The myelin water imaging transcriptome: myelin water fraction regionally varies with oligodendrocyte-specific gene expression.

Identifying sensitive and specific measures that can quantify myelin are instrumental in characterizing microstructural changes in neurological conditions. Neuroimaging transcriptomics is emerging as a valuable technique in this regard, offering insights into the molecular basis of promising candidates for myelin quantification, such as myelin water fraction (MWF). We aimed to demonstrate the utility of neuroimaging transcriptomics by validating MWF as a myelin measure. We utilized data from a normative MWF brain atlas, comprised of 50 healthy subjects (mean age = 25 years, range = 17-42 years) scanned at 3 Tesla. Magnetic resonance imaging data included myelin water imaging to extract MWF and T1 anatomical scans for image registration and segmentation. We investigated the inter-regional distributions of gene expression data from the Allen Human Brain Atlas in conjunction with inter-regional MWF distribution patterns. Pearson correlations were used to identify genes with expression profiles mirroring MWF. The Single Cell Type Atlas from the Human Protein Atlas was leveraged to classify genes into gene sets with high cell type specificity, and a control gene set with low cell type specificity. Then, we compared the Pearson correlation coefficients for each gene set to determine if cell type-specific gene expression signatures correlate with MWF. Pearson correlation coefficients between MWF and gene expression for oligodendrocytes and adipocytes were significantly higher than for the control gene set, whereas correlations between MWF and inhibitory/excitatory neurons were significantly lower. Our approach in integrating transcriptomics with neuroimaging measures supports an emerging technique for understanding and validating MRI-derived markers such as MWF.

Changing your mind about the data: Updating sampling assumptions in inductive inference.

When people use samples of evidence to make inferences, they consider both the sample contents and how the sample was generated ("sampling assumptions"). The current studies examined whether people can update their sampling assumptions - whether they can revise a belief about sample generation that is discovered to be incorrect, and reinterpret old data in light of the new belief. We used a property induction task where learners saw a sample of instances that shared a novel property and then inferred whether it generalized to other items. Assumptions about how the sample was selected were manipulated between conditions: in the property sampling frame condition, items were selected because they shared a property, while in the category sampling frame condition, items were selected because they belonged to a particular category. Experiment 1 found that these frames affected patterns of property generalization regardless of whether they were presented before or after the sample data was observed: in both cases, generalization was narrower under a property than a category frame. In Experiments 2 and 3, an initial category or property frame was presented before the sample, and was later retracted and replaced with the complementary frame. Learners were able to update their beliefs about sample generation, basing their property generalization on the more recent correct frame. These results show that learners can revise incorrect beliefs about data selection and adjust their inductive inferences accordingly.

Management of ovarian and breast cancer risk in non-BRCA HBOC pathogenic variant carriers in a large California health care system.

PURPOSE: To describe breast and ovarian cancer risk reduction strategies in the clinical management of women who test positive for non-BRCA hereditary breast and ovarian cancer (HBOC) pathogenic variants compared to those who test positive for pathogenic BRCA variants or have negative germline panel testing. METHODS: Examination of imaging and preventive surgeries in women undergoing HBOC genetic testing from 1/1/2015 to 12/31/2018, with follow up to 03/31/2020 in Kaiser Permanente Northern California. RESULTS: A total of 13,271 tests which included HBOC genes were identified. Rate of bilateral salpingo-oophorectomy after genetic testing were similar for BRCA and the non-BRCA moderate risk ovarian pathogenic variants (PVs) (47.4% vs 54%, p = 0.25). Rates were lower for low risk or unknownrisk non-BRCA PVs (12.8%, p < 0.001, 5.3% (p < 0.001). Rates of surveillance for ovarian cancer with ultrasound and CA 125 in the first year was 63.3% and 64.7% for BRCA PV, 37.5% and 27.1%, for non-BRCA moderate risk PVs and 13.7% and 4.6%, for low-risk PVs. Bilateral mastectomy rates were 19.7% for BRCA PV, 10.1% (p = 0.028) for non-BRCA breast high risk PVs, for moderate risk PVs 7.7% (p < 0.001) and for unknown risk 0.4% (p < 0.001). MRI surveillance rates in the first year similarly were 47.4% for non-BRCA BRCA PV, 43% for breast high risk PV, 39.4% for moderate risk and 4.9% for unknown risk PV. CONCLUSION: Surgical and surveillance strategies are underutilized for HBOC PV, however there is concordance of uptake of preventive strategies with specific risk associated with non-BRCA PVs.

Factors influencing genetic counseling and testing for hereditary breast and ovarian cancer syndrome in a large US health care system.

Investigate whether disparities and other factors influence referral to genetic counseling and testing for hereditary breast and ovarian cancer syndrome (HBOC) in a large health care system. Examination of clinical, demographic, and socioeconomic factors from electronic health records associated with genetic referral and testing within 12 months after a new cancer diagnosed between August 1, 2013 and December 31, 2018. For patients meeting institutional criteria for HBOC testing, 60.6% were referred for genetic counseling, 88% of whom underwent germline testing; at least one pathogenic variant was found in 15.3%. Referral rates for patients with breast (69%) or ovarian cancer (65.7%) were much higher than for metastatic prostate (11.1%, p < 0.0001) or pancreatic cancer (22.3%, p < 0.0001); referral criteria were implemented more recently for the latter two cancers. Younger age, family history, and chemotherapy were associated with referral. Higher Elixhauser comorbidity score and prior cancer were associated with non-referral. No other factors were associated with genetic referral for all eligible cancers combined, although differences were seen in specific cancers. Race was a significant factor only for breast cancer, with fewer Asians than Whites referred. Health disparities in referral to genetics for HBOC cancers are mitigated in a comprehensive integrated health care system.

Streamlining genetic testing for women with ovarian cancer in a Northern California health care system.

OBJECTIVE: Referral to Genetics for pre-testing counseling may be inefficient for women with ovarian cancer. This study assesses feasibility of gynecologic oncologists directly offering genetic testing. METHODS: A prospective pilot study was conducted at two gynecologic oncology hubs in an integrated healthcare system from May 1 to November 6, 2019. Gynecologic oncologists offered multigene panel testing to women with newly diagnosed ovarian cancer, followed by selective genetic counseling. Outcomes were compared between study participants and women from other hubs in the health system. RESULTS: Of ovarian cancer patients at study sites, 40 participated and all underwent genetic testing. Of 101 patients diagnosed at other sites, 85% were referred to genetics (p = .0061 compared to pilot participants) and 67% completed testing (p < .0001). The time from diagnosis to blood draw and notification of result was 18.5 and 34 days for the pilot group compared to 25.5 and 53 days at other sites. Panel testing detected 9 (22.5%) and 7 (10.3%, p = .08) pathogenic mutations in each group, respectively. Patients and providers were highly satisfied with the streamlined process. CONCLUSION: Genetic testing performed at the gynecologic oncology point of care for patients with ovarian cancer is feasible, increases uptake of testing, and improves time to results.

Prenatal detection of congenital heart disease.

OBJECTIVES: To define current frequency of prenatal detection of congenital heart disease (CHD), factors affecting prenatal detection, and its influence on postnatal course. STUDY DESIGN: We prospectively identified all fetuses and infants < or =6 months of age with major CHD at 3 referral centers in Northern California over 1 year; we obtained prenatal and demographic data, reviewed prenatal ultrasound (US) and postnatal records, and used logistic regression to analyze maternal, fetal, and prenatal-care provider risk factors for prenatal diagnosis. RESULTS: Ninety-eight of 309 infants with major CHD had prenatal diagnosis (36% accounting for 27 pregnancy terminations); 185 infant-families participated in the postnatal survey, and although 99% had prenatal US, only 28% were prenatally diagnosed. Anomalous pulmonary venous return (0%), transposition of the great arteries (19%), and left obstructive lesions (23%) had the lowest prenatal detection. Heterotaxy (82%), single ventricle (64%), and HLHS (61%) had the highest. Prenatal diagnosis was higher at university versus community practices (P = .001). Sociodemographics were not associated with prenatal diagnosis. Infants diagnosed prenatally were less frequently ventilated (P < .01) or treated with prostaglandin (P < .05). CONCLUSIONS: Prenatal detection of major CHD significantly alters postnatal course but remains low despite nearly universal US. CHD type and US practice type are important determinants of prenatal detection.