Novel commercially available genomic tests for prostate cancer: a roadmap to understanding their clinical impact

A 17-Gene Panel Genomic Prostate Score Has Similar Predictive Accuracy for Adverse Pathology at Radical Prostatectomy in African American and European American Men

OBJECTIVE

To validate the 17-gene Oncotype DX Genomic Prostate Score (GPS) as a predictor of adverse pathology (AP) in African American (AA) men and to assess the distribution of GPS in AA and European American (EA) men with localized prostate cancer.

METHODS

The study populations were derived from 2 multi-institutional observational studies. Between February 2009 and September 2014, AA and EA men who elected immediate radical prostatectomy after a ≥10-core transrectal ultrasound biopsy were included in the study. Logistic regressions, area under the receiver operating characteristics curves (AUC), calibration curves, and predictive values were used to compare the accuracy of GPS. AP was defined as primary Gleason grade 4, presence of any Gleason pattern 5, and/or non-organ-confined disease (≥pT3aN0M0) at radical prostatectomy.

RESULTS

Overall, 96 AA and 76 EA men were selected and 46 (26.7%) had AP. GPS result was a significant predictor of AP (odds ratio per 20 GPS units [OR/20 units] in AA: 4.58; 95% confidence interval (CI) 1.8-11.5, P = .001; and EA: 4.88; 95% CI 1.8-13.5, P = .002). On multivariate analysis, there was no significant interaction between GPS and race (P >.10). GPS remained significant in models adjusted for either National Comprehensive Cancer Network (NCCN) risk group or Cancer of the Prostate Risk Assessment (CAPRA) score. In race-stratified models, area under the receiver operating characteristics curves for GPS/20 units was 0.69 for AAs vs 0.74 for EAs (P = .79). The GPS distributions were not statistically different by race (all P >.05).

CONCLUSION

In this clinical validation study, the Oncotype DX GPS is an independent predictor of AP at prostatectomy in AA and EA men with similar predictive accuracy and distributions.

Precision medicine applications in prostate cancer

Aided by developments in diagnostics and therapeutics, healthcare is increasingly moving toward precision medicine, in which treatment is customized to each individual. We discuss the relevance of precision medicine in prostate cancer, including gene targets, therapeutics and resistance mechanisms. We foresee precision medicine becoming an integral component of prostate cancer management to increase response to therapy and prolong survival.

Quality of care and economic considerations of active surveillance of men with prostate cancer

The current health care climate mandates the delivery of high-value care for patients considering active surveillance for newly-diagnosed prostate cancer. Value is defined by increasing benefits (e.g., quality) for acceptable costs. This review discusses quality of care considerations for men contemplating active surveillance, and highlights cost implications at the patient, health-system, and societal level related to pursuit of non-interventional management of men diagnosed with localized prostate cancer. In general, most quality measures are focused on prostate cancer care in general, rather that active surveillance patients specifically. However, most prostate cancer quality measures are pertinent to men seeking close observation of their prostate tumors with active surveillance. These include accurate documentation of clinical stage, informed discussion of all treatment options, and appropriate use of imaging for less-aggressive prostate cancer. Furthermore, interventions that may help improve the quality of care for active surveillance patients are reviewed (e.g., quality collaboratives, judicious antibiotic use, etc.). Finally, the potential economic impact and benefits of broad acceptance of active surveillance strategies are highlighted.

Automated analysis of co-localized protein expression in histologic sections of prostate cancer

An automated approach based on routinely-processed, whole-slide immunohistochemistry (IHC) was implemented to study co-localized protein expression in tissue samples. Expression of two markers was chosen to represent stromal (CD31) and epithelial (Ki-67) compartments in prostate cancer. IHC was performed on whole-slide sections representing low-, intermediate-, and high-grade disease from 15 patients. The automated workflow was developed using a training set of regions-of-interest in sequential tissue sections. Protein expression was studied on digital representations of IHC images across entire slides representing formalin-fixed paraffin embedded blocks. Using the training-set, the known association between Ki-67 and Gleason grade was confirmed. CD31 expression was more heterogeneous across samples and remained invariant with grade in this cohort. Interestingly, the Ki-67/CD31 ratio was significantly increased in high (Gleason ≥ 8) versus low/intermediate (Gleason ≤7) samples when assessed in the training-set and the whole-tissue block images. Further, the feasibility of the automated approach to process Tissue Microarray (TMA) samples in high throughput was evaluated. This work establishes an initial framework for automated analysis of co-localized protein expression and distribution in high-resolution digital microscopy images based on standard IHC techniques. Applied to a larger sample population, the approach may help to elucidate the biologic basis for the Gleason grade, which is the strongest, single factor distinguishing clinically aggressive from indolent prostate cancer.

LINE-1 methylation status in prostate cancer and non-neoplastic tissue adjacent to tumor in association with mortality

Aberrant DNA methylation seems to be associated with prostate cancer behavior. We investigated LINE-1 methylation in prostate cancer and non-neoplastic tissue adjacent to tumor (NTAT) in association with mortality from prostate cancer. We selected 157 prostate cancer patients with available NTAT from 2 cohorts of patients diagnosed between 1982-1988 and 1993-1996, followed up until 2010. An association between LINE-1 hypomethylation and prostate cancer mortality in tumor was suggested [hazard ratio per 5% decrease in LINE-1 methylation levels: 1.40, 95% confidence interval (CI): 0.95-2.01]. After stratification of the patients for Gleason score, the association was present only for those with a Gleason score of at least 8. Among these, low (<75%) vs. high (>80%) LINE-1 methylation was associated with a hazard ratio of 4.68 (95% CI: 1.03-21.34). LINE-1 methylation in the NTAT was not associated with prostate cancer mortality. Our results are consistent with the hypothesis that tumor tissue global hypomethylation may be a late event in prostate cancerogenesis and is associated with tumor progression.

Characterization of 1577 primary prostate cancers reveals novel biological and clinicopathologic insights into molecular subtypes

BACKGROUND

Prostate cancer (PCa) molecular subtypes have been defined by essentially mutually exclusive events, including ETS gene fusions (most commonly involving ERG) and SPINK1 overexpression. Clinical assessment may aid in disease stratification, complementing available prognostic tests.

OBJECTIVE

To determine the analytical validity and clinicopatholgic associations of microarray-based molecular subtyping.

DESIGN, SETTING, AND PARTICIPANTS

We analyzed Affymetrix GeneChip expression profiles for 1577 patients from eight radical prostatectomy cohorts, including 1351 cases assessed using the Decipher prognostic assay (GenomeDx Biosciences, San Diego, CA, USA) performed in a laboratory with Clinical Laboratory Improvements Amendment certification. A microarray-based (m-) random forest ERG classification model was trained and validated. Outlier expression analysis was used to predict other mutually exclusive non-ERG ETS gene rearrangements (ETS(+)) or SPINK1 overexpression (SPINK1(+)).

OUTCOME MEASUREMENTS

Associations with clinical features and outcomes by multivariate logistic regression analysis and receiver operating curves.

RESULTS AND LIMITATIONS

The m-ERG classifier showed 95% accuracy in an independent validation subset (155 samples). Across cohorts, 45% of PCas were classified as m-ERG(+), 9% as m-ETS(+), 8% as m-SPINK1(+), and 38% as triple negative (m-ERG(-)/m-ETS(-)/m-SPINK1(-)). Gene expression profiling supports three underlying molecularly defined groups: m-ERG(+), m-ETS(+), and m-SPINK1(+)/triple negative. On multivariate analysis, m-ERG(+) tumors were associated with lower preoperative serum prostate-specific antigen and Gleason scores, but greater extraprostatic extension (p<0.001). m-ETS(+) tumors were associated with seminal vesicle invasion (p=0.01), while m-SPINK1(+)/triple negative tumors had higher Gleason scores and were more frequent in Black/African American patients (p<0.001). Clinical outcomes were not significantly different among subtypes.

CONCLUSIONS

A clinically available prognostic test (Decipher) can also assess PCa molecular subtypes, obviating the need for additional testing. Clinicopathologic differences were found among subtypes based on global expression patterns.

PATIENT SUMMARY

Molecular subtyping of prostate cancer can be achieved using extra data generated from a clinical-grade, genome-wide expression-profiling prognostic assay (Decipher). Transcriptomic and clinical analysis support three distinct molecular subtypes: (1) m-ERG(+), (2) m-ETS(+), and (3) m-SPINK1(+)/triple negative (m-ERG(-)/m-ETS(-)/m-SPINK1(-)). Incorporation of subtyping into a clinically available assay may facilitate additional applications beyond routine prognosis.

Evolving transcriptomic fingerprint based on genome-wide data as prognostic tools in prostate cancer

Background Information

Prostate cancer (PCa) is a common disease but only a small subset of patients are at risk of developing metastasis and lethal disease, and identifying which patients will progress is challenging because of the heterogeneity underlying tumour progression. Understanding this heterogeneity at the molecular level and the resulting clinical impact is a critical step necessary for risk stratification. Defining genomic fingerprint elucidates molecular variation and may improve PCa risk stratification, providing more accurate prognostic information of tumour aggressiveness (or lethality) for prognostic biomarker development. Therefore, we explored transcriptomic differences between patients with indolent disease outcome and patients who developed metastasis post‐radical prostatectomy using genome‐wide expression data in the post radical prostatectomy clinical space before metastatic spread.

Results

Based on differential expression analysis, patients with adverse pathological findings who are at higher risk of developing metastasis have a distinct transcriptomic fingerprint that can be detected on surgically removed prostate specimens several years before metastasis detection. Nearly half of the transcriptomic fingerprint features were non‐coding RNA highlighting their pivotal role in PCa progression. Protein‐coding RNA features in the fingerprint are involved in multiple pathways including cell cycle, chromosome structure maintenance and cytoskeleton organisation. The metastatic transcriptomic fingerprint was determined in independent cohorts verifying the association between the fingerprint and metastatic patients. Further, the fingerprint was confirmed in metastasis lesions demonstrating that the fingerprint represents early metastatic transcriptomic changes, suggesting its utility as a prognostic tool to predict metastasis and provide clinical value in the early radical prostatectomy setting.

Conclusions

Here, we show that transcriptomic patterns of metastatic PCa exist that can be detected early after radical prostatectomy. This metastatic fingerprint has potential prognostic ability that can impact PCa treatment management potentially circumventing the requirements for unnecessary therapies.