Stratification of aggressive prostate cancer from indolent disease—Prospective controlled trial utilizing expression of 11 genes in apparently benign tissue

Prostate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine-Learning Techniques

BACKGROUND

Accurate risk stratification of men with a clinical suspicion of prostate cancer (cSPCa) remains challenging despite the increasing use of MRI.

PURPOSE

To evaluate the diagnostic accuracy of a unique biparametric MRI protocol (IMPROD bpMRI) combined with clinical and molecular markers in men with cSPCa.

STUDY TYPE

Prospective single-institutional clinical trial (NCT01864135).

SUBJECTS

Eighty men with cSPCa.

FIELD STRENGTH/SEQUENCE

3T, surface array coils. Two T₂ -weighted and three diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm² ; 2) b-values 0,1500 s/mm² ; 3) b-values 0, 2000 s/mm² .

ASSESSMENT

IMPROD bpMRI examinations were qualitatively (IMPROD bpMRI Likert score) and quantitatively (DWI-based Gleason grade score) prospectively reported. Men with IMPROD bpMRI Likert 3-5 had two targeted biopsies followed by 12-core systematic biopsies (SB); those with IMPROD bpMRI Likert 1-2 had only SB. Additionally, 2-core from normal-appearing prostate areas were obtained for the mRNA expression of ACSM1, AMACR, CACNA1D, DLX1, PCA3, PLA2G7, RHOU, SPINK1, SPON2, TMPRSS2-ERG, and TDRD1 measured by quantitative reverse-transcription polymerase chain reaction.

STATISTICAL TESTS

Univariate and multivariate analysis using regularized least-squares, feature selection and tournament leave-pair-out cross-validation (TLPOCV), as well as 10 random splits of the data in training-testing sets, were used to evaluate the mRNA, clinical and IMPROD bpMRI parameters in detecting clinically significant prostate cancer (SPCa) defined as Gleason score ≥ 3 + 4. The evaluation metric was the area under the curve (AUC).

RESULTS

IMPROD bpMRI Likert demonstrated the highest TLPOCV AUC of 0.92. The tested clinical variables had AUC 0.56-0.73, while the mRNA and additional IMPROD bpMRI parameters had AUC 0.50-0.67 and 0.65-0.89 respectively. The combination of clinical and mRNA biomarkers produced TLPOCV AUC of 0.87, the highest TLPOCV performance without including IMPROD bpMRI Likert.

DATA CONCLUSION

The qualitative IMPROD bpMRI Likert score demonstrated the highest accuracy for SPCa detection compared with the tested clinical variables and mRNA biomarkers.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1540-1553.

RNA-based markers in biopsy cores with atypical small acinar proliferation: Predictive effect of T2E fusion positivity and MMP-2 upregulation for a subsequent prostate cancer diagnosis

BACKGROUND

Atypical small acinar proliferation (ASAP) is a precursor lesion of prostate cancer (PC), and PC develops from this suspicious focus or an unsampled malignant gland nearby. However, PC-related molecular alterations that could guide the timing of repeat biopsies and help monitor PC risk in ASAP-diagnosed patients have not been investigated. The purpose of this study was to first investigate the expression of seven different PC-related RNAs that included serine 2 (TMPRSS2): erythroblastosis virus E26 oncogene homolog (ERG) gene (TMPRSS2-ERG, T2E) fusion, alpha-methylacyl-CoA racemase (AMACR), kallikrein related peptidase 3 (KLK3), androgen receptor (AR), prostate cancer specific antigen 3 (PCA3), and matrix metalloproteinases (MMP)-2 and 9.

METHODS

PC-related RNAs were evaluated using a real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) system in pathologically ASAP-diagnosed prostate biopsy cores from 55 patients presenting with a normal digital rectal examination and a PSA level of 4-10 ng/mL.

RESULTS

We detected that positive T2E fusion status (P = 0.013) and the expression of AMACR (P = 0.016), AR (P = 0.016) and MMP-2 (P = 0.013) were independently and significantly associated with PC risk in ASAP patients. There were also several statistically significant correlations between expression levels. Additionally, we demonstrated that T2E fusion positive ASAP patients with higher MMP-2 expression were more likely to be diagnosed with PC at a subsequent biopsy during the follow-up period (P = 0.003).

CONCLUSIONS

Although, more clinical validations are needed for the stratification of PC risk in ASAP-diagnosed biopsy cores, our current results indicate that the coexistence of T2E fusion positivity with MMP-2 upregulation may help clinicians adjust their biopsy timetable and/or assessment of PC risk in ASAP-diagnosed patients with a PSA level of 4-10 ng/mL.

DLX1, a binding protein of beta-catenin, promoted the growth and migration of prostate cancer cells

Several studies have indicated the involvement of DLX1 in the progression of prostate cancer. However, the functions of DLX1 in the prostate cancer and the underlying molecular mechanism remains largely unknown. In this study, we have shown that DLX1 was up-regulated in the prostate clinical samples. DLX1 promoted the growth, migration and colony formation of prostate cancer cells by activating beta-catenin/TCF signaling. DLX1 interacted with beta-catenin and enhanced the interaction between beta-catenin and TCF4. Taken together, this study demonstrated that DLX1 exerted the oncogenic roles on the prostate cancer by activating beta-catenin/TCF signaling.

Identification of gene pairs through penalized regression subject to constraints

Background

This article concerns the identification of gene pairs or combinations of gene pairs associated with biological phenotype or clinical outcome, allowing for building predictive models that are not only robust to normalization but also easily validated and measured by qPCR techniques. However, given a small number of biological samples yet a large number of genes, this problem suffers from the difficulty of high computational complexity and imposes challenges to the accuracy of identification statistically.

Results

In this paper, we propose a parsimonious model representation and develop efficient algorithms for identification. Particularly, we derive an equivalent model subject to a sum-to-zero constraint in penalized linear regression, where the correspondence between nonzero coefficients in these models is established. Most importantly, it reduces the model complexity of the traditional approach from the quadratic order to the linear order in the number of candidate genes, while overcoming the difficulty of model nonidentifiablity. Computationally, we develop an algorithm using the alternating direction method of multipliers (ADMM) to deal with the constraint. Numerically, we demonstrate that the proposed method outperforms the traditional method in terms of the statistical accuracy. Moreover, we demonstrate that our ADMM algorithm is more computationally efficient than a coordinate descent algorithm with a local search. Finally, we illustrate the proposed method on a prostate cancer dataset to identify gene pairs that are associated with pre-operative prostate-specific antigen.

Conclusion

Our findings demonstrate the feasibility and utility of using gene pairs as biomarkers.

Evaluation of a short RNA within Prostate Cancer Gene 3 in the predictive role for future cancer using non-malignant prostate biopsies

Background

Prostate Cancer 3 (PCA3) is a long non-coding RNA (ncRNA) upregulated in prostate cancer (PCa). We recently identified a short ncRNA expressed from intron 1 of PCA3. Here we test the ability of this ncRNA to predict the presence of cancer in men with a biopsy without PCa.

Methods

We selected men whose initial biopsy did not identify PCa and selected matched cohorts whose subsequent biopsies revealed PCa or benign tissue. We extracted RNA from the initial biopsy and measured PCA3-shRNA2, PCA3 and PSA (qRT-PCR).

Results

We identified 116 men with and 94 men without an eventual diagnosis of PCa in 2–5 biopsies (mean 26 months), collected from 2002–2008. The cohorts were similar for age, PSA and surveillance period. We detected PSA and PCA3-shRNA2 RNA in all samples, and PCA3 RNA in 90% of biopsies. The expression of PCA3 and PCA3-shRNA2 were correlated (Pearson’s r = 0.37, p<0.01). There was upregulation of PCA3 (2.1-fold, t-test p = 0.02) and PCA3-shRNA2 (1.5-fold) in men with PCa on subsequent biopsy, although this was not significant for the latter RNA (p = 0.2). PCA3 was associated with the future detection of PCa (C-index 0.61, p = 0.01). This was not the case for PCA3-shRNA2 (C-index 0.55, p = 0.2).

Conclusions

PCA3 and PCA3-shRNA2 expression are detectable in historic biopsies and their expression is correlated suggesting co-expression. PCA3 expression was upregulated in men with PCa diagnosed at a future date, the same did not hold for PCA3-shRNA2. Futures studies should explore expression in urine and look at a time course between biopsy and PCa detection.