Marshall D. Brown

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 mans 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.

Evaluating incremental values from new predictors with net reclassification improvement in survival analysis

Developing individualized prediction rules for disease risk and prognosis has played a key role in modern medicine. When new genomic or biological markers become available to assist in risk prediction, it is essential to assess the improvement in clinical usefulness of the new markers over existing routine variables. Net reclassification improvement (NRI) has been proposed to assess improvement in risk reclassification in the context of comparing two risk models and the concept has been quickly adopted in medical journals (Pencina et al., Stat Med 27:157–172, 2008). We propose both nonparametric and semiparametric procedures for calculating NRI as a function of a future prediction time

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

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Effect of age on serum prostate-specific antigen in women

with a censored failure time outcome. The proposed methods accommodate covariate-dependent censoring, therefore providing more robust and sometimes more efficient procedures compared with the existing nonparametric-based estimators (Pencina et al., Stat Med 30:11–21, 2011; Uno et al., Comparing risk scoring systems beyond the roc paradigm in survival analysis, 2009). Simulation results indicate that the proposed procedures perform well in finite samples. We illustrate these procedures by evaluating a new risk model for predicting the onset of cardiovascular disease.

Neuronal pentraxin receptor-1 is a new cerebrospinal fluid biomarker of Alzheimer’s disease progression

Abstract 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.

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

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.