Qualitative development of the PROMIS Profile v1.0-Familial Chylomicronemia Syndrome (FCS) 28.

PURPOSE: Familial chylomicronemia syndrome (FCS) is a rare genetic disorder characterized by high triglyceride levels, significant disease burden, and negative impacts on health-related quality of life. This project aimed to create a PROMIS-based patient-reported outcome measure that represents valid and important concerns for patients with FCS. METHODS: We reviewed the literature and data from a previous qualitative study of FCS to identify key FCS symptoms and impacts, which were mapped to PROMIS domains to create a pool of eligible items. Candidate items were reduced per expert feedback and patients with FCS completed cognitive interviews to confirm content validity and measure content. RESULTS: Literature and qualitative data review identified ten key symptoms and 12 key impacts of FCS, including abdominal pain, fatigue, difficulty thinking, and worry about pancreatitis attacks. We identified 96 items primarily from PROMIS, supplemented with items from the Quality of Life in Neurological Disorders™ (Neuro-QoL™) and the Functional Assessment of Chronic Illness Therapy (FACIT) measurement systems. This pool was reduced to 32 candidate items, which were assessed via cognitive interviews with eight participants with FCS. Cognitive interview results and additional expert feedback led to the removal of four items and finalization of the PROMIS Profile v1.0-familial chylomicronemia syndrome (FCS) 28. CONCLUSIONS: The PROMIS Profile v1.0-familial chylomicronemia syndrome (FCS) 28 provides strong content validity for assessing quality of life among patients with FCS. The benefits of PROMIS, including norm-referenced mean values for each measure, will facilitate comparison of patients with FCS to other clinical populations.

Concept and benchmarks for assessing narrow-sense validity of genetic risk score values.

BACKGROUND: While higher genetic risk score (GRS) has been statistically associated with increased disease risk (broad-sense validity), the concept and tools for assessing the validity of reported GRS values from tests (narrow-sense validity) are underdeveloped. METHODS: We propose two benchmarks for assessing the narrow-sense validity of GRS. The baseline benchmark requires that the mean GRS value in a general population approximates 1.0. The calibration benchmark assesses the agreement between observed risks and estimated risks (GRS values). We assessed benchmark performance for three prostate cancer (PCa) GRS tests, derived from three SNP panels with increasing stringency of selection criteria, in a PCa chemoprevention trial where 714 of 3225 men were diagnosed with PCa during the 4-year follow-up. RESULTS: GRS from Panels 1, 2, and 3 were all statistically associated with PCa risk; P = 5.58 × 10-3 , P = 1 × 10-3 , and P = 1.5 × 10-13 , respectively (broad-sense validity). For narrow-sense validity, the mean GRS value among men without PCa was 1.33, 1.09, and 0.98 for Panels 1, 2, and 3, respectively (baseline benchmark). For assessing the calibration benchmark, observed risks were calculated for seven groups of men with GRS values <0.3, 0.3-0.79, 0.8-1.19, 1.2-1.49, 1.5-1.99, 2-2.99, and ≥3. The calibration slope (higher is better) was 0.15, 0.12, and 0.60, and the bias score (lower is better) between the observed risks and GRS values was 0.08, 0.08, and 0.02 for Panels 1, 2, and 3, respectively. CONCLUSION: Performance differed considerably among GRS tests. We recommend that all GRS tests be evaluated using the two benchmarks before clinical implementation for individual risk assessment.

Genetic risk score modifies the effect of APOE on risk and age onset of Alzheimer's disease.

Single nucleotide polymorphism (SNP)-based genetic risk score (GRS) and APOE genotype are both important in risk prediction of Alzheimer's disease (AD); however, the interaction between GRS and APOE has not been extensively investigated. Our objective was to determine whether GRS modifies the APOE effect on AD risk and age at onset (AAO). The study included 774 AD cases and 767 controls of European descent. Population standardized GRS was calculated based on 17 previously implicated AD risk-associated SNPs. Association was analyzed using logistic regression, Cox proportional hazards model and Kaplan-Meier curve. We found that GRS was significantly associated with AD risk and the association was stronger among APOE ε4 carriers. Compared to ε4 non-carriers, the Odds Ratio (OR) for AD was 8.09 (95% Confidence Interval [CI]: 4.98-13.63) for ε4 carriers with high-GRS (≥1.5). In contrast, the OR was 2.55 (95% CI: 1.46-4.49) for ε4 carriers with low-GRS (<0.6). In conclusion, these results suggest SNP-based GRS may supplement APOE for better assessment of inherited risk and age of onset of AD.

HOXA9, PCDH17, POU4F2, and ONECUT2 as a Urinary Biomarker Combination for the Detection of Bladder Cancer in Chinese Patients with Hematuria.

BACKGROUND: DNA methylation biomarkers for bladder cancer (BCa) have not been evaluated extensively in the Chinese population. OBJECTIVE: To develop and validate a urinary biomarker combination of methylation assays in a group of Chinese patients with hematuria. DESIGN, SETTING, AND PARTICIPANTS: A total of 192 urine samples were collected and evaluated from patients with microscopic or gross hematuria, including 97 BCa patients and 95 controls with benign diseases. A two-stage study was conducted: the first stage being assay construction and the second stage being assay validation. Eighty-one urine samples were analyzed for the hypermethylation of eight selected genes in stage 1 and then a four-gene panel was constructed. An additional 111 urine samples were analyzed using the four-gene panel (including HOXA9, PCDH17, POU4F2, and ONECUT2) for independent validation. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The positive predictive value (PPV) and negative predictive value (NPV) were calculated for the combination methylation assay. Uni- and multivariate binary logistic regression analyses (backward elimination, conditional) were performed to calculate the association between BCa and each predictor variable. RESULTS AND LIMITATIONS: The combination assay of HOXA9, PCDH17, POU4F2, and ONECUT2 was selected based on the results of multivariate logistic regression analysis in stage 1. Using a strategy of three-level risk stratification, the assay yielded a consistent PPV of 100%. With an estimated BCa prevalence of 10% in a general hematuria population, the assay would result in an overall NPV of 98%. This combined methylation biomarker would yield an overall area under the receiver operating characteristic curve of 0.871 (with a sensitivity of 90.5% and a specificity of 73.2%) if using the prediction model from multivariate regression analysis. In addition, over half of BCa cases would be predicted accurately and ∼60% of unnecessary cystoscopies could be spared. This study had several limitations. First, the sample size was relatively small. Second, it was performed in a case-control population rather than in a natural hematuria cohort. CONCLUSIONS: A combination methylation assay of HOXA9, PCDH17, POU4F2, and ONECUT2 resulted in high PPV and NPV in Chinese patients with hematuria. With accurate risk prediction, the urinary biomarker combination could spare a sizeable proportion of low-risk patients from extensive and invasive examination. PATIENT SUMMARY: In the present study, we looked at the predictive performance of a urinary biomarker combination of HOXA9, PCDH17, POU4F2, and ONECUT2. We found that this urinary biomarker combination may help discriminate bladder cancer from other benign diseases in patients with hematuria, resulting in a reduction of unnecessary invasive examination in patients at low risk.

Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts.

BACKGROUND: Genetic risk score (GRS) is an odds ratio (OR)-weighted and population-standardized method for measuring cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers. METHODS: Utilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer-specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer-specific GRSs were calculated, for the first time in these cohorts, based on previously published risk-associated SNPs using the Caucasian subjects in these two cohorts. RESULTS: Mean cancer-specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer-specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low-, average-, and high-risk groups based on cancer-specific GRS (<0.5, 0.5-1.5, and >1.5, respectively), significant dose-response associations of higher cancer-specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P-trend  < 0.05). More than 64% subjects in the population controls of eMERGE can be classified as high risk for at least one type of these cancers. CONCLUSION: Validity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer-specific GRS may have the potential for developing personalized cancer screening strategy.