Identification and evaluation of clinically significant prostate cancer: a step towards personalized diagnosis

Dual-Tracer PET-MRI-Derived Imaging Biomarkers for Prediction of Clinically Significant Prostate Cancer

PURPOSE

To investigate if imaging biomarkers derived from 3-Tesla dual-tracer [(18)F]fluoromethylcholine (FMC) and [⁶⁸Ga]Ga-PSMA^HBED-CC conjugate 11 (PSMA)-positron emission tomography can adequately predict clinically significant prostate cancer (csPC).

METHODS

We assessed 77 biopsy-proven PC patients who underwent 3T dual-tracer PET/mpMRI followed by radical prostatectomy (RP) between 2014 and 2017. We performed a retrospective lesion-based analysis of all cancer foci and compared it to whole-mount histopathology of the RP specimen. The primary aim was to investigate the pretherapeutic role of the imaging biomarkers FMC- and PSMA-maximum standardized uptake values (SUVmax) for the prediction of csPC and to compare it to the mpMRI-methods and PI-RADS score.

RESULTS

Overall, we identified 104 cancer foci, 69 were clinically significant (66.3%) and 35 were clinically insignificant (33.7%). We found that the combined FMC+PSMA SUVmax were the only significant parameters (p < 0.001 and p = 0.049) for the prediction of csPC. ROC analysis showed an AUC for the prediction of csPC of 0.695 for PI-RADS scoring (95% CI 0.591 to 0.786), 0.792 for FMC SUVmax (95% CI 0.696 to 0.869), 0.852 for FMC+PSMA SUVmax (95% CI 0.764 to 0.917), and 0.852 for the multivariable CHAID model (95% CI 0.763 to 0.916). Comparing the AUCs, we found that FMC+PSMA SUVmax and the multivariable model were significantly more accurate for the prediction of csPC compared to PI-RADS scoring (p = 0.0123, p = 0.0253, respectively).

CONCLUSIONS

Combined FMC+PSMA SUVmax seems to be a reliable parameter for the prediction of csPC and might overcome the limitations of PI-RADS scoring. Further prospective studies are necessary to confirm these promising preliminary results.

Magnetic resonance imaging-ultrasound fusion-targeted biopsy combined with systematic 12-core ultrasound-guided biopsy improves the detection of clinically significant prostate cancer: Are we ready to abandon the systematic approach?

Background:

Multiparametric (mp) magnetic resonance imaging (MRI)–ultrasound fusion-targeted biopsy (TB) has improved the detection of clinically significant prostate cancer (csCaP) using the Prostate Imaging Reporting and Data System (PI-RADS) reporting system, leading some authors to conclude that TB can replace the 12-core systematic biopsy (SB). We compared the diagnostic performance of TB with SB at our institution.

Methods:

Eighty-three men with elevated prostate-specific antigen levels (6.6 ng/mL, interquartile range [IQR] 4.5–9.2) and abnormal mp-MRI (127 lesions, PI-RADS ≥3, median size: 1.1 cm, IQR 0.8–1.6) underwent simultaneous TB and SB. Diagnosis of any CaP (Gleason score, [GS] ≥6) and csCaP (GS ≥7) was compared using the McNemar's exact test.

Results:

SB showed higher, but not statistically significant, detection rates of any CaP and csCaP (51.8% and 34.9%) versus TB (44.6% and 28.9%) (P = 0.286 and P = 0.359, respectively). TB outperformed SB in the quantification of 56.6% CaP and detecting cancer in anterior sectors (7.2%). Compared to SB, TB missed twice the amount of any CaP and csCaP. SB alone detected 22.2% of all csCaPs and upgraded 20.6% of TB-detected CaP. SB identified cancer invisible on mp-MRI (13.7% of all CaP) or missed by TB due to a small size (<1 cm) and sampling error (7% of lesions).

Conclusion:

A combination of SB with TB remained necessary for achieving the highest cancer detection rates. Limiting prostate biopsy to TB alone can miss csCaP due to the presence of synchronous high-grade cancer invisible on MRI or failure to hit the target. TB is the best approach for anterior lesions and tumor quantification.

Identification of Clinically Significant Prostate Cancer by Combined PCA3 and AMACR mRNA Detection in Urine Samples

Purpose

Preclinical evaluation of PCA3 and AMACR transcript simultaneous detection in urine to diagnose clinical significant prostate cancer (prostate cancer with Gleason score ≥7) in a Russian cohort.

Patients and Methods

We analyzed urine samples of patients with a total serum PSA ≥2 ng/mL: 31 men with prostate cancer scheduled for radical prostatectomy, 128 men scheduled for first diagnostic biopsy (prebiopsy cohort). PCA3, AMACR, PSA and GPI transcripts were detected by multiplex reverse transcription quantitative polymerase chain reaction, and the results were used for scores for calculation and statistical analysis.

Results

There was no significant difference between clinically significant and nonsignificant prostate cancer PCA3 scores. However, there was a significant difference in the AMACR score (patients scheduled for radical prostatectomy p=0.0088, prebiopsy cohort p=0.029). We estimated AUCs, optimal cutoffs, sensitivities and specificities for PCa and csPCa detection in the prebiopsy cohort by tPSA, PCA3 score, PCPT Risk Calculator and classification models based on tPSA, PCA3 score and AMACR score. In the clinically significant prostate cancer ROC analysis, the PCA3 score AUC was 0.632 (95%CI: 0.511–0.752), the AMACR score AUC was 0.711 (95%CI: 0.617–0.806) and AUC of classification model based on the PCA3 score, the AMACR score and total PSA was 0.72 (95%CI: 0.58–0.83). In addition, the correlation of the AMACR score with the ratio of total RNA and RNA of prostate cells in urine was shown (tau=0.347, p=6.542e–09). Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use.

Conclusion

The AMACR score is a good predictor of clinically significant prostate cancer. Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use. Evaluation of the AMACR score and classification models based on it for clinically significant prostate cancer detection with larger samples and a follow-up analysis is promising.

Diagnostic Accuracy and Value of Magnetic Resonance Imaging-Ultrasound Fusion Transperineal Targeted and Template Systematic Prostate Biopsy Based on Bi-parametric Magnetic Resonance Imaging

Purpose

The purpose of this study was to investigate the diagnostic value of magnetic resonance imaging (MRI)–ultrasound (US) fusion transperineal targeted biopsy (FTB) and fusion template systematic biopsy (FSB) for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) (intermediate/high grade [Gleason score ≥ 3+4]) based on bi-parametric MRI (bpMRI).

Materials and methods

Retrospectively, we analyzed 300 patients with elevated prostate-specific antigen (≥ 4.0 ng/mL) and/or abnormal findings in a digital rectal examination at the Korea University Hospital. All 300 men underwent bpMRI-US fusion transperineal FTB and FSB in the period from April 2017 to March 2019.

Results

PCas were detected in 158 of 300 men (52.7%), and the prevalence of csPCa was 34.0%. CsPCas were detected in 12 of 102 (11.8%) with Prostate Imaging-Reporting and Data System (PI-RADS) 3, 42 of 92 (45.7%) with PI-RADS 4, respectively; and 45 of 62 (72.6%) men with PI-RADS 5, respectively. BpMRI showed a sensitivity of 95.1% and negative predictive value of 89.6% for csPCa. FTB detected additional csPCa in 33 men (12.9%) compared to FSB. Compared to FTB, FSB detected additional csPCa in 10 men (3.9%).

Conclusion

BpMRI-US FTB and FSB improved detection of PCa and csPCa. The accuracy of bi-parametric MRI is comparable with that of multi-parametric MRI. Further, it is rapid, simpler, cheaper, and no side effects of contrast media. Therefore, it is expected that bpMRI-US transperineal FTB and FSB could be a good alternative to conventional US-guided transrectal biopsy, which is the current gold standard.

Genomic Evaluation of Multiparametric Magnetic Resonance Imaging-visible and -nonvisible Lesions in Clinically Localised Prostate Cancer

BACKGROUND

The prostate cancer (PCa) diagnostic pathway is undergoing a radical change with the introduction of multiparametric magnetic resonance imaging (mpMRI), genomic testing, and different prostate biopsy techniques. It has been proposed that these tests should be used in a sequential manner to optimise risk stratification.

OBJECTIVE

To characterise the genomic, epigenomic, and transcriptomic features of mpMRI-visible and -nonvisible PCa in clinically localised disease.

DESIGN, SETTING, AND PARTICIPANTS

Multicore analysis of fresh prostate tissue sampled immediately after radical prostatectomy was performed for intermediate- to high-risk PCa.

INTERVENTION

Low-pass whole-genome, exome, methylation, and transcriptome profiling of patient tissue cores taken from microscopically benign and cancerous areas in the same prostate. Circulating free and germline DNA was assessed from the blood of five patients.

OUTCOME MEASUREMENT AND STATISTICAL ANALYSIS

Correlations between preoperative mpMRI and genomic characteristics of tumour and benign prostate samples were assessed. Gene profiles for individual tumour cores were correlated with existing genomic classifiers currently used for prognostication.

RESULTS AND LIMITATIONS

A total of 43 prostate cores (22 tumour and 21 benign) were profiled from six whole prostate glands. Of the 22 tumour cores, 16 were tumours visible and six were tumours nonvisible on mpMRI. Intratumour genomic, epigenomic, and transcriptomic heterogeneity was found within mpMRI-visible lesions. This could potentially lead to misclassification of patients using signatures based on copy number or RNA expression. Moreover, three of the six cores obtained from mpMRI-nonvisible tumours harboured one or more genetic alterations commonly observed in metastatic castration-resistant PCa. No circulating free DNA alterations were found. Limitations include the small cohort size and lack of follow-up.

CONCLUSIONS

Our study supports the continued use of systematic prostate sampling in addition to mpMRI, as avoidance of systematic biopsies in patients with negative mpMRI may mean that clinically significant tumours harbouring genetic alterations commonly seen in metastatic PCa are missed. Furthermore, there is inconsistency in individual genomics when genomic classifiers are applied.

PATIENT SUMMARY

Our study shows that tumour heterogeneity within prostate tumours visible on multiparametric magnetic resonance imaging (mpMRI) can lead to misclassification of patients if only one core is used for genomic analysis. In addition, some cancers that were missed by mpMRI had genomic aberrations that are commonly seen in advanced metastatic prostate cancer. Avoiding biopsies in mpMRI-negative cases may mean that such potentially lethal cancers are missed.

Small nucleolar RNA host gene 1: A new biomarker and therapeutic target for cancers

OBJECTIVES

Long non-coding RNAs (lncRNAs), a group of transcripts with length greater than 200 nucleotides, have been involved in multiple pathophysiological processes of the human body, especially in tumorigenesis and progression of cancers. The aberrant expression of lncRNAs processes crucial functions involved in proliferation, apoptosis and metastatic capacity of cancers. Recent studies have revealed that small nucleolar RNA host gene 1 (SNHG1), a long non-coding RNA transcribed from UHG, was located in chromosome 11. Aberrant expression of SNHG1 has been demonstrated to be associated with various sites of cancers such as glioma, esophageal cancer, gastric cancer and many others, and its deregulation could be related to survival and prognosis of cancer patients. Pertinent to clinical practice, SNHG1 might act as a prognostic biomarker for tumors and might even serve as potential target for therapy. In this review, we summarized current researches concerning the role of SNHG1 in tumor progression and discussed its mechanisms involved.

MATERIALS AND METHODS

In this review, we summarized and figured out recent studies concerning the expression and biological mechanisms of SNHG1in tumor development. The related studies were obtained through a systematic search of PubMed, Embase and Cochrane Library.

RESULTS

SNHG1 was a valuable cancer-related lncRNA that the expression level was up-regulation in a variety of malignancies, including glioma, esophageal cancer, lung cancer, gastric cancer, hepatocellular carcinoma, colorectal carcinoma, prostate cancer, cervical cancer, osteosarcoma, neuroblastoma, nasopharyngeal carcinoma. The aberrant expressions of SNHG1 have shown to contribute to proliferation, migration, and invasion of cancer cells.

CONCLUSIONS

SNHG1 represents promising novel biomarkers for various cancer types and have a great potential to be effectively used in clinical practice in the near future.

SNHG1 lncRNA negatively regulates miR-199a-3p to enhance CDK7 expression and promote cell proliferation in prostate cancer

Long noncoding RNAs (lncRNAs) have been reported to play vital roles in the development of human cancers, but our understandings of most lncRNAs in cancers are still limited. Recently, accumlating evidences have showed that many RNA transcripts could function as competing endogenous RNAs (ceRNAs) by competitively binding common microRNAs. In this study, we demonstrated that a lncRNA, Small Nucleolar RNA Host Gene 1 (SNHG1), as a ceRNA for miR-199a-3p, played a critical role in prostate cancer cell proliferation. We found that SNHG1 was aberrantly up-regulated in prostate carcinoma tissues; while, miR-199a-3p was abnormally down-regulated. The level of SNHG1 in prostate cancer was significantly negatively correlated with that of miR-199a-3p. Our data indicated that SNHG1 could interact with miR-199a-3p and inhibit the activity of miR-199a-3p in prostate cancer cells. In addition, miR-199a-3p could target the 3' UTR of CDK7 and suppress CDK7 expression. More importantly, SNHG1 increased CDK7 expression by competitively binding miR-199a-3p, and then promoted cell proliferation and cell cycle progression in prostate cancer. Taken together, these findings elucidated a novel mechanism of prostate cancer progression. Thus, SNHG1 might serve as a potential target for prostate cancer therapies.