Effects of clinical covariates on serum miRNA expression among women without ovarian cancer.

BACKGROUND: Serum microRNAs (miRNAs) are potential biomarkers for ovarian cancer; however, many factors may influence miRNA expression. To understand potential confounders in miRNA analysis, we examined how sociodemographic factors and comorbidities, including known ovarian cancer risk factors, influence serum miRNA levels in women without ovarian cancer. METHODS: Data from 1,576 women from the Mass General Brigham Biobank collected between 2012 and 2019, excluding subjects previously or subsequently diagnosed with ovarian cancer, were examined. Using a focused panel of 179 miRNA probes optimized for serum profiling, miRNA expression was measured by flow cytometry using the Abcam Fireplex® assay and correlated with subjects' electronic medical records. RESULTS: The study population broadly reflected the New England population. The median age of subjects was 49 years, 34% were current or prior smokers, 33% were obese (BMI >30kg/m2), 49% were postmenopausal, and 11% had undergone prior bilateral oophorectomy. Significant differences in miRNA expression were observed among ovarian risk factors such as age, obesity, menopause, BRCA1 or BRCA2 germline mutations or breast cancer in family history. Additionally, miRNA expression was significantly altered by prior bilateral oophorectomy, hypertension, and hypercholesterolemia. Other variables, such as smoking, parity, age at menarche, hormonal replacement therapy, oral contraception, breast, endometrial, or colon cancer, and diabetes were not associated with significant changes in the panel when corrected for multiple testing. CONCLUSIONS: Serum miRNA expression patterns are significantly affected by patient demographics, exposure history, and medical comorbidities. IMPACT: Understanding confounders in serum miRNA expression is important for refining clinical assays for cancer screening.

Differences in Serum miRNA Profiles by Race, Ethnicity, and Socioeconomic Status: Implications for Developing an Equitable Ovarian Cancer Screening Test.

Serum miRNAs are promising biomarkers for several clinical conditions, including ovarian cancer. To inform equitable implementation of these tests, we investigated the effects of race, ethnicity, and socioeconomic status on serum miRNA profiles. Serum samples from a large institutional biobank were analyzed using a custom panel of 179 miRNA species highly expressed in human serum, measured using the Abcam Fireplex assay via flow cytometry. Data were log-transformed prior to analysis. Differences in miRNA by race and ethnicity were assessed using logistic regression. Pairwise t tests analyzed racial and ethnic differences among eight miRNAs previously associated with ovarian cancer risk. Pearson correlations determined the relationship between mean miRNA expression and the social deprivation index (SDI) for Massachusetts residents. Of 1,586 patients (76.9% white, non-Hispanic), compared with white, non-Hispanic patients, those from other racial and ethnic groups were younger (41.9 years ± 13.2 vs. 51.3 ± 15.1, P < 0.01) and had fewer comorbidities (3.5 comorbidities ± 2.7 vs. 4.6 ± 2.8, P < 0.01). On logistic regression, miRNAs predicted race and ethnicity at an AUC of 0.69 (95% confidence interval, 0.66-0.72), which remained consistent when stratified by most comorbidities. Among eight miRNAs previously associated with ovarian cancer risk, seven significantly varied by race and ethnicity (all P < 0.01). There were no significant differences in SDI for any of these eight miRNAs. miRNA expression is significantly influenced by race and ethnicity, which remained consistent after controlling for confounders. Understanding baseline differences in biomarker test characteristics prior to clinical implementation is essential to ensure instruments perform comparably across diverse populations. PREVENTION RELEVANCE: This study aimed to understand factors affecting miRNA expression, to ensure we create equitable screening tests for ovarian cancer that perform well in diverse populations. The goal is to ensure that we are detecting ovarian cancer cases earlier (secondary prevention) in women of all races, ethnic backgrounds, and socioeconomic means.

Fast and robust imputation for miRNA expression data using constrained least squares.

BACKGROUND: High dimensional transcriptome profiling, whether through next generation sequencing techniques or high-throughput arrays, may result in scattered variables with missing data. Data imputation is a common strategy to maximize the inclusion of samples by using statistical techniques to fill in missing values. However, many data imputation methods are cumbersome and risk introduction of systematic bias. RESULTS: We present a new data imputation method using constrained least squares and algorithms from the inverse problems literature and present applications for this technique in miRNA expression analysis. The proposed technique is shown to offer an imputation orders of magnitude faster, with greater than or equal accuracy when compared to similar methods from the literature. CONCLUSIONS: This study offers a robust and efficient algorithm for data imputation, which can be used, e.g., to improve cancer prediction accuracy in the presence of missing data.

Compressed sensing two-dimensional Bragg scatter imaging.

Here we introduce a new reconstruction technique for two-dimensional Bragg scattering tomography (BST), based on the Radon transform models of Webber and Miller [Inverse Probl. Imaging15, 683 (2021).10.3934/ipi.2021010]. Our method uses a combination of ideas from multibang control and microlocal analysis to construct an objective function which can regularize the BST artifacts; specifically the boundary artifacts due to sharp cutoff in sinogram space (as observed in [arXiv preprint, arXiv:2007.00208 (2020)]), and artifacts arising from approximations made in constructing the model used for inversion. We then test our algorithm in a variety of Monte Carlo (MC) simulated examples of practical interest in airport baggage screening and threat detection. The data used in our studies is generated with a novel Monte-Carlo code presented here. The model, which is available from the authors upon request, captures both the Bragg scatter effects described by BST as well as beam attenuation and Compton scatter.