Searching for prognostic biomarkers for small renal masses in the urinary proteome.

Renal cell carcinoma (RCC) is frequently diagnosed incidentally as an early-stage small renal mass (SRM; pT1a, ≤4 cm). Overtreatment of patients with benign or clinically indolent SRMs is increasingly common and has resulted in a recent shift in treatment recommendations. There are currently no available biomarkers that can accurately predict clinical behavior. Therefore, we set out to identify early biomarkers of RCC progression. We employed a quantitative label-free liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) proteomics approach and targeted parallel-reaction monitoring to identify and validate early, noninvasive urinary biomarkers for RCC-SRMs. In total, we evaluated 115 urine samples, including 33 renal oncocytoma (≤4 cm) cases, 30 progressive and 26 nonprogressive clear cell RCC (ccRCC)-SRM cases, in addition to 26 healthy controls. We identified six proteins, which displayed significantly elevated expression in clear cell RCC-SRMs (ccRCC-SRMs) relative to healthy controls. Proteins C12ORF49 and EHD4 showed significantly elevated expression in ccRCC-SRMs compared to renal oncocytoma (≤4 cm). Additionally, proteins EPS8L2, CHMP2A, PDCD6IP, CNDP2 and CEACAM1 displayed significantly elevated expression in progressive relative to nonprogressive ccRCC-SRMs. A two-protein signature (EPS8L2 and CCT6A) showed significant discriminatory ability (areas under the curve: 0.81, 95% CI: 0.70-0.93) in distinguishing progressive from nonprogressive ccRCC-SRMs. Patients (Stage I-IV) with EPS8L2 and CCT6A mRNA alterations showed significantly shorter overall survival (p = 1.407 × 10-6 ) compared to patients with no alterations. Our in-depth proteomic analysis identified novel biomarkers for early-stage RCC-SRMs. Pretreatment characterization of urinary proteins may provide insight into early RCC progression and could potentially help assign patients to appropriate management strategies.

Identification of Prognostic Biomarkers in the Urinary Peptidome of the Small Renal Mass.

Renal cell carcinoma (RCC) is often diagnosed incidentally as a small renal mass (SRM; pT1a, ≤4 cm). Increasing concerns surrounding the overtreatment of patients with benign or clinically silent SRMs has resulted in a recent shift in treatment recommendations, especially in elderly and infirm patients. There are currently no biomarkers that can predict progression. We used a quantitative label-free liquid chromatography-tandem mass spectrometry peptidomics approach and targeted parallel-reaction monitoring to identify early, noninvasive diagnostic and prognostic biomarkers for early-stage RCC-SRMs. In total, 115 urine samples, including 33 renal oncocytoma (≤4 cm) cases, 30 progressive and 26 nonprogressive clear cell RCC-SRM cases, and 26 healthy controls were evaluated. Nine endogenous peptides that displayed significantly elevated expression in clear cell RCC-SRMs relative to healthy controls were identified. Peptides NVINGGSHAGNKLAMQEF, VNVDEVGGEALGRL, and VVAGVANALAHKYH showed significantly elevated expression in clear cell RCC-SRMs relative to renal oncocytoma. Additionally, peptides SHTSDSDVPSGVTEVVVKL and IVDNNILFLGKVNRP displayed significantly elevated expression in progressive relative to nonprogressive clear cell RCC-SRMs. Peptide SHTSDSDVPSGVTEVVVKL showed the most significant discriminatory utility (area under the curve, 0.76; 95% CI, 0.62-0.90; P = 0.0027). Patients with elevated SHTSDSDVPSGVTEVVVKL expression had significantly shorter overall survival (hazard ratio, 4.13; 95% CI, 1.09-15.65; P = 0.024) compared to patients with low expression. Pretreatment characterization of urinary peptides can provide insight into early RCC progression and may aid clinical decision-making and improve disease management.

Serum complexed and free prostate-specific antigen (PSA) for the diagnosis of the polycystic ovarian syndrome (PCOS).

BACKGROUND: Polycystic ovarian syndrome (PCOS) is a common cause of reproductive and metabolic dysfunction. We hypothesized that serum prostate-specific antigen (PSA) may constitute a new biomarker for hyperandrogenism in PCOS. METHODS: We conducted a cross-sectional study of 45 women with PCOS and 40 controls. Serum from these women was analyzed for androgenic steroids and for complexed PSA (cPSA) and free PSA (fPSA) with a novel fifth- generation assay with a sensitivity of ~10 fg/mL for cPSA and 140 fg/mL for fPSA. RESULTS: cPSA and fPSA levels were about three times higher in PCOS compared to controls. However, in PCOS, cPSA and fPSA did not differ according to waist-to-hip ratio, Ferriman-Gallwey score, or degree of hyperandrogenemia or oligo-ovulation. In PCOS and control women, serum cPSA and fPSA levels were highly correlated with each other, and with free and total testosterone levels, but not with other hormones. Adjusting for age, body mass index (BMI) and race, cPSA was significantly associated with PCOS, with an odds ratio (OR) of 5.67 (95% confidence interval [CI]: 1.86, 22.0). The OR of PCOS for fPSA was 7.04 (95% CI: 1.65, 40.4). A multivariate model that included age, BMI, race and cPSA yielded an area-under-the-receiver-operating-characteristic curve of 0.89. CONCLUSIONS: Serum cPSA and fPSA are novel biomarkers for hyperandrogenism in PCOS and may have value for disease diagnosis.

Designing a study to evaluate the benefit of a biomarker for selecting patient treatment.

Biomarkers that predict the efficacy of treatment can potentially improve clinical outcomes and decrease medical costs by allowing treatment to be provided only to those most likely to benefit. We consider the design of a randomized clinical trial in which one objective is to evaluate a treatment selection marker. The marker may be measured prospectively or retrospectively using samples collected at baseline. We describe and contrast criteria around which the trial can be designed. An existing approach focuses on determining if there is a statistical interaction between the marker and treatment. We propose three alternative approaches based on estimating clinically relevant measures of improvement in outcomes with use of the marker. Importantly, our approaches accommodate the common scenario in which the marker-based rule for recommending treatment is developed with data from the trial. Sample sizes are calculated for powering a trial to assess these criteria in the context of adjuvant chemotherapy for the treatment of estrogen-receptor-positive, node-positive breast cancer. In this example, we find that larger sample sizes are generally required for assessing clinical impact than for simply evaluating if there is a statistical interaction between marker and treatment. We also find that retrospectively selecting a case-control subset of subjects for marker evaluation can lead to large efficiency gains, especially if cases and controls are matched on treatment assignment.

Prognostic urinary miRNAs for the assessment of small renal masses.

BACKGROUND: Renal cell carcinoma (RCC) is often detected incidentally as a small renal mass (SRM; pT1a, ≤4 cm). It is clinically challenging to predict progression in patients with SRMs. This is largely due to the recent recognition of clinically progressive and non-progressive RCC-SRMs. It is critical to accurately stratify SRM patients according to risk to avoid unnecessary treatment. This is especially significant for elderly and infirm patients, where the risk of surgery outweighs mortality from SRMs. METHODS: We employed a qRT-PCR array-based approach and targeted qRT-PCR to identify and validate early, non-invasive diagnostic and prognostic biomarkers of RCC-SRMs. In total, we evaluated eighty urine samples, including 30 renal oncocytoma (≤4 cm) cases, 26 progressive and 24 non-progressive clear cell RCC-SRM (ccRCC-SRM) cases. RESULTS: We identified nine urinary miRNAs which displayed significantly elevated expression in ccRCC-SRMs (pT1a; ≤4 cm) relative to renal oncocytoma (≤4 cm). Additionally, miR-328-3p displayed significantly down-regulated expression in progressive relative to non-progressive ccRCC-SRMs. Patients with elevated miR-328-3p expression had significantly longer overall survival (HR = 0.29, 95% CI = 0.08-1.03, p = 0.042) compared to patients with low miR-328-3p expression. We also found no significant association between miR-328-3p expression levels and gender, age, laterality, tumor size, or grade, suggesting that miR-328-3p is an independent prognostic biomarker. CONCLUSIONS: Our in-depth miRNA profiling approach identified novel biomarkers for early-stage ccRCC-SRMs. Pretreatment characterization of urinary miRNAs may provide insight into early RCC progression and could potentially aid clinical decision-making, improving patient management and reducing overtreatment.

17-Gene Genomic Prostate Score Test Results in the Canary Prostate Active Surveillance Study (PASS) Cohort.

PURPOSE: The 17-gene Oncotype DX Genomic Prostate Score (GPS) test predicts adverse pathology (AP) in patients with low-risk prostate cancer treated with immediate surgery. We evaluated the GPS test as a predictor of outcomes in a multicenter active surveillance cohort. MATERIALS AND METHODS: Diagnostic biopsy tissue was obtained from men enrolled at 8 sites in the Canary Prostate Active Surveillance Study. The primary endpoint was AP (Gleason Grade Group [GG] ≥ 3, ≥ pT3a) in men who underwent radical prostatectomy (RP) after initial surveillance. Multivariable regression models for interval-censored data were used to evaluate the association between AP and GPS. Inverse probability of censoring weighting was applied to adjust for informative censoring. Predictiveness curves were used to evaluate how models stratified risk of AP. Association between GPS and time to upgrade on surveillance biopsy was evaluated using Cox proportional hazards models. RESULTS: GPS results were obtained for 432 men (median follow-up, 4.6 years); 101 underwent RP after a median 2.1 years of surveillance, and 52 had AP. A total of 167 men (39%) upgraded at a subsequent biopsy. GPS was significantly associated with AP when adjusted for diagnostic GG (hazards ratio [HR]/5 GPS units, 1.18; 95% CI, 1.04 to 1.44; P = .030), but not when also adjusted for prostate-specific antigen density (PSAD; HR, 1.85; 95% CI, 0.99 to 4.19; P = .066). Models containing PSAD and GG, or PSAD, GG, and GPS may stratify risk better than a model with GPS and GG. No association was observed between GPS and subsequent biopsy upgrade (P = .48). CONCLUSION: In our study, the independent association of GPS with AP after initial active surveillance was not statistically significant, and there was no association with upgrading in surveillance biopsy. Adding GPS to a model containing PSAD and diagnostic GG did not significantly improve stratification of risk for AP over the clinical variables alone.

Assessing the Clinical Impact of Risk Models for Opting Out of Treatment.

Decision curves are a tool for evaluating the population impact of using a risk model for deciding whether to undergo some intervention, which might be a treatment to help prevent an unwanted clinical event or invasive diagnostic testing such as biopsy. The common formulation of decision curves is based on an opt-in framework. That is, a risk model is evaluated based on the population impact of using the model to opt high-risk patients into treatment in a setting where the standard of care is not to treat. Opt-in decision curves display the population net benefit of the risk model in comparison to the reference policy of treating no patients. In some contexts, however, the standard of care in the absence of a risk model is to treat everyone, and the potential use of the risk model would be to opt low-risk patients out of treatment. Although opt-out settings were discussed in the original decision curve paper, opt-out decision curves are underused. We review the formulation of opt-out decision curves and discuss their advantages for interpretation and inference when treat-all is the standard.

Evaluating the impact of policies recommending PrEP to subpopulations of men and transgender women who have sex with men based on demographic and behavioral risk factors.

INTRODUCTION: Developing guidelines to inform the use of antiretroviral pre-exposure prophylaxis (PrEP) for HIV prevention in resource-limited settings must necessarily be informed by considering the resources and infrastructure needed for PrEP delivery. We describe an approach that identifies subpopulations of cisgender men who have sex with men (MSM) and transgender women (TGW) to prioritize for the rollout of PrEP in resource-limited settings. METHODS: We use data from the iPrEx study, a multi-national phase III study of PrEP for HIV prevention in MSM/TGW, to build statistical models that identify subpopulations at high risk of HIV acquisition without PrEP, and with high expected PrEP benefit. We then evaluate empirically the population impact of policies recommending PrEP to these subpopulations, and contrast these with existing policies. RESULTS: A policy recommending PrEP to a high risk subpopulation of MSM/TGW reporting condomless receptive anal intercourse over the last 3 months (estimated 3.3% 1-year HIV incidence) yields an estimated 1.95% absolute reduction in 1-year HIV incidence at the population level, and 3.83% reduction over 2 years. Importantly, such a policy requires rolling PrEP out to just 59.7% of MSM/TGW in the iPrEx population. We find that this policy is identical to that which prioritizes MSM/TGW with high expected PrEP benefit. It is estimated to achieve nearly the same reduction in HIV incidence as the PrEP guideline put forth by the US Centers for Disease Control, which relies on the measurement of more behavioral risk factors and which would recommend PrEP to a larger subset of the MSM/TGW population (86% vs. 60%). CONCLUSIONS: These findings may be used to focus future mathematical modelling studies of PrEP in resource-limited settings on prioritizing PrEP for high-risk subpopulations of MSM/TGW. The statistical approach we took could be employed to develop PrEP policies for other at-risk populations and resource-limited settings.

Performance of PCA3 and TMPRSS2:ERG urinary biomarkers in prediction of biopsy outcome in the Canary Prostate Active Surveillance Study (PASS).

BACKGROUND: For men on active surveillance for prostate cancer, biomarkers may improve prediction of reclassification to higher grade or volume cancer. This study examined the association of urinary PCA3 and TMPRSS2:ERG (T2:ERG) with biopsy-based reclassification. METHODS: Urine was collected at baseline, 6, 12, and 24 months in the multi-institutional Canary Prostate Active Surveillance Study (PASS), and PCA3 and T2:ERG levels were quantitated. Reclassification was an increase in Gleason score or ratio of biopsy cores with cancer to ≥34%. The association of biomarker scores, adjusted for common clinical variables, with short- and long-term reclassification was evaluated. Discriminatory capacity of models with clinical variables alone or with biomarkers was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: Seven hundred and eighty-two men contributed 2069 urine specimens. After adjusting for PSA, prostate size, and ratio of biopsy cores with cancer, PCA3 but not T2:ERG was associated with short-term reclassification at the first surveillance biopsy (OR = 1.3; 95% CI 1.0-1.7, p = 0.02). The addition of PCA3 to a model with clinical variables improved area under the curve from 0.743 to 0.753 and increased net benefit minimally. After adjusting for clinical variables, neither marker nor marker kinetics was associated with time to reclassification in subsequent biopsies. CONCLUSIONS: PCA3 but not T2:ERG was associated with cancer reclassification in the first surveillance biopsy but has negligible improvement over clinical variables alone in ROC or DCA analyses. Neither marker was associated with reclassification in subsequent biopsies.

Adjusting for covariates in evaluating markers for selecting treatment, with application to guiding chemotherapy for treating estrogen-receptor-positive, node-positive breast cancer.

In many clinical contexts, biomarkers that predict treatment efficacy are highly sought after. Such treatment selection or predictive biomarkers have the potential to identify subgroups most likely to benefit from the treatment, and may therefore be used to improve clinical outcomes and reduce medical costs. A methodological challenge in evaluating these biomarkers is determining how to take into account other variables that predict clinical outcomes, or that influence the biomarker distribution, generically termed covariates. We distinguish between two questions that arise when evaluating markers in the context of covariates. First, what is the biomarker's added value for selecting treatment, over and above the covariates? Second, what is the marker's performance within covariate strata-does performance vary? We lay out statistical methodology for addressing each of these questions. We argue that the common approach of testing for the marker's statistical interaction with treatment, in the context of a multivariate model that includes the covariates as predictors, does not directly address either question. We illustrate the methodology in new analyses of the Oncotype DX Recurrence Score, a marker used to select adjuvant chemotherapy for the treatment of estrogen-receptor-positive breast cancer.

Serum complexed and free prostate specific antigen levels are lower in female elite athletes in comparison to control women.

BACKGROUND: We hypothesize that prostate specific antigen (PSA), a protein that it is under regulation by androgens, may be differentially expressed in female elite athletes in comparison to control women. METHODS: We conducted a cross-sectional study of 106 female athletes and 114 sedentary age-matched controls.  Serum from these women was analyzed for complexed prostate specific antigen (cPSA) and free prostate specific antigen (fPSA), by fifth generation assays with limits of detection of around 6 and 140 fg/mL, respectively.  A panel of estrogens, androgens and progesterone in the same serum was also quantified by tandem mass spectrometry.  Results: Both components of serum PSA (cPSA and fPSA) were lower in the elite athletes vs the control group (P=0.033 and 0.013, respectively).  Furthermore, estrone (p=0.003) and estradiol (p=0.004) were significantly lower, and dehydroepiandrosterone  (p=0.095) and 5-androstene-3ß, 17ß-diol (p=0.084) tended to be higher in the athletes vs controls. Oral contraceptive use was similar between groups and significantly associated with increased cPSA and fPSA in athletes (p= 0.046 and 0.009, respectively).  PSA fractions were not significantly associated with progesterone changes. The Spearman correlation between cPSA and fPSA in both athletes and controls was 0.75 (P < 0.0001) and 0.64 (P < 0.0001), respectively.  Conclusions: Elite athletes have lower complexed and free PSA, higher levels of androgen precursors and lower levels of estrogen in their serum than sedentary control women. ABBREVIATIONS: cPSA, complexed PSA; fPSA, free PSA; PCOS, polycystic ovarian syndrome; E1, estrone; E2, estradiol; DHEA, dehydroepiandrosterone, Testo, testosterone; DHT, dihydrotestosterone; PROG, progesterone; Delta 4, androstenedione; Delta 5, androst-5-ene-3ß, 17ß-diol; BMD, body mineral density; LLOQ, lower limit of quantification; ULOQ, upper limit of quantification; LOD, limit of detection; ACT, α 1-antichymotrypsin.

Evaluating the Four Kallikrein Panel of the 4Kscore for Prediction of High-grade Prostate Cancer in Men in the Canary Prostate Active Surveillance Study.

BACKGROUND: Diagnosis of Gleason 6 prostate cancer can leave uncertainty about the presence of undetected aggressive disease. OBJECTIVE: To evaluate the utility of a four kallikrein (4K) panel in predicting the presence of high-grade cancer in men on active surveillance. DESIGN, SETTING, AND PARTICIPANTS: Plasma collected before the first and subsequent surveillance biopsies was assessed for 718 men prospectively enrolled in the multi-institutional Canary PASS trial. Biopsy data were split 2:1 into training and test sets. We developed statistical models that included clinical information and either the 4Kpanel or serum prostate-specific antigen (PSA). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The endpoint was reclassification to Gleason ≥7. We used receiver operating characteristic (ROC) curve analyses and area under the curve (AUC) to assess discriminatory capacity, and decision curve analysis (DCA) to report clinical net benefit. RESULTS AND LIMITATIONS: Significant predictors for reclassification were 4Kpanel (odds ratio [OR] 1.54, 95% confidence interval [CI] 1.31-1.81) or PSA (OR 2.11, 95% CI 1.53-2.91), ≥20% cores positive (OR 2.10, 95% CI 1.33-3.32), two or more prior negative biopsies (OR 0.19, 95% CI 0.04-0.85), prostate volume (OR 0.47, 95% CI 0.31-0.70), and body mass index (OR 1.09, 95% CI 1.04-1.14). ROC curve analysis comparing 4K and base models indicated that the 4Kpanel improved accuracy for predicting reclassification (AUC 0.78 vs 0.74) at the first surveillance biopsy. Both models performed comparably for prediction of reclassification at subsequent biopsies (AUC 0.75 vs 0.76). In DCA, both models showed higher net benefit compared to biopsy-all and biopsy-none strategies. Limitations include the single cohort nature of the study and the small numbers; results should be validated in another cohort before clinical use. CONCLUSIONS: The 4Kpanel provided incremental value over routine clinical information in predicting high-grade cancer in the first biopsy after diagnosis. The 4Kpanel did not add predictive value to the base model at subsequent surveillance biopsies. PATIENT SUMMARY: Active surveillance is a management strategy for many low-grade prostate cancers. Repeat biopsies monitor for previously undetected high-grade cancer. We show that a model with clinical variables, including a panel of four kallikreins, indicates the presence of high-grade cancer before a biopsy is performed.

Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use.

The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man's risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics.

Validation of a Novel Biomarker Panel for the Detection of Ovarian Cancer.

BACKGROUND: Ovarian cancer is the most lethal gynecological malignancy. Our integrated -omics approach to ovarian cancer biomarker discovery has identified kallikrein 6 (KLK6) and folate-receptor 1 (FOLR1) as promising candidates but these markers require further validation. METHODS: KLK6, FOLR1, CA125, and HE4 were investigated in three independent serum cohorts with a total of 20 healthy controls, 150 benign controls, and 216 ovarian cancer patients. The serum biomarker levels were determined by ELISA or automated immunoassay. RESULTS: All biomarkers demonstrated elevations in the sera of ovarian cancer patients compared with controls (P < 0.01). Overall, CA125 and HE4 displayed the strongest ability (AUC 0.80 and 0.82, respectively) to identify ovarian cancer patients and the addition of HE4 to CA125 improved the sensitivity from 36% to 67% at a set specificity of 95%. In addition, the combination of HE4 and FOLR1 was a strong predictor of ovarian cancer diagnosis, displaying comparable sensitivity (65%) to the best-performing CA125-based models (67%) at a set specificity of 95%. CONCLUSIONS: The markers identified through our integrated -omics approach performed similarly to the clinically approved markers CA125 and HE4. Furthermore, HE4 represents a powerful diagnostic marker for ovarian cancer and should be used more routinely in a clinical setting. IMPACT: The implications of our study are 2-fold: (i) we have demonstrated the strengths of HE4 alone and in combination with CA125, lending credence to increasing its usage in the clinic; and (ii) we have demonstrated the clinical utility of our integrated -omics approach to identifying novel serum markers with comparable performance to clinical markers. Cancer Epidemiol Biomarkers Prev; 25(9); 1333-40. ©2016 AACR.

An approach to evaluating and comparing biomarkers for patient treatment selection.

Despite the heightened interest in developing biomarkers predicting treatment response that are used to optimize patient treatment decisions, there has been relatively little development of statistical methodology to evaluate these markers. There is currently no unified statistical framework for marker evaluation. This paper proposes a suite of descriptive and inferential methods designed to evaluate individual markers and to compare candidate markers. An R software package has been developed which implements these methods. Their utility is illustrated in the breast cancer treatment context, where candidate markers are evaluated for their ability to identify a subset of women who do not benefit from adjuvant chemotherapy and can therefore avoid its toxicity.