Deanna S. Cross

Population based allele frequencies of disease associated polymorphisms in the Personalized Medicine Research Project

BackgroundThere is a lack of knowledge regarding the frequency of disease associated polymorphisms in populations and population attributable risk for many populations remains unknown. Factors that could affect the association of the allele with disease, either positively or negatively, such as race, ethnicity, and gender, may not be possible to determine without population based allele frequencies.Here we used a panel of 51 polymorphisms previously associated with at least one disease and determined the allele frequencies within the entire Personalized Medicine Research Project population based cohort. We compared these allele frequencies to those in dbSNP and other data sources stratified by race. Differences in allele frequencies between self reported race, region of origin, and sex were determined.ResultsThere were 19544 individuals who self reported a single racial category, 19027 or (97.4%) self reported white Caucasian, and 11205 (57.3%) individuals were female. Of the 11,208 (57%) individuals with an identifiable region of origin 8337 or (74.4%) were German.41 polymorphisms were significantly different between self reported race at the 0.05 level. Stratification of our Caucasian population by self reported region of origin revealed 19 polymorphisms that were significantly different (p = 0.05) between individuals of different origins. Further stratification of the population by gender revealed few significant differences in allele frequencies between the genders.ConclusionsThis represents one of the largest population based allele frequency studies to date. Stratification by self reported race and region of origin revealed wide differences in allele frequencies not only by race but also by region of origin within a single racial group. We report allele frequencies for our Asian/Hmong and American Indian populations; these two minority groups are not typically selected for population allele frequency detection. Population wide allele frequencies are important for the design and implementation of studies and for determining the relevance of a disease associated polymorphism for a given population.

Underutilization of Lynch syndrome screening in a multisite study of patients with colorectal cancer

Purpose:The aim of this study was to examine Lynch syndrome screening of patients with metastatic colorectal cancer in integrated health-care-delivery organizations.Methods:We determined the availability of Lynch syndrome screening criteria and actual Lynch syndrome screening in the medical records of 1,188 patients diagnosed with metastatic colorectal cancer between 2004 and 2009 at seven institutions in the Cancer Research Network.Results:We found infrequent use of Lynch syndrome screening (41/1,188). Family history was available for 937 of the 1,188 patients (79%). There was sufficient information to assess Lynch syndrome risk using family history–based criteria in 719 of the 937 patients (77%) with family history documentation. In 391 individuals with a family history of a Lynch syndrome–associated cancer, 107 (27%) could not be evaluated due to missing information such as age of cancer onset. Eleven percent of patients who met the Bethesda criteria and 25% of individuals who met the Amsterdam II criteria were screened for Lynch syndrome. Recommended guidelines were adhered to during screening, but no testing method was preferred.Conclusion:The information required for Lynch syndrome screening decisions is routinely collected but seldom used. There is a critical gap between collection of family history and its use to guide Lynch syndrome screening, which may support a case for implementation of universal screening guidelines.Genet Med 15 12, 933–940.Genetics in Medicine (2013); 15 12, 933–940. doi:10.1038/gim.2013.43

KRAS Testing and Epidermal Growth Factor Receptor Inhibitor Treatment for Colorectal Cancer in Community Settings

Background: In metastatic colorectal cancer (mCRC), mutations in the KRAS gene predict poor response to EGF receptor (EGFR) inhibitors. Clinical treatment guidelines now recommend KRAS testing if EGFR inhibitors are considered. Our study investigates the clinical uptake and utilization of KRAS testing. Methods: We included 1,188 patients with mCRCs diagnosed from 2004 to 2009, from seven integrated health care delivery systems with a combined membership of 5.5 million. We used electronic medical records and targeted manual chart review to capture the complexity and breadth of real-world clinical oncology care. Results: Overall, 428 patients (36%) received KRAS testing during their clinical care, and 266 (22%) were treated with EGFR inhibitors. Age at diagnosis (P = 0.0034), comorbid conditions (P = 0.0316), and survival time from diagnosis (P < 0.0001) influence KRAS testing and EGFR inhibitor prescribing. The proportion who received KRAS testing increased from 7% to 97% for those treated in 2006 and 2010, respectively, and 83% of all treated patients had a KRAS wild-type genotype. Most patients with a KRAS mutation (86%) were not treated with EGFR inhibitors. The interval between mCRC diagnosis and receipt of KRAS testing decreased from 26 months (2006) to 10 months (2009). Conclusions: These findings show rapid uptake and incorporation of this predictive biomarker into clinical oncology care. Impact: In this delivery setting, KRAS testing is widely used to guide treatment decisions with EGFR inhibitors in patients with mCRCs. An important future research goal is to evaluate utilization of KRAS testing in other delivery settings in the United States. Cancer Epidemiol Biomarkers Prev; 22(1); 91–101. ©2012 AACR.

Dietary intake in the Personalized Medicine Research Project: a resource for studies of gene-diet interaction

BackgroundTo describe the dietary intake of participants in the Personalized Medicine Research Project (PMRP), and to quantify differences in nutrient intake by smoking status and APOE4-a genetic marker that has been shown to modify the association between risk factors and outcomes.MethodsThe PMRP is a population-based DNA, plasma and serum biobank of more than 20,000 adults aged 18 years and older in central Wisconsin. A questionnaire at enrollment captures demographic information as well as self-reported smoking and alcohol intake. The protocol was amended to include the collection of dietary intake and physical activity via self-reported questionnaires: the National Cancer Institute 124-item Diet History Questionnaire and the Baecke Physical Activity Questionnaire. These questionnaires were mailed out to previously enrolled participants. APOE was genotyped in all subjects.ResultsThe response rate to the mailed questionnaires was 68.2% for subjects who could still be contacted (alive with known address). Participants ranged in age from 18 to 98 years (mean 54.7) and 61% were female. Dietary intake is variable when comparing gender, age, smoking, and APOE4. Over 50% of females are dietary supplement users; females have higher supplement intake than males, but both have increasing supplement use as age increases. Food energy, total fat, cholesterol, protein, and alcohol intake decreases as both males and females age. Female smokers had higher macronutrient intake, whereas male nonsmokers had higher macronutrient intake. Nonsmokers in both genders use more supplements. In females, nonsmokers and smokers with APOE4 had higher supplement use. In males, nonsmokers with APOE4 had higher supplement use between ages 18-39 only, and lower supplement use at ages above 39. Male smokers with APOE4 had lower supplement use.ConclusionDietary intake in PMRP subjects is relatively consistent with data from the National Health and Nutrition Examination Survey (NHANES). Findings suggest a possible correlation between the use of supplements and APOE4. The PMRP dietary data can benefit studies of gene-environment interactions and the development of common diseases.

Development of a fingerprinting panel using medically relevant polymorphisms

BackgroundFor population based biorepositories to be of use, rigorous quality control and assurance must be maintained. We have designed and validated a panel of polymorphisms for individual sample identification consisting of 36 common polymorphisms that have been implicated in a wide range of diseases and an additional sex marker. This panel uniquely identifies our biorepository of approximately 20,000 samples and would continue to uniquely identify samples in biorepositories of over 100 million samples.MethodsA panel of polymorphisms associated with at least one disease state in multiple populations was constructed using a cut-off of 0.20 or greater confirmed minor allele frequency in a European Caucasian population. The fingerprinting assay was tested using the MALDI-TOF mass spectrometry method of allele determination on a Sequenom platform with a panel of 28 Caucasian HapMap samples; the results were compared with known genotypes to ensure accuracy. The frequencies of the alleles were compared to the expected frequencies from dbSNP and any genotype that did not achieve Hardy Weinberg equilibrium was excluded from the final assay.ResultsThe final assay consisted of the AMG sex marker and 36 medically relevant polymorphisms with representation on each chromosome, encompassing polymorphisms on both the Illumina 550K bead array and the Affymetrix 6.0 chip (with over a million polymorphisms) platform. The validated assay has a P(ID) of 6.132 × 10-15 and a Psib(ID) of 3.077 × 10-8. This assay allows unique identification of our biorepository of 20,000 individuals as well and ensures that as we continue to recruit individuals they can be uniquely fingerprinted. In addition, diseases such as cancer, heart disease diabetes, obesity, and respiratory disease are well represented in the fingerprinting assay.ConclusionThe polymorphisms in this panel are currently represented on a number of common genotyping platforms making QA/QC flexible enough to accommodate a large number of studies. In addition, this panel can serve as a resource for investigators who are interested in the effects of disease in a population, particularly for common diseases.

Protocol for the “Implementation, adoption, and utility of family history in diverse care settings” study

BackgroundRisk assessment with a thorough family health history is recommended by numerous organizations and is now a required component of the annual physical for Medicare beneficiaries under the Affordable Care Act. However, there are several barriers to incorporating robust risk assessments into routine care. MeTree, a web-based patient-facing health risk assessment tool, was developed with the aim of overcoming these barriers. In order to better understand what factors will be instrumental for broader adoption of risk assessment programs like MeTree in clinical settings, we obtained funding to perform a type III hybrid implementation-effectiveness study in primary care clinics at five diverse healthcare systems. Here, we describe the study’s protocol.Methods/designMeTree collects personal medical information and a three-generation family health history from patients on 98 conditions. Using algorithms built entirely from current clinical guidelines, it provides clinical decision support to providers and patients on 30 conditions. All adult patients with an upcoming well-visit appointment at one of the 20 intervention clinics are eligible to participate. Patient-oriented risk reports are provided in real time. Provider-oriented risk reports are uploaded to the electronic medical record for review at the time of the appointment. Implementation outcomes are enrollment rate of clinics, providers, and patients (enrolled vs approached) and their representativeness compared to the underlying population. Primary effectiveness outcomes are the percent of participants newly identified as being at increased risk for one of the clinical decision support conditions and the percent with appropriate risk-based screening. Secondary outcomes include percent change in those meeting goals for a healthy lifestyle (diet, exercise, and smoking). Outcomes are measured through electronic medical record data abstraction, patient surveys, and surveys/qualitative interviews of clinical staff.DiscussionThis study evaluates factors that are critical to successful implementation of a web-based risk assessment tool into routine clinical care in a variety of healthcare settings. The result will identify resource needs and potential barriers and solutions to implementation in each setting as well as an understanding potential effectiveness.Trial registrationNCT01956773

Role of genomics in eliminating health disparities.

The Texas Center for Health Disparities, a National Institute on Minority Health and Health Disparities Center of Excellence, presents an annual conference to discuss prevention, awareness education, and ongoing research about health disparities both in Texas and among the national population. The 2014 Annual Texas Conference on Health Disparities brought together experts in research, patient care, and community outreach on the “Role of Genomics in Eliminating Health Disparities.” Rapid advances in genomics and pharmacogenomics are leading the field of medicine to use genetics and genetic risk to build personalized or individualized medicine strategies. We are at a critical juncture of ensuring such rapid advances benefit diverse populations. Relatively few forums have been organized around the theme of the role of genomics in eliminating health disparities. The conference consisted of three sessions addressing “Gene-Environment Interactions and Health Disparities,” “Personalized Medicine and Elimination of Health Disparities,” and “Ethics and Public Policy in the Genomic Era.” This article summarizes the basic science, clinical correlates, and public health data presented by the speakers.

Historical Prostate Cancer Screening and Treatment Outcomes from a Single Institution

Objective To quantify outcomes of individuals diagnosed and treated for prostate cancer in a single institution. Design Retrospective electronic chart abstraction. Setting Marshfield Clinic, the largest private multispecialty group practice in Wisconsin, and one of the largest in the United States, provides health care services annually to approximately 385,000 unique patients through 1.8 million annual patient encounters. Participants Individuals within the Marshfield Clinic cancer registry who had been diagnosed with prostate cancer between 1960 and 2009. Methods Electronic chart abstraction from the cancer registry and the electronic medical record was conducted (N=6,181). Data abstracted included age at diagnosis; stage and grade of tumor; prostate specific antigen (PSA) values before, at, and after diagnosis; initial cancer treatment; follow-up time; subsequent cancer treatments; evidence of metastasis; age of death; and cause of death, if known. Results The average age of prostate cancer diagnosis has decreased from 70–71 years in the 1960’s and 1970’s to an average age at diagnosis of 67 years in the 2000’s (P<0.001). This decrease in age occurred within the decades of implementation of PSA screening. Approximately 74% of men diagnosed with prostate cancer within the PSA screening era had at least one PSA test, and the presence of a PSA test did not appear to change treatment outcome. Age, grade, and stage were the biggest predictors of prostate cancer outcome. There was no difference in event-free survival between current treatment types (radical prostatectomy, brachytherapy, photon treatment, or intensity-modulated radiation therapy) (2003 or later) when stratified by age (greater than 85%, 5-year event-free survival P=0.85); however, more events occurred with older external beam radiation treatment regimens (1993–2003) (70% to 75%, 5-year event-free survival P=0.001). Conclusion Individuals diagnosed and treated for prostate cancer within the Marshfield Clinic comprehensive care setting follow national trends with a decreased age of diagnosis since the advent of PSA screening. Outcomes for individuals treated within the Clinic system are also comparable to national trends.

Expression and initial promoter characterization of PCAN1 in retinal tissue and prostate cell lines

Prostate cancer is the most frequently diagnosed neoplasia in men and one of the leading causes of cancer-related deaths in men over 60. In an effort to understand the molecular events leading to prostate cancer, we have identified PCAN1 (prostate cancer gene 1) (also known as GDEP), a gene that is highly expressed in prostate epithelial tissue and frequently mutated in prostate tumors. Here we demonstrate its expression in neural retina, and retinoblastoma cell culture but not retinal pigment epithelial cell culture. We further characterize PCAN1 expression in the prostate cell lines RWPE1, RWPE2, and LnCAP PGC. We demonstrate an increase in expression when the cells are grown in the presence of Matrigel, an artificial extracellular basement membrane. Expression was time dependent, with expression observed on d 6 and little or no expression on d 12. Testosterone was not found to increase PCAN1 expression in this culture system. In addition, normal prostate epithelial cells co-cultured with normal prostate stromal cells did not exhibit PCAN1 expression at any time. To definitively locate the transcription initiation sites, we performed restriction-ligase-mediated 5′ RACE, to selectively amplify only mRNA with a 5′ cap. An initial characterization of the sequence upstream of the initiation sites determined six possible binding sites for the prostate specific regulatory protein NKX3. 1 and four potential binding sites for the PPAR/RXR heterodimer that is involved in the control of cell differentiation and apoptosis.

Development of a Multi‐institutional Cohort to Facilitate Cardiovascular Disease Biomarker Validation Using Existing Biorepository Samples Linked to Electronic Health Records

Emerging biomarkers for acute myocardial infarction (AMI) may enhance conventional risk‐prediction algorithms if they are informative and associated with risk independently of established predictors. In this study, we constructed a cohort for testing emerging biomarkers for AMI in managed‐care populations using existing biospecimen repositories linked to electronic health records (EHR).