Utilization of biopsy-based genomic classifier to predict distant metastasis after definitive radiation and short-course ADT for intermediate and high-risk prostate cancer

Integration of Genomic Tests in Prostate Cancer Care: Implications for Clinical Practice and Patient Outcomes

Prostate cancer (PCa) is a common malignancy in men and is among the leading causes of cancer-related death worldwide. Genomic tests assess disease aggressiveness and guide treatment, particularly in low- and intermediate-risk PCa. We reviewed the literature on the use of four genomic tests (Prolaris®, Promark®, Oncotype DX®, and Decipher®) in assessing the prognosis of PCa and their use in treatment decision-making. Most of the studies showed that Prolaris® has a strong correlation with biochemical recurrence, metastasis risk, PCa-specific mortality (PCSM), and pathological features. Similarly, three studies on Promark® indicated a connection between results and pathological features in the subsequent prostatectomy, time to metastasis, and biochemical recurrence. Fourteen studies on Oncotype DX® showed a clear correlation between high scores, death, and PCSM. One study found that routine biopsy pathology reports, combined with serum PSA levels, provide a risk assessment comparable to Oncotype DX® testing. Results from 22 studies on Decipher® were controversial. The test was associated with conservative management, suggesting that patients with a high GC score are more likely to need radiation after surgery. Comparative studies indicated that Oncotype DX® is preferable for assessing PCSM, Decipher® for predicting metastasis, and Prolaris® for predicting recurrence. With the incidence rate of PCa dramatically increasing, genomic tests appear to be useful adjunctive precision medicine tools with significant potential in improving prognostic discrimination, facilitating better risk stratification, and guiding personalized treatment, especially in the intermediate-risk patient group. Large-scale, prospective, multi-sectional studies are required to validate the utility of these tests prior to their integration into clinical practice.

Biopsy-based Basal-luminal Subtyping Classifier in High-risk Prostate Cancer: A Combined Analysis of the NRG Oncology/RTOG 9202, 9413, and 9902 Phase 3 Trials

BACKGROUND AND OBJECTIVE

Long-term (LT) androgen deprivation therapy (ADT) has been found to be beneficial to patients with high-risk prostate cancer (PCa). However, administration of LT-ADT to all patients with high-risk PCa may lead to overtreatment. Enhanced risk stratification using genomic classifiers (such as the recently developed prostate subtyping classifier [PSC]) might be useful. This study aims to characterize the prognostic and predictive ability of the PSC in patients with high-risk PCa undergoing radiotherapy long-term (LT; 24-28 mo) versus short-term (ST; 4 mo) ADT.

METHODS

Biopsy samples from three randomized, phase 3 trials-NRG/RTOG 9202, 9413, and 9902-were classified as either PSC basal or luminal. The prognostic and predictive values of PSC for each oncologic endpoint (biochemical failure [BF], distant metastasis [DM], metastasis-free survival [MFS], PCa-specific mortality [PCSM], overall survival [OS]) and other cause-mortality (OCM) were assessed with Cox proportional hazards (MFS, OCM, and OS), Fine-Gray (BF, DM, and PCSM), and restricted mean survival time (RMST) models.

KEY FINDINGS AND LIMITATIONS

On a multivariable analysis, the basal subtype was found to have a worse prognosis for MFS (hazard ratio [HR] 1.8 [1.3-2.5], p < 0.001), PCSM (subdistribution HR 2.4 [95% confidence interval {CI} 1.4-4.1], p = 0.001), and OS (HR 1.8 [1.3-2.6], p < 0.001). Ten-year PCSM was 15% better for the luminal subtype than for the basal subtype (11% [95% CI 6-15%] vs 26% [95% CI 17-35%]). A significant interaction between ADT duration (LT vs ST) and PSC subtype (basal vs luminal) was observed for PCSM (pinteraction = 0.008), leading to the observation that 10-yr PCSM was improved with LT-ADT only in patients with basal-type tumors (5% [95% CI 0-11%] vs 42% [29-56%], p < 0.001). Improvements in 10-yr RMST with LT-ADT were greater for basal tumors for oncologic endpoints with the exception of OCM.

CONCLUSIONS AND CLINICAL IMPLICATIONS

PSC is both a prognostic and a predictive biomarker for patients who benefit from LT-ADT. PSC subtypes may be used to personalize ADT recommendations for patients with high-risk PCa, pending further validation in a prospective study.

PATIENT SUMMARY

In this study, we tried to understand the usefulness of a new genomic test in patients with high-risk, nonmetastatic prostate cancer who underwent radiation therapy and hormonal therapy (HT). We found that this test can help determine a patient's prognosis (eg, a patient's chance of having the cancer return) and, more importantly, personalize treatment decisions by understanding which patients may benefit from long-term HT. This has the potential to save many patients who may not benefit from prolonged HT from "overtreatment" or the unnecessary side effects of such treatment.

Prostate Cancer Risk Stratification in NRG Oncology Phase III Randomized Trials Using Multimodal Deep Learning With Digital Histopathology

PURPOSE

Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups.

MATERIALS AND METHODS

The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups.

RESULTS

The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively.

CONCLUSION

The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

Molecular analysis of archival diagnostic prostate cancer biopsies identifies genomic similarities in cases with progression post-radiotherapy, and those with de novo metastatic disease

BACKGROUND

It is important to identify molecular features that improve prostate cancer (PCa) risk stratification before radical treatment with curative intent. Molecular analysis of historical diagnostic formalin-fixed paraffin-embedded (FFPE) prostate biopsies from cohorts with post-radiotherapy (RT) long-term clinical follow-up has been limited. Utilizing parallel sequencing modalities, we performed a proof-of-principle sequencing analysis of historical diagnostic FFPE prostate biopsies. We compared patients with (i) stable PCa (sPCa) postprimary or salvage RT, (ii) progressing PCa (pPCa) post-RT, and (iii) de novo metastatic PCa (mPCa).

METHODS

A cohort of 19 patients with diagnostic prostate biopsies (n = 6 sPCa, n = 5 pPCa, n = 8 mPCa) and mean 4 years 10 months follow-up (diagnosed 2009-2016) underwent nucleic acid extraction from demarcated malignancy. Samples underwent 3'RNA sequencing (3'RNAseq) (n = 19), nanoString analysis (n = 12), and Illumina 850k methylation (n = 8) sequencing. Bioinformatic analysis was performed to coherently identify differentially expressed genes and methylated genomic regions (MGRs).

RESULTS

Eighteen of 19 samples provided useable 3'RNAseq data. Principal component analysis (PCA) demonstrated similar expression profiles between pPCa and mPCa cases, versus sPCa. Coherently differentially methylated probes between these groups identified ~600 differentially MGRs. The top 50 genes with increased expression in pPCa patients were associated with reduced progression-free survival post-RT (p < 0.0001) in an external cohort.

CONCLUSIONS

3'RNAseq, nanoString and 850k-methylation analyses are each achievable from historical FFPE diagnostic pretreatment prostate biopsies, unlocking the potential to utilize large cohorts of historic clinical samples. Profiling similarities between individuals with pPCa and mPCa suggests biological similarities and historical radiological staging limitations, which warrant further investigation.

Validation of the Combined Clinical Cell-Cycle Risk Score to Prognosticate Early Prostate Cancer Metastasis From Biopsy Specimens and Comparison With Other Routinely Used Risk Classifiers

PURPOSE

We aim to independently validate the prognostic utility of the combined cell-cycle risk (CCR) multimodality threshold to estimate risk of early metastasis after definitive treatment of prostate cancer and compare this prognostic ability with other validated biomarkers.

METHODS

Patients diagnosed with localized prostate cancer were enrolled into a single-institutional registry for the prospective observational cohort study. The primary end point was risk of metastasis within 3 years of diagnostic biopsy. Secondary end points included time to definitive treatment, time to subsequent therapy, and metastasis after completion of initial definitive treatment. Multivariable cause-specific Cox proportional hazards regression models were produced accounting for competing risk of death and stratified on the basis of the CCR active surveillance and multimodality (MM) thresholds. Time-dependent areas under the receiver operating characteristic curve were calculated.

RESULTS

The cohort consisted of 554 men with prostate cancer and available CCR score from biopsy. The CCR score was prognostic for metastasis (hazard ratio [HR], 2.32 [95% CI, 1.17 to 4.59]; P = .02), with scores above the MM threshold having a higher risk than those below the threshold (HR, 5.44 [95% CI, 2.72 to 10.91]; P < .001). The AUC for 3-year risk of metastasis on the basis of CCR was 0.736. When men with CCR above the MM threshold received MM therapy, their 3-year risk of metastasis was significantly lower than those receiving single-modality therapy (3% v 14%). Similarly, a CCR score above the active surveillance threshold portended a faster time to first definitive treatment.

CONCLUSION

CCR outperforms other commonly used biomarkers for prediction of early metastasis. We illustrate the clinical utility of the CCR active surveillance and multimodality thresholds. Molecular genomic tests can inform patient selection and personalization of treatment for localized prostate cancer.

How to Integrate Prostate Cancer Biomarkers in Urology Clinical Practice: An Update

Nowadays, the management of prostate cancer has become more and more challenging due to the increasing number of available treatment options, therapeutic agents, and our understanding of its carcinogenesis and disease progression. Moreover, currently available risk stratification systems used to facilitate clinical decision-making have limitations, particularly in providing a personalized and patient-centered management strategy. Although prognosis and prostate cancer-specific survival have improved in recent years, the heterogenous behavior of the disease among patients included in the same risk prognostic group negatively impacts not only our clinical decision-making but also oncological outcomes, irrespective of the treatment strategy. Several biomarkers, along with available tests, have been developed to help clinicians in difficult decision-making scenarios and guide management strategies. In this review article, we focus on the scientific evidence that supports the clinical use of several biomarkers considered by professional urological societies (and included in uro-oncological guidelines) in the diagnosis process and specific difficult management strategies for clinically localized or advanced prostate cancer.

Genomic classifiers and prognosis of localized prostate cancer: a systematic review

BACKGROUND

Refinement of the risk classification for localized prostate cancer is warranted to aid in clinical decision making. A systematic analysis was undertaken to evaluate the prognostic ability of three genomic classifiers, Decipher, GPS, and Prolaris, for biochemical recurrence, development of metastases and prostate cancer-specific mortality in patients with localized prostate cancer.

METHODS

Data sources: MEDLINE, Embase, and Web of Science were queried for reports published from January 2010 to April 2022.

STUDY SELECTION

prospective or retrospective studies reporting prognosis for patients with localized prostate cancer.

DATA EXTRACTION

relevant data were extracted into a customized database by one researcher with a second overreading. Risk of bias was assessed using a validated tool for prognostic studies, Quality in Prognosis Studies (QUIPS). Disagreements were resolved by consensus or by input from a third reviewer. We assessed the certainty of evidence by GRADE incorporating adaptation for prognostic studies.

RESULTS

Data synthesis: a total of 39 studies (37 retrospective) involving over 10,000 patients were identified. Twenty-two assessed Decipher, 5 GPS, and 14 Prolaris. Thirty-four studies included patients who underwent prostatectomy. Based on very low to low certainty of evidence, each of the three genomic classifiers modestly improved upon the prognostic ability for biochemical recurrence, development of metastases, and prostate cancer-specific mortality compared to standard clinical risk-classification schemes.

LIMITATIONS

downgrading of confidence in the evidence stemmed largely from bias due to the retrospective nature of the studies, heterogeneity in treatment received, and era in which patients were treated (i.e., prior to the 2000s).

CONCLUSIONS

Genomic classifiers provide a small but consistent improvement upon the prognostic ability of clinical classification schemes, which may be helpful when treatment decisions are uncertain. However, evidence from current management-era data and of the predictive ability of these tests is needed.

Genomic Classifier Performance in Intermediate-Risk Prostate Cancer: Results From NRG Oncology/RTOG 0126 Randomized Phase 3 Trial

PURPOSE

Intermediate-risk prostate cancer is a heterogeneous disease state with diverse treatment options. The 22-gene Decipher genomic classifier (GC) retrospectively has shown to improve risk stratification in these patients. We assessed the performance of the GC in men with intermediate-risk disease enrolled in NRG Oncology/RTOG 01-26 with updated follow-up.

METHODS AND MATERIALS

After National Cancer Institute approval, biopsy slides were collected from NRG Oncology/RTOG 01-26, a randomized phase 3 trial of men with intermediate-risk prostate cancer randomized to 70.2 Gy versus 79.2 Gy of radiation therapy without androgen deprivation therapy. RNA was extracted from the highest-grade tumor foci to generate the locked 22-gene GC model. The primary endpoint for this ancillary project was disease progression (composite of biochemical failure, local failure, distant metastasis, prostate cancer-specific mortality, and use of salvage therapy). Individual endpoints were also assessed. Fine-Gray or cause-specific Cox multivariable models were constructed adjusting for randomization arm and trial stratification factors.

RESULTS

Two-hundred fifteen patient samples passed quality control for analysis. The median follow-up was 12.8 years (range, 2.4-17.7). On multivariable analysis, the 22-gene GC (per 0.1 unit) was independently prognostic for disease progression (subdistribution hazard ratio [sHR], 1.12; 95% confidence interval [CI], 1.00-1.26; P = .04), biochemical failure (sHR, 1.22; 95% CI, 1.10-1.37; P < .001), distant metastasis (sHR, 1.28; 95% CI, 1.06-1.55; P = .01), and prostate cancer-specific mortality (sHR, 1.45; 95% CI, 1.20-1.76; P < .001). Ten-year distant metastasis in GC low-risk patients was 4% compared with 16% for GC high-risk patients. In patients with lower GC scores, the 10-year difference in metastasis-free survival rate between arms was -7%, compared with 21% for higher GC patients (P-interaction = .04).

CONCLUSIONS

This study represents the first validation of a biopsy-based gene expression classifier, assessing both its prognostic and predictive value, using data from a randomized phase 3 trial of intermediate-risk prostate cancer. Decipher improves risk stratification and can aid in treatment decision-making in men with intermediate-risk disease.

Histology Specific Molecular Biomarkers: Ushering in a New Era of Precision Radiation Oncology

Histopathology and clinical staging have historically formed the backbone for allocation of treatment decisions in oncology. Although this has provided an extremely practical and fruitful approach for decades, it has long been evident that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. As efficient and affordable DNA and RNA sequencing have become available, the ability to provide precision therapy has become within grasp. This has been realized with systemic oncologic therapy, as targeted therapies have demonstrated immense promise for subsets of patients with oncogene-driver mutations. Further, several studies have evaluated predictive biomarkers for response to systemic therapy within a variety of malignancies. Within radiation oncology, the use of genomics/transcriptomics to guide the use, dose, and fractionation of radiation therapy is rapidly evolving but still in its infancy. The genomic adjusted radiation dose/radiation sensitivity index is one such early and exciting effort to provide genomically guided radiation dosing with a pan-cancer approach. In addition to this broad method, a histology specific approach to precision radiation therapy is also underway. Herein we review select literature surrounding the use of histology specific, molecular biomarkers to allow for precision radiotherapy with the greatest emphasis on commercially available and prospectively validated biomarkers.

Analysis of a Biopsy-Based Genomic Classifier in High-Risk Prostate Cancer: Meta-Analysis of the NRG Oncology/Radiation Therapy Oncology Group 9202, 9413, and 9902 Phase 3 Randomized Trials

PURPOSE

Decipher is a genomic classifier (GC) prospectively validated postprostatectomy. We validated the performance of the GC in pretreatment biopsy samples within the context of 3 randomized phase 3 high-risk definitive radiation therapy trials.

METHODS AND MATERIALS

A prespecified analysis plan (NRG-GU-TS006) was approved to obtain formalin-fixed paraffin-embedded tissue from biopsy specimens from the NRG biobank from patients enrolled in the NRG/Radiation Therapy Oncology Group (RTOG) 9202, 9413, and 9902 phase 3 randomized trials. After central review, the highest-grade tumors were profiled on clinical-grade whole-transcriptome arrays and GC scores were obtained. The primary objective was to validate the independent prognostic ability for the GC for distant metastases (DM), and secondary for prostate cancer-specific mortality (PCSM) and overall survival (OS) with Cox univariable and multivariable analyses.

RESULTS

GC scores were obtained on 385 samples, of which 265 passed microarray quality control (69%) and had a median follow-up of 11 years (interquartile range, 9-13). In the pooled cohort, on univariable analysis, the GC was shown to be a prognostic factor for DM (per 0.1 unit; subdistribution hazard ratio [sHR], 1.29; 95% confidence interval [CI], 1.18-1.41; P < .001), PCSM (sHR, 1.28; 95% CI, 1.16-1.41; P < .001), and OS (hazard ratio [HR], 1.16; 95% CI, 1.08-1.22; P < .001). On multivariable analyses, the GC (per 0.1 unit) was independently associated with DM (sHR, 1.22; 95% CI, 1.09-1.36), PCSM (sHR, 1.23; 95% CI, 1.09-1.39), and OS (HR, 1.12; 95% CI, 1.05-1.20) after adjusting for age, Prostate Specific Antigen, Gleason score, cT stage, trial, and randomized treatment arm. GC had similar prognostic ability in patients receiving short-term or long-term androgen-deprivation therapy, but the absolute improvement in outcome varied by GC risk.

CONCLUSIONS

This is the first validation of a gene expression biomarker on pretreatment prostate cancer biopsy samples from prospective randomized trials and demonstrates an independent association of GC score with DM, PCSM, and OS. High-risk prostate cancer is a heterogeneous disease state, and GC can improve risk stratification to help personalize shared decision making.

Prostate Cancer Tumor Volume and Genomic Risk

Background

Despite the historic association of higher prostate cancer volume with worse oncologic outcomes, little is known about the impact of tumor volume on cancer biology.

Objective

To characterize the relationship between tumor volume (measured by percent positive cores [PPC]) and cancer biology (measured by Decipher genomic risk classifier [GC]) in men who underwent prostate biopsy.

Design setting and participants

Prostate biopsies from 52 272 men profiled with Decipher captured in a population-based prospective tumor registry were collected from 2016 to 2021.

Outcome measurements and statistical analysis

The degree of distribution and correlation of PPC with a GC score across grade group (GG) strata were examined using the Mann-Whitney U test, Pearson correlation coefficient, and multivariable linear regression controlled for clinicopathologic characteristics.

Results and limitations

A total of 38 921 (74%) biopsies passed quality control (14 331 GG1, 16 159 GG2, 5661 GG3, 1775 GG4, and 995 GG5). Median PPC and GC increased with sequentially higher GG. There was an increasingly positive correlation (r) between PPC and GC in GG2-5 prostate cancer (r [95% confidence interval {CI}]: 0.07 [0.5, 0.8] in GG2, 0.15 [0.12, 0.17] in GG3, 0.20 [0.15, 0.24] in GG4, and 0.25 [0.19, 0.31] in GG5), with no correlation in GG1 disease (r = 0.01, 95% CI [-0.001, 0.03]). In multivariable linear regression, GC was significantly associated with higher PPC for GG2-5 (all p < 0.05) but was not significantly associated with PPC for GG1. Limitations include retrospective design and a lack of final pathology from radical prostatectomy specimens.

Conclusions

Higher tumor volume was associated with worse GC for GG2-5 prostate cancer, whereas tumor volume was not associated with worse GC for GG1 disease. The finding that tumor volume is not associated with worse cancer biology in GG1 disease encourages active surveillance for most patients irrespective of tumor volume.

Patient summary

We studied the relationship between prostate cancer tumor volume and cancer biology, as measured by the Decipher genomic risk score, in men who underwent prostate biopsy. We found that tumor volume was not associated with worse cancer biology for low-grade prostate cancer. Our findings reassuringly support recent guidelines to recommend active surveillance for grade group 1 prostate cancer in most patients, irrespective of tumor volume.

Genomic Classifiers in Personalized Prostate Cancer Radiation Therapy Approaches: A Systematic Review and Future Perspectives Based on International Consensus

Current risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect the disease heterogeneity. Genomic classifiers (GC) enable improved risk stratification after surgery, but less data exist for patients treated with definitive radiation therapy (RT) or RT in oligo-/metastatic disease stages. To guide future perspectives of GCs for RT, we conducted (1) a systematic review on the evidence of GCs for patients treated with RT and (2) a survey of experts using the Delphi method, addressing the role of GCs in personalized treatments to identify relevant fields of future clinical and translational research. We performed a systematic review and screened ongoing clinical trials on ClinicalTrials.gov. Based on these results, a multidisciplinary international team of experts received an adapted Delphi method survey. Thirty-one and 30 experts answered round 1 and round 2, respectively. Questions with ≥75% agreement were considered relevant and included in the qualitative synthesis. Evidence for GCs as predictive biomarkers is mainly available to the postoperative RT setting. Validation of GCs as prognostic markers in the definitive RT setting is emerging. Experts used GCs in patients with PCa with extensive metastases (30%), in postoperative settings (27%), and in newly diagnosed PCa (23%). Forty-seven percent of experts do not currently use GCs in clinical practice. Expert consensus demonstrates that GCs are promising tools to improve risk-stratification in primary and oligo-/metastatic patients in addition to existing classifications. Experts were convinced that GCs might guide treatment decisions in terms of RT-field definition and intensification/deintensification in various disease stages. This work confirms the value of GCs and the promising evidence of GC utility in the setting of RT. Additional studies of GCs as prognostic biomarkers are anticipated and form the basis for future studies addressing predictive capabilities of GCs to optimize RT and systemic therapy. The expert consensus points out future directions for GC research in the management of PCa.

Genomic biomarkers to guide precision radiotherapy in prostate cancer

Our ability to prognosticate the clinical course of patients with cancer has historically been limited to clinical, histopathological, and radiographic features. It has long been clear however, that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. The advent of efficient genomic sequencing has led to a revolution in cancer care as we try to understand and personalize treatment specific to patient clinico-genomic phenotypes. Within prostate cancer, emerging evidence suggests that tumor genomics (e.g., DNA, RNA, and epigenetics) can be utilized to inform clinical decision making. In addition to providing discriminatory information about prognosis, it is likely tumor genomics also hold a key in predicting response to oncologic therapies which could be used to further tailor treatment recommendations. Herein we review select literature surrounding the use of tumor genomics within the management of prostate cancer, specifically leaning toward analytically validated and clinically tested genomic biomarkers utilized in radiotherapy and/or adjunctive therapies given with radiotherapy.

Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Immune checkpoint inhibitors (ICI) have revolutionized the management of locally advanced and advanced non-small lung cancer (NSCLC). With an improvement in the overall survival (OS) as both first- and second-line treatments, ICIs, and especially programmed-death 1 (PD-1) and programmed-death ligands 1 (PD-L1), changed the landscape of thoracic oncology. The PD-L1 level of expression is commonly accepted as the most used biomarker, with both prognostic and predictive values. However, even in a low expression level of PD-L1, response rates remain significant while a significant number of patients will experience hyperprogression or adverse events. The dentification of such subtypes is thus of paramount importance. While several studies focused mainly on the prediction of the PD-L1 expression status, others aimed directly at the development of prediction/prognostic models. The response to ICIs depends on a complex physiopathological cascade, intricating multiple mechanisms from the molecular to the macroscopic level. With the high-throughput extraction of features, omics approaches aim for the most comprehensive assessment of each patient. In this article, we will review the place of the different biomarkers (clinical, biological, genomics, transcriptomics, proteomics and radiomics), their clinical implementation and discuss the most recent trends projecting on the future steps in prediction modeling in NSCLC patients treated with ICI.

Impact of Decipher Biopsy testing on clinical outcomes in localized prostate cancer in a prospective statewide collaborative

BACKGROUND

Decipher Biopsy is a commercially available gene expression classifier used in risk stratification of newly diagnosed prostate cancer (PCa). Currently, there are no prospective data evaluating its clinical utility. We seek to assess the clinical utility of Decipher Biopsy in localized PCa patients.

METHODS

A multi-institutional study of 855 men who underwent Decipher Biopsy testing between February 2015 and October 2019. All patients were tracked through the prospective Michigan Urological Surgery Improvement Collaborative and linked to the Decipher Genomics Resource Information Database (GRID^®; NCT02609269). Patient matching was performed by an independent third-party (ArborMetrix Inc.) using two or more unique identifiers. Cumulative incidence curves for time to treatment (TTT) and time to failure (TTF) were constructed using Kaplan-Meier estimates. Multivariable Cox proportional hazard models were used to evaluate the independent association of high-risk Decipher scores with the conversion from AS to radical therapy and treatment failure (biochemical failure or receipt of salvage therapy).

RESULTS AND LIMITATIONS

Eight hundred fifty-five patients underwent Decipher Biopsy testing during the study period. Of the 855 men, 264 proceeded to AS (31%), and 454 (53%) received radical therapy. In men electing AS, after adjusting for NCCN risk group, age, PSA, prostate volume, body mass index, and percent positive cores, a high-risk Decipher score was independently associated with shorter TTT (HR 2.51, 95% CI 1.52-4.13 p < 0.001). Similarly, in patients that underwent radical therapy, a high-risk Decipher score was independently associated with TTF (HR 2.98, 95% CI 1.22-7.29, p = 0.01) on multivariable analysis. Follow-up time was a limitation.

CONCLUSION

In a prospective statewide registry, high-risk Decipher Biopsy score was strongly and independently associated with conversion from AS to definitive treatment and treatment failure. These real-world data support the clinical utility of Decipher Biopsy. An ongoing phase 3 randomized trial (NCT04396808) will provide level 1 evidence of the clinical impact of Decipher biopsy testing.

Intraductal Carcinoma of the Prostate as a Cause of Prostate Cancer Metastasis: A Molecular Portrait

Intraductal carcinoma of the prostate (IDC-P) is one of the most aggressive types of prostate cancer (PCa). IDC-P is identified in approximately 20% of PCa patients and is associated with recurrence, metastasis, and PCa-specific death. The main feature of this histological variant is the colonization of benign glands by PCa cells. Although IDC-P is a well-recognized independent parameter for metastasis, mechanisms by which IDC-P cells can spread and colonize other tissues are not fully known. In this review, we discuss the molecular portraits of IDC-P determined by immunohistochemistry and genomic approaches and highlight the areas in which more research is needed.

Biomarkers for prostate cancer detection and risk stratification

Although prostate cancer (PCa) is the most commonly diagnosed cancer in men, most patients do not die from the disease. Prostate specific antigen (PSA), the most widely used oncologic biomarker, has revolutionized screening and early detection, resulting in reduced proportion of patients presenting with advanced disease. However, given the inherent limitations of PSA, additional diagnostic and prognostic tools are needed to facilitate early detection and accurate risk stratification of disease. Serum, urine, and tissue-based biomarkers are increasingly being incorporated into the clinical care paradigm, but there is still a limited understanding of how to use them most effectively. In the current article, we review test characteristics and clinical performance data for both serum [4 K score, prostate health index (phi)] and urine [SelectMDx, ExoDx Prostate Intelliscore, MyProstateScore (MPS), and PCa antigen 3 (PCA3)] biomarkers to aid decisions regarding initial or repeat biopsies as well as tissue-based biomarkers (Confirm MDx, Decipher, Oncotype Dx, and Polaris) aimed at risk stratifying patients and identifying those patients most likely to benefit from treatment versus surveillance or monotherapy versus multi-modal therapy.

Stereotactic pelvic radiotherapy with HDR boost for dose escalation in intermediate and high-risk prostate cancer (SPARE): Efficacy, toxicity and quality of life

BACKGROUND

The ASCO/CCO guidelines recommend brachytherapy (BT) boost for eligible intermediate- (IR) or high-risk (HR) prostate cancer (PCa) patients. We present efficacy, toxicity and quality-of-life (QoL) outcomes in patients treated on a prospective protocol of MRI dose-painted high-dose-rate BT boost (HDR-BT) followed by 5-fraction pelvic radiotherapy (RT) and 6-18 months of androgen deprivation therapy (ADT).

METHODS

In this phase I/II study, IR or HR PCa patients received HDR-BT 15 Gy × 1 to prostate and up to 22.5 Gy to MRI nodule, followed by 25 Gy in 5, weekly fractions to pelvis. Toxicity was assessed using CTCAEv3.0, and QoL was captured using EPIC questionnaire. Biochemical failure (BF; nadir + 2.0), and proportion of patients with PSA < 0.4 ng/ml at 4-years (4yPSARR) were evaluated. A minimally clinically important change (MCIC) was recorded if QoL score decreased >0.5 standard deviation of baseline scores.

RESULTS

Thirty-one patients (NCCN 3.2% favorable IR, 48.4% unfavorable IR and 48.4% HR) completed treatment with a median follow-up of 61 months. Median D90 to MR nodule was 19.0 Gy and median prostate V100% was 96.5%. The actuarial 5-year BF rate was 18.2%, and the 4yPSARR was 71%. One patient died of PCa. Acute grade 2 and 3 toxicities: GU: 50%, 7%, and GI: 3%, none, respectively. Late grade 2 and 3 toxicities were: GU: 23%, 3%, and GI: 7%, none, respectively. Proportion of patients with MCIC was 7.7% for urinary domain and 32.0% for bowel domain.

CONCLUSIONS

This novel treatment protocol incorporating MRI dose-painted HDR-BT boost and 5-fraction pelvic RT with ADT is well tolerated.

Tissue-Based Biomarkers for the Risk Stratification of Men With Clinically Localized Prostate Cancer

Risk stratification of men with clinically localized prostate cancer has historically relied on basic clinicopathologic parameters such as prostate specific antigen level, grade group, and clinical stage. However, prostate cancer often behaves in ways that cannot be accurately predicted by these parameters. Thus, recent efforts have focused on developing tissue-based genomic tests that provide greater insights into the risk of a given patient's disease. Multiple tests are now commercially available and provide additional prognostic information at various stages of the care pathway for prostate cancer. Indeed, early evidence suggests that these assays may have a significant impact on patient and physician decision-making. However, the impact of these tests on oncologic outcomes remains less clear. In this review, we highlight recent advances in the use of tissue-based biomarkers in the treatment of prostate cancer and identify the existing evidence supporting their clinical use.

Grading Evolution and Contemporary Prognostic Biomarkers of Clinically Significant Prostate Cancer

Simple Summary

Prostate cancer treatment decisions are based on clinical stage and histological diagnosis, including Gleason grading assessed by a pathologist, in biopsies. Prior to staging and grading, serum or blood prostate-specific antigen (PSA) levels are measured and often trigger diagnostic examinations. However, PSA is best suited as a marker of cancer relapse after initial treatment. In this review, we first narratively describe the evolution of histological grading, the current status of Gleason pattern-based diagnostics and glance into future methodology of risk assessment by histological examination. In the second part, we systematically review the biomarkers that have been shown, independent from clinical characteristics, to correlate with clinically relevant end-points, i.e., occurrence of metastases, disease-specific mortality and overall survival after initial treatment of localized prostate cancer.

Abstract

Gleason grading remains the strongest prognostic parameter in localized prostate adenocarcinoma. We have here outlined the evolution and contemporary practices in pathological evaluation of prostate tissue samples for Gleason score and Grade group. The state of more observer-independent grading methods with the aid of artificial intelligence is also reviewed. Additionally, we conducted a systematic review of biomarkers that hold promise in adding independent prognostic or predictive value on top of clinical parameters, Grade group and PSA. We especially focused on hard end points during the follow-up, i.e., occurrence of metastasis, disease-specific mortality and overall mortality. In peripheral blood, biopsy-detected prostate cancer or in surgical specimens, we can conclude that there are more than sixty biomarkers that have been shown to have independent prognostic significance when adjusted to conventional risk assessment or grouping. Our search brought up some known putative markers and panels, as expected. Also, the synthesis in the systematic review indicated markers that ought to be further studied as part of prospective trials and in well characterized patient cohorts in order to increase the resolution of the current clinico-pathological prognostic factors.

Current and emerging therapies for localized high-risk prostate cancer

Introduction: Despite progress in the field of high-risk localized prostate cancer (HRPCa) treatments, high-risk patients treated with curative intent are at increased risk of biochemical recurrence, metastatic progression and cancer-related death. The optimal treatment strategy remains a topic of debate. This review provides an overview of the current and investigational therapeutic options for HRPCa.Areas covered: A PubMed search was performed for papers on the current perspectives on the multimodality treatment of HRPCa. We focus on both primary local treatment as well as systemic treatment options. Finally, relevant ongoing trials focusing on systemic treatments (including [neo]adjuvant treatments) enrolling at least 50 patients were retrieved, to highlight ongoing research and treatment optimization.Expert opinion: Disease progression in HRPCa patients is driven by local tumor extension and subclinical metastases. Therefore, the main treatment concept is a multimodal approach targeting the primary tumor with extended surgery or RT with long-term ADT and simultaneously targeting micro-metastatic deposits. However, there is still room for optimization. Upcoming clinical trials comparing surgery versus RT as local treatment, trials with (neo)adjuvant chemotherapy or androgen receptor signaling inhibitors will likely change the treatment landscape. However, a multimodal treatment strategy will stay as the cornerstone in the treatment of HRPCa.

Development and Validation of a Novel TP53 Mutation Signature That Predicts Risk of Metastasis in Primary Prostate Cancer

INTRODUCTION

Prostate tumors with TP53 gene mutations are molecularly heterogenous, and the presence of TP53 gene mutations has been linked to inferior outcomes. We developed an RNA-based gene signature that detects underlying TP53 gene mutations and identifies wild-type prostate tumors that are analogous to TP53-mutant tumors.

MATERIALS AND METHODS

Using genomic expression profiles from The Cancer Genome Atlas, we developed a mutation signature score to predict prostatic tumors with a molecular fingerprint similar to tumors with TP53 mutations. Area under the receiver operating characteristic curve assessed model accuracy in predicting TP53 mutations, and Cox regression models measured association between the signature and progression-free survival and metastasis-free survival (MFS).

RESULTS

The TP53 signature score achieved an area under the receiver operating characteristic curve of 0.84 in the training and 0.82 in the validation cohorts for predicting an underlying mutation. In three retrospective cohorts, a high score was prognostic for poor 5-year MFS: 46% versus 81% (hazard ratio [HR], 3.05; P < .0001; Johns Hopkins University cohort), 64% versus 83% (HR, 2.77; P < .0001; Mayo Clinic cohort), and 71% versus 97% (HR, 6.8; P = .0001; Brigham and Women's Hospital cohort). The signature also identified TP53 wild-type tumors molecularly analogous to TP53 mutant tumors, wherein high signature score correlated with worse 5-year MFS (50% vs. 82%; HR, 3.05; P < .0001).

CONCLUSIONS

This novel mutational signature predicted tumors with TP53 mutations, identified TP53 wild-type tumors analogous to mutant tumors, and was independently associated with poor MFS. This signature can therefore be used to strengthen existing clinical risk-stratification tools.

Recent Advances in the Management of High-Risk Localized Prostate Cancer: Local Therapy, Systemic Therapy, and Biomarkers to Guide Treatment Decisions

High-risk prostate cancer accounts for approximately 15% of all prostate cancer diagnoses. Patients with high-risk disease have an increased risk of developing biochemical recurrence, metastases, and death from prostate cancer. As the optimal management of high-risk disease in patients with prostate cancer continues to evolve, the contemporary treatment paradigm is moving toward a multidisciplinary integrated approach of systemic and local therapy for patients with high-risk disease. The strategies for definitive, adjuvant, and salvage local treatment, including radical prostatectomy or radiation therapy, serve as the backbone of therapy for patients with localized disease. Systemic therapy decisions regarding use in combination with surgery, choice of therapy (hormone therapy, chemotherapy), and treatment duration continue to be refined. As more effective hormonal agents populate the treatment landscape for advanced prostate cancer, including abiraterone and next-generation antiandrogens, an opportunity is provided to explore these treatments in patients with localized disease in the hope of improving the long-term outcome for patients. Integration of innovative blood and tissue-based biomarkers to guide therapy selection for patients with high-risk disease is an area of active research. Contemporary studies are using such biomarkers to stratify patients and select therapies. In this review, we summarize contemporary evidence for local treatment strategies, systemic therapy options, and biomarkers in development for the management of high-risk prostate cancer in patients.

Why the UK Should Consider Gene Expression Testing in Prostate Cancer

There is an increase in the use of prognostic gene expression biomarkers in the USA for the personalisation of treatment for men with localised and recurrent prostate cancer. However, these are not available in the UK. This overview will cover the need to shift from subjective histological phenotypes (e.g. Gleason grade) to more objective biological genotypes, review the suboptimal performance of clinical and pathological variables to accurately risk stratify patients and discuss the growing body of consistent work that has shown that genomic classifiers more accurately discriminate which men harbour indolent or biologically aggressive disease, independently of grade or stage. Overall, we will discuss the need for improved prognostic biomarkers and why the UK and the National Institute for Health and Care Excellence guidelines should move beyond the now 20-year-old three-tier D'Amico risk classification schema to guide the management of prostate cancer in the modern era.

Long-Term Dynamics of Three Dimensional Telomere Profiles in Circulating Tumor Cells in High-Risk Prostate Cancer Patients Undergoing Androgen-Deprivation and Radiation Therapy

Patient-specific assessment, disease monitoring, and the development of an accurate early surrogate of the therapeutic efficacy of locally advanced prostate cancer still remain a clinical challenge. Contrary to prostate biopsies, circulating tumor cell (CTC) collection from blood is a less-invasive method and has potential as a real-time liquid biopsy and as a surrogate marker for treatment efficacy. In this study, we used size-based filtration to isolate CTCs from the blood of 100 prostate cancer patients with high-risk localized disease. CTCs from five time points: +0, +2, +6, +12 and +24 months were analyzed. Consenting treatment-naïve patients with cT3, Gleason 8-10, or prostate-specific antigen > 20 ng/mL and non-metastatic prostate cancer were included. For all time points, we performed 3D telomere-specific quantitative fluorescence in situ hybridization on a minimum of thirty isolated CTCs. The patients were divided into five groups based on the changes of number of telomeres vs. telomere lengths over time and into three clusters based on all telomere parameters found on diagnosis. Group 2 was classified as non-respondent to treatment and the Cluster 3 presented more aggressive phenotype. Additionally, we compared our telomere results with the PSA levels for each patient at 6 months of ADT, at 6 months of completed RT, and at 36 months post-initial therapy. CTCs of patients with PSA levels above or equal to 0.1 ng/mL presented significant increases of nuclear volume, number of telomeres, and telomere aggregates. The 3D telomere analysis of CTCs identified disease heterogeneity among a clinically homogeneous group of patients, which suggests differences in therapeutic responses. Our finding suggests a new opportunity for better treatment monitoring of patients with localized high-risk prostate cancer.

Validation of a Metastatic Assay using biopsies to improve risk stratification in patients with prostate cancer treated with radical radiation therapy

Background

Radiotherapy is an effective treatment of intermediate/high-risk locally advanced prostate cancer, however, >30% of patients relapse within 5 years. Clinicopathological parameters currently fail to identify patients prone to systemic relapse and those whom treatment intensification may be beneficial. The purpose of this study was to independently validate the performance of a 70-gene Metastatic Assay in a cohort of diagnostic biopsies from patients treated with radical radiotherapy and androgen deprivation therapy.

Patients and methods

A bridging cohort of prostate cancer diagnostic biopsy specimens was profiled to enable optimization of the Metastatic Assay threshold before further independent clinical validation in a cohort of diagnostic biopsies from patients treated with radical radiotherapy and androgen deprivation therapy. Multivariable Cox proportional hazard regression analysis was used to assess assay performance in predicting biochemical failure-free survival (BFFS) and metastasis-free survival (MFS).

Results

Gene expression analysis was carried out in 248 patients from the independent validation cohort and the Metastatic Assay applied. Ten-year MFS was 72% for Metastatic Assay positive patients and 94% for Metastatic Assay negative patients [HR = 3.21 (1.35–7.67); P = 0.003]. On multivariable analysis the Metastatic Assay remained predictive for development of distant metastases [HR = 2.71 (1.11–6.63); P = 0.030]. The assay retained independent prognostic performance for MFS when assessed with the Cancer of the Prostate Assessment Score (CAPRA) [HR = 3.23 (1.22–8.59); P = 0.019] whilst CAPRA itself was not significant [HR = 1.88, (0.52–6.77); P = 0.332]. A high concordance [100% (61.5–100)] for the assay result was noted between two separate foci taken from 11 tumours, whilst Gleason score had low concordance.

Conclusions

The Metastatic Assay demonstrated significant prognostic performance in patients treated with radical radiotherapy both alone and independent of standard clinical and pathological variables. The Metastatic Assay could have clinical utility when deciding upon treatment intensification in high-risk patients. Genomic and clinical data are available as a public resource.

Prostate Cancer Transcriptomic Subtypes

While the DNA of a tumor is often equated to a fingerprint or its unique genetic identify, a tumor's RNA profile represents a complex dynamic state more akin to a tumors personality or distinct behavior. Of the 11 types of RNA, the translational and clinical focus in prostate cancer has been primarily on mRNA and lncRNA. The most common use of RNA-based biomarkers is to assess a tumor's aggressiveness or treatment sensitivity. However, multiple gene expression signatures have been developed to capture the functional state that results from canonical DNA alterations, including ERG fusions, SPOP mutations, and Rb loss. More commonly, these biomarkers have been used to develop over 30 prognostic gene expression signatures, three of which are now commercially available and being increasingly incorporated into clinical trials. In parallel, the ability to use microarray and RNAseq technologies have allowed high throughput methods of performing whole transcriptomic analyses. This has enabled the discovery and training of numerous predictive biomarker signatures rooted in biologically informed pathways to determine which tumors are more sensitive or resistant to various treatments, including androgen-deprivation therapy, radiotherapy, chemotherapy, and PARP inhibition. This chapter will review the various types of RNA, technologies available to assess gene expression, and describe the available gene expression signatures for prostate cancer.

Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance

Background

Many men diagnosed with prostate cancer are active surveillance (AS) candidates. However, AS may be associated with increased risk of disease progression and metastasis due to delayed therapy. Genomic classifiers, e.g., Decipher, may allow better risk-stratify newly diagnosed prostate cancers for AS.

Methods

Decipher was initially assessed in a prospective cohort of prostatectomies to explore the correlation with clinically meaningful biologic characteristics and then assessed in diagnostic biopsies from a retrospective multicenter cohort of 266 men with National Comprehensive Cancer Network (NCCN) very low/low and favorable-intermediate risk prostate cancer. Decipher and Cancer of the Prostate Risk Assessment (CAPRA) were compared as predictors of adverse pathology (AP) for which there is universal agreement that patients with long life-expectancy are not suitable candidates for AS (primary pattern 4 or 5, advanced local stage [pT3b or greater] or lymph node involvement).

Results

Decipher from prostatectomies was significantly associated with adverse pathologic features (p-values < 0.001). Decipher from the 266 diagnostic biopsies (64.7% NCCN-very-low/low and 35.3% favorable-intermediate) was an independent predictor of AP (odds ratio 1.29 per 10% increase, 95% confidence interval [CI] 1.03–1.61, p-value 0.025) when adjusting for CAPRA. CAPRA area under curve (AUC) was 0.57, (95% CI 0.47–0.68). Adding Decipher to CAPRA increased the AUC to 0.65 (95% CI 0.58–0.70). NPV, which determines the degree of confidence in the absence of AP for patients, was 91% (95% CI 87–94%) and 96% (95% CI 90–99%) for Decipher thresholds of 0.45 and 0.2, respectively. Using a threshold of 0.2, Decipher was a significant predictor of AP when adjusting for CAPRA (p-value 0.016).

Conclusion

Decipher can be applied to prostate biopsies from NCCN-very-low/low and favorable-intermediate risk patients to predict absence of adverse pathologic features. These patients are predicted to be good candidates for active surveillance.

National Comprehensive Cancer Network (NCCN) risk classification in predicting biochemical recurrence after radical prostatectomy: a retrospective cohort study in Chinese prostate cancer patients

This study aimed to assess the role of the National Comprehensive Cancer Network (NCCN) risk classification in predicting biochemical recurrence (BCR) after radical prostatectomy (RP) in Chinese prostate cancer patients. We included a consecutive cohort of 385 patients with prostate cancer who underwent RP at Fudan University Shanghai Cancer Center (Shanghai, China) from March 2011 to December 2014. Gleason grade groups were applied at analysis according to the 2014 International Society of Urological Pathology Consensus. Risk groups were stratified according to the NCCN Clinical Practice Guidelines in Oncology: Prostate Cancer version 1, 2017. All 385 patients were divided into BCR and non-BCR groups. The clinicopathological characteristics were compared using an independent sample t-test, Chi-squared test, and Fisher's exact test. BCR-free survival was compared using the log-rank test and multivariable Cox proportional hazard analysis. During median follow-up of 48 months (range: 1-78 months), 31 (8.05%) patients experienced BCR. The BCR group had higher prostate-specific antigen level at diagnosis (46.54 ± 39.58 ng ml-1 vs 21.02 ± 21.06 ng ml-1, P= 0.001), more advanced pT stage (P = 0.002), and higher pN1 rate (P < 0.001). NCCN risk classification was a significant predictor of BCR (P = 0.0006) and BCR-free survival (P = 0.003) after RP. As NCCN risk level increased, there was a significant decreasing trend in BCR-free survival rate (Ptrend = 0.0002). This study confirmed and validated that NCCN risk classification was a significant predictor of BCR and BCR-free survival after RP.

Are localized prostate cancer biomarkers useful in the clinical practice?

Prostate cancer presents itself in a heterogeneous way with both aggressive and indolent forms. Despite the controversy surrounding its use, prostate-specific antigen screening ultimately leads to a greater number of diagnosed patients. One of the biggest challenges in clinical practice is to select the right patients for biopsy and, among diagnosed patients, to differentiate tumors with an indolent course from those with an unfavorable prognosis, in order to determine the best therapeutic decision for each case, avoiding unnecessary interventions. Currently, several types of biomarkers are available for clinical use in patients with prostate cancer, which include blood-based (prostate-specific antigen, Prostate Health Index®, 4K score®); urine sample-based (PCA3, SelectMDx®, ExoDx Prostate IntelliScore®); and biopsy, transurethral resection, or radical prostatectomy tissue-based (ConfirmMDx®, Oncotype®, Prolaris®, Decipher®). The aim of this review is to provide an overview of the current state of evidence and to highlight recent advances in the evaluation and diagnosis of prostate cancer, with emphasis on biomarkers related to diagnosis and to prognostic evaluation of localized prostate cancer.

Unfavorable Pathology, Tissue Biomarkers and Genomic Tests With Clinical Implications in Prostate Cancer Management

Prostate cancer management has traditionally relied upon risk stratification of patients based on Gleason score, pretreatment prostate-specific antigen and clinical tumor stage. However, these factors alone do not adequately reflect the inherent complexity and heterogeneity of prostate cancer. Accurate and individualized risk stratification at the time of diagnosis is instrumental to facilitate clinical decision-making and treatment selection tailored to each patient. The incorporation of tissue and genetic biomarkers into current prostate cancer prediction models may optimize decision-making and improve patient outcomes. In this review we discuss the clinical significance of unfavorable morphologic features such as cribriform architecture and intraductal carcinoma of the prostate, tissue biomarkers and genomic tests and assess their potential use in prostate cancer risk assessment and treatment selection.

Tissue-based multigene expression tests for pretreatment prostate cancer risk assessment: current status and future perspectives

Prostate cancer is a highly prevalent disease with ample spectrum of aggressiveness and treatment options. Low-risk disease can be safely managed by nonintervention strategies, such as active surveillance; however, accurate risk assessment is warranted. Molecular tests have been developed and validated to complement standard clinicopathological parameters and help to improve risk stratification in prostate cancer. Herein, we review selected tissue-based assays, including genomic prostate score, cell cycle progression score and genomic classifier, with particular emphasis on their role in patient risk assessment in a pretreatment setting, in view of their current or potential utilization in active surveillance.

Genomic biomarkers in prostate cancer

Prostate cancer is the most common non-cutaneous cancer among men in the United States. In the last decade there has been a rapid expansion in the field of biomarker assays for diagnosis, prognosis, and treatment prediction in prostate cancer. The evidence base for these assays is rapidly evolving. With several commercial assays available at each stage of the disease, deciding which genomic assays are appropriate for which patients can be nuanced for physicians. In an effort to help guide these decisions in clinical practice, we aim to give an update on the current status of the biomarker field of prostate cancer.

Applying Precision Oncology Principles in Radiation Oncology

Radiation therapy is a critical component in the curative management of many solid tumor types, and advances in radiation delivery techniques during the past decade have led to improved disease control and quality of life for patients. During the same period, remarkable advances have also been made in understanding the genomic landscape of tumors; however, treatment decisions in radiation oncology continue to depend primarily on clinical and histopathologic characteristics rather than on the genetic features of the tumor or the patient. With the development of novel genomic techniques and their increasing use in clinical practice, radiation oncology is uniquely positioned to leverage these advances to identify novel biomarkers that could inform radiation dose, field, and the use of concurrent systemic agents. Here, we summarize efforts to use genomic techniques to guide radiation decisions, and we highlight some of the current opportunities and challenges that exist in attempting to apply precision oncology principles in radiation oncology.

The use of Hormonal Therapy to Augment Radiation Therapy in Prostate Cancer: An Update

PURPOSE OF REVIEW

Androgen deprivation therapy (ADT) is an important adjunctive therapy to external beam radiation therapy (RT) for the definitive management of prostate cancer. The role of ADT is well-established for locally advanced or high-risk disease in conjunction with standard doses of RT, but less defined for intermediate-risk disease or with dose-escalated RT. The goal of this review is to summarize evidence evaluating the combination of ADT/RT, focusing on recent trials and current controversies as they pertain to the practicing clinician.

RECENT FINDINGS

The benefit of ADT on biochemical control is maintained with dose-escalated RT according to recently reported phase III studies. Furthermore, there is now prospective, randomized evidence to support the addition of ADT to RT in the post-prostatectomy setting. ADT continues to play an important role for prostate cancer patients receiving dose-escalated RT. Future research is needed to identify subgroups most likely to benefit from this combination.

Prostate Cancer Markers

Diagnostic biomarkers derived from blood, urine, or prostate tissue provide additional information beyond clinical calculators to determine the risk of detecting high-grade prostate cancer. Once diagnosed, multiple markers leverage prostate cancer biopsy tissue to prognosticate clinical outcomes, including adverse pathology at radical prostatectomy, disease recurrence, and prostate cancer mortality; however the clinical utility of some outcomes to patient decision making is unclear. Markers using tissue from radical prostatectomy specimens provide additional information about the risk of biochemical recurrence, development of metastatic disease, and subsequent mortality beyond existing multivariable clinical calculators (the use of a marker to simply sub-stratify risk groups such as the NCCN groups is of minimal value). No biomarkers currently available for prostate cancer have been prospectively validated to be predict an improved clinical outcome for a specific therapy based on the test result; however, further research and development of these tests may produce a truly predictive biomarker for prostate cancer treatment.

Optimizing androgen deprivation therapy with radiation therapy for aggressive localized and locally advanced prostate cancer

Radiation therapy with androgen deprivation therapy (ADT) has historically been one of the mainstays of treatment for intermediate- and high-risk prostate cancer. The benefit of ADT likely derives from both enhancing local control and inhibiting micrometastatic disease. While level 1 evidence has demonstrated the benefits of 4-6 months of ADT for all men with intermediate-risk disease, further stratification of intermediate-risk prostate cancer into favorable and unfavorable subgroups indicates that ADT may not be necessary for favorable intermediate-risk disease but likely still provides a survival advantage for unfavorable intermediate-risk disease, even in the dose escalation era. Long-course ADT, consisting of 2-3 years of treatment, is the standard of care for high-risk prostate cancer managed with RT based on phase III trials. However, emerging data from a randomized trial raises the possibility that 18 months of ADT could be sufficient for select high-risk patients. The desire to minimize exposure to ADT lies in its many adverse effects, including the potential for cardiovascular harm in certain patients with significant coexisting comorbidity, possibly increased risk for neurocognitive and psychiatric events, and the well-documented metabolic changes. Providers need to carefully weigh these potential risks with the known survival benefits of ADT in aggressive localized and locally advanced prostate cancer.

Tissue-based biomarkers in prostate cancer

INTRODUCTION

Prostate cancer is a heterogeneous disease. Existing risk stratification tools based on standard clinlicopathologic variables (prostate specific antigen [PSA], Gleason score, and tumor stage) provide a modest degree of predictive ability. Advances in high-throughput sequencing has led to the development of several novel tissue-based biomarkers that can improve prognostication in prostate cancer management.

AREAS COVERED

The authors review commercially-available, tissue-based biomarker assays that improve upon existing risk-stratification tools in several areas of prostate cancer management, including the appropriateness of active surveillance and aiding in decision making regarding the use of adjuvant therapy. Additionally, some of the obstacles to the widespread adoption of these biomarkers and discuss several investigational sources of new biomarkers are discussed.

EXPERT COMMENTARY

Work is ongoing to answer pertinent clinical questions in prostate cancer management including which patients should undergo biopsy, active surveillance, receive adjuvant therapy, and what systemic therapy is best in the first-line. Incorporation into novel biomarkers may allow for the incorporation of a 'personalized' approach to management. Further validation will be required and questions of cost must be considered before wide scale adoption of these biomarkers. Tumor heterogeneity may impose a ceiling on the prognostic ability of biomarkers using currently available techniques.

Genomic testing for localized prostate cancer: where do we go from here?

PURPOSE OF REVIEW

The goal of this article is to discuss current genomic testing options in localized prostate cancer.

RECENT FINDINGS

There are multiple genomic tests currently available for men with localized prostate cancer. Prolaris, OncotypeDx, and Decipher can all be tested using biopsy tissue. Prolaris and Decipher are also available for men undergoing radical prostatectomy to predict subsequent disease progression.

SUMMARY

The Prolaris cell cycle progression score measured on biopsy predicts the risk of prostate cancer death in 10 years with conservative management, whereas, the primary endpoint for the OncotypeDx genomic prostate score is the risk of adverse disease at radical prostatectomy. Decipher measures genome-wide RNA expression, and its Genomic Classifier signature was initially designed to predict the risk of metastasis for men with adverse disease at radical prostatectomy, and more recently, a biopsy version was released. Recently, Decipher signatures predicting prostate cancer cell lineage and postoperative radiation sensitivity have also been described. Any of these tests can be used by men with localized prostate cancer to provide additional prognostic risk stratification to aid in treatment decisions.

Active Surveillance for Intermediate Risk Prostate Cancer

PURPOSE OF REVIEW

Active surveillance is now widely utilized for the management of low-risk prostate cancer (PCa). The limits of surveillance for men with intermediate risk cancer are controversial. While there is a broad consensus that men with low-risk disease can be safely managed with AS, many potential candidates, including those with Gleason 3 + 4 disease, PSA >10, younger men and African-Americans are often excluded.

RECENT FINDINGS

Outcome data for intermediate-risk patients managed by active surveillance demonstrate reasonable outcomes, but these men clearly are at higher risk for progression to metastatic disease. The use of biomarkers and multiparametric MRI will enable a more precise and personalized risk assessment. Literature describing the effects of young age on outcomes is limited, but the experience reported in prospective series with 15-20 year follow-up suggests it is a safe approach. African-American men are at greater risk for occult co-existent higher-grade disease, but in the absence of this their outcome is favorable. Patients with intermediate-risk PCa should not be excluded from active surveillance based on a single criterion. Treatment decisions should be based on multiple parameters, including percent Gleason 4, PSA density, cancer volume on biopsy, MRI findings, and patient age and co-morbidity. Genetic tissue-based biomarkers are also likely to play a role in enhancing decision making.