Molecular Profiles of Prostate Cancer: To Treat or Not to Treat

Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models

Radical prostatectomy is the gold standard treatment for prostate cancer (PCa); however, it does not always completely cure PCa, and patients often experience a recurrence of the disease. In addition, the clinical and pathological parameters used to assess the prognosis and choose further tactics for treating a patient are insufficiently informative and need to be supplemented with new markers. In this study, we performed RNA-Seq of PCa tissue samples, aimed at identifying potential prognostic markers at the level of gene expression and miRNAs associated with one of the key signs of cancer aggressiveness-lymphatic dissemination. The relative expression of candidate markers was validated by quantitative PCR, including an independent sample of patients based on archival material. Statistically significant results, derived from an independent set of samples, were confirmed for miR-148a-3p and miR-615-3p, as well as for the CST2, OCLN, and PCAT4 genes. Considering the obtained validation data, we also analyzed the predictive value of models based on various combinations of identified markers using algorithms based on machine learning. The highest predictive potential was shown for the "CST2 + OCLN + pT" model (AUC = 0.863) based on the CatBoost Classifier algorithm.

ALDH3A2, ODF2, QSOX2, and MicroRNA-503-5p Expression to Forecast Recurrence in TMPRSS2-ERG-Positive Prostate Cancer

Following radical surgery, patients may suffer a relapse. It is important to identify such patients so that therapy tactics can be modified appropriately. Existing stratification schemes do not display the probability of recurrence with enough precision since locally advanced prostate cancer (PCa) is classified as high-risk but is not ranked in greater detail. Between 40 and 50% of PCa cases belong to the TMPRSS2-ERG subtype that is a sufficiently homogeneous group for high-precision prognostic marker search to be possible. This study includes two independent cohorts and is based on high throughput sequencing and qPCR data. As a result, we have been able to suggest a perspective-trained model involving a deep neural network based on both qPCR data for mRNA and miRNA and clinicopathological criteria that can be used for recurrence risk forecasts in patients with TMPRSS2-ERG-positive, locally advanced PCa (the model uses ALDH3A2 + ODF2 + QSOX2 + hsa-miR-503-5p + ISUP + pT, with an AUC = 0.944). In addition to the prognostic model’s use of identified differentially expressed genes and miRNAs, miRNA–target pairs were found that correlate with the prognosis and can be presented as an interactome network.

miRNAs expression signature potentially associated with lymphatic dissemination in locally advanced prostate cancer

Background

Prostate cancer is one of the most common and socially significant cancers among men. The aim of our study was to reveal changes in miRNA expression profiles associated with lymphatic dissemination in prostate cancer and to identify the most prominent miRNAs as potential prognostic markers for future studies.

Methods

High-throughput miRNA sequencing was performed for 44 prostate cancer specimens taken from Russian patients, with and without lymphatic dissemination (N1 – 20 samples; N0 – 24 samples).

Results

We found at least 18 microRNAs with differential expression between N0 and N1 sample groups: miR-182-5p, miR-183-5p, miR-96-5p, miR-25-3p, miR-93-5p, miR-7-5p, miR-615-3p, miR-10b, miR-1248 (N1-miRs; elevated expression in N1 cohort; p < 0.05); miR-1271-5p, miR-184, miR-222-3p, miR-221-5p, miR-221-3p, miR-455-3p, miR-143-5p, miR-181c-3p and miR-455-5p (N0-miRs; elevated expression in N0; p < 0.05). The expression levels of N1-miRs were highly correlated between each other (the same is applied for N0-miRs) and the expression levels of N0-miRs and N1-miRs were anti-correlated. The tumor samples can be divided into two groups depending on the expression ratio between N0-miRs and N1-miRs.

Conclusions

We found the miRNA expression signature associated with lymphatic dissemination, in particular on the Russian patient cohort. Many of these miRNAs are well-known players in either oncogenic transformation or tumor suppression. Further experimental studies with extended sampling are required to validate these results.

A pre-specified model based on four kallikrein markers in blood improves predictions of adverse pathology and biochemical recurrence after radical prostatectomy

Background

A pre-specified model based on four kallikrein markers in blood, commercially available as 4Kscore, predicts Gleason Grade (GG) 3 + 4 or higher prostate cancer on biopsy. However, sampling error and variation in pathology reporting may miss aggressive disease.

Methods

The 4Kscore was measured in cryopreserved blood from 2330 men obtained before prostatectomy at a single institution between 2002 and 2010. Adverse surgical pathology and biochemical recurrence (BCR) were pre-specified to be assessed in all men, biopsy GG 3 + 3, and 3 + 4.

Results

Adjusted for established clinical predictors, the 4Kscore was significantly associated with adverse pathology (OR 1.49; 95% CI 1.32, 1.67; p < 0.0001). Adding 4Kscore increased discrimination from (AUC) 0.672 to 0.718 and 0.644 to 0.659 within biopsy GG 3 + 3 and 3 + 4, respectively. Higher 4Kscore was associated with higher risk of BCR (HR 1.16, 95% CI 1.06, 1.26; p = 0.001). Adding 4Kscore improved the prediction of BCR (C-index 0.630–0.660) within GG 3 + 3, but not GG 3 + 4.

Conclusions

The 4Kscore can help guide the clinical decision whether additional risk assessment—such as confirmatory biopsy—is needed to decide between active surveillance versus curative therapy. Evidence that the panel could influence management in biopsy GG 3 + 4 is less strong and requires further investigation.

Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

Precision Medicine: Functional Advancements

Precision medicine was conceptualized on the strength of genomic sequence analysis. High-throughput functional metrics have enhanced sequence interpretation and clinical precision. These technologies include metabolomics, magnetic resonance imaging, and I rhythm (cardiac monitoring), among others. These technologies are discussed and placed in clinical context for the medical specialties of internal medicine, pediatrics, obstetrics, and gynecology. Publications in these fields support the concept of a higher level of precision in identifying disease risk. Precise disease risk identification has the potential to enable intervention with greater specificity, resulting in disease prevention-an important goal of precision medicine.

Magnetic Resonance Imaging-Guided Confirmatory Biopsy for Initiating Active Surveillance of Prostate Cancer

IMPORTANCE

Transrectal, ultrasonography-guided prostate biopsy often fails to disclose the severity of underlying pathologic findings for prostate cancer. Magnetic resonance imaging (MRI)-guided biopsy may improve the characterization of prostate pathologic results, but few studies have examined its use for the decision to enter active surveillance.

OBJECTIVE

To evaluate whether confirmatory biopsy findings by MRI guidance are associated with the risk of pathologic disease upgrading among patients with prostate cancer during active surveillance.

DESIGN, SETTINGS, AND PARTICIPANTS

This retrospective cohort study used prospectively obtained registry data from 332 men with prostate cancer of Gleason grade group (GG) 2 or lower who were referred for active surveillance at a large academic medical center from January 1, 2009, through December 31, 2017.

EXPOSURES

All confirmatory and follow-up biopsies were performed using MRI guidance with an MRI-ultrasonography fusion device. Patients underwent repeated MRI-guided biopsies every 12 to 24 months. At follow-up sessions, in addition to obtaining systematic samples, lesions seen on MRI were targeted and foci of low-grade prostate cancer were obtained again using tracking technology. Active surveillance was terminated with detection of at least GG3 disease or receipt of treatment.

MAIN OUTCOMES AND MEASURES

The primary outcome was upgrading to at least GG3 disease during active surveillance. Secondary outcomes were the associations of MRI lesion grade, prostate-specific antigen (PSA) level, PSA density, and biopsy method (targeted, systematic, or tracked) with the primary outcome.

RESULTS

Of 332 patients (mean [SD] age, 62.8 [7.6] years), 39 (11.7%) upgraded to at least GG3 disease during follow-up. The incidence of upgrading was 7.9% (9 of 114) when the confirmatory biopsy finding was normal, 11.4% (20 of 175) when the finding showed GG1 disease, and 23.3% (10 of 43) when the finding was GG2 disease (P = .03). Men with GG2 disease were almost 8 times more likely to upgrade during surveillance compared with those with normal findings but only among those with low PSA density (hazard ratio [HR], 7.82; 95% CI, 2.29-26.68). A PSA density of at least 0.15 ng/mL/mL was associated with increased risk of upgrading among patients with normal findings (HR, 7.21; 95% CI, 1.98-26.24) or GG1 disease (HR, 2.86; 95% CI, 1.16 to 7.03) on confirmatory biopsy. A total of 46% of pathologic disease upgrades would have been missed if only the targeted biopsy was performed and 65% of disease upgrades were detected only with tracked biopsy.

CONCLUSIONS AND RELEVANCE

The findings suggest that confirmatory biopsy with MRI guidance is significantly associated with future disease upgrading of prostate cancer, especially when combined with PSA density, and should be considered as an appropriate entry point for active surveillance. Systematic and targeted biopsies were additive in detection of clinically significant cancers. Repeated biopsy at sites at which findings were previously abnormal (tracking biopsy) facilitated detection of cancers not suitable for continued active surveillance.

Immunoreactivity for prostate specific antigen and Ki67 differentiates subgroups of prostate cancer related to outcome

Based on gene-expression profiles, prostate tumors can be subdivided into subtypes with different aggressiveness and response to treatment. We investigated if similar clinically relevant subgroups can be identified simply by the combination of two immunohistochemistry markers: one for tumor cell differentiation (prostate specific antigen, PSA) and one for proliferation (Ki67). This was analyzed in men with prostate cancer diagnosed at transurethral resection of the prostate 1975-1991 (n = 331) where the majority was managed by watchful waiting. Ki67 and PSA immunoreactivity was related to outcome and to tumor characteristics previously associated with prognosis. Increased Ki67 and decreased PSA were associated with poor outcome, and they provided independent prognostic information from Gleason score. A combinatory score for PSA and Ki67 immunoreactivity was produced using the median PSA and Ki67 levels as cut-off (for Ki67 the upper quartile was also evaluated) for differentiation into subgroups. Patients with PSA low/Ki67 high tumors showed higher Gleason score, more advanced tumor stage, and higher risk of prostate cancer death compared to other patients. Their tumor epithelial cells were often ERG positive and expressed higher levels of ErbB2, phosphorylated epidermal growth factor receptor (pEGF-R) and protein kinase B (pAkt), and their tumor stroma showed a reactive response with type 2 macrophage infiltration, high density of blood vessels and hyaluronic acid, and with reduced levels of caveolin-1, androgen receptors, and mast cells. In contrast, men with PSA high/Ki67 low tumors were characterized by low Gleason score, and the most favorable outcome amongst PSA/Ki67-defined subgroups. Men with PSA low/Ki67 low tumors showed clinical and tumor characteristics intermediate of the two groups above. A combinatory PSA/Ki67 immunoreactivity score identifies subgroups of prostate cancers with different epithelial and stroma phenotypes and highly different outcome but the clinical usefulness of this approach needs to be validated in other cohorts.

Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4)

OBJECTIVE

To examine the ability of a novel live primary-cell phenotypic (LPCP) test to predict postsurgical adverse pathology (P-SAP) features and risk stratify patients based on SAP features in a blinded study utilizing radical prostatectomy (RP) surgical specimens.

METHODS

Two hundred fifty-one men undergoing RP were enrolled in a prospective, multicenter (10), and proof-of-concept study in the United States. Fresh prostate samples were taken from known areas of cancer in the operating room immediately after RP. Samples were shipped and tested at a central laboratory. Utilizing the LPCP test, a suite of phenotypic biomarkers was analyzed and quantified using objective machine vision software. Biomarkers were objectively ranked via machine learning-derived statistical algorithms (MLDSA) to predict postsurgical adverse pathological features. Sensitivity and specificity were determined by comparing blinded predictions and unblinded RP surgical pathology reports, training MLDSAs on 70% of biopsy cells and testing MLDSAs on the remaining 30% of biopsy cells across the tested patient population.

RESULTS

The LPCP test predicted adverse pathologies post-RP with area under the curve (AUC) via receiver operating characteristics analysis of greater than 0.80 and distinguished between Prostate Cancer Grade Groups 1, 2, and 3/Gleason Scores 3 + 3, 3 + 4, and 4 + 3. Further, LPCP derived-biomarker scores predicted Gleason pattern, stage, and adverse pathology with high precision-AUCs>0.80.

CONCLUSION

Using MLDSA-derived phenotypic biomarker scores, the LPCP test successfully risk stratified Prostate Cancer Grade Groups 1, 2, and 3 (Gleason 3 + 3 and 7) into distinct subgroups predicted to have surgical adverse pathologies or not with high performance (>0.85 AUC).

Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.

Point-of-Care Testing and Personalized Medicine for Metabolic Disorders

This chapter describes innovations in biomarker testing that can facilitate earlier and better treatment of patients who suffer from metabolic disorders. The use of new microfluidic devices along with miniaturized biosensors and transducers enables analysis of a single drop of a blood within the time frame of a typical visit to a doctor's office. Steps are underway so that these approaches will incorporate both biochemical and clinical data, resulting in unique bioprofiles for each patient. This will allow earlier, personalized, and more effective therapeutic options. In addition, smartphone apps for self-monitoring will be used increasingly for the best possible patient outcomes.

Canadian Men's perspectives about active surveillance in prostate cancer: need for guidance and resources

Background

In prostate cancer, men diagnosed with low risk disease may be monitored through an active surveillance. This research explored the perspectives of men with prostate cancer regarding their decision-making process for active surveillance to identify factors that influence their decision and assist health professionals in having conversations about this option.

Methods

Focus group interviews (n = 7) were held in several Canadian cities with men (N = 52) diagnosed with prostate cancer and eligible for active surveillance. The men’s viewpoints were captured regarding their understanding of active surveillance, the factors that influenced their decision, and their experience with the approach. A content and theme analysis was performed on the verbatim transcripts from the sessions.

Results

Patients described their concerns of living with their disease without intervention, but were reassured by the close monitoring under AS while avoiding harmful side effects associated with treatments. Conversations with their doctor and how AS was described were cited as key influences in their decision, in addition to availability of information on treatment options, distrust in the health system, personality, experiences and opinions of others, and personal perspectives on quality of life.

Conclusions

Men require a thorough explanation on AS as a safe and valid option, as well as guidance towards supportive resources in their decision-making.

Aggressive rat prostate tumors reprogram the benign parts of the prostate and regional lymph nodes prior to metastasis

In order to grow and spread tumors need to interact with adjacent tissues. We therefore hypothesized that small but aggressive prostate cancers influence the rest of the prostate and regional lymph nodes differently than tumors that are more indolent. Poorly metastatic (Dunning AT1) or highly metastatic (Dunning MLL) rat prostate tumor cells were injected into the ventral prostate lobe of immunocompetent rats. After 10 days—when the tumors occupied about 30% of the prostate lobe and lymph node metastases were undetectable—the global gene expression in tumors, benign parts of the prostate, and regional iliac lymph nodes were examined to define tumor-induced changes related to preparation for future metastasis. The tumors induced profound effects on the gene expression profiles in the benign parts of the prostate and these were strikingly different in the two tumor models. Gene ontology enrichment analysis suggested that tumors with high metastatic capacity were more successful than less metastatic tumors in inducing tumor-promoting changes and suppressing anti-tumor immune responses in the entire prostate. Some of these differences such as altered angiogenesis, nerve density, accumulation of T-cells and macrophages were verified by immunohistochemistry. Gene expression alterations in the regional lymph nodes suggested decreased quantity and activation of immune cells in MLL-lymph nodes that were also verified by immunostaining. In summary, even when small highly metastatic prostate tumors can affect the entire tumor-bearing organ and pre-metastatic lymph nodes differently than less metastatic tumors. When the kinetics of these extratumoral influences (by us named TINT = tumor instructed normal tissue) are more precisely defined they could potentially be used as markers of disease aggressiveness and become therapeutic targets.

On Risk Estimation versus Risk Stratification in Early Prostate Cancer

In a Perspective article, Sigrid Carlsson and Michael Kattan discuss Gnanapragasam and colleagues' accompanying research study on refining risk stratification in early prostate cancer.

Incorporation of tissue-based genomic biomarkers into localized prostate cancer clinics

Localized prostate cancer (PCa) is a clinically heterogeneous disease, which presents with variability in patient outcomes within the same risk stratification (low, intermediate or high) and even within the same Gleason scores. Genomic tools have been developed with the purpose of stratifying patients affected by this disease to help physicians personalize therapies and follow-up schemes. This review focuses on these tissue-based tools. At present, four genomic tools are commercially available: Decipher™, Oncotype DX®, Prolaris® and ProMark®. Decipher™ is a tool based on 22 genes and evaluates the risk of adverse outcomes (metastasis) after radical prostatectomy (RP). Oncotype DX® is based on 17 genes and focuses on the ability to predict outcomes (adverse pathology) in very low-low and low-intermediate PCa patients, while Prolaris® is built on a panel of 46 genes and is validated to evaluate outcomes for patients at low risk as well as patients who are affected by high risk PCa and post-RP. Finally, ProMark® is based on a multiplexed proteomics assay and predicts PCa aggressiveness in patients found with similar features to Oncotype DX®. These biomarkers can be helpful for post-biopsy decision-making in low risk patients and post-radical prostatectomy in selected risk groups. Further studies are needed to investigate the clinical benefit of these new technologies, the financial ramifications and how they should be utilized in clinics.