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Many cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate these studies, one must specify the levels of accuracy sought. However, justified target levels are rarely available. We describe a way to calculate target levels of sensitivity and specificity for a biomarker intended to be applied in a defined clinical context. The calculation requires knowledge of the prevalence or incidence of cases in the clinical population and the ratio of benefit associated with the clinical consequences of a positive biomarker test in cases (true positive) to cost associated with a positive biomarker test in controls (false positive). Guidance is offered on soliciting the cost/benefit ratio. The calculations are based on the longstanding decision theory concept of providing a net benefit on average in the population, and they rely on some assumptions about uniformity of costs and benefits to those tested. Calculations are illustrated with 3 applications: predicting colon cancer recurrence in stage 1 patients; predicting interval breast cancer (between mammography screenings); and screening for ovarian cancer. It is feasible to specify target levels of biomarker performance that enable evaluation of the potential clinical impact of biomarkers in early-phase studies. Nevertheless, biomarkers meeting the criteria should still be tested rigorously in studies that measure the actual impact on patient outcomes of using the biomarker to make clinical decisions.

We aimed to combine the fibrosis (FIB)-4 score and fibroscan-derived liver stiffness (LS) into a single score (FIB-5) that predicts incident complications of portal hypertension (PH) in persons with compensated liver disease. In this retrospective cohort study, we identified 5849 US veterans who underwent LS measurement from May 01, 2014 to June 30, 2019, and laboratory tests enabling FIB-4 calculation within 6 months of LS measurement. Patients were followed up from the LS measurement date until February 05, 2020, for incident complications of PH. We combined LS values and the individual components of the FIB-4 score (i.e. age, aspartate aminotransferase, alanine aminotransferase, and platelet count) using multivariable Cox proportional hazards modeling and the machine learning algorithm eXtreme gradient boosting to develop the C-FIB-5 and X-FIB-5 models, respectively. Models were internally validated using optimism-corrected measures. Among 5,849 patients, the mean age was 62.8 years, 95.9% were men, and the mean follow-up time was 2.14 ± 1.21 years. Within 3 years after LS measurement date, 116 (2.0%) patients developed complications of PH. The X-FIB-5 (area under the receiver operating characteristic [AUROC] 0.845) and C-FIB-5 scores (AUROC 0.868) demonstrated superior discrimination over LS (AUROC 0.688) and FIB-4 (AUROC 0.672) for predicting incident complications of PH. Both the X-FIB-5 and C-FIB-5 models demonstrated higher classification accuracy across all sensitivity cutoffs when compared with LS or FIB-4 alone. We combined LS and the individual components of the FIB-4 into a single scoring system (FIB-5, www.fib5.net ), which can help identify patients with compensated liver disease at risk of developing complications of PH.

The Texas Hepatocellular Carcinoma Consortium cohort study investigates risk factors of hepatocellular carcinoma (HCC) and biomarkers for early HCC detection in patients with liver cirrhosis. Adult patients with liver cirrhosis are enrolled at 5 clinical centers from 3 cities in Texas, with a target of 5,000 patients. Clinical history, risk factor questionnaires, liver imaging, laboratory data, and blood samples were collected at enrollment and at each 6-month follow-up visit. The primary outcome was the development of HCC. Biomarkers were tested in banked blood samples using prospective specimen collection, retrospective blinded evaluation design. We describe study design, eligibility criteria, recruitment, study cores, and sample size and analysis considerations.

Accurate risk prediction has been the central goal in many studies of survival outcomes. In the presence of multiple risk factors, a censored regression model can be employed to estimate a risk prediction rule. Before the prediction tool can be popularized for practical use, it is crucial to rigorously assess its prediction performance. In our motivating example, researchers are interested in developing and validating a risk prediction tool to identify future lung cancer cases by integrating demographic information, disease characteristics and smoking-related data. Considering the long latency period of cancer, it is desirable for a prediction tool to achieve discriminative performance that does not weaken over time. We propose estimation and inferential procedures to comprehensively assess both the overall predictive discrimination and the temporal pattern of an estimated prediction rule. The proposed methods readily accommodate commonly used censored regression models, including the Cox proportional hazards model and the accelerated failure time model. The estimators are consistent and asymptotically normal, and reliable variance estimators are also developed. The proposed methods offer an informative tool for inferring time-dependent predictive discrimination, as well as for comparing the discrimination performance between candidate models. Applications of the proposed methods demonstrate enduring performance of the risk prediction tool in the PLCO study and detected decaying performance in a study of liver disease.

Cribriform growth pattern is well-established as an adverse pathologic feature in prostate cancer. The literature suggests “large” cribriform glands associate with aggressive behavior; however, published studies use varying definitions for “large”. We aimed to identify an outcome-based quantitative cut-off for “large” vs “small” cribriform glands. We conducted an initial training phase using the tissue microarray based Canary retrospective radical prostatectomy cohort. Of 1287 patients analyzed, cribriform growth was observed in 307 (24%). Using Kaplan–Meier estimates of recurrence-free survival curves (RFS) that were stratified by cribriform gland size, we identified 0.25 mm as the optimal cutoff to identify more aggressive disease. In univariable and multivariable Cox proportional hazard analyses, size >0.25 mm was a significant predictor of worse RFS compared to patients with cribriform glands ≤0.25 mm, independent of pre-operative PSA, grade, stage and margin status (p < 0.001). In addition, two different subset analyses of low-intermediate risk cases (cases with Gleason score ≤ 3 + 4 = 7; and cases with Gleason score = 3 + 4 = 7/4 + 3 = 7) likewise demonstrated patients with largest cribriform diameter >0.25 mm had a significantly lower RFS relative to patients with cribriform glands ≤0.25 mm (each subset p = 0.004). Furthermore, there was no significant difference in outcomes between patients with cribriform glands ≤ 0.25 mm and patients without cribriform glands. The >0.25 mm cut-off was validated as statistically significant in a separate 419 patient, completely embedded whole-section radical prostatectomy cohort by biochemical recurrence, metastasis-free survival, and disease specific death, even when cases with admixed Gleason pattern 5 carcinoma were excluded. In summary, our findings support reporting cribriform gland size and identify 0.25 mm as an optimal outcome-based quantitative measure for defining “large” cribriform glands. Moreover, cribriform glands >0.25 mm are associated with potential for metastatic disease independent of Gleason pattern 5 adenocarcinoma.

Diagnostic biomarkers are often measured with errors due to imperfect lab conditions or analytic variability of the assay. The ability of a diagnostic biomarker to discriminate between cases and controls is often measured by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, among others. Ignoring measurement error can cause biased estimation of a diagnostic accuracy measure, which results in misleading interpretation of the efficacy of a diagnostic biomarker. Existing assays available are either research grade or clinical grade. Research assays are cost effective, often multiplex, but they may be associated with moderate measurement errors leading to poorer diagnostic performance. In comparison, clinical assays may provide better diagnostic ability, but with higher cost since they are usually developed by industry. Correction for attenuation methods are often valid when biomarkers are from a normal distribution, but may be biased with skewed biomarkers. In this paper, we develop a flexible method based on skew–normal biomarker distributions to correct for bias in estimating diagnostic performance measures including AUC, sensitivity, and specificity. Finite sample performance of the proposed method is examined via extensive simulation studies. The methods are applied to a pancreatic cancer biomarker study.

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

Mutations identified in the epidermal growth factor receptor (EGFR) predict sensitivity to EGFR-targeted therapy for non-small cell lung carcinoma (NSCLC). We previously reported that Electric Field-Induced Release and Measurement (EFIRM)-based liquid biopsy could detect EGFR ctDNA with >94% concordance with tissue-based genotyping. A side-by-side comparison of concordance of EFIRM and droplet digital PCR (ddPCR) for the detection of the two front-line actionable EFGR mutations was performed with paired plasma and saliva samples from 13 NSCLC patients. Deep sequencing analysis based on single-strand DNA library preparation was employed to determine the size distributions of EGFR L858R ctDNA in plasma and saliva samples. EFIRM detected both EGFR mutations with 100% sensitivity in both plasma and saliva samples, whereas ddPCR detected EGFR mutations with sensitivities of 84.6% and 15.4%, respectively. In saliva samples, the majority of EGFR L858R ctDNA fragments detected were <80 bp. Deep sequencing analysis of ctDNA enriched for the EGFR L858R mutation revealed the significant presence of EGFR L858R ctDNA as ultra-short circulating tumor DNA (usctDNA) with the size of 40–60 bp in patient plasma and saliva. Most of usctDNAs are not amplifiable with the current ddPCR assay. Further examination using cell lines and patient biofluids revealed that the majority of usctDNAs were predominately localized in the exosomal fraction. Our study revealed the abundant existence of EGFR ctDNA in the plasma and saliva of NSCLC patients is usctDNA. usctDNA is a novel type of targets for liquid biopsy that can be efficiently detected by EFIRM technology.

Background Hepatocellular carcinoma (HCC) has limited treatment options in patients with advanced stage disease and early detection of HCC through surveillance programs is a key component towards reducing mortality. The current practice guidelines recommend that high-risk cirrhosis patients are screened every six months with ultrasonography but these are done in local hospitals with variable quality leading to disagreement about the benefit of HCC surveillance. The well-established diagnostic biomarker α -Fetoprotein (AFP) is used widely in screening but the reported performance varies widely across studies. We evaluate two biomarker screening approaches, a six-month risk prediction model and a parametric empirical Bayes (PEB) algorithm, in terms of their ability to improve the likelihood of early detection of HCC compared to current AFP alone when applied prospectively in a future study. Methods We used electronic medical records from the Department of Veterans Affairs Hepatitis C Clinical Case Registry to construct our analysis cohort, which consists of serial AFP tests in 11,222 cirrhosis control patients and 902 HCC cases prior to their HCC diagnosis. The six-month risk prediction model incorporates routinely measured laboratory tests, age, the rate of change in AFP over the past year with the current AFP. The PEB algorithm incorporates prior AFP screening values to identify patients with a significant elevated level of AFP at their current screen. We split the analysis cohort into independent training and validation datasets. All model fitting and parameter estimation was performed using the training data and the algorithm performance was assessed by applying each approach to patients in the validation dataset. Results When the screening-level false positive rate was set at 10%, the patient-level true positive rate using current AFP alone was 53.88% while the patient-level true positive rate for the six-month risk prediction model was 58.09% (4.21% increase) and PEB approach was 63.64% (9.76% increase). Both screening approaches identify a greater proportion of HCC cases earlier than using AFP alone. Conclusions The two approaches show greater potential to improve early detection of HCC compared to using the current AFP only and are worthy of further study.

Advanced hepatocellular carcinoma (HCC) has limited treatment options and poor survival, therefore early detection is critical to improving the survival of patients with HCC. Current guidelines for high-risk patients include ultrasound screenings every six months, but ultrasounds are operator dependent and not sensitive for early HCC. Serum α-Fetoprotein (AFP) is a widely used diagnostic biomarker, but it has limited sensitivity and is not elevated in all HCC cases so, we incorporate a second blood-based biomarker, des-γ carboxy-prothrombin (DCP), that has shown potential as a screening marker for HCC. The data from the Hepatitis Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial is a valuable source of data to study biomarker screening for HCC. We assume the trajectories of AFP and DCP follow a joint hierarchical mixture model with random changepoints that allows for distinct changepoint times and subsequent trajectories of each biomarker. The changepoint indicators are jointly modeled with a Markov Random Field distribution to help detect borderline changepoints. Markov chain Monte Carlo methods are used to calculate posterior distributions, which are used in risk calculations among future patients and determine whether a patient has a positive screen. The screening algorithm was compared to alternatives in simulations studies under a range of possible scenarios and in the HALT-C Trial using cross-validation.