verdict of whether or not to screen not yet unanimous, we hope to aid in resolution of the long-standing debate.

Urinary oestrogen steroidome as an indicator of the risk of localised prostate cancer progression

BACKGROUND

Prostate cancer (PCa) is the most common cancer in North American men. Beyond the established contribution of androgens to disease progression, growing evidence suggest that oestrogen-related pathways might also be of clinical importance. The aim of this study was to explore the association of urinary oestrogen levels with clinical outcomes.

METHODS

Urine samples from the prospective multi-institutional PROCURE cohort were collected before RP for discovery (n = 259) and validation (n = 253). Urinary total oestrogens (unconjugated + conjugated), including oestrone and oestradiol, their bioactive and inactive catechol and methyl derivatives (n = 15), were measured using mass spectrometry (MS).

RESULTS

The median follow-up time for the discovery and replication cohorts was 7.6 and 6.5 years, respectively. Highly significant correlations between urinary oestrogens were observed; however, correlations with circulating oestrogens were modest. Our findings indicate that higher levels of urinary oestriol and 16-ketoestradiol were associated with lower risk of BCR. In contrast, higher levels of 2-methoxyestrone were associated with an increased risk of development of metastasis/deaths.

CONCLUSIONS

Our data suggest that urinary levels of oestriol and 16-ketoestradiol metabolites are associated with a more favourable outcome, whereas those of 2-methoxyestrone are associated with an elevated risk of metastasis after RP. Further studies are required to better understand the impact of oestrogens on disease biology and as easily accessible urine-based risk-stratification markers.

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.

Circular RNAs and Their Linear Transcripts as Diagnostic and Prognostic Tissue Biomarkers in Prostate Cancer after Prostatectomy in Combination with Clinicopathological Factors

As new biomarkers, circular RNAs (circRNAs) have been largely unexplored in prostate cancer (PCa). Using an integrative approach, we aimed to evaluate the potential of circRNAs and their linear transcripts (linRNAs) to act as (i) diagnostic biomarkers for differentiation between normal and tumor tissue and (ii) prognostic biomarkers for the prediction of biochemical recurrence (BCR) after radical prostatectomy. In a first step, eight circRNAs (circATXN10, circCRIM1, circCSNK1G3, circGUCY1A2, circLPP, circNEAT1, circRHOBTB3, and circSTIL) were identified as differentially expressed via a genome-wide circRNA-based microarray analysis of six PCa samples. Additional bioinformatics and literature data were applied for this selection process. In total, 115 malignant PCa and 79 adjacent normal tissue samples were examined using robust RT-qPCR assays specifically established for the circRNAs and their linear counterparts. Their diagnostic and prognostic potential was evaluated using receiver operating characteristic curves, Cox regressions, decision curve analyses, and C-statistic calculations of prognostic indices. The combination of circATXN10 and linSTIL showed a high discriminative ability between malignant and adjacent normal tissue PCa. The combination of linGUCY1A2, linNEAT1, and linSTIL proved to be the best predictive RNA-signature for BCR. The combination of this RNA signature with five established reference models based on only clinicopathological factors resulted in an improved predictive accuracy for BCR in these models. This is an encouraging study for PCa to evaluate circRNAs and their linRNAs in an integrative approach, and the results showed their clinical potential in combination with standard clinicopathological variables.

A 38-gene model comprised of key TET2-associated genes shows additive utility to high-risk prostate cancer cases in the prognostication of biochemical recurrence

BACKGROUND

Early treatment of patients at risk for developing aggressive prostate cancer is able to delay metastasis and reduce mortality; as such, up-front identification of these patients is critical. Several risk classification systems, including CAPRA-S, are currently used for disease prognostication. However, high-risk patients identified by these systems can still exhibit wide-ranging disease outcomes, leading to overtreatment of some patients in this group.

METHODS

The master methylation regulator TET2 is downregulated in prostate cancer, where its loss is linked to aggressive disease and poor outcome. Using a random forest strategy, we developed a model based on the expression of 38 genes associated with TET2 utilizing 100 radical prostatectomy samples (training cohort) with a 49% biochemical recurrence rate. This 38-gene model was comprised of both upregulated and downregulated TET2-associated genes with a binary outcome, and was further assessed in an independent validation (n = 423) dataset for association with biochemical recurrence.

RESULTS

38-gene model status was able to correctly identify patients exhibiting recurrence with 81.4% sensitivity in the validation cohort, and added significant prognostic utility to the high-risk CAPRA-S classification group. Patients considered high-risk by CAPRA-S with negative 38-gene model status exhibited no statistically significant difference in time to recurrence from low-risk CAPRA-S patients, indicating that the expression of TET2-associated genes is able to separate truly high-risk cases from those which have a more benign disease course.

CONCLUSIONS

The 38-gene model may hold potential in determining which patients would truly benefit from aggressive treatment course, demonstrating a novel role for genes linked to TET2 in the prognostication of PCa and indicating the importance of TET2 dysregulation among high-risk patient groups.

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.

Methylation Markers in Prostate Biopsies Are Prognosticators for Late Biochemical Recurrence and Therapy after Surgery in Prostate Cancer Patients

After diagnosis of prostate cancer is confirmed by a positive biopsy, the tumor may be surgically removed via radical prostatectomy (RP). However, many prostate cancer patients experience biochemical recurrence after surgery and/or undergo salvage radiotherapy or hormone therapy. Timely treatment is required to prevent the spread of disease in these cases, and biopsy tissue may hold potential for disease prognostication before surgery is ever performed. We previously developed a prognostic multigene methylation panel in RP specimens, including APC, CRIP3, HOXD3, and TGFB2. In the current study, this panel was applied to a cohort of biopsy specimens (n = 86), which were assessed for DNA methylation using the real-time quantitative PCR-based multiplex MethyLight. The biopsy-based methylation panel is significantly associated with biochemical recurrence when combined with the current clinical parameter of prostate-specific antigen (PSA) levels at diagnosis and is able to prognosticate the initiation of salvage radiotherapy, where it outperforms PSA, and/or hormone therapy after RP. In addition, this methylation panel is significantly associated with late recurrence occurring within 5 and 7 years after surgery, when combined with PSA at diagnosis. Combining DNA methylation and clinicopathologic markers at the biopsy stage will not only increase their prognostic ability but will also ensure effective patient management.

Models predicting survival to guide treatment decision-making in newly diagnosed primary non-metastatic prostate cancer: a systematic review

OBJECTIVES

Men diagnosed with non-metastatic prostate cancer require standardised and robust long-term prognostic information to help them decide on management. Most currently-used tools use short-term and surrogate outcomes. We explored the evidence base in the literature on available pre-treatment, prognostic models built around long-term survival and assess the accuracy, generalisability and clinical availability of these models.

DESIGN

Systematic literature review, pre-specified and registered on PROSPERO (CRD42018086394).

DATA SOURCES

MEDLINE, Embase and The Cochrane Library were searched from January 2000 through February 2018, using previously-tested search terms.

ELIGIBILITY CRITERIA

Inclusion required a multivariable model prognostic model for non-metastatic prostate cancer, using long-term survival data (defined as ≥5 years), which was not treatment-specific and usable at the point of diagnosis.

DATA EXTRACTION AND SYNTHESIS

Title, abstract and full-text screening were sequentially performed by three reviewers. Data extraction was performed for items in the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Individual studies were assessed using the new Prediction model Risk Of Bias ASsessment Tool.

RESULTS

Database searches yielded 6581 studies after deduplication. Twelve studies were included in the final review. Nine were model development studies using data from over 231 888 men. However, only six of the nine studies included any conservatively managed cases and only three of the nine included treatment as a predictor variable. Every included study had at least one parameter for which there was high risk of bias, with failure to report accuracy, and inadequate reporting of missing data common failings. Three external validation studies were included, reporting two available models: The University of California San Francisco (UCSF) Cancer of the Prostate Risk Assessment score and the Cambridge Prognostic Groups. Neither included treatment effect, and both had potential flaws in design, but represent the most robust and usable prognostic models currently available.

CONCLUSION

Few long-term prognostic models exist to inform decision-making at diagnosis of non-metastatic prostate cancer. Improved models are required to inform management and avoid undertreatment and overtreatment of non-metastatic prostate cancer.