Decipher correlation patterns post prostatectomy: initial experience from 2 342 prospective patients

Usefulness of Tissue Biomarkers versus Prostate-Specific Membrane Antigen-Positron Emission Tomography for Prostate Cancer Biochemical Recurrence after Radical Prostatectomy

Despite curative-intent local therapy, approximately 27% to 53% of prostate cancer (PCa) patients experience prostate-specific antigen (PSA) recurrence, known as biochemical recurrence (BCR). BCR significantly raises the risk of PCa-related morbidity and mortality, yet there is no consensus on optimal management. Prostate-specific membrane antigen-positron emission tomography (PSMA PET) has emerged as highly sensitive imaging, distinguishing local recurrences from distant metastases, crucially influencing treatment decisions. Genomic biomarkers such as Decipher, Prolaris, and Oncotype DX contribute to refining recurrence risk profiles, guiding decisions on intensifying adjuvant therapies, like radiotherapy and androgen deprivation therapy (ADT). This review assesses PSMA PET and biomarker utility in post-radical prostatectomy BCR scenarios, highlighting their impact on clinical decision-making. Despite their promising roles, the routine integration of biomarkers is limited by availability and cost, requiring further evidence. PSMA PET remains indispensable for restaging and treatment evaluation in these patients. Integrating biomarkers and PSMA PET promises to optimize personalized management strategies for BCR, though more comprehensive consensus-building studies are needed to define their standardized utility in clinical practice.

Morphologic patterns observed in prostate biopsy cases with discrepant grade group and molecular risk classification

BACKGROUND

Molecular-based risk classifier tests are increasingly being utilized by urologists and radiation oncologists to guide clinical decision making. The Decipher prostate biopsy test is a 22-gene RNA biomarker assay designed to predict likelihood of high-grade disease at radical prostatectomy and risk of metastasis and mortality. The test provides a risk category of low, intermediate, or high. We investigated histologic features of biopsies in which the Grade Group (GG) and Decipher risk category (molecular risk) were discrepant.

METHODS

Our institutional urologic outcomes database was searched for men who underwent prostate biopsies with subsequent Decipher testing from 2016 to 2020. We defined discrepant GG and molecular risk as either GG1-2 with high Decipher risk category or GG ≥ 3 with low Decipher risk category. The biopsy slide on which Decipher testing was performed was re-reviewed for GG and various histologic features, including % Gleason pattern 4, types of Gleason pattern 4 and 5, other "high risk" features (e.g., complex papillary, ductal carcinoma, intraductal carcinoma [IDC]), and other unusual and often "difficult to grade" patterns (e.g., atrophic carcinoma, mucin rupture, pseudohyperplastic carcinoma, collagenous fibroplasia, foamy gland carcinoma, carcinoma with basal cell marker expression, carcinoma with prominent vacuoles, and stromal reaction). Follow-up data was also obtained from the electronic medical record.

RESULTS

Of 178 men who underwent prostate biopsies and had Decipher testing performed, 41 (23%) had discrepant GG and molecular risk. Slides were available for review for 33/41 (80%). Of these 33 patients, 23 (70%) had GG1-2 (GG1 n = 5, GG2 n = 18) with high Decipher risk, and 10 (30%) had GG ≥ 3 with low Decipher risk. Of the 5 GG1 cases, one case was considered GG2 on re-review; no other high risk features were identified but each case showed at least one of the following "difficult to grade" patterns: 3 atrophic carcinoma, 1 collagenous fibroplasia, 1 carcinoma with mucin rupture, and 1 carcinoma with basal cell marker expression. Of the 18 GG2 high Decipher risk cases, 2 showed GG3 on re-review, 5 showed large cribriform and/or other high risk features, and 10 showed a "difficult to grade" pattern. Of the 10 GG ≥ 3 low Decipher risk cases, 5 had known high risk features including 2 with large cribriform, 1 with IDC, and 1 with Gleason pattern 5.

CONCLUSIONS

In GG1-2 high Decipher risk cases, difficult to grade patterns were frequently seen in the absence of other known high risk morphologic features; whether these constitute true high risk cases requires further study. In the GG ≥ 3 low Decipher risk cases, aggressive histologic patterns such as large cribriform and IDC were observed in half (50%) of cases; therefore, the molecular classifier may not capture all high risk histologic patterns.

Decipher Score predicts prostate specific antigen persistence after prostatectomy

BACKGROUND

The aim of this study was to evaluate genomic risk of patients with persistent prostate specific antigen (PSA) using mRNA expression analysis and a validated prognostic genomic-risk classifier.

METHODS

Monocentric retrospective study including all patients who underwent radical prostatectomy (RP) by one surgeon and Decipher Test from October 2013 to December 2018. PSA persistent population was defined as all patients with two consecutive PSA>0.1 ng/mL at follow-up after the surgery. Neurovascular Structure-adjacent Frozen-section Examination (NeuroSAFE) was performed intraoperatively for research of positive surgical margins. Multivariate analysis was performed for persistent PSA (pPSA) predictors. A specific localized, organ-confined, and negative margins sub-population with PSA persistence was compared to a similar sub-population without PSA persistence for genomic differential expression analyses.

RESULTS

A total of 564 patients were included and 61 of them had pPSA. Preoperative PSA was higher in the PSA persistent group (11.6 [6.4, 21.2] vs. 6.2 [4.7, 9.2] P=0.00010), as well as PSA density (PSAd) (0.3 [0.2, 0.5] vs. 0.2 [0.1, 0.3] P=0.0001). Postoperative characteristics, Gleason Score, and positive surgical margins were significantly higher in the PSA persistent population. 31 patients had pPSA in our specific subpopulation and were compared to 217 patients with no pPSA. On multivariate analysis, only Decipher Score (OR=5.64 [1.28; 24.89], P=0.022) and preoperative PSA (OR=1.06, [1.02; 1.09], P=0.001) were significant predictors for PSA persistence. We found two genes to be significantly upregulated with a 2.5-fold change in our specific subpopulation (SERPINB11 and PDE11A).

CONCLUSIONS

We found unique genomic features of patients with pPSA, whilst confirming previous clinical findings that this condition behaves to a worse prognosis. Given this high genomic risk, further imaging studies should be performed to select patients for early treatment intensification.

Association between Expression of Connective Tissue Genes and Prostate Cancer Growth and Progression

To find an association between genomic features of connective tissue and pejorative clinical outcomes on radical prostatectomy specimens. We performed a retrospective analysis of patients who underwent radical prostatectomy and underwent a Decipher transcriptomic test for localized prostate cancer in our institution (n = 695). The expression results of selected connective tissue genes were analyzed after multiple t tests, revealing significant differences in the transcriptomic expression (over- or under-expression). We investigated the association between transcript results and clinical features such as extra-capsular extension (ECE), clinically significant cancer, lymph node (LN) invasion and early biochemical recurrence (eBCR), defined as earlier than 3 years after surgery). The Cancer Genome Atlas (TCGA) was used to evaluate the prognostic role of genes on progression-free survival (PFS) and overall survival (OS). Out of 528 patients, we found that 189 had ECE and 27 had LN invasion. The Decipher score was higher in patients with ECE, LN invasion, and eBCR. Our gene selection microarray analysis showed an overexpression in both ECE and LN invasion, and in clinically significant cancer for COL1A1, COL1A2, COL3A1, LUM, VCAN, FN1, AEBP1, ASPN, TIMP1, TIMP3, BGN, and underexpression in FMOD and FLNA. In the TCGA population, overexpression of these genes was correlated with worse PFS. Significant co-occurrence of these genes was observed. When presenting overexpression of our gene selection, the 5-year PFS rate was 53% vs. 68% (p = 0.0315). Transcriptomic overexpression of connective tissue genes correlated to worse clinical features, such as ECE, clinically significant cancer and BCR, identifying the potential prognostic value of the gene signature of the connective tissue in prostate cancer. TCGAp cohort analysis showed a worse PFS in case of overexpression of the connective tissue genes.

Fusion Gene Detection in Prostate Cancer Samples Enhances the Prediction of Prostate Cancer Clinical Outcomes from Radical Prostatectomy through Machine Learning in a Multi-Institutional Analysis

Prostate cancer remains one of the most fatal malignancies in men in the United States. Predicting the course of prostate cancer is challenging given that only a fraction of prostate cancer patients experience cancer recurrence after radical prostatectomy or radiation therapy. This study examined the expressions of 14 fusion genes in 607 prostate cancer samples from the University of Pittsburgh, Stanford University, and the University of Wisconsin-Madison. The profiling of 14 fusion genes was integrated with Gleason score of the primary prostate cancer and serum prostate-specific antigen level to develop machine-learning models to predict the recurrence of prostate cancer after radical prostatectomy. Machine-learning algorithms were developed by analysis of the data from the University of Pittsburgh cohort as a training set using the leave-one-out cross-validation method. These algorithms were then applied to the data set from the combined Stanford/Wisconsin cohort (testing set). The results showed that the addition of fusion gene profiling consistently improved the prediction accuracy rate of prostate cancer recurrence by Gleason score, serum prostate-specific antigen level, or a combination of both. These improvements occurred in both the training and testing cohorts and were corroborated by multiple models.

When to order genomic tests: development and external validation of a model to predict high-risk prostate cancer at the genotypic level

PURPOSE

The aim of this study was to develop a model to predict high-genomic-risk prostate cancer (PCa) according to Decipher score, a validated 22 gene prognostic panel. By doing so, one might select the individuals who are likely to benefit from genomic testing and improve pre-op counseling about the need for adjuvant treatments.

METHODS

We retrospectively reviewed IRB-approved databases at two institutions. All patients had preoperative magnetic resonance imaging (MRI) and Decipher prostate radical prostatectomy (RP), a validated 22 gene prognostic panel. We used binary logistic regression to estimate high-risk Decipher (Decipher score > 0.60) probability on RP specimen. Area under the curve (AUC) and calibration were used to assess the accuracy of the model in the development and validation cohort. Decision curve analysis (DCA) was performed to assess the clinical benefit of the model.

RESULTS

The development and validation cohort included 622 and 185 patients with 283 (35%) and 80 (43%) of those with high-risk Decipher. The multivariable model included PSA density, biopsy Gleason Grade Group, percentage of positive cores and MRI extracapsular extension. AUC was 0.73 after leave-one-out cross-validation. DCA showed a clinical benefit in a range of probabilities between 15 and 60%. In the external validation cohort, AUC was 0.70 and calibration showed that the model underestimates the actual probability of the outcome.

CONCLUSIONS

The proposed model to predict high-risk Decipher score at RP is helpful to improve risk stratification of patients with PCa and to assess the need for additional testing and treatments.

Differences in Prostate Cancer Transcriptomes by Age at Diagnosis: Are Primary Tumors from Older Men Inherently Different?

Older age at diagnosis is consistently associated with worse clinical outcomes in prostate cancer. We sought to characterize gene expression profiles of prostate tumor tissue by age at diagnosis. We conducted a discovery analysis in The Cancer Genome Atlas prostate cancer dataset (n = 320; 29% of men >65 years at diagnosis), using linear regressions of age at diagnosis and mRNA expression and adjusting for TMPRSS2:ERG fusion status and race. This analysis identified 13 age-related candidate genes at FDR < 0.1, six of which were also found in an analysis additionally adjusted for Gleason score. We then validated the 13 age-related genes in a transcriptome study nested in the Health Professionals Follow-up Study and Physicians' Health Study (n = 374; 53% of men >65 years). Gene expression differences by age in the 13 candidate genes were directionally consistent, and age at diagnosis was weakly associated with the 13-gene score. However, the age-related genes were not consistently associated with risk of metastases and prostate cancer-specific death. Collectively, these findings argue against tumor genomic differences as a main explanation for age-related differences in prostate cancer prognosis.

PREVENTION RELEVANCE

Older age at diagnosis is consistently associated with worse clinical outcomes in prostate cancer. This study with independent discovery and validation sets and long-term follow-up suggests that prevention of lethal prostate cancer should focus on implementing appropriate screening, staging, and treatment among older men without expecting fundamentally different tumor biology.

Association between Incidental Pelvic Inflammation and Aggressive Prostate Cancer

The impact of pelvic inflammation on prostate cancer (PCa) biology and aggressive phenotype has never been studied. Our study objective was to evaluate the role of pelvic inflammation on PCa aggressiveness and its association with clinical outcomes in patients following radical prostatectomy (RP). This study has been conducted on a retrospective single-institutional consecutive cohort of 2278 patients who underwent robot-assisted laparoscopic prostatectomy (RALP) between 01/2013 and 10/2019. Data from 2085 patients were analyzed to study the association between pelvic inflammation and adverse pathology (AP), defined as Gleason Grade Group (GGG) > 2 and ≥ pT3 stage, at resection. In a subset of 1997 patients, the association between pelvic inflammation and biochemical recurrence (BCR) was studied. Alteration in tumor transcriptome and inflammatory markers in patients with and without pelvic inflammation were studied using microarray analysis, immunohistochemistry, and culture supernatants derived from inflamed sites used in functional assays. Changes in blood inflammatory markers in the study cohort were analyzed by O-link. In univariate analyses, pelvic inflammation emerged as a significant predictor of AP. Multivariate cox proportional-hazards regression analyses showed that high pelvic inflammation with pT3 stage and positive surgical margins significantly affected the time to BCR (p ≤ 0.05). PCa patients with high inflammation had elevated levels of pro-inflammatory cytokines in their tissues and in blood. Genes involved in epithelial-to-mesenchymal transition (EMT) and DNA damage response were upregulated in patients with pelvic inflammation. Attenuation of STAT and IL-6 signaling decreased tumor driving properties of conditioned medium from inflamed sites. Pelvic inflammation exacerbates the progression of prostate cancer and drives an aggressive phenotype.

Novel Transcriptomic Interactions Between Immune Content and Genomic Classifier Predict Lethal Outcomes in High-grade Prostate Cancer

Grade group 4 and 5 (GG-45) prostate cancer (PCa) patients are at the highest risk of lethal outcomes, yet lack genomic risk stratification for prognosis and treatment selection. Here, we assess whether transcriptomic interactions between tumor immune content score (ICS) and the Decipher genomic classifier can identify most lethal subsets of GG-45 PCa. We utilized whole transcriptome data from 8071 tumor tissue (6071 prostatectomy and 2000 treatment-naïve biopsy samples) to derive four immunogenomic subtypes using ICS and Decipher. When compared across all grade groups, GG-45 samples had the highest proportion of most aggressive subtype-ICS^High/Decipher^High. Subsequent analyses within the GG-45 patient samples (n = 1420) revealed that the ICS^High/Decipher^High subtype was associated with increased genomic radiosensitivity. Additionally, in a multivariable model (n = 335), ICS^High/Decipher^High subtype had a significantly higher risk of distant metastasis (hazard ratio [HR] = 5.41; 95% confidence interval [CI], 2.76-10.6; p ≤  0.0001) and PCa-specific mortality (HR = 10.6; 95% CI, 4.18-26.94; p ≤  0.0001) as compared with ICS^Low/Decipher^Low. The novel immunogenomic subtypes establish a very strong synergistic interaction between ICS and Decipher in identifying GG-45 patients who experience the most lethal outcomes. PATIENT SUMMARY: In this analysis, we identified a novel interaction between the total immune content of prostate tumors and genomic classifier to identify the most lethal subset of patients with grade groups 4 and 5. Our results will aid in the subtyping of aggressive prostate cancer patients who may benefit from combined immune-radiotherapy modalities.

Prostate zones and cancer: lost in transition?

Localized prostate cancer shows great clinical, genetic and environmental heterogeneity; however, prostate cancer treatment is currently guided solely by clinical staging, serum PSA levels and histology. Increasingly, the roles of differential genomics, multifocality and spatial distribution in tumorigenesis are being considered to further personalize treatment. The human prostate is divided into three zones based on its histological features: the peripheral zone (PZ), the transition zone (TZ) and the central zone (CZ). Each zone has variable prostate cancer incidence, prognosis and outcomes, with TZ prostate tumours having better clinical outcomes than PZ and CZ tumours. Molecular and cell biological studies can improve understanding of the unique molecular, genomic and zonal cell type features that underlie the differences in tumour progression and aggression between the zones. The unique biology of each zonal tumour type could help to guide individualized treatment and patient risk stratification.

Molecular risk classifier score and biochemical recurrence risk are associated with cribriform pattern type in Gleason 3+4=7 prostate cancer

PURPOSE

Among Gleason pattern 4 types, cribriform pattern is associated with the worst outcomes. We hypothesized that larger cribriform patterns would be associated with increased Decipher scores and higher biochemical recurrence (BCR) risk in Gleason 3+4=7 prostatectomy patients.

MATERIALS AND METHODS

The slide from patients who underwent prostatectomy from January 2016 to March 2020 on which Decipher was performed was re-reviewed for Gleason score and cribriform patterns, with large cribriform defined as cribriform acini with greater than 12 lumens and simple cribriform as 12 or fewer lumens. Differences in Decipher score were analyzed in a generalized linear model controlling for pathology stage and tumor margin status. A multivariable Cox proportional hazards model was performed for BCR-free survival.

RESULTS

Of 337 cases, 118 were Gleason 3+4=7. The mean Decipher scores in 3+4=7 cases without cribriform, with simple cribriform, and with large cribriform were 0.41, 0.54, and 0.62, respectively. In a multivariable model with pathology stage, margin tumor length, and percentage pattern 4 as covariates, compared to cases without cribriform, simple cribriform was associated with 0.10 increase in Decipher (p=0.03) and 4.7-fold hazard ratio of BCR (95% confidence interval [CI], 0.4-56.5; p=0.22) and large cribriform was associated with 0.17 increase in Decipher (p<0.001) and 16.0-fold hazard ratio of BCR (95% CI, 1.4-181.2; p=0.02).

CONCLUSIONS

Among Gleason 3+4=7 carcinomas, large cribriform was associated with higher Decipher scores and greater BCR risk. Our results support that large cribriform is an aggressive pattern 4 subtype and should be considered a contraindication for active surveillance.

Tumour markers of prostate cancer: The post-PSA era

Although PSA-based prostate cancer (PCa) screening had a positive impact in reducing PCa mortality, it also led to overdiagnosis, overtreatment and to a significant number of unnecessary biopsies. In the post-PSA era, new biomarkers have emerged that can complement the information given by PSA, towards a better cancer diagnostic specificity, and also allow a better estimate of the aggressiveness of the disease and its clinical outcome. That means those markers have the potential to assist the clinician in the decision-making processes, such as whether or not to perform a biopsy, and to make the best treatment choice among the new therapeutic options available, including active surveillance (AS) in lower risk disease. In this article, we will review several of those more recent diagnostic markers (4Kscore®, [-2]proPSA and Prostate Health Index (PHI), SelectMDx®, ConfirmMDx®, Progensa® Prostate Cancer Antigen 3, Mi-Prostate Score, ExoDx™ Prostate Test, the Stockholm-3 test and ERSPC risk calculators) and prognostic markers (OncotypeDX® Genomic Prostate Score, Prolaris®, Decipher® and ProMark®). We will also address some new liquid biopsy approaches - circulating tumour cells and cell-free DNA (cfDNA) - with a potential role in metastatic castration-resistant PCa and will briefly give some future perspectives, mostly outlooking epigenetic markers.

Comparative analysis of 1152 African-American and European-American men with prostate cancer identifies distinct genomic and immunological differences

Racial disparities in prostate cancer have not been well characterized on a genomic level. Here we show the results of a multi-institutional retrospective analysis of 1,152 patients (596 African-American men (AAM) and 556 European-American men (EAM)) who underwent radical prostatectomy. Comparative analyses between the race groups were conducted at the clinical, genomic, pathway, molecular subtype, and prognostic levels. The EAM group had increased ERG (P < 0.001) and ETS (P = 0.02) expression, decreased SPINK1 expression (P < 0.001), and basal-like (P < 0.001) molecular subtypes. After adjusting for confounders, the AAM group was associated with higher expression of CRYBB2, GSTM3, and inflammation genes (IL33, IFNG, CCL4, CD3, ICOSLG), and lower expression of mismatch repair genes (MSH2, MSH6) (p < 0.001 for all). At the pathway level, the AAM group had higher expression of genes sets related to the immune response, apoptosis, hypoxia, and reactive oxygen species. EAM group was associated with higher levels of fatty acid metabolism, DNA repair, and WNT/beta-catenin signaling. Based on cell lines data, AAM were predicted to have higher potential response to DNA damage. In conclusion, biological characteristics of prostate tumor were substantially different in AAM when compared to EAM.

Walter Rayford, Alp Tuna Beksac et al. investigated gene expression alterations in African-American and European-American men who underwent radical prostatectomy for prostate cancer. The observed differences include higher expression of inflammation genes and lower expression of mismatch repair genes in African-American men.

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.

Validation of a 22-Gene Genomic Classifier in Patients With Recurrent Prostate Cancer: An Ancillary Study of the NRG/RTOG 9601 Randomized Clinical Trial

Question

Can a genomic biomarker estimate the risk of prostate cancer clinical end points in men who received salvage radiation for rising prostate-specific antigen levels after surgery?

Findings

In this ancillary study of 352 men randomized to placebo or hormone therapy in the NRG/RTOG 9601 clinical trial of salvage radiation, the Decipher genomic classifier was independently associated with the risk of metastasis, prostate cancer–specific mortality, and overall survival.

Meaning

These findings suggest that the Decipher genomic classifier is a promising biomarker to risk stratify men to better enable hormone therapy treatment decisions for biochemical recurrence of their prostate cancer after surgery.

Importance

Decipher (Decipher Biosciences Inc) is a genomic classifier (GC) developed to estimate the risk of distant metastasis (DM) after radical prostatectomy (RP) in patients with prostate cancer.

Objective

To validate the GC in the context of a randomized phase 3 trial.

Design, Setting, and Participants

This ancillary study used RP specimens from the phase 3 placebo-controlled NRG/RTOG 9601 randomized clinical trial conducted from March 1998 to March 2003. The specimens were centrally reviewed, and RNA was extracted from the highest-grade tumor available in 2019 with a median follow-up of 13 years. Clinical-grade whole transcriptomes from samples passing quality control were assigned GC scores (scale, 0-1). A National Clinical Trials Network–approved prespecified statistical plan included the primary objective of validating the independent prognostic ability of GC for DM, with secondary end points of prostate cancer–specific mortality (PCSM) and overall survival (OS). Data were analyzed from September 2019 to December 2019.

Intervention

Salvage radiotherapy (sRT) with or without 2 years of bicalutamide.

Main Outcomes and Measures

The preplanned primary end point of this study was the independent association of the GC with the development of DM.

Results

In this ancillary study of specimens from a phase 3 randomized clinical trial, GC scores were generated from 486 of 760 randomized patients with a median follow-up of 13 years; samples from a total of 352 men (median [interquartile range] age, 64.5 (60-70) years; 314 White [89.2%] participants) passed microarray quality control and comprised the final cohort for analysis. On multivariable analysis, the GC (continuous variable, per 0.1 unit) was independently associated with DM (hazard ratio [HR], 1.17; 95% CI, 1.05-1.32; P = .006), PCSM (HR, 1.39; 95% CI, 1.20-1.63; P < .001), and OS (HR, 1.17; 95% CI, 1.06-1.29; P = .002) after adjusting for age, race/ethnicity, Gleason score, T stage, margin status, entry prostate-specific antigen, and treatment arm. Although the original planned analysis was not powered to detect a treatment effect interaction by GC score, the estimated absolute effect of bicalutamide on 12-year OS was less when comparing patients with lower vs higher GC scores (2.4% vs 8.9%), which was further demonstrated in men receiving early sRT at a prostate-specific antigen level lower than 0.7 ng/mL (−7.8% vs 4.6%).

Conclusions and Relevance

This ancillary validation study of the Decipher GC in a randomized trial cohort demonstrated association of the GC with DM, PCSM, and OS independent of standard clinicopathologic variables. These results suggest that not all men with biochemically recurrent prostate cancer after surgery benefit equally from the addition of hormone therapy to sRT.

Trial Registration

ClinicalTrials.gov identifier: NCT00002874

Clinical-genomic Characterization Unveils More Aggressive Disease Features in Elderly Prostate Cancer Patients with Low-grade Disease

BACKGROUND

Over 20% of men diagnosed with prostate cancer (PC) are ≥75 yr old. More objective disease-specific indices for predicting outcomes beyond chronological age are necessary.

OBJECTIVE

To analyze age-related differences in clinical-genomic prognostic features of aggressiveness in localized PC.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective multicenter cross-sectional study reported the use of the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) guidelines. Clinical-genomic data of patients who underwent a prostate biopsy or radical prostatectomy (RP) were obtained from the Decipher Genomic Resource Information Database (NCT02609269).

INTERVENTION

Our analyses focused on the 22-gene Decipher genomic classifier (GC) and 50-gene (PAM50) models in the biopsy and RP cohorts stratified by age.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary endpoint was the impact of age on GC scores and PAM50 molecular subtypes. Prognostic indices including Decipher GC scores, PAM50 molecular subtypes, National Comprehensive Cancer Network risk categories, and ISUP grade groups (IGGs) were stratified by age using multivariable logistic regression analyses.

RESULTS AND LIMITATIONS

Within histological low-risk IGGs, there were a higher proportion of patients with high-risk Decipher biopsy scores with age (age <60 yr: 10.1% IGG 1 and 29.9% IGG 2 vs age ≥80 yr: 22% IGG 1 and 37.7% IGG 2). The prevalence of the adverse phenotype luminal B (PAM50-defined) increased with age (age <60 yr: 22.7% and 40.2% vs age ≥80 yr: 29.7% and 49.1%, in patients with IGG 1 and IGG 2, respectively). In IGGs 3-5, no age differences were observed. Multivariable models demonstrated that each age decile entailed a 19% (odds ratio [OR] 1.19, 95% confidence interval [CI] 1.10-1.29, p < 0.001) and a 10% (OR 1.1, 95% CI 1.05-1.16) increased probability for a high-risk Decipher biopsy and RP score, respectively. Aside from an obvious selection bias, data on race, family history, prostate volume, and long-term follow-up outcomes were unavailable.

CONCLUSIONS

These data demonstrated that elderly men with favorable pathology (IGG 1-2), might harbor more aggressive disease than younger patients based on validated GC scores.

PATIENT SUMMARY

The presented clinical-genomic data demonstrate that elderly patients with low-risk prostate cancer might harbor more aggressive disease than their younger counterparts. This suggests that standard well-accepted paradigm of elderly prostate cancer patients not being aggressively treated, based solely on their chronological age, might need to be reconsidered.

Correlation between cribriform/intraductal prostatic adenocarcinoma and percent Gleason pattern 4 to a 22-gene genomic classifier

BACKGROUND

The Decipher test measures expression of 22 RNA biomarkers associated with aggressive prostate cancer used to improve risk stratification of patients to help guide management. To date, Decipher's genomic classification has not been extensively correlated with specific histologic growth patterns in prostatic adenocarcinoma. With a growing understanding of the clinical aggressiveness associated with cribriform growth pattern (CF), intraductal carcinoma (IDC), and percent Gleason pattern 4 (G4%), we sought to determine if their presence was associated with an increased genomic risk as measured by the Decipher assay.

DESIGN

Clinical use of the Decipher assay was performed on the highest Gleason score (GS) tumor nodule of prostatectomy specimens from a prospective cohort of 48 patients, with GS varying from 7 through 9 to help guide clinical risk stratification. The tumors were reviewed for CF, IDC, and G4%, which were then compared to the Decipher score (0-1) and risk stratification (high vs not high).

RESULTS

The presence of CF/IDC was significantly associated with Decipher risk score (P = .007), with a high-risk Decipher score in 22% vs 56% of patients without or with CF/IDC. On binary logistic regression analysis, G4% (odds ratio [OR] 1.04 per percent increase [95% confidence interval [CI], 1.02-1.06]; P = .0004) and CF predominant (OR, 9.60 [95%CI, 1.48-62.16]; P = .02) were significantly associated with a high-risk GC score. IDC did not reach significance (OR, 1.92 [95%CI, 0.65-5.67]; P = .24).

CONCLUSIONS

Our findings add to an expanding knowledge base that supports G4% and CF/IDC as molecularly unique and clinically relevant features in prostatic adenocarcinoma. These histologic features should be standardly reported as they are associated with more aggressive prostate cancer. Future work should determine the independent information of these histologic findings that are relative to genomic assessment on long-term outcomes.

Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline

PURPOSE

This guideline provides recommendations for available tissue-based prostate cancer biomarkers geared toward patient selection for active surveillance, identification of clinically significant disease, choice of postprostatectomy adjuvant versus salvage radiotherapy, and to address emerging questions such as the relative value of tissue biomarkers compared with magnetic resonance imaging.

METHODS

An ASCO multidisciplinary Expert Panel, with representatives from the European Association of Urology, American Urological Association, and the College of American Pathologists, conducted a systematic literature review of localized prostate cancer biomarker studies between January 2013 and January 2019. Numerous tissue-based molecular biomarkers were evaluated for their prognostic capabilities and potential for improving management decisions. Here, the Panel makes recommendations regarding the clinical use and indications of these biomarkers.

RESULTS

Of 555 studies identified, 77 were selected for inclusion plus 32 additional references selected by the Expert Panel. Few biomarkers had rigorous testing involving multiple cohorts and only 5 of these tests are commercially available currently: Oncotype Dx Prostate, Prolaris, Decipher, Decipher PORTOS, and ProMark. With various degrees of value and validation, multiple biomarkers have been shown to refine risk stratification and can be considered for select men to improve management decisions. There is a paucity of prospective studies assessing short- and long-term outcomes of patients when these markers are integrated into clinical decision making.

RECOMMENDATIONS

Tissue-based molecular biomarkers (evaluating the sample with the highest volume of the highest Gleason pattern) may improve risk stratification when added to standard clinical parameters, but the Expert Panel endorses their use only in situations in which the assay results, when considered as a whole with routine clinical factors, are likely to affect a clinical decision. These assays are not recommended for routine use as they have not been prospectively tested or shown to improve long-term outcomes-for example, quality of life, need for treatment, or survival. Additional information is available at www.asco.org/genitourinary-cancer-guidelines.

Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score

BACKGROUND

Biochemical recurrence (BCR) affects a significant proportion of patients who undergo robotic-assisted laparoscopic prostatectomy (RALP).

PURPOSE

To evaluate the performance of a routine clinical prostate multiparametric magnetic resonance imaging (mpMRI) and Decipher genomic classifier score for prediction of biochemical recurrence in patients who underwent RALP.

STUDY TYPE

Retrospective cohort study.

SUBJECTS

Ninety-one patients who underwent RALP performed by a single surgeon, had mpMRI before RALP, Decipher taken from RALP samples, and prostate specific antigen (PSA) follow-up for >3 years or BCR within 3 years, defined as PSA >0.2 mg/ml. FIELD STRENGTH/SEQUENCE: mpMRI was performed at 27 different institutions using 1.5T (n = 10) or 3T scanners and included T₂ w, diffusion-weighted imaging (DWI), or dynamic contrast-enhanced (DCE) MRI.

ASSESSMENT

All mpMRI studies were reported by one reader using Prostate Imaging Reporting and Data System v. 2.1 (PI-RADsv2.1) without knowledge of other findings. Eighteen (20%) randomly selected cases were re-reported by reader B to evaluate interreader variability.

STATISTICAL TESTS

Univariate and multivariate analysis using greedy feature selection and tournament leave-pair-out cross-validation (TLPOCV) were used to evaluate the performance of various variables for prediction of BCR, which included clinical (three), systematic biopsy (three), surgical (six: RALP Gleason Grade Group [GGG], extracapsular extension, seminal vesicle invasion, intraoperative surgical margins [PSM], final PSM, pTNM), Decipher (two: Decipher score, Decipher risk category), and mpMRI (eight: prostate volume, PSA density, PI-RADv2.1 score, MRI largest lesion size, summed MRI lesions' volume and relative volume [MRI-lesion-percentage], mpMRI ECE, mpMRI seminal vesicle invasion [SVI]) variables. The evaluation metric was the area under the curve (AUC).

RESULTS

Forty-eight (53%) patients developed BCR. The best-performing individual features with TLPOCV AUC of 0.73 (95% confidence interval [CI] 0.64-0.82) were RALP GGG, MRI-lesion-percentage followed by biopsy GGG (0.72, 0.62-0.82), and Decipher score (0.71, 0.60-0.82). The best performance was achieved by feature selection of Decipher+Surgery and MRI + Surgery variables with TLPOCV AUC of 0.82 and 0.81, respectively DATA CONCLUSION: Relative lesion volume measured on a routine clinical mpMRI failed to outperform Decipher score in BCR prediction.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1075-1085.

Associations of Luminal and Basal Subtyping of Prostate Cancer With Prognosis and Response to Androgen Deprivation Therapy

Importance

There is a clear need for a molecular subtyping approach in prostate cancer to identify clinically distinct subgroups that benefit from specific therapies.

Objectives

To identify prostate cancer subtypes based on luminal and basal lineage and to determine associations with clinical outcomes and response to treatment.

Design, Setting, and Participants

The PAM50 classifier was used to subtype 1567 retrospectively collected (median follow-up, 10 years) and 2215 prospectively collected prostate cancer samples into luminal- and basal-like subtypes.

Main Outcomes and Measures

Metastasis, biochemical recurrence, overall survival, prostate cancer–specific survival, associations with biological pathways, and clinicopathologic variables were the main outcomes.

Results

Among the 3782 samples, the PAM50 classifier consistently segregated prostate cancer into 3 subtypes in both the retrospective and prospective cohorts: luminal A (retrospective, 538 [34.3%]; prospective, 737 [33.3%]), luminal B (retrospective, 447 [28.5%]; prospective, 723 [32.6%]), and basal (retrospective, 582 [37.1%]; prospective, 755 [34.1%]). Known luminal lineage markers, such as NKX3.1 and KRT18, were enriched in luminal-like cancers, and the basal lineage CD49f signature was enriched in basal-like cancers, demonstrating the connection between these subtypes and established prostate cancer biology. In the retrospective cohort, luminal B prostate cancers exhibited the poorest clinical prognoses on both univariable and multivariable analyses accounting for standard clinicopathologic prognostic factors (10-year biochemical recurrence-free survival [bRFS], 29%; distant metastasis-free survival [DMFS], 53%; prostate cancer-specific survival [PCSS], 78%; overall survival [OS], 69%), followed by basal prostate cancers (10-year bRFS, 39%; DMFS, 73%; PCSS, 86%; OS, 80%) and luminal A prostate cancers (10-year bRFS, 41%; DMFS, 73%; PCSS, 89%; OS, 82%). Although both luminal-like subtypes were associated with increased androgen receptor expression and signaling, only luminal B prostate cancers were significantly associated with postoperative response to androgen deprivation therapy (ADT) in a subset analysis in our retrospective cohorts (n = 315) matching patients based on clinicopathologic variables (luminal B 10-year metastasis: treated, 33% vs untreated, 55%; nonluminal B 10-year metastasis: treated, 37% vs untreated, 21%; P = .006 for interaction).

Conclusions and Relevance

Luminal- and basal-like prostate cancers demonstrate divergent clinical behavior, and patients with luminal B tumors respond better to postoperative ADT than do patients with non–luminal B tumors. These findings contribute novel insight into prostate cancer biology, providing a potential clinical tool to personalize ADT treatment for prostate cancer by predicting which men may benefit from ADT after surgery.

Genomic Risk Predicts Molecular Imaging-detected Metastatic Nodal Disease in Prostate Cancer

BACKGROUND

The Decipher genomic classifier (GC) is increasingly being used to determine metastasis risk in men with localized prostate cancer (PCa). Whether GCs predict for the presence of occult metastatic disease at presentation or subsequent metastatic progression is unknown.

OBJECTIVE

To determine if GC scores predict extraprostatic ⁶⁸Ga prostate-specific membrane antigen (⁶⁸Ga-PSMA-11) positron emission tomography (PET) positivity at presentation.

DESIGN, SETTING, AND PARTICIPANTS

Between December 2015 and September 2018, 91 PCa patients with both GC scores and pretreatment ⁶⁸Ga-PSMA-11 PET scans were identified. Risk stratification was performed using the National Comprehensive Cancer Network (NCCN), Cancer of the Prostate Risk Assessment (CAPRA), and GC scores.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Logistic regression was used to identify factors correlated with PSMA-positive disease.

RESULTS AND LIMITATIONS

The NCCN criteria identified 23 (25.3%) and 68 patients (74.7%) as intermediate and high risk, while CAPRA scores revealed 28 (30.8%) and 63 (69.2%) as low/intermediate and high risk, respectively. By contrast, only 45 patients (49.4%) had high-risk GC scores. PSMA-avid pelvic nodal involvement was identified in 27 patients (29.7%). Higher GC score was significantly associated with pelvic nodal involvement (odds ratio [OR] 1.38 per 0.1 units; p=0.009) and any PSMA-avid nodal involvement (pelvic or distant; OR 1.40 per 0.1 units; p=0.007). However, higher GC score was not significantly associated with PSMA-avid osseous metastases (OR 1.11 per 0.1 units; p=0.50). Limitations include selection bias for patients able to receive both tests and the sample size.

CONCLUSIONS

Each 0.1-unit increase in GC score was associated with an approximate 40% increase in the odds of PSMA-avid lymph node involvement. These data suggest that patients with GC high risk might benefit from more nodal imaging and treatment intensification, potentially via pelvic nodal dissection, pelvic nodal irradiation, and/or the addition of chemohormonal agents.

PATIENT SUMMARY

Patients with higher genomic classifier scores were found to have more metastatic lymph node involvement on prostate-specific membrane antigen imaging.

Multiparametric Magnetic Resonance Imaging Features Identify Aggressive Prostate Cancer at the Phenotypic and Transcriptomic Level

PURPOSE

Multiparametric magnetic resonance imaging is a diagnostic tool for prostate cancer with limited data on prognostic use. We sought to determine whether multiparametric magnetic resonance could predict aggressive prostate cancer features.

MATERIALS AND METHODS

We retrospectively analyzed the records of 206 patients who underwent radical prostatectomy between 2013 and 2017. All patients had available RNA expression data on the final pathology specimen obtained from a location corresponding to a lesion location on multiparametric magnetic resonance imaging. The association between the PIRADS™ (Prostate Imaging Reporting and Data System) score and adverse pathology features were analyzed. We also performed differential transcriptomic analysis between the PIRADS groups. Factors associated with adverse pathology were analyzed using a multivariable logistic regression model.

RESULTS

Lesion size (p = 0.03), PIRADS score (p = 0.02) and extraprostatic extension (p = 0.01) associated significantly with the Decipher® score. Multivariable analysis showed that the PIRADS score (referent PIRADS 3, OR 8.1, 95% CI 1.2-57.5, p = 0.04), the Gleason Grade Group (referent 3, OR 5.6, 95% CI 1.5-21.1, p = 0.01) and prostate specific antigen (OR 1.103, 95% CI 1.011-1.203) were risk factors for adverse pathology findings. The difference between PIRADS 4 and 5 did not reach significance (OR 1.9, 95% CI 0.8-4.5, p = 0.12). However, the PI3K-AKT-mTOR, WNT-β and E2F signaling pathways were more active in PIRADS 5 than in PIRADS 4 cases.

CONCLUSIONS

The PIRADS score is associated with adverse pathology results, increased metastatic risk and differential genomic pathway activation.

Markers of clinical utility in the differential diagnosis and prognosis of prostate cancer

Molecular diagnostics is a rapidly evolving area of surgical pathology, that is gradually beginning to transform our diagnostical procedures for a variety of tumors. Next to molecular prognostication that has begun to complement our histological diagnosis in breast cancer, additional testing to detect targets and to predict therapy response has become common practice in breast and lung cancer. Prostate cancer is a bit slower in this respect, as it is still largely diagnosed and classified on morphological grounds. Our diagnostic immunohistochemical armamentarium of basal cell markers and positive markers of malignancy now allows to clarify the majority of lesions, if applied to the appropriate morphological context (and step sections). Prognostic immunohistochemistry remains a problematic and erratic yet tempting research field that provides information on tumor relevance of proteins, but little hard data to integrate into our diagnostic workflow. Main reasons are various issues of standardization that hamper the reproducibility of cut-off values to delineate risk categories. Molecular testing of DNA-methylation or transcript profiling may be much better standardized and this review discusses a couple of commercially available tests: The ConfirmDX test measures DNA-methylation to estimate the likelihood of cancer detection on a repeat biopsy and may help to reduce unnecessary biopsies. The tests Prolaris, OncotypeDX Prostate, and Decipher all are transcript tests that have shown to provide prognostic data independent of clinico-pathological parameters and that may aid in therapy planning. However, further validation and more comparative studies will be needed to clarify the many open questions concerning sampling bias and tumor heterogeneity.

Performance of a Prostate Cancer Genomic Classifier in Predicting Metastasis in Men with Prostate-specific Antigen Persistence Postprostatectomy

BACKGROUND

Prostate cancer patients who have a detectable prostate-specific antigen (PSA) postprostatectomy may harbor pre-existing metastatic disease. To our knowledge, none of the commercially available genomic biomarkers have been investigated in such men.

OBJECTIVE

To evaluate if a 22-gene genomic classifier can independently predict development of metastasis in men with PSA persistence postoperatively.

DESIGN, SETTING, AND PARTICIPANTS

A multi-institutional study of 477 men who underwent radical prostatectomy (RP) between 1990 and 2015 from three academic centers. Patients were categorized as detectable PSA (n=150) or undetectable (n=327) based on post-RP PSA nadir ≥0.1 ng/ml.

OUTCOME MEASUREMENTS AND STATISITICAL ANALYSIS

Cumulative incidence curves for metastasis were constructed using Fine-Gray competing risks analysis. Penalized Cox univariable and multivariable (MVA) proportional hazards models were performed to evaluate the association of the genomic classifier with metastasis.

RESULTS AND LIMITATIONS

The median follow-up for censored patients was 57 mo. The median time from RP to first postoperative PSA was 1.4 mo. Detectable PSA patients were more likely to have higher adverse pathologic features compared with undetectable PSA patients. On MVA, only genomic high-risk (hazard ratio [HR]: 5.95, 95% confidence interval [CI]: 2.02-19.41, p=0.001), detectable PSA (HR: 4.26, 95% CI: 1.16-21.8, p=0.03), and lymph node invasion (HR: 12.2, 95% CI: 2.46-70.7, p=0.003) remained prognostic factors for metastasis. Among detectable PSA patients, the 5-yr metastasis rate was 0.90% for genomic low/intermediate and 18% for genomic high risk (p<0.001). Genomic high risk remained independently prognostic on MVA (HR: 5.61, 95% CI: 1.48-22.7, p=0.01) among detectable PSA patients. C-index for Cancer of the Prostate Risk Assessment Postsurgical score, Gandaglia nomogram, and the genomic classifier plus either Cancer of the Prostate Risk Assessment Postsurgical score or Gandaglia were 0.69, 0.68, and 0.82 or 0.81, respectively. Sample size was a limitation.

CONCLUSIONS

Despite patients with a detectable PSA harboring significantly higher rates of aggressive clinicopathologic features, Decipher independently predicts for metastasis. Prospective validation of these findings is warranted and will be collected as part of the ongoing randomized trial NRG GU-002.

PATIENT SUMMARY

Decipher independently predicted metastasis for patients with detectable prostate-specific antigen after prostatectomy.

Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Drug development for noncastrate prostate cancer in a changed therapeutic landscape

The unprecedented progress in the treatment of metastatic castration-resistant prostate cancer is only beginning to be realized in patients with noncastrate disease. This slow progress in part reflects the use of trial objectives focused on time-to-event end points, such as time to metastasis and overall survival, which require long follow-up durations and large sample sizes, and has been further delayed by the use of approved therapies that are effective at the time of progression. Our central hypotheses are that progress can be accelerated, and that outcomes can be improved by shifting trial objectives to response measures occurring early that solely reflect the effects of the treatment. To test these hypotheses, a continuously enrolling multi-arm, multi-stage randomized trial design, analogous to that used in the STAMPEDE trial, has been developed. Eligibility is focused on patients with incurable disease or those with a high risk of death with any form of monotherapy alone. The primary objective is to eliminate all disease using a multimodality treatment strategy. End points include pathological complete response and an undetectable level of serum prostate-specific antigen, with recovery of serum testosterone levels. Both are binary, objective, and provide an early, quantitative indication of efficacy.

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.

Transcriptome evaluation of the relation between body mass index and prostate cancer outcomes

BACKGROUND

Large epidemiological studies indicate that an increased body mass index (BMI) is associated with increased prostate cancer (PCa) mortality. Data indicate that there is no association between elevated metabolic pathway proteins and PCa mortality. There are no published studies evaluating the relation between BMI and metabolic pathways with respect to PCa outcomes with a genomics approach.

METHODS

The Decipher Genomic Resource Information Database was queried for patients who had undergone prostatectomy and had BMI information available. These patients came from Thomas Jefferson University (TJU) and Johns Hopkins Medical Institution (JHMI); the latter provided 2 cohorts (I and II). A high-BMI group (≥30 kg/m² ) and a low-BMI group (<25 kg/m² ) were identified, and genomic data were interrogated for differentially expressed genes with an interquartile range filter and a Wilcoxon test. P values were adjusted for multiple testing with the Benjamini-Hochberg false-discovery rate method.

RESULTS

A total of 477 patients with a median follow-up of 108 months had BMI information available. Two genes were found to interact with BMI in both the JHMI I cohort and the TJU cohort, but there was no statistical significance after adjustments for multiple comparisons. Aberrant metabolic gene expression was significantly correlated with distant metastases (P < .05). No relation was found between BMI and metastases or overall survival (both P values > .05).

CONCLUSIONS

In a genomic analysis of prostatectomy specimens, metabolic gene expression, but not BMI, was associated with PCa metastases. Cancer 2017;123:2240-2247. © 2017 American Cancer Society.

Pan-Cancer Analysis of Genomic Sequencing Among the Elderly

PURPOSE

We hypothesized that elderly patients might have age-specific genetic abnormalities yet be underrepresented in currently available sequencing repositories, which could limit the effect of sequencing efforts for this population.

METHODS AND MATERIALS

Leveraging The Cancer Genome Atlas (TCGA) data portal, 9 tumor types were analyzed. The frequency distribution of cancer by age was determined and compared with Surveillance, Epidemiology, and End Results data. Using the estimated median somatic mutational frequency of each tumor type, the samples needed beyond TCGA to detect a 10% mutational frequency were calculated. Microarray data from a separate prospective cohort were obtained from primary prostatectomy samples to determine whether elderly-specific transcriptomic alterations could be identified.

RESULTS

Of the 5236 TCGA samples, 73% were from patients aged <70 years. Comparing the distribution of TCGA samples by age to the Surveillance, Epidemiology, and End Results data, patients <70 years were well represented across most tumor types, but patients aged 80 to 99 years were underrepresented in all cancers (median TCGA underrepresentation of 167%). All cancers (except for colorectal) contained enough samples to detect a 10% mutational frequency in patients aged <60 years. In contrast, no cancer type had enough samples for which a 10% mutational frequency could be detected in patients aged ≥80 years. To further interrogate whether elderly patients with cancer were likely to harbor age-specific molecular abnormalities, we accessed transcriptomic data from a separate, larger database of >2000 prostate cancer samples. That analysis revealed significant differences in the expression of 10 genes in patients aged ≥70 years compared with those <70 years, of which 7 are involved in androgen signaling and/or DNA repair.

CONCLUSIONS

Elderly patients have been underrepresented in genomic sequencing studies. Our data suggest the presence of elderly-specific molecular alterations. Further dedicated efforts to understand the biology of cancer among the elderly will be important moving forward.